diff --git a/advanced/others/fuzzy-name-matching.ipynb b/advanced/others/fuzzy-name-matching.ipynb
new file mode 100644
index 0000000..dd93a3e
--- /dev/null
+++ b/advanced/others/fuzzy-name-matching.ipynb
@@ -0,0 +1,283 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# The Fuzz\n",
+    "- `theFuzz` uses the Levenshtein edit distance to calculate the degree of closeness between two strings. \n",
+    "    - It also provides features for determining string similarity in various situations\n",
+    "- [Reference](https://www.datacamp.com/tutorial/fuzzy-string-python)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !conda install thefuzz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from thefuzz import fuzz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## String Matching Methods\n",
+    "|Technique|\tDescription|\tCode Example|\n",
+    "|:------:|:------|:------|\n",
+    "|Simple Ratio|\tCalculates similarity considering the order of input strings.\t|`fuzz.ratio(name, full_name)`|\n",
+    "|Partial Ratio|\tFinds partial similarity by comparing the shortest string with sub-strings.|\t`fuzz.partial_ratio(name, full_name)`\n",
+    "|Token Sort Ratio|\tIgnores order of words in strings.|\t`fuzz.token_sort_ratio(full_name_reordered, full_name)`|\n",
+    "|Token Set Ratio|\tRemoves common tokens before calculating similarity.|\t`fuzz.token_set_ratio(name, full_name)`|"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple Ratio \n",
+    "- `ratio()` calculates the edit distance based on the ordering of both input strings\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Similarity score: 86\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the similarity score\n",
+    "name = \"Kurtis Pykes\"\n",
+    "full_name = \"Kurtis K D Pykes\"\n",
+    "\n",
+    "print(f\"Similarity score: {fuzz.ratio(name, full_name)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Partial Ratio\n",
+    "- `partial_ratio()` seeks to find how partially similar two strings are.\n",
+    "    - it calculates the similarity by taking the **shortest** string, which in this scenario is stored in the variable `name`, then compares it against the **sub-strings** of the same length in the longer string, which is stored in `full_name`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Similarity score: 67\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Similarity score: {fuzz.partial_ratio(name, full_name)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Since order matters in partial ratio, our score dropped to 67 in this instance. \n",
+    "- Therefore, to get a 100% similarity match, you would have to move the \"K D\" part to the end of the string"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Partial ratio similarity score: 100\n",
+      "Simple ratio similarity score: 86\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Order matters with partial ratio\n",
+    "# Check the similarity score\n",
+    "name = \"Kurtis Pykes\"\n",
+    "full_name = \"Kurtis Pykes K D\" # move K D to the end \n",
+    "\n",
+    "print(f\"Partial ratio similarity score: {fuzz.partial_ratio(name, full_name)}\")\n",
+    "\n",
+    "# But order will not effect simple ratio if strings do not match\n",
+    "print(f\"Simple ratio similarity score: {fuzz.ratio(name, full_name)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Token Sort Ratio\n",
+    "- Token sort doesn’t care about what order words occur in. It accounts for similar strings that aren’t in order as expressed above"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Token sort ratio similarity score: 100\n",
+      "Partial ratio similarity score: 75\n",
+      "Simple ratio similarity score: 86\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the similarity score\n",
+    "full_name = \"Kurtis K D Pykes\"\n",
+    "full_name_reordered = \"Kurtis Pykes K D\"\n",
+    "\n",
+    "# Order does not matter for token sort ratio\n",
+    "print(f\"Token sort ratio similarity score: {fuzz.token_sort_ratio(full_name_reordered, full_name)}\")\n",
+    "\n",
+    "# Order matters for partial ratio\n",
+    "print(f\"Partial ratio similarity score: {fuzz.partial_ratio(full_name, full_name_reordered)}\")\n",
+    "\n",
+    "# Order will not effect simple ratio if strings do not match\n",
+    "print(f\"Simple ratio similarity score: {fuzz.ratio(name, full_name)}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-  If there are words that are dissimilar words in the strings, it will negatively impact the similarity ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Token sort ratio similarity score: 86\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the similarity score\n",
+    "name = \"Kurtis Pykes\"\n",
+    "full_name = \"Kurtis K D Pykes\" # \"Kurtis Pykes K D\"\n",
+    "\n",
+    "print(f\"Token sort ratio similarity score: {fuzz.token_sort_ratio(name, full_name)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Token set ratio\n",
+    "- The `token_set_ratio()` method is pretty similar to the token_sort_ratio(), except it takes out common tokens before calculating how similar the strings are: this is extremely helpful when the strings are significantly different in length. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Token sort ratio similarity score: 100\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check the similarity score\n",
+    "name = \"Kurtis Pykes\"\n",
+    "full_name = \"Kurtis K D Pykes\"\n",
+    "\n",
+    "print(f\"Token sort ratio similarity score: {fuzz.token_set_ratio(name, full_name)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Process\n",
+    "- The process module enables users to extract text from a collection using fuzzy string matching. Calling the extract() method on the process module returns the strings with a similarity score in a vector. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('barcelona fc', 86), ('AFC Barcelona', 82)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from thefuzz import process\n",
+    "\n",
+    "collection = [\"AFC Barcelona\", \"Barcelona AFC\", \"barcelona fc\", \"afc barcalona\"]\n",
+    "print(process.extract(\"barcelona\", collection, scorer=fuzz.ratio, limit=2))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python_tutorial",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/basics/notebooks/pandas-pivot-melt-crosstab.ipynb b/basics/notebooks/pandas-pivot-melt-crosstab.ipynb
new file mode 100644
index 0000000..8c91038
--- /dev/null
+++ b/basics/notebooks/pandas-pivot-melt-crosstab.ipynb
@@ -0,0 +1,941 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reshaping Dataframes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pivot\n",
+    "- In pandas, there are two methods `.pivot()` and `.pivot_table()` (RECOMMENDED)\n",
+    "- However, `.pivot()` unable to handle duplicate values in the index column, in this case, the index column is `cusid` which contains multiple rows of `cusid=1`, and `cusid=2`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>cusid</th>\n",
+       "      <th>payment_method</th>\n",
+       "      <th>merchant</th>\n",
+       "      <th>total_txn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>DEBIT</td>\n",
+       "      <td>SHOPEE</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>DEBIT</td>\n",
+       "      <td>GRAB</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>CREDIT</td>\n",
+       "      <td>SHOPEE</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2</td>\n",
+       "      <td>CREDIT</td>\n",
+       "      <td>SHOPEE</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>CREDIT</td>\n",
+       "      <td>LAZADA</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2</td>\n",
+       "      <td>DEBIT</td>\n",
+       "      <td>GSM</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   cusid payment_method merchant  total_txn\n",
+       "0      1          DEBIT   SHOPEE          1\n",
+       "1      1          DEBIT     GRAB          2\n",
+       "2      1         CREDIT   SHOPEE          3\n",
+       "3      2         CREDIT   SHOPEE          4\n",
+       "4      2         CREDIT   LAZADA          5\n",
+       "5      2          DEBIT      GSM          6"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({'cusid': [1,1,1,2,2,2],\n",
+    "                   'payment_method': ['DEBIT', 'DEBIT', 'CREDIT', 'CREDIT', 'CREDIT', 'DEBIT'],\n",
+    "                   'merchant': ['SHOPEE', 'GRAB', 'SHOPEE', 'SHOPEE', 'LAZADA', 'GSM'],\n",
+    "                   'total_txn': [1, 2, 3, 4, 5, 6],\n",
+    "})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th>payment_method</th>\n",
+       "      <th colspan=\"2\" halign=\"left\">CREDIT</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">DEBIT</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>merchant</th>\n",
+       "      <th>LAZADA</th>\n",
+       "      <th>SHOPEE</th>\n",
+       "      <th>GRAB</th>\n",
+       "      <th>GSM</th>\n",
+       "      <th>SHOPEE</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cusid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "payment_method CREDIT        DEBIT            \n",
+       "merchant       LAZADA SHOPEE  GRAB  GSM SHOPEE\n",
+       "cusid                                         \n",
+       "1                 NaN    3.0   2.0  NaN    1.0\n",
+       "2                 5.0    4.0   NaN  6.0    NaN"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pivot_df = df.pivot_table(index=[\"cusid\"], columns=[\"payment_method\", \"merchant\"], values=[\"total_txn\"])\n",
+    "pivot_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MultiIndex([('CREDIT', 'LAZADA'),\n",
+       "            ('CREDIT', 'SHOPEE'),\n",
+       "            ( 'DEBIT',   'GRAB'),\n",
+       "            ( 'DEBIT',    'GSM'),\n",
+       "            ( 'DEBIT', 'SHOPEE')],\n",
+       "           names=['payment_method', 'merchant'])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# MultiIndex\n",
+    "pivot_df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CREDIT_LAZADA</th>\n",
+       "      <th>CREDIT_SHOPEE</th>\n",
+       "      <th>DEBIT_GRAB</th>\n",
+       "      <th>DEBIT_GSM</th>\n",
+       "      <th>DEBIT_SHOPEE</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>cusid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       CREDIT_LAZADA  CREDIT_SHOPEE  DEBIT_GRAB  DEBIT_GSM  DEBIT_SHOPEE\n",
+       "cusid                                                                   \n",
+       "1                NaN            3.0         2.0        NaN           1.0\n",
+       "2                5.0            4.0         NaN        6.0           NaN"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flatten_name_df = pivot_df.copy()\n",
+    "flatten_name_df.columns = list(map(\"_\".join, pivot_df.columns))\n",
+    "flatten_name_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Melt (Unpivot)\n",
+    "- Unpivot a DataFrame from **wide** to **long** format, optionally leaving identifiers set.\n",
+    "- For example, we want to melt the dataframe `df` below into `subjects` and `grades` for each student instead of having multiple subjects columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Math</th>\n",
+       "      <th>English</th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>A+</td>\n",
+       "      <td>C</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>John</td>\n",
+       "      <td>B</td>\n",
+       "      <td>B</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Foo</td>\n",
+       "      <td>A</td>\n",
+       "      <td>B</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Bar</td>\n",
+       "      <td>F</td>\n",
+       "      <td>A+</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alex</td>\n",
+       "      <td>D</td>\n",
+       "      <td>F</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Tom</td>\n",
+       "      <td>C</td>\n",
+       "      <td>A</td>\n",
+       "      <td>13</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Name Math English  Age\n",
+       "0   Bob   A+       C   13\n",
+       "1  John    B       B   16\n",
+       "2   Foo    A       B   16\n",
+       "3   Bar    F      A+   15\n",
+       "4  Alex    D       F   15\n",
+       "5   Tom    C       A   13"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({'Name': ['Bob', 'John', 'Foo', 'Bar', 'Alex', 'Tom'],\n",
+    "                   'Math': ['A+', 'B', 'A', 'F', 'D', 'C'],\n",
+    "                   'English': ['C', 'B', 'B', 'A+', 'F', 'A'],\n",
+    "                   'Age': [13, 16, 16, 15, 15, 13]})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Subject</th>\n",
+       "      <th>Grades</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alex</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Math</td>\n",
+       "      <td>D</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Alex</td>\n",
+       "      <td>15</td>\n",
+       "      <td>English</td>\n",
+       "      <td>F</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Bar</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Math</td>\n",
+       "      <td>F</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Bar</td>\n",
+       "      <td>15</td>\n",
+       "      <td>English</td>\n",
+       "      <td>A+</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Math</td>\n",
+       "      <td>A+</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Bob</td>\n",
+       "      <td>13</td>\n",
+       "      <td>English</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Foo</td>\n",
+       "      <td>16</td>\n",
+       "      <td>Math</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Foo</td>\n",
+       "      <td>16</td>\n",
+       "      <td>English</td>\n",
+       "      <td>B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>John</td>\n",
+       "      <td>16</td>\n",
+       "      <td>Math</td>\n",
+       "      <td>B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>John</td>\n",
+       "      <td>16</td>\n",
+       "      <td>English</td>\n",
+       "      <td>B</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Tom</td>\n",
+       "      <td>13</td>\n",
+       "      <td>Math</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>Tom</td>\n",
+       "      <td>13</td>\n",
+       "      <td>English</td>\n",
+       "      <td>A</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Name  Age  Subject Grades\n",
+       "4   Alex   15     Math      D\n",
+       "10  Alex   15  English      F\n",
+       "3    Bar   15     Math      F\n",
+       "9    Bar   15  English     A+\n",
+       "0    Bob   13     Math     A+\n",
+       "6    Bob   13  English      C\n",
+       "2    Foo   16     Math      A\n",
+       "8    Foo   16  English      B\n",
+       "1   John   16     Math      B\n",
+       "7   John   16  English      B\n",
+       "5    Tom   13     Math      C\n",
+       "11   Tom   13  English      A"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.melt(\n",
+    "    id_vars=[\"Name\", \"Age\"],\n",
+    "    value_vars=[\"Math\", \"English\"],\n",
+    "    var_name=\"Subject\",\n",
+    "    value_name=\"Grades\",\n",
+    ").sort_values(by=[\"Name\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Crosstab\n",
+    "- Crosstab: displays the relationship between two or more categorical variables by showing the frequency of different combinations of those variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Education</th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Graduate</td>\n",
+       "      <td>27</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Graduate</td>\n",
+       "      <td>24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Graduate</td>\n",
+       "      <td>29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Graduate</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Undergraduate</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender      Education  Age\n",
+       "0    Male       Graduate   27\n",
+       "1  Female  Undergraduate   18\n",
+       "2  Female  Undergraduate   19\n",
+       "3    Male       Graduate   24\n",
+       "4    Male       Graduate   29\n",
+       "5  Female       Graduate   23\n",
+       "6    Male  Undergraduate   18"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({'Gender': ['Male', 'Female', 'Female', 'Male', 'Male','Female', 'Male'],\n",
+    "        'Education': ['Graduate', 'Undergraduate', 'Undergraduate', 'Graduate', 'Graduate', 'Graduate', 'Undergraduate'],\n",
+    "        'Age': [27, 18, 19, 24, 29, 23,18]})\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Education</th>\n",
+       "      <th>Graduate</th>\n",
+       "      <th>Undergraduate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Education  Graduate  Undergraduate\n",
+       "Gender                            \n",
+       "Female            0              2\n",
+       "Male              3              0"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Basic crosstab\n",
+    "cross_tab = pd.crosstab(df['Gender'], df['Education'])\n",
+    "cross_tab"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Education</th>\n",
+       "      <th>Graduate</th>\n",
+       "      <th>Undergraduate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>0.142857</td>\n",
+       "      <td>0.285714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.142857</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Education  Graduate  Undergraduate\n",
+       "Gender                            \n",
+       "Female     0.142857       0.285714\n",
+       "Male       0.428571       0.142857"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Crosstab with normalization: shows the proportion of each combination relative to the total.\n",
+    "cross_tab_normalized = pd.crosstab(df['Gender'], df['Education'], normalize='all')\n",
+    "cross_tab_normalized"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th>Education</th>\n",
+       "      <th>Graduate</th>\n",
+       "      <th>Undergraduate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Female</th>\n",
+       "      <td>23.000000</td>\n",
+       "      <td>18.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>26.666667</td>\n",
+       "      <td>18.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Education   Graduate  Undergraduate\n",
+       "Gender                             \n",
+       "Female     23.000000           18.5\n",
+       "Male       26.666667           18.0"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Crosstab with aggregation for each combination\n",
+    "cross_tab_agg = pd.crosstab(df['Gender'], df['Education'], values=df['Age'], aggfunc='mean')\n",
+    "cross_tab_agg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "# Crosstab with margins\n",
+    "cross_tab_margins = pd.crosstab(df['Gender'], df['Education'], margins=True, margins_name=\"Total\")\n",
+    "print(\"\\nCrosstab with Margins:\")\n",
+    "print(cross_tab_margins)\n",
+    "\n",
+    "\n",
+    "print(\"\\nCrosstab with Normalization:\")\n",
+    "print(cross_tab_normalized)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ml_env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/basics/notebooks/subprocess.ipynb b/basics/notebooks/subprocess.ipynb
index 180151e..1461054 100644
--- a/basics/notebooks/subprocess.ipynb
+++ b/basics/notebooks/subprocess.ipynb
@@ -244,13 +244,6 @@
     "\n",
     "print(result.stdout.decode()) # decode() to convert from bytes to strings\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {