diff --git a/README.md b/README.md
index fc8d44b1..055c00a5 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,39 @@
-# python-ml-course
+# [Curso completo de Machine Learning: Data Science en Python](https://www.udemy.com/course/machinelearningpython/)
+
+## Requisitos
+* Se necesitan conocimientos de matemáticas de bachillerato o conocimientos básicos de estadística
+* Se recomienda saber programar un poco para enfocarse en aprender las técnicas de análisis en Python aunque no es totalmente necesario
+## Descripción
+¿Te suenan las palabras Machine Learning o Data Scientist? ¿Te pica la curiosidad de para qué sirven estas técnicas o por qué empresas de todo el mundo pagan un sueldo de 120.000 hasta 200.000$ al año a un científico de datos? 
+
+Pues este curso está pensado y diseñado por todo un profesional del mundo del Data Science como es Juan Gabriel Gomila, de modo que os va a compartir todo su conocimiento y ayudaros a entender la teoría tan compleja sobre las matemáticas que tiene detrás, los algoritmos y librerías de programación con Python para convertiros en todo unos expertos a pesar de que no tengáis experiencia previa. 
+
+Veremos paso a paso como empezar a trabajar con conceptos y algoritmos del mundo del Machine Learning. Con cada nueva clase y sección que completes tendrás unas nuevas habilidades que te ayudarán a entender este mundo tan completo y lucrativo que puede ser esta rama del Data Science.
+
+También decirte que este curso es muy divertido, en la línea de Juan Gabriel Gomila y que aprenderás y te divertirás mientras vas aprendiendo acerca de técnicas de Machine Learning con Python. En particular, los temas que trabajaremos serán los siguientes:
+
+- Parte 1 - Instalación de Python y paquetes necesarios para data science, machine learning y visualización de los datos
+- Parte 2 - Evolución histórica del análisis predictivo y el machine learning
+- Parte 3 - Pre procesado y limpieza de los datos 
+- Parte 4 - Manejo de datos y data wrangling, operaciones con datasets y distribuciones de probabilidad más famosas
+- Parte 5 - Repaso de estadística básica, intervalos de confianza, contrastes de hipótesis, correlación,...
+- Parte 6 - Regression lineal simple, regresión lineal múltiple y regresión polinomial, variables categóricas y tratamiento de outliers.
+- Parte 7 - Clasificación con regresión logística, estimación con máxima verosimititud, validación cruzada, K-fold cross validation, curvas ROC 
+- Parte 8 - Clustering, K-means, K-medoides, dendrogramas y clustering jerárquico, técnica del codo y análisis de la silueta
+- Parte 9 - Clasificación con árboles, bosques aleatorios, técnicas de poda, entropía, maximización de la información
+- Parte 10 - Support Vector Machines para problemas de clasificación y regresión, kernels no lineales, reconocimiento facial (cómo funciona CSI)
+- Parte 11 - Los K vecinos más cercanos, decisión por mayoría, programación de algoritmos de Machine Learning vs librerías de Python
+- Parte 12 - Análisis de componentes principales, reducción de la dimensión, LDA
+- Parte 13 - Deep learning, Reinforcement Learning, Redes neuronales artificiales y convolucionales y Tensor Flow
+
+Además, en el curso encontrarás ejercicios, datasets para practicar basados en ejemplos de la vida real, de modo que no solo aprenderás la teoría con los vídeos, si no también a practicar para construir tus propios modelos de Machine Learning. Y como no olvidar que tendrás un github con todo el código fuente en Python para descargar y utilizar en todos tus proyectos. Así que no esperes más y apúntate al curso de Machine Learning más completo y útil del mercado español!
+
+## ¿Para quién es este curso?
+- Cualquiera interesado en aprender Machine Learning
+- Estudiantes que tienen un conocimiento de matemáticas que quieran aprender acerca del Machine Learning con Python
+- Usuarios intermedios que conocen los fundamentos de Machine learning como los algoritmos clásicos de regresión lineal o logística pero buscan aprender más y explorar otros campos del aprendizaje estadístico
+- Programadores que les guste el código y que estén interesados en aprender Machine Learning para aplicar dichas técnicas a sus datasets
+- Estudiantes de universidad que busquen especializarse y aprender a ser Data Scientists
+- Analistas de datos que quieran ir más allá gracias al Machine Learning
+- Cualquier persona que no esté satisfecha con su propio trabajo y busque empezar a trabajar como un Data Scientist profesional
+- Cualquier persona que quiera dar valor añadido a su propia empresa utilizando las potentes herramientas de Machine Learning
diff --git a/environment.yml b/environment.yml
new file mode 100644
index 00000000..90897198
--- /dev/null
+++ b/environment.yml
@@ -0,0 +1,132 @@
+name: python-ml-2020
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - absl-py=0.5.0=py_0
+  - appnope=0.1.0=py35_0
+  - astor=0.7.1=py_0
+  - backcall=0.1.0=py_0
+  - blas=1.1=openblas
+  - bleach=3.1.5=pyh9f0ad1d_0
+  - brewer2mpl=1.4.1=py_3
+  - bzip2=1.0.8=h0b31af3_2
+  - c-ares=1.15.0=h01d97ff_1001
+  - ca-certificates=2020.4.5.1=hecc5488_0
+  - certifi=2018.8.24=py35_1001
+  - cloudpickle=1.4.1=py_0
+  - cycler=0.10.0=py_2
+  - dask-core=2.6.0=py_0
+  - decorator=4.4.2=py_0
+  - defusedxml=0.6.0=py_0
+  - entrypoints=0.2.3=py35_2
+  - freetype=2.8.1=hfa320df_1
+  - gast=0.3.3=py_0
+  - ggplot=0.11.5=py_4
+  - grpcio=1.14.1=py35hd60e7a3_0
+  - imageio=2.8.0=py_0
+  - ipykernel=5.1.0=pyh24bf2e0_0
+  - ipython=7.0.1=py35h24bf2e0_0
+  - ipython_genutils=0.2.0=py_1
+  - jedi=0.12.1=py35_0
+  - jinja2=2.11.2=pyh9f0ad1d_0
+  - jpeg=9c=h1de35cc_1001
+  - json5=0.9.0=py_0
+  - jsonschema=2.6.0=py35_2
+  - jupyter_client=5.3.3=py_0
+  - jupyter_core=4.5.0=py_0
+  - jupyterlab=2.1.0=py_0
+  - jupyterlab_server=1.0.0=py_0
+  - libcxx=10.0.0=h1af66ff_2
+  - libffi=3.2.1=h4a8c4bd_1007
+  - libgfortran=3.0.1=0
+  - libpng=1.6.37=hbbe82c9_1
+  - libprotobuf=3.6.0=hd9629dc_1000
+  - libsodium=1.0.18=h01d97ff_0
+  - libtiff=4.1.0=h2ae36a8_6
+  - libwebp-base=1.1.0=h0b31af3_3
+  - lz4-c=1.9.2=h4a8c4bd_1
+  - markdown=3.2.1=py_0
+  - markupsafe=1.0=py35h470a237_1
+  - matplotlib=2.1.2=py35h6d6146d_0
+  - mistune=0.8.3=py35h470a237_2
+  - nbconvert=5.6.0=py_0
+  - nbformat=5.0.6=py_0
+  - ncurses=6.1=h0a44026_1002
+  - networkx=2.4=py_1
+  - notebook=5.7.0=py35_0
+  - numpy=1.14.6=py35_blas_openblashd3ea46f_200
+  - olefile=0.46=py_0
+  - openblas=0.2.20=8
+  - openssl=1.0.2u=h0b31af3_0
+  - packaging=20.4=pyh9f0ad1d_0
+  - pandas=0.22.0=py35_1
+  - pandoc=2.9.2.1=0
+  - pandocfilters=1.4.2=py_1
+  - parso=0.7.0=pyh9f0ad1d_0
+  - patsy=0.5.1=py_0
+  - pexpect=4.6.0=py35_0
+  - pickleshare=0.7.5=py35_0
+  - pillow=5.2.0=py35h2dc6135_1
+  - pip=20.1.1=py_1
+  - plotly=2.7.0=py35_0
+  - prometheus_client=0.8.0=pyh9f0ad1d_0
+  - prompt_toolkit=2.0.10=py_0
+  - protobuf=3.6.0=py35hfc679d8_0
+  - ptyprocess=0.6.0=py_1001
+  - pygments=2.6.1=py_0
+  - pyparsing=2.4.7=pyh9f0ad1d_0
+  - python=3.5.5=h5001a0f_2
+  - python-dateutil=2.8.1=py_0
+  - pytz=2020.1=pyh9f0ad1d_0
+  - pywavelets=1.0.1=py35h7eb728f_0
+  - pyzmq=17.1.2=py35hae99301_0
+  - readline=7.0=hcfe32e1_1001
+  - requests=2.12.5=py35_0
+  - scikit-image=0.14.0=py35hfc679d8_1
+  - scikit-learn=0.20.0=py35_blas_openblasha84fab4_201
+  - scipy=1.1.0=py35_blas_openblash7943236_201
+  - send2trash=1.5.0=py_0
+  - setuptools=40.4.3=py35_0
+  - simplegeneric=0.8.1=py_1
+  - six=1.15.0=pyh9f0ad1d_0
+  - sqlite=3.28.0=h9721f7c_0
+  - statsmodels=0.9.0=py35_0
+  - tensorboard=1.10.0=py35_0
+  - tensorflow=1.10.0=py35_0
+  - termcolor=1.1.0=py_2
+  - terminado=0.8.1=py35_1
+  - testpath=0.4.4=py_0
+  - tk=8.6.10=hbbe82c9_0
+  - toolz=0.10.0=py_0
+  - tornado=5.1.1=py35h470a237_0
+  - traitlets=4.3.2=py35_0
+  - wcwidth=0.1.9=pyh9f0ad1d_0
+  - webencodings=0.5.1=py_1
+  - werkzeug=1.0.1=pyh9f0ad1d_0
+  - wheel=0.34.2=py_1
+  - xz=5.2.5=h0b31af3_0
+  - zeromq=4.2.5=hfc679d8_4
+  - zlib=1.2.11=h0b31af3_1006
+  - zstd=1.4.4=h4b3e974_3
+  - pip:
+    - attrs==19.3.0
+    - cffi==1.14.0
+    - future==0.18.2
+    - importlib-metadata==1.6.0
+    - ipywidgets==7.5.1
+    - more-itertools==8.3.0
+    - pathlib2==2.3.5
+    - pluggy==0.13.1
+    - py==1.8.1
+    - pyclust==0.2.0
+    - pycparser==2.20
+    - pytest==5.4.2
+    - python-graphviz==0.14
+    - seaborn==0.9.1
+    - treelib==1.6.1
+    - tzlocal==2.1
+    - widgetsnbextension==3.5.1
+    - zipp==1.2.0
+prefix: /opt/anaconda3/envs/python-ml-2020
+
diff --git a/notebooks/T1 - 1 - Data Cleaning - Carga de datos-Colab.ipynb b/notebooks/T1 - 1 - Data Cleaning - Carga de datos-Colab.ipynb
new file mode 100644
index 00000000..5fe95776
--- /dev/null
+++ b/notebooks/T1 - 1 - Data Cleaning - Carga de datos-Colab.ipynb	
@@ -0,0 +1,1597 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/T1%20-%201%20-%20Data%20Cleaning%20-%20Carga%20de%20datos-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 122
+    },
+    "colab_type": "code",
+    "id": "DMwj_ddZhbjD",
+    "outputId": "ee612eed-e8bd-4072-d9b9-f7ddf0eda3da"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+      "\n",
+      "Enter your authorization code:\n",
+      "··········\n",
+      "Mounted at /content/drive\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "zUFcR_TkX8qK"
+   },
+   "source": [
+    "# Carga de datos a través de la función read_csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "RBr31uxHX8qM"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Y_s6i9FIX8qR"
+   },
+   "outputs": [],
+   "source": [
+    "mainpath = \"/content/drive/My Drive/Curso Machine Learning con Python/datasets/\"\n",
+    "filename = \"titanic/titanic3.csv\"\n",
+    "fullpath = os.path.join(mainpath, filename)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "u8CuQabhX8qV"
+   },
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(fullpath)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 425
+    },
+    "colab_type": "code",
+    "id": "oN4kxj87X8qY",
+    "outputId": "70f44e14-0dbc-4bb3-b8e7-1fc74771e7c0"
+   },
+   "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>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pclass  survived  ...   body                        home.dest\n",
+       "0       1         1  ...    NaN                     St Louis, MO\n",
+       "1       1         1  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+       "2       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+       "3       1         0  ...  135.0  Montreal, PQ / Chesterville, ON\n",
+       "4       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+       "\n",
+       "[5 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "albOkDT1X8qd"
+   },
+   "source": [
+    "### Ejemplos de los parámetros de la función read_csv\n",
+    "```\n",
+    "read.csv(filepath=\"/Users/JuanGabriel/Developer/AnacondaProjects/python-ml-course/datasets/titanic/titanic3.csv\",\n",
+    "        sep = \",\", \n",
+    "        dtype={\"ingresos\":np.float64, \"edad\":np.int32}, \n",
+    "        header=0,names={\"ingresos\", \"edad\"},\n",
+    "        skiprows=12, index_col=None, \n",
+    "        skip_blank_lines=False, na_filter=False\n",
+    "        )\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "zNN7tZGkX8qd"
+   },
+   "outputs": [],
+   "source": [
+    "data2 = pd.read_csv(mainpath + \"/\" + \"customer-churn-model/Customer Churn Model.txt\", sep=\",\") #CUIDADO: ES EL TXT; NO EL CSV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 326
+    },
+    "colab_type": "code",
+    "id": "yhTDGrdZX8qg",
+    "outputId": "77a81442-9d9d-499a-feb9-748b2e27b98d"
+   },
+   "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>State</th>\n",
+       "      <th>Account Length</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KS</td>\n",
+       "      <td>128</td>\n",
+       "      <td>415</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>25</td>\n",
+       "      <td>265.1</td>\n",
+       "      <td>110</td>\n",
+       "      <td>45.07</td>\n",
+       "      <td>197.4</td>\n",
+       "      <td>99</td>\n",
+       "      <td>16.78</td>\n",
+       "      <td>244.7</td>\n",
+       "      <td>91</td>\n",
+       "      <td>11.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>107</td>\n",
+       "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>26</td>\n",
+       "      <td>161.6</td>\n",
+       "      <td>123</td>\n",
+       "      <td>27.47</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.45</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>137</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>243.4</td>\n",
+       "      <td>114</td>\n",
+       "      <td>41.38</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>84</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299.4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>50.90</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.86</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>75</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>166.7</td>\n",
+       "      <td>113</td>\n",
+       "      <td>28.34</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.41</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS             128        415  ...        2.70              1  False.\n",
+       "1    OH             107        415  ...        3.70              1  False.\n",
+       "2    NJ             137        415  ...        3.29              0  False.\n",
+       "3    OH              84        408  ...        1.78              2  False.\n",
+       "4    OK              75        415  ...        2.73              3  False.\n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 102
+    },
+    "colab_type": "code",
+    "id": "JqEOEFxfX8ql",
+    "outputId": "ebec79ef-929a-4927-dfc5-da61f9e392d3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['State', 'Account Length', 'Area Code', 'Phone', \"Int'l Plan\",\n",
+       "       'VMail Plan', 'VMail Message', 'Day Mins', 'Day Calls',\n",
+       "       'Day Charge', 'Eve Mins', 'Eve Calls', 'Eve Charge', 'Night Mins',\n",
+       "       'Night Calls', 'Night Charge', 'Intl Mins', 'Intl Calls',\n",
+       "       'Intl Charge', 'CustServ Calls', 'Churn?'], dtype=object)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data2.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 51
+    },
+    "colab_type": "code",
+    "id": "LyWQSIinX8qp",
+    "outputId": "460c9b1f-f1dd-4cd9-a29a-7a8009b3b3b2"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',\n",
+       "       'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U'], dtype=object)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_cols = pd.read_csv(mainpath + \"/\" + \"customer-churn-model/Customer Churn Columns.csv\")\n",
+    "data_col_list = data_cols[\"Column_Names\"].tolist()\n",
+    "data2 = pd.read_csv(mainpath + \"/\" + \"customer-churn-model/Customer Churn Model.txt\", \n",
+    "                    header = None, names = data_col_list)\n",
+    "data2.columns.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "TTZUkFrOX8qr"
+   },
+   "source": [
+    "# Carga de datos a través de la función open"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "X-L6HB-YX8qs"
+   },
+   "outputs": [],
+   "source": [
+    "data3 = open(mainpath + \"/\" + \"customer-churn-model/Customer Churn Model.txt\",'r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "4GD_rhV0X8qv"
+   },
+   "outputs": [],
+   "source": [
+    "cols = data3.readline().strip().split(\",\")\n",
+    "n_cols = len(cols)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Qf8-2mp8X8qx"
+   },
+   "outputs": [],
+   "source": [
+    "counter = 0\n",
+    "main_dict = {}\n",
+    "for col in cols:\n",
+    "    main_dict[col] = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "Dp2exBAvX8qz",
+    "outputId": "c66b11a4-5ba6-488d-df84-d35187a2ff92"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "El data set tiene 3333 filas y 21 columnas\n"
+     ]
+    }
+   ],
+   "source": [
+    "for line in data3:\n",
+    "    values = line.strip().split(\",\")\n",
+    "    for i in range(len(cols)):\n",
+    "        main_dict[cols[i]].append(values[i])\n",
+    "    counter += 1\n",
+    "\n",
+    "print(\"El data set tiene %d filas y %d columnas\"%(counter-1, n_cols))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 326
+    },
+    "colab_type": "code",
+    "id": "4pPopSNXX8q1",
+    "outputId": "be7496e8-c0b4-4731-9925-c39e9e843502"
+   },
+   "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>State</th>\n",
+       "      <th>Account Length</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KS</td>\n",
+       "      <td>128</td>\n",
+       "      <td>415</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>25</td>\n",
+       "      <td>265.100000</td>\n",
+       "      <td>110</td>\n",
+       "      <td>45.070000</td>\n",
+       "      <td>197.400000</td>\n",
+       "      <td>99</td>\n",
+       "      <td>16.780000</td>\n",
+       "      <td>244.700000</td>\n",
+       "      <td>91</td>\n",
+       "      <td>11.010000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.700000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>107</td>\n",
+       "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>26</td>\n",
+       "      <td>161.600000</td>\n",
+       "      <td>123</td>\n",
+       "      <td>27.470000</td>\n",
+       "      <td>195.500000</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.620000</td>\n",
+       "      <td>254.400000</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.450000</td>\n",
+       "      <td>13.700000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.700000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>137</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>243.400000</td>\n",
+       "      <td>114</td>\n",
+       "      <td>41.380000</td>\n",
+       "      <td>121.200000</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.300000</td>\n",
+       "      <td>162.600000</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.320000</td>\n",
+       "      <td>12.200000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.290000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>84</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299.400000</td>\n",
+       "      <td>71</td>\n",
+       "      <td>50.900000</td>\n",
+       "      <td>61.900000</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.260000</td>\n",
+       "      <td>196.900000</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.860000</td>\n",
+       "      <td>6.600000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.780000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>75</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>166.700000</td>\n",
+       "      <td>113</td>\n",
+       "      <td>28.340000</td>\n",
+       "      <td>148.300000</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.610000</td>\n",
+       "      <td>186.900000</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.410000</td>\n",
+       "      <td>10.100000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.730000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  State Account Length Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS            128       415  ...    2.700000              1  False.\n",
+       "1    OH            107       415  ...    3.700000              1  False.\n",
+       "2    NJ            137       415  ...    3.290000              0  False.\n",
+       "3    OH             84       408  ...    1.780000              2  False.\n",
+       "4    OK             75       415  ...    2.730000              3  False.\n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df3 = pd.DataFrame(main_dict)\n",
+    "df3.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "K0pSjfa2X8q4"
+   },
+   "source": [
+    "## Lectura y escritura de ficheros"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "tEswrpyjX8q4"
+   },
+   "outputs": [],
+   "source": [
+    "infile = mainpath + \"/\" + \"customer-churn-model/Customer Churn Model.txt\"\n",
+    "outfile = mainpath + \"/\" + \"customer-churn-model/Table Customer Churn Model.txt\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "zIZxH1N1X8q6"
+   },
+   "outputs": [],
+   "source": [
+    "with open(infile, \"r\") as infile1:\n",
+    "    with open(outfile, \"w\") as outfile1:\n",
+    "        for line in infile1:\n",
+    "            fields = line.strip().split(\",\")\n",
+    "            outfile1.write(\"\\t\".join(fields))\n",
+    "            outfile1.write(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 326
+    },
+    "colab_type": "code",
+    "id": "5pmQO3dFX8q8",
+    "outputId": "96d01623-0b80-4a16-e7c8-21a3768e369f"
+   },
+   "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>State</th>\n",
+       "      <th>Account Length</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KS</td>\n",
+       "      <td>128</td>\n",
+       "      <td>415</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>25</td>\n",
+       "      <td>265.1</td>\n",
+       "      <td>110</td>\n",
+       "      <td>45.07</td>\n",
+       "      <td>197.4</td>\n",
+       "      <td>99</td>\n",
+       "      <td>16.78</td>\n",
+       "      <td>244.7</td>\n",
+       "      <td>91</td>\n",
+       "      <td>11.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>107</td>\n",
+       "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>26</td>\n",
+       "      <td>161.6</td>\n",
+       "      <td>123</td>\n",
+       "      <td>27.47</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.45</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>137</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>243.4</td>\n",
+       "      <td>114</td>\n",
+       "      <td>41.38</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>84</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299.4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>50.90</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.86</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>75</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>166.7</td>\n",
+       "      <td>113</td>\n",
+       "      <td>28.34</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.41</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS             128        415  ...        2.70              1  False.\n",
+       "1    OH             107        415  ...        3.70              1  False.\n",
+       "2    NJ             137        415  ...        3.29              0  False.\n",
+       "3    OH              84        408  ...        1.78              2  False.\n",
+       "4    OK              75        415  ...        2.73              3  False.\n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df4 = pd.read_csv(outfile, sep = \"\\t\")\n",
+    "df4.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "kMmXP2lhX8q9"
+   },
+   "source": [
+    "# Leer datos desde una URL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "KqJtfLuPX8q-"
+   },
+   "outputs": [],
+   "source": [
+    "medals_url = \"http://winterolympicsmedals.com/medals.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "iC4Q0KHiX8rA"
+   },
+   "outputs": [],
+   "source": [
+    "medals_data = pd.read_csv(medals_url)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "vrrSZxrQX8rB",
+    "outputId": "b1e487d3-dfca-42dd-efb7-3b685a204471"
+   },
+   "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>Year</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Sport</th>\n",
+       "      <th>Discipline</th>\n",
+       "      <th>NOC</th>\n",
+       "      <th>Event</th>\n",
+       "      <th>Event gender</th>\n",
+       "      <th>Medal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1924</td>\n",
+       "      <td>Chamonix</td>\n",
+       "      <td>Skating</td>\n",
+       "      <td>Figure skating</td>\n",
+       "      <td>AUT</td>\n",
+       "      <td>individual</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Silver</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1924</td>\n",
+       "      <td>Chamonix</td>\n",
+       "      <td>Skating</td>\n",
+       "      <td>Figure skating</td>\n",
+       "      <td>AUT</td>\n",
+       "      <td>individual</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Gold</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1924</td>\n",
+       "      <td>Chamonix</td>\n",
+       "      <td>Skating</td>\n",
+       "      <td>Figure skating</td>\n",
+       "      <td>AUT</td>\n",
+       "      <td>pairs</td>\n",
+       "      <td>X</td>\n",
+       "      <td>Gold</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1924</td>\n",
+       "      <td>Chamonix</td>\n",
+       "      <td>Bobsleigh</td>\n",
+       "      <td>Bobsleigh</td>\n",
+       "      <td>BEL</td>\n",
+       "      <td>four-man</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Bronze</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1924</td>\n",
+       "      <td>Chamonix</td>\n",
+       "      <td>Ice Hockey</td>\n",
+       "      <td>Ice Hockey</td>\n",
+       "      <td>CAN</td>\n",
+       "      <td>ice hockey</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Gold</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year      City       Sport  ...       Event Event gender   Medal\n",
+       "0  1924  Chamonix     Skating  ...  individual            M  Silver\n",
+       "1  1924  Chamonix     Skating  ...  individual            W    Gold\n",
+       "2  1924  Chamonix     Skating  ...       pairs            X    Gold\n",
+       "3  1924  Chamonix   Bobsleigh  ...    four-man            M  Bronze\n",
+       "4  1924  Chamonix  Ice Hockey  ...  ice hockey            M    Gold\n",
+       "\n",
+       "[5 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "medals_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "RHofBSM6X8rE"
+   },
+   "source": [
+    "#### Ejercicio de descarga de datos con urllib3\n",
+    "Vamos a hacer un ejemplo usando la librería urllib3 para leer los datos desde una URL externa, procesarlos y convertirlos a un data frame de *python* antes de guardarlos en un CSV local. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "_QtQyIPyX8rE"
+   },
+   "outputs": [],
+   "source": [
+    "def downloadFromURL(url, filename, sep = \",\", delim = \"\\n\", encoding=\"utf-8\", \n",
+    "                   mainpath = \"/content/drive/My Drive/Curso Machine Learning con Python/datasets\"):\n",
+    "    #primero importamos la librería y hacemos la conexión con la web de los datos\n",
+    "    import urllib3\n",
+    "    http = urllib3.PoolManager()\n",
+    "    r = http.request('GET', url)\n",
+    "    print(\"El estado de la respuesta es %d\" %(r.status))\n",
+    "    response = r.data ## CORREGIDO: eliminado un doble decode que daba error\n",
+    "    \n",
+    "    #El objeto reponse contiene un string binario, así que lo convertimos a un string descodificándolo en UTF-8\n",
+    "    str_data = response.decode(encoding)\n",
+    "\n",
+    "    #Dividimos el string en un array de filas, separándolo por intros\n",
+    "    lines = str_data.split(delim)\n",
+    "\n",
+    "    #La primera línea contiene la cabecera, así que la extraemos\n",
+    "    col_names = lines[0].split(sep)\n",
+    "    n_cols = len(col_names)\n",
+    "\n",
+    "    #Generamos un diccionario vacío donde irá la información procesada desde la URL externa\n",
+    "    counter = 0\n",
+    "    main_dict = {}\n",
+    "    for col in col_names:\n",
+    "        main_dict[col] = []\n",
+    "\n",
+    "    #Procesamos fila a fila la información para ir rellenando el diccionario con los datos como hicimos antes\n",
+    "    for line in lines:\n",
+    "        #Nos saltamos la primera línea que es la que contiene la cabecera y ya tenemos procesada\n",
+    "        if(counter > 0):\n",
+    "            #Dividimos cada string por las comas como elemento separador\n",
+    "            values = line.strip().split(sep)\n",
+    "            #Añadimos cada valor a su respectiva columna del diccionario\n",
+    "            for i in range(len(col_names)):\n",
+    "                main_dict[col_names[i]].append(values[i])\n",
+    "        counter += 1\n",
+    "\n",
+    "    print(\"El data set tiene %d filas y %d columnas\"%(counter-1, n_cols))\n",
+    "\n",
+    "    #Convertimos el diccionario procesado a Data Frame y comprobamos que los datos son correctos\n",
+    "    df = pd.DataFrame(main_dict)\n",
+    "    print(df.head())\n",
+    "\n",
+    "    #Elegimos donde guardarlo (en la carpeta athletes es donde tiene más sentido por el contexto del análisis)\n",
+    "    fullpath = os.path.join(mainpath, filename)\n",
+    "\n",
+    "    #Lo guardamos en CSV, en JSON o en Excel según queramos\n",
+    "    df.to_csv(fullpath+\".csv\")\n",
+    "    df.to_json(fullpath+\".json\")\n",
+    "    df.to_excel(fullpath+\".xls\")\n",
+    "    print(\"Los ficheros se han guardado correctamente en: \"+fullpath)\n",
+    "    \n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 411
+    },
+    "colab_type": "code",
+    "id": "t2KjFoyAX8rF",
+    "outputId": "3f84129c-04bd-41e8-fd83-6f51d46b7287"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "El estado de la respuesta es 200\n",
+      "El data set tiene 2312 filas y 8 columnas\n",
+      "   Year      City       Sport  ...       Event Event gender   Medal\n",
+      "0  1924  Chamonix     Skating  ...  individual            M  Silver\n",
+      "1  1924  Chamonix     Skating  ...  individual            W    Gold\n",
+      "2  1924  Chamonix     Skating  ...       pairs            X    Gold\n",
+      "3  1924  Chamonix   Bobsleigh  ...    four-man            M  Bronze\n",
+      "4  1924  Chamonix  Ice Hockey  ...  ice hockey            M    Gold\n",
+      "\n",
+      "[5 rows x 8 columns]\n",
+      "Los ficheros se han guardado correctamente en: /content/drive/My Drive/Curso Machine Learning con Python/datasets/athletes/downloaded_medals\n"
+     ]
+    },
+    {
+     "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>Year</th>\n",
+       "      <th>City</th>\n",
+       "      <th>Sport</th>\n",
+       "      <th>Discipline</th>\n",
+       "      <th>NOC</th>\n",
+       "      <th>Event</th>\n",
+       "      <th>Event gender</th>\n",
+       "      <th>Medal</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1924</td>\n",
+       "      <td>Chamonix</td>\n",
+       "      <td>Skating</td>\n",
+       "      <td>Figure skating</td>\n",
+       "      <td>AUT</td>\n",
+       "      <td>individual</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Silver</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1924</td>\n",
+       "      <td>Chamonix</td>\n",
+       "      <td>Skating</td>\n",
+       "      <td>Figure skating</td>\n",
+       "      <td>AUT</td>\n",
+       "      <td>individual</td>\n",
+       "      <td>W</td>\n",
+       "      <td>Gold</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1924</td>\n",
+       "      <td>Chamonix</td>\n",
+       "      <td>Skating</td>\n",
+       "      <td>Figure skating</td>\n",
+       "      <td>AUT</td>\n",
+       "      <td>pairs</td>\n",
+       "      <td>X</td>\n",
+       "      <td>Gold</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1924</td>\n",
+       "      <td>Chamonix</td>\n",
+       "      <td>Bobsleigh</td>\n",
+       "      <td>Bobsleigh</td>\n",
+       "      <td>BEL</td>\n",
+       "      <td>four-man</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Bronze</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1924</td>\n",
+       "      <td>Chamonix</td>\n",
+       "      <td>Ice Hockey</td>\n",
+       "      <td>Ice Hockey</td>\n",
+       "      <td>CAN</td>\n",
+       "      <td>ice hockey</td>\n",
+       "      <td>M</td>\n",
+       "      <td>Gold</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Year      City       Sport  ...       Event Event gender   Medal\n",
+       "0  1924  Chamonix     Skating  ...  individual            M  Silver\n",
+       "1  1924  Chamonix     Skating  ...  individual            W    Gold\n",
+       "2  1924  Chamonix     Skating  ...       pairs            X    Gold\n",
+       "3  1924  Chamonix   Bobsleigh  ...    four-man            M  Bronze\n",
+       "4  1924  Chamonix  Ice Hockey  ...  ice hockey            M    Gold\n",
+       "\n",
+       "[5 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "medals_df = downloadFromURL(medals_url, \"athletes/downloaded_medals\")\n",
+    "medals_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "uoHukxi_X8rH"
+   },
+   "source": [
+    "## Ficheros XLS y XLSX"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "97Ag6nMmX8rH"
+   },
+   "outputs": [],
+   "source": [
+    "mainpath = \"/content/drive/My Drive/Curso Machine Learning con Python/datasets\"\n",
+    "filename = \"titanic/titanic3.xls\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "nKeAlrTZX8rJ"
+   },
+   "outputs": [],
+   "source": [
+    "titanic2 = pd.read_excel(mainpath + \"/\" + filename, \"titanic3\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "wq8hH1QJX8rK"
+   },
+   "outputs": [],
+   "source": [
+    "filename = \"titanic/titanic3.xlsx\"\n",
+    "titanic3 = pd.read_excel(mainpath + \"/\" + filename, \"titanic3\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "L8mZNJ27X8rL"
+   },
+   "outputs": [],
+   "source": [
+    "titanic3.to_csv(mainpath + \"/titanic/titanic_custom.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "6QrbnM17X8rM"
+   },
+   "outputs": [],
+   "source": [
+    "titanic3.to_excel(mainpath + \"/titanic/titanic_custom.xls\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "FgiMhbvHX8rN"
+   },
+   "outputs": [],
+   "source": [
+    "titanic3.to_json(mainpath + \"/titanic/titanic_custom.json\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "Copia de T1 - 1 - Data Cleaning - Carga de datos.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.5.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/notebooks/T1 - 1 - Data Cleaning - Carga de datos.ipynb b/notebooks/T1 - 1 - Data Cleaning - Carga de datos.ipynb
index 04773574..251c38be 100644
--- a/notebooks/T1 - 1 - Data Cleaning - Carga de datos.ipynb	
+++ b/notebooks/T1 - 1 - Data Cleaning - Carga de datos.ipynb	
@@ -2,15 +2,62 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/Update_T1_1_Data_Cleaning_Carga_de_datos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 122
+    },
+    "colab_type": "code",
+    "id": "DMwj_ddZhbjD",
+    "outputId": "ee612eed-e8bd-4072-d9b9-f7ddf0eda3da"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+      "\n",
+      "Enter your authorization code:\n",
+      "··········\n",
+      "Mounted at /content/drive\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "zUFcR_TkX8qK"
+   },
    "source": [
     "# Carga de datos a través de la función read_csv"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "RBr31uxHX8qM"
+   },
    "outputs": [],
    "source": [
     "import pandas as pd\n",
@@ -19,19 +66,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Y_s6i9FIX8qR"
+   },
    "outputs": [],
    "source": [
-    "mainpath = \"/Users/JuanGabriel/Developer/AnacondaProjects/python-ml-course/datasets\"\n",
+    "mainpath = \"/content/drive/My Drive/Curso Machine Learning con Python/datasets/\"\n",
     "filename = \"titanic/titanic3.csv\"\n",
     "fullpath = os.path.join(mainpath, filename)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "u8CuQabhX8qV"
+   },
    "outputs": [],
    "source": [
     "data = pd.read_csv(fullpath)"
@@ -39,8 +94,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 425
+    },
+    "colab_type": "code",
+    "id": "oN4kxj87X8qY",
+    "outputId": "70f44e14-0dbc-4bb3-b8e7-1fc74771e7c0"
+   },
    "outputs": [
     {
      "data": {
@@ -82,13 +145,13 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "      <td>Allen, Miss. Elisabeth Walton</td>\n",
        "      <td>female</td>\n",
        "      <td>29.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>24160</td>\n",
        "      <td>211.3375</td>\n",
        "      <td>B5</td>\n",
@@ -99,13 +162,13 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "      <td>Allison, Master. Hudson Trevor</td>\n",
        "      <td>male</td>\n",
        "      <td>0.9167</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>113781</td>\n",
        "      <td>151.5500</td>\n",
        "      <td>C22 C26</td>\n",
@@ -116,13 +179,13 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "      <td>Allison, Miss. Helen Loraine</td>\n",
        "      <td>female</td>\n",
        "      <td>2.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>113781</td>\n",
        "      <td>151.5500</td>\n",
        "      <td>C22 C26</td>\n",
@@ -133,13 +196,13 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
        "      <td>male</td>\n",
        "      <td>30.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>113781</td>\n",
        "      <td>151.5500</td>\n",
        "      <td>C22 C26</td>\n",
@@ -150,13 +213,13 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
        "      <td>female</td>\n",
        "      <td>25.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
        "      <td>113781</td>\n",
        "      <td>151.5500</td>\n",
        "      <td>C22 C26</td>\n",
@@ -170,30 +233,20 @@
        "</div>"
       ],
       "text/plain": [
-       "   pclass  survived                                             name     sex  \\\n",
-       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
-       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
-       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
-       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
-       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
-       "\n",
-       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
-       "0  29.0000    0.0    0.0   24160  211.3375       B5        S    2    NaN   \n",
-       "1   0.9167    1.0    2.0  113781  151.5500  C22 C26        S   11    NaN   \n",
-       "2   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
-       "3  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN  135.0   \n",
-       "4  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    NaN   \n",
+       "   pclass  survived  ...   body                        home.dest\n",
+       "0       1         1  ...    NaN                     St Louis, MO\n",
+       "1       1         1  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+       "2       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+       "3       1         0  ...  135.0  Montreal, PQ / Chesterville, ON\n",
+       "4       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
        "\n",
-       "                         home.dest  \n",
-       "0                     St Louis, MO  \n",
-       "1  Montreal, PQ / Chesterville, ON  \n",
-       "2  Montreal, PQ / Chesterville, ON  \n",
-       "3  Montreal, PQ / Chesterville, ON  \n",
-       "4  Montreal, PQ / Chesterville, ON  "
+       "[5 rows x 14 columns]"
       ]
      },
-     "execution_count": 7,
-     "metadata": {},
+     "execution_count": 4,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -203,31 +256,48 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "albOkDT1X8qd"
+   },
    "source": [
     "### Ejemplos de los parámetros de la función read_csv\n",
+    "```\n",
     "read.csv(filepath=\"/Users/JuanGabriel/Developer/AnacondaProjects/python-ml-course/datasets/titanic/titanic3.csv\",\n",
     "        sep = \",\", \n",
     "        dtype={\"ingresos\":np.float64, \"edad\":np.int32}, \n",
     "        header=0,names={\"ingresos\", \"edad\"},\n",
     "        skiprows=12, index_col=None, \n",
     "        skip_blank_lines=False, na_filter=False\n",
-    "        )"
+    "        )\n",
+    "```"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "zNN7tZGkX8qd"
+   },
    "outputs": [],
    "source": [
-    "data2 = pd.read_csv(mainpath + \"/\" + \"customer-churn-model/Customer Churn Model.txt\")"
+    "data2 = pd.read_csv(mainpath + \"/\" + \"customer-churn-model/Customer Churn Model.txt\", sep=\",\") #CUIDADO: ES EL TXT; NO EL CSV"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 326
+    },
+    "colab_type": "code",
+    "id": "yhTDGrdZX8qg",
+    "outputId": "77a81442-9d9d-499a-feb9-748b2e27b98d"
+   },
    "outputs": [
     {
      "data": {
@@ -260,7 +330,7 @@
        "      <th>Day Mins</th>\n",
        "      <th>Day Calls</th>\n",
        "      <th>Day Charge</th>\n",
-       "      <th>...</th>\n",
+       "      <th>Eve Mins</th>\n",
        "      <th>Eve Calls</th>\n",
        "      <th>Eve Charge</th>\n",
        "      <th>Night Mins</th>\n",
@@ -286,7 +356,7 @@
        "      <td>265.1</td>\n",
        "      <td>110</td>\n",
        "      <td>45.07</td>\n",
-       "      <td>...</td>\n",
+       "      <td>197.4</td>\n",
        "      <td>99</td>\n",
        "      <td>16.78</td>\n",
        "      <td>244.7</td>\n",
@@ -310,7 +380,7 @@
        "      <td>161.6</td>\n",
        "      <td>123</td>\n",
        "      <td>27.47</td>\n",
-       "      <td>...</td>\n",
+       "      <td>195.5</td>\n",
        "      <td>103</td>\n",
        "      <td>16.62</td>\n",
        "      <td>254.4</td>\n",
@@ -334,7 +404,7 @@
        "      <td>243.4</td>\n",
        "      <td>114</td>\n",
        "      <td>41.38</td>\n",
-       "      <td>...</td>\n",
+       "      <td>121.2</td>\n",
        "      <td>110</td>\n",
        "      <td>10.30</td>\n",
        "      <td>162.6</td>\n",
@@ -358,7 +428,7 @@
        "      <td>299.4</td>\n",
        "      <td>71</td>\n",
        "      <td>50.90</td>\n",
-       "      <td>...</td>\n",
+       "      <td>61.9</td>\n",
        "      <td>88</td>\n",
        "      <td>5.26</td>\n",
        "      <td>196.9</td>\n",
@@ -382,7 +452,7 @@
        "      <td>166.7</td>\n",
        "      <td>113</td>\n",
        "      <td>28.34</td>\n",
-       "      <td>...</td>\n",
+       "      <td>148.3</td>\n",
        "      <td>122</td>\n",
        "      <td>12.61</td>\n",
        "      <td>186.9</td>\n",
@@ -396,43 +466,23 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
-       "0    KS             128        415  382-4657         no        yes   \n",
-       "1    OH             107        415  371-7191         no        yes   \n",
-       "2    NJ             137        415  358-1921         no         no   \n",
-       "3    OH              84        408  375-9999        yes         no   \n",
-       "4    OK              75        415  330-6626        yes         no   \n",
-       "\n",
-       "   VMail Message  Day Mins  Day Calls  Day Charge   ...    Eve Calls  \\\n",
-       "0             25     265.1        110       45.07   ...           99   \n",
-       "1             26     161.6        123       27.47   ...          103   \n",
-       "2              0     243.4        114       41.38   ...          110   \n",
-       "3              0     299.4         71       50.90   ...           88   \n",
-       "4              0     166.7        113       28.34   ...          122   \n",
-       "\n",
-       "   Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  \\\n",
-       "0       16.78       244.7           91         11.01       10.0           3   \n",
-       "1       16.62       254.4          103         11.45       13.7           3   \n",
-       "2       10.30       162.6          104          7.32       12.2           5   \n",
-       "3        5.26       196.9           89          8.86        6.6           7   \n",
-       "4       12.61       186.9          121          8.41       10.1           3   \n",
-       "\n",
-       "   Intl Charge  CustServ Calls  Churn?  \n",
-       "0         2.70               1  False.  \n",
-       "1         3.70               1  False.  \n",
-       "2         3.29               0  False.  \n",
-       "3         1.78               2  False.  \n",
-       "4         2.73               3  False.  \n",
+       "  State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS             128        415  ...        2.70              1  False.\n",
+       "1    OH             107        415  ...        3.70              1  False.\n",
+       "2    NJ             137        415  ...        3.29              0  False.\n",
+       "3    OH              84        408  ...        1.78              2  False.\n",
+       "4    OK              75        415  ...        2.73              3  False.\n",
        "\n",
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 20,
-     "metadata": {},
+     "execution_count": 19,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -442,8 +492,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 102
+    },
+    "colab_type": "code",
+    "id": "JqEOEFxfX8ql",
+    "outputId": "ebec79ef-929a-4927-dfc5-da61f9e392d3"
+   },
    "outputs": [
     {
      "data": {
@@ -455,8 +513,10 @@
        "       'Intl Charge', 'CustServ Calls', 'Churn?'], dtype=object)"
       ]
      },
-     "execution_count": 21,
-     "metadata": {},
+     "execution_count": 20,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -466,8 +526,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 51
+    },
+    "colab_type": "code",
+    "id": "LyWQSIinX8qp",
+    "outputId": "460c9b1f-f1dd-4cd9-a29a-7a8009b3b3b2"
+   },
    "outputs": [
     {
      "data": {
@@ -476,8 +544,10 @@
        "       'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U'], dtype=object)"
       ]
      },
-     "execution_count": 26,
-     "metadata": {},
+     "execution_count": 22,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -491,15 +561,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "TTZUkFrOX8qr"
+   },
    "source": [
     "# Carga de datos a través de la función open"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "X-L6HB-YX8qs"
+   },
    "outputs": [],
    "source": [
     "data3 = open(mainpath + \"/\" + \"customer-churn-model/Customer Churn Model.txt\",'r')"
@@ -507,8 +584,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "4GD_rhV0X8qv"
+   },
    "outputs": [],
    "source": [
     "cols = data3.readline().strip().split(\",\")\n",
@@ -517,8 +598,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Qf8-2mp8X8qx"
+   },
    "outputs": [],
    "source": [
     "counter = 0\n",
@@ -529,8 +614,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "Dp2exBAvX8qz",
+    "outputId": "c66b11a4-5ba6-488d-df84-d35187a2ff92"
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -547,13 +640,21 @@
     "        main_dict[cols[i]].append(values[i])\n",
     "    counter += 1\n",
     "\n",
-    "print(\"El data set tiene %d filas y %d columnas\"%(counter, n_cols))"
+    "print(\"El data set tiene %d filas y %d columnas\"%(counter-1, n_cols))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 326
+    },
+    "colab_type": "code",
+    "id": "4pPopSNXX8q1",
+    "outputId": "be7496e8-c0b4-4731-9925-c39e9e843502"
+   },
    "outputs": [
     {
      "data": {
@@ -576,189 +677,169 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>State</th>\n",
        "      <th>Account Length</th>\n",
        "      <th>Area Code</th>\n",
-       "      <th>Churn?</th>\n",
-       "      <th>CustServ Calls</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
        "      <th>Day Calls</th>\n",
        "      <th>Day Charge</th>\n",
-       "      <th>Day Mins</th>\n",
+       "      <th>Eve Mins</th>\n",
        "      <th>Eve Calls</th>\n",
        "      <th>Eve Charge</th>\n",
-       "      <th>Eve Mins</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Intl Calls</th>\n",
-       "      <th>Intl Charge</th>\n",
-       "      <th>Intl Mins</th>\n",
+       "      <th>Night Mins</th>\n",
        "      <th>Night Calls</th>\n",
        "      <th>Night Charge</th>\n",
-       "      <th>Night Mins</th>\n",
-       "      <th>Phone</th>\n",
-       "      <th>State</th>\n",
-       "      <th>VMail Message</th>\n",
-       "      <th>VMail Plan</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>KS</td>\n",
        "      <td>128</td>\n",
        "      <td>415</td>\n",
-       "      <td>False.</td>\n",
-       "      <td>1</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>25</td>\n",
+       "      <td>265.100000</td>\n",
        "      <td>110</td>\n",
        "      <td>45.070000</td>\n",
-       "      <td>265.100000</td>\n",
+       "      <td>197.400000</td>\n",
        "      <td>99</td>\n",
        "      <td>16.780000</td>\n",
-       "      <td>197.400000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.700000</td>\n",
-       "      <td>10.000000</td>\n",
+       "      <td>244.700000</td>\n",
        "      <td>91</td>\n",
        "      <td>11.010000</td>\n",
-       "      <td>244.700000</td>\n",
-       "      <td>382-4657</td>\n",
-       "      <td>KS</td>\n",
-       "      <td>25</td>\n",
-       "      <td>yes</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.700000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>OH</td>\n",
        "      <td>107</td>\n",
        "      <td>415</td>\n",
-       "      <td>False.</td>\n",
-       "      <td>1</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>26</td>\n",
+       "      <td>161.600000</td>\n",
        "      <td>123</td>\n",
        "      <td>27.470000</td>\n",
-       "      <td>161.600000</td>\n",
+       "      <td>195.500000</td>\n",
        "      <td>103</td>\n",
        "      <td>16.620000</td>\n",
-       "      <td>195.500000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3.700000</td>\n",
-       "      <td>13.700000</td>\n",
+       "      <td>254.400000</td>\n",
        "      <td>103</td>\n",
        "      <td>11.450000</td>\n",
-       "      <td>254.400000</td>\n",
-       "      <td>371-7191</td>\n",
-       "      <td>OH</td>\n",
-       "      <td>26</td>\n",
-       "      <td>yes</td>\n",
+       "      <td>13.700000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.700000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
        "      <td>137</td>\n",
        "      <td>415</td>\n",
-       "      <td>False.</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
        "      <td>0</td>\n",
+       "      <td>243.400000</td>\n",
        "      <td>114</td>\n",
        "      <td>41.380000</td>\n",
-       "      <td>243.400000</td>\n",
+       "      <td>121.200000</td>\n",
        "      <td>110</td>\n",
        "      <td>10.300000</td>\n",
-       "      <td>121.200000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3.290000</td>\n",
-       "      <td>12.200000</td>\n",
+       "      <td>162.600000</td>\n",
        "      <td>104</td>\n",
        "      <td>7.320000</td>\n",
-       "      <td>162.600000</td>\n",
-       "      <td>358-1921</td>\n",
-       "      <td>NJ</td>\n",
+       "      <td>12.200000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.290000</td>\n",
        "      <td>0</td>\n",
-       "      <td>no</td>\n",
+       "      <td>False.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
+       "      <td>OH</td>\n",
        "      <td>84</td>\n",
        "      <td>408</td>\n",
-       "      <td>False.</td>\n",
-       "      <td>2</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299.400000</td>\n",
        "      <td>71</td>\n",
        "      <td>50.900000</td>\n",
-       "      <td>299.400000</td>\n",
+       "      <td>61.900000</td>\n",
        "      <td>88</td>\n",
        "      <td>5.260000</td>\n",
-       "      <td>61.900000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1.780000</td>\n",
-       "      <td>6.600000</td>\n",
+       "      <td>196.900000</td>\n",
        "      <td>89</td>\n",
        "      <td>8.860000</td>\n",
-       "      <td>196.900000</td>\n",
-       "      <td>375-9999</td>\n",
-       "      <td>OH</td>\n",
-       "      <td>0</td>\n",
-       "      <td>no</td>\n",
+       "      <td>6.600000</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.780000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
+       "      <td>OK</td>\n",
        "      <td>75</td>\n",
        "      <td>415</td>\n",
-       "      <td>False.</td>\n",
-       "      <td>3</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>166.700000</td>\n",
        "      <td>113</td>\n",
        "      <td>28.340000</td>\n",
-       "      <td>166.700000</td>\n",
+       "      <td>148.300000</td>\n",
        "      <td>122</td>\n",
        "      <td>12.610000</td>\n",
-       "      <td>148.300000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.730000</td>\n",
-       "      <td>10.100000</td>\n",
+       "      <td>186.900000</td>\n",
        "      <td>121</td>\n",
        "      <td>8.410000</td>\n",
-       "      <td>186.900000</td>\n",
-       "      <td>330-6626</td>\n",
-       "      <td>OK</td>\n",
-       "      <td>0</td>\n",
-       "      <td>no</td>\n",
+       "      <td>10.100000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.730000</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "  Account Length Area Code  Churn? CustServ Calls Day Calls Day Charge  \\\n",
-       "0            128       415  False.              1       110  45.070000   \n",
-       "1            107       415  False.              1       123  27.470000   \n",
-       "2            137       415  False.              0       114  41.380000   \n",
-       "3             84       408  False.              2        71  50.900000   \n",
-       "4             75       415  False.              3       113  28.340000   \n",
-       "\n",
-       "     Day Mins Eve Calls Eve Charge    Eve Mins    ...     Intl Calls  \\\n",
-       "0  265.100000        99  16.780000  197.400000    ...              3   \n",
-       "1  161.600000       103  16.620000  195.500000    ...              3   \n",
-       "2  243.400000       110  10.300000  121.200000    ...              5   \n",
-       "3  299.400000        88   5.260000   61.900000    ...              7   \n",
-       "4  166.700000       122  12.610000  148.300000    ...              3   \n",
-       "\n",
-       "  Intl Charge  Intl Mins Night Calls Night Charge  Night Mins     Phone State  \\\n",
-       "0    2.700000  10.000000          91    11.010000  244.700000  382-4657    KS   \n",
-       "1    3.700000  13.700000         103    11.450000  254.400000  371-7191    OH   \n",
-       "2    3.290000  12.200000         104     7.320000  162.600000  358-1921    NJ   \n",
-       "3    1.780000   6.600000          89     8.860000  196.900000  375-9999    OH   \n",
-       "4    2.730000  10.100000         121     8.410000  186.900000  330-6626    OK   \n",
-       "\n",
-       "  VMail Message VMail Plan  \n",
-       "0            25        yes  \n",
-       "1            26        yes  \n",
-       "2             0         no  \n",
-       "3             0         no  \n",
-       "4             0         no  \n",
+       "  State Account Length Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS            128       415  ...    2.700000              1  False.\n",
+       "1    OH            107       415  ...    3.700000              1  False.\n",
+       "2    NJ            137       415  ...    3.290000              0  False.\n",
+       "3    OH             84       408  ...    1.780000              2  False.\n",
+       "4    OK             75       415  ...    2.730000              3  False.\n",
        "\n",
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 71,
-     "metadata": {},
+     "execution_count": 8,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -769,25 +850,36 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "K0pSjfa2X8q4"
+   },
    "source": [
     "## Lectura y escritura de ficheros"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "tEswrpyjX8q4"
+   },
    "outputs": [],
    "source": [
     "infile = mainpath + \"/\" + \"customer-churn-model/Customer Churn Model.txt\"\n",
-    "outfile = mainpath + \"/\" + \"customer-churn-model/Tab Customer Churn Model.txt\""
+    "outfile = mainpath + \"/\" + \"customer-churn-model/Table Customer Churn Model.txt\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "zIZxH1N1X8q6"
+   },
    "outputs": [],
    "source": [
     "with open(infile, \"r\") as infile1:\n",
@@ -800,8 +892,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 326
+    },
+    "colab_type": "code",
+    "id": "5pmQO3dFX8q8",
+    "outputId": "96d01623-0b80-4a16-e7c8-21a3768e369f"
+   },
    "outputs": [
     {
      "data": {
@@ -834,7 +934,7 @@
        "      <th>Day Mins</th>\n",
        "      <th>Day Calls</th>\n",
        "      <th>Day Charge</th>\n",
-       "      <th>...</th>\n",
+       "      <th>Eve Mins</th>\n",
        "      <th>Eve Calls</th>\n",
        "      <th>Eve Charge</th>\n",
        "      <th>Night Mins</th>\n",
@@ -860,7 +960,7 @@
        "      <td>265.1</td>\n",
        "      <td>110</td>\n",
        "      <td>45.07</td>\n",
-       "      <td>...</td>\n",
+       "      <td>197.4</td>\n",
        "      <td>99</td>\n",
        "      <td>16.78</td>\n",
        "      <td>244.7</td>\n",
@@ -884,7 +984,7 @@
        "      <td>161.6</td>\n",
        "      <td>123</td>\n",
        "      <td>27.47</td>\n",
-       "      <td>...</td>\n",
+       "      <td>195.5</td>\n",
        "      <td>103</td>\n",
        "      <td>16.62</td>\n",
        "      <td>254.4</td>\n",
@@ -908,7 +1008,7 @@
        "      <td>243.4</td>\n",
        "      <td>114</td>\n",
        "      <td>41.38</td>\n",
-       "      <td>...</td>\n",
+       "      <td>121.2</td>\n",
        "      <td>110</td>\n",
        "      <td>10.30</td>\n",
        "      <td>162.6</td>\n",
@@ -932,7 +1032,7 @@
        "      <td>299.4</td>\n",
        "      <td>71</td>\n",
        "      <td>50.90</td>\n",
-       "      <td>...</td>\n",
+       "      <td>61.9</td>\n",
        "      <td>88</td>\n",
        "      <td>5.26</td>\n",
        "      <td>196.9</td>\n",
@@ -956,7 +1056,7 @@
        "      <td>166.7</td>\n",
        "      <td>113</td>\n",
        "      <td>28.34</td>\n",
-       "      <td>...</td>\n",
+       "      <td>148.3</td>\n",
        "      <td>122</td>\n",
        "      <td>12.61</td>\n",
        "      <td>186.9</td>\n",
@@ -970,43 +1070,23 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
-       "0    KS             128        415  382-4657         no        yes   \n",
-       "1    OH             107        415  371-7191         no        yes   \n",
-       "2    NJ             137        415  358-1921         no         no   \n",
-       "3    OH              84        408  375-9999        yes         no   \n",
-       "4    OK              75        415  330-6626        yes         no   \n",
-       "\n",
-       "   VMail Message  Day Mins  Day Calls  Day Charge   ...    Eve Calls  \\\n",
-       "0             25     265.1        110       45.07   ...           99   \n",
-       "1             26     161.6        123       27.47   ...          103   \n",
-       "2              0     243.4        114       41.38   ...          110   \n",
-       "3              0     299.4         71       50.90   ...           88   \n",
-       "4              0     166.7        113       28.34   ...          122   \n",
-       "\n",
-       "   Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  \\\n",
-       "0       16.78       244.7           91         11.01       10.0           3   \n",
-       "1       16.62       254.4          103         11.45       13.7           3   \n",
-       "2       10.30       162.6          104          7.32       12.2           5   \n",
-       "3        5.26       196.9           89          8.86        6.6           7   \n",
-       "4       12.61       186.9          121          8.41       10.1           3   \n",
-       "\n",
-       "   Intl Charge  CustServ Calls  Churn?  \n",
-       "0         2.70               1  False.  \n",
-       "1         3.70               1  False.  \n",
-       "2         3.29               0  False.  \n",
-       "3         1.78               2  False.  \n",
-       "4         2.73               3  False.  \n",
+       "  State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS             128        415  ...        2.70              1  False.\n",
+       "1    OH             107        415  ...        3.70              1  False.\n",
+       "2    NJ             137        415  ...        3.29              0  False.\n",
+       "3    OH              84        408  ...        1.78              2  False.\n",
+       "4    OK              75        415  ...        2.73              3  False.\n",
        "\n",
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 82,
-     "metadata": {},
+     "execution_count": 12,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -1017,15 +1097,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "kMmXP2lhX8q9"
+   },
    "source": [
     "# Leer datos desde una URL"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "KqJtfLuPX8q-"
+   },
    "outputs": [],
    "source": [
     "medals_url = \"http://winterolympicsmedals.com/medals.csv\""
@@ -1033,8 +1120,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "iC4Q0KHiX8rA"
+   },
    "outputs": [],
    "source": [
     "medals_data = pd.read_csv(medals_url)"
@@ -1042,8 +1133,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "vrrSZxrQX8rB",
+    "outputId": "b1e487d3-dfca-42dd-efb7-3b685a204471"
+   },
    "outputs": [
     {
      "data": {
@@ -1137,23 +1236,20 @@
        "</div>"
       ],
       "text/plain": [
-       "   Year      City       Sport      Discipline  NOC       Event Event gender  \\\n",
-       "0  1924  Chamonix     Skating  Figure skating  AUT  individual            M   \n",
-       "1  1924  Chamonix     Skating  Figure skating  AUT  individual            W   \n",
-       "2  1924  Chamonix     Skating  Figure skating  AUT       pairs            X   \n",
-       "3  1924  Chamonix   Bobsleigh       Bobsleigh  BEL    four-man            M   \n",
-       "4  1924  Chamonix  Ice Hockey      Ice Hockey  CAN  ice hockey            M   \n",
+       "   Year      City       Sport  ...       Event Event gender   Medal\n",
+       "0  1924  Chamonix     Skating  ...  individual            M  Silver\n",
+       "1  1924  Chamonix     Skating  ...  individual            W    Gold\n",
+       "2  1924  Chamonix     Skating  ...       pairs            X    Gold\n",
+       "3  1924  Chamonix   Bobsleigh  ...    four-man            M  Bronze\n",
+       "4  1924  Chamonix  Ice Hockey  ...  ice hockey            M    Gold\n",
        "\n",
-       "    Medal  \n",
-       "0  Silver  \n",
-       "1    Gold  \n",
-       "2    Gold  \n",
-       "3  Bronze  \n",
-       "4    Gold  "
+       "[5 rows x 8 columns]"
       ]
      },
-     "execution_count": 85,
-     "metadata": {},
+     "execution_count": 41,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -1163,7 +1259,10 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "RHofBSM6X8rE"
+   },
    "source": [
     "#### Ejercicio de descarga de datos con urllib3\n",
     "Vamos a hacer un ejemplo usando la librería urllib3 para leer los datos desde una URL externa, procesarlos y convertirlos a un data frame de *python* antes de guardarlos en un CSV local. "
@@ -1171,12 +1270,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "_QtQyIPyX8rE"
+   },
    "outputs": [],
    "source": [
     "def downloadFromURL(url, filename, sep = \",\", delim = \"\\n\", encoding=\"utf-8\", \n",
-    "                   mainpath = \"/Users/JuanGabriel/Developer/AnacondaProjects/python-ml-course/datasets\"):\n",
+    "                   mainpath = \"/content/drive/My Drive/Curso Machine Learning con Python/datasets\"):\n",
     "    #primero importamos la librería y hacemos la conexión con la web de los datos\n",
     "    import urllib3\n",
     "    http = urllib3.PoolManager()\n",
@@ -1211,7 +1314,7 @@
     "                main_dict[col_names[i]].append(values[i])\n",
     "        counter += 1\n",
     "\n",
-    "    print(\"El data set tiene %d filas y %d columnas\"%(counter, n_cols))\n",
+    "    print(\"El data set tiene %d filas y %d columnas\"%(counter-1, n_cols))\n",
     "\n",
     "    #Convertimos el diccionario procesado a Data Frame y comprobamos que los datos son correctos\n",
     "    df = pd.DataFrame(main_dict)\n",
@@ -1231,8 +1334,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 411
+    },
+    "colab_type": "code",
+    "id": "t2KjFoyAX8rF",
+    "outputId": "3f84129c-04bd-41e8-fd83-6f51d46b7287"
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1240,20 +1351,15 @@
      "text": [
       "El estado de la respuesta es 200\n",
       "El data set tiene 2312 filas y 8 columnas\n",
-      "       City      Discipline       Event Event gender   Medal  NOC       Sport  \\\n",
-      "0  Chamonix  Figure skating  individual            M  Silver  AUT     Skating   \n",
-      "1  Chamonix  Figure skating  individual            W    Gold  AUT     Skating   \n",
-      "2  Chamonix  Figure skating       pairs            X    Gold  AUT     Skating   \n",
-      "3  Chamonix       Bobsleigh    four-man            M  Bronze  BEL   Bobsleigh   \n",
-      "4  Chamonix      Ice Hockey  ice hockey            M    Gold  CAN  Ice Hockey   \n",
+      "   Year      City       Sport  ...       Event Event gender   Medal\n",
+      "0  1924  Chamonix     Skating  ...  individual            M  Silver\n",
+      "1  1924  Chamonix     Skating  ...  individual            W    Gold\n",
+      "2  1924  Chamonix     Skating  ...       pairs            X    Gold\n",
+      "3  1924  Chamonix   Bobsleigh  ...    four-man            M  Bronze\n",
+      "4  1924  Chamonix  Ice Hockey  ...  ice hockey            M    Gold\n",
       "\n",
-      "   Year  \n",
-      "0  1924  \n",
-      "1  1924  \n",
-      "2  1924  \n",
-      "3  1924  \n",
-      "4  1924  \n",
-      "Los ficheros se han guardado correctamente en: /Users/JuanGabriel/Developer/AnacondaProjects/python-ml-course/datasets/athletes/downloaded_medals\n"
+      "[5 rows x 8 columns]\n",
+      "Los ficheros se han guardado correctamente en: /content/drive/My Drive/Curso Machine Learning con Python/datasets/athletes/downloaded_medals\n"
      ]
     },
     {
@@ -1277,94 +1383,91 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>Year</th>\n",
        "      <th>City</th>\n",
+       "      <th>Sport</th>\n",
        "      <th>Discipline</th>\n",
+       "      <th>NOC</th>\n",
        "      <th>Event</th>\n",
        "      <th>Event gender</th>\n",
        "      <th>Medal</th>\n",
-       "      <th>NOC</th>\n",
-       "      <th>Sport</th>\n",
-       "      <th>Year</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
+       "      <td>1924</td>\n",
        "      <td>Chamonix</td>\n",
+       "      <td>Skating</td>\n",
        "      <td>Figure skating</td>\n",
+       "      <td>AUT</td>\n",
        "      <td>individual</td>\n",
        "      <td>M</td>\n",
        "      <td>Silver</td>\n",
-       "      <td>AUT</td>\n",
-       "      <td>Skating</td>\n",
-       "      <td>1924</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>1924</td>\n",
        "      <td>Chamonix</td>\n",
+       "      <td>Skating</td>\n",
        "      <td>Figure skating</td>\n",
+       "      <td>AUT</td>\n",
        "      <td>individual</td>\n",
        "      <td>W</td>\n",
        "      <td>Gold</td>\n",
-       "      <td>AUT</td>\n",
-       "      <td>Skating</td>\n",
-       "      <td>1924</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
+       "      <td>1924</td>\n",
        "      <td>Chamonix</td>\n",
+       "      <td>Skating</td>\n",
        "      <td>Figure skating</td>\n",
+       "      <td>AUT</td>\n",
        "      <td>pairs</td>\n",
        "      <td>X</td>\n",
        "      <td>Gold</td>\n",
-       "      <td>AUT</td>\n",
-       "      <td>Skating</td>\n",
-       "      <td>1924</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
+       "      <td>1924</td>\n",
        "      <td>Chamonix</td>\n",
        "      <td>Bobsleigh</td>\n",
+       "      <td>Bobsleigh</td>\n",
+       "      <td>BEL</td>\n",
        "      <td>four-man</td>\n",
        "      <td>M</td>\n",
        "      <td>Bronze</td>\n",
-       "      <td>BEL</td>\n",
-       "      <td>Bobsleigh</td>\n",
-       "      <td>1924</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
+       "      <td>1924</td>\n",
        "      <td>Chamonix</td>\n",
        "      <td>Ice Hockey</td>\n",
+       "      <td>Ice Hockey</td>\n",
+       "      <td>CAN</td>\n",
        "      <td>ice hockey</td>\n",
        "      <td>M</td>\n",
        "      <td>Gold</td>\n",
-       "      <td>CAN</td>\n",
-       "      <td>Ice Hockey</td>\n",
-       "      <td>1924</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "       City      Discipline       Event Event gender   Medal  NOC       Sport  \\\n",
-       "0  Chamonix  Figure skating  individual            M  Silver  AUT     Skating   \n",
-       "1  Chamonix  Figure skating  individual            W    Gold  AUT     Skating   \n",
-       "2  Chamonix  Figure skating       pairs            X    Gold  AUT     Skating   \n",
-       "3  Chamonix       Bobsleigh    four-man            M  Bronze  BEL   Bobsleigh   \n",
-       "4  Chamonix      Ice Hockey  ice hockey            M    Gold  CAN  Ice Hockey   \n",
+       "   Year      City       Sport  ...       Event Event gender   Medal\n",
+       "0  1924  Chamonix     Skating  ...  individual            M  Silver\n",
+       "1  1924  Chamonix     Skating  ...  individual            W    Gold\n",
+       "2  1924  Chamonix     Skating  ...       pairs            X    Gold\n",
+       "3  1924  Chamonix   Bobsleigh  ...    four-man            M  Bronze\n",
+       "4  1924  Chamonix  Ice Hockey  ...  ice hockey            M    Gold\n",
        "\n",
-       "   Year  \n",
-       "0  1924  \n",
-       "1  1924  \n",
-       "2  1924  \n",
-       "3  1924  \n",
-       "4  1924  "
+       "[5 rows x 8 columns]"
       ]
      },
-     "execution_count": 171,
-     "metadata": {},
+     "execution_count": 6,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -1375,25 +1478,36 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "uoHukxi_X8rH"
+   },
    "source": [
     "## Ficheros XLS y XLSX"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "97Ag6nMmX8rH"
+   },
    "outputs": [],
    "source": [
-    "mainpath = \"/Users/JuanGabriel/Developer/AnacondaProjects/python-ml-course/datasets\"\n",
+    "mainpath = \"/content/drive/My Drive/Curso Machine Learning con Python/datasets\"\n",
     "filename = \"titanic/titanic3.xls\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "nKeAlrTZX8rJ"
+   },
    "outputs": [],
    "source": [
     "titanic2 = pd.read_excel(mainpath + \"/\" + filename, \"titanic3\")"
@@ -1401,17 +1515,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "wq8hH1QJX8rK"
+   },
    "outputs": [],
    "source": [
+    "filename = \"titanic/titanic3.xlsx\"\n",
     "titanic3 = pd.read_excel(mainpath + \"/\" + filename, \"titanic3\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "L8mZNJ27X8rL"
+   },
    "outputs": [],
    "source": [
     "titanic3.to_csv(mainpath + \"/titanic/titanic_custom.csv\")"
@@ -1419,8 +1542,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "6QrbnM17X8rM"
+   },
    "outputs": [],
    "source": [
     "titanic3.to_excel(mainpath + \"/titanic/titanic_custom.xls\")"
@@ -1428,22 +1555,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "FgiMhbvHX8rN"
+   },
    "outputs": [],
    "source": [
     "titanic3.to_json(mainpath + \"/titanic/titanic_custom.json\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "Copia de T1 - 1 - Data Cleaning - Carga de datos.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -1463,5 +1593,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 1
 }
diff --git "a/notebooks/T1 - 2 - Data Cleaning - An\303\241lisis Preliminar de los Datos-Colab.ipynb" "b/notebooks/T1 - 2 - Data Cleaning - An\303\241lisis Preliminar de los Datos-Colab.ipynb"
new file mode 100644
index 00000000..706dde66
--- /dev/null
+++ "b/notebooks/T1 - 2 - Data Cleaning - An\303\241lisis Preliminar de los Datos-Colab.ipynb"	
@@ -0,0 +1,2623 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/Collab---v-3.8/notebooks/T1%20-%202%20-%20Data%20Cleaning%20-%20Análisis%20Preliminar%20de%20los%20Datos-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 122
+    },
+    "colab_type": "code",
+    "id": "KuWKn2xNsus2",
+    "outputId": "08abfa93-52fe-4af1-dfd8-a46bc2ed5852"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+      "\n",
+      "Enter your authorization code:\n",
+      "··········\n",
+      "Mounted at /content/drive\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "yENsfUcZstT8"
+   },
+   "source": [
+    "# Resumen de los datos: dimensiones y estructuras"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "aMpKkWS_stT9"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "32SJ41cgstUC"
+   },
+   "outputs": [],
+   "source": [
+    "mainpath = \"/content/drive/My Drive/Curso Machine Learning con Python/datasets\"\n",
+    "filename = \"titanic/titanic3.csv\"\n",
+    "fullpath = os.path.join(mainpath, filename)\n",
+    "\n",
+    "urldata = \"https://raw.githubusercontent.com/joanby/python-ml-course/master/datasets/titanic/titanic3.csv\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "VHNXSK_dstUH"
+   },
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(urldata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 359
+    },
+    "colab_type": "code",
+    "id": "ScRUdcNvstUL",
+    "outputId": "48db5b76-0feb-42f7-b251-e860a559a1e9"
+   },
+   "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>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Anderson, Mr. Harry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>48.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19952</td>\n",
+       "      <td>26.5500</td>\n",
+       "      <td>E12</td>\n",
+       "      <td>S</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>New York, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
+       "      <td>female</td>\n",
+       "      <td>63.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13502</td>\n",
+       "      <td>77.9583</td>\n",
+       "      <td>D7</td>\n",
+       "      <td>S</td>\n",
+       "      <td>10</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Hudson, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Andrews, Mr. Thomas Jr</td>\n",
+       "      <td>male</td>\n",
+       "      <td>39.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>112050</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>A36</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Belfast, NI</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Appleton, Mrs. Edward Dale (Charlotte Lamson)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>53.0000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11769</td>\n",
+       "      <td>51.4792</td>\n",
+       "      <td>C101</td>\n",
+       "      <td>S</td>\n",
+       "      <td>D</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Bayside, Queens, NY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Artagaveytia, Mr. Ramon</td>\n",
+       "      <td>male</td>\n",
+       "      <td>71.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17609</td>\n",
+       "      <td>49.5042</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>Montevideo, Uruguay</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pclass  survived  ...   body                        home.dest\n",
+       "0       1         1  ...    NaN                     St Louis, MO\n",
+       "1       1         1  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+       "2       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+       "3       1         0  ...  135.0  Montreal, PQ / Chesterville, ON\n",
+       "4       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+       "5       1         1  ...    NaN                     New York, NY\n",
+       "6       1         1  ...    NaN                       Hudson, NY\n",
+       "7       1         0  ...    NaN                      Belfast, NI\n",
+       "8       1         1  ...    NaN              Bayside, Queens, NY\n",
+       "9       1         0  ...   22.0              Montevideo, Uruguay\n",
+       "\n",
+       "[10 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 297
+    },
+    "colab_type": "code",
+    "id": "bmZKLfVFstUO",
+    "outputId": "76e2ee20-47dd-448c-fa5a-65713c682ba0"
+   },
+   "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>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Youseff, Mr. Gerious</td>\n",
+       "      <td>male</td>\n",
+       "      <td>45.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2628</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>312.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1302</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yousif, Mr. Wazli</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2647</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1303</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yousseff, Mr. Gerious</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2627</td>\n",
+       "      <td>14.4583</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived                       name  ... boat   body  home.dest\n",
+       "1301       3         0       Youseff, Mr. Gerious  ...  NaN  312.0        NaN\n",
+       "1302       3         0          Yousif, Mr. Wazli  ...  NaN    NaN        NaN\n",
+       "1303       3         0      Yousseff, Mr. Gerious  ...  NaN    NaN        NaN\n",
+       "1304       3         0       Zabour, Miss. Hileni  ...  NaN  328.0        NaN\n",
+       "1305       3         0      Zabour, Miss. Thamine  ...  NaN    NaN        NaN\n",
+       "1306       3         0  Zakarian, Mr. Mapriededer  ...  NaN  304.0        NaN\n",
+       "1307       3         0        Zakarian, Mr. Ortin  ...  NaN    NaN        NaN\n",
+       "1308       3         0         Zimmerman, Mr. Leo  ...  NaN    NaN        NaN\n",
+       "\n",
+       "[8 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.tail(8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "AP0t_Q3vstUR",
+    "outputId": "58457385-8d5b-44af-a351-823157941b77"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1309, 14)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 68
+    },
+    "colab_type": "code",
+    "id": "TLencTV4stUT",
+    "outputId": "d7e3ea14-be61-4690-d270-d43257b7371b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch',\n",
+       "       'ticket', 'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "w6kdlahLstUV"
+   },
+   "source": [
+    "Vamos a hacer un resumen de los estadísticos básicos de las variables numéricas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 297
+    },
+    "colab_type": "code",
+    "id": "mdrPIOXfstUW",
+    "outputId": "52ae8db0-21b3-4589-860b-701959a4a0e6"
+   },
+   "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>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>body</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1309.000000</td>\n",
+       "      <td>1309.000000</td>\n",
+       "      <td>1046.000000</td>\n",
+       "      <td>1309.000000</td>\n",
+       "      <td>1309.000000</td>\n",
+       "      <td>1308.000000</td>\n",
+       "      <td>121.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2.294882</td>\n",
+       "      <td>0.381971</td>\n",
+       "      <td>29.881135</td>\n",
+       "      <td>0.498854</td>\n",
+       "      <td>0.385027</td>\n",
+       "      <td>33.295479</td>\n",
+       "      <td>160.809917</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.837836</td>\n",
+       "      <td>0.486055</td>\n",
+       "      <td>14.413500</td>\n",
+       "      <td>1.041658</td>\n",
+       "      <td>0.865560</td>\n",
+       "      <td>51.758668</td>\n",
+       "      <td>97.696922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.166700</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.895800</td>\n",
+       "      <td>72.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.454200</td>\n",
+       "      <td>155.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.275000</td>\n",
+       "      <td>256.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "      <td>328.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            pclass     survived  ...         fare        body\n",
+       "count  1309.000000  1309.000000  ...  1308.000000  121.000000\n",
+       "mean      2.294882     0.381971  ...    33.295479  160.809917\n",
+       "std       0.837836     0.486055  ...    51.758668   97.696922\n",
+       "min       1.000000     0.000000  ...     0.000000    1.000000\n",
+       "25%       2.000000     0.000000  ...     7.895800   72.000000\n",
+       "50%       3.000000     0.000000  ...    14.454200  155.000000\n",
+       "75%       3.000000     1.000000  ...    31.275000  256.000000\n",
+       "max       3.000000     1.000000  ...   512.329200  328.000000\n",
+       "\n",
+       "[8 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 272
+    },
+    "colab_type": "code",
+    "id": "PqJiBFt-stUX",
+    "outputId": "2df150a7-c5b7-457a-8aa1-5596908ba94e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pclass         int64\n",
+       "survived       int64\n",
+       "name          object\n",
+       "sex           object\n",
+       "age          float64\n",
+       "sibsp          int64\n",
+       "parch          int64\n",
+       "ticket        object\n",
+       "fare         float64\n",
+       "cabin         object\n",
+       "embarked      object\n",
+       "boat          object\n",
+       "body         float64\n",
+       "home.dest     object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "dUyJy_0wstUZ"
+   },
+   "source": [
+    "# Missing values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 221
+    },
+    "colab_type": "code",
+    "id": "B_8ZxrsdstUa",
+    "outputId": "e650b150-5cc5-48e2-d9d1-ad07ed67befc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        True\n",
+       "1        True\n",
+       "2        True\n",
+       "3       False\n",
+       "4        True\n",
+       "        ...  \n",
+       "1304    False\n",
+       "1305     True\n",
+       "1306    False\n",
+       "1307     True\n",
+       "1308     True\n",
+       "Name: body, Length: 1309, dtype: bool"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.isnull(data[\"body\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 221
+    },
+    "colab_type": "code",
+    "id": "86tfBnHnstUb",
+    "outputId": "f690dc35-fd64-4795-ed25-f26cd44b9be0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       False\n",
+       "1       False\n",
+       "2       False\n",
+       "3        True\n",
+       "4       False\n",
+       "        ...  \n",
+       "1304     True\n",
+       "1305    False\n",
+       "1306     True\n",
+       "1307    False\n",
+       "1308    False\n",
+       "Name: body, Length: 1309, dtype: bool"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.notnull(data[\"body\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "CBd8kJZZstUd",
+    "outputId": "7a014a8e-3059-4715-a33a-070d34391cba"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1188"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.isnull(data[\"body\"]).values.ravel().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "xeqolLYRstUe",
+    "outputId": "55cb2272-ca1c-4d8a-89e4-666ae004f488"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "121"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.notnull(data[\"body\"]).values.ravel().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "4phNNBtistUg"
+   },
+   "source": [
+    "Los valores que faltan en un data set pueden venir por dos razones:\n",
+    "* Extracción de los datos\n",
+    "* Recolección de los datos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "x-cgFFVbstUg"
+   },
+   "source": [
+    "#### Borrado de valores que faltan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 419
+    },
+    "colab_type": "code",
+    "id": "YCS7PRF-stUg",
+    "outputId": "15345cdb-7bf6-4e31-c022-eb4a39605ae4"
+   },
+   "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>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived  ...   body                        home.dest\n",
+       "0          1         1  ...    NaN                     St Louis, MO\n",
+       "1          1         1  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+       "2          1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+       "3          1         0  ...  135.0  Montreal, PQ / Chesterville, ON\n",
+       "4          1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+       "...      ...       ...  ...    ...                              ...\n",
+       "1304       3         0  ...  328.0                              NaN\n",
+       "1305       3         0  ...    NaN                              NaN\n",
+       "1306       3         0  ...  304.0                              NaN\n",
+       "1307       3         0  ...    NaN                              NaN\n",
+       "1308       3         0  ...    NaN                              NaN\n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.dropna(axis=0, how=\"all\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "udKUHN8vstUi"
+   },
+   "outputs": [],
+   "source": [
+    "data2 = data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 48
+    },
+    "colab_type": "code",
+    "id": "8y71Q4sestUj",
+    "outputId": "087cf1ad-88a1-45f6-a343-d011750f803f"
+   },
+   "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>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [pclass, survived, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked, boat, body, home.dest]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data2.dropna(axis=0, how=\"any\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "bhTm7KoastUk"
+   },
+   "source": [
+    "#### Cómputo de los valores fantantes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "hz_2oSgastUk"
+   },
+   "outputs": [],
+   "source": [
+    "data3 = data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 419
+    },
+    "colab_type": "code",
+    "id": "qWVJbqDFstUm",
+    "outputId": "5c4b0162-a64d-48ee-df66-94be0d1a9e42"
+   },
+   "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>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>male</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>male</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>C</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>0</td>\n",
+       "      <td>S</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 14 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived  ...   body                        home.dest\n",
+       "0          1         1  ...    0.0                     St Louis, MO\n",
+       "1          1         1  ...    0.0  Montreal, PQ / Chesterville, ON\n",
+       "2          1         0  ...    0.0  Montreal, PQ / Chesterville, ON\n",
+       "3          1         0  ...  135.0  Montreal, PQ / Chesterville, ON\n",
+       "4          1         0  ...    0.0  Montreal, PQ / Chesterville, ON\n",
+       "...      ...       ...  ...    ...                              ...\n",
+       "1304       3         0  ...  328.0                                0\n",
+       "1305       3         0  ...    0.0                                0\n",
+       "1306       3         0  ...  304.0                                0\n",
+       "1307       3         0  ...    0.0                                0\n",
+       "1308       3         0  ...    0.0                                0\n",
+       "\n",
+       "[1309 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data3.fillna(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "dNmxfKrVstUn"
+   },
+   "outputs": [],
+   "source": [
+    "data4 = data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Fuvj3a5mstUp"
+   },
+   "outputs": [],
+   "source": [
+    "data4= data4.fillna(\"Desconocido\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "zjtGoMDlstUq"
+   },
+   "outputs": [],
+   "source": [
+    "data5 = data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "5xVfZZjqstUr",
+    "outputId": "97b5a74b-d38f-4c49-8f5b-0fee381e20a1"
+   },
+   "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>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>female</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>female</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>male</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pclass  survived  ...   body                        home.dest\n",
+       "0       1         1  ...    0.0                     St Louis, MO\n",
+       "1       1         1  ...    0.0  Montreal, PQ / Chesterville, ON\n",
+       "2       1         0  ...    0.0  Montreal, PQ / Chesterville, ON\n",
+       "3       1         0  ...  135.0  Montreal, PQ / Chesterville, ON\n",
+       "4       1         0  ...    0.0  Montreal, PQ / Chesterville, ON\n",
+       "\n",
+       "[5 rows x 14 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data5[\"body\"] = data5[\"body\"].fillna(0)\n",
+    "data5[\"home.dest\"] = data5[\"home.dest\"].fillna(\"Desconocido\")\n",
+    "data5.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "8-k1TYRAstUt",
+    "outputId": "ab51c187-7239-430d-98b3-8e73317e6b6d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "263"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.isnull(data5[\"age\"]).values.ravel().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 221
+    },
+    "colab_type": "code",
+    "id": "W-H6DyvrstUu",
+    "outputId": "8e325f64-7eea-47ae-be29-4e0227e1b8cf"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       29.000000\n",
+       "1        0.916700\n",
+       "2        2.000000\n",
+       "3       30.000000\n",
+       "4       25.000000\n",
+       "          ...    \n",
+       "1304    14.500000\n",
+       "1305    29.881135\n",
+       "1306    26.500000\n",
+       "1307    27.000000\n",
+       "1308    29.000000\n",
+       "Name: age, Length: 1309, dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data5[\"age\"].fillna(data[\"age\"].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "wlWPK6RnstUv",
+    "outputId": "8f88b6b7-65c0-44cd-f54a-d24e831f0343"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data5[\"age\"][1291]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 221
+    },
+    "colab_type": "code",
+    "id": "2dTk5NWLstUw",
+    "outputId": "282c385b-d877-4534-b991-090507dea7ef"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       29.0000\n",
+       "1        0.9167\n",
+       "2        2.0000\n",
+       "3       30.0000\n",
+       "4       25.0000\n",
+       "         ...   \n",
+       "1304    14.5000\n",
+       "1305    14.5000\n",
+       "1306    26.5000\n",
+       "1307    27.0000\n",
+       "1308    29.0000\n",
+       "Name: age, Length: 1309, dtype: float64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data5[\"age\"].fillna(method=\"ffill\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 221
+    },
+    "colab_type": "code",
+    "id": "HheokQ1xstUy",
+    "outputId": "01bb1478-279b-4775-da4d-5c2bfd75f9ee"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0       29.0000\n",
+       "1        0.9167\n",
+       "2        2.0000\n",
+       "3       30.0000\n",
+       "4       25.0000\n",
+       "         ...   \n",
+       "1304    14.5000\n",
+       "1305    26.5000\n",
+       "1306    26.5000\n",
+       "1307    27.0000\n",
+       "1308    29.0000\n",
+       "Name: age, Length: 1309, dtype: float64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data5[\"age\"].fillna(method=\"backfill\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "QbaXaBqLstUz"
+   },
+   "source": [
+    "# Variables dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "xSwVk0FFstUz",
+    "outputId": "9483dc25-1d42-4a36-e94b-70cd5f475107"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    female\n",
+       "1      male\n",
+       "2    female\n",
+       "3      male\n",
+       "4    female\n",
+       "5      male\n",
+       "6    female\n",
+       "7      male\n",
+       "8    female\n",
+       "9      male\n",
+       "Name: sex, dtype: object"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"sex\"].head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "cVI6XnwAstU0"
+   },
+   "outputs": [],
+   "source": [
+    "dummy_sex = pd.get_dummies(data[\"sex\"], prefix=\"sex\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 359
+    },
+    "colab_type": "code",
+    "id": "UACeCYkVstU1",
+    "outputId": "07c6753b-9760-4356-fc91-e14aab5d7ac2"
+   },
+   "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>sex_female</th>\n",
+       "      <th>sex_male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   sex_female  sex_male\n",
+       "0           1         0\n",
+       "1           0         1\n",
+       "2           1         0\n",
+       "3           0         1\n",
+       "4           1         0\n",
+       "5           0         1\n",
+       "6           1         0\n",
+       "7           0         1\n",
+       "8           1         0\n",
+       "9           0         1"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy_sex.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 255
+    },
+    "colab_type": "code",
+    "id": "XfaDOjjPstU2",
+    "outputId": "d06566ca-23c1-46ae-cdf5-63bd07c4e892"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['pclass',\n",
+       " 'survived',\n",
+       " 'name',\n",
+       " 'sex',\n",
+       " 'age',\n",
+       " 'sibsp',\n",
+       " 'parch',\n",
+       " 'ticket',\n",
+       " 'fare',\n",
+       " 'cabin',\n",
+       " 'embarked',\n",
+       " 'boat',\n",
+       " 'body',\n",
+       " 'home.dest']"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "column_name=data.columns.values.tolist()\n",
+    "column_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ZnQJ01axstU3"
+   },
+   "outputs": [],
+   "source": [
+    "data = data.drop([\"sex\"], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "sRnzCHnOstU4"
+   },
+   "outputs": [],
+   "source": [
+    "data = pd.concat([data, dummy_sex], axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "VzRD_sPQstU5"
+   },
+   "outputs": [],
+   "source": [
+    "def createDummies(df, var_name):\n",
+    "    dummy = pd.get_dummies(df[var_name], prefix=var_name)\n",
+    "    df = df.drop(var_name, axis = 1)\n",
+    "    df = pd.concat([df, dummy ], axis = 1)\n",
+    "    return df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 487
+    },
+    "colab_type": "code",
+    "id": "_0Fp6A5DstU6",
+    "outputId": "67b6367f-51c8-4fdc-84fc-8b6f60669a02"
+   },
+   "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>pclass</th>\n",
+       "      <th>survived</th>\n",
+       "      <th>name</th>\n",
+       "      <th>age</th>\n",
+       "      <th>sibsp</th>\n",
+       "      <th>parch</th>\n",
+       "      <th>ticket</th>\n",
+       "      <th>fare</th>\n",
+       "      <th>cabin</th>\n",
+       "      <th>embarked</th>\n",
+       "      <th>boat</th>\n",
+       "      <th>body</th>\n",
+       "      <th>home.dest</th>\n",
+       "      <th>sex_female</th>\n",
+       "      <th>sex_male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24160</td>\n",
+       "      <td>211.3375</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>S</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>St Louis, MO</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Allison, Master. Hudson Trevor</td>\n",
+       "      <td>0.9167</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Miss. Helen Loraine</td>\n",
+       "      <td>2.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+       "      <td>30.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+       "      <td>25.0000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113781</td>\n",
+       "      <td>151.5500</td>\n",
+       "      <td>C22 C26</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Hileni</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>328.0</td>\n",
+       "      <td>Desconocido</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zabour, Miss. Thamine</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2665</td>\n",
+       "      <td>14.4542</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Desconocido</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Mapriededer</td>\n",
+       "      <td>26.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2656</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>Desconocido</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zakarian, Mr. Ortin</td>\n",
+       "      <td>27.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2670</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Desconocido</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Zimmerman, Mr. Leo</td>\n",
+       "      <td>29.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>315082</td>\n",
+       "      <td>7.8750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Desconocido</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 15 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      pclass  survived  ... sex_female  sex_male\n",
+       "0          1         1  ...          1         0\n",
+       "1          1         1  ...          0         1\n",
+       "2          1         0  ...          1         0\n",
+       "3          1         0  ...          0         1\n",
+       "4          1         0  ...          1         0\n",
+       "...      ...       ...  ...        ...       ...\n",
+       "1304       3         0  ...          1         0\n",
+       "1305       3         0  ...          1         0\n",
+       "1306       3         0  ...          0         1\n",
+       "1307       3         0  ...          0         1\n",
+       "1308       3         0  ...          0         1\n",
+       "\n",
+       "[1309 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "createDummies(data3, \"sex\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "T1 - 2 - Data Cleaning - Análisis Preliminar de los Datos.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git "a/notebooks/T1 - 2 - Data Cleaning - An\303\241lisis Preliminar de los Datos.ipynb" "b/notebooks/T1 - 2 - Data Cleaning - An\303\241lisis Preliminar de los Datos.ipynb"
index b87652ef..cb720c86 100644
--- "a/notebooks/T1 - 2 - Data Cleaning - An\303\241lisis Preliminar de los Datos.ipynb"	
+++ "b/notebooks/T1 - 2 - Data Cleaning - An\303\241lisis Preliminar de los Datos.ipynb"	
@@ -1,5713 +1,2623 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Resumen de los datos: dimensiones y estructuras"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import os"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mainpath = \"/Users/JuanGabriel/Developer/AnacondaProjects/python-ml-course/datasets\"\n",
-    "filename = \"titanic/titanic3.csv\"\n",
-    "fullpath = os.path.join(mainpath, filename)\n",
-    "\n",
-    "urldata = \"https://raw.githubusercontent.com/joanby/python-ml-course/master/datasets/titanic/titanic3.csv\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv(urldata)"
-   ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.6.4"
+    },
+    "colab": {
+      "name": "T1 - 2 - Data Cleaning - Análisis Preliminar de los Datos.ipynb",
+      "provenance": [],
+      "toc_visible": true,
+      "include_colab_link": true
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/html": [
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "      <th>boat</th>\n",
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-       "      <th>home.dest</th>\n",
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-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>24160</td>\n",
-       "      <td>211.3375</td>\n",
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-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>St Louis, MO</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Allison, Master. Hudson Trevor</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.9167</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
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-       "      <td>151.5500</td>\n",
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-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Allison, Miss. Helen Loraine</td>\n",
-       "      <td>female</td>\n",
-       "      <td>2.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
-       "      <td>male</td>\n",
-       "      <td>30.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>25.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Anderson, Mr. Harry</td>\n",
-       "      <td>male</td>\n",
-       "      <td>48.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19952</td>\n",
-       "      <td>26.5500</td>\n",
-       "      <td>E12</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
-       "      <td>female</td>\n",
-       "      <td>63.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13502</td>\n",
-       "      <td>77.9583</td>\n",
-       "      <td>D7</td>\n",
-       "      <td>S</td>\n",
-       "      <td>10</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Hudson, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Andrews, Mr. Thomas Jr</td>\n",
-       "      <td>male</td>\n",
-       "      <td>39.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>112050</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>A36</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Belfast, NI</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Appleton, Mrs. Edward Dale (Charlotte Lamson)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>53.0000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11769</td>\n",
-       "      <td>51.4792</td>\n",
-       "      <td>C101</td>\n",
-       "      <td>S</td>\n",
-       "      <td>D</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Bayside, Queens, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Artagaveytia, Mr. Ramon</td>\n",
-       "      <td>male</td>\n",
-       "      <td>71.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17609</td>\n",
-       "      <td>49.5042</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>Montevideo, Uruguay</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/Collab---v-3.8/notebooks/T1%20-%202%20-%20Data%20Cleaning%20-%20An%C3%A1lisis%20Preliminar%20de%20los%20Datos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KuWKn2xNsus2",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 122
+        },
+        "outputId": "08abfa93-52fe-4af1-dfd8-a46bc2ed5852"
+      },
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
       ],
-      "text/plain": [
-       "   pclass  survived                                             name     sex  \\\n",
-       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
-       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
-       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
-       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
-       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
-       "5     1.0       1.0                              Anderson, Mr. Harry    male   \n",
-       "6     1.0       1.0                Andrews, Miss. Kornelia Theodosia  female   \n",
-       "7     1.0       0.0                           Andrews, Mr. Thomas Jr    male   \n",
-       "8     1.0       1.0    Appleton, Mrs. Edward Dale (Charlotte Lamson)  female   \n",
-       "9     1.0       0.0                          Artagaveytia, Mr. Ramon    male   \n",
-       "\n",
-       "       age  sibsp  parch    ticket      fare    cabin embarked boat   body  \\\n",
-       "0  29.0000    0.0    0.0     24160  211.3375       B5        S    2    NaN   \n",
-       "1   0.9167    1.0    2.0    113781  151.5500  C22 C26        S   11    NaN   \n",
-       "2   2.0000    1.0    2.0    113781  151.5500  C22 C26        S  NaN    NaN   \n",
-       "3  30.0000    1.0    2.0    113781  151.5500  C22 C26        S  NaN  135.0   \n",
-       "4  25.0000    1.0    2.0    113781  151.5500  C22 C26        S  NaN    NaN   \n",
-       "5  48.0000    0.0    0.0     19952   26.5500      E12        S    3    NaN   \n",
-       "6  63.0000    1.0    0.0     13502   77.9583       D7        S   10    NaN   \n",
-       "7  39.0000    0.0    0.0    112050    0.0000      A36        S  NaN    NaN   \n",
-       "8  53.0000    2.0    0.0     11769   51.4792     C101        S    D    NaN   \n",
-       "9  71.0000    0.0    0.0  PC 17609   49.5042      NaN        C  NaN   22.0   \n",
-       "\n",
-       "                         home.dest  \n",
-       "0                     St Louis, MO  \n",
-       "1  Montreal, PQ / Chesterville, ON  \n",
-       "2  Montreal, PQ / Chesterville, ON  \n",
-       "3  Montreal, PQ / Chesterville, ON  \n",
-       "4  Montreal, PQ / Chesterville, ON  \n",
-       "5                     New York, NY  \n",
-       "6                       Hudson, NY  \n",
-       "7                      Belfast, NI  \n",
-       "8              Bayside, Queens, NY  \n",
-       "9              Montevideo, Uruguay  "
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+            "\n",
+            "Enter your authorization code:\n",
+            "··········\n",
+            "Mounted at /content/drive\n"
+          ],
+          "name": "stdout"
+        }
       ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yENsfUcZstT8",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Resumen de los datos: dimensiones y estructuras"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aMpKkWS_stT9",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import os"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "32SJ41cgstUC",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "mainpath = \"/content/drive/My Drive/Curso Machine Learning con Python/datasets\"\n",
+        "filename = \"titanic/titanic3.csv\"\n",
+        "fullpath = os.path.join(mainpath, filename)\n",
+        "\n",
+        "urldata = \"https://raw.githubusercontent.com/joanby/python-ml-course/master/datasets/titanic/titanic3.csv\""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VHNXSK_dstUH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data = pd.read_csv(urldata)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/html": [
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-       "      <th>1302</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Yousif, Mr. Wazli</td>\n",
-       "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1303</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Yousseff, Mr. Gerious</td>\n",
-       "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>2627</td>\n",
-       "      <td>14.4583</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1304</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zabour, Miss. Hileni</td>\n",
-       "      <td>female</td>\n",
-       "      <td>14.5</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2665</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>328.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1305</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zabour, Miss. Thamine</td>\n",
-       "      <td>female</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
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-       "      <td>2665</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>NaN</td>\n",
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-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1306</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zakarian, Mr. Mapriededer</td>\n",
-       "      <td>male</td>\n",
-       "      <td>26.5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2656</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>304.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1307</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zakarian, Mr. Ortin</td>\n",
-       "      <td>male</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2670</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1308</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zimmerman, Mr. Leo</td>\n",
-       "      <td>male</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>315082</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1309</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "ScRUdcNvstUL",
+        "colab_type": "code",
+        "colab": {
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+          "height": 359
+        },
+        "outputId": "48db5b76-0feb-42f7-b251-e860a559a1e9"
+      },
+      "source": [
+        "data.head(10)"
       ],
-      "text/plain": [
-       "      pclass  survived                       name     sex   age  sibsp  parch  \\\n",
-       "1302     3.0       0.0          Yousif, Mr. Wazli    male   NaN    0.0    0.0   \n",
-       "1303     3.0       0.0      Yousseff, Mr. Gerious    male   NaN    0.0    0.0   \n",
-       "1304     3.0       0.0       Zabour, Miss. Hileni  female  14.5    1.0    0.0   \n",
-       "1305     3.0       0.0      Zabour, Miss. Thamine  female   NaN    1.0    0.0   \n",
-       "1306     3.0       0.0  Zakarian, Mr. Mapriededer    male  26.5    0.0    0.0   \n",
-       "1307     3.0       0.0        Zakarian, Mr. Ortin    male  27.0    0.0    0.0   \n",
-       "1308     3.0       0.0         Zimmerman, Mr. Leo    male  29.0    0.0    0.0   \n",
-       "1309     NaN       NaN                        NaN     NaN   NaN    NaN    NaN   \n",
-       "\n",
-       "      ticket     fare cabin embarked boat   body home.dest  \n",
-       "1302    2647   7.2250   NaN        C  NaN    NaN       NaN  \n",
-       "1303    2627  14.4583   NaN        C  NaN    NaN       NaN  \n",
-       "1304    2665  14.4542   NaN        C  NaN  328.0       NaN  \n",
-       "1305    2665  14.4542   NaN        C  NaN    NaN       NaN  \n",
-       "1306    2656   7.2250   NaN        C  NaN  304.0       NaN  \n",
-       "1307    2670   7.2250   NaN        C  NaN    NaN       NaN  \n",
-       "1308  315082   7.8750   NaN        S  NaN    NaN       NaN  \n",
-       "1309     NaN      NaN   NaN      NaN  NaN    NaN       NaN  "
+      "execution_count": 5,
+      "outputs": [
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+          "data": {
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+              "      <th>pclass</th>\n",
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+              "      <td>Allison, Miss. Helen Loraine</td>\n",
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+              "      <td>NaN</td>\n",
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+              "      <th>3</th>\n",
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+              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+              "      <td>male</td>\n",
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+              "      <td>NaN</td>\n",
+              "      <td>135.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
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+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>25.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
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+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Anderson, Mr. Harry</td>\n",
+              "      <td>male</td>\n",
+              "      <td>48.0000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>19952</td>\n",
+              "      <td>26.5500</td>\n",
+              "      <td>E12</td>\n",
+              "      <td>S</td>\n",
+              "      <td>3</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>New York, NY</td>\n",
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+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
+              "      <td>female</td>\n",
+              "      <td>63.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>13502</td>\n",
+              "      <td>77.9583</td>\n",
+              "      <td>D7</td>\n",
+              "      <td>S</td>\n",
+              "      <td>10</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Hudson, NY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Andrews, Mr. Thomas Jr</td>\n",
+              "      <td>male</td>\n",
+              "      <td>39.0000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>112050</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>A36</td>\n",
+              "      <td>S</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Belfast, NI</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Appleton, Mrs. Edward Dale (Charlotte Lamson)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>53.0000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>11769</td>\n",
+              "      <td>51.4792</td>\n",
+              "      <td>C101</td>\n",
+              "      <td>S</td>\n",
+              "      <td>D</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Bayside, Queens, NY</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Artagaveytia, Mr. Ramon</td>\n",
+              "      <td>male</td>\n",
+              "      <td>71.0000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>PC 17609</td>\n",
+              "      <td>49.5042</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>Montevideo, Uruguay</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   pclass  survived  ...   body                        home.dest\n",
+              "0       1         1  ...    NaN                     St Louis, MO\n",
+              "1       1         1  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+              "2       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+              "3       1         0  ...  135.0  Montreal, PQ / Chesterville, ON\n",
+              "4       1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+              "5       1         1  ...    NaN                     New York, NY\n",
+              "6       1         1  ...    NaN                       Hudson, NY\n",
+              "7       1         0  ...    NaN                      Belfast, NI\n",
+              "8       1         1  ...    NaN              Bayside, Queens, NY\n",
+              "9       1         0  ...   22.0              Montevideo, Uruguay\n",
+              "\n",
+              "[10 rows x 14 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
       ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.tail(8)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(1310, 14)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "bmZKLfVFstUO",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "76e2ee20-47dd-448c-fa5a-65713c682ba0"
+      },
+      "source": [
+        "data.tail(8)"
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+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
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+              "      <th>1303</th>\n",
+              "      <td>3</td>\n",
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+              "      <th>1304</th>\n",
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+              "      <td>1</td>\n",
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+              "      <td>2665</td>\n",
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+              "      <td>328.0</td>\n",
+              "      <td>NaN</td>\n",
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+              "      <th>1305</th>\n",
+              "      <td>3</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>2665</td>\n",
+              "      <td>14.4542</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1306</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zakarian, Mr. Mapriededer</td>\n",
+              "      <td>male</td>\n",
+              "      <td>26.5</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2656</td>\n",
+              "      <td>7.2250</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>304.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1307</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zakarian, Mr. Ortin</td>\n",
+              "      <td>male</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2670</td>\n",
+              "      <td>7.2250</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1308</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zimmerman, Mr. Leo</td>\n",
+              "      <td>male</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>315082</td>\n",
+              "      <td>7.8750</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      pclass  survived                       name  ... boat   body  home.dest\n",
+              "1301       3         0       Youseff, Mr. Gerious  ...  NaN  312.0        NaN\n",
+              "1302       3         0          Yousif, Mr. Wazli  ...  NaN    NaN        NaN\n",
+              "1303       3         0      Yousseff, Mr. Gerious  ...  NaN    NaN        NaN\n",
+              "1304       3         0       Zabour, Miss. Hileni  ...  NaN  328.0        NaN\n",
+              "1305       3         0      Zabour, Miss. Thamine  ...  NaN    NaN        NaN\n",
+              "1306       3         0  Zakarian, Mr. Mapriededer  ...  NaN  304.0        NaN\n",
+              "1307       3         0        Zakarian, Mr. Ortin  ...  NaN    NaN        NaN\n",
+              "1308       3         0         Zimmerman, Mr. Leo  ...  NaN    NaN        NaN\n",
+              "\n",
+              "[8 rows x 14 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
       ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch',\n",
-       "       'ticket', 'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'],\n",
-       "      dtype=object)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "AP0t_Q3vstUR",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "58457385-8d5b-44af-a351-823157941b77"
+      },
+      "source": [
+        "data.shape"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1309, 14)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
       ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.columns.values"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Vamos a hacer un resumen de los estadísticos básicos de las variables numéricas."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "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>pclass</th>\n",
-       "      <th>survived</th>\n",
-       "      <th>age</th>\n",
-       "      <th>sibsp</th>\n",
-       "      <th>parch</th>\n",
-       "      <th>fare</th>\n",
-       "      <th>body</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>1309.000000</td>\n",
-       "      <td>1309.000000</td>\n",
-       "      <td>1046.000000</td>\n",
-       "      <td>1309.000000</td>\n",
-       "      <td>1309.000000</td>\n",
-       "      <td>1308.000000</td>\n",
-       "      <td>121.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>2.294882</td>\n",
-       "      <td>0.381971</td>\n",
-       "      <td>29.881135</td>\n",
-       "      <td>0.498854</td>\n",
-       "      <td>0.385027</td>\n",
-       "      <td>33.295479</td>\n",
-       "      <td>160.809917</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.837836</td>\n",
-       "      <td>0.486055</td>\n",
-       "      <td>14.413500</td>\n",
-       "      <td>1.041658</td>\n",
-       "      <td>0.865560</td>\n",
-       "      <td>51.758668</td>\n",
-       "      <td>97.696922</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.166700</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>21.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>7.895800</td>\n",
-       "      <td>72.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>28.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>14.454200</td>\n",
-       "      <td>155.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>39.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>31.275000</td>\n",
-       "      <td>256.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>80.000000</td>\n",
-       "      <td>8.000000</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>512.329200</td>\n",
-       "      <td>328.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "TLencTV4stUT",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 68
+        },
+        "outputId": "d7e3ea14-be61-4690-d270-d43257b7371b"
+      },
+      "source": [
+        "data.columns.values"
       ],
-      "text/plain": [
-       "            pclass     survived          age        sibsp        parch  \\\n",
-       "count  1309.000000  1309.000000  1046.000000  1309.000000  1309.000000   \n",
-       "mean      2.294882     0.381971    29.881135     0.498854     0.385027   \n",
-       "std       0.837836     0.486055    14.413500     1.041658     0.865560   \n",
-       "min       1.000000     0.000000     0.166700     0.000000     0.000000   \n",
-       "25%       2.000000     0.000000    21.000000     0.000000     0.000000   \n",
-       "50%       3.000000     0.000000    28.000000     0.000000     0.000000   \n",
-       "75%       3.000000     1.000000    39.000000     1.000000     0.000000   \n",
-       "max       3.000000     1.000000    80.000000     8.000000     9.000000   \n",
-       "\n",
-       "              fare        body  \n",
-       "count  1308.000000  121.000000  \n",
-       "mean     33.295479  160.809917  \n",
-       "std      51.758668   97.696922  \n",
-       "min       0.000000    1.000000  \n",
-       "25%       7.895800   72.000000  \n",
-       "50%      14.454200  155.000000  \n",
-       "75%      31.275000  256.000000  \n",
-       "max     512.329200  328.000000  "
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch',\n",
+              "       'ticket', 'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'],\n",
+              "      dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
       ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "pclass       float64\n",
-       "survived     float64\n",
-       "name          object\n",
-       "sex           object\n",
-       "age          float64\n",
-       "sibsp        float64\n",
-       "parch        float64\n",
-       "ticket        object\n",
-       "fare         float64\n",
-       "cabin         object\n",
-       "embarked      object\n",
-       "boat          object\n",
-       "body         float64\n",
-       "home.dest     object\n",
-       "dtype: object"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w6kdlahLstUV",
+        "colab_type": "text"
+      },
+      "source": [
+        "Vamos a hacer un resumen de los estadísticos básicos de las variables numéricas."
       ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.dtypes"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Missing values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "0        True\n",
-       "1        True\n",
-       "2        True\n",
-       "3       False\n",
-       "4        True\n",
-       "5        True\n",
-       "6        True\n",
-       "7        True\n",
-       "8        True\n",
-       "9       False\n",
-       "10      False\n",
-       "11       True\n",
-       "12       True\n",
-       "13       True\n",
-       "14       True\n",
-       "15       True\n",
-       "16       True\n",
-       "17       True\n",
-       "18       True\n",
-       "19       True\n",
-       "20       True\n",
-       "21       True\n",
-       "22       True\n",
-       "23       True\n",
-       "24       True\n",
-       "25      False\n",
-       "26       True\n",
-       "27       True\n",
-       "28       True\n",
-       "29       True\n",
-       "        ...  \n",
-       "1280     True\n",
-       "1281     True\n",
-       "1282     True\n",
-       "1283     True\n",
-       "1284     True\n",
-       "1285    False\n",
-       "1286     True\n",
-       "1287     True\n",
-       "1288    False\n",
-       "1289     True\n",
-       "1290     True\n",
-       "1291     True\n",
-       "1292     True\n",
-       "1293     True\n",
-       "1294    False\n",
-       "1295     True\n",
-       "1296    False\n",
-       "1297     True\n",
-       "1298     True\n",
-       "1299     True\n",
-       "1300     True\n",
-       "1301    False\n",
-       "1302     True\n",
-       "1303     True\n",
-       "1304    False\n",
-       "1305     True\n",
-       "1306    False\n",
-       "1307     True\n",
-       "1308     True\n",
-       "1309     True\n",
-       "Name: body, Length: 1310, dtype: bool"
+      "cell_type": "code",
+      "metadata": {
+        "id": "mdrPIOXfstUW",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "52ae8db0-21b3-4589-860b-701959a4a0e6"
+      },
+      "source": [
+        "data.describe()"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>pclass</th>\n",
+              "      <th>survived</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>fare</th>\n",
+              "      <th>body</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>1309.000000</td>\n",
+              "      <td>1309.000000</td>\n",
+              "      <td>1046.000000</td>\n",
+              "      <td>1309.000000</td>\n",
+              "      <td>1309.000000</td>\n",
+              "      <td>1308.000000</td>\n",
+              "      <td>121.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>2.294882</td>\n",
+              "      <td>0.381971</td>\n",
+              "      <td>29.881135</td>\n",
+              "      <td>0.498854</td>\n",
+              "      <td>0.385027</td>\n",
+              "      <td>33.295479</td>\n",
+              "      <td>160.809917</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.837836</td>\n",
+              "      <td>0.486055</td>\n",
+              "      <td>14.413500</td>\n",
+              "      <td>1.041658</td>\n",
+              "      <td>0.865560</td>\n",
+              "      <td>51.758668</td>\n",
+              "      <td>97.696922</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.166700</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>21.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>7.895800</td>\n",
+              "      <td>72.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>28.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>14.454200</td>\n",
+              "      <td>155.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>39.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>31.275000</td>\n",
+              "      <td>256.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>80.000000</td>\n",
+              "      <td>8.000000</td>\n",
+              "      <td>9.000000</td>\n",
+              "      <td>512.329200</td>\n",
+              "      <td>328.000000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            pclass     survived  ...         fare        body\n",
+              "count  1309.000000  1309.000000  ...  1308.000000  121.000000\n",
+              "mean      2.294882     0.381971  ...    33.295479  160.809917\n",
+              "std       0.837836     0.486055  ...    51.758668   97.696922\n",
+              "min       1.000000     0.000000  ...     0.000000    1.000000\n",
+              "25%       2.000000     0.000000  ...     7.895800   72.000000\n",
+              "50%       3.000000     0.000000  ...    14.454200  155.000000\n",
+              "75%       3.000000     1.000000  ...    31.275000  256.000000\n",
+              "max       3.000000     1.000000  ...   512.329200  328.000000\n",
+              "\n",
+              "[8 rows x 7 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
       ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isnull(data[\"body\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "0       False\n",
-       "1       False\n",
-       "2       False\n",
-       "3        True\n",
-       "4       False\n",
-       "5       False\n",
-       "6       False\n",
-       "7       False\n",
-       "8       False\n",
-       "9        True\n",
-       "10       True\n",
-       "11      False\n",
-       "12      False\n",
-       "13      False\n",
-       "14      False\n",
-       "15      False\n",
-       "16      False\n",
-       "17      False\n",
-       "18      False\n",
-       "19      False\n",
-       "20      False\n",
-       "21      False\n",
-       "22      False\n",
-       "23      False\n",
-       "24      False\n",
-       "25       True\n",
-       "26      False\n",
-       "27      False\n",
-       "28      False\n",
-       "29      False\n",
-       "        ...  \n",
-       "1280    False\n",
-       "1281    False\n",
-       "1282    False\n",
-       "1283    False\n",
-       "1284    False\n",
-       "1285     True\n",
-       "1286    False\n",
-       "1287    False\n",
-       "1288     True\n",
-       "1289    False\n",
-       "1290    False\n",
-       "1291    False\n",
-       "1292    False\n",
-       "1293    False\n",
-       "1294     True\n",
-       "1295    False\n",
-       "1296     True\n",
-       "1297    False\n",
-       "1298    False\n",
-       "1299    False\n",
-       "1300    False\n",
-       "1301     True\n",
-       "1302    False\n",
-       "1303    False\n",
-       "1304     True\n",
-       "1305    False\n",
-       "1306     True\n",
-       "1307    False\n",
-       "1308    False\n",
-       "1309    False\n",
-       "Name: body, Length: 1310, dtype: bool"
+      "cell_type": "code",
+      "metadata": {
+        "id": "PqJiBFt-stUX",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 272
+        },
+        "outputId": "2df150a7-c5b7-457a-8aa1-5596908ba94e"
+      },
+      "source": [
+        "data.dtypes"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "pclass         int64\n",
+              "survived       int64\n",
+              "name          object\n",
+              "sex           object\n",
+              "age          float64\n",
+              "sibsp          int64\n",
+              "parch          int64\n",
+              "ticket        object\n",
+              "fare         float64\n",
+              "cabin         object\n",
+              "embarked      object\n",
+              "boat          object\n",
+              "body         float64\n",
+              "home.dest     object\n",
+              "dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        }
       ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.notnull(data[\"body\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "1189"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dUyJy_0wstUZ",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Missing values"
       ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isnull(data[\"body\"]).values.ravel().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "121"
+      "cell_type": "code",
+      "metadata": {
+        "id": "B_8ZxrsdstUa",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "e650b150-5cc5-48e2-d9d1-ad07ed67befc"
+      },
+      "source": [
+        "pd.isnull(data[\"body\"])"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0        True\n",
+              "1        True\n",
+              "2        True\n",
+              "3       False\n",
+              "4        True\n",
+              "        ...  \n",
+              "1304    False\n",
+              "1305     True\n",
+              "1306    False\n",
+              "1307     True\n",
+              "1308     True\n",
+              "Name: body, Length: 1309, dtype: bool"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
       ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.notnull(data[\"body\"]).values.ravel().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Los valores que faltan en un data set pueden venir por dos razones:\n",
-    "* Extracción de los datos\n",
-    "* Recolección de los datos"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Borrado de valores que faltan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "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>pclass</th>\n",
-       "      <th>survived</th>\n",
-       "      <th>name</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>age</th>\n",
-       "      <th>sibsp</th>\n",
-       "      <th>parch</th>\n",
-       "      <th>ticket</th>\n",
-       "      <th>fare</th>\n",
-       "      <th>cabin</th>\n",
-       "      <th>embarked</th>\n",
-       "      <th>boat</th>\n",
-       "      <th>body</th>\n",
-       "      <th>home.dest</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
-       "      <td>female</td>\n",
-       "      <td>29.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>24160</td>\n",
-       "      <td>211.3375</td>\n",
-       "      <td>B5</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>St Louis, MO</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Allison, Master. Hudson Trevor</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.9167</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>11</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Allison, Miss. Helen Loraine</td>\n",
-       "      <td>female</td>\n",
-       "      <td>2.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
-       "      <td>male</td>\n",
-       "      <td>30.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>25.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Anderson, Mr. Harry</td>\n",
-       "      <td>male</td>\n",
-       "      <td>48.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19952</td>\n",
-       "      <td>26.5500</td>\n",
-       "      <td>E12</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
-       "      <td>female</td>\n",
-       "      <td>63.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13502</td>\n",
-       "      <td>77.9583</td>\n",
-       "      <td>D7</td>\n",
-       "      <td>S</td>\n",
-       "      <td>10</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Hudson, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Andrews, Mr. Thomas Jr</td>\n",
-       "      <td>male</td>\n",
-       "      <td>39.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>112050</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>A36</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Belfast, NI</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Appleton, Mrs. Edward Dale (Charlotte Lamson)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>53.0000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11769</td>\n",
-       "      <td>51.4792</td>\n",
-       "      <td>C101</td>\n",
-       "      <td>S</td>\n",
-       "      <td>D</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Bayside, Queens, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Artagaveytia, Mr. Ramon</td>\n",
-       "      <td>male</td>\n",
-       "      <td>71.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17609</td>\n",
-       "      <td>49.5042</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>Montevideo, Uruguay</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Astor, Col. John Jacob</td>\n",
-       "      <td>male</td>\n",
-       "      <td>47.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17757</td>\n",
-       "      <td>227.5250</td>\n",
-       "      <td>C62 C64</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>124.0</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Astor, Mrs. John Jacob (Madeleine Talmadge Force)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>18.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17757</td>\n",
-       "      <td>227.5250</td>\n",
-       "      <td>C62 C64</td>\n",
-       "      <td>C</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Aubart, Mme. Leontine Pauline</td>\n",
-       "      <td>female</td>\n",
-       "      <td>24.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17477</td>\n",
-       "      <td>69.3000</td>\n",
-       "      <td>B35</td>\n",
-       "      <td>C</td>\n",
-       "      <td>9</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Paris, France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Barber, Miss. Ellen \"Nellie\"</td>\n",
-       "      <td>female</td>\n",
-       "      <td>26.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19877</td>\n",
-       "      <td>78.8500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
-       "      <td>male</td>\n",
-       "      <td>80.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>27042</td>\n",
-       "      <td>30.0000</td>\n",
-       "      <td>A23</td>\n",
-       "      <td>S</td>\n",
-       "      <td>B</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Hessle, Yorks</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Baumann, Mr. John D</td>\n",
-       "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17318</td>\n",
-       "      <td>25.9250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Baxter, Mr. Quigg Edmond</td>\n",
-       "      <td>male</td>\n",
-       "      <td>24.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>PC 17558</td>\n",
-       "      <td>247.5208</td>\n",
-       "      <td>B58 B60</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Montreal, PQ</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Baxter, Mrs. James (Helene DeLaudeniere Chaput)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>50.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>PC 17558</td>\n",
-       "      <td>247.5208</td>\n",
-       "      <td>B58 B60</td>\n",
-       "      <td>C</td>\n",
-       "      <td>6</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Montreal, PQ</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bazzani, Miss. Albina</td>\n",
-       "      <td>female</td>\n",
-       "      <td>32.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11813</td>\n",
-       "      <td>76.2917</td>\n",
-       "      <td>D15</td>\n",
-       "      <td>C</td>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Beattie, Mr. Thomson</td>\n",
-       "      <td>male</td>\n",
-       "      <td>36.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13050</td>\n",
-       "      <td>75.2417</td>\n",
-       "      <td>C6</td>\n",
-       "      <td>C</td>\n",
-       "      <td>A</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Winnipeg, MN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Beckwith, Mr. Richard Leonard</td>\n",
-       "      <td>male</td>\n",
-       "      <td>37.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11751</td>\n",
-       "      <td>52.5542</td>\n",
-       "      <td>D35</td>\n",
-       "      <td>S</td>\n",
-       "      <td>5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>47.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11751</td>\n",
-       "      <td>52.5542</td>\n",
-       "      <td>D35</td>\n",
-       "      <td>S</td>\n",
-       "      <td>5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Behr, Mr. Karl Howell</td>\n",
-       "      <td>male</td>\n",
-       "      <td>26.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>111369</td>\n",
-       "      <td>30.0000</td>\n",
-       "      <td>C148</td>\n",
-       "      <td>C</td>\n",
-       "      <td>5</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bidois, Miss. Rosalie</td>\n",
-       "      <td>female</td>\n",
-       "      <td>42.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17757</td>\n",
-       "      <td>227.5250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bird, Miss. Ellen</td>\n",
-       "      <td>female</td>\n",
-       "      <td>29.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17483</td>\n",
-       "      <td>221.7792</td>\n",
-       "      <td>C97</td>\n",
-       "      <td>S</td>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Birnbaum, Mr. Jakob</td>\n",
-       "      <td>male</td>\n",
-       "      <td>25.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13905</td>\n",
-       "      <td>26.0000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>148.0</td>\n",
-       "      <td>San Francisco, CA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bishop, Mr. Dickinson H</td>\n",
-       "      <td>male</td>\n",
-       "      <td>25.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11967</td>\n",
-       "      <td>91.0792</td>\n",
-       "      <td>B49</td>\n",
-       "      <td>C</td>\n",
-       "      <td>7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Dowagiac, MI</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bishop, Mrs. Dickinson H (Helen Walton)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>19.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11967</td>\n",
-       "      <td>91.0792</td>\n",
-       "      <td>B49</td>\n",
-       "      <td>C</td>\n",
-       "      <td>7</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Dowagiac, MI</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bissette, Miss. Amelia</td>\n",
-       "      <td>female</td>\n",
-       "      <td>35.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17760</td>\n",
-       "      <td>135.6333</td>\n",
-       "      <td>C99</td>\n",
-       "      <td>S</td>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bjornstrom-Steffansson, Mr. Mauritz Hakan</td>\n",
-       "      <td>male</td>\n",
-       "      <td>28.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>110564</td>\n",
-       "      <td>26.5500</td>\n",
-       "      <td>C52</td>\n",
-       "      <td>S</td>\n",
-       "      <td>D</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Stockholm, Sweden / Washington, DC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1279</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Vestrom, Miss. Hulda Amanda Adolfina</td>\n",
-       "      <td>female</td>\n",
-       "      <td>14.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>350406</td>\n",
-       "      <td>7.8542</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1280</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Vovk, Mr. Janko</td>\n",
-       "      <td>male</td>\n",
-       "      <td>22.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>349252</td>\n",
-       "      <td>7.8958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1281</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Waelens, Mr. Achille</td>\n",
-       "      <td>male</td>\n",
-       "      <td>22.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>345767</td>\n",
-       "      <td>9.0000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Antwerp, Belgium / Stanton, OH</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1282</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Ware, Mr. Frederick</td>\n",
-       "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>359309</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1283</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Warren, Mr. Charles William</td>\n",
-       "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>C.A. 49867</td>\n",
-       "      <td>7.5500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1284</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Webber, Mr. James</td>\n",
-       "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>SOTON/OQ 3101316</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1285</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wenzel, Mr. Linhart</td>\n",
-       "      <td>male</td>\n",
-       "      <td>32.5000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>345775</td>\n",
-       "      <td>9.5000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>298.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1286</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Whabee, Mrs. George Joseph (Shawneene Abi-Saab)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>38.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2688</td>\n",
-       "      <td>7.2292</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1287</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Widegren, Mr. Carl/Charles Peter</td>\n",
-       "      <td>male</td>\n",
-       "      <td>51.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>347064</td>\n",
-       "      <td>7.7500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1288</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wiklund, Mr. Jakob Alfred</td>\n",
-       "      <td>male</td>\n",
-       "      <td>18.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3101267</td>\n",
-       "      <td>6.4958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>314.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1289</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wiklund, Mr. Karl Johan</td>\n",
-       "      <td>male</td>\n",
-       "      <td>21.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3101266</td>\n",
-       "      <td>6.4958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1290</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>47.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>363272</td>\n",
-       "      <td>7.0000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1291</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Willer, Mr. Aaron (\"Abi Weller\")</td>\n",
-       "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3410</td>\n",
-       "      <td>8.7125</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1292</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Willey, Mr. Edward</td>\n",
-       "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>S.O./P.P. 751</td>\n",
-       "      <td>7.5500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1293</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Williams, Mr. Howard Hugh \"Harry\"</td>\n",
-       "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>A/5 2466</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1294</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Williams, Mr. Leslie</td>\n",
-       "      <td>male</td>\n",
-       "      <td>28.5000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>54636</td>\n",
-       "      <td>16.1000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1295</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Windelov, Mr. Einar</td>\n",
-       "      <td>male</td>\n",
-       "      <td>21.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>SOTON/OQ 3101317</td>\n",
-       "      <td>7.2500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1296</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wirz, Mr. Albert</td>\n",
-       "      <td>male</td>\n",
-       "      <td>27.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>315154</td>\n",
-       "      <td>8.6625</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1297</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wiseman, Mr. Phillippe</td>\n",
-       "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>A/4. 34244</td>\n",
-       "      <td>7.2500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1298</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wittevrongel, Mr. Camille</td>\n",
-       "      <td>male</td>\n",
-       "      <td>36.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>345771</td>\n",
-       "      <td>9.5000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1299</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Yasbeck, Mr. Antoni</td>\n",
-       "      <td>male</td>\n",
-       "      <td>27.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2659</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1300</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Yasbeck, Mrs. Antoni (Selini Alexander)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>15.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2659</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1301</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Youseff, Mr. Gerious</td>\n",
-       "      <td>male</td>\n",
-       "      <td>45.5000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2628</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>312.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1302</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Yousif, Mr. Wazli</td>\n",
-       "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2647</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1303</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Yousseff, Mr. Gerious</td>\n",
-       "      <td>male</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2627</td>\n",
-       "      <td>14.4583</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1304</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zabour, Miss. Hileni</td>\n",
-       "      <td>female</td>\n",
-       "      <td>14.5000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2665</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>328.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1305</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zabour, Miss. Thamine</td>\n",
-       "      <td>female</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2665</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1306</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zakarian, Mr. Mapriededer</td>\n",
-       "      <td>male</td>\n",
-       "      <td>26.5000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2656</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>304.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1307</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zakarian, Mr. Ortin</td>\n",
-       "      <td>male</td>\n",
-       "      <td>27.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2670</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1308</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zimmerman, Mr. Leo</td>\n",
-       "      <td>male</td>\n",
-       "      <td>29.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>315082</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1309 rows × 14 columns</p>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "86tfBnHnstUb",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "f690dc35-fd64-4795-ed25-f26cd44b9be0"
+      },
+      "source": [
+        "pd.notnull(data[\"body\"])"
       ],
-      "text/plain": [
-       "      pclass  survived                                               name  \\\n",
-       "0        1.0       1.0                      Allen, Miss. Elisabeth Walton   \n",
-       "1        1.0       1.0                     Allison, Master. Hudson Trevor   \n",
-       "2        1.0       0.0                       Allison, Miss. Helen Loraine   \n",
-       "3        1.0       0.0               Allison, Mr. Hudson Joshua Creighton   \n",
-       "4        1.0       0.0    Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
-       "5        1.0       1.0                                Anderson, Mr. Harry   \n",
-       "6        1.0       1.0                  Andrews, Miss. Kornelia Theodosia   \n",
-       "7        1.0       0.0                             Andrews, Mr. Thomas Jr   \n",
-       "8        1.0       1.0      Appleton, Mrs. Edward Dale (Charlotte Lamson)   \n",
-       "9        1.0       0.0                            Artagaveytia, Mr. Ramon   \n",
-       "10       1.0       0.0                             Astor, Col. John Jacob   \n",
-       "11       1.0       1.0  Astor, Mrs. John Jacob (Madeleine Talmadge Force)   \n",
-       "12       1.0       1.0                      Aubart, Mme. Leontine Pauline   \n",
-       "13       1.0       1.0                       Barber, Miss. Ellen \"Nellie\"   \n",
-       "14       1.0       1.0               Barkworth, Mr. Algernon Henry Wilson   \n",
-       "15       1.0       0.0                                Baumann, Mr. John D   \n",
-       "16       1.0       0.0                           Baxter, Mr. Quigg Edmond   \n",
-       "17       1.0       1.0    Baxter, Mrs. James (Helene DeLaudeniere Chaput)   \n",
-       "18       1.0       1.0                              Bazzani, Miss. Albina   \n",
-       "19       1.0       0.0                               Beattie, Mr. Thomson   \n",
-       "20       1.0       1.0                      Beckwith, Mr. Richard Leonard   \n",
-       "21       1.0       1.0   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)   \n",
-       "22       1.0       1.0                              Behr, Mr. Karl Howell   \n",
-       "23       1.0       1.0                              Bidois, Miss. Rosalie   \n",
-       "24       1.0       1.0                                  Bird, Miss. Ellen   \n",
-       "25       1.0       0.0                                Birnbaum, Mr. Jakob   \n",
-       "26       1.0       1.0                            Bishop, Mr. Dickinson H   \n",
-       "27       1.0       1.0            Bishop, Mrs. Dickinson H (Helen Walton)   \n",
-       "28       1.0       1.0                             Bissette, Miss. Amelia   \n",
-       "29       1.0       1.0          Bjornstrom-Steffansson, Mr. Mauritz Hakan   \n",
-       "...      ...       ...                                                ...   \n",
-       "1279     3.0       0.0               Vestrom, Miss. Hulda Amanda Adolfina   \n",
-       "1280     3.0       0.0                                    Vovk, Mr. Janko   \n",
-       "1281     3.0       0.0                               Waelens, Mr. Achille   \n",
-       "1282     3.0       0.0                                Ware, Mr. Frederick   \n",
-       "1283     3.0       0.0                        Warren, Mr. Charles William   \n",
-       "1284     3.0       0.0                                  Webber, Mr. James   \n",
-       "1285     3.0       0.0                                Wenzel, Mr. Linhart   \n",
-       "1286     3.0       1.0    Whabee, Mrs. George Joseph (Shawneene Abi-Saab)   \n",
-       "1287     3.0       0.0                   Widegren, Mr. Carl/Charles Peter   \n",
-       "1288     3.0       0.0                          Wiklund, Mr. Jakob Alfred   \n",
-       "1289     3.0       0.0                            Wiklund, Mr. Karl Johan   \n",
-       "1290     3.0       1.0                   Wilkes, Mrs. James (Ellen Needs)   \n",
-       "1291     3.0       0.0                   Willer, Mr. Aaron (\"Abi Weller\")   \n",
-       "1292     3.0       0.0                                 Willey, Mr. Edward   \n",
-       "1293     3.0       0.0                  Williams, Mr. Howard Hugh \"Harry\"   \n",
-       "1294     3.0       0.0                               Williams, Mr. Leslie   \n",
-       "1295     3.0       0.0                                Windelov, Mr. Einar   \n",
-       "1296     3.0       0.0                                   Wirz, Mr. Albert   \n",
-       "1297     3.0       0.0                             Wiseman, Mr. Phillippe   \n",
-       "1298     3.0       0.0                          Wittevrongel, Mr. Camille   \n",
-       "1299     3.0       0.0                                Yasbeck, Mr. Antoni   \n",
-       "1300     3.0       1.0            Yasbeck, Mrs. Antoni (Selini Alexander)   \n",
-       "1301     3.0       0.0                               Youseff, Mr. Gerious   \n",
-       "1302     3.0       0.0                                  Yousif, Mr. Wazli   \n",
-       "1303     3.0       0.0                              Yousseff, Mr. Gerious   \n",
-       "1304     3.0       0.0                               Zabour, Miss. Hileni   \n",
-       "1305     3.0       0.0                              Zabour, Miss. Thamine   \n",
-       "1306     3.0       0.0                          Zakarian, Mr. Mapriededer   \n",
-       "1307     3.0       0.0                                Zakarian, Mr. Ortin   \n",
-       "1308     3.0       0.0                                 Zimmerman, Mr. Leo   \n",
-       "\n",
-       "         sex      age  sibsp  parch            ticket      fare    cabin  \\\n",
-       "0     female  29.0000    0.0    0.0             24160  211.3375       B5   \n",
-       "1       male   0.9167    1.0    2.0            113781  151.5500  C22 C26   \n",
-       "2     female   2.0000    1.0    2.0            113781  151.5500  C22 C26   \n",
-       "3       male  30.0000    1.0    2.0            113781  151.5500  C22 C26   \n",
-       "4     female  25.0000    1.0    2.0            113781  151.5500  C22 C26   \n",
-       "5       male  48.0000    0.0    0.0             19952   26.5500      E12   \n",
-       "6     female  63.0000    1.0    0.0             13502   77.9583       D7   \n",
-       "7       male  39.0000    0.0    0.0            112050    0.0000      A36   \n",
-       "8     female  53.0000    2.0    0.0             11769   51.4792     C101   \n",
-       "9       male  71.0000    0.0    0.0          PC 17609   49.5042      NaN   \n",
-       "10      male  47.0000    1.0    0.0          PC 17757  227.5250  C62 C64   \n",
-       "11    female  18.0000    1.0    0.0          PC 17757  227.5250  C62 C64   \n",
-       "12    female  24.0000    0.0    0.0          PC 17477   69.3000      B35   \n",
-       "13    female  26.0000    0.0    0.0             19877   78.8500      NaN   \n",
-       "14      male  80.0000    0.0    0.0             27042   30.0000      A23   \n",
-       "15      male      NaN    0.0    0.0          PC 17318   25.9250      NaN   \n",
-       "16      male  24.0000    0.0    1.0          PC 17558  247.5208  B58 B60   \n",
-       "17    female  50.0000    0.0    1.0          PC 17558  247.5208  B58 B60   \n",
-       "18    female  32.0000    0.0    0.0             11813   76.2917      D15   \n",
-       "19      male  36.0000    0.0    0.0             13050   75.2417       C6   \n",
-       "20      male  37.0000    1.0    1.0             11751   52.5542      D35   \n",
-       "21    female  47.0000    1.0    1.0             11751   52.5542      D35   \n",
-       "22      male  26.0000    0.0    0.0            111369   30.0000     C148   \n",
-       "23    female  42.0000    0.0    0.0          PC 17757  227.5250      NaN   \n",
-       "24    female  29.0000    0.0    0.0          PC 17483  221.7792      C97   \n",
-       "25      male  25.0000    0.0    0.0             13905   26.0000      NaN   \n",
-       "26      male  25.0000    1.0    0.0             11967   91.0792      B49   \n",
-       "27    female  19.0000    1.0    0.0             11967   91.0792      B49   \n",
-       "28    female  35.0000    0.0    0.0          PC 17760  135.6333      C99   \n",
-       "29      male  28.0000    0.0    0.0            110564   26.5500      C52   \n",
-       "...      ...      ...    ...    ...               ...       ...      ...   \n",
-       "1279  female  14.0000    0.0    0.0            350406    7.8542      NaN   \n",
-       "1280    male  22.0000    0.0    0.0            349252    7.8958      NaN   \n",
-       "1281    male  22.0000    0.0    0.0            345767    9.0000      NaN   \n",
-       "1282    male      NaN    0.0    0.0            359309    8.0500      NaN   \n",
-       "1283    male      NaN    0.0    0.0        C.A. 49867    7.5500      NaN   \n",
-       "1284    male      NaN    0.0    0.0  SOTON/OQ 3101316    8.0500      NaN   \n",
-       "1285    male  32.5000    0.0    0.0            345775    9.5000      NaN   \n",
-       "1286  female  38.0000    0.0    0.0              2688    7.2292      NaN   \n",
-       "1287    male  51.0000    0.0    0.0            347064    7.7500      NaN   \n",
-       "1288    male  18.0000    1.0    0.0           3101267    6.4958      NaN   \n",
-       "1289    male  21.0000    1.0    0.0           3101266    6.4958      NaN   \n",
-       "1290  female  47.0000    1.0    0.0            363272    7.0000      NaN   \n",
-       "1291    male      NaN    0.0    0.0              3410    8.7125      NaN   \n",
-       "1292    male      NaN    0.0    0.0     S.O./P.P. 751    7.5500      NaN   \n",
-       "1293    male      NaN    0.0    0.0          A/5 2466    8.0500      NaN   \n",
-       "1294    male  28.5000    0.0    0.0             54636   16.1000      NaN   \n",
-       "1295    male  21.0000    0.0    0.0  SOTON/OQ 3101317    7.2500      NaN   \n",
-       "1296    male  27.0000    0.0    0.0            315154    8.6625      NaN   \n",
-       "1297    male      NaN    0.0    0.0        A/4. 34244    7.2500      NaN   \n",
-       "1298    male  36.0000    0.0    0.0            345771    9.5000      NaN   \n",
-       "1299    male  27.0000    1.0    0.0              2659   14.4542      NaN   \n",
-       "1300  female  15.0000    1.0    0.0              2659   14.4542      NaN   \n",
-       "1301    male  45.5000    0.0    0.0              2628    7.2250      NaN   \n",
-       "1302    male      NaN    0.0    0.0              2647    7.2250      NaN   \n",
-       "1303    male      NaN    0.0    0.0              2627   14.4583      NaN   \n",
-       "1304  female  14.5000    1.0    0.0              2665   14.4542      NaN   \n",
-       "1305  female      NaN    1.0    0.0              2665   14.4542      NaN   \n",
-       "1306    male  26.5000    0.0    0.0              2656    7.2250      NaN   \n",
-       "1307    male  27.0000    0.0    0.0              2670    7.2250      NaN   \n",
-       "1308    male  29.0000    0.0    0.0            315082    7.8750      NaN   \n",
-       "\n",
-       "     embarked boat   body                           home.dest  \n",
-       "0           S    2    NaN                        St Louis, MO  \n",
-       "1           S   11    NaN     Montreal, PQ / Chesterville, ON  \n",
-       "2           S  NaN    NaN     Montreal, PQ / Chesterville, ON  \n",
-       "3           S  NaN  135.0     Montreal, PQ / Chesterville, ON  \n",
-       "4           S  NaN    NaN     Montreal, PQ / Chesterville, ON  \n",
-       "5           S    3    NaN                        New York, NY  \n",
-       "6           S   10    NaN                          Hudson, NY  \n",
-       "7           S  NaN    NaN                         Belfast, NI  \n",
-       "8           S    D    NaN                 Bayside, Queens, NY  \n",
-       "9           C  NaN   22.0                 Montevideo, Uruguay  \n",
-       "10          C  NaN  124.0                        New York, NY  \n",
-       "11          C    4    NaN                        New York, NY  \n",
-       "12          C    9    NaN                       Paris, France  \n",
-       "13          S    6    NaN                                 NaN  \n",
-       "14          S    B    NaN                       Hessle, Yorks  \n",
-       "15          S  NaN    NaN                        New York, NY  \n",
-       "16          C  NaN    NaN                        Montreal, PQ  \n",
-       "17          C    6    NaN                        Montreal, PQ  \n",
-       "18          C    8    NaN                                 NaN  \n",
-       "19          C    A    NaN                        Winnipeg, MN  \n",
-       "20          S    5    NaN                        New York, NY  \n",
-       "21          S    5    NaN                        New York, NY  \n",
-       "22          C    5    NaN                        New York, NY  \n",
-       "23          C    4    NaN                                 NaN  \n",
-       "24          S    8    NaN                                 NaN  \n",
-       "25          C  NaN  148.0                   San Francisco, CA  \n",
-       "26          C    7    NaN                        Dowagiac, MI  \n",
-       "27          C    7    NaN                        Dowagiac, MI  \n",
-       "28          S    8    NaN                                 NaN  \n",
-       "29          S    D    NaN  Stockholm, Sweden / Washington, DC  \n",
-       "...       ...  ...    ...                                 ...  \n",
-       "1279        S  NaN    NaN                                 NaN  \n",
-       "1280        S  NaN    NaN                                 NaN  \n",
-       "1281        S  NaN    NaN      Antwerp, Belgium / Stanton, OH  \n",
-       "1282        S  NaN    NaN                                 NaN  \n",
-       "1283        S  NaN    NaN                                 NaN  \n",
-       "1284        S  NaN    NaN                                 NaN  \n",
-       "1285        S  NaN  298.0                                 NaN  \n",
-       "1286        C    C    NaN                                 NaN  \n",
-       "1287        S  NaN    NaN                                 NaN  \n",
-       "1288        S  NaN  314.0                                 NaN  \n",
-       "1289        S  NaN    NaN                                 NaN  \n",
-       "1290        S  NaN    NaN                                 NaN  \n",
-       "1291        S  NaN    NaN                                 NaN  \n",
-       "1292        S  NaN    NaN                                 NaN  \n",
-       "1293        S  NaN    NaN                                 NaN  \n",
-       "1294        S  NaN   14.0                                 NaN  \n",
-       "1295        S  NaN    NaN                                 NaN  \n",
-       "1296        S  NaN  131.0                                 NaN  \n",
-       "1297        S  NaN    NaN                                 NaN  \n",
-       "1298        S  NaN    NaN                                 NaN  \n",
-       "1299        C    C    NaN                                 NaN  \n",
-       "1300        C  NaN    NaN                                 NaN  \n",
-       "1301        C  NaN  312.0                                 NaN  \n",
-       "1302        C  NaN    NaN                                 NaN  \n",
-       "1303        C  NaN    NaN                                 NaN  \n",
-       "1304        C  NaN  328.0                                 NaN  \n",
-       "1305        C  NaN    NaN                                 NaN  \n",
-       "1306        C  NaN  304.0                                 NaN  \n",
-       "1307        C  NaN    NaN                                 NaN  \n",
-       "1308        S  NaN    NaN                                 NaN  \n",
-       "\n",
-       "[1309 rows x 14 columns]"
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0       False\n",
+              "1       False\n",
+              "2       False\n",
+              "3        True\n",
+              "4       False\n",
+              "        ...  \n",
+              "1304     True\n",
+              "1305    False\n",
+              "1306     True\n",
+              "1307    False\n",
+              "1308    False\n",
+              "Name: body, Length: 1309, dtype: bool"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
       ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.dropna(axis=0, how=\"all\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data2 = data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "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>pclass</th>\n",
-       "      <th>survived</th>\n",
-       "      <th>name</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>age</th>\n",
-       "      <th>sibsp</th>\n",
-       "      <th>parch</th>\n",
-       "      <th>ticket</th>\n",
-       "      <th>fare</th>\n",
-       "      <th>cabin</th>\n",
-       "      <th>embarked</th>\n",
-       "      <th>boat</th>\n",
-       "      <th>body</th>\n",
-       "      <th>home.dest</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "CBd8kJZZstUd",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "7a014a8e-3059-4715-a33a-070d34391cba"
+      },
+      "source": [
+        "pd.isnull(data[\"body\"]).values.ravel().sum()"
       ],
-      "text/plain": [
-       "Empty DataFrame\n",
-       "Columns: [pclass, survived, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked, boat, body, home.dest]\n",
-       "Index: []"
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1188"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
       ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data2.dropna(axis=0, how=\"any\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Cómputo de los valores fantantes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data3 = data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>pclass</th>\n",
-       "      <th>survived</th>\n",
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-       "      <th>sex</th>\n",
-       "      <th>age</th>\n",
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-       "      <th>ticket</th>\n",
-       "      <th>fare</th>\n",
-       "      <th>cabin</th>\n",
-       "      <th>embarked</th>\n",
-       "      <th>boat</th>\n",
-       "      <th>body</th>\n",
-       "      <th>home.dest</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
-       "      <td>female</td>\n",
-       "      <td>29.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>24160</td>\n",
-       "      <td>211.3375</td>\n",
-       "      <td>B5</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>St Louis, MO</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Allison, Master. Hudson Trevor</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.9167</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>11</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Allison, Miss. Helen Loraine</td>\n",
-       "      <td>female</td>\n",
-       "      <td>2.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
-       "      <td>male</td>\n",
-       "      <td>30.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>25.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Anderson, Mr. Harry</td>\n",
-       "      <td>male</td>\n",
-       "      <td>48.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19952</td>\n",
-       "      <td>26.5500</td>\n",
-       "      <td>E12</td>\n",
-       "      <td>S</td>\n",
-       "      <td>3</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Andrews, Miss. Kornelia Theodosia</td>\n",
-       "      <td>female</td>\n",
-       "      <td>63.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13502</td>\n",
-       "      <td>77.9583</td>\n",
-       "      <td>D7</td>\n",
-       "      <td>S</td>\n",
-       "      <td>10</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Hudson, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Andrews, Mr. Thomas Jr</td>\n",
-       "      <td>male</td>\n",
-       "      <td>39.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>112050</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>A36</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Belfast, NI</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Appleton, Mrs. Edward Dale (Charlotte Lamson)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>53.0000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11769</td>\n",
-       "      <td>51.4792</td>\n",
-       "      <td>C101</td>\n",
-       "      <td>S</td>\n",
-       "      <td>D</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Bayside, Queens, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Artagaveytia, Mr. Ramon</td>\n",
-       "      <td>male</td>\n",
-       "      <td>71.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17609</td>\n",
-       "      <td>49.5042</td>\n",
-       "      <td>0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>Montevideo, Uruguay</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Astor, Col. John Jacob</td>\n",
-       "      <td>male</td>\n",
-       "      <td>47.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17757</td>\n",
-       "      <td>227.5250</td>\n",
-       "      <td>C62 C64</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>124.0</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Astor, Mrs. John Jacob (Madeleine Talmadge Force)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>18.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17757</td>\n",
-       "      <td>227.5250</td>\n",
-       "      <td>C62 C64</td>\n",
-       "      <td>C</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Aubart, Mme. Leontine Pauline</td>\n",
-       "      <td>female</td>\n",
-       "      <td>24.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17477</td>\n",
-       "      <td>69.3000</td>\n",
-       "      <td>B35</td>\n",
-       "      <td>C</td>\n",
-       "      <td>9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Paris, France</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Barber, Miss. Ellen \"Nellie\"</td>\n",
-       "      <td>female</td>\n",
-       "      <td>26.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19877</td>\n",
-       "      <td>78.8500</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
-       "      <td>male</td>\n",
-       "      <td>80.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>27042</td>\n",
-       "      <td>30.0000</td>\n",
-       "      <td>A23</td>\n",
-       "      <td>S</td>\n",
-       "      <td>B</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Hessle, Yorks</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Baumann, Mr. John D</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17318</td>\n",
-       "      <td>25.9250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Baxter, Mr. Quigg Edmond</td>\n",
-       "      <td>male</td>\n",
-       "      <td>24.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>PC 17558</td>\n",
-       "      <td>247.5208</td>\n",
-       "      <td>B58 B60</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Montreal, PQ</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Baxter, Mrs. James (Helene DeLaudeniere Chaput)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>50.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>PC 17558</td>\n",
-       "      <td>247.5208</td>\n",
-       "      <td>B58 B60</td>\n",
-       "      <td>C</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Montreal, PQ</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bazzani, Miss. Albina</td>\n",
-       "      <td>female</td>\n",
-       "      <td>32.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11813</td>\n",
-       "      <td>76.2917</td>\n",
-       "      <td>D15</td>\n",
-       "      <td>C</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Beattie, Mr. Thomson</td>\n",
-       "      <td>male</td>\n",
-       "      <td>36.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13050</td>\n",
-       "      <td>75.2417</td>\n",
-       "      <td>C6</td>\n",
-       "      <td>C</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Winnipeg, MN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Beckwith, Mr. Richard Leonard</td>\n",
-       "      <td>male</td>\n",
-       "      <td>37.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11751</td>\n",
-       "      <td>52.5542</td>\n",
-       "      <td>D35</td>\n",
-       "      <td>S</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>47.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11751</td>\n",
-       "      <td>52.5542</td>\n",
-       "      <td>D35</td>\n",
-       "      <td>S</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Behr, Mr. Karl Howell</td>\n",
-       "      <td>male</td>\n",
-       "      <td>26.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>111369</td>\n",
-       "      <td>30.0000</td>\n",
-       "      <td>C148</td>\n",
-       "      <td>C</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>New York, NY</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bidois, Miss. Rosalie</td>\n",
-       "      <td>female</td>\n",
-       "      <td>42.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17757</td>\n",
-       "      <td>227.5250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bird, Miss. Ellen</td>\n",
-       "      <td>female</td>\n",
-       "      <td>29.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17483</td>\n",
-       "      <td>221.7792</td>\n",
-       "      <td>C97</td>\n",
-       "      <td>S</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Birnbaum, Mr. Jakob</td>\n",
-       "      <td>male</td>\n",
-       "      <td>25.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13905</td>\n",
-       "      <td>26.0000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>148.0</td>\n",
-       "      <td>San Francisco, CA</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bishop, Mr. Dickinson H</td>\n",
-       "      <td>male</td>\n",
-       "      <td>25.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11967</td>\n",
-       "      <td>91.0792</td>\n",
-       "      <td>B49</td>\n",
-       "      <td>C</td>\n",
-       "      <td>7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Dowagiac, MI</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bishop, Mrs. Dickinson H (Helen Walton)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>19.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11967</td>\n",
-       "      <td>91.0792</td>\n",
-       "      <td>B49</td>\n",
-       "      <td>C</td>\n",
-       "      <td>7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Dowagiac, MI</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bissette, Miss. Amelia</td>\n",
-       "      <td>female</td>\n",
-       "      <td>35.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17760</td>\n",
-       "      <td>135.6333</td>\n",
-       "      <td>C99</td>\n",
-       "      <td>S</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bjornstrom-Steffansson, Mr. Mauritz Hakan</td>\n",
-       "      <td>male</td>\n",
-       "      <td>28.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>110564</td>\n",
-       "      <td>26.5500</td>\n",
-       "      <td>C52</td>\n",
-       "      <td>S</td>\n",
-       "      <td>D</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Stockholm, Sweden / Washington, DC</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1280</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Vovk, Mr. Janko</td>\n",
-       "      <td>male</td>\n",
-       "      <td>22.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>349252</td>\n",
-       "      <td>7.8958</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1281</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Waelens, Mr. Achille</td>\n",
-       "      <td>male</td>\n",
-       "      <td>22.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>345767</td>\n",
-       "      <td>9.0000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Antwerp, Belgium / Stanton, OH</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1282</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Ware, Mr. Frederick</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>359309</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1283</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Warren, Mr. Charles William</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>C.A. 49867</td>\n",
-       "      <td>7.5500</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1284</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Webber, Mr. James</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>SOTON/OQ 3101316</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1285</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wenzel, Mr. Linhart</td>\n",
-       "      <td>male</td>\n",
-       "      <td>32.5000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>345775</td>\n",
-       "      <td>9.5000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>298.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1286</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Whabee, Mrs. George Joseph (Shawneene Abi-Saab)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>38.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2688</td>\n",
-       "      <td>7.2292</td>\n",
-       "      <td>0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1287</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Widegren, Mr. Carl/Charles Peter</td>\n",
-       "      <td>male</td>\n",
-       "      <td>51.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>347064</td>\n",
-       "      <td>7.7500</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1288</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wiklund, Mr. Jakob Alfred</td>\n",
-       "      <td>male</td>\n",
-       "      <td>18.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3101267</td>\n",
-       "      <td>6.4958</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>314.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1289</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wiklund, Mr. Karl Johan</td>\n",
-       "      <td>male</td>\n",
-       "      <td>21.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3101266</td>\n",
-       "      <td>6.4958</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1290</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>47.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>363272</td>\n",
-       "      <td>7.0000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1291</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Willer, Mr. Aaron (\"Abi Weller\")</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3410</td>\n",
-       "      <td>8.7125</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1292</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Willey, Mr. Edward</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>S.O./P.P. 751</td>\n",
-       "      <td>7.5500</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1293</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Williams, Mr. Howard Hugh \"Harry\"</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>A/5 2466</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1294</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Williams, Mr. Leslie</td>\n",
-       "      <td>male</td>\n",
-       "      <td>28.5000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>54636</td>\n",
-       "      <td>16.1000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1295</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Windelov, Mr. Einar</td>\n",
-       "      <td>male</td>\n",
-       "      <td>21.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>SOTON/OQ 3101317</td>\n",
-       "      <td>7.2500</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1296</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wirz, Mr. Albert</td>\n",
-       "      <td>male</td>\n",
-       "      <td>27.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>315154</td>\n",
-       "      <td>8.6625</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1297</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wiseman, Mr. Phillippe</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>A/4. 34244</td>\n",
-       "      <td>7.2500</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1298</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wittevrongel, Mr. Camille</td>\n",
-       "      <td>male</td>\n",
-       "      <td>36.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>345771</td>\n",
-       "      <td>9.5000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1299</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Yasbeck, Mr. Antoni</td>\n",
-       "      <td>male</td>\n",
-       "      <td>27.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2659</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1300</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Yasbeck, Mrs. Antoni (Selini Alexander)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>15.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2659</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1301</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Youseff, Mr. Gerious</td>\n",
-       "      <td>male</td>\n",
-       "      <td>45.5000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2628</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>312.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1302</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Yousif, Mr. Wazli</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2647</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1303</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Yousseff, Mr. Gerious</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2627</td>\n",
-       "      <td>14.4583</td>\n",
-       "      <td>0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1304</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zabour, Miss. Hileni</td>\n",
-       "      <td>female</td>\n",
-       "      <td>14.5000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2665</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>328.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1305</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zabour, Miss. Thamine</td>\n",
-       "      <td>female</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2665</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1306</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zakarian, Mr. Mapriededer</td>\n",
-       "      <td>male</td>\n",
-       "      <td>26.5000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2656</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>304.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1307</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zakarian, Mr. Ortin</td>\n",
-       "      <td>male</td>\n",
-       "      <td>27.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2670</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>0</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1308</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zimmerman, Mr. Leo</td>\n",
-       "      <td>male</td>\n",
-       "      <td>29.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>315082</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>0</td>\n",
-       "      <td>S</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1309</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1310 rows × 14 columns</p>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "xeqolLYRstUe",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "55cb2272-ca1c-4d8a-89e4-666ae004f488"
+      },
+      "source": [
+        "pd.notnull(data[\"body\"]).values.ravel().sum()"
       ],
-      "text/plain": [
-       "      pclass  survived                                               name  \\\n",
-       "0        1.0       1.0                      Allen, Miss. Elisabeth Walton   \n",
-       "1        1.0       1.0                     Allison, Master. Hudson Trevor   \n",
-       "2        1.0       0.0                       Allison, Miss. Helen Loraine   \n",
-       "3        1.0       0.0               Allison, Mr. Hudson Joshua Creighton   \n",
-       "4        1.0       0.0    Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
-       "5        1.0       1.0                                Anderson, Mr. Harry   \n",
-       "6        1.0       1.0                  Andrews, Miss. Kornelia Theodosia   \n",
-       "7        1.0       0.0                             Andrews, Mr. Thomas Jr   \n",
-       "8        1.0       1.0      Appleton, Mrs. Edward Dale (Charlotte Lamson)   \n",
-       "9        1.0       0.0                            Artagaveytia, Mr. Ramon   \n",
-       "10       1.0       0.0                             Astor, Col. John Jacob   \n",
-       "11       1.0       1.0  Astor, Mrs. John Jacob (Madeleine Talmadge Force)   \n",
-       "12       1.0       1.0                      Aubart, Mme. Leontine Pauline   \n",
-       "13       1.0       1.0                       Barber, Miss. Ellen \"Nellie\"   \n",
-       "14       1.0       1.0               Barkworth, Mr. Algernon Henry Wilson   \n",
-       "15       1.0       0.0                                Baumann, Mr. John D   \n",
-       "16       1.0       0.0                           Baxter, Mr. Quigg Edmond   \n",
-       "17       1.0       1.0    Baxter, Mrs. James (Helene DeLaudeniere Chaput)   \n",
-       "18       1.0       1.0                              Bazzani, Miss. Albina   \n",
-       "19       1.0       0.0                               Beattie, Mr. Thomson   \n",
-       "20       1.0       1.0                      Beckwith, Mr. Richard Leonard   \n",
-       "21       1.0       1.0   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)   \n",
-       "22       1.0       1.0                              Behr, Mr. Karl Howell   \n",
-       "23       1.0       1.0                              Bidois, Miss. Rosalie   \n",
-       "24       1.0       1.0                                  Bird, Miss. Ellen   \n",
-       "25       1.0       0.0                                Birnbaum, Mr. Jakob   \n",
-       "26       1.0       1.0                            Bishop, Mr. Dickinson H   \n",
-       "27       1.0       1.0            Bishop, Mrs. Dickinson H (Helen Walton)   \n",
-       "28       1.0       1.0                             Bissette, Miss. Amelia   \n",
-       "29       1.0       1.0          Bjornstrom-Steffansson, Mr. Mauritz Hakan   \n",
-       "...      ...       ...                                                ...   \n",
-       "1280     3.0       0.0                                    Vovk, Mr. Janko   \n",
-       "1281     3.0       0.0                               Waelens, Mr. Achille   \n",
-       "1282     3.0       0.0                                Ware, Mr. Frederick   \n",
-       "1283     3.0       0.0                        Warren, Mr. Charles William   \n",
-       "1284     3.0       0.0                                  Webber, Mr. James   \n",
-       "1285     3.0       0.0                                Wenzel, Mr. Linhart   \n",
-       "1286     3.0       1.0    Whabee, Mrs. George Joseph (Shawneene Abi-Saab)   \n",
-       "1287     3.0       0.0                   Widegren, Mr. Carl/Charles Peter   \n",
-       "1288     3.0       0.0                          Wiklund, Mr. Jakob Alfred   \n",
-       "1289     3.0       0.0                            Wiklund, Mr. Karl Johan   \n",
-       "1290     3.0       1.0                   Wilkes, Mrs. James (Ellen Needs)   \n",
-       "1291     3.0       0.0                   Willer, Mr. Aaron (\"Abi Weller\")   \n",
-       "1292     3.0       0.0                                 Willey, Mr. Edward   \n",
-       "1293     3.0       0.0                  Williams, Mr. Howard Hugh \"Harry\"   \n",
-       "1294     3.0       0.0                               Williams, Mr. Leslie   \n",
-       "1295     3.0       0.0                                Windelov, Mr. Einar   \n",
-       "1296     3.0       0.0                                   Wirz, Mr. Albert   \n",
-       "1297     3.0       0.0                             Wiseman, Mr. Phillippe   \n",
-       "1298     3.0       0.0                          Wittevrongel, Mr. Camille   \n",
-       "1299     3.0       0.0                                Yasbeck, Mr. Antoni   \n",
-       "1300     3.0       1.0            Yasbeck, Mrs. Antoni (Selini Alexander)   \n",
-       "1301     3.0       0.0                               Youseff, Mr. Gerious   \n",
-       "1302     3.0       0.0                                  Yousif, Mr. Wazli   \n",
-       "1303     3.0       0.0                              Yousseff, Mr. Gerious   \n",
-       "1304     3.0       0.0                               Zabour, Miss. Hileni   \n",
-       "1305     3.0       0.0                              Zabour, Miss. Thamine   \n",
-       "1306     3.0       0.0                          Zakarian, Mr. Mapriededer   \n",
-       "1307     3.0       0.0                                Zakarian, Mr. Ortin   \n",
-       "1308     3.0       0.0                                 Zimmerman, Mr. Leo   \n",
-       "1309     0.0       0.0                                                  0   \n",
-       "\n",
-       "         sex      age  sibsp  parch            ticket      fare    cabin  \\\n",
-       "0     female  29.0000    0.0    0.0             24160  211.3375       B5   \n",
-       "1       male   0.9167    1.0    2.0            113781  151.5500  C22 C26   \n",
-       "2     female   2.0000    1.0    2.0            113781  151.5500  C22 C26   \n",
-       "3       male  30.0000    1.0    2.0            113781  151.5500  C22 C26   \n",
-       "4     female  25.0000    1.0    2.0            113781  151.5500  C22 C26   \n",
-       "5       male  48.0000    0.0    0.0             19952   26.5500      E12   \n",
-       "6     female  63.0000    1.0    0.0             13502   77.9583       D7   \n",
-       "7       male  39.0000    0.0    0.0            112050    0.0000      A36   \n",
-       "8     female  53.0000    2.0    0.0             11769   51.4792     C101   \n",
-       "9       male  71.0000    0.0    0.0          PC 17609   49.5042        0   \n",
-       "10      male  47.0000    1.0    0.0          PC 17757  227.5250  C62 C64   \n",
-       "11    female  18.0000    1.0    0.0          PC 17757  227.5250  C62 C64   \n",
-       "12    female  24.0000    0.0    0.0          PC 17477   69.3000      B35   \n",
-       "13    female  26.0000    0.0    0.0             19877   78.8500        0   \n",
-       "14      male  80.0000    0.0    0.0             27042   30.0000      A23   \n",
-       "15      male   0.0000    0.0    0.0          PC 17318   25.9250        0   \n",
-       "16      male  24.0000    0.0    1.0          PC 17558  247.5208  B58 B60   \n",
-       "17    female  50.0000    0.0    1.0          PC 17558  247.5208  B58 B60   \n",
-       "18    female  32.0000    0.0    0.0             11813   76.2917      D15   \n",
-       "19      male  36.0000    0.0    0.0             13050   75.2417       C6   \n",
-       "20      male  37.0000    1.0    1.0             11751   52.5542      D35   \n",
-       "21    female  47.0000    1.0    1.0             11751   52.5542      D35   \n",
-       "22      male  26.0000    0.0    0.0            111369   30.0000     C148   \n",
-       "23    female  42.0000    0.0    0.0          PC 17757  227.5250        0   \n",
-       "24    female  29.0000    0.0    0.0          PC 17483  221.7792      C97   \n",
-       "25      male  25.0000    0.0    0.0             13905   26.0000        0   \n",
-       "26      male  25.0000    1.0    0.0             11967   91.0792      B49   \n",
-       "27    female  19.0000    1.0    0.0             11967   91.0792      B49   \n",
-       "28    female  35.0000    0.0    0.0          PC 17760  135.6333      C99   \n",
-       "29      male  28.0000    0.0    0.0            110564   26.5500      C52   \n",
-       "...      ...      ...    ...    ...               ...       ...      ...   \n",
-       "1280    male  22.0000    0.0    0.0            349252    7.8958        0   \n",
-       "1281    male  22.0000    0.0    0.0            345767    9.0000        0   \n",
-       "1282    male   0.0000    0.0    0.0            359309    8.0500        0   \n",
-       "1283    male   0.0000    0.0    0.0        C.A. 49867    7.5500        0   \n",
-       "1284    male   0.0000    0.0    0.0  SOTON/OQ 3101316    8.0500        0   \n",
-       "1285    male  32.5000    0.0    0.0            345775    9.5000        0   \n",
-       "1286  female  38.0000    0.0    0.0              2688    7.2292        0   \n",
-       "1287    male  51.0000    0.0    0.0            347064    7.7500        0   \n",
-       "1288    male  18.0000    1.0    0.0           3101267    6.4958        0   \n",
-       "1289    male  21.0000    1.0    0.0           3101266    6.4958        0   \n",
-       "1290  female  47.0000    1.0    0.0            363272    7.0000        0   \n",
-       "1291    male   0.0000    0.0    0.0              3410    8.7125        0   \n",
-       "1292    male   0.0000    0.0    0.0     S.O./P.P. 751    7.5500        0   \n",
-       "1293    male   0.0000    0.0    0.0          A/5 2466    8.0500        0   \n",
-       "1294    male  28.5000    0.0    0.0             54636   16.1000        0   \n",
-       "1295    male  21.0000    0.0    0.0  SOTON/OQ 3101317    7.2500        0   \n",
-       "1296    male  27.0000    0.0    0.0            315154    8.6625        0   \n",
-       "1297    male   0.0000    0.0    0.0        A/4. 34244    7.2500        0   \n",
-       "1298    male  36.0000    0.0    0.0            345771    9.5000        0   \n",
-       "1299    male  27.0000    1.0    0.0              2659   14.4542        0   \n",
-       "1300  female  15.0000    1.0    0.0              2659   14.4542        0   \n",
-       "1301    male  45.5000    0.0    0.0              2628    7.2250        0   \n",
-       "1302    male   0.0000    0.0    0.0              2647    7.2250        0   \n",
-       "1303    male   0.0000    0.0    0.0              2627   14.4583        0   \n",
-       "1304  female  14.5000    1.0    0.0              2665   14.4542        0   \n",
-       "1305  female   0.0000    1.0    0.0              2665   14.4542        0   \n",
-       "1306    male  26.5000    0.0    0.0              2656    7.2250        0   \n",
-       "1307    male  27.0000    0.0    0.0              2670    7.2250        0   \n",
-       "1308    male  29.0000    0.0    0.0            315082    7.8750        0   \n",
-       "1309       0   0.0000    0.0    0.0                 0    0.0000        0   \n",
-       "\n",
-       "     embarked boat   body                           home.dest  \n",
-       "0           S    2    0.0                        St Louis, MO  \n",
-       "1           S   11    0.0     Montreal, PQ / Chesterville, ON  \n",
-       "2           S    0    0.0     Montreal, PQ / Chesterville, ON  \n",
-       "3           S    0  135.0     Montreal, PQ / Chesterville, ON  \n",
-       "4           S    0    0.0     Montreal, PQ / Chesterville, ON  \n",
-       "5           S    3    0.0                        New York, NY  \n",
-       "6           S   10    0.0                          Hudson, NY  \n",
-       "7           S    0    0.0                         Belfast, NI  \n",
-       "8           S    D    0.0                 Bayside, Queens, NY  \n",
-       "9           C    0   22.0                 Montevideo, Uruguay  \n",
-       "10          C    0  124.0                        New York, NY  \n",
-       "11          C    4    0.0                        New York, NY  \n",
-       "12          C    9    0.0                       Paris, France  \n",
-       "13          S    6    0.0                                   0  \n",
-       "14          S    B    0.0                       Hessle, Yorks  \n",
-       "15          S    0    0.0                        New York, NY  \n",
-       "16          C    0    0.0                        Montreal, PQ  \n",
-       "17          C    6    0.0                        Montreal, PQ  \n",
-       "18          C    8    0.0                                   0  \n",
-       "19          C    A    0.0                        Winnipeg, MN  \n",
-       "20          S    5    0.0                        New York, NY  \n",
-       "21          S    5    0.0                        New York, NY  \n",
-       "22          C    5    0.0                        New York, NY  \n",
-       "23          C    4    0.0                                   0  \n",
-       "24          S    8    0.0                                   0  \n",
-       "25          C    0  148.0                   San Francisco, CA  \n",
-       "26          C    7    0.0                        Dowagiac, MI  \n",
-       "27          C    7    0.0                        Dowagiac, MI  \n",
-       "28          S    8    0.0                                   0  \n",
-       "29          S    D    0.0  Stockholm, Sweden / Washington, DC  \n",
-       "...       ...  ...    ...                                 ...  \n",
-       "1280        S    0    0.0                                   0  \n",
-       "1281        S    0    0.0      Antwerp, Belgium / Stanton, OH  \n",
-       "1282        S    0    0.0                                   0  \n",
-       "1283        S    0    0.0                                   0  \n",
-       "1284        S    0    0.0                                   0  \n",
-       "1285        S    0  298.0                                   0  \n",
-       "1286        C    C    0.0                                   0  \n",
-       "1287        S    0    0.0                                   0  \n",
-       "1288        S    0  314.0                                   0  \n",
-       "1289        S    0    0.0                                   0  \n",
-       "1290        S    0    0.0                                   0  \n",
-       "1291        S    0    0.0                                   0  \n",
-       "1292        S    0    0.0                                   0  \n",
-       "1293        S    0    0.0                                   0  \n",
-       "1294        S    0   14.0                                   0  \n",
-       "1295        S    0    0.0                                   0  \n",
-       "1296        S    0  131.0                                   0  \n",
-       "1297        S    0    0.0                                   0  \n",
-       "1298        S    0    0.0                                   0  \n",
-       "1299        C    C    0.0                                   0  \n",
-       "1300        C    0    0.0                                   0  \n",
-       "1301        C    0  312.0                                   0  \n",
-       "1302        C    0    0.0                                   0  \n",
-       "1303        C    0    0.0                                   0  \n",
-       "1304        C    0  328.0                                   0  \n",
-       "1305        C    0    0.0                                   0  \n",
-       "1306        C    0  304.0                                   0  \n",
-       "1307        C    0    0.0                                   0  \n",
-       "1308        S    0    0.0                                   0  \n",
-       "1309        0    0    0.0                                   0  \n",
-       "\n",
-       "[1310 rows x 14 columns]"
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "121"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
       ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data3.fillna(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data4 = data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data4= data4.fillna(\"Desconocido\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data5 = data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4phNNBtistUg",
+        "colab_type": "text"
+      },
+      "source": [
+        "Los valores que faltan en un data set pueden venir por dos razones:\n",
+        "* Extracción de los datos\n",
+        "* Recolección de los datos"
+      ]
+    },
     {
-     "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>pclass</th>\n",
-       "      <th>survived</th>\n",
-       "      <th>name</th>\n",
-       "      <th>sex</th>\n",
-       "      <th>age</th>\n",
-       "      <th>sibsp</th>\n",
-       "      <th>parch</th>\n",
-       "      <th>ticket</th>\n",
-       "      <th>fare</th>\n",
-       "      <th>cabin</th>\n",
-       "      <th>embarked</th>\n",
-       "      <th>boat</th>\n",
-       "      <th>body</th>\n",
-       "      <th>home.dest</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
-       "      <td>female</td>\n",
-       "      <td>29.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>24160</td>\n",
-       "      <td>211.3375</td>\n",
-       "      <td>B5</td>\n",
-       "      <td>S</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>St Louis, MO</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Allison, Master. Hudson Trevor</td>\n",
-       "      <td>male</td>\n",
-       "      <td>0.9167</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>11</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Allison, Miss. Helen Loraine</td>\n",
-       "      <td>female</td>\n",
-       "      <td>2.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
-       "      <td>male</td>\n",
-       "      <td>30.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
-       "      <td>female</td>\n",
-       "      <td>25.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>113781</td>\n",
-       "      <td>151.5500</td>\n",
-       "      <td>C22 C26</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Montreal, PQ / Chesterville, ON</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x-cgFFVbstUg",
+        "colab_type": "text"
+      },
+      "source": [
+        "#### Borrado de valores que faltan"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YCS7PRF-stUg",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        },
+        "outputId": "15345cdb-7bf6-4e31-c022-eb4a39605ae4"
+      },
+      "source": [
+        "data.dropna(axis=0, how=\"all\")"
       ],
-      "text/plain": [
-       "   pclass  survived                                             name     sex  \\\n",
-       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
-       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
-       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
-       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
-       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
-       "\n",
-       "       age  sibsp  parch  ticket      fare    cabin embarked boat   body  \\\n",
-       "0  29.0000    0.0    0.0   24160  211.3375       B5        S    2    0.0   \n",
-       "1   0.9167    1.0    2.0  113781  151.5500  C22 C26        S   11    0.0   \n",
-       "2   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    0.0   \n",
-       "3  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN  135.0   \n",
-       "4  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  NaN    0.0   \n",
-       "\n",
-       "                         home.dest  \n",
-       "0                     St Louis, MO  \n",
-       "1  Montreal, PQ / Chesterville, ON  \n",
-       "2  Montreal, PQ / Chesterville, ON  \n",
-       "3  Montreal, PQ / Chesterville, ON  \n",
-       "4  Montreal, PQ / Chesterville, ON  "
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>pclass</th>\n",
+              "      <th>survived</th>\n",
+              "      <th>name</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>ticket</th>\n",
+              "      <th>fare</th>\n",
+              "      <th>cabin</th>\n",
+              "      <th>embarked</th>\n",
+              "      <th>boat</th>\n",
+              "      <th>body</th>\n",
+              "      <th>home.dest</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+              "      <td>female</td>\n",
+              "      <td>29.0000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>24160</td>\n",
+              "      <td>211.3375</td>\n",
+              "      <td>B5</td>\n",
+              "      <td>S</td>\n",
+              "      <td>2</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>St Louis, MO</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Allison, Master. Hudson Trevor</td>\n",
+              "      <td>male</td>\n",
+              "      <td>0.9167</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>11</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Miss. Helen Loraine</td>\n",
+              "      <td>female</td>\n",
+              "      <td>2.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+              "      <td>male</td>\n",
+              "      <td>30.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>135.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>25.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1304</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zabour, Miss. Hileni</td>\n",
+              "      <td>female</td>\n",
+              "      <td>14.5000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2665</td>\n",
+              "      <td>14.4542</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>328.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1305</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zabour, Miss. Thamine</td>\n",
+              "      <td>female</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2665</td>\n",
+              "      <td>14.4542</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1306</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zakarian, Mr. Mapriededer</td>\n",
+              "      <td>male</td>\n",
+              "      <td>26.5000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2656</td>\n",
+              "      <td>7.2250</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>304.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1307</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zakarian, Mr. Ortin</td>\n",
+              "      <td>male</td>\n",
+              "      <td>27.0000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2670</td>\n",
+              "      <td>7.2250</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1308</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zimmerman, Mr. Leo</td>\n",
+              "      <td>male</td>\n",
+              "      <td>29.0000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>315082</td>\n",
+              "      <td>7.8750</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1309 rows × 14 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      pclass  survived  ...   body                        home.dest\n",
+              "0          1         1  ...    NaN                     St Louis, MO\n",
+              "1          1         1  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+              "2          1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+              "3          1         0  ...  135.0  Montreal, PQ / Chesterville, ON\n",
+              "4          1         0  ...    NaN  Montreal, PQ / Chesterville, ON\n",
+              "...      ...       ...  ...    ...                              ...\n",
+              "1304       3         0  ...  328.0                              NaN\n",
+              "1305       3         0  ...    NaN                              NaN\n",
+              "1306       3         0  ...  304.0                              NaN\n",
+              "1307       3         0  ...    NaN                              NaN\n",
+              "1308       3         0  ...    NaN                              NaN\n",
+              "\n",
+              "[1309 rows x 14 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
       ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data5[\"body\"] = data5[\"body\"].fillna(0)\n",
-    "data5[\"home.dest\"] = data5[\"home.dest\"].fillna(\"Desconocido\")\n",
-    "data5.head(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "264"
+      "cell_type": "code",
+      "metadata": {
+        "id": "udKUHN8vstUi",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data2 = data"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "8y71Q4sestUj",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 48
+        },
+        "outputId": "087cf1ad-88a1-45f6-a343-d011750f803f"
+      },
+      "source": [
+        "data2.dropna(axis=0, how=\"any\")"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>pclass</th>\n",
+              "      <th>survived</th>\n",
+              "      <th>name</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>ticket</th>\n",
+              "      <th>fare</th>\n",
+              "      <th>cabin</th>\n",
+              "      <th>embarked</th>\n",
+              "      <th>boat</th>\n",
+              "      <th>body</th>\n",
+              "      <th>home.dest</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "Empty DataFrame\n",
+              "Columns: [pclass, survived, name, sex, age, sibsp, parch, ticket, fare, cabin, embarked, boat, body, home.dest]\n",
+              "Index: []"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
       ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.isnull(data5[\"age\"]).values.ravel().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "0       29.000000\n",
-       "1        0.916700\n",
-       "2        2.000000\n",
-       "3       30.000000\n",
-       "4       25.000000\n",
-       "5       48.000000\n",
-       "6       63.000000\n",
-       "7       39.000000\n",
-       "8       53.000000\n",
-       "9       71.000000\n",
-       "10      47.000000\n",
-       "11      18.000000\n",
-       "12      24.000000\n",
-       "13      26.000000\n",
-       "14      80.000000\n",
-       "15      29.881135\n",
-       "16      24.000000\n",
-       "17      50.000000\n",
-       "18      32.000000\n",
-       "19      36.000000\n",
-       "20      37.000000\n",
-       "21      47.000000\n",
-       "22      26.000000\n",
-       "23      42.000000\n",
-       "24      29.000000\n",
-       "25      25.000000\n",
-       "26      25.000000\n",
-       "27      19.000000\n",
-       "28      35.000000\n",
-       "29      28.000000\n",
-       "          ...    \n",
-       "1280    22.000000\n",
-       "1281    22.000000\n",
-       "1282    29.881135\n",
-       "1283    29.881135\n",
-       "1284    29.881135\n",
-       "1285    32.500000\n",
-       "1286    38.000000\n",
-       "1287    51.000000\n",
-       "1288    18.000000\n",
-       "1289    21.000000\n",
-       "1290    47.000000\n",
-       "1291    29.881135\n",
-       "1292    29.881135\n",
-       "1293    29.881135\n",
-       "1294    28.500000\n",
-       "1295    21.000000\n",
-       "1296    27.000000\n",
-       "1297    29.881135\n",
-       "1298    36.000000\n",
-       "1299    27.000000\n",
-       "1300    15.000000\n",
-       "1301    45.500000\n",
-       "1302    29.881135\n",
-       "1303    29.881135\n",
-       "1304    14.500000\n",
-       "1305    29.881135\n",
-       "1306    26.500000\n",
-       "1307    27.000000\n",
-       "1308    29.000000\n",
-       "1309    29.881135\n",
-       "Name: age, Length: 1310, dtype: float64"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bhTm7KoastUk",
+        "colab_type": "text"
+      },
+      "source": [
+        "#### Cómputo de los valores fantantes"
       ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data5[\"age\"].fillna(data[\"age\"].mean())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hz_2oSgastUk",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data3 = data"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "nan"
+      "cell_type": "code",
+      "metadata": {
+        "id": "qWVJbqDFstUm",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        },
+        "outputId": "5c4b0162-a64d-48ee-df66-94be0d1a9e42"
+      },
+      "source": [
+        "data3.fillna(0)"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>pclass</th>\n",
+              "      <th>survived</th>\n",
+              "      <th>name</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>ticket</th>\n",
+              "      <th>fare</th>\n",
+              "      <th>cabin</th>\n",
+              "      <th>embarked</th>\n",
+              "      <th>boat</th>\n",
+              "      <th>body</th>\n",
+              "      <th>home.dest</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+              "      <td>female</td>\n",
+              "      <td>29.0000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>24160</td>\n",
+              "      <td>211.3375</td>\n",
+              "      <td>B5</td>\n",
+              "      <td>S</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>St Louis, MO</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Allison, Master. Hudson Trevor</td>\n",
+              "      <td>male</td>\n",
+              "      <td>0.9167</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>11</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Miss. Helen Loraine</td>\n",
+              "      <td>female</td>\n",
+              "      <td>2.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+              "      <td>male</td>\n",
+              "      <td>30.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>0</td>\n",
+              "      <td>135.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>25.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1304</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zabour, Miss. Hileni</td>\n",
+              "      <td>female</td>\n",
+              "      <td>14.5000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2665</td>\n",
+              "      <td>14.4542</td>\n",
+              "      <td>0</td>\n",
+              "      <td>C</td>\n",
+              "      <td>0</td>\n",
+              "      <td>328.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1305</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zabour, Miss. Thamine</td>\n",
+              "      <td>female</td>\n",
+              "      <td>0.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2665</td>\n",
+              "      <td>14.4542</td>\n",
+              "      <td>0</td>\n",
+              "      <td>C</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1306</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zakarian, Mr. Mapriededer</td>\n",
+              "      <td>male</td>\n",
+              "      <td>26.5000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2656</td>\n",
+              "      <td>7.2250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>C</td>\n",
+              "      <td>0</td>\n",
+              "      <td>304.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1307</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zakarian, Mr. Ortin</td>\n",
+              "      <td>male</td>\n",
+              "      <td>27.0000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2670</td>\n",
+              "      <td>7.2250</td>\n",
+              "      <td>0</td>\n",
+              "      <td>C</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1308</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zimmerman, Mr. Leo</td>\n",
+              "      <td>male</td>\n",
+              "      <td>29.0000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>315082</td>\n",
+              "      <td>7.8750</td>\n",
+              "      <td>0</td>\n",
+              "      <td>S</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1309 rows × 14 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      pclass  survived  ...   body                        home.dest\n",
+              "0          1         1  ...    0.0                     St Louis, MO\n",
+              "1          1         1  ...    0.0  Montreal, PQ / Chesterville, ON\n",
+              "2          1         0  ...    0.0  Montreal, PQ / Chesterville, ON\n",
+              "3          1         0  ...  135.0  Montreal, PQ / Chesterville, ON\n",
+              "4          1         0  ...    0.0  Montreal, PQ / Chesterville, ON\n",
+              "...      ...       ...  ...    ...                              ...\n",
+              "1304       3         0  ...  328.0                                0\n",
+              "1305       3         0  ...    0.0                                0\n",
+              "1306       3         0  ...  304.0                                0\n",
+              "1307       3         0  ...    0.0                                0\n",
+              "1308       3         0  ...    0.0                                0\n",
+              "\n",
+              "[1309 rows x 14 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 19
+        }
       ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data5[\"age\"][1291]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dNmxfKrVstUn",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data4 = data"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Fuvj3a5mstUp",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data4= data4.fillna(\"Desconocido\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "0       29.0000\n",
-       "1        0.9167\n",
-       "2        2.0000\n",
-       "3       30.0000\n",
-       "4       25.0000\n",
-       "5       48.0000\n",
-       "6       63.0000\n",
-       "7       39.0000\n",
-       "8       53.0000\n",
-       "9       71.0000\n",
-       "10      47.0000\n",
-       "11      18.0000\n",
-       "12      24.0000\n",
-       "13      26.0000\n",
-       "14      80.0000\n",
-       "15      80.0000\n",
-       "16      24.0000\n",
-       "17      50.0000\n",
-       "18      32.0000\n",
-       "19      36.0000\n",
-       "20      37.0000\n",
-       "21      47.0000\n",
-       "22      26.0000\n",
-       "23      42.0000\n",
-       "24      29.0000\n",
-       "25      25.0000\n",
-       "26      25.0000\n",
-       "27      19.0000\n",
-       "28      35.0000\n",
-       "29      28.0000\n",
-       "         ...   \n",
-       "1280    22.0000\n",
-       "1281    22.0000\n",
-       "1282    22.0000\n",
-       "1283    22.0000\n",
-       "1284    22.0000\n",
-       "1285    32.5000\n",
-       "1286    38.0000\n",
-       "1287    51.0000\n",
-       "1288    18.0000\n",
-       "1289    21.0000\n",
-       "1290    47.0000\n",
-       "1291    47.0000\n",
-       "1292    47.0000\n",
-       "1293    47.0000\n",
-       "1294    28.5000\n",
-       "1295    21.0000\n",
-       "1296    27.0000\n",
-       "1297    27.0000\n",
-       "1298    36.0000\n",
-       "1299    27.0000\n",
-       "1300    15.0000\n",
-       "1301    45.5000\n",
-       "1302    45.5000\n",
-       "1303    45.5000\n",
-       "1304    14.5000\n",
-       "1305    14.5000\n",
-       "1306    26.5000\n",
-       "1307    27.0000\n",
-       "1308    29.0000\n",
-       "1309    29.0000\n",
-       "Name: age, Length: 1310, dtype: float64"
+      "cell_type": "code",
+      "metadata": {
+        "id": "zjtGoMDlstUq",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data5 = data"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5xVfZZjqstUr",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "97b5a74b-d38f-4c49-8f5b-0fee381e20a1"
+      },
+      "source": [
+        "data5[\"body\"] = data5[\"body\"].fillna(0)\n",
+        "data5[\"home.dest\"] = data5[\"home.dest\"].fillna(\"Desconocido\")\n",
+        "data5.head(5)"
+      ],
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>pclass</th>\n",
+              "      <th>survived</th>\n",
+              "      <th>name</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>ticket</th>\n",
+              "      <th>fare</th>\n",
+              "      <th>cabin</th>\n",
+              "      <th>embarked</th>\n",
+              "      <th>boat</th>\n",
+              "      <th>body</th>\n",
+              "      <th>home.dest</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+              "      <td>female</td>\n",
+              "      <td>29.0000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>24160</td>\n",
+              "      <td>211.3375</td>\n",
+              "      <td>B5</td>\n",
+              "      <td>S</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>St Louis, MO</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Allison, Master. Hudson Trevor</td>\n",
+              "      <td>male</td>\n",
+              "      <td>0.9167</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>11</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Miss. Helen Loraine</td>\n",
+              "      <td>female</td>\n",
+              "      <td>2.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+              "      <td>male</td>\n",
+              "      <td>30.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>135.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+              "      <td>female</td>\n",
+              "      <td>25.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   pclass  survived  ...   body                        home.dest\n",
+              "0       1         1  ...    0.0                     St Louis, MO\n",
+              "1       1         1  ...    0.0  Montreal, PQ / Chesterville, ON\n",
+              "2       1         0  ...    0.0  Montreal, PQ / Chesterville, ON\n",
+              "3       1         0  ...  135.0  Montreal, PQ / Chesterville, ON\n",
+              "4       1         0  ...    0.0  Montreal, PQ / Chesterville, ON\n",
+              "\n",
+              "[5 rows x 14 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 23
+        }
       ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data5[\"age\"].fillna(method=\"ffill\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "0       29.0000\n",
-       "1        0.9167\n",
-       "2        2.0000\n",
-       "3       30.0000\n",
-       "4       25.0000\n",
-       "5       48.0000\n",
-       "6       63.0000\n",
-       "7       39.0000\n",
-       "8       53.0000\n",
-       "9       71.0000\n",
-       "10      47.0000\n",
-       "11      18.0000\n",
-       "12      24.0000\n",
-       "13      26.0000\n",
-       "14      80.0000\n",
-       "15      24.0000\n",
-       "16      24.0000\n",
-       "17      50.0000\n",
-       "18      32.0000\n",
-       "19      36.0000\n",
-       "20      37.0000\n",
-       "21      47.0000\n",
-       "22      26.0000\n",
-       "23      42.0000\n",
-       "24      29.0000\n",
-       "25      25.0000\n",
-       "26      25.0000\n",
-       "27      19.0000\n",
-       "28      35.0000\n",
-       "29      28.0000\n",
-       "         ...   \n",
-       "1280    22.0000\n",
-       "1281    22.0000\n",
-       "1282    32.5000\n",
-       "1283    32.5000\n",
-       "1284    32.5000\n",
-       "1285    32.5000\n",
-       "1286    38.0000\n",
-       "1287    51.0000\n",
-       "1288    18.0000\n",
-       "1289    21.0000\n",
-       "1290    47.0000\n",
-       "1291    28.5000\n",
-       "1292    28.5000\n",
-       "1293    28.5000\n",
-       "1294    28.5000\n",
-       "1295    21.0000\n",
-       "1296    27.0000\n",
-       "1297    36.0000\n",
-       "1298    36.0000\n",
-       "1299    27.0000\n",
-       "1300    15.0000\n",
-       "1301    45.5000\n",
-       "1302    14.5000\n",
-       "1303    14.5000\n",
-       "1304    14.5000\n",
-       "1305    26.5000\n",
-       "1306    26.5000\n",
-       "1307    27.0000\n",
-       "1308    29.0000\n",
-       "1309        NaN\n",
-       "Name: age, Length: 1310, dtype: float64"
+      "cell_type": "code",
+      "metadata": {
+        "id": "8-k1TYRAstUt",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "ab51c187-7239-430d-98b3-8e73317e6b6d"
+      },
+      "source": [
+        "pd.isnull(data5[\"age\"]).values.ravel().sum()"
+      ],
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "263"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 24
+        }
       ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data5[\"age\"].fillna(method=\"backfill\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Variables dummy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "0    female\n",
-       "1      male\n",
-       "2    female\n",
-       "3      male\n",
-       "4    female\n",
-       "5      male\n",
-       "6    female\n",
-       "7      male\n",
-       "8    female\n",
-       "9      male\n",
-       "Name: sex, dtype: object"
+      "cell_type": "code",
+      "metadata": {
+        "id": "W-H6DyvrstUu",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "8e325f64-7eea-47ae-be29-4e0227e1b8cf"
+      },
+      "source": [
+        "data5[\"age\"].fillna(data[\"age\"].mean())"
+      ],
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0       29.000000\n",
+              "1        0.916700\n",
+              "2        2.000000\n",
+              "3       30.000000\n",
+              "4       25.000000\n",
+              "          ...    \n",
+              "1304    14.500000\n",
+              "1305    29.881135\n",
+              "1306    26.500000\n",
+              "1307    27.000000\n",
+              "1308    29.000000\n",
+              "Name: age, Length: 1309, dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 25
+        }
       ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[\"sex\"].head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dummy_sex = pd.get_dummies(data[\"sex\"], prefix=\"sex\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "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>sex_female</th>\n",
-       "      <th>sex_male</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "wlWPK6RnstUv",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "8f88b6b7-65c0-44cd-f54a-d24e831f0343"
+      },
+      "source": [
+        "data5[\"age\"][1291]"
       ],
-      "text/plain": [
-       "   sex_female  sex_male\n",
-       "0           1         0\n",
-       "1           0         1\n",
-       "2           1         0\n",
-       "3           0         1\n",
-       "4           1         0\n",
-       "5           0         1\n",
-       "6           1         0\n",
-       "7           0         1\n",
-       "8           1         0\n",
-       "9           0         1"
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "nan"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 26
+        }
       ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dummy_sex.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "['pclass',\n",
-       " 'survived',\n",
-       " 'name',\n",
-       " 'sex',\n",
-       " 'age',\n",
-       " 'sibsp',\n",
-       " 'parch',\n",
-       " 'ticket',\n",
-       " 'fare',\n",
-       " 'cabin',\n",
-       " 'embarked',\n",
-       " 'boat',\n",
-       " 'body',\n",
-       " 'home.dest']"
+      "cell_type": "code",
+      "metadata": {
+        "id": "2dTk5NWLstUw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "282c385b-d877-4534-b991-090507dea7ef"
+      },
+      "source": [
+        "data5[\"age\"].fillna(method=\"ffill\")"
+      ],
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0       29.0000\n",
+              "1        0.9167\n",
+              "2        2.0000\n",
+              "3       30.0000\n",
+              "4       25.0000\n",
+              "         ...   \n",
+              "1304    14.5000\n",
+              "1305    14.5000\n",
+              "1306    26.5000\n",
+              "1307    27.0000\n",
+              "1308    29.0000\n",
+              "Name: age, Length: 1309, dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 27
+        }
       ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "column_name=data.columns.values.tolist()\n",
-    "column_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = data.drop([\"sex\"], axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.concat([data, dummy_sex], axis = 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def createDummies(df, var_name):\n",
-    "    dummy = pd.get_dummies(df[var_name], prefix=var_name)\n",
-    "    df = df.drop(var_name, axis = 1)\n",
-    "    df = pd.concat([df, dummy ], axis = 1)\n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "HheokQ1xstUy",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "01bb1478-279b-4775-da4d-5c2bfd75f9ee"
+      },
+      "source": [
+        "data5[\"age\"].fillna(method=\"backfill\")"
+      ],
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0       29.0000\n",
+              "1        0.9167\n",
+              "2        2.0000\n",
+              "3       30.0000\n",
+              "4       25.0000\n",
+              "         ...   \n",
+              "1304    14.5000\n",
+              "1305    26.5000\n",
+              "1306    26.5000\n",
+              "1307    27.0000\n",
+              "1308    29.0000\n",
+              "Name: age, Length: 1309, dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QbaXaBqLstUz",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Variables dummy"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xSwVk0FFstUz",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "9483dc25-1d42-4a36-e94b-70cd5f475107"
+      },
+      "source": [
+        "data[\"sex\"].head(10)"
+      ],
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0    female\n",
+              "1      male\n",
+              "2    female\n",
+              "3      male\n",
+              "4    female\n",
+              "5      male\n",
+              "6    female\n",
+              "7      male\n",
+              "8    female\n",
+              "9      male\n",
+              "Name: sex, dtype: object"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 29
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cVI6XnwAstU0",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "dummy_sex = pd.get_dummies(data[\"sex\"], prefix=\"sex\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "UACeCYkVstU1",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 359
+        },
+        "outputId": "07c6753b-9760-4356-fc91-e14aab5d7ac2"
+      },
+      "source": [
+        "dummy_sex.head(10)"
+      ],
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>sex_female</th>\n",
+              "      <th>sex_male</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   sex_female  sex_male\n",
+              "0           1         0\n",
+              "1           0         1\n",
+              "2           1         0\n",
+              "3           0         1\n",
+              "4           1         0\n",
+              "5           0         1\n",
+              "6           1         0\n",
+              "7           0         1\n",
+              "8           1         0\n",
+              "9           0         1"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 31
+        }
+      ]
+    },
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-       "      <td>Anderson, Mr. Harry</td>\n",
-       "      <td>48.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19952</td>\n",
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-       "      <td>63.0000</td>\n",
-       "      <td>1.0</td>\n",
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-       "      <td>39.0000</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>53.0000</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>S</td>\n",
-       "      <td>D</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Bayside, Queens, NY</td>\n",
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-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>9</th>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>Artagaveytia, Mr. Ramon</td>\n",
-       "      <td>71.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>49.5042</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>Montevideo, Uruguay</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
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-       "    <tr>\n",
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-       "      <td>0</td>\n",
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-       "      <td>1.0</td>\n",
-       "      <td>Astor, Mrs. John Jacob (Madeleine Talmadge Force)</td>\n",
-       "      <td>18.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>227.5250</td>\n",
-       "      <td>C62 C64</td>\n",
-       "      <td>C</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>New York, NY</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
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-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Aubart, Mme. Leontine Pauline</td>\n",
-       "      <td>24.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17477</td>\n",
-       "      <td>69.3000</td>\n",
-       "      <td>B35</td>\n",
-       "      <td>C</td>\n",
-       "      <td>9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Paris, France</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Barber, Miss. Ellen \"Nellie\"</td>\n",
-       "      <td>26.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>19877</td>\n",
-       "      <td>78.8500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Barkworth, Mr. Algernon Henry Wilson</td>\n",
-       "      <td>80.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>27042</td>\n",
-       "      <td>30.0000</td>\n",
-       "      <td>A23</td>\n",
-       "      <td>S</td>\n",
-       "      <td>B</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Hessle, Yorks</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Baumann, Mr. John D</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17318</td>\n",
-       "      <td>25.9250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>New York, NY</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Baxter, Mr. Quigg Edmond</td>\n",
-       "      <td>24.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>PC 17558</td>\n",
-       "      <td>247.5208</td>\n",
-       "      <td>B58 B60</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Montreal, PQ</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Baxter, Mrs. James (Helene DeLaudeniere Chaput)</td>\n",
-       "      <td>50.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>PC 17558</td>\n",
-       "      <td>247.5208</td>\n",
-       "      <td>B58 B60</td>\n",
-       "      <td>C</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Montreal, PQ</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bazzani, Miss. Albina</td>\n",
-       "      <td>32.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11813</td>\n",
-       "      <td>76.2917</td>\n",
-       "      <td>D15</td>\n",
-       "      <td>C</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Beattie, Mr. Thomson</td>\n",
-       "      <td>36.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13050</td>\n",
-       "      <td>75.2417</td>\n",
-       "      <td>C6</td>\n",
-       "      <td>C</td>\n",
-       "      <td>A</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Winnipeg, MN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Beckwith, Mr. Richard Leonard</td>\n",
-       "      <td>37.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11751</td>\n",
-       "      <td>52.5542</td>\n",
-       "      <td>D35</td>\n",
-       "      <td>S</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>New York, NY</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Beckwith, Mrs. Richard Leonard (Sallie Monypeny)</td>\n",
-       "      <td>47.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>11751</td>\n",
-       "      <td>52.5542</td>\n",
-       "      <td>D35</td>\n",
-       "      <td>S</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>New York, NY</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Behr, Mr. Karl Howell</td>\n",
-       "      <td>26.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>111369</td>\n",
-       "      <td>30.0000</td>\n",
-       "      <td>C148</td>\n",
-       "      <td>C</td>\n",
-       "      <td>5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>New York, NY</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bidois, Miss. Rosalie</td>\n",
-       "      <td>42.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17757</td>\n",
-       "      <td>227.5250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bird, Miss. Ellen</td>\n",
-       "      <td>29.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17483</td>\n",
-       "      <td>221.7792</td>\n",
-       "      <td>C97</td>\n",
-       "      <td>S</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Birnbaum, Mr. Jakob</td>\n",
-       "      <td>25.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13905</td>\n",
-       "      <td>26.0000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>148.0</td>\n",
-       "      <td>San Francisco, CA</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bishop, Mr. Dickinson H</td>\n",
-       "      <td>25.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11967</td>\n",
-       "      <td>91.0792</td>\n",
-       "      <td>B49</td>\n",
-       "      <td>C</td>\n",
-       "      <td>7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Dowagiac, MI</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bishop, Mrs. Dickinson H (Helen Walton)</td>\n",
-       "      <td>19.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11967</td>\n",
-       "      <td>91.0792</td>\n",
-       "      <td>B49</td>\n",
-       "      <td>C</td>\n",
-       "      <td>7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Dowagiac, MI</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bissette, Miss. Amelia</td>\n",
-       "      <td>35.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>PC 17760</td>\n",
-       "      <td>135.6333</td>\n",
-       "      <td>C99</td>\n",
-       "      <td>S</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Bjornstrom-Steffansson, Mr. Mauritz Hakan</td>\n",
-       "      <td>28.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>110564</td>\n",
-       "      <td>26.5500</td>\n",
-       "      <td>C52</td>\n",
-       "      <td>S</td>\n",
-       "      <td>D</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Stockholm, Sweden / Washington, DC</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1280</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Vovk, Mr. Janko</td>\n",
-       "      <td>22.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>349252</td>\n",
-       "      <td>7.8958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1281</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Waelens, Mr. Achille</td>\n",
-       "      <td>22.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>345767</td>\n",
-       "      <td>9.0000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Antwerp, Belgium / Stanton, OH</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1282</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Ware, Mr. Frederick</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>359309</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1283</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Warren, Mr. Charles William</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>C.A. 49867</td>\n",
-       "      <td>7.5500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1284</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Webber, Mr. James</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>SOTON/OQ 3101316</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1285</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wenzel, Mr. Linhart</td>\n",
-       "      <td>32.5000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>345775</td>\n",
-       "      <td>9.5000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>298.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1286</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Whabee, Mrs. George Joseph (Shawneene Abi-Saab)</td>\n",
-       "      <td>38.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2688</td>\n",
-       "      <td>7.2292</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1287</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Widegren, Mr. Carl/Charles Peter</td>\n",
-       "      <td>51.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>347064</td>\n",
-       "      <td>7.7500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1288</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wiklund, Mr. Jakob Alfred</td>\n",
-       "      <td>18.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3101267</td>\n",
-       "      <td>6.4958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>314.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1289</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wiklund, Mr. Karl Johan</td>\n",
-       "      <td>21.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3101266</td>\n",
-       "      <td>6.4958</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1290</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
-       "      <td>47.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>363272</td>\n",
-       "      <td>7.0000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1291</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Willer, Mr. Aaron (\"Abi Weller\")</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3410</td>\n",
-       "      <td>8.7125</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1292</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Willey, Mr. Edward</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>S.O./P.P. 751</td>\n",
-       "      <td>7.5500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1293</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Williams, Mr. Howard Hugh \"Harry\"</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>A/5 2466</td>\n",
-       "      <td>8.0500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1294</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Williams, Mr. Leslie</td>\n",
-       "      <td>28.5000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>54636</td>\n",
-       "      <td>16.1000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1295</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Windelov, Mr. Einar</td>\n",
-       "      <td>21.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>SOTON/OQ 3101317</td>\n",
-       "      <td>7.2500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1296</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wirz, Mr. Albert</td>\n",
-       "      <td>27.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>315154</td>\n",
-       "      <td>8.6625</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>131.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1297</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wiseman, Mr. Phillippe</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>A/4. 34244</td>\n",
-       "      <td>7.2500</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1298</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Wittevrongel, Mr. Camille</td>\n",
-       "      <td>36.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>345771</td>\n",
-       "      <td>9.5000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1299</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Yasbeck, Mr. Antoni</td>\n",
-       "      <td>27.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2659</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>C</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1300</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Yasbeck, Mrs. Antoni (Selini Alexander)</td>\n",
-       "      <td>15.0000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2659</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1301</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Youseff, Mr. Gerious</td>\n",
-       "      <td>45.5000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2628</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>312.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1302</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Yousif, Mr. Wazli</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2647</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1303</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Yousseff, Mr. Gerious</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2627</td>\n",
-       "      <td>14.4583</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1304</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zabour, Miss. Hileni</td>\n",
-       "      <td>14.5000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2665</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>328.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1305</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zabour, Miss. Thamine</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2665</td>\n",
-       "      <td>14.4542</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1306</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zakarian, Mr. Mapriededer</td>\n",
-       "      <td>26.5000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2656</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>304.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1307</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zakarian, Mr. Ortin</td>\n",
-       "      <td>27.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2670</td>\n",
-       "      <td>7.2250</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>C</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1308</th>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Zimmerman, Mr. Leo</td>\n",
-       "      <td>29.0000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>315082</td>\n",
-       "      <td>7.8750</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>S</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1309</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>Desconocido</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1310 rows × 15 columns</p>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "XfaDOjjPstU2",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 255
+        },
+        "outputId": "d06566ca-23c1-46ae-cdf5-63bd07c4e892"
+      },
+      "source": [
+        "column_name=data.columns.values.tolist()\n",
+        "column_name"
       ],
-      "text/plain": [
-       "      pclass  survived                                               name  \\\n",
-       "0        1.0       1.0                      Allen, Miss. Elisabeth Walton   \n",
-       "1        1.0       1.0                     Allison, Master. Hudson Trevor   \n",
-       "2        1.0       0.0                       Allison, Miss. Helen Loraine   \n",
-       "3        1.0       0.0               Allison, Mr. Hudson Joshua Creighton   \n",
-       "4        1.0       0.0    Allison, Mrs. Hudson J C (Bessie Waldo Daniels)   \n",
-       "5        1.0       1.0                                Anderson, Mr. Harry   \n",
-       "6        1.0       1.0                  Andrews, Miss. Kornelia Theodosia   \n",
-       "7        1.0       0.0                             Andrews, Mr. Thomas Jr   \n",
-       "8        1.0       1.0      Appleton, Mrs. Edward Dale (Charlotte Lamson)   \n",
-       "9        1.0       0.0                            Artagaveytia, Mr. Ramon   \n",
-       "10       1.0       0.0                             Astor, Col. John Jacob   \n",
-       "11       1.0       1.0  Astor, Mrs. John Jacob (Madeleine Talmadge Force)   \n",
-       "12       1.0       1.0                      Aubart, Mme. Leontine Pauline   \n",
-       "13       1.0       1.0                       Barber, Miss. Ellen \"Nellie\"   \n",
-       "14       1.0       1.0               Barkworth, Mr. Algernon Henry Wilson   \n",
-       "15       1.0       0.0                                Baumann, Mr. John D   \n",
-       "16       1.0       0.0                           Baxter, Mr. Quigg Edmond   \n",
-       "17       1.0       1.0    Baxter, Mrs. James (Helene DeLaudeniere Chaput)   \n",
-       "18       1.0       1.0                              Bazzani, Miss. Albina   \n",
-       "19       1.0       0.0                               Beattie, Mr. Thomson   \n",
-       "20       1.0       1.0                      Beckwith, Mr. Richard Leonard   \n",
-       "21       1.0       1.0   Beckwith, Mrs. Richard Leonard (Sallie Monypeny)   \n",
-       "22       1.0       1.0                              Behr, Mr. Karl Howell   \n",
-       "23       1.0       1.0                              Bidois, Miss. Rosalie   \n",
-       "24       1.0       1.0                                  Bird, Miss. Ellen   \n",
-       "25       1.0       0.0                                Birnbaum, Mr. Jakob   \n",
-       "26       1.0       1.0                            Bishop, Mr. Dickinson H   \n",
-       "27       1.0       1.0            Bishop, Mrs. Dickinson H (Helen Walton)   \n",
-       "28       1.0       1.0                             Bissette, Miss. Amelia   \n",
-       "29       1.0       1.0          Bjornstrom-Steffansson, Mr. Mauritz Hakan   \n",
-       "...      ...       ...                                                ...   \n",
-       "1280     3.0       0.0                                    Vovk, Mr. Janko   \n",
-       "1281     3.0       0.0                               Waelens, Mr. Achille   \n",
-       "1282     3.0       0.0                                Ware, Mr. Frederick   \n",
-       "1283     3.0       0.0                        Warren, Mr. Charles William   \n",
-       "1284     3.0       0.0                                  Webber, Mr. James   \n",
-       "1285     3.0       0.0                                Wenzel, Mr. Linhart   \n",
-       "1286     3.0       1.0    Whabee, Mrs. George Joseph (Shawneene Abi-Saab)   \n",
-       "1287     3.0       0.0                   Widegren, Mr. Carl/Charles Peter   \n",
-       "1288     3.0       0.0                          Wiklund, Mr. Jakob Alfred   \n",
-       "1289     3.0       0.0                            Wiklund, Mr. Karl Johan   \n",
-       "1290     3.0       1.0                   Wilkes, Mrs. James (Ellen Needs)   \n",
-       "1291     3.0       0.0                   Willer, Mr. Aaron (\"Abi Weller\")   \n",
-       "1292     3.0       0.0                                 Willey, Mr. Edward   \n",
-       "1293     3.0       0.0                  Williams, Mr. Howard Hugh \"Harry\"   \n",
-       "1294     3.0       0.0                               Williams, Mr. Leslie   \n",
-       "1295     3.0       0.0                                Windelov, Mr. Einar   \n",
-       "1296     3.0       0.0                                   Wirz, Mr. Albert   \n",
-       "1297     3.0       0.0                             Wiseman, Mr. Phillippe   \n",
-       "1298     3.0       0.0                          Wittevrongel, Mr. Camille   \n",
-       "1299     3.0       0.0                                Yasbeck, Mr. Antoni   \n",
-       "1300     3.0       1.0            Yasbeck, Mrs. Antoni (Selini Alexander)   \n",
-       "1301     3.0       0.0                               Youseff, Mr. Gerious   \n",
-       "1302     3.0       0.0                                  Yousif, Mr. Wazli   \n",
-       "1303     3.0       0.0                              Yousseff, Mr. Gerious   \n",
-       "1304     3.0       0.0                               Zabour, Miss. Hileni   \n",
-       "1305     3.0       0.0                              Zabour, Miss. Thamine   \n",
-       "1306     3.0       0.0                          Zakarian, Mr. Mapriededer   \n",
-       "1307     3.0       0.0                                Zakarian, Mr. Ortin   \n",
-       "1308     3.0       0.0                                 Zimmerman, Mr. Leo   \n",
-       "1309     NaN       NaN                                                NaN   \n",
-       "\n",
-       "          age  sibsp  parch            ticket      fare    cabin embarked  \\\n",
-       "0     29.0000    0.0    0.0             24160  211.3375       B5        S   \n",
-       "1      0.9167    1.0    2.0            113781  151.5500  C22 C26        S   \n",
-       "2      2.0000    1.0    2.0            113781  151.5500  C22 C26        S   \n",
-       "3     30.0000    1.0    2.0            113781  151.5500  C22 C26        S   \n",
-       "4     25.0000    1.0    2.0            113781  151.5500  C22 C26        S   \n",
-       "5     48.0000    0.0    0.0             19952   26.5500      E12        S   \n",
-       "6     63.0000    1.0    0.0             13502   77.9583       D7        S   \n",
-       "7     39.0000    0.0    0.0            112050    0.0000      A36        S   \n",
-       "8     53.0000    2.0    0.0             11769   51.4792     C101        S   \n",
-       "9     71.0000    0.0    0.0          PC 17609   49.5042      NaN        C   \n",
-       "10    47.0000    1.0    0.0          PC 17757  227.5250  C62 C64        C   \n",
-       "11    18.0000    1.0    0.0          PC 17757  227.5250  C62 C64        C   \n",
-       "12    24.0000    0.0    0.0          PC 17477   69.3000      B35        C   \n",
-       "13    26.0000    0.0    0.0             19877   78.8500      NaN        S   \n",
-       "14    80.0000    0.0    0.0             27042   30.0000      A23        S   \n",
-       "15        NaN    0.0    0.0          PC 17318   25.9250      NaN        S   \n",
-       "16    24.0000    0.0    1.0          PC 17558  247.5208  B58 B60        C   \n",
-       "17    50.0000    0.0    1.0          PC 17558  247.5208  B58 B60        C   \n",
-       "18    32.0000    0.0    0.0             11813   76.2917      D15        C   \n",
-       "19    36.0000    0.0    0.0             13050   75.2417       C6        C   \n",
-       "20    37.0000    1.0    1.0             11751   52.5542      D35        S   \n",
-       "21    47.0000    1.0    1.0             11751   52.5542      D35        S   \n",
-       "22    26.0000    0.0    0.0            111369   30.0000     C148        C   \n",
-       "23    42.0000    0.0    0.0          PC 17757  227.5250      NaN        C   \n",
-       "24    29.0000    0.0    0.0          PC 17483  221.7792      C97        S   \n",
-       "25    25.0000    0.0    0.0             13905   26.0000      NaN        C   \n",
-       "26    25.0000    1.0    0.0             11967   91.0792      B49        C   \n",
-       "27    19.0000    1.0    0.0             11967   91.0792      B49        C   \n",
-       "28    35.0000    0.0    0.0          PC 17760  135.6333      C99        S   \n",
-       "29    28.0000    0.0    0.0            110564   26.5500      C52        S   \n",
-       "...       ...    ...    ...               ...       ...      ...      ...   \n",
-       "1280  22.0000    0.0    0.0            349252    7.8958      NaN        S   \n",
-       "1281  22.0000    0.0    0.0            345767    9.0000      NaN        S   \n",
-       "1282      NaN    0.0    0.0            359309    8.0500      NaN        S   \n",
-       "1283      NaN    0.0    0.0        C.A. 49867    7.5500      NaN        S   \n",
-       "1284      NaN    0.0    0.0  SOTON/OQ 3101316    8.0500      NaN        S   \n",
-       "1285  32.5000    0.0    0.0            345775    9.5000      NaN        S   \n",
-       "1286  38.0000    0.0    0.0              2688    7.2292      NaN        C   \n",
-       "1287  51.0000    0.0    0.0            347064    7.7500      NaN        S   \n",
-       "1288  18.0000    1.0    0.0           3101267    6.4958      NaN        S   \n",
-       "1289  21.0000    1.0    0.0           3101266    6.4958      NaN        S   \n",
-       "1290  47.0000    1.0    0.0            363272    7.0000      NaN        S   \n",
-       "1291      NaN    0.0    0.0              3410    8.7125      NaN        S   \n",
-       "1292      NaN    0.0    0.0     S.O./P.P. 751    7.5500      NaN        S   \n",
-       "1293      NaN    0.0    0.0          A/5 2466    8.0500      NaN        S   \n",
-       "1294  28.5000    0.0    0.0             54636   16.1000      NaN        S   \n",
-       "1295  21.0000    0.0    0.0  SOTON/OQ 3101317    7.2500      NaN        S   \n",
-       "1296  27.0000    0.0    0.0            315154    8.6625      NaN        S   \n",
-       "1297      NaN    0.0    0.0        A/4. 34244    7.2500      NaN        S   \n",
-       "1298  36.0000    0.0    0.0            345771    9.5000      NaN        S   \n",
-       "1299  27.0000    1.0    0.0              2659   14.4542      NaN        C   \n",
-       "1300  15.0000    1.0    0.0              2659   14.4542      NaN        C   \n",
-       "1301  45.5000    0.0    0.0              2628    7.2250      NaN        C   \n",
-       "1302      NaN    0.0    0.0              2647    7.2250      NaN        C   \n",
-       "1303      NaN    0.0    0.0              2627   14.4583      NaN        C   \n",
-       "1304  14.5000    1.0    0.0              2665   14.4542      NaN        C   \n",
-       "1305      NaN    1.0    0.0              2665   14.4542      NaN        C   \n",
-       "1306  26.5000    0.0    0.0              2656    7.2250      NaN        C   \n",
-       "1307  27.0000    0.0    0.0              2670    7.2250      NaN        C   \n",
-       "1308  29.0000    0.0    0.0            315082    7.8750      NaN        S   \n",
-       "1309      NaN    NaN    NaN               NaN       NaN      NaN      NaN   \n",
-       "\n",
-       "     boat   body                           home.dest  sex_female  sex_male  \n",
-       "0       2    0.0                        St Louis, MO           1         0  \n",
-       "1      11    0.0     Montreal, PQ / Chesterville, ON           0         1  \n",
-       "2     NaN    0.0     Montreal, PQ / Chesterville, ON           1         0  \n",
-       "3     NaN  135.0     Montreal, PQ / Chesterville, ON           0         1  \n",
-       "4     NaN    0.0     Montreal, PQ / Chesterville, ON           1         0  \n",
-       "5       3    0.0                        New York, NY           0         1  \n",
-       "6      10    0.0                          Hudson, NY           1         0  \n",
-       "7     NaN    0.0                         Belfast, NI           0         1  \n",
-       "8       D    0.0                 Bayside, Queens, NY           1         0  \n",
-       "9     NaN   22.0                 Montevideo, Uruguay           0         1  \n",
-       "10    NaN  124.0                        New York, NY           0         1  \n",
-       "11      4    0.0                        New York, NY           1         0  \n",
-       "12      9    0.0                       Paris, France           1         0  \n",
-       "13      6    0.0                         Desconocido           1         0  \n",
-       "14      B    0.0                       Hessle, Yorks           0         1  \n",
-       "15    NaN    0.0                        New York, NY           0         1  \n",
-       "16    NaN    0.0                        Montreal, PQ           0         1  \n",
-       "17      6    0.0                        Montreal, PQ           1         0  \n",
-       "18      8    0.0                         Desconocido           1         0  \n",
-       "19      A    0.0                        Winnipeg, MN           0         1  \n",
-       "20      5    0.0                        New York, NY           0         1  \n",
-       "21      5    0.0                        New York, NY           1         0  \n",
-       "22      5    0.0                        New York, NY           0         1  \n",
-       "23      4    0.0                         Desconocido           1         0  \n",
-       "24      8    0.0                         Desconocido           1         0  \n",
-       "25    NaN  148.0                   San Francisco, CA           0         1  \n",
-       "26      7    0.0                        Dowagiac, MI           0         1  \n",
-       "27      7    0.0                        Dowagiac, MI           1         0  \n",
-       "28      8    0.0                         Desconocido           1         0  \n",
-       "29      D    0.0  Stockholm, Sweden / Washington, DC           0         1  \n",
-       "...   ...    ...                                 ...         ...       ...  \n",
-       "1280  NaN    0.0                         Desconocido           0         1  \n",
-       "1281  NaN    0.0      Antwerp, Belgium / Stanton, OH           0         1  \n",
-       "1282  NaN    0.0                         Desconocido           0         1  \n",
-       "1283  NaN    0.0                         Desconocido           0         1  \n",
-       "1284  NaN    0.0                         Desconocido           0         1  \n",
-       "1285  NaN  298.0                         Desconocido           0         1  \n",
-       "1286    C    0.0                         Desconocido           1         0  \n",
-       "1287  NaN    0.0                         Desconocido           0         1  \n",
-       "1288  NaN  314.0                         Desconocido           0         1  \n",
-       "1289  NaN    0.0                         Desconocido           0         1  \n",
-       "1290  NaN    0.0                         Desconocido           1         0  \n",
-       "1291  NaN    0.0                         Desconocido           0         1  \n",
-       "1292  NaN    0.0                         Desconocido           0         1  \n",
-       "1293  NaN    0.0                         Desconocido           0         1  \n",
-       "1294  NaN   14.0                         Desconocido           0         1  \n",
-       "1295  NaN    0.0                         Desconocido           0         1  \n",
-       "1296  NaN  131.0                         Desconocido           0         1  \n",
-       "1297  NaN    0.0                         Desconocido           0         1  \n",
-       "1298  NaN    0.0                         Desconocido           0         1  \n",
-       "1299    C    0.0                         Desconocido           0         1  \n",
-       "1300  NaN    0.0                         Desconocido           1         0  \n",
-       "1301  NaN  312.0                         Desconocido           0         1  \n",
-       "1302  NaN    0.0                         Desconocido           0         1  \n",
-       "1303  NaN    0.0                         Desconocido           0         1  \n",
-       "1304  NaN  328.0                         Desconocido           1         0  \n",
-       "1305  NaN    0.0                         Desconocido           1         0  \n",
-       "1306  NaN  304.0                         Desconocido           0         1  \n",
-       "1307  NaN    0.0                         Desconocido           0         1  \n",
-       "1308  NaN    0.0                         Desconocido           0         1  \n",
-       "1309  NaN    0.0                         Desconocido           0         0  \n",
-       "\n",
-       "[1310 rows x 15 columns]"
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['pclass',\n",
+              " 'survived',\n",
+              " 'name',\n",
+              " 'sex',\n",
+              " 'age',\n",
+              " 'sibsp',\n",
+              " 'parch',\n",
+              " 'ticket',\n",
+              " 'fare',\n",
+              " 'cabin',\n",
+              " 'embarked',\n",
+              " 'boat',\n",
+              " 'body',\n",
+              " 'home.dest']"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZnQJ01axstU3",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data = data.drop([\"sex\"], axis = 1)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "sRnzCHnOstU4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data = pd.concat([data, dummy_sex], axis = 1)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "VzRD_sPQstU5",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "def createDummies(df, var_name):\n",
+        "    dummy = pd.get_dummies(df[var_name], prefix=var_name)\n",
+        "    df = df.drop(var_name, axis = 1)\n",
+        "    df = pd.concat([df, dummy ], axis = 1)\n",
+        "    return df"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_0Fp6A5DstU6",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 487
+        },
+        "outputId": "67b6367f-51c8-4fdc-84fc-8b6f60669a02"
+      },
+      "source": [
+        "createDummies(data3, \"sex\")"
+      ],
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>pclass</th>\n",
+              "      <th>survived</th>\n",
+              "      <th>name</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>ticket</th>\n",
+              "      <th>fare</th>\n",
+              "      <th>cabin</th>\n",
+              "      <th>embarked</th>\n",
+              "      <th>boat</th>\n",
+              "      <th>body</th>\n",
+              "      <th>home.dest</th>\n",
+              "      <th>sex_female</th>\n",
+              "      <th>sex_male</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Allen, Miss. Elisabeth Walton</td>\n",
+              "      <td>29.0000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>24160</td>\n",
+              "      <td>211.3375</td>\n",
+              "      <td>B5</td>\n",
+              "      <td>S</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>St Louis, MO</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Allison, Master. Hudson Trevor</td>\n",
+              "      <td>0.9167</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>11</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Miss. Helen Loraine</td>\n",
+              "      <td>2.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
+              "      <td>30.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>135.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
+              "      <td>25.0000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>113781</td>\n",
+              "      <td>151.5500</td>\n",
+              "      <td>C22 C26</td>\n",
+              "      <td>S</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Montreal, PQ / Chesterville, ON</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1304</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zabour, Miss. Hileni</td>\n",
+              "      <td>14.5000</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2665</td>\n",
+              "      <td>14.4542</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>328.0</td>\n",
+              "      <td>Desconocido</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1305</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zabour, Miss. Thamine</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2665</td>\n",
+              "      <td>14.4542</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Desconocido</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1306</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zakarian, Mr. Mapriededer</td>\n",
+              "      <td>26.5000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2656</td>\n",
+              "      <td>7.2250</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>304.0</td>\n",
+              "      <td>Desconocido</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1307</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zakarian, Mr. Ortin</td>\n",
+              "      <td>27.0000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2670</td>\n",
+              "      <td>7.2250</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>C</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Desconocido</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1308</th>\n",
+              "      <td>3</td>\n",
+              "      <td>0</td>\n",
+              "      <td>Zimmerman, Mr. Leo</td>\n",
+              "      <td>29.0000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>315082</td>\n",
+              "      <td>7.8750</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>S</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>Desconocido</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1309 rows × 15 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      pclass  survived  ... sex_female  sex_male\n",
+              "0          1         1  ...          1         0\n",
+              "1          1         1  ...          0         1\n",
+              "2          1         0  ...          1         0\n",
+              "3          1         0  ...          0         1\n",
+              "4          1         0  ...          1         0\n",
+              "...      ...       ...  ...        ...       ...\n",
+              "1304       3         0  ...          1         0\n",
+              "1305       3         0  ...          1         0\n",
+              "1306       3         0  ...          0         1\n",
+              "1307       3         0  ...          0         1\n",
+              "1308       3         0  ...          0         1\n",
+              "\n",
+              "[1309 rows x 15 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 36
+        }
       ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "createDummies(data3, \"sex\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  ]
+}
\ No newline at end of file
diff --git a/notebooks/T1 - 3 - Data Cleaning - Plots-Colab.ipynb b/notebooks/T1 - 3 - Data Cleaning - Plots-Colab.ipynb
new file mode 100644
index 00000000..23f39787
--- /dev/null
+++ b/notebooks/T1 - 3 - Data Cleaning - Plots-Colab.ipynb	
@@ -0,0 +1,874 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/Collab---v-3.8/notebooks/T1%20-%203%20-%20Data%20Cleaning%20-%20Plots-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 122
+    },
+    "colab_type": "code",
+    "id": "SEJnb_CWuzZ6",
+    "outputId": "91ffe2d6-7009-464e-a6ec-b6c5704da429"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+      "\n",
+      "Enter your authorization code:\n",
+      "··········\n",
+      "Mounted at /content/drive\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "L_BUppvGuwx2"
+   },
+   "source": [
+    "# Plots y visualización de los datos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "jdYlSTiRuwx4"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "3hwU_OkRuwx9"
+   },
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/customer-churn-model/Customer Churn Model.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 606
+    },
+    "colab_type": "code",
+    "id": "IWQGFbEKuwyB",
+    "outputId": "d608e205-5687-4bbe-d0b3-3dd02ca66dba"
+   },
+   "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>State</th>\n",
+       "      <th>Account Length</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KS</td>\n",
+       "      <td>128</td>\n",
+       "      <td>415</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>25</td>\n",
+       "      <td>265.1</td>\n",
+       "      <td>110</td>\n",
+       "      <td>45.07</td>\n",
+       "      <td>197.4</td>\n",
+       "      <td>99</td>\n",
+       "      <td>16.78</td>\n",
+       "      <td>244.7</td>\n",
+       "      <td>91</td>\n",
+       "      <td>11.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>107</td>\n",
+       "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>26</td>\n",
+       "      <td>161.6</td>\n",
+       "      <td>123</td>\n",
+       "      <td>27.47</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.45</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>137</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>243.4</td>\n",
+       "      <td>114</td>\n",
+       "      <td>41.38</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>84</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299.4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>50.90</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.86</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>75</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>166.7</td>\n",
+       "      <td>113</td>\n",
+       "      <td>28.34</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.41</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3328</th>\n",
+       "      <td>AZ</td>\n",
+       "      <td>192</td>\n",
+       "      <td>415</td>\n",
+       "      <td>414-4276</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>36</td>\n",
+       "      <td>156.2</td>\n",
+       "      <td>77</td>\n",
+       "      <td>26.55</td>\n",
+       "      <td>215.5</td>\n",
+       "      <td>126</td>\n",
+       "      <td>18.32</td>\n",
+       "      <td>279.1</td>\n",
+       "      <td>83</td>\n",
+       "      <td>12.56</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3329</th>\n",
+       "      <td>WV</td>\n",
+       "      <td>68</td>\n",
+       "      <td>415</td>\n",
+       "      <td>370-3271</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>231.1</td>\n",
+       "      <td>57</td>\n",
+       "      <td>39.29</td>\n",
+       "      <td>153.4</td>\n",
+       "      <td>55</td>\n",
+       "      <td>13.04</td>\n",
+       "      <td>191.3</td>\n",
+       "      <td>123</td>\n",
+       "      <td>8.61</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3330</th>\n",
+       "      <td>RI</td>\n",
+       "      <td>28</td>\n",
+       "      <td>510</td>\n",
+       "      <td>328-8230</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>180.8</td>\n",
+       "      <td>109</td>\n",
+       "      <td>30.74</td>\n",
+       "      <td>288.8</td>\n",
+       "      <td>58</td>\n",
+       "      <td>24.55</td>\n",
+       "      <td>191.9</td>\n",
+       "      <td>91</td>\n",
+       "      <td>8.64</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.81</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3331</th>\n",
+       "      <td>CT</td>\n",
+       "      <td>184</td>\n",
+       "      <td>510</td>\n",
+       "      <td>364-6381</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>213.8</td>\n",
+       "      <td>105</td>\n",
+       "      <td>36.35</td>\n",
+       "      <td>159.6</td>\n",
+       "      <td>84</td>\n",
+       "      <td>13.57</td>\n",
+       "      <td>139.2</td>\n",
+       "      <td>137</td>\n",
+       "      <td>6.26</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3332</th>\n",
+       "      <td>TN</td>\n",
+       "      <td>74</td>\n",
+       "      <td>415</td>\n",
+       "      <td>400-4344</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>25</td>\n",
+       "      <td>234.4</td>\n",
+       "      <td>113</td>\n",
+       "      <td>39.85</td>\n",
+       "      <td>265.9</td>\n",
+       "      <td>82</td>\n",
+       "      <td>22.60</td>\n",
+       "      <td>241.4</td>\n",
+       "      <td>77</td>\n",
+       "      <td>10.86</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3333 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "0       KS             128        415  ...        2.70              1  False.\n",
+       "1       OH             107        415  ...        3.70              1  False.\n",
+       "2       NJ             137        415  ...        3.29              0  False.\n",
+       "3       OH              84        408  ...        1.78              2  False.\n",
+       "4       OK              75        415  ...        2.73              3  False.\n",
+       "...    ...             ...        ...  ...         ...            ...     ...\n",
+       "3328    AZ             192        415  ...        2.67              2  False.\n",
+       "3329    WV              68        415  ...        2.59              3  False.\n",
+       "3330    RI              28        510  ...        3.81              2  False.\n",
+       "3331    CT             184        510  ...        1.35              2  False.\n",
+       "3332    TN              74        415  ...        3.70              0  False.\n",
+       "\n",
+       "[3333 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "b1dfR0FruwyF"
+   },
+   "outputs": [],
+   "source": [
+    "%matplotlib inline "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "EKmumRn-uwyI"
+   },
+   "outputs": [],
+   "source": [
+    "#savefig(\"path_donde_guardar_im.jpeg\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "b5C_T5hXuwyJ"
+   },
+   "source": [
+    "### Scatter Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 296
+    },
+    "colab_type": "code",
+    "id": "Dy0NYV1ouwyK",
+    "outputId": "bcebb7ae-6c3e-4ab3-a5e0-e9ff3cfe820f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f019b3ac6a0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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VNKwj6Ralx38WcKC71yX42nXAce5ea2bFBCt8/p1gtc9J7v6Qmd0FXAz8IcHXFkk79fIlWxVEaLMKKE70hT1QG35bHP5z4DhgRnj8foJfLCJZ48ppixMO/W8e1FOhLxmjxR6/md1JENSfAhVmNp+gFw+Au/+4rRc3s0KC4Zz+wO+Bt4CP3H1r2GQtsHcL544FxgKUlpZG+W8RiVV1bR1DJ8xL+DwFvmSa1oZ6FoZfFwGPtefF3X0bMDicDvoIX67pH+XcKcAUgPLy8gRvdBdJruET57K+JrEbsb4/Yl+uOf2QmCoSab/Wgv8JoJe7b/fJlZkdTHATV2Tu/pGZPQ0MB7qZWVHY6+8LrEuwZpGUmb9iAxc/sCjh89TLl0zW2hj/nUDPZo53B25v64XNrFfY08fMOgLfAFYCTxMsAwEwBpiVSMEiqTJg3OyEQ/+2UYcq9CXjtdbj79/cXH13/6eZRZmF0we4PxznLwCmu/vfzGwF8JCZTQBeAe5pT+EicamsquGESYndptK5Ayy/QYEv2aG14O/aynNtzvIJ7+49rJnjq4BhbZcmknqHXT+HDz/b1nbDRuZdfgz9e7f2v4tIZmltqKfSzE5petDMTiaY4imSMx5d/A5l42YnFPrH9u/O2zefqtCXrNNaj/8yYLaZjSKY2QNQTvAB7WlxFyaSKroRS/JNiz1+d38T+ArwLFAW/nsWONTdE1uJSiQDTXx8WcKhf/oheyj0Jeu1umRDuEzDH1NUi0jKqJcv+SzS6pwiueKsO5+lYl1t2w0bGXlYH3597pCYKhJJPQW/5A318kUCUZZlPh2Y7e71KahHJOnOn/wCL67+KKFztNyC5LIoPf5zgdvMbCZwr7u/FnNNIkmjXr7IjqJsvfidcJP1bwP3mZkTfOD7oLvXxF2gSHscOWEuG2oTW1RtxiVHUt6vR0wViWSOKOvx4+4fE6yh/xDBUgxnA4vN7Ecx1iaSsMqqGsrGzU449N+++VSFvuSNKGP8ZwAXEayp/wAwzN03mlkngu0Y74y3RJFohlw/hw8SXG7htlGHctaQfWKqSCQzRRnjP4dgq8TtVq1y90/N7OJ4yhKJ7tHF73DZ9KUJnbP3biW8MP6EmCoSyWxRxvjHtPLc/OSWI5KY/cfPZluC2/RoUTXJd22O8ZvZkWb2LzOrNbPNZrbNzD5ORXEiLZn89JuUjUss9LWomkggylDP74DzgIcJFmkbDRwQZ1EirdEUTZGdE3VWTyVQ6O7b3P2PwEnxliWyo0lPrkw49MefeIBCX6SJKD3+T82sA1BhZrcC64n4C0MkWdTLF0meKAF+Ydjuh8AnwD4EM31EYjd/xYaEQ79hLF9EmhdlVs8aM+sVPr4+/pJEAokGvgGrFfgibWox+M3MgGsJevoF4aGtwJ3ufkOK6pM8dOW0xcx4ZX1C50w4YyDfOapfTBWJ5JbWevyXAyOAw919NYCZ7Qf8wcwud/dJqShQ8kd1bR1DJ8xL6JwenYpY9IsTY6pIJDe1FvwXAt9w9/cbDrj7KjP7DjAXUPBL0vx0egUPL16X0DlaVE2kfVoL/uLGod/A3d8zs+IYa5I8UllVwwmTnmu7YSMdCuCNmzSWL9JerQX/5nY+JxLJD/70L+Ys35jQOfeMHsrxA/eMqSKR/NBa8A9qYWkGA3Zp64XNbB+C1Tx7Aw5Mcffbzaw7MA0oA94GRrn7hwnWLVls/ooNfO+BRSSyxI72vRVJnhaD390Ld/K1twJXuPtiM+sKLDKzp4DvAvPd/WYzGweMA67eyfeSLDHo2jlsqkts6WTNyRdJrtg2W3f39QR3+eLuNWa2EtgbOBM4Nmx2P/AMCv6ct3B1NSMnL0joHE3RFIlHbMHfmJmVAYcBLwO9w18KABsIhoIkhw2+7u989Hl9Queoly8Sn9iD38y6ADOBy9z94+C+sIC7e7iHb3PnjQXGApSWlsZdpsTgzy+u5uePrUjonPEnHsAlXx8QU0UiAjEHfzjtcyYw1d3/Gh6uMrM+7r7ezPoAzU7rcPcpwBSA8vLyBLfakHRLdLmFQuAt9fJFUiK2VTbDJR/uAVa6+28bPfUY0LCr1xhgVlw1SOotXF2dcOhPOGOgQl8kheLs8Y8guPv3VTOrCI/9DLgZmB7u17sGGBVjDZJCh984l/c+2RK5fdeSAl69/uQYKxKR5sQ5q+d5gjn/zTk+rveV1GvPjB3diCWSPimZ1SO5q/yGJ3n/062R239/xL5cc/ohMVYkIm1R8Eu7JTqWrymaIplBwS8Jqa6tY8qzbzH5n6sjn6MpmiKZRcEvkf1o6iIef3VD5Padio0VN54SY0Ui0h4KfmnT/BUbuPiBRQmdc9uoQzlryD4xVSQiO0PBL606cuJTbKhJbBVujeWLZDYFvzSruraOYRPmkcg6mhrLF8kOCn7ZwQWTX+SF1dG3SOhYBCsnqJcvki0U/PIFjeWL5AcFvwBw+I1P8t4n0W/E2rWkgKVabkEkKyn481x1bR2HT5hHIqvla7kFkeym4M9jY+//F3NXRt/s/Iiybkz7wYgYKxKRVFDw56Hq2jqGTpgXuX1JIcz+8TH07901xqpEJFUU/HlmVsU6fvJQRdsNCX44JmtYRyTnKPjzRHVtHbc+sZJpi9dFat+7azEvX/PNmKsSkXRQ8Oe46to6bp3zGtMWro18znEH9uLei4bFWJWIpJOCP4dNXbCGax5dFrl9j05FTLvkKI3li+Q4BX+O+q9HlvKnl9+J3H7GJUdS3q9HjBWJSKZQ8OeYhaurGX3PAqJuitW32y48P047YYrkEwV/jqisquGC/3mJqppom50XGkwbq16+SD5S8OeAXzz6Kg8s+L9IbTsUwhUnaBVNkXym4M9i1bV1PLV8Q+TQH7hnZ5647Nh4ixKRjKfgz1IT/7aC/3l+NR6x/U+O68/l3zww1ppEJDso+LPM/BUbuHTqIuoi7pDStYPxzFXH06NLSbyFiUjWUPBnka/eMp93Pvw8UttC4DdaK19EmhFb8JvZvcBpwEZ3PyQ81h2YBpQBbwOj3D36Vk95qrq2jm/89hk+iDhH8+j9e/Dn7x8Zc1Uikq0KYnzt+4CTmhwbB8x39wHA/PB7acWsinUMnTAvUuifc9hezLv8GIW+iLQqth6/uz9nZmVNDp8JHBs+vh94Brg6rhqyWWVVDb9/+k0eqVgfqf3PTjmIscfsH3NVIpILUj3G39vdG5JsA9C7pYZmNhYYC1BaWpqC0jJHIvPyASaefQgXHLFvjBWJSC5J24e77u5m1uJsRHefAkwBKC8vjzprMasFvfxKHql4N1L7swf34eenHawZOyKSkFQHf5WZ9XH39WbWB4i+71+O+8mDi5m1JNqwTocCeOlnJyjwRaRd4vxwtzmPAWPCx2OAWSl+/4xTWVXDSZOeiRz6R5Ttzhs3narQF5F2i3M654MEH+T2NLO1wLXAzcB0M7sYWAOMiuv9s8FVM5YwPeIGKbuVFDLz0hFaK19Edlqcs3q+3cJTWgOYYMmFKKF/4B6d+fdj99eNWCKSNLpzN4Wqa+tY++Fn3Pv8qkhDOwf27syTlx8bf2EiklcU/CkydcEarnt8OQV4m+vs7Nm1AxPP/grHD9wzNcWJSF5R8KfA5Gff4pd/fy1S2zMG7ckd3x4ac0Uiks8U/DGbNPd1bv9HZZvtvjagJ/912kB9eCsisVPwx6BhLP+e51fxWISx/FHlfbl15KAUVCYiouBPulkV67hqxlIM5/OtLd9wbMC1pw/k6P491csXkZRS8CdRdW0dVz68hC3bWl9hotBg0rmDOWPw3imqTETkSwr+JKiurWP5u5t4de2mNkP/vPK+/PSkg3TnrYikjYJ/J82qWBeplw/BWP7NGssXkTRT8LdTZVUNz1e+z01PvNZq6BcAvzj93zi6fy+N5YtIRlDwt0OU9fJLigoA51cjB2ksX0QyioI/QZVVNW2GfodCuHv0UA7eazeN5YtIxlHwJ6jinY9afb6oAH79rcEcc8AeKapIRCQxCv5WNMzWAePgvXalR5cSBu/Trdm2N599CHvt3umLdiIimUrB34KpC9Zw7WPL2VoffHBbVAC/HRXMvR89vJQHXvpyuGf08FLO0563IpIlzD3zt7MtLy/3hQsXpuz9pi5YwzWPLtvheEmR8eK44+nRpYTKqhoq3vmIwft002wdEclIZrbI3cubHlePv4nq2jquf3x5s88VWgFrP/yMHl1K6N+7qwJfRLKSgj/UsLDaps82U1xYwOZtOy6av83r6bt7xzRUJyKSPAp+grtvr565lOKCIPDrmxn9KjT41chB+uBWRLJe3gZ/Qw+/c4dCrp65lM+31PM59UDwQW5JUQFFBcbmbfV87+h+fO+r+yn0RSQn5GXwB0snL6HQCthSX09Bk+c7Fhfx+wuGsFvHYvru3lGBLyI5Je+Cv7q2jiumV7C1HqD5zW+31NdrPr6I5Ky8CP6GYZ2+u3dk+bsfh6G/veIC2KW4iC319dx6zqEKfRHJWTkf/I0/uN1SX89FI8qabTfp3MPYp3snDe2ISM5rOrydEmZ2kpm9bmaVZjYurvdZuLqaKx9ewudb6qmp28rnW+q59/nVFBfadu2KC43h+/dg0D7dFPoikvNSHvxmVgj8HjgZGAh828wGJvt9fvHoq4ycvGCHtfI7FBby4+MGUFJUQKcOhZQUFfCbb2mapojkj3QM9QwDKt19FYCZPQScCaxI1hu0tnTylvp6zj+ilPOPKP1i3F+hLyL5JB3BvzfwTqPv1wJHNG1kZmOBsQClpaUJvUFLSycXF9p2H9wq8EUkH6VljD8Kd5/i7uXuXt6rV6+Ezm1p6eQHv3eEdsMSkbyXjuBfB+zT6Pu+4bGk6d+7K6OHb/9XwujhpZT365HMtxERyUrpGOr5FzDAzPoRBP55wPnJfpMbzvwKo48s09LJIiJNpDz43X2rmf0QeBIoBO519+bXQd5JWjpZRGRHabmBy92fAJ5Ix3uLiOS7jP1wV0RE4qHgFxHJMwp+EZE8o+AXEckz5t7MPoMZxszeA9a08/SewPtJLCdO2VQrZFe92VQrZFe9qjU+O1vvvu6+wx2wWRH8O8PMFrp7ebrriCKbaoXsqjebaoXsqle1xieuejXUIyKSZxT8IiJ5Jh+Cf0q6C0hANtUK2VVvNtUK2VWvao1PLPXm/Bi/iIhsLx96/CIi0oiCX0Qkz+R08KdqU/f2MrO3zexVM6sws4Xhse5m9pSZvRl+3T1Ntd1rZhvNbFmjY83WZoE7wuu81MyGZEi915nZuvD6VpjZKY2eGx/W+7qZnZjiWvcxs6fNbIWZLTezn4THM+76tlJrpl7bXczsf81sSVjv9eHxfmb2cljXNDPrEB4vCb+vDJ8vy4Ba7zOz1Y2u7eDwePJ+Dtw9J/8RLPn8FrAf0AFYAgxMd11Nanwb6Nnk2K3AuPDxOOCWNNV2DDAEWNZWbcApwN8BA44EXs6Qeq8Drmym7cDw56EE6Bf+nBSmsNY+wJDwcVfgjbCmjLu+rdSaqdfWgC7h42Lg5fCaTQfOC4/fBfx7+PhS4K7w8XnAtAyo9T5gZDPtk/ZzkMs9/i82dXf3zUDDpu6Z7kzg/vDx/cBZ6SjC3Z8DPmhyuKXazgQe8MACoJuZ9UlNpYEW6m3JmcBD7l7n7quBSoKfl5Rw9/Xuvjh8XAOsJNiLOuOubyu1tiTd19bdvTb8tjj858BxwIzweNNr23DNZwDHm5mludaWJO3nIJeDv7lN3TNtw10H5prZonBzeYDe7r4+fLwB6J2e0prVUm2ZfK1/GP5ZfG+jYbOMqTccWjiMoLeX0de3Sa2QodfWzArNrALYCDxF8FfHR+6+tZmavqg3fH4TkLI9WpvW6u4N13ZieG0nmVlJ01pD7b62uRz82eBodx8CnAz8h5kd0/hJD/6+y8j5tplcWyN/APYHBgPrgd+kt5ztmVkXYCZwmbt/3Pi5TLu+zdSasdfW3be5+2CC/byHAQeluaQWNa3VzA4BxhPUfDjQHbg62e+by8Ef+6buO8vd14VfNwKPEPyQVjX8+RZ+3Zi+CnfQUm0Zea3dvSr8H6seuJsvhxzSXq+ZFRME6VR3/2t4OCOvb3O1ZvK1beDuHzx1lGkAAAO5SURBVAFPA8MJhkUadhxsXNMX9YbP7wZUp7jUxrWeFA6vubvXAX8khmuby8H/xabu4Sf45wGPpbmmL5hZZzPr2vAY+CawjKDGMWGzMcCs9FTYrJZqewwYHc46OBLY1GjIIm2ajH+eTXB9Iaj3vHBGRz9gAPC/KazLgHuAle7+20ZPZdz1banWDL62vcysW/i4I/ANgs8lngZGhs2aXtuGaz4S+Ef411a6an2t0S9/I/gsovG1Tc7PQao+wU7HP4JPwd8gGOO7Jt31NKltP4LZD0uA5Q31EYwvzgfeBOYB3dNU34MEf8JvIRhLvLil2ghmGfw+vM6vAuUZUu+fwnqWhv/T9GnU/pqw3teBk1Nc69EEwzhLgYrw3ymZeH1bqTVTr+2hwCthXcuAX4TH9yP4BVQJPAyUhMd3Cb+vDJ/fLwNq/Ud4bZcBf+bLmT9J+znQkg0iInkml4d6RESkGQp+EZE8o+AXEckzCn4RkTyj4BcRyTMKfskLZrYtXOlwebga4hVmttM//2ZWZmZuZhMaHetpZlvM7Hfh9z8ws9E7+14iyVLUdhORnPCZB7fGY2Z7AH8BdgWuTcJrrwZOBX4efv8tgnszAHD3u5LwHiJJox6/5B0PlsgYS7DImIW99n+a2eLw31EAZvaAmX2xOqqZTTWz5lZ4/RRYaWbl4ffnEiwD3HDedWZ2Zfj4GTO7JVyH/Q0z+2p4/ODwWEW4ONeAeP7rRRT8kqfcfRXBng17EKyJ8w0PFsw7F7gjbHYP8F0AM9sNOAqY3cJLPkSwVME+wDbg3VbevsjdhwGX8eVfHD8Abg//KiknuPtYJBYa6hEJ1kH/XbjT0TbgAAB3f9bM/tvMegHnADP9y6V9m5oD3AhUAdPaeL+GRdkWAWXh45eAa8ysL/BXd3+zvf8xIm1Rj1/ykpntRxDyG4HLCQJ7EEFvu0Ojpg8A3wEuAu5t6fU82OxnEXAFX2740ZK68Os2ws6Xu/8FOAP4DHjCzI5L7L9IJDr1+CXvhD34u4DfubuHwzhr3b3ezMYQDAE1uI9g8a4N7r6ijZf+DfCsu3+Q6CZO4S+iVe5+h5mVEizg9Y+EXkQkIgW/5IuO4U5HxcBWgtUlG5YZ/m9gZjjlcg7wScNJ7l5lZiuBR9t6A3dfTqPZPAkaBVxoZlsIdt+6qZ2vI9Imrc4p0goz60SwBO4Qd9+U7npEkkFj/CItMLMTCDbxuFOhL7lEPX4RkTyjHr+ISJ5R8IuI5BkFv4hInlHwi4jkGQW/iEie+f9E3Jz15xh9sAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.plot(kind=\"scatter\", x=\"Day Mins\", y=\"Day Charge\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 296
+    },
+    "colab_type": "code",
+    "id": "DqeqcFc-uwyL",
+    "outputId": "d111c7cc-fac3-4577-e6c7-b910dd89eaeb"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f019b34bc18>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data.plot(kind=\"scatter\", x=\"Night Mins\", y=\"Night Charge\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 296
+    },
+    "colab_type": "code",
+    "id": "aGWlvG_CuwyN",
+    "outputId": "e601cc62-a962-471d-f766-e5794b7a6cdd"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0198552b38>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "figure, axs = plt.subplots(2,2, sharey=True, sharex=True)\n",
+    "data.plot(kind=\"scatter\", x=\"Day Mins\", y =\"Day Charge\", ax=axs[0][0])\n",
+    "data.plot(kind=\"scatter\", x=\"Night Mins\", y=\"Night Charge\", ax=axs[0][1])\n",
+    "data.plot(kind=\"scatter\", x=\"Day Calls\", y =\"Day Charge\", ax=axs[1][0])\n",
+    "data.plot(kind=\"scatter\", x=\"Night Calls\", y=\"Night Charge\", ax=axs[1][1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "MfMWlcyzuwyP"
+   },
+   "source": [
+    "### Histogramas de frecuencias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 312
+    },
+    "colab_type": "code",
+    "id": "WQuFiRiHuwyP",
+    "outputId": "505bc479-b210-45d7-dab0-4c212fad5d2e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Histograma del número de llamadas al día')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "k = int(np.ceil(1+np.log2(3333)))\n",
+    "plt.hist(data[\"Day Calls\"], bins = k) #bins = [0,30,60,...,200]\n",
+    "plt.xlabel(\"Número de llamadas al día\")\n",
+    "plt.ylabel(\"Frecuencia\")\n",
+    "plt.title(\"Histograma del número de llamadas al día\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "lQ0MTWkEuwyQ"
+   },
+   "source": [
+    "### Boxplot, diagrama de caja y bigotes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 298
+    },
+    "colab_type": "code",
+    "id": "QUKSN0KluwyR",
+    "outputId": "5dce1e8d-24f0-4134-cb22-282035da73fa"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Boxplot de las llamadas diarias')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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2krSQZMrtvSWtBS4nGW3UG7gvHcz0WDrS6Fjgu5LeBt4FpkTEpkYPbGZmJZMlKaxNrydYRPJhvhlY3dJOETG+keIbm3ju7cDtGWIxM7MSyjIh3unp4gxJD5LcS+GekkZlZma5aG5CvH4RsUXSwKLip9Ofe9LgGgQzM+v6mjtTWEAySmg5yQR4xVc0B3BgCeMyM7McNDch3inpzxHlC8fMzPLUXPPRe+6jUMz3UzAz636aaz6qv49CJVAN/JGkCenDJBeVHV3a0MzMrNyavHgtIsakF6utBw6PiOqIOILk4rWXyhWgmZmVT5brFA6JiPpRR0TECkmHljAms5IbOHAgmzdvLnk9rZ9xvnWqqqrYtMkDAa3jZEkKT0n6N+DWdP2LwFOlC8ms9DZv3kw69VaXVuqkY7ufLEnhXOACkjmQAB4GflqyiMzMLDdZrmjeBlyXPszMrBvLMkuqmZntJpwUzMysoFVJQVIPSR1xK04zM+uEWkwKkhZI6iepD7ACeEbSN0sfmpmZlVuWM4WR6b2STwOWAiOAL5c0KjMzy0WWpNBLUi+SpHBXRLxNMkuqmZl1M1mSwr8CLwJ9gIclHQBsKWVQZmaWjyzXKdwA3FBUtFrSmNKFZGZmeclyRTOSPgN8kGTG1HrfLUlEZmaWmyyjj2qAM4FpJFNnfw44oMRxmZlZDrL0KYyOiAnA5oi4guQ+CgdnObik+ZI2SlpRVDZQ0n2S/pL+rErLJekGSc9Keqqlm/yYmVnHy5IU3kp//lXSEOBtYHDG498EnNyg7FvA/RFxEHB/ug4wFjgofUzGk+6ZmZVdlj6FxZIGAD8AniAZjvpvWQ4eEQ9LGt6g+FTguHT5ZuB3wCVp+S2RzGf8mKQBkgZHxPosdZm1RlzeD2b0zzuMdovLPcGAdawso4+uTBdvl7QYqIyI19tR575FH/QvA/umy/sBa4qetzYt2yUpSJpMcibBsGHD2hGG7c50xZZucz+FmJF3FNadNJkUJH22mW1ExB3trTwiQlKr/jMjYi4wF6C6urrr/1ebmXUizZ0pjEt/7gOMBh5I18cAjwBtTQob6puFJA0GNqblLwH7Fz1vKL4XtJlZWTXZ0RwR50bEuUAvkvmPzoiIM0iuV+jVjjrvAs5Ol88G/qOofEI6Cuko4HX3J5iZlVeWjub9G3w4bwAyNeZLWkjSqby3pLXA5cD3gdskTQJWA59Pn3438GngWeCvJLcBNTOzMsqSFO6X9BtgYbp+JvDbLAePiPFNbDqhkecG8NUsxzUzs9LIMvrowrTT+RNp0dyIuLO0YZmZWR4yzX2UjjRq92gjMzPr3LLMfXSUpMclvSlph6Sdkjx1tplZN5RlmosfA+OBvwB7AF8BflLKoMzMLB9ZkgIR8SzQMyJ2RsTPeO98RmZm1g1k6VP4q6QK4ElJs0imnciUTMzMrGvJ8uH+ZaAncCGwleSq4zNKGZSZmeUjy5DU1eniW8AVpQ3HzMzylGX00SmS/iBpk6Qtkt7w6CMzs+4pS5/Cj4DPAk9Hd5hr2MzMmpQlKawBVjghWHcjKe8Q2q2qqirvEKybyZIUpgN3S3oI2F5fGBHXliwqsxIrx3ec9L4jJa/HrCNlSQozgTeBSqCitOGYmVmesiSFIRExquSRmJlZ7rJcp3C3pBNLHomZmeUuS1K4ALhH0lsekmpm1r1luXitbzkCMTOz/GW6n4KkKuAgks5mACLi4VIFZWZm+WgxKUj6CnARMBR4EjgKeBQ4vrShmZlZuWXpU7gI+BiwOiLGAB8FXitpVGZmlossSWFbRGwDkNQ7IlYBh5Q2LDMzy0OWPoW1kgYAi4D7JG0GVrewT5MkHQL8qqjoQOA7wADgPKAuLb80Iu5uaz1mZtZ6WUYfnZ4uzpD0INAfuKetFUbEn4HDACT1BF4C7gTOBa6LiB+29dhmZtY+TSYFSQMbKX46/bknsKkD6j8BeC4iVneHycnMzLq65s4UlgMBNPZpHSTNPu31BWBh0fqFkiYAtcDXI2Jzwx0kTQYmAwwbNqwDQjAzs3rKaxbH9L7P64APRsQGSfsCr5AknCuBwRExsbljVFdXR21tbemDNWsDz5JqnZWk5RFR3di25pqPDm/uoBHxRDvjGgs8EREb0uNtKKp7HrC4ncc3M7NWaq756JpmtgXtv3htPEVNR5IGR8T6dPV0YEU7j29mZq3UZFJIL1QrCUl9gH8Ezi8qniXpMJKE82KDbWZmVgaZ5j7qaBGxFdirQdmX84jFzMz+JssVzWZmtptwUjAzs4IWk4ISX5L0nXR9mKQjSx+amZmVW5YzhTnA0SSjhQDeAH5SsojMzCw3WTqaPx4Rh0v6A0BEbE4vPDMzs24my5nC2+nEdQEgaRDwbkmjMjOzXGRJCjeQzGK6j6SZwDLgqpJGZWZmucgydfYvJC0nmdFUwGkRsbLkkZmZWdllnTp7I7tOSTEwIjpi6mwzM+tEsk6dPQzYnC4PAP4HGFHy6MzMrKya7FOIiBERcSDwW2BcROwdEXsBpwD3litAMzMrnywdzUcV3ys5IpYCo0sXkpmZ5SXLdQrrJH0buDVd/yLJzXHMzKybyXKmMB4YRDIs9Y50eXyze5iZWZeUZUjqJuCiMsRiZmY58yypZmZW4KRgZmYFTgpmZlaQ5X4KQyXdKalO0kZJt0saWo7gzMysvLKcKfwMuAsYDAwB/jMtMzOzbiZLUhgUET+LiHfSx00kw1LNzKybyZIUXk1vx9kzfXwJeLW9FUt6UdLTkp6UVJuWDZR0n6S/pD+r2luPmZlllyUpTAQ+D7wMrAf+GTi3g+ofExGHRUR1uv4t4P6IOAi4P103M7MyafbitfSOa1dFxD+VKZ5TgePS5ZuB3wGXlKluM7PdXrNnChGxEzigRPdkDuBeScslTU7L9o2I9enyy8C+DXeSNFlSraTaurq6EoRlZrb7yjIh3vPA7yXdBWytL4yIa9tZ9zER8ZKkfYD7JK0q3hgRISka7hQRc4G5ANXV1e/ZbmZmbZclKTyXPnoAfTuq4oh4Kf25UdKdwJHABkmDI2K9pMEkd3wzM7MyabGjOSKuiIgrgB/UL6frbSapj6S+9cvAicAKkushzk6fdjbwH+2pxywPJ510Ej16JP9aPXr04KSTTso5IrPsWjxTkHQ0cCOwJzBM0keA8yNiajvq3Re4U1J9DAsi4h5JjwO3SZoErCYZ9WSWu/RvtdUignvvvTfz/hFuEbV8ZRmS+iPgJNJrEyLij8Cx7ak0Ip6PiI+kjw9GxMy0/NWIOCEiDoqIT6XTdpvlLiIyPepdcMEFvPbaa1xwwQWtOoZZ3rL0KRARaxp809lZmnDMur6JEycyZ84cAObMmcP27duZP39+zlGZZZMlKayRNBoISb1IbrizsrRhmXVdjzzyCJWVlWzfvp3evXszYsSIvEMyyyxL89EU4KvAfsBLwGHpupk1YtWqVRxxxBGsW7eOI444glWrVrW8k1knkeV2nK8AXyxDLGZdniQigkceeYQhQ4bsUm7WFWQZfTQCmAYML35+Gae+MOsyIqKQGOo1XDfrzLI0Hy0CXgRmA9cUPcysEePGjdtlRNG4cePyDskssywdzdsi4oaSR2LWTSxZsoRrr72WKVOmUFNTw5IlS/IOySwztXRaK+ks4CDgXmB7fXlEPFHa0FpWXV0dtbW1eYdhVlBZWUl1dTW1tbWF0Uf169u2bcs7PDMAJC0vumXBLrKcKXwI+DJwPPBuWhbpupkVOe+885gzZw6DBg1iw4YNDBgwgEcffZSpU9szAYBZ+WTpU/gccGBEfDIixqQPJwSzRowePZqKigo2bNgAwIYNG6ioqGD06NE5R2aWTZaksAIYUOpAzLqD6dOnU1VVxQMPPMCOHTt44IEHqKqqYvr06XmHZpZJluajAcCqdLK64j4FD0k1a2Dt2rXce++9jBkzBoAxY8Zw8803c+KJJ+YcmVk2WZLC5SWPwszMOoUsVzQ/VI5AzLqDoUOHMmHCBBYsWMAxxxzDsmXLmDBhAkOHDs07NLNMGu1TkPT+ouU3JG1JH9sk7ZS0pXwhmnUds2bNYufOnUycOJHevXszceJEdu7cyaxZs/IOzSyTpjqaz5H0bYCI6BsR/SKiH7AHcAYwp1wBmnUl48eP58wzz2T9+vVEBOvXr+fMM89k/PjxeYdmlkmjSSEi5gAvSPpyg/KIiEUkN90xswYWLlzIkiVLWLp0KTt27GDp0qUsWbKEhQsX5h2aWSZZrmj+bNFqD6Aa+GREHF3KwLLwFc3W2YwaNYrZs2cXRh8BPPjgg0ybNo0VK1bkGJnZ3zR3RXOWpPCzotV3SCbHmxcRGzsswjZyUrDOpmfPnmzbto1evXoVyt5++20qKyvZudM3LLTOoV3TXETEuR0fkln3dOihh7Js2bJdzhSWLVvGoYcemmNUZtk1mRQkfaeZ/SIirixBPGZd2mWXXcakSZO48cYbC0NSJ02axMyZM/MOzSyT5s4UtjZS1geYBOwFtCkpSNofuAXYl2RivbkRcb2kGcB5QF361Esj4u621GGWl/pRRtOmTWPlypUceuihzJw506OPrMtosU8BQFJf4CKShHAbcE1b+xQkDQYGR8QT6XGXA6cBnwfejIgfZj2W+xTMzFqvzX0KkgYCF5Pco/lm4PCI2NyeYCJiPbA+XX5D0kpgv/Yc08zMOkaTs6RK+gHwOPAG8KGImNHehNBIHcOBjwL/Ny26UNJTkuZLqmpin8mSaiXV1tXVNfYUMzNroyabjyS9SzIr6jskbf+FTSQdzf3aVbG0J/AQMDMi7pC0L/BKWteVJE1ME5s7hpuPzMxar03NRxGR5V4LbQ2oF3A78IuIuCOtb0PR9nnA4lLVb2ZmjSvZB39TJAm4EVgZEdcWlQ8uetrpJDf3MTOzMspyP4WO9r9I7vn8tKQn07JLgfGSDiNpPnoROD+H2MzMdmtlTwoRsYykX6IhX5NgZpazsjcfmZlZ5+WkYGZmBU4KZmZW4KRgZmYFTgpmZlbgpGBmZgVOCmZmVuCkYGZmBU4KZmZW4KRgZmYFTgpmZlbgpGBmZgVOCmZmVuCkYGZmBU4KZmZW4KRgZmYFTgpmZlbgpGBmZgVOCmZmVuCkYGZmBU4KZmZW0OmSgqSTJf1Z0rOSvpV3PGatNW3aNCorK5FEZWUl06ZNyzsks8w6VVKQ1BP4CTAWGAmMlzQy36jMsps2bRo1NTVcddVVbN26lauuuoqamhonBusyOlVSAI4Eno2I5yNiB/BL4NScYzLLbN68eVx99dVcfPHFvP/97+fiiy/m6quvZt68eXmHZpZJZ0sK+wFritbXpmUFkiZLqpVUW1dXV9bgzFqyfft2pkyZskvZlClT2L59e04RmbVOZ0sKLYqIuRFRHRHVgwYNyjscs1307t2bmpqaXcpqamro3bt3ThGZtc778g6ggZeA/YvWh6ZlZl3CeeedxyWXXAIkZwg1NTVccskl7zl7MOusOltSeBw4SNIIkmTwBeCsfEMyy2727NkAXHrppXz961+nd+/eTJkypVBu1tkpIvKOYReSPg38COgJzI+ImU09t7q6Ompra8sWm5lZdyBpeURUN7ats50pEBF3A3fnHYeZ2e6oy3U0m5lZ6TgpmJlZgZOCmZkVOCmYmVlBpxt91BqS6oDVecdh1oS9gVfyDsKsEQdERKNX/3bppGDWmUmqbWrYn1ln5eYjMzMrcFIwM7MCJwWz0pmbdwBmreU+BTMzK/CZgpmZFTgpmJlZgZOCWQeTNF/SRkkr8o7FrLWcFMw63k3AyXkHYdYWTgpmHSwiHgY25R2HWVs4KZiZWYGTgpmZFTgpmJlZgZOCmZkVOCmYdTBJC4FHgUMkrZU0Ke+YzLLyNBdmZlbgMwUzMytwUjAzswInBTMzK3BSMDOzAicFMzMrcFIwM7MCJwUzMyv4/56tleL+3pGXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.boxplot(data[\"Day Calls\"])\n",
+    "plt.ylabel(\"Número de llamadas diarias\")\n",
+    "plt.title(\"Boxplot de las llamadas diarias\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 170
+    },
+    "colab_type": "code",
+    "id": "PqSFRzMQuwyS",
+    "outputId": "1aa33895-a7e3-49b1-b27c-25c00d7db030"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    3333.000000\n",
+       "mean      100.435644\n",
+       "std        20.069084\n",
+       "min         0.000000\n",
+       "25%        87.000000\n",
+       "50%       101.000000\n",
+       "75%       114.000000\n",
+       "max       165.000000\n",
+       "Name: Day Calls, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"Day Calls\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "uz1oKSpIuwyT",
+    "outputId": "41332730-2975-4765-f791-7d5350fae1c6"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "IQR=data[\"Day Calls\"].quantile(0.75)-data[\"Day Calls\"].quantile(0.25)\n",
+    "IQR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "wDKapxuLuwyV",
+    "outputId": "5c045b44-7a2f-4494-95a3-f1878affd6aa"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "46.5"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"Day Calls\"].quantile(0.25) - 1.5*IQR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "uMBjtBUDuwyW",
+    "outputId": "de4ec136-5cf2-496c-c9bf-67ba241650c0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "154.5"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"Day Calls\"].quantile(0.75) + 1.5*IQR"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "T1 - 3 - Data Cleaning - Plots.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/notebooks/T1 - 3 - Data Cleaning - Plots.ipynb b/notebooks/T1 - 3 - Data Cleaning - Plots.ipynb
index 6b3f8863..1a6a640f 100644
--- a/notebooks/T1 - 3 - Data Cleaning - Plots.ipynb	
+++ b/notebooks/T1 - 3 - Data Cleaning - Plots.ipynb	
@@ -1,2124 +1,874 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Plots y visualización de los datos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv(\"../datasets/customer-churn-model/Customer Churn Model.txt\")"
-   ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.6.4"
+    },
+    "colab": {
+      "name": "T1 - 3 - Data Cleaning - Plots.ipynb",
+      "provenance": [],
+      "toc_visible": true,
+      "include_colab_link": true
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/Collab---v-3.8/notebooks/T1%20-%203%20-%20Data%20Cleaning%20-%20Plots.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
     {
-     "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>State</th>\n",
-       "      <th>Account Length</th>\n",
-       "      <th>Area Code</th>\n",
-       "      <th>Phone</th>\n",
-       "      <th>Int'l Plan</th>\n",
-       "      <th>VMail Plan</th>\n",
-       "      <th>VMail Message</th>\n",
-       "      <th>Day Mins</th>\n",
-       "      <th>Day Calls</th>\n",
-       "      <th>Day Charge</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Eve Calls</th>\n",
-       "      <th>Eve Charge</th>\n",
-       "      <th>Night Mins</th>\n",
-       "      <th>Night Calls</th>\n",
-       "      <th>Night Charge</th>\n",
-       "      <th>Intl Mins</th>\n",
-       "      <th>Intl Calls</th>\n",
-       "      <th>Intl Charge</th>\n",
-       "      <th>CustServ Calls</th>\n",
-       "      <th>Churn?</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>KS</td>\n",
-       "      <td>128</td>\n",
-       "      <td>415</td>\n",
-       "      <td>382-4657</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>25</td>\n",
-       "      <td>265.1</td>\n",
-       "      <td>110</td>\n",
-       "      <td>45.07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>99</td>\n",
-       "      <td>16.78</td>\n",
-       "      <td>244.7</td>\n",
-       "      <td>91</td>\n",
-       "      <td>11.01</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.70</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>OH</td>\n",
-       "      <td>107</td>\n",
-       "      <td>415</td>\n",
-       "      <td>371-7191</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>26</td>\n",
-       "      <td>161.6</td>\n",
-       "      <td>123</td>\n",
-       "      <td>27.47</td>\n",
-       "      <td>...</td>\n",
-       "      <td>103</td>\n",
-       "      <td>16.62</td>\n",
-       "      <td>254.4</td>\n",
-       "      <td>103</td>\n",
-       "      <td>11.45</td>\n",
-       "      <td>13.7</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3.70</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>NJ</td>\n",
-       "      <td>137</td>\n",
-       "      <td>415</td>\n",
-       "      <td>358-1921</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>243.4</td>\n",
-       "      <td>114</td>\n",
-       "      <td>41.38</td>\n",
-       "      <td>...</td>\n",
-       "      <td>110</td>\n",
-       "      <td>10.30</td>\n",
-       "      <td>162.6</td>\n",
-       "      <td>104</td>\n",
-       "      <td>7.32</td>\n",
-       "      <td>12.2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3.29</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>OH</td>\n",
-       "      <td>84</td>\n",
-       "      <td>408</td>\n",
-       "      <td>375-9999</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>299.4</td>\n",
-       "      <td>71</td>\n",
-       "      <td>50.90</td>\n",
-       "      <td>...</td>\n",
-       "      <td>88</td>\n",
-       "      <td>5.26</td>\n",
-       "      <td>196.9</td>\n",
-       "      <td>89</td>\n",
-       "      <td>8.86</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1.78</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>OK</td>\n",
-       "      <td>75</td>\n",
-       "      <td>415</td>\n",
-       "      <td>330-6626</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>166.7</td>\n",
-       "      <td>113</td>\n",
-       "      <td>28.34</td>\n",
-       "      <td>...</td>\n",
-       "      <td>122</td>\n",
-       "      <td>12.61</td>\n",
-       "      <td>186.9</td>\n",
-       "      <td>121</td>\n",
-       "      <td>8.41</td>\n",
-       "      <td>10.1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.73</td>\n",
-       "      <td>3</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>AL</td>\n",
-       "      <td>118</td>\n",
-       "      <td>510</td>\n",
-       "      <td>391-8027</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>223.4</td>\n",
-       "      <td>98</td>\n",
-       "      <td>37.98</td>\n",
-       "      <td>...</td>\n",
-       "      <td>101</td>\n",
-       "      <td>18.75</td>\n",
-       "      <td>203.9</td>\n",
-       "      <td>118</td>\n",
-       "      <td>9.18</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1.70</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>MA</td>\n",
-       "      <td>121</td>\n",
-       "      <td>510</td>\n",
-       "      <td>355-9993</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>24</td>\n",
-       "      <td>218.2</td>\n",
-       "      <td>88</td>\n",
-       "      <td>37.09</td>\n",
-       "      <td>...</td>\n",
-       "      <td>108</td>\n",
-       "      <td>29.62</td>\n",
-       "      <td>212.6</td>\n",
-       "      <td>118</td>\n",
-       "      <td>9.57</td>\n",
-       "      <td>7.5</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2.03</td>\n",
-       "      <td>3</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>MO</td>\n",
-       "      <td>147</td>\n",
-       "      <td>415</td>\n",
-       "      <td>329-9001</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>157.0</td>\n",
-       "      <td>79</td>\n",
-       "      <td>26.69</td>\n",
-       "      <td>...</td>\n",
-       "      <td>94</td>\n",
-       "      <td>8.76</td>\n",
-       "      <td>211.8</td>\n",
-       "      <td>96</td>\n",
-       "      <td>9.53</td>\n",
-       "      <td>7.1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1.92</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>LA</td>\n",
-       "      <td>117</td>\n",
-       "      <td>408</td>\n",
-       "      <td>335-4719</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>184.5</td>\n",
-       "      <td>97</td>\n",
-       "      <td>31.37</td>\n",
-       "      <td>...</td>\n",
-       "      <td>80</td>\n",
-       "      <td>29.89</td>\n",
-       "      <td>215.8</td>\n",
-       "      <td>90</td>\n",
-       "      <td>9.71</td>\n",
-       "      <td>8.7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.35</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>WV</td>\n",
-       "      <td>141</td>\n",
-       "      <td>415</td>\n",
-       "      <td>330-8173</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>37</td>\n",
-       "      <td>258.6</td>\n",
-       "      <td>84</td>\n",
-       "      <td>43.96</td>\n",
-       "      <td>...</td>\n",
-       "      <td>111</td>\n",
-       "      <td>18.87</td>\n",
-       "      <td>326.4</td>\n",
-       "      <td>97</td>\n",
-       "      <td>14.69</td>\n",
-       "      <td>11.2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3.02</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>IN</td>\n",
-       "      <td>65</td>\n",
-       "      <td>415</td>\n",
-       "      <td>329-6603</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>129.1</td>\n",
-       "      <td>137</td>\n",
-       "      <td>21.95</td>\n",
-       "      <td>...</td>\n",
-       "      <td>83</td>\n",
-       "      <td>19.42</td>\n",
-       "      <td>208.8</td>\n",
-       "      <td>111</td>\n",
-       "      <td>9.40</td>\n",
-       "      <td>12.7</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3.43</td>\n",
-       "      <td>4</td>\n",
-       "      <td>True.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>RI</td>\n",
-       "      <td>74</td>\n",
-       "      <td>415</td>\n",
-       "      <td>344-9403</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>187.7</td>\n",
-       "      <td>127</td>\n",
-       "      <td>31.91</td>\n",
-       "      <td>...</td>\n",
-       "      <td>148</td>\n",
-       "      <td>13.89</td>\n",
-       "      <td>196.0</td>\n",
-       "      <td>94</td>\n",
-       "      <td>8.82</td>\n",
-       "      <td>9.1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.46</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>IA</td>\n",
-       "      <td>168</td>\n",
-       "      <td>408</td>\n",
-       "      <td>363-1107</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>128.8</td>\n",
-       "      <td>96</td>\n",
-       "      <td>21.90</td>\n",
-       "      <td>...</td>\n",
-       "      <td>71</td>\n",
-       "      <td>8.92</td>\n",
-       "      <td>141.1</td>\n",
-       "      <td>128</td>\n",
-       "      <td>6.35</td>\n",
-       "      <td>11.2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3.02</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>MT</td>\n",
-       "      <td>95</td>\n",
-       "      <td>510</td>\n",
-       "      <td>394-8006</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>156.6</td>\n",
-       "      <td>88</td>\n",
-       "      <td>26.62</td>\n",
-       "      <td>...</td>\n",
-       "      <td>75</td>\n",
-       "      <td>21.05</td>\n",
-       "      <td>192.3</td>\n",
-       "      <td>115</td>\n",
-       "      <td>8.65</td>\n",
-       "      <td>12.3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3.32</td>\n",
-       "      <td>3</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>IA</td>\n",
-       "      <td>62</td>\n",
-       "      <td>415</td>\n",
-       "      <td>366-9238</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>120.7</td>\n",
-       "      <td>70</td>\n",
-       "      <td>20.52</td>\n",
-       "      <td>...</td>\n",
-       "      <td>76</td>\n",
-       "      <td>26.11</td>\n",
-       "      <td>203.0</td>\n",
-       "      <td>99</td>\n",
-       "      <td>9.14</td>\n",
-       "      <td>13.1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3.54</td>\n",
-       "      <td>4</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>NY</td>\n",
-       "      <td>161</td>\n",
-       "      <td>415</td>\n",
-       "      <td>351-7269</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>332.9</td>\n",
-       "      <td>67</td>\n",
-       "      <td>56.59</td>\n",
-       "      <td>...</td>\n",
-       "      <td>97</td>\n",
-       "      <td>27.01</td>\n",
-       "      <td>160.6</td>\n",
-       "      <td>128</td>\n",
-       "      <td>7.23</td>\n",
-       "      <td>5.4</td>\n",
-       "      <td>9</td>\n",
-       "      <td>1.46</td>\n",
-       "      <td>4</td>\n",
-       "      <td>True.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>ID</td>\n",
-       "      <td>85</td>\n",
-       "      <td>408</td>\n",
-       "      <td>350-8884</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>27</td>\n",
-       "      <td>196.4</td>\n",
-       "      <td>139</td>\n",
-       "      <td>33.39</td>\n",
-       "      <td>...</td>\n",
-       "      <td>90</td>\n",
-       "      <td>23.88</td>\n",
-       "      <td>89.3</td>\n",
-       "      <td>75</td>\n",
-       "      <td>4.02</td>\n",
-       "      <td>13.8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.73</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>VT</td>\n",
-       "      <td>93</td>\n",
-       "      <td>510</td>\n",
-       "      <td>386-2923</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>190.7</td>\n",
-       "      <td>114</td>\n",
-       "      <td>32.42</td>\n",
-       "      <td>...</td>\n",
-       "      <td>111</td>\n",
-       "      <td>18.55</td>\n",
-       "      <td>129.6</td>\n",
-       "      <td>121</td>\n",
-       "      <td>5.83</td>\n",
-       "      <td>8.1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.19</td>\n",
-       "      <td>3</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>VA</td>\n",
-       "      <td>76</td>\n",
-       "      <td>510</td>\n",
-       "      <td>356-2992</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>33</td>\n",
-       "      <td>189.7</td>\n",
-       "      <td>66</td>\n",
-       "      <td>32.25</td>\n",
-       "      <td>...</td>\n",
-       "      <td>65</td>\n",
-       "      <td>18.09</td>\n",
-       "      <td>165.7</td>\n",
-       "      <td>108</td>\n",
-       "      <td>7.46</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.70</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>TX</td>\n",
-       "      <td>73</td>\n",
-       "      <td>415</td>\n",
-       "      <td>373-2782</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>224.4</td>\n",
-       "      <td>90</td>\n",
-       "      <td>38.15</td>\n",
-       "      <td>...</td>\n",
-       "      <td>88</td>\n",
-       "      <td>13.56</td>\n",
-       "      <td>192.8</td>\n",
-       "      <td>74</td>\n",
-       "      <td>8.68</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3.51</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>FL</td>\n",
-       "      <td>147</td>\n",
-       "      <td>415</td>\n",
-       "      <td>396-5800</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>155.1</td>\n",
-       "      <td>117</td>\n",
-       "      <td>26.37</td>\n",
-       "      <td>...</td>\n",
-       "      <td>93</td>\n",
-       "      <td>20.37</td>\n",
-       "      <td>208.8</td>\n",
-       "      <td>133</td>\n",
-       "      <td>9.40</td>\n",
-       "      <td>10.6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.86</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>CO</td>\n",
-       "      <td>77</td>\n",
-       "      <td>408</td>\n",
-       "      <td>393-7984</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>62.4</td>\n",
-       "      <td>89</td>\n",
-       "      <td>10.61</td>\n",
-       "      <td>...</td>\n",
-       "      <td>121</td>\n",
-       "      <td>14.44</td>\n",
-       "      <td>209.6</td>\n",
-       "      <td>64</td>\n",
-       "      <td>9.43</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1.54</td>\n",
-       "      <td>5</td>\n",
-       "      <td>True.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>AZ</td>\n",
-       "      <td>130</td>\n",
-       "      <td>415</td>\n",
-       "      <td>358-1958</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>183.0</td>\n",
-       "      <td>112</td>\n",
-       "      <td>31.11</td>\n",
-       "      <td>...</td>\n",
-       "      <td>99</td>\n",
-       "      <td>6.20</td>\n",
-       "      <td>181.8</td>\n",
-       "      <td>78</td>\n",
-       "      <td>8.18</td>\n",
-       "      <td>9.5</td>\n",
-       "      <td>19</td>\n",
-       "      <td>2.57</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>SC</td>\n",
-       "      <td>111</td>\n",
-       "      <td>415</td>\n",
-       "      <td>350-2565</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>110.4</td>\n",
-       "      <td>103</td>\n",
-       "      <td>18.77</td>\n",
-       "      <td>...</td>\n",
-       "      <td>102</td>\n",
-       "      <td>11.67</td>\n",
-       "      <td>189.6</td>\n",
-       "      <td>105</td>\n",
-       "      <td>8.53</td>\n",
-       "      <td>7.7</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2.08</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>VA</td>\n",
-       "      <td>132</td>\n",
-       "      <td>510</td>\n",
-       "      <td>343-4696</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>81.1</td>\n",
-       "      <td>86</td>\n",
-       "      <td>13.79</td>\n",
-       "      <td>...</td>\n",
-       "      <td>72</td>\n",
-       "      <td>20.84</td>\n",
-       "      <td>237.0</td>\n",
-       "      <td>115</td>\n",
-       "      <td>10.67</td>\n",
-       "      <td>10.3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2.78</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>NE</td>\n",
-       "      <td>174</td>\n",
-       "      <td>415</td>\n",
-       "      <td>331-3698</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>124.3</td>\n",
-       "      <td>76</td>\n",
-       "      <td>21.13</td>\n",
-       "      <td>...</td>\n",
-       "      <td>112</td>\n",
-       "      <td>23.55</td>\n",
-       "      <td>250.7</td>\n",
-       "      <td>115</td>\n",
-       "      <td>11.28</td>\n",
-       "      <td>15.5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>4.19</td>\n",
-       "      <td>3</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>WY</td>\n",
-       "      <td>57</td>\n",
-       "      <td>408</td>\n",
-       "      <td>357-3817</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>39</td>\n",
-       "      <td>213.0</td>\n",
-       "      <td>115</td>\n",
-       "      <td>36.21</td>\n",
-       "      <td>...</td>\n",
-       "      <td>112</td>\n",
-       "      <td>16.24</td>\n",
-       "      <td>182.7</td>\n",
-       "      <td>115</td>\n",
-       "      <td>8.22</td>\n",
-       "      <td>9.5</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.57</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>MT</td>\n",
-       "      <td>54</td>\n",
-       "      <td>408</td>\n",
-       "      <td>418-6412</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>134.3</td>\n",
-       "      <td>73</td>\n",
-       "      <td>22.83</td>\n",
-       "      <td>...</td>\n",
-       "      <td>100</td>\n",
-       "      <td>13.22</td>\n",
-       "      <td>102.1</td>\n",
-       "      <td>68</td>\n",
-       "      <td>4.59</td>\n",
-       "      <td>14.7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.97</td>\n",
-       "      <td>3</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>MO</td>\n",
-       "      <td>20</td>\n",
-       "      <td>415</td>\n",
-       "      <td>353-2630</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>190.0</td>\n",
-       "      <td>109</td>\n",
-       "      <td>32.30</td>\n",
-       "      <td>...</td>\n",
-       "      <td>84</td>\n",
-       "      <td>21.95</td>\n",
-       "      <td>181.5</td>\n",
-       "      <td>102</td>\n",
-       "      <td>8.17</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>1.70</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>HI</td>\n",
-       "      <td>49</td>\n",
-       "      <td>510</td>\n",
-       "      <td>410-7789</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>119.3</td>\n",
-       "      <td>117</td>\n",
-       "      <td>20.28</td>\n",
-       "      <td>...</td>\n",
-       "      <td>109</td>\n",
-       "      <td>18.28</td>\n",
-       "      <td>178.7</td>\n",
-       "      <td>90</td>\n",
-       "      <td>8.04</td>\n",
-       "      <td>11.1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3303</th>\n",
-       "      <td>WI</td>\n",
-       "      <td>114</td>\n",
-       "      <td>415</td>\n",
-       "      <td>373-7308</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>26</td>\n",
-       "      <td>137.1</td>\n",
-       "      <td>88</td>\n",
-       "      <td>23.31</td>\n",
-       "      <td>...</td>\n",
-       "      <td>125</td>\n",
-       "      <td>13.23</td>\n",
-       "      <td>247.6</td>\n",
-       "      <td>94</td>\n",
-       "      <td>11.14</td>\n",
-       "      <td>11.5</td>\n",
-       "      <td>7</td>\n",
-       "      <td>3.11</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3304</th>\n",
-       "      <td>IL</td>\n",
-       "      <td>71</td>\n",
-       "      <td>510</td>\n",
-       "      <td>330-7137</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>186.1</td>\n",
-       "      <td>114</td>\n",
-       "      <td>31.64</td>\n",
-       "      <td>...</td>\n",
-       "      <td>140</td>\n",
-       "      <td>16.88</td>\n",
-       "      <td>206.5</td>\n",
-       "      <td>80</td>\n",
-       "      <td>9.29</td>\n",
-       "      <td>13.8</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3.73</td>\n",
-       "      <td>4</td>\n",
-       "      <td>True.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3305</th>\n",
-       "      <td>IN</td>\n",
-       "      <td>58</td>\n",
-       "      <td>415</td>\n",
-       "      <td>406-8445</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>22</td>\n",
-       "      <td>224.1</td>\n",
-       "      <td>127</td>\n",
-       "      <td>38.10</td>\n",
-       "      <td>...</td>\n",
-       "      <td>85</td>\n",
-       "      <td>20.30</td>\n",
-       "      <td>174.2</td>\n",
-       "      <td>86</td>\n",
-       "      <td>7.84</td>\n",
-       "      <td>11.5</td>\n",
-       "      <td>7</td>\n",
-       "      <td>3.11</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3306</th>\n",
-       "      <td>AL</td>\n",
-       "      <td>106</td>\n",
-       "      <td>408</td>\n",
-       "      <td>404-5283</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>29</td>\n",
-       "      <td>83.6</td>\n",
-       "      <td>131</td>\n",
-       "      <td>14.21</td>\n",
-       "      <td>...</td>\n",
-       "      <td>131</td>\n",
-       "      <td>17.33</td>\n",
-       "      <td>229.5</td>\n",
-       "      <td>73</td>\n",
-       "      <td>10.33</td>\n",
-       "      <td>8.1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.19</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3307</th>\n",
-       "      <td>OK</td>\n",
-       "      <td>172</td>\n",
-       "      <td>408</td>\n",
-       "      <td>398-3632</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>203.9</td>\n",
-       "      <td>109</td>\n",
-       "      <td>34.66</td>\n",
-       "      <td>...</td>\n",
-       "      <td>123</td>\n",
-       "      <td>19.89</td>\n",
-       "      <td>160.7</td>\n",
-       "      <td>65</td>\n",
-       "      <td>7.23</td>\n",
-       "      <td>17.8</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4.81</td>\n",
-       "      <td>4</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3308</th>\n",
-       "      <td>IA</td>\n",
-       "      <td>45</td>\n",
-       "      <td>415</td>\n",
-       "      <td>399-5763</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>211.3</td>\n",
-       "      <td>87</td>\n",
-       "      <td>35.92</td>\n",
-       "      <td>...</td>\n",
-       "      <td>97</td>\n",
-       "      <td>14.08</td>\n",
-       "      <td>265.9</td>\n",
-       "      <td>72</td>\n",
-       "      <td>11.97</td>\n",
-       "      <td>13.3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3.59</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3309</th>\n",
-       "      <td>VT</td>\n",
-       "      <td>100</td>\n",
-       "      <td>408</td>\n",
-       "      <td>340-9449</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>219.4</td>\n",
-       "      <td>112</td>\n",
-       "      <td>37.30</td>\n",
-       "      <td>...</td>\n",
-       "      <td>102</td>\n",
-       "      <td>19.18</td>\n",
-       "      <td>255.3</td>\n",
-       "      <td>95</td>\n",
-       "      <td>11.49</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.24</td>\n",
-       "      <td>4</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3310</th>\n",
-       "      <td>NY</td>\n",
-       "      <td>94</td>\n",
-       "      <td>415</td>\n",
-       "      <td>363-1123</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>190.4</td>\n",
-       "      <td>91</td>\n",
-       "      <td>32.37</td>\n",
-       "      <td>...</td>\n",
-       "      <td>107</td>\n",
-       "      <td>7.82</td>\n",
-       "      <td>224.8</td>\n",
-       "      <td>108</td>\n",
-       "      <td>10.12</td>\n",
-       "      <td>13.6</td>\n",
-       "      <td>17</td>\n",
-       "      <td>3.67</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3311</th>\n",
-       "      <td>LA</td>\n",
-       "      <td>128</td>\n",
-       "      <td>415</td>\n",
-       "      <td>361-2170</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>147.7</td>\n",
-       "      <td>94</td>\n",
-       "      <td>25.11</td>\n",
-       "      <td>...</td>\n",
-       "      <td>83</td>\n",
-       "      <td>24.08</td>\n",
-       "      <td>188.3</td>\n",
-       "      <td>124</td>\n",
-       "      <td>8.47</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1.86</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3312</th>\n",
-       "      <td>SC</td>\n",
-       "      <td>181</td>\n",
-       "      <td>408</td>\n",
-       "      <td>406-6304</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>229.9</td>\n",
-       "      <td>130</td>\n",
-       "      <td>39.08</td>\n",
-       "      <td>...</td>\n",
-       "      <td>93</td>\n",
-       "      <td>12.27</td>\n",
-       "      <td>262.4</td>\n",
-       "      <td>110</td>\n",
-       "      <td>11.81</td>\n",
-       "      <td>14.2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.83</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3313</th>\n",
-       "      <td>ID</td>\n",
-       "      <td>127</td>\n",
-       "      <td>408</td>\n",
-       "      <td>392-5090</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>102.8</td>\n",
-       "      <td>128</td>\n",
-       "      <td>17.48</td>\n",
-       "      <td>...</td>\n",
-       "      <td>95</td>\n",
-       "      <td>12.21</td>\n",
-       "      <td>191.4</td>\n",
-       "      <td>97</td>\n",
-       "      <td>8.61</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.70</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3314</th>\n",
-       "      <td>MO</td>\n",
-       "      <td>89</td>\n",
-       "      <td>415</td>\n",
-       "      <td>373-7713</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>178.7</td>\n",
-       "      <td>81</td>\n",
-       "      <td>30.38</td>\n",
-       "      <td>...</td>\n",
-       "      <td>74</td>\n",
-       "      <td>19.86</td>\n",
-       "      <td>131.9</td>\n",
-       "      <td>120</td>\n",
-       "      <td>5.94</td>\n",
-       "      <td>9.1</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.46</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3315</th>\n",
-       "      <td>ME</td>\n",
-       "      <td>149</td>\n",
-       "      <td>415</td>\n",
-       "      <td>392-1376</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>18</td>\n",
-       "      <td>148.5</td>\n",
-       "      <td>106</td>\n",
-       "      <td>25.25</td>\n",
-       "      <td>...</td>\n",
-       "      <td>106</td>\n",
-       "      <td>9.73</td>\n",
-       "      <td>178.3</td>\n",
-       "      <td>98</td>\n",
-       "      <td>8.02</td>\n",
-       "      <td>6.5</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.76</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3316</th>\n",
-       "      <td>MS</td>\n",
-       "      <td>103</td>\n",
-       "      <td>510</td>\n",
-       "      <td>390-6388</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>29</td>\n",
-       "      <td>164.1</td>\n",
-       "      <td>111</td>\n",
-       "      <td>27.90</td>\n",
-       "      <td>...</td>\n",
-       "      <td>96</td>\n",
-       "      <td>18.62</td>\n",
-       "      <td>220.3</td>\n",
-       "      <td>108</td>\n",
-       "      <td>9.91</td>\n",
-       "      <td>12.3</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3.32</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3317</th>\n",
-       "      <td>SD</td>\n",
-       "      <td>163</td>\n",
-       "      <td>415</td>\n",
-       "      <td>379-7290</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>197.2</td>\n",
-       "      <td>90</td>\n",
-       "      <td>33.52</td>\n",
-       "      <td>...</td>\n",
-       "      <td>113</td>\n",
-       "      <td>16.02</td>\n",
-       "      <td>211.1</td>\n",
-       "      <td>94</td>\n",
-       "      <td>9.50</td>\n",
-       "      <td>7.8</td>\n",
-       "      <td>8</td>\n",
-       "      <td>2.11</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3318</th>\n",
-       "      <td>OK</td>\n",
-       "      <td>52</td>\n",
-       "      <td>415</td>\n",
-       "      <td>397-9928</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>124.9</td>\n",
-       "      <td>131</td>\n",
-       "      <td>21.23</td>\n",
-       "      <td>...</td>\n",
-       "      <td>118</td>\n",
-       "      <td>25.54</td>\n",
-       "      <td>192.5</td>\n",
-       "      <td>106</td>\n",
-       "      <td>8.66</td>\n",
-       "      <td>11.6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.13</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3319</th>\n",
-       "      <td>WY</td>\n",
-       "      <td>89</td>\n",
-       "      <td>415</td>\n",
-       "      <td>378-6924</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>115.4</td>\n",
-       "      <td>99</td>\n",
-       "      <td>19.62</td>\n",
-       "      <td>...</td>\n",
-       "      <td>115</td>\n",
-       "      <td>17.84</td>\n",
-       "      <td>280.9</td>\n",
-       "      <td>112</td>\n",
-       "      <td>12.64</td>\n",
-       "      <td>15.9</td>\n",
-       "      <td>6</td>\n",
-       "      <td>4.29</td>\n",
-       "      <td>3</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3320</th>\n",
-       "      <td>GA</td>\n",
-       "      <td>122</td>\n",
-       "      <td>510</td>\n",
-       "      <td>411-5677</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>101</td>\n",
-       "      <td>23.80</td>\n",
-       "      <td>...</td>\n",
-       "      <td>77</td>\n",
-       "      <td>16.69</td>\n",
-       "      <td>120.1</td>\n",
-       "      <td>133</td>\n",
-       "      <td>5.40</td>\n",
-       "      <td>9.7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.62</td>\n",
-       "      <td>4</td>\n",
-       "      <td>True.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3321</th>\n",
-       "      <td>VT</td>\n",
-       "      <td>60</td>\n",
-       "      <td>415</td>\n",
-       "      <td>400-2738</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>193.9</td>\n",
-       "      <td>118</td>\n",
-       "      <td>32.96</td>\n",
-       "      <td>...</td>\n",
-       "      <td>110</td>\n",
-       "      <td>7.23</td>\n",
-       "      <td>210.1</td>\n",
-       "      <td>134</td>\n",
-       "      <td>9.45</td>\n",
-       "      <td>13.2</td>\n",
-       "      <td>8</td>\n",
-       "      <td>3.56</td>\n",
-       "      <td>3</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3322</th>\n",
-       "      <td>MD</td>\n",
-       "      <td>62</td>\n",
-       "      <td>408</td>\n",
-       "      <td>409-1856</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>321.1</td>\n",
-       "      <td>105</td>\n",
-       "      <td>54.59</td>\n",
-       "      <td>...</td>\n",
-       "      <td>122</td>\n",
-       "      <td>22.57</td>\n",
-       "      <td>180.5</td>\n",
-       "      <td>72</td>\n",
-       "      <td>8.12</td>\n",
-       "      <td>11.5</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3.11</td>\n",
-       "      <td>4</td>\n",
-       "      <td>True.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3323</th>\n",
-       "      <td>IN</td>\n",
-       "      <td>117</td>\n",
-       "      <td>415</td>\n",
-       "      <td>362-5899</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>118.4</td>\n",
-       "      <td>126</td>\n",
-       "      <td>20.13</td>\n",
-       "      <td>...</td>\n",
-       "      <td>97</td>\n",
-       "      <td>21.19</td>\n",
-       "      <td>227.0</td>\n",
-       "      <td>56</td>\n",
-       "      <td>10.22</td>\n",
-       "      <td>13.6</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3.67</td>\n",
-       "      <td>5</td>\n",
-       "      <td>True.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3324</th>\n",
-       "      <td>WV</td>\n",
-       "      <td>159</td>\n",
-       "      <td>415</td>\n",
-       "      <td>377-1164</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>169.8</td>\n",
-       "      <td>114</td>\n",
-       "      <td>28.87</td>\n",
-       "      <td>...</td>\n",
-       "      <td>105</td>\n",
-       "      <td>16.80</td>\n",
-       "      <td>193.7</td>\n",
-       "      <td>82</td>\n",
-       "      <td>8.72</td>\n",
-       "      <td>11.6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.13</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3325</th>\n",
-       "      <td>OH</td>\n",
-       "      <td>78</td>\n",
-       "      <td>408</td>\n",
-       "      <td>368-8555</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>193.4</td>\n",
-       "      <td>99</td>\n",
-       "      <td>32.88</td>\n",
-       "      <td>...</td>\n",
-       "      <td>88</td>\n",
-       "      <td>9.94</td>\n",
-       "      <td>243.3</td>\n",
-       "      <td>109</td>\n",
-       "      <td>10.95</td>\n",
-       "      <td>9.3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.51</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3326</th>\n",
-       "      <td>OH</td>\n",
-       "      <td>96</td>\n",
-       "      <td>415</td>\n",
-       "      <td>347-6812</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>106.6</td>\n",
-       "      <td>128</td>\n",
-       "      <td>18.12</td>\n",
-       "      <td>...</td>\n",
-       "      <td>87</td>\n",
-       "      <td>24.21</td>\n",
-       "      <td>178.9</td>\n",
-       "      <td>92</td>\n",
-       "      <td>8.05</td>\n",
-       "      <td>14.9</td>\n",
-       "      <td>7</td>\n",
-       "      <td>4.02</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3327</th>\n",
-       "      <td>SC</td>\n",
-       "      <td>79</td>\n",
-       "      <td>415</td>\n",
-       "      <td>348-3830</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>134.7</td>\n",
-       "      <td>98</td>\n",
-       "      <td>22.90</td>\n",
-       "      <td>...</td>\n",
-       "      <td>68</td>\n",
-       "      <td>16.12</td>\n",
-       "      <td>221.4</td>\n",
-       "      <td>128</td>\n",
-       "      <td>9.96</td>\n",
-       "      <td>11.8</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3.19</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3328</th>\n",
-       "      <td>AZ</td>\n",
-       "      <td>192</td>\n",
-       "      <td>415</td>\n",
-       "      <td>414-4276</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>36</td>\n",
-       "      <td>156.2</td>\n",
-       "      <td>77</td>\n",
-       "      <td>26.55</td>\n",
-       "      <td>...</td>\n",
-       "      <td>126</td>\n",
-       "      <td>18.32</td>\n",
-       "      <td>279.1</td>\n",
-       "      <td>83</td>\n",
-       "      <td>12.56</td>\n",
-       "      <td>9.9</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2.67</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3329</th>\n",
-       "      <td>WV</td>\n",
-       "      <td>68</td>\n",
-       "      <td>415</td>\n",
-       "      <td>370-3271</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>231.1</td>\n",
-       "      <td>57</td>\n",
-       "      <td>39.29</td>\n",
-       "      <td>...</td>\n",
-       "      <td>55</td>\n",
-       "      <td>13.04</td>\n",
-       "      <td>191.3</td>\n",
-       "      <td>123</td>\n",
-       "      <td>8.61</td>\n",
-       "      <td>9.6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.59</td>\n",
-       "      <td>3</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3330</th>\n",
-       "      <td>RI</td>\n",
-       "      <td>28</td>\n",
-       "      <td>510</td>\n",
-       "      <td>328-8230</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>180.8</td>\n",
-       "      <td>109</td>\n",
-       "      <td>30.74</td>\n",
-       "      <td>...</td>\n",
-       "      <td>58</td>\n",
-       "      <td>24.55</td>\n",
-       "      <td>191.9</td>\n",
-       "      <td>91</td>\n",
-       "      <td>8.64</td>\n",
-       "      <td>14.1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>3.81</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3331</th>\n",
-       "      <td>CT</td>\n",
-       "      <td>184</td>\n",
-       "      <td>510</td>\n",
-       "      <td>364-6381</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>213.8</td>\n",
-       "      <td>105</td>\n",
-       "      <td>36.35</td>\n",
-       "      <td>...</td>\n",
-       "      <td>84</td>\n",
-       "      <td>13.57</td>\n",
-       "      <td>139.2</td>\n",
-       "      <td>137</td>\n",
-       "      <td>6.26</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>1.35</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3332</th>\n",
-       "      <td>TN</td>\n",
-       "      <td>74</td>\n",
-       "      <td>415</td>\n",
-       "      <td>400-4344</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>25</td>\n",
-       "      <td>234.4</td>\n",
-       "      <td>113</td>\n",
-       "      <td>39.85</td>\n",
-       "      <td>...</td>\n",
-       "      <td>82</td>\n",
-       "      <td>22.60</td>\n",
-       "      <td>241.4</td>\n",
-       "      <td>77</td>\n",
-       "      <td>10.86</td>\n",
-       "      <td>13.7</td>\n",
-       "      <td>4</td>\n",
-       "      <td>3.70</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>3333 rows × 21 columns</p>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "SEJnb_CWuzZ6",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 122
+        },
+        "outputId": "91ffe2d6-7009-464e-a6ec-b6c5704da429"
+      },
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
       ],
-      "text/plain": [
-       "     State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
-       "0       KS             128        415  382-4657         no        yes   \n",
-       "1       OH             107        415  371-7191         no        yes   \n",
-       "2       NJ             137        415  358-1921         no         no   \n",
-       "3       OH              84        408  375-9999        yes         no   \n",
-       "4       OK              75        415  330-6626        yes         no   \n",
-       "5       AL             118        510  391-8027        yes         no   \n",
-       "6       MA             121        510  355-9993         no        yes   \n",
-       "7       MO             147        415  329-9001        yes         no   \n",
-       "8       LA             117        408  335-4719         no         no   \n",
-       "9       WV             141        415  330-8173        yes        yes   \n",
-       "10      IN              65        415  329-6603         no         no   \n",
-       "11      RI              74        415  344-9403         no         no   \n",
-       "12      IA             168        408  363-1107         no         no   \n",
-       "13      MT              95        510  394-8006         no         no   \n",
-       "14      IA              62        415  366-9238         no         no   \n",
-       "15      NY             161        415  351-7269         no         no   \n",
-       "16      ID              85        408  350-8884         no        yes   \n",
-       "17      VT              93        510  386-2923         no         no   \n",
-       "18      VA              76        510  356-2992         no        yes   \n",
-       "19      TX              73        415  373-2782         no         no   \n",
-       "20      FL             147        415  396-5800         no         no   \n",
-       "21      CO              77        408  393-7984         no         no   \n",
-       "22      AZ             130        415  358-1958         no         no   \n",
-       "23      SC             111        415  350-2565         no         no   \n",
-       "24      VA             132        510  343-4696         no         no   \n",
-       "25      NE             174        415  331-3698         no         no   \n",
-       "26      WY              57        408  357-3817         no        yes   \n",
-       "27      MT              54        408  418-6412         no         no   \n",
-       "28      MO              20        415  353-2630         no         no   \n",
-       "29      HI              49        510  410-7789         no         no   \n",
-       "...    ...             ...        ...       ...        ...        ...   \n",
-       "3303    WI             114        415  373-7308         no        yes   \n",
-       "3304    IL              71        510  330-7137        yes         no   \n",
-       "3305    IN              58        415  406-8445         no        yes   \n",
-       "3306    AL             106        408  404-5283         no        yes   \n",
-       "3307    OK             172        408  398-3632         no         no   \n",
-       "3308    IA              45        415  399-5763         no         no   \n",
-       "3309    VT             100        408  340-9449        yes         no   \n",
-       "3310    NY              94        415  363-1123         no         no   \n",
-       "3311    LA             128        415  361-2170         no         no   \n",
-       "3312    SC             181        408  406-6304         no         no   \n",
-       "3313    ID             127        408  392-5090         no         no   \n",
-       "3314    MO              89        415  373-7713         no         no   \n",
-       "3315    ME             149        415  392-1376         no        yes   \n",
-       "3316    MS             103        510  390-6388         no        yes   \n",
-       "3317    SD             163        415  379-7290        yes         no   \n",
-       "3318    OK              52        415  397-9928         no         no   \n",
-       "3319    WY              89        415  378-6924         no         no   \n",
-       "3320    GA             122        510  411-5677        yes         no   \n",
-       "3321    VT              60        415  400-2738         no         no   \n",
-       "3322    MD              62        408  409-1856         no         no   \n",
-       "3323    IN             117        415  362-5899         no         no   \n",
-       "3324    WV             159        415  377-1164         no         no   \n",
-       "3325    OH              78        408  368-8555         no         no   \n",
-       "3326    OH              96        415  347-6812         no         no   \n",
-       "3327    SC              79        415  348-3830         no         no   \n",
-       "3328    AZ             192        415  414-4276         no        yes   \n",
-       "3329    WV              68        415  370-3271         no         no   \n",
-       "3330    RI              28        510  328-8230         no         no   \n",
-       "3331    CT             184        510  364-6381        yes         no   \n",
-       "3332    TN              74        415  400-4344         no        yes   \n",
-       "\n",
-       "      VMail Message  Day Mins  Day Calls  Day Charge   ...    Eve Calls  \\\n",
-       "0                25     265.1        110       45.07   ...           99   \n",
-       "1                26     161.6        123       27.47   ...          103   \n",
-       "2                 0     243.4        114       41.38   ...          110   \n",
-       "3                 0     299.4         71       50.90   ...           88   \n",
-       "4                 0     166.7        113       28.34   ...          122   \n",
-       "5                 0     223.4         98       37.98   ...          101   \n",
-       "6                24     218.2         88       37.09   ...          108   \n",
-       "7                 0     157.0         79       26.69   ...           94   \n",
-       "8                 0     184.5         97       31.37   ...           80   \n",
-       "9                37     258.6         84       43.96   ...          111   \n",
-       "10                0     129.1        137       21.95   ...           83   \n",
-       "11                0     187.7        127       31.91   ...          148   \n",
-       "12                0     128.8         96       21.90   ...           71   \n",
-       "13                0     156.6         88       26.62   ...           75   \n",
-       "14                0     120.7         70       20.52   ...           76   \n",
-       "15                0     332.9         67       56.59   ...           97   \n",
-       "16               27     196.4        139       33.39   ...           90   \n",
-       "17                0     190.7        114       32.42   ...          111   \n",
-       "18               33     189.7         66       32.25   ...           65   \n",
-       "19                0     224.4         90       38.15   ...           88   \n",
-       "20                0     155.1        117       26.37   ...           93   \n",
-       "21                0      62.4         89       10.61   ...          121   \n",
-       "22                0     183.0        112       31.11   ...           99   \n",
-       "23                0     110.4        103       18.77   ...          102   \n",
-       "24                0      81.1         86       13.79   ...           72   \n",
-       "25                0     124.3         76       21.13   ...          112   \n",
-       "26               39     213.0        115       36.21   ...          112   \n",
-       "27                0     134.3         73       22.83   ...          100   \n",
-       "28                0     190.0        109       32.30   ...           84   \n",
-       "29                0     119.3        117       20.28   ...          109   \n",
-       "...             ...       ...        ...         ...   ...          ...   \n",
-       "3303             26     137.1         88       23.31   ...          125   \n",
-       "3304              0     186.1        114       31.64   ...          140   \n",
-       "3305             22     224.1        127       38.10   ...           85   \n",
-       "3306             29      83.6        131       14.21   ...          131   \n",
-       "3307              0     203.9        109       34.66   ...          123   \n",
-       "3308              0     211.3         87       35.92   ...           97   \n",
-       "3309              0     219.4        112       37.30   ...          102   \n",
-       "3310              0     190.4         91       32.37   ...          107   \n",
-       "3311              0     147.7         94       25.11   ...           83   \n",
-       "3312              0     229.9        130       39.08   ...           93   \n",
-       "3313              0     102.8        128       17.48   ...           95   \n",
-       "3314              0     178.7         81       30.38   ...           74   \n",
-       "3315             18     148.5        106       25.25   ...          106   \n",
-       "3316             29     164.1        111       27.90   ...           96   \n",
-       "3317              0     197.2         90       33.52   ...          113   \n",
-       "3318              0     124.9        131       21.23   ...          118   \n",
-       "3319              0     115.4         99       19.62   ...          115   \n",
-       "3320              0     140.0        101       23.80   ...           77   \n",
-       "3321              0     193.9        118       32.96   ...          110   \n",
-       "3322              0     321.1        105       54.59   ...          122   \n",
-       "3323              0     118.4        126       20.13   ...           97   \n",
-       "3324              0     169.8        114       28.87   ...          105   \n",
-       "3325              0     193.4         99       32.88   ...           88   \n",
-       "3326              0     106.6        128       18.12   ...           87   \n",
-       "3327              0     134.7         98       22.90   ...           68   \n",
-       "3328             36     156.2         77       26.55   ...          126   \n",
-       "3329              0     231.1         57       39.29   ...           55   \n",
-       "3330              0     180.8        109       30.74   ...           58   \n",
-       "3331              0     213.8        105       36.35   ...           84   \n",
-       "3332             25     234.4        113       39.85   ...           82   \n",
-       "\n",
-       "      Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  \\\n",
-       "0          16.78       244.7           91         11.01       10.0   \n",
-       "1          16.62       254.4          103         11.45       13.7   \n",
-       "2          10.30       162.6          104          7.32       12.2   \n",
-       "3           5.26       196.9           89          8.86        6.6   \n",
-       "4          12.61       186.9          121          8.41       10.1   \n",
-       "5          18.75       203.9          118          9.18        6.3   \n",
-       "6          29.62       212.6          118          9.57        7.5   \n",
-       "7           8.76       211.8           96          9.53        7.1   \n",
-       "8          29.89       215.8           90          9.71        8.7   \n",
-       "9          18.87       326.4           97         14.69       11.2   \n",
-       "10         19.42       208.8          111          9.40       12.7   \n",
-       "11         13.89       196.0           94          8.82        9.1   \n",
-       "12          8.92       141.1          128          6.35       11.2   \n",
-       "13         21.05       192.3          115          8.65       12.3   \n",
-       "14         26.11       203.0           99          9.14       13.1   \n",
-       "15         27.01       160.6          128          7.23        5.4   \n",
-       "16         23.88        89.3           75          4.02       13.8   \n",
-       "17         18.55       129.6          121          5.83        8.1   \n",
-       "18         18.09       165.7          108          7.46       10.0   \n",
-       "19         13.56       192.8           74          8.68       13.0   \n",
-       "20         20.37       208.8          133          9.40       10.6   \n",
-       "21         14.44       209.6           64          9.43        5.7   \n",
-       "22          6.20       181.8           78          8.18        9.5   \n",
-       "23         11.67       189.6          105          8.53        7.7   \n",
-       "24         20.84       237.0          115         10.67       10.3   \n",
-       "25         23.55       250.7          115         11.28       15.5   \n",
-       "26         16.24       182.7          115          8.22        9.5   \n",
-       "27         13.22       102.1           68          4.59       14.7   \n",
-       "28         21.95       181.5          102          8.17        6.3   \n",
-       "29         18.28       178.7           90          8.04       11.1   \n",
-       "...          ...         ...          ...           ...        ...   \n",
-       "3303       13.23       247.6           94         11.14       11.5   \n",
-       "3304       16.88       206.5           80          9.29       13.8   \n",
-       "3305       20.30       174.2           86          7.84       11.5   \n",
-       "3306       17.33       229.5           73         10.33        8.1   \n",
-       "3307       19.89       160.7           65          7.23       17.8   \n",
-       "3308       14.08       265.9           72         11.97       13.3   \n",
-       "3309       19.18       255.3           95         11.49       12.0   \n",
-       "3310        7.82       224.8          108         10.12       13.6   \n",
-       "3311       24.08       188.3          124          8.47        6.9   \n",
-       "3312       12.27       262.4          110         11.81       14.2   \n",
-       "3313       12.21       191.4           97          8.61       10.0   \n",
-       "3314       19.86       131.9          120          5.94        9.1   \n",
-       "3315        9.73       178.3           98          8.02        6.5   \n",
-       "3316       18.62       220.3          108          9.91       12.3   \n",
-       "3317       16.02       211.1           94          9.50        7.8   \n",
-       "3318       25.54       192.5          106          8.66       11.6   \n",
-       "3319       17.84       280.9          112         12.64       15.9   \n",
-       "3320       16.69       120.1          133          5.40        9.7   \n",
-       "3321        7.23       210.1          134          9.45       13.2   \n",
-       "3322       22.57       180.5           72          8.12       11.5   \n",
-       "3323       21.19       227.0           56         10.22       13.6   \n",
-       "3324       16.80       193.7           82          8.72       11.6   \n",
-       "3325        9.94       243.3          109         10.95        9.3   \n",
-       "3326       24.21       178.9           92          8.05       14.9   \n",
-       "3327       16.12       221.4          128          9.96       11.8   \n",
-       "3328       18.32       279.1           83         12.56        9.9   \n",
-       "3329       13.04       191.3          123          8.61        9.6   \n",
-       "3330       24.55       191.9           91          8.64       14.1   \n",
-       "3331       13.57       139.2          137          6.26        5.0   \n",
-       "3332       22.60       241.4           77         10.86       13.7   \n",
-       "\n",
-       "      Intl Calls  Intl Charge  CustServ Calls  Churn?  \n",
-       "0              3         2.70               1  False.  \n",
-       "1              3         3.70               1  False.  \n",
-       "2              5         3.29               0  False.  \n",
-       "3              7         1.78               2  False.  \n",
-       "4              3         2.73               3  False.  \n",
-       "5              6         1.70               0  False.  \n",
-       "6              7         2.03               3  False.  \n",
-       "7              6         1.92               0  False.  \n",
-       "8              4         2.35               1  False.  \n",
-       "9              5         3.02               0  False.  \n",
-       "10             6         3.43               4   True.  \n",
-       "11             5         2.46               0  False.  \n",
-       "12             2         3.02               1  False.  \n",
-       "13             5         3.32               3  False.  \n",
-       "14             6         3.54               4  False.  \n",
-       "15             9         1.46               4   True.  \n",
-       "16             4         3.73               1  False.  \n",
-       "17             3         2.19               3  False.  \n",
-       "18             5         2.70               1  False.  \n",
-       "19             2         3.51               1  False.  \n",
-       "20             4         2.86               0  False.  \n",
-       "21             6         1.54               5   True.  \n",
-       "22            19         2.57               0  False.  \n",
-       "23             6         2.08               2  False.  \n",
-       "24             2         2.78               0  False.  \n",
-       "25             5         4.19               3  False.  \n",
-       "26             3         2.57               0  False.  \n",
-       "27             4         3.97               3  False.  \n",
-       "28             6         1.70               0  False.  \n",
-       "29             1         3.00               1  False.  \n",
-       "...          ...          ...             ...     ...  \n",
-       "3303           7         3.11               2  False.  \n",
-       "3304           5         3.73               4   True.  \n",
-       "3305           7         3.11               2  False.  \n",
-       "3306           3         2.19               1  False.  \n",
-       "3307           4         4.81               4  False.  \n",
-       "3308           6         3.59               1  False.  \n",
-       "3309           4         3.24               4  False.  \n",
-       "3310          17         3.67               2  False.  \n",
-       "3311           5         1.86               2  False.  \n",
-       "3312           4         3.83               2  False.  \n",
-       "3313           5         2.70               1  False.  \n",
-       "3314           4         2.46               1  False.  \n",
-       "3315           4         1.76               0  False.  \n",
-       "3316           9         3.32               0  False.  \n",
-       "3317           8         2.11               1  False.  \n",
-       "3318           4         3.13               2  False.  \n",
-       "3319           6         4.29               3  False.  \n",
-       "3320           4         2.62               4   True.  \n",
-       "3321           8         3.56               3  False.  \n",
-       "3322           2         3.11               4   True.  \n",
-       "3323           3         3.67               5   True.  \n",
-       "3324           4         3.13               1  False.  \n",
-       "3325           4         2.51               2  False.  \n",
-       "3326           7         4.02               1  False.  \n",
-       "3327           5         3.19               2  False.  \n",
-       "3328           6         2.67               2  False.  \n",
-       "3329           4         2.59               3  False.  \n",
-       "3330           6         3.81               2  False.  \n",
-       "3331          10         1.35               2  False.  \n",
-       "3332           4         3.70               0  False.  \n",
-       "\n",
-       "[3333 rows x 21 columns]"
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+            "\n",
+            "Enter your authorization code:\n",
+            "··········\n",
+            "Mounted at /content/drive\n"
+          ],
+          "name": "stdout"
+        }
       ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "% matplotlib inline "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#savefig(\"path_donde_guardar_im.jpeg\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Scatter Plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1137e7470>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L_BUppvGuwx2",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Plots y visualización de los datos"
       ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x113319160>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "jdYlSTiRuwx4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3hwU_OkRuwx9",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/customer-churn-model/Customer Churn Model.txt\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IWQGFbEKuwyB",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 606
+        },
+        "outputId": "d608e205-5687-4bbe-d0b3-3dd02ca66dba"
+      },
+      "source": [
+        "data"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>State</th>\n",
+              "      <th>Account Length</th>\n",
+              "      <th>Area Code</th>\n",
+              "      <th>Phone</th>\n",
+              "      <th>Int'l Plan</th>\n",
+              "      <th>VMail Plan</th>\n",
+              "      <th>VMail Message</th>\n",
+              "      <th>Day Mins</th>\n",
+              "      <th>Day Calls</th>\n",
+              "      <th>Day Charge</th>\n",
+              "      <th>Eve Mins</th>\n",
+              "      <th>Eve Calls</th>\n",
+              "      <th>Eve Charge</th>\n",
+              "      <th>Night Mins</th>\n",
+              "      <th>Night Calls</th>\n",
+              "      <th>Night Charge</th>\n",
+              "      <th>Intl Mins</th>\n",
+              "      <th>Intl Calls</th>\n",
+              "      <th>Intl Charge</th>\n",
+              "      <th>CustServ Calls</th>\n",
+              "      <th>Churn?</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>KS</td>\n",
+              "      <td>128</td>\n",
+              "      <td>415</td>\n",
+              "      <td>382-4657</td>\n",
+              "      <td>no</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>25</td>\n",
+              "      <td>265.1</td>\n",
+              "      <td>110</td>\n",
+              "      <td>45.07</td>\n",
+              "      <td>197.4</td>\n",
+              "      <td>99</td>\n",
+              "      <td>16.78</td>\n",
+              "      <td>244.7</td>\n",
+              "      <td>91</td>\n",
+              "      <td>11.01</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2.70</td>\n",
+              "      <td>1</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>OH</td>\n",
+              "      <td>107</td>\n",
+              "      <td>415</td>\n",
+              "      <td>371-7191</td>\n",
+              "      <td>no</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>26</td>\n",
+              "      <td>161.6</td>\n",
+              "      <td>123</td>\n",
+              "      <td>27.47</td>\n",
+              "      <td>195.5</td>\n",
+              "      <td>103</td>\n",
+              "      <td>16.62</td>\n",
+              "      <td>254.4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>11.45</td>\n",
+              "      <td>13.7</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3.70</td>\n",
+              "      <td>1</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>NJ</td>\n",
+              "      <td>137</td>\n",
+              "      <td>415</td>\n",
+              "      <td>358-1921</td>\n",
+              "      <td>no</td>\n",
+              "      <td>no</td>\n",
+              "      <td>0</td>\n",
+              "      <td>243.4</td>\n",
+              "      <td>114</td>\n",
+              "      <td>41.38</td>\n",
+              "      <td>121.2</td>\n",
+              "      <td>110</td>\n",
+              "      <td>10.30</td>\n",
+              "      <td>162.6</td>\n",
+              "      <td>104</td>\n",
+              "      <td>7.32</td>\n",
+              "      <td>12.2</td>\n",
+              "      <td>5</td>\n",
+              "      <td>3.29</td>\n",
+              "      <td>0</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>OH</td>\n",
+              "      <td>84</td>\n",
+              "      <td>408</td>\n",
+              "      <td>375-9999</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>no</td>\n",
+              "      <td>0</td>\n",
+              "      <td>299.4</td>\n",
+              "      <td>71</td>\n",
+              "      <td>50.90</td>\n",
+              "      <td>61.9</td>\n",
+              "      <td>88</td>\n",
+              "      <td>5.26</td>\n",
+              "      <td>196.9</td>\n",
+              "      <td>89</td>\n",
+              "      <td>8.86</td>\n",
+              "      <td>6.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1.78</td>\n",
+              "      <td>2</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>OK</td>\n",
+              "      <td>75</td>\n",
+              "      <td>415</td>\n",
+              "      <td>330-6626</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>no</td>\n",
+              "      <td>0</td>\n",
+              "      <td>166.7</td>\n",
+              "      <td>113</td>\n",
+              "      <td>28.34</td>\n",
+              "      <td>148.3</td>\n",
+              "      <td>122</td>\n",
+              "      <td>12.61</td>\n",
+              "      <td>186.9</td>\n",
+              "      <td>121</td>\n",
+              "      <td>8.41</td>\n",
+              "      <td>10.1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2.73</td>\n",
+              "      <td>3</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3328</th>\n",
+              "      <td>AZ</td>\n",
+              "      <td>192</td>\n",
+              "      <td>415</td>\n",
+              "      <td>414-4276</td>\n",
+              "      <td>no</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>36</td>\n",
+              "      <td>156.2</td>\n",
+              "      <td>77</td>\n",
+              "      <td>26.55</td>\n",
+              "      <td>215.5</td>\n",
+              "      <td>126</td>\n",
+              "      <td>18.32</td>\n",
+              "      <td>279.1</td>\n",
+              "      <td>83</td>\n",
+              "      <td>12.56</td>\n",
+              "      <td>9.9</td>\n",
+              "      <td>6</td>\n",
+              "      <td>2.67</td>\n",
+              "      <td>2</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3329</th>\n",
+              "      <td>WV</td>\n",
+              "      <td>68</td>\n",
+              "      <td>415</td>\n",
+              "      <td>370-3271</td>\n",
+              "      <td>no</td>\n",
+              "      <td>no</td>\n",
+              "      <td>0</td>\n",
+              "      <td>231.1</td>\n",
+              "      <td>57</td>\n",
+              "      <td>39.29</td>\n",
+              "      <td>153.4</td>\n",
+              "      <td>55</td>\n",
+              "      <td>13.04</td>\n",
+              "      <td>191.3</td>\n",
+              "      <td>123</td>\n",
+              "      <td>8.61</td>\n",
+              "      <td>9.6</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2.59</td>\n",
+              "      <td>3</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3330</th>\n",
+              "      <td>RI</td>\n",
+              "      <td>28</td>\n",
+              "      <td>510</td>\n",
+              "      <td>328-8230</td>\n",
+              "      <td>no</td>\n",
+              "      <td>no</td>\n",
+              "      <td>0</td>\n",
+              "      <td>180.8</td>\n",
+              "      <td>109</td>\n",
+              "      <td>30.74</td>\n",
+              "      <td>288.8</td>\n",
+              "      <td>58</td>\n",
+              "      <td>24.55</td>\n",
+              "      <td>191.9</td>\n",
+              "      <td>91</td>\n",
+              "      <td>8.64</td>\n",
+              "      <td>14.1</td>\n",
+              "      <td>6</td>\n",
+              "      <td>3.81</td>\n",
+              "      <td>2</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3331</th>\n",
+              "      <td>CT</td>\n",
+              "      <td>184</td>\n",
+              "      <td>510</td>\n",
+              "      <td>364-6381</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>no</td>\n",
+              "      <td>0</td>\n",
+              "      <td>213.8</td>\n",
+              "      <td>105</td>\n",
+              "      <td>36.35</td>\n",
+              "      <td>159.6</td>\n",
+              "      <td>84</td>\n",
+              "      <td>13.57</td>\n",
+              "      <td>139.2</td>\n",
+              "      <td>137</td>\n",
+              "      <td>6.26</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>10</td>\n",
+              "      <td>1.35</td>\n",
+              "      <td>2</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3332</th>\n",
+              "      <td>TN</td>\n",
+              "      <td>74</td>\n",
+              "      <td>415</td>\n",
+              "      <td>400-4344</td>\n",
+              "      <td>no</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>25</td>\n",
+              "      <td>234.4</td>\n",
+              "      <td>113</td>\n",
+              "      <td>39.85</td>\n",
+              "      <td>265.9</td>\n",
+              "      <td>82</td>\n",
+              "      <td>22.60</td>\n",
+              "      <td>241.4</td>\n",
+              "      <td>77</td>\n",
+              "      <td>10.86</td>\n",
+              "      <td>13.7</td>\n",
+              "      <td>4</td>\n",
+              "      <td>3.70</td>\n",
+              "      <td>0</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>3333 rows × 21 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+              "0       KS             128        415  ...        2.70              1  False.\n",
+              "1       OH             107        415  ...        3.70              1  False.\n",
+              "2       NJ             137        415  ...        3.29              0  False.\n",
+              "3       OH              84        408  ...        1.78              2  False.\n",
+              "4       OK              75        415  ...        2.73              3  False.\n",
+              "...    ...             ...        ...  ...         ...            ...     ...\n",
+              "3328    AZ             192        415  ...        2.67              2  False.\n",
+              "3329    WV              68        415  ...        2.59              3  False.\n",
+              "3330    RI              28        510  ...        3.81              2  False.\n",
+              "3331    CT             184        510  ...        1.35              2  False.\n",
+              "3332    TN              74        415  ...        3.70              0  False.\n",
+              "\n",
+              "[3333 rows x 21 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data.plot(kind=\"scatter\", x=\"Day Mins\", y=\"Day Charge\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x113968cf8>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "b1dfR0FruwyF",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "%matplotlib inline "
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EKmumRn-uwyI",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "#savefig(\"path_donde_guardar_im.jpeg\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b5C_T5hXuwyJ",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Scatter Plot"
       ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x113dfca58>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "Dy0NYV1ouwyK",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 296
+        },
+        "outputId": "bcebb7ae-6c3e-4ab3-a5e0-e9ff3cfe820f"
+      },
+      "source": [
+        "data.plot(kind=\"scatter\", x=\"Day Mins\", y=\"Day Charge\")"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f019b3ac6a0>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data.plot(kind=\"scatter\", x=\"Night Mins\", y=\"Night Charge\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x11e01e240>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "DqeqcFc-uwyL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 296
+        },
+        "outputId": "d111c7cc-fac3-4577-e6c7-b910dd89eaeb"
+      },
+      "source": [
+        "data.plot(kind=\"scatter\", x=\"Night Mins\", y=\"Night Charge\")"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f019b34bc18>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11de4da90>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "aGWlvG_CuwyN",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 296
+        },
+        "outputId": "e601cc62-a962-471d-f766-e5794b7a6cdd"
+      },
+      "source": [
+        "figure, axs = plt.subplots(2,2, sharey=True, sharex=True)\n",
+        "data.plot(kind=\"scatter\", x=\"Day Mins\", y =\"Day Charge\", ax=axs[0][0])\n",
+        "data.plot(kind=\"scatter\", x=\"Night Mins\", y=\"Night Charge\", ax=axs[0][1])\n",
+        "data.plot(kind=\"scatter\", x=\"Day Calls\", y =\"Day Charge\", ax=axs[1][0])\n",
+        "data.plot(kind=\"scatter\", x=\"Night Calls\", y=\"Night Charge\", ax=axs[1][1])"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0198552b38>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 4 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "figure, axs = plt.subplots(2,2, sharey=True, sharex=True)\n",
-    "data.plot(kind=\"scatter\", x=\"Day Mins\", y =\"Day Charge\", ax=axs[0][0])\n",
-    "data.plot(kind=\"scatter\", x=\"Night Mins\", y=\"Night Charge\", ax=axs[0][1])\n",
-    "data.plot(kind=\"scatter\", x=\"Day Calls\", y =\"Day Charge\", ax=axs[1][0])\n",
-    "data.plot(kind=\"scatter\", x=\"Night Calls\", y=\"Night Charge\", ax=axs[1][1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Histogramas de frecuencias"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Text(0.5,1,'Histograma del número de llamadas al día')"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MfMWlcyzuwyP",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Histogramas de frecuencias"
       ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11f8faa20>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "WQuFiRiHuwyP",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 312
+        },
+        "outputId": "505bc479-b210-45d7-dab0-4c212fad5d2e"
+      },
+      "source": [
+        "k = int(np.ceil(1+np.log2(3333)))\n",
+        "plt.hist(data[\"Day Calls\"], bins = k) #bins = [0,30,60,...,200]\n",
+        "plt.xlabel(\"Número de llamadas al día\")\n",
+        "plt.ylabel(\"Frecuencia\")\n",
+        "plt.title(\"Histograma del número de llamadas al día\")"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Histograma del número de llamadas al día')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "k = int(np.ceil(1+np.log2(3333)))\n",
-    "plt.hist(data[\"Day Calls\"], bins = k) #bins = [0,30,60,...,200]\n",
-    "plt.xlabel(\"Número de llamadas al día\")\n",
-    "plt.ylabel(\"Frecuencia\")\n",
-    "plt.title(\"Histograma del número de llamadas al día\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Boxplot, diagrama de caja y bigotes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Text(0.5,1,'Boxplot de las llamadas diarias')"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lQ0MTWkEuwyQ",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Boxplot, diagrama de caja y bigotes"
       ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11fdb49e8>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "QUKSN0KluwyR",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 298
+        },
+        "outputId": "5dce1e8d-24f0-4134-cb22-282035da73fa"
+      },
+      "source": [
+        "plt.boxplot(data[\"Day Calls\"])\n",
+        "plt.ylabel(\"Número de llamadas diarias\")\n",
+        "plt.title(\"Boxplot de las llamadas diarias\")"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Boxplot de las llamadas diarias')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.boxplot(data[\"Day Calls\"])\n",
-    "plt.ylabel(\"Número de llamadas diarias\")\n",
-    "plt.title(\"Boxplot de las llamadas diarias\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "count    3333.000000\n",
-       "mean      100.435644\n",
-       "std        20.069084\n",
-       "min         0.000000\n",
-       "25%        87.000000\n",
-       "50%       101.000000\n",
-       "75%       114.000000\n",
-       "max       165.000000\n",
-       "Name: Day Calls, dtype: float64"
+      "cell_type": "code",
+      "metadata": {
+        "id": "PqSFRzMQuwyS",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 170
+        },
+        "outputId": "1aa33895-a7e3-49b1-b27c-25c00d7db030"
+      },
+      "source": [
+        "data[\"Day Calls\"].describe()"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count    3333.000000\n",
+              "mean      100.435644\n",
+              "std        20.069084\n",
+              "min         0.000000\n",
+              "25%        87.000000\n",
+              "50%       101.000000\n",
+              "75%       114.000000\n",
+              "max       165.000000\n",
+              "Name: Day Calls, dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        }
       ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[\"Day Calls\"].describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "27.0"
+      "cell_type": "code",
+      "metadata": {
+        "id": "uz1oKSpIuwyT",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "41332730-2975-4765-f791-7d5350fae1c6"
+      },
+      "source": [
+        "IQR=data[\"Day Calls\"].quantile(0.75)-data[\"Day Calls\"].quantile(0.25)\n",
+        "IQR"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "27.0"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
       ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "IQR=data[\"Day Calls\"].quantile(0.75)-data[\"Day Calls\"].quantile(0.25)\n",
-    "IQR"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "46.5"
+      "cell_type": "code",
+      "metadata": {
+        "id": "wDKapxuLuwyV",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "5c045b44-7a2f-4494-95a3-f1878affd6aa"
+      },
+      "source": [
+        "data[\"Day Calls\"].quantile(0.25) - 1.5*IQR"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "46.5"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
       ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data[\"Day Calls\"].quantile(0.25) - 1.5*IQR"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "154.5"
+      "cell_type": "code",
+      "metadata": {
+        "id": "uMBjtBUDuwyW",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "de4ec136-5cf2-496c-c9bf-67ba241650c0"
+      },
+      "source": [
+        "data[\"Day Calls\"].quantile(0.75) + 1.5*IQR"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "154.5"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
       ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "data[\"Day Calls\"].quantile(0.75) + 1.5*IQR"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  ]
+}
\ No newline at end of file
diff --git a/notebooks/T10 - 1 - Analisis de Componentes Principales-Colab.ipynb b/notebooks/T10 - 1 - Analisis de Componentes Principales-Colab.ipynb
new file mode 100644
index 00000000..62ab3d95
--- /dev/null
+++ b/notebooks/T10 - 1 - Analisis de Componentes Principales-Colab.ipynb	
@@ -0,0 +1,585 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T10%20-%201%20-%20Analisis%20de%20Componentes%20Principales-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Análisis de Componentes Principales - Paso a Paso"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Estandarizar los datos (para cada una de las m observaciones)\n",
+    "* Obtener los vectores y valores propios a partir de la matriz de covarianzas o de correlaciones o incluso la técnica de singular vector decomposition.\n",
+    "* Ordenar los valores propios en orden descendente y quedarnos con los *p* que se correpondan a los *p* mayores y así disminuir el número de variables del dataset (p<m)\n",
+    "* Constrir la matriz de proyección W a partir de los p vectores propios\n",
+    "* Transformar el dataset original X a través de W para así obtener dadtos en el subespacio dimensional de dimensión *p*, que será Y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"/content/python-ml-course/datasets/iris/iris.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df.iloc[:,0:4].values\n",
+    "y = df.iloc[:,4].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import chart_studio.plotly as py\n",
+    "import plotly.graph_objects as go\n",
+    "import chart_studio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chart_studio.tools.set_credentials_file(username='JuanGabriel', api_key='6mEfSXf8XNyIzpxwb8z7')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "traces = []\n",
+    "legend = {0:True, 1:True, 2:True, 3:True}\n",
+    "\n",
+    "colors = {'setosa': 'rgb(255,127,20)',\n",
+    "         'versicolor': 'rgb(31, 220, 120)',\n",
+    "         'virginica': 'rgb(44, 50, 180)'}\n",
+    "\n",
+    "\n",
+    "for col in range(4): \n",
+    "    for key in colors:\n",
+    "        traces.append(go.Histogram(x=X[y==key, col],\n",
+    "                                   opacity = 0.7, \n",
+    "                                   xaxis=\"x%s\"%(col+1),\n",
+    "                                   marker={\"color\":colors[key]},\n",
+    "                                   name = key, showlegend=legend[col])\n",
+    "                     )\n",
+    "        \n",
+    "    legend = {0:False, 1:False, 2:False, 3:False}\n",
+    "\n",
+    "layout = go.Layout(\n",
+    "    title={\"text\":\"Distribución de los rasgos de las diferentes flores Iris\",\n",
+    "           \"xref\" : \"paper\",\"x\" : 0.5},\n",
+    "    barmode=\"overlay\",\n",
+    "    xaxis= {\"domain\" : [0,0.25], \"title\":\"Long. Sépalos (cm)\"},\n",
+    "    xaxis2= {\"domain\" : [0.3, 0.5], \"title\" : \"Anch. Sépalos (cm)\"},\n",
+    "    xaxis3= {\"domain\" : [0.55, 0.75], \"title\" : \"Long. Pétalos (cm)\"},\n",
+    "    xaxis4= {\"domain\" : [0.8,1.0], \"title\" : \"Anch. Pétalos (cm)\"},\n",
+    "    yaxis={\"title\":\"Número de ejemplares\"}\n",
+    ")\n",
+    "\n",
+    "fig = go.Figure(data=traces, layout=layout)\n",
+    "py.iplot(fig)\n",
+    "#fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import StandardScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_std = StandardScaler().fit_transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "traces = []\n",
+    "legend = {0:True, 1:True, 2:True, 3:True}\n",
+    "\n",
+    "colors = {'setosa': 'rgb(255,127,20)',\n",
+    "         'versicolor': 'rgb(31, 220, 120)',\n",
+    "         'virginica': 'rgb(44, 50, 180)'}\n",
+    "\n",
+    "\n",
+    "for col in range(4): \n",
+    "    for key in colors:\n",
+    "        traces.append(go.Histogram(x=X_std[y==key, col],\n",
+    "                                   opacity = 0.7, \n",
+    "                                   xaxis=\"x%s\"%(col+1),\n",
+    "                                   marker={\"color\":colors[key]},\n",
+    "                                   name = key, showlegend=legend[col])\n",
+    "                     )\n",
+    "        \n",
+    "    legend = {0:False, 1:False, 2:False, 3:False}\n",
+    "\n",
+    "layout = go.Layout(\n",
+    "    title={\"text\":\"Distribución de los rasgos de las diferentes flores Iris\",\n",
+    "           \"xref\" : \"paper\",\"x\" : 0.5},\n",
+    "    barmode=\"overlay\",\n",
+    "    xaxis= {\"domain\" : [0,0.25], \"title\":\"Long. Sépalos (cm)\"},\n",
+    "    xaxis2= {\"domain\" : [0.3, 0.5], \"title\" : \"Anch. Sépalos (cm)\"},\n",
+    "    xaxis3= {\"domain\" : [0.55, 0.75], \"title\" : \"Long. Pétalos (cm)\"},\n",
+    "    xaxis4= {\"domain\" : [0.8,1.0], \"title\" : \"Anch. Pétalos (cm)\"},\n",
+    "    yaxis={\"title\":\"Número de ejemplares\"}\n",
+    ")\n",
+    "\n",
+    "fig = go.Figure(data=traces, layout=layout)\n",
+    "py.iplot(fig)\n",
+    "#fig.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1- Calculamos la descomposición de valores y vectores propios\n",
+    "##### a) Usando la Matriz de Covarianzas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import display, Math, Latex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(Math(r'\\sigma_{jk} = \\frac{1}{n-1}\\sum_{i=1}^m (x_{ij} - \\overline{x_j})(x_{ik} - \\overline{x_k})'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(Math(r'\\Sigma = \\frac{1}{n-1}((X-\\overline{x})^T(X-\\overline{x}))'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(Math(r'\\overline{x} = \\sum_{i=1}^n x_i\\in \\mathbb R^m'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_vect = np.mean(X_std, axis=0)\n",
+    "mean_vect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cov_matrix = (X_std - mean_vect).T.dot((X_std - mean_vect))/(X_std.shape[0]-1)\n",
+    "print(\"La matriz de covarianzas es \\n%s\"%cov_matrix)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.cov(X_std.T)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eig_vals, eig_vectors = np.linalg.eig(cov_matrix)\n",
+    "print(\"Valores propios \\n%s\"%eig_vals)\n",
+    "print(\"Vectores propios \\n%s\"%eig_vectors)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### b) Usando la Matriz de Correlaciones"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr_matrix = np.corrcoef(X_std.T)\n",
+    "corr_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eig_vals_corr, eig_vectors_corr = np.linalg.eig(corr_matrix)\n",
+    "print(\"Valores propios \\n%s\"%eig_vals_corr)\n",
+    "print(\"Vectores propios \\n%s\"%eig_vectors_corr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corr_matrix = np.corrcoef(X.T)\n",
+    "corr_matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### c) Singular Value Decomposition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "u,s,v = np.linalg.svd(X_std.T)\n",
+    "u"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2 - Las componentes principales"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for ev in eig_vectors:\n",
+    "    print(\"La longitud del VP es: %s\"%np.linalg.norm(ev))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eigen_pairs = [(np.abs(eig_vals[i]), eig_vectors[:,i]) for i in range(len(eig_vals))]\n",
+    "eigen_pairs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ordenamos los vectores propios con valor propio de mayor a menor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eigen_pairs.sort()\n",
+    "eigen_pairs.reverse()\n",
+    "eigen_pairs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Valores propios en orden descendente:\")\n",
+    "for ep in eigen_pairs:\n",
+    "    print(ep[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "total_sum = sum(eig_vals)\n",
+    "var_exp = [(i/total_sum)*100 for i in sorted(eig_vals, reverse=True)]\n",
+    "cum_var_exp = np.cumsum(var_exp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot1 = go.Bar(x=[f\"CP {i}\" for i in range(1,5)], y=var_exp, showlegend= True)\n",
+    "plot2 = go.Scatter(x=[f\"CP {i}\" for i in range(1,5)], y=cum_var_exp, showlegend= True)\n",
+    "\n",
+    "data = [plot1,plot2]\n",
+    "\n",
+    "layout = go.Layout(xaxis= {\"title\": \"Componentes principales\"},\n",
+    "                  yaxis ={\"title\": \"Porcentaje de varianza explicada\"},\n",
+    "                  title = \"Porcentaje de variabilidad explicada por cada componente principal\")\n",
+    "\n",
+    "fig = go.Figure(data=data,layout=layout)\n",
+    "py.iplot(fig)\n",
+    "#fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W = np.hstack((eigen_pairs[0][1].reshape(4,1), \n",
+    "               eigen_pairs[1][1].reshape(4,1)))\n",
+    "W"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3- Proyectando las variables en el nuevo subespacio vectorial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "display(Math(r'Y = X \\cdot W, X \\in M(\\mathbb R)_{150, 4}, W \\in M(\\mathbb R)_{4,2}, Y \\in M(\\mathbb R)_{150, 2}'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y = X_std.dot(W)\n",
+    "Y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = []\n",
+    "for name in ('setosa', 'versicolor', 'virginica'):\n",
+    "    result = go.Scatter(x= Y[y==name,0], y =Y[y==name, 1],\n",
+    "                       mode = \"markers\", name=name,\n",
+    "    marker= { \"size\": 12, \"line\" : { \"color\" : 'rgba(220,220,220,0.15)', \"width\":0.5},\n",
+    "           \"opacity\": 0.8})\n",
+    "    results.append(result)\n",
+    "    \n",
+    "layout = go.Layout(showlegend = True, \n",
+    "                   scene ={ \"xaxis\" :{\"title\": \"Componente Principal 1\"},\n",
+    "                            \"yaxis\" : {\"title\": \"Componente Principal 2\"}},\n",
+    "                  xaxis ={ \"zerolinecolor\": \"gray\"},\n",
+    "                  yaxis={ \"zerolinecolor\": \"gray\"})\n",
+    "fig = go.Figure(data=results,layout=layout)\n",
+    "py.iplot(fig)\n",
+    "#fig.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T10 - 1 - Analisis de Componentes Principales.ipynb b/notebooks/T10 - 1 - Analisis de Componentes Principales.ipynb
index 81a65772..f7a7037c 100644
--- a/notebooks/T10 - 1 - Analisis de Componentes Principales.ipynb	
+++ b/notebooks/T10 - 1 - Analisis de Componentes Principales.ipynb	
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,103 +38,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "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></th>\n",
-       "      <th>Sepal.Length</th>\n",
-       "      <th>Sepal.Width</th>\n",
-       "      <th>Petal.Length</th>\n",
-       "      <th>Petal.Width</th>\n",
-       "      <th>Species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species\n",
-       "0           5.1          3.5           1.4          0.2  setosa\n",
-       "1           4.9          3.0           1.4          0.2  setosa\n",
-       "2           4.7          3.2           1.3          0.2  setosa\n",
-       "3           4.6          3.1           1.5          0.2  setosa\n",
-       "4           5.0          3.6           1.4          0.2  setosa"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -144,63 +57,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([5.1, 3.5, 1.4, 0.2])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "X[0]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import plotly.plotly as py\n",
-    "from plotly.graph_objs import * \n",
-    "import plotly.tools as tls"
+    "import chart_studio.plotly as py\n",
+    "import plotly.graph_objects as go\n",
+    "import chart_studio"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "tls.set_credentials_file(username='JuanGabriel', api_key='6mEfSXf8XNyIzpxwb8z7')"
+    "chart_studio.tools.set_credentials_file(username='JuanGabriel', api_key='6mEfSXf8XNyIzpxwb8z7')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/20.embed\" height=\"525px\" width=\"100%\"></iframe>"
-      ],
-      "text/plain": [
-       "<plotly.tools.PlotlyDisplay object>"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "traces = []\n",
     "legend = {0:True, 1:True, 2:True, 3:True}\n",
@@ -209,29 +97,37 @@
     "         'versicolor': 'rgb(31, 220, 120)',\n",
     "         'virginica': 'rgb(44, 50, 180)'}\n",
     "\n",
+    "\n",
     "for col in range(4): \n",
     "    for key in colors:\n",
-    "        traces.append(Histogram(x=X[y==key, col], opacity = 0.7, \n",
-    "                                xaxis=\"x%s\"%(col+1), marker=Marker(color=colors[key]),\n",
-    "                               name = key, showlegend=legend[col]))\n",
-    "    legend = {0:False, 1:False, 2:False, 3:False}\n",
+    "        traces.append(go.Histogram(x=X[y==key, col],\n",
+    "                                   opacity = 0.7, \n",
+    "                                   xaxis=\"x%s\"%(col+1),\n",
+    "                                   marker={\"color\":colors[key]},\n",
+    "                                   name = key, showlegend=legend[col])\n",
+    "                     )\n",
     "        \n",
-    "data = Data(traces)\n",
-    "layout = Layout(barmode=\"overlay\", \n",
-    "                xaxis=XAxis(domain=[0,0.25], title=\"Long. Sépalos (cm)\"),\n",
-    "                xaxis2=XAxis(domain=[0.3, 0.5], title = \"Anch. Sépalos (cm)\"),\n",
-    "                xaxis3=XAxis(domain = [0.55, 0.75], title = \"Long. Pétalos (cm)\"),\n",
-    "                xaxis4=XAxis(domain=[0.8,1.0], title = \"Anch. Pétalos (cm)\"),\n",
-    "                yaxis=YAxis(title=\"Número de ejemplares\"),\n",
-    "                title=\"Distribución de los rasgos de las diferentes flores Iris\")\n",
+    "    legend = {0:False, 1:False, 2:False, 3:False}\n",
+    "\n",
+    "layout = go.Layout(\n",
+    "    title={\"text\":\"Distribución de los rasgos de las diferentes flores Iris\",\n",
+    "           \"xref\" : \"paper\",\"x\" : 0.5},\n",
+    "    barmode=\"overlay\",\n",
+    "    xaxis= {\"domain\" : [0,0.25], \"title\":\"Long. Sépalos (cm)\"},\n",
+    "    xaxis2= {\"domain\" : [0.3, 0.5], \"title\" : \"Anch. Sépalos (cm)\"},\n",
+    "    xaxis3= {\"domain\" : [0.55, 0.75], \"title\" : \"Long. Pétalos (cm)\"},\n",
+    "    xaxis4= {\"domain\" : [0.8,1.0], \"title\" : \"Anch. Pétalos (cm)\"},\n",
+    "    yaxis={\"title\":\"Número de ejemplares\"}\n",
+    ")\n",
     "\n",
-    "fig = Figure(data = data, layout = layout)\n",
-    "py.iplot(fig)"
+    "fig = go.Figure(data=traces, layout=layout)\n",
+    "py.iplot(fig)\n",
+    "#fig.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -249,23 +145,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/22.embed\" height=\"525px\" width=\"100%\"></iframe>"
-      ],
-      "text/plain": [
-       "<plotly.tools.PlotlyDisplay object>"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "traces = []\n",
     "legend = {0:True, 1:True, 2:True, 3:True}\n",
@@ -274,24 +156,32 @@
     "         'versicolor': 'rgb(31, 220, 120)',\n",
     "         'virginica': 'rgb(44, 50, 180)'}\n",
     "\n",
+    "\n",
     "for col in range(4): \n",
     "    for key in colors:\n",
-    "        traces.append(Histogram(x=X_std[y==key, col], opacity = 0.7, \n",
-    "                                xaxis=\"x%s\"%(col+1), marker=Marker(color=colors[key]),\n",
-    "                               name = key, showlegend=legend[col]))\n",
-    "    legend = {0:False, 1:False, 2:False, 3:False}\n",
+    "        traces.append(go.Histogram(x=X_std[y==key, col],\n",
+    "                                   opacity = 0.7, \n",
+    "                                   xaxis=\"x%s\"%(col+1),\n",
+    "                                   marker={\"color\":colors[key]},\n",
+    "                                   name = key, showlegend=legend[col])\n",
+    "                     )\n",
     "        \n",
-    "data = Data(traces)\n",
-    "layout = Layout(barmode=\"overlay\", \n",
-    "                xaxis=XAxis(domain=[0,0.25], title=\"Long. Sépalos (cm)\"),\n",
-    "                xaxis2=XAxis(domain=[0.3, 0.5], title = \"Anch. Sépalos (cm)\"),\n",
-    "                xaxis3=XAxis(domain = [0.55, 0.75], title = \"Long. Pétalos (cm)\"),\n",
-    "                xaxis4=XAxis(domain=[0.8,1.0], title = \"Anch. Pétalos (cm)\"),\n",
-    "                yaxis=YAxis(title=\"Número de ejemplares\"),\n",
-    "                title=\"Distribución de los rasgos de las diferentes flores Iris\")\n",
+    "    legend = {0:False, 1:False, 2:False, 3:False}\n",
     "\n",
-    "fig = Figure(data = data, layout = layout)\n",
-    "py.iplot(fig)"
+    "layout = go.Layout(\n",
+    "    title={\"text\":\"Distribución de los rasgos de las diferentes flores Iris\",\n",
+    "           \"xref\" : \"paper\",\"x\" : 0.5},\n",
+    "    barmode=\"overlay\",\n",
+    "    xaxis= {\"domain\" : [0,0.25], \"title\":\"Long. Sépalos (cm)\"},\n",
+    "    xaxis2= {\"domain\" : [0.3, 0.5], \"title\" : \"Anch. Sépalos (cm)\"},\n",
+    "    xaxis3= {\"domain\" : [0.55, 0.75], \"title\" : \"Long. Pétalos (cm)\"},\n",
+    "    xaxis4= {\"domain\" : [0.8,1.0], \"title\" : \"Anch. Pétalos (cm)\"},\n",
+    "    yaxis={\"title\":\"Número de ejemplares\"}\n",
+    ")\n",
+    "\n",
+    "fig = go.Figure(data=traces, layout=layout)\n",
+    "py.iplot(fig)\n",
+    "#fig.show()"
    ]
   },
   {
@@ -304,7 +194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,73 +203,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\sigma_{jk} = \\frac{1}{n-1}\\sum_{i=1}^m (x_{ij} - \\overline{x_j})(x_{ik} - \\overline{x_k})$$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(Math(r'\\sigma_{jk} = \\frac{1}{n-1}\\sum_{i=1}^m (x_{ij} - \\overline{x_j})(x_{ik} - \\overline{x_k})'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\Sigma = \\frac{1}{n-1}((X-\\overline{x})^T(X-\\overline{x}))$$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(Math(r'\\Sigma = \\frac{1}{n-1}((X-\\overline{x})^T(X-\\overline{x}))'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$\\overline{x} = \\sum_{i=1}^n x_i\\in \\mathbb R^m$$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(Math(r'\\overline{x} = \\sum_{i=1}^n x_i\\in \\mathbb R^m'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -388,20 +239,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([-4.73695157e-16, -7.81597009e-16, -4.26325641e-16, -4.73695157e-16])"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "mean_vect = np.mean(X_std, axis=0)\n",
     "mean_vect"
@@ -409,21 +249,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "La matriz de covarianzas es \n",
-      "[[ 1.00671141 -0.11835884  0.87760447  0.82343066]\n",
-      " [-0.11835884  1.00671141 -0.43131554 -0.36858315]\n",
-      " [ 0.87760447 -0.43131554  1.00671141  0.96932762]\n",
-      " [ 0.82343066 -0.36858315  0.96932762  1.00671141]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cov_matrix = (X_std - mean_vect).T.dot((X_std - mean_vect))/(X_std.shape[0]-1)\n",
     "print(\"La matriz de covarianzas es \\n%s\"%cov_matrix)\n"
@@ -431,46 +259,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.00671141, -0.11835884,  0.87760447,  0.82343066],\n",
-       "       [-0.11835884,  1.00671141, -0.43131554, -0.36858315],\n",
-       "       [ 0.87760447, -0.43131554,  1.00671141,  0.96932762],\n",
-       "       [ 0.82343066, -0.36858315,  0.96932762,  1.00671141]])"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "np.cov(X_std.T)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Valores propios \n",
-      "[2.93808505 0.9201649  0.14774182 0.02085386]\n",
-      "Vectores propios \n",
-      "[[ 0.52106591 -0.37741762 -0.71956635  0.26128628]\n",
-      " [-0.26934744 -0.92329566  0.24438178 -0.12350962]\n",
-      " [ 0.5804131  -0.02449161  0.14212637 -0.80144925]\n",
-      " [ 0.56485654 -0.06694199  0.63427274  0.52359713]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "eig_vals, eig_vectors = np.linalg.eig(cov_matrix)\n",
     "print(\"Valores propios \\n%s\"%eig_vals)\n",
@@ -486,23 +286,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.        , -0.11756978,  0.87175378,  0.81794113],\n",
-       "       [-0.11756978,  1.        , -0.4284401 , -0.36612593],\n",
-       "       [ 0.87175378, -0.4284401 ,  1.        ,  0.96286543],\n",
-       "       [ 0.81794113, -0.36612593,  0.96286543,  1.        ]])"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "corr_matrix = np.corrcoef(X_std.T)\n",
     "corr_matrix"
@@ -510,23 +296,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Valores propios \n",
-      "[2.91849782 0.91403047 0.14675688 0.02071484]\n",
-      "Vectores propios \n",
-      "[[ 0.52106591 -0.37741762 -0.71956635  0.26128628]\n",
-      " [-0.26934744 -0.92329566  0.24438178 -0.12350962]\n",
-      " [ 0.5804131  -0.02449161  0.14212637 -0.80144925]\n",
-      " [ 0.56485654 -0.06694199  0.63427274  0.52359713]]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "eig_vals_corr, eig_vectors_corr = np.linalg.eig(corr_matrix)\n",
     "print(\"Valores propios \\n%s\"%eig_vals_corr)\n",
@@ -535,23 +307,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.        , -0.11756978,  0.87175378,  0.81794113],\n",
-       "       [-0.11756978,  1.        , -0.4284401 , -0.36612593],\n",
-       "       [ 0.87175378, -0.4284401 ,  1.        ,  0.96286543],\n",
-       "       [ 0.81794113, -0.36612593,  0.96286543,  1.        ]])"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "corr_matrix = np.corrcoef(X.T)\n",
     "corr_matrix"
@@ -566,23 +324,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-0.52106591, -0.37741762,  0.71956635,  0.26128628],\n",
-       "       [ 0.26934744, -0.92329566, -0.24438178, -0.12350962],\n",
-       "       [-0.5804131 , -0.02449161, -0.14212637, -0.80144925],\n",
-       "       [-0.56485654, -0.06694199, -0.63427274,  0.52359713]])"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "u,s,v = np.linalg.svd(X_std.T)\n",
     "u"
@@ -590,52 +334,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([20.92306556, 11.7091661 ,  4.69185798,  1.76273239])"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "s"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1.08239531e-01,  9.94577561e-02,  1.12996303e-01, ...,\n",
-       "        -7.27030413e-02, -6.56112167e-02, -4.59137323e-02],\n",
-       "       [-4.09957970e-02,  5.75731483e-02,  2.92000319e-02, ...,\n",
-       "        -2.29793601e-02, -8.63643414e-02,  2.07800179e-03],\n",
-       "       [ 2.72186462e-02,  5.00034005e-02, -9.42089147e-03, ...,\n",
-       "        -3.84023516e-02, -1.98939364e-01, -1.12588405e-01],\n",
-       "       ...,\n",
-       "       [ 5.43380310e-02,  5.12936114e-03,  2.75184277e-02, ...,\n",
-       "         9.89532683e-01, -1.41206665e-02, -8.30595907e-04],\n",
-       "       [ 1.96438400e-03,  8.48544595e-02,  1.78604309e-01, ...,\n",
-       "        -1.25488246e-02,  9.52049996e-01, -2.19201906e-02],\n",
-       "       [ 2.46978090e-03,  5.83496936e-03,  1.49419118e-01, ...,\n",
-       "        -7.17729676e-04, -2.32048811e-02,  9.77300244e-01]])"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "v"
    ]
@@ -649,20 +359,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "La longitud del VP es: 0.9999999999999997\n",
-      "La longitud del VP es: 1.0\n",
-      "La longitud del VP es: 1.0\n",
-      "La longitud del VP es: 1.0000000000000002\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for ev in eig_vectors:\n",
     "    print(\"La longitud del VP es: %s\"%np.linalg.norm(ev))"
@@ -670,27 +369,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(2.938085050199995,\n",
-       "  array([ 0.52106591, -0.26934744,  0.5804131 ,  0.56485654])),\n",
-       " (0.9201649041624871,\n",
-       "  array([-0.37741762, -0.92329566, -0.02449161, -0.06694199])),\n",
-       " (0.14774182104494807,\n",
-       "  array([-0.71956635,  0.24438178,  0.14212637,  0.63427274])),\n",
-       " (0.020853862176462023,\n",
-       "  array([ 0.26128628, -0.12350962, -0.80144925,  0.52359713]))]"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "eigen_pairs = [(np.abs(eig_vals[i]), eig_vectors[:,i]) for i in range(len(eig_vals))]\n",
     "eigen_pairs"
@@ -705,27 +386,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(2.938085050199995,\n",
-       "  array([ 0.52106591, -0.26934744,  0.5804131 ,  0.56485654])),\n",
-       " (0.9201649041624871,\n",
-       "  array([-0.37741762, -0.92329566, -0.02449161, -0.06694199])),\n",
-       " (0.14774182104494807,\n",
-       "  array([-0.71956635,  0.24438178,  0.14212637,  0.63427274])),\n",
-       " (0.020853862176462023,\n",
-       "  array([ 0.26128628, -0.12350962, -0.80144925,  0.52359713]))]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "eigen_pairs.sort()\n",
     "eigen_pairs.reverse()\n",
@@ -734,21 +397,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Valores propios en orden descendente:\n",
-      "2.938085050199995\n",
-      "0.9201649041624871\n",
-      "0.14774182104494807\n",
-      "0.020853862176462023\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Valores propios en orden descendente:\")\n",
     "for ep in eigen_pairs:\n",
@@ -757,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -768,56 +419,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/24.embed\" height=\"525px\" width=\"100%\"></iframe>"
-      ],
-      "text/plain": [
-       "<plotly.tools.PlotlyDisplay object>"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "plot1 = Bar(x=[\"CP %s\"%i for i in range(1,5)], y = var_exp, showlegend=False)\n",
-    "plot2 = Scatter(x=[\"CP %s\"%i for i in range(1,5)], y = cum_var_exp, showlegend=True, name = \"% de Varianza Explicada Acumulada\")\n",
+    "plot1 = go.Bar(x=[f\"CP {i}\" for i in range(1,5)], y=var_exp, showlegend= True)\n",
+    "plot2 = go.Scatter(x=[f\"CP {i}\" for i in range(1,5)], y=cum_var_exp, showlegend= True)\n",
     "\n",
-    "data = Data([plot1, plot2])\n",
+    "data = [plot1,plot2]\n",
     "\n",
-    "layout = Layout(xaxis = XAxis(title=\"Componentes principales\"), \n",
-    "               yaxis = YAxis(title = \"Porcentaje de varianza explicada\"),\n",
-    "               title = \"Porcentaje de variabilidad explicada por cada componente principal\")\n",
+    "layout = go.Layout(xaxis= {\"title\": \"Componentes principales\"},\n",
+    "                  yaxis ={\"title\": \"Porcentaje de varianza explicada\"},\n",
+    "                  title = \"Porcentaje de variabilidad explicada por cada componente principal\")\n",
     "\n",
-    "fig = Figure(data = data, layout = layout)\n",
-    "py.iplot(fig)"
+    "fig = go.Figure(data=data,layout=layout)\n",
+    "py.iplot(fig)\n",
+    "#fig.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0.52106591, -0.37741762],\n",
-       "       [-0.26934744, -0.92329566],\n",
-       "       [ 0.5804131 , -0.02449161],\n",
-       "       [ 0.56485654, -0.06694199]])"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "W = np.hstack((eigen_pairs[0][1].reshape(4,1), \n",
     "               eigen_pairs[1][1].reshape(4,1)))\n",
@@ -826,20 +450,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([5.1, 3.5, 1.4, 0.2])"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "X[0]"
    ]
@@ -853,191 +466,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/latex": [
-       "$$Y = X \\cdot W, X \\in M(\\mathbb R)_{150, 4}, W \\in M(\\mathbb R)_{4,2}, Y \\in M(\\mathbb R)_{150, 2}$$"
-      ],
-      "text/plain": [
-       "<IPython.core.display.Math object>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "display(Math(r'Y = X \\cdot W, X \\in M(\\mathbb R)_{150, 4}, W \\in M(\\mathbb R)_{4,2}, Y \\in M(\\mathbb R)_{150, 2}'))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[-2.26470281, -0.4800266 ],\n",
-       "       [-2.08096115,  0.67413356],\n",
-       "       [-2.36422905,  0.34190802],\n",
-       "       [-2.29938422,  0.59739451],\n",
-       "       [-2.38984217, -0.64683538],\n",
-       "       [-2.07563095, -1.48917752],\n",
-       "       [-2.44402884, -0.0476442 ],\n",
-       "       [-2.23284716, -0.22314807],\n",
-       "       [-2.33464048,  1.11532768],\n",
-       "       [-2.18432817,  0.46901356],\n",
-       "       [-2.1663101 , -1.04369065],\n",
-       "       [-2.32613087, -0.13307834],\n",
-       "       [-2.2184509 ,  0.72867617],\n",
-       "       [-2.6331007 ,  0.96150673],\n",
-       "       [-2.1987406 , -1.86005711],\n",
-       "       [-2.26221453, -2.68628449],\n",
-       "       [-2.2075877 , -1.48360936],\n",
-       "       [-2.19034951, -0.48883832],\n",
-       "       [-1.898572  , -1.40501879],\n",
-       "       [-2.34336905, -1.12784938],\n",
-       "       [-1.914323  , -0.40885571],\n",
-       "       [-2.20701284, -0.92412143],\n",
-       "       [-2.7743447 , -0.45834367],\n",
-       "       [-1.81866953, -0.08555853],\n",
-       "       [-2.22716331, -0.13725446],\n",
-       "       [-1.95184633,  0.62561859],\n",
-       "       [-2.05115137, -0.24216355],\n",
-       "       [-2.16857717, -0.52714953],\n",
-       "       [-2.13956345, -0.31321781],\n",
-       "       [-2.26526149,  0.3377319 ],\n",
-       "       [-2.14012214,  0.50454069],\n",
-       "       [-1.83159477, -0.42369507],\n",
-       "       [-2.61494794, -1.79357586],\n",
-       "       [-2.44617739, -2.15072788],\n",
-       "       [-2.10997488,  0.46020184],\n",
-       "       [-2.2078089 ,  0.2061074 ],\n",
-       "       [-2.04514621, -0.66155811],\n",
-       "       [-2.52733191, -0.59229277],\n",
-       "       [-2.42963258,  0.90418004],\n",
-       "       [-2.16971071, -0.26887896],\n",
-       "       [-2.28647514, -0.44171539],\n",
-       "       [-1.85812246,  2.33741516],\n",
-       "       [-2.5536384 ,  0.47910069],\n",
-       "       [-1.96444768, -0.47232667],\n",
-       "       [-2.13705901, -1.14222926],\n",
-       "       [-2.0697443 ,  0.71105273],\n",
-       "       [-2.38473317, -1.1204297 ],\n",
-       "       [-2.39437631,  0.38624687],\n",
-       "       [-2.22944655, -0.99795976],\n",
-       "       [-2.20383344, -0.00921636],\n",
-       "       [ 1.10178118, -0.86297242],\n",
-       "       [ 0.73133743, -0.59461473],\n",
-       "       [ 1.24097932, -0.61629765],\n",
-       "       [ 0.40748306,  1.75440399],\n",
-       "       [ 1.0754747 ,  0.20842105],\n",
-       "       [ 0.38868734,  0.59328364],\n",
-       "       [ 0.74652974, -0.77301931],\n",
-       "       [-0.48732274,  1.85242909],\n",
-       "       [ 0.92790164, -0.03222608],\n",
-       "       [ 0.01142619,  1.03401828],\n",
-       "       [-0.11019628,  2.65407282],\n",
-       "       [ 0.44069345,  0.06329519],\n",
-       "       [ 0.56210831,  1.76472438],\n",
-       "       [ 0.71956189,  0.18622461],\n",
-       "       [-0.0333547 ,  0.43900321],\n",
-       "       [ 0.87540719, -0.50906396],\n",
-       "       [ 0.35025167,  0.19631173],\n",
-       "       [ 0.15881005,  0.79209574],\n",
-       "       [ 1.22509363,  1.6222438 ],\n",
-       "       [ 0.1649179 ,  1.30260923],\n",
-       "       [ 0.73768265, -0.39657156],\n",
-       "       [ 0.47628719,  0.41732028],\n",
-       "       [ 1.2341781 ,  0.93332573],\n",
-       "       [ 0.6328582 ,  0.41638772],\n",
-       "       [ 0.70266118,  0.06341182],\n",
-       "       [ 0.87427365, -0.25079339],\n",
-       "       [ 1.25650912,  0.07725602],\n",
-       "       [ 1.35840512, -0.33131168],\n",
-       "       [ 0.66480037,  0.22592785],\n",
-       "       [-0.04025861,  1.05871855],\n",
-       "       [ 0.13079518,  1.56227183],\n",
-       "       [ 0.02345269,  1.57247559],\n",
-       "       [ 0.24153827,  0.77725638],\n",
-       "       [ 1.06109461,  0.63384324],\n",
-       "       [ 0.22397877,  0.28777351],\n",
-       "       [ 0.42913912, -0.84558224],\n",
-       "       [ 1.04872805, -0.5220518 ],\n",
-       "       [ 1.04453138,  1.38298872],\n",
-       "       [ 0.06958832,  0.21950333],\n",
-       "       [ 0.28347724,  1.32932464],\n",
-       "       [ 0.27907778,  1.12002852],\n",
-       "       [ 0.62456979, -0.02492303],\n",
-       "       [ 0.33653037,  0.98840402],\n",
-       "       [-0.36218338,  2.01923787],\n",
-       "       [ 0.28858624,  0.85573032],\n",
-       "       [ 0.09136066,  0.18119213],\n",
-       "       [ 0.22771687,  0.38492008],\n",
-       "       [ 0.57638829,  0.1548736 ],\n",
-       "       [-0.44766702,  1.54379203],\n",
-       "       [ 0.25673059,  0.5988518 ],\n",
-       "       [ 1.84456887, -0.87042131],\n",
-       "       [ 1.15788161,  0.69886986],\n",
-       "       [ 2.20526679, -0.56201048],\n",
-       "       [ 1.44015066,  0.04698759],\n",
-       "       [ 1.86781222, -0.29504482],\n",
-       "       [ 2.75187334, -0.8004092 ],\n",
-       "       [ 0.36701769,  1.56150289],\n",
-       "       [ 2.30243944, -0.42006558],\n",
-       "       [ 2.00668647,  0.71143865],\n",
-       "       [ 2.25977735, -1.92101038],\n",
-       "       [ 1.36417549, -0.69275645],\n",
-       "       [ 1.60267867,  0.42170045],\n",
-       "       [ 1.8839007 , -0.41924965],\n",
-       "       [ 1.2601151 ,  1.16226042],\n",
-       "       [ 1.4676452 ,  0.44227159],\n",
-       "       [ 1.59007732, -0.67624481],\n",
-       "       [ 1.47143146, -0.25562182],\n",
-       "       [ 2.42632899, -2.55666125],\n",
-       "       [ 3.31069558, -0.01778095],\n",
-       "       [ 1.26376667,  1.70674538],\n",
-       "       [ 2.0377163 , -0.91046741],\n",
-       "       [ 0.97798073,  0.57176432],\n",
-       "       [ 2.89765149, -0.41364106],\n",
-       "       [ 1.33323218,  0.48181122],\n",
-       "       [ 1.7007339 , -1.01392187],\n",
-       "       [ 1.95432671, -1.0077776 ],\n",
-       "       [ 1.17510363,  0.31639447],\n",
-       "       [ 1.02095055, -0.06434603],\n",
-       "       [ 1.78834992,  0.18736121],\n",
-       "       [ 1.86364755, -0.56229073],\n",
-       "       [ 2.43595373, -0.25928443],\n",
-       "       [ 2.30492772, -2.62632347],\n",
-       "       [ 1.86270322,  0.17854949],\n",
-       "       [ 1.11414774,  0.29292262],\n",
-       "       [ 1.2024733 ,  0.81131527],\n",
-       "       [ 2.79877045, -0.85680333],\n",
-       "       [ 1.57625591, -1.06858111],\n",
-       "       [ 1.3462921 , -0.42243061],\n",
-       "       [ 0.92482492, -0.0172231 ],\n",
-       "       [ 1.85204505, -0.67612817],\n",
-       "       [ 2.01481043, -0.61388564],\n",
-       "       [ 1.90178409, -0.68957549],\n",
-       "       [ 1.15788161,  0.69886986],\n",
-       "       [ 2.04055823, -0.8675206 ],\n",
-       "       [ 1.9981471 , -1.04916875],\n",
-       "       [ 1.87050329, -0.38696608],\n",
-       "       [ 1.56458048,  0.89668681],\n",
-       "       [ 1.5211705 , -0.26906914],\n",
-       "       [ 1.37278779, -1.01125442],\n",
-       "       [ 0.96065603,  0.02433167]])"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "Y = X_std.dot(W)\n",
     "Y"
@@ -1045,38 +485,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/44.embed\" height=\"525px\" width=\"100%\"></iframe>"
-      ],
-      "text/plain": [
-       "<plotly.tools.PlotlyDisplay object>"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "results = []\n",
-    "\n",
     "for name in ('setosa', 'versicolor', 'virginica'):\n",
-    "    result = Scatter(x=Y[y==name,0], y = Y[y==name, 1], \n",
-    "                    mode = \"markers\", name=name, \n",
-    "                     marker=Marker(size = 12, line = Line(color='rgba(220,220,220,0.15)', width=0.5), opacity = 0.8))\n",
+    "    result = go.Scatter(x= Y[y==name,0], y =Y[y==name, 1],\n",
+    "                       mode = \"markers\", name=name,\n",
+    "    marker= { \"size\": 12, \"line\" : { \"color\" : 'rgba(220,220,220,0.15)', \"width\":0.5},\n",
+    "           \"opacity\": 0.8})\n",
     "    results.append(result)\n",
-    "\n",
-    "data = Data(results)\n",
-    "layout = Layout(showlegend=True, scene =Scene(xaxis=XAxis(title=\"Componente Principal 1\"),\n",
-    "                                             yaxis=YAxis(title=\"Componente Principal 2\")))\n",
-    "\n",
-    "fig = Figure(data=data, layout=layout)\n",
-    "py.iplot(fig)"
+    "    \n",
+    "layout = go.Layout(showlegend = True, \n",
+    "                   scene ={ \"xaxis\" :{\"title\": \"Componente Principal 1\"},\n",
+    "                            \"yaxis\" : {\"title\": \"Componente Principal 2\"}},\n",
+    "                  xaxis ={ \"zerolinecolor\": \"gray\"},\n",
+    "                  yaxis={ \"zerolinecolor\": \"gray\"})\n",
+    "fig = go.Figure(data=results,layout=layout)\n",
+    "py.iplot(fig)\n",
+    "#fig.show()"
    ]
   },
   {
@@ -1103,7 +531,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T10 - 2 - Analisis de Componentes Principales SK Learn-Colab.ipynb b/notebooks/T10 - 2 - Analisis de Componentes Principales SK Learn-Colab.ipynb
new file mode 100644
index 00000000..54618856
--- /dev/null
+++ b/notebooks/T10 - 2 - Analisis de Componentes Principales SK Learn-Colab.ipynb	
@@ -0,0 +1,345 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T10%20-%202%20-%20Analisis%20de%20Componentes%20Principales%20SK%20Learn-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Análisis de Componentes Principales - SkLearn\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install chart_studio\n",
+    "import pandas as pd\n",
+    "\n",
+    "import chart_studio.plotly as py\n",
+    "from plotly.graph_objs import * \n",
+    "from chart_studio import tools as tls\n",
+    "\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "\n",
+    "tls.set_credentials_file(username='JuanGabriel', api_key='6mEfSXf8XNyIzpxwb8z7')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"/content/python-ml-course/datasets/iris/iris.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df.iloc[:,0:4].values\n",
+    "y = df.iloc[:,4].values\n",
+    "X_std = StandardScaler().fit_transform(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.decomposition import PCA as sk_pca"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "acp = sk_pca(n_components=2)\n",
+    "Y = acp.fit_transform(X_std)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-2.26470281,  0.4800266 ],\n",
+       "       [-2.08096115, -0.67413356],\n",
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+       "       [-2.29938422, -0.59739451],\n",
+       "       [-2.38984217,  0.64683538],\n",
+       "       [-2.07563095,  1.48917752],\n",
+       "       [-2.44402884,  0.0476442 ],\n",
+       "       [-2.23284716,  0.22314807],\n",
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+       "       [-1.914323  ,  0.40885571],\n",
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+       "       [-1.96444768,  0.47232667],\n",
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+       "       [ 1.24097932,  0.61629765],\n",
+       "       [ 0.40748306, -1.75440399],\n",
+       "       [ 1.0754747 , -0.20842105],\n",
+       "       [ 0.38868734, -0.59328364],\n",
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+       "       [-0.48732274, -1.85242909],\n",
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+       "       [ 0.01142619, -1.03401828],\n",
+       "       [-0.11019628, -2.65407282],\n",
+       "       [ 0.44069345, -0.06329519],\n",
+       "       [ 0.56210831, -1.76472438],\n",
+       "       [ 0.71956189, -0.18622461],\n",
+       "       [-0.0333547 , -0.43900321],\n",
+       "       [ 0.87540719,  0.50906396],\n",
+       "       [ 0.35025167, -0.19631173],\n",
+       "       [ 0.15881005, -0.79209574],\n",
+       "       [ 1.22509363, -1.6222438 ],\n",
+       "       [ 0.1649179 , -1.30260923],\n",
+       "       [ 0.73768265,  0.39657156],\n",
+       "       [ 0.47628719, -0.41732028],\n",
+       "       [ 1.2341781 , -0.93332573],\n",
+       "       [ 0.6328582 , -0.41638772],\n",
+       "       [ 0.70266118, -0.06341182],\n",
+       "       [ 0.87427365,  0.25079339],\n",
+       "       [ 1.25650912, -0.07725602],\n",
+       "       [ 1.35840512,  0.33131168],\n",
+       "       [ 0.66480037, -0.22592785],\n",
+       "       [-0.04025861, -1.05871855],\n",
+       "       [ 0.13079518, -1.56227183],\n",
+       "       [ 0.02345269, -1.57247559],\n",
+       "       [ 0.24153827, -0.77725638],\n",
+       "       [ 1.06109461, -0.63384324],\n",
+       "       [ 0.22397877, -0.28777351],\n",
+       "       [ 0.42913912,  0.84558224],\n",
+       "       [ 1.04872805,  0.5220518 ],\n",
+       "       [ 1.04453138, -1.38298872],\n",
+       "       [ 0.06958832, -0.21950333],\n",
+       "       [ 0.28347724, -1.32932464],\n",
+       "       [ 0.27907778, -1.12002852],\n",
+       "       [ 0.62456979,  0.02492303],\n",
+       "       [ 0.33653037, -0.98840402],\n",
+       "       [-0.36218338, -2.01923787],\n",
+       "       [ 0.28858624, -0.85573032],\n",
+       "       [ 0.09136066, -0.18119213],\n",
+       "       [ 0.22771687, -0.38492008],\n",
+       "       [ 0.57638829, -0.1548736 ],\n",
+       "       [-0.44766702, -1.54379203],\n",
+       "       [ 0.25673059, -0.5988518 ],\n",
+       "       [ 1.84456887,  0.87042131],\n",
+       "       [ 1.15788161, -0.69886986],\n",
+       "       [ 2.20526679,  0.56201048],\n",
+       "       [ 1.44015066, -0.04698759],\n",
+       "       [ 1.86781222,  0.29504482],\n",
+       "       [ 2.75187334,  0.8004092 ],\n",
+       "       [ 0.36701769, -1.56150289],\n",
+       "       [ 2.30243944,  0.42006558],\n",
+       "       [ 2.00668647, -0.71143865],\n",
+       "       [ 2.25977735,  1.92101038],\n",
+       "       [ 1.36417549,  0.69275645],\n",
+       "       [ 1.60267867, -0.42170045],\n",
+       "       [ 1.8839007 ,  0.41924965],\n",
+       "       [ 1.2601151 , -1.16226042],\n",
+       "       [ 1.4676452 , -0.44227159],\n",
+       "       [ 1.59007732,  0.67624481],\n",
+       "       [ 1.47143146,  0.25562182],\n",
+       "       [ 2.42632899,  2.55666125],\n",
+       "       [ 3.31069558,  0.01778095],\n",
+       "       [ 1.26376667, -1.70674538],\n",
+       "       [ 2.0377163 ,  0.91046741],\n",
+       "       [ 0.97798073, -0.57176432],\n",
+       "       [ 2.89765149,  0.41364106],\n",
+       "       [ 1.33323218, -0.48181122],\n",
+       "       [ 1.7007339 ,  1.01392187],\n",
+       "       [ 1.95432671,  1.0077776 ],\n",
+       "       [ 1.17510363, -0.31639447],\n",
+       "       [ 1.02095055,  0.06434603],\n",
+       "       [ 1.78834992, -0.18736121],\n",
+       "       [ 1.86364755,  0.56229073],\n",
+       "       [ 2.43595373,  0.25928443],\n",
+       "       [ 2.30492772,  2.62632347],\n",
+       "       [ 1.86270322, -0.17854949],\n",
+       "       [ 1.11414774, -0.29292262],\n",
+       "       [ 1.2024733 , -0.81131527],\n",
+       "       [ 2.79877045,  0.85680333],\n",
+       "       [ 1.57625591,  1.06858111],\n",
+       "       [ 1.3462921 ,  0.42243061],\n",
+       "       [ 0.92482492,  0.0172231 ],\n",
+       "       [ 1.85204505,  0.67612817],\n",
+       "       [ 2.01481043,  0.61388564],\n",
+       "       [ 1.90178409,  0.68957549],\n",
+       "       [ 1.15788161, -0.69886986],\n",
+       "       [ 2.04055823,  0.8675206 ],\n",
+       "       [ 1.9981471 ,  1.04916875],\n",
+       "       [ 1.87050329,  0.38696608],\n",
+       "       [ 1.56458048, -0.89668681],\n",
+       "       [ 1.5211705 ,  0.26906914],\n",
+       "       [ 1.37278779,  1.01125442],\n",
+       "       [ 0.96065603, -0.02433167]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/48.embed\" height=\"525px\" width=\"100%\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = []\n",
+    "\n",
+    "for name in ('setosa', 'versicolor', 'virginica'):\n",
+    "    result = go.Scatter (x = Y[y==name,0], y = Y[y==name,1],\n",
+    "    mode = \"markers\", name = name, marker = {\"size\":8, \"line\": {\"color\": \"rgba(225,225,225,0.2)\",\"width\": 0.5}}, opacity= 0.75)\n",
+    "    results.append(result)\n",
+    "\n",
+    "layout = go.Layout(xaxis = {\"title\":'CP1', \"showline\" :False, \"zerolinecolor\" : \"gray\"}, yaxis = {\"title\" :'CP2', \"showline\" :False, \"zerolinecolor\" : \"gray\"})\n",
+    "\n",
+    "fig = go.Figure(data=results, layout=layout)\n",
+    "py.iplot(fig)\n",
+    "#fig.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T10 - 2 - Analisis de Componentes Principales SK Learn.ipynb b/notebooks/T10 - 2 - Analisis de Componentes Principales SK Learn.ipynb
index e16ed618..69fca363 100644
--- a/notebooks/T10 - 2 - Analisis de Componentes Principales SK Learn.ipynb	
+++ b/notebooks/T10 - 2 - Analisis de Componentes Principales SK Learn.ipynb	
@@ -255,18 +255,15 @@
     "results = []\n",
     "\n",
     "for name in ('setosa', 'versicolor', 'virginica'):\n",
-    "    result = Scatter(x = Y[y==name,0], y = Y[y==name, 1],\n",
-    "                    mode = \"markers\", name = name,\n",
-    "                    marker = Marker(size=8, line=Line(color=\"rgba(225,225,225,0.2)\", width=0.5),\n",
-    "                                   opacity = 0.75))\n",
+    "    result = go.Scatter (x = Y[y==name,0], y = Y[y==name,1],\n",
+    "    mode = \"markers\", name = name, marker = {\"size\":8, \"line\": {\"color\": \"rgba(225,225,225,0.2)\",\"width\": 0.5}}, opacity= 0.75)\n",
     "    results.append(result)\n",
-    "    \n",
-    "data = Data(results)\n",
-    "layout = Layout(xaxis = XAxis(title=\"CP1\", showline=False),\n",
-    "               yaxis = YAxis(title=\"CP2\", showline=False))\n",
     "\n",
-    "fig = Figure(data = data, layout = layout)\n",
-    "py.iplot(fig)"
+    "layout = go.Layout(xaxis = {\"title\":'CP1', \"showline\" :False, \"zerolinecolor\" : \"gray\"}, yaxis = {\"title\" :'CP2', \"showline\" :False, \"zerolinecolor\" : \"gray\"})\n",
+    "\n",
+    "fig = go.Figure(data=results, layout=layout)\n",
+    "py.iplot(fig)\n",
+    "#fig.show()"
    ]
   },
   {
@@ -293,7 +290,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T10 - 3 - Plotly para dibujar-Colab.ipynb b/notebooks/T10 - 3 - Plotly para dibujar-Colab.ipynb
new file mode 100644
index 00000000..10ea30eb
--- /dev/null
+++ b/notebooks/T10 - 3 - Plotly para dibujar-Colab.ipynb	
@@ -0,0 +1,4599 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T10%20-%203%20-%20Plotly%20para%20dibujar-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gráficos con PlotLy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "No module named 'plotly'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-a3ba4c2438ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotly\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_objs\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtls\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_credentials_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0musername\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'JuanGabriel'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapi_key\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'6mEfSXf8XNyIzpxwb8z7'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mImportError\u001b[0m: No module named 'plotly'"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install chart_studio\n",
+    "import chart_studio.plotly as py\n",
+    "import plotly.graph_objects as go\n",
+    "from chart_studio import tools as tls\n",
+    "\n",
+    "tls.set_credentials_file(username='JuanGabriel', api_key='6mEfSXf8XNyIzpxwb8z7')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "No module named 'plotly'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-3d478176c797>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mImportError\u001b[0m: No module named 'plotly'"
+     ]
+    }
+   ],
+   "source": [
+    "import plotly\n",
+    "plotly.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on package plotly:\n",
+      "\n",
+      "NAME\n",
+      "    plotly - https://plot.ly/python/\n",
+      "\n",
+      "DESCRIPTION\n",
+      "    Plotly's Python API allows users to programmatically access Plotly's\n",
+      "    server resources.\n",
+      "    \n",
+      "    This package is organized as follows:\n",
+      "    \n",
+      "    Subpackages:\n",
+      "    \n",
+      "    - plotly: all functionality that requires access to Plotly's servers\n",
+      "    \n",
+      "    - graph_objs: objects for designing figures and visualizing data\n",
+      "    \n",
+      "    - matplotlylib: tools to convert matplotlib figures\n",
+      "    \n",
+      "    Modules:\n",
+      "    \n",
+      "    - tools: some helpful tools that do not require access to Plotly's servers\n",
+      "    \n",
+      "    - utils: functions that you probably won't need, but that subpackages use\n",
+      "    \n",
+      "    - version: holds the current API version\n",
+      "    \n",
+      "    - exceptions: defines our custom exception classes\n",
+      "\n",
+      "PACKAGE CONTENTS\n",
+      "    api (package)\n",
+      "    colors\n",
+      "    config\n",
+      "    dashboard_objs (package)\n",
+      "    exceptions\n",
+      "    figure_factory (package)\n",
+      "    files\n",
+      "    graph_objs (package)\n",
+      "    graph_reference\n",
+      "    grid_objs (package)\n",
+      "    matplotlylib (package)\n",
+      "    offline (package)\n",
+      "    optional_imports\n",
+      "    plotly (package)\n",
+      "    presentation_objs (package)\n",
+      "    session\n",
+      "    tools\n",
+      "    utils\n",
+      "    version\n",
+      "    widgets (package)\n",
+      "\n",
+      "DATA\n",
+      "    absolute_import = _Feature((2, 5, 0, 'alpha', 1), (3, 0, 0, 'alpha', 0...\n",
+      "\n",
+      "VERSION\n",
+      "    2.5.1\n",
+      "\n",
+      "FILE\n",
+      "    /Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/plotly/__init__.py\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(plotly)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on package numpy.random in numpy:\n",
+      "\n",
+      "NAME\n",
+      "    numpy.random\n",
+      "\n",
+      "DESCRIPTION\n",
+      "    ========================\n",
+      "    Random Number Generation\n",
+      "    ========================\n",
+      "    \n",
+      "    ==================== =========================================================\n",
+      "    Utility functions\n",
+      "    ==============================================================================\n",
+      "    random_sample        Uniformly distributed floats over ``[0, 1)``.\n",
+      "    random               Alias for `random_sample`.\n",
+      "    bytes                Uniformly distributed random bytes.\n",
+      "    random_integers      Uniformly distributed integers in a given range.\n",
+      "    permutation          Randomly permute a sequence / generate a random sequence.\n",
+      "    shuffle              Randomly permute a sequence in place.\n",
+      "    seed                 Seed the random number generator.\n",
+      "    choice               Random sample from 1-D array.\n",
+      "    \n",
+      "    ==================== =========================================================\n",
+      "    \n",
+      "    ==================== =========================================================\n",
+      "    Compatibility functions\n",
+      "    ==============================================================================\n",
+      "    rand                 Uniformly distributed values.\n",
+      "    randn                Normally distributed values.\n",
+      "    ranf                 Uniformly distributed floating point numbers.\n",
+      "    randint              Uniformly distributed integers in a given range.\n",
+      "    ==================== =========================================================\n",
+      "    \n",
+      "    ==================== =========================================================\n",
+      "    Univariate distributions\n",
+      "    ==============================================================================\n",
+      "    beta                 Beta distribution over ``[0, 1]``.\n",
+      "    binomial             Binomial distribution.\n",
+      "    chisquare            :math:`\\chi^2` distribution.\n",
+      "    exponential          Exponential distribution.\n",
+      "    f                    F (Fisher-Snedecor) distribution.\n",
+      "    gamma                Gamma distribution.\n",
+      "    geometric            Geometric distribution.\n",
+      "    gumbel               Gumbel distribution.\n",
+      "    hypergeometric       Hypergeometric distribution.\n",
+      "    laplace              Laplace distribution.\n",
+      "    logistic             Logistic distribution.\n",
+      "    lognormal            Log-normal distribution.\n",
+      "    logseries            Logarithmic series distribution.\n",
+      "    negative_binomial    Negative binomial distribution.\n",
+      "    noncentral_chisquare Non-central chi-square distribution.\n",
+      "    noncentral_f         Non-central F distribution.\n",
+      "    normal               Normal / Gaussian distribution.\n",
+      "    pareto               Pareto distribution.\n",
+      "    poisson              Poisson distribution.\n",
+      "    power                Power distribution.\n",
+      "    rayleigh             Rayleigh distribution.\n",
+      "    triangular           Triangular distribution.\n",
+      "    uniform              Uniform distribution.\n",
+      "    vonmises             Von Mises circular distribution.\n",
+      "    wald                 Wald (inverse Gaussian) distribution.\n",
+      "    weibull              Weibull distribution.\n",
+      "    zipf                 Zipf's distribution over ranked data.\n",
+      "    ==================== =========================================================\n",
+      "    \n",
+      "    ==================== =========================================================\n",
+      "    Multivariate distributions\n",
+      "    ==============================================================================\n",
+      "    dirichlet            Multivariate generalization of Beta distribution.\n",
+      "    multinomial          Multivariate generalization of the binomial distribution.\n",
+      "    multivariate_normal  Multivariate generalization of the normal distribution.\n",
+      "    ==================== =========================================================\n",
+      "    \n",
+      "    ==================== =========================================================\n",
+      "    Standard distributions\n",
+      "    ==============================================================================\n",
+      "    standard_cauchy      Standard Cauchy-Lorentz distribution.\n",
+      "    standard_exponential Standard exponential distribution.\n",
+      "    standard_gamma       Standard Gamma distribution.\n",
+      "    standard_normal      Standard normal distribution.\n",
+      "    standard_t           Standard Student's t-distribution.\n",
+      "    ==================== =========================================================\n",
+      "    \n",
+      "    ==================== =========================================================\n",
+      "    Internal functions\n",
+      "    ==============================================================================\n",
+      "    get_state            Get tuple representing internal state of generator.\n",
+      "    set_state            Set state of generator.\n",
+      "    ==================== =========================================================\n",
+      "\n",
+      "PACKAGE CONTENTS\n",
+      "    info\n",
+      "    mtrand\n",
+      "    setup\n",
+      "    tests (package)\n",
+      "\n",
+      "FUNCTIONS\n",
+      "    beta(...) method of mtrand.RandomState instance\n",
+      "        beta(a, b, size=None)\n",
+      "        \n",
+      "        Draw samples from a Beta distribution.\n",
+      "        \n",
+      "        The Beta distribution is a special case of the Dirichlet distribution,\n",
+      "        and is related to the Gamma distribution.  It has the probability\n",
+      "        distribution function\n",
+      "        \n",
+      "        .. math:: f(x; a,b) = \\frac{1}{B(\\alpha, \\beta)} x^{\\alpha - 1}\n",
+      "                                                         (1 - x)^{\\beta - 1},\n",
+      "        \n",
+      "        where the normalisation, B, is the beta function,\n",
+      "        \n",
+      "        .. math:: B(\\alpha, \\beta) = \\int_0^1 t^{\\alpha - 1}\n",
+      "                                     (1 - t)^{\\beta - 1} dt.\n",
+      "        \n",
+      "        It is often seen in Bayesian inference and order statistics.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        a : float or array_like of floats\n",
+      "            Alpha, non-negative.\n",
+      "        b : float or array_like of floats\n",
+      "            Beta, non-negative.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``a`` and ``b`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(a, b).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized beta distribution.\n",
+      "    \n",
+      "    binomial(...) method of mtrand.RandomState instance\n",
+      "        binomial(n, p, size=None)\n",
+      "        \n",
+      "        Draw samples from a binomial distribution.\n",
+      "        \n",
+      "        Samples are drawn from a binomial distribution with specified\n",
+      "        parameters, n trials and p probability of success where\n",
+      "        n an integer >= 0 and p is in the interval [0,1]. (n may be\n",
+      "        input as a float, but it is truncated to an integer in use)\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        n : int or array_like of ints\n",
+      "            Parameter of the distribution, >= 0. Floats are also accepted,\n",
+      "            but they will be truncated to integers.\n",
+      "        p : float or array_like of floats\n",
+      "            Parameter of the distribution, >= 0 and <=1.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``n`` and ``p`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(n, p).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized binomial distribution, where\n",
+      "            each sample is equal to the number of successes over the n trials.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.binom : probability density function, distribution or\n",
+      "            cumulative density function, etc.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density for the binomial distribution is\n",
+      "        \n",
+      "        .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n",
+      "        \n",
+      "        where :math:`n` is the number of trials, :math:`p` is the probability\n",
+      "        of success, and :math:`N` is the number of successes.\n",
+      "        \n",
+      "        When estimating the standard error of a proportion in a population by\n",
+      "        using a random sample, the normal distribution works well unless the\n",
+      "        product p*n <=5, where p = population proportion estimate, and n =\n",
+      "        number of samples, in which case the binomial distribution is used\n",
+      "        instead. For example, a sample of 15 people shows 4 who are left\n",
+      "        handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n",
+      "        so the binomial distribution should be used in this case.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n",
+      "               Springer-Verlag, 2002.\n",
+      "        .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n",
+      "               Fifth Edition, 2002.\n",
+      "        .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n",
+      "               and Quigley, 1972.\n",
+      "        .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n",
+      "               Wolfram Web Resource.\n",
+      "               http://mathworld.wolfram.com/BinomialDistribution.html\n",
+      "        .. [5] Wikipedia, \"Binomial distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Binomial_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> n, p = 10, .5  # number of trials, probability of each trial\n",
+      "        >>> s = np.random.binomial(n, p, 1000)\n",
+      "        # result of flipping a coin 10 times, tested 1000 times.\n",
+      "        \n",
+      "        A real world example. A company drills 9 wild-cat oil exploration\n",
+      "        wells, each with an estimated probability of success of 0.1. All nine\n",
+      "        wells fail. What is the probability of that happening?\n",
+      "        \n",
+      "        Let's do 20,000 trials of the model, and count the number that\n",
+      "        generate zero positive results.\n",
+      "        \n",
+      "        >>> sum(np.random.binomial(9, 0.1, 20000) == 0)/20000.\n",
+      "        # answer = 0.38885, or 38%.\n",
+      "    \n",
+      "    bytes(...) method of mtrand.RandomState instance\n",
+      "        bytes(length)\n",
+      "        \n",
+      "        Return random bytes.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        length : int\n",
+      "            Number of random bytes.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : str\n",
+      "            String of length `length`.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> np.random.bytes(10)\n",
+      "        ' eh\\x85\\x022SZ\\xbf\\xa4' #random\n",
+      "    \n",
+      "    chisquare(...) method of mtrand.RandomState instance\n",
+      "        chisquare(df, size=None)\n",
+      "        \n",
+      "        Draw samples from a chi-square distribution.\n",
+      "        \n",
+      "        When `df` independent random variables, each with standard normal\n",
+      "        distributions (mean 0, variance 1), are squared and summed, the\n",
+      "        resulting distribution is chi-square (see Notes).  This distribution\n",
+      "        is often used in hypothesis testing.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        df : float or array_like of floats\n",
+      "             Number of degrees of freedom, should be > 0.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``df`` is a scalar.  Otherwise,\n",
+      "            ``np.array(df).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized chi-square distribution.\n",
+      "        \n",
+      "        Raises\n",
+      "        ------\n",
+      "        ValueError\n",
+      "            When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)\n",
+      "            is given.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The variable obtained by summing the squares of `df` independent,\n",
+      "        standard normally distributed random variables:\n",
+      "        \n",
+      "        .. math:: Q = \\sum_{i=0}^{\\mathtt{df}} X^2_i\n",
+      "        \n",
+      "        is chi-square distributed, denoted\n",
+      "        \n",
+      "        .. math:: Q \\sim \\chi^2_k.\n",
+      "        \n",
+      "        The probability density function of the chi-squared distribution is\n",
+      "        \n",
+      "        .. math:: p(x) = \\frac{(1/2)^{k/2}}{\\Gamma(k/2)}\n",
+      "                         x^{k/2 - 1} e^{-x/2},\n",
+      "        \n",
+      "        where :math:`\\Gamma` is the gamma function,\n",
+      "        \n",
+      "        .. math:: \\Gamma(x) = \\int_0^{-\\infty} t^{x - 1} e^{-t} dt.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] NIST \"Engineering Statistics Handbook\"\n",
+      "               http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> np.random.chisquare(2,4)\n",
+      "        array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272])\n",
+      "    \n",
+      "    choice(...) method of mtrand.RandomState instance\n",
+      "        choice(a, size=None, replace=True, p=None)\n",
+      "        \n",
+      "        Generates a random sample from a given 1-D array\n",
+      "        \n",
+      "                .. versionadded:: 1.7.0\n",
+      "        \n",
+      "        Parameters\n",
+      "        -----------\n",
+      "        a : 1-D array-like or int\n",
+      "            If an ndarray, a random sample is generated from its elements.\n",
+      "            If an int, the random sample is generated as if a were np.arange(a)\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
+      "            single value is returned.\n",
+      "        replace : boolean, optional\n",
+      "            Whether the sample is with or without replacement\n",
+      "        p : 1-D array-like, optional\n",
+      "            The probabilities associated with each entry in a.\n",
+      "            If not given the sample assumes a uniform distribution over all\n",
+      "            entries in a.\n",
+      "        \n",
+      "        Returns\n",
+      "        --------\n",
+      "        samples : single item or ndarray\n",
+      "            The generated random samples\n",
+      "        \n",
+      "        Raises\n",
+      "        -------\n",
+      "        ValueError\n",
+      "            If a is an int and less than zero, if a or p are not 1-dimensional,\n",
+      "            if a is an array-like of size 0, if p is not a vector of\n",
+      "            probabilities, if a and p have different lengths, or if\n",
+      "            replace=False and the sample size is greater than the population\n",
+      "            size\n",
+      "        \n",
+      "        See Also\n",
+      "        ---------\n",
+      "        randint, shuffle, permutation\n",
+      "        \n",
+      "        Examples\n",
+      "        ---------\n",
+      "        Generate a uniform random sample from np.arange(5) of size 3:\n",
+      "        \n",
+      "        >>> np.random.choice(5, 3)\n",
+      "        array([0, 3, 4])\n",
+      "        >>> #This is equivalent to np.random.randint(0,5,3)\n",
+      "        \n",
+      "        Generate a non-uniform random sample from np.arange(5) of size 3:\n",
+      "        \n",
+      "        >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n",
+      "        array([3, 3, 0])\n",
+      "        \n",
+      "        Generate a uniform random sample from np.arange(5) of size 3 without\n",
+      "        replacement:\n",
+      "        \n",
+      "        >>> np.random.choice(5, 3, replace=False)\n",
+      "        array([3,1,0])\n",
+      "        >>> #This is equivalent to np.random.permutation(np.arange(5))[:3]\n",
+      "        \n",
+      "        Generate a non-uniform random sample from np.arange(5) of size\n",
+      "        3 without replacement:\n",
+      "        \n",
+      "        >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n",
+      "        array([2, 3, 0])\n",
+      "        \n",
+      "        Any of the above can be repeated with an arbitrary array-like\n",
+      "        instead of just integers. For instance:\n",
+      "        \n",
+      "        >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n",
+      "        >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n",
+      "        array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],\n",
+      "              dtype='|S11')\n",
+      "    \n",
+      "    dirichlet(...) method of mtrand.RandomState instance\n",
+      "        dirichlet(alpha, size=None)\n",
+      "        \n",
+      "        Draw samples from the Dirichlet distribution.\n",
+      "        \n",
+      "        Draw `size` samples of dimension k from a Dirichlet distribution. A\n",
+      "        Dirichlet-distributed random variable can be seen as a multivariate\n",
+      "        generalization of a Beta distribution. Dirichlet pdf is the conjugate\n",
+      "        prior of a multinomial in Bayesian inference.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        alpha : array\n",
+      "            Parameter of the distribution (k dimension for sample of\n",
+      "            dimension k).\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
+      "            single value is returned.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        samples : ndarray,\n",
+      "            The drawn samples, of shape (size, alpha.ndim).\n",
+      "        \n",
+      "        Raises\n",
+      "        -------\n",
+      "        ValueError\n",
+      "            If any value in alpha is less than or equal to zero\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n",
+      "        \n",
+      "        Uses the following property for computation: for each dimension,\n",
+      "        draw a random sample y_i from a standard gamma generator of shape\n",
+      "        `alpha_i`, then\n",
+      "        :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n",
+      "        Dirichlet distributed.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] David McKay, \"Information Theory, Inference and Learning\n",
+      "               Algorithms,\" chapter 23,\n",
+      "               http://www.inference.phy.cam.ac.uk/mackay/\n",
+      "        .. [2] Wikipedia, \"Dirichlet distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Dirichlet_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Taking an example cited in Wikipedia, this distribution can be used if\n",
+      "        one wanted to cut strings (each of initial length 1.0) into K pieces\n",
+      "        with different lengths, where each piece had, on average, a designated\n",
+      "        average length, but allowing some variation in the relative sizes of\n",
+      "        the pieces.\n",
+      "        \n",
+      "        >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n",
+      "        \n",
+      "        >>> plt.barh(range(20), s[0])\n",
+      "        >>> plt.barh(range(20), s[1], left=s[0], color='g')\n",
+      "        >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n",
+      "        >>> plt.title(\"Lengths of Strings\")\n",
+      "    \n",
+      "    exponential(...) method of mtrand.RandomState instance\n",
+      "        exponential(scale=1.0, size=None)\n",
+      "        \n",
+      "        Draw samples from an exponential distribution.\n",
+      "        \n",
+      "        Its probability density function is\n",
+      "        \n",
+      "        .. math:: f(x; \\frac{1}{\\beta}) = \\frac{1}{\\beta} \\exp(-\\frac{x}{\\beta}),\n",
+      "        \n",
+      "        for ``x > 0`` and 0 elsewhere. :math:`\\beta` is the scale parameter,\n",
+      "        which is the inverse of the rate parameter :math:`\\lambda = 1/\\beta`.\n",
+      "        The rate parameter is an alternative, widely used parameterization\n",
+      "        of the exponential distribution [3]_.\n",
+      "        \n",
+      "        The exponential distribution is a continuous analogue of the\n",
+      "        geometric distribution.  It describes many common situations, such as\n",
+      "        the size of raindrops measured over many rainstorms [1]_, or the time\n",
+      "        between page requests to Wikipedia [2]_.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        scale : float or array_like of floats\n",
+      "            The scale parameter, :math:`\\beta = 1/\\lambda`.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``scale`` is a scalar.  Otherwise,\n",
+      "            ``np.array(scale).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized exponential distribution.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Peyton Z. Peebles Jr., \"Probability, Random Variables and\n",
+      "               Random Signal Principles\", 4th ed, 2001, p. 57.\n",
+      "        .. [2] Wikipedia, \"Poisson process\",\n",
+      "               http://en.wikipedia.org/wiki/Poisson_process\n",
+      "        .. [3] Wikipedia, \"Exponential distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Exponential_distribution\n",
+      "    \n",
+      "    f(...) method of mtrand.RandomState instance\n",
+      "        f(dfnum, dfden, size=None)\n",
+      "        \n",
+      "        Draw samples from an F distribution.\n",
+      "        \n",
+      "        Samples are drawn from an F distribution with specified parameters,\n",
+      "        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of\n",
+      "        freedom in denominator), where both parameters should be greater than\n",
+      "        zero.\n",
+      "        \n",
+      "        The random variate of the F distribution (also known as the\n",
+      "        Fisher distribution) is a continuous probability distribution\n",
+      "        that arises in ANOVA tests, and is the ratio of two chi-square\n",
+      "        variates.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        dfnum : float or array_like of floats\n",
+      "            Degrees of freedom in numerator, should be > 0.\n",
+      "        dfden : float or array_like of float\n",
+      "            Degrees of freedom in denominator, should be > 0.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``dfnum`` and ``dfden`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(dfnum, dfden).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized Fisher distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.f : probability density function, distribution or\n",
+      "            cumulative density function, etc.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The F statistic is used to compare in-group variances to between-group\n",
+      "        variances. Calculating the distribution depends on the sampling, and\n",
+      "        so it is a function of the respective degrees of freedom in the\n",
+      "        problem.  The variable `dfnum` is the number of samples minus one, the\n",
+      "        between-groups degrees of freedom, while `dfden` is the within-groups\n",
+      "        degrees of freedom, the sum of the number of samples in each group\n",
+      "        minus the number of groups.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n",
+      "               Fifth Edition, 2002.\n",
+      "        .. [2] Wikipedia, \"F-distribution\",\n",
+      "               http://en.wikipedia.org/wiki/F-distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        An example from Glantz[1], pp 47-40:\n",
+      "        \n",
+      "        Two groups, children of diabetics (25 people) and children from people\n",
+      "        without diabetes (25 controls). Fasting blood glucose was measured,\n",
+      "        case group had a mean value of 86.1, controls had a mean value of\n",
+      "        82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n",
+      "        data consistent with the null hypothesis that the parents diabetic\n",
+      "        status does not affect their children's blood glucose levels?\n",
+      "        Calculating the F statistic from the data gives a value of 36.01.\n",
+      "        \n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> dfnum = 1. # between group degrees of freedom\n",
+      "        >>> dfden = 48. # within groups degrees of freedom\n",
+      "        >>> s = np.random.f(dfnum, dfden, 1000)\n",
+      "        \n",
+      "        The lower bound for the top 1% of the samples is :\n",
+      "        \n",
+      "        >>> sort(s)[-10]\n",
+      "        7.61988120985\n",
+      "        \n",
+      "        So there is about a 1% chance that the F statistic will exceed 7.62,\n",
+      "        the measured value is 36, so the null hypothesis is rejected at the 1%\n",
+      "        level.\n",
+      "    \n",
+      "    gamma(...) method of mtrand.RandomState instance\n",
+      "        gamma(shape, scale=1.0, size=None)\n",
+      "        \n",
+      "        Draw samples from a Gamma distribution.\n",
+      "        \n",
+      "        Samples are drawn from a Gamma distribution with specified parameters,\n",
+      "        `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n",
+      "        \"theta\"), where both parameters are > 0.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        shape : float or array_like of floats\n",
+      "            The shape of the gamma distribution. Should be greater than zero.\n",
+      "        scale : float or array_like of floats, optional\n",
+      "            The scale of the gamma distribution. Should be greater than zero.\n",
+      "            Default is equal to 1.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``shape`` and ``scale`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(shape, scale).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized gamma distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.gamma : probability density function, distribution or\n",
+      "            cumulative density function, etc.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density for the Gamma distribution is\n",
+      "        \n",
+      "        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n",
+      "        \n",
+      "        where :math:`k` is the shape and :math:`\\theta` the scale,\n",
+      "        and :math:`\\Gamma` is the Gamma function.\n",
+      "        \n",
+      "        The Gamma distribution is often used to model the times to failure of\n",
+      "        electronic components, and arises naturally in processes for which the\n",
+      "        waiting times between Poisson distributed events are relevant.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n",
+      "               Wolfram Web Resource.\n",
+      "               http://mathworld.wolfram.com/GammaDistribution.html\n",
+      "        .. [2] Wikipedia, \"Gamma distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Gamma_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> shape, scale = 2., 2.  # mean=4, std=2*sqrt(2)\n",
+      "        >>> s = np.random.gamma(shape, scale, 1000)\n",
+      "        \n",
+      "        Display the histogram of the samples, along with\n",
+      "        the probability density function:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> import scipy.special as sps\n",
+      "        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n",
+      "        >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n",
+      "        ...                      (sps.gamma(shape)*scale**shape))\n",
+      "        >>> plt.plot(bins, y, linewidth=2, color='r')\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    geometric(...) method of mtrand.RandomState instance\n",
+      "        geometric(p, size=None)\n",
+      "        \n",
+      "        Draw samples from the geometric distribution.\n",
+      "        \n",
+      "        Bernoulli trials are experiments with one of two outcomes:\n",
+      "        success or failure (an example of such an experiment is flipping\n",
+      "        a coin).  The geometric distribution models the number of trials\n",
+      "        that must be run in order to achieve success.  It is therefore\n",
+      "        supported on the positive integers, ``k = 1, 2, ...``.\n",
+      "        \n",
+      "        The probability mass function of the geometric distribution is\n",
+      "        \n",
+      "        .. math:: f(k) = (1 - p)^{k - 1} p\n",
+      "        \n",
+      "        where `p` is the probability of success of an individual trial.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        p : float or array_like of floats\n",
+      "            The probability of success of an individual trial.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``p`` is a scalar.  Otherwise,\n",
+      "            ``np.array(p).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized geometric distribution.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw ten thousand values from the geometric distribution,\n",
+      "        with the probability of an individual success equal to 0.35:\n",
+      "        \n",
+      "        >>> z = np.random.geometric(p=0.35, size=10000)\n",
+      "        \n",
+      "        How many trials succeeded after a single run?\n",
+      "        \n",
+      "        >>> (z == 1).sum() / 10000.\n",
+      "        0.34889999999999999 #random\n",
+      "    \n",
+      "    get_state(...) method of mtrand.RandomState instance\n",
+      "        get_state()\n",
+      "        \n",
+      "        Return a tuple representing the internal state of the generator.\n",
+      "        \n",
+      "        For more details, see `set_state`.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : tuple(str, ndarray of 624 uints, int, int, float)\n",
+      "            The returned tuple has the following items:\n",
+      "        \n",
+      "            1. the string 'MT19937'.\n",
+      "            2. a 1-D array of 624 unsigned integer keys.\n",
+      "            3. an integer ``pos``.\n",
+      "            4. an integer ``has_gauss``.\n",
+      "            5. a float ``cached_gaussian``.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        set_state\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        `set_state` and `get_state` are not needed to work with any of the\n",
+      "        random distributions in NumPy. If the internal state is manually altered,\n",
+      "        the user should know exactly what he/she is doing.\n",
+      "    \n",
+      "    gumbel(...) method of mtrand.RandomState instance\n",
+      "        gumbel(loc=0.0, scale=1.0, size=None)\n",
+      "        \n",
+      "        Draw samples from a Gumbel distribution.\n",
+      "        \n",
+      "        Draw samples from a Gumbel distribution with specified location and\n",
+      "        scale.  For more information on the Gumbel distribution, see\n",
+      "        Notes and References below.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        loc : float or array_like of floats, optional\n",
+      "            The location of the mode of the distribution. Default is 0.\n",
+      "        scale : float or array_like of floats, optional\n",
+      "            The scale parameter of the distribution. Default is 1.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``loc`` and ``scale`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized Gumbel distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.gumbel_l\n",
+      "        scipy.stats.gumbel_r\n",
+      "        scipy.stats.genextreme\n",
+      "        weibull\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme\n",
+      "        Value Type I) distribution is one of a class of Generalized Extreme\n",
+      "        Value (GEV) distributions used in modeling extreme value problems.\n",
+      "        The Gumbel is a special case of the Extreme Value Type I distribution\n",
+      "        for maximums from distributions with \"exponential-like\" tails.\n",
+      "        \n",
+      "        The probability density for the Gumbel distribution is\n",
+      "        \n",
+      "        .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n",
+      "                  \\beta}},\n",
+      "        \n",
+      "        where :math:`\\mu` is the mode, a location parameter, and\n",
+      "        :math:`\\beta` is the scale parameter.\n",
+      "        \n",
+      "        The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n",
+      "        very early in the hydrology literature, for modeling the occurrence of\n",
+      "        flood events. It is also used for modeling maximum wind speed and\n",
+      "        rainfall rates.  It is a \"fat-tailed\" distribution - the probability of\n",
+      "        an event in the tail of the distribution is larger than if one used a\n",
+      "        Gaussian, hence the surprisingly frequent occurrence of 100-year\n",
+      "        floods. Floods were initially modeled as a Gaussian process, which\n",
+      "        underestimated the frequency of extreme events.\n",
+      "        \n",
+      "        It is one of a class of extreme value distributions, the Generalized\n",
+      "        Extreme Value (GEV) distributions, which also includes the Weibull and\n",
+      "        Frechet.\n",
+      "        \n",
+      "        The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance\n",
+      "        of :math:`\\frac{\\pi^2}{6}\\beta^2`.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Gumbel, E. J., \"Statistics of Extremes,\"\n",
+      "               New York: Columbia University Press, 1958.\n",
+      "        .. [2] Reiss, R.-D. and Thomas, M., \"Statistical Analysis of Extreme\n",
+      "               Values from Insurance, Finance, Hydrology and Other Fields,\"\n",
+      "               Basel: Birkhauser Verlag, 2001.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> mu, beta = 0, 0.1 # location and scale\n",
+      "        >>> s = np.random.gumbel(mu, beta, 1000)\n",
+      "        \n",
+      "        Display the histogram of the samples, along with\n",
+      "        the probability density function:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n",
+      "        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n",
+      "        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n",
+      "        ...          linewidth=2, color='r')\n",
+      "        >>> plt.show()\n",
+      "        \n",
+      "        Show how an extreme value distribution can arise from a Gaussian process\n",
+      "        and compare to a Gaussian:\n",
+      "        \n",
+      "        >>> means = []\n",
+      "        >>> maxima = []\n",
+      "        >>> for i in range(0,1000) :\n",
+      "        ...    a = np.random.normal(mu, beta, 1000)\n",
+      "        ...    means.append(a.mean())\n",
+      "        ...    maxima.append(a.max())\n",
+      "        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n",
+      "        >>> beta = np.std(maxima) * np.sqrt(6) / np.pi\n",
+      "        >>> mu = np.mean(maxima) - 0.57721*beta\n",
+      "        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n",
+      "        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n",
+      "        ...          linewidth=2, color='r')\n",
+      "        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n",
+      "        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n",
+      "        ...          linewidth=2, color='g')\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    hypergeometric(...) method of mtrand.RandomState instance\n",
+      "        hypergeometric(ngood, nbad, nsample, size=None)\n",
+      "        \n",
+      "        Draw samples from a Hypergeometric distribution.\n",
+      "        \n",
+      "        Samples are drawn from a hypergeometric distribution with specified\n",
+      "        parameters, ngood (ways to make a good selection), nbad (ways to make\n",
+      "        a bad selection), and nsample = number of items sampled, which is less\n",
+      "        than or equal to the sum ngood + nbad.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        ngood : int or array_like of ints\n",
+      "            Number of ways to make a good selection.  Must be nonnegative.\n",
+      "        nbad : int or array_like of ints\n",
+      "            Number of ways to make a bad selection.  Must be nonnegative.\n",
+      "        nsample : int or array_like of ints\n",
+      "            Number of items sampled.  Must be at least 1 and at most\n",
+      "            ``ngood + nbad``.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``ngood``, ``nbad``, and ``nsample``\n",
+      "            are all scalars.  Otherwise, ``np.broadcast(ngood, nbad, nsample).size``\n",
+      "            samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized hypergeometric distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.hypergeom : probability density function, distribution or\n",
+      "            cumulative density function, etc.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density for the Hypergeometric distribution is\n",
+      "        \n",
+      "        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n",
+      "        \n",
+      "        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n",
+      "        \n",
+      "        for P(x) the probability of x successes, n = ngood, m = nbad, and\n",
+      "        N = number of samples.\n",
+      "        \n",
+      "        Consider an urn with black and white marbles in it, ngood of them\n",
+      "        black and nbad are white. If you draw nsample balls without\n",
+      "        replacement, then the hypergeometric distribution describes the\n",
+      "        distribution of black balls in the drawn sample.\n",
+      "        \n",
+      "        Note that this distribution is very similar to the binomial\n",
+      "        distribution, except that in this case, samples are drawn without\n",
+      "        replacement, whereas in the Binomial case samples are drawn with\n",
+      "        replacement (or the sample space is infinite). As the sample space\n",
+      "        becomes large, this distribution approaches the binomial.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n",
+      "               and Quigley, 1972.\n",
+      "        .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n",
+      "               MathWorld--A Wolfram Web Resource.\n",
+      "               http://mathworld.wolfram.com/HypergeometricDistribution.html\n",
+      "        .. [3] Wikipedia, \"Hypergeometric distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Hypergeometric_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> ngood, nbad, nsamp = 100, 2, 10\n",
+      "        # number of good, number of bad, and number of samples\n",
+      "        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n",
+      "        >>> hist(s)\n",
+      "        #   note that it is very unlikely to grab both bad items\n",
+      "        \n",
+      "        Suppose you have an urn with 15 white and 15 black marbles.\n",
+      "        If you pull 15 marbles at random, how likely is it that\n",
+      "        12 or more of them are one color?\n",
+      "        \n",
+      "        >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n",
+      "        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n",
+      "        #   answer = 0.003 ... pretty unlikely!\n",
+      "    \n",
+      "    laplace(...) method of mtrand.RandomState instance\n",
+      "        laplace(loc=0.0, scale=1.0, size=None)\n",
+      "        \n",
+      "        Draw samples from the Laplace or double exponential distribution with\n",
+      "        specified location (or mean) and scale (decay).\n",
+      "        \n",
+      "        The Laplace distribution is similar to the Gaussian/normal distribution,\n",
+      "        but is sharper at the peak and has fatter tails. It represents the\n",
+      "        difference between two independent, identically distributed exponential\n",
+      "        random variables.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        loc : float or array_like of floats, optional\n",
+      "            The position, :math:`\\mu`, of the distribution peak. Default is 0.\n",
+      "        scale : float or array_like of floats, optional\n",
+      "            :math:`\\lambda`, the exponential decay. Default is 1.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``loc`` and ``scale`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized Laplace distribution.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        It has the probability density function\n",
+      "        \n",
+      "        .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n",
+      "                                       \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n",
+      "        \n",
+      "        The first law of Laplace, from 1774, states that the frequency\n",
+      "        of an error can be expressed as an exponential function of the\n",
+      "        absolute magnitude of the error, which leads to the Laplace\n",
+      "        distribution. For many problems in economics and health\n",
+      "        sciences, this distribution seems to model the data better\n",
+      "        than the standard Gaussian distribution.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). \"Handbook of\n",
+      "               Mathematical Functions with Formulas, Graphs, and Mathematical\n",
+      "               Tables, 9th printing,\" New York: Dover, 1972.\n",
+      "        .. [2] Kotz, Samuel, et. al. \"The Laplace Distribution and\n",
+      "               Generalizations, \" Birkhauser, 2001.\n",
+      "        .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n",
+      "               From MathWorld--A Wolfram Web Resource.\n",
+      "               http://mathworld.wolfram.com/LaplaceDistribution.html\n",
+      "        .. [4] Wikipedia, \"Laplace distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Laplace_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution\n",
+      "        \n",
+      "        >>> loc, scale = 0., 1.\n",
+      "        >>> s = np.random.laplace(loc, scale, 1000)\n",
+      "        \n",
+      "        Display the histogram of the samples, along with\n",
+      "        the probability density function:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n",
+      "        >>> x = np.arange(-8., 8., .01)\n",
+      "        >>> pdf = np.exp(-abs(x-loc)/scale)/(2.*scale)\n",
+      "        >>> plt.plot(x, pdf)\n",
+      "        \n",
+      "        Plot Gaussian for comparison:\n",
+      "        \n",
+      "        >>> g = (1/(scale * np.sqrt(2 * np.pi)) *\n",
+      "        ...      np.exp(-(x - loc)**2 / (2 * scale**2)))\n",
+      "        >>> plt.plot(x,g)\n",
+      "    \n",
+      "    logistic(...) method of mtrand.RandomState instance\n",
+      "        logistic(loc=0.0, scale=1.0, size=None)\n",
+      "        \n",
+      "        Draw samples from a logistic distribution.\n",
+      "        \n",
+      "        Samples are drawn from a logistic distribution with specified\n",
+      "        parameters, loc (location or mean, also median), and scale (>0).\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        loc : float or array_like of floats, optional\n",
+      "            Parameter of the distribution. Default is 0.\n",
+      "        scale : float or array_like of floats, optional\n",
+      "            Parameter of the distribution. Should be greater than zero.\n",
+      "            Default is 1.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``loc`` and ``scale`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized logistic distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.logistic : probability density function, distribution or\n",
+      "            cumulative density function, etc.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density for the Logistic distribution is\n",
+      "        \n",
+      "        .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n",
+      "        \n",
+      "        where :math:`\\mu` = location and :math:`s` = scale.\n",
+      "        \n",
+      "        The Logistic distribution is used in Extreme Value problems where it\n",
+      "        can act as a mixture of Gumbel distributions, in Epidemiology, and by\n",
+      "        the World Chess Federation (FIDE) where it is used in the Elo ranking\n",
+      "        system, assuming the performance of each player is a logistically\n",
+      "        distributed random variable.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Reiss, R.-D. and Thomas M. (2001), \"Statistical Analysis of\n",
+      "               Extreme Values, from Insurance, Finance, Hydrology and Other\n",
+      "               Fields,\" Birkhauser Verlag, Basel, pp 132-133.\n",
+      "        .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n",
+      "               MathWorld--A Wolfram Web Resource.\n",
+      "               http://mathworld.wolfram.com/LogisticDistribution.html\n",
+      "        .. [3] Wikipedia, \"Logistic-distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Logistic_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> loc, scale = 10, 1\n",
+      "        >>> s = np.random.logistic(loc, scale, 10000)\n",
+      "        >>> count, bins, ignored = plt.hist(s, bins=50)\n",
+      "        \n",
+      "        #   plot against distribution\n",
+      "        \n",
+      "        >>> def logist(x, loc, scale):\n",
+      "        ...     return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n",
+      "        >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n",
+      "        ... logist(bins, loc, scale).max())\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    lognormal(...) method of mtrand.RandomState instance\n",
+      "        lognormal(mean=0.0, sigma=1.0, size=None)\n",
+      "        \n",
+      "        Draw samples from a log-normal distribution.\n",
+      "        \n",
+      "        Draw samples from a log-normal distribution with specified mean,\n",
+      "        standard deviation, and array shape.  Note that the mean and standard\n",
+      "        deviation are not the values for the distribution itself, but of the\n",
+      "        underlying normal distribution it is derived from.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        mean : float or array_like of floats, optional\n",
+      "            Mean value of the underlying normal distribution. Default is 0.\n",
+      "        sigma : float or array_like of floats, optional\n",
+      "            Standard deviation of the underlying normal distribution. Should\n",
+      "            be greater than zero. Default is 1.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``mean`` and ``sigma`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(mean, sigma).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized log-normal distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.lognorm : probability density function, distribution,\n",
+      "            cumulative density function, etc.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        A variable `x` has a log-normal distribution if `log(x)` is normally\n",
+      "        distributed.  The probability density function for the log-normal\n",
+      "        distribution is:\n",
+      "        \n",
+      "        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n",
+      "                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n",
+      "        \n",
+      "        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n",
+      "        deviation of the normally distributed logarithm of the variable.\n",
+      "        A log-normal distribution results if a random variable is the *product*\n",
+      "        of a large number of independent, identically-distributed variables in\n",
+      "        the same way that a normal distribution results if the variable is the\n",
+      "        *sum* of a large number of independent, identically-distributed\n",
+      "        variables.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal\n",
+      "               Distributions across the Sciences: Keys and Clues,\"\n",
+      "               BioScience, Vol. 51, No. 5, May, 2001.\n",
+      "               http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n",
+      "        .. [2] Reiss, R.D. and Thomas, M., \"Statistical Analysis of Extreme\n",
+      "               Values,\" Basel: Birkhauser Verlag, 2001, pp. 31-32.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> mu, sigma = 3., 1. # mean and standard deviation\n",
+      "        >>> s = np.random.lognormal(mu, sigma, 1000)\n",
+      "        \n",
+      "        Display the histogram of the samples, along with\n",
+      "        the probability density function:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n",
+      "        \n",
+      "        >>> x = np.linspace(min(bins), max(bins), 10000)\n",
+      "        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n",
+      "        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n",
+      "        \n",
+      "        >>> plt.plot(x, pdf, linewidth=2, color='r')\n",
+      "        >>> plt.axis('tight')\n",
+      "        >>> plt.show()\n",
+      "        \n",
+      "        Demonstrate that taking the products of random samples from a uniform\n",
+      "        distribution can be fit well by a log-normal probability density\n",
+      "        function.\n",
+      "        \n",
+      "        >>> # Generate a thousand samples: each is the product of 100 random\n",
+      "        >>> # values, drawn from a normal distribution.\n",
+      "        >>> b = []\n",
+      "        >>> for i in range(1000):\n",
+      "        ...    a = 10. + np.random.random(100)\n",
+      "        ...    b.append(np.product(a))\n",
+      "        \n",
+      "        >>> b = np.array(b) / np.min(b) # scale values to be positive\n",
+      "        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='mid')\n",
+      "        >>> sigma = np.std(np.log(b))\n",
+      "        >>> mu = np.mean(np.log(b))\n",
+      "        \n",
+      "        >>> x = np.linspace(min(bins), max(bins), 10000)\n",
+      "        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n",
+      "        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n",
+      "        \n",
+      "        >>> plt.plot(x, pdf, color='r', linewidth=2)\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    logseries(...) method of mtrand.RandomState instance\n",
+      "        logseries(p, size=None)\n",
+      "        \n",
+      "        Draw samples from a logarithmic series distribution.\n",
+      "        \n",
+      "        Samples are drawn from a log series distribution with specified\n",
+      "        shape parameter, 0 < ``p`` < 1.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        p : float or array_like of floats\n",
+      "            Shape parameter for the distribution.  Must be in the range (0, 1).\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``p`` is a scalar.  Otherwise,\n",
+      "            ``np.array(p).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized logarithmic series distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.logser : probability density function, distribution or\n",
+      "            cumulative density function, etc.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density for the Log Series distribution is\n",
+      "        \n",
+      "        .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n",
+      "        \n",
+      "        where p = probability.\n",
+      "        \n",
+      "        The log series distribution is frequently used to represent species\n",
+      "        richness and occurrence, first proposed by Fisher, Corbet, and\n",
+      "        Williams in 1943 [2].  It may also be used to model the numbers of\n",
+      "        occupants seen in cars [3].\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Buzas, Martin A.; Culver, Stephen J.,  Understanding regional\n",
+      "               species diversity through the log series distribution of\n",
+      "               occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n",
+      "               Volume 5, Number 5, September 1999 , pp. 187-195(9).\n",
+      "        .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n",
+      "               relation between the number of species and the number of\n",
+      "               individuals in a random sample of an animal population.\n",
+      "               Journal of Animal Ecology, 12:42-58.\n",
+      "        .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n",
+      "               Data Sets, CRC Press, 1994.\n",
+      "        .. [4] Wikipedia, \"Logarithmic distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Logarithmic_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> a = .6\n",
+      "        >>> s = np.random.logseries(a, 10000)\n",
+      "        >>> count, bins, ignored = plt.hist(s)\n",
+      "        \n",
+      "        #   plot against distribution\n",
+      "        \n",
+      "        >>> def logseries(k, p):\n",
+      "        ...     return -p**k/(k*log(1-p))\n",
+      "        >>> plt.plot(bins, logseries(bins, a)*count.max()/\n",
+      "                     logseries(bins, a).max(), 'r')\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    multinomial(...) method of mtrand.RandomState instance\n",
+      "        multinomial(n, pvals, size=None)\n",
+      "        \n",
+      "        Draw samples from a multinomial distribution.\n",
+      "        \n",
+      "        The multinomial distribution is a multivariate generalisation of the\n",
+      "        binomial distribution.  Take an experiment with one of ``p``\n",
+      "        possible outcomes.  An example of such an experiment is throwing a dice,\n",
+      "        where the outcome can be 1 through 6.  Each sample drawn from the\n",
+      "        distribution represents `n` such experiments.  Its values,\n",
+      "        ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the\n",
+      "        outcome was ``i``.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        n : int\n",
+      "            Number of experiments.\n",
+      "        pvals : sequence of floats, length p\n",
+      "            Probabilities of each of the ``p`` different outcomes.  These\n",
+      "            should sum to 1 (however, the last element is always assumed to\n",
+      "            account for the remaining probability, as long as\n",
+      "            ``sum(pvals[:-1]) <= 1)``.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
+      "            single value is returned.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray\n",
+      "            The drawn samples, of shape *size*, if that was provided.  If not,\n",
+      "            the shape is ``(N,)``.\n",
+      "        \n",
+      "            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n",
+      "            value drawn from the distribution.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Throw a dice 20 times:\n",
+      "        \n",
+      "        >>> np.random.multinomial(20, [1/6.]*6, size=1)\n",
+      "        array([[4, 1, 7, 5, 2, 1]])\n",
+      "        \n",
+      "        It landed 4 times on 1, once on 2, etc.\n",
+      "        \n",
+      "        Now, throw the dice 20 times, and 20 times again:\n",
+      "        \n",
+      "        >>> np.random.multinomial(20, [1/6.]*6, size=2)\n",
+      "        array([[3, 4, 3, 3, 4, 3],\n",
+      "               [2, 4, 3, 4, 0, 7]])\n",
+      "        \n",
+      "        For the first run, we threw 3 times 1, 4 times 2, etc.  For the second,\n",
+      "        we threw 2 times 1, 4 times 2, etc.\n",
+      "        \n",
+      "        A loaded die is more likely to land on number 6:\n",
+      "        \n",
+      "        >>> np.random.multinomial(100, [1/7.]*5 + [2/7.])\n",
+      "        array([11, 16, 14, 17, 16, 26])\n",
+      "        \n",
+      "        The probability inputs should be normalized. As an implementation\n",
+      "        detail, the value of the last entry is ignored and assumed to take\n",
+      "        up any leftover probability mass, but this should not be relied on.\n",
+      "        A biased coin which has twice as much weight on one side as on the\n",
+      "        other should be sampled like so:\n",
+      "        \n",
+      "        >>> np.random.multinomial(100, [1.0 / 3, 2.0 / 3])  # RIGHT\n",
+      "        array([38, 62])\n",
+      "        \n",
+      "        not like:\n",
+      "        \n",
+      "        >>> np.random.multinomial(100, [1.0, 2.0])  # WRONG\n",
+      "        array([100,   0])\n",
+      "    \n",
+      "    multivariate_normal(...) method of mtrand.RandomState instance\n",
+      "        multivariate_normal(mean, cov[, size, check_valid, tol])\n",
+      "        \n",
+      "        Draw random samples from a multivariate normal distribution.\n",
+      "        \n",
+      "        The multivariate normal, multinormal or Gaussian distribution is a\n",
+      "        generalization of the one-dimensional normal distribution to higher\n",
+      "        dimensions.  Such a distribution is specified by its mean and\n",
+      "        covariance matrix.  These parameters are analogous to the mean\n",
+      "        (average or \"center\") and variance (standard deviation, or \"width,\"\n",
+      "        squared) of the one-dimensional normal distribution.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        mean : 1-D array_like, of length N\n",
+      "            Mean of the N-dimensional distribution.\n",
+      "        cov : 2-D array_like, of shape (N, N)\n",
+      "            Covariance matrix of the distribution. It must be symmetric and\n",
+      "            positive-semidefinite for proper sampling.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n",
+      "            generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because\n",
+      "            each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n",
+      "            If no shape is specified, a single (`N`-D) sample is returned.\n",
+      "        check_valid : { 'warn', 'raise', 'ignore' }, optional\n",
+      "            Behavior when the covariance matrix is not positive semidefinite.\n",
+      "        tol : float, optional\n",
+      "            Tolerance when checking the singular values in covariance matrix.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray\n",
+      "            The drawn samples, of shape *size*, if that was provided.  If not,\n",
+      "            the shape is ``(N,)``.\n",
+      "        \n",
+      "            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n",
+      "            value drawn from the distribution.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The mean is a coordinate in N-dimensional space, which represents the\n",
+      "        location where samples are most likely to be generated.  This is\n",
+      "        analogous to the peak of the bell curve for the one-dimensional or\n",
+      "        univariate normal distribution.\n",
+      "        \n",
+      "        Covariance indicates the level to which two variables vary together.\n",
+      "        From the multivariate normal distribution, we draw N-dimensional\n",
+      "        samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix\n",
+      "        element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n",
+      "        The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n",
+      "        \"spread\").\n",
+      "        \n",
+      "        Instead of specifying the full covariance matrix, popular\n",
+      "        approximations include:\n",
+      "        \n",
+      "          - Spherical covariance (`cov` is a multiple of the identity matrix)\n",
+      "          - Diagonal covariance (`cov` has non-negative elements, and only on\n",
+      "            the diagonal)\n",
+      "        \n",
+      "        This geometrical property can be seen in two dimensions by plotting\n",
+      "        generated data-points:\n",
+      "        \n",
+      "        >>> mean = [0, 0]\n",
+      "        >>> cov = [[1, 0], [0, 100]]  # diagonal covariance\n",
+      "        \n",
+      "        Diagonal covariance means that points are oriented along x or y-axis:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> x, y = np.random.multivariate_normal(mean, cov, 5000).T\n",
+      "        >>> plt.plot(x, y, 'x')\n",
+      "        >>> plt.axis('equal')\n",
+      "        >>> plt.show()\n",
+      "        \n",
+      "        Note that the covariance matrix must be positive semidefinite (a.k.a.\n",
+      "        nonnegative-definite). Otherwise, the behavior of this method is\n",
+      "        undefined and backwards compatibility is not guaranteed.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Papoulis, A., \"Probability, Random Variables, and Stochastic\n",
+      "               Processes,\" 3rd ed., New York: McGraw-Hill, 1991.\n",
+      "        .. [2] Duda, R. O., Hart, P. E., and Stork, D. G., \"Pattern\n",
+      "               Classification,\" 2nd ed., New York: Wiley, 2001.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> mean = (1, 2)\n",
+      "        >>> cov = [[1, 0], [0, 1]]\n",
+      "        >>> x = np.random.multivariate_normal(mean, cov, (3, 3))\n",
+      "        >>> x.shape\n",
+      "        (3, 3, 2)\n",
+      "        \n",
+      "        The following is probably true, given that 0.6 is roughly twice the\n",
+      "        standard deviation:\n",
+      "        \n",
+      "        >>> list((x[0,0,:] - mean) < 0.6)\n",
+      "        [True, True]\n",
+      "    \n",
+      "    negative_binomial(...) method of mtrand.RandomState instance\n",
+      "        negative_binomial(n, p, size=None)\n",
+      "        \n",
+      "        Draw samples from a negative binomial distribution.\n",
+      "        \n",
+      "        Samples are drawn from a negative binomial distribution with specified\n",
+      "        parameters, `n` trials and `p` probability of success where `n` is an\n",
+      "        integer > 0 and `p` is in the interval [0, 1].\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        n : int or array_like of ints\n",
+      "            Parameter of the distribution, > 0. Floats are also accepted,\n",
+      "            but they will be truncated to integers.\n",
+      "        p : float or array_like of floats\n",
+      "            Parameter of the distribution, >= 0 and <=1.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``n`` and ``p`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(n, p).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized negative binomial distribution,\n",
+      "            where each sample is equal to N, the number of trials it took to\n",
+      "            achieve n - 1 successes, N - (n - 1) failures, and a success on the,\n",
+      "            (N + n)th trial.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density for the negative binomial distribution is\n",
+      "        \n",
+      "        .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n",
+      "        \n",
+      "        where :math:`n-1` is the number of successes, :math:`p` is the\n",
+      "        probability of success, and :math:`N+n-1` is the number of trials.\n",
+      "        The negative binomial distribution gives the probability of n-1\n",
+      "        successes and N failures in N+n-1 trials, and success on the (N+n)th\n",
+      "        trial.\n",
+      "        \n",
+      "        If one throws a die repeatedly until the third time a \"1\" appears,\n",
+      "        then the probability distribution of the number of non-\"1\"s that\n",
+      "        appear before the third \"1\" is a negative binomial distribution.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n",
+      "               MathWorld--A Wolfram Web Resource.\n",
+      "               http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n",
+      "        .. [2] Wikipedia, \"Negative binomial distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Negative_binomial_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        A real world example. A company drills wild-cat oil\n",
+      "        exploration wells, each with an estimated probability of\n",
+      "        success of 0.1.  What is the probability of having one success\n",
+      "        for each successive well, that is what is the probability of a\n",
+      "        single success after drilling 5 wells, after 6 wells, etc.?\n",
+      "        \n",
+      "        >>> s = np.random.negative_binomial(1, 0.1, 100000)\n",
+      "        >>> for i in range(1, 11):\n",
+      "        ...    probability = sum(s<i) / 100000.\n",
+      "        ...    print i, \"wells drilled, probability of one success =\", probability\n",
+      "    \n",
+      "    noncentral_chisquare(...) method of mtrand.RandomState instance\n",
+      "        noncentral_chisquare(df, nonc, size=None)\n",
+      "        \n",
+      "        Draw samples from a noncentral chi-square distribution.\n",
+      "        \n",
+      "        The noncentral :math:`\\chi^2` distribution is a generalisation of\n",
+      "        the :math:`\\chi^2` distribution.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        df : float or array_like of floats\n",
+      "            Degrees of freedom, should be > 0.\n",
+      "        \n",
+      "            .. versionchanged:: 1.10.0\n",
+      "               Earlier NumPy versions required dfnum > 1.\n",
+      "        nonc : float or array_like of floats\n",
+      "            Non-centrality, should be non-negative.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``df`` and ``nonc`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(df, nonc).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized noncentral chi-square distribution.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density function for the noncentral Chi-square\n",
+      "        distribution is\n",
+      "        \n",
+      "        .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n",
+      "                               \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}\n",
+      "                               \\P_{Y_{df+2i}}(x),\n",
+      "        \n",
+      "        where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n",
+      "        \n",
+      "        In Delhi (2007), it is noted that the noncentral chi-square is\n",
+      "        useful in bombing and coverage problems, the probability of\n",
+      "        killing the point target given by the noncentral chi-squared\n",
+      "        distribution.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in\n",
+      "               the analysis of weapon systems effectiveness\", Metrika,\n",
+      "               Volume 15, Number 1 / December, 1970.\n",
+      "        .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n",
+      "               http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw values from the distribution and plot the histogram\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n",
+      "        ...                   bins=200, normed=True)\n",
+      "        >>> plt.show()\n",
+      "        \n",
+      "        Draw values from a noncentral chisquare with very small noncentrality,\n",
+      "        and compare to a chisquare.\n",
+      "        \n",
+      "        >>> plt.figure()\n",
+      "        >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n",
+      "        ...                   bins=np.arange(0., 25, .1), normed=True)\n",
+      "        >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n",
+      "        ...                    bins=np.arange(0., 25, .1), normed=True)\n",
+      "        >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n",
+      "        >>> plt.show()\n",
+      "        \n",
+      "        Demonstrate how large values of non-centrality lead to a more symmetric\n",
+      "        distribution.\n",
+      "        \n",
+      "        >>> plt.figure()\n",
+      "        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n",
+      "        ...                   bins=200, normed=True)\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    noncentral_f(...) method of mtrand.RandomState instance\n",
+      "        noncentral_f(dfnum, dfden, nonc, size=None)\n",
+      "        \n",
+      "        Draw samples from the noncentral F distribution.\n",
+      "        \n",
+      "        Samples are drawn from an F distribution with specified parameters,\n",
+      "        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of\n",
+      "        freedom in denominator), where both parameters > 1.\n",
+      "        `nonc` is the non-centrality parameter.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        dfnum : float or array_like of floats\n",
+      "            Numerator degrees of freedom, should be > 0.\n",
+      "        \n",
+      "            .. versionchanged:: 1.14.0\n",
+      "               Earlier NumPy versions required dfnum > 1.\n",
+      "        dfden : float or array_like of floats\n",
+      "            Denominator degrees of freedom, should be > 0.\n",
+      "        nonc : float or array_like of floats\n",
+      "            Non-centrality parameter, the sum of the squares of the numerator\n",
+      "            means, should be >= 0.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``dfnum``, ``dfden``, and ``nonc``\n",
+      "            are all scalars.  Otherwise, ``np.broadcast(dfnum, dfden, nonc).size``\n",
+      "            samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized noncentral Fisher distribution.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        When calculating the power of an experiment (power = probability of\n",
+      "        rejecting the null hypothesis when a specific alternative is true) the\n",
+      "        non-central F statistic becomes important.  When the null hypothesis is\n",
+      "        true, the F statistic follows a central F distribution. When the null\n",
+      "        hypothesis is not true, then it follows a non-central F statistic.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Weisstein, Eric W. \"Noncentral F-Distribution.\"\n",
+      "               From MathWorld--A Wolfram Web Resource.\n",
+      "               http://mathworld.wolfram.com/NoncentralF-Distribution.html\n",
+      "        .. [2] Wikipedia, \"Noncentral F-distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Noncentral_F-distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        In a study, testing for a specific alternative to the null hypothesis\n",
+      "        requires use of the Noncentral F distribution. We need to calculate the\n",
+      "        area in the tail of the distribution that exceeds the value of the F\n",
+      "        distribution for the null hypothesis.  We'll plot the two probability\n",
+      "        distributions for comparison.\n",
+      "        \n",
+      "        >>> dfnum = 3 # between group deg of freedom\n",
+      "        >>> dfden = 20 # within groups degrees of freedom\n",
+      "        >>> nonc = 3.0\n",
+      "        >>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)\n",
+      "        >>> NF = np.histogram(nc_vals, bins=50, normed=True)\n",
+      "        >>> c_vals = np.random.f(dfnum, dfden, 1000000)\n",
+      "        >>> F = np.histogram(c_vals, bins=50, normed=True)\n",
+      "        >>> plt.plot(F[1][1:], F[0])\n",
+      "        >>> plt.plot(NF[1][1:], NF[0])\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    normal(...) method of mtrand.RandomState instance\n",
+      "        normal(loc=0.0, scale=1.0, size=None)\n",
+      "        \n",
+      "        Draw random samples from a normal (Gaussian) distribution.\n",
+      "        \n",
+      "        The probability density function of the normal distribution, first\n",
+      "        derived by De Moivre and 200 years later by both Gauss and Laplace\n",
+      "        independently [2]_, is often called the bell curve because of\n",
+      "        its characteristic shape (see the example below).\n",
+      "        \n",
+      "        The normal distributions occurs often in nature.  For example, it\n",
+      "        describes the commonly occurring distribution of samples influenced\n",
+      "        by a large number of tiny, random disturbances, each with its own\n",
+      "        unique distribution [2]_.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        loc : float or array_like of floats\n",
+      "            Mean (\"centre\") of the distribution.\n",
+      "        scale : float or array_like of floats\n",
+      "            Standard deviation (spread or \"width\") of the distribution.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``loc`` and ``scale`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized normal distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.norm : probability density function, distribution or\n",
+      "            cumulative density function, etc.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density for the Gaussian distribution is\n",
+      "        \n",
+      "        .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n",
+      "                         e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n",
+      "        \n",
+      "        where :math:`\\mu` is the mean and :math:`\\sigma` the standard\n",
+      "        deviation. The square of the standard deviation, :math:`\\sigma^2`,\n",
+      "        is called the variance.\n",
+      "        \n",
+      "        The function has its peak at the mean, and its \"spread\" increases with\n",
+      "        the standard deviation (the function reaches 0.607 times its maximum at\n",
+      "        :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n",
+      "        `numpy.random.normal` is more likely to return samples lying close to\n",
+      "        the mean, rather than those far away.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Wikipedia, \"Normal distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Normal_distribution\n",
+      "        .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability,\n",
+      "               Random Variables and Random Signal Principles\", 4th ed., 2001,\n",
+      "               pp. 51, 51, 125.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> mu, sigma = 0, 0.1 # mean and standard deviation\n",
+      "        >>> s = np.random.normal(mu, sigma, 1000)\n",
+      "        \n",
+      "        Verify the mean and the variance:\n",
+      "        \n",
+      "        >>> abs(mu - np.mean(s)) < 0.01\n",
+      "        True\n",
+      "        \n",
+      "        >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n",
+      "        True\n",
+      "        \n",
+      "        Display the histogram of the samples, along with\n",
+      "        the probability density function:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n",
+      "        >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n",
+      "        ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n",
+      "        ...          linewidth=2, color='r')\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    pareto(...) method of mtrand.RandomState instance\n",
+      "        pareto(a, size=None)\n",
+      "        \n",
+      "        Draw samples from a Pareto II or Lomax distribution with\n",
+      "        specified shape.\n",
+      "        \n",
+      "        The Lomax or Pareto II distribution is a shifted Pareto\n",
+      "        distribution. The classical Pareto distribution can be\n",
+      "        obtained from the Lomax distribution by adding 1 and\n",
+      "        multiplying by the scale parameter ``m`` (see Notes).  The\n",
+      "        smallest value of the Lomax distribution is zero while for the\n",
+      "        classical Pareto distribution it is ``mu``, where the standard\n",
+      "        Pareto distribution has location ``mu = 1``.  Lomax can also\n",
+      "        be considered as a simplified version of the Generalized\n",
+      "        Pareto distribution (available in SciPy), with the scale set\n",
+      "        to one and the location set to zero.\n",
+      "        \n",
+      "        The Pareto distribution must be greater than zero, and is\n",
+      "        unbounded above.  It is also known as the \"80-20 rule\".  In\n",
+      "        this distribution, 80 percent of the weights are in the lowest\n",
+      "        20 percent of the range, while the other 20 percent fill the\n",
+      "        remaining 80 percent of the range.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        a : float or array_like of floats\n",
+      "            Shape of the distribution. Should be greater than zero.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``a`` is a scalar.  Otherwise,\n",
+      "            ``np.array(a).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized Pareto distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.lomax : probability density function, distribution or\n",
+      "            cumulative density function, etc.\n",
+      "        scipy.stats.genpareto : probability density function, distribution or\n",
+      "            cumulative density function, etc.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density for the Pareto distribution is\n",
+      "        \n",
+      "        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n",
+      "        \n",
+      "        where :math:`a` is the shape and :math:`m` the scale.\n",
+      "        \n",
+      "        The Pareto distribution, named after the Italian economist\n",
+      "        Vilfredo Pareto, is a power law probability distribution\n",
+      "        useful in many real world problems.  Outside the field of\n",
+      "        economics it is generally referred to as the Bradford\n",
+      "        distribution. Pareto developed the distribution to describe\n",
+      "        the distribution of wealth in an economy.  It has also found\n",
+      "        use in insurance, web page access statistics, oil field sizes,\n",
+      "        and many other problems, including the download frequency for\n",
+      "        projects in Sourceforge [1]_.  It is one of the so-called\n",
+      "        \"fat-tailed\" distributions.\n",
+      "        \n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n",
+      "               Sourceforge projects.\n",
+      "        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n",
+      "        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n",
+      "               Values, Birkhauser Verlag, Basel, pp 23-30.\n",
+      "        .. [4] Wikipedia, \"Pareto distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Pareto_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> a, m = 3., 2.  # shape and mode\n",
+      "        >>> s = (np.random.pareto(a, 1000) + 1) * m\n",
+      "        \n",
+      "        Display the histogram of the samples, along with the probability\n",
+      "        density function:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> count, bins, _ = plt.hist(s, 100, normed=True)\n",
+      "        >>> fit = a*m**a / bins**(a+1)\n",
+      "        >>> plt.plot(bins, max(count)*fit/max(fit), linewidth=2, color='r')\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    permutation(...) method of mtrand.RandomState instance\n",
+      "        permutation(x)\n",
+      "        \n",
+      "        Randomly permute a sequence, or return a permuted range.\n",
+      "        \n",
+      "        If `x` is a multi-dimensional array, it is only shuffled along its\n",
+      "        first index.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        x : int or array_like\n",
+      "            If `x` is an integer, randomly permute ``np.arange(x)``.\n",
+      "            If `x` is an array, make a copy and shuffle the elements\n",
+      "            randomly.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray\n",
+      "            Permuted sequence or array range.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> np.random.permutation(10)\n",
+      "        array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])\n",
+      "        \n",
+      "        >>> np.random.permutation([1, 4, 9, 12, 15])\n",
+      "        array([15,  1,  9,  4, 12])\n",
+      "        \n",
+      "        >>> arr = np.arange(9).reshape((3, 3))\n",
+      "        >>> np.random.permutation(arr)\n",
+      "        array([[6, 7, 8],\n",
+      "               [0, 1, 2],\n",
+      "               [3, 4, 5]])\n",
+      "    \n",
+      "    poisson(...) method of mtrand.RandomState instance\n",
+      "        poisson(lam=1.0, size=None)\n",
+      "        \n",
+      "        Draw samples from a Poisson distribution.\n",
+      "        \n",
+      "        The Poisson distribution is the limit of the binomial distribution\n",
+      "        for large N.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        lam : float or array_like of floats\n",
+      "            Expectation of interval, should be >= 0. A sequence of expectation\n",
+      "            intervals must be broadcastable over the requested size.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``lam`` is a scalar. Otherwise,\n",
+      "            ``np.array(lam).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized Poisson distribution.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The Poisson distribution\n",
+      "        \n",
+      "        .. math:: f(k; \\lambda)=\\frac{\\lambda^k e^{-\\lambda}}{k!}\n",
+      "        \n",
+      "        For events with an expected separation :math:`\\lambda` the Poisson\n",
+      "        distribution :math:`f(k; \\lambda)` describes the probability of\n",
+      "        :math:`k` events occurring within the observed\n",
+      "        interval :math:`\\lambda`.\n",
+      "        \n",
+      "        Because the output is limited to the range of the C long type, a\n",
+      "        ValueError is raised when `lam` is within 10 sigma of the maximum\n",
+      "        representable value.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Weisstein, Eric W. \"Poisson Distribution.\"\n",
+      "               From MathWorld--A Wolfram Web Resource.\n",
+      "               http://mathworld.wolfram.com/PoissonDistribution.html\n",
+      "        .. [2] Wikipedia, \"Poisson distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Poisson_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> import numpy as np\n",
+      "        >>> s = np.random.poisson(5, 10000)\n",
+      "        \n",
+      "        Display histogram of the sample:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> count, bins, ignored = plt.hist(s, 14, normed=True)\n",
+      "        >>> plt.show()\n",
+      "        \n",
+      "        Draw each 100 values for lambda 100 and 500:\n",
+      "        \n",
+      "        >>> s = np.random.poisson(lam=(100., 500.), size=(100, 2))\n",
+      "    \n",
+      "    power(...) method of mtrand.RandomState instance\n",
+      "        power(a, size=None)\n",
+      "        \n",
+      "        Draws samples in [0, 1] from a power distribution with positive\n",
+      "        exponent a - 1.\n",
+      "        \n",
+      "        Also known as the power function distribution.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        a : float or array_like of floats\n",
+      "            Parameter of the distribution. Should be greater than zero.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``a`` is a scalar.  Otherwise,\n",
+      "            ``np.array(a).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized power distribution.\n",
+      "        \n",
+      "        Raises\n",
+      "        ------\n",
+      "        ValueError\n",
+      "            If a < 1.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density function is\n",
+      "        \n",
+      "        .. math:: P(x; a) = ax^{a-1}, 0 \\le x \\le 1, a>0.\n",
+      "        \n",
+      "        The power function distribution is just the inverse of the Pareto\n",
+      "        distribution. It may also be seen as a special case of the Beta\n",
+      "        distribution.\n",
+      "        \n",
+      "        It is used, for example, in modeling the over-reporting of insurance\n",
+      "        claims.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Christian Kleiber, Samuel Kotz, \"Statistical size distributions\n",
+      "               in economics and actuarial sciences\", Wiley, 2003.\n",
+      "        .. [2] Heckert, N. A. and Filliben, James J. \"NIST Handbook 148:\n",
+      "               Dataplot Reference Manual, Volume 2: Let Subcommands and Library\n",
+      "               Functions\", National Institute of Standards and Technology\n",
+      "               Handbook Series, June 2003.\n",
+      "               http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> a = 5. # shape\n",
+      "        >>> samples = 1000\n",
+      "        >>> s = np.random.power(a, samples)\n",
+      "        \n",
+      "        Display the histogram of the samples, along with\n",
+      "        the probability density function:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> count, bins, ignored = plt.hist(s, bins=30)\n",
+      "        >>> x = np.linspace(0, 1, 100)\n",
+      "        >>> y = a*x**(a-1.)\n",
+      "        >>> normed_y = samples*np.diff(bins)[0]*y\n",
+      "        >>> plt.plot(x, normed_y)\n",
+      "        >>> plt.show()\n",
+      "        \n",
+      "        Compare the power function distribution to the inverse of the Pareto.\n",
+      "        \n",
+      "        >>> from scipy import stats\n",
+      "        >>> rvs = np.random.power(5, 1000000)\n",
+      "        >>> rvsp = np.random.pareto(5, 1000000)\n",
+      "        >>> xx = np.linspace(0,1,100)\n",
+      "        >>> powpdf = stats.powerlaw.pdf(xx,5)\n",
+      "        \n",
+      "        >>> plt.figure()\n",
+      "        >>> plt.hist(rvs, bins=50, normed=True)\n",
+      "        >>> plt.plot(xx,powpdf,'r-')\n",
+      "        >>> plt.title('np.random.power(5)')\n",
+      "        \n",
+      "        >>> plt.figure()\n",
+      "        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n",
+      "        >>> plt.plot(xx,powpdf,'r-')\n",
+      "        >>> plt.title('inverse of 1 + np.random.pareto(5)')\n",
+      "        \n",
+      "        >>> plt.figure()\n",
+      "        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n",
+      "        >>> plt.plot(xx,powpdf,'r-')\n",
+      "        >>> plt.title('inverse of stats.pareto(5)')\n",
+      "    \n",
+      "    rand(...) method of mtrand.RandomState instance\n",
+      "        rand(d0, d1, ..., dn)\n",
+      "        \n",
+      "        Random values in a given shape.\n",
+      "        \n",
+      "        Create an array of the given shape and populate it with\n",
+      "        random samples from a uniform distribution\n",
+      "        over ``[0, 1)``.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        d0, d1, ..., dn : int, optional\n",
+      "            The dimensions of the returned array, should all be positive.\n",
+      "            If no argument is given a single Python float is returned.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray, shape ``(d0, d1, ..., dn)``\n",
+      "            Random values.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        random\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        This is a convenience function. If you want an interface that\n",
+      "        takes a shape-tuple as the first argument, refer to\n",
+      "        np.random.random_sample .\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> np.random.rand(3,2)\n",
+      "        array([[ 0.14022471,  0.96360618],  #random\n",
+      "               [ 0.37601032,  0.25528411],  #random\n",
+      "               [ 0.49313049,  0.94909878]]) #random\n",
+      "    \n",
+      "    randint(...) method of mtrand.RandomState instance\n",
+      "        randint(low, high=None, size=None, dtype='l')\n",
+      "        \n",
+      "        Return random integers from `low` (inclusive) to `high` (exclusive).\n",
+      "        \n",
+      "        Return random integers from the \"discrete uniform\" distribution of\n",
+      "        the specified dtype in the \"half-open\" interval [`low`, `high`). If\n",
+      "        `high` is None (the default), then results are from [0, `low`).\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        low : int\n",
+      "            Lowest (signed) integer to be drawn from the distribution (unless\n",
+      "            ``high=None``, in which case this parameter is one above the\n",
+      "            *highest* such integer).\n",
+      "        high : int, optional\n",
+      "            If provided, one above the largest (signed) integer to be drawn\n",
+      "            from the distribution (see above for behavior if ``high=None``).\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
+      "            single value is returned.\n",
+      "        dtype : dtype, optional\n",
+      "            Desired dtype of the result. All dtypes are determined by their\n",
+      "            name, i.e., 'int64', 'int', etc, so byteorder is not available\n",
+      "            and a specific precision may have different C types depending\n",
+      "            on the platform. The default value is 'np.int'.\n",
+      "        \n",
+      "            .. versionadded:: 1.11.0\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : int or ndarray of ints\n",
+      "            `size`-shaped array of random integers from the appropriate\n",
+      "            distribution, or a single such random int if `size` not provided.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        random.random_integers : similar to `randint`, only for the closed\n",
+      "            interval [`low`, `high`], and 1 is the lowest value if `high` is\n",
+      "            omitted. In particular, this other one is the one to use to generate\n",
+      "            uniformly distributed discrete non-integers.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> np.random.randint(2, size=10)\n",
+      "        array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])\n",
+      "        >>> np.random.randint(1, size=10)\n",
+      "        array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
+      "        \n",
+      "        Generate a 2 x 4 array of ints between 0 and 4, inclusive:\n",
+      "        \n",
+      "        >>> np.random.randint(5, size=(2, 4))\n",
+      "        array([[4, 0, 2, 1],\n",
+      "               [3, 2, 2, 0]])\n",
+      "    \n",
+      "    randn(...) method of mtrand.RandomState instance\n",
+      "        randn(d0, d1, ..., dn)\n",
+      "        \n",
+      "        Return a sample (or samples) from the \"standard normal\" distribution.\n",
+      "        \n",
+      "        If positive, int_like or int-convertible arguments are provided,\n",
+      "        `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n",
+      "        with random floats sampled from a univariate \"normal\" (Gaussian)\n",
+      "        distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n",
+      "        floats, they are first converted to integers by truncation). A single\n",
+      "        float randomly sampled from the distribution is returned if no\n",
+      "        argument is provided.\n",
+      "        \n",
+      "        This is a convenience function.  If you want an interface that takes a\n",
+      "        tuple as the first argument, use `numpy.random.standard_normal` instead.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        d0, d1, ..., dn : int, optional\n",
+      "            The dimensions of the returned array, should be all positive.\n",
+      "            If no argument is given a single Python float is returned.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        Z : ndarray or float\n",
+      "            A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from\n",
+      "            the standard normal distribution, or a single such float if\n",
+      "            no parameters were supplied.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        random.standard_normal : Similar, but takes a tuple as its argument.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n",
+      "        \n",
+      "        ``sigma * np.random.randn(...) + mu``\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> np.random.randn()\n",
+      "        2.1923875335537315 #random\n",
+      "        \n",
+      "        Two-by-four array of samples from N(3, 6.25):\n",
+      "        \n",
+      "        >>> 2.5 * np.random.randn(2, 4) + 3\n",
+      "        array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random\n",
+      "               [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random\n",
+      "    \n",
+      "    random = random_sample(...) method of mtrand.RandomState instance\n",
+      "        random_sample(size=None)\n",
+      "        \n",
+      "        Return random floats in the half-open interval [0.0, 1.0).\n",
+      "        \n",
+      "        Results are from the \"continuous uniform\" distribution over the\n",
+      "        stated interval.  To sample :math:`Unif[a, b), b > a` multiply\n",
+      "        the output of `random_sample` by `(b-a)` and add `a`::\n",
+      "        \n",
+      "          (b - a) * random_sample() + a\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
+      "            single value is returned.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : float or ndarray of floats\n",
+      "            Array of random floats of shape `size` (unless ``size=None``, in which\n",
+      "            case a single float is returned).\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> np.random.random_sample()\n",
+      "        0.47108547995356098\n",
+      "        >>> type(np.random.random_sample())\n",
+      "        <type 'float'>\n",
+      "        >>> np.random.random_sample((5,))\n",
+      "        array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])\n",
+      "        \n",
+      "        Three-by-two array of random numbers from [-5, 0):\n",
+      "        \n",
+      "        >>> 5 * np.random.random_sample((3, 2)) - 5\n",
+      "        array([[-3.99149989, -0.52338984],\n",
+      "               [-2.99091858, -0.79479508],\n",
+      "               [-1.23204345, -1.75224494]])\n",
+      "    \n",
+      "    random_integers(...) method of mtrand.RandomState instance\n",
+      "        random_integers(low, high=None, size=None)\n",
+      "        \n",
+      "        Random integers of type np.int between `low` and `high`, inclusive.\n",
+      "        \n",
+      "        Return random integers of type np.int from the \"discrete uniform\"\n",
+      "        distribution in the closed interval [`low`, `high`].  If `high` is\n",
+      "        None (the default), then results are from [1, `low`]. The np.int\n",
+      "        type translates to the C long type used by Python 2 for \"short\"\n",
+      "        integers and its precision is platform dependent.\n",
+      "        \n",
+      "        This function has been deprecated. Use randint instead.\n",
+      "        \n",
+      "        .. deprecated:: 1.11.0\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        low : int\n",
+      "            Lowest (signed) integer to be drawn from the distribution (unless\n",
+      "            ``high=None``, in which case this parameter is the *highest* such\n",
+      "            integer).\n",
+      "        high : int, optional\n",
+      "            If provided, the largest (signed) integer to be drawn from the\n",
+      "            distribution (see above for behavior if ``high=None``).\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
+      "            single value is returned.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : int or ndarray of ints\n",
+      "            `size`-shaped array of random integers from the appropriate\n",
+      "            distribution, or a single such random int if `size` not provided.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        random.randint : Similar to `random_integers`, only for the half-open\n",
+      "            interval [`low`, `high`), and 0 is the lowest value if `high` is\n",
+      "            omitted.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        To sample from N evenly spaced floating-point numbers between a and b,\n",
+      "        use::\n",
+      "        \n",
+      "          a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> np.random.random_integers(5)\n",
+      "        4\n",
+      "        >>> type(np.random.random_integers(5))\n",
+      "        <type 'int'>\n",
+      "        >>> np.random.random_integers(5, size=(3,2))\n",
+      "        array([[5, 4],\n",
+      "               [3, 3],\n",
+      "               [4, 5]])\n",
+      "        \n",
+      "        Choose five random numbers from the set of five evenly-spaced\n",
+      "        numbers between 0 and 2.5, inclusive (*i.e.*, from the set\n",
+      "        :math:`{0, 5/8, 10/8, 15/8, 20/8}`):\n",
+      "        \n",
+      "        >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.\n",
+      "        array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ])\n",
+      "        \n",
+      "        Roll two six sided dice 1000 times and sum the results:\n",
+      "        \n",
+      "        >>> d1 = np.random.random_integers(1, 6, 1000)\n",
+      "        >>> d2 = np.random.random_integers(1, 6, 1000)\n",
+      "        >>> dsums = d1 + d2\n",
+      "        \n",
+      "        Display results as a histogram:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    random_sample(...) method of mtrand.RandomState instance\n",
+      "        random_sample(size=None)\n",
+      "        \n",
+      "        Return random floats in the half-open interval [0.0, 1.0).\n",
+      "        \n",
+      "        Results are from the \"continuous uniform\" distribution over the\n",
+      "        stated interval.  To sample :math:`Unif[a, b), b > a` multiply\n",
+      "        the output of `random_sample` by `(b-a)` and add `a`::\n",
+      "        \n",
+      "          (b - a) * random_sample() + a\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
+      "            single value is returned.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : float or ndarray of floats\n",
+      "            Array of random floats of shape `size` (unless ``size=None``, in which\n",
+      "            case a single float is returned).\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> np.random.random_sample()\n",
+      "        0.47108547995356098\n",
+      "        >>> type(np.random.random_sample())\n",
+      "        <type 'float'>\n",
+      "        >>> np.random.random_sample((5,))\n",
+      "        array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])\n",
+      "        \n",
+      "        Three-by-two array of random numbers from [-5, 0):\n",
+      "        \n",
+      "        >>> 5 * np.random.random_sample((3, 2)) - 5\n",
+      "        array([[-3.99149989, -0.52338984],\n",
+      "               [-2.99091858, -0.79479508],\n",
+      "               [-1.23204345, -1.75224494]])\n",
+      "    \n",
+      "    ranf = random_sample(...) method of mtrand.RandomState instance\n",
+      "        random_sample(size=None)\n",
+      "        \n",
+      "        Return random floats in the half-open interval [0.0, 1.0).\n",
+      "        \n",
+      "        Results are from the \"continuous uniform\" distribution over the\n",
+      "        stated interval.  To sample :math:`Unif[a, b), b > a` multiply\n",
+      "        the output of `random_sample` by `(b-a)` and add `a`::\n",
+      "        \n",
+      "          (b - a) * random_sample() + a\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
+      "            single value is returned.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : float or ndarray of floats\n",
+      "            Array of random floats of shape `size` (unless ``size=None``, in which\n",
+      "            case a single float is returned).\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> np.random.random_sample()\n",
+      "        0.47108547995356098\n",
+      "        >>> type(np.random.random_sample())\n",
+      "        <type 'float'>\n",
+      "        >>> np.random.random_sample((5,))\n",
+      "        array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])\n",
+      "        \n",
+      "        Three-by-two array of random numbers from [-5, 0):\n",
+      "        \n",
+      "        >>> 5 * np.random.random_sample((3, 2)) - 5\n",
+      "        array([[-3.99149989, -0.52338984],\n",
+      "               [-2.99091858, -0.79479508],\n",
+      "               [-1.23204345, -1.75224494]])\n",
+      "    \n",
+      "    rayleigh(...) method of mtrand.RandomState instance\n",
+      "        rayleigh(scale=1.0, size=None)\n",
+      "        \n",
+      "        Draw samples from a Rayleigh distribution.\n",
+      "        \n",
+      "        The :math:`\\chi` and Weibull distributions are generalizations of the\n",
+      "        Rayleigh.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        scale : float or array_like of floats, optional\n",
+      "            Scale, also equals the mode. Should be >= 0. Default is 1.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``scale`` is a scalar.  Otherwise,\n",
+      "            ``np.array(scale).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized Rayleigh distribution.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density function for the Rayleigh distribution is\n",
+      "        \n",
+      "        .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n",
+      "        \n",
+      "        The Rayleigh distribution would arise, for example, if the East\n",
+      "        and North components of the wind velocity had identical zero-mean\n",
+      "        Gaussian distributions.  Then the wind speed would have a Rayleigh\n",
+      "        distribution.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Brighton Webs Ltd., \"Rayleigh Distribution,\"\n",
+      "               http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n",
+      "        .. [2] Wikipedia, \"Rayleigh distribution\"\n",
+      "               http://en.wikipedia.org/wiki/Rayleigh_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw values from the distribution and plot the histogram\n",
+      "        \n",
+      "        >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n",
+      "        \n",
+      "        Wave heights tend to follow a Rayleigh distribution. If the mean wave\n",
+      "        height is 1 meter, what fraction of waves are likely to be larger than 3\n",
+      "        meters?\n",
+      "        \n",
+      "        >>> meanvalue = 1\n",
+      "        >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n",
+      "        >>> s = np.random.rayleigh(modevalue, 1000000)\n",
+      "        \n",
+      "        The percentage of waves larger than 3 meters is:\n",
+      "        \n",
+      "        >>> 100.*sum(s>3)/1000000.\n",
+      "        0.087300000000000003\n",
+      "    \n",
+      "    sample = random_sample(...) method of mtrand.RandomState instance\n",
+      "        random_sample(size=None)\n",
+      "        \n",
+      "        Return random floats in the half-open interval [0.0, 1.0).\n",
+      "        \n",
+      "        Results are from the \"continuous uniform\" distribution over the\n",
+      "        stated interval.  To sample :math:`Unif[a, b), b > a` multiply\n",
+      "        the output of `random_sample` by `(b-a)` and add `a`::\n",
+      "        \n",
+      "          (b - a) * random_sample() + a\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
+      "            single value is returned.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : float or ndarray of floats\n",
+      "            Array of random floats of shape `size` (unless ``size=None``, in which\n",
+      "            case a single float is returned).\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> np.random.random_sample()\n",
+      "        0.47108547995356098\n",
+      "        >>> type(np.random.random_sample())\n",
+      "        <type 'float'>\n",
+      "        >>> np.random.random_sample((5,))\n",
+      "        array([ 0.30220482,  0.86820401,  0.1654503 ,  0.11659149,  0.54323428])\n",
+      "        \n",
+      "        Three-by-two array of random numbers from [-5, 0):\n",
+      "        \n",
+      "        >>> 5 * np.random.random_sample((3, 2)) - 5\n",
+      "        array([[-3.99149989, -0.52338984],\n",
+      "               [-2.99091858, -0.79479508],\n",
+      "               [-1.23204345, -1.75224494]])\n",
+      "    \n",
+      "    seed(...) method of mtrand.RandomState instance\n",
+      "        seed(seed=None)\n",
+      "        \n",
+      "        Seed the generator.\n",
+      "        \n",
+      "        This method is called when `RandomState` is initialized. It can be\n",
+      "        called again to re-seed the generator. For details, see `RandomState`.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        seed : int or 1-d array_like, optional\n",
+      "            Seed for `RandomState`.\n",
+      "            Must be convertible to 32 bit unsigned integers.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        RandomState\n",
+      "    \n",
+      "    set_state(...) method of mtrand.RandomState instance\n",
+      "        set_state(state)\n",
+      "        \n",
+      "        Set the internal state of the generator from a tuple.\n",
+      "        \n",
+      "        For use if one has reason to manually (re-)set the internal state of the\n",
+      "        \"Mersenne Twister\"[1]_ pseudo-random number generating algorithm.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        state : tuple(str, ndarray of 624 uints, int, int, float)\n",
+      "            The `state` tuple has the following items:\n",
+      "        \n",
+      "            1. the string 'MT19937', specifying the Mersenne Twister algorithm.\n",
+      "            2. a 1-D array of 624 unsigned integers ``keys``.\n",
+      "            3. an integer ``pos``.\n",
+      "            4. an integer ``has_gauss``.\n",
+      "            5. a float ``cached_gaussian``.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : None\n",
+      "            Returns 'None' on success.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        get_state\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        `set_state` and `get_state` are not needed to work with any of the\n",
+      "        random distributions in NumPy. If the internal state is manually altered,\n",
+      "        the user should know exactly what he/she is doing.\n",
+      "        \n",
+      "        For backwards compatibility, the form (str, array of 624 uints, int) is\n",
+      "        also accepted although it is missing some information about the cached\n",
+      "        Gaussian value: ``state = ('MT19937', keys, pos)``.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] M. Matsumoto and T. Nishimura, \"Mersenne Twister: A\n",
+      "           623-dimensionally equidistributed uniform pseudorandom number\n",
+      "           generator,\" *ACM Trans. on Modeling and Computer Simulation*,\n",
+      "           Vol. 8, No. 1, pp. 3-30, Jan. 1998.\n",
+      "    \n",
+      "    shuffle(...) method of mtrand.RandomState instance\n",
+      "        shuffle(x)\n",
+      "        \n",
+      "        Modify a sequence in-place by shuffling its contents.\n",
+      "        \n",
+      "        This function only shuffles the array along the first axis of a\n",
+      "        multi-dimensional array. The order of sub-arrays is changed but\n",
+      "        their contents remains the same.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        x : array_like\n",
+      "            The array or list to be shuffled.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        None\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> arr = np.arange(10)\n",
+      "        >>> np.random.shuffle(arr)\n",
+      "        >>> arr\n",
+      "        [1 7 5 2 9 4 3 6 0 8]\n",
+      "        \n",
+      "        Multi-dimensional arrays are only shuffled along the first axis:\n",
+      "        \n",
+      "        >>> arr = np.arange(9).reshape((3, 3))\n",
+      "        >>> np.random.shuffle(arr)\n",
+      "        >>> arr\n",
+      "        array([[3, 4, 5],\n",
+      "               [6, 7, 8],\n",
+      "               [0, 1, 2]])\n",
+      "    \n",
+      "    standard_cauchy(...) method of mtrand.RandomState instance\n",
+      "        standard_cauchy(size=None)\n",
+      "        \n",
+      "        Draw samples from a standard Cauchy distribution with mode = 0.\n",
+      "        \n",
+      "        Also known as the Lorentz distribution.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
+      "            single value is returned.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        samples : ndarray or scalar\n",
+      "            The drawn samples.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density function for the full Cauchy distribution is\n",
+      "        \n",
+      "        .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n",
+      "                  (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n",
+      "        \n",
+      "        and the Standard Cauchy distribution just sets :math:`x_0=0` and\n",
+      "        :math:`\\gamma=1`\n",
+      "        \n",
+      "        The Cauchy distribution arises in the solution to the driven harmonic\n",
+      "        oscillator problem, and also describes spectral line broadening. It\n",
+      "        also describes the distribution of values at which a line tilted at\n",
+      "        a random angle will cut the x axis.\n",
+      "        \n",
+      "        When studying hypothesis tests that assume normality, seeing how the\n",
+      "        tests perform on data from a Cauchy distribution is a good indicator of\n",
+      "        their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n",
+      "        very much like a Gaussian distribution, but with heavier tails.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n",
+      "              Distribution\",\n",
+      "              http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n",
+      "        .. [2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n",
+      "              Wolfram Web Resource.\n",
+      "              http://mathworld.wolfram.com/CauchyDistribution.html\n",
+      "        .. [3] Wikipedia, \"Cauchy distribution\"\n",
+      "              http://en.wikipedia.org/wiki/Cauchy_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples and plot the distribution:\n",
+      "        \n",
+      "        >>> s = np.random.standard_cauchy(1000000)\n",
+      "        >>> s = s[(s>-25) & (s<25)]  # truncate distribution so it plots well\n",
+      "        >>> plt.hist(s, bins=100)\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    standard_exponential(...) method of mtrand.RandomState instance\n",
+      "        standard_exponential(size=None)\n",
+      "        \n",
+      "        Draw samples from the standard exponential distribution.\n",
+      "        \n",
+      "        `standard_exponential` is identical to the exponential distribution\n",
+      "        with a scale parameter of 1.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
+      "            single value is returned.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : float or ndarray\n",
+      "            Drawn samples.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Output a 3x8000 array:\n",
+      "        \n",
+      "        >>> n = np.random.standard_exponential((3, 8000))\n",
+      "    \n",
+      "    standard_gamma(...) method of mtrand.RandomState instance\n",
+      "        standard_gamma(shape, size=None)\n",
+      "        \n",
+      "        Draw samples from a standard Gamma distribution.\n",
+      "        \n",
+      "        Samples are drawn from a Gamma distribution with specified parameters,\n",
+      "        shape (sometimes designated \"k\") and scale=1.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        shape : float or array_like of floats\n",
+      "            Parameter, should be > 0.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``shape`` is a scalar.  Otherwise,\n",
+      "            ``np.array(shape).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized standard gamma distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.gamma : probability density function, distribution or\n",
+      "            cumulative density function, etc.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density for the Gamma distribution is\n",
+      "        \n",
+      "        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n",
+      "        \n",
+      "        where :math:`k` is the shape and :math:`\\theta` the scale,\n",
+      "        and :math:`\\Gamma` is the Gamma function.\n",
+      "        \n",
+      "        The Gamma distribution is often used to model the times to failure of\n",
+      "        electronic components, and arises naturally in processes for which the\n",
+      "        waiting times between Poisson distributed events are relevant.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n",
+      "               Wolfram Web Resource.\n",
+      "               http://mathworld.wolfram.com/GammaDistribution.html\n",
+      "        .. [2] Wikipedia, \"Gamma distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Gamma_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> shape, scale = 2., 1. # mean and width\n",
+      "        >>> s = np.random.standard_gamma(shape, 1000000)\n",
+      "        \n",
+      "        Display the histogram of the samples, along with\n",
+      "        the probability density function:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> import scipy.special as sps\n",
+      "        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n",
+      "        >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\\n",
+      "        ...                       (sps.gamma(shape) * scale**shape))\n",
+      "        >>> plt.plot(bins, y, linewidth=2, color='r')\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    standard_normal(...) method of mtrand.RandomState instance\n",
+      "        standard_normal(size=None)\n",
+      "        \n",
+      "        Draw samples from a standard Normal distribution (mean=0, stdev=1).\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  Default is None, in which case a\n",
+      "            single value is returned.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : float or ndarray\n",
+      "            Drawn samples.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        >>> s = np.random.standard_normal(8000)\n",
+      "        >>> s\n",
+      "        array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311, #random\n",
+      "               -0.38672696, -0.4685006 ])                               #random\n",
+      "        >>> s.shape\n",
+      "        (8000,)\n",
+      "        >>> s = np.random.standard_normal(size=(3, 4, 2))\n",
+      "        >>> s.shape\n",
+      "        (3, 4, 2)\n",
+      "    \n",
+      "    standard_t(...) method of mtrand.RandomState instance\n",
+      "        standard_t(df, size=None)\n",
+      "        \n",
+      "        Draw samples from a standard Student's t distribution with `df` degrees\n",
+      "        of freedom.\n",
+      "        \n",
+      "        A special case of the hyperbolic distribution.  As `df` gets\n",
+      "        large, the result resembles that of the standard normal\n",
+      "        distribution (`standard_normal`).\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        df : float or array_like of floats\n",
+      "            Degrees of freedom, should be > 0.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``df`` is a scalar.  Otherwise,\n",
+      "            ``np.array(df).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized standard Student's t distribution.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density function for the t distribution is\n",
+      "        \n",
+      "        .. math:: P(x, df) = \\frac{\\Gamma(\\frac{df+1}{2})}{\\sqrt{\\pi df}\n",
+      "                  \\Gamma(\\frac{df}{2})}\\Bigl( 1+\\frac{x^2}{df} \\Bigr)^{-(df+1)/2}\n",
+      "        \n",
+      "        The t test is based on an assumption that the data come from a\n",
+      "        Normal distribution. The t test provides a way to test whether\n",
+      "        the sample mean (that is the mean calculated from the data) is\n",
+      "        a good estimate of the true mean.\n",
+      "        \n",
+      "        The derivation of the t-distribution was first published in\n",
+      "        1908 by William Gosset while working for the Guinness Brewery\n",
+      "        in Dublin. Due to proprietary issues, he had to publish under\n",
+      "        a pseudonym, and so he used the name Student.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Dalgaard, Peter, \"Introductory Statistics With R\",\n",
+      "               Springer, 2002.\n",
+      "        .. [2] Wikipedia, \"Student's t-distribution\"\n",
+      "               http://en.wikipedia.org/wiki/Student's_t-distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        From Dalgaard page 83 [1]_, suppose the daily energy intake for 11\n",
+      "        women in Kj is:\n",
+      "        \n",
+      "        >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \\\n",
+      "        ...                    7515, 8230, 8770])\n",
+      "        \n",
+      "        Does their energy intake deviate systematically from the recommended\n",
+      "        value of 7725 kJ?\n",
+      "        \n",
+      "        We have 10 degrees of freedom, so is the sample mean within 95% of the\n",
+      "        recommended value?\n",
+      "        \n",
+      "        >>> s = np.random.standard_t(10, size=100000)\n",
+      "        >>> np.mean(intake)\n",
+      "        6753.636363636364\n",
+      "        >>> intake.std(ddof=1)\n",
+      "        1142.1232221373727\n",
+      "        \n",
+      "        Calculate the t statistic, setting the ddof parameter to the unbiased\n",
+      "        value so the divisor in the standard deviation will be degrees of\n",
+      "        freedom, N-1.\n",
+      "        \n",
+      "        >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))\n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> h = plt.hist(s, bins=100, normed=True)\n",
+      "        \n",
+      "        For a one-sided t-test, how far out in the distribution does the t\n",
+      "        statistic appear?\n",
+      "        \n",
+      "        >>> np.sum(s<t) / float(len(s))\n",
+      "        0.0090699999999999999  #random\n",
+      "        \n",
+      "        So the p-value is about 0.009, which says the null hypothesis has a\n",
+      "        probability of about 99% of being true.\n",
+      "    \n",
+      "    triangular(...) method of mtrand.RandomState instance\n",
+      "        triangular(left, mode, right, size=None)\n",
+      "        \n",
+      "        Draw samples from the triangular distribution over the\n",
+      "        interval ``[left, right]``.\n",
+      "        \n",
+      "        The triangular distribution is a continuous probability\n",
+      "        distribution with lower limit left, peak at mode, and upper\n",
+      "        limit right. Unlike the other distributions, these parameters\n",
+      "        directly define the shape of the pdf.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        left : float or array_like of floats\n",
+      "            Lower limit.\n",
+      "        mode : float or array_like of floats\n",
+      "            The value where the peak of the distribution occurs.\n",
+      "            The value should fulfill the condition ``left <= mode <= right``.\n",
+      "        right : float or array_like of floats\n",
+      "            Upper limit, should be larger than `left`.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``left``, ``mode``, and ``right``\n",
+      "            are all scalars.  Otherwise, ``np.broadcast(left, mode, right).size``\n",
+      "            samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized triangular distribution.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density function for the triangular distribution is\n",
+      "        \n",
+      "        .. math:: P(x;l, m, r) = \\begin{cases}\n",
+      "                  \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n",
+      "                  \\frac{2(r-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n",
+      "                  0& \\text{otherwise}.\n",
+      "                  \\end{cases}\n",
+      "        \n",
+      "        The triangular distribution is often used in ill-defined\n",
+      "        problems where the underlying distribution is not known, but\n",
+      "        some knowledge of the limits and mode exists. Often it is used\n",
+      "        in simulations.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Wikipedia, \"Triangular distribution\"\n",
+      "               http://en.wikipedia.org/wiki/Triangular_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw values from the distribution and plot the histogram:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=200,\n",
+      "        ...              normed=True)\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    uniform(...) method of mtrand.RandomState instance\n",
+      "        uniform(low=0.0, high=1.0, size=None)\n",
+      "        \n",
+      "        Draw samples from a uniform distribution.\n",
+      "        \n",
+      "        Samples are uniformly distributed over the half-open interval\n",
+      "        ``[low, high)`` (includes low, but excludes high).  In other words,\n",
+      "        any value within the given interval is equally likely to be drawn\n",
+      "        by `uniform`.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        low : float or array_like of floats, optional\n",
+      "            Lower boundary of the output interval.  All values generated will be\n",
+      "            greater than or equal to low.  The default value is 0.\n",
+      "        high : float or array_like of floats\n",
+      "            Upper boundary of the output interval.  All values generated will be\n",
+      "            less than high.  The default value is 1.0.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``low`` and ``high`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(low, high).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized uniform distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        randint : Discrete uniform distribution, yielding integers.\n",
+      "        random_integers : Discrete uniform distribution over the closed\n",
+      "                          interval ``[low, high]``.\n",
+      "        random_sample : Floats uniformly distributed over ``[0, 1)``.\n",
+      "        random : Alias for `random_sample`.\n",
+      "        rand : Convenience function that accepts dimensions as input, e.g.,\n",
+      "               ``rand(2,2)`` would generate a 2-by-2 array of floats,\n",
+      "               uniformly distributed over ``[0, 1)``.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density function of the uniform distribution is\n",
+      "        \n",
+      "        .. math:: p(x) = \\frac{1}{b - a}\n",
+      "        \n",
+      "        anywhere within the interval ``[a, b)``, and zero elsewhere.\n",
+      "        \n",
+      "        When ``high`` == ``low``, values of ``low`` will be returned.\n",
+      "        If ``high`` < ``low``, the results are officially undefined\n",
+      "        and may eventually raise an error, i.e. do not rely on this\n",
+      "        function to behave when passed arguments satisfying that\n",
+      "        inequality condition.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> s = np.random.uniform(-1,0,1000)\n",
+      "        \n",
+      "        All values are within the given interval:\n",
+      "        \n",
+      "        >>> np.all(s >= -1)\n",
+      "        True\n",
+      "        >>> np.all(s < 0)\n",
+      "        True\n",
+      "        \n",
+      "        Display the histogram of the samples, along with the\n",
+      "        probability density function:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> count, bins, ignored = plt.hist(s, 15, normed=True)\n",
+      "        >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    vonmises(...) method of mtrand.RandomState instance\n",
+      "        vonmises(mu, kappa, size=None)\n",
+      "        \n",
+      "        Draw samples from a von Mises distribution.\n",
+      "        \n",
+      "        Samples are drawn from a von Mises distribution with specified mode\n",
+      "        (mu) and dispersion (kappa), on the interval [-pi, pi].\n",
+      "        \n",
+      "        The von Mises distribution (also known as the circular normal\n",
+      "        distribution) is a continuous probability distribution on the unit\n",
+      "        circle.  It may be thought of as the circular analogue of the normal\n",
+      "        distribution.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        mu : float or array_like of floats\n",
+      "            Mode (\"center\") of the distribution.\n",
+      "        kappa : float or array_like of floats\n",
+      "            Dispersion of the distribution, has to be >=0.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``mu`` and ``kappa`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(mu, kappa).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized von Mises distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.vonmises : probability density function, distribution, or\n",
+      "            cumulative density function, etc.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density for the von Mises distribution is\n",
+      "        \n",
+      "        .. math:: p(x) = \\frac{e^{\\kappa cos(x-\\mu)}}{2\\pi I_0(\\kappa)},\n",
+      "        \n",
+      "        where :math:`\\mu` is the mode and :math:`\\kappa` the dispersion,\n",
+      "        and :math:`I_0(\\kappa)` is the modified Bessel function of order 0.\n",
+      "        \n",
+      "        The von Mises is named for Richard Edler von Mises, who was born in\n",
+      "        Austria-Hungary, in what is now the Ukraine.  He fled to the United\n",
+      "        States in 1939 and became a professor at Harvard.  He worked in\n",
+      "        probability theory, aerodynamics, fluid mechanics, and philosophy of\n",
+      "        science.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). \"Handbook of\n",
+      "               Mathematical Functions with Formulas, Graphs, and Mathematical\n",
+      "               Tables, 9th printing,\" New York: Dover, 1972.\n",
+      "        .. [2] von Mises, R., \"Mathematical Theory of Probability\n",
+      "               and Statistics\", New York: Academic Press, 1964.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> mu, kappa = 0.0, 4.0 # mean and dispersion\n",
+      "        >>> s = np.random.vonmises(mu, kappa, 1000)\n",
+      "        \n",
+      "        Display the histogram of the samples, along with\n",
+      "        the probability density function:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> from scipy.special import i0\n",
+      "        >>> plt.hist(s, 50, normed=True)\n",
+      "        >>> x = np.linspace(-np.pi, np.pi, num=51)\n",
+      "        >>> y = np.exp(kappa*np.cos(x-mu))/(2*np.pi*i0(kappa))\n",
+      "        >>> plt.plot(x, y, linewidth=2, color='r')\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    wald(...) method of mtrand.RandomState instance\n",
+      "        wald(mean, scale, size=None)\n",
+      "        \n",
+      "        Draw samples from a Wald, or inverse Gaussian, distribution.\n",
+      "        \n",
+      "        As the scale approaches infinity, the distribution becomes more like a\n",
+      "        Gaussian. Some references claim that the Wald is an inverse Gaussian\n",
+      "        with mean equal to 1, but this is by no means universal.\n",
+      "        \n",
+      "        The inverse Gaussian distribution was first studied in relationship to\n",
+      "        Brownian motion. In 1956 M.C.K. Tweedie used the name inverse Gaussian\n",
+      "        because there is an inverse relationship between the time to cover a\n",
+      "        unit distance and distance covered in unit time.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        mean : float or array_like of floats\n",
+      "            Distribution mean, should be > 0.\n",
+      "        scale : float or array_like of floats\n",
+      "            Scale parameter, should be >= 0.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``mean`` and ``scale`` are both scalars.\n",
+      "            Otherwise, ``np.broadcast(mean, scale).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized Wald distribution.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density function for the Wald distribution is\n",
+      "        \n",
+      "        .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n",
+      "                                    \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n",
+      "        \n",
+      "        As noted above the inverse Gaussian distribution first arise\n",
+      "        from attempts to model Brownian motion. It is also a\n",
+      "        competitor to the Weibull for use in reliability modeling and\n",
+      "        modeling stock returns and interest rate processes.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Brighton Webs Ltd., Wald Distribution,\n",
+      "               http://www.brighton-webs.co.uk/distributions/wald.asp\n",
+      "        .. [2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n",
+      "               Distribution: Theory : Methodology, and Applications\", CRC Press,\n",
+      "               1988.\n",
+      "        .. [3] Wikipedia, \"Wald distribution\"\n",
+      "               http://en.wikipedia.org/wiki/Wald_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw values from the distribution and plot the histogram:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    weibull(...) method of mtrand.RandomState instance\n",
+      "        weibull(a, size=None)\n",
+      "        \n",
+      "        Draw samples from a Weibull distribution.\n",
+      "        \n",
+      "        Draw samples from a 1-parameter Weibull distribution with the given\n",
+      "        shape parameter `a`.\n",
+      "        \n",
+      "        .. math:: X = (-ln(U))^{1/a}\n",
+      "        \n",
+      "        Here, U is drawn from the uniform distribution over (0,1].\n",
+      "        \n",
+      "        The more common 2-parameter Weibull, including a scale parameter\n",
+      "        :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        a : float or array_like of floats\n",
+      "            Shape of the distribution. Should be greater than zero.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``a`` is a scalar.  Otherwise,\n",
+      "            ``np.array(a).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized Weibull distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.weibull_max\n",
+      "        scipy.stats.weibull_min\n",
+      "        scipy.stats.genextreme\n",
+      "        gumbel\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The Weibull (or Type III asymptotic extreme value distribution\n",
+      "        for smallest values, SEV Type III, or Rosin-Rammler\n",
+      "        distribution) is one of a class of Generalized Extreme Value\n",
+      "        (GEV) distributions used in modeling extreme value problems.\n",
+      "        This class includes the Gumbel and Frechet distributions.\n",
+      "        \n",
+      "        The probability density for the Weibull distribution is\n",
+      "        \n",
+      "        .. math:: p(x) = \\frac{a}\n",
+      "                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n",
+      "        \n",
+      "        where :math:`a` is the shape and :math:`\\lambda` the scale.\n",
+      "        \n",
+      "        The function has its peak (the mode) at\n",
+      "        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n",
+      "        \n",
+      "        When ``a = 1``, the Weibull distribution reduces to the exponential\n",
+      "        distribution.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Waloddi Weibull, Royal Technical University, Stockholm,\n",
+      "               1939 \"A Statistical Theory Of The Strength Of Materials\",\n",
+      "               Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n",
+      "               Generalstabens Litografiska Anstalts Forlag, Stockholm.\n",
+      "        .. [2] Waloddi Weibull, \"A Statistical Distribution Function of\n",
+      "               Wide Applicability\", Journal Of Applied Mechanics ASME Paper\n",
+      "               1951.\n",
+      "        .. [3] Wikipedia, \"Weibull distribution\",\n",
+      "               http://en.wikipedia.org/wiki/Weibull_distribution\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> a = 5. # shape\n",
+      "        >>> s = np.random.weibull(a, 1000)\n",
+      "        \n",
+      "        Display the histogram of the samples, along with\n",
+      "        the probability density function:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> x = np.arange(1,100.)/50.\n",
+      "        >>> def weib(x,n,a):\n",
+      "        ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n",
+      "        \n",
+      "        >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n",
+      "        >>> x = np.arange(1,100.)/50.\n",
+      "        >>> scale = count.max()/weib(x, 1., 5.).max()\n",
+      "        >>> plt.plot(x, weib(x, 1., 5.)*scale)\n",
+      "        >>> plt.show()\n",
+      "    \n",
+      "    zipf(...) method of mtrand.RandomState instance\n",
+      "        zipf(a, size=None)\n",
+      "        \n",
+      "        Draw samples from a Zipf distribution.\n",
+      "        \n",
+      "        Samples are drawn from a Zipf distribution with specified parameter\n",
+      "        `a` > 1.\n",
+      "        \n",
+      "        The Zipf distribution (also known as the zeta distribution) is a\n",
+      "        continuous probability distribution that satisfies Zipf's law: the\n",
+      "        frequency of an item is inversely proportional to its rank in a\n",
+      "        frequency table.\n",
+      "        \n",
+      "        Parameters\n",
+      "        ----------\n",
+      "        a : float or array_like of floats\n",
+      "            Distribution parameter. Should be greater than 1.\n",
+      "        size : int or tuple of ints, optional\n",
+      "            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n",
+      "            ``m * n * k`` samples are drawn.  If size is ``None`` (default),\n",
+      "            a single value is returned if ``a`` is a scalar. Otherwise,\n",
+      "            ``np.array(a).size`` samples are drawn.\n",
+      "        \n",
+      "        Returns\n",
+      "        -------\n",
+      "        out : ndarray or scalar\n",
+      "            Drawn samples from the parameterized Zipf distribution.\n",
+      "        \n",
+      "        See Also\n",
+      "        --------\n",
+      "        scipy.stats.zipf : probability density function, distribution, or\n",
+      "            cumulative density function, etc.\n",
+      "        \n",
+      "        Notes\n",
+      "        -----\n",
+      "        The probability density for the Zipf distribution is\n",
+      "        \n",
+      "        .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)},\n",
+      "        \n",
+      "        where :math:`\\zeta` is the Riemann Zeta function.\n",
+      "        \n",
+      "        It is named for the American linguist George Kingsley Zipf, who noted\n",
+      "        that the frequency of any word in a sample of a language is inversely\n",
+      "        proportional to its rank in the frequency table.\n",
+      "        \n",
+      "        References\n",
+      "        ----------\n",
+      "        .. [1] Zipf, G. K., \"Selected Studies of the Principle of Relative\n",
+      "               Frequency in Language,\" Cambridge, MA: Harvard Univ. Press,\n",
+      "               1932.\n",
+      "        \n",
+      "        Examples\n",
+      "        --------\n",
+      "        Draw samples from the distribution:\n",
+      "        \n",
+      "        >>> a = 2. # parameter\n",
+      "        >>> s = np.random.zipf(a, 1000)\n",
+      "        \n",
+      "        Display the histogram of the samples, along with\n",
+      "        the probability density function:\n",
+      "        \n",
+      "        >>> import matplotlib.pyplot as plt\n",
+      "        >>> from scipy import special\n",
+      "        \n",
+      "        Truncate s values at 50 so plot is interesting:\n",
+      "        \n",
+      "        >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)\n",
+      "        >>> x = np.arange(1., 50.)\n",
+      "        >>> y = x**(-a) / special.zetac(a)\n",
+      "        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n",
+      "        >>> plt.show()\n",
+      "\n",
+      "DATA\n",
+      "    __all__ = ['beta', 'binomial', 'bytes', 'chisquare', 'choice', 'dirich...\n",
+      "\n",
+      "FILE\n",
+      "    /Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/numpy/random/__init__.py\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "help(np.random)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scatter Plots sencillos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 2000\n",
+    "random_x = np.random.randn(N)\n",
+    "random_y = np.random.randn(N)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trace = go.Scatter(x = random_x, y = random_y, mode = \"markers\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/50.embed\" height=\"525px\" width=\"100%\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "py.iplot([trace], filename = \"basic-scatter\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://plot.ly/~JuanGabriel/52'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot_url = py.plot([trace], filename = \"basic-scatter-inline\")\n",
+    "plot_url"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Gráficos combinados\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 200\n",
+    "rand_x = np.linspace(0,1, N)\n",
+    "rand_y0 = np.random.randn(N) + 3\n",
+    "rand_y1 = np.random.randn(N)\n",
+    "rand_y2 = np.random.randn(N) - 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trace0 = go.Scatter(x = rand_x, y = rand_y0, mode=\"markers\", name=\"Puntos\")\n",
+    "trace1 = go.Scatter(x = rand_x, y = rand_y1, mode=\"lines\", name=\"Líneas\")\n",
+    "trace2 = go.Scatter(x = rand_x, y = rand_y2, mode=\"lines+markers\", name=\"Puntos y líneas\")\n",
+    "data = [trace0, trace1, trace2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/54.embed\" height=\"525px\" width=\"100%\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "py.iplot(data, filename = \"scatter-line-plot\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Estilizado de gráficos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trace = go.Scatter(x = random_x, y = random_y, name = \"Puntos de estilo guay\", mode=\"markers\",\n",
+    "                  marker = dict(size = 12, color = \"rgba(140,20,20,0.8)\", line = dict(width=2, color=\"rgb(10,10,10)\")))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layout = dict(title = \"Scatter Plot Estilizado\", xaxis = dict(zeroline = False), yaxis = dict(zeroline=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/56.embed\" height=\"525px\" width=\"100%\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = dict(data = [trace], layout = layout)\n",
+    "py.iplot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/62.embed\" height=\"525px\" width=\"100%\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trace = go.Scatter(x = random_x, y = random_y, name = \"Puntos de estilo guay\", mode=\"markers\",\n",
+    "                  marker = dict(size = 8, color = \"rgba(10,80,220,0.25)\", line = dict(width=1, color=\"rgb(10,10,80)\")))\n",
+    "\n",
+    "\n",
+    "fig = dict(data = [trace], layout = layout)\n",
+    "py.iplot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/64.embed\" height=\"525px\" width=\"100%\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trace = go.Histogram(x = random_x, name = \"Puntos de estilo guay\")\n",
+    "\n",
+    "\n",
+    "fig = dict(data = [trace], layout = layout)\n",
+    "py.iplot(fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/52.embed\" height=\"525px\" width=\"100%\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trace = go.Box(x = random_x, name = \"Puntos de estilo guay\", fillcolor = \"rgba(180,25,95,0.6)\")\n",
+    "\n",
+    "\n",
+    "fig = dict(data = [trace], layout = layout)\n",
+    "py.iplot(fig,  filename = \"basic-scatter-inline\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on class Box in module plotly.graph_objs.graph_objs:\n",
+      "\n",
+      "class Box(PlotlyDict)\n",
+      " |  Valid attributes for 'box' at path [] under parents ():\n",
+      " |  \n",
+      " |      ['boxmean', 'boxpoints', 'customdata', 'customdatasrc', 'fillcolor',\n",
+      " |      'hoverinfo', 'hoverinfosrc', 'hoverlabel', 'hoveron', 'ids', 'idssrc',\n",
+      " |      'jitter', 'legendgroup', 'line', 'marker', 'name', 'notched',\n",
+      " |      'notchwidth', 'opacity', 'orientation', 'pointpos', 'selected',\n",
+      " |      'selectedpoints', 'showlegend', 'stream', 'text', 'textsrc', 'type',\n",
+      " |      'uid', 'unselected', 'visible', 'whiskerwidth', 'x', 'x0', 'xaxis',\n",
+      " |      'xcalendar', 'xsrc', 'y', 'y0', 'yaxis', 'ycalendar', 'ysrc']\n",
+      " |  \n",
+      " |  Run `<box-object>.help('attribute')` on any of the above.\n",
+      " |  '<box-object>' is the object at []\n",
+      " |  \n",
+      " |  Method resolution order:\n",
+      " |      Box\n",
+      " |      PlotlyDict\n",
+      " |      builtins.dict\n",
+      " |      PlotlyBase\n",
+      " |      builtins.object\n",
+      " |  \n",
+      " |  Methods inherited from PlotlyDict:\n",
+      " |  \n",
+      " |  __copy__(self)\n",
+      " |  \n",
+      " |  __deepcopy__(self, memodict={})\n",
+      " |  \n",
+      " |  __dir__(self)\n",
+      " |      Dynamically return the existing and possible attributes.\n",
+      " |  \n",
+      " |  __getattr__(self, key)\n",
+      " |      Python only calls this when key is missing!\n",
+      " |  \n",
+      " |  __getitem__(self, key)\n",
+      " |      Calls __missing__ when key is not found. May mutate object.\n",
+      " |  \n",
+      " |  __init__(self, *args, **kwargs)\n",
+      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
+      " |  \n",
+      " |  __missing__(self, key)\n",
+      " |      Mimics defaultdict. This is called from __getitem__ when key DNE.\n",
+      " |  \n",
+      " |  __setattr__(self, key, value)\n",
+      " |      Maps __setattr__ onto __setitem__\n",
+      " |  \n",
+      " |  __setitem__(self, key, value, _raise=True)\n",
+      " |      Validates/Converts values which should be Graph Objects.\n",
+      " |  \n",
+      " |  force_clean(self, **kwargs)\n",
+      " |      Recursively remove empty/None values.\n",
+      " |  \n",
+      " |  get_data(self, flatten=False)\n",
+      " |      Returns the JSON for the plot with non-data elements stripped.\n",
+      " |  \n",
+      " |  get_ordered(self, **kwargs)\n",
+      " |      Return a predictable, OrderedDict version of self.\n",
+      " |  \n",
+      " |  help(self, attribute=None, return_help=False)\n",
+      " |      Print help string for this object or an attribute of this object.\n",
+      " |      \n",
+      " |      :param (str) attribute: A valid attribute string for this object.\n",
+      " |      :param (bool) return_help: Return help_string instead of printing it?\n",
+      " |      :return: (None|str)\n",
+      " |  \n",
+      " |  strip_style(self)\n",
+      " |      Recursively strip style from the current representation.\n",
+      " |      \n",
+      " |      All PlotlyDicts and PlotlyLists are guaranteed to survive the\n",
+      " |      stripping process, though they made be left empty. This is allowable.\n",
+      " |      \n",
+      " |      Keys that will be stripped in this process are tagged with\n",
+      " |      `'type': 'style'` in graph_objs_meta.json. Note that a key tagged as\n",
+      " |      style, but with an array as a value may still be considered data.\n",
+      " |  \n",
+      " |  to_string(self, level=0, indent=4, eol='\\n', pretty=True, max_chars=80)\n",
+      " |      Returns a formatted string showing graph_obj constructors.\n",
+      " |      \n",
+      " |      :param (int) level: The number of indentations to start with.\n",
+      " |      :param (int) indent: The indentation amount.\n",
+      " |      :param (str) eol: The end of line character(s).\n",
+      " |      :param (bool) pretty: Curtail long list output with a '..' ?\n",
+      " |      :param (int) max_chars: The max characters per line.\n",
+      " |      \n",
+      " |      Example:\n",
+      " |      \n",
+      " |          print(obj.to_string())\n",
+      " |  \n",
+      " |  update(self, dict1=None, **dict2)\n",
+      " |      Update current dict with dict1 and then dict2.\n",
+      " |      \n",
+      " |      This recursively updates the structure of the original dictionary-like\n",
+      " |      object with the new entries in the second and third objects. This\n",
+      " |      allows users to update with large, nested structures.\n",
+      " |      \n",
+      " |      Note, because the dict2 packs up all the keyword arguments, you can\n",
+      " |      specify the changes as a list of keyword agruments.\n",
+      " |      \n",
+      " |      Examples:\n",
+      " |      # update with dict\n",
+      " |      obj = Layout(title='my title', xaxis=XAxis(range=[0,1], domain=[0,1]))\n",
+      " |      update_dict = dict(title='new title', xaxis=dict(domain=[0,.8]))\n",
+      " |      obj.update(update_dict)\n",
+      " |      obj\n",
+      " |      {'title': 'new title', 'xaxis': {'range': [0,1], 'domain': [0,.8]}}\n",
+      " |      \n",
+      " |      # update with list of keyword arguments\n",
+      " |      obj = Layout(title='my title', xaxis=XAxis(range=[0,1], domain=[0,1]))\n",
+      " |      obj.update(title='new title', xaxis=dict(domain=[0,.8]))\n",
+      " |      obj\n",
+      " |      {'title': 'new title', 'xaxis': {'range': [0,1], 'domain': [0,.8]}}\n",
+      " |      \n",
+      " |      This 'fully' supports duck-typing in that the call signature is\n",
+      " |      identical, however this differs slightly from the normal update\n",
+      " |      method provided by Python's dictionaries.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors inherited from PlotlyDict:\n",
+      " |  \n",
+      " |  __dict__\n",
+      " |      dictionary for instance variables (if defined)\n",
+      " |  \n",
+      " |  __weakref__\n",
+      " |      list of weak references to the object (if defined)\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Methods inherited from builtins.dict:\n",
+      " |  \n",
+      " |  __contains__(self, key, /)\n",
+      " |      True if D has a key k, else False.\n",
+      " |  \n",
+      " |  __delitem__(self, key, /)\n",
+      " |      Delete self[key].\n",
+      " |  \n",
+      " |  __eq__(self, value, /)\n",
+      " |      Return self==value.\n",
+      " |  \n",
+      " |  __ge__(self, value, /)\n",
+      " |      Return self>=value.\n",
+      " |  \n",
+      " |  __getattribute__(self, name, /)\n",
+      " |      Return getattr(self, name).\n",
+      " |  \n",
+      " |  __gt__(self, value, /)\n",
+      " |      Return self>value.\n",
+      " |  \n",
+      " |  __iter__(self, /)\n",
+      " |      Implement iter(self).\n",
+      " |  \n",
+      " |  __le__(self, value, /)\n",
+      " |      Return self<=value.\n",
+      " |  \n",
+      " |  __len__(self, /)\n",
+      " |      Return len(self).\n",
+      " |  \n",
+      " |  __lt__(self, value, /)\n",
+      " |      Return self<value.\n",
+      " |  \n",
+      " |  __ne__(self, value, /)\n",
+      " |      Return self!=value.\n",
+      " |  \n",
+      " |  __new__(*args, **kwargs) from builtins.type\n",
+      " |      Create and return a new object.  See help(type) for accurate signature.\n",
+      " |  \n",
+      " |  __repr__(self, /)\n",
+      " |      Return repr(self).\n",
+      " |  \n",
+      " |  __sizeof__(...)\n",
+      " |      D.__sizeof__() -> size of D in memory, in bytes\n",
+      " |  \n",
+      " |  clear(...)\n",
+      " |      D.clear() -> None.  Remove all items from D.\n",
+      " |  \n",
+      " |  copy(...)\n",
+      " |      D.copy() -> a shallow copy of D\n",
+      " |  \n",
+      " |  fromkeys(iterable, value=None, /) from builtins.type\n",
+      " |      Returns a new dict with keys from iterable and values equal to value.\n",
+      " |  \n",
+      " |  get(...)\n",
+      " |      D.get(k[,d]) -> D[k] if k in D, else d.  d defaults to None.\n",
+      " |  \n",
+      " |  items(...)\n",
+      " |      D.items() -> a set-like object providing a view on D's items\n",
+      " |  \n",
+      " |  keys(...)\n",
+      " |      D.keys() -> a set-like object providing a view on D's keys\n",
+      " |  \n",
+      " |  pop(...)\n",
+      " |      D.pop(k[,d]) -> v, remove specified key and return the corresponding value.\n",
+      " |      If key is not found, d is returned if given, otherwise KeyError is raised\n",
+      " |  \n",
+      " |  popitem(...)\n",
+      " |      D.popitem() -> (k, v), remove and return some (key, value) pair as a\n",
+      " |      2-tuple; but raise KeyError if D is empty.\n",
+      " |  \n",
+      " |  setdefault(...)\n",
+      " |      D.setdefault(k[,d]) -> D.get(k,d), also set D[k]=d if k not in D\n",
+      " |  \n",
+      " |  values(...)\n",
+      " |      D.values() -> an object providing a view on D's values\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data and other attributes inherited from builtins.dict:\n",
+      " |  \n",
+      " |  __hash__ = None\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Methods inherited from PlotlyBase:\n",
+      " |  \n",
+      " |  to_graph_objs(self, **kwargs)\n",
+      " |      Everything is cast into graph_objs. Here for backwards compat.\n",
+      " |  \n",
+      " |  validate(self)\n",
+      " |      Everything is *always* validated now. Keep for backwards compat.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(go.Box)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Información al hacer Hover"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "data = pd.read_csv(\"/content/python-ml-course/datasets/usa-population/usa_states_population.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "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>Rank</th>\n",
+       "      <th>State</th>\n",
+       "      <th>Postal</th>\n",
+       "      <th>Population</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>AL</td>\n",
+       "      <td>4849377.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Alaska</td>\n",
+       "      <td>AK</td>\n",
+       "      <td>736732.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Arizona</td>\n",
+       "      <td>AZ</td>\n",
+       "      <td>6731484.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Arkansas</td>\n",
+       "      <td>AR</td>\n",
+       "      <td>2966369.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>California</td>\n",
+       "      <td>CA</td>\n",
+       "      <td>38802500.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>Colorado</td>\n",
+       "      <td>CO</td>\n",
+       "      <td>5355866.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>Connecticut</td>\n",
+       "      <td>CT</td>\n",
+       "      <td>3596677.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>Delaware</td>\n",
+       "      <td>DE</td>\n",
+       "      <td>935614.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>District of Columbia</td>\n",
+       "      <td>DC</td>\n",
+       "      <td>658893.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>Florida</td>\n",
+       "      <td>FL</td>\n",
+       "      <td>19893297.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>Georgia</td>\n",
+       "      <td>GA</td>\n",
+       "      <td>10097343.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>Hawaii</td>\n",
+       "      <td>HI</td>\n",
+       "      <td>1419561.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>Idaho</td>\n",
+       "      <td>ID</td>\n",
+       "      <td>1634464.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>Illinois</td>\n",
+       "      <td>IL</td>\n",
+       "      <td>12880580.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>Indiana</td>\n",
+       "      <td>IN</td>\n",
+       "      <td>6596855.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>Iowa</td>\n",
+       "      <td>IA</td>\n",
+       "      <td>3107126.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>Kansas</td>\n",
+       "      <td>KS</td>\n",
+       "      <td>2904021.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>Kentucky</td>\n",
+       "      <td>KY</td>\n",
+       "      <td>4413457.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>Louisiana</td>\n",
+       "      <td>LA</td>\n",
+       "      <td>4649676.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>Maine</td>\n",
+       "      <td>ME</td>\n",
+       "      <td>1330089.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>21</td>\n",
+       "      <td>Maryland</td>\n",
+       "      <td>MD</td>\n",
+       "      <td>5976407.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>22</td>\n",
+       "      <td>Massachusetts</td>\n",
+       "      <td>MA</td>\n",
+       "      <td>6745408.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
+       "      <td>Michigan</td>\n",
+       "      <td>MI</td>\n",
+       "      <td>9909877.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>24</td>\n",
+       "      <td>Minnesota</td>\n",
+       "      <td>MN</td>\n",
+       "      <td>5457173.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>25</td>\n",
+       "      <td>Mississippi</td>\n",
+       "      <td>MS</td>\n",
+       "      <td>2994079.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>26</td>\n",
+       "      <td>Missouri</td>\n",
+       "      <td>MO</td>\n",
+       "      <td>6063589.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>27</td>\n",
+       "      <td>Montana</td>\n",
+       "      <td>MT</td>\n",
+       "      <td>1023579.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>28</td>\n",
+       "      <td>Nebraska</td>\n",
+       "      <td>NE</td>\n",
+       "      <td>1881503.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
+       "      <td>Nevada</td>\n",
+       "      <td>NV</td>\n",
+       "      <td>2839098.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>30</td>\n",
+       "      <td>New Hampshire</td>\n",
+       "      <td>NH</td>\n",
+       "      <td>1326813.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>31</td>\n",
+       "      <td>New Jersey</td>\n",
+       "      <td>NJ</td>\n",
+       "      <td>8938175.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>32</td>\n",
+       "      <td>New Mexico</td>\n",
+       "      <td>NM</td>\n",
+       "      <td>2085572.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>33</td>\n",
+       "      <td>New York</td>\n",
+       "      <td>NY</td>\n",
+       "      <td>19746227.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>34</td>\n",
+       "      <td>North Carolina</td>\n",
+       "      <td>NC</td>\n",
+       "      <td>9943964.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>35</td>\n",
+       "      <td>North Dakota</td>\n",
+       "      <td>ND</td>\n",
+       "      <td>739482.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>36</td>\n",
+       "      <td>Ohio</td>\n",
+       "      <td>OH</td>\n",
+       "      <td>11594163.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>37</td>\n",
+       "      <td>Oklahoma</td>\n",
+       "      <td>OK</td>\n",
+       "      <td>3878051.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>38</td>\n",
+       "      <td>Oregon</td>\n",
+       "      <td>OR</td>\n",
+       "      <td>3970239.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>38</th>\n",
+       "      <td>39</td>\n",
+       "      <td>Pennsylvania</td>\n",
+       "      <td>PA</td>\n",
+       "      <td>12787209.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>40</td>\n",
+       "      <td>Puerto Rico</td>\n",
+       "      <td>PR</td>\n",
+       "      <td>3548397.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>41</td>\n",
+       "      <td>Rhode Island</td>\n",
+       "      <td>RI</td>\n",
+       "      <td>1055173.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>41</th>\n",
+       "      <td>42</td>\n",
+       "      <td>South Carolina</td>\n",
+       "      <td>SC</td>\n",
+       "      <td>4832482.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>43</td>\n",
+       "      <td>South Dakota</td>\n",
+       "      <td>SD</td>\n",
+       "      <td>853175.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>44</td>\n",
+       "      <td>Tennessee</td>\n",
+       "      <td>TN</td>\n",
+       "      <td>6549352.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>45</td>\n",
+       "      <td>Texas</td>\n",
+       "      <td>TX</td>\n",
+       "      <td>26956958.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>46</td>\n",
+       "      <td>Utah</td>\n",
+       "      <td>UT</td>\n",
+       "      <td>2942902.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>47</td>\n",
+       "      <td>Vermont</td>\n",
+       "      <td>VT</td>\n",
+       "      <td>626562.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>48</td>\n",
+       "      <td>Virginia</td>\n",
+       "      <td>VA</td>\n",
+       "      <td>8326289.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>49</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>WA</td>\n",
+       "      <td>7061530.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>50</td>\n",
+       "      <td>West Virginia</td>\n",
+       "      <td>WV</td>\n",
+       "      <td>1850326.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>51</td>\n",
+       "      <td>Wisconsin</td>\n",
+       "      <td>WI</td>\n",
+       "      <td>5757564.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>52</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>WY</td>\n",
+       "      <td>584153.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Rank                 State Postal  Population\n",
+       "0      1               Alabama     AL   4849377.0\n",
+       "1      2                Alaska     AK    736732.0\n",
+       "2      3               Arizona     AZ   6731484.0\n",
+       "3      4              Arkansas     AR   2966369.0\n",
+       "4      5            California     CA  38802500.0\n",
+       "5      6              Colorado     CO   5355866.0\n",
+       "6      7           Connecticut     CT   3596677.0\n",
+       "7      8              Delaware     DE    935614.0\n",
+       "8      9  District of Columbia     DC    658893.0\n",
+       "9     10               Florida     FL  19893297.0\n",
+       "10    11               Georgia     GA  10097343.0\n",
+       "11    12                Hawaii     HI   1419561.0\n",
+       "12    13                 Idaho     ID   1634464.0\n",
+       "13    14              Illinois     IL  12880580.0\n",
+       "14    15               Indiana     IN   6596855.0\n",
+       "15    16                  Iowa     IA   3107126.0\n",
+       "16    17                Kansas     KS   2904021.0\n",
+       "17    18              Kentucky     KY   4413457.0\n",
+       "18    19             Louisiana     LA   4649676.0\n",
+       "19    20                 Maine     ME   1330089.0\n",
+       "20    21              Maryland     MD   5976407.0\n",
+       "21    22         Massachusetts     MA   6745408.0\n",
+       "22    23              Michigan     MI   9909877.0\n",
+       "23    24             Minnesota     MN   5457173.0\n",
+       "24    25           Mississippi     MS   2994079.0\n",
+       "25    26              Missouri     MO   6063589.0\n",
+       "26    27               Montana     MT   1023579.0\n",
+       "27    28              Nebraska     NE   1881503.0\n",
+       "28    29                Nevada     NV   2839098.0\n",
+       "29    30         New Hampshire     NH   1326813.0\n",
+       "30    31            New Jersey     NJ   8938175.0\n",
+       "31    32            New Mexico     NM   2085572.0\n",
+       "32    33              New York     NY  19746227.0\n",
+       "33    34        North Carolina     NC   9943964.0\n",
+       "34    35          North Dakota     ND    739482.0\n",
+       "35    36                  Ohio     OH  11594163.0\n",
+       "36    37              Oklahoma     OK   3878051.0\n",
+       "37    38                Oregon     OR   3970239.0\n",
+       "38    39          Pennsylvania     PA  12787209.0\n",
+       "39    40           Puerto Rico     PR   3548397.0\n",
+       "40    41          Rhode Island     RI   1055173.0\n",
+       "41    42        South Carolina     SC   4832482.0\n",
+       "42    43          South Dakota     SD    853175.0\n",
+       "43    44             Tennessee     TN   6549352.0\n",
+       "44    45                 Texas     TX  26956958.0\n",
+       "45    46                  Utah     UT   2942902.0\n",
+       "46    47               Vermont     VT    626562.0\n",
+       "47    48              Virginia     VA   8326289.0\n",
+       "48    49            Washington     WA   7061530.0\n",
+       "49    50         West Virginia     WV   1850326.0\n",
+       "50    51             Wisconsin     WI   5757564.0\n",
+       "51    52               Wyoming     WY    584153.0"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 53\n",
+    "c = ['hsl('+str(h)+', 50%, 50%)' for h in np.linspace(0,360,N)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'data' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-77c31e686f7e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2000\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Rank\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Population\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m1000000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"markers\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "l = []\n",
+    "y = []\n",
+    "for i in range(int(N)):\n",
+    "    y.append((2000+i))\n",
+    "    trace0 = go.Scatter(\n",
+    "        x = data[\"Rank\"], \n",
+    "        y = data[\"Population\"]+ i*1000000,\n",
+    "        mode = \"markers\",\n",
+    "        marker = dict(size = 14, line = dict(width=1), color = c[i], opacity = 0.3),\n",
+    "        name = data[\"State\"]\n",
+    "    )\n",
+    "    l.append(trace0)\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "layout = go.Layout(title = \"Población de los estados de USA\",\n",
+    "                  hovermode = \"closest\", \n",
+    "                  xaxis = dict(title=\"ID\", ticklen=5, zeroline=False, gridwidth=2),\n",
+    "                  yaxis = dict(title=\"Población\", ticklen=5, gridwidth=2),\n",
+    "                  showlegend = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/52.embed\" height=\"525px\" width=\"100%\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fig = go.Figure(data = l, layout = layout)\n",
+    "py.iplot(fig, filename = \"basic-scatter-inline\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trace = go.Scatter(y = np.random.randn(1000),\n",
+    "                  mode = \"markers\", marker = dict(size = 16, color = np.random.randn(1000), \n",
+    "                                                  colorscale = \"Viridis\", showscale=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/52.embed\" height=\"525px\" width=\"100%\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "py.iplot([trace],  filename = \"basic-scatter-inline\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Datasets muy grandes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 100000\n",
+    "trace = go.Scattergl(x = np.random.randn(N), y = np.random.randn(N), mode = \"markers\",\n",
+    "                    marker = dict(color=\"#BAD5FF\", line = dict(width=1)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~JuanGabriel/52.embed\" height=\"525px\" width=\"100%\"></iframe>"
+      ],
+      "text/plain": [
+       "<plotly.tools.PlotlyDisplay object>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "py.iplot([trace],  filename = \"basic-scatter-inline\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T10 - 3 - Plotly para dibujar.ipynb b/notebooks/T10 - 3 - Plotly para dibujar.ipynb
index 8211ac7f..0898e31a 100644
--- a/notebooks/T10 - 3 - Plotly para dibujar.ipynb	
+++ b/notebooks/T10 - 3 - Plotly para dibujar.ipynb	
@@ -9,9 +9,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "No module named 'plotly'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-1-a3ba4c2438ce>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotly\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_objs\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtools\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtls\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_credentials_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0musername\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'JuanGabriel'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapi_key\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'6mEfSXf8XNyIzpxwb8z7'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mImportError\u001b[0m: No module named 'plotly'"
+     ]
+    }
+   ],
    "source": [
     "import plotly.plotly as py\n",
     "import plotly.graph_objs as go \n",
@@ -22,18 +34,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "'2.5.1'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "ImportError",
+     "evalue": "No module named 'plotly'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-3d478176c797>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__version__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mImportError\u001b[0m: No module named 'plotly'"
+     ]
     }
    ],
    "source": [
@@ -41,6 +54,13 @@
     "plotly.__version__"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 5,
@@ -4339,7 +4359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4349,9 +4369,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'data' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-77c31e686f7e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2000\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Rank\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Population\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m1000000\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0mmode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"markers\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
+     ]
+    }
+   ],
    "source": [
     "l = []\n",
     "y = []\n",
@@ -4369,6 +4401,13 @@
     "    "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 38,
@@ -4505,7 +4544,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T11 - 1 - TensorFlow101-Colab.ipynb b/notebooks/T11 - 1 - TensorFlow101-Colab.ipynb
new file mode 100644
index 00000000..42761d3e
--- /dev/null
+++ b/notebooks/T11 - 1 - TensorFlow101-Colab.ipynb	
@@ -0,0 +1,187 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T11%20-%201%20-%20TensorFlow101-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%tensorflow_version 1.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introducción a Tensor Flow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "print(tensorflow.__version__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1 = tf.constant([1,2,3,4,5])\n",
+    "x2 = tf.constant([6,7,8,9,10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tensor(\"Mul:0\", shape=(5,), dtype=int32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "res = tf.multiply(x1,x2)\n",
+    "print(res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 6 14 24 36 50]\n"
+     ]
+    }
+   ],
+   "source": [
+    "sess = tf.Session()\n",
+    "print(sess.run(res))\n",
+    "sess.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 6 14 24 36 50]\n"
+     ]
+    }
+   ],
+   "source": [
+    "with tf.Session() as sess:\n",
+    "    output = sess.run(res)\n",
+    "    print(output)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config = tf.ConfigProto(log_device_placement = True)\n",
+    "config = tf.ConfigProto(allow_soft_placement = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T11 - 1 - TensorFlow101.ipynb b/notebooks/T11 - 1 - TensorFlow101.ipynb
index c3597caa..8c715ca8 100644
--- a/notebooks/T11 - 1 - TensorFlow101.ipynb	
+++ b/notebooks/T11 - 1 - TensorFlow101.ipynb	
@@ -116,7 +116,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T11 - 2 - Se\303\261ales de tr\303\241fico-Colab.ipynb" "b/notebooks/T11 - 2 - Se\303\261ales de tr\303\241fico-Colab.ipynb"
new file mode 100644
index 00000000..b3cd4357
--- /dev/null
+++ "b/notebooks/T11 - 2 - Se\303\261ales de tr\303\241fico-Colab.ipynb"	
@@ -0,0 +1,1482 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T11%20-%202%20-%20Señales%20de%20tráfico-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%tensorflow_version 1.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reconocimiento de las señales de tráfico"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "print(tf.__version__)\n",
+    "import os\n",
+    "import skimage.io as imd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_ml_data(data_directory):\n",
+    "    dirs = [d for d in os.listdir(data_directory)\n",
+    "            if os.path.isdir(os.path.join(data_directory,d))]\n",
+    "    \n",
+    "    labels = []\n",
+    "    images = []\n",
+    "    for d in dirs:\n",
+    "        label_dir = os.path.join(data_directory, d)\n",
+    "        file_names = [os.path.join(label_dir, f)\n",
+    "                     for f in os.listdir(label_dir)\n",
+    "                     if f.endswith(\".ppm\")]\n",
+    "        \n",
+    "        for f in file_names:\n",
+    "            images.append(imd.imread(f))\n",
+    "            labels.append(int(d))\n",
+    "        \n",
+    "    return images, labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "main_dir = \"/content/python-ml-course/datasets/belgian/\"\n",
+    "train_data_dir = os.path.join(main_dir, \"Training\")\n",
+    "test_data_dir = os.path.join(main_dir, \"Testing\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "images, labels = load_ml_data(train_data_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "images = np.array(images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = np.array(labels)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "images.ndim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4575"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "images.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[ 47,  52,  59],\n",
+       "        [ 35,  64,  72],\n",
+       "        [ 53, 104, 112],\n",
+       "        ...,\n",
+       "        [ 75,  82,  60],\n",
+       "        [ 72,  77,  55],\n",
+       "        [ 68,  71,  50]],\n",
+       "\n",
+       "       [[ 46,  51,  57],\n",
+       "        [ 38,  66,  74],\n",
+       "        [ 58, 109, 115],\n",
+       "        ...,\n",
+       "        [ 74,  84,  62],\n",
+       "        [ 76,  84,  62],\n",
+       "        [ 76,  83,  61]],\n",
+       "\n",
+       "       [[ 44,  50,  56],\n",
+       "        [ 44,  71,  79],\n",
+       "        [ 59, 109, 115],\n",
+       "        ...,\n",
+       "        [ 71,  83,  61],\n",
+       "        [ 74,  84,  63],\n",
+       "        [ 75,  84,  63]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[120, 141, 139],\n",
+       "        [119, 144, 138],\n",
+       "        [114, 141, 131],\n",
+       "        ...,\n",
+       "        [ 33,  30,  27],\n",
+       "        [ 32,  30,  28],\n",
+       "        [ 30,  30,  28]],\n",
+       "\n",
+       "       [[133, 151, 145],\n",
+       "        [124, 146, 138],\n",
+       "        [111, 137, 127],\n",
+       "        ...,\n",
+       "        [ 31,  30,  25],\n",
+       "        [ 32,  32,  27],\n",
+       "        [ 33,  33,  30]],\n",
+       "\n",
+       "       [[139, 158, 147],\n",
+       "        [124, 146, 137],\n",
+       "        [107, 132, 123],\n",
+       "        ...,\n",
+       "        [ 31,  30,  23],\n",
+       "        [ 33,  33,  27],\n",
+       "        [ 35,  36,  31]]], dtype=uint8)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "images[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels.ndim"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4575"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "62"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(set(labels))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "  C_CONTIGUOUS : True\n",
+       "  F_CONTIGUOUS : True\n",
+       "  OWNDATA : True\n",
+       "  WRITEABLE : True\n",
+       "  ALIGNED : True\n",
+       "  WRITEBACKIFCOPY : False\n",
+       "  UPDATEIFCOPY : False"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "images.flags"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "images.itemsize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36600"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "images.nbytes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4575.0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "images.nbytes/images.itemsize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(labels, len(set(labels)))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[2658, 905, 2776, 730, 3094, 3993]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rand_signs = random.sample(range(0, len(labels)), 6)\n",
+    "rand_signs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/h02JXAyNkRACnS7TT1KkVqSG3Z0Rw2HAg0EEiY7EAnTEkFEy0RLBlfRRfM7K/YOG8egUDz90nqgtSouZUYrjLiQNUPpBUNd9gseGMyvsOHhxcl8e9DMlgdy1FFHGAtn6+Wlz1IELTSqg/dUhlQpdrnGiQ6z6+wh1rMi5YFZom2OqsbCzOyFsCS0dFCNrRSWBGJzsBfNCWd5GjHIyj5XND0GsRmsRCn4XtQG7Im3ArkgbsCvSBuyKtAG7Im3ArkgbsCvSBuyKtAG7Im3ArkgbsCvSBuyKtAG7Im3ArkgbsCvSBuyKtAG7Im3ArkgbsCvSBuyKtAG7Im3Arkj/C/ZJae4LILLlAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(105, 108, 3), min:8, max:194\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(254, 216, 3), min:2, max:255\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(128, 124, 3), min:2, max:255\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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tG9bFGCs07YimjcQAWjKNCBHocJqcyQIeEl4GRl1LiIE+JbIX3DLZM2UIvPsvP0a/c0BLQbnOpkbuhaCKaiTlTNN2FFOyC4Vqrm+66Rxdt8blK7vEdkTJmSEnNra3EQMQPIRqdeaHFgxFQ1O3NmJDkFi3hTXWricnpkPPxUnicGp0sdASeP/vfohz529nq4PR9XYKLnRNPQJTnBDqgZQwbxcVRzRysLfL5bTD1vYakz6xtr7J7mSX4kbbNGiIqCij6Eg2ihvZMyErQUMVMubdUtcxksD29gZeMuErD/D+n38pd5y/jRhaPBV4w1lKzsRv4Q8EZPU/OhaHpSjz7xSsyFwgVmQuECsyF4gVmQvEiswFYkXmArEic4FYkblArMhcIFZkLhArMheIFZkLxIrMBWJF5gKxInOBWJG5QKzIXCBWZC4QKzIXiBWZC8SKzAViReYC8b9vPiHrJJYMOwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(141, 135, 3), min:7, max:255\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAEwAAABJCAYAAACXWsCYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEKdJREFUeJztm9ePXNd9xz+/c26Zsn25y7IkRYqkRFGiaDWrWMW2bFmK4yYHsOMU5yEIkgBByl+R9wDJU4C8JUGKgQB+cOTARZGYyKIdirIkilRjX3LrtDv3nvLLw10JyEPgnQdvFGC+M8C8zdzfB+fXz4iqMtb2Zf6vH+D/m8bARtQY2IgaAxtRY2AjagxsRI2BjagxsBE1Bjaikp38sScful0np9qEEPBBCVGIGmm2m2jpMcZSBc/i3n20JqcZDDwbqyXDYUmIHu88IXoEAwgRwRqLxWCtweQpWSNhcqrJ/HyTGAbE/iZVdwOvkTIq6iNFvySowWQNpnctcOL4HeyZafP7f/bn8ots2FFgWZ6hGun1OzSbE6ARESH6SKdbsLiwl5nJaaKH65dX6PULioHDYFBVYojEELe+TQgawRjEGkxisc5CHzY3hE6nza75aSYaizSTaTqbN1DfQVXIGjlgwFh8WXHjxg16a7+QFbDDwJLEgCh7di9ibMLa6iaV80je4uDho6Ap6+sFRX9I5YY474ghUPqA95HgIzGCsQbvHZIYVCNGBFCSJCHNckySEjx0Noa0J9rs3r3A9Px+BptXWd/YpNlqIQpFUTHodrnmKiba6fZs+OUi+p/KsgTnhySpAYV+r09zYo7J6QWCEzbX1+n3C2JQqrKi8p4yKsamJGmLqakWk602WZ4jIiBCWQwYFn2Koke/7OMGAywGn5U0Wi16ITLoD5ifn2TP4gHoDQkhEL0nqmLE4oeODVdty4YdBeb9kDxLKcuSYeGZmZ5jcm4fVUxYW17BDYe4ECgrRwiApOzZd4D5pSVm9u4hm2gxMTVJWTlUwYpBUCQ6XG+TjStXWL9xndWVFdY31vDekbdamCTlphoKn3Jo721cu/Q2EpUqRLyrqHoVLtle/ttRYK0sJ282cAF6WrBr9xKDvmdzo0t/0Me7QOkA22RybpYDx46x/8QJXBkYdAasXt7gcucag2JICHX8y7OUZqvB9Pw0C3c9yNJJGNy8wZuvvMT1K5coOh2SRoOIoDGy2drFgcP31tBcD+dKooG52cVt2bCjwEIIdDYLsA12LRxkbbNgY22Tqqjo90s0bbH79mPc99CjNGdm+OCDy5x56XU63T5V6cBmBCyqIKaOh6oVsjJALq2TnH0fmwpTc02e+Oo32Fy+zPmfvsra1Su49TViI+eyCi4ssO/AXVz/4DwTUxHFMCzCtmzY0TqsjApJg9bkPP1uSXdlk2pY4bwnzWY5dOd9nHrqKWze4NzLP+Hy6xe5tdqn0AQxDQRLKgaLQRSMKgnyUdD3qpTDwMr1Lj/80Tl8tpsnnvkKB48eY6LRpipKBsUmy9evcXOlx+K+I4DB4DA63JYNOwqs8kprYgZIWV/dwFeeUASqUjhw4iQPP/FpBje7vPrvp5nfM8dDj5xiMU/IQgRJ+LCiECMooAgRJaJozQwRi5Bya7nHyy+f4+y717n388+x987jTDabuEGPqhxy69YGhVOStAkKzcbHMku2MSbh1s01yqqqywVnSFq7uPfhh7h17ToXzr7F/jtu49NfeISjS7tpDDd48acX2XAp0ViCKEEVRFAx1IcrUmeB2l2DRoy1uGHgzfNXiZJy3+e+gPiCC2+coygGGJty48YyS3tm8CLEqtiWDTsKrNGcotMZ0O108T5S+YBPJjl68m66N9Z4/SdnWDp2hKeefZyTR5eYSiNf/tpn0CTj9Jn3WC8CXlJCjLVviEGhhkZENQIGRBCNaFBiaXjr5+9hG8rJJ5/m3fNv4ash1TClZ5TNdpNW3kL5OLpkBZ3NPtFFvBecFyb3HeT+x0/xxplz7Dt0gKeee5hT9+xnup3TaLZYOnKQr331Mzx2ah8LEwYTlNRYRBUjdfwSDEYsRgwCCBYNjjyH6WZC9CXn3/iA5SLnwNE7aIjBDYdUpefWWo8gOWF7hf7OAiuGnnLo8D7ig5A0ZjjxiXvx3QI7M8OTX/kidxw7yGwKmQVNDNHA0qFFvvEbv8IXnn6QPVMWG0sSmxCjgkpdxG6hMsYiCnkz4eFP3snRffM0EkNVRM6cvcgdn3iIZrONeo+rKoqiojcoUbs9Z9tRYN3NLlXlKL1HbJPDdz/MbUf28uaPz9Nxwns31rCT86w7T2U8Gqs6iDcz5m9b5Bvf/hx/8qe/yoMn95E7jxVBDUTVus6ShMoFslT4rd/9Jn/we89y78E5GtZiNaF3Y5N86Qh33PcAjaxBWQ4IwwGrN28RaGzLhh2u9CucVyIWSZscPH4bvRsdbna7rEblB9/7MZlGnnvmEbJGThNHdA5USLMGmiqnTt3DVDrHP8sLvPj6JQY0CGKxJiX6QKud8+hj9/DZR08wLRsUxYDKB1QygkbevHCFe0/cx8Vzb9BbL4nB45xlfb23LRt29IRpDKiCSkJjcpZd++e49c5NOtFhxdJd7vKv3z3Nd757movLXboecpsgAUIAVwZSm3HiviM8/ztPszCbMZWnSATnHI0s5bFHH+Cbv/klFtueCZOSNxqISVAiUZT3L92kOb/EzOw8idi6rwwR77Znw84CC0JUj7E5M4u7CUPPyuYmmAzBkKQpvU6P77/wIn//Ly9yftWz5iPGGBJRkjzBpRbJLHcfP8pv//pzHJo1LDSFZlN54LHj/NqXn+T4nhmypsVMNRBrSJMMI0JihN76gDKmTC8skCVCjJ4YIsOy3JYNO+qSGgSVgLU57ZkZkmgZOI9iIRocEWsF16k482+vY6uUA3/4LJOZIpTYKKRJTmoEEuGJZ++n2bT84z/8kInbb+Or3/och2bbzFmliIayqhAXcc5hJCGqUvUq1rtDJud30WikDPqDrXJke9pRYIhgtwZ3zclJqu4AFyMBQflwrlUXpINBwUunz7D/zj0898jdLLSERgIGj4glijA3PcUTT32SyflFktlpbt87y0xq6MYhQSIqikidOUOo67QsbbDZ6TE3M02apgiChkgM2+sldxSYcx5JLJiERruNG1b4CJgEDRUgH5UJYiOuX/F3f/09Xj97gW9+6xnuO7ybNA4JpSdrt4nqySdSnnj0BE4DKkoiFusNeM+67+NcRYwBEYsi+OgZlgXSSBgWJYlJ6jZrm5dydhSYAFEgN7YuBUSwYtEgiKQ1LwxCPRxEwVUVP331PEUVSb79Je48OMViloAYjEkoQwXqScQQERCDDyUaPMEHjLUk1iJeEBVEIklqSG1Cq9GkX3Xr343bA7azWyMxRFVEYdgfkjYzEkBRkHqxAVA/utRvUdTB+dfe5y/+6m/52XsrrHqhinXcMYklEFHVGoiCEShdhYsR7wMat3pNFCXSbjborm+gQCASVetn2IZ2NkuiiCghOnqbPSZm2zRSi4pna9iAiqISCRqJGoBIIiAhcOnCdf7mL/+JC1dWISrBVUBEBUr1uODwwYEoRsFikagYMUQNKIpqYG5yis7KCqUvtwhsTTu2oZ0N+kSIoKGiv9khbTWZnGpiixIVhVgH6bqjZuvUKRYleoczysTEFD5GhlVJK6tjniqEECHGrQWcEoWPgn3U+hSBMjndZqqV0Fm5ReU9IgYRwZrtEdvRE2aMYqIBHdJfX6Ez8EzPtWiKRQ3I1msrmNVZU5XoA0aVu+4+xNef/zSHZ9toDPgI0SkEkFi7eoih3gvESIwRFzxBI2LqxnzPgT1UvWV66yv4UMc1g5Ca7aHYWWAC9djPUw42eP/iBxy66xhTrRSN4aMmuv6kHghGCMZx9K79fP0rT3F8/wS7MsVGjwZPv98jOL81PNSt+diW+2v4yNVFLCEq995zjOX33qYY9AhRUQQjIPjt2fBLIfO/KEkNiMO7AcZ3uPjKz+iL5cFHTpCpoqbeMwqKsbWpzTTn6ece4Y/++Hk+dXI3Sy2HNX2UihgdeZ4iiSUAldZtjqglVo5YDhENBFWCDzTabaaly/s/+y8K58AqxkDSMCSNj2HQT1KDMVIXkX6IH67z1rmz7Fraz+xkTuILBAgqhKC4WPHY5z/B859/nGPz0zTFQN7CmZxgGnibg83JNCXVDBcyKtPEpzmaTjCgSc9bMpsR0sA9997Oz0+/TKffodpKAsYqQiDZpkvu7OY7FdJ0q0UpumR5zrV3LnDl8DF2L0zhioKOj0S1qCqLe3fx+DMPsrhnkuudVRpZg7Zp0hv0SbKU0g2IqvSHA0JQooNyWDIoe5gQWVvtsDL0FMWA/Xft4/DheU6/9A4bgy5eA0bqZ8pSoRx+DHvJLDM4J5SFI8Hi+h3ytMXZ0y/yxS99nc3eD6lWNhk4h8+aNCdmeO0H5zgXChwOCRArCM5RFiWV91TOUbmKGBWNyrAscd5jjcWFwM1bq8wdmOOxU8fpf/AORdHbKvGExEKaRISIMR/DSl+1opEnxEaCqxRXlfj+JiC8/c4FHn3qU7z2H69w7epNut5x89INvn91mWAiWBAMGgWxBucDQWvDQ4yAIiaiClEtEjzqhyzs381DD5+gd+V9Lv7naYZ+iI8eI4YstbSbgrWxLku2oR0FZiQwOdEiliWqQlChGvaIGrhw9lVShE8+8Slef+1NLr17mW7RY9M2wIMtFdlqqRCpx9PUwD5sA1UjxIAVj6fk+F1HOH5kL8W1S5x75WWKzhqReheQJkqWgrWAxm0H8x0FdnDfPHnWwJU9Su8Qm2HUE8ou3Wvv8urGGhcunOf+Rx7lnvuPc/Htd/j52XeoQr1hcls7SLaWtzFGdOtkCEKep+QTKYv75njg1B3ceO8CZ174DsO1W/SKPo6AJZKn0GwmNJsWQ6Ryge3mv51ds6UgEpiZnaA32KgLSy9ELL7sEmNF52rJy9+7ycL+27nzxEme/Ow8168ts3zjJr3ugLKsiKF2ITGCpAabGqZnp1jav5d9excwseTcj16gs3yNbm+DoRsSQ6hPVg5ZLlgb0KhEADXE7U13dhZYd7ND0miSNxtMTLWIWuG9w5qt8bUP+H4glEOu9QesXr3CoTuPsHvvEgcP3oMxCd5F+kVBFTwYQ7vVpJWnhKqgt7HGpXOvsrl8ndVbV6l8QQwBjR4jEWOg3cpIE4jBoVFJ0pQYFVdtb0a9o8AGRaBhlRgcWSPFFkNm0pxQKYPS4IMQvUNDDx9KyuE6F/obXExeoz01SXtqmqnZOcQaokYQWL3SZ7CxwbDbIQwLqmHBsOgj1KBUI2ggy4XEBvKkvtjnEKwV0twQ1GPTj6FL2jRDxNDvdckaLdLMMNFoYFTY6HgGhaMs614wliViU3ouYG1C2d9gY/kqN5IUa0x93bOeP9TbKFfWpzSGj0Y8GgJJAlkzpdWypAkk1pClKbFwiCg2MeSNHGM/hmVFFEWMItETgyexFsUzNT1B5Tze1yEdJ3hv6g2TL1C1OFdfQvmwz1RVjLWgkRBCfb9iKwMiQhRD0siYmLBk1pGntm7ircFmhqamOFchmHqPsM210Y4Cq5yjkWdYYxkOCkIUgoHKVSgVNvG00owWKeUgMBhURGOIISBiUF9nSRFBxBCC25oL1uM/Y2pYxgg2NeSZZbqdEv0QDZGy8uRp3a+mWYI1QgiBygWc2x4wGf/BdDSN/9gwosbARtQY2IgaAxtRY2AjagxsRI2BjagxsBE1BjaixsBG1BjYiBoDG1FjYCNqDGxEjYGNqDGwETUGNqLGwEbUGNiIGgMbUWNgI2oMbESNgY2o/wZLff8SCt5KGAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(84, 86, 3), min:27, max:254\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(107, 105, 3), min:6, max:197\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(len(rand_signs)):\n",
+    "    temp_im = images[rand_signs[i]]\n",
+    "    plt.subplot(1,6,i+1)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.imshow(temp_im)\n",
+    "    plt.subplots_adjust(wspace = 0.5)\n",
+    "    plt.show()\n",
+    "    print(\"Forma:{0}, min:{1}, max:{2}\".format(temp_im.shape,\n",
+    "                                               temp_im.min(),\n",
+    "                                               temp_im.max()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x1152 with 62 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "unique_labels = set(labels)\n",
+    "plt.figure(figsize=(16,16))\n",
+    "i = 1\n",
+    "for label in unique_labels:\n",
+    "    temp_im = images[list(labels).index(label)]\n",
+    "    plt.subplot(8,8, i)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.title(\"Clase {0} ({1})\".format(label, list(labels).count(label)))\n",
+    "    i +=1\n",
+    "    plt.imshow(temp_im)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(labels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modelo de Red Neuronal con TensorFlow\n",
+    "* Las imágenes no todas son del mismo tamaño\n",
+    "* Hay 62 clases de imágenes (desde la 0 hasta la 61)\n",
+    "* La distribución de señales de tráfico no es uniforme (algunas salen más veces que otras)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skimage import transform"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tamaño mínimo: 22x20\n"
+     ]
+    }
+   ],
+   "source": [
+    "w = 9999 \n",
+    "h = 9999\n",
+    "for image in images:\n",
+    "    if image.shape[0] < h:\n",
+    "        h = image.shape[0]\n",
+    "    if image.shape[1] < w:\n",
+    "        w = image.shape[1]\n",
+    "print(\"Tamaño mínimo: {0}x{1}\".format(h,w))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda3/lib/python3.5/site-packages/skimage/transform/_warps.py:105: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
+      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n",
+      "/anaconda3/lib/python3.5/site-packages/skimage/transform/_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
+      "  warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "images30 = [transform.resize(image, (30,30)) for image in images]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[0.22186275, 0.40480392, 0.4295098 ],\n",
+       "        [0.42978431, 0.54390196, 0.53941176],\n",
+       "        [0.44313725, 0.54921569, 0.53343137],\n",
+       "        ...,\n",
+       "        [0.26784314, 0.29166667, 0.2227451 ],\n",
+       "        [0.2385098 , 0.26913725, 0.19303922],\n",
+       "        [0.28131373, 0.32641176, 0.24066667]],\n",
+       "\n",
+       "       [[0.21217647, 0.39062745, 0.40141176],\n",
+       "        [0.43011765, 0.55227451, 0.54468627],\n",
+       "        [0.44098039, 0.53431373, 0.49598039],\n",
+       "        ...,\n",
+       "        [0.3272549 , 0.34117647, 0.27245098],\n",
+       "        [0.29258824, 0.32743137, 0.24454902],\n",
+       "        [0.28129412, 0.32186275, 0.23892157]],\n",
+       "\n",
+       "       [[0.26254902, 0.45235294, 0.47558824],\n",
+       "        [0.45901961, 0.58      , 0.55627451],\n",
+       "        [0.45882353, 0.5377451 , 0.51421569],\n",
+       "        ...,\n",
+       "        [0.35686275, 0.31421569, 0.25343137],\n",
+       "        [0.34009804, 0.29872549, 0.25539216],\n",
+       "        [0.33205882, 0.30039216, 0.27931373]],\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "       [[0.39833333, 0.52656863, 0.50019608],\n",
+       "        [0.42921569, 0.55666667, 0.52352941],\n",
+       "        [0.43676471, 0.54705882, 0.53333333],\n",
+       "        ...,\n",
+       "        [0.19019608, 0.18480392, 0.14068627],\n",
+       "        [0.19392157, 0.18715686, 0.16058824],\n",
+       "        [0.09519608, 0.08764706, 0.07617647]],\n",
+       "\n",
+       "       [[0.44809804, 0.55633333, 0.5267451 ],\n",
+       "        [0.39123529, 0.50823529, 0.48954902],\n",
+       "        [0.38284314, 0.4995098 , 0.48921569],\n",
+       "        ...,\n",
+       "        [0.19411765, 0.1845098 , 0.14666667],\n",
+       "        [0.14231373, 0.12609804, 0.10943137],\n",
+       "        [0.10939216, 0.09311765, 0.09037255]],\n",
+       "\n",
+       "       [[0.44929412, 0.5534902 , 0.5165098 ],\n",
+       "        [0.39137255, 0.48690196, 0.47747059],\n",
+       "        [0.37941176, 0.47872549, 0.46754902],\n",
+       "        ...,\n",
+       "        [0.17666667, 0.17509804, 0.1545098 ],\n",
+       "        [0.14196078, 0.12605882, 0.11821569],\n",
+       "        [0.12815686, 0.11776471, 0.1057451 ]]])"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "images30[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(30, 30, 3), min:0.0, max:1.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAE0AAABKCAYAAAD+DNkIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADPNJREFUeJztmmvQXdVZx39rrX0598ubvIlJaS4UKQQoCFNodSwIYy0FAw3lEnDAWpSKAxSnamcEhIqDOI5tp2ltiyi3FiQkAobWApIALWVCKZdwGYiQkIQk7/19z3Vf1/LD2uGrh/3hiM75f3rPe/Y+ez3//Tz/9V/PWsIYwwjvD/J/ewD/FzEiLQdGpOXAiLQcGJGWAyPScmBEWg6MSMuBEWk54AzzYbfe820TJwkAUimkMYT9EAAlJcpROL5rPyvNc9u3sW/3WwAEvYTWdAsdxAD0W/MoIEzt9Ua5pCZFIQCIOwHjjSbtoAVAoVoiiAJc3weg1iyjXEOpaD8f3HuAt19riUHiGCppYa+LVPaRSRyRJClutoorl+sI12F+dr+9tt8l6YZM7p0GoDUxjxsqxg+rAvA7v38mfWKe/sGzAPQWuni+wmQRlRo+bRWCZ/+hU4PnFDGp5WV+ugMiJaoVAVh1+KqB4xiVZw4MNdOa9QZxHAEglMDxPCJSALqtDu++spPVKxYDMLakiU4rXLz2QgBWNpeyf/YgjlsDIImLuK7HZWeeDMDBiQOMN8eY3bsAwLf+ZjO7Wx36iQYglgbH9/CzzAv6MZWCj5ltA1Afqw8cx1BJ63a7yEIBgATNzDu7YN9uAA5fVufsSz5NPygDMDU7ySc/cy5ze21QE50Wi1Yvpt3rAyDSFC0dOlmXZumaKnMzPU4+93gAHll/E//4+bUc7M4DIFPF+Mol9Hx7fVP5LG8sJi7Z8az41CcGjmOopCUqJZ7dY/+eOshRH/koi4+2g22uWsVeVcHRPQCOO+087lm3Hv/V7fZmodA6AWkVxVEKRyiMtpkUAiYx/Cyyn8/bdAfXbnqKV5/7TwB2TL2B8qs4Ost0CVXXY7FvX5J0BqdipGk5MNRMO6a+iPJHPgzAbCFCdQLKixoAeH6ZZlqhdvgaAO69+BIqb7yAX7DlJOKQ0JMokWa/FhMLTcEoAIrGIS1CIcu8X3zhanq33MDx684FoPzaGC+2dtFU9nkNIagUCmhlLYsQA7kNYMikuSubVOvZoI2BJeAXbXlMdmJWjy/jJ1++HgD/pe04qk+QceQpiYMgNZYUAzgoIpkFq8EVEikyH8gsz1/z5/RmZwE47fI/5NQ9VfZMWt/newKJQqSW9EAeehn/M4ZK2rFHH02irCKkKESiaWubSSurNTZfeR3yyQcA8B1JagROJiBG2LtE5usEgIFDsRqpSRKNEpaE0DVQjXntxpsBiKI+n77yWo4sW1+2e9frzMqYKAkAKDJ423+kaTkw1ExrY3CyzFJC0kt8CsIO4embv0HtmZ8S+IdWDH0ct0Bi7HuVOkUp+d5sidZIA0rZ8tTGoKUkwWaamyogxK3b+w9869s83G5x7p/9FQDjScRrrzxL3bGWo1gsDhyHGOZu1G590MwF1jeN+WPsn+zyXxvuAmDvxjtx1QzoEgC+jDFCo7UN2hiFkKAyktM0QZBisnKUKLSJMcqSKlDIxEVl9S2SkJaGD1/wBQBOvel6+v0p5tvvAKC8GieNn/jBW3tOzEzjV63z3jc5h95zgP2b7wCgVm6xet3pqJKdzX76vS2sqo0jjxkHwBhNGBvisAtAcW+LTgsqxy4HYNFRv8KbP3+Vxj4rcsmYg1zSIMH6sl7bwZvWTDxwNwBPjhVYfek6jGcnot78AowPFsdI03JgqJmWJiFB27ZqojCh4SsWZ9PfaXd9l/SocyC1Q/qNL77NN9dexOXff9he7y2jAxTtZMdjX/1TfnO1oXb1XwIwv6vPb32tyb+c91kAjjxuDWtu/B5epoFGaJxd2/nh2ecAUAnbLHTbyMznmVgPHMdwfVqlAKkdXBQvIB2Hece2ehrHncjfn3kRYs/zAFz+yP2UI8GGNScBcMpXrmbNWedw++k26JrX4sgnfsTG674PwOyd36H9jb/gwq/fDsC2726gGE7znRM/BYD5UJNrHn2c4hEfBaDdDigLSdm3z+/0ewPHMVTSHtj0EBrbRFy9dCkfOux4gp5NndaeCc684lKeueEZAH585u8iTcSirP9WDFOE22eZY81rIrv0VREVTgHgV0P0/ikKi5cBUNYJRZGyRNgFftTRJKaF21wEgGd8RGiI9tl+ne7MwTGDxTHStBwYaqZVKRNkXYaSLKFKLiXPtrv/df2XuHTzT3CuuwGAX17/NVJHYGxi0u/PgygQZe9ZASLWyIr1WSZJiaIQ37XfR1FE7Bb42FeuAGD8Vw8HtYTpt3bZ+2tFxtI2bxx4FwAvG9cgGCppRH1Kmc/qtXo4wpBmY63MH+Cuyz7LHzy8DYCDM1Ps/4cNpI7VwH7QpaBqxId8pbHLpmLJWoaFRBDphETa73XQwYgKK061vizsTHL/BefjTk8CUBRrwPNxldW0hNbAYQyVNMf1EVgfVio1iF2fYubgO1WXwjuT3H/pxQBc8MM7uPM/tqF2/BIAEUOkwkPtNBxSMBpl7O9plSDbEZFrNc+VLiKYY9Pnfts+LzgIqYMSVkPjOKEmPeanrKYlqj9wHCNNy4Hhkua5JAoSBWESIRzFfMFlvuByzdOPEqw5Fv/1rfivb0UFkuVHfhw0oMFLQUlQjkI5CpGUoNentrRObWmdSEVIYyiZAiVToB8ExLKHI0IcEYLukcouwpUIVxKFMa+8/CKuL3B9QalYGDiMoZanVPK919QJe2ijUR1rbp956Cm+tHEL/f5uAHSxwf4XX6Dg2vLtzc5QVC4xVuPcuMhLGx/k1796FQBB+CanXHApLz76JABzUxOUnBJh5lk9XECCtsvLpB+w6vCVTLxp+23yfaTPcDdW5trUm0sAMHFKWhbIkg3ihZtu5fWtP6N+jN0Y2f/I4xTeepmwYKfPuR072PK3f4cKrSYlfped997N7nesTzvjqi+z8bZ/o32bbQCo5QWevet2HGFZS7QBT+BkGqrR9KOY6Qm7e1Us+QPHMdK0HBhqa+jr9/2TiRNbjkoJjvm1U1gmbKZsWbueopQQ2eWMq1yMkOisHIUWpAgc196fkuIYnyjxAIjQuFLjZ34rIkWbKlJYHyhkik5dvOU2o87a9GPue/BxnKLNm5Jb56oLL/vgtYYcR9JP7NQuC0Wee347H/+E3cI7b9MmfrBuLWOeDcqYQz37rJyUxgN01tN3pEtiDAVpSXFNgk4NSbZB4ggPJUOiQ03P1BAvK3DqnZsAuHfbUzSaZYxnSe8uhIPHkTP+XHjn7Z2ML7NNq5mD0zhukU132gX22eefy/otP+LBC+2OeqXdxbhwqBKkhhSNzJqOSaLBkcTpoQM1LiiBzroaSWqQ2uC4lsRw+TK+uHkjG7bahoDuhkzHc/QCm5krVx81cBwjTcuBoWraeZevNZ7KeviOwK02MUkHgKCVsvaP/oQjHduZvfv83+MIoQiysx4KCchsV8ruTmmtkdnRKoRDrFNMpoEYUKmmd9gYAJfc/s988+mXaL/xsn2+FPTDLn52tGvZiiO4+cobBtK0oZJ22R9/zrRn7B5BpVimvPww5ietT0p6LRJlWH/tjQCcUE647/MXsSi1luNdHeCrIk5GUhIGuK5DnGkWjoOIYpysPGPh4DQE6+7fDMAtDz9BZ/sLVKpZaymNqTWaeNmGyoGJKTbetuWDR9rpnznZlDzrvKVxWHHUx3j11R0ALGpU6PZaKGkz8YrrbmXi7V/wbtb1mCxCz3fwp2wmVgpFZj1DP9tT8KVLoR1QiG1mep7Lmlv+mse2vgRAe+dO/MIsUWh5kUpRqddJUxt/p9fl4fu2DUTaSNNyYKiZdsZZJ5hSzWZauTrGzOQCcWAdfrVWodPvUPPtsYVUVjnixBNYv/YMALY/tpGdO96iUbEWQbmwd/8U+r19T0gjQ6NuO7MrPnkSW594FtOzvs9JE8I4pl6394dBjE4kjYbVPKThnjse+eD5tKLnkx0o4MCBfbjCp1KxmiJJkUlE6NiJoT01w4uPT/HKsz8HQGsDXoA7b0lvVutMtPv4mSb5QhH2A9wxW64Pbf53KqlCeVYTO/0OFbdJmu1RzM0tMN5cSq9tyz1OPqhNSCTtWUtKHCeESYgpVbJvDMJIPCc7BeQJXBccka0QnBiMx8ys7bQu9OZI2hEsWGHvJDG1eoXWjF1h1NKUKA6ItdW4armMBwRda2LrpSpBr0/Yt2QV3keXY6RpOTDUTDMpqKxzG3W7+J6Hzhw5joNbLBL2bDlFQQ9EQj+b3aQjqDWgttiWY9ANKZYEBd9+jvqSRCfoQxFJQ6VZee8IfdkpEaMpSbtM63X69Hs9Cr49BtFpdQaOY6gTwf8XjMozB0ak5cCItBwYkZYDI9JyYERaDoxIy4ERaTkwIi0HRqTlwIi0HBiRlgMj0nJgRFoOjEjLgRFpOTAiLQdGpOXAiLQcGJGWAyPScmBEWg6MSMuB/wbmwjW4bq0m5QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(30, 30, 3), min:0.026627450980392278, max:1.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(30, 30, 3), min:0.05225490196078381, max:1.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(30, 30, 3), min:0.06715686274509822, max:0.6456644880174299\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(30, 30, 3), min:0.03203921568627577, max:1.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(30, 30, 3), min:0.024101307189542488, max:1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "rand_signs = random.sample(range(0, len(labels)), 6)\n",
+    "rand_signs\n",
+    "for i in range(len(rand_signs)):\n",
+    "    temp_im = images30[rand_signs[i]]\n",
+    "    plt.subplot(1,6,i+1)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.imshow(temp_im)\n",
+    "    plt.subplots_adjust(wspace = 0.5)\n",
+    "    plt.show()\n",
+    "    print(\"Forma:{0}, min:{1}, max:{2}\".format(temp_im.shape,\n",
+    "                                               temp_im.min(),\n",
+    "                                               temp_im.max()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skimage.color import rgb2gray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "images30 = np.array(images30)\n",
+    "images30 = rgb2gray(images30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(30, 30), min:0.0, max:0.6131553464052293\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(30, 30), min:0.10036139215686302, max:0.9990383098039217\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(30, 30), min:0.07573926143790845, max:0.85074481372549\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(30, 30), min:0.0, max:0.8775040408496722\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(30, 30), min:0.14224938997821343, max:0.7879190535947712\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Forma:(30, 30), min:0.11080798039215436, max:0.9999073529411765\n"
+     ]
+    }
+   ],
+   "source": [
+    "rand_signs = random.sample(range(0, len(labels)), 6)\n",
+    "rand_signs\n",
+    "for i in range(len(rand_signs)):\n",
+    "    temp_im = images30[rand_signs[i]]\n",
+    "    plt.subplot(1,6,i+1)\n",
+    "    plt.axis(\"off\")\n",
+    "    plt.imshow(temp_im, cmap=\"gray\")\n",
+    "    plt.subplots_adjust(wspace = 0.5)\n",
+    "    plt.show()\n",
+    "    print(\"Forma:{0}, min:{1}, max:{2}\".format(temp_im.shape,\n",
+    "                                               temp_im.min(),\n",
+    "                                               temp_im.max()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = tf.placeholder(dtype = tf.float32, shape = [None, 30,30])\n",
+    "y = tf.placeholder(dtype = tf.int32, shape = [None])\n",
+    "\n",
+    "images_flat = tf.contrib.layers.flatten(x)\n",
+    "logits = tf.contrib.layers.fully_connected(images_flat, 62, tf.nn.relu)\n",
+    "\n",
+    "loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(labels = y, logits=logits))\n",
+    "\n",
+    "train_opt = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)\n",
+    "\n",
+    "final_pred = tf.argmax(logits,1)\n",
+    "\n",
+    "accuracy = tf.reduce_mean(tf.cast(final_pred, tf.float32))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor 'Flatten/flatten/Reshape:0' shape=(?, 900) dtype=float32>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "images_flat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor 'fully_connected/Relu:0' shape=(?, 62) dtype=float32>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor 'Mean:0' shape=() dtype=float32>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<tf.Tensor 'ArgMax:0' shape=(?,) dtype=int64>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EPOCH 0\n",
+      "Eficacia:  37.650494\n",
+      "EPOCH 50\n",
+      "Eficacia:  35.907978\n",
+      "EPOCH 100\n",
+      "Eficacia:  35.659233\n",
+      "EPOCH 150\n",
+      "Eficacia:  35.29814\n",
+      "EPOCH 200\n",
+      "Eficacia:  35.14011\n",
+      "EPOCH 250\n",
+      "Eficacia:  35.06142\n",
+      "EPOCH 300\n",
+      "Eficacia:  34.930275\n",
+      "EPOCH 350\n",
+      "Eficacia:  34.835846\n",
+      "EPOCH 400\n",
+      "Eficacia:  34.6988\n",
+      "EPOCH 450\n",
+      "Eficacia:  34.59825\n",
+      "EPOCH 500\n",
+      "Eficacia:  34.495518\n",
+      "EPOCH 550\n",
+      "Eficacia:  34.460327\n",
+      "EPOCH 600\n",
+      "Eficacia:  34.397377\n"
+     ]
+    }
+   ],
+   "source": [
+    "tf.set_random_seed(1234)\n",
+    "\n",
+    "sess = tf.Session()\n",
+    "\n",
+    "sess.run(tf.global_variables_initializer())\n",
+    "\n",
+    "for i in range(601):\n",
+    "    \n",
+    "    _, accuracy_val = sess.run([train_opt, accuracy],\n",
+    "                              feed_dict= {\n",
+    "                                  x: images30,\n",
+    "                                  y: list(labels)\n",
+    "                              })\n",
+    "    #_, loss_val = sess.run([train_opt, loss],\n",
+    "    #                          feed_dict= {\n",
+    "    #                              x: images30,\n",
+    "    #                              y: list(labels)\n",
+    "    #                          })\n",
+    "    if i%50 == 0:\n",
+    "        print(\"EPOCH\", i)\n",
+    "        print(\"Eficacia: \", accuracy_val)\n",
+    "        #print(\"Pérdidas:\", loss_val)\n",
+    "    #print(\"Fin del Ecpoh \", i)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Evaluación de la red neuronal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_idx = random.sample(range(len(images30)), 40)\n",
+    "sample_images = [images30[i] for i in sample_idx]\n",
+    "sample_labels = [labels[i] for i in sample_idx]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = sess.run([final_pred], feed_dict={x:sample_images})[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([37, 22, 22,  0, 32, 56, 47, 35, 40, 22, 38, 38,  1, 34, 56, 32, 32,\n",
+       "        7, 28, 61, 38, 39, 61, 37, 47, 61, 32, 38, 32, 41, 32, 39, 32, 31,\n",
+       "       47, 28, 40, 47, 57, 54])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[37,\n",
+       " 22,\n",
+       " 22,\n",
+       " 19,\n",
+       " 32,\n",
+       " 56,\n",
+       " 47,\n",
+       " 35,\n",
+       " 40,\n",
+       " 22,\n",
+       " 38,\n",
+       " 38,\n",
+       " 4,\n",
+       " 34,\n",
+       " 56,\n",
+       " 9,\n",
+       " 32,\n",
+       " 7,\n",
+       " 28,\n",
+       " 61,\n",
+       " 38,\n",
+       " 44,\n",
+       " 61,\n",
+       " 37,\n",
+       " 47,\n",
+       " 61,\n",
+       " 32,\n",
+       " 38,\n",
+       " 45,\n",
+       " 41,\n",
+       " 32,\n",
+       " 39,\n",
+       " 32,\n",
+       " 31,\n",
+       " 47,\n",
+       " 19,\n",
+       " 40,\n",
+       " 47,\n",
+       " 57,\n",
+       " 53]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x1440 with 40 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16,20))\n",
+    "for i in range(len(sample_images)):\n",
+    "    truth = sample_labels[i]\n",
+    "    predi = prediction[i]\n",
+    "    plt.subplot(10,4,i+1)\n",
+    "    plt.axis(\"off\")\n",
+    "    color = \"green\" if truth==predi else \"red\"\n",
+    "    plt.text(32,15, \"Real:         {0}\\nPrediccion:{1}\".format(truth, predi),\n",
+    "            fontsize = 14, color = color)\n",
+    "    plt.imshow(sample_images[i], cmap=\"gray\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_images, test_labels = load_ml_data(test_data_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/anaconda3/lib/python3.5/site-packages/skimage/transform/_warps.py:105: UserWarning: The default mode, 'constant', will be changed to 'reflect' in skimage 0.15.\n",
+      "  warn(\"The default mode, 'constant', will be changed to 'reflect' in \"\n",
+      "/anaconda3/lib/python3.5/site-packages/skimage/transform/_warps.py:110: UserWarning: Anti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.\n",
+      "  warn(\"Anti-aliasing will be enabled by default in skimage 0.15 to \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_images30 = [transform.resize(im,(30,30)) for im in test_images]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_images30 = rgb2gray(np.array(test_images30))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = sess.run([final_pred], feed_dict={x:test_images30})[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1603"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "match_count = sum([int(l0 == lp) for l0, lp in zip(test_labels, prediction)])\n",
+    "match_count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eficacia de la red neuronal: 63.61\n"
+     ]
+    }
+   ],
+   "source": [
+    "acc = match_count/len(test_labels)*100\n",
+    "print(\"Eficacia de la red neuronal: {:.2f}\".format(acc))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T11 - 2 - Se\303\261ales de tr\303\241fico.ipynb" "b/notebooks/T11 - 2 - Se\303\261ales de tr\303\241fico.ipynb"
index 571c2430..9db20659 100644
--- "a/notebooks/T11 - 2 - Se\303\261ales de tr\303\241fico.ipynb"	
+++ "b/notebooks/T11 - 2 - Se\303\261ales de tr\303\241fico.ipynb"	
@@ -1411,7 +1411,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.5"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T11 - 3 - Reconocimiento de texto escrito-Colab.ipynb b/notebooks/T11 - 3 - Reconocimiento de texto escrito-Colab.ipynb
new file mode 100644
index 00000000..42d80e02
--- /dev/null
+++ b/notebooks/T11 - 3 - Reconocimiento de texto escrito-Colab.ipynb	
@@ -0,0 +1,465 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T11%20-%203%20-%20Reconocimiento%20de%20texto%20escrito-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%tensorflow_version 1.x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# El dataset de MNIST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "print(tf.__version__)\n",
+    "from tensorflow.examples.tutorials.mnist import input_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING:tensorflow:From <ipython-input-3-b46be0f6b76f>:1: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
+      "WARNING:tensorflow:From /Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please write your own downloading logic.\n",
+      "WARNING:tensorflow:From /Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:262: extract_images (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use tf.data to implement this functionality.\n",
+      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
+      "WARNING:tensorflow:From /Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:267: extract_labels (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use tf.data to implement this functionality.\n",
+      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
+      "WARNING:tensorflow:From /Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:110: dense_to_one_hot (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use tf.one_hot on tensors.\n",
+      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
+      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
+      "WARNING:tensorflow:From /Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/datasets/mnist.py:290: DataSet.__init__ (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
+      "Instructions for updating:\n",
+      "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n"
+     ]
+    }
+   ],
+   "source": [
+    "mnist = input_data.read_data_sets(\"MNIST_data\", one_hot = True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55000"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(mnist.train.images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10000"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(mnist.test.images)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im_temp = mnist.train.images[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from skimage import io\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/skimage/io/_plugins/matplotlib_plugin.py:51: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  out_of_range_float = (np.issubdtype(image.dtype, np.float) and\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1c345290f0>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1c3448bcf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "io.imshow(np.reshape(im_temp, (28,28)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mnist.train.labels[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Una red neuronal con Tensor Flow - v1\n",
+    "* Las imágenes de entrenamiento de MNIST viven en un espacio vectorial de dimensión 784.\n",
+    "* El dataset se puede pensar como 55000 filas y 784 columnas.\n",
+    "* Cada dato del datset es un número real entre 0 y 1.\n",
+    "\n",
+    "y = softmax(W * x + b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dim_input = 784\n",
+    "n_categories = 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = tf.placeholder(tf.float32, [None, dim_input])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "W = tf.Variable(tf.zeros([dim_input,n_categories])) \n",
+    "b = tf.Variable(tf.zeros([n_categories]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "softmax_args = tf.matmul(x,W) + b\n",
+    "y_hat = tf.nn.softmax(softmax_args)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Entrenando la red neuronal\n",
+    "* Loss / Cost <- objetivo minimizar las pérdidas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import display, Math, Latex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$$H_{y}(\\hat{y}) = -\\sum_{i} y_i log(\\hat{y_i})$$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r\"H_{y}(\\hat{y}) = -\\sum_{i} y_i log(\\hat{y_i})\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_ = tf.placeholder(tf.float32, [None, 10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y_hat), reduction_indices=[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#tf.nn.softmax_cross_entropy_with_logits(softmax_args, y_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "session = tf.InteractiveSession()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tf.global_variables_initializer().run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for _ in range(10000):\n",
+    "    batch_x, batch_y = mnist.train.next_batch(150)\n",
+    "    session.run(train_step, feed_dict={x:batch_x, y_: batch_y})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Evaluando la red neuronal\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "correct_predictions = tf.equal(tf.argmax(y_hat, 1), tf.argmax(y_,1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.9254\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(session.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T11 - 3 - Reconocimiento de texto escrito.ipynb b/notebooks/T11 - 3 - Reconocimiento de texto escrito.ipynb
index 6359b074..a57d2565 100644
--- a/notebooks/T11 - 3 - Reconocimiento de texto escrito.ipynb	
+++ b/notebooks/T11 - 3 - Reconocimiento de texto escrito.ipynb	
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T11%20-%203%20-%20Reconocimiento%20de%20texto%20escrito-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -394,7 +401,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T12 - 1 - R y Python-Colab.ipynb b/notebooks/T12 - 1 - R y Python-Colab.ipynb
new file mode 100644
index 00000000..538980e1
--- /dev/null
+++ b/notebooks/T12 - 1 - R y Python-Colab.ipynb	
@@ -0,0 +1,12256 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T12%20-%201%20-%20R%20y%20Python-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cloning into 'python-ml-course'...\n",
+      "remote: Enumerating objects: 90, done.\u001b[K\n",
+      "remote: Counting objects: 100% (90/90), done.\u001b[K\n",
+      "remote: Compressing objects: 100% (64/64), done.\u001b[K\n",
+      "^Cceiving objects:  11% (1937/17605), 56.04 MiB | 22.20 MiB/s\n"
+     ]
+    }
+   ],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Juntando R y Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install rpy2\n",
+    "import rpy2.robjects as ro\n",
+    "import rpy2.robjects.numpy2ri"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rpy2.robjects.numpy2ri.activate()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "codigo_r = \"\"\"\n",
+    "saludar <- function(cadena){\n",
+    "    return(paste(\"Hola, \", cadena))\n",
+    "}\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "R object with classes: ('function',) mapped to:\n",
+       "<SignatureTranslatedFunction - Python:0x11322d7c8 / R:0x7f9101b37e60>"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ro.r(codigo_r)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "saludar_py = ro.globalenv[\"saludar\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hola,  Antonio Banderas'"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res = saludar_py(\"Antonio Banderas\")\n",
+    "res[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "rpy2.robjects.vectors.StrVector"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "function (cadena) \n",
+      "{\n",
+      "    return(paste(\"Hola, \", cadena))\n",
+      "}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(saludar_py.r_repr())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "var_from_python = ro.FloatVector(np.arange(1,5,0.1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <span>FloatVector with 40 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      <tr>\n",
+       "      \n",
+       "      <td>\n",
+       "        1.000000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        1.100000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        1.200000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        1.300000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        ...\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        4.600000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        4.700000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        4.800000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        4.900000\n",
+       "      </td>\n",
+       "      \n",
+       "      </tr>\n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    "
+      ],
+      "text/plain": [
+       "R object with classes: ('numeric',) mapped to:\n",
+       "<FloatVector - Python:0x115408e48 / R:0x7f90fe696ce0>\n",
+       "[1.000000, 1.100000, 1.200000, 1.300000, ..., 4.600000, 4.700000, 4.800000, 4.900000]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "var_from_python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "c(1, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2, 2.1, 2.2, \n",
+      "2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.1, 3.2, 3.3, 3.4, 3.5, \n",
+      "3.6, 3.7, 3.8, 3.9, 4, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, \n",
+      "4.9)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(var_from_python.r_repr())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ro.globalenv[\"var_to_r\"] = var_from_python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1. , 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2. , 2.1, 2.2,\n",
+       "       2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3. , 3.1, 3.2, 3.3, 3.4, 3.5,\n",
+       "       3.6, 3.7, 3.8, 3.9, 4. , 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8,\n",
+       "       4.9])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ro.r(\"var_to_r\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([118.])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ro.r(\"sum(var_to_r)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2.95])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ro.r(\"mean(var_to_r)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.16904519])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ro.r(\"sd(var_to_r)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "118.00000000000007"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.sum(var_from_python)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.950000000000002"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(var_from_python)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.   , 1.975, 2.95 , 2.95 , 3.925, 4.9  ])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ro.r(\"summary(var_to_r)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <span>ListVector with 6 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      \n",
+       "      <tr>\n",
+       "      <th>\n",
+       "        breaks\n",
+       "      </th>\n",
+       "      <td>\n",
+       "        \n",
+       "    <span>FloatVector with 5 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      <tr>\n",
+       "      \n",
+       "      <td>\n",
+       "        1.000000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        2.000000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        3.000000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        4.000000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        5.000000\n",
+       "      </td>\n",
+       "      \n",
+       "      </tr>\n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    \n",
+       "      </td>\n",
+       "      </tr>\n",
+       "      \n",
+       "      <tr>\n",
+       "      <th>\n",
+       "        counts\n",
+       "      </th>\n",
+       "      <td>\n",
+       "        \n",
+       "    <span>IntVector with 4 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      <tr>\n",
+       "      \n",
+       "      <td>\n",
+       "        11\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        10\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        10\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        9\n",
+       "      </td>\n",
+       "      \n",
+       "      </tr>\n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    \n",
+       "      </td>\n",
+       "      </tr>\n",
+       "      \n",
+       "      <tr>\n",
+       "      <th>\n",
+       "        density\n",
+       "      </th>\n",
+       "      <td>\n",
+       "        \n",
+       "    <span>FloatVector with 4 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      <tr>\n",
+       "      \n",
+       "      <td>\n",
+       "        0.275000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        0.250000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        0.250000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        0.225000\n",
+       "      </td>\n",
+       "      \n",
+       "      </tr>\n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    \n",
+       "      </td>\n",
+       "      </tr>\n",
+       "      \n",
+       "      <tr>\n",
+       "      <th>\n",
+       "        mids\n",
+       "      </th>\n",
+       "      <td>\n",
+       "        \n",
+       "    <span>FloatVector with 4 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      <tr>\n",
+       "      \n",
+       "      <td>\n",
+       "        1.500000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        2.500000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        3.500000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        4.500000\n",
+       "      </td>\n",
+       "      \n",
+       "      </tr>\n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    \n",
+       "      </td>\n",
+       "      </tr>\n",
+       "      \n",
+       "      <tr>\n",
+       "      <th>\n",
+       "        xname\n",
+       "      </th>\n",
+       "      <td>\n",
+       "        \n",
+       "    <span>StrVector with 1 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      <tr>\n",
+       "      \n",
+       "      <td>\n",
+       "        'var_to_r'\n",
+       "      </td>\n",
+       "      \n",
+       "      </tr>\n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    \n",
+       "      </td>\n",
+       "      </tr>\n",
+       "      \n",
+       "      <tr>\n",
+       "      <th>\n",
+       "        equidist\n",
+       "      </th>\n",
+       "      <td>\n",
+       "        \n",
+       "    <span>BoolVector with 1 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      <tr>\n",
+       "      \n",
+       "      <td>\n",
+       "               1\n",
+       "      </td>\n",
+       "      \n",
+       "      </tr>\n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    \n",
+       "      </td>\n",
+       "      </tr>\n",
+       "      \n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    "
+      ],
+      "text/plain": [
+       "R object with classes: ('histogram',) mapped to:\n",
+       "<ListVector - Python:0x115283888 / R:0x7fc8dc7de940>\n",
+       "[Float..., IntVe..., Float..., Float..., StrVe..., BoolV...]\n",
+       "  breaks: <class 'rpy2.robjects.vectors.FloatVector'>\n",
+       "  R object with classes: ('numeric',) mapped to:\n",
+       "<FloatVector - Python:0x118818348 / R:0x7fc8dc7dece8>\n",
+       "[1.000000, 2.000000, 3.000000, 4.000000, 5.000000]\n",
+       "  counts: <class 'rpy2.robjects.vectors.IntVector'>\n",
+       "  R object with classes: ('integer',) mapped to:\n",
+       "<IntVector - Python:0x118816148 / R:0x7fc8def2e470>\n",
+       "[11, 10, 10, 9]\n",
+       "  density: <class 'rpy2.robjects.vectors.FloatVector'>\n",
+       "  R object with classes: ('numeric',) mapped to:\n",
+       "<FloatVector - Python:0x118820288 / R:0x7fc8dc7df2d8>\n",
+       "[0.275000, 0.250000, 0.250000, 0.225000]\n",
+       "  mids: <class 'rpy2.robjects.vectors.FloatVector'>\n",
+       "  R object with classes: ('numeric',) mapped to:\n",
+       "<FloatVector - Python:0x118820c88 / R:0x7fc8dc7df128>\n",
+       "[1.500000, 2.500000, 3.500000, 4.500000]\n",
+       "  xname: <class 'rpy2.robjects.vectors.StrVector'>\n",
+       "  R object with classes: ('character',) mapped to:\n",
+       "<StrVector - Python:0x1188201c8 / R:0x7fc8dc7d7478>\n",
+       "['var_to_r']\n",
+       "  equidist: <class 'rpy2.robjects.vectors.BoolVector'>\n",
+       "  R object with classes: ('logical',) mapped to:\n",
+       "<BoolVector - Python:0x118820b48 / R:0x7fc8de98f358>\n",
+       "[       1]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ro.r(\"hist(var_to_r, breaks = 4)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Trabajar de forma conjunta entre R y Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rpy2.robjects.packages import importr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ro.r(\"install.packages('extRemes')\")# si os falla decidle 'n' al hacer la instalación"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "extremes = importr(\"extRemes\") # library(extRemes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fevd = extremes.fevd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python representation of an R function.\n",
+      "description\n",
+      "-----------\n",
+      "\n",
+      "\n",
+      " Fit a univariate extreme value distribution functions (e.g., GEV, GP, PP, Gumbel, or Exponential) to data; possibly with covariates in the parameters.\n",
+      " \n",
+      "\n",
+      "\n",
+      "fevd(\n",
+      "    x,\n",
+      "    data,\n",
+      "    threshold = rinterface.NULL,\n",
+      "    threshold_fun = ~,\n",
+      "    location_fun = ~,\n",
+      "    scale_fun = ~,\n",
+      "    shape_fun = ~,\n",
+      "    use_phi = False,\n",
+      "    type = c,\n",
+      "    method = c,\n",
+      "    initial = rinterface.NULL,\n",
+      "    span,\n",
+      "    units = rinterface.NULL,\n",
+      "    time_units = days,\n",
+      "    period_basis = year,\n",
+      "    na_action = <rpy2.rinterface.ListSexpVector - Python:0x10ff108b8 / R:0x7f90fdf77678>,\n",
+      "    optim_args = rinterface.NULL,\n",
+      "    priorFun = rinterface.NULL,\n",
+      "    priorParams = rinterface.NULL,\n",
+      "    proposalFun = rinterface.NULL,\n",
+      "    proposalParams = rinterface.NULL,\n",
+      "    iter = 9999.0,\n",
+      "    weights = 1.0,\n",
+      "    blocks = rinterface.NULL,\n",
+      "    verbose = False,\n",
+      ")\n",
+      "\n",
+      "x :  `fevd`: `x` can be a numeric vector, the name of a column of `data` or a formula giving the data to which the EVD is to be fit.  In the case of the latter two, the `data` argument must be specified, and must have appropriately named columns.`plot` and `print` method functions: any list object returned by `fevd`. ,\n",
+      "\n",
+      "object :  A list object of class \\dQuote{fevd} as returned by `fevd`. ,\n",
+      "\n",
+      "data :  A data frame object with named columns giving the data to be fit, as well as any data necessary for modeling non-stationarity through the threshold and/or any of the parameters. ,\n",
+      "\n",
+      "threshold :  numeric (single or vector).  If fitting a peak over threshold (POT) model (i.e., `type` = \\dQuote{PP}, \\dQuote{GP}, \\dQuote{Exponential}) this is the threshold over which (non-inclusive) data (or excesses) are used to estimate the parameters of the distribution function.  If the length is greater than 1, then the length must be equal to either the length of `x` (or number of rows of `data`) or to the number of unique arguments in `threshold.fun`. ,\n",
+      "\n",
+      "threshold.fun :  formula describing a model for the thresholds using columns from `data`.  Any valid formula will work.  `data` must be supplied if this argument is anything other than ~ 1.  Not for use with `method` \\dQuote{Lmoments}. ,\n",
+      "\n",
+      "location.fun :  formula describing a model for each parameter using columns from `data`.  `data` must be supplied if any of these arguments are anything other than ~ 1. ,\n",
+      "\n",
+      "scale.fun :  formula describing a model for each parameter using columns from `data`.  `data` must be supplied if any of these arguments are anything other than ~ 1. ,\n",
+      "\n",
+      "shape.fun :  formula describing a model for each parameter using columns from `data`.  `data` must be supplied if any of these arguments are anything other than ~ 1. ,\n",
+      "\n",
+      "use.phi :  logical; should the log of the scale parameter be used in the numerical optimization (for `method` \\dQuote{MLE}, \\dQuote{GMLE} and \\dQuote{Bayesian} only)?  For the ML and GML estimation, this may make things more stable for some data. ,\n",
+      "\n",
+      "type :  `fevd`: character stating which EVD to fit.  Default is to fit the generalized extreme value (GEV) distribution function (df).`plot` method function: character describing which plot(s) is (are) desired.  Default is \\dQuote{primary}, which makes a 2 by 2 panel of plots including the QQ plot of the data quantiles against the fitted model quantiles (`type` \\dQuote{qq}), a QQ plot (\\dQuote{qq2}) of quantiles from model-simulated data against the data, a density plot of the data along with the model fitted density (`type` \\dQuote{density}) and a return level plot (`type` \\dQuote{rl}). In the case of a stationary (fixed) model, the return level plot will show return levels calculated for return periods given by `return.period`, along with associated CIs (calculated using default `method` arguments depending on the estimation method used in the fit.  For non-stationary models, the data are plotted as a line along with associated effective return levels for return periods of 2, 20 and 100 years (unless `return.period` is specified by the user to other values.  Other possible values for `type` include \\dQuote{hist}, which is similar to \\dQuote{density}, but shows the histogram for the data and \\dQuote{trace}, which is not used for L-moment fits.  In the case of MLE/GMLE, the trace yields a panel of plots that show the negative log-likelihood and gradient negative log-likelihood (note that the MLE gradient is currently used even for GMLE) for each of the estimated parameter(s); allowing one parameter to vary according to `prange`, while the others remain fixed at their estimated values.  In the case of Bayesian estimation, the \\dQuote{trace} option creates a panel of plots showing the posterior df and MCMC trace for each parameter. ,\n",
+      "\n",
+      "method :  `fevd`: character naming which type of estimation method to use.  Default is to use maximum likelihood estimation (MLE). ,\n",
+      "\n",
+      "initial :  A list object with any named parameter component giving the initial value estimates for starting the numerical optimization (MLE/GMLE) or the MCMC iterations (Bayesian).  In the case of MLE/GMLE, it is best to obtain a good intial guess, and in the Bayesian case, it is perhaps better to choose poor initial estimates.  If NULL (default), then L-moments estimates and estimates based on Gumbel moments will be calculated, and whichever yields the lowest negative log-likelihood is used.  In the case of `type` \\dQuote{PP}, an additional MLE/GMLE estimate is made for the generalized Pareto (GP) df, and parameters are converted to those of the Poisson Process (PP) model.  Again, the initial estimates yielding the lowest negative log-likelihoo value are used for the initial guess. ,\n",
+      "\n",
+      "span :  single numeric giving the number of years (or other desired temporal unit) in the data set.  Only used for POT models, and only important in the estimation for the PP model, but important for subsequent estimates of return levels for any POT model.  If missing, it will be calculated using information from `time.units`. ,\n",
+      "\n",
+      "units :  (optional) character giving the units of the data, which if given may be used subsequently (e.g., on plot axis labels, etc.). ,\n",
+      "\n",
+      "time.units :  character string that must be one of \\dQuote{hours}, \\dQuote{minutes}, \\dQuote{seconds}, \\dQuote{days}, \\dQuote{months}, \\dQuote{years}, \\dQuote{m/hour}, \\dQuote{m/minute}, \\dQuote{m/second}, \\dQuote{m/day}, \\dQuote{m/month}, or \\dQuote{m/year}; where m is a number.  If `span` is missing, then this argument is used in determining the value of `span`.  It is also returned with the output and used subsequently for plot labelling, etc. ,\n",
+      "\n",
+      "period.basis :  character string giving the units for the period.  Used only for plot labelling and naming output vectors from some of the method functions (e.g., for establishing what the period represents for the return period). ,\n",
+      "\n",
+      "rperiods :  numeric vector giving the return period(s) for which it is desired to calculate the corresponding return levels. ,\n",
+      "\n",
+      "period :  character string naming the units for the return period. ,\n",
+      "\n",
+      "burn.in :  The first `burn.in` values are thrown out before calculating anything from the MCMC sample. ,\n",
+      "\n",
+      "a :  when plotting empirical probabilies and such, the function `ppoints` is called, which has this argument `a`. ,\n",
+      "\n",
+      "d :  numeric determining how to scale the rate parameter for the point process.  If NULL, the function will attempt to scale based on the values of `period.basis` and `time.units`, the first of which must be \\dQuote{year} and the second of which must be one of \\dQuote{days}, \\dQuote{months}, \\dQuote{years}, \\dQuote{hours}, \\dQuote{minutes} or \\dQuote{seconds}.  If none of these are the case, then `d` should be specified, otherwise, it is not necessary. ,\n",
+      "\n",
+      "density.args :  named list object containing arguments to the `density` and `hist` functions, respectively. ,\n",
+      "\n",
+      "hist.args :  named list object containing arguments to the `density` and `hist` functions, respectively. ,\n",
+      "\n",
+      "na.action :  function to be called to handle missing values.  Generally, this should remain at the default (na.fail), and the user should take care to impute missing values in an appropriate manner as it may have serious consequences on the results. ,\n",
+      "\n",
+      "optim.args :  A list with named components matching exactly any arguments that the user wishes to specify to `optim`, which is used only for MLE and GMLE methods.  By default, the \\dQuote{BFGS} method is used along with `grlevd` for the gradient argument.  Generally, the `grlevd` function is used for the `gr` option unless the user specifies otherwise, or the optimization method does not take gradient information. ,\n",
+      "\n",
+      "priorFun :  character naming a prior df to use for methods GMLE and Bayesian.  The default for GMLE (not including Gumbel or Exponential types) is to use the one suggested by Martins and Stedinger (2000, 2001) on the shape parameter; a beta df on -0.5 to 0.5 with parameters `p` and `q`.  Must take `x` as its first argument for `method` \\dQuote{GMLE}.  Optional arguments for the default function are `p` and `q` (see details section).The default for Bayesian estimation is to use normal distribution functions.  For Bayesian estimation, this function must take `theta` as its first argument.Note: if this argument is not NULL and `method` is set to \\dQuote{MLE}, it will be changed to \\dQuote{GMLE}. ,\n",
+      "\n",
+      "priorParams :  named list containing any prior df parameters (where the list names are the same as the function argument names).  Default for GMLE (assuming the default function is used) is to use `q` = 6 and `p` = 9.  Note that in the Martins and Stedinger (2000, 2001) papers, they use a different EVD parametrization than is used here such that a positive shape parameter gives the upper bounded distribution instead of the heavy-tail one (as emloyed here).  To be consistent with these papers, `p` and `q` are reversed inside the code so that they have the same interpretation as in the papers.Default for Bayesian estimation is to use ML estimates for the means of each parameter (may be changed using `m`, which must be a vector of same length as the number of parameters to be estimated (i.e., if using the default prior df)) and a standard deviation of 10 for all other parameters (again, if using the default prior df, may be changed using `v`, which must be a vector of length equal to the number of parameters). ,\n",
+      "\n",
+      "proposalFun :  For Bayesian estimation only, this is a character naming a function used to generate proposal parameters at each iteration of the MCMC.  If NULL (default), a random walk chain is used whereby if theta.i is the current value of the parameter, the proposed new parameter theta.star is given by theta.i + z, where z is drawn at random from a normal df. ,\n",
+      "\n",
+      "proposalParams :  A named list object describing any optional arguments to the `proposalFun` function.  All functions must take argument `p`, which must be a vector of the parameters, and `ind`, which is used to identify which parameter is to be proposed.  The default `proposalFun` function takes additional arguments `mean` and `sd`, which must be vectors of length equal to the number of parameters in the model (default is to use zero for the mean of z for every parameter and 0.1 for its standard deviation). ,\n",
+      "\n",
+      "iter :  Used only for Bayesian estimation, this is the number of MCMC iterations to do. ,\n",
+      "\n",
+      "weights :  numeric of length 1 or n giving weights to be applied     in the likelihood calculations (e.g., if there are data points to     be weighted more/less heavily than others). ,\n",
+      "\n",
+      "blocks :  An optional list containing information required to fit point process models in a computationally-efficient manner by using only the exceedances and not the observations below the threshold(s). See details for further information.       ,\n",
+      "\n",
+      "FUN :  character string naming a function to use to estimate the parameters from the MCMC sample.  The function is applied to each column of the `results` component of the returned `fevd` object. ,\n",
+      "\n",
+      "verbose :  logical; should progress information be printed to the screen?  If TRUE, for MLE/GMLE, the argument `trace` will be set to 6 in the call to `optim`. ,\n",
+      "\n",
+      "prange :  matrix whose columns are numeric vectors of length two for each parameter in the model giving the parameter range over which trace plots should be made.  Default is to use either +/- 2 * std. err. of the parameter (first choice) or, if the standard error cannot be calculated, then +/- 2 * log2(abs(parameter)).  Typically, these values seem to work very well for these plots. ,\n",
+      "\n",
+      "... :  Not used by most functions here.  Optional arguments to `plot` for the various `plot` method functions.In the case of the `summary` method functions, the logical argument `silent` may be passed to suppress (if TRUE) printing any information to the screen. ,\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(fevd.__doc__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:4: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
+      "  after removing the cwd from sys.path.\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py:2227: FutureWarning: split() requires a non-empty pattern match.\n",
+      "  yield pat.split(line.strip())\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/pandas/io/parsers.py:2229: FutureWarning: split() requires a non-empty pattern match.\n",
+      "  yield pat.split(line.strip())\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"/content/python-ml-course/datasets/time/time_series.txt\", \n",
+    "                   sep = \"\\s*\", skiprows = 1, parse_dates = [[0,1]],\n",
+    "                   names = [\"date\", \"time\", \"wind_speed\"],\n",
+    "                   index_col = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>wind_speed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date_time</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1983-01-01 00:00:00</th>\n",
+       "      <td>7.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1983-01-01 01:00:00</th>\n",
+       "      <td>8.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1983-01-01 02:00:00</th>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1983-01-01 03:00:00</th>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1983-01-01 04:00:00</th>\n",
+       "      <td>9.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                     wind_speed\n",
+       "date_time                      \n",
+       "1983-01-01 00:00:00         7.9\n",
+       "1983-01-01 01:00:00         8.2\n",
+       "1983-01-01 02:00:00         8.5\n",
+       "1983-01-01 03:00:00         9.0\n",
+       "1983-01-01 04:00:00         9.9"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(275352, 1)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: pd.TimeGrouper is deprecated and will be removed; Please use pd.Grouper(freq=...)\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    }
+   ],
+   "source": [
+    "max_ws = data.wind_speed.groupby(pd.Grouper(freq=\"A\")).max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date_time\n",
+       "1983-12-31    22.2\n",
+       "1984-12-31    25.5\n",
+       "1985-12-31    21.5\n",
+       "1986-12-31    22.5\n",
+       "1987-12-31    23.7\n",
+       "1988-12-31    22.5\n",
+       "1989-12-31    21.7\n",
+       "1990-12-31    29.7\n",
+       "1991-12-31    24.2\n",
+       "1992-12-31    23.8\n",
+       "1993-12-31    28.1\n",
+       "1994-12-31    23.4\n",
+       "1995-12-31    23.7\n",
+       "1996-12-31    25.6\n",
+       "1997-12-31    23.2\n",
+       "1998-12-31    24.9\n",
+       "1999-12-31    22.8\n",
+       "2000-12-31    24.6\n",
+       "2001-12-31    22.3\n",
+       "2002-12-31    25.5\n",
+       "2003-12-31    22.6\n",
+       "2004-12-31    24.0\n",
+       "2005-12-31    20.8\n",
+       "2006-12-31    23.5\n",
+       "2007-12-31    24.4\n",
+       "2008-12-31    24.1\n",
+       "2009-12-31    25.1\n",
+       "2010-12-31    19.4\n",
+       "2011-12-31    22.8\n",
+       "2012-12-31    24.2\n",
+       "2013-12-31    25.0\n",
+       "2014-12-31    25.3\n",
+       "Freq: A-DEC, Name: wind_speed, dtype: float64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max_ws"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1113e16d8>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1113dc390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "max_ws.plot(kind=\"bar\", figsize=(16,9))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = fevd(max_ws.values, type=\"GEV\", method = \"GMLE\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'rpy2.robjects.vectors.ListVector'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(result))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method RObjectMixin.r_repr of R object with classes: ('fevd',) mapped to:\n",
+       "<ListVector - Python:0x115c53308 / R:0x7f90fe4f48d0>\n",
+       "[Vector, StrVector, FloatVector, BoolVector, ..., StrVector, StrVector, ListVector, ListVector]\n",
+       "  call: <class 'rpy2.robjects.vectors.Vector'>\n",
+       "  R object with classes: ('fevd',) mapped to:\n",
+       "<Vector - Python:0x11141ab88 / R:0x7f90fdf4bce8>\n",
+       "[SignatureT..., Array, StrVector, StrVector]\n",
+       "  data.name: <class 'rpy2.robjects.vectors.StrVector'>\n",
+       "  R object with classes: ('character',) mapped to:\n",
+       "<StrVector - Python:0x11141a7c8 / R:0x7f910162d340>\n",
+       "['struct..., '23.8, ..., '22.6, ..., '25.3),..., '']\n",
+       "  weights: <class 'rpy2.robjects.vectors.FloatVector'>\n",
+       "  R object with classes: ('numeric',) mapped to:\n",
+       "<FloatVector - Python:0x11141af48 / R:0x7f910260beb8>\n",
+       "[1.000000]\n",
+       "  in.data: <class 'rpy2.robjects.vectors.BoolVector'>\n",
+       "  R object with classes: ('logical',) mapped to:\n",
+       "<BoolVector - Python:0x11141af08 / R:0x7f9102164fb8>\n",
+       "[       0]\n",
+       "...\n",
+       "  priorFun: <class 'rpy2.robjects.vectors.StrVector'>\n",
+       "  R object with classes: ('character',) mapped to:\n",
+       "<StrVector - Python:0x111424f48 / R:0x7f90fdb51d88>\n",
+       "['na.fail']\n",
+       "  priorParams: <class 'rpy2.robjects.vectors.StrVector'>\n",
+       "  R object with classes: ('character',) mapped to:\n",
+       "<StrVector - Python:0x111424788 / R:0x7f91016a8760>\n",
+       "['location', 'scale', 'shape']\n",
+       "R object with classes: ('fevd',) mapped to:\n",
+       "<ListVector - Python:0x115c53308 / R:0x7f90fe4f48d0>\n",
+       "[Vector, StrVector, FloatVector, BoolVector, ..., StrVector, StrVector, ListVector, ListVector]\n",
+       "R object with classes: ('fevd',) mapped to:\n",
+       "<ListVector - Python:0x115c53308 / R:0x7f90fe4f48d0>\n",
+       "[Vector, StrVector, FloatVector, BoolVector, ..., StrVector, StrVector, ListVector, ListVector]>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result.r_repr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " [1] \"call\"            \"data.name\"       \"weights\"         \"in.data\"        \n",
+      " [5] \"x\"               \"priorFun\"        \"priorParams\"     \"method\"         \n",
+      " [9] \"type\"            \"period.basis\"    \"par.models\"      \"const.loc\"      \n",
+      "[13] \"const.scale\"     \"const.shape\"     \"n\"               \"na.action\"      \n",
+      "[17] \"parnames\"        \"results\"         \"initial.results\"\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(result.names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = result.rx(\"results\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "$par\n",
+      "  location      scale      shape \n",
+      "23.0639415  1.7576913 -0.1628816 \n",
+      "\n",
+      "$value\n",
+      "[1] 1e+16\n",
+      "\n",
+      "$counts\n",
+      "function gradient \n",
+      "       1        1 \n",
+      "\n",
+      "$convergence\n",
+      "[1] 0\n",
+      "\n",
+      "$message\n",
+      "NULL\n",
+      "\n",
+      "$hessian\n",
+      "         location scale shape\n",
+      "location        0     0     0\n",
+      "scale           0     0     0\n",
+      "shape           0     0     0\n",
+      "\n",
+      "$num.pars\n",
+      "$num.pars$location\n",
+      "[1] 1\n",
+      "\n",
+      "$num.pars$scale\n",
+      "[1] 1\n",
+      "\n",
+      "$num.pars$shape\n",
+      "[1] 1\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(res[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loc, scale, shape = res[0].rx(\"par\")[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23.06394151991562"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.7576912874286912"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scale"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.1628816367715244"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Función mágica para R"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/rpy2/ipython/rmagic.py:73: UserWarning: The Python package 'pandas' is stronglyrecommended when using `rpy2.ipython`. Unfortunately it could not be loaded, but at least we found 'numpy'.\n",
+      "  \"but at least we found 'numpy'.\")))\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function R in module rpy2.ipython.rmagic:\n",
+      "\n",
+      "R(self, line, cell=None, local_ns=None)\n",
+      "    ::\n",
+      "    \n",
+      "      %R [-i INPUT] [-o OUTPUT] [-n] [-w WIDTH] [-h HEIGHT] [-p POINTSIZE]\n",
+      "             [-b BG] [--noisolation] [-u {px,in,cm,mm}] [-r RES] [-c CONVERTER]\n",
+      "             [code [code ...]]\n",
+      "    \n",
+      "    Execute code in R, optionally returning results to the Python runtime.\n",
+      "    \n",
+      "    In line mode, this will evaluate an expression and convert the returned\n",
+      "    value to a Python object.  The return value is determined by rpy2's\n",
+      "    behaviour of returning the result of evaluating the final expression.\n",
+      "    \n",
+      "    Multiple R expressions can be executed by joining them with semicolons::\n",
+      "    \n",
+      "        In [9]: %R X=c(1,4,5,7); sd(X); mean(X)\n",
+      "        Out[9]: array([ 4.25])\n",
+      "    \n",
+      "    In cell mode, this will run a block of R code. The resulting value\n",
+      "    is printed if it would be printed when evaluating the same code\n",
+      "    within a standard R REPL.\n",
+      "    \n",
+      "    Nothing is returned to python by default in cell mode::\n",
+      "    \n",
+      "        In [10]: %%R\n",
+      "           ....: Y = c(2,4,3,9)\n",
+      "           ....: summary(lm(Y~X))\n",
+      "    \n",
+      "        Call:\n",
+      "        lm(formula = Y ~ X)\n",
+      "    \n",
+      "        Residuals:\n",
+      "            1     2     3     4\n",
+      "         0.88 -0.24 -2.28  1.64\n",
+      "    \n",
+      "        Coefficients:\n",
+      "                    Estimate Std. Error t value Pr(>|t|)\n",
+      "        (Intercept)   0.0800     2.3000   0.035    0.975\n",
+      "        X             1.0400     0.4822   2.157    0.164\n",
+      "    \n",
+      "        Residual standard error: 2.088 on 2 degrees of freedom\n",
+      "        Multiple R-squared: 0.6993,Adjusted R-squared: 0.549\n",
+      "        F-statistic: 4.651 on 1 and 2 DF,  p-value: 0.1638\n",
+      "    \n",
+      "    In the notebook, plots are published as the output of the cell::\n",
+      "    \n",
+      "        %R plot(X, Y)\n",
+      "    \n",
+      "    will create a scatter plot of X bs Y.\n",
+      "    \n",
+      "    If cell is not None and line has some R code, it is prepended to\n",
+      "    the R code in cell.\n",
+      "    \n",
+      "    Objects can be passed back and forth between rpy2 and python via the -i -o flags in line::\n",
+      "    \n",
+      "        In [14]: Z = np.array([1,4,5,10])\n",
+      "    \n",
+      "        In [15]: %R -i Z mean(Z)\n",
+      "        Out[15]: array([ 5.])\n",
+      "    \n",
+      "        In [16]: %R -o W W=Z*mean(Z)\n",
+      "        Out[16]: array([  5.,  20.,  25.,  50.])\n",
+      "    \n",
+      "        In [17]: W\n",
+      "        Out[17]: array([  5.,  20.,  25.,  50.])\n",
+      "    \n",
+      "    The return value is determined by these rules:\n",
+      "    \n",
+      "    * If the cell is not None (i.e., has contents), the magic returns None.\n",
+      "    \n",
+      "    * If the final line results in a NULL value when evaluated\n",
+      "      by rpy2, then None is returned.\n",
+      "    \n",
+      "    * No attempt is made to convert the final value to a structured array.\n",
+      "      Use %Rget to push a structured array.\n",
+      "    \n",
+      "    * If the -n flag is present, there is no return value.\n",
+      "    \n",
+      "    * A trailing ';' will also result in no return value as the last\n",
+      "      value in the line is an empty string.\n",
+      "    \n",
+      "    optional arguments:\n",
+      "      -i INPUT, --input INPUT\n",
+      "                            Names of input variable from shell.user_ns to be\n",
+      "                            assigned to R variables of the same names after using\n",
+      "                            the Converter self.converter. Multiple names can be\n",
+      "                            passed separated only by commas with no whitespace.\n",
+      "      -o OUTPUT, --output OUTPUT\n",
+      "                            Names of variables to be pushed from rpy2 to\n",
+      "                            shell.user_ns after executing cell body (rpy2's\n",
+      "                            internal facilities will apply ri2ro as appropriate).\n",
+      "                            Multiple names can be passed separated only by commas\n",
+      "                            with no whitespace.\n",
+      "      -n, --noreturn        Force the magic to not return anything.\n",
+      "    \n",
+      "    Plot:\n",
+      "      Arguments to plotting device\n",
+      "    \n",
+      "      -w WIDTH, --width WIDTH\n",
+      "                            Width of plotting device in R.\n",
+      "      -h HEIGHT, --height HEIGHT\n",
+      "                            Height of plotting device in R.\n",
+      "      -p POINTSIZE, --pointsize POINTSIZE\n",
+      "                            Pointsize of plotting device in R.\n",
+      "      -b BG, --bg BG        Background of plotting device in R.\n",
+      "    \n",
+      "    SVG:\n",
+      "      SVG specific arguments\n",
+      "    \n",
+      "      --noisolation         Disable SVG isolation in the Notebook. By default,\n",
+      "                            SVGs are isolated to avoid namespace collisions\n",
+      "                            between figures. Disabling SVG isolation allows to\n",
+      "                            reference previous figures or share CSS rules across a\n",
+      "                            set of SVGs.\n",
+      "    \n",
+      "    PNG:\n",
+      "      PNG specific arguments\n",
+      "    \n",
+      "      -u <{px,in,cm,mm}>, --units <{px,in,cm,mm}>\n",
+      "                            Units of png plotting device sent as an argument to\n",
+      "                            *png* in R. One of [\"px\", \"in\", \"cm\", \"mm\"].\n",
+      "      -r RES, --res RES     Resolution of png plotting device sent as an argument\n",
+      "                            to *png* in R. Defaults to 72 if *units* is one of\n",
+      "                            [\"in\", \"cm\", \"mm\"].\n",
+      "      -c CONVERTER, --converter CONVERTER\n",
+      "                            Name of local converter to use. A converter contains\n",
+      "                            the rules to convert objects back and forth between\n",
+      "                            Python and R. If not specified/None, the defaut\n",
+      "                            converter for the magic's module is used (that is\n",
+      "                            rpy2's default converter + numpy converter + pandas\n",
+      "                            converter if all three are available).\n",
+      "      code\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(rpy2.ipython.rmagic.RMagics.R)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([4.25])"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%R X=c(1,4,5,7); sd(X); mean(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\n",
+       "Call:\n",
+       "lm(formula = Y ~ X)\n",
+       "\n",
+       "Residuals:\n",
+       "    1     2     3     4 \n",
+       " 0.88 -0.24 -2.28  1.64 \n",
+       "\n",
+       "Coefficients:\n",
+       "            Estimate Std. Error t value Pr(>|t|)\n",
+       "(Intercept)   0.0800     2.3000   0.035    0.975\n",
+       "X             1.0400     0.4822   2.157    0.164\n",
+       "\n",
+       "Residual standard error: 2.088 on 2 degrees of freedom\n",
+       "Multiple R-squared:  0.6993,\tAdjusted R-squared:  0.549 \n",
+       "F-statistic: 4.651 on 1 and 2 DF,  p-value: 0.1638\n",
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%R\n",
+    "Y = c(2,4,3,9)\n",
+    "lm = lm(Y~X)\n",
+    "summary(lm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZPz2RnghtzJMu4JIaAlM+9cC/4CFEL2PudsGmZOjQofLQQw9pOuokwhnRxnbccUfla0Q+g08RLQ+exnxOOfA8+A3CJRHf4B3wJPgc/IgIgUQ84zfzPxGziEoID3zyyScVDyJh0Rf4AhHbSBg4pqmRPhyT1WAXDjh1MHbh47itdOCBBwa9e/fWaFn+WtrPXXfdNejbt69Gq3JhCTWyFmU65hs4kLR41/iAe56IVEJUK6J2NUcu7J9GP4mmIwqJAyaIlh397fP5cogK5hYc/rJ+uj3yYNVVV9Xv0fwuXFuw0047BY6patCSXXbZRftL5BbHfNVpe5999tHgH24g6DWCh7gBFoA30b+IQkYgFIKQ4LztFi9alxsEAYFHCOhB/fTJMV51+sYpHHyuvvrqgHSOcWqUF6Kc0V7qohwisfDM3cuizuduQOunY9q5aGdc839u4ZP77q8R8cx/pxz/nU9+008c2/mj7vD9Qt/d6jJRukL57frfz8ywMCzKPQai7zXvPZGjfKAm5jSCODnBRecyJrA11lgjcAxP50aCGRF9kMAULtSlzmWOCWpgJMekAyfoBMccc0xAEA7K2nPPPTUQhu8Xc4tjrhr4wjFb5VPMnQTMcExT5z4XrlXnTXgO86EL66mRDAnMBBFt0Qlo+r25f5RJkI1iyPMQtyjQIB8uRKkG/2g7zBGrjp133lmlWVYZZ511lq5EWE2wsoAcmLnYvS6ak15L+48YqUhr6NiJWUrMWaRCYssS39aBnZMIHYPRlZGPtcqqp7l2kIbYrZQVJlylHNPKrYB82V769L99Hl8OdbP6ChMrHyRn4shG8yMBs+IhHuzw4cPFPWztL3kcQ9UVGWmQnFkFutCW4iKNqbbBLUw0n2PaqnFA4mXFiKaBFRQ4UScrKfAjL3GHkXr5zT1iI5OO8tES8JuVHJI/0jN1EKeVVR0rS1aJxHrljzp8vGVWlpSJJE9sVdpKGeRzCwAdI/SRP2K/EueU1R0rWLDkOxgxllgV02e0JHxCrEL5o03QkCFDNFYwmgvaxXOmLrQB3gbAveSaNrxHQxl+64P77gXRNOF/jHWfJnzdvhsCpSDA+wU5JqTjzY+5QuMvPEbJx9xSjCrT10feQoR0yHtCWt8ODGB533w7mSuI104caTRb2Mfw7vo8qFhpK3Mwn1z3+XmHmQcgpEo0m7y7lMU8hGRIrGliOaMdRVPIO+wYrGrymA8cI9T5Eo0p8cYdwxQndIgT8pQXoP0j5jOxuDs5jSft2W233bSNaABdRETVbJLfRSnUWOjMW7SReQgeRj1IoPxmTuE3PAeeQCxrx9T1Olpd5j7mtyREmdRFeUnJ8xDSgw28Ak1vA1w8aSGWzhAwBAwBQ8AQMASyQSDnhpRNcVaKIWAIGAKGgCFgCCRBwBhwEpQsjSFgCBgChoAhkDECxoAzBtSKMwQMAUPAEDAEkiBgDDgJSpbGEDAEDAFDwBDIGAFjwBkDasUZAoaAIWAIGAJJEDAGnAQlS2MIGAKGgCFgCGSMgDHgjAG14gwBQ8AQMAQMgSQIGANOgpKlMQQMAUPAEDAEMkbAGHDGgFpxhoAhYAgYAoZAEgSMASdBydIYAoaAIWAIGAIZI2AMOGNArThDwBAwBAwBQyAJAsaAk6BkaQwBQ8AQMAQMgYwRMAacMaBWnCFgCBgChoAhkAQBY8BJULI0hoAhYAgYAoZAxggYA84YUCvOEDAEDAFDwBBIgoAx4CQoWRpDwBAwBAwBQyBjBIwBZwyoFWcIGAKGgCFgCCRBwBhwEpQsjSFgCBgChoAhkDECxoAzBtSKMwQMAUPAEDAEkiBgDDgJSpbGEDAEDAFDwBDIGAFjwBkDasUZAoaAIWAIGAJJEDAGnAQlS2MIGAKGgCFgCGSMgDHgjAG14gwBQ8AQMAQMgSQIGANOgpKlMQQMAUPAEDAEMkbAGHDGgFpxhoAhYAgYAoZAEgSMASdBydIYAoaAIWAIGAIZI2AMOGNArThDwBAwBAwBQyAJAsaAk6BkaQwBQ8AQMAQMgYwRMAacMaBWnCFgCBgChoAhkAQBY8BJULI0hkCNI/DHH3/UeA+s+YZA/SEwQ/11yXpkCLRuBL755hvZfffd5fPPP5fp06cLzLdHjx7yz3/+s3UDY703BKoMAWPAVfZArDmGQFoEYLSrrbaarLvuurLMMsvI1KlTZcqUKWmLtfyGgCGQMQKmgs4YUCvOEGhpBH7++Wfp3bu3rLnmmvL222/LwIED5ZlnnmnpZln9hoAhEEHAJOAIIPbTEKh1BDbYYAM57LDD5JZbbtHPBRZYQGaeeeZa75a13xCoOwQaAkd11yvrkCHQyhEYOXKkqqGff/55efzxx2XQoEGyxBJLtHJUrPuGQHUhYAy4up6HtcYQSI3Ac889J0i97P9CGGKdcsopctJJJ6Uu2wowBAyB7BAwFXR2WFpJhkCLIsDe79577y3vvvuuzD777NK+fXttz48//ijzzDNPi7bNKjcEDIHGCJgE3BgTu2II1CwCn3zyidx7773SoUMH6dKli/aD/d9OnTrZPnDNPlVreL0iYAy4Xp+s9csQCCGAL/AMM5jCKwSJfTUEWhwBeyNb/BFYAwyBbBGwQBzZ4mmlGQLlQsAYcLmQtXINgRZCwAJxtBDwVq0hUCQCFoijSMAsuSFQ7QhYII5qf0LWPkPgLwRMAraRYAjUGQIWiKPOHqh1p24RMCOsun201rHWjIAF4mjNT9/6XisIGAOulSdl7TQEEiJw2223yUsvvaSHMCy33HKyxx575HyCExZhyQwBQ6ACCNgecAVAtioMgUohMGDAALn66qtlscUWk/XWW08mTpwo3bp1kw8++KBSTbB6DAFDICECtgecEChLZghUOwIvvviiTJs2TR599FFp0+avtfVuu+0mq6++ulx00UVy6aWXVnsXrH2GQKtCwCTgVvW4rbP1jMBnn30m66yzTo75+r727NlTPvroI//TPg0BQ6BKEDAGXCUPwpphCKRF4L///a/MOuusjYrh2u+//97oul0wBAyBlkWgxVTQTAioydq2bduyCFjthkAdIXDttdfK008/ndej3377Le+3/TAEDIHqQKCiVtDEoz3mmGPk7rvv1t7DgAkUv+OOO8rRRx8tM844Y3WgYq0wBGoQgcmTJxdUNc8111y5wxlqsGvWZEOgLhGoqAR8/vnnK4jvv/9+jtmyOh8yZIjccsst6i5RlyhbpwyBCiDAGcD8GRkChkBtIFBRBvzll1/Kdtttl2O+QDTTTDPJFltsIa+88kpJiP36668CY0eSNjIEqgUBtlYGDRqk47ta2lSN7bD3txqfSnW3qaGhQRsYBIHMEPwm06WNTG/IlpVV6v3NttXNPLddd91VDjjgANlmm23UT5Hk48ePl5tuukmeeOKJJnPjz/jGG280SoM6GyOTPn36NLpnFwyBlkLg8ssvlyWXXFL69+/fUk2oiXpvvfVWXaQsvfTSNdFea2TLIQDD9eSZcLtfJ8rPs3TwlzP7rNT7W1EGTECAe+65Rx544AF56623ZPr06bLIIoso821Odfb999/L6NGjGwE8atQoZeZI0UaGQCUR+Pzzz+XVV1+VOeaYQzbeeOO8qj/++OO835X4ce+998qJJ54YWxW+wAToqEaab775VAtWjW2zNlUHAj/99JO888470rFjR1mgXSANc2TPdMM9rdT7W1EG/Oeffwori7Fjx8rQoUNl4MCB+h3JlutNGWEtv/zywl+UPvnkEwmvjKL37bchUA4EmAxY9A0ePFjuvPNOueqqq4QQkD4ARjnqbK7MzTffXNZff315/fXX5YILLpBTTjlFF7gcTzjnnHM2l72k+7g+2fZPSdBZpoQITJ06VbAbQqM076f3iizaTSQDBoxv/HHHHScdOnSQCy+8sEXe3Yr6AV9yySXCSqZfv34aJm/fffeVL774QiVYpGIjQ6BWENhpp53kxhtvlEMOOUSeffZZmXvuueWhhx5q0ebPMMMMKo2//PLLQgSsLl26yDzzzCP77LOPwITTEsccnnTSSfr+vv3229K7d28h1vT2228vv/zyS9riLb8h0AiBH374Qd577z1ZeqmlZN63rhX56k2RDl0bpSv2wpQpU2SZZZaRO+64Q6688ko544wzii0ik/QVZcBIq3vvvbe+sLgebbXVVoIun5X7a6+9lkmHrBBDoBIIEGt5tdVWy1WFipeXuhpoww03lNNOO00uu+wyYY+Vdw3JOC39+9//1iKOPfZY6du3r/zf//2f+OhbbC0ZGQJZIvDtt99qDHM0n/P8Mlbkt5+kYfNzpKFN+tgRn376qSy44ILaXDxxvGtslu1PUlZFGTAW0Ly0iP5YLiM1sIeLtSiGWUaGQK0g0L17d9lrr72Elxf12P777y9rrbVWVTSfhQEqcfaon3nmGdl5553Vzz5t49599119T3v16qWnK/n+cugDGBgZAlkhgE87AtuKK6741/bJtInSsNEwaZhhpkyqWHXVVWXllVeW2WefXbVGBLBpCaroHjBxalHbhcPlYYjFtbj93ZYAxOo0BJIggA0DqtcNNthAFl54YXnhhRd0jypJ3kqkgQmvssoqqhpu165dJlWidke1jcEkBpVsIe2yyy5yzTXXKMPPpBIrpNUjgLsqW5Ndluss7WabTfFoWH7zTHFB8/rII48IB5gsuuiiOa+cTCtJUFhFGTDtiboboLozMgRqDQEMBltKbZUEqyOPPFIefPBBDXJDO//xj38IUnsagukiUSOZwOAfe+wxXXhgXc1ecFM0cuRIOffccxslQaqmXAwyjQwB3FInTZokXReaRWb+3h2hOecaZQVlzTXXLGv5zRVecQbcXIPsviFgCKRDAGmcFf7JJ5+s+9JYROOe9K9//StdwS4320YwTGijjTbSP7wQ+PO+mXoz8o+JDn//KB188MEtYn0abUfcb7YXfvzxR5l33nnjbus19v1nmWUW/QsnIh/x7gk0RDkYw2VJWAaj2cDwrl4IewLwXGme36Ttk2eKbHdVvXStYD8qugdcsBV2wxAwBDJDABepNdZYI8cQF1poIcFdKC3hi48aGreN/fbbL2f5jPsVzL4pglHgChX9g0FV44EsuEyyx496vRCBB/vgxDSIEkZpw4cPl6eeekpGjBgRvZ36N66cWKXXA7F4wy6IRUWX+UXavniRtNn0DGmYNdtFSzViZQy4Gp+KtckQSIEAVs/4AMMEnn/+eTWcwgAyLWFRjcEVkgr73ttuu21dHnMIY8VepakzlAnAAhbYsDRF6667rjJyGDrqVYyL2HckBKcnGJAPTOSvtZZP+v7BBx/oAhG3ubYTXpY2WwyXhvadFQLU0TDmeiVjwPX6ZK1frRYBH9gGJgKjxCXpww8/TI0H0YFQJWNEiT9wjx491K2QSbScFEz/U6Y7lWTu75n8veTE95+/NFEz6Q9WsXvssUfB9EjySLcrrbRSwTTcePjhh+W8886Tb775Rhk2PtnXX3+9rL322qq2f/PNN2WTTTZRQ1Tcx+Ki/TVZQQ3fZPGCDQB4r7DCCqoJaei+hzTMubD2Cu0Dlsqc5AVO9Uj1s4FQj0/H+mQIFIEAKkn87JnUcK9o37695iYwRxZ7kFtvvbWqntlTRsUNE8bYi/1lrKHLRg1tpGHxHn8X3xDxA016f4aZ/y6jiW/s+fL35JNP5lLBLJBiIdTpyy67bO5e0i9Iv2gR2DOGscCUYTK4sLFYGjNmjDL+iy++uNkiWQDU8v4vR9MyTmeZaUZZeo7/5uwAGmacNdd3XFY9sXBhHNcbGQOutydq/Wm1CGCUc+qppwoxodmnRaUHESqyU6dO+j3NP/Y7OTaU6ESezjnnHMGnkr3ccpEady29fsHi094vWHDoBgZkqPWhm2++WRcgoduJvuLTCvOFWCBhpMUWAS43hDOFcIlJQlk8zyT1lCMN/cZOYY5ZZ5Ql3r1UGtY6ILYa4j7jy87znTBhQmyaWr9oDLjWn6C13xAIIbCUC9nH+dpRQuLIgojHGyUCfdQ7oY5uSiWdpP9xVuKonXHpwqecrQMs2OuZMAYkjOn8c88mi712lshSfaRhwRViu4yfLqp6orgRLrIeyRhwPT5V61OrRgDV5u67767SA6pTmC/7tVnEg27VwJah81iTH3XUUeoihvEXhwLUKxEvHOaLXcJC4x6QhpW2laYCbKDm//rrr+sVDu2XMeC6frzWudaIAIwWqQoLXALOY0VaLXGqa+l5cHZ5c4QLVhxx9rknpFvopZde8pdU9ex/3H777epSVEzEslrzAw4fJ0gM5mDmDaVh/qU8BK32s6AVtN/wJpoOUXRYnZWDsvBPLEe7rExDoFYRwBiLk4qwWEbiIMoUEayMqheBYpgvvWBvFKZWC8RigXG45IJz5g5AMOb715OLZcCY1x922GHqs8YqELcDfqclO84sLYKWP0sEODcXaZHjBOuJiE+NhTKGOvgCX3HFFXZmbx09YLYViLSFIVe1E5oXjhPsHIyV+f6YVO3NrXj7Yhkwlnn4DnJGLw787FFgqZeW7DiztAha/qwQQHWI+weLQk7iOussZxBSJ8R+75lnninzzz+/fnL0Wkudd1onkFZVN6ZNmyazuUMKqjGCWBgojhPE/3y5X0fL3F88JdKxZeMuh9tWLd9j94CxdEQywPn5oosuUgu06CEKpXQAvy+MQ3Bex0cxfJyZN8MvpVzLYwgUgwCrcoxfCOeHkz8hB2HCrNRr+VQugjjcddddOSgeffRR/Y4bElqtJZZYInfPvlQ3AqiXC8WQxh95kUUWabIDPhY143vixIlq+NRkhoxv4vPM+7X8gjPLbG9/Jg1bnJ/ZUYIZN7VFi4tlwMR75QFi/t21a1c9szeLFXSa48yw5AyHb/Oo4VPWpk2sIO+T2KchkIcAEsTmm2+uzJcbSBL4HIb9W/MylPADS+TOnf8Kp1dC9pKyEJyhUIAIf/h4SQVbpoojwIKJfdNjjjmmUd1NHQ7hE7P1QIjHQw89VNhG5HdThJ8zPt3M93xPM6f64wQpa5ZxI0U2PV0a2rRtqvpWey+WAeOvRhxUDLBQ0T300EPSs2fPsh1nxuHhzR1nhn8cAyRK+M7h4G5kCCRFADcImC6BFY477jj5z3/+o5qeLBaZRDO64YYbVHtU6aPO8AHmzygdAl999ZXumY8bN04jVvn5EKkzbCyFIRTaBX+gBGphpE0WeJyZPN9886nQgCQYnt+ICc01AqX4CGXRa1iws5XgiUAUjFkO1oBoIwuu999/X8uI888mHadXnX/++XzVGN4sxJgzWRyyRYE2iHp83Gs+MeAj8lQpBGZfT5rotJwr/2V3sOxGpRTTavLEMuByHWeGJTWrMVZ3W265pRDSjiPNCM/GnhyGI4WIAclflBgo5Y5FG63Tftc2Akxkl156qW6BPP3003oYNxNgeHIttoejRo1Sxvvdd9/pNstiiy1WbBGZped9IiJWmAh1eNlll4Uv1cx3MM3CBoUOo4ZvzniJeQjpEwZKqEjCe5KPWNg77LCDBuQAX0JV4ubFwQyDBw+Wvn376hwFM9xqq62UMWL3Ql4YG9sDGPyx8MNQbtCgQSqZcr5y9BoGgowpbBOwVWDuxPiKtl933XV6+hTMFymTdtAe6owS+7AczoFXy4ABA2TxxRdX5suWH22BEaPq9oRXCnWXQnqc4KTx0nXOX2QmtzAxah6BWAbsjzPzjC2r48z8aSoEOj/77LP1NJXmVCPNd8FSGALFI8BElkWA91deeUWD67Nnh30D2zZITEzgLUXEbN5ss820eiZUjClRidcq8axgHFmQDwXZXFlEvcJO4O6771a1PqpgtsEwSmUrjYAZSKHEY95iiy1yxYE3e++ocLFxwZZmjjnm0AUfmkQkX5ge5TNekIAJHRq95gvkNCAYNFGh7r//fjn33HO1DO7DxIcOHaptfOyxx2IZsC+HT9qGQS3RpeiLdzUNpwEfDo8ohuATLE5+/ma8rPDRlTLjVsOLyd6q08YyYFZMSJuoSBhgOIoPdL6EaYmHtNtuu+VOU8G/mNWlnyzSlm/5DYFKIcCRcpxqgw0CkylH01ULzTjjjMIfxORP+3ifjzjiiGppYlHtQMVbzljTcY3BhQvCI4RFFnupELYCMFE+/WEISKGeWCjAfFFDY3R6wgkn+FtCmQcffLCeE0xwDoyp2G+Nu+YzjRw5UqVcVNAYCK6++uq5IB7dunXTZCxQGIdJKJoHZuwPnWBvGY2kP3QiSXlI5Vg6/zHtW1n+Y8d8NxqaO80oSf7WnibWeomXlhWVP86MvTFWfWnJn6biI8Kg6mFPgtNUjAyBWkCACZFTWlBNsphEm1NNzBcMYRhM/Pwdf/zxKhlx3JtRcgS8ERKxmhFEOIABf2q0gXiEwKSQTpEkn3jiiVzBPh9zKLYpaPrI269fP2XaSLvMq6+99pqeIPWvf/1LJeDoNV9gnz595LnnnhMMm2DuBFTxlvpoWoqlaB5Os+JcZ9rFnjaHQbCXnYTAAM8BaLmGT2XGfmdJQ4e/DgBJkt/SiORJwFE3BgBiQKGGwbgEd4001FKnqaRps+U1BFCxMfFhXIXRDVqbShtYFfMUMLwJW0Mj5aCuNCoeARgw8x9qZjxD0AQyJ2JDsO+++6pkDsMiWFGUhg0bpmOFe3hwoEmEkTJ+MNLC7oD9XLQV0Wt+C4N9aAQXVM1ElOLsYMKMZkW+f1dffbXa5Pj+NVc+Cw8kfPqmLqq/LyYNMyVj3M2V3ZruN7jJJfAdZq8B1VocIaliZFBt5I2wGEBGhkCWCKBeQz2HmhA1H6rcsGVqU3UNHz5c9/X69+/fVLKy3CPoPZ4F7PHRBwgJOKwOLUvFJRRaK+8vDJTFl5cgkYbZI+Y3EuSxxx4rXr0bhQFPkqiBH8wUK+YwxV3z92F4PMtyqeKj/fP1xn2i7n77rbdkzulTZKnu68clqflrlXp/8yRgLO14wKg6WPWFqVbijobbbN8NgVIQQLXGFgwGNOyLYeFaaHItpfxy58GjANXggQceqK4r1Bed7LNqAxa05WIKWbUxi3KixlswLGxXkF/YD+ZM5EIUZb6ki3secdd8mX6/2f/O+jPav0LlY8j19ptjZP6vnpXFV167UDK7nhCBPAaMXxirLNQe0QkHlYmRIVDPCMB4H374YWW87PUdfvjhssoqq9Rcl3mHkdR9pLmsOsDRcIcccoi6vGBBjhEl1tVrrLGGXO8M0pLuHWbVnpYsB6MsjKcYM+VcgKD2Rs2L61xLkx4n+NYbsvD4h9xeeAdp6GihJdM+kzwGjJUef2z6M/Hg4wbxQuOn1ts5aBsZAvWGAOo9XERuueUW1f4QnAPDm1qlnXfeWRfQGNb4CFi8yywo0hD7lWxDIfFhxYu7DYt2AuSMGDFChgwZkqb4mssLUywnY0S6ZqFTDfYGLATY8138z3GywNJdpc3K29Xc86rGBucxYPYqMAYAaPa8iNcMAb6P2FKNnbA2GQKlIID6lGhvMF4MSbDK9xampZRXLXmQRukHBjXeHSlJ+MLm2k8wDIx22ANEZennBxbmGBjVGxEAJIwbCzX+PHm1LYZVzI/eEIvgF4wtH7XKpy/2E4kzvO9MfpgycRqYn5G8ieoWJtpMu+LU3uF0xXxnb5qgH0RZm1fml4b5ltTsuEZhNZ0F0S9wbC7GdRZ1VVMZeQyYh0ZEFVbOHTp0yEkBDAIkYyNDoB4QYHK87777hChFhAg8/fTT1WCqHvpGHzAMImAE8a6zJKx+ObQCNyy2qTh0fuWVV1brcNxpmiL2DuNibcNk/CKhqfyVvIdfK9GnsCanzUSN4jsq56dd5DTP3IgkhZEegU4IwcjeO+5HGGQ1FdUvaV/ijLcwDsI+BwMw2hUNZMQpdli9E/UqC8Iu6MN33pBlu6yiGIjMlysWy/q3nDFWFip4hDz21AmT2ZoojwHTcVY5rU2V1JoeeGvuK4YzTFhEZONELsL81WPsZAyCsMzFkAxGCREeEQaahvBrxUvCawxgnmxPob5vLvQmYRWJHhUlwjhWm9aBkJ0wYNTsGOKdfPLJGjwD5kBgjrBUjAYF1T6fMGTCTmJM1ZQkR7xofIhZvHgicAcaBh8zGqkTaZYFAKEhcT2CGXo/dMJOwmg9EQaS8R0lQmCGY1iTjm0DYi94NygWDUj29I/3gcUGxP7+Uw/cLut0njd3LVo+kivPFrc3pHIIZgpjZqHCe8aCkHqjcahJy8IMF9dwpDPagi87ONbyVhD9a44aMWAy8KBRX3GaBi8YxGEMuGMYGQK1hgCMgji8qEkxLiQGOfuY9UqoQwkAEaYsjCiZF6Kx3FG7sqBhf7gpqQ+DsDijMO+GFG5r9LtXu/rrTOjhA1iS3seS2DM4X1bcJxbkSPlQJ6f5Q6XPPAjD4nhWGBMSKBGwYIzEtsflC4ZN4JMo9uE6cF8CKwJvYNCGBI1/+Y033qiuYkjfSLYcEMKZzjBpGDZuZARD4mxn6oMxYeVOvUi9BANBHc0iAMYMM0YLEo1hjWQM88egDgMyAoKwqCI2P4Z7+B8fdthhagO0y/ZbS5d5f5drg4Vkx51+VTe8cF/4TtREFl/Uz4KPdw0cNt10U2WibO2woImLQ41mlS0Ngo0Qg8ITdga0hcAgLBYuueQSf6vuPmMZMAOEVTQrXa9eYNAbGQK1hACuc0xm/LGAvPjii5uV1Gqpf9G2MpkyQSNNRAPqIwEz0aWhlozlHt5rjJuLktz3UaqawwCmQsxkGBzMF4aLNArThKkRAxoJFGa15557quoZq2gYG9ImCwLiEpAGBhImJE9C8iLdsQ1CuTBlVNssZrAsR8XMPi7aC+pn4QNTRf2McSyLFi8xIi1ynTYxV/uAKzBwpNJwDGv82JE4/alNtIs2X3nllWpgy8KBc3zJe+s/b5JuC7WRoZfeJ3+2nUX7Tf4oYbBIeEz6RXQ4sMMHncUJ0jqLDIh6o3Go2VdmUUEZpD3ooIN0oYO0TIQubAt8pK1ovfXyO5YBMzhQs7Qmt4J6eaDWj79UYOzHoW7u1auXrsCbUgnWC2ZM9khq5XIjbKlY7jBcrxaNe1Zp70fLhKkiORL9DCaM6hxsEUggpG/GFdIr++y77LKLXsf6nAhZMCGiYKEtYBx68saszK8Q0bVg7jBRb8CF4MOhC0iG3hUU1W6hWM9IxzBjLyj5KFlxMaxRc0O0zcfkR7JmoerV4UjOSNejn3lQXv92Zjns6KGaB40R+VFXe8IC3LvpwXB9iGGYJ/v6XEPK9hTtDxKuD1Tj/ahZJLHQQ+pFy+At+n0Z9fbZJq5DgEroM47eYgXFH6ukchAGMUaGQBYIsF/G6puXFqmBMXv00Uc3uR+XRb3VUgbqUlR37OOhBuQPwxY+kSbSUmuJ5Y7UiXoUlToSGH7OGFnBFCEkXPZLu3fvnoMUhsdetmeuuA4h3YYJRso+KFImBHPHYhpG5tMiCVMOC6m4Pd1weXxnYYn0y3iHPBOkrTDScAxrL1BhH8C+K2pn1M0cwIAaesK4sao54R3aZPu9HANdKTa/VuT+wVzZ/4VYrLAYQd0M38CfHg1AmAFHNRfsD6Myh/zJTKiwWTiz3cmCD21sEhy0kBr8FysBs9qMHknljTnS9NEc+dOgZ3kLIcCh4lg0YwzEcYCo/7LY8yxUXzVfZ38bdw7UiagFYSZM/FjqFjq0PWl/Wkssd/qJ9OXHEEwMiQ6mCpPBWAprcO9jDX7gzsEcMFOYIvu3cQZEhAPlGEJUzEiuqImRSJGaYbpIwniiIAV6ibWp5wNTwweb065gaCwcIBhwXAxrXxYSL+OB/BwmcuXll8iuO24nbWedU40TEcKayk85SN30GSM1+kw54IQETCwJ7iPd8xdHYMzeMPGt2ZunLfSfBQ4LR6R+LO29u1dcGTV/zXW2ETlGGbhN9MCpJwIHTOA20gMnVTRKV+wFZ3UauAcWuAcSOHVE4FaCgRt0Adcdwy+2OE3vjCUC57tcUl7LVNsIOOkhcPu6gTtpJnDGMQHjtlqI8ez2+CraHCeFBY55BE5aC8aOHRs4tWHgXFkCZ7QTOKmkom1JWlk1v79OZdyoG24vM3ASWd51p8UL3H5w7hpzmjNgzf2O+8JziVK4PqfNCZyUGk1S8LdjVgHtiJKTKHWOddJk4BZkgbMDCNy+by6Zk1CDd199Nnjr1tODHz//uw8+gc/vf8d9RvGgTLd4iEsaey0OC/oSdz22gDJcrNT7GysBY3rPXgKrKiLoYBKPlJGWWpsjf1q8LH88AmhS2I97/PHHdaWMFWnYNSQ+V/1fRY3HfiRWqRj2oGJEomC/knjWRsUh4FW24Vx+rzV8DakvbJWNJOejCIbThb/7Pd/wtbj6wveb+l4oVjTSIyph1Lp+u4+5HWtr7r075lVp88nTssKaG0ubxVdsVEUS6dNL3T4z+7jeJclfa+ozDgsw5a/eKZYBu5WH7hkBALp9VFmoCvyh1KWCksaRv9Q6LV/9IIDFJotDJ9Gp8Qaqwbnmmqt+OpiyJxgKeatRVM7s70FY62JEY1Q7CMDo4xhTsT1gLzXMfMnPfjP7xjDtmX6eKEv32V7aLLBssUVb+gwQiGXAmLLz8iJl8MleSHSVU0rdhRz5MXtvzpEfPzPiUUcJX2VvwRe9Z7/rAwEmC5gtrgpIdYzLsDVmffQyfS+wqiW4CAY1SDvsyaElYOHclG9q+pqthKwRQPLs3LlzqmIx6MIS20u+FAZTx/UHrSa2Pp1X30AaZv4rgEaqyixzSQjEMmAsKTEiYEXNJy/xGWecUVIF0UwECcD6L0xOha8b76huChGLAu/jFk6TxJE/nN6+1w4CxJqF8cJQ0MDAeItRbdVOT7NpKdoAfIGReHEB8WpJDGV4p42qGwHclLAa9lqdpubDJD3BnYoyIdTC+DAfMGh/aSe/6NzenNCTpA5Lkw6BWDckisTpnD0JTNRZPeMXl5YwlWf1RZxpLOAwOYfwlcOSzsgQAAFcPrAExTEf60riDA90kdmSMF8sN7EyJZoODLu1EZIT7jFeY4U1rDHf2hgF+MUWshgupQdYavt9VCysL7/4Apl16meycLvpzWocS6nP8hSPQKwEjAk8E2CYYMj4eKWhloykk6bdlrcyCBABh5B8L7zwglYIM2GLIWwIwkEhuBvhS0lknfBRbaio8RskcALuIEToQZrI+lCCyqBhtbQmBGCQqIWThMpMigvbEQT6QKoeduLx8uHz90un2X+TBbpvlrQIS1dmBGIZMA73+GFBhBDDoIMHmZZaKpJO2nZb/vIiwLiA8aI6ZR8Tv0hiN8NAiSnLtgUB7zl/FnUq588SdAPrXpi1D4gAYz7iiCNy2hrGLdoVY8DlfX5WenoEGM9oeFg4ZkWUdb3zA4exv//qM7Jk+1mlfa99sireyskAgVgGHDYBx9iFGKC4JDG5pSEfSQendaLLEGkGp22srH04tzTlW97aQoBzpzHy++STTzTsHirjRx55RBkwe74QrjW4UcCAienMmPEuHlh3EgDeM2CiEBFflrEKsYdcKISfJqizfwS0Z+ESR2wh7bXXXnG37FoVIACz7OQimWVNbPtxItIynd0pR4uvkHXxVl5KBGIZMEdBEY8UQjWCZELM0LTUWiLppMWp3vPDePHdxaUIS0/2eL21rguqkXduLC4TLqiEQoLVJhbRnrgXNlRxAVnUvgB/cyK3wbALMSRfRj19sgAhspIn3l0sYNkPxu7CqHoR8CEss2zh12Pflc8m/6ihLc1rIEtksysrlgEz0YVfZE7iiLNALqUZceHwiN1rVN8IwAywqMflDOK4N0Lx4dMLoW4e6AytkNSID4uxHuH5OE2FI+AgJDj2yIjxDGPxErPedP9wl8PqE6YLY+b4wSxCqPryq/2TGND+fGM0S5xRy9nehOnklB2j1oPAxNcflnFfT5MV197MDtWp4scey4BR8bFiwtAFRowRDGqMhRZaKJElahX315pWIQSQcokHi98h2hOYKcYgSKlsOWBdj4VzmLB+xnUGDQwSMkZZMFk0JxBjkTEJg0Vlx2fUMppDwIkf25qJfXEWIHgWEMGOLR8wR9NgVH0IsDhNelRiktZPeP42+WriZOmy/nbSznmyGFUvAo0Y8JtvvqmnpzBZIj1wvijuIEymqPSMDIE4BLBAZpHGAdxE8SFgBuMGNTMqYc5rxaKZiYYTinCRIDoTJ21xOgsnsJDPa17wYYyj9u3bqwtb3D279hcC77zzjtpY4F8PsXDGmNKo+hB466231CeXZ1QKEVISjxWEJg4iuetf10ufFReSPtvtJ+3mWbCUIi1PBRHIY8AcjcXRZQQ/8IYtqPSQVjjM2/YRKvhkaqgqVMT438I0GSvEZSYKE8ZVuK5h4Uz8Zr/KR8pl/xaLZr8XTOAIzlI1So8A2gYM0TiNB6xRxaPeN6oeBFgc8U7g91vq/jyeKX7BiuEsAWtWX76j7D30Ijl82uxq4Fo9PbaWxCGQx4A5YBprZM98yYB6BBU0KzUjQyCKANLt6aefrsEziMLEQd9IvRwx5q1uOTsV9Sd/2BOwH8liDg0Le5RG2SIAtliHo6InuMPBBx+sh6tkW4uVlhQBmC1n2oZjO48fP16wWse2JmxImKRM7B+wnfjpp5/U7mHKt5OlX//+gnbojIuvUfsInj92AEbVjUAeA2ZFFQ56QNOxzrvmmms0ulB1d8VaV0kEmECwTmYFjwEVMWeZSHBZg+ESPQ3/W3x8YbJIw8cdd5z6lCOZpQ3qUsm+1mJdhJHl8JNyE0wgzSk+5W5fNZTPIogtAC+t0ibCQGKEWCyxp7/qqquqLQSH5qy1cmfZbKue8tmf8+gWD9s4zNn4/7Y2YqFDdEXmJLZQ+R0+JY2FC1uq3CMNWoMsvHvS4JzHgHv37q2BEPDN9HFkKRwGjErRyBAAgaddkHcOFsdimdNWOJicrYtzzz1X1c3sQXIAACppDK2wK8D6HWtco/IhQJQwjK3iiEXS1VdfHXcr8TWCRfjwsWRCO0bAnocffliN4aIGcYkLruOELFDYxsNCP0zFSr3kxSiRowT90bCd288kx+7aV9p17iU7dV1D3zH84NlP9vGkw3Vm+Z1nD/ltJb7D8GByfPIHhaV+8oCFvw8G4XbCGNkG9fcxtESq94RPPwwU5skfPCp8DCRaBk4D4zp/GGSGGTBtRevm7/swnb78lvjMY8DLL7+8HpTAA8QfE8MZgh0wePg0ar0IMOCxUuYFwx4AIypW2ky+qKA5thKJFzcYpOPwwG+9qFW25+CPIc7rr7+uls+cHsYCiC2BLPxMCW1IFLJu3bqptoOJEg0HQVNwK8PC3SgfgS+//FKxjzLg/FTJfoGzZ74d5ppFzj5ioLw2/icZdsTfbpxpQlkipbPV6Bkg0vQqq6ySaxwMbtSoUXofZoa2lAWBJ+YGFtswVv7wP0fb5Yk5BK1Z+H6YAcOgCRzi71N/mLjOvAIDhTlH78PsGZuFiHxhhl4oXSWv5zFgKkZyYV8CX0xWI0g1TLjRzlaykVZXyyIwbdo03c9FAmLviYUZ6mT/8hECEiMfVGNGLYcAEwz7v0QPYy/eT36cGEaITw6pSEMstHj22IpcfPHFqvno2bOnGv+kKbee86Y9UjCMDdoGtEvMxQftv6c8MOoz2XbPg8NJUn2nXMaMZ4B8hgmGi0tg9LpPA8NF01KIKH+llVYqdFtVwmE1fTRhNTLQaBuL/d2IAVMAFqn8VYJsD6kSKJdeB2ohzltGgmJhduyxx6rEgwEJDBhpCytoO82qdIyzzskJSDDdiRMnqsoN1T+ScRbEXj9lseAyibcxol6KI355WD3bOGXxVzBexL+eRc/cs88qGw7YTb8XX1J8Dhhr1AYomrIQ842ms9/JEIhlwMmyFp/K9pCKx6xSOdjbI9Y3amasmffff3+NpITxFKqpO+64Iyfx4ruLyvFptxdMoHf8e8OqpEq12eqJR4CFEX7VBDEhqAmR5jgLNitCzclBF+w3L7zwwomKRXLDVS1KLORKMUaKllMtv/0+ZVOSYJK2EneB94ugKqhdWfjcMfwId0zsNhobHU1EVoTKGTLmmhWiycupKAO2PaTkD6YSKdm/43AD4iujtkRqgtEOHTpUhg0bphbNHO/H3uJ5552ne4lMMGeccYYemoC6iL2tsKFFJdptdTSPAD7WxNdGCnv++edVi+EPsWg+d/MpMGBhHEBJJnCsssNHR/oaYDDNSV0+bbV/Iv1imezxKLW9vIPhBdPMM84gy01xpxmtsbNc71z52BpkOyAt8S6zuEYwQhOZ5fhI27bWkj+PAZf7NBXbQ2r5YcX+HS4KuEawz88qG9/cTu4kFqQUpCUsFZkA+jvfQqwMkXbYkkDKxe8X5kzADaPqRACGy34Z6koYMNIUWwdpI9lhIIMkhh0Akc4IccniiyMfsb5lMVeIsJTnL0qox9MyrGiZlfgN84JxsbDwBDPDADGtdLrffvv5IqWN24Y9d8DiMmePHeWMq+90OH+g97JgwLSVPkBEsDOqPAJ5DJh9vkKb4OGBlqaZtoeUBr10eTHRZx+XiZk/LB65hqUmkyChJGHQRNQ58MAD9Y8aUUn37dtX1c4Y+WRpWJKuR5Y7DoExY8boWcl+DxKVNBaqaenWW29Vd0Ska/y8CSHa2rwjeFeIl4DEyJwYnhdZjDRlZJQU/x49eqh3Aen79+ws7VbfUZ77YKJzwflMo2YRYKVY4v1m8Yw6G/ccKO5gnGLLtfTpEMhjwP40FQYYFpNISahVMB9nUBDdKAsqZQ8JJoA6NEqs9m31FkUl/zdRcTheEnUfVu24m6HGIhgAK2kCNuBegvSLRIImhBOxiDGLhMw1pJ5SQ+blt8Z+lRsBzt3eeOONZdKkSSp1Elo2rNIstX62LLCuhtEg7WIUhCEW1rmthVjI8N6gDfILnKz7jgqafXFUwtvus6f06tNX9nI+99he4EbDIrhYYuGAtJtWOi+2XkvfNAJ5DNgnxW+QVTPxZBkEGNp4/zOfJu1nsXtItAOpLEpMMrYH+RcqMEr2clk0rbPOOvodIypePBgvEYsuvPBCXb3jz4fRBfFoWcVzVCDPnYkFtzP262C+TDgcCQhDNqoNBPDj59kR+pPxwMS9xhprpG48jB31KKpnyoMJE+4QYyxC2LYGYqumXDRixAi577775NLTj9XtIFTEbAGllViZD5B+s5DOy9X31lpuLAPGkKC3i4oFkyTAAi8YTuD4CKehNHtIqMfjwoZh7l+Le0hpcIzL66NQcbgBKkKkWYJkoCrk4ANWzax+cU0hItKgQYN0UcV3VvIwX+836stnDBjVJgKoGuMWrGl6gw8oltV+35Cy0Jzg/12PcQIQOtiWIRhFuaMmoU1AG9Vjgd/ljot/kZ57/EN9arMwUKMflJNFWWnGj+VtjEAsA2afkHCUvGx8YumKk3Vasj2ktAg2zs+h68ReZvXMni4LFVSFMFgWUqibiWCDChL1IRIw0hEqLpiuqaQaY1qrV9im4R2LI7aXcC1LS3H7hiz26o0wsoL5onkrN/MFOwwgN+34h+y6ZV8Z07araqeyYpgIKfwZVR8CbeKaxH4vKkmvmiT+pnc5iEuf9BqMAdWm30OiHvaQTIJNiuBf6ZBsYaT44rJy5vQqmCyWqaib2KtF5QRzZj8XLQbSLVIRRwNeccUVqjI05lsc7tWeGu0GRzq++uqruk+JytH/xTHOau9PS7YP33YEj0r5t5982N6y49b95HVZQR57aqQa0WXVf7aazMc3KzSzLSeWAWN0hSXs5MmTNbYmKswsVmN+D+mll17SXrCHhGtCoQDy2Xa1PkpjYYRaGQkWdXOvXr3UJYjrEyZMkOHDh6tlM5aSxEtl6wCCAbMFgMuIrYbrYyxEe0H8dny6OQQDa3YOZ0A1TNxucxuLopX/OywEoLLl3alUgBAk7bvvu1/GztFNNtikn/ptZ8EwWYwTyc6oehGIVUEjYRERiUDuvMBM4khaSE5pqLXtIaXBKpoXa++3335bzj//fHUbYoLA+hRDDVbrTL7sx2Egg2YBS0pUkrhLIBnF+WBG67DftY8AEhtHQvKHNwO+vxjV4UaGVbtRPgL4vCNo8D517NhRb2JwWAmjw+CP32TKNHeYwrBhaodx553X6r49W4BpCSEKF0O2oIyqF4FYBsyEjgGON6wg9uuVV16ZSS/iVGH1uIeUCVj/KwSJFzcgLJuZLB599FHZdNNN1Tjk9ttvV8ZLyEGOK8N1zJ+8gsbBqHUiwDhgbOCny1YS2xRGfyPAHi8LWhgVi1MsxytJwS/fyx+jb5O3/lhetYsEx0FixQqahXTsd3WhAABAAElEQVQaIiAIfWPLiYAsRtWLQOzTIfTgTjvtpMY8TOaoM3fcccfq7UUdt4xVLPu9HOPFy4SxBq4guBzhSvThhx+qhTOrZoyreOmMWicCuJSxx8/7issZZzQzobfmMYEbHQyJT3Dwql2YHcJApfZ4wyMy+GGC/P7QUPlsiZ00ljbqb4QdtvkKGdGF8zf3nTmCGN2VXlQ01y673xiBWAZMoAwsoHmRUUWfdtppGku2cXa7Um4EcPnAx9JbYnLGK24RGFxx9iWM2Yypyv0UaqN8Qobi87veeuup1Ms+MH8QLnzE+K4n8rGMPZNFkg0Hx8DWhN8wN7w4wgsRfmfh2ZEEz5EjR6pWiiiDwX9/lF/vO1ZemnMTufmq2zReNwxz8ODBajyZRbCbJVzQDrPzSPJkWj5NHgNmzxCLZ4wPwieXwIwZzOEYpS3f9PpsAYZUrIiJtoO6mUmCmM3XXHONGlmxtwvxnEqJiFOfqFmvQIBIdY888kgsGJXY04ytOMVFTujizzNYpDq/EKVY3O6QaAsxU47t8xJvimakyrrDDjuoPz7BjDg3u/fy88vohXaQYaeco+81C4SDDjpI47OXWhFq9LCq2ZhvqUhWPl8eA2avCNXMfPPNp9JVuDl+XzF8zb5niwCH3LP/jipx2rRpumpGLcWZuwcffLD6JDKpsKI25pst9vVQGirHelI7YtPAYtQz2LB0y/MiWl9T1FLMl60hXARxu8QI0ltYH3300XLNVSNktvaL6XtO25lvcRMslpD+WayjIeN72uMPi63f0meDQB4DJswaf0hXDG6LsZwNyElKwcKc1T5Wq/hxYsmM8Rsr27322kulACJcGRkCrQWBuMh3tdD3qMHbhp3ayLu/dZAttthCOi6xlC6sjz/+eFU7EyAFA8tiCekflTr72Oz3G9UmAnkM2HfBq58PP/xwf8k+y4AADBeMCZrB2a1YOyMBs8+L+wCuEcTz5ROrZyNDwBCofgTQQuCP3eCaOqTnDDLjvItJj1V30oZ7TSIaLf5Kpeak/1LLtXyVRSCWARNofdddd1ULW9TSEBv7qFWMikMAVTKqJ9RpMFeYLMEwOJ0EVwGkWwImcODBkCFDVOrFBYzjAgmegQEJp9ngVmJkCCRBgIXdVVddlTuYgzxIkyeccEKS7JYmJQL46uM1ctmABWTjbfeQZ39zrkazthP2g40MgTACsQyYVRqWz2GyQA5hNJJ9Z3+HUIDE4L3kkkvkpptu0pjMRBljBYuhFcYZSMEEy8DIBEbNCpqFD8ZvRLKBUbPva2QIJEGABR7uaxzGgPYEIka4UWUQgNFuv/6q8vUbj8pnc6wu27gTjWafffbKVG611BQCeQw4bAVNgP8wsdeQRYSWcJn1+B2GyQqYfVzUUGgOkIAh9nOJXoVfIq4HTIpIvzDmDTfcUHD/whALQysOvUfqxcgC9wU7crEeR0t5+sTCDxsCIs8ZtQwCk378U8bP1VO6ho4TzKolaDjQjPnFVVblWjmVRyCPAUetoDH/x8KOTX6/d1GOJnorwZayWkzTJ6wczzrrLLV4BCcmP9TLxGkmYhWH3aNORgUIw8Uvk0/cKbbddls99IIoVhhYEfJz9913zzUH5m1kCBSLAJHl8BFncec1V5zqYzYdxSJZXHoCbDTMtajadHz17U/StWvXTGLoh1vBfDxmzJhMzncOl2vfWwaBPAbsraA5xg5DrKddZCVUJzAMwtqlpTTnAaetuxz5kXaZ6Ig4xGlRBC6BGY91wTFwH2IfHR9eDtU+4ogjdO+X62+++abup3NyDRizoiVYfhaHppejn1ZmbSFAWEOM+AYOHJjzmw0Hoci6N7W8gM4Ki2DCKAnevV/GLbunfPvtt7r1hJSaNbFlRfSuWhRWssaiHsqLPQ0JZsuKGUmOkzouuugiZSBpO+zPA2afk/1OJEBWdLVKhIFklXvyySdrIHfOTualw0XAE6rmtddeW8NGwoRZhHBgAsFOkIaJWkQABWO+HjH7TIsAkzPnPxNSloUdf1HXmFLqYOwSopZoTQTlYeEIsefMO9BaKfjwcZn+1Fny2cL99P1mTigH8wVfjDotBkD9jLQ8Cdh3i4cMA/arLNSnnBqSlnBM51B4fx4wJyxxHjD7orVI4BPeh0ES4I/9t2HDhqnWgNUwCxqYsCdOpjEyBMqFwKqrrqqL28cee0yD6lAPNhxsh6Qhv4BmuwRrfhbQHPbQmin48WuZPuoGmbD6cfLz723dCXIdVMPFM4gGDskCJ+accmozsmijlZEcgVgGzGp5q6220tBpSKqEUMNilzizGAfBNEshfx4wEiASH+cBH3nkkXoeMPGOmyIMkjiSL0ow9XLuT0frC//GaIqwbyxQ6BN/a665pvz666/q18sEhcN9OHxeOL99NwTKgQBjEgZJiEIWzoShzOIdqbcFdBbYT//5B/lgxcNcSKuZpH37eYXAGhhbEk0Q7VfWkrA/MjGLtlsZLY9ALAPGGIsTeDyF1VfeqMPfK+az0HnAHC7Q3EBlMsHgKUpcx/CpUoR6GaMpJjUOOr/88sv1BBoCZtA+XkDOYg1jVqm2WT2GAAgQwY7FIMFdkMLY5nnwwQdTg5NmAZ268ioqIPjzD2loO4O6CH707e/SdubZVGNInAS0hxCSKkaYnNNtZAgUQiCWAfMClyMMJXtISISEXuM0HyYJ1NGsGLEUbopgbPxFCWbojUCi97L+TbsJnMF+14gRI+Tqq6/WQOoEwG/fvr32B9WTkSHQkgigKfLvFAz4ueeek2OPPVbDnKZpV6EFNGO+uQX0E088oRHeovUTnGbllVeOXq7a35xmFLxyjfyxxgE6Z+H5wLwEwyV0LEFz8B757rvv1H2wajtiDasKBGIZcLki6dTSHhJMnSDpvEyoy7EEHzRokLCv1snFy0bNd+ihh6r7EPu9GF4YGQLVgABuKr169crtQRL0hXGcBbGXHCUMs5ojYgjExRHYZ599KraAbq6Nzd0PfpwswUPHyu/LbSHv/C8WM3OBJ7bROCDhhRde0Kh2bNdlScQEYKFjMQGyRLVly4plwOWKpFNLe0iol9nHQeW+8cYba1AM/HJHjx6twTL4TnB1jKuM+bbsILba8xFAVcyYnTRpko5fpDKsotNSvbkRFoNH8MdvMv3eQ+W3LjvIO78t4izBF2h0DjduiWjyWLjjnoiHQ1YE88Xrgsh6RvWDQKwbUjiSDlGZ+MOvMC35PSTOFoYwwoLBnXjiiWmLzjQ/CxCsl4mne+qppwpn8LK/gyEYhmhHHXWUWm4T8WqdddbJtG4rzBBIiwChTJ92/uWoRV9++WUds3gfpCWvwaoXN8Ki8HBBNn7tto+8/dvCztJ5kUbMl7KYF7AFgVkyN2QVPhaN5Pvvv68qbY5mNKofBGIl4HJF0kmzh1RJyGG+xNElZCTRhG688UY9ohFDljPPPFMnNPx4eSnY+zUyBKoJAY4TZRHNGM6SakmDlWW/KevHNnPKe1Nnc6FlOxV858PuQVifZ6H2R6pm4Y86m20wo/pCIJYBlzOSTtweEgy/mgi3ItoEDhx0jXUzqmZiMl933XXV1FRriyHQCIFyHSfqNVgYTxbrRtiokTVwIfjoCWnovIFKtO9/+IkywTCTpQssdAjAg3Zg8ODBGqQEiZX94Lg972K7TZwB3B2N6hOBWAaM6spH0qnPbsf3ihCcd955p/6xf4MKCStSJHeChhgZAtWMAP7nqChhjuU4TrRWNFhZPKPpL13pwk5Nku/nW1XjvMMECQEZJeYFXBFx9SIM7f333y/rrrtuNJn9NgRiEYhlwOWKpBPbgiq4iLqIfV4kBwJpcHTgoosuWgUtsyYYAskRIGQsWyNTpkxRmwUMBT2l8d/3ZfBZCxqscHtL+T79idNEfvpWvll9iIx1gX7QiBU6ThB3L08sgOJiFfj79mkIRBH4e/SE7vhIOv4SapZ6NH3HaR6DK/Z5MaZiJUvkLyNDoBYRwFgS1SfMol27dnkhC/G1zUIlWou4FNPmYPqf6JVlcvfDZfyEL6VLly6KZaEyOGGKBTtzJDG3N91000JJi76OLzEUVXsXXZBlqFoE8hjwP//5T/nmm2/kkEMO0bNsJ0+erNGecGfYcccdNTxl1fakiIZhpcieDe4ZrFhR23HofRbh+opohiU1BDJFgG0TxjEBYggdi/2Cp3pcQPu+Zfr56w/y5fJ7yqSvJiY6TpCFDvNjOWj8+PES9jMuRx1WZssikMeAsdrD8hfi4eNuUE/EivLf//63/Oc//9FQkezxjho1So0cOFLw9NNPrzqXqHrC3/pSfgQIkcqpW0bJEQimfikNc/6l+fp88lSNYlXOE42StIw9ZWxSzPI5CVq1mybWD7h2uxPfctyKLr74Yo3RTISrG264QS2a2TMjShCrWE4swmfSyBAwBFoPAsHEtyV4+hyNxvXJJ5+oxXNLM1/Qx5KarQQMYo3qF4E8CbjeuokK/ZZbbhHi0HI26k033aSHKNBPBjfBCjwRk3bcuHH+p30aAoZAnSMQfPacTB95gTRsdLJGmUIDyJ5v+IjR5iDgwAW0DhxBmiWxZYBBnVF9I9CIAbN/RLzjiRMn6ioMJsXALMfZluWClrazv/vss89K//799XvUhQBjicsuu0w23HBD2WGHHeS0005TKbhcbbJyDYFKIoA2B6Msgse89tpr6qOKcaXRXwgEv/0k05+/VGTTM+X9r39zF/+UFVdcsdE8hyQ6duxYPe0oypixi2GuZGuLebPUY1rjnglHmEbnrLh0dq22EchjwDArBmEc1cJexJdffqlSLqfBDBgwQKXfQu4DvEycc3zfffep2omj28pxAlQclnbNECgnApzaxalj99xzjxxwwAEaEYtgEQSWMfofApxq1G+4vDf+Gz3gYJlllmmk7mUPljmB/Vi2qYh8FzZme+CBB3JuR/gAZ8mA7Tm1DgTyGDDnAPNXCWJQI1VHV5Wl1I0lMwZUrPo5RIF4rBwTloQ4UMHIEKgnBFiAotGBQRBQhxjFffv2racultQXXIwa2rTVvL/PPI8enMACvdDCG6b69ddfC2eOI4Dcdddd6ublKyc8JOeD4wsc9rn29+3TEGgOgYoaYTGQsdDk/Ewiy/DHngsHHsCQS6UXX3xROnbsqBbOu+++e2LmW2p9ls8QqGYECJaBS+EVV1wh2267rVx55ZUFmUw19yPLtgW//yLBs8O1SLbUcNdCJV+I+ZKQgEQ+0MbUqVM1FGW4TSx09txzTzn77LNVmxa+l+Y7AT3eeOONTGJJp2mH5S0/AnkScLmr44QQCFUOexwQL8OQIUNUXbzHHnvotWL/9enTJ29lWmx+S28I1BMCnM9LdDfsGwhL+frrr8sZZ5xRT10sqi/BL9+7c3yPE1m8p8DcYL4dOnRoNtrdVlttpSe24baIKj9qaIVamhPTsiS8NDAIJXIZZ/8a1TcCFWXA7NGiEvPMF2gZZKiBX3nllZKRzkKNXXLlltEQqDIEcF3Zd999NToT2zODBg1qtL9ZZU0uW3NQOwf3DBZZdhP5dbkB8o5TGROkBAachDh+lL9KEXMkW3NJ21epdlk95UGgogyYAPGsJNmn5SWACPiBexCuQk0RBhHsx0SJcJJ2RmYUFfvd2hE48sgj1QIa7RIxzjk0oHv37q0Pll+mSMNqu8mPC68p7znJF/V8pexcigWbLTooi7PXi63b0rcMAhVlwN26dVPLTIxDMF4gfiq+blggN3euLumJ1Ryld999V4hza2QIGAJ/IfDCCy+oxHvyySfrwQwcH3jiiSeqcWKrw2jmOWRqh57yvpsnMJqq5rjK7Dcvssgire4RteYOV5QBf//997rfi5vElltuKUwMmPUTl5l94ZNOOqngs8Cnkb8oEdWKfRMjQ8AQ+AuBd955R/d+/Xux0EIL5ULMtgaMgo+fkoal+2hXv5/6o3z00Udq8JnUrxYvihtvvFHdt/r169caILM+thACFbWChtESMJ4Y05w6hIVmGuvnFsLMqjUEqhoBAkSccsopqm3CUpctH2wvsiTe2z//dCcHVRlNH3WTBO/cq61iy+rj/x0niFHa+uuvrwt+Qk4Woocfflh23nln4RPc0CYYGQLlQqCiDJiXgfN2kXqRdrEqxHndr9TL1Ukr1xBoTQjMMcccGqGJIzZZ6GIBjWV0WiqXG2Hadvn80591XhbjXpKGTU7R09yIYIWbI3hgc4LmDStorJsLEVta3kgUlTCauXIT0bZoGyfRGbUuBCrKgLfeemvZb7/95KWXXlKUYcKY27M/lSVh1EVUr7XWWkstrHF1MjIE6h2B0aNHywknnKB/F154oRo4YlGLEda1116buvthN0KkSFS7uDgR+pWY61nSq6++Wrxv7Y9fS0O/8+SLr3+QL774Qo8TxFUICtuJ4IZUiHCFRLrHJoW8u+22W6Gkqa8jeLBIwOeXYwfnm2++1GVaAbWFQEX3gGGIvKicx+vpnHPOUYf3rHzeWEkSjAP3C14gXijqIDSfkSFQzwgQrWnZZZeN7SIL3bRULjfCaLuI4463BBI3Pv7Es26OCLSB5Pv5uPEam3mllVbK86O97rrrdJ6B0bG/W4hggt5XGGtkLw0XSp/mOpIvwgEnsmU1/6Vpj+WtPAIVZcB0DzeAKLHnkhWNGTNG9778yhfme+CBB2ZVvJVjCFQtAhg5MpHjWYDUGyYWpGkpjRshUjKSXpQmTJiQO6HM3xs2bJjgXghxXjeuit5t0afhM5g2SRrm+N/CYoZZBKmcfnKcYJRxrrLKKom3unBrxGOj3MQcRQxqo9aLQMUZcLmhxuKTAxawsoYw/Mpi8il3u618QyAtAvi34tqHFBdlIAsssEDa4rVMDngoxY2QxQEq8ihhyBVVvW6//fZy7rnnal84y5t3OkrBNx9L8NRZ0rDdVcpYP/zwQ5Umiz1OMFqu/TYEKolA3TFgrD1vu+02WXfddXUPmPM68TM2MgTqHQHUq/w999xzgsTnpSuYMlbRvXv3TgVBGjdC1LlxASZgrlEjTA6SQJJHFXz00Ufn4jH7xgcTRsn0J8+QNn2OVibtDaXijhP0eezTEKhGBOqOAROWklNLOBkJY4qhQ4dKoSMJq/GBWJsMgVIRIFocXgVY8jLmfXAbXHCyOAvYuxFi0MUBBLgRIhFnTYTSRA0dR4SWnO4k3zYbnyLT519G3nU+zzDruOME4/LbNUOgmhCoeQbMy4qBRSHC9SmOmKww7ohTb8WlT3oNlRluVn4POmm+5tKxV7booos2l6yo+6jm//vf/2YeHQhjHYx+sozRjapy8uTJmT8vDlOfZZZZMn9eBPAfMWJEUc8jbWLGHCeL3XvvvRpLGHUsxJ4mknFa4l3CKti7ERLeEoa/2WablVx0c+9vtOA203+XWf5cU37+z8vOD/kF+fTTT2XSpEnRZGX9jSaAPeZKL+zZR0ddH93fLmtnXeHlmHuStLkl6sUojjHJgRgIb+WmirohlaMzWDzvtddeChrAJf3DAvG9995LnD5puePGjVMr76Tpk6bDgCVp2qTpmEgY5EnTJ02HBAbDTJo+STosYik3Sdpi0mDgAw7F5EmSFvefloiixFGfHJGHtMpeKlIqLnn/93//l/r1K4cbYbHvb9B2Jvllpnn1eeGqxBZTkueRZRremXKMmebayD43C+bm0mV9vxxzT5I2tkS9bHu8+eabcvjhh1fm/XX7L62S3Goy2GGHHTLvu4u/G7iDJTIv10UQy7zMhx56KHCHjmde7oABAwInWWZarotqFDhmkmmZFOb2GwM3iWdebjmeV9JGnnfeeYHzsQ+c6jlwK3r9c9sxSbM3mc5ZGgfO9zcvjTt7OLj99tvzrlXih4tWFbggI5WoKq8OZyAW3H///XnXKvHDuVQGzm+4ElXl1dFSY7kl6nW+7YFbrOb1v5w/al4FnXpZbwUYAnWGAP7AHDow22yzZd6zcrsRZt5gK9AQqGIEjAFX8cOxphkCpSCABTThFtmHXmKJJbQIGCdnBBsZAoZA9SBgDLh6noW1xBDIBIG5555bnBo6r6ws/IDzCrQfhoAhkBoBY8CpIbQCDIHqQmDppZcW/sLkD3sPX7PvhoAh0LIItHX+dsNatgktUzsnnRCyLy7EXZoWEY0Il4+sXRRoZ9z+W5q20saOHTvm/EXTlBXOy6HitDVLNyT/vHhmWRJuHbXyvJL2m1N1sH4+/fTT5ZJLLhEOZsBimOP16onKNX6bw4j9dcYMpyxVknCZZEuh0m5I5Zh7kuDWEvX6UK7MYZWgBiy8KlGR1WEIGAKVQQCGy1m4MCgCVEydOlWmTJkihx56aGUaYLUYAoZAIgRq3g84US8tkSHQihAgyAxhJzl7m6P3Bg4cKM8880wrQsC6agjUBgK2B1wbz8laaQgkRmCDDTaQww47TI/+5BMDLKJhGRkChkB1IWAq6Op6HtYaQyATBEaOHKnnzD7//PN6GMmgQYNyLkmZVGCFGAKGQGoETAJODaEVYAhUFwKEoSQmdJg4kOGyyy4LX7LvhoAh0MIImATcwg/AqjcEskaAU8CIaQsRO5jze7GMPuKII7KuysozBAyBFAgYA04BnmU1BGoBARwdOB8btbSRIWAIVA8CrUoFzUSEO0bc2ahfffVVyUfdIW1w6Hn0CEKOJsTPtFSKyz9t2jT1r43WlbSOpjDguD/aW4r/LrgSg5hTgCDw4GgvT/jX+Xv+WpLPOAw4yQrJjohPpVD0eVEW7fVEO0sxWirUVk5+4cjDStErr7wi7rAArY5+YQm9wgorVKr6stZTaPxyrCR+ueXykY2OGd/JuGfu72XxGX2vwmWmmbPC5cR9j6uXE87obzmjqsXhyftezneIIDU//PBDo7m63GMK3FuNBHzXXXfJRRddpIOHCfe2227LTbKjRo2STTfdVM+bjRuMTV3jzMjRo0crc2GSc6cLiTtRQ+PuciYu/pjUu+KKKzZVTN69QvlPOOEEPSqLMjmXFcOaYqgpDD7//HNZeeWV5f3339ezZJOWy+Al6AMTHwOWeMPulCk9Dg+Vp39Zzz//fJXCkpZbCAMOg7/qqqv0ZcHHdfjw4UmL1HRxzws3nbfeekvv0wcOsoeJJaVCbaXPjz32mBbD+Dr44IOTFpkqHcfWhdvPogrLaBZBtUyFxi9HGvJOcIbrTTfdJL169cq0m3FjptAzz6riQu+VL/+ee+6RY445Rt9Xfy2Lz0L1Mkfut99+GmCHoxjdSWqZLnYK4Vnud4izsy+44AIVHuaaay658cYbFcZyj6ncs3IrylZBzicycKsr7euRRx4Z3Hzzzfr96quvDnr06BG4SbdoHDjScJ111snlW2ONNQLHyIIhQ4bkjivjyL9ij7eKy+9WpMHOO++sdTkpOFh00UVz9Sb9UggDt7IN3Nm1gYuKFbhVddLiNJ2bCALwhNyLGbhFiH53k0Nw55136vdS/hXCYNVVVw1++umnXB0cuZeUCj0vn99Ji8H6668fPP300/5Sos+4toKpi1oUcAwgRwLynWuVoi+//DIYM2ZM4KQHrdIx5YBxU8sUN345ktCdB67dcpN44Jhvpl0sNGbinnmWFRd6r6iDd5Rxuuyyy2ZZpZZVqN4+ffoE7jxuTeMWwY2OpEzbkDg8K/EOOUYbOPsIbX7Pnj0DJ4AE5R5TYaxajQr6hRdeyC06Pv30U+nevbv+XmmlleS5556Tzp075+4n/YKE++yzz2pyDF+++OILIWRiOBC+Oz+16JViXH5WZ+7cVZXSR4wYIY7xJ21mLl0hDM4++2zZbrvt1FAnlzjhly233FL4gzio3A0u/e4mf/1khbnTTjvJJptsor+T/ovDAE1D165dBSmYVfjgwYOLOnKv0PPybbr88suF8eDOIfWXEn3GtRU19vLLLy9XXHGFqs/cZFmSCj5RA0KJOEx8++23Fzd5qZaAsX7QQQfpGHcLolDK2vsaN345tH2ttdbSzhD/mncwSyo0ZuKeeZb1FnqvqOOAAw5QzQ+apqwprl7G0vjx44W57PrrrxcnCKi2LMu64/CsxDt0ww03CCputCts7xHqljmrnGMqjFurYcC+08THdRKUqk25tvrqq/tbJX+i0oaBoRJeeOGFc+U8/vjj6vrx5JNP5q4V8yUuP4Y0TERM6DA79kaKpTAG7nB1YRJDnQUDKpXGjRunjPbSSy/VIhjIMDLwQC3Nfq3TEBRdfBgDJ5nKfffdp+WiJt5www1VdVzs3nLc8wJLQjg+8cQTRbfRZwi3lfJgwG41rcwQxl7q8/LlN/fJ/hnM12l3cgtM3I822mgjcRJMxWMXN9feUu+Hxy/bHOEY6WyFsF+b9Z573Jih/eFnXmp/msoXfa8uvvhicdKvLLfcck1lS30vXC/qffabmcfYSmH7i8VcKUJLcw0L41mpd4gtm7vvvlvHEUzYaT0qMqYUC9fJVkPHH398sPXWWwfuBW3UZ9SvpRDqUDfBBU6KzMvuGEXgzmUN3EDOu570R3P5UX2//PLLSYvLpYtigPrdLRwCt78cuADkgYucFItProCYL6j+unTpEjjmFXM3CK655prATZSx95q6GMXAnW8b9O3bN5fFLXoCp73I/U7ypdDzcnu1wYABA5IUEZsm2laejVsg5NLyvZTnlSsgwRdn1xCcfPLJeSmdcUmA+rB///5512v1R3T8On9nHV++P44x+a+ZfRYaM9FnnlmF/yso+l6xhcZWmduTDo499tjA7e0Hzrc762qDaL0utGng4ooHbhGidblFdgDuWVMUz0q/Q8yBzoZH+8ac5akcY8qX/ZfJanPLkjq4f+KJJ6qhxu23354zvkrbLSxM3d6p7LLLLuL2QXPFPfLII2qMxWqulNOW4vKzIu3du3euDqT4YsuOw+DMM8/UPiCdErx/tdVWK8oK2u01ilvUqPECK3MIXDCEwfcUwoDD7a/o96T/4jDgoHlWp966mpUrKsKkVOh5kR9jqS222CJpUXnp4tpKu9iO8IQaj5OyykngHZX8sEx3k4mq7MtZdyXKjhu/bgEpaEag9957L08DpRdT/is0ZuKeecqq8rLHvVezzjqrqoCJ8U2/+c2WTJZUqN6llloqp94v9r1L0r44PMv9DjkmqPMSmiOIbS36We4xlYeH58T1/InRgpsMA6cyCVjN8OfUjXldLkUCdsw8cJaluTIp98UXXwycxbNKk74ux6Dz6mruR6H8J510UrD55purxO2sf5srJu9+EgwwIiNdMXTggQcGzq0rDwNnSRnccccdaiiC9OUYW84YKGnZhTBwx+sFGONgbMOKtRgq9LwoAyM0d2RfMcXl0hZq6ymnnBI4C/Fgs802C/7xj3/k0pfrC0Yybh9UDb/CdSA18lfL1NT4Rbuy8cYbB07NH7jtlEy7WWjMFHrmWVVe6L3y5aPFK4cRVqF60TR169YtcCpofVfitIi+baV8FsKz3O+QUz2rpgoNEcZ8zF1QOcdUGJ9W44aUt+qo4R/sRSFZleKr2xLdZk8l63NTkYCRKJEAqp0wzoPK5Z8a7T8uFaeddppqNZylvOCugisYn1k/h2jdLfkbQ5paGA8tiVHauh3jUEMltCqVpHK/Q/QLu4Ho+KnEmDIGXMmRZHUZAhVAAJ9mrPNRraH6xwCr1n2AKwCbVWEIVBwBY8AVh9wqNAQMAUPAEDAERFqNEZY9bEPAEDAEDAFDoJoQMAZcTU/D2mIIGAKGgCHQahAwBtxqHrV11BAwBAwBQ6CaEDAGXE1Pw9piCBgChoAh0GoQMAbcah61ddQQMAQMAUOgmhAwBlxNT8PaYggYAoaAIdBqEDAG3GoetXXUEDAEDAFDoJoQMAZcTU/D2mIIGAKGgCHQahAwBtxqHrV11BAwBAwBQ6CaEDAGXE1Pw9piCBgChoAh0GoQMAbcah61ddQQMAQMAUOgmhAwBlxNT8PaYggYAoaAIdBqEDAG3GoetXXUEDAEDAFDoJoQMAbcwk9jypQpMm7cuEat+Oyzz4TzKJMQ6b777ruCSb/55hs97zIuAWdtjhkzRn7++ee425lc41g82vjjjz/K999/n0mZVshfCHCOKcSZpnHYcr+pZ1so31+l5/+PS8vZzNTBH9+j5A44z933bY2mCf+Oq8Pf/+qrr/zXvM9i6yiECe/i9OnT88r2P7KqY/Lkyb7IvE/eQ96R8F9egpgfhfrB+1aImnte0XyFMKGcuDmnlH7E1cFz4HoccSZ6GCd+N0dxdZCH9jaFV1P34ur84YcfhLk7/FfomZPfjiOMQ7GC10488UQ59dRT9YF17NhRax49erSsttpq8uCDD8pmm23WbGvuv/9+efzxx+XCCy+MTft///d/stNOO8kGG2yQd//888+Xs846S8+L/eSTT6Rbt25y2WWXSUNDQ166Un5MmjRJ23P66afL0KFDZcMNN5QJEybIBx98IKecckopRVqeGARgWHfffbdcdNFFssACCwiT0W233SYzzzyz4s5YatOmjaywwgr6rMNF3HXXXbH5wmn890Jpu3TponWRbrnllpN//vOfPot+Dho0SJ5++mlp166d/uac4tlmmy0vjf9RqA7uX3HFFcI4552IUjF1MBajmMBct912W5lxxhmVqey7776yww475FWTtg4m5D322EMWXnhh+fLLL4W+zj///Lk6LrnkErnuuuv0N0zhvffeExbOc801Vy5N+EtcPz766COh7QsuuKB8/fXX+mxXXHHFcDZp7nn5xE1h8ueff2pfVl55ZTnyyCN9Fv0sph+F6rj33nt1rLZv3146dOggI0aMyKtj4MCBwpnXEIsA0r3yyit5afyPQnX4+4ceeqguEBlfYUqCZTi9/3722WfLNddcI0sttZS/JGussYYwz8eSe4GNWhCBE044IVh11VWDc845J9eKo446Klh++eWDBx54IHdt/PjxgXtxc7/54gZf4AZe4CbgYPDgwbl7jvkFX3zxRe733nvvHTgGnfvNl/vuuy/o3r174FZsueu77757cNppp+lvNwBz1ydOnBhMnTpVf0+bNi144YUXAic1B26Vqtc+/fTT4KefftLr7sXXaw899FCw/vrrB+R1kktAvptuuilwE0eu3Gg7ufHmm28GL730UuBW97l09qVpBNZcc83ArdQ1kZsQg5tvvllxX2eddXIZ3SQQfP7557nffInLl5cg9CMuLc98lVVWCaVq/DWcr/Hd/CvhtL4fpGD89urVK3CL0fwM//sVzheb4H8XGYtxmNxzzz0B9UFOixC4xcr/cvz9kbaOCy64IHj00Ue1wKOPPjpwE/7fhUe+uck6cIvUyNW/fxbqx5AhQwK3SNGEvH9u4f13JvctyfPyGQphwpwDFj169Agcs/HJYz+b60ehOpzwkZvr6INjyLHlM/8wx7gFXux9Lhaqg3s8D7eICPbbbz9+5lFzWOYlDv1wAk3gBKrQlb++uoVA4DSd+l6++uqrAb/dAiuYIZYr28WKIrDNNtuoFHPEEUeoKpHVHKsmL4nuv//+ql5EkmFFzGoNqeLAAw8UNwDliSeekL59+2qb3cDRVSEqnHnmmaegVOwGpuy4444y55xz5vp67LHHym677SbHHXecbLLJJiqttm3bVljVrbXWWuImW9l+++1l00031RXn0ksvrRLzgAEDZPHFF5fOnTvLnXfeKUg5jkmrxDtq1ChxCwnp379/rh6+xLWTVf3HH3+sZdE3cKDPRk0jANae3GJI3MJKpSCeA4RE5RZkMsMM+a97XD5fTvQzLi1SyKyzzqrjEEnFS18+L2PQLRxVEkOacxOddO3a1d9u9BlXB4n23HNPHctRaYt7xdSBZBiHyZZbbin8QWhp3JSp3/2/LOo45JBDtDjqf/LJJxtJdb4utwAVxxjkueee85cafRbqx3nnnZdLi0YLiT5MzT2vcNpCmIDNtddeq31wDD2cJe97kn7E1cFYRV1MH6FOnTqptLvFFlvklc+Pyy+/XFZaaSVZb731Gt3zF+Lq4B6qZea1YcOGycMPP+yT5z6bwzKXMOaLE1ZUy+FvMWez/bbuuutKz549dUsILQJaqvw30uewz4oigApl9tlnl7Fjxwr7XG6FKahwIT55mR577DH9DcMlHWrcq666Shkj35ng+INpwVwhJ9FKof2HDz/8UPbaay9N5/+hNkFFXIiYiKiTSXTkyJHiJxXUnk5ql7XXXltQ+bz88suy3Xbbydtvv60qdBhwmAq184033tCFxK677io777yzMEiNAYeRa/o76n4mRdSpnng2PAtUqKg/4yguX1w6roXTwiRYKDIOUA2jYg1PZqgHncSp9TOut9pqKx0TMO2mKFwH6Zz0K++++25sllLqKIQJthhs1Vx66aV5dWVVB3vxt9xyizJG3pM4YlvIabOEhW9zVKgfbEexlQSjDxPpm3pe4bT+exSTeeedV/iLlu3T+89i+hGug0UD8wjzCfMgC/o4BstCgC03hI8kFK6D9AcddJCquVHVN0WFsGwqD21+/fXXc0moC9zBn+ePrcZiiy2m87Ux4BxMLfsFyfKOO+5QicGp3HKSK4wuLDUghT7//PNquOX3d9gvZtXsVLe698O+BsSLArOOI1aODF4mN08w+iWXXNL/zH3CeD0hhfCS0CYYpCf2jyEWEqxim6JC7WSvx6nixam3dM+S/cToKr6pclvzPbQH7Buy+PKLFiZ8tBPsv8dJj+AVl68QjtG0rOj5gw444ACVKFj9e60Ke5xMOBBjlbHGxL355pvrtbh/0Tri0oSvFVtHIUzQvIAVk3qfPn3CVehebTH9KFQH++Bor8DgpJNOEqcmzqsH7JCQr7zyyrzrcT8K1cE+OfuNjzzyiO6fhvM297zCafneFCbRtOHfxfQjrg4W+bfffrvaxeyzzz6xxn3MXYypRRddNFx17PdoHf/5z38ECZ13hTnv/fff14UjWr8wNYVlOF30Oxqb448/Pu8yghRMl3dzjjnm0O+MB9Pv5cHUcj94+TGmYRKFOXpiMgir5p555hlx+8PKAL06DYYGIW1g4OL2WsXtA6pKmIceR0iYDHIkXrcnoWo+VOCou6FZZpklJz0jyUKsqjFOQcpBmgkzYK8u14TuHyv48H1/nc9C7bz66qt18uCFYYXr+xXOa98bI8CEy0qe54laC2LR1K9fP9lll10KMt+4fI1L/+tKXNpbb701N9G4fUndHvHMl1xIHTB/iOeJhgP1eCGKq6NQWn+9mDoKYYJR1NZbby033nijbun4sv1nFnWwQPHaASSgsJGOr4eFNe9Gc4vOQv2A6WJUidQW994397x8O/hsDpNw2uj3pP0oVAfMiy0r5iPmO9S2UUIjGKeWjqaLq4P5lYU+UimGg6i7vQGsz98clj5dMZ/ROZK8JgEXg2AZ0yKtzj333Kp2CVcz33zzCXvEG220ke6NsEpD4j333HN1xc6kMdNMM+kqnb2Ggc5CkDRMxEsssYQstNBC4eJy35FG/vGPf2jZTJq4NPBio8JEPcZ+HvvK7O16iYpJCkkK1SN1kq6QKo0JgD0nLLyjVKidWOqyEGHvmnLZdzZqGgEY3xlnnKHP2mtEvLqZiRDVL/chrGzRiDgDPBk+fHhsPvBHBegMtnIVF6qDPV0srlmMoSJ2hkaah/qdYaGOIdSISLzsQTOOmezYF05aB+rYOGquDhahMKIbbrghlx3L4zhMWKyy98ui1BOLTlSHTfWjmDrQStEXtDwsRjxWLKax+l1mmWUUQ6yUw1RMHYcffrjOEWALoZWib74OnlPc88JLgvkAyc0T2wBxmBRSjfs6CvWjmDpguIwVJEUsrWGUjEHmPRgqxHhjayVMxdSxyCKLaFb6w9ik/eE6CmGJ3QvMn7m1EKFFAXdPMPfwOPTX9dMNBqMaQMCpdQO3h9Copc4frtE1J3kWZUUctoR2+xcB+SFnOBBQb5i4h0VzEiKt8xcsmLRQO8PtKZjZbpQFAbcIC7DSLYYYD37MxOVj3Iat2kupI67c8LVoHVgzOyYSTpL6exZ18E4VQ+XoR/R5OWkvcEylmGYVnbbYOph3wmMmSYXF1pGkzGgat3Wg1uTR66X+Nj/gvOWI/TAEagsBxxRyaucsWk55SAJRlVwWZfsyKlEH0htaJe9/7OvO8rNe6sAgE8m1nFQvdbBlt+yyy2YGlTHgzKC0ggyByiOAqj7qXlT5VliNhoAhUAoCxoBLQc3yGAKGgCFgCBgCKREwK+iUAFp2Q8AQMAQMAUOgFASMAZeCmuUxBAwBQ8AQMARSImAMOCWAlt0QMAQMAUPAECgFAWPApaBmeQwBQ8AQMAQMgZQIGANOCaBlNwQMAUPAEDAESkHAGHApqFkeQ8AQMAQMAUMgJQLGgFMCaNkNAUPAEDAEDIFSEDAGXApqlscQMAQMAUPAEEiJgDHglABadkPAEDAEDAFDoBQEjAGXgprlMQQMAUPAEDAEUiJgDDglgJbdEDAEDAFDwBAoBQFjwKWgZnkMAUPAEDAEDIGUCBgDTgmgZTcEDAFDwBAwBEpBwBhwKahZHkPAEDAEDAFDICUCxoBTAmjZDQFDwBAwBAyBUhAwBlwKapbHEDAEDAFDwBBIiYAx4JQAWnZDwBAwBAwBQ6AUBIwBl4Ka5TEEDAFDwBAwBFIiYAw4JYCW3RAwBAwBQ8AQKAUBY8CloGZ5DAFDwBAwBAyBlAgYA04JoGU3BAwBQ8AQMARKQcAYcCmoWR5DwBAwBAwBQyAlAsaAUwJo2Q0BQ8AQMAQMgVIQMAZcCmqWxxAwBAwBQ8AQSImAMeCUAFp2Q8AQMAQMAUOgFASMAZeCmuUxBAwBQ8AQMARSImAMOCWAlt0QMAQMAUPAECgFAWPApaBmeQwBQ8AQMAQMgZQIGANOCaBlNwQMAUPAEDAESkHAGHApqFkeQ8AQMAQMAUMgJQLGgFMCaNkNAUPAEDAEDIFSEDAGXApqlscQMAQMAUPAEEiJgDHglABadkPAEDAEDAFDoBQEjAGXgprlMQQMAUPAEDAEUiJgDDglgJbdEDAEDAFDwBAoBQFjwKWgZnkMAUPAEDAEDIGUCBgDTgmgZTcEDAFDwBAwBEpBwBhwKahZHkPAEDAEDAFDICUCxoBTAmjZDQFDwBAwBAyBUhAwBlwKapbHEDAEDAFDwBBIiYAx4JQAWnZDwBAwBAwBQ6AUBIwBl4Ka5TEEDAFDwBAwBFIiYAw4JYCW3RAwBAwBQ8AQKAUBY8CloGZ5DAFDwBAwBAyBlAgYA04JoGU3BAwBQ8AQMARKQcAYcCmoWR5DwBAwBAwBQyAlAsaAUwJo2Q0BQ8AQMAQMgVIQMAZcCmqWxxAwBAwBQ8AQSImAMeCUAFp2Q8AQMAQMAUOgFASMAZeCmuUxBAwBQ8AQMARSImAMOCWAlt0QMAQMAUPAECgFAWPApaBmeQwBQ8AQMAQMgZQIGANOCaBlNwQMAUPAEDAESkHAGHApqFkeQ8AQMAQMAUMgJQLGgFMCaNkNAUPAEDAEDIFSEDAGXApqlscQMAQMAUPAEEiJgDHglABadkPAEDAEDAFDoBQEjAGXgprlMQQMAUPAEDAEUiJgDDglgJbdEDAEDAFDwBAoBYEZ/r+9M4G/Ytz/+JOs92YN175edHMR15YsqWRLi1BZyxoRKtmSUFLIPxQSEpJboiyFSrguIcvlcnWLFGVLlJ1r/t/38zOnOXPm/H5zzpkzZ8453+/rdX6/OXNmnnnmM/M83+e753NSks753//+Z26//Xazyiq6lkjSc6n2vqy55prm5JNPNvXr1692KGq9fx2/tcKjP5YIgbjGb9kz4Pvvv9989913Zuutty7Ro9LLKgKZCNx2221mww03NEcddVTmj7onhYCO3xQUupEgBOIav2XPgHlmG2+8senUqVOCHp92pdoR+OSTT6odgtD3r+M3NFR6YEwIxDV+VW8b0wPVyygCioAioAgoAl4ElAF70aiC7YULF5oFCxZUwZ3qLSoCioAiUDsCixcvNj/++GPtBxXx14pQQRcRn4ppetKkSeaWW24xjuPYz2+//Wb69Olj2rVrVzH3qDeiCCgCikBYBL7//nvz6quvml133dVsu+22YU+L9LiSMeBffvnFei6rl2ikzzOwsQcffNDce++9ZsSIEaZx48b2mLfeess0adLEPPLII6Z9+/aB5+lORUARUAQqFYE//OEPpnXr1mattdYq2S3GyoB//fVXc8kll9hJnzsmdGiNNdYwnTt3NhdffLFZbbXVSgZEpV6YEK0hQ4aY//73v2bVVVc+7t12280sW7bMtGzZ0qxYscKcdNJJlQqB3pcioAhUMQKEumF6Q+jbcccd05AoJfOlI7HagG+66SZ78//5z3/M/PnzLVN4/fXXzaeffmrGjRuXBox+KRwBJN/77rvPvPvuu2nM1215vfXWM1OnTrXS8bx589zd+l8RyAkB16yR00l6sCJQZARgvB988IGZPn26+de//mW+/vrrIl8x9+ZjZcAYvI8++ug0SXf11Vc3bdu2NYsWLcq993pGVgQGDfrC9O27rsRHzxAmu5b5+efgQwkB6dWrl7n22muDD0jI3o8++sh8+OGHaZ+fs91UiD4vXbrU/PDDD2lHYhfnHc2Fvv32W6tJyOWccj0WjUmXLl3MJptsYs4666wUfn//+9/N1VdfXa63pf2uMARgvAh4MN63337b/PGPfzT77bef2XvvvRN3pyt1kjF07cQTTzTnnHOO6dixo9lyyy3tFWG8SGkzZsyIoQfVcQnRKJuRIz8xzZo1Mptvvqbp3duYgQONefFFI/aOTAyOOOIIM2XKFPPUU0+ZQw89NPOABOzZeeedzYEHHpjWE5zKtt9++7R9Yb+gjWnVqpVp3rx56hQYDO/no48+mtpX1wbHvv/+++aaa66p69Cy//2hhx4yBx10kLn77rvN0KFDzTHHHJMTVmUPgN5AWSDw3HPPWbMaiXD+9re/2YQ4Se14rAwYMJiwHn/8cbsyQeLYaqutLPNFElOKBoFp0ybIIucFc/PNN9sGBw0yRhKGiRo6e/vnnXeeGTBggHVKqFevXvYDS/QLznpPPvlkxtWXL19u8C348ssvrXf3TjvtZNVO+Bdss802htUwUi3fOQaPR+7v3HPPNQ0aNDCcj22I0KxGjRoZ10zChTCNfPbZZwZ7uUuo6pcsWWKd2Ro2bOjuror/3Du+AtjNrrzySnPVVVeZ0047zbCAU1IEkoIAdl7e0XIYn7VMycWBc9NNNzVnnHFGWuOuDSmJE39aRxP8RTzqzeefkxXse3PPPfeYO+64I9Vb0fKbU09NfQ3cQMIknSdqm0MOOSTwGO9OvKphUIXSuuuua7p37x6qGa96GIaKKhQb9iBZYRxwwAHmhRdesAwWZz6YRc+ePa3UjPoJJrrZZptZhornNypT0kQi9Q4U9QCS9Omnn25V8bNnz7b5xcGRds8//3wzc+ZM6ygIoyZkAUbEvmoizEeonv/v//7P7LvvvpYJX3TRRaZ///7mhBNOqBUKFttBJoOffvpJ87jXipz+mA0B3ikW0PiyeGmLLbbwfk30dqwM2FXxPfvsszb+lIHMSgUbEo5ZrKqV8kNAeIfYOozZa6/7zWGHHZZS8Qe1Jn5vEoKEF3r6r9j3yIEahgGzyvzTn/6U3kAe38J6ISLJnnLKKakrrLPOOubhhx+23wmtIsSK70j9qKD4jBkzxjLgb775xnrew5hbtGhhYLBegjnjsPY5K5jfCaYCE+c6qOe/+uora0PCo/yLL74wc+fONW+88YZ7eFX8ZyGDsyR4uoTvwO67727w5aiNWBxdd911GYe888475q9//atd/GT8qDsUgQAEENg+/vhja/qhDgCmJOy85UixMmC1IRXnFRGNvpk2zZiXXloujldPWMZT25W6djVSqcdIIo70ozARoM7FcWGXXXZJ/9H3rWnTpr49xf2KCvqZZ54JvIhbiGPttde2amQOopqJm+EGtbMb4oak73f4Q1XtJRyr1l9/fct82Y+TIIStd//997ehDDBkFgXVRNwvixq0AP369TNd5UViGxU0C7faCNsxHz+hDWNCVVIE6kKA9wQtGMIaYxTt2T777FO2zJf79clAdUFQ2O9IFEzcrg0JrzRsSGEG4HvvvWfjWZFAvB9W5HPmzCmsY2V8tmhhzKWXGrHHGfPss+NsXC/MozZirkTZIB76GdShQwfz2GOPZexP8o66TBdIqi4zdtXU3vtBne0lbMNI965E3KNHD/Paa69Ztf4//vEPy4SIX682BnzrrbfaymNt2rSxzPTMM880JK3HoRK/DiVFoFgIsOCdNWuWHYeM97322su+g5ihypnSZ54i34lrQ3r55ZftlVA5M9Gh7quL0POjKvR/eBg411QrTZhgJL7NiE3yB+vFfPzxx9cJRbNmRpKfGLFpZh6KOuell16yE23mr6Xbg2T+l7/8Je0TdqGAGrl58+Z28YfnLs5WddEVV1xh6/mSJQzpe8899xSv8ma26hZe/GTRQQ1WTURoBwvm4447zibPARvG35FHHmknxmrCQu81XgRcJ0u0dAcffLD156hr4R1vD/O7Wj2RPmPV/xAYjQ0Ju5FLSLHYkJgccyVXhTV69OhcT62I48lfIrzA/PbbJEOs7IUXXhjqvgh3JSkMWl2/NpmQmj322MNOrKEaS/BBeDGT7/pFicHCCSiszdm9JWKFveeQNQwJua7BP2zYMLPDDjtUVD1gtAdkrMMBj3uD0D7hRIfTH+9MrlTt4zdXvPT4eBCIa/zGagMGuu222y4DwTBSW8ZJusMi4Aq8xx8/0YaFhIVFHILNqFFGGEnmGThh4RiHZFMJhP0XKdbLSMPel/8c7MzVSniEjx07Ng1HPFHZh3ZCSREoFAGyVZE2l/DUKJw8C+1Psc+PnQEH3RCrDQTx3mSMUMoZAeyTqOhdqSRsAy7z9h+PbR7vVuwuG2ywgf/nsvrOIEZyU4oGgT//+c9pDWGLU1IECkUApyr8fHCyYsHsOlYW2m7Szy8ZA/ZWQ8KZQyk3BMiiKCGsYgsxNkzG9dTNrZXgo3FKwt75/PPPa6WkYIh0ryKgCESAAM6RRBdgPmPeIbyRRZ4btRDBJRLdRKxOWDjSUIOWpAc4wvAhBpB4YLxKlcIjQOrdyy/HAetrQ2lB4lvzJeKCxZk1jUj7mCTJkTzQBN17iXv3Jufw/ha0zbGoTIMIj2YyXIUhtDV4/yopAopA/ggg9ZL4B+a7jYQCYvrClFEtzBfkYmXAbpo/rYaU/0vLmbjNPfCAkcIWxr7A2ObqSoRQ2xUlB0VGTDBevySbINC91ATTxHfgggsuSOsKiTlyqWVM5itvEglvYzBfvHvDEBOHpl8Mg5QeowhkR4BYfSReSqISq1+NQlisDBgJRKshZX8hw/4immFD6klJeGWTUxRaQEGyC5pJk4yE1azsAatQPNXdkLGVv5RmC1vuK6+8YvM20wOkX2p8ep34kWLRBnilXH5/8803M6Rb9pNwxHus985QjbFQ9BJpE8miBQNWUgQUgfAIMN7QgHqJ+uQw4HLNYuW9l3y3Y7UBazWkfB9T+nlIvwhrS5YstI5SdWWtSj878xt+NRRBkjwLki5w5e/k+4UBs0L1E0zbr4UlHbBks0xRmGNSB9exgX2IfqCyOvzww21qSRZzbiww9T6JUUUbgFPaDTfcYGN3sY1TFQUJFzU2xLHkMGbVTRjNjTfeaDbaaKNUDx4QgMePH29zPuOROUlWJ6i/iZEmBrHaUlCmgNENRSAPBEho8+9//9sy4DBpbvO4RNmeEisD1mpI0bwnOPWOGUPmq2cDmWM+V5F6A5JkoiajlmuOJ83bKGKVAoh6DRJim0Zizk+jMMeknVDHl07SwTvvvNMyYKpqUYTBZcDsh+mS7pA87l3mTgAAQABJREFU0KRGPFUqUBD7O0bAQnp1M4TdddddNnYVZo10THk94ltdIq80TJiQI7apwvTuu+/aWriXXXaZtY1TTUlJEVAEsiOAlopxQ+50Etfg76OUjkCsDJhLB1VDSu+SfqsLAdGyis3XCJN5VtJQSh7KCAgfrubNjZE69dazmiYp58UHWzCqIi+FyZkS5hhvm3Vtk8b07LPPtg5Q2IvIBesSixEYMoTqnMpGSKzkfoY43t0mKQdOVG4xB2/1FBJtMGmQCcslHERwFMGGDHmTyLjH6H9FQBGoQeB7sY8RUkSmOHxTYLxUEPOnfFW8pESsglB+CMB8sX9ip6T+bRRUTxJyTJyY2RJxnqh0/Qw488ji7yH7FCoscjP7y9+1bt3aSr4wyVmzZllvSlTzruMfzmTYfCEYM1mbcLrCZvzPf/4z1XmkXhg1BecpX4gqmnyzOG8RlkVWLX81pdTJuqEIKALWREMOAfIS8Kkmr+ZcH78y4FwRS8jxhAihQi02YTagVF9SspWhhsYWTJ+8OcCRjPv27Wvr+OLsRyYvBj8OapTRYwHhBvdT05ZjaYMSmcOHD0+DccCAAdaeTBYsFjkTJOE20jeFKij1SPhcXako0xrUL4pAFSHA4pbxgZezUu0IKAOuHZ9E/Sp+DKI2NVK8whiq8sB0ikHCc2Tw1LSMFAlDInFKqVayqK7ceF8kcjcemCo8eEZDMNtHHnnEhk15vSpRS8NEvZMBtmCYKqoybFMuuXHPSNl8/L9jC/bnhnbP1f+KQLUhgGcz9l2y5eHR7JI/fau7X/9nIhBrGFLm5XVPLgiIr5B48hrr+QxDwos3ahKnX4m5NeK8VNMyjIvEKRROLwfyMl+3v17m6+7jv5f5eve720G/6+RSgw4Lsmorx+i+F/rfWO0TJhkqp3366acKSZ4IKAPOE7hSnEbJVfyAeOkJESoGEZKE9DtlysrWmzRpYr2FV+7RrWpEIFsmu4EDB6bis6sRl2q6Z2Lg8YHAkZHIAtTNm2++eTVBEOm9rtQbRNqsNhY1AlKK1Uq/hPYMHRocmxvFNXHG6tbNSGiOkfKQNS1Sg5mKN0rJRoD4ZGKWgwgvVMKyCiHXoY0EJa45gjCvXr16GUqKkplMqTIRQNtBdACx9FQWI2UkmjG2y5lwrvRGU8R9L8qA40Y8z+uJ+dHsv7+RUnC/Wmn0kksuybOluk9jHpWSwJK8grAxY8MISEjBICz3AVf33ZfvEeuss05Wr/goSrth9jj22GNTzBekCDMh2Ylriy9f9LTntSGA3wXMlzKBMN9yTRvJHEYiHt5XJHkiHdDglIqUAZcK+RyvK++9lGusyeKENBNk68yxyayHcy2pxWCeeMJIPG1NDO02EgvLCrjQrFtZL6o/FIwAEgkfl0izidMYtuwovLY1k52LbPX9x3GR+uDluADHUYyMfjBctEREQ1BylWx4pa5jrQy4TMaShJ9aGjXqNfvyFLvbEsWT8oTmWjh8kU5OGXCxkY+mfSaXJ2QFhXoY7/CrrrrKpuYspPVCMtkRb42a2k9IIiwolZKDANIutl6kQy+VC/PFV4H3jXeLDzHJZPUjjSzhh2iKkkLKgJPyJEL2g1UcL1GxSSIL0qhx48Zm2rRppnPnzmn79UvyECCxCBLv1VKzknSAlPvsL7FrxD0XSv5MdmHD0wgZ69ixY8blqQXboEGDjP26IzwCZJzCI5lnjonAGxIUvhVjnaqw7y9YsMBq2PwMOJe24j6WRQNSLs5h5HdHYwfTxVQXVbKiYtyTMuBioBpxm4QEkf2KpBEknyjFC4UEPGzYsIjvTJsrBgJoKvCSJ04TgmnisVooYT9DkmaC7tevn+natavdpjQjubddx6yg6xAritrPTxTBcPvp/02/140AKVJPPvlkyePeyWZ6Q/MBE81lUYOp4oMPPjAshnjGlP4sxRxT992mH0GBFfIhwHTnzZtn0NA0a9bMCijFNNGl96Kwb8qAC8MvlrMlkZNU7DES6jHHvmSxXFQuIqGekmvamCFDjFlvvfXsqpjVtjd3clx90euERwAtxYFixCcHL9IQSUdgloXSrVIui5Sebdq0sUUvBg8ebD2fkbQflxg5MoUpxYvAhRdeaJ2I3Kx4jE0KiZDtLQyxoKcgCc914403tu8M6ViTSiwuYLgwXjyY9xfPVN53mG9tC8Ck3o8y4KQ+md/7RYy7+A1IfmJjRo9+LdZCAPXrG7HbkTu5pvYwamikK2XAyX5pmECfeeYZG5KEhHTeeefZeM1Cez1fYuG6d+9ueA+IRW/fvr1Ve+Kcg51ZGXChCOd+PiE03vHIs0FDEZYWLVpkiySgMYnCUz7sdcMehz339ddfT0m62G9dKbfUDlRh76G248qGAc+YMUNCY67JuBfUJsSpVipJ+VsjeTCkpq2xL2KcsZaSAdLW973vvhoGzAsPAya/slJyEaDKE96eSL1/JrNKRIR9kSpT9957b6rIBfY2UqLecccdEV1Fm8kFAcpvMh6RCpFmqZFNtEJYSmJlL8wl+LpQVhQnKlTiMN1bbrklwzEs7H0m9biyYcAk4A8qDH/GGWdUtA1JBBnJS2xs+TzUidjz4qSTTjLy8hvxijRW8nn66afjvLxeKw8EKBiBSvGcc86xGaqwEcI8c7ELBl0WNScJWbzpOLEfsq8SpJGge076PhZZhNl06dLFzg2Mz6BngZ0d8xGmpCSqmGG6OA/CdF999VU717C4OF8KlSfJaznq96FsGHDUN14u7YlzoxShr5F+SQkZN3FJcSg0jz6KNLyDrYtL9iMSMCglEwEcUGC6fJjMmKTxgI5i8eSXqCmOoVRaBHC84pONCMMh+QQ2U0ptJoUBM49gyqCWN+8pPgsIWX369Cl4sZgNi6TtVwactCfi649o+2wGrEGD3ixa/mffJTO+UudeKvPZIHzUQWTFcovbZxysO0qOwIoVK6Rq1sPWGYdJrrdkcKH2sVJ1IUAVMNTR2HkpSLLnnnuWPG8zXtbUF58utjWYL9I68bm8o0lZGMT5lgQyYGqponvH3qiJF+J8HJnXIiMVhE0PlWIpSHxtUkRd3ffee08ZcAqR5G089dRTZu7cuWbkyJG2TGPyeqg9KjYChBUxTmF4lOpk3OYbHxxFX+kLTBdpl+INrcSzE+fASlYvh8EtkAHj3YhLN8H7lJqiGDufhg0bhmlTj4kYATxZUSsmAX+8LFmcKSUXgWOkigYf7LPYgqNKRZncO9aeeRGA6SL5Ml8gQBVq+/e2ncv2J598YpkuHvnU9KbG9ogRI2L3Y8mlz3EfK36umYR9j5ACQgvIonPXXXfZHLMw4YULF2aeoHuKggAe0BDSbzFq/9a0Hv4vIVEE6BOLp5RsBLAJYlPD9kvcLmo/pepAgJSRhx9+uGnatGnszBc7M3wDbR0SLuaQK664wjrqnSQenXE7kSb9iQdKwBjt77//fpu7Fb08gxn3dgYxDh0zZ85M+n2Vff/eftsY8j/jfUxeUwLOS0kiTEk4gJGg/S3N0qVLba7YUq2sS4lDOVy7mKkoy+H+q6mPaDkIPyKjmLfgRpx5m/EzIDEGku7bMnERMtRNapqSHAPJVyk7AoEMmLyi5JB96KGHbOUI93SCtUtlh3T7UC3/xVRiw38kvatlwD169CjprTOOCIcaN66elYKxMVKMWyl5CBQrFWXy7rS6e/T5559bhkfhhObNm8de1xa77tSpU82sWbOstgW77oABA8q2VGEp3qZABryhZH0gvRdlmyBWWSTBuPLKK61tqRQdrbZryjstg8pYlT/2X55JqemEE4y5/HLKIu5knXyUAZf6iQRfv1ipKIOvpnvjRuD7778377zzjtTrXmJ9QxCM4ioqj4qZoiw4+lGIA1X3Pffckwj/lLifQxTXS2PAPNjTTjvNGvBRL6LWgFhhUQ9SKR4EyKEv8egSD4fK983EZPo66ihjJO+J+fXXv4kdWIoFKyUSAcxG3lSUZKpKggNfIsEqo04hCBECiPYJdTMhPMRlF1vNSxIP4nQpb0nmM/KME6uLQ6ZSYQikMWC8JQcOHGgmT55sNtlkE6tWoPk11lhDkjFsU9iV9OzQCODshOqZHAdDhvzLltUKfXIRDyQWmHDSjz7aSVbfw4t4JW26UATQmJx55pm2mc8++8xqtAgBUSpfBJB4cYCkTCAOdt6MZMW4K3w9YLoU2mABR85vyvsV+7rFuJektpnGgHGyIn5sPym/gycbzj8ukYAhbIUN9xz9nx8CZJtcvNhIKFhNBizSbSaFpCCOpP5cR2q7LjNoTFi0KSkCikDxEYDxkimq2M6PRF08KqnvKIJAnukhUg5t2223Lf4NVuEV0hgwq2bUHKx28GDzEqWqlOJDgEyPxNGRwSZJVUpEGWIJ1ReFMJKYzD2+p6RXIocvGjKlaBEglhfvYq+0idq5WMyXxTS2XRgvCTvIBaHSbrTPNKi1NAaMmpkPhGv7PvvsY1UQSMYEUSvFiwAaiKRWeiIeGFuUMuB434naroaKmUQHfoJJRkFM0khD2AGvu+46c+6559rc4OSDpkKSl1lEcb1qaAPbKhIni+x2xB0KoWomnAdq3bq1/V+sP5SYhOny7jDfE3Kq2Q+LhXZmu2kM2P2Zh3G5uLvyYAg7IgSGws9jxoxxD9H/RUJAhF5JnlDjgEUC9aQy4CeeaCtaEumoUmIQYFGUzUy0wQYbFNzP8ePH2zYuvfRSuyC//vrrzYknnmjLxDFXUJFHKTwC5OsmbAdJk/+TJk2y8y3ZB0nRWKyxj3RNLgd8fQhlatu2rblPao6qo234ZxfVkYEMmNqSgyQDP8Z3ypj17ds3cgkYF3a89+IMGI8KtGK2I2NSqtas9ICOs/5vLvf14YcbmWXL1jdXX53LWXpsMRHARsinWER6QyoskZWNCAl8RSDKxsFMlHJDoGfPntbPBpMfoUTUWiYHA+rf7bffPnLvZkKIYPKPPfaY9Z5mwUS2rGJ7UeeGSnUdHciAcbh64IEHbAmrm2++2YwaNSqSwt6//vqrXe3h4AXx4LEfEbd48cUX2/zT1QV/5t3OmlUT/8sqGLySmrrt9NPXkEXaPkZtgJnPsFL3MGGTThB/EHxE8LI+QYLDSVV7JzUzayGkLqrz+Il33JvByf97JX8HQ5gvC5t58+aZLbfc0oYWUTwhSmIuIakSoWktWrTQfMxRgltgW4EMmIFG7C+ZTViZ4Q03ePDgAi9lzE033WTbwJWeYg8Qjga9evWyaS+TKu3Zjsbwh/hf6v9KZS67+ClF/d+wtylpwcVe1Fiy4PxXPCUbhT1NjytjBGAYFEzHbkgSFiZ00l7CfBs1qv0dQKs2dOjQjLvHzFKtpS1htKjwUeUjnaIGDrLhZ4AWcgfPiVzg2JmPkiB+0guvt956Ic/Ww+JAIJABk4aS+C+8XKmIBFG7kRekEFossTWotF3mS1sUfsAGoRV2jGS3MeaHH4zZe28jRTDeSrSDk4SJS0rKheJ885sw4ELeCj03agSWLVtmc7YzfolqgHCwKXT80i4+IfiI4DBEoRachJCuKGFKprxsRPIGPn4ixI5ED9VICDXUaUatj9aRqmdR1MQleQ+Ml5BSqmJRa1cd5JL5hgUyYFKL4d06btw4yyDpehRqIlZ7DOCOHTtadQvtUiyaiWHGjBl8rWoSs7io9WrifxlEqOaTTGeeuVgydX0mXdSMOEl6TsUavzBa7L133323lWaZ3HG+UgqHwA+yuiZPN7HzZJFC+IgKPxYxFESA8aK9ZO7Aj6eUNYDDoRLfUSxG0bjif0Q4VxQ8rdDeBzJgPPDwmiQHcZSECosXDucu3OwBZKuttrLMV+OMjaj1aj5ffPGFtZdhE0oyHXHERiK13yNdPDrJ3ay6vhVr/GKnxAaMNIW0e9VVV9nUtUcccUTVYZzLDcMcP/zwQ0PxAua8KEuLYkMnLzOLI5gKJWNLXTktF2wKPRZmim8BvMrvTEZ41/Llyy3ThfGClUs8gyQkFwlkwNge8cSj0oXbSVQkbmo79yby+Y9TUZIyO+VzD8U+B+k3yfZf9/7x1CRZCC+2rrRdVEr/v1jjl5KkhDmhesY3BCZM3ChmKpyxlDIRwLbLeMash+c4oUVRCDbEZE+ZMsVMmDDBYEtGzVyssKXMuyrNHhYxlMpFkwDT5YNzH4QfAcmBvMTCBy0DCxP+ez9JEfgCGTCG+htvvNF7L9bzMW1HHl9uuOEGaz8KOpUBzOpNqSb9ZDkMJkLIkNKffvpjsQNvIyFl+vSSgECxxi9hR5ilYCou4UCEuYrJTSkdAQonIPWCDdq/LbbYIv2APL7BgCZOnGh9dPYWZxHmVFdIyqO5kp6CNoCFxHfffWfV5vznA4Nlce9W43M7iQ8Rx5MdkOpP1CtgG41MEEMth2ptgQyYlQQfAAIMbBZR6MtRXzGAWa35Myi5lZdcsP3/CXdBneAn+ud16vL/Xi7fJepLBquRYhg1FZDKRaLgPendex3J3mNEHVkuaFd2P1HFMeH7JYIo7hpNmJ904exHpOY7cxZMBHtvoXMUmiYSocySOEUc3wgNTVKK2mAEavbCR+Affh6CYy8ZF11Ci4Z2AEe0IC1Bs2bN3EMr5n8gA+buUC3hCU2IEHG72Hv23HPPgm6cFwZX+CuuuCJnlRXpMIcPz6zAw37KcpU7kccAeKlcg0o3itVyHJig+tlll39LLOgByoDjADzENdw4exa6SqVDgIpFhRKe7AgtpKXFLEh+Bmz8SSRUvkioCEorVqyw/9nGKWzzzTe3i0Jvv8nchgYNFTEMtxpzigcyYGL7WK1cLWmOsF1g88HOg4ddocRqMJ+sOax+glZAlRDGIAtEW/9X1jw25rocVCfue4D6a+2175F+HyAJBcQfWh2iXWhK9h/7LBEH5OqmwArEcyLTklJxECClI5pCmEkUhGAB40Xy7dSpk7nssssSz6DekThKQp9cQjXMYgFVcVCGNvfddI+vxv+BDBhXeQYxKxoIx6moErpXI8h13TNVH8VJ7/f6v69nrBTrOr+Uv9c4Yr0tq/PfRC22iizWStkbvTYIYA8jBMVL5aKu9Pa5HLbxriWZCIwSlX+hSUVgYnfccYe1hZIQicxVSUjXi6kP+zOx4PxHpU4aSy8R0QLD5YMaWR0zvegEbwcyYGLICJpHhQKIeNp17do1uIUI9g4bNswy+2pVmUleA8HbCNY1EnAU3uYRPJZQTaA2YnV75JGfSqazzUKdowcVF4GgzHUHHHCArSVb3CtXV+tULUI1DBMmE1ghKSTx8CWjGBLkqaeeWvQqSGGeFA5RpMmE4bppRFkM4OQX5PSEYxQfpfAIBDJgVi+kmSNxN9lZzjvvPJt6LnyzdR/pLcZQTgyn7jvL/Qhy2rOYXLBggbWF1OWQlvsVinvGjjvuKKq3tyURgDLg4iIdrnXChdzYXDRXxN17nV3CtaJHZUMAhkseg48//tgyHLzD87XLkqeZxCaki8RsgNmvFJIj87HfUcy156IqplISuSG4T3+8bTacdH/dCAQyYNLKvfzyy9aIzsouqoQQOBdRest1EvEXY6i7u5V5hGj7LU2c+FpZqZ/dp4Hqjfzeh2pOSheSkv5nInUnUxbT5FhHo9WnT5+S9qtSLk7aXKRCnIhYfObDkPCtGTt2rJk+fbrNDEi51zjTRaJSJuEPCzM+fG/evHmaBIsg0LJly0p5bIm8jwwG3KFDB2t/OPzww60DByoWQhqQiHnhCiEtxlA7egxs8mKXG/FeEB6hlAwEeI8oOQcRAoJdEedHpWgQAEuk1HykXqRKwonICMiClTS8captUSkTT4t6GSJGGQmXhQSLNaV4EUhjwMRl4T7+tBSkdVd1xO7utddehrKEhVbq0GIMmQ936VIjLz6lGX+1eWIJ9yo3YvBiu8Jp795769miEpIfQKlECGCj8y6WSU2okkx0DwNVbK6Eihemi2czDq6jR48OtKPm2m4uxzM+kXphtHjFI+Gy7Y/PzaVNPbYwBNIYMI4AOGu4zNdtmkoq1PwslLQYQyaCks9ewkOMOHG8ZbO/xKmGyuxNfntwxMIpAybcpMn2UnDDmMsvN2I3yq89PaswBAiJIUsSCyMIKfiaa66ptVpRYVeszLNhmkiM2D/x8M2HwB6BBjsvz4OQTn+Gp3zazXYOTBZPZYQdQqK22Wab1KEwWoppKCUHgTQGjMNGEANgHy9joaTFGNIRlPh0I2VSZTVsRMX/qi0Zl35E+XwjGQqp9w47bHtbVIJ7Iq5ZKT4EUG+eJunIYBpMvq4zH4kQYCJK4REgIQ45nJkT0SjkQ1QnwrMZ7AcMGFBUMwA2aZguH+y5CFGFeGXnc796Tu4IpDFgTmel5rfn4fUXFWkxhpVIYjal4JGkPZUUlLNtsP3KX8tri9U9WXsOO+wwI/4kqQ+hVUrxIEAiiIGSy3Ty5Mk2+YGbiQkNhVcSirI3zA2VlAcaQQOb+cKFC616Fu1frgwYxk0sL2316NHDaiOixNzbFtIuHtQu02XRxWKY5BeuI573eN1OFgJp0+ORRx6ZUlv5uxmno4D/2pX6XTRT5pBDatJPYnsv5xUrNsdp06bZRyUZ88Tj1kiZNCPxwZX69JJ5XyRGIX0shPqTiTmqXO7YD88//3zL5Ek+gb8CHrTYNMeMGROYvzeZKAX3ivsjhhqplwUl77TfHBd8Zs1epM9bb73VhhOSdYwkGsUm4nKxSZNoRZlusdGOvv00BowdLyjAOvrLaosgIBk/ra2U1J/EEpYzMWEhNVAejElh5kwjKtByvqPy7nsxcrnfc889smA8xNowjzvuOGvbxIOWikhIfC7jL1fkMKEgNWI/z0VljxaAHM04WVFEBXt7lNmrCFki0xa2faqkeZ3A8MQuNEd/uT6vSuj3KpVwE+V6D5JgTFI4Ygd+MSOtW7ndE2EZqDmZxCBxshQ7ZLndRWX015vLnTvC8cdfXjSfO4UJwABQrVIGDnUnjj3Ej5IVqtwJSf7ggw/Oifkydk8++WSzaNEia75jYRIF86XkI7Z8wj+fe+45M3/+fIt5NRYsKPf3qrb+p0nAtR2ov0WPAEwKxxmSWFTCKhYpmEmD5C1KpUOgWLncyVjXsWNHW9ShYcOGNnMTDPnee++NpFBL6RCruXKu6mZCM1l4kFyoSZMmkXUfGzIZCFncsMhhXOE7U0m29sjAKvOGlAGX+AEirVD9qBIcJnD6mT17tiEVoktXXmnM8ccbsae5e/R/sREoVi53Cg2QK4BYVrKfYV/Gzjx16tQ6s+XBVChF6ifel7gLymMmwdEKYgGRC6Fu5j5wdKMOMkUvopB4vX3ADIjPDSX8lOl6kam8bWXAJXimRHQRpnP22cY8//zzNk1gCboR+SXxvsRO6CXqbUv5ZyPZTZViQoDkCsXK5Y5tFM9eLxF7yqe2hA7EvlLdx0+oVoOKr/uPi+o7IVl4DVOnNtfsYKibb7nlFlvxiGgRtACFEH3ArssCxMvEg0r3FXIdPTe5CCgDLsGzIfyIanHduv1o3njjDXPppZeWoBfRX3KLLbawKnWcRtzQDdHOiQrNSJYvIxNX9NfUFtMRwAb/rJTXwlnKLXJCvmHigwtNpkPIyzmSZYX227VrZ23L5AggdzxmlCtRd2QhGDd5APyEExfMOw7Cho0kjqqZUnphHU7xbs5V3QyT5zy0Qnimu8S9wnRZeOB1DeOF4eKprlR9CKgTVgme+RNPGImXxQu6Rv0clPykBN2K5JJIFYSouEScs9QCEM9Qd4/+LxYCTPitWrWyTAYGzHfChmDEQdJnrv146KGHbCYlMubBNI6RNG5RJOjJtR+5Ho+anJz2FLnHaxinsTDMF3UzixY8m/E8HjVqVMrWS5vY2l1VtrdPN0geVhJvkBkOVT0JOShEA24zJTyAQjeEHaIxat26tTJfL3hVtq0ScAke+JQpeKaS/eqZiqsgtMsuu1hHLKrvuISAj1/WvHlGJiR3r/6PGgEYDEwRj+cHH3zQMhpSD1I6Lwo17zx5gOSGZ8GItEscMJK1W/ow6vuJqj3KBi6QUp8wQ5heGGcrmCbqZs6FWbOoxMsfSR8V/xlnnGFV7oTesQh5SoLesdeCNfjjRMV3ngcqe0K08GxGM4QmgAVMmH5EhYG2k0wEVAKO+bnIQtxIljuJNawJMyj3+F8/fDBgJiEvifnPPPmkEU9O717djhqBpVLZw03mQgnR7bbbzqZCjIL50lec68466ywrwfEdJkwCiP79+/M1sYRphKQYOJHVxfRQU1988cW2WALxwKSkpHwqjld9+/a1Mb7U7CVJBwUVyPOMmvn222+3909iku7du6ecp3gO2MbpA3n2WRCxXVc/EgumdixSBJQBRwpn3Y2J2UdUgsa88MLThio1leblSAgSqjayCXlJwitFCvPu0e1iIsCk77U9RnEtFot4QHtjUUnCgboV5pZUgtkhtdZGvK9XiLcgld8o5Yj6GG9p1MkuoYpGC4AtHBW/S127drUSNvZdVNVoH3CuhLAdYw/G0cqbQMM9V/9XNwKqgo75+YuJTux0xpx66lTTu3fvmK9e/MuRkIO4RWxjQU43xe9BdV+BCX/SpEkGR7hPP/00lRiFcBuYZaGEVO0nwnGSQsTV4+CEhBp2cYvGpl+/fmaK2IbwdCb/c8+ePW3IFelVjzrqKOsshWSM0xRq7Ouuu85gE4dYlKBaJmEGamaOIyEHsf2kh3zvvffSvJyTgpX2o/QIKAMuwTOAOeHEkWSpoRBYSEpATt0gBnzbbUakgRontEKuoedmItCmTZusoTX5FI/PvEKy95AUg6gCJFGk/7oYMI5ZMMu5c+da5zUyhrkmoeHDh5u2bdtaSZdFB9oqPJWRbmHC2IhRUWMfRrLF5wFbMDH9qJi7deuWbLC0d4lAoGwYMKtaPAj9hFMEq8xyInLGticHZYUSk5BrE/PfovjviB3RiDOLEWce/6/6vRAEyJrEp9qIxSwZ2JgjkESRPIPCemCQxKnDqPkdm26nTp3sPrYJ4cJrHEIqZm55UpwXcDTjXByn3FhnbMKvvPKKvS7zDxI3sc5q2622t6+w+y0bBoxqM8iZhP3l8tJjNrrssq/twO1DuaAKJSR7vENRB/onQkmba0aOrImDlsp5SopAQQiQjQsbK3ZZ7Kw4RHnnA1TxT0jcH1IxtXlxEmSRQgYrHKLGjx9vr08xCWy8u+++u4TNnWJuE1XNww8/bH/LFrKEqYVkHDBe5iElRSBXBMrmreEl5+MnkgIwuJJOYgYSxysjKrKJUqLvSJtYPel9zrd/qOh23XVXq4ZGdecl8YeRyc2IR6iRkBZNUenFRrdzRwC7K85SSL2kbvQSjlUdOnSwoX7EROMYhaSKZzNhQRMnTrQZsVDPE/PLO0vMNHZcwooIO3IJCZj32svckbb5KCkC+SJQNgw43xtMynliOhJ70i9m1qwpNnl9UvpVrH7gTYqKzs+AuR4JkUSrZ7OBjR1brB5ou9WAANoWbLBB2jHsuCeeeKJVISPREjJFmBFezjDkoUOHWiaMahmnqptuuslKwF7cYLx4PqPexhaca/pKb1u6rQj4EVAG7EekCN8R0MlD36LFdFGBtcyp3FkRuhNLk3iSuuq9oAsOG2aMhK0qKQIFIUCcbRARj4sk63osk0gDFTW5oAkxooIRCUTwFCc1pJ/5IlVzHHZhmDDSNecrKQJRIqAMOEo0s7QlkQ2i6vpNnD9GSZacMVmOqqzdSBXYf/EwxVbmJ6l7Ls5z/r36XRHIjsBXX31lq4bVFdNLhi688PGIRrLF3wKbLx7PMObBgwfbpBjnnntuYHgQWbPef/998+OPP1ovZ6ReqhMpKQJRI6AMOGpEA9oTvw/Tps1tkvf12KoayKifXxDDdxAD9sKEJFxgYRlvc7pdYQjg44FHMos5/EBqq71LHDSMGj8LSnzCSIk6QPVMWBEq6doIx0HCk1BrY1cutOJRbdfS3xQBZcAxvANz5syUzFD/MJ07PxDD1ZJzCWIjr5EqDIRx1EbifCoxlcZIhj8lRSANAaTQOXPmWMl1q622sl7MaQf8/oVjsPOSOAMJuUGDBraQAlnZCD3CHhyG0NoceuihFe0kGQYHPSYeBMQnVamYCFByDDUYeXO9HpTFvGZS2nbzEuPEUhuJL4zBJix13ZUUgRQCjJ1ZUruTECPCg/jgiewnCiYgrVJsAcbLd5JhoDaeMWNGVq0TFYrIn+2nNddc079LvysCRUFAJeCiwFrT6Lff/mRatpwqtqdTpBqQlAOqQiJnLlIJ9rZsJNEfNjQJSVgK+oi3abYjdX/SEPjuu+8CPZAL7eeiRYusHRdpluxU3kxeVBqaMGGCvQQSKykksdNyDuFDHTt2FG3K1bYsI3HCSMB+grlTGxiVM85YqKuVFIG4EVAJuEiIE1e43XaXSMxhBxvgX6TLJL7Zw6TwMVIIeNRGxATzkWyKEtdZ25H6W6kQwKsYr2H3QyhPs2bN7He8i6MkGCJhP5gxXOaLmnmYqEpY1BFSRAwwjlXkWibmFw0TNuIRI0ZYr2XKAMJ8vVIzHs04Z1GLm6xW9F+Zb5RPTtvKBQGVgHNBK+SxZN9p2nQ/qRrzgkwQa4c8qzIPI+sQsZrUPsYxpjZCDU1++wAtY22n6W8xITBkyBBb0IEc3zAvnKMwL5Ds4vTTT6/T1p9LN0nv6KaYJdMVjlQPPPCALZQAQyUVJIyUfMyEIpGbmbSReN9TwWj69Ok2sYb3miwYCEdiMYh5hKQcXubsPVa3FYE4EFAJOGKUWZWzQm/a9EaR5BrahBMRX6LsmqMyjFs5prbOw3gTVFintq5W5W/XXnutdWbCvkrFJaRI4mxfeumlSJmvF1wKJFAmEAcrVM/Ydc8++2wr9bI9UPKZUnQBb2c8lpFw6Rf2Yi/BfGHk2HdJQYnKWpmvFyHdLgUCKgFHhDohEmTWwbaECuyUU/aWEmdacAB4SfHHZImEgr0tLFFStWlTIyrCsGfoccVG4JxzzrHF7bt27ZoT08XhCXusn5BGszFCGCkMlhAi7LqMq1ZSy5MczdiFUYmfLMnF8bTHyZH2KSsY5Oy44YYbWkctb0EFf1/0uyIQNwLKgAtEHEcOVufYp6ghehKGTFNPypEZc8YZBTZeQafjhEX+XaSPoLSBQbd66aVG4jGN+fvfdSEThE+p9uFQ+Pjjj5v+/ftbla+bi92tFBTUL5ipt7i9ewxVjILKVv5dHjrS7/rrr2+oZcx7c4AkEF9jjTXMeeedZ0i20b17d1tYASc/PKBJfRrEfLkWUrI/V7TbB/2vCJQKAWXAeSLPhMIEQRjDGcJpYcLewS/VzZQ8CGBvI76SmODaUlR6TpFJ3kjyBGOaNyeV5zciCS+zKkZ3wgdvtl17JN+RpghFQeJRihYBwoGQgCmA0q5dO/v+ryU1JTEvkCgDKTQb4UzFx0/33nuvfYajRo2yOZqx0fI8UTczrrDz8jzxIUB9TJYqvJ1JExmUGtK1SzMuSYda26LA3xf9rgjEjUDJGDC2GnfCjPumC7keHpfYwsgfy0qcIuhK4RBACqYizfFi6B03blzWk7DX4VSDbXG11f4jmYnOFUeuPcWuN0hSA35qqP/Kh4mZMBjUm0g45O/FGxZVJNmQcAAjPpSMXK1bt05502a9sP5QKwIwWiTRu+++25pbjjnmGOscVetJIX7ENks4EO8HXvNIqlQleuedd6yH8uzZs6V29Fq2hjbbSMVBhPMjGilU07ShzDcIJd2XJARiZcBMlCRBf+SRRywGMGBUSp07d7bhBEkOB2CCIIfsVMkWcanoRrFJ+UnCEYUxG/EI9f+i30GA580kTngICxckHOzDvBdkLEKNP3PmTJt44eCDD7YhIkzKSDojR64iYSl3ig05PJbUJCatIG0OGDDA2p+R4LbxlJkL35oeicczJhaYIdIuamA0GrnY9YNQZExRpxcfAdTJFEBAu4SHM/Zf0ktSr/euu+4KOt0uvHDCon8sxKjEha1XSRFIOgKxMmAqjkCoq1xmixMGtkEkIgphJ5FQfzGBo0JFMgvKlCPjX/LMGgmVSOIdJKdPqIhR14MpC7GRI0daSYU0g0irTLZ41vpJ+HDORJt8jjrqKCsV33fffaZTp04G5k78aDZJKucLVckJRx99tDnrrLMsc9x3330tE77ooousLZhi9vkSYUQwTBjwihUrrKqaXMwsnKi3O2bMGLvwDWp/+fLl1vaLJoRn/de//jU1twQdr/sUgSQhECsDRrV47LHHpg0QVqwkSceBImnEZIBXJVIUjAJnkCCSRbqozoxIdkYm+6AjdJ8XAVSDqIT55Evi/GoLOEiEinXUqqsdpDbUmnjNwuQJFbvgggvqTM5fV7vV9DuexyyUUfG6dP3119uQH8ZxvoSmgkU54wsNCRIxccUkcEHKJlkGGa+CCAbMoo6+YXJQUgTKCYFYGTCVSFABor5163jiUIFkwmBLErEaJ50dSQbIvpMtVEISA0lohJHQDCO24STdQWX3RQRY0ZwYUU8bsR0ac/75RuzDdd8z2ou+4iGH2ePyyy+3Ez4JJrTQet3YcQTZqfyETb8Qcu2+mCJwnuPZoCGjjCDha7URMcl8lBSBckQgVgZMuAEZbQhhePvtt60jDWojmO/GYWbPGBBmQUDg/5IlS8zo0aOtSqu2y4ozpvXUhfmKYKcUEwKSXEvy/hopd2gkVtSI+rEmj7RoIEMR7x0Lv8mTJ5uuEtOKNM6Ej8pTKV4EGGvU6aWIAmYJNwOWvxeug50+Iz8y+r1cEYiVAQPSpptuap1vkuYFTX5bAv1J5k6mnW4E8oYg0XyJ+ivEgXpIURCQ0FDLiEWLKZLQykuIH484dhmxJxqx2a/c798inAbmi1q6ucQ74YeA+rOuou/+dvR7/gi8+OKLdrzh5JiN+RKW9O9//9tKxpgPlBSBSkBglThvAhUTUsb2229vqwMR0I/TBNluYMhxE6EsSN945aIGY2VNjGM25ishiVbtKb5YSglDAAlYnKxTJOGk4qFrRIVpJIGDMeIrJPmBUz+nbWAfxikL2yPxoyR8QE1NGJRS8RFgwUsWOZJp+AnnKmLuCS+iKENTUqMpKQIVgkCsEnCpvKBhtKi4SBNJuAtpIwn4J/6QMBgcw+hb/fqrSxxp+pOVRbekuqtRdeJ70r69Fo5PRyiZ34gS4yOPWmKPjZHwUUlhWGOvd3t8661GEj0Y8cA1Ul3HiP1xU7E/DrShcqQ7xMFo/vyP5B3ZSxyNGkss8k7W3oiURt5hpWgQCLIrk1Bj/vz51jkLpz0ctKh0pLG90WCurSQDgVgZcCFe0CRwYJXsJxjpHnvskVHububMeuJA9Y3ECP5mT6lXbxVRX20kSSD+LhPqRlbV2L79CJF2/yAZeIxlvCKgCxNm4P8iqvKaK7GvceNV5DhHVt9OSp1ZR3U9fzf1e4kQIOSXD17TkPe5NWpUzyxevIpkVjISg1xPFmnkrXbE431106VLF/vp1MmRLFxryIezHZGyfxV/hTnyftSoQVnAkcXJT8Q8K+WPAHG9fFjsgDFaCiVFoNIQiJUBF+IFTSgCIQp+Gjt2rFl11VVtAL73t+bNjaiXN5Q0dzV7cZCSnB/CrK9KOUuh9Z41q6b8Hb8xxtddt57Y/1aGVBB5lCX6yHs53S5DBPBe55NJ9VO7qPsuCZbsAu3nn+sJA19NpN99RVp+PXVM0AZaF6X8ESBKgjht6v4qKQKVikCsDLgQL2jCgIKS+BMf6OYG9j4kHG/qco6iyo4yVy9quu1HQNZ2wmz9e/V7sRFgrAeN92JfV9tXBOJEIFYGzI25XtDemyTOFibau3dv727dVgQUAUVAEVAEKhaB2BlwEJJkKCqEKIwQpJ4upM1CziXWOVs4RSHtluJcYjMrJdEB90IBgTgIp78ddtghjkuV/TXyGb+EJVHKsBjpRAt950mvS2GIQnIb4P1NZEgxYp7Jr11IacYo7u/bb7+1OeCjvj8EOXyNCr0/MIpj/NaTDv9uJY13HEcVB0wFHBK1J8nphbzRxJRWApGPtwVpviqACDGjiEAc7wpFRkh7mS2DWgXAGckt5Dt+CREjNCkob3ihHSv0nSeFLak1KQqRL8EAaIdQzSgJ3wQcWguZn0hFSuGLoDrOYfvK/cGEKVMaJfE+ka+fql35EosnImZIj1z08QsDjouE6TqiZnYk7MB+JO7P2XnnnZ1rrrnGkVVVXN0o+nXk4Rf9GnFdoJLuRRYSjgzQuKDT6xQRAUkV60gK0civIAzKkWIdBbUr3tuOaPUKauOxxx5zbrjhhoLaCDqZObhVq1ZBP4XeJwlRHEkpHPr4oANFS+hI6GfQTwXtk1KkjuR0KKgNiaxxpNRsQW2EPTnWWAlvHDAxfpQde/311w1ZqGqrD5vvSkbPUwQUAUVAEVAEkopArAwY3TwlzdxShIDiVkMiB7OSIqAIKAKKgCJQLQjE6oRVSBxwtTwQvU9FQBFQBBSB6kAgVgnYjQPGc5FqSNTZbdCgQaKqIVXHY9e7VAQUAUVAESg1AvXFY3dAnJ2gygyMuGXLlpKFqJX1FKy0gHtqmu64445xwlq0a1XSvVCwvVKeS9EeeJk0TGpKQk2izpRFrulC33ky8xGGSMnLfIm61YT/Fev+CgmxwYRY6P3x/Li/QkK1grDl+THOgwp7BB0ftA+zKPkq3Jr1QcdEta9kYUhR3YC2owgoAoqAIqAIlCMCsaqgyxEg7bMioAgoAoqAIlAMBJQBFwNVbVMRUAQUAUVAEagDAWXAdQCkPysCioAioAgoAsVAQBlwMVDVNhUBRUARUAQUgToQUAZcB0D6syKgCCgCioAiUAwElAEXA1VtUxFQBBQBRUARqAMBZcB1ABT256VLl2Yc+v3339uKJhk/JHwH1UComuIS2z/++GPq4/3NPSap/3kuVEjxEv2n2olS+SLA+8j4ipIKfc8ZN/TLS1999ZUtK+jdF/e2d+z++uuvqcvHNT/lMjd+9tlntjZ8qpNF2liyZElay9nmhKDnl+3YtAZDflEGHBKobIdRUEKqp5gePXrY5CJSKcQeOnLkSHPIIYeYpk2bmuHDh2c7PVH7GZzt27c3Z5xxhu27W2N5woQJtjZms2bNDB/KfSWdYLrt2rUzUrXFJnuZPXu27TL/d999d3PcccfZj1QtSfqtaP98CPTr18907NjRdOrUyVx88cW+X/P/Wsh7vmzZMrPffvvZDH9uDyhHecIJJ9iSey+++KK7O9b/MDSSZrhj9+abb7bXj2N+ymVupMThvvvua0477TQ7PqkRXSy6/fbbzemnn55qPtucEPT8sh2baizXjbBlk/S4YAR69erlUDoMokSaPFhHBqOzyy67OLJScij/RclFWR0HN5CgvZQIu+iii2yPuIfGjRvb7UsuucR5+OGHE9TTurvywgsvOIMHD7YHTp061encubPdlknSWbBggd2W3OTO008/XXdjekRiEJDKac4BBxyQ6o9M2s5HH32U+l7IRr7vudSNteNdats6bEPTpk1zTj31VLstjMgRBmi34/5DP/yl9eKan3KZG8F+7NixFp7Ro0c7l112WVGgEgZvn8URRxyRaj9oTsj2/IKOTTWUx4ZKwLmuWHzH33jjjaZNmzZ2LyUWSdM2d+5cs9tuuxnSopGWTpixee+993xnJu8rEuPQoUNtxz7++OOUKojC56+++qo55ZRTjLyYyet4QI/2339/I4PaFka/6667rJaCw1CHbb311vYMjpkzZ07A2borqQiQmpGC8pAsbg2F3RljUVC+7/k666xjnn32WbPrrrumukGeeyRiiLSI9LMUxD2hRmXsMg5QR8c1P+UyN3rxKua47Natmxk1alTaowiaE7z98T6/oGPTGsvxSzRvbo4XrcTDp0+fblDrzJw508gq2Ky77rqp2yT/NfahcqGFCxeaLl26mBEjRtguk9P2oIMOMptttpmRQuNmvfXWs+qicrifyZMnG0pdknt2+fLlaZM1z+XDDz8sh9vQPvoQ+Omnn8yxxx5rzj77bPte+n7O62u+77lIvhnXo8b5dtttl9rPwhzmR47nOIliN/vss48VEgYNGmR9H0SzFev8FGZuBC93zizmfIkq/t133009gmxzws8//5zx/FCLexd7UcwfyoBTjyL/DVFBm/79+5unnnrK2lsaNmxoJ3u3RR4yzKscaN68eaZDhw7Wbo1tG7rjjjtSXYcBizq6bBgwNkLswEySooZOc5Ipp+eSegC6YZ2veEcp5iImk8gQifI9988B9evXj535Agy+KS6JWtfazUWFH9v8FHZudPGiEEac4xIm6nWcc6+9YsWKNIx4fhR5CDrWxTef/6qCzgc1zzkw3SFDhhhWeW71DLH52lKLrKJYqeOY5V0Ne05P1ObixYvN0UcfbcQWY1q0aGH7hscfq8Yvv/zSfkdly4o66TR+/HjDhAN99913dmHkVnHBVADNmjXLNGnSxG7rn/JAgPcRkw/OTVEy36jf87333tu+X6CK+alUC/CePXsa8YGwD9cdu3HNT7nMjV684hyXmAlxUvPPCd7+uM8v27EFjZw87MZ6igcBHKykLJrTqFEj+5GJwf6KQ4GUXHSk9KIzZswYzxnJ3ZTVsiO1mlP3wj2JZ7QzceJERxiyIxKx07ZtW+eHH35I7k383jNZ+DiionTEq9sRb3RHJgP7i3gxOq1bt3YOPPBAh/tVKi8ExFPZkXJxae/oSy+9FMlNFPqe8765Tlh0qE+fPs6hhx7qiG3YEZtiJH3MtRFRt9r3XxYtjiw2HdFw2SbimJ9ymRvFW9sRr3bbV8bmt99+m+uthj5eBCLH64SVbU4Ien7Zjg19cd+BWo6woOVL7ScT1iN4W8es2o8sj19Ry6CyKSdC+g2qNy2LCGsXLqd70b7Gg0CU73lS3jNUqziLeamU81O2a5cSr6BrB+0Dw2z7vfiG2VYGHAYlPUYRUAQUAUVAEYgYAbUBRwyoNqcIKAKKgCKgCIRBQBlwGJT0GEVAEVAEFAFFIGIElAFHDKg2pwgoAoqAIqAIhEFAGXAYlPQYRUARUAQUAUUgYgSUAUcMqDanCCgCioAioAiEQUAZcBiU9BhFQBFQBBQBRSBiBJQBRwyoNqcIKAKKgCKgCIRBQBlwGJT0GEVAEVAEFAFFIGIElAFHDKg2pwgoAoqAIqAIhEFAGXAYlPQYRUARUAQUAUUgYgSUAUcMqDanCCgCioAioAiEQUAZcBiU9BhFQBFQBBQBRSBiBJQBRwyoNqcIKAKKgCKgCIRBQBlwGJT0GEVAEVAEFAFFIGIElAEHAPr111+bZcuWpf2yZMkSQw3LfIj6ot9//33GqUuXLs3YV9sO+vThhx/az1dffVXboaF/W7x4sfntt99qPZ6aun48OCHo3E8++cTWQK61wRA/0qcvvvgi8MiffvrJ1uOkJicfvrvE9x9//NH9mvofhBe1moPuK3WSblQlAgsWLEiNs88//zx2DHif3XG+cOFCI8Xpc+pDLmMwaAxzsW+++cYw5xWbGOO5zoPF7lOs7cskpORD4Oqrr3b+/Oc/O8J4Ur/su+++jgyK1PewG/369XPatm3rNG3a1Bk5cqQ9be7cuU7z5s2dTp06OS1atHDeeeedUM1ddtllTuPGjZ3DDz/c2W+//Zw999zT+fTTT0Odm+2gPfbYwxHmlO1nu/+xxx5zBg8enHGMe+6sWbOcsWPH2t8bNWrkCBPMODaXHS+//LKz6667WoyOPfZYR5hx2umnnHKKw7X5bLPNNs5ee+1lfx82bJjFBnxuvvlmu+/99993Dj74YKdDhw4Wa2G4dv/DDz/sHHTQQQ7t83yEaaddQ79ULwJ/+MMf7Nh0x+dWW23lvPrqq1kBYQxeeumlWX/P9Yd//OMfzkYbbWTf5UMPPdTZaaednAsuuCB0My1btgw9Bt0x7G/8oosucoQB+3dH/p3xeO6550bebrk0iLSi5EMABrz11ls7PXv2TP0SxIBhDP/73//SPqkTZEMkaef444+3u0QKdrbYYgu73atXLwemBj355JPO6aefbrfr+gMDvuWWW1KH8X348OH2+y+//OK88cYbzksvveSItG33yerWkdWz89prrznz589PnUe/OZbf3QEIo3Lpv//9ryPSvv3KogMG/dlnn9nv/nNlBWsnn0suucRZvny54zLgN99805GVrdtk6n9dmHEgiwuRQuw5J554ovP000+nzvdu0BYLGBYAPIdtt93WAYeff/7ZbrPv/PPPdx566CF72v333+9ceOGFdpsFkds/Jht+U1IEQGCdddZJA+LOO+90TjrppNQ+Fpjvvfde6jtjmPcQRuyOOX5kzMPEeCcXLVrkvPvuu86XX37pfPDBB3Zx/89//tNh/PgJBswi0iWRiJ3NNtvMtsE+//VFWrXtMs75bd68ealFK2PS21fO949h/wL8P//5jx03jB+EBZeYS7g/iDb+9a9/2XHn/p5tDmKcMd9wDgz3ueeec0Syd09zLr/8cjtHpXZU0caqsYrbZXQxWXGae+65xzz//PPmwAMPDOy5MFIjL1PabzIIzCqr1Gj21113XfPAAw8Y1Fh33HGHOeCAA+yxN954Y+ocYYxmtdVWS32vawN1jQxgIwPLzJkzx1x55ZVGBooRKc+IJGjVVcKEzVtvvWVkIWFkMJlddtnFzJw50wwcONCItGc/G264oVUxCYO1lzz11FPNuHHjzAYbbGBEyrb3tc8++9hjZYCY119/3YgUnHGuMDvbDxkz5uOPP7Ztde/e3ay//vpmypQpth1ZeKRuCzxEUk19Z+P22283e++9d2of9ygLIPt9//33t+0fcsghqd/djdtuu82IpGxEkrW7/vKXv9i26tWrZ0RqsM9BJh8jCxz7u0jLZsyYMXZbJj/7nz/gKdqE1HfdUARcBFD/CvOwY4J9vL/jx483stgzslA1kyZNMrxLvPuMR7536dLFiBRqnn32WSOLRyPSsZ1DtttuO9O+fXszevRoI1K12WGHHYxoYuwcw/dsxBhjTmFMBV3/8ccft2N7++23N7KYNIw/+kI/RTNl+y6M1F6rQYMGGWPYf13GFfME1zz66KPNU089ZWQBYGSxbzbeeGM7VrkOY4/rMJ+xHTQHMT+Jhs80bNjQjvvjjjvOtv3MM88Y5li+g4kIFqmx6e9PJX9XBpzl6a6xxhqWAYsEa5lP0GE33XRT0O6MfS+88IIdpDAFGBUMApo+fboRtbRljhknZdkxYcIE88orr1hGCxOGyWOjFWnYHHbYYQaGLqvxlG2TiUDU4OaRRx4xvPQMBAY0jAhbE4MaYsAxWfzpT3+yAwyGTV9FGk3198UXX8w4d6211jJHHnmkPRYGCDHhcK/YYumrlwGLRGv4ZCPuadVVV76Wa6+9trWH+Y+nb0wIM2bMsD/xnetPmzbNLkiYENjXuXNn07dvX9OjRw97zyKBpDV17bXXWvyOOeaYtP36pXoREEnO7LbbbnaRy+J5yJAh5rTTTrOAjBgxwjJB3ku2Rfo1YsawTOaII46wDDgIOcYa4wumxnm8kywu8SuZPXu2Zcje8xg3jF3OYxHJmPrjH/9oz4UJe6/PefT3wQcf9DZhF6OiHTKM0auuusrOASwCgsa/98S3337bMnL2nXzyybbd3r17m4kTJxrau+KKKyyTR6AQTZe5++67zaBBg7LOQWIyMmeddZZl1ggbbdq0MaJ2NvjGQKI1M1yzGmnlTFeNd1/HPYt61g4uUa8GHinqTftCen9ktexKwO7+jh07Gj5I0mJLsgxOVNCmf//+dnW5ySabuIfW+Z/VLS8vxIBGCoe5stIdOnSolXZhPEjF0N/+9jf7n5UvEwtOHTvvvLPdxyLD3W7Xrp1l1K4B3IkAAAUVSURBVJtuuqkdrNddd51lTKxOYYpQtnPtj54/rvSKlO13PrvvvvvMDTfc4DnaGFHxpSRgJhavExXXZvXtJxgvfXeZO7gi9bPIgJCY2detWzcrraCpgAnDtF1iYcLk9uijj2Y8M/cY/V99CMAkeJcgpDS2YWIwDFEjWwbkooJWJRuJyjX1ExKud17wj8vUgb9vwFDFLGLq169vGEecm+369NffDxaaMHf6De2+++6GOYfFrTvmveP/98vaf0jzaMIgUb2bo446ys5dLHBFPW9YiOPohfQOMQbpQ7Y5yO0b98x8BQMXk5dhjmFBwNyUzeHSXqCC/6gXdB0Pl5cFNTQvjJ+YzBmc3o93kMGwmjdvnjoNSXXLLbe0TJdVNRIw3/MlBiSDHEaMVI3Ki5Uo13EZsCttu9dAHY1EDnGcu/JEYhU7lb0XJGh+o39su5TtXCYJ93rusdn+M6C9eLHtVT/TXxYkSPIQK+4mTZpkNAejRWp3CcndKznTHyauMSLpM9GgCoPZii3fnsLih0GPRoHflRSBIASuv/56g1nJlTphXix0YY5Iciw2ve8/DM/1nHbHFu165wW++8cl+7zEO8liGJWvey6L06Drc557jNsG7z79cr3/GUcw0Gxj2D2P/1wDswzEWOQDs+zatavd16pVK6s6BgNU0WBQ2xzk9g08mFfQUjHuxFHStgfDRx1fjaQScB1PffXVV7cqFuyhuRKrXhgwAxXpE9UrgwrpjFAnVFAQK0Ne5jCEHRcVFoOLleOoUaOs+gr7LO2jshIP7pQ91t8mqlnxrLSq5R133DFla+U4VEoMBiYH+sYCgvt3Kdu57OfaMPEoCBvxOeecYyVhJgw0B+LgYtBIiBOIvQSSCKo/l5gEmjVrZvchdYM7q2ukANRfTGQQONMWeGHHc6WBs88+24jTnduc/lcELAJIdthsGcOMmwEDBlh1NIwWTQ2MhMUezIWxif0XtS3+FOJNnXrvooIz6PqTJ08ObJ5jMcGwSKe/9I8+ZRv/biOMM8aXOzbQIqHt414hxhMqdFTehPEhiDD+6pqDUDVjKnviiSfsPNOnTx/bHtfimtVI9URd6VTjjcd5zzBFpDOYZjGJ2D1swmGIyWPNNdcMc2jGMUHnssDg/tzVbsZJeewQj86UCi3s6fQDYuL0EitvbGhKikAUCLDIg5m5BJODEfPesehjzBfzffNf3+1H0P+gdz9oDLvnsvCWaIGUihlVs3g8W9OUewz/g/oQZg6iP8w97nzIggUbt+tD4r1GpW+rBBzDE45LxRmW+XLL+TLfbOf6GV4UsLr2q1zaytaPYk6GufRPj60MBLzMlzti4ekuPllse80hxbhj//Vru0bQu1/b+Edzh/QL00WSnzp1qjVH+a8R1Icwc5C3P6i6UZdXI/MFT5WA/W+VflcEFAFFoMoRQIrHWxoplUWtu7iIGha0XFzDa+qK+hpJbk8ZcJKfjvZNEVAEFAFFoGIRWKVi70xvTBFQBBQBRUARSDACyoAT/HC0a4qAIqAIKAKVi4Ay4Mp9tnpnioAioAgoAglGQBlwgh+Odk0RUAQUAUWgchFQBly5z1bvTBFQBBQBRSDBCCgDTvDD0a4pAoqAIqAIVC4CyoAr99nqnSkCioAioAgkGAFlwAl+ONo1RUARUAQUgcpFQBlw5T5bvTNFQBFQBBSBBCOgDDjBD0e7pggoAoqAIlC5CCgDrtxnq3emCCgCioAikGAE/h8G70Om8/oy+gAAAABJRU5ErkJggg==\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%R -i result plot.fevd(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <span>ListVector with 6 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      \n",
+       "      <tr>\n",
+       "      <th>\n",
+       "        breaks\n",
+       "      </th>\n",
+       "      <td>\n",
+       "        \n",
+       "    <span>FloatVector with 9 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      <tr>\n",
+       "      \n",
+       "      <td>\n",
+       "        1.000000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        1.500000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        2.000000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        2.500000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        ...\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        3.500000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        4.000000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        4.500000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        5.000000\n",
+       "      </td>\n",
+       "      \n",
+       "      </tr>\n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    \n",
+       "      </td>\n",
+       "      </tr>\n",
+       "      \n",
+       "      <tr>\n",
+       "      <th>\n",
+       "        counts\n",
+       "      </th>\n",
+       "      <td>\n",
+       "        \n",
+       "    <span>IntVector with 8 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      <tr>\n",
+       "      \n",
+       "      <td>\n",
+       "        6\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        5\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        5\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        5\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        5\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        5\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        5\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        4\n",
+       "      </td>\n",
+       "      \n",
+       "      </tr>\n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    \n",
+       "      </td>\n",
+       "      </tr>\n",
+       "      \n",
+       "      <tr>\n",
+       "      <th>\n",
+       "        density\n",
+       "      </th>\n",
+       "      <td>\n",
+       "        \n",
+       "    <span>FloatVector with 8 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      <tr>\n",
+       "      \n",
+       "      <td>\n",
+       "        0.300000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        0.250000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        0.250000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        0.250000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        0.250000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        0.250000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        0.250000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        0.200000\n",
+       "      </td>\n",
+       "      \n",
+       "      </tr>\n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    \n",
+       "      </td>\n",
+       "      </tr>\n",
+       "      \n",
+       "      <tr>\n",
+       "      <th>\n",
+       "        mids\n",
+       "      </th>\n",
+       "      <td>\n",
+       "        \n",
+       "    <span>FloatVector with 8 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      <tr>\n",
+       "      \n",
+       "      <td>\n",
+       "        1.250000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        1.750000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        2.250000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        2.750000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        3.250000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        3.750000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        4.250000\n",
+       "      </td>\n",
+       "      \n",
+       "      <td>\n",
+       "        4.750000\n",
+       "      </td>\n",
+       "      \n",
+       "      </tr>\n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    \n",
+       "      </td>\n",
+       "      </tr>\n",
+       "      \n",
+       "      <tr>\n",
+       "      <th>\n",
+       "        xname\n",
+       "      </th>\n",
+       "      <td>\n",
+       "        \n",
+       "    <span>StrVector with 1 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      <tr>\n",
+       "      \n",
+       "      <td>\n",
+       "        'var_from_python'\n",
+       "      </td>\n",
+       "      \n",
+       "      </tr>\n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    \n",
+       "      </td>\n",
+       "      </tr>\n",
+       "      \n",
+       "      <tr>\n",
+       "      <th>\n",
+       "        equidist\n",
+       "      </th>\n",
+       "      <td>\n",
+       "        \n",
+       "    <span>BoolVector with 1 elements.</span>\n",
+       "    <table>\n",
+       "      <tbody>\n",
+       "      <tr>\n",
+       "      \n",
+       "      <td>\n",
+       "               1\n",
+       "      </td>\n",
+       "      \n",
+       "      </tr>\n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    \n",
+       "      </td>\n",
+       "      </tr>\n",
+       "      \n",
+       "      </tbody>\n",
+       "    </table>\n",
+       "    "
+      ],
+      "text/plain": [
+       "R object with classes: ('histogram',) mapped to:\n",
+       "<ListVector - Python:0x116034d08 / R:0x7f9102174c80>\n",
+       "[Float..., IntVe..., Float..., Float..., StrVe..., BoolV...]\n",
+       "  breaks: <class 'rpy2.robjects.vectors.FloatVector'>\n",
+       "  R object with classes: ('numeric',) mapped to:\n",
+       "<FloatVector - Python:0x116034c88 / R:0x7f9101193350>\n",
+       "[1.000000, 1.500000, 2.000000, 2.500000, ..., 3.500000, 4.000000, 4.500000, 5.000000]\n",
+       "  counts: <class 'rpy2.robjects.vectors.IntVector'>\n",
+       "  R object with classes: ('integer',) mapped to:\n",
+       "<IntVector - Python:0x113253d48 / R:0x7f9101e2f120>\n",
+       "[6, 5, 5, 5, 5, 5, 5, 4]\n",
+       "  density: <class 'rpy2.robjects.vectors.FloatVector'>\n",
+       "  R object with classes: ('numeric',) mapped to:\n",
+       "<FloatVector - Python:0x11324d288 / R:0x7f9101b6f818>\n",
+       "[0.300000, 0.250000, 0.250000, 0.250000, 0.250000, 0.250000, 0.250000, 0.200000]\n",
+       "  mids: <class 'rpy2.robjects.vectors.FloatVector'>\n",
+       "  R object with classes: ('numeric',) mapped to:\n",
+       "<FloatVector - Python:0x113250688 / R:0x7f910161a3a8>\n",
+       "[1.250000, 1.750000, 2.250000, 2.750000, 3.250000, 3.750000, 4.250000, 4.750000]\n",
+       "  xname: <class 'rpy2.robjects.vectors.StrVector'>\n",
+       "  R object with classes: ('character',) mapped to:\n",
+       "<StrVector - Python:0x115408388 / R:0x7f90fefdce78>\n",
+       "['var_from_python']\n",
+       "  equidist: <class 'rpy2.robjects.vectors.BoolVector'>\n",
+       "  R object with classes: ('logical',) mapped to:\n",
+       "<BoolVector - Python:0x11554e948 / R:0x7f90fe9a8558>\n",
+       "[       1]"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%R -i var_from_python hist(var_from_python)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "rpy2.rinterface.NULL"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ro.globalenv[\"result\"] = result\n",
+    "ro.r(\"plot.fevd(result)\") ## puede dar error y generar un objeto rpy2.rinterface.NULL"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Un ejemplo complejo de R, Python y Rmagic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "metodos = [\"MLE\", \"GMLE\", \"Bayesian\", \"Lmoments\"]\n",
+    "tipos = [\"GEV\", \"Gumbel\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tipo de Ajuste:  GEV\n",
+      "Método del Ajuste:  MLE\n",
+      "$par\n",
+      "  location      scale      shape \n",
+      "23.0517078  1.8085853 -0.1497984 \n",
+      "\n",
+      "$value\n",
+      "[1] 66.22729\n",
+      "\n",
+      "$counts\n",
+      "function gradient \n",
+      "      27        6 \n",
+      "\n",
+      "$convergence\n",
+      "[1] 0\n",
+      "\n",
+      "$message\n",
+      "NULL\n",
+      "\n",
+      "$hessian\n",
+      "           location      scale     shape\n",
+      "location  9.1825628 -0.3982934  11.22038\n",
+      "scale    -0.3982934 21.4422632  19.81854\n",
+      "shape    11.2203805 19.8185358 172.17411\n",
+      "\n",
+      "$num.pars\n",
+      "$num.pars$location\n",
+      "[1] 1\n",
+      "\n",
+      "$num.pars$scale\n",
+      "[1] 1\n",
+      "\n",
+      "$num.pars$shape\n",
+      "[1] 1\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tipo de Ajuste:  GEV\n",
+      "Método del Ajuste:  GMLE\n",
+      "$par\n",
+      "  location      scale      shape \n",
+      "23.0639415  1.7576913 -0.1628816 \n",
+      "\n",
+      "$value\n",
+      "[1] 1e+16\n",
+      "\n",
+      "$counts\n",
+      "function gradient \n",
+      "       1        1 \n",
+      "\n",
+      "$convergence\n",
+      "[1] 0\n",
+      "\n",
+      "$message\n",
+      "NULL\n",
+      "\n",
+      "$hessian\n",
+      "         location scale shape\n",
+      "location        0     0     0\n",
+      "scale           0     0     0\n",
+      "shape           0     0     0\n",
+      "\n",
+      "$num.pars\n",
+      "$num.pars$location\n",
+      "[1] 1\n",
+      "\n",
+      "$num.pars$scale\n",
+      "[1] 1\n",
+      "\n",
+      "$num.pars$shape\n",
+      "[1] 1\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tipo de Ajuste:  GEV\n",
+      "Método del Ajuste:  Bayesian\n",
+      "         location log.scale         shape new\n",
+      "    [1,] 23.06394 0.5640012 -0.1628816368   0\n",
+      "    [2,] 23.29770 0.5640012 -0.1628816368   1\n",
+      "    [3,] 23.27768 0.5696776 -0.2283441364   3\n",
+      "    [4,] 23.22454 0.5029564 -0.1549580874   2\n",
+      "    [5,] 23.12634 0.5607213 -0.1434951100   3\n",
+      "    [6,] 23.21428 0.5025826 -0.1858128567   3\n",
+      "    [7,] 23.13304 0.4764981 -0.0463744442   3\n",
+      "    [8,] 23.04952 0.4764981 -0.0463744442   1\n",
+      "    [9,] 23.04952 0.4764981 -0.0463744442   0\n",
+      "   [10,] 22.85024 0.5522314 -0.0670262359   3\n",
+      "   [11,] 22.78250 0.5495687 -0.0073933229   2\n",
+      "   [12,] 22.78250 0.5495687 -0.0073933229   0\n",
+      "   [13,] 22.82030 0.5495687  0.0194413245   2\n",
+      "   [14,] 22.82030 0.5495687  0.0194413245   0\n",
+      "   [15,] 22.79760 0.6415186  0.0182682272   3\n",
+      "   [16,] 22.73757 0.6585723  0.0083780861   3\n",
+      "   [17,] 22.81066 0.6392593 -0.0489819980   3\n",
+      "   [18,] 22.83781 0.6275736 -0.1103175659   3\n",
+      "   [19,] 22.85318 0.7287007 -0.1042880827   3\n",
+      "   [20,] 22.74847 0.7287007 -0.1042880827   1\n",
+      "   [21,] 22.78969 0.7332724 -0.0001580664   3\n",
+      "   [22,] 22.95550 0.7260511 -0.0828487363   3\n",
+      "   [23,] 22.94475 0.7663420 -0.0198831760   2\n",
+      "   [24,] 22.77116 0.8120957 -0.2727487392   3\n",
+      "   [25,] 22.91986 0.8120957 -0.2737381782   2\n",
+      "   [26,] 22.81046 0.8413046 -0.2737381782   2\n",
+      "   [27,] 22.81046 0.8413046 -0.2737381782   0\n",
+      "   [28,] 23.03953 0.7842097 -0.2598567741   3\n",
+      "   [29,] 23.04893 0.8133500 -0.2583749113   2\n",
+      "   [30,] 23.09049 0.8480430 -0.0931230658   2\n",
+      "   [31,] 23.10207 0.8461731 -0.0961834070   3\n",
+      "   [32,] 23.21257 0.9076092 -0.2608045049   3\n",
+      "   [33,] 23.24853 0.9076092 -0.2577728686   1\n",
+      "   [34,] 23.20222 0.9234535 -0.2995659501   3\n",
+      "   [35,] 23.34970 0.9736305 -0.2751800822   2\n",
+      "   [36,] 23.37681 0.9248343 -0.2901201737   3\n",
+      "   [37,] 23.44048 1.0145250 -0.2150416264   3\n",
+      "   [38,] 23.39097 0.9015265 -0.2284171898   3\n",
+      "   [39,] 23.40145 0.9200874 -0.0208037701   2\n",
+      "   [40,] 23.42015 0.8878770 -0.0385155147   3\n",
+      "   [41,] 23.46269 0.8602646 -0.0718942698   3\n",
+      "   [42,] 23.44409 0.8485925 -0.2286224238   3\n",
+      "   [43,] 23.46744 0.7471957 -0.2347505028   3\n",
+      "   [44,] 23.54367 0.7819573 -0.2146048201   3\n",
+      "   [45,] 23.54879 0.9328888 -0.2430019388   2\n",
+      "   [46,] 23.40023 0.7450359 -0.1607761854   3\n",
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+      " [9876,] 22.45313 0.6378090 -0.0530395815   2\n",
+      " [9877,] 22.49770 0.6543623  0.0389397402   2\n",
+      " [9878,] 22.49770 0.6562260  0.0389397402   1\n",
+      " [9879,] 22.47473 0.6070971  0.1011903699   3\n",
+      " [9880,] 22.67484 0.7018574  0.1011903699   2\n",
+      " [9881,] 22.61723 0.6598348  0.0054812118   3\n",
+      " [9882,] 22.53176 0.8022291  0.0054812118   2\n",
+      " [9883,] 22.54010 0.7104786  0.2367519066   3\n",
+      " [9884,] 22.50528 0.7104786  0.2367519066   1\n",
+      " [9885,] 22.42985 0.7477523  0.1724013453   2\n",
+      " [9886,] 22.32811 0.6826833  0.1200226216   3\n",
+      " [9887,] 22.26907 0.6171186  0.1314058666   3\n",
+      " [9888,] 22.20566 0.7237514 -0.1825915892   3\n",
+      " [9889,] 22.23034 0.7705316 -0.1842865001   3\n",
+      " [9890,] 22.20782 0.7982961 -0.2208349921   3\n",
+      " [9891,] 22.06414 0.9229186 -0.1706961907   2\n",
+      " [9892,] 22.11187 0.8972430 -0.1706961907   2\n",
+      " [9893,] 21.99379 0.8972430  0.0222353148   2\n",
+      " [9894,] 22.13678 0.9230028  0.0745544970   3\n",
+      " [9895,] 22.04647 0.8591362  0.1145175295   3\n",
+      " [9896,] 22.12221 0.9340163  0.0786891748   2\n",
+      " [9897,] 22.08825 0.8541672  0.1562148545   2\n",
+      " [9898,] 22.09296 0.8759933 -0.0254447748   3\n",
+      " [9899,] 22.02360 0.8759933 -0.0254447748   1\n",
+      " [9900,] 22.02360 0.8759933 -0.0254447748   0\n",
+      " [9901,] 22.11665 0.7101860 -0.0542508104   3\n",
+      " [9902,] 22.11896 0.7101860 -0.0542508104   1\n",
+      " [9903,] 22.21740 0.7325898 -0.0755946522   3\n",
+      " [9904,] 22.43740 0.6005034 -0.0264099157   3\n",
+      " [9905,] 22.56806 0.6477722 -0.1990764504   2\n",
+      " [9906,] 22.55836 0.6347881 -0.0988764498   3\n",
+      " [9907,] 22.49428 0.7760331 -0.0138406064   3\n",
+      " [9908,] 22.55902 0.8143085 -0.0044569684   3\n",
+      " [9909,] 22.55902 0.8143085 -0.0044569684   0\n",
+      " [9910,] 22.73795 0.7452525 -0.0611445720   3\n",
+      " [9911,] 22.73511 0.6700122 -0.0037143151   2\n",
+      " [9912,] 22.64530 0.5755708 -0.0037143151   2\n",
+      " [9913,] 22.64846 0.7336578 -0.1131022501   3\n",
+      " [9914,] 22.82218 0.7336578 -0.1131022501   1\n",
+      " [9915,] 22.81065 0.6561009 -0.1973038336   3\n",
+      " [9916,] 22.90212 0.7304754 -0.1661109552   3\n",
+      " [9917,] 22.79703 0.7742430 -0.2382027651   3\n",
+      " [9918,] 22.85582 0.7742430 -0.2382027651   1\n",
+      " [9919,] 22.70662 0.6893184 -0.0961302658   3\n",
+      " [9920,] 22.79160 0.6768093 -0.1936656365   3\n",
+      " [9921,] 22.79160 0.6001924 -0.1967516861   2\n",
+      " [9922,] 22.75619 0.6126986 -0.1660158629   3\n",
+      " [9923,] 22.72583 0.7436455 -0.1889440110   3\n",
+      " [9924,] 22.79740 0.7436455 -0.1365685743   2\n",
+      " [9925,] 22.87607 0.6463024 -0.0937372633   2\n",
+      " [9926,] 22.92776 0.6002123 -0.2435874219   1\n",
+      " [9927,] 22.94450 0.6002123 -0.2435874219   1\n",
+      " [9928,] 22.92981 0.6002123 -0.2435874219   1\n",
+      " [9929,] 22.94207 0.5995365 -0.1181435187   3\n",
+      " [9930,] 22.99631 0.7256399 -0.0505676323   1\n",
+      " [9931,] 22.99631 0.7256399 -0.0505676323   0\n",
+      " [9932,] 23.08308 0.6962412 -0.0350468584   3\n",
+      " [9933,] 23.19665 0.5873834 -0.1107952885   3\n",
+      " [9934,] 23.28346 0.7237686 -0.1849872949   3\n",
+      " [9935,] 23.42721 0.7237686 -0.1849872949   1\n",
+      " [9936,] 23.55131 0.6295930 -0.2202892257   3\n",
+      " [9937,] 23.41983 0.6295930 -0.2202892257   1\n",
+      " [9938,] 23.30305 0.5848582 -0.2202892257   2\n",
+      " [9939,] 23.29451 0.5848582 -0.2241537081   2\n",
+      " [9940,] 23.06018 0.7000667 -0.1511858914   3\n",
+      " [9941,] 22.99395 0.8089044 -0.1700841086   3\n",
+      " [9942,] 22.79170 0.9042613 -0.1307157055   2\n",
+      " [9943,] 22.66794 0.8915552 -0.1376646578   3\n",
+      " [9944,] 22.54449 0.9364844 -0.1553478979   2\n",
+      " [9945,] 22.54110 0.9729048 -0.1453249189   3\n",
+      " [9946,] 22.68062 1.0355826 -0.2493135712   3\n",
+      " [9947,] 22.72230 0.9539462 -0.2176991308   3\n",
+      " [9948,] 22.60747 0.9717891  0.0269724727   2\n",
+      " [9949,] 22.60747 0.9717891  0.0269724727   0\n",
+      " [9950,] 22.60747 0.9717891  0.0269724727   0\n",
+      " [9951,] 22.52337 0.9210581 -0.0550384740   3\n",
+      " [9952,] 22.49858 1.0006564 -0.0468070927   3\n",
+      " [9953,] 22.29576 1.0158889 -0.1474353379   3\n",
+      " [9954,] 22.27916 0.9611276 -0.1274441198   3\n",
+      " [9955,] 22.33734 0.9611276 -0.1274441198   1\n",
+      " [9956,] 22.32682 0.9765297 -0.0994100089   3\n",
+      " [9957,] 22.30709 0.9765297 -0.0219775257   2\n",
+      " [9958,] 22.30709 0.9168346 -0.2153493151   2\n",
+      " [9959,] 22.32482 0.9584534 -0.0950202499   2\n",
+      " [9960,] 22.32482 0.9584534 -0.1086741772   1\n",
+      " [9961,] 22.23858 0.9706661 -0.2717656155   2\n",
+      " [9962,] 22.23858 0.9706661 -0.2717656155   0\n",
+      " [9963,] 22.22065 0.9601861 -0.2717656155   2\n",
+      " [9964,] 22.23193 0.9601861 -0.2717656155   1\n",
+      " [9965,] 22.23193 0.9601861 -0.3113905891   1\n",
+      " [9966,] 22.42308 1.0002692 -0.2151321703   3\n",
+      " [9967,] 22.42308 1.0002692 -0.2151321703   0\n",
+      " [9968,] 22.46342 0.8236392 -0.2199059579   3\n",
+      " [9969,] 22.49470 0.6997021 -0.2383344094   3\n",
+      " [9970,] 22.50729 0.8259550 -0.2244213926   3\n",
+      " [9971,] 22.50729 0.8259550 -0.2244213926   0\n",
+      " [9972,] 22.33727 0.8270290 -0.1836915580   3\n",
+      " [9973,] 22.23446 0.8270290 -0.1926596583   1\n",
+      " [9974,] 22.34362 0.9583651 -0.3358579677   1\n",
+      " [9975,] 22.34362 0.9583651 -0.3358579677   0\n",
+      " [9976,] 22.34362 0.9583651 -0.3358579677   0\n",
+      " [9977,] 22.38966 1.0364850 -0.3313261635   3\n",
+      " [9978,] 22.39211 0.9930749 -0.2914965072   3\n",
+      " [9979,] 22.54778 0.9697036 -0.2914965072   2\n",
+      " [9980,] 22.49274 0.9525773 -0.2329931852   3\n",
+      " [9981,] 22.49216 0.9185248 -0.2329931852   2\n",
+      " [9982,] 22.49216 0.9185248 -0.2329931852   0\n",
+      " [9983,] 22.49216 0.9185248 -0.2329931852   0\n",
+      " [9984,] 22.49216 0.9185248 -0.2329931852   0\n",
+      " [9985,] 22.46621 0.9185248 -0.2329931852   1\n",
+      " [9986,] 22.46621 0.9185248 -0.2329931852   0\n",
+      " [9987,] 22.46621 0.9185248 -0.2329931852   0\n",
+      " [9988,] 22.47800 0.9185248 -0.2376642712   2\n",
+      " [9989,] 22.41514 0.9365761 -0.2760391749   3\n",
+      " [9990,] 22.36362 1.0782503 -0.1872810746   2\n",
+      " [9991,] 22.30713 1.1302192 -0.2650061863   2\n",
+      " [9992,] 22.49763 1.1302192 -0.2875581141   2\n",
+      " [9993,] 22.49763 1.1302192 -0.2783327541   1\n",
+      " [9994,] 22.71897 1.1333392 -0.2469581219   2\n",
+      " [9995,] 22.66204 0.9569227 -0.2469581219   2\n",
+      " [9996,] 22.67852 1.0921902 -0.1936115515   3\n",
+      " [9997,] 22.76029 1.1144082 -0.1551722215   1\n",
+      " [9998,] 22.91158 1.0744887 -0.2134631212   3\n",
+      " [9999,] 22.90096 1.0066271 -0.2099472568   3\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tipo de Ajuste:  GEV\n",
+      "Método del Ajuste:  Lmoments\n",
+      "  location      scale      shape \n",
+      "23.0639415  1.7576913 -0.1628816 \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tipo de Ajuste:  Gumbel\n",
+      "Método del Ajuste:  MLE\n",
+      "$par\n",
+      " location     scale \n",
+      "22.905876  1.814452 \n",
+      "\n",
+      "$value\n",
+      "[1] 67.38487\n",
+      "\n",
+      "$counts\n",
+      "function gradient \n",
+      "      14        5 \n",
+      "\n",
+      "$convergence\n",
+      "[1] 0\n",
+      "\n",
+      "$message\n",
+      "NULL\n",
+      "\n",
+      "$hessian\n",
+      "          location     scale\n",
+      "location  9.719762 -4.762621\n",
+      "scale    -4.762621 21.535378\n",
+      "\n",
+      "$num.pars\n",
+      "$num.pars$location\n",
+      "[1] 1\n",
+      "\n",
+      "$num.pars$scale\n",
+      "[1] 1\n",
+      "\n",
+      "$num.pars$shape\n",
+      "NULL\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tipo de Ajuste:  Gumbel\n",
+      "Método del Ajuste:  GMLE\n",
+      "$par\n",
+      " location     scale \n",
+      "22.905876  1.814452 \n",
+      "\n",
+      "$value\n",
+      "[1] 67.38487\n",
+      "\n",
+      "$counts\n",
+      "function gradient \n",
+      "      14        5 \n",
+      "\n",
+      "$convergence\n",
+      "[1] 0\n",
+      "\n",
+      "$message\n",
+      "NULL\n",
+      "\n",
+      "$hessian\n",
+      "          location     scale\n",
+      "location  9.719762 -4.762621\n",
+      "scale    -4.762621 21.535378\n",
+      "\n",
+      "$num.pars\n",
+      "$num.pars$location\n",
+      "[1] 1\n",
+      "\n",
+      "$num.pars$scale\n",
+      "[1] 1\n",
+      "\n",
+      "$num.pars$shape\n",
+      "NULL\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tipo de Ajuste:  Gumbel\n",
+      "Método del Ajuste:  Bayesian\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: Error in res[1, (nloc + nsc + 1):np] <- initial$shape : \n",
+      "  número de items para para sustituir no es un múltiplo de la longitud del reemplazo\n",
+      "\n",
+      "  warnings.warn(x, RRuntimeWarning)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: Además: \n",
+      "  warnings.warn(x, RRuntimeWarning)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: Warning messages:\n",
+      "\n",
+      "  warnings.warn(x, RRuntimeWarning)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: 1: \n",
+      "  warnings.warn(x, RRuntimeWarning)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: In (function (x, data, threshold = NULL, threshold.fun = ~1, location.fun = ~1,  :\n",
+      "  warnings.warn(x, RRuntimeWarning)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: \n",
+      " \n",
+      "  warnings.warn(x, RRuntimeWarning)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning:  fevd: Using method MLE.  No default for specified arguments.\n",
+      "\n",
+      "  warnings.warn(x, RRuntimeWarning)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/rpy2/rinterface/__init__.py:145: RRuntimeWarning: 2: \n",
+      "  warnings.warn(x, RRuntimeWarning)\n"
+     ]
+    },
+    {
+     "ename": "RRuntimeError",
+     "evalue": "Error in res[1, (nloc + nsc + 1):np] <- initial$shape : \n  número de items para para sustituir no es un múltiplo de la longitud del reemplazo\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRRuntimeError\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-91-205ca498076b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Tipo de Ajuste: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Método del Ajuste: \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfevd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_ws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrx\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"results\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'R'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-i result plot.fevd(result)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/rpy2/robjects/functions.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m                 \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mr_k\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSignatureTranslatedFunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0mpattern_link\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mr'\\\\link\\{(.+?)\\}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/rpy2/robjects/functions.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    104\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m             \u001b[0mnew_kwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpy2ri\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 106\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mFunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnew_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    107\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconversion\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mri2ro\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRRuntimeError\u001b[0m: Error in res[1, (nloc + nsc + 1):np] <- initial$shape : \n  número de items para para sustituir no es un múltiplo de la longitud del reemplazo\n"
+     ]
+    }
+   ],
+   "source": [
+    "for t in tipos:\n",
+    "    for m in metodos:\n",
+    "        print(\"Tipo de Ajuste: \", t)\n",
+    "        print(\"Método del Ajuste: \", m)\n",
+    "        result = fevd(max_ws.values, method = m, type = t)\n",
+    "        print(result.rx(\"results\")[0])\n",
+    "        %R -i result plot.fevd(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T12 - 1 - R y Python.ipynb b/notebooks/T12 - 1 - R y Python.ipynb
index 618f5a71..e75335fc 100644
--- a/notebooks/T12 - 1 - R y Python.ipynb	
+++ b/notebooks/T12 - 1 - R y Python.ipynb	
@@ -12182,7 +12182,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T2 - 1 - Data Cleaning - Data Wrangling-Colab.ipynb b/notebooks/T2 - 1 - Data Cleaning - Data Wrangling-Colab.ipynb
new file mode 100644
index 00000000..5d575206
--- /dev/null
+++ b/notebooks/T2 - 1 - Data Cleaning - Data Wrangling-Colab.ipynb	
@@ -0,0 +1,5565 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/Collab---v-3.8/notebooks/T2%20-%201%20-%20Data%20Cleaning%20-%20Data%20Wrangling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "SMxTLuTc-NLS"
+   },
+   "source": [
+    "# Data Wrangling - La cirugía de los datos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 122
+    },
+    "colab_type": "code",
+    "id": "WMXo2rua-i2A",
+    "outputId": "e83f8f80-b976-4208-d4ba-ccbb92b53d24"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+      "\n",
+      "Enter your authorization code:\n",
+      "··········\n",
+      "Mounted at /content/drive\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "8uA3DRdv-NLT"
+   },
+   "source": [
+    "El **data wrangling**, a veces denominada **data munging**, es el proceso de transformar y mapear datos de un dataset *raw* (en bruto) en otro formato con la intención de hacerlo más apropiado y valioso para una variedad de propósitos posteriores, como el análisis. Un **data wrangler** es una persona que realiza estas operaciones de transformación.\n",
+    "\n",
+    "Esto puede incluir munging, visualización de datos, agregación de datos, entrenamiento de un modelo estadístico, así como muchos otros usos potenciales. La oscilación de datos como proceso generalmente sigue un conjunto de pasos generales que comienzan extrayendo los datos en forma cruda del origen de datos, dividiendo los datos en bruto usando algoritmos (por ejemplo, clasificación) o analizando los datos en estructuras de datos predefinidas, y finalmente depositando el contenido resultante en un sistema de almacenamiento (o silo) para su uso futuro."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Tn3uN9w6-NLU"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "HejeQ93y-NLY"
+   },
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/customer-churn-model/Customer Churn Model.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "JG55qLHb-NLb",
+    "outputId": "a10b1e9d-2e00-4fa6-b691-497c05124e4e"
+   },
+   "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>State</th>\n",
+       "      <th>Account Length</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KS</td>\n",
+       "      <td>128</td>\n",
+       "      <td>415</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>25</td>\n",
+       "      <td>265.1</td>\n",
+       "      <td>110</td>\n",
+       "      <td>45.07</td>\n",
+       "      <td>197.4</td>\n",
+       "      <td>99</td>\n",
+       "      <td>16.78</td>\n",
+       "      <td>244.7</td>\n",
+       "      <td>91</td>\n",
+       "      <td>11.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>107</td>\n",
+       "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>26</td>\n",
+       "      <td>161.6</td>\n",
+       "      <td>123</td>\n",
+       "      <td>27.47</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.45</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>137</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>243.4</td>\n",
+       "      <td>114</td>\n",
+       "      <td>41.38</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>84</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299.4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>50.90</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.86</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>75</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>166.7</td>\n",
+       "      <td>113</td>\n",
+       "      <td>28.34</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.41</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS             128        415  ...        2.70              1  False.\n",
+       "1    OH             107        415  ...        3.70              1  False.\n",
+       "2    NJ             137        415  ...        3.29              0  False.\n",
+       "3    OH              84        408  ...        1.78              2  False.\n",
+       "4    OK              75        415  ...        2.73              3  False.\n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "DLXD3g5j-NLe"
+   },
+   "source": [
+    "### Crear un subconjunto de datos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "lAEQlD7b-NLf"
+   },
+   "source": [
+    "#### Subconjunto de columna o columnas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "0qXIL9iu-NLf"
+   },
+   "outputs": [],
+   "source": [
+    "account_length = data[\"Account Length\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 119
+    },
+    "colab_type": "code",
+    "id": "G-SKobd4-NLh",
+    "outputId": "6bdfa6e5-e945-4a2b-afbd-c8ed587ac5a4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    128\n",
+       "1    107\n",
+       "2    137\n",
+       "3     84\n",
+       "4     75\n",
+       "Name: Account Length, dtype: int64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "account_length.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "4CksBm4N-NLk",
+    "outputId": "3f762849-dc1e-46e5-f13d-54bc6f6e2bda"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.series.Series"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(account_length)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "34W1Dmuo-NLn"
+   },
+   "outputs": [],
+   "source": [
+    "subset = data[[\"Account Length\", \"Phone\", \"Eve Charge\", \"Day Calls\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "zeFRzWhp-NLq",
+    "outputId": "72a021f8-a5c9-493b-ca18-b2401068b8d8"
+   },
+   "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>Account Length</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Day Calls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>128</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>16.78</td>\n",
+       "      <td>110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>107</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>137</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>75</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>113</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Account Length     Phone  Eve Charge  Day Calls\n",
+       "0             128  382-4657       16.78        110\n",
+       "1             107  371-7191       16.62        123\n",
+       "2             137  358-1921       10.30        114\n",
+       "3              84  375-9999        5.26         71\n",
+       "4              75  330-6626       12.61        113"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "pOiG-qk1-NLs",
+    "outputId": "a2a355d8-7a1e-40ec-cc88-69413fa9d92a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pandas.core.frame.DataFrame"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(subset)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "9xaANT1k-NLu",
+    "outputId": "fb7d536c-3f48-4270-f9a0-f836ab00259b"
+   },
+   "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>Account Length</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Calls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>128</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>16.78</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>107</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>103</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>137</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>104</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>84</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>89</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>75</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>121</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Account Length     Phone  Eve Charge  Night Calls\n",
+       "0             128  382-4657       16.78           91\n",
+       "1             107  371-7191       16.62          103\n",
+       "2             137  358-1921       10.30          104\n",
+       "3              84  375-9999        5.26           89\n",
+       "4              75  330-6626       12.61          121"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "desired_columns = [\"Account Length\", \"Phone\", \"Eve Charge\", \"Night Calls\"]\n",
+    "subset = data[desired_columns]\n",
+    "subset.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "SLaaTCkk-NLw",
+    "outputId": "8bba1dc1-055a-494b-9d82-c7d3d7a8c47a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Account Length', 'VMail Message', 'Day Calls']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "desired_columns = [\"Account Length\", \"VMail Message\", \"Day Calls\"]\n",
+    "desired_columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 374
+    },
+    "colab_type": "code",
+    "id": "LwqEtdkD-NLy",
+    "outputId": "dede79c7-5567-41c6-83e6-b6e879fd543a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['State',\n",
+       " 'Account Length',\n",
+       " 'Area Code',\n",
+       " 'Phone',\n",
+       " \"Int'l Plan\",\n",
+       " 'VMail Plan',\n",
+       " 'VMail Message',\n",
+       " 'Day Mins',\n",
+       " 'Day Calls',\n",
+       " 'Day Charge',\n",
+       " 'Eve Mins',\n",
+       " 'Eve Calls',\n",
+       " 'Eve Charge',\n",
+       " 'Night Mins',\n",
+       " 'Night Calls',\n",
+       " 'Night Charge',\n",
+       " 'Intl Mins',\n",
+       " 'Intl Calls',\n",
+       " 'Intl Charge',\n",
+       " 'CustServ Calls',\n",
+       " 'Churn?']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_columns_list = data.columns.values.tolist()\n",
+    "all_columns_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 323
+    },
+    "colab_type": "code",
+    "id": "2pJn5sdI-NL0",
+    "outputId": "5845fdb5-10ef-49d3-c217-a9c506952452"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['State',\n",
+       " 'Area Code',\n",
+       " 'Phone',\n",
+       " \"Int'l Plan\",\n",
+       " 'VMail Plan',\n",
+       " 'Day Mins',\n",
+       " 'Day Charge',\n",
+       " 'Eve Mins',\n",
+       " 'Eve Calls',\n",
+       " 'Eve Charge',\n",
+       " 'Night Mins',\n",
+       " 'Night Calls',\n",
+       " 'Night Charge',\n",
+       " 'Intl Mins',\n",
+       " 'Intl Calls',\n",
+       " 'Intl Charge',\n",
+       " 'CustServ Calls',\n",
+       " 'Churn?']"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sublist = [x for x in all_columns_list if x not in desired_columns]\n",
+    "sublist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "BPtSQxkz-NL2",
+    "outputId": "7210b64f-fa0e-44a7-af4f-3b4b6e8dc23f"
+   },
+   "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>State</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KS</td>\n",
+       "      <td>415</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>265.1</td>\n",
+       "      <td>45.07</td>\n",
+       "      <td>197.4</td>\n",
+       "      <td>99</td>\n",
+       "      <td>16.78</td>\n",
+       "      <td>244.7</td>\n",
+       "      <td>91</td>\n",
+       "      <td>11.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>161.6</td>\n",
+       "      <td>27.47</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.45</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>243.4</td>\n",
+       "      <td>41.38</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>299.4</td>\n",
+       "      <td>50.90</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.86</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>166.7</td>\n",
+       "      <td>28.34</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.41</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  State  Area Code     Phone  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS        415  382-4657  ...        2.70              1  False.\n",
+       "1    OH        415  371-7191  ...        3.70              1  False.\n",
+       "2    NJ        415  358-1921  ...        3.29              0  False.\n",
+       "3    OH        408  375-9999  ...        1.78              2  False.\n",
+       "4    OK        415  330-6626  ...        2.73              3  False.\n",
+       "\n",
+       "[5 rows x 18 columns]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subset = data[sublist]\n",
+    "subset.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "haxlKpjS-NL4"
+   },
+   "source": [
+    "#### Subconjunto de filas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 793
+    },
+    "colab_type": "code",
+    "id": "buNFSltI-NL4",
+    "outputId": "08a76b7a-69c1-40a5-8163-54d64f8b6795"
+   },
+   "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>State</th>\n",
+       "      <th>Account Length</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>107</td>\n",
+       "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>26</td>\n",
+       "      <td>161.6</td>\n",
+       "      <td>123</td>\n",
+       "      <td>27.47</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.45</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>137</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>243.4</td>\n",
+       "      <td>114</td>\n",
+       "      <td>41.38</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>84</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299.4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>50.90</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.86</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>75</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>166.7</td>\n",
+       "      <td>113</td>\n",
+       "      <td>28.34</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.41</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>118</td>\n",
+       "      <td>510</td>\n",
+       "      <td>391-8027</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>223.4</td>\n",
+       "      <td>98</td>\n",
+       "      <td>37.98</td>\n",
+       "      <td>220.6</td>\n",
+       "      <td>101</td>\n",
+       "      <td>18.75</td>\n",
+       "      <td>203.9</td>\n",
+       "      <td>118</td>\n",
+       "      <td>9.18</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>MA</td>\n",
+       "      <td>121</td>\n",
+       "      <td>510</td>\n",
+       "      <td>355-9993</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>24</td>\n",
+       "      <td>218.2</td>\n",
+       "      <td>88</td>\n",
+       "      <td>37.09</td>\n",
+       "      <td>348.5</td>\n",
+       "      <td>108</td>\n",
+       "      <td>29.62</td>\n",
+       "      <td>212.6</td>\n",
+       "      <td>118</td>\n",
+       "      <td>9.57</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2.03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MO</td>\n",
+       "      <td>147</td>\n",
+       "      <td>415</td>\n",
+       "      <td>329-9001</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>79</td>\n",
+       "      <td>26.69</td>\n",
+       "      <td>103.1</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.76</td>\n",
+       "      <td>211.8</td>\n",
+       "      <td>96</td>\n",
+       "      <td>9.53</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.92</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>LA</td>\n",
+       "      <td>117</td>\n",
+       "      <td>408</td>\n",
+       "      <td>335-4719</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>184.5</td>\n",
+       "      <td>97</td>\n",
+       "      <td>31.37</td>\n",
+       "      <td>351.6</td>\n",
+       "      <td>80</td>\n",
+       "      <td>29.89</td>\n",
+       "      <td>215.8</td>\n",
+       "      <td>90</td>\n",
+       "      <td>9.71</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.35</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>WV</td>\n",
+       "      <td>141</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-8173</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>37</td>\n",
+       "      <td>258.6</td>\n",
+       "      <td>84</td>\n",
+       "      <td>43.96</td>\n",
+       "      <td>222.0</td>\n",
+       "      <td>111</td>\n",
+       "      <td>18.87</td>\n",
+       "      <td>326.4</td>\n",
+       "      <td>97</td>\n",
+       "      <td>14.69</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>IN</td>\n",
+       "      <td>65</td>\n",
+       "      <td>415</td>\n",
+       "      <td>329-6603</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>129.1</td>\n",
+       "      <td>137</td>\n",
+       "      <td>21.95</td>\n",
+       "      <td>228.5</td>\n",
+       "      <td>83</td>\n",
+       "      <td>19.42</td>\n",
+       "      <td>208.8</td>\n",
+       "      <td>111</td>\n",
+       "      <td>9.40</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.43</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>RI</td>\n",
+       "      <td>74</td>\n",
+       "      <td>415</td>\n",
+       "      <td>344-9403</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>187.7</td>\n",
+       "      <td>127</td>\n",
+       "      <td>31.91</td>\n",
+       "      <td>163.4</td>\n",
+       "      <td>148</td>\n",
+       "      <td>13.89</td>\n",
+       "      <td>196.0</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.82</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>IA</td>\n",
+       "      <td>168</td>\n",
+       "      <td>408</td>\n",
+       "      <td>363-1107</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>128.8</td>\n",
+       "      <td>96</td>\n",
+       "      <td>21.90</td>\n",
+       "      <td>104.9</td>\n",
+       "      <td>71</td>\n",
+       "      <td>8.92</td>\n",
+       "      <td>141.1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>6.35</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>MT</td>\n",
+       "      <td>95</td>\n",
+       "      <td>510</td>\n",
+       "      <td>394-8006</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>156.6</td>\n",
+       "      <td>88</td>\n",
+       "      <td>26.62</td>\n",
+       "      <td>247.6</td>\n",
+       "      <td>75</td>\n",
+       "      <td>21.05</td>\n",
+       "      <td>192.3</td>\n",
+       "      <td>115</td>\n",
+       "      <td>8.65</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.32</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>IA</td>\n",
+       "      <td>62</td>\n",
+       "      <td>415</td>\n",
+       "      <td>366-9238</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>120.7</td>\n",
+       "      <td>70</td>\n",
+       "      <td>20.52</td>\n",
+       "      <td>307.2</td>\n",
+       "      <td>76</td>\n",
+       "      <td>26.11</td>\n",
+       "      <td>203.0</td>\n",
+       "      <td>99</td>\n",
+       "      <td>9.14</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.54</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>NY</td>\n",
+       "      <td>161</td>\n",
+       "      <td>415</td>\n",
+       "      <td>351-7269</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>332.9</td>\n",
+       "      <td>67</td>\n",
+       "      <td>56.59</td>\n",
+       "      <td>317.8</td>\n",
+       "      <td>97</td>\n",
+       "      <td>27.01</td>\n",
+       "      <td>160.6</td>\n",
+       "      <td>128</td>\n",
+       "      <td>7.23</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>85</td>\n",
+       "      <td>408</td>\n",
+       "      <td>350-8884</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>27</td>\n",
+       "      <td>196.4</td>\n",
+       "      <td>139</td>\n",
+       "      <td>33.39</td>\n",
+       "      <td>280.9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>23.88</td>\n",
+       "      <td>89.3</td>\n",
+       "      <td>75</td>\n",
+       "      <td>4.02</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.73</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>VT</td>\n",
+       "      <td>93</td>\n",
+       "      <td>510</td>\n",
+       "      <td>386-2923</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>190.7</td>\n",
+       "      <td>114</td>\n",
+       "      <td>32.42</td>\n",
+       "      <td>218.2</td>\n",
+       "      <td>111</td>\n",
+       "      <td>18.55</td>\n",
+       "      <td>129.6</td>\n",
+       "      <td>121</td>\n",
+       "      <td>5.83</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>VA</td>\n",
+       "      <td>76</td>\n",
+       "      <td>510</td>\n",
+       "      <td>356-2992</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>33</td>\n",
+       "      <td>189.7</td>\n",
+       "      <td>66</td>\n",
+       "      <td>32.25</td>\n",
+       "      <td>212.8</td>\n",
+       "      <td>65</td>\n",
+       "      <td>18.09</td>\n",
+       "      <td>165.7</td>\n",
+       "      <td>108</td>\n",
+       "      <td>7.46</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>TX</td>\n",
+       "      <td>73</td>\n",
+       "      <td>415</td>\n",
+       "      <td>373-2782</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>224.4</td>\n",
+       "      <td>90</td>\n",
+       "      <td>38.15</td>\n",
+       "      <td>159.5</td>\n",
+       "      <td>88</td>\n",
+       "      <td>13.56</td>\n",
+       "      <td>192.8</td>\n",
+       "      <td>74</td>\n",
+       "      <td>8.68</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>FL</td>\n",
+       "      <td>147</td>\n",
+       "      <td>415</td>\n",
+       "      <td>396-5800</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>155.1</td>\n",
+       "      <td>117</td>\n",
+       "      <td>26.37</td>\n",
+       "      <td>239.7</td>\n",
+       "      <td>93</td>\n",
+       "      <td>20.37</td>\n",
+       "      <td>208.8</td>\n",
+       "      <td>133</td>\n",
+       "      <td>9.40</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.86</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>CO</td>\n",
+       "      <td>77</td>\n",
+       "      <td>408</td>\n",
+       "      <td>393-7984</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>62.4</td>\n",
+       "      <td>89</td>\n",
+       "      <td>10.61</td>\n",
+       "      <td>169.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>14.44</td>\n",
+       "      <td>209.6</td>\n",
+       "      <td>64</td>\n",
+       "      <td>9.43</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.54</td>\n",
+       "      <td>5</td>\n",
+       "      <td>True.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>AZ</td>\n",
+       "      <td>130</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1958</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>183.0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>31.11</td>\n",
+       "      <td>72.9</td>\n",
+       "      <td>99</td>\n",
+       "      <td>6.20</td>\n",
+       "      <td>181.8</td>\n",
+       "      <td>78</td>\n",
+       "      <td>8.18</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>19</td>\n",
+       "      <td>2.57</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>SC</td>\n",
+       "      <td>111</td>\n",
+       "      <td>415</td>\n",
+       "      <td>350-2565</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>110.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>18.77</td>\n",
+       "      <td>137.3</td>\n",
+       "      <td>102</td>\n",
+       "      <td>11.67</td>\n",
+       "      <td>189.6</td>\n",
+       "      <td>105</td>\n",
+       "      <td>8.53</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.08</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>VA</td>\n",
+       "      <td>132</td>\n",
+       "      <td>510</td>\n",
+       "      <td>343-4696</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>81.1</td>\n",
+       "      <td>86</td>\n",
+       "      <td>13.79</td>\n",
+       "      <td>245.2</td>\n",
+       "      <td>72</td>\n",
+       "      <td>20.84</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>115</td>\n",
+       "      <td>10.67</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.78</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "1     OH             107        415  ...        3.70              1  False.\n",
+       "2     NJ             137        415  ...        3.29              0  False.\n",
+       "3     OH              84        408  ...        1.78              2  False.\n",
+       "4     OK              75        415  ...        2.73              3  False.\n",
+       "5     AL             118        510  ...        1.70              0  False.\n",
+       "6     MA             121        510  ...        2.03              3  False.\n",
+       "7     MO             147        415  ...        1.92              0  False.\n",
+       "8     LA             117        408  ...        2.35              1  False.\n",
+       "9     WV             141        415  ...        3.02              0  False.\n",
+       "10    IN              65        415  ...        3.43              4   True.\n",
+       "11    RI              74        415  ...        2.46              0  False.\n",
+       "12    IA             168        408  ...        3.02              1  False.\n",
+       "13    MT              95        510  ...        3.32              3  False.\n",
+       "14    IA              62        415  ...        3.54              4  False.\n",
+       "15    NY             161        415  ...        1.46              4   True.\n",
+       "16    ID              85        408  ...        3.73              1  False.\n",
+       "17    VT              93        510  ...        2.19              3  False.\n",
+       "18    VA              76        510  ...        2.70              1  False.\n",
+       "19    TX              73        415  ...        3.51              1  False.\n",
+       "20    FL             147        415  ...        2.86              0  False.\n",
+       "21    CO              77        408  ...        1.54              5   True.\n",
+       "22    AZ             130        415  ...        2.57              0  False.\n",
+       "23    SC             111        415  ...        2.08              2  False.\n",
+       "24    VA             132        510  ...        2.78              0  False.\n",
+       "\n",
+       "[24 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[1:25]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 824
+    },
+    "colab_type": "code",
+    "id": "B6yso4ho-NL5",
+    "outputId": "e9ac1877-fc96-470f-f66d-7a506d3d542c"
+   },
+   "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>State</th>\n",
+       "      <th>Account Length</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>IN</td>\n",
+       "      <td>65</td>\n",
+       "      <td>415</td>\n",
+       "      <td>329-6603</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>129.1</td>\n",
+       "      <td>137</td>\n",
+       "      <td>21.95</td>\n",
+       "      <td>228.5</td>\n",
+       "      <td>83</td>\n",
+       "      <td>19.42</td>\n",
+       "      <td>208.8</td>\n",
+       "      <td>111</td>\n",
+       "      <td>9.40</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.43</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>RI</td>\n",
+       "      <td>74</td>\n",
+       "      <td>415</td>\n",
+       "      <td>344-9403</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>187.7</td>\n",
+       "      <td>127</td>\n",
+       "      <td>31.91</td>\n",
+       "      <td>163.4</td>\n",
+       "      <td>148</td>\n",
+       "      <td>13.89</td>\n",
+       "      <td>196.0</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.82</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.46</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>IA</td>\n",
+       "      <td>168</td>\n",
+       "      <td>408</td>\n",
+       "      <td>363-1107</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>128.8</td>\n",
+       "      <td>96</td>\n",
+       "      <td>21.90</td>\n",
+       "      <td>104.9</td>\n",
+       "      <td>71</td>\n",
+       "      <td>8.92</td>\n",
+       "      <td>141.1</td>\n",
+       "      <td>128</td>\n",
+       "      <td>6.35</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>MT</td>\n",
+       "      <td>95</td>\n",
+       "      <td>510</td>\n",
+       "      <td>394-8006</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>156.6</td>\n",
+       "      <td>88</td>\n",
+       "      <td>26.62</td>\n",
+       "      <td>247.6</td>\n",
+       "      <td>75</td>\n",
+       "      <td>21.05</td>\n",
+       "      <td>192.3</td>\n",
+       "      <td>115</td>\n",
+       "      <td>8.65</td>\n",
+       "      <td>12.3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.32</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>IA</td>\n",
+       "      <td>62</td>\n",
+       "      <td>415</td>\n",
+       "      <td>366-9238</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>120.7</td>\n",
+       "      <td>70</td>\n",
+       "      <td>20.52</td>\n",
+       "      <td>307.2</td>\n",
+       "      <td>76</td>\n",
+       "      <td>26.11</td>\n",
+       "      <td>203.0</td>\n",
+       "      <td>99</td>\n",
+       "      <td>9.14</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.54</td>\n",
+       "      <td>4</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>NY</td>\n",
+       "      <td>161</td>\n",
+       "      <td>415</td>\n",
+       "      <td>351-7269</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>332.9</td>\n",
+       "      <td>67</td>\n",
+       "      <td>56.59</td>\n",
+       "      <td>317.8</td>\n",
+       "      <td>97</td>\n",
+       "      <td>27.01</td>\n",
+       "      <td>160.6</td>\n",
+       "      <td>128</td>\n",
+       "      <td>7.23</td>\n",
+       "      <td>5.4</td>\n",
+       "      <td>9</td>\n",
+       "      <td>1.46</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>ID</td>\n",
+       "      <td>85</td>\n",
+       "      <td>408</td>\n",
+       "      <td>350-8884</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>27</td>\n",
+       "      <td>196.4</td>\n",
+       "      <td>139</td>\n",
+       "      <td>33.39</td>\n",
+       "      <td>280.9</td>\n",
+       "      <td>90</td>\n",
+       "      <td>23.88</td>\n",
+       "      <td>89.3</td>\n",
+       "      <td>75</td>\n",
+       "      <td>4.02</td>\n",
+       "      <td>13.8</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.73</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>VT</td>\n",
+       "      <td>93</td>\n",
+       "      <td>510</td>\n",
+       "      <td>386-2923</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>190.7</td>\n",
+       "      <td>114</td>\n",
+       "      <td>32.42</td>\n",
+       "      <td>218.2</td>\n",
+       "      <td>111</td>\n",
+       "      <td>18.55</td>\n",
+       "      <td>129.6</td>\n",
+       "      <td>121</td>\n",
+       "      <td>5.83</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>VA</td>\n",
+       "      <td>76</td>\n",
+       "      <td>510</td>\n",
+       "      <td>356-2992</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>33</td>\n",
+       "      <td>189.7</td>\n",
+       "      <td>66</td>\n",
+       "      <td>32.25</td>\n",
+       "      <td>212.8</td>\n",
+       "      <td>65</td>\n",
+       "      <td>18.09</td>\n",
+       "      <td>165.7</td>\n",
+       "      <td>108</td>\n",
+       "      <td>7.46</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>TX</td>\n",
+       "      <td>73</td>\n",
+       "      <td>415</td>\n",
+       "      <td>373-2782</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>224.4</td>\n",
+       "      <td>90</td>\n",
+       "      <td>38.15</td>\n",
+       "      <td>159.5</td>\n",
+       "      <td>88</td>\n",
+       "      <td>13.56</td>\n",
+       "      <td>192.8</td>\n",
+       "      <td>74</td>\n",
+       "      <td>8.68</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>FL</td>\n",
+       "      <td>147</td>\n",
+       "      <td>415</td>\n",
+       "      <td>396-5800</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>155.1</td>\n",
+       "      <td>117</td>\n",
+       "      <td>26.37</td>\n",
+       "      <td>239.7</td>\n",
+       "      <td>93</td>\n",
+       "      <td>20.37</td>\n",
+       "      <td>208.8</td>\n",
+       "      <td>133</td>\n",
+       "      <td>9.40</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.86</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>CO</td>\n",
+       "      <td>77</td>\n",
+       "      <td>408</td>\n",
+       "      <td>393-7984</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>62.4</td>\n",
+       "      <td>89</td>\n",
+       "      <td>10.61</td>\n",
+       "      <td>169.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>14.44</td>\n",
+       "      <td>209.6</td>\n",
+       "      <td>64</td>\n",
+       "      <td>9.43</td>\n",
+       "      <td>5.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.54</td>\n",
+       "      <td>5</td>\n",
+       "      <td>True.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>AZ</td>\n",
+       "      <td>130</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1958</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>183.0</td>\n",
+       "      <td>112</td>\n",
+       "      <td>31.11</td>\n",
+       "      <td>72.9</td>\n",
+       "      <td>99</td>\n",
+       "      <td>6.20</td>\n",
+       "      <td>181.8</td>\n",
+       "      <td>78</td>\n",
+       "      <td>8.18</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>19</td>\n",
+       "      <td>2.57</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>SC</td>\n",
+       "      <td>111</td>\n",
+       "      <td>415</td>\n",
+       "      <td>350-2565</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>110.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>18.77</td>\n",
+       "      <td>137.3</td>\n",
+       "      <td>102</td>\n",
+       "      <td>11.67</td>\n",
+       "      <td>189.6</td>\n",
+       "      <td>105</td>\n",
+       "      <td>8.53</td>\n",
+       "      <td>7.7</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.08</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>VA</td>\n",
+       "      <td>132</td>\n",
+       "      <td>510</td>\n",
+       "      <td>343-4696</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>81.1</td>\n",
+       "      <td>86</td>\n",
+       "      <td>13.79</td>\n",
+       "      <td>245.2</td>\n",
+       "      <td>72</td>\n",
+       "      <td>20.84</td>\n",
+       "      <td>237.0</td>\n",
+       "      <td>115</td>\n",
+       "      <td>10.67</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.78</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>NE</td>\n",
+       "      <td>174</td>\n",
+       "      <td>415</td>\n",
+       "      <td>331-3698</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>124.3</td>\n",
+       "      <td>76</td>\n",
+       "      <td>21.13</td>\n",
+       "      <td>277.1</td>\n",
+       "      <td>112</td>\n",
+       "      <td>23.55</td>\n",
+       "      <td>250.7</td>\n",
+       "      <td>115</td>\n",
+       "      <td>11.28</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4.19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>WY</td>\n",
+       "      <td>57</td>\n",
+       "      <td>408</td>\n",
+       "      <td>357-3817</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>39</td>\n",
+       "      <td>213.0</td>\n",
+       "      <td>115</td>\n",
+       "      <td>36.21</td>\n",
+       "      <td>191.1</td>\n",
+       "      <td>112</td>\n",
+       "      <td>16.24</td>\n",
+       "      <td>182.7</td>\n",
+       "      <td>115</td>\n",
+       "      <td>8.22</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.57</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>MT</td>\n",
+       "      <td>54</td>\n",
+       "      <td>408</td>\n",
+       "      <td>418-6412</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>134.3</td>\n",
+       "      <td>73</td>\n",
+       "      <td>22.83</td>\n",
+       "      <td>155.5</td>\n",
+       "      <td>100</td>\n",
+       "      <td>13.22</td>\n",
+       "      <td>102.1</td>\n",
+       "      <td>68</td>\n",
+       "      <td>4.59</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.97</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>MO</td>\n",
+       "      <td>20</td>\n",
+       "      <td>415</td>\n",
+       "      <td>353-2630</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>190.0</td>\n",
+       "      <td>109</td>\n",
+       "      <td>32.30</td>\n",
+       "      <td>258.2</td>\n",
+       "      <td>84</td>\n",
+       "      <td>21.95</td>\n",
+       "      <td>181.5</td>\n",
+       "      <td>102</td>\n",
+       "      <td>8.17</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>HI</td>\n",
+       "      <td>49</td>\n",
+       "      <td>510</td>\n",
+       "      <td>410-7789</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>119.3</td>\n",
+       "      <td>117</td>\n",
+       "      <td>20.28</td>\n",
+       "      <td>215.1</td>\n",
+       "      <td>109</td>\n",
+       "      <td>18.28</td>\n",
+       "      <td>178.7</td>\n",
+       "      <td>90</td>\n",
+       "      <td>8.04</td>\n",
+       "      <td>11.1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>IL</td>\n",
+       "      <td>142</td>\n",
+       "      <td>415</td>\n",
+       "      <td>416-8428</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84.8</td>\n",
+       "      <td>95</td>\n",
+       "      <td>14.42</td>\n",
+       "      <td>136.7</td>\n",
+       "      <td>63</td>\n",
+       "      <td>11.62</td>\n",
+       "      <td>250.5</td>\n",
+       "      <td>148</td>\n",
+       "      <td>11.27</td>\n",
+       "      <td>14.2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.83</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>NH</td>\n",
+       "      <td>75</td>\n",
+       "      <td>510</td>\n",
+       "      <td>370-3359</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>226.1</td>\n",
+       "      <td>105</td>\n",
+       "      <td>38.44</td>\n",
+       "      <td>201.5</td>\n",
+       "      <td>107</td>\n",
+       "      <td>17.13</td>\n",
+       "      <td>246.2</td>\n",
+       "      <td>98</td>\n",
+       "      <td>11.08</td>\n",
+       "      <td>10.3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2.78</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>LA</td>\n",
+       "      <td>172</td>\n",
+       "      <td>408</td>\n",
+       "      <td>383-1121</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>212.0</td>\n",
+       "      <td>121</td>\n",
+       "      <td>36.04</td>\n",
+       "      <td>31.2</td>\n",
+       "      <td>115</td>\n",
+       "      <td>2.65</td>\n",
+       "      <td>293.3</td>\n",
+       "      <td>78</td>\n",
+       "      <td>13.20</td>\n",
+       "      <td>12.6</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3.40</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>33</th>\n",
+       "      <td>AZ</td>\n",
+       "      <td>12</td>\n",
+       "      <td>408</td>\n",
+       "      <td>360-1596</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>249.6</td>\n",
+       "      <td>118</td>\n",
+       "      <td>42.43</td>\n",
+       "      <td>252.4</td>\n",
+       "      <td>119</td>\n",
+       "      <td>21.45</td>\n",
+       "      <td>280.2</td>\n",
+       "      <td>90</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>1</td>\n",
+       "      <td>True.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>57</td>\n",
+       "      <td>408</td>\n",
+       "      <td>395-2854</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>25</td>\n",
+       "      <td>176.8</td>\n",
+       "      <td>94</td>\n",
+       "      <td>30.06</td>\n",
+       "      <td>195.0</td>\n",
+       "      <td>75</td>\n",
+       "      <td>16.58</td>\n",
+       "      <td>213.5</td>\n",
+       "      <td>116</td>\n",
+       "      <td>9.61</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.24</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "10    IN              65        415  ...        3.43              4   True.\n",
+       "11    RI              74        415  ...        2.46              0  False.\n",
+       "12    IA             168        408  ...        3.02              1  False.\n",
+       "13    MT              95        510  ...        3.32              3  False.\n",
+       "14    IA              62        415  ...        3.54              4  False.\n",
+       "15    NY             161        415  ...        1.46              4   True.\n",
+       "16    ID              85        408  ...        3.73              1  False.\n",
+       "17    VT              93        510  ...        2.19              3  False.\n",
+       "18    VA              76        510  ...        2.70              1  False.\n",
+       "19    TX              73        415  ...        3.51              1  False.\n",
+       "20    FL             147        415  ...        2.86              0  False.\n",
+       "21    CO              77        408  ...        1.54              5   True.\n",
+       "22    AZ             130        415  ...        2.57              0  False.\n",
+       "23    SC             111        415  ...        2.08              2  False.\n",
+       "24    VA             132        510  ...        2.78              0  False.\n",
+       "25    NE             174        415  ...        4.19              3  False.\n",
+       "26    WY              57        408  ...        2.57              0  False.\n",
+       "27    MT              54        408  ...        3.97              3  False.\n",
+       "28    MO              20        415  ...        1.70              0  False.\n",
+       "29    HI              49        510  ...        3.00              1  False.\n",
+       "30    IL             142        415  ...        3.83              2  False.\n",
+       "31    NH              75        510  ...        2.78              1  False.\n",
+       "32    LA             172        408  ...        3.40              3  False.\n",
+       "33    AZ              12        408  ...        3.19              1   True.\n",
+       "34    OK              57        408  ...        2.24              0  False.\n",
+       "\n",
+       "[25 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[10:35]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 297
+    },
+    "colab_type": "code",
+    "id": "qti5Qah2-NL6",
+    "outputId": "64cecb49-d016-4772-b8e5-ca067a780560"
+   },
+   "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>State</th>\n",
+       "      <th>Account Length</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KS</td>\n",
+       "      <td>128</td>\n",
+       "      <td>415</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>25</td>\n",
+       "      <td>265.1</td>\n",
+       "      <td>110</td>\n",
+       "      <td>45.07</td>\n",
+       "      <td>197.4</td>\n",
+       "      <td>99</td>\n",
+       "      <td>16.78</td>\n",
+       "      <td>244.7</td>\n",
+       "      <td>91</td>\n",
+       "      <td>11.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>107</td>\n",
+       "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>26</td>\n",
+       "      <td>161.6</td>\n",
+       "      <td>123</td>\n",
+       "      <td>27.47</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.45</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>137</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>243.4</td>\n",
+       "      <td>114</td>\n",
+       "      <td>41.38</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>84</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299.4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>50.90</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.86</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>75</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>166.7</td>\n",
+       "      <td>113</td>\n",
+       "      <td>28.34</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.41</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>118</td>\n",
+       "      <td>510</td>\n",
+       "      <td>391-8027</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>223.4</td>\n",
+       "      <td>98</td>\n",
+       "      <td>37.98</td>\n",
+       "      <td>220.6</td>\n",
+       "      <td>101</td>\n",
+       "      <td>18.75</td>\n",
+       "      <td>203.9</td>\n",
+       "      <td>118</td>\n",
+       "      <td>9.18</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>MA</td>\n",
+       "      <td>121</td>\n",
+       "      <td>510</td>\n",
+       "      <td>355-9993</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>24</td>\n",
+       "      <td>218.2</td>\n",
+       "      <td>88</td>\n",
+       "      <td>37.09</td>\n",
+       "      <td>348.5</td>\n",
+       "      <td>108</td>\n",
+       "      <td>29.62</td>\n",
+       "      <td>212.6</td>\n",
+       "      <td>118</td>\n",
+       "      <td>9.57</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2.03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MO</td>\n",
+       "      <td>147</td>\n",
+       "      <td>415</td>\n",
+       "      <td>329-9001</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>79</td>\n",
+       "      <td>26.69</td>\n",
+       "      <td>103.1</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.76</td>\n",
+       "      <td>211.8</td>\n",
+       "      <td>96</td>\n",
+       "      <td>9.53</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.92</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS             128        415  ...        2.70              1  False.\n",
+       "1    OH             107        415  ...        3.70              1  False.\n",
+       "2    NJ             137        415  ...        3.29              0  False.\n",
+       "3    OH              84        408  ...        1.78              2  False.\n",
+       "4    OK              75        415  ...        2.73              3  False.\n",
+       "5    AL             118        510  ...        1.70              0  False.\n",
+       "6    MA             121        510  ...        2.03              3  False.\n",
+       "7    MO             147        415  ...        1.92              0  False.\n",
+       "\n",
+       "[8 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[:8] # CORRECCIÓN: es lo mismo que data[0:8]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 452
+    },
+    "colab_type": "code",
+    "id": "RQSc-Wa3-NL8",
+    "outputId": "8f00de7a-f053-4594-dd3e-7fd398733d46"
+   },
+   "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>State</th>\n",
+       "      <th>Account Length</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3320</th>\n",
+       "      <td>GA</td>\n",
+       "      <td>122</td>\n",
+       "      <td>510</td>\n",
+       "      <td>411-5677</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>101</td>\n",
+       "      <td>23.80</td>\n",
+       "      <td>196.4</td>\n",
+       "      <td>77</td>\n",
+       "      <td>16.69</td>\n",
+       "      <td>120.1</td>\n",
+       "      <td>133</td>\n",
+       "      <td>5.40</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.62</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3321</th>\n",
+       "      <td>VT</td>\n",
+       "      <td>60</td>\n",
+       "      <td>415</td>\n",
+       "      <td>400-2738</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>193.9</td>\n",
+       "      <td>118</td>\n",
+       "      <td>32.96</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>110</td>\n",
+       "      <td>7.23</td>\n",
+       "      <td>210.1</td>\n",
+       "      <td>134</td>\n",
+       "      <td>9.45</td>\n",
+       "      <td>13.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3.56</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3322</th>\n",
+       "      <td>MD</td>\n",
+       "      <td>62</td>\n",
+       "      <td>408</td>\n",
+       "      <td>409-1856</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>321.1</td>\n",
+       "      <td>105</td>\n",
+       "      <td>54.59</td>\n",
+       "      <td>265.5</td>\n",
+       "      <td>122</td>\n",
+       "      <td>22.57</td>\n",
+       "      <td>180.5</td>\n",
+       "      <td>72</td>\n",
+       "      <td>8.12</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.11</td>\n",
+       "      <td>4</td>\n",
+       "      <td>True.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3323</th>\n",
+       "      <td>IN</td>\n",
+       "      <td>117</td>\n",
+       "      <td>415</td>\n",
+       "      <td>362-5899</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>118.4</td>\n",
+       "      <td>126</td>\n",
+       "      <td>20.13</td>\n",
+       "      <td>249.3</td>\n",
+       "      <td>97</td>\n",
+       "      <td>21.19</td>\n",
+       "      <td>227.0</td>\n",
+       "      <td>56</td>\n",
+       "      <td>10.22</td>\n",
+       "      <td>13.6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.67</td>\n",
+       "      <td>5</td>\n",
+       "      <td>True.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3324</th>\n",
+       "      <td>WV</td>\n",
+       "      <td>159</td>\n",
+       "      <td>415</td>\n",
+       "      <td>377-1164</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>169.8</td>\n",
+       "      <td>114</td>\n",
+       "      <td>28.87</td>\n",
+       "      <td>197.7</td>\n",
+       "      <td>105</td>\n",
+       "      <td>16.80</td>\n",
+       "      <td>193.7</td>\n",
+       "      <td>82</td>\n",
+       "      <td>8.72</td>\n",
+       "      <td>11.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3325</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>78</td>\n",
+       "      <td>408</td>\n",
+       "      <td>368-8555</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>193.4</td>\n",
+       "      <td>99</td>\n",
+       "      <td>32.88</td>\n",
+       "      <td>116.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>9.94</td>\n",
+       "      <td>243.3</td>\n",
+       "      <td>109</td>\n",
+       "      <td>10.95</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.51</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3326</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>96</td>\n",
+       "      <td>415</td>\n",
+       "      <td>347-6812</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>106.6</td>\n",
+       "      <td>128</td>\n",
+       "      <td>18.12</td>\n",
+       "      <td>284.8</td>\n",
+       "      <td>87</td>\n",
+       "      <td>24.21</td>\n",
+       "      <td>178.9</td>\n",
+       "      <td>92</td>\n",
+       "      <td>8.05</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>7</td>\n",
+       "      <td>4.02</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3327</th>\n",
+       "      <td>SC</td>\n",
+       "      <td>79</td>\n",
+       "      <td>415</td>\n",
+       "      <td>348-3830</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>134.7</td>\n",
+       "      <td>98</td>\n",
+       "      <td>22.90</td>\n",
+       "      <td>189.7</td>\n",
+       "      <td>68</td>\n",
+       "      <td>16.12</td>\n",
+       "      <td>221.4</td>\n",
+       "      <td>128</td>\n",
+       "      <td>9.96</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3328</th>\n",
+       "      <td>AZ</td>\n",
+       "      <td>192</td>\n",
+       "      <td>415</td>\n",
+       "      <td>414-4276</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>36</td>\n",
+       "      <td>156.2</td>\n",
+       "      <td>77</td>\n",
+       "      <td>26.55</td>\n",
+       "      <td>215.5</td>\n",
+       "      <td>126</td>\n",
+       "      <td>18.32</td>\n",
+       "      <td>279.1</td>\n",
+       "      <td>83</td>\n",
+       "      <td>12.56</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3329</th>\n",
+       "      <td>WV</td>\n",
+       "      <td>68</td>\n",
+       "      <td>415</td>\n",
+       "      <td>370-3271</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>231.1</td>\n",
+       "      <td>57</td>\n",
+       "      <td>39.29</td>\n",
+       "      <td>153.4</td>\n",
+       "      <td>55</td>\n",
+       "      <td>13.04</td>\n",
+       "      <td>191.3</td>\n",
+       "      <td>123</td>\n",
+       "      <td>8.61</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.59</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3330</th>\n",
+       "      <td>RI</td>\n",
+       "      <td>28</td>\n",
+       "      <td>510</td>\n",
+       "      <td>328-8230</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>180.8</td>\n",
+       "      <td>109</td>\n",
+       "      <td>30.74</td>\n",
+       "      <td>288.8</td>\n",
+       "      <td>58</td>\n",
+       "      <td>24.55</td>\n",
+       "      <td>191.9</td>\n",
+       "      <td>91</td>\n",
+       "      <td>8.64</td>\n",
+       "      <td>14.1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3.81</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3331</th>\n",
+       "      <td>CT</td>\n",
+       "      <td>184</td>\n",
+       "      <td>510</td>\n",
+       "      <td>364-6381</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>213.8</td>\n",
+       "      <td>105</td>\n",
+       "      <td>36.35</td>\n",
+       "      <td>159.6</td>\n",
+       "      <td>84</td>\n",
+       "      <td>13.57</td>\n",
+       "      <td>139.2</td>\n",
+       "      <td>137</td>\n",
+       "      <td>6.26</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1.35</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3332</th>\n",
+       "      <td>TN</td>\n",
+       "      <td>74</td>\n",
+       "      <td>415</td>\n",
+       "      <td>400-4344</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>25</td>\n",
+       "      <td>234.4</td>\n",
+       "      <td>113</td>\n",
+       "      <td>39.85</td>\n",
+       "      <td>265.9</td>\n",
+       "      <td>82</td>\n",
+       "      <td>22.60</td>\n",
+       "      <td>241.4</td>\n",
+       "      <td>77</td>\n",
+       "      <td>10.86</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "3320    GA             122        510  ...        2.62              4   True.\n",
+       "3321    VT              60        415  ...        3.56              3  False.\n",
+       "3322    MD              62        408  ...        3.11              4   True.\n",
+       "3323    IN             117        415  ...        3.67              5   True.\n",
+       "3324    WV             159        415  ...        3.13              1  False.\n",
+       "3325    OH              78        408  ...        2.51              2  False.\n",
+       "3326    OH              96        415  ...        4.02              1  False.\n",
+       "3327    SC              79        415  ...        3.19              2  False.\n",
+       "3328    AZ             192        415  ...        2.67              2  False.\n",
+       "3329    WV              68        415  ...        2.59              3  False.\n",
+       "3330    RI              28        510  ...        3.81              2  False.\n",
+       "3331    CT             184        510  ...        1.35              2  False.\n",
+       "3332    TN              74        415  ...        3.70              0  False.\n",
+       "\n",
+       "[13 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[3320:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "8Z6R4bxt-NL9"
+   },
+   "source": [
+    "#### Subconjuntos de filas con condiciones booleanas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "Wb62I5i6-NL-",
+    "outputId": "c8712e83-cd21-4657-b490-f1f9df3d44b0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(43, 21)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "##Usuarios con Day Mins > 300\n",
+    "data1 = data[data[\"Day Mins\"]>300]\n",
+    "data1.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "V1S_csPL-NL_",
+    "outputId": "1d93a643-7310-43de-c515-f214caec9274"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(83, 21)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "##Usuarios de Nueva York (State = \"NY\")\n",
+    "data2 = data[data[\"State\"]==\"NY\"]\n",
+    "data2.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "V55Hp0tc-NMA",
+    "outputId": "8052d52c-dcc7-4171-88df-9964a1605007"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 21)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "##AND -> &\n",
+    "data3 = data[(data[\"Day Mins\"]>300) & (data[\"State\"]==\"NY\")]\n",
+    "data3.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "NA-MO0DB-NMB",
+    "outputId": "a68c21a5-0628-4357-ae0e-e973eeaa27bc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(124, 21)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "##OR -> |\n",
+    "data4 = data[(data[\"Day Mins\"]>300) | (data[\"State\"]==\"NY\")]\n",
+    "data4.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "hXf8QEJM-NMC",
+    "outputId": "b8ed4887-1307-46b5-f563-7f057dcdf9b1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1626, 21)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data5 = data[data[\"Day Calls\"]<data[\"Night Calls\"]]\n",
+    "data5.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "8_OXaGHI-NMD",
+    "outputId": "8c9dbabc-3647-4cde-9b36-0a61c758b61f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2051, 21)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data6 = data[data[\"Day Mins\"]<data[\"Night Mins\"]]\n",
+    "data6.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "BIdhM5eh-NME",
+    "outputId": "5127d67c-d7ea-4956-f218-3a7cac878413"
+   },
+   "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>Day Mins</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Account Length</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>265.1</td>\n",
+       "      <td>244.7</td>\n",
+       "      <td>128</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>161.6</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>243.4</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>299.4</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>166.7</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>75</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Day Mins  Night Mins  Account Length\n",
+       "0     265.1       244.7             128\n",
+       "1     161.6       254.4             107\n",
+       "2     243.4       162.6             137\n",
+       "3     299.4       196.9              84\n",
+       "4     166.7       186.9              75"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "##Minutos de día, de noche y Longitud de la Cuenta de los primeros 50 individuos\n",
+    "subset_first_50 = data[[\"Day Mins\", \"Night Mins\", \"Account Length\"]][:50]\n",
+    "subset_first_50.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 359
+    },
+    "colab_type": "code",
+    "id": "2xYGI76f-NMF",
+    "outputId": "19abf650-e77b-4f5a-e2c8-b46bafadb806"
+   },
+   "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>State</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KS</td>\n",
+       "      <td>415</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>265.1</td>\n",
+       "      <td>45.07</td>\n",
+       "      <td>197.4</td>\n",
+       "      <td>99</td>\n",
+       "      <td>16.78</td>\n",
+       "      <td>244.7</td>\n",
+       "      <td>91</td>\n",
+       "      <td>11.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>161.6</td>\n",
+       "      <td>27.47</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.45</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>243.4</td>\n",
+       "      <td>41.38</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>299.4</td>\n",
+       "      <td>50.90</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.86</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>166.7</td>\n",
+       "      <td>28.34</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.41</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>510</td>\n",
+       "      <td>391-8027</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>223.4</td>\n",
+       "      <td>37.98</td>\n",
+       "      <td>220.6</td>\n",
+       "      <td>101</td>\n",
+       "      <td>18.75</td>\n",
+       "      <td>203.9</td>\n",
+       "      <td>118</td>\n",
+       "      <td>9.18</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>MA</td>\n",
+       "      <td>510</td>\n",
+       "      <td>355-9993</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>218.2</td>\n",
+       "      <td>37.09</td>\n",
+       "      <td>348.5</td>\n",
+       "      <td>108</td>\n",
+       "      <td>29.62</td>\n",
+       "      <td>212.6</td>\n",
+       "      <td>118</td>\n",
+       "      <td>9.57</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2.03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MO</td>\n",
+       "      <td>415</td>\n",
+       "      <td>329-9001</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>26.69</td>\n",
+       "      <td>103.1</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.76</td>\n",
+       "      <td>211.8</td>\n",
+       "      <td>96</td>\n",
+       "      <td>9.53</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.92</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>LA</td>\n",
+       "      <td>408</td>\n",
+       "      <td>335-4719</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>184.5</td>\n",
+       "      <td>31.37</td>\n",
+       "      <td>351.6</td>\n",
+       "      <td>80</td>\n",
+       "      <td>29.89</td>\n",
+       "      <td>215.8</td>\n",
+       "      <td>90</td>\n",
+       "      <td>9.71</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.35</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>WV</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-8173</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>258.6</td>\n",
+       "      <td>43.96</td>\n",
+       "      <td>222.0</td>\n",
+       "      <td>111</td>\n",
+       "      <td>18.87</td>\n",
+       "      <td>326.4</td>\n",
+       "      <td>97</td>\n",
+       "      <td>14.69</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  State  Area Code     Phone  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS        415  382-4657  ...        2.70              1  False.\n",
+       "1    OH        415  371-7191  ...        3.70              1  False.\n",
+       "2    NJ        415  358-1921  ...        3.29              0  False.\n",
+       "3    OH        408  375-9999  ...        1.78              2  False.\n",
+       "4    OK        415  330-6626  ...        2.73              3  False.\n",
+       "5    AL        510  391-8027  ...        1.70              0  False.\n",
+       "6    MA        510  355-9993  ...        2.03              3  False.\n",
+       "7    MO        415  329-9001  ...        1.92              0  False.\n",
+       "8    LA        408  335-4719  ...        2.35              1  False.\n",
+       "9    WV        415  330-8173  ...        3.02              0  False.\n",
+       "\n",
+       "[10 rows x 18 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "subset[:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "9fOFwprb-NMG"
+   },
+   "source": [
+    "#### Filtrado con ix -> loc e iloc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "A9ojz-2u-NMG",
+    "outputId": "290cc3ae-4a98-4fa7-c26c-f9221a913b63"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: DeprecationWarning: \n",
+      ".ix is deprecated. Please use\n",
+      ".loc for label based indexing or\n",
+      ".iloc for positional indexing\n",
+      "\n",
+      "See the documentation here:\n",
+      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
+      "  \"\"\"Entry point for launching an IPython kernel.\n"
+     ]
+    },
+    {
+     "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>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>391-8027</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>355-9993</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>329-9001</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>335-4719</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>330-8173</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>329-6603</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Phone Int'l Plan VMail Plan\n",
+       "1   371-7191         no        yes\n",
+       "2   358-1921         no         no\n",
+       "3   375-9999        yes         no\n",
+       "4   330-6626        yes         no\n",
+       "5   391-8027        yes         no\n",
+       "6   355-9993         no        yes\n",
+       "7   329-9001        yes         no\n",
+       "8   335-4719         no         no\n",
+       "9   330-8173        yes        yes\n",
+       "10  329-6603         no         no"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.ix[1:10, 3:6] ## Primeras 10 filas, columnas de la 3 a la 6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "colab_type": "code",
+    "id": "_AafoPsa-NMH",
+    "outputId": "97d8102d-1657-4bd3-ce1c-800d9fcfc1ad"
+   },
+   "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",
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+       "\n",
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+       "        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>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>391-8027</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>355-9993</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>329-9001</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>335-4719</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>330-8173</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Phone Int'l Plan VMail Plan\n",
+       "1  371-7191         no        yes\n",
+       "2  358-1921         no         no\n",
+       "3  375-9999        yes         no\n",
+       "4  330-6626        yes         no\n",
+       "5  391-8027        yes         no\n",
+       "6  355-9993         no        yes\n",
+       "7  329-9001        yes         no\n",
+       "8  335-4719         no         no\n",
+       "9  330-8173        yes        yes"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.iloc[1:10, 3:6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "colab_type": "code",
+    "id": "8oDWI-Lk-NMI",
+    "outputId": "8d1c4ccd-0f88-4356-9b24-91b1c1a040de"
+   },
+   "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>State</th>\n",
+       "      <th>Account Length</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>107</td>\n",
+       "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>26</td>\n",
+       "      <td>161.6</td>\n",
+       "      <td>123</td>\n",
+       "      <td>27.47</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.45</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>137</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>243.4</td>\n",
+       "      <td>114</td>\n",
+       "      <td>41.38</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>84</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299.4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>50.90</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.86</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>75</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>166.7</td>\n",
+       "      <td>113</td>\n",
+       "      <td>28.34</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.41</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>118</td>\n",
+       "      <td>510</td>\n",
+       "      <td>391-8027</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>223.4</td>\n",
+       "      <td>98</td>\n",
+       "      <td>37.98</td>\n",
+       "      <td>220.6</td>\n",
+       "      <td>101</td>\n",
+       "      <td>18.75</td>\n",
+       "      <td>203.9</td>\n",
+       "      <td>118</td>\n",
+       "      <td>9.18</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>MA</td>\n",
+       "      <td>121</td>\n",
+       "      <td>510</td>\n",
+       "      <td>355-9993</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>24</td>\n",
+       "      <td>218.2</td>\n",
+       "      <td>88</td>\n",
+       "      <td>37.09</td>\n",
+       "      <td>348.5</td>\n",
+       "      <td>108</td>\n",
+       "      <td>29.62</td>\n",
+       "      <td>212.6</td>\n",
+       "      <td>118</td>\n",
+       "      <td>9.57</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2.03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MO</td>\n",
+       "      <td>147</td>\n",
+       "      <td>415</td>\n",
+       "      <td>329-9001</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>79</td>\n",
+       "      <td>26.69</td>\n",
+       "      <td>103.1</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.76</td>\n",
+       "      <td>211.8</td>\n",
+       "      <td>96</td>\n",
+       "      <td>9.53</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.92</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>LA</td>\n",
+       "      <td>117</td>\n",
+       "      <td>408</td>\n",
+       "      <td>335-4719</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>184.5</td>\n",
+       "      <td>97</td>\n",
+       "      <td>31.37</td>\n",
+       "      <td>351.6</td>\n",
+       "      <td>80</td>\n",
+       "      <td>29.89</td>\n",
+       "      <td>215.8</td>\n",
+       "      <td>90</td>\n",
+       "      <td>9.71</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.35</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>WV</td>\n",
+       "      <td>141</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-8173</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>37</td>\n",
+       "      <td>258.6</td>\n",
+       "      <td>84</td>\n",
+       "      <td>43.96</td>\n",
+       "      <td>222.0</td>\n",
+       "      <td>111</td>\n",
+       "      <td>18.87</td>\n",
+       "      <td>326.4</td>\n",
+       "      <td>97</td>\n",
+       "      <td>14.69</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "1    OH             107        415  ...        3.70              1  False.\n",
+       "2    NJ             137        415  ...        3.29              0  False.\n",
+       "3    OH              84        408  ...        1.78              2  False.\n",
+       "4    OK              75        415  ...        2.73              3  False.\n",
+       "5    AL             118        510  ...        1.70              0  False.\n",
+       "6    MA             121        510  ...        2.03              3  False.\n",
+       "7    MO             147        415  ...        1.92              0  False.\n",
+       "8    LA             117        408  ...        2.35              1  False.\n",
+       "9    WV             141        415  ...        3.02              0  False.\n",
+       "\n",
+       "[9 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.iloc[:,3:6] ##Todas las filas para las columnas entre la 3 y la 6\n",
+    "data.iloc[1:10,:] ##Todas las columnas para las filas de la 1 a la 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "colab_type": "code",
+    "id": "4q2QyqM3-NMJ",
+    "outputId": "ea48cf26-115e-4621-84b8-6fd4c804a048"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>Day Mins</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>415</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>161.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>415</td>\n",
+       "      <td>no</td>\n",
+       "      <td>243.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>408</td>\n",
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+       "      <td>299.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>415</td>\n",
+       "      <td>no</td>\n",
+       "      <td>166.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>510</td>\n",
+       "      <td>no</td>\n",
+       "      <td>223.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>510</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>218.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>415</td>\n",
+       "      <td>no</td>\n",
+       "      <td>157.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>408</td>\n",
+       "      <td>no</td>\n",
+       "      <td>184.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>415</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>258.6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Area Code VMail Plan  Day Mins\n",
+       "1        415        yes     161.6\n",
+       "2        415         no     243.4\n",
+       "3        408         no     299.4\n",
+       "4        415         no     166.7\n",
+       "5        510         no     223.4\n",
+       "6        510        yes     218.2\n",
+       "7        415         no     157.0\n",
+       "8        408         no     184.5\n",
+       "9        415        yes     258.6"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.iloc[1:10, [2,5,7]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 173
+    },
+    "colab_type": "code",
+    "id": "NmOSEz3A-NMK",
+    "outputId": "9b8835f6-1489-4954-9e3e-89f927cf74af"
+   },
+   "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>Area Code</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>Day Mins</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>415</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>161.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>510</td>\n",
+       "      <td>no</td>\n",
+       "      <td>223.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>408</td>\n",
+       "      <td>no</td>\n",
+       "      <td>184.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>408</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>146.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Area Code VMail Plan  Day Mins\n",
+       "1         415        yes     161.6\n",
+       "5         510         no     223.4\n",
+       "8         408         no     184.5\n",
+       "36        408        yes     146.3"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.iloc[[1,5,8,36], [2,5,7]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 173
+    },
+    "colab_type": "code",
+    "id": "1aZxP683-NML",
+    "outputId": "5eea626a-93cb-4d4c-e49d-5362962643b5"
+   },
+   "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>Area Code</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>Day Mins</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>415</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>161.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>510</td>\n",
+       "      <td>no</td>\n",
+       "      <td>223.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>408</td>\n",
+       "      <td>no</td>\n",
+       "      <td>184.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>408</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>146.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Area Code VMail Plan  Day Mins\n",
+       "1         415        yes     161.6\n",
+       "5         510         no     223.4\n",
+       "8         408         no     184.5\n",
+       "36        408        yes     146.3"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.loc[[1,5,8,36], [\"Area Code\", \"VMail Plan\", \"Day Mins\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "jKj3sOSf-NMM"
+   },
+   "source": [
+    "#### Insertar nuevas filas en el dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "WbPrEZVu-NMM"
+   },
+   "outputs": [],
+   "source": [
+    "data[\"Total Mins\"] = data[\"Day Mins\"] + data[\"Night Mins\"] + data[\"Eve Mins\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 119
+    },
+    "colab_type": "code",
+    "id": "S41vxiTL-NMN",
+    "outputId": "77783b76-081d-410a-a700-2783b8115943"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    707.2\n",
+       "1    611.5\n",
+       "2    527.2\n",
+       "3    558.2\n",
+       "4    501.9\n",
+       "Name: Total Mins, dtype: float64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"Total Mins\"].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "kz04R87g-NMO"
+   },
+   "outputs": [],
+   "source": [
+    "data[\"Total Calls\"] = data[\"Day Calls\"] + data[\"Night Calls\"] + data[\"Eve Calls\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 119
+    },
+    "colab_type": "code",
+    "id": "cx5JFZa3-NMP",
+    "outputId": "7a10ebcf-ed97-46fe-d5ae-ad99131bdff9"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    300\n",
+       "1    329\n",
+       "2    328\n",
+       "3    248\n",
+       "4    356\n",
+       "Name: Total Calls, dtype: int64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"Total Calls\"].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "AmR-jcnF-NMP",
+    "outputId": "641d25d4-6d48-4649-ba4c-8e57d4b36750"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3333, 23)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "MB472-er-NMQ",
+    "outputId": "7f421f9a-1c06-4313-dc84-1eb3e71bbe72"
+   },
+   "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>State</th>\n",
+       "      <th>Account Length</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "      <th>Total Mins</th>\n",
+       "      <th>Total Calls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KS</td>\n",
+       "      <td>128</td>\n",
+       "      <td>415</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>25</td>\n",
+       "      <td>265.1</td>\n",
+       "      <td>110</td>\n",
+       "      <td>45.07</td>\n",
+       "      <td>197.4</td>\n",
+       "      <td>99</td>\n",
+       "      <td>16.78</td>\n",
+       "      <td>244.7</td>\n",
+       "      <td>91</td>\n",
+       "      <td>11.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "      <td>707.2</td>\n",
+       "      <td>300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>107</td>\n",
+       "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>26</td>\n",
+       "      <td>161.6</td>\n",
+       "      <td>123</td>\n",
+       "      <td>27.47</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.45</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "      <td>611.5</td>\n",
+       "      <td>329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>137</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>243.4</td>\n",
+       "      <td>114</td>\n",
+       "      <td>41.38</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "      <td>527.2</td>\n",
+       "      <td>328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>84</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299.4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>50.90</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.86</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "      <td>558.2</td>\n",
+       "      <td>248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>75</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>166.7</td>\n",
+       "      <td>113</td>\n",
+       "      <td>28.34</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.41</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "      <td>501.9</td>\n",
+       "      <td>356</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  State  Account Length  Area Code  ...  Churn? Total Mins Total Calls\n",
+       "0    KS             128        415  ...  False.      707.2         300\n",
+       "1    OH             107        415  ...  False.      611.5         329\n",
+       "2    NJ             137        415  ...  False.      527.2         328\n",
+       "3    OH              84        408  ...  False.      558.2         248\n",
+       "4    OK              75        415  ...  False.      501.9         356\n",
+       "\n",
+       "[5 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "u6QeuXs_-NMR"
+   },
+   "source": [
+    "### Generación aleatoria de números"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "K_yOfvJl-NMR"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "UXCbacXE-NMS",
+    "outputId": "a8bfd98a-ac49-4148-fc05-990081951e43"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "##Generar un número aleatorio entero entre 1 y 100\n",
+    "np.random.randint(1,100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "a2wx0lEK-NMT",
+    "outputId": "485c8b49-a01a-4159-d7d6-b0659c76ac4d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6010490980853076"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "##La forma más clásica de generar un número aleatorio es entre 0 y 1 (con decimales)\n",
+    "np.random.random()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "-xlzcWw7-NMU"
+   },
+   "outputs": [],
+   "source": [
+    "##Función que genera una lista de n números aleatorios enteros dentro del intervalo [a,b]\n",
+    "def randint_list(n, a, b):\n",
+    "    x = []\n",
+    "    for i in range(n):\n",
+    "        x.append(np.random.randint(a,b))\n",
+    "    return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 442
+    },
+    "colab_type": "code",
+    "id": "K6CxKlDj-NMU",
+    "outputId": "03d4ade8-bb35-462b-a95b-475d11f04201"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[9,\n",
+       " 11,\n",
+       " 26,\n",
+       " 19,\n",
+       " 19,\n",
+       " 11,\n",
+       " 28,\n",
+       " 8,\n",
+       " 49,\n",
+       " 46,\n",
+       " 16,\n",
+       " 33,\n",
+       " 49,\n",
+       " 20,\n",
+       " 43,\n",
+       " 27,\n",
+       " 46,\n",
+       " 48,\n",
+       " 11,\n",
+       " 7,\n",
+       " 46,\n",
+       " 35,\n",
+       " 8,\n",
+       " 37,\n",
+       " 26]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "randint_list(25, 1, 50)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Rk9OkLbS-NMV"
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 187
+    },
+    "colab_type": "code",
+    "id": "zNLCJ6G8-NMW",
+    "outputId": "06f304a4-cf4b-4b1a-fa0f-2f8493c6de39"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "49\n",
+      "42\n",
+      "56\n",
+      "7\n",
+      "28\n",
+      "77\n",
+      "42\n",
+      "14\n",
+      "98\n",
+      "84\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(10):\n",
+    "    print(random.randrange(0, 100,7))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "XrB-zDrP-NMX"
+   },
+   "source": [
+    "#### Shuffling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 119
+    },
+    "colab_type": "code",
+    "id": "5lfMHVIO-NMX",
+    "outputId": "c194c366-5de2-4f46-dfd0-40835a95d119"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
+       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
+       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
+       "       51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67,\n",
+       "       68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,\n",
+       "       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = np.arange(100)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "O-rvs3AL-NMY"
+   },
+   "outputs": [],
+   "source": [
+    "np.random.shuffle(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 119
+    },
+    "colab_type": "code",
+    "id": "E4VsNWz--NMZ",
+    "outputId": "321583bb-30e5-49e3-fbeb-c1c9ee906d58"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([40, 26, 52, 35, 42,  6, 14, 47, 60,  8, 15, 36, 27, 29, 62, 44, 81,\n",
+       "       72, 21, 20, 11, 54, 58, 75, 80, 59, 67, 79, 61,  2,  0,  9, 49, 32,\n",
+       "       50, 64, 33, 91, 45, 97, 89, 74,  3, 95, 94,  5, 55, 93, 71, 16, 31,\n",
+       "       51, 48, 46, 92, 18, 77, 10, 96, 88, 13, 30, 28, 66, 86, 56, 23, 19,\n",
+       "        4, 83, 87, 69,  1, 22, 63, 85, 68, 78, 90, 99, 43, 82, 70, 41, 65,\n",
+       "       38, 34,  7, 57, 98, 37, 39, 25, 53, 17, 73, 76, 84, 24, 12])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "nVxDud5W-NMZ"
+   },
+   "source": [
+    "#### Choice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "bXMy1YjU-NMZ",
+    "outputId": "adb24c16-382b-4e44-e886-e1d9d49b8b72"
+   },
+   "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>State</th>\n",
+       "      <th>Account Length</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "      <th>Total Mins</th>\n",
+       "      <th>Total Calls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KS</td>\n",
+       "      <td>128</td>\n",
+       "      <td>415</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>25</td>\n",
+       "      <td>265.1</td>\n",
+       "      <td>110</td>\n",
+       "      <td>45.07</td>\n",
+       "      <td>197.4</td>\n",
+       "      <td>99</td>\n",
+       "      <td>16.78</td>\n",
+       "      <td>244.7</td>\n",
+       "      <td>91</td>\n",
+       "      <td>11.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "      <td>707.2</td>\n",
+       "      <td>300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>107</td>\n",
+       "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>26</td>\n",
+       "      <td>161.6</td>\n",
+       "      <td>123</td>\n",
+       "      <td>27.47</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.45</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "      <td>611.5</td>\n",
+       "      <td>329</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>137</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>243.4</td>\n",
+       "      <td>114</td>\n",
+       "      <td>41.38</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "      <td>527.2</td>\n",
+       "      <td>328</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>84</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299.4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>50.90</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.86</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "      <td>558.2</td>\n",
+       "      <td>248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>75</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>166.7</td>\n",
+       "      <td>113</td>\n",
+       "      <td>28.34</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.41</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "      <td>501.9</td>\n",
+       "      <td>356</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  State  Account Length  Area Code  ...  Churn? Total Mins Total Calls\n",
+       "0    KS             128        415  ...  False.      707.2         300\n",
+       "1    OH             107        415  ...  False.      611.5         329\n",
+       "2    NJ             137        415  ...  False.      527.2         328\n",
+       "3    OH              84        408  ...  False.      558.2         248\n",
+       "4    OK              75        415  ...  False.      501.9         356\n",
+       "\n",
+       "[5 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "D_BQW3A6-NMa",
+    "outputId": "9af1227a-b6ce-46a0-d789-23ede85431c9"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3333, 23)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 408
+    },
+    "colab_type": "code",
+    "id": "dDl3G-dr-NMb",
+    "outputId": "47d7487c-860b-4c20-d2ea-09f5eba971c3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['State',\n",
+       " 'Account Length',\n",
+       " 'Area Code',\n",
+       " 'Phone',\n",
+       " \"Int'l Plan\",\n",
+       " 'VMail Plan',\n",
+       " 'VMail Message',\n",
+       " 'Day Mins',\n",
+       " 'Day Calls',\n",
+       " 'Day Charge',\n",
+       " 'Eve Mins',\n",
+       " 'Eve Calls',\n",
+       " 'Eve Charge',\n",
+       " 'Night Mins',\n",
+       " 'Night Calls',\n",
+       " 'Night Charge',\n",
+       " 'Intl Mins',\n",
+       " 'Intl Calls',\n",
+       " 'Intl Charge',\n",
+       " 'CustServ Calls',\n",
+       " 'Churn?',\n",
+       " 'Total Mins',\n",
+       " 'Total Calls']"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "column_list = data.columns.values.tolist()\n",
+    "column_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "gWWpBhGN-NMc",
+    "outputId": "c837ad96-aa26-4465-8a2d-6b0104886eeb"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'VMail Message'"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.random.choice(column_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "ggTVQLbC-NMd"
+   },
+   "source": [
+    "#### Seed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 102
+    },
+    "colab_type": "code",
+    "id": "PYEZWOcu-NMd",
+    "outputId": "a0954dc4-0a28-41e8-f732-ad2f4e23d578"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.8823493117539459\n",
+      "0.10432773786047767\n",
+      "0.9070093335163405\n",
+      "0.3063988986063515\n",
+      "0.446408872427422\n"
+     ]
+    }
+   ],
+   "source": [
+    "np.random.seed(2018)\n",
+    "for i in range(5):\n",
+    "    print(np.random.random())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "I6hUoptf_gqj"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "T2 - 1 - Data Cleaning - Data Wrangling.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/notebooks/T2 - 1 - Data Cleaning - Data Wrangling.ipynb b/notebooks/T2 - 1 - Data Cleaning - Data Wrangling.ipynb
index f1a88aa0..5d575206 100644
--- a/notebooks/T2 - 1 - Data Cleaning - Data Wrangling.ipynb	
+++ b/notebooks/T2 - 1 - Data Cleaning - Data Wrangling.ipynb	
@@ -2,14 +2,60 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/Collab---v-3.8/notebooks/T2%20-%201%20-%20Data%20Cleaning%20-%20Data%20Wrangling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "SMxTLuTc-NLS"
+   },
    "source": [
     "# Data Wrangling - La cirugía de los datos"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 122
+    },
+    "colab_type": "code",
+    "id": "WMXo2rua-i2A",
+    "outputId": "e83f8f80-b976-4208-d4ba-ccbb92b53d24"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+      "\n",
+      "Enter your authorization code:\n",
+      "··········\n",
+      "Mounted at /content/drive\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "8uA3DRdv-NLT"
+   },
    "source": [
     "El **data wrangling**, a veces denominada **data munging**, es el proceso de transformar y mapear datos de un dataset *raw* (en bruto) en otro formato con la intención de hacerlo más apropiado y valioso para una variedad de propósitos posteriores, como el análisis. Un **data wrangler** es una persona que realiza estas operaciones de transformación.\n",
     "\n",
@@ -18,8 +64,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Tn3uN9w6-NLU"
+   },
    "outputs": [],
    "source": [
     "import pandas as pd"
@@ -27,17 +77,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "HejeQ93y-NLY"
+   },
    "outputs": [],
    "source": [
-    "data = pd.read_csv(\"../datasets/customer-churn-model/Customer Churn Model.txt\")"
+    "data = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/customer-churn-model/Customer Churn Model.txt\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "JG55qLHb-NLb",
+    "outputId": "a10b1e9d-2e00-4fa6-b691-497c05124e4e"
+   },
    "outputs": [
     {
      "data": {
@@ -70,7 +132,7 @@
        "      <th>Day Mins</th>\n",
        "      <th>Day Calls</th>\n",
        "      <th>Day Charge</th>\n",
-       "      <th>...</th>\n",
+       "      <th>Eve Mins</th>\n",
        "      <th>Eve Calls</th>\n",
        "      <th>Eve Charge</th>\n",
        "      <th>Night Mins</th>\n",
@@ -96,7 +158,7 @@
        "      <td>265.1</td>\n",
        "      <td>110</td>\n",
        "      <td>45.07</td>\n",
-       "      <td>...</td>\n",
+       "      <td>197.4</td>\n",
        "      <td>99</td>\n",
        "      <td>16.78</td>\n",
        "      <td>244.7</td>\n",
@@ -120,7 +182,7 @@
        "      <td>161.6</td>\n",
        "      <td>123</td>\n",
        "      <td>27.47</td>\n",
-       "      <td>...</td>\n",
+       "      <td>195.5</td>\n",
        "      <td>103</td>\n",
        "      <td>16.62</td>\n",
        "      <td>254.4</td>\n",
@@ -144,7 +206,7 @@
        "      <td>243.4</td>\n",
        "      <td>114</td>\n",
        "      <td>41.38</td>\n",
-       "      <td>...</td>\n",
+       "      <td>121.2</td>\n",
        "      <td>110</td>\n",
        "      <td>10.30</td>\n",
        "      <td>162.6</td>\n",
@@ -168,7 +230,7 @@
        "      <td>299.4</td>\n",
        "      <td>71</td>\n",
        "      <td>50.90</td>\n",
-       "      <td>...</td>\n",
+       "      <td>61.9</td>\n",
        "      <td>88</td>\n",
        "      <td>5.26</td>\n",
        "      <td>196.9</td>\n",
@@ -192,7 +254,7 @@
        "      <td>166.7</td>\n",
        "      <td>113</td>\n",
        "      <td>28.34</td>\n",
-       "      <td>...</td>\n",
+       "      <td>148.3</td>\n",
        "      <td>122</td>\n",
        "      <td>12.61</td>\n",
        "      <td>186.9</td>\n",
@@ -206,43 +268,23 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
-       "0    KS             128        415  382-4657         no        yes   \n",
-       "1    OH             107        415  371-7191         no        yes   \n",
-       "2    NJ             137        415  358-1921         no         no   \n",
-       "3    OH              84        408  375-9999        yes         no   \n",
-       "4    OK              75        415  330-6626        yes         no   \n",
-       "\n",
-       "   VMail Message  Day Mins  Day Calls  Day Charge   ...    Eve Calls  \\\n",
-       "0             25     265.1        110       45.07   ...           99   \n",
-       "1             26     161.6        123       27.47   ...          103   \n",
-       "2              0     243.4        114       41.38   ...          110   \n",
-       "3              0     299.4         71       50.90   ...           88   \n",
-       "4              0     166.7        113       28.34   ...          122   \n",
-       "\n",
-       "   Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  \\\n",
-       "0       16.78       244.7           91         11.01       10.0           3   \n",
-       "1       16.62       254.4          103         11.45       13.7           3   \n",
-       "2       10.30       162.6          104          7.32       12.2           5   \n",
-       "3        5.26       196.9           89          8.86        6.6           7   \n",
-       "4       12.61       186.9          121          8.41       10.1           3   \n",
-       "\n",
-       "   Intl Charge  CustServ Calls  Churn?  \n",
-       "0         2.70               1  False.  \n",
-       "1         3.70               1  False.  \n",
-       "2         3.29               0  False.  \n",
-       "3         1.78               2  False.  \n",
-       "4         2.73               3  False.  \n",
+       "  State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS             128        415  ...        2.70              1  False.\n",
+       "1    OH             107        415  ...        3.70              1  False.\n",
+       "2    NJ             137        415  ...        3.29              0  False.\n",
+       "3    OH              84        408  ...        1.78              2  False.\n",
+       "4    OK              75        415  ...        2.73              3  False.\n",
        "\n",
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 6,
-     "metadata": {},
+     "execution_count": 5,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -252,22 +294,32 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "DLXD3g5j-NLe"
+   },
    "source": [
     "### Crear un subconjunto de datos"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "lAEQlD7b-NLf"
+   },
    "source": [
     "#### Subconjunto de columna o columnas"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "0qXIL9iu-NLf"
+   },
    "outputs": [],
    "source": [
     "account_length = data[\"Account Length\"]"
@@ -275,8 +327,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 119
+    },
+    "colab_type": "code",
+    "id": "G-SKobd4-NLh",
+    "outputId": "6bdfa6e5-e945-4a2b-afbd-c8ed587ac5a4"
+   },
    "outputs": [
     {
      "data": {
@@ -289,8 +349,10 @@
        "Name: Account Length, dtype: int64"
       ]
      },
-     "execution_count": 9,
-     "metadata": {},
+     "execution_count": 7,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -300,8 +362,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "4CksBm4N-NLk",
+    "outputId": "3f762849-dc1e-46e5-f13d-54bc6f6e2bda"
+   },
    "outputs": [
     {
      "data": {
@@ -309,8 +379,10 @@
        "pandas.core.series.Series"
       ]
      },
-     "execution_count": 10,
-     "metadata": {},
+     "execution_count": 8,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -320,8 +392,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "34W1Dmuo-NLn"
+   },
    "outputs": [],
    "source": [
     "subset = data[[\"Account Length\", \"Phone\", \"Eve Charge\", \"Day Calls\"]]"
@@ -329,8 +405,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "zeFRzWhp-NLq",
+    "outputId": "72a021f8-a5c9-493b-ca18-b2401068b8d8"
+   },
    "outputs": [
     {
      "data": {
@@ -408,8 +492,10 @@
        "4              75  330-6626       12.61        113"
       ]
      },
-     "execution_count": 12,
-     "metadata": {},
+     "execution_count": 10,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -419,8 +505,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "pOiG-qk1-NLs",
+    "outputId": "a2a355d8-7a1e-40ec-cc88-69413fa9d92a"
+   },
    "outputs": [
     {
      "data": {
@@ -428,8 +522,10 @@
        "pandas.core.frame.DataFrame"
       ]
      },
-     "execution_count": 13,
-     "metadata": {},
+     "execution_count": 11,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -439,8 +535,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "9xaANT1k-NLu",
+    "outputId": "fb7d536c-3f48-4270-f9a0-f836ab00259b"
+   },
    "outputs": [
     {
      "data": {
@@ -518,8 +622,10 @@
        "4              75  330-6626       12.61          121"
       ]
      },
-     "execution_count": 15,
-     "metadata": {},
+     "execution_count": 12,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -531,8 +637,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "SLaaTCkk-NLw",
+    "outputId": "8bba1dc1-055a-494b-9d82-c7d3d7a8c47a"
+   },
    "outputs": [
     {
      "data": {
@@ -540,8 +654,10 @@
        "['Account Length', 'VMail Message', 'Day Calls']"
       ]
      },
-     "execution_count": 18,
-     "metadata": {},
+     "execution_count": 13,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -552,8 +668,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 374
+    },
+    "colab_type": "code",
+    "id": "LwqEtdkD-NLy",
+    "outputId": "dede79c7-5567-41c6-83e6-b6e879fd543a"
+   },
    "outputs": [
     {
      "data": {
@@ -581,8 +705,10 @@
        " 'Churn?']"
       ]
      },
-     "execution_count": 17,
-     "metadata": {},
+     "execution_count": 14,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -593,8 +719,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 323
+    },
+    "colab_type": "code",
+    "id": "2pJn5sdI-NL0",
+    "outputId": "5845fdb5-10ef-49d3-c217-a9c506952452"
+   },
    "outputs": [
     {
      "data": {
@@ -619,8 +753,10 @@
        " 'Churn?']"
       ]
      },
-     "execution_count": 20,
-     "metadata": {},
+     "execution_count": 15,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -631,8 +767,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "BPtSQxkz-NL2",
+    "outputId": "7210b64f-fa0e-44a7-af4f-3b4b6e8dc23f"
+   },
    "outputs": [
     {
      "data": {
@@ -786,30 +930,20 @@
        "</div>"
       ],
       "text/plain": [
-       "  State  Area Code     Phone Int'l Plan VMail Plan  Day Mins  Day Charge  \\\n",
-       "0    KS        415  382-4657         no        yes     265.1       45.07   \n",
-       "1    OH        415  371-7191         no        yes     161.6       27.47   \n",
-       "2    NJ        415  358-1921         no         no     243.4       41.38   \n",
-       "3    OH        408  375-9999        yes         no     299.4       50.90   \n",
-       "4    OK        415  330-6626        yes         no     166.7       28.34   \n",
-       "\n",
-       "   Eve Mins  Eve Calls  Eve Charge  Night Mins  Night Calls  Night Charge  \\\n",
-       "0     197.4         99       16.78       244.7           91         11.01   \n",
-       "1     195.5        103       16.62       254.4          103         11.45   \n",
-       "2     121.2        110       10.30       162.6          104          7.32   \n",
-       "3      61.9         88        5.26       196.9           89          8.86   \n",
-       "4     148.3        122       12.61       186.9          121          8.41   \n",
+       "  State  Area Code     Phone  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS        415  382-4657  ...        2.70              1  False.\n",
+       "1    OH        415  371-7191  ...        3.70              1  False.\n",
+       "2    NJ        415  358-1921  ...        3.29              0  False.\n",
+       "3    OH        408  375-9999  ...        1.78              2  False.\n",
+       "4    OK        415  330-6626  ...        2.73              3  False.\n",
        "\n",
-       "   Intl Mins  Intl Calls  Intl Charge  CustServ Calls  Churn?  \n",
-       "0       10.0           3         2.70               1  False.  \n",
-       "1       13.7           3         3.70               1  False.  \n",
-       "2       12.2           5         3.29               0  False.  \n",
-       "3        6.6           7         1.78               2  False.  \n",
-       "4       10.1           3         2.73               3  False.  "
+       "[5 rows x 18 columns]"
       ]
      },
-     "execution_count": 21,
-     "metadata": {},
+     "execution_count": 16,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -820,15 +954,26 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "haxlKpjS-NL4"
+   },
    "source": [
     "#### Subconjunto de filas"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 793
+    },
+    "colab_type": "code",
+    "id": "buNFSltI-NL4",
+    "outputId": "08a76b7a-69c1-40a5-8163-54d64f8b6795"
+   },
    "outputs": [
     {
      "data": {
@@ -861,7 +1006,7 @@
        "      <th>Day Mins</th>\n",
        "      <th>Day Calls</th>\n",
        "      <th>Day Charge</th>\n",
-       "      <th>...</th>\n",
+       "      <th>Eve Mins</th>\n",
        "      <th>Eve Calls</th>\n",
        "      <th>Eve Charge</th>\n",
        "      <th>Night Mins</th>\n",
@@ -887,7 +1032,7 @@
        "      <td>161.6</td>\n",
        "      <td>123</td>\n",
        "      <td>27.47</td>\n",
-       "      <td>...</td>\n",
+       "      <td>195.5</td>\n",
        "      <td>103</td>\n",
        "      <td>16.62</td>\n",
        "      <td>254.4</td>\n",
@@ -911,7 +1056,7 @@
        "      <td>243.4</td>\n",
        "      <td>114</td>\n",
        "      <td>41.38</td>\n",
-       "      <td>...</td>\n",
+       "      <td>121.2</td>\n",
        "      <td>110</td>\n",
        "      <td>10.30</td>\n",
        "      <td>162.6</td>\n",
@@ -935,7 +1080,7 @@
        "      <td>299.4</td>\n",
        "      <td>71</td>\n",
        "      <td>50.90</td>\n",
-       "      <td>...</td>\n",
+       "      <td>61.9</td>\n",
        "      <td>88</td>\n",
        "      <td>5.26</td>\n",
        "      <td>196.9</td>\n",
@@ -959,7 +1104,7 @@
        "      <td>166.7</td>\n",
        "      <td>113</td>\n",
        "      <td>28.34</td>\n",
-       "      <td>...</td>\n",
+       "      <td>148.3</td>\n",
        "      <td>122</td>\n",
        "      <td>12.61</td>\n",
        "      <td>186.9</td>\n",
@@ -983,7 +1128,7 @@
        "      <td>223.4</td>\n",
        "      <td>98</td>\n",
        "      <td>37.98</td>\n",
-       "      <td>...</td>\n",
+       "      <td>220.6</td>\n",
        "      <td>101</td>\n",
        "      <td>18.75</td>\n",
        "      <td>203.9</td>\n",
@@ -1007,7 +1152,7 @@
        "      <td>218.2</td>\n",
        "      <td>88</td>\n",
        "      <td>37.09</td>\n",
-       "      <td>...</td>\n",
+       "      <td>348.5</td>\n",
        "      <td>108</td>\n",
        "      <td>29.62</td>\n",
        "      <td>212.6</td>\n",
@@ -1031,7 +1176,7 @@
        "      <td>157.0</td>\n",
        "      <td>79</td>\n",
        "      <td>26.69</td>\n",
-       "      <td>...</td>\n",
+       "      <td>103.1</td>\n",
        "      <td>94</td>\n",
        "      <td>8.76</td>\n",
        "      <td>211.8</td>\n",
@@ -1055,7 +1200,7 @@
        "      <td>184.5</td>\n",
        "      <td>97</td>\n",
        "      <td>31.37</td>\n",
-       "      <td>...</td>\n",
+       "      <td>351.6</td>\n",
        "      <td>80</td>\n",
        "      <td>29.89</td>\n",
        "      <td>215.8</td>\n",
@@ -1079,7 +1224,7 @@
        "      <td>258.6</td>\n",
        "      <td>84</td>\n",
        "      <td>43.96</td>\n",
-       "      <td>...</td>\n",
+       "      <td>222.0</td>\n",
        "      <td>111</td>\n",
        "      <td>18.87</td>\n",
        "      <td>326.4</td>\n",
@@ -1103,7 +1248,7 @@
        "      <td>129.1</td>\n",
        "      <td>137</td>\n",
        "      <td>21.95</td>\n",
-       "      <td>...</td>\n",
+       "      <td>228.5</td>\n",
        "      <td>83</td>\n",
        "      <td>19.42</td>\n",
        "      <td>208.8</td>\n",
@@ -1127,7 +1272,7 @@
        "      <td>187.7</td>\n",
        "      <td>127</td>\n",
        "      <td>31.91</td>\n",
-       "      <td>...</td>\n",
+       "      <td>163.4</td>\n",
        "      <td>148</td>\n",
        "      <td>13.89</td>\n",
        "      <td>196.0</td>\n",
@@ -1151,7 +1296,7 @@
        "      <td>128.8</td>\n",
        "      <td>96</td>\n",
        "      <td>21.90</td>\n",
-       "      <td>...</td>\n",
+       "      <td>104.9</td>\n",
        "      <td>71</td>\n",
        "      <td>8.92</td>\n",
        "      <td>141.1</td>\n",
@@ -1175,7 +1320,7 @@
        "      <td>156.6</td>\n",
        "      <td>88</td>\n",
        "      <td>26.62</td>\n",
-       "      <td>...</td>\n",
+       "      <td>247.6</td>\n",
        "      <td>75</td>\n",
        "      <td>21.05</td>\n",
        "      <td>192.3</td>\n",
@@ -1199,7 +1344,7 @@
        "      <td>120.7</td>\n",
        "      <td>70</td>\n",
        "      <td>20.52</td>\n",
-       "      <td>...</td>\n",
+       "      <td>307.2</td>\n",
        "      <td>76</td>\n",
        "      <td>26.11</td>\n",
        "      <td>203.0</td>\n",
@@ -1223,7 +1368,7 @@
        "      <td>332.9</td>\n",
        "      <td>67</td>\n",
        "      <td>56.59</td>\n",
-       "      <td>...</td>\n",
+       "      <td>317.8</td>\n",
        "      <td>97</td>\n",
        "      <td>27.01</td>\n",
        "      <td>160.6</td>\n",
@@ -1247,7 +1392,7 @@
        "      <td>196.4</td>\n",
        "      <td>139</td>\n",
        "      <td>33.39</td>\n",
-       "      <td>...</td>\n",
+       "      <td>280.9</td>\n",
        "      <td>90</td>\n",
        "      <td>23.88</td>\n",
        "      <td>89.3</td>\n",
@@ -1271,7 +1416,7 @@
        "      <td>190.7</td>\n",
        "      <td>114</td>\n",
        "      <td>32.42</td>\n",
-       "      <td>...</td>\n",
+       "      <td>218.2</td>\n",
        "      <td>111</td>\n",
        "      <td>18.55</td>\n",
        "      <td>129.6</td>\n",
@@ -1295,7 +1440,7 @@
        "      <td>189.7</td>\n",
        "      <td>66</td>\n",
        "      <td>32.25</td>\n",
-       "      <td>...</td>\n",
+       "      <td>212.8</td>\n",
        "      <td>65</td>\n",
        "      <td>18.09</td>\n",
        "      <td>165.7</td>\n",
@@ -1319,7 +1464,7 @@
        "      <td>224.4</td>\n",
        "      <td>90</td>\n",
        "      <td>38.15</td>\n",
-       "      <td>...</td>\n",
+       "      <td>159.5</td>\n",
        "      <td>88</td>\n",
        "      <td>13.56</td>\n",
        "      <td>192.8</td>\n",
@@ -1343,7 +1488,7 @@
        "      <td>155.1</td>\n",
        "      <td>117</td>\n",
        "      <td>26.37</td>\n",
-       "      <td>...</td>\n",
+       "      <td>239.7</td>\n",
        "      <td>93</td>\n",
        "      <td>20.37</td>\n",
        "      <td>208.8</td>\n",
@@ -1367,7 +1512,7 @@
        "      <td>62.4</td>\n",
        "      <td>89</td>\n",
        "      <td>10.61</td>\n",
-       "      <td>...</td>\n",
+       "      <td>169.9</td>\n",
        "      <td>121</td>\n",
        "      <td>14.44</td>\n",
        "      <td>209.6</td>\n",
@@ -1391,7 +1536,7 @@
        "      <td>183.0</td>\n",
        "      <td>112</td>\n",
        "      <td>31.11</td>\n",
-       "      <td>...</td>\n",
+       "      <td>72.9</td>\n",
        "      <td>99</td>\n",
        "      <td>6.20</td>\n",
        "      <td>181.8</td>\n",
@@ -1415,7 +1560,7 @@
        "      <td>110.4</td>\n",
        "      <td>103</td>\n",
        "      <td>18.77</td>\n",
-       "      <td>...</td>\n",
+       "      <td>137.3</td>\n",
        "      <td>102</td>\n",
        "      <td>11.67</td>\n",
        "      <td>189.6</td>\n",
@@ -1439,7 +1584,7 @@
        "      <td>81.1</td>\n",
        "      <td>86</td>\n",
        "      <td>13.79</td>\n",
-       "      <td>...</td>\n",
+       "      <td>245.2</td>\n",
        "      <td>72</td>\n",
        "      <td>20.84</td>\n",
        "      <td>237.0</td>\n",
@@ -1453,119 +1598,42 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>24 rows × 21 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
-       "1     OH             107        415  371-7191         no        yes   \n",
-       "2     NJ             137        415  358-1921         no         no   \n",
-       "3     OH              84        408  375-9999        yes         no   \n",
-       "4     OK              75        415  330-6626        yes         no   \n",
-       "5     AL             118        510  391-8027        yes         no   \n",
-       "6     MA             121        510  355-9993         no        yes   \n",
-       "7     MO             147        415  329-9001        yes         no   \n",
-       "8     LA             117        408  335-4719         no         no   \n",
-       "9     WV             141        415  330-8173        yes        yes   \n",
-       "10    IN              65        415  329-6603         no         no   \n",
-       "11    RI              74        415  344-9403         no         no   \n",
-       "12    IA             168        408  363-1107         no         no   \n",
-       "13    MT              95        510  394-8006         no         no   \n",
-       "14    IA              62        415  366-9238         no         no   \n",
-       "15    NY             161        415  351-7269         no         no   \n",
-       "16    ID              85        408  350-8884         no        yes   \n",
-       "17    VT              93        510  386-2923         no         no   \n",
-       "18    VA              76        510  356-2992         no        yes   \n",
-       "19    TX              73        415  373-2782         no         no   \n",
-       "20    FL             147        415  396-5800         no         no   \n",
-       "21    CO              77        408  393-7984         no         no   \n",
-       "22    AZ             130        415  358-1958         no         no   \n",
-       "23    SC             111        415  350-2565         no         no   \n",
-       "24    VA             132        510  343-4696         no         no   \n",
-       "\n",
-       "    VMail Message  Day Mins  Day Calls  Day Charge   ...    Eve Calls  \\\n",
-       "1              26     161.6        123       27.47   ...          103   \n",
-       "2               0     243.4        114       41.38   ...          110   \n",
-       "3               0     299.4         71       50.90   ...           88   \n",
-       "4               0     166.7        113       28.34   ...          122   \n",
-       "5               0     223.4         98       37.98   ...          101   \n",
-       "6              24     218.2         88       37.09   ...          108   \n",
-       "7               0     157.0         79       26.69   ...           94   \n",
-       "8               0     184.5         97       31.37   ...           80   \n",
-       "9              37     258.6         84       43.96   ...          111   \n",
-       "10              0     129.1        137       21.95   ...           83   \n",
-       "11              0     187.7        127       31.91   ...          148   \n",
-       "12              0     128.8         96       21.90   ...           71   \n",
-       "13              0     156.6         88       26.62   ...           75   \n",
-       "14              0     120.7         70       20.52   ...           76   \n",
-       "15              0     332.9         67       56.59   ...           97   \n",
-       "16             27     196.4        139       33.39   ...           90   \n",
-       "17              0     190.7        114       32.42   ...          111   \n",
-       "18             33     189.7         66       32.25   ...           65   \n",
-       "19              0     224.4         90       38.15   ...           88   \n",
-       "20              0     155.1        117       26.37   ...           93   \n",
-       "21              0      62.4         89       10.61   ...          121   \n",
-       "22              0     183.0        112       31.11   ...           99   \n",
-       "23              0     110.4        103       18.77   ...          102   \n",
-       "24              0      81.1         86       13.79   ...           72   \n",
-       "\n",
-       "    Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  \\\n",
-       "1        16.62       254.4          103         11.45       13.7           3   \n",
-       "2        10.30       162.6          104          7.32       12.2           5   \n",
-       "3         5.26       196.9           89          8.86        6.6           7   \n",
-       "4        12.61       186.9          121          8.41       10.1           3   \n",
-       "5        18.75       203.9          118          9.18        6.3           6   \n",
-       "6        29.62       212.6          118          9.57        7.5           7   \n",
-       "7         8.76       211.8           96          9.53        7.1           6   \n",
-       "8        29.89       215.8           90          9.71        8.7           4   \n",
-       "9        18.87       326.4           97         14.69       11.2           5   \n",
-       "10       19.42       208.8          111          9.40       12.7           6   \n",
-       "11       13.89       196.0           94          8.82        9.1           5   \n",
-       "12        8.92       141.1          128          6.35       11.2           2   \n",
-       "13       21.05       192.3          115          8.65       12.3           5   \n",
-       "14       26.11       203.0           99          9.14       13.1           6   \n",
-       "15       27.01       160.6          128          7.23        5.4           9   \n",
-       "16       23.88        89.3           75          4.02       13.8           4   \n",
-       "17       18.55       129.6          121          5.83        8.1           3   \n",
-       "18       18.09       165.7          108          7.46       10.0           5   \n",
-       "19       13.56       192.8           74          8.68       13.0           2   \n",
-       "20       20.37       208.8          133          9.40       10.6           4   \n",
-       "21       14.44       209.6           64          9.43        5.7           6   \n",
-       "22        6.20       181.8           78          8.18        9.5          19   \n",
-       "23       11.67       189.6          105          8.53        7.7           6   \n",
-       "24       20.84       237.0          115         10.67       10.3           2   \n",
-       "\n",
-       "    Intl Charge  CustServ Calls  Churn?  \n",
-       "1          3.70               1  False.  \n",
-       "2          3.29               0  False.  \n",
-       "3          1.78               2  False.  \n",
-       "4          2.73               3  False.  \n",
-       "5          1.70               0  False.  \n",
-       "6          2.03               3  False.  \n",
-       "7          1.92               0  False.  \n",
-       "8          2.35               1  False.  \n",
-       "9          3.02               0  False.  \n",
-       "10         3.43               4   True.  \n",
-       "11         2.46               0  False.  \n",
-       "12         3.02               1  False.  \n",
-       "13         3.32               3  False.  \n",
-       "14         3.54               4  False.  \n",
-       "15         1.46               4   True.  \n",
-       "16         3.73               1  False.  \n",
-       "17         2.19               3  False.  \n",
-       "18         2.70               1  False.  \n",
-       "19         3.51               1  False.  \n",
-       "20         2.86               0  False.  \n",
-       "21         1.54               5   True.  \n",
-       "22         2.57               0  False.  \n",
-       "23         2.08               2  False.  \n",
-       "24         2.78               0  False.  \n",
+       "   State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "1     OH             107        415  ...        3.70              1  False.\n",
+       "2     NJ             137        415  ...        3.29              0  False.\n",
+       "3     OH              84        408  ...        1.78              2  False.\n",
+       "4     OK              75        415  ...        2.73              3  False.\n",
+       "5     AL             118        510  ...        1.70              0  False.\n",
+       "6     MA             121        510  ...        2.03              3  False.\n",
+       "7     MO             147        415  ...        1.92              0  False.\n",
+       "8     LA             117        408  ...        2.35              1  False.\n",
+       "9     WV             141        415  ...        3.02              0  False.\n",
+       "10    IN              65        415  ...        3.43              4   True.\n",
+       "11    RI              74        415  ...        2.46              0  False.\n",
+       "12    IA             168        408  ...        3.02              1  False.\n",
+       "13    MT              95        510  ...        3.32              3  False.\n",
+       "14    IA              62        415  ...        3.54              4  False.\n",
+       "15    NY             161        415  ...        1.46              4   True.\n",
+       "16    ID              85        408  ...        3.73              1  False.\n",
+       "17    VT              93        510  ...        2.19              3  False.\n",
+       "18    VA              76        510  ...        2.70              1  False.\n",
+       "19    TX              73        415  ...        3.51              1  False.\n",
+       "20    FL             147        415  ...        2.86              0  False.\n",
+       "21    CO              77        408  ...        1.54              5   True.\n",
+       "22    AZ             130        415  ...        2.57              0  False.\n",
+       "23    SC             111        415  ...        2.08              2  False.\n",
+       "24    VA             132        510  ...        2.78              0  False.\n",
        "\n",
        "[24 rows x 21 columns]"
       ]
      },
-     "execution_count": 22,
-     "metadata": {},
+     "execution_count": 17,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -1575,8 +1643,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 824
+    },
+    "colab_type": "code",
+    "id": "B6yso4ho-NL5",
+    "outputId": "e9ac1877-fc96-470f-f66d-7a506d3d542c"
+   },
    "outputs": [
     {
      "data": {
@@ -1609,7 +1685,7 @@
        "      <th>Day Mins</th>\n",
        "      <th>Day Calls</th>\n",
        "      <th>Day Charge</th>\n",
-       "      <th>...</th>\n",
+       "      <th>Eve Mins</th>\n",
        "      <th>Eve Calls</th>\n",
        "      <th>Eve Charge</th>\n",
        "      <th>Night Mins</th>\n",
@@ -1635,7 +1711,7 @@
        "      <td>129.1</td>\n",
        "      <td>137</td>\n",
        "      <td>21.95</td>\n",
-       "      <td>...</td>\n",
+       "      <td>228.5</td>\n",
        "      <td>83</td>\n",
        "      <td>19.42</td>\n",
        "      <td>208.8</td>\n",
@@ -1659,7 +1735,7 @@
        "      <td>187.7</td>\n",
        "      <td>127</td>\n",
        "      <td>31.91</td>\n",
-       "      <td>...</td>\n",
+       "      <td>163.4</td>\n",
        "      <td>148</td>\n",
        "      <td>13.89</td>\n",
        "      <td>196.0</td>\n",
@@ -1683,7 +1759,7 @@
        "      <td>128.8</td>\n",
        "      <td>96</td>\n",
        "      <td>21.90</td>\n",
-       "      <td>...</td>\n",
+       "      <td>104.9</td>\n",
        "      <td>71</td>\n",
        "      <td>8.92</td>\n",
        "      <td>141.1</td>\n",
@@ -1707,7 +1783,7 @@
        "      <td>156.6</td>\n",
        "      <td>88</td>\n",
        "      <td>26.62</td>\n",
-       "      <td>...</td>\n",
+       "      <td>247.6</td>\n",
        "      <td>75</td>\n",
        "      <td>21.05</td>\n",
        "      <td>192.3</td>\n",
@@ -1731,7 +1807,7 @@
        "      <td>120.7</td>\n",
        "      <td>70</td>\n",
        "      <td>20.52</td>\n",
-       "      <td>...</td>\n",
+       "      <td>307.2</td>\n",
        "      <td>76</td>\n",
        "      <td>26.11</td>\n",
        "      <td>203.0</td>\n",
@@ -1755,7 +1831,7 @@
        "      <td>332.9</td>\n",
        "      <td>67</td>\n",
        "      <td>56.59</td>\n",
-       "      <td>...</td>\n",
+       "      <td>317.8</td>\n",
        "      <td>97</td>\n",
        "      <td>27.01</td>\n",
        "      <td>160.6</td>\n",
@@ -1779,7 +1855,7 @@
        "      <td>196.4</td>\n",
        "      <td>139</td>\n",
        "      <td>33.39</td>\n",
-       "      <td>...</td>\n",
+       "      <td>280.9</td>\n",
        "      <td>90</td>\n",
        "      <td>23.88</td>\n",
        "      <td>89.3</td>\n",
@@ -1803,7 +1879,7 @@
        "      <td>190.7</td>\n",
        "      <td>114</td>\n",
        "      <td>32.42</td>\n",
-       "      <td>...</td>\n",
+       "      <td>218.2</td>\n",
        "      <td>111</td>\n",
        "      <td>18.55</td>\n",
        "      <td>129.6</td>\n",
@@ -1827,7 +1903,7 @@
        "      <td>189.7</td>\n",
        "      <td>66</td>\n",
        "      <td>32.25</td>\n",
-       "      <td>...</td>\n",
+       "      <td>212.8</td>\n",
        "      <td>65</td>\n",
        "      <td>18.09</td>\n",
        "      <td>165.7</td>\n",
@@ -1851,7 +1927,7 @@
        "      <td>224.4</td>\n",
        "      <td>90</td>\n",
        "      <td>38.15</td>\n",
-       "      <td>...</td>\n",
+       "      <td>159.5</td>\n",
        "      <td>88</td>\n",
        "      <td>13.56</td>\n",
        "      <td>192.8</td>\n",
@@ -1875,7 +1951,7 @@
        "      <td>155.1</td>\n",
        "      <td>117</td>\n",
        "      <td>26.37</td>\n",
-       "      <td>...</td>\n",
+       "      <td>239.7</td>\n",
        "      <td>93</td>\n",
        "      <td>20.37</td>\n",
        "      <td>208.8</td>\n",
@@ -1899,7 +1975,7 @@
        "      <td>62.4</td>\n",
        "      <td>89</td>\n",
        "      <td>10.61</td>\n",
-       "      <td>...</td>\n",
+       "      <td>169.9</td>\n",
        "      <td>121</td>\n",
        "      <td>14.44</td>\n",
        "      <td>209.6</td>\n",
@@ -1923,7 +1999,7 @@
        "      <td>183.0</td>\n",
        "      <td>112</td>\n",
        "      <td>31.11</td>\n",
-       "      <td>...</td>\n",
+       "      <td>72.9</td>\n",
        "      <td>99</td>\n",
        "      <td>6.20</td>\n",
        "      <td>181.8</td>\n",
@@ -1947,7 +2023,7 @@
        "      <td>110.4</td>\n",
        "      <td>103</td>\n",
        "      <td>18.77</td>\n",
-       "      <td>...</td>\n",
+       "      <td>137.3</td>\n",
        "      <td>102</td>\n",
        "      <td>11.67</td>\n",
        "      <td>189.6</td>\n",
@@ -1971,7 +2047,7 @@
        "      <td>81.1</td>\n",
        "      <td>86</td>\n",
        "      <td>13.79</td>\n",
-       "      <td>...</td>\n",
+       "      <td>245.2</td>\n",
        "      <td>72</td>\n",
        "      <td>20.84</td>\n",
        "      <td>237.0</td>\n",
@@ -1995,7 +2071,7 @@
        "      <td>124.3</td>\n",
        "      <td>76</td>\n",
        "      <td>21.13</td>\n",
-       "      <td>...</td>\n",
+       "      <td>277.1</td>\n",
        "      <td>112</td>\n",
        "      <td>23.55</td>\n",
        "      <td>250.7</td>\n",
@@ -2019,7 +2095,7 @@
        "      <td>213.0</td>\n",
        "      <td>115</td>\n",
        "      <td>36.21</td>\n",
-       "      <td>...</td>\n",
+       "      <td>191.1</td>\n",
        "      <td>112</td>\n",
        "      <td>16.24</td>\n",
        "      <td>182.7</td>\n",
@@ -2043,7 +2119,7 @@
        "      <td>134.3</td>\n",
        "      <td>73</td>\n",
        "      <td>22.83</td>\n",
-       "      <td>...</td>\n",
+       "      <td>155.5</td>\n",
        "      <td>100</td>\n",
        "      <td>13.22</td>\n",
        "      <td>102.1</td>\n",
@@ -2067,7 +2143,7 @@
        "      <td>190.0</td>\n",
        "      <td>109</td>\n",
        "      <td>32.30</td>\n",
-       "      <td>...</td>\n",
+       "      <td>258.2</td>\n",
        "      <td>84</td>\n",
        "      <td>21.95</td>\n",
        "      <td>181.5</td>\n",
@@ -2091,7 +2167,7 @@
        "      <td>119.3</td>\n",
        "      <td>117</td>\n",
        "      <td>20.28</td>\n",
-       "      <td>...</td>\n",
+       "      <td>215.1</td>\n",
        "      <td>109</td>\n",
        "      <td>18.28</td>\n",
        "      <td>178.7</td>\n",
@@ -2115,7 +2191,7 @@
        "      <td>84.8</td>\n",
        "      <td>95</td>\n",
        "      <td>14.42</td>\n",
-       "      <td>...</td>\n",
+       "      <td>136.7</td>\n",
        "      <td>63</td>\n",
        "      <td>11.62</td>\n",
        "      <td>250.5</td>\n",
@@ -2139,7 +2215,7 @@
        "      <td>226.1</td>\n",
        "      <td>105</td>\n",
        "      <td>38.44</td>\n",
-       "      <td>...</td>\n",
+       "      <td>201.5</td>\n",
        "      <td>107</td>\n",
        "      <td>17.13</td>\n",
        "      <td>246.2</td>\n",
@@ -2163,7 +2239,7 @@
        "      <td>212.0</td>\n",
        "      <td>121</td>\n",
        "      <td>36.04</td>\n",
-       "      <td>...</td>\n",
+       "      <td>31.2</td>\n",
        "      <td>115</td>\n",
        "      <td>2.65</td>\n",
        "      <td>293.3</td>\n",
@@ -2187,7 +2263,7 @@
        "      <td>249.6</td>\n",
        "      <td>118</td>\n",
        "      <td>42.43</td>\n",
-       "      <td>...</td>\n",
+       "      <td>252.4</td>\n",
        "      <td>119</td>\n",
        "      <td>21.45</td>\n",
        "      <td>280.2</td>\n",
@@ -2211,7 +2287,7 @@
        "      <td>176.8</td>\n",
        "      <td>94</td>\n",
        "      <td>30.06</td>\n",
-       "      <td>...</td>\n",
+       "      <td>195.0</td>\n",
        "      <td>75</td>\n",
        "      <td>16.58</td>\n",
        "      <td>213.5</td>\n",
@@ -2225,123 +2301,43 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>25 rows × 21 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "   State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
-       "10    IN              65        415  329-6603         no         no   \n",
-       "11    RI              74        415  344-9403         no         no   \n",
-       "12    IA             168        408  363-1107         no         no   \n",
-       "13    MT              95        510  394-8006         no         no   \n",
-       "14    IA              62        415  366-9238         no         no   \n",
-       "15    NY             161        415  351-7269         no         no   \n",
-       "16    ID              85        408  350-8884         no        yes   \n",
-       "17    VT              93        510  386-2923         no         no   \n",
-       "18    VA              76        510  356-2992         no        yes   \n",
-       "19    TX              73        415  373-2782         no         no   \n",
-       "20    FL             147        415  396-5800         no         no   \n",
-       "21    CO              77        408  393-7984         no         no   \n",
-       "22    AZ             130        415  358-1958         no         no   \n",
-       "23    SC             111        415  350-2565         no         no   \n",
-       "24    VA             132        510  343-4696         no         no   \n",
-       "25    NE             174        415  331-3698         no         no   \n",
-       "26    WY              57        408  357-3817         no        yes   \n",
-       "27    MT              54        408  418-6412         no         no   \n",
-       "28    MO              20        415  353-2630         no         no   \n",
-       "29    HI              49        510  410-7789         no         no   \n",
-       "30    IL             142        415  416-8428         no         no   \n",
-       "31    NH              75        510  370-3359         no         no   \n",
-       "32    LA             172        408  383-1121         no         no   \n",
-       "33    AZ              12        408  360-1596         no         no   \n",
-       "34    OK              57        408  395-2854         no        yes   \n",
-       "\n",
-       "    VMail Message  Day Mins  Day Calls  Day Charge   ...    Eve Calls  \\\n",
-       "10              0     129.1        137       21.95   ...           83   \n",
-       "11              0     187.7        127       31.91   ...          148   \n",
-       "12              0     128.8         96       21.90   ...           71   \n",
-       "13              0     156.6         88       26.62   ...           75   \n",
-       "14              0     120.7         70       20.52   ...           76   \n",
-       "15              0     332.9         67       56.59   ...           97   \n",
-       "16             27     196.4        139       33.39   ...           90   \n",
-       "17              0     190.7        114       32.42   ...          111   \n",
-       "18             33     189.7         66       32.25   ...           65   \n",
-       "19              0     224.4         90       38.15   ...           88   \n",
-       "20              0     155.1        117       26.37   ...           93   \n",
-       "21              0      62.4         89       10.61   ...          121   \n",
-       "22              0     183.0        112       31.11   ...           99   \n",
-       "23              0     110.4        103       18.77   ...          102   \n",
-       "24              0      81.1         86       13.79   ...           72   \n",
-       "25              0     124.3         76       21.13   ...          112   \n",
-       "26             39     213.0        115       36.21   ...          112   \n",
-       "27              0     134.3         73       22.83   ...          100   \n",
-       "28              0     190.0        109       32.30   ...           84   \n",
-       "29              0     119.3        117       20.28   ...          109   \n",
-       "30              0      84.8         95       14.42   ...           63   \n",
-       "31              0     226.1        105       38.44   ...          107   \n",
-       "32              0     212.0        121       36.04   ...          115   \n",
-       "33              0     249.6        118       42.43   ...          119   \n",
-       "34             25     176.8         94       30.06   ...           75   \n",
-       "\n",
-       "    Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  \\\n",
-       "10       19.42       208.8          111          9.40       12.7           6   \n",
-       "11       13.89       196.0           94          8.82        9.1           5   \n",
-       "12        8.92       141.1          128          6.35       11.2           2   \n",
-       "13       21.05       192.3          115          8.65       12.3           5   \n",
-       "14       26.11       203.0           99          9.14       13.1           6   \n",
-       "15       27.01       160.6          128          7.23        5.4           9   \n",
-       "16       23.88        89.3           75          4.02       13.8           4   \n",
-       "17       18.55       129.6          121          5.83        8.1           3   \n",
-       "18       18.09       165.7          108          7.46       10.0           5   \n",
-       "19       13.56       192.8           74          8.68       13.0           2   \n",
-       "20       20.37       208.8          133          9.40       10.6           4   \n",
-       "21       14.44       209.6           64          9.43        5.7           6   \n",
-       "22        6.20       181.8           78          8.18        9.5          19   \n",
-       "23       11.67       189.6          105          8.53        7.7           6   \n",
-       "24       20.84       237.0          115         10.67       10.3           2   \n",
-       "25       23.55       250.7          115         11.28       15.5           5   \n",
-       "26       16.24       182.7          115          8.22        9.5           3   \n",
-       "27       13.22       102.1           68          4.59       14.7           4   \n",
-       "28       21.95       181.5          102          8.17        6.3           6   \n",
-       "29       18.28       178.7           90          8.04       11.1           1   \n",
-       "30       11.62       250.5          148         11.27       14.2           6   \n",
-       "31       17.13       246.2           98         11.08       10.3           5   \n",
-       "32        2.65       293.3           78         13.20       12.6          10   \n",
-       "33       21.45       280.2           90         12.61       11.8           3   \n",
-       "34       16.58       213.5          116          9.61        8.3           4   \n",
-       "\n",
-       "    Intl Charge  CustServ Calls  Churn?  \n",
-       "10         3.43               4   True.  \n",
-       "11         2.46               0  False.  \n",
-       "12         3.02               1  False.  \n",
-       "13         3.32               3  False.  \n",
-       "14         3.54               4  False.  \n",
-       "15         1.46               4   True.  \n",
-       "16         3.73               1  False.  \n",
-       "17         2.19               3  False.  \n",
-       "18         2.70               1  False.  \n",
-       "19         3.51               1  False.  \n",
-       "20         2.86               0  False.  \n",
-       "21         1.54               5   True.  \n",
-       "22         2.57               0  False.  \n",
-       "23         2.08               2  False.  \n",
-       "24         2.78               0  False.  \n",
-       "25         4.19               3  False.  \n",
-       "26         2.57               0  False.  \n",
-       "27         3.97               3  False.  \n",
-       "28         1.70               0  False.  \n",
-       "29         3.00               1  False.  \n",
-       "30         3.83               2  False.  \n",
-       "31         2.78               1  False.  \n",
-       "32         3.40               3  False.  \n",
-       "33         3.19               1   True.  \n",
-       "34         2.24               0  False.  \n",
+       "   State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "10    IN              65        415  ...        3.43              4   True.\n",
+       "11    RI              74        415  ...        2.46              0  False.\n",
+       "12    IA             168        408  ...        3.02              1  False.\n",
+       "13    MT              95        510  ...        3.32              3  False.\n",
+       "14    IA              62        415  ...        3.54              4  False.\n",
+       "15    NY             161        415  ...        1.46              4   True.\n",
+       "16    ID              85        408  ...        3.73              1  False.\n",
+       "17    VT              93        510  ...        2.19              3  False.\n",
+       "18    VA              76        510  ...        2.70              1  False.\n",
+       "19    TX              73        415  ...        3.51              1  False.\n",
+       "20    FL             147        415  ...        2.86              0  False.\n",
+       "21    CO              77        408  ...        1.54              5   True.\n",
+       "22    AZ             130        415  ...        2.57              0  False.\n",
+       "23    SC             111        415  ...        2.08              2  False.\n",
+       "24    VA             132        510  ...        2.78              0  False.\n",
+       "25    NE             174        415  ...        4.19              3  False.\n",
+       "26    WY              57        408  ...        2.57              0  False.\n",
+       "27    MT              54        408  ...        3.97              3  False.\n",
+       "28    MO              20        415  ...        1.70              0  False.\n",
+       "29    HI              49        510  ...        3.00              1  False.\n",
+       "30    IL             142        415  ...        3.83              2  False.\n",
+       "31    NH              75        510  ...        2.78              1  False.\n",
+       "32    LA             172        408  ...        3.40              3  False.\n",
+       "33    AZ              12        408  ...        3.19              1   True.\n",
+       "34    OK              57        408  ...        2.24              0  False.\n",
        "\n",
        "[25 rows x 21 columns]"
       ]
      },
-     "execution_count": 23,
-     "metadata": {},
+     "execution_count": 18,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -2351,8 +2347,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 297
+    },
+    "colab_type": "code",
+    "id": "qti5Qah2-NL6",
+    "outputId": "64cecb49-d016-4772-b8e5-ca067a780560"
+   },
    "outputs": [
     {
      "data": {
@@ -2385,7 +2389,7 @@
        "      <th>Day Mins</th>\n",
        "      <th>Day Calls</th>\n",
        "      <th>Day Charge</th>\n",
-       "      <th>...</th>\n",
+       "      <th>Eve Mins</th>\n",
        "      <th>Eve Calls</th>\n",
        "      <th>Eve Charge</th>\n",
        "      <th>Night Mins</th>\n",
@@ -2411,7 +2415,7 @@
        "      <td>265.1</td>\n",
        "      <td>110</td>\n",
        "      <td>45.07</td>\n",
-       "      <td>...</td>\n",
+       "      <td>197.4</td>\n",
        "      <td>99</td>\n",
        "      <td>16.78</td>\n",
        "      <td>244.7</td>\n",
@@ -2435,7 +2439,7 @@
        "      <td>161.6</td>\n",
        "      <td>123</td>\n",
        "      <td>27.47</td>\n",
-       "      <td>...</td>\n",
+       "      <td>195.5</td>\n",
        "      <td>103</td>\n",
        "      <td>16.62</td>\n",
        "      <td>254.4</td>\n",
@@ -2459,7 +2463,7 @@
        "      <td>243.4</td>\n",
        "      <td>114</td>\n",
        "      <td>41.38</td>\n",
-       "      <td>...</td>\n",
+       "      <td>121.2</td>\n",
        "      <td>110</td>\n",
        "      <td>10.30</td>\n",
        "      <td>162.6</td>\n",
@@ -2483,7 +2487,7 @@
        "      <td>299.4</td>\n",
        "      <td>71</td>\n",
        "      <td>50.90</td>\n",
-       "      <td>...</td>\n",
+       "      <td>61.9</td>\n",
        "      <td>88</td>\n",
        "      <td>5.26</td>\n",
        "      <td>196.9</td>\n",
@@ -2507,7 +2511,7 @@
        "      <td>166.7</td>\n",
        "      <td>113</td>\n",
        "      <td>28.34</td>\n",
-       "      <td>...</td>\n",
+       "      <td>148.3</td>\n",
        "      <td>122</td>\n",
        "      <td>12.61</td>\n",
        "      <td>186.9</td>\n",
@@ -2531,7 +2535,7 @@
        "      <td>223.4</td>\n",
        "      <td>98</td>\n",
        "      <td>37.98</td>\n",
-       "      <td>...</td>\n",
+       "      <td>220.6</td>\n",
        "      <td>101</td>\n",
        "      <td>18.75</td>\n",
        "      <td>203.9</td>\n",
@@ -2555,7 +2559,7 @@
        "      <td>218.2</td>\n",
        "      <td>88</td>\n",
        "      <td>37.09</td>\n",
-       "      <td>...</td>\n",
+       "      <td>348.5</td>\n",
        "      <td>108</td>\n",
        "      <td>29.62</td>\n",
        "      <td>212.6</td>\n",
@@ -2579,7 +2583,7 @@
        "      <td>157.0</td>\n",
        "      <td>79</td>\n",
        "      <td>26.69</td>\n",
-       "      <td>...</td>\n",
+       "      <td>103.1</td>\n",
        "      <td>94</td>\n",
        "      <td>8.76</td>\n",
        "      <td>211.8</td>\n",
@@ -2593,55 +2597,26 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>8 rows × 21 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
-       "0    KS             128        415  382-4657         no        yes   \n",
-       "1    OH             107        415  371-7191         no        yes   \n",
-       "2    NJ             137        415  358-1921         no         no   \n",
-       "3    OH              84        408  375-9999        yes         no   \n",
-       "4    OK              75        415  330-6626        yes         no   \n",
-       "5    AL             118        510  391-8027        yes         no   \n",
-       "6    MA             121        510  355-9993         no        yes   \n",
-       "7    MO             147        415  329-9001        yes         no   \n",
-       "\n",
-       "   VMail Message  Day Mins  Day Calls  Day Charge   ...    Eve Calls  \\\n",
-       "0             25     265.1        110       45.07   ...           99   \n",
-       "1             26     161.6        123       27.47   ...          103   \n",
-       "2              0     243.4        114       41.38   ...          110   \n",
-       "3              0     299.4         71       50.90   ...           88   \n",
-       "4              0     166.7        113       28.34   ...          122   \n",
-       "5              0     223.4         98       37.98   ...          101   \n",
-       "6             24     218.2         88       37.09   ...          108   \n",
-       "7              0     157.0         79       26.69   ...           94   \n",
-       "\n",
-       "   Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  \\\n",
-       "0       16.78       244.7           91         11.01       10.0           3   \n",
-       "1       16.62       254.4          103         11.45       13.7           3   \n",
-       "2       10.30       162.6          104          7.32       12.2           5   \n",
-       "3        5.26       196.9           89          8.86        6.6           7   \n",
-       "4       12.61       186.9          121          8.41       10.1           3   \n",
-       "5       18.75       203.9          118          9.18        6.3           6   \n",
-       "6       29.62       212.6          118          9.57        7.5           7   \n",
-       "7        8.76       211.8           96          9.53        7.1           6   \n",
-       "\n",
-       "   Intl Charge  CustServ Calls  Churn?  \n",
-       "0         2.70               1  False.  \n",
-       "1         3.70               1  False.  \n",
-       "2         3.29               0  False.  \n",
-       "3         1.78               2  False.  \n",
-       "4         2.73               3  False.  \n",
-       "5         1.70               0  False.  \n",
-       "6         2.03               3  False.  \n",
-       "7         1.92               0  False.  \n",
+       "  State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS             128        415  ...        2.70              1  False.\n",
+       "1    OH             107        415  ...        3.70              1  False.\n",
+       "2    NJ             137        415  ...        3.29              0  False.\n",
+       "3    OH              84        408  ...        1.78              2  False.\n",
+       "4    OK              75        415  ...        2.73              3  False.\n",
+       "5    AL             118        510  ...        1.70              0  False.\n",
+       "6    MA             121        510  ...        2.03              3  False.\n",
+       "7    MO             147        415  ...        1.92              0  False.\n",
        "\n",
        "[8 rows x 21 columns]"
       ]
      },
-     "execution_count": 24,
-     "metadata": {},
+     "execution_count": 19,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -2651,8 +2626,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
+   "execution_count": 20,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 452
+    },
+    "colab_type": "code",
+    "id": "RQSc-Wa3-NL8",
+    "outputId": "8f00de7a-f053-4594-dd3e-7fd398733d46"
+   },
    "outputs": [
     {
      "data": {
@@ -2685,7 +2668,7 @@
        "      <th>Day Mins</th>\n",
        "      <th>Day Calls</th>\n",
        "      <th>Day Charge</th>\n",
-       "      <th>...</th>\n",
+       "      <th>Eve Mins</th>\n",
        "      <th>Eve Calls</th>\n",
        "      <th>Eve Charge</th>\n",
        "      <th>Night Mins</th>\n",
@@ -2711,7 +2694,7 @@
        "      <td>140.0</td>\n",
        "      <td>101</td>\n",
        "      <td>23.80</td>\n",
-       "      <td>...</td>\n",
+       "      <td>196.4</td>\n",
        "      <td>77</td>\n",
        "      <td>16.69</td>\n",
        "      <td>120.1</td>\n",
@@ -2735,7 +2718,7 @@
        "      <td>193.9</td>\n",
        "      <td>118</td>\n",
        "      <td>32.96</td>\n",
-       "      <td>...</td>\n",
+       "      <td>85.0</td>\n",
        "      <td>110</td>\n",
        "      <td>7.23</td>\n",
        "      <td>210.1</td>\n",
@@ -2759,7 +2742,7 @@
        "      <td>321.1</td>\n",
        "      <td>105</td>\n",
        "      <td>54.59</td>\n",
-       "      <td>...</td>\n",
+       "      <td>265.5</td>\n",
        "      <td>122</td>\n",
        "      <td>22.57</td>\n",
        "      <td>180.5</td>\n",
@@ -2783,7 +2766,7 @@
        "      <td>118.4</td>\n",
        "      <td>126</td>\n",
        "      <td>20.13</td>\n",
-       "      <td>...</td>\n",
+       "      <td>249.3</td>\n",
        "      <td>97</td>\n",
        "      <td>21.19</td>\n",
        "      <td>227.0</td>\n",
@@ -2807,7 +2790,7 @@
        "      <td>169.8</td>\n",
        "      <td>114</td>\n",
        "      <td>28.87</td>\n",
-       "      <td>...</td>\n",
+       "      <td>197.7</td>\n",
        "      <td>105</td>\n",
        "      <td>16.80</td>\n",
        "      <td>193.7</td>\n",
@@ -2831,7 +2814,7 @@
        "      <td>193.4</td>\n",
        "      <td>99</td>\n",
        "      <td>32.88</td>\n",
-       "      <td>...</td>\n",
+       "      <td>116.9</td>\n",
        "      <td>88</td>\n",
        "      <td>9.94</td>\n",
        "      <td>243.3</td>\n",
@@ -2855,7 +2838,7 @@
        "      <td>106.6</td>\n",
        "      <td>128</td>\n",
        "      <td>18.12</td>\n",
-       "      <td>...</td>\n",
+       "      <td>284.8</td>\n",
        "      <td>87</td>\n",
        "      <td>24.21</td>\n",
        "      <td>178.9</td>\n",
@@ -2879,7 +2862,7 @@
        "      <td>134.7</td>\n",
        "      <td>98</td>\n",
        "      <td>22.90</td>\n",
-       "      <td>...</td>\n",
+       "      <td>189.7</td>\n",
        "      <td>68</td>\n",
        "      <td>16.12</td>\n",
        "      <td>221.4</td>\n",
@@ -2903,7 +2886,7 @@
        "      <td>156.2</td>\n",
        "      <td>77</td>\n",
        "      <td>26.55</td>\n",
-       "      <td>...</td>\n",
+       "      <td>215.5</td>\n",
        "      <td>126</td>\n",
        "      <td>18.32</td>\n",
        "      <td>279.1</td>\n",
@@ -2927,7 +2910,7 @@
        "      <td>231.1</td>\n",
        "      <td>57</td>\n",
        "      <td>39.29</td>\n",
-       "      <td>...</td>\n",
+       "      <td>153.4</td>\n",
        "      <td>55</td>\n",
        "      <td>13.04</td>\n",
        "      <td>191.3</td>\n",
@@ -2951,7 +2934,7 @@
        "      <td>180.8</td>\n",
        "      <td>109</td>\n",
        "      <td>30.74</td>\n",
-       "      <td>...</td>\n",
+       "      <td>288.8</td>\n",
        "      <td>58</td>\n",
        "      <td>24.55</td>\n",
        "      <td>191.9</td>\n",
@@ -2975,7 +2958,7 @@
        "      <td>213.8</td>\n",
        "      <td>105</td>\n",
        "      <td>36.35</td>\n",
-       "      <td>...</td>\n",
+       "      <td>159.6</td>\n",
        "      <td>84</td>\n",
        "      <td>13.57</td>\n",
        "      <td>139.2</td>\n",
@@ -2999,7 +2982,7 @@
        "      <td>234.4</td>\n",
        "      <td>113</td>\n",
        "      <td>39.85</td>\n",
-       "      <td>...</td>\n",
+       "      <td>265.9</td>\n",
        "      <td>82</td>\n",
        "      <td>22.60</td>\n",
        "      <td>241.4</td>\n",
@@ -3013,75 +2996,31 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>13 rows × 21 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "     State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
-       "3320    GA             122        510  411-5677        yes         no   \n",
-       "3321    VT              60        415  400-2738         no         no   \n",
-       "3322    MD              62        408  409-1856         no         no   \n",
-       "3323    IN             117        415  362-5899         no         no   \n",
-       "3324    WV             159        415  377-1164         no         no   \n",
-       "3325    OH              78        408  368-8555         no         no   \n",
-       "3326    OH              96        415  347-6812         no         no   \n",
-       "3327    SC              79        415  348-3830         no         no   \n",
-       "3328    AZ             192        415  414-4276         no        yes   \n",
-       "3329    WV              68        415  370-3271         no         no   \n",
-       "3330    RI              28        510  328-8230         no         no   \n",
-       "3331    CT             184        510  364-6381        yes         no   \n",
-       "3332    TN              74        415  400-4344         no        yes   \n",
-       "\n",
-       "      VMail Message  Day Mins  Day Calls  Day Charge   ...    Eve Calls  \\\n",
-       "3320              0     140.0        101       23.80   ...           77   \n",
-       "3321              0     193.9        118       32.96   ...          110   \n",
-       "3322              0     321.1        105       54.59   ...          122   \n",
-       "3323              0     118.4        126       20.13   ...           97   \n",
-       "3324              0     169.8        114       28.87   ...          105   \n",
-       "3325              0     193.4         99       32.88   ...           88   \n",
-       "3326              0     106.6        128       18.12   ...           87   \n",
-       "3327              0     134.7         98       22.90   ...           68   \n",
-       "3328             36     156.2         77       26.55   ...          126   \n",
-       "3329              0     231.1         57       39.29   ...           55   \n",
-       "3330              0     180.8        109       30.74   ...           58   \n",
-       "3331              0     213.8        105       36.35   ...           84   \n",
-       "3332             25     234.4        113       39.85   ...           82   \n",
-       "\n",
-       "      Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  \\\n",
-       "3320       16.69       120.1          133          5.40        9.7   \n",
-       "3321        7.23       210.1          134          9.45       13.2   \n",
-       "3322       22.57       180.5           72          8.12       11.5   \n",
-       "3323       21.19       227.0           56         10.22       13.6   \n",
-       "3324       16.80       193.7           82          8.72       11.6   \n",
-       "3325        9.94       243.3          109         10.95        9.3   \n",
-       "3326       24.21       178.9           92          8.05       14.9   \n",
-       "3327       16.12       221.4          128          9.96       11.8   \n",
-       "3328       18.32       279.1           83         12.56        9.9   \n",
-       "3329       13.04       191.3          123          8.61        9.6   \n",
-       "3330       24.55       191.9           91          8.64       14.1   \n",
-       "3331       13.57       139.2          137          6.26        5.0   \n",
-       "3332       22.60       241.4           77         10.86       13.7   \n",
-       "\n",
-       "      Intl Calls  Intl Charge  CustServ Calls  Churn?  \n",
-       "3320           4         2.62               4   True.  \n",
-       "3321           8         3.56               3  False.  \n",
-       "3322           2         3.11               4   True.  \n",
-       "3323           3         3.67               5   True.  \n",
-       "3324           4         3.13               1  False.  \n",
-       "3325           4         2.51               2  False.  \n",
-       "3326           7         4.02               1  False.  \n",
-       "3327           5         3.19               2  False.  \n",
-       "3328           6         2.67               2  False.  \n",
-       "3329           4         2.59               3  False.  \n",
-       "3330           6         3.81               2  False.  \n",
-       "3331          10         1.35               2  False.  \n",
-       "3332           4         3.70               0  False.  \n",
+       "     State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "3320    GA             122        510  ...        2.62              4   True.\n",
+       "3321    VT              60        415  ...        3.56              3  False.\n",
+       "3322    MD              62        408  ...        3.11              4   True.\n",
+       "3323    IN             117        415  ...        3.67              5   True.\n",
+       "3324    WV             159        415  ...        3.13              1  False.\n",
+       "3325    OH              78        408  ...        2.51              2  False.\n",
+       "3326    OH              96        415  ...        4.02              1  False.\n",
+       "3327    SC              79        415  ...        3.19              2  False.\n",
+       "3328    AZ             192        415  ...        2.67              2  False.\n",
+       "3329    WV              68        415  ...        2.59              3  False.\n",
+       "3330    RI              28        510  ...        3.81              2  False.\n",
+       "3331    CT             184        510  ...        1.35              2  False.\n",
+       "3332    TN              74        415  ...        3.70              0  False.\n",
        "\n",
        "[13 rows x 21 columns]"
       ]
      },
-     "execution_count": 26,
-     "metadata": {},
+     "execution_count": 20,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -3091,15 +3030,26 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "8Z6R4bxt-NL9"
+   },
    "source": [
     "#### Subconjuntos de filas con condiciones booleanas"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "Wb62I5i6-NL-",
+    "outputId": "c8712e83-cd21-4657-b490-f1f9df3d44b0"
+   },
    "outputs": [
     {
      "data": {
@@ -3107,8 +3057,10 @@
        "(43, 21)"
       ]
      },
-     "execution_count": 36,
-     "metadata": {},
+     "execution_count": 21,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -3120,8 +3072,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "V1S_csPL-NL_",
+    "outputId": "1d93a643-7310-43de-c515-f214caec9274"
+   },
    "outputs": [
     {
      "data": {
@@ -3129,8 +3089,10 @@
        "(83, 21)"
       ]
      },
-     "execution_count": 35,
-     "metadata": {},
+     "execution_count": 22,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -3142,8 +3104,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "V55Hp0tc-NMA",
+    "outputId": "8052d52c-dcc7-4171-88df-9964a1605007"
+   },
    "outputs": [
     {
      "data": {
@@ -3151,8 +3121,10 @@
        "(2, 21)"
       ]
      },
-     "execution_count": 34,
-     "metadata": {},
+     "execution_count": 23,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -3164,8 +3136,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "NA-MO0DB-NMB",
+    "outputId": "a68c21a5-0628-4357-ae0e-e973eeaa27bc"
+   },
    "outputs": [
     {
      "data": {
@@ -3173,8 +3153,10 @@
        "(124, 21)"
       ]
      },
-     "execution_count": 37,
-     "metadata": {},
+     "execution_count": 24,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -3186,8 +3168,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "hXf8QEJM-NMC",
+    "outputId": "b8ed4887-1307-46b5-f563-7f057dcdf9b1"
+   },
    "outputs": [
     {
      "data": {
@@ -3195,8 +3185,10 @@
        "(1626, 21)"
       ]
      },
-     "execution_count": 38,
-     "metadata": {},
+     "execution_count": 25,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -3207,8 +3199,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "8_OXaGHI-NMD",
+    "outputId": "8c9dbabc-3647-4cde-9b36-0a61c758b61f"
+   },
    "outputs": [
     {
      "data": {
@@ -3216,8 +3216,10 @@
        "(2051, 21)"
       ]
      },
-     "execution_count": 39,
-     "metadata": {},
+     "execution_count": 26,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -3228,8 +3230,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
+   "execution_count": 27,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "BIdhM5eh-NME",
+    "outputId": "5127d67c-d7ea-4956-f218-3a7cac878413"
+   },
    "outputs": [
     {
      "data": {
@@ -3301,8 +3311,10 @@
        "4     166.7       186.9              75"
       ]
      },
-     "execution_count": 41,
-     "metadata": {},
+     "execution_count": 27,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -3314,8 +3326,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 359
+    },
+    "colab_type": "code",
+    "id": "2xYGI76f-NMF",
+    "outputId": "19abf650-e77b-4f5a-e2c8-b46bafadb806"
+   },
    "outputs": [
     {
      "data": {
@@ -3574,45 +3594,25 @@
        "</div>"
       ],
       "text/plain": [
-       "  State  Area Code     Phone Int'l Plan VMail Plan  Day Mins  Day Charge  \\\n",
-       "0    KS        415  382-4657         no        yes     265.1       45.07   \n",
-       "1    OH        415  371-7191         no        yes     161.6       27.47   \n",
-       "2    NJ        415  358-1921         no         no     243.4       41.38   \n",
-       "3    OH        408  375-9999        yes         no     299.4       50.90   \n",
-       "4    OK        415  330-6626        yes         no     166.7       28.34   \n",
-       "5    AL        510  391-8027        yes         no     223.4       37.98   \n",
-       "6    MA        510  355-9993         no        yes     218.2       37.09   \n",
-       "7    MO        415  329-9001        yes         no     157.0       26.69   \n",
-       "8    LA        408  335-4719         no         no     184.5       31.37   \n",
-       "9    WV        415  330-8173        yes        yes     258.6       43.96   \n",
+       "  State  Area Code     Phone  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS        415  382-4657  ...        2.70              1  False.\n",
+       "1    OH        415  371-7191  ...        3.70              1  False.\n",
+       "2    NJ        415  358-1921  ...        3.29              0  False.\n",
+       "3    OH        408  375-9999  ...        1.78              2  False.\n",
+       "4    OK        415  330-6626  ...        2.73              3  False.\n",
+       "5    AL        510  391-8027  ...        1.70              0  False.\n",
+       "6    MA        510  355-9993  ...        2.03              3  False.\n",
+       "7    MO        415  329-9001  ...        1.92              0  False.\n",
+       "8    LA        408  335-4719  ...        2.35              1  False.\n",
+       "9    WV        415  330-8173  ...        3.02              0  False.\n",
        "\n",
-       "   Eve Mins  Eve Calls  Eve Charge  Night Mins  Night Calls  Night Charge  \\\n",
-       "0     197.4         99       16.78       244.7           91         11.01   \n",
-       "1     195.5        103       16.62       254.4          103         11.45   \n",
-       "2     121.2        110       10.30       162.6          104          7.32   \n",
-       "3      61.9         88        5.26       196.9           89          8.86   \n",
-       "4     148.3        122       12.61       186.9          121          8.41   \n",
-       "5     220.6        101       18.75       203.9          118          9.18   \n",
-       "6     348.5        108       29.62       212.6          118          9.57   \n",
-       "7     103.1         94        8.76       211.8           96          9.53   \n",
-       "8     351.6         80       29.89       215.8           90          9.71   \n",
-       "9     222.0        111       18.87       326.4           97         14.69   \n",
-       "\n",
-       "   Intl Mins  Intl Calls  Intl Charge  CustServ Calls  Churn?  \n",
-       "0       10.0           3         2.70               1  False.  \n",
-       "1       13.7           3         3.70               1  False.  \n",
-       "2       12.2           5         3.29               0  False.  \n",
-       "3        6.6           7         1.78               2  False.  \n",
-       "4       10.1           3         2.73               3  False.  \n",
-       "5        6.3           6         1.70               0  False.  \n",
-       "6        7.5           7         2.03               3  False.  \n",
-       "7        7.1           6         1.92               0  False.  \n",
-       "8        8.7           4         2.35               1  False.  \n",
-       "9       11.2           5         3.02               0  False.  "
+       "[10 rows x 18 columns]"
       ]
      },
-     "execution_count": 43,
-     "metadata": {},
+     "execution_count": 28,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -3622,15 +3622,23 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "9fOFwprb-NMG"
+   },
    "source": [
     "#### Filtrado con ix -> loc e iloc"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "A9ojz-2u-NMG",
+    "outputId": "290cc3ae-4a98-4fa7-c26c-f9221a913b63"
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -3752,7 +3760,9 @@
       ]
      },
      "execution_count": 44,
-     "metadata": {},
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -3762,8 +3772,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
+   "execution_count": 29,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "colab_type": "code",
+    "id": "_AafoPsa-NMH",
+    "outputId": "97d8102d-1657-4bd3-ce1c-800d9fcfc1ad"
+   },
    "outputs": [
     {
      "data": {
@@ -3863,8 +3881,10 @@
        "9  330-8173        yes        yes"
       ]
      },
-     "execution_count": 45,
-     "metadata": {},
+     "execution_count": 29,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -3874,18 +3894,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data.iloc[:,3:6] ##Todas las filas para las columnas entre la 3 y la 6\n",
-    "data.iloc[1:10,:] ##Todas las columnas para las filas de la 1 a la 10"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "colab_type": "code",
+    "id": "8oDWI-Lk-NMI",
+    "outputId": "8d1c4ccd-0f88-4356-9b24-91b1c1a040de"
+   },
    "outputs": [
     {
      "data": {
@@ -3908,20 +3926,325 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>State</th>\n",
+       "      <th>Account Length</th>\n",
        "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
        "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
        "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>107</td>\n",
        "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
        "      <td>yes</td>\n",
+       "      <td>26</td>\n",
        "      <td>161.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
+       "      <td>123</td>\n",
+       "      <td>27.47</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.45</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>137</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>243.4</td>\n",
+       "      <td>114</td>\n",
+       "      <td>41.38</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>84</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299.4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>50.90</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.86</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>75</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>166.7</td>\n",
+       "      <td>113</td>\n",
+       "      <td>28.34</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.41</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>AL</td>\n",
+       "      <td>118</td>\n",
+       "      <td>510</td>\n",
+       "      <td>391-8027</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>223.4</td>\n",
+       "      <td>98</td>\n",
+       "      <td>37.98</td>\n",
+       "      <td>220.6</td>\n",
+       "      <td>101</td>\n",
+       "      <td>18.75</td>\n",
+       "      <td>203.9</td>\n",
+       "      <td>118</td>\n",
+       "      <td>9.18</td>\n",
+       "      <td>6.3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>MA</td>\n",
+       "      <td>121</td>\n",
+       "      <td>510</td>\n",
+       "      <td>355-9993</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>24</td>\n",
+       "      <td>218.2</td>\n",
+       "      <td>88</td>\n",
+       "      <td>37.09</td>\n",
+       "      <td>348.5</td>\n",
+       "      <td>108</td>\n",
+       "      <td>29.62</td>\n",
+       "      <td>212.6</td>\n",
+       "      <td>118</td>\n",
+       "      <td>9.57</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2.03</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>MO</td>\n",
+       "      <td>147</td>\n",
+       "      <td>415</td>\n",
+       "      <td>329-9001</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>157.0</td>\n",
+       "      <td>79</td>\n",
+       "      <td>26.69</td>\n",
+       "      <td>103.1</td>\n",
+       "      <td>94</td>\n",
+       "      <td>8.76</td>\n",
+       "      <td>211.8</td>\n",
+       "      <td>96</td>\n",
+       "      <td>9.53</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1.92</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>LA</td>\n",
+       "      <td>117</td>\n",
+       "      <td>408</td>\n",
+       "      <td>335-4719</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>184.5</td>\n",
+       "      <td>97</td>\n",
+       "      <td>31.37</td>\n",
+       "      <td>351.6</td>\n",
+       "      <td>80</td>\n",
+       "      <td>29.89</td>\n",
+       "      <td>215.8</td>\n",
+       "      <td>90</td>\n",
+       "      <td>9.71</td>\n",
+       "      <td>8.7</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2.35</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>WV</td>\n",
+       "      <td>141</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-8173</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>37</td>\n",
+       "      <td>258.6</td>\n",
+       "      <td>84</td>\n",
+       "      <td>43.96</td>\n",
+       "      <td>222.0</td>\n",
+       "      <td>111</td>\n",
+       "      <td>18.87</td>\n",
+       "      <td>326.4</td>\n",
+       "      <td>97</td>\n",
+       "      <td>14.69</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.02</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "1    OH             107        415  ...        3.70              1  False.\n",
+       "2    NJ             137        415  ...        3.29              0  False.\n",
+       "3    OH              84        408  ...        1.78              2  False.\n",
+       "4    OK              75        415  ...        2.73              3  False.\n",
+       "5    AL             118        510  ...        1.70              0  False.\n",
+       "6    MA             121        510  ...        2.03              3  False.\n",
+       "7    MO             147        415  ...        1.92              0  False.\n",
+       "8    LA             117        408  ...        2.35              1  False.\n",
+       "9    WV             141        415  ...        3.02              0  False.\n",
+       "\n",
+       "[9 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.iloc[:,3:6] ##Todas las filas para las columnas entre la 3 y la 6\n",
+    "data.iloc[1:10,:] ##Todas las columnas para las filas de la 1 a la 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 328
+    },
+    "colab_type": "code",
+    "id": "4q2QyqM3-NMJ",
+    "outputId": "ea48cf26-115e-4621-84b8-6fd4c804a048"
+   },
+   "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>Area Code</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>Day Mins</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>415</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>161.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
        "      <td>415</td>\n",
        "      <td>no</td>\n",
        "      <td>243.4</td>\n",
@@ -3985,8 +4308,10 @@
        "9        415        yes     258.6"
       ]
      },
-     "execution_count": 46,
-     "metadata": {},
+     "execution_count": 31,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -3996,8 +4321,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 173
+    },
+    "colab_type": "code",
+    "id": "NmOSEz3A-NMK",
+    "outputId": "9b8835f6-1489-4954-9e3e-89f927cf74af"
+   },
    "outputs": [
     {
      "data": {
@@ -4062,8 +4395,10 @@
        "36        408        yes     146.3"
       ]
      },
-     "execution_count": 47,
-     "metadata": {},
+     "execution_count": 32,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4073,8 +4408,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
+   "execution_count": 33,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 173
+    },
+    "colab_type": "code",
+    "id": "1aZxP683-NML",
+    "outputId": "5eea626a-93cb-4d4c-e49d-5362962643b5"
+   },
    "outputs": [
     {
      "data": {
@@ -4139,8 +4482,10 @@
        "36        408        yes     146.3"
       ]
      },
-     "execution_count": 49,
-     "metadata": {},
+     "execution_count": 33,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4150,15 +4495,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "jKj3sOSf-NMM"
+   },
    "source": [
     "#### Insertar nuevas filas en el dataframe"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "WbPrEZVu-NMM"
+   },
    "outputs": [],
    "source": [
     "data[\"Total Mins\"] = data[\"Day Mins\"] + data[\"Night Mins\"] + data[\"Eve Mins\"]"
@@ -4166,8 +4518,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
+   "execution_count": 35,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 119
+    },
+    "colab_type": "code",
+    "id": "S41vxiTL-NMN",
+    "outputId": "77783b76-081d-410a-a700-2783b8115943"
+   },
    "outputs": [
     {
      "data": {
@@ -4180,8 +4540,10 @@
        "Name: Total Mins, dtype: float64"
       ]
      },
-     "execution_count": 52,
-     "metadata": {},
+     "execution_count": 35,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4191,8 +4553,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "kz04R87g-NMO"
+   },
    "outputs": [],
    "source": [
     "data[\"Total Calls\"] = data[\"Day Calls\"] + data[\"Night Calls\"] + data[\"Eve Calls\"]"
@@ -4200,8 +4566,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
+   "execution_count": 37,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 119
+    },
+    "colab_type": "code",
+    "id": "cx5JFZa3-NMP",
+    "outputId": "7a10ebcf-ed97-46fe-d5ae-ad99131bdff9"
+   },
    "outputs": [
     {
      "data": {
@@ -4214,8 +4588,10 @@
        "Name: Total Calls, dtype: int64"
       ]
      },
-     "execution_count": 55,
-     "metadata": {},
+     "execution_count": 37,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4225,8 +4601,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
+   "execution_count": 38,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "AmR-jcnF-NMP",
+    "outputId": "641d25d4-6d48-4649-ba4c-8e57d4b36750"
+   },
    "outputs": [
     {
      "data": {
@@ -4234,8 +4618,10 @@
        "(3333, 23)"
       ]
      },
-     "execution_count": 56,
-     "metadata": {},
+     "execution_count": 38,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4245,8 +4631,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
+   "execution_count": 39,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "MB472-er-NMQ",
+    "outputId": "7f421f9a-1c06-4313-dc84-1eb3e71bbe72"
+   },
    "outputs": [
     {
      "data": {
@@ -4279,7 +4673,9 @@
        "      <th>Day Mins</th>\n",
        "      <th>Day Calls</th>\n",
        "      <th>Day Charge</th>\n",
-       "      <th>...</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
        "      <th>Night Mins</th>\n",
        "      <th>Night Calls</th>\n",
        "      <th>Night Charge</th>\n",
@@ -4305,7 +4701,9 @@
        "      <td>265.1</td>\n",
        "      <td>110</td>\n",
        "      <td>45.07</td>\n",
-       "      <td>...</td>\n",
+       "      <td>197.4</td>\n",
+       "      <td>99</td>\n",
+       "      <td>16.78</td>\n",
        "      <td>244.7</td>\n",
        "      <td>91</td>\n",
        "      <td>11.01</td>\n",
@@ -4329,7 +4727,9 @@
        "      <td>161.6</td>\n",
        "      <td>123</td>\n",
        "      <td>27.47</td>\n",
-       "      <td>...</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
        "      <td>254.4</td>\n",
        "      <td>103</td>\n",
        "      <td>11.45</td>\n",
@@ -4353,7 +4753,9 @@
        "      <td>243.4</td>\n",
        "      <td>114</td>\n",
        "      <td>41.38</td>\n",
-       "      <td>...</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
        "      <td>162.6</td>\n",
        "      <td>104</td>\n",
        "      <td>7.32</td>\n",
@@ -4377,7 +4779,9 @@
        "      <td>299.4</td>\n",
        "      <td>71</td>\n",
        "      <td>50.90</td>\n",
-       "      <td>...</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
        "      <td>196.9</td>\n",
        "      <td>89</td>\n",
        "      <td>8.86</td>\n",
@@ -4401,7 +4805,9 @@
        "      <td>166.7</td>\n",
        "      <td>113</td>\n",
        "      <td>28.34</td>\n",
-       "      <td>...</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
        "      <td>186.9</td>\n",
        "      <td>121</td>\n",
        "      <td>8.41</td>\n",
@@ -4415,43 +4821,23 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 23 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
-       "0    KS             128        415  382-4657         no        yes   \n",
-       "1    OH             107        415  371-7191         no        yes   \n",
-       "2    NJ             137        415  358-1921         no         no   \n",
-       "3    OH              84        408  375-9999        yes         no   \n",
-       "4    OK              75        415  330-6626        yes         no   \n",
-       "\n",
-       "   VMail Message  Day Mins  Day Calls  Day Charge     ...       Night Mins  \\\n",
-       "0             25     265.1        110       45.07     ...            244.7   \n",
-       "1             26     161.6        123       27.47     ...            254.4   \n",
-       "2              0     243.4        114       41.38     ...            162.6   \n",
-       "3              0     299.4         71       50.90     ...            196.9   \n",
-       "4              0     166.7        113       28.34     ...            186.9   \n",
-       "\n",
-       "   Night Calls  Night Charge  Intl Mins  Intl Calls  Intl Charge  \\\n",
-       "0           91         11.01       10.0           3         2.70   \n",
-       "1          103         11.45       13.7           3         3.70   \n",
-       "2          104          7.32       12.2           5         3.29   \n",
-       "3           89          8.86        6.6           7         1.78   \n",
-       "4          121          8.41       10.1           3         2.73   \n",
-       "\n",
-       "   CustServ Calls  Churn?  Total Mins  Total Calls  \n",
-       "0               1  False.       707.2          300  \n",
-       "1               1  False.       611.5          329  \n",
-       "2               0  False.       527.2          328  \n",
-       "3               2  False.       558.2          248  \n",
-       "4               3  False.       501.9          356  \n",
+       "  State  Account Length  Area Code  ...  Churn? Total Mins Total Calls\n",
+       "0    KS             128        415  ...  False.      707.2         300\n",
+       "1    OH             107        415  ...  False.      611.5         329\n",
+       "2    NJ             137        415  ...  False.      527.2         328\n",
+       "3    OH              84        408  ...  False.      558.2         248\n",
+       "4    OK              75        415  ...  False.      501.9         356\n",
        "\n",
        "[5 rows x 23 columns]"
       ]
      },
-     "execution_count": 58,
-     "metadata": {},
+     "execution_count": 39,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4461,15 +4847,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "u6QeuXs_-NMR"
+   },
    "source": [
     "### Generación aleatoria de números"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "K_yOfvJl-NMR"
+   },
    "outputs": [],
    "source": [
     "import numpy as np"
@@ -4477,17 +4870,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
+   "execution_count": 41,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "UXCbacXE-NMS",
+    "outputId": "a8bfd98a-ac49-4148-fc05-990081951e43"
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "91"
+       "7"
       ]
      },
-     "execution_count": 72,
-     "metadata": {},
+     "execution_count": 41,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4498,17 +4901,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
+   "execution_count": 42,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "a2wx0lEK-NMT",
+    "outputId": "485c8b49-a01a-4159-d7d6-b0659c76ac4d"
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.00305328030781038"
+       "0.6010490980853076"
       ]
      },
-     "execution_count": 76,
-     "metadata": {},
+     "execution_count": 42,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4519,8 +4932,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "-xlzcWw7-NMU"
+   },
    "outputs": [],
    "source": [
     "##Función que genera una lista de n números aleatorios enteros dentro del intervalo [a,b]\n",
@@ -4533,41 +4950,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
+   "execution_count": 44,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 442
+    },
+    "colab_type": "code",
+    "id": "K6CxKlDj-NMU",
+    "outputId": "03d4ade8-bb35-462b-a95b-475d11f04201"
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[28,\n",
-       " 13,\n",
-       " 33,\n",
-       " 46,\n",
-       " 27,\n",
+       "[9,\n",
+       " 11,\n",
+       " 26,\n",
+       " 19,\n",
+       " 19,\n",
+       " 11,\n",
        " 28,\n",
-       " 29,\n",
-       " 13,\n",
        " 8,\n",
-       " 14,\n",
-       " 28,\n",
+       " 49,\n",
        " 46,\n",
-       " 24,\n",
-       " 34,\n",
-       " 36,\n",
-       " 41,\n",
-       " 9,\n",
-       " 14,\n",
-       " 25,\n",
+       " 16,\n",
+       " 33,\n",
+       " 49,\n",
        " 20,\n",
-       " 4,\n",
+       " 43,\n",
+       " 27,\n",
+       " 46,\n",
+       " 48,\n",
+       " 11,\n",
+       " 7,\n",
+       " 46,\n",
        " 35,\n",
-       " 13,\n",
-       " 26,\n",
-       " 12]"
+       " 8,\n",
+       " 37,\n",
+       " 26]"
       ]
      },
-     "execution_count": 80,
-     "metadata": {},
+     "execution_count": 44,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4577,8 +5004,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Rk9OkLbS-NMV"
+   },
    "outputs": [],
    "source": [
     "import random"
@@ -4586,23 +5017,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {},
+   "execution_count": 46,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 187
+    },
+    "colab_type": "code",
+    "id": "zNLCJ6G8-NMW",
+    "outputId": "06f304a4-cf4b-4b1a-fa0f-2f8493c6de39"
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "84\n",
-      "63\n",
-      "42\n",
-      "0\n",
-      "35\n",
       "49\n",
-      "98\n",
+      "42\n",
       "56\n",
-      "91\n",
-      "7\n"
+      "7\n",
+      "28\n",
+      "77\n",
+      "42\n",
+      "14\n",
+      "98\n",
+      "84\n"
      ]
     }
    ],
@@ -4613,15 +5052,26 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "XrB-zDrP-NMX"
+   },
    "source": [
     "#### Shuffling"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {},
+   "execution_count": 47,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 119
+    },
+    "colab_type": "code",
+    "id": "5lfMHVIO-NMX",
+    "outputId": "c194c366-5de2-4f46-dfd0-40835a95d119"
+   },
    "outputs": [
     {
      "data": {
@@ -4634,8 +5084,10 @@
        "       85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99])"
       ]
      },
-     "execution_count": 114,
-     "metadata": {},
+     "execution_count": 47,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4646,8 +5098,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "O-rvs3AL-NMY"
+   },
    "outputs": [],
    "source": [
     "np.random.shuffle(a)"
@@ -4655,22 +5111,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
+   "execution_count": 49,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 119
+    },
+    "colab_type": "code",
+    "id": "E4VsNWz--NMZ",
+    "outputId": "321583bb-30e5-49e3-fbeb-c1c9ee906d58"
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([36, 42, 13, 58, 62, 38, 33, 85, 41, 51, 25, 34, 49, 12, 16, 96, 86,\n",
-       "       90, 69, 10, 59, 75, 54,  9, 77, 22, 21, 83, 17,  2, 76, 70, 24, 11,\n",
-       "       68, 55,  6, 94, 45, 88, 26, 30, 44, 65, 50, 37, 98,  4,  1, 35, 64,\n",
-       "       72, 61, 81, 23, 32,  7, 63,  0, 40,  5, 66, 82, 92, 29, 43, 93, 57,\n",
-       "       20, 74, 19, 39, 14, 99, 87, 48, 15, 95, 73, 78, 52, 46, 18, 67, 60,\n",
-       "        8, 56,  3, 91, 80, 89, 31, 84, 79, 71, 97, 28, 53, 27, 47])"
+       "array([40, 26, 52, 35, 42,  6, 14, 47, 60,  8, 15, 36, 27, 29, 62, 44, 81,\n",
+       "       72, 21, 20, 11, 54, 58, 75, 80, 59, 67, 79, 61,  2,  0,  9, 49, 32,\n",
+       "       50, 64, 33, 91, 45, 97, 89, 74,  3, 95, 94,  5, 55, 93, 71, 16, 31,\n",
+       "       51, 48, 46, 92, 18, 77, 10, 96, 88, 13, 30, 28, 66, 86, 56, 23, 19,\n",
+       "        4, 83, 87, 69,  1, 22, 63, 85, 68, 78, 90, 99, 43, 82, 70, 41, 65,\n",
+       "       38, 34,  7, 57, 98, 37, 39, 25, 53, 17, 73, 76, 84, 24, 12])"
       ]
      },
-     "execution_count": 120,
-     "metadata": {},
+     "execution_count": 49,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4680,15 +5146,26 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "nVxDud5W-NMZ"
+   },
    "source": [
     "#### Choice"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
+   "execution_count": 50,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "bXMy1YjU-NMZ",
+    "outputId": "adb24c16-382b-4e44-e886-e1d9d49b8b72"
+   },
    "outputs": [
     {
      "data": {
@@ -4721,7 +5198,9 @@
        "      <th>Day Mins</th>\n",
        "      <th>Day Calls</th>\n",
        "      <th>Day Charge</th>\n",
-       "      <th>...</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
        "      <th>Night Mins</th>\n",
        "      <th>Night Calls</th>\n",
        "      <th>Night Charge</th>\n",
@@ -4747,7 +5226,9 @@
        "      <td>265.1</td>\n",
        "      <td>110</td>\n",
        "      <td>45.07</td>\n",
-       "      <td>...</td>\n",
+       "      <td>197.4</td>\n",
+       "      <td>99</td>\n",
+       "      <td>16.78</td>\n",
        "      <td>244.7</td>\n",
        "      <td>91</td>\n",
        "      <td>11.01</td>\n",
@@ -4771,7 +5252,9 @@
        "      <td>161.6</td>\n",
        "      <td>123</td>\n",
        "      <td>27.47</td>\n",
-       "      <td>...</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
        "      <td>254.4</td>\n",
        "      <td>103</td>\n",
        "      <td>11.45</td>\n",
@@ -4795,7 +5278,9 @@
        "      <td>243.4</td>\n",
        "      <td>114</td>\n",
        "      <td>41.38</td>\n",
-       "      <td>...</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
        "      <td>162.6</td>\n",
        "      <td>104</td>\n",
        "      <td>7.32</td>\n",
@@ -4819,7 +5304,9 @@
        "      <td>299.4</td>\n",
        "      <td>71</td>\n",
        "      <td>50.90</td>\n",
-       "      <td>...</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
        "      <td>196.9</td>\n",
        "      <td>89</td>\n",
        "      <td>8.86</td>\n",
@@ -4843,7 +5330,9 @@
        "      <td>166.7</td>\n",
        "      <td>113</td>\n",
        "      <td>28.34</td>\n",
-       "      <td>...</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
        "      <td>186.9</td>\n",
        "      <td>121</td>\n",
        "      <td>8.41</td>\n",
@@ -4857,43 +5346,23 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 23 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
-       "0    KS             128        415  382-4657         no        yes   \n",
-       "1    OH             107        415  371-7191         no        yes   \n",
-       "2    NJ             137        415  358-1921         no         no   \n",
-       "3    OH              84        408  375-9999        yes         no   \n",
-       "4    OK              75        415  330-6626        yes         no   \n",
-       "\n",
-       "   VMail Message  Day Mins  Day Calls  Day Charge     ...       Night Mins  \\\n",
-       "0             25     265.1        110       45.07     ...            244.7   \n",
-       "1             26     161.6        123       27.47     ...            254.4   \n",
-       "2              0     243.4        114       41.38     ...            162.6   \n",
-       "3              0     299.4         71       50.90     ...            196.9   \n",
-       "4              0     166.7        113       28.34     ...            186.9   \n",
-       "\n",
-       "   Night Calls  Night Charge  Intl Mins  Intl Calls  Intl Charge  \\\n",
-       "0           91         11.01       10.0           3         2.70   \n",
-       "1          103         11.45       13.7           3         3.70   \n",
-       "2          104          7.32       12.2           5         3.29   \n",
-       "3           89          8.86        6.6           7         1.78   \n",
-       "4          121          8.41       10.1           3         2.73   \n",
-       "\n",
-       "   CustServ Calls  Churn?  Total Mins  Total Calls  \n",
-       "0               1  False.       707.2          300  \n",
-       "1               1  False.       611.5          329  \n",
-       "2               0  False.       527.2          328  \n",
-       "3               2  False.       558.2          248  \n",
-       "4               3  False.       501.9          356  \n",
+       "  State  Account Length  Area Code  ...  Churn? Total Mins Total Calls\n",
+       "0    KS             128        415  ...  False.      707.2         300\n",
+       "1    OH             107        415  ...  False.      611.5         329\n",
+       "2    NJ             137        415  ...  False.      527.2         328\n",
+       "3    OH              84        408  ...  False.      558.2         248\n",
+       "4    OK              75        415  ...  False.      501.9         356\n",
        "\n",
        "[5 rows x 23 columns]"
       ]
      },
-     "execution_count": 121,
-     "metadata": {},
+     "execution_count": 50,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4903,8 +5372,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
+   "execution_count": 51,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "D_BQW3A6-NMa",
+    "outputId": "9af1227a-b6ce-46a0-d789-23ede85431c9"
+   },
    "outputs": [
     {
      "data": {
@@ -4912,8 +5389,10 @@
        "(3333, 23)"
       ]
      },
-     "execution_count": 122,
-     "metadata": {},
+     "execution_count": 51,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4923,8 +5402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
+   "execution_count": 52,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 408
+    },
+    "colab_type": "code",
+    "id": "dDl3G-dr-NMb",
+    "outputId": "47d7487c-860b-4c20-d2ea-09f5eba971c3"
+   },
    "outputs": [
     {
      "data": {
@@ -4954,8 +5441,10 @@
        " 'Total Calls']"
       ]
      },
-     "execution_count": 124,
-     "metadata": {},
+     "execution_count": 52,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4966,8 +5455,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
+   "execution_count": 53,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "gWWpBhGN-NMc",
+    "outputId": "c837ad96-aa26-4465-8a2d-6b0104886eeb"
+   },
    "outputs": [
     {
      "data": {
@@ -4975,8 +5472,10 @@
        "'VMail Message'"
       ]
      },
-     "execution_count": 126,
-     "metadata": {},
+     "execution_count": 53,
+     "metadata": {
+      "tags": []
+     },
      "output_type": "execute_result"
     }
    ],
@@ -4986,15 +5485,26 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "colab_type": "text",
+    "id": "ggTVQLbC-NMd"
+   },
    "source": [
     "#### Seed"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
+   "execution_count": 54,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 102
+    },
+    "colab_type": "code",
+    "id": "PYEZWOcu-NMd",
+    "outputId": "a0954dc4-0a28-41e8-f732-ad2f4e23d578"
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -5013,9 +5523,25 @@
     "for i in range(5):\n",
     "    print(np.random.random())"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "I6hUoptf_gqj"
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "T2 - 1 - Data Cleaning - Data Wrangling.ipynb",
+   "provenance": []
+  },
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -5031,9 +5557,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.5"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 1
 }
diff --git "a/notebooks/T2 - 2 - Data Cleaning - Funciones de distribuci\303\263n de probabilidad-Colab.ipynb" "b/notebooks/T2 - 2 - Data Cleaning - Funciones de distribuci\303\263n de probabilidad-Colab.ipynb"
new file mode 100644
index 00000000..fe0960b0
--- /dev/null
+++ "b/notebooks/T2 - 2 - Data Cleaning - Funciones de distribuci\303\263n de probabilidad-Colab.ipynb"	
@@ -0,0 +1,1301 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/Collab---v-3.8/notebooks/T2%20-%202%20-%20Data%20Cleaning%20-%20Funciones%20de%20distribuci%C3%B3n%20de%20probabilidad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "76tSTLpx_6tj"
+   },
+   "source": [
+    "# Funciones de distribución de probabilidades\n",
+    "## Distribución Uniforme"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 122
+    },
+    "colab_type": "code",
+    "id": "tOrouqly_9oY",
+    "outputId": "dfe35705-3b1a-452c-de31-3b8025787095"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+      "\n",
+      "Enter your authorization code:\n",
+      "··········\n",
+      "Mounted at /content/drive\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "24f0E2tZ_6tk"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "lXlMO8RS_6tp"
+   },
+   "outputs": [],
+   "source": [
+    "a = 1\n",
+    "b = 100\n",
+    "n = 1000000\n",
+    "data = np.random.uniform(a, b, n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 367
+    },
+    "colab_type": "code",
+    "id": "tiC5WseY_6tt",
+    "outputId": "b0abc9de-891c-4789-f4d5-42f4dc86c5a8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([100219.,  99664., 100096.,  99826., 100078., 100598.,  99562.,\n",
+       "        100101.,  99596., 100260.]),\n",
+       " array([ 1.00019609, 10.90016559, 20.80013509, 30.70010458, 40.60007408,\n",
+       "        50.50004358, 60.40001307, 70.29998257, 80.19995207, 90.09992157,\n",
+       "        99.99989106]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.hist(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "XubDFO_f_6tx"
+   },
+   "source": [
+    "## Distribución Normal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "X1N2_WO1_6tx"
+   },
+   "outputs": [],
+   "source": [
+    "data = np.random.randn(1000000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "colab_type": "code",
+    "id": "qhOyLRGN_6tz",
+    "outputId": "459205ba-9d27-4da8-ac42-1a03c499bd39"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f85136a29e8>]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = range(1,1000001)\n",
+    "plt.plot(x, data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 367
+    },
+    "colab_type": "code",
+    "id": "UnsfVULW_6t1",
+    "outputId": "1631f469-3f20-4b50-af70-d5a1c4931ecb"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1.10000e+02, 3.40700e+03, 3.83800e+04, 1.84609e+05, 3.64728e+05,\n",
+       "        2.95298e+05, 9.92620e+04, 1.34940e+04, 6.98000e+02, 1.40000e+01]),\n",
+       " array([-4.67212058, -3.69181253, -2.71150449, -1.73119645, -0.75088841,\n",
+       "         0.22941963,  1.20972767,  2.19003571,  3.17034375,  4.15065179,\n",
+       "         5.13095983]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.hist(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 295
+    },
+    "colab_type": "code",
+    "id": "XdW96vvx_6t3",
+    "outputId": "a1f36bac-4ba7-495d-ecd3-56f1ee5c2d7d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f8511e4d240>]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(x,sorted(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 368
+    },
+    "colab_type": "code",
+    "id": "QQZ1SxuG_6t5",
+    "outputId": "8bbe5f6f-ab4b-4b71-88b9-fd3960af2cb1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  10.,   89.,  466., 1766., 2896., 2861., 1414.,  429.,   65.,\n",
+       "           4.]),\n",
+       " array([-4.24623314, -2.2667752 , -0.28731726,  1.69214068,  3.67159862,\n",
+       "         5.65105656,  7.6305145 ,  9.60997244, 11.58943037, 13.56888831,\n",
+       "        15.54834625]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mu = 5.5\n",
+    "sd = 2.5\n",
+    "Z_10000 = np.random.randn(10000)\n",
+    "data = mu + sd * Z_10000 # Z = (X - mu) / sd -> N(0,1), X = mu + sd * Z\n",
+    "plt.hist(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 51
+    },
+    "colab_type": "code",
+    "id": "n8siHqnN_6t6",
+    "outputId": "9aeff760-7b7d-4196-d02d-658c2150d85f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 0.00239305,  0.66424699, -0.46608439, -2.06345436],\n",
+       "       [ 0.10321101,  0.10132709, -0.70695038, -0.87042236]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = np.random.randn(2,4)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "qirIn8au_6t8"
+   },
+   "source": [
+    "## La simulación de Monte Carlo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "s4ySxEBn_6t8"
+   },
+   "source": [
+    "* Generamos dos números aleatorios uniforme x e y entre 0 y 1 en total 1000 veces.\n",
+    "* Calcularemos $z = x^2 + y^2$:\n",
+    "    * Si $z < 1 \\rightarrow$ estamos dentro del círculo.\n",
+    "    * Si $z \\geq 1 \\rightarrow$ estamos fuera del círculo.\n",
+    "* Calculamos el número total de veces que están dentro del círculo y lo dividimos entre el número total de intentos para obtener una aproximación de la probabilidad de caer dentro del círculo.\n",
+    "* Usamos dicha probabilidad para aproximar el valor de π.\n",
+    "* Repetimos el experimento un número suficiente de veces (por ejemplo 100), para obtener (100) diferentes aproximaciones de π. \n",
+    "* Calculamos el promedio de los 100 experimentos anteriores para dar un valor final de π.\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "IxAP8lvL_6t_"
+   },
+   "outputs": [],
+   "source": [
+    "def pi_montecarlo(n, n_exp):\n",
+    "    pi_avg = 0\n",
+    "    pi_value_list = []\n",
+    "    for i in range(n_exp):\n",
+    "        value = 0\n",
+    "        x = np.random.uniform(0,1,n).tolist()\n",
+    "        y = np.random.uniform(0,1,n).tolist()\n",
+    "        for j in range(n):\n",
+    "            z = np.sqrt(x[j] * x[j] + y[j] * y[j])\n",
+    "            if z<=1:\n",
+    "                value += 1\n",
+    "        float_value = float(value)\n",
+    "        pi_value = float_value * 4 / n\n",
+    "        pi_value_list.append(pi_value)\n",
+    "        pi_avg += pi_value\n",
+    "\n",
+    "    pi = pi_avg/n_exp\n",
+    "\n",
+    "    print(pi)\n",
+    "    fig = plt.plot(pi_value_list)\n",
+    "    return (pi, fig)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 301
+    },
+    "colab_type": "code",
+    "id": "36fV9Wf9_6uA",
+    "outputId": "472dd2bc-a99d-4d73-cb97-5ff12e9220c8"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.143076000000002\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(3.143076000000002, [<matplotlib.lines.Line2D at 0x7f85134a1ac8>])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pi_montecarlo(10000, 200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "T367QBnb_6uB"
+   },
+   "source": [
+    "### Dummy Data Sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Eu_1AXII_6uC"
+   },
+   "outputs": [],
+   "source": [
+    "n = 1000000\n",
+    "data = pd.DataFrame(\n",
+    "    {\n",
+    "        'A' : np.random.randn(n),\n",
+    "        'B' : 1.5 + 2.5 * np.random.randn(n),\n",
+    "        'C' : np.random.uniform(5, 32, n)\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 297
+    },
+    "colab_type": "code",
+    "id": "BvB2sFTs_6uD",
+    "outputId": "555a43d1-bee1-467f-8154-11a0b9efea82"
+   },
+   "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>A</th>\n",
+       "      <th>B</th>\n",
+       "      <th>C</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1000000.000000</td>\n",
+       "      <td>1000000.000000</td>\n",
+       "      <td>1000000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.002164</td>\n",
+       "      <td>1.499644</td>\n",
+       "      <td>18.508191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.999902</td>\n",
+       "      <td>2.501756</td>\n",
+       "      <td>7.794876</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-4.614127</td>\n",
+       "      <td>-11.344276</td>\n",
+       "      <td>5.000005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-0.673665</td>\n",
+       "      <td>-0.187840</td>\n",
+       "      <td>11.749997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.002129</td>\n",
+       "      <td>1.502898</td>\n",
+       "      <td>18.506363</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.678266</td>\n",
+       "      <td>3.187172</td>\n",
+       "      <td>25.261393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>4.553337</td>\n",
+       "      <td>13.301662</td>\n",
+       "      <td>31.999969</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    A               B               C\n",
+       "count  1000000.000000  1000000.000000  1000000.000000\n",
+       "mean         0.002164        1.499644       18.508191\n",
+       "std          0.999902        2.501756        7.794876\n",
+       "min         -4.614127      -11.344276        5.000005\n",
+       "25%         -0.673665       -0.187840       11.749997\n",
+       "50%          0.002129        1.502898       18.506363\n",
+       "75%          0.678266        3.187172       25.261393\n",
+       "max          4.553337       13.301662       31.999969"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 367
+    },
+    "colab_type": "code",
+    "id": "1GCmP9H6_6uE",
+    "outputId": "389e26c7-7030-4d80-b48a-a360080b878c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1.22000e+02, 2.50800e+03, 2.81180e+04, 1.40963e+05, 3.15165e+05,\n",
+       "        3.24594e+05, 1.52739e+05, 3.25730e+04, 3.09000e+03, 1.28000e+02]),\n",
+       " array([-4.61412738, -3.69738095, -2.78063452, -1.86388809, -0.94714166,\n",
+       "        -0.03039522,  0.88635121,  1.80309764,  2.71984407,  3.6365905 ,\n",
+       "         4.55333693]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data[\"A\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 367
+    },
+    "colab_type": "code",
+    "id": "N9nUkcxA_6uF",
+    "outputId": "e9fae6ec-8936-4406-e6c4-c36075e781f3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([1.20000e+01, 8.29000e+02, 1.39770e+04, 1.01506e+05, 3.00478e+05,\n",
+       "        3.64624e+05, 1.79885e+05, 3.57100e+04, 2.90300e+03, 7.60000e+01]),\n",
+       " array([-11.34427637,  -8.87968257,  -6.41508878,  -3.95049498,\n",
+       "         -1.48590119,   0.9786926 ,   3.4432864 ,   5.90788019,\n",
+       "          8.37247399,  10.83706778,  13.30166157]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data[\"B\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 367
+    },
+    "colab_type": "code",
+    "id": "W1Y5s-qY_6uG",
+    "outputId": "8078d044-b46c-4183-b68e-0a75194160b3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 99574., 100661.,  99456.,  99788., 100310.,  99741.,  99869.,\n",
+       "        100111., 100293., 100197.]),\n",
+       " array([ 5.00000455,  7.70000101, 10.39999746, 13.09999392, 15.79999037,\n",
+       "        18.49998682, 21.19998328, 23.89997973, 26.59997619, 29.29997264,\n",
+       "        31.99996909]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data[\"C\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "KPDL3dga_6uH"
+   },
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/customer-churn-model/Customer Churn Model.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "HA94Q1BK_6uI",
+    "outputId": "404b0219-bace-4f83-ee13-d83c9207d107"
+   },
+   "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>State</th>\n",
+       "      <th>Account Length</th>\n",
+       "      <th>Area Code</th>\n",
+       "      <th>Phone</th>\n",
+       "      <th>Int'l Plan</th>\n",
+       "      <th>VMail Plan</th>\n",
+       "      <th>VMail Message</th>\n",
+       "      <th>Day Mins</th>\n",
+       "      <th>Day Calls</th>\n",
+       "      <th>Day Charge</th>\n",
+       "      <th>Eve Mins</th>\n",
+       "      <th>Eve Calls</th>\n",
+       "      <th>Eve Charge</th>\n",
+       "      <th>Night Mins</th>\n",
+       "      <th>Night Calls</th>\n",
+       "      <th>Night Charge</th>\n",
+       "      <th>Intl Mins</th>\n",
+       "      <th>Intl Calls</th>\n",
+       "      <th>Intl Charge</th>\n",
+       "      <th>CustServ Calls</th>\n",
+       "      <th>Churn?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KS</td>\n",
+       "      <td>128</td>\n",
+       "      <td>415</td>\n",
+       "      <td>382-4657</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>25</td>\n",
+       "      <td>265.1</td>\n",
+       "      <td>110</td>\n",
+       "      <td>45.07</td>\n",
+       "      <td>197.4</td>\n",
+       "      <td>99</td>\n",
+       "      <td>16.78</td>\n",
+       "      <td>244.7</td>\n",
+       "      <td>91</td>\n",
+       "      <td>11.01</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>107</td>\n",
+       "      <td>415</td>\n",
+       "      <td>371-7191</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>26</td>\n",
+       "      <td>161.6</td>\n",
+       "      <td>123</td>\n",
+       "      <td>27.47</td>\n",
+       "      <td>195.5</td>\n",
+       "      <td>103</td>\n",
+       "      <td>16.62</td>\n",
+       "      <td>254.4</td>\n",
+       "      <td>103</td>\n",
+       "      <td>11.45</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>NJ</td>\n",
+       "      <td>137</td>\n",
+       "      <td>415</td>\n",
+       "      <td>358-1921</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>243.4</td>\n",
+       "      <td>114</td>\n",
+       "      <td>41.38</td>\n",
+       "      <td>121.2</td>\n",
+       "      <td>110</td>\n",
+       "      <td>10.30</td>\n",
+       "      <td>162.6</td>\n",
+       "      <td>104</td>\n",
+       "      <td>7.32</td>\n",
+       "      <td>12.2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>OH</td>\n",
+       "      <td>84</td>\n",
+       "      <td>408</td>\n",
+       "      <td>375-9999</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>299.4</td>\n",
+       "      <td>71</td>\n",
+       "      <td>50.90</td>\n",
+       "      <td>61.9</td>\n",
+       "      <td>88</td>\n",
+       "      <td>5.26</td>\n",
+       "      <td>196.9</td>\n",
+       "      <td>89</td>\n",
+       "      <td>8.86</td>\n",
+       "      <td>6.6</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1.78</td>\n",
+       "      <td>2</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>OK</td>\n",
+       "      <td>75</td>\n",
+       "      <td>415</td>\n",
+       "      <td>330-6626</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>0</td>\n",
+       "      <td>166.7</td>\n",
+       "      <td>113</td>\n",
+       "      <td>28.34</td>\n",
+       "      <td>148.3</td>\n",
+       "      <td>122</td>\n",
+       "      <td>12.61</td>\n",
+       "      <td>186.9</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.41</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2.73</td>\n",
+       "      <td>3</td>\n",
+       "      <td>False.</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+       "0    KS             128        415  ...        2.70              1  False.\n",
+       "1    OH             107        415  ...        3.70              1  False.\n",
+       "2    NJ             137        415  ...        3.29              0  False.\n",
+       "3    OH              84        408  ...        1.78              2  False.\n",
+       "4    OK              75        415  ...        2.73              3  False.\n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "hN3pktK9_6uJ"
+   },
+   "outputs": [],
+   "source": [
+    "colum_names = data.columns.values.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "321RtjMG_6uK",
+    "outputId": "153b3032-42cb-42da-ee53-df9aa52d72a4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "21"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = len(colum_names)\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "gFJSTVfp_6uL"
+   },
+   "outputs": [],
+   "source": [
+    "new_data = pd.DataFrame(\n",
+    "    {\n",
+    "        'Column Name': colum_names,\n",
+    "        'A' : np.random.randn(a),\n",
+    "        'B' : np.random.uniform(0,1,a)\n",
+    "    }, index = range(42, 42 + a)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 700
+    },
+    "colab_type": "code",
+    "id": "ovmUIoXZ_6uM",
+    "outputId": "8baf6056-944f-404d-94e0-b02247d348e3"
+   },
+   "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>Column Name</th>\n",
+       "      <th>A</th>\n",
+       "      <th>B</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>State</td>\n",
+       "      <td>0.009601</td>\n",
+       "      <td>0.571675</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43</th>\n",
+       "      <td>Account Length</td>\n",
+       "      <td>1.127091</td>\n",
+       "      <td>0.203394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>44</th>\n",
+       "      <td>Area Code</td>\n",
+       "      <td>1.413309</td>\n",
+       "      <td>0.426407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Phone</td>\n",
+       "      <td>1.335081</td>\n",
+       "      <td>0.190180</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>Int'l Plan</td>\n",
+       "      <td>-0.828382</td>\n",
+       "      <td>0.156801</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>VMail Plan</td>\n",
+       "      <td>0.608314</td>\n",
+       "      <td>0.255236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>VMail Message</td>\n",
+       "      <td>-1.529998</td>\n",
+       "      <td>0.688072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49</th>\n",
+       "      <td>Day Mins</td>\n",
+       "      <td>0.463638</td>\n",
+       "      <td>0.638551</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50</th>\n",
+       "      <td>Day Calls</td>\n",
+       "      <td>-0.419050</td>\n",
+       "      <td>0.900912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>51</th>\n",
+       "      <td>Day Charge</td>\n",
+       "      <td>0.180764</td>\n",
+       "      <td>0.204704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52</th>\n",
+       "      <td>Eve Mins</td>\n",
+       "      <td>-0.569117</td>\n",
+       "      <td>0.213367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>53</th>\n",
+       "      <td>Eve Calls</td>\n",
+       "      <td>-1.260274</td>\n",
+       "      <td>0.206531</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>Eve Charge</td>\n",
+       "      <td>0.241916</td>\n",
+       "      <td>0.431233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55</th>\n",
+       "      <td>Night Mins</td>\n",
+       "      <td>-0.105272</td>\n",
+       "      <td>0.562286</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>Night Calls</td>\n",
+       "      <td>-0.788214</td>\n",
+       "      <td>0.735034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57</th>\n",
+       "      <td>Night Charge</td>\n",
+       "      <td>1.190481</td>\n",
+       "      <td>0.578185</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>58</th>\n",
+       "      <td>Intl Mins</td>\n",
+       "      <td>-1.857890</td>\n",
+       "      <td>0.029463</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>Intl Calls</td>\n",
+       "      <td>-0.085863</td>\n",
+       "      <td>0.238753</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>Intl Charge</td>\n",
+       "      <td>-0.622193</td>\n",
+       "      <td>0.324741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>CustServ Calls</td>\n",
+       "      <td>0.839831</td>\n",
+       "      <td>0.839604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>Churn?</td>\n",
+       "      <td>0.570857</td>\n",
+       "      <td>0.642823</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Column Name         A         B\n",
+       "42           State  0.009601  0.571675\n",
+       "43  Account Length  1.127091  0.203394\n",
+       "44       Area Code  1.413309  0.426407\n",
+       "45           Phone  1.335081  0.190180\n",
+       "46      Int'l Plan -0.828382  0.156801\n",
+       "47      VMail Plan  0.608314  0.255236\n",
+       "48   VMail Message -1.529998  0.688072\n",
+       "49        Day Mins  0.463638  0.638551\n",
+       "50       Day Calls -0.419050  0.900912\n",
+       "51      Day Charge  0.180764  0.204704\n",
+       "52        Eve Mins -0.569117  0.213367\n",
+       "53       Eve Calls -1.260274  0.206531\n",
+       "54      Eve Charge  0.241916  0.431233\n",
+       "55      Night Mins -0.105272  0.562286\n",
+       "56     Night Calls -0.788214  0.735034\n",
+       "57    Night Charge  1.190481  0.578185\n",
+       "58       Intl Mins -1.857890  0.029463\n",
+       "59      Intl Calls -0.085863  0.238753\n",
+       "60     Intl Charge -0.622193  0.324741\n",
+       "61  CustServ Calls  0.839831  0.839604\n",
+       "62          Churn?  0.570857  0.642823"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "97ZGkX3z_6uN"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "T2 - 2 - Data Cleaning - Funciones de distribución de probabilidad.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git "a/notebooks/T2 - 2 - Data Cleaning - Funciones de distribuci\303\263n de probabilidad.ipynb" "b/notebooks/T2 - 2 - Data Cleaning - Funciones de distribuci\303\263n de probabilidad.ipynb"
index 6478c1a8..d38473a2 100644
--- "a/notebooks/T2 - 2 - Data Cleaning - Funciones de distribuci\303\263n de probabilidad.ipynb"	
+++ "b/notebooks/T2 - 2 - Data Cleaning - Funciones de distribuci\303\263n de probabilidad.ipynb"	
@@ -1,1081 +1,1303 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Funciones de distribución de probabilidades\n",
-    "## Distribución Uniforme"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = 1\n",
-    "b = 100\n",
-    "n = 1000000\n",
-    "data = np.random.uniform(a, b, n)"
-   ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.6.4"
+    },
+    "colab": {
+      "name": "T2 - 2 - Data Cleaning - Funciones de distribución de probabilidad.ipynb",
+      "provenance": [],
+      "include_colab_link": true
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "(array([100159.,  99606.,  99421.,  99864.,  99804.,  99915., 100297.,\n",
-       "        100348., 100225., 100361.]),\n",
-       " array([ 1.00000292, 10.89996049, 20.79991807, 30.69987565, 40.59983322,\n",
-       "        50.4997908 , 60.39974838, 70.29970595, 80.19966353, 90.09962111,\n",
-       "        99.99957868]),\n",
-       " <a list of 10 Patch objects>)"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/Collab---v-3.8/notebooks/T2%20-%202%20-%20Data%20Cleaning%20-%20Funciones%20de%20distribuci%C3%B3n%20de%20probabilidad.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a038b00>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "76tSTLpx_6tj",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Funciones de distribución de probabilidades\n",
+        "## Distribución Uniforme"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "plt.hist(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Distribución Normal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = np.random.randn(1000000)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x11815d4e0>]"
+      "cell_type": "code",
+      "metadata": {
+        "id": "tOrouqly_9oY",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 122
+        },
+        "outputId": "dfe35705-3b1a-452c-de31-3b8025787095"
+      },
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+            "\n",
+            "Enter your authorization code:\n",
+            "··········\n",
+            "Mounted at /content/drive\n"
+          ],
+          "name": "stdout"
+        }
       ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a980550>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x = range(1,1000001)\n",
-    "plt.plot(x, data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "id": "24f0E2tZ_6tk",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import pandas as pd"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "(array([7.00000e+00, 1.76000e+02, 6.10100e+03, 6.69350e+04, 2.65940e+05,\n",
-       "        3.94335e+05, 2.17925e+05, 4.51050e+04, 3.39500e+03, 8.10000e+01]),\n",
-       " array([-5.60316173, -4.56527912, -3.52739651, -2.4895139 , -1.45163129,\n",
-       "        -0.41374868,  0.62413394,  1.66201655,  2.69989916,  3.73778177,\n",
-       "         4.77566438]),\n",
-       " <a list of 10 Patch objects>)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
+      "cell_type": "code",
+      "metadata": {
+        "id": "lXlMO8RS_6tp",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "a = 1\n",
+        "b = 100\n",
+        "n = 1000000\n",
+        "data = np.random.uniform(a, b, n)"
+      ],
+      "execution_count": 0,
+      "outputs": []
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a91f1d0>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "tiC5WseY_6tt",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 367
+        },
+        "outputId": "b0abc9de-891c-4789-f4d5-42f4dc86c5a8"
+      },
+      "source": [
+        "%matplotlib inline\n",
+        "plt.hist(data)"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([100219.,  99664., 100096.,  99826., 100078., 100598.,  99562.,\n",
+              "        100101.,  99596., 100260.]),\n",
+              " array([ 1.00019609, 10.90016559, 20.80013509, 30.70010458, 40.60007408,\n",
+              "        50.50004358, 60.40001307, 70.29998257, 80.19995207, 90.09992157,\n",
+              "        99.99989106]),\n",
+              " <a list of 10 Patch objects>)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "plt.hist(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x116e85eb8>]"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XubDFO_f_6tx",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Distribución Normal"
       ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x116e85d30>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "X1N2_WO1_6tx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data = np.random.randn(1000000)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qhOyLRGN_6tz",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "outputId": "459205ba-9d27-4da8-ac42-1a03c499bd39"
+      },
+      "source": [
+        "x = range(1,1000001)\n",
+        "plt.plot(x, data)"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f85136a29e8>]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(x,sorted(data))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(array([   6.,   32.,  281., 1180., 2576., 3118., 1960.,  711.,  123.,\n",
-       "          13.]),\n",
-       " array([-5.12280629, -3.11658208, -1.11035788,  0.89586633,  2.90209053,\n",
-       "         4.90831474,  6.91453894,  8.92076315, 10.92698735, 12.93321156,\n",
-       "        14.93943576]),\n",
-       " <a list of 10 Patch objects>)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "UnsfVULW_6t1",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 367
+        },
+        "outputId": "1631f469-3f20-4b50-af70-d5a1c4931ecb"
+      },
+      "source": [
+        "%matplotlib inline\n",
+        "plt.hist(data)"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([1.10000e+02, 3.40700e+03, 3.83800e+04, 1.84609e+05, 3.64728e+05,\n",
+              "        2.95298e+05, 9.92620e+04, 1.34940e+04, 6.98000e+02, 1.40000e+01]),\n",
+              " array([-4.67212058, -3.69181253, -2.71150449, -1.73119645, -0.75088841,\n",
+              "         0.22941963,  1.20972767,  2.19003571,  3.17034375,  4.15065179,\n",
+              "         5.13095983]),\n",
+              " <a list of 10 Patch objects>)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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Wb75nIuM+edN7JjKuTp4TvnHdzgDqJMzluMeoqtuqanVVrZ6amlrIqUjSGeV4Q+KZdqmI9vpsq+8Hlg21W9pqs9WXjqjPNoYkaUyONyS2A0eeUNoA3D1Uv7Y95bQWeKFdMtoJXJbk3HbD+jJgZ9v2YpK17amma4/a16gxJEljMuc9iSR/AbwLOD/JPgZPKd0E3JVkI/Bj4AOt+Q7gSmAG+DnwIYCqOpjkM8Du1u7TVXXkZvhHGDxBdTbwrbYwyxiSpDGZMySq6prOpktHtC3gus5+tgBbRtSngYtH1J8bNYYkaXz8xrUkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkrhMKiSRPJnkkyZ4k0612XpJdSR5vr+e2epLckmQmycNJ3ja0nw2t/eNJNgzV3972P9P65kTmK0k6NifjTOKfV9Wqqlrd3m8G7q2qFcC97T3AFcCKtmwCboVBqAA3AO8A1gA3HAmW1ubDQ/3WnYT5SpLmaSEuN60Htrb1rcBVQ/U7a+B+4JwkFwKXA7uq6mBVHQJ2AevattdV1f1VVcCdQ/uSJI3BiYZEAX+Z5KEkm1rtgqp6uq3/BLigrS8Bnhrqu6/VZqvvG1H/DUk2JZlOMn3gwIETOR5J0pDFJ9j/n1bV/iR/F9iV5AfDG6uqktQJjjGnqroNuA1g9erVCz6eJJ0pTuhMoqr2t9dngW8wuKfwTLtURHt9tjXfDywb6r601WarLx1RlySNyXGHRJK/k+S3j6wDlwHfB7YDR55Q2gDc3da3A9e2p5zWAi+0y1I7gcuSnNtuWF8G7GzbXkyytj3VdO3QviRJY3Ail5suAL7RnkpdDPx5Vf33JLuBu5JsBH4MfKC13wFcCcwAPwc+BFBVB5N8Btjd2n26qg629Y8AdwBnA99qiyRpTI47JKrqCeAtI+rPAZeOqBdwXWdfW4AtI+rTwMXHO0dJ0onxG9eSpK4TfbpJr3DLN98z6SlIOoV5JiFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQufwVW0oKZ5K8MP3nTeyY29unEMwlJUpchIUnqMiQkSV2GhCSp65QPiSTrkvwwyUySzZOejySdSU7pp5uSLAK+ALwb2AfsTrK9qh6d7MxOPv+taUmnolM6JIA1wExVPQGQZBuwHjjtQkLSyTWpP7xOt0dvT/WQWAI8NfR+H/COoxsl2QRsam9/luSHY5jbfJ0P/M2kJzEBHveZxeNu8rkJzeTE/YNRxVM9JOalqm4Dbpv0PEZJMl1Vqyc9j3HzuM8sHvfp61S/cb0fWDb0fmmrSZLG4FQPid3AiiQXJTkLuBrYPuE5SdIZ45S+3FRVh5NcD+wEFgFbqmrvhKd1rE7Jy2Bj4HGfWTzu01SqatJzkCSdok71y02SpAkyJCRJXYbEGCX5eJJKcv6k5zIOSf5jkh8keTjJN5KcM+k5LaQz8SdkkixLcl+SR5PsTfLRSc9pnJIsSvLdJN+c9FwWiiExJkmWAZcB/2fScxmjXcDFVfWPgP8FfHLC81kwQz8hcwWwErgmycrJzmosDgMfr6qVwFrgujPkuI/4KPDYpCexkAyJ8bkZ+A/AGfOkQFX9ZVUdbm/vZ/A9l9PV//8Jmar6JXDkJ2ROa1X1dFV9p63/lMEH5pLJzmo8kiwF3gP82aTnspAMiTFIsh7YX1Xfm/RcJujfAN+a9CQW0KifkDkjPiyPSLIceCvwwGRnMjb/icEffr+a9EQW0in9PYlXkiT/A/h7Izb9IfApBpeaTjuzHXdV3d3a/CGDyxJfHufcND5JXgt8DfhYVb046fkstCTvBZ6tqoeSvGvS81lIhsRJUlX/YlQ9yT8ELgK+lwQGl1y+k2RNVf1kjFNcEL3jPiLJvwbeC1xap/eXcs7Yn5BJ8ioGAfHlqvr6pOczJu8E/mWSK4HXAK9L8t+q6l9NeF4nnV+mG7MkTwKrq+q0/8XMJOuAzwP/rKoOTHo+CynJYgY35y9lEA67gd9/Bf5CwDHJ4C+frcDBqvrYpOczCe1M4t9V1XsnPZeF4D0JLaT/DPw2sCvJniT/ZdITWijtBv2Rn5B5DLjrdA+I5p3AB4FL2v/jPe2va50mPJOQJHV5JiFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkrr+H+ftn/bo/QjGAAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1180efac8>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "XdW96vvx_6t3",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "outputId": "a1f36bac-4ba7-495d-ecd3-56f1ee5c2d7d"
+      },
+      "source": [
+        "plt.plot(x,sorted(data))"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f8511e4d240>]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAEFCAYAAAD+A2xwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAbCklEQVR4nO3deXTd5X3n8fdXixdZu7VLluVF3m1sLMwWdgIuQyApaQoJTUo4YdJMQmamGbownfSkZ3om00M67SSZ4El7knQ6hCxAPAmlQDAhARyQV2zZloXxIlurrdW21vudP+6VbIQX2brbT/q8zvGRdO+P3/P9WdKHx8/v+T2PuTsiIhJcKYkuQEREJkZBLiIScApyEZGAU5CLiAScglxEJODSEtFoQUGBV1VVJaJpEZHA2rJlS7u7F459PSFBXlVVRW1tbSKaFhEJLDM7dK7XNbQiIhJwCnIRkYBTkIuIBJyCXEQk4BTkIiIBpyAXEQk4BbmISMApyEVE4uBY52m+8eI+3ms/GfVzK8hFROKgubuPv3+lgUPHFeQiIjKGglxEJOAU5CIiAacgFxEJOAW5iEjAKchFRAJOQS4iEnAKchGRgFOQi4gEnIJcRCTgFOQiIgGnIBcRCTgFuYhIwCnIRUTiwD1251aQi4jERTjJU8yifmYFuYhIHAyHwh8V5CIiARWKjK2kxCB1FeQiInEQCmloRUQk0CI5TmpKEge5maWa2TYz+3m0zikiMlmMDq1EP8ej2iP/MrAniucTEZk0hiNBbsk6tGJmFcC/Ab4bjfOJiEw2Hgny1GQNcuB/AI8BofMdYGaPmFmtmdW2tbVFqVkRkWBI6umHZnY30OruWy50nLtvcPcad68pLCycaLMiIoGS7NMPrwfuMbODwA+BW83s/0ThvCIik0ZSTz909z9z9wp3rwLuB15x9wcnXJmIyCQyMv0wKYNcREQubmRoJTUGqZsWzZO5+6vAq9E8p4jIZBBK9umHIiJyYaEATD8UEZELSOrphyIicnHJPv1QREQuwj2Jpx+KiMjFaWhFRCTgNLQiIhJwIQ2tiIgE28gj+pp+KCISUMN6RF9EJNhGZq2YxshFRIJJT3aKiATc4PDIolkKchGRQGrv7Sdzehoz0lOjfm4FuYhIHLT3DlCQOS0m51aQi4jEQVtPH4VZ02NybgW5iEgctPX0U5CpIBcRCaz23gH1yEVEgqp/aJiu04PqkYuIBNXx3gEA9chFRIKqracfgEL1yEVEgmkkyAvUIxcRCab61h4AqmZnxOT8CnIRkRjbfriTeQWzyM3QA0EiIoHj7uxo7OSKipyYtaEgFxGJoXfbemnp7mdtVX7M2lCQi4jE0M+2HyPF4M7lxTFrQ0EuIhIj7s5z249y/cICirJmxKwdBbmISIxsPdzBkROn+ejq8pi2oyAXEYmRH9c2MiM9hTtXlMS0HQW5iEgMNHac4qdbG/ndKyvInJ4W07YU5CIiMfCNl+oxjC/esjDmbU04yM1sjpltMrM6M9ttZl+ORmEiIkH1xrvtPLP1KJ/90DzKcmfGvL1o9PeHgD92961mlgVsMbOX3L0uCucWEQmU3v4hHn92F5X5GXz5tuq4tDnhHrm7N7n71sjnPcAeILa3aEVEktBwyPnyU9s4fOIUX79vFTOnRX+j5XOJ6hi5mVUBa4DfnuO9R8ys1sxq29raotmsiEhS+O8v7OWXe1v56keWce2C2XFrN2pBbmaZwE+Bf+/u3WPfd/cN7l7j7jWFhYXRalZEJCn86O0jPPnaAR68ppJPX1sV17ajEuRmlk44xP/Z3Z+JxjlFRILi6bcP8yfP7ORDCwv46keWx739Cd/sNDMD/gHY4+7fmHhJIiLB4O5885UGnnipnhsXFfLkg2tJT43/rO5ozFq5HvgD4B0z2x557c/d/fkonFtEJCmdHhjm8Wff4ZltR/nYmnK+ft8qpqUl5tGcCQe5u/8GsCjUIiISCHubu3n0qW3sb+3lP9y+iEdvW0h4cCIxYvvcqIjIJDI4HGLDawf4u5f3kz0zne8/tI4bFyV+8oaCXERkHGoPnuA/P7eLvc093LWyhL+6dwWzM2OzmfKlUpCLiFxAc1cfX39hL89uO0pZzgy+8+Ba1sd4NcNLpSAXETmHxo5TPPmrAzxdewQcvnDzAv7dLQuZFeOVDC9H8lUkIpJAB9tP8u1XG3hm61HM4ONrK/jCzQuZk5+R6NLOS0EuIgLUt/TwrU0N/L8dx0hPTeHBa+byyI3z47J64UQpyEVkygqFnNffbecHbx7ipboWMqal8rkb5vPwDfNiusdmtCnIRWTKOdZ5mp9uaeRHW45w5MRp8mdN49FbF/LQ9fPImzUt0eVdMgW5iEwJpwaGeGFXM89uO8pvGtpxh2vnz+Yrdyxm/YoSpqfFZ8nZWFCQi8ik1Tc4zK/q2/j5ziZermvh9OAwFXkz+dKt1Xz8ygoqZyfvDcxLoSAXkUmlb3CY1+rb+MU7TfxyTyu9/UPkz5rGx64s56Ory6mZm0dKyuRaVURBLiKB13lqgE37WnmproVX97VxamCY3Ix07lpZwt2ryrh2weyErEoYLwpyEQkcd2f3sW5+Vd/Gr+rb2HKog+GQU5g1nY+uKWf98pJJH95nU5CLSCB0nhrg1/vbeXVfG6/tb6Otpx+A5WXZfP6m+Xx4WQmrynMm3bDJeCjIRSQp9Q8Ns+1wJ2+8e5zf7G9j+5FOQg65GencUF3ITYsKuXFRQaDme8eKglxEksLgcIidjZ1sPnCCN989ztsHT9A/FCLFYGV5Dl+8tZqbFhWyek4uqVOw130hCnIRSYje/iF2HOnkrfdO8NZ7J9h2pIO+wRAAS0qy+OTVlVy3oIB18/LJmZme4GqTm4JcRGLO3TnW1UftwRNsPdRB7aEO9jR1E3JIMVhams39V1Vy9bx8rp4/m/wAPl2ZSApyEYm6weEQe5q6qT3YwZbDHWw52EFzdx8AGdNSWT0nly/espC1Vfmsqcwle4Z63BOhIBeRCWvr6Wfb4Q62H+lk6+EOdhzp4vTgMADluTO5al4+NXPzWDs3jyUlWaRNkWmB8aIgF5FL0t03yK6jXexs7GJnYyc7jnRxtPM0AGkpxtLSbH7/qjmsnZtHTVUepTnJvwxs0CnIReS8evoG2XW0m11Hu3gn8ue99pOj78/Jn8maylweur6K1XNyWVGew4z04C4+FVQKchEBoOvUILuPhcN617FweJ8d2mU5M1hRnsN9V5azojyHVRW5uimZJBTkIlPMyAySumPd1B3rZvexLuqaumnsOD16THnuTJaXZY+G9oryHAqSZMd4+SAFucgk1j80zP6WXvY0dbOnqYc9Td3UNXXTdXpw9Jj5BbNYPSeXT109l+Vl2awoz1FPO2AU5CKTRFtPfySwu0eD+922XoZCDsDM9FQWl2Rx18pSlpVmsawsmyUl2Um5K7xcGn0HRQKmb3CYhtZe9jb3sK+5m73NPexp6qG9t3/0mNKcGSwtzeb2ZUUsLc1maWk2VbNn6dH2SUpBLpKkQiGnseM09S097G3uZk9zD/uae3iv/STDkV72tLQUqosyuXlxIUtKslgWCe0g7jspl09BLpJg7k5bbz/1zb3Ut/REgruH/S09nBwYHj2uMj+DRcVZ/M6KEpaUZLO4JIuq2Rl6uEYU5CLx1HlqgPqWM4G9rzn8sePUmZuPeRnpLC7J4vdq5rC4JItFxVksLskiU2PZch76yRCJgZ6+Qfa39tLQ0su+SGjXt/TQ0n1mHDtzehrVxZncubyERcVZLCnJYmFxJoWZ0zHTWLaMn4JcZAJODQzR0NrL/pZe6lt7qG/uob6ld/SRdYAZ6SlUF2Vx/cICFheHe9jVxZmU585UYEtURCXIzWw98HdAKvBdd/9v0TivSLI42R8J7NZe9kd61/tbe9/3EM201BTmF86ipiqPTxZXUl2UyaLiLObkZ2i2iMTUhIPczFKBbwEfBhqBt81so7vXTfTcIvF2sn9oNKzPhPb7e9gjgb2mMo/fr5lDdXEmC4t041ESJxo98nVAg7sfADCzHwL3AgpySVpdpwdpaO0ZHRbZ39pLQ+u5A3vt3Dzuv2oO1cVZLCrOpDJfgS3JJRpBXg4cOevrRuDqsQeZ2SPAIwCVlZVRaFbk4rpODVLf2hMJ63Bwj73pOD0thfmFmaydm8cD68KBXV2kwJbgiNvNTnffAGwAqKmp8Xi1K1NDd9/g6DBIfUs4uOtbemjtORPYGdNSWViUyfULCkbDuro4k4o8jWFLsEUjyI8Cc876uiLymkjUjUzrGxvaI9uIQXhNkeriTG6oLmRRceboLJGynJmkKLBlEopGkL8NVJvZPMIBfj/wySicV6awkZuO4aAOh/b+lh6OdZ0J7BnpKSwsyuS6BbNHx68XFWdRnqvAlqllwkHu7kNm9kXgXwlPP/xHd9894cpkShhZAGrkCcf6c80SSUthYWEm6+blRwI7HNoaEhEJi8oYubs/DzwfjXPJ5BQKOYdPnIqs2NfDvpbwqn0H208SWf/pfbNERm46LirOolLzsEUuSE92StR1nRpkT3M3e5siS6w2h594HNlV3Sy8ANTi4izuXlnKYi0AJTIhCnK5bEPDId5rP0ldJLBHgrvprHHsvIx0lpZm88C6SpaUZLGoJDwskjFNP3oi0aLfJhmX3v4h9jZ1s/tY9+h2YXubexgYCgGQnmosKMzk6nn5LCkN97CXlWZTlKUFoERiTUEuH9Da0xfZlDe8OW9dUzcHj5/EI2PZeRnpLC/L4TPXzmVpaXi7sIVFmUxL07CISCIoyKcw9/AONLuOdvHO0fBO6ruPddN21kM0c/Jnsrw0h99dU86ysmyWl+VQnK1etkgyUZBPEe7O0c7T7GzsYmdj12h4j+ymnpZiLCzK5IbqApaX5bC8LLxlWM7M9ARXLiIXoyCfpNp7+9nZ2MmOI13saOzkncYujp8cAMLj2eHd1EtYUZ7D8rIclpRkMSM9NcFVi8jlUJBPAv1Dw9Qd62bb4U62H+lk25EOjpwIP1CTYlBdlMWtS4pYVZHDqopclpRmMT1NoS0yWSjIA6ip6zRbD3Wy5VAHWw93UHesm4Hh8OyRkuwZrKnM5Q+umcuqilxWlucwS3s9ikxq+g1Pcu7OweOn2HzgOJsPHKf2YMfo4+sz0lNYVZ7LQ9dXsaYyl9Vz8ijJmZHgikUk3hTkSWZscG8+cHx07ezCrOmsq8rn4Q/NY+3cPJaWZmvKn4goyBPN3Tl0/BRvnie4r5k/m2vm53PN/NnML5ilaX8i8gEK8jgbCe4zPe4To2tpK7hF5HIoyOOg4+QAr+1v47X6dl5vaB8N7oLM6aOhfe0CBbeIXB4FeQy4O3uaenhlbwub9rWx7XAHIYfcjHSuX1jAtfNnc8382SwoVHCLyMQpyKNkcDjEbw+c4OU9LbxU1zI6s+SKihy+dGs1Ny8uZFVFrtbVFpGoU5BPQP/QML+ub+f5XU28XNdCd98Q09NSuKG6gEdvW8gtS4ooytJ0QBGJLQX5JRoOOZsPHOe5bUd5YXczPX1DZM9I48PLSrhzeTE3VBcyc5qemhSR+FGQj9Oepm6e2drIxh3HaOnuJ3N6GncsL+aeK8q4bkGB5nOLSMIoyC/gZP8QG3cc46m3DrOzsYv0VOOmRUX8xd1l3L60WItMiUhSUJCfw77mHn7w5kGe23aUkwPDLC7O4qsfWca9q8vJnzUt0eWJiLyPgjzC3Xm1vo3v/voArzccZ3paCnevKuOTV1dyZWWupgmKSNKa8kE+HHJe2NXMNzc1sKepm5LsGTy2fjEPXFVJnnrfIhIAUzbI3Z0X61p44sV91Lf0Mr9wFn/z8VXcu7pcNy5FJFCmZJDvbe7mvzy3m7cOnmB+4Sz+5wNruGtlqR7WEZFAmlJB3tM3yN++tJ/vv3mQnJnp/PXHVvKJmgrSUtUDF5HgmhJB7u5s3HGM//qLPbT19vPAukoeu3MxuRkaAxeR4Jv0Qd7a08ef/GQnm/a1saoih//96RqumJOb6LJERKJmUgf5Gw3tPPrDbfT2D/HVjyzj09dWaRxcRCadSRnk7s4//OY9/vr5PcwvzOSpz11DdXFWossSEYmJSRfkoZDztZ/X8b03DnLn8mKe+MRqMrWLvIhMYhOarmFmf2Nme81sp5k9a2YJHXx2d766cTffe+Mgn71+Hv/rU2sV4iIy6U103t1LwAp3XwXUA3828ZIu3xMv1vNPmw/xb2+az1/cvZQUjYeLyBQwoSB39xfdfSjy5WagYuIlXZ6fbT/KNzc1cP9Vc/jT9Uu0NoqITBnRfBLms8C/nO9NM3vEzGrNrLatrS2KzYbXCn/sJztZV5XP1+5doRAXkSnlogPIZvYyUHKOtx53959FjnkcGAL++XzncfcNwAaAmpoav6xqz6FvcJhHn9pG9sx0vv3glVonRUSmnIsGubvffqH3zewPgbuB29w9agE9Xt/e1MD+1l6+99BVFGROj3fzIiIJN6EpHWa2HngMuMndT0WnpPFr6e7jydcOcM8VZdy8uCjezYuIJIWJjkN8E8gCXjKz7Wb2nSjUNP7GX2kg5M5X7lgcz2ZFRJLKhHrk7r4wWoVcqhMnB3i69gj3XVlB5eyMRJUhIpJwgb0z+Pw7TQwMhfjMdVWJLkVEJKECG+Qv7GpmXsEslpRoDRURmdoCGeQdJwd488Bx1q8o0ZxxEZnyAhnkL+1pYTjk/M6Kc01vFxGZWgIZ5C/saqY8dyYry3MSXYqISMIFLsgHh0O83tDOh5cVa1hFRIQABvm+5h76h0KsnZuX6FJERJJC4IK8rqkbgOVl2QmuREQkOQQuyN9t62VaagpzZ89KdCkiIkkhcEHe2HGairyZ2kRZRCQicEHe1HmakpwZiS5DRCRpBC/Iu/oozZmZ6DJERJJGoIJ8aDhES3cfpeqRi4iMClSQHz85QMihWEEuIjIqUEHeeWoQgPyMaQmuREQkeQQqyLtOh4M8Z2Z6gisREUkegQry3v5wkGfOmNB+GCIik0qggvxk/zAAs6alJrgSEZHkEaggPz0QDvIZ6QpyEZERgQry/uEQANPTA1W2iEhMBSoRB4YiQZ6qHrmIyIhABflgpEeenqZ1VkRERgQqyIdDDkCKNpQQERkVqCAfoRwXETkjUEHuHu6RG0pyEZERAQvy8EctRS4ickaggjwyRK5Nl0VEzhKoIHdGhlZERGREsIJ8tEee2DpERJJJsII88lFDKyIiZwQqyEe75CIiMioqQW5mf2xmbmYF0Tjf+TgaVhERGWvCQW5mc4A7gMMTL+fC3PVUp4jIWNHokf8t8BhnhrBjJuSuGSsiImNMKMjN7F7gqLvvGMexj5hZrZnVtrW1XVZ7GloREfmgi+6ZZmYvAyXneOtx4M8JD6tclLtvADYA1NTUXFbv3V2P54uIjHXRIHf328/1upmtBOYBOyLTASuArWa2zt2bo1rlSC24ngYSERnjsncxdvd3gKKRr83sIFDj7u1RqOs8jSrHRUTGCtQ8co2Ri4h80GX3yMdy96ponesCbWj6oYjIGIHqkYc0tCIi8gGBCnJ3rbMiIjJWsIIcPRAkIjJWsILc0diKiMgYgQpyUI6LiIwVqCB3d42Ri4iMEawgRxsvi4iMFaggD6lHLiLyAVF7ICgeVpTlMDikXYJERM4WqCC/f10l96+rTHQZIiJJJVBDKyIi8kEKchGRgFOQi4gEnIJcRCTgFOQiIgGnIBcRCTgFuYhIwCnIRUQCztzj/6SkmbUBhy7zPy8AYrfBc3LSNU8NuuapYSLXPNfdC8e+mJAgnwgzq3X3mkTXEU+65qlB1zw1xOKaNbQiIhJwCnIRkYALYpBvSHQBCaBrnhp0zVND1K85cGPkIiLyfkHskYuIyFkU5CIiAZe0QW5m681sn5k1mNmfnuP96Wb2dOT935pZVfyrjK5xXPN/NLM6M9tpZr80s7mJqDOaLnbNZx13n5m5mQV6qtp4rtfMPhH5Pu82s/8b7xqjbRw/15VmtsnMtkV+tu9KRJ3RZGb/aGatZrbrPO+bmf195O9kp5ldOaEG3T3p/gCpwLvAfGAasANYNuaYLwDfiXx+P/B0ouuOwzXfAmREPv+jqXDNkeOygNeAzUBNouuO8fe4GtgG5EW+Lkp03XG45g3AH0U+XwYcTHTdUbjuG4ErgV3nef8u4F8AA64BfjuR9pK1R74OaHD3A+4+APwQuHfMMfcC3498/hPgNgv2zswXvWZ33+TupyJfbgYq4lxjtI3n+wzwV8DXgb54FhcD47nezwHfcvcOAHdvjXON0Taea3YgO/J5DnAsjvXFhLu/Bpy4wCH3Aj/wsM1ArpmVXm57yRrk5cCRs75ujLx2zmPcfQjoAmbHpbrYGM81n+1hwv9HD7KLXnPkn5xz3P0X8SwsRsbzPV4ELDKz181ss5mtj1t1sTGea/5L4EEzawSeB74Un9IS6lJ/3y8oUJsvS5iZPQjUADclupZYMrMU4BvAHya4lHhKIzy8cjPhf3G9ZmYr3b0zoVXF1gPA99z9CTO7FvgnM1vh7qFEFxYUydojPwrMOevrishr5zzGzNII/5PseFyqi43xXDNmdjvwOHCPu/fHqbZYudg1ZwErgFfN7CDhscSNAb7hOZ7vcSOw0d0H3f09oJ5wsAfVeK75YeBHAO7+JjCD8MJSk9m4ft/HK1mD/G2g2szmmdk0wjczN445ZiPwmcjnHwde8chdhIC66DWb2RrgScIhHvSxU7jINbt7l7sXuHuVu1cRvi9wj7vXJqbcCRvPz/VzhHvjmFkB4aGWA/EsMsrGc82HgdsAzGwp4SBvi2uV8bcR+HRk9so1QJe7N1322RJ9d/cCd33vItwbeRd4PPLa1wj/IkP4m/1joAF4C5if6JrjcM0vAy3A9sifjYmuOdbXPObYVwnwrJVxfo+N8HBSHfAOcH+ia47DNS8DXic8o2U7cEeia47CNT8FNAGDhP+V9TDweeDzZ32fvxX5O3lnoj/XekRfRCTgknVoRURExklBLiIScApyEZGAU5CLiAScglxEJMYutojWOY6/pIXTNGtFRCTGzOxGoJfw+iorLnJsNeEHpG519w4zK/KLPDeiHrmISIz5ORbRMrMFZvaCmW0xs1+b2ZLIW5e8cJqCXEQkMTYAX3L3tcBXgG9HXr/khdO0aJaISJyZWSZwHfDjs1bfnh75eMkLpynIRUTiLwXodPfV53ivkfBGE4PAe2Y2snDa2xc6mYiIxJG7dxMO6d+D0a3froi8fckLpynIRURizMyeAt4EFptZo5k9DHwKeNjMdgC7ObNz0r8Cx82sDtgE/Cd3v+AS3Zp+KCIScOqRi4gEnIJcRCTgFOQiIgGnIBcRCTgFuYhIwCnIRUQCTkEuIhJw/x9deGL2m7meSAAAAABJRU5ErkJggg==\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mu = 5.5\n",
-    "sd = 2.5\n",
-    "Z_10000 = np.random.randn(10000)\n",
-    "data = mu + sd * Z_10000 # Z = (X - mu) / sd -> N(0,1), X = mu + sd * Z\n",
-    "plt.hist(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([[-2.0037831 , -0.21771535, -1.90002375,  0.88870408],\n",
-       "       [-0.93380982, -0.94088418,  0.38366571, -0.0243514 ]])"
+      "cell_type": "code",
+      "metadata": {
+        "id": "QQZ1SxuG_6t5",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 368
+        },
+        "outputId": "8bbe5f6f-ab4b-4b71-88b9-fd3960af2cb1"
+      },
+      "source": [
+        "mu = 5.5\n",
+        "sd = 2.5\n",
+        "Z_10000 = np.random.randn(10000)\n",
+        "data = mu + sd * Z_10000 # Z = (X - mu) / sd -> N(0,1), X = mu + sd * Z\n",
+        "plt.hist(data)"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([  10.,   89.,  466., 1766., 2896., 2861., 1414.,  429.,   65.,\n",
+              "           4.]),\n",
+              " array([-4.24623314, -2.2667752 , -0.28731726,  1.69214068,  3.67159862,\n",
+              "         5.65105656,  7.6305145 ,  9.60997244, 11.58943037, 13.56888831,\n",
+              "        15.54834625]),\n",
+              " <a list of 10 Patch objects>)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD5CAYAAADLL+UrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAASrklEQVR4nO3dbYxc133f8e+vlKwUtmFREcvSJBEqLtOCfhFZXchKYwRq1OiBLkIZCBzqhc06ApigEmqjKQo6ASrDqQG5rW3AhSODrohQgWtG9UNFWHRlWnEh+IVkrVRaEqW4WskURIISN6Yj2zDglOq/L+ZQHdGz3Afuzk5yvh9gMHfOPXfuf+7M/ObuuXdmU1VIkvrwd1a7AEnS+Bj6ktQRQ1+SOmLoS1JHDH1J6oihL0kduWi+Dkl+DngIuKT1/2JV3ZHkCuAA8PPAY8D7quqvk1wC3AP8Y+D7wG9X1bF2Xx8GbgVeBf5VVT1wvnVffvnltWXLliU+NEnq02OPPfaXVbVu1Lx5Qx/4KfDrVfXjJBcD30ryNeBfA5+qqgNJPssgzO9q1z+oqn+QZCfwceC3k2wDdgJvB94KfCPJL1XVq3OteMuWLUxPTy/ioUqSkrww17x5h3dq4Mft5sXtUsCvA19s7fuBm9v0jnabNv+6JGntB6rqp1X1PWAGuHqRj0WSdAEWNKafZE2SI8Ap4DDwHPBXVXWmdTkObGzTG4EXAdr8VxgMAb3WPmKZ4XXtTjKdZHp2dnbxj0iSNKcFhX5VvVpVVwKbGOyd/6OVKqiq9lbVVFVNrVs3ckhKkrREizp7p6r+Cvgm8CvApUnOHhPYBJxo0yeAzQBt/lsYHNB9rX3EMpKkMZg39JOsS3Jpm/67wG8AzzAI/99q3XYB97Xpg+02bf6f1+BX3Q4CO5Nc0s782Qp8e7keiCRpfgs5e2cDsD/JGgYfEvdW1VeTPA0cSPLvgf8F3N363w38aZIZ4DSDM3aoqqNJ7gWeBs4At53vzB1J0vLLJP+08tTUVHnKpiQtTpLHqmpq1Dy/kStJHTH0JakjCxnTlybWlj33r9q6j9357lVbt7RU7ulLUkfc05eWaLX+yvAvDF0I9/QlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JH5g39JJuTfDPJ00mOJvlga/9IkhNJjrTL9qFlPpxkJsl3k9ww1H5ja5tJsmdlHpIkaS4XLaDPGeD3q+rxJG8GHktyuM37VFX9p+HOSbYBO4G3A28FvpHkl9rszwC/ARwHHk1ysKqeXo4HIkma37yhX1UngZNt+kdJngE2nmeRHcCBqvop8L0kM8DVbd5MVT0PkORA62voS9KYLGpMP8kW4B3AI63p9iRPJNmXZG1r2wi8OLTY8dY2V/u569idZDrJ9Ozs7GLKkyTNY8Ghn+RNwJeAD1XVD4G7gLcBVzL4S+ATy1FQVe2tqqmqmlq3bt1y3KUkqVnImD5JLmYQ+J+vqi8DVNXLQ/M/B3y13TwBbB5afFNr4zztkqQxWMjZOwHuBp6pqk8OtW8Y6vYe4Kk2fRDYmeSSJFcAW4FvA48CW5NckeQNDA72HlyehyFJWoiF7On/KvA+4MkkR1rbHwC3JLkSKOAY8LsAVXU0yb0MDtCeAW6rqlcBktwOPACsAfZV1dFlfCySpHks5OydbwEZMevQeZb5GPCxEe2HzrecJGll+Y1cSeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakj84Z+ks1Jvpnk6SRHk3ywtV+W5HCSZ9v12taeJJ9OMpPkiSRXDd3Xrtb/2SS7Vu5hSZJGWcie/hng96tqG3ANcFuSbcAe4MGq2go82G4D3ARsbZfdwF0w+JAA7gDeCVwN3HH2g0KSNB7zhn5Vnayqx9v0j4BngI3ADmB/67YfuLlN7wDuqYGHgUuTbABuAA5X1emq+gFwGLhxWR+NJOm8FjWmn2QL8A7gEWB9VZ1ss14C1rfpjcCLQ4sdb21ztZ+7jt1JppNMz87OLqY8SdI8Fhz6Sd4EfAn4UFX9cHheVRVQy1FQVe2tqqmqmlq3bt1y3KUkqVlQ6Ce5mEHgf76qvtyaX27DNrTrU639BLB5aPFNrW2udknSmCzk7J0AdwPPVNUnh2YdBM6egbMLuG+o/f3tLJ5rgFfaMNADwPVJ1rYDuNe3NknSmFy0gD6/CrwPeDLJkdb2B8CdwL1JbgVeAN7b5h0CtgMzwE+ADwBU1ekkfwQ82vp9tKpOL8ujkCQtyLyhX1XfAjLH7OtG9C/gtjnuax+wbzEFSpKWj9/IlaSOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOrKQH1yT5rVlz/2rXYKkBXBPX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1ZN7QT7IvyakkTw21fSTJiSRH2mX70LwPJ5lJ8t0kNwy139jaZpLsWf6HIkmaz0L29P8EuHFE+6eq6sp2OQSQZBuwE3h7W+aPk6xJsgb4DHATsA24pfWVJI3RvP9EpaoeSrJlgfe3AzhQVT8FvpdkBri6zZupqucBkhxofZ9edMWSpCW7kDH925M80YZ/1ra2jcCLQ32Ot7a52iVJY7TU0L8LeBtwJXAS+MRyFZRkd5LpJNOzs7PLdbeSJJYY+lX1clW9WlX/F/gc/38I5wSweajrptY2V/uo+95bVVNVNbVu3bqllCdJmsOSQj/JhqGb7wHOntlzENiZ5JIkVwBbgW8DjwJbk1yR5A0MDvYeXHrZkqSlmPdAbpIvANcClyc5DtwBXJvkSqCAY8DvAlTV0ST3MjhAewa4rapebfdzO/AAsAbYV1VHl/3RSJLOayFn79wyovnu8/T/GPCxEe2HgEOLqk6StKz8Rq4kdWTePX1Jk2XLnvtXbd3H7nz3qq1by8M9fUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqyLyhn2RfklNJnhpquyzJ4STPtuu1rT1JPp1kJskTSa4aWmZX6/9skl0r83AkSeezkD39PwFuPKdtD/BgVW0FHmy3AW4CtrbLbuAuGHxIAHcA7wSuBu44+0EhSRqfeUO/qh4CTp/TvAPY36b3AzcPtd9TAw8DlybZANwAHK6q01X1A+AwP/tBIklaYUsd019fVSfb9EvA+ja9EXhxqN/x1jZX+89IsjvJdJLp2dnZJZYnSRrlgg/kVlUBtQy1nL2/vVU1VVVT69atW667lSSx9NB/uQ3b0K5PtfYTwOahfpta21ztkqQxWmroHwTOnoGzC7hvqP397Syea4BX2jDQA8D1Sda2A7jXtzZJ0hhdNF+HJF8ArgUuT3KcwVk4dwL3JrkVeAF4b+t+CNgOzAA/AT4AUFWnk/wR8Gjr99GqOvfgsCRphc0b+lV1yxyzrhvRt4Db5riffcC+RVUnSVpWfiNXkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SerIBYV+kmNJnkxyJMl0a7ssyeEkz7brta09ST6dZCbJE0muWo4HIElauOXY0/+nVXVlVU2123uAB6tqK/Bguw1wE7C1XXYDdy3DuiVJi7ASwzs7gP1tej9w81D7PTXwMHBpkg0rsH5J0hwuNPQL+HqSx5Lsbm3rq+pkm34JWN+mNwIvDi17vLW9TpLdSaaTTM/Ozl5geZKkYRdd4PLvqqoTSf4ecDjJXwzPrKpKUou5w6raC+wFmJqaWtSykqTzu6DQr6oT7fpUkq8AVwMvJ9lQVSfb8M2p1v0EsHlo8U2tTctoy577V7sESRNsycM7Sd6Y5M1np4HrgaeAg8Cu1m0XcF+bPgi8v53Fcw3wytAwkCRpDC5kT3898JUkZ+/nv1bV/0jyKHBvkluBF4D3tv6HgO3ADPAT4AMXsG5J0hIsOfSr6nngl0e0fx+4bkR7AbctdX2SVt9qDR8eu/Pdq7Lev438Rq4kdcTQl6SOGPqS1BFDX5I6YuhLUkcMfUnqiKEvSR0x9CWpI4a+JHXE0Jekjhj6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHblotQv422jLnvtXuwRJGsk9fUnqiHv6kibeav71fOzOd6/auleCe/qS1JGxh36SG5N8N8lMkj3jXr8k9WysoZ9kDfAZ4CZgG3BLkm3jrEGSejbuMf2rgZmqeh4gyQFgB/D0SqzMs2gk6fXGHfobgReHbh8H3jncIcluYHe7+eMk3x2afTnwlyta4dJNam3WtXiTWtuk1gWTW9sF15WPL1MlP2slt9kvzDVj4s7eqaq9wN5R85JMV9XUmEtakEmtzboWb1Jrm9S6YHJrm9S6YPVqG/eB3BPA5qHbm1qbJGkMxh36jwJbk1yR5A3ATuDgmGuQpG6NdXinqs4kuR14AFgD7Kuqo4u4i5HDPhNiUmuzrsWb1NomtS6Y3NomtS5YpdpSVauxXknSKvAbuZLUEUNfkjoy0aGf5CNJTiQ50i7b5+g31p92SPIfk/xFkieSfCXJpXP0O5bkyVb79ArXdN5tkOSSJH/W5j+SZMtK1tPWuTnJN5M8neRokg+O6HNtkleGnuN/t9J1Da37vM9PBj7dttkTSa4aQ03/cGhbHEnywyQfOqfP2LZZkn1JTiV5aqjtsiSHkzzbrtfOseyu1ufZJLvGUNdEvC/nqG1ysqyqJvYCfAT4N/P0WQM8B/wi8AbgO8C2Fa7reuCiNv1x4ONz9DsGXD6G7TTvNgD+JfDZNr0T+LMx1LUBuKpNvxn43yPquhb46iq9vs77/ADbga8BAa4BHhlzfWuAl4BfWK1tBvwacBXw1FDbfwD2tOk9o17/wGXA8+16bZteu8J1TcT7co7aJibLJnpPf4Fe+2mHqvpr4OxPO6yYqvp6VZ1pNx9m8H2D1bSQbbAD2N+mvwhclyQrWVRVnayqx9v0j4BnGHwr+2+KHcA9NfAwcGmSDWNc/3XAc1X1whjX+TpV9RBw+pzm4dfSfuDmEYveAByuqtNV9QPgMHDjStY1Ke/LObbZQowly/4mhP7t7c+1fXP8GTnqpx3GGSy/w2BvcJQCvp7ksfbzEitlIdvgtT7tjfEK8PMrWNPrtOGkdwCPjJj9K0m+k+RrSd4+rpqY//lZ7dfWTuALc8xbrW0GsL6qTrbpl4D1I/qs9rabhPfluSYiy1Y99JN8I8lTIy47gLuAtwFXAieBT0xIXWf7/CFwBvj8HHfzrqq6isGvit6W5NfGUPrESfIm4EvAh6rqh+fMfpzB8MUvA/8Z+O9jLG1in58Mvrz4m8B/GzF7NbfZ69RgXGKizvue0PflqmXZuVb9t3eq6p8tpF+SzwFfHTFrRX7aYb66kvwL4J8D17UX/qj7ONGuTyX5CoM/3x660NpGWMg2ONvneJKLgLcA31+BWl4nycUMAv/zVfXlc+cPfwhU1aEkf5zk8qpa8R/vWsDzs5o/G3IT8HhVvXzujNXcZs3LSTZU1ck23HVqRJ8TDI49nLUJ+J8rXdiEvS+H1/na8zjuLDvXqu/pn88546fvAZ4a0W3sP+2Q5Ebg3wK/WVU/maPPG5O8+ew0g4NMo+pfDgvZBgeBs2dQ/Bbw53O9KZZLO2ZwN/BMVX1yjj5//+yxhSRXM3hNjuPDaCHPz0Hg/Rm4BnhlaFhjpd3CHEM7q7XNhgy/lnYB943o8wBwfZK1bSjj+ta2YibwfTm83snJspU6gr0cF+BPgSeBJ9qD39Da3wocGuq3ncGZIc8BfziGumYYjL0daZfPnlsXgyPw32mXoytd16htAHyUwRsA4OcYDBXMAN8GfnEM2+ldDP70f2JoW20Hfg/4vdbn9rZ9vsPg4Ns/GdNra+Tzc05tYfBPf55rr8OpMdX2RgYh/pahtlXZZgw+eE4C/4fBGPOtDI4FPQg8C3wDuKz1nQL+y9Cyv9NebzPAB8ZQ10S8L+eobWKyzJ9hkKSOTPTwjiRpeRn6ktQRQ1+SOmLoS1JHDH1J6oihL0kdMfQlqSP/Dx9fT8OTZWJ8AAAAAElFTkSuQmCC\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data = np.random.randn(2,4)\n",
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## La simulación de Monte Carlo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* Generamos dos números aleatorios uniforme x e y entre 0 y 1 en total 1000 veces.\n",
-    "* Calcularemos $z = x^2 + y^2$:\n",
-    "    * Si $z < 1 \\rightarrow$ estamos dentro del círculo.\n",
-    "    * Si $z \\geq 1 \\rightarrow$ estamos fuera del círculo.\n",
-    "* Calculamos el número total de veces que están dentro del círculo y lo dividimos entre el número total de intentos para obtener una aproximación de la probabilidad de caer dentro del círculo.\n",
-    "* Usamos dicha probabilidad para aproximar el valor de π.\n",
-    "* Repetimos el experimento un número suficiente de veces (por ejemplo 100), para obtener (100) diferentes aproximaciones de π. \n",
-    "* Calculamos el promedio de los 100 experimentos anteriores para dar un valor final de π.\n",
-    "    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def pi_montecarlo(n, n_exp):\n",
-    "    pi_avg = 0\n",
-    "    pi_value_list = []\n",
-    "    for i in range(n_exp):\n",
-    "        value = 0\n",
-    "        x = np.random.uniform(0,1,n).tolist()\n",
-    "        y = np.random.uniform(0,1,n).tolist()\n",
-    "        for j in range(n):\n",
-    "            z = np.sqrt(x[j] * x[j] + y[j] * y[j])\n",
-    "            if z<=1:\n",
-    "                value += 1\n",
-    "        float_value = float(value)\n",
-    "        pi_value = float_value * 4 / n\n",
-    "        pi_value_list.append(pi_value)\n",
-    "        pi_avg += pi_value\n",
-    "\n",
-    "    pi = pi_avg/n_exp\n",
-    "\n",
-    "    print(pi)\n",
-    "    fig = plt.plot(pi_value_list)\n",
-    "    return (pi, fig)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.141549999999997\n"
-     ]
+      "cell_type": "code",
+      "metadata": {
+        "id": "n8siHqnN_6t6",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "9aeff760-7b7d-4196-d02d-658c2150d85f"
+      },
+      "source": [
+        "data = np.random.randn(2,4)\n",
+        "data"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[ 0.00239305,  0.66424699, -0.46608439, -2.06345436],\n",
+              "       [ 0.10321101,  0.10132709, -0.70695038, -0.87042236]])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        }
+      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "(3.141549999999997, [<matplotlib.lines.Line2D at 0x116d47a20>])"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "qirIn8au_6t8",
+        "colab_type": "text"
+      },
+      "source": [
+        "## La simulación de Monte Carlo"
       ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x117c2f7b8>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s4ySxEBn_6t8",
+        "colab_type": "text"
+      },
+      "source": [
+        "* Generamos dos números aleatorios uniforme x e y entre 0 y 1 en total 1000 veces.\n",
+        "* Calcularemos $z = x^2 + y^2$:\n",
+        "    * Si $z < 1 \\rightarrow$ estamos dentro del círculo.\n",
+        "    * Si $z \\geq 1 \\rightarrow$ estamos fuera del círculo.\n",
+        "* Calculamos el número total de veces que están dentro del círculo y lo dividimos entre el número total de intentos para obtener una aproximación de la probabilidad de caer dentro del círculo.\n",
+        "* Usamos dicha probabilidad para aproximar el valor de π.\n",
+        "* Repetimos el experimento un número suficiente de veces (por ejemplo 100), para obtener (100) diferentes aproximaciones de π. \n",
+        "* Calculamos el promedio de los 100 experimentos anteriores para dar un valor final de π.\n",
+        "    "
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "pi_montecarlo(10000, 200)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Dummy Data Sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n = 1000000\n",
-    "data = pd.DataFrame(\n",
-    "    {\n",
-    "        'A' : np.random.randn(n),\n",
-    "        'B' : 1.5 + 2.5 * np.random.randn(n),\n",
-    "        'C' : np.random.uniform(5, 32, n)\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "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>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>C</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>1000000.000000</td>\n",
-       "      <td>1000000.000000</td>\n",
-       "      <td>1000000.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>0.000239</td>\n",
-       "      <td>1.499385</td>\n",
-       "      <td>18.469332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.999032</td>\n",
-       "      <td>2.498255</td>\n",
-       "      <td>7.792416</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>-4.808515</td>\n",
-       "      <td>-10.816448</td>\n",
-       "      <td>5.000018</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>-0.673233</td>\n",
-       "      <td>-0.185435</td>\n",
-       "      <td>11.709981</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>0.001055</td>\n",
-       "      <td>1.498496</td>\n",
-       "      <td>18.461919</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>0.675847</td>\n",
-       "      <td>3.183562</td>\n",
-       "      <td>25.217279</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>5.064791</td>\n",
-       "      <td>13.143170</td>\n",
-       "      <td>32.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "IxAP8lvL_6t_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "def pi_montecarlo(n, n_exp):\n",
+        "    pi_avg = 0\n",
+        "    pi_value_list = []\n",
+        "    for i in range(n_exp):\n",
+        "        value = 0\n",
+        "        x = np.random.uniform(0,1,n).tolist()\n",
+        "        y = np.random.uniform(0,1,n).tolist()\n",
+        "        for j in range(n):\n",
+        "            z = np.sqrt(x[j] * x[j] + y[j] * y[j])\n",
+        "            if z<=1:\n",
+        "                value += 1\n",
+        "        float_value = float(value)\n",
+        "        pi_value = float_value * 4 / n\n",
+        "        pi_value_list.append(pi_value)\n",
+        "        pi_avg += pi_value\n",
+        "\n",
+        "    pi = pi_avg/n_exp\n",
+        "\n",
+        "    print(pi)\n",
+        "    fig = plt.plot(pi_value_list)\n",
+        "    return (pi, fig)"
       ],
-      "text/plain": [
-       "                    A               B               C\n",
-       "count  1000000.000000  1000000.000000  1000000.000000\n",
-       "mean         0.000239        1.499385       18.469332\n",
-       "std          0.999032        2.498255        7.792416\n",
-       "min         -4.808515      -10.816448        5.000018\n",
-       "25%         -0.673233       -0.185435       11.709981\n",
-       "50%          0.001055        1.498496       18.461919\n",
-       "75%          0.675847        3.183562       25.217279\n",
-       "max          5.064791       13.143170       32.000000"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "(array([5.90000e+01, 2.24800e+03, 2.99250e+04, 1.62936e+05, 3.55487e+05,\n",
-       "        3.17335e+05, 1.14403e+05, 1.66540e+04, 9.30000e+02, 2.30000e+01]),\n",
-       " array([-4.80851462, -3.82118406, -2.83385349, -1.84652292, -0.85919236,\n",
-       "         0.12813821,  1.11546877,  2.10279934,  3.09012991,  4.07746047,\n",
-       "         5.06479104]),\n",
-       " <a list of 10 Patch objects>)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "36fV9Wf9_6uA",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 301
+        },
+        "outputId": "472dd2bc-a99d-4d73-cb97-5ff12e9220c8"
+      },
+      "source": [
+        "pi_montecarlo(10000, 200)"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "3.143076000000002\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(3.143076000000002, [<matplotlib.lines.Line2D at 0x7f85134a1ac8>])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAD8CAYAAACLrvgBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAFSNJREFUeJzt3X/MXmWd5/H3ZwooWUcBeXBJW7dkbLIiu1O0C038Y11woYCZMgkkZbLSuCSdNZBo4uxanD+YUUkwm5ENWWXDLF2K6w4S1NBI2U4HmUxM5MeDVqCiy7PISoWFYhExRkzxu3/cV9fbcvd5rj7t01Pa9ys5uc/5nuu6znUg6afnx303VYUkST1+b+gJSJLeOAwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndjht6AofaqaeeWsuWLRt6GpL0hvLII4+8WFVTc7U76kJj2bJlTE9PDz0NSXpDSfJ/etp5e0qS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUrc5QyPJm5M8lOR7SXYk+ctWvy3Jj5Jsb8uKVk+Sm5LMJHk0yXvHxlqX5Mm2rBurvy/JY63PTUnS6qck2dbab0ty8qH/TyBJ6tXzjfBXgfOq6hdJjge+leTetu/fV9Vd+7S/CFjelnOBm4Fzk5wCXAesBAp4JMnmqnqptVkPPABsAVYD9wIbgPuq6oYkG9r2J+d/uhIs23DPIMd9+oZLBjmudCjNeaVRI79om8e3pWbpsga4vfV7ADgpyenAhcC2qtrdgmIbsLrte2tVfbuqCrgduHRsrE1tfdNYXZI0gK5nGkkWJdkOvMDoD/4H267r2y2oG5O8qdUWA8+Mdd/ZarPVd06oA7yjqp4DaJ+ndZ+ZJOmQ6wqNqnqtqlYAS4BzkpwFXAv8U+BfAKfw29tGmTTEPOrdkqxPMp1keteuXQfSVZJ0AA7o7amq+hnw98Dqqnqu3YJ6FfhvwDmt2U5g6Vi3JcCzc9SXTKgDPN9uX9E+X9jPvG6pqpVVtXJqas5f9pUkzVPP21NTSU5q6ycCHwR+MPaHeRg9a3i8ddkMXNneoloFvNxuLW0FLkhycnsL6gJga9v3SpJVbawrgbvHxtr7ltW6sbokaQA9b0+dDmxKsohRyNxZVd9I8s0kU4xuL20H/l1rvwW4GJgBfgl8BKCqdif5DPBwa/fpqtrd1j8K3AacyOitqb1vZ90A3JnkKuDHwOXzPVFJ0sGbMzSq6lHg7An18/bTvoCr97NvI7BxQn0aOGtC/afA+XPNUZJ0ePiNcElSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktSt59/TkHQILNtwz2DHfvqGSwY7to4uXmlIkroZGpKkboaGJKmboSFJ6mZoSJK6zRkaSd6c5KEk30uyI8lftvoZSR5M8mSSryQ5odXf1LZn2v5lY2Nd2+o/THLhWH11q80k2TBWn3gMSdIweq40XgXOq6o/BFYAq5OsAj4H3FhVy4GXgKta+6uAl6rqXcCNrR1JzgTWAu8BVgNfTLIoySLgC8BFwJnAFa0tsxxDkjSAOUOjRn7RNo9vSwHnAXe1+ibg0ra+pm3T9p+fJK1+R1W9WlU/AmaAc9oyU1VPVdWvgTuANa3P/o4hSRpA1zONdkWwHXgB2Ab8b+BnVbWnNdkJLG7ri4FnANr+l4G3j9f36bO/+ttnOYYkaQBdoVFVr1XVCmAJoyuDd09q1j6zn32Hqv46SdYnmU4yvWvXrklNJEmHwAG9PVVVPwP+HlgFnJRk78+QLAGebes7gaUAbf/bgN3j9X367K/+4izH2Hdet1TVyqpaOTU1dSCnJEk6AD1vT00lOamtnwh8EHgCuB+4rDVbB9zd1je3bdr+b1ZVtfra9nbVGcBy4CHgYWB5e1PqBEYPyze3Pvs7hiRpAD0/WHg6sKm95fR7wJ1V9Y0k3wfuSPJZ4LvAra39rcCXkswwusJYC1BVO5LcCXwf2ANcXVWvASS5BtgKLAI2VtWONtYn93MMSdIA5gyNqnoUOHtC/SlGzzf2rf8KuHw/Y10PXD+hvgXY0nsMSdIw/Ea4JKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqducoZFkaZL7kzyRZEeSj7X6XyT5SZLtbbl4rM+1SWaS/DDJhWP11a02k2TDWP2MJA8meTLJV5Kc0Opvatszbf+yQ3nykqQD03OlsQf4RFW9G1gFXJ3kzLbvxqpa0ZYtAG3fWuA9wGrgi0kWJVkEfAG4CDgTuGJsnM+1sZYDLwFXtfpVwEtV9S7gxtZOkjSQOUOjqp6rqu+09VeAJ4DFs3RZA9xRVa9W1Y+AGeCctsxU1VNV9WvgDmBNkgDnAXe1/puAS8fG2tTW7wLOb+0lSQM4oGca7fbQ2cCDrXRNkkeTbExycqstBp4Z67az1fZXfzvws6ras0/9d8Zq+19u7fed1/ok00mmd+3adSCnJEk6AN2hkeQtwFeBj1fVz4GbgT8AVgDPAX+1t+mE7jWP+mxj/W6h6paqWllVK6empmY9D0nS/HWFRpLjGQXGl6vqawBV9XxVvVZVvwH+mtHtJxhdKSwd674EeHaW+ovASUmO26f+O2O1/W8Ddh/ICUqSDp2et6cC3Ao8UVWfH6ufPtbsj4HH2/pmYG178+kMYDnwEPAwsLy9KXUCo4flm6uqgPuBy1r/dcDdY2Ota+uXAd9s7SVJAzhu7ia8H/gw8FiS7a32KUZvP61gdLvoaeBPAapqR5I7ge8zevPq6qp6DSDJNcBWYBGwsap2tPE+CdyR5LPAdxmFFO3zS0lmGF1hrD2Ic5UkHaQ5Q6OqvsXkZwtbZulzPXD9hPqWSf2q6il+e3trvP4r4PK55ihJOjz8RrgkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZuhIUnqZmhIkroZGpKkboaGJKmboSFJ6mZoSJK6GRqSpG5zhkaSpUnuT/JEkh1JPtbqpyTZluTJ9nlyqyfJTUlmkjya5L1jY61r7Z9Msm6s/r4kj7U+NyXJbMeQJA2j50pjD/CJqno3sAq4OsmZwAbgvqpaDtzXtgEuApa3ZT1wM4wCALgOOBc4B7huLARubm339lvd6vs7hiRpAHOGRlU9V1XfaeuvAE8Ai4E1wKbWbBNwaVtfA9xeIw8AJyU5HbgQ2FZVu6vqJWAbsLrte2tVfbuqCrh9n7EmHUOSNIADeqaRZBlwNvAg8I6qeg5GwQKc1potBp4Z67az1War75xQZ5Zj7Duv9Ummk0zv2rXrQE5JknQAukMjyVuArwIfr6qfz9Z0Qq3mUe9WVbdU1cqqWjk1NXUgXSVJB6ArNJIczygwvlxVX2vl59utJdrnC62+E1g61n0J8Owc9SUT6rMdQ5I0gJ63pwLcCjxRVZ8f27UZ2PsG1Drg7rH6le0tqlXAy+3W0lbggiQntwfgFwBb275Xkqxqx7pyn7EmHUOSNIDjOtq8H/gw8FiS7a32KeAG4M4kVwE/Bi5v+7YAFwMzwC+BjwBU1e4knwEebu0+XVW72/pHgduAE4F728Isx5AkDWDO0KiqbzH5uQPA+RPaF3D1fsbaCGycUJ8GzppQ/+mkY0iShuE3wiVJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR16/mX+6RDbtmGe4aegqR58EpDktTN0JAkdTM0JEnd5gyNJBuTvJDk8bHaXyT5SZLtbbl4bN+1SWaS/DDJhWP11a02k2TDWP2MJA8meTLJV5Kc0Opvatszbf+yQ3XSkqT56bnSuA1YPaF+Y1WtaMsWgCRnAmuB97Q+X0yyKMki4AvARcCZwBWtLcDn2ljLgZeAq1r9KuClqnoXcGNrJ0ka0JyhUVX/AOzuHG8NcEdVvVpVPwJmgHPaMlNVT1XVr4E7gDVJApwH3NX6bwIuHRtrU1u/Czi/tZckDeRgnmlck+TRdvvq5FZbDDwz1mZnq+2v/nbgZ1W1Z5/674zV9r/c2r9OkvVJppNM79q16yBOSZI0m/mGxs3AHwArgOeAv2r1SVcCNY/6bGO9vlh1S1WtrKqVU1NTs81bknQQ5hUaVfV8Vb1WVb8B/prR7ScYXSksHWu6BHh2lvqLwElJjtun/jtjtf1vo/82mSRpAcwrNJKcPrb5x8DeN6s2A2vbm09nAMuBh4CHgeXtTakTGD0s31xVBdwPXNb6rwPuHhtrXVu/DPhmay9JGsicPyOS5G+ADwCnJtkJXAd8IMkKRreLngb+FKCqdiS5E/g+sAe4uqpea+NcA2wFFgEbq2pHO8QngTuSfBb4LnBrq98KfCnJDKMrjLUHfbaSpIMyZ2hU1RUTyrdOqO1tfz1w/YT6FmDLhPpT/Pb21nj9V8Dlc81PknT4+I1wSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1m/Pf05D0xrdswz2DHPfpGy4Z5LhaOF5pSJK6GRqSpG6GhiSpm6EhSeo2Z2gk2ZjkhSSPj9VOSbItyZPt8+RWT5KbkswkeTTJe8f6rGvtn0yybqz+viSPtT43Jclsx5AkDafnSuM2YPU+tQ3AfVW1HLivbQNcBCxvy3rgZhgFAHAdcC5wDnDdWAjc3Nru7bd6jmNIkgYyZ2hU1T8Au/cprwE2tfVNwKVj9dtr5AHgpCSnAxcC26pqd1W9BGwDVrd9b62qb1dVAbfvM9akY0iSBjLfZxrvqKrnANrnaa2+GHhmrN3OVputvnNCfbZjSJIGcqgfhGdCreZRP7CDJuuTTCeZ3rVr14F2lyR1mm9oPN9uLdE+X2j1ncDSsXZLgGfnqC+ZUJ/tGK9TVbdU1cqqWjk1NTXPU5IkzWW+obEZ2PsG1Drg7rH6le0tqlXAy+3W0lbggiQntwfgFwBb275Xkqxqb01duc9Yk44hSRrInL89leRvgA8ApybZyegtqBuAO5NcBfwYuLw13wJcDMwAvwQ+AlBVu5N8Bni4tft0Ve19uP5RRm9onQjc2xZmOYYkaSBzhkZVXbGfXedPaFvA1fsZZyOwcUJ9GjhrQv2nk44hSRqO3wiXJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTN0JAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1MzQkSd0MDUlSN0NDktTtoEIjydNJHkuyPcl0q52SZFuSJ9vnya2eJDclmUnyaJL3jo2zrrV/Msm6sfr72vgzrW8OZr6SpINzKK40/lVVraiqlW17A3BfVS0H7mvbABcBy9uyHrgZRiEDXAecC5wDXLc3aFqb9WP9Vh+C+UqS5mkhbk+tATa19U3ApWP122vkAeCkJKcDFwLbqmp3Vb0EbANWt31vrapvV1UBt4+NJUkawMGGRgF/m+SRJOtb7R1V9RxA+zyt1RcDz4z13dlqs9V3TqhLkgZy3EH2f39VPZvkNGBbkh/M0nbS84iaR/31A48Caz3AO9/5ztlnLEmat4O60qiqZ9vnC8DXGT2TeL7dWqJ9vtCa7wSWjnVfAjw7R33JhPqkedxSVSurauXU1NTBnJIkaRbzDo0k/yjJ7+9dBy4AHgc2A3vfgFoH3N3WNwNXtreoVgEvt9tXW4ELkpzcHoBfAGxt+15Jsqq9NXXl2FiSpAEczO2pdwBfb2/BHgf8j6r6n0keBu5MchXwY+Dy1n4LcDEwA/wS+AhAVe1O8hng4dbu01W1u61/FLgNOBG4ty2SpIHMOzSq6ingDyfUfwqcP6FewNX7GWsjsHFCfRo4a75zlCQdWn4jXJLUzdCQJHUzNCRJ3QwNSVI3Q0OS1M3QkCR1O9ifEdEb3LIN9ww9BUlvIF5pSJK6GRqSpG6GhiSpm6EhSepmaEiSuhkakqRuhoYkqZvf05C0YIb8HtDTN1wy2LGPZl5pSJK6GRqSpG6GhiSpm6EhSepmaEiSuh3xoZFkdZIfJplJsmHo+UjSseyIDo0ki4AvABcBZwJXJDlz2FlJ0rHrSP+exjnATFU9BZDkDmAN8P1BZ7UA/HctJL0RHOmhsRh4Zmx7J3DuQHOR9AYy1F/EjvYvFR7poZEJtXpdo2Q9sL5t/iLJDxd0VgfnVODFoScxgGPxvI/Fc4Zj/LzzuaGnMW//pKfRkR4aO4GlY9tLgGf3bVRVtwC3HK5JHYwk01W1cuh5HG7H4nkfi+cMnvfQ81hoR/SDcOBhYHmSM5KcAKwFNg88J0k6Zh3RVxpVtSfJNcBWYBGwsap2DDwtSTpmHdGhAVBVW4AtQ8/jEHpD3EZbAMfieR+L5wye91EtVa97rixJ0kRH+jMNSdIRxNAYUJI/S1JJTh16LgstyX9M8oMkjyb5epKThp7TQjoWf/4mydIk9yd5IsmOJB8bek6HS5JFSb6b5BtDz2WhGRoDSbIU+NfAj4eey2GyDTirqv458L+Aaweez4I5hn/+Zg/wiap6N7AKuPoYOW+AjwFPDD2Jw8HQGM6NwH9gwpcVj0ZV9bdVtadtPsDoOzdHq///8zdV9Wtg78/fHNWq6rmq+k5bf4XRH6KLh53VwkuyBLgE+K9Dz+VwMDQGkOSPgJ9U1feGnstA/i1w79CTWECTfv7mqP/Dc1ySZcDZwIPDzuSw+E+M/gL4m6Encjgc8a/cvlEl+TvgH0/Y9efAp4ALDu+MFt5s51xVd7c2f87oNsaXD+fcDrOun785WiV5C/BV4ONV9fOh57OQknwIeKGqHknygaHnczgYGgukqj44qZ7knwFnAN9LAqPbNN9Jck5V/d/DOMVDbn/nvFeSdcCHgPPr6H7Xu+vnb45GSY5nFBhfrqqvDT2fw+D9wB8luRh4M/DWJP+9qv7NwPNaMH5PY2BJngZWVtVR/QNvSVYDnwf+ZVXtGno+CynJcYwe9p8P/ITRz+H8ydH+awYZ/S1oE7C7qj4+9HwOt3al8WdV9aGh57KQfKahw+U/A78PbEuyPcl/GXpCC6U98N/78zdPAHce7YHRvB/4MHBe+3+8vf0NXEcRrzQkSd280pAkdTM0JEndDA1JUjdDQ5LUzdCQJHUzNCRJ3QwNSVI3Q0OS1O3/AXmnrmvZ+MkgAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x12ad02e10>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "T367QBnb_6uB",
+        "colab_type": "text"
+      },
+      "source": [
+        "### Dummy Data Sets"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(data[\"A\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Eu_1AXII_6uC",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "n = 1000000\n",
+        "data = pd.DataFrame(\n",
+        "    {\n",
+        "        'A' : np.random.randn(n),\n",
+        "        'B' : 1.5 + 2.5 * np.random.randn(n),\n",
+        "        'C' : np.random.uniform(5, 32, n)\n",
+        "    }\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "(array([2.70000e+01, 1.20700e+03, 1.87870e+04, 1.17181e+05, 3.09940e+05,\n",
-       "        3.48185e+05, 1.67357e+05, 3.42040e+04, 3.00800e+03, 1.04000e+02]),\n",
-       " array([-10.81644789,  -8.4204861 ,  -6.02452431,  -3.62856252,\n",
-       "         -1.23260073,   1.16336106,   3.55932285,   5.95528464,\n",
-       "          8.35124643,  10.74720821,  13.14317   ]),\n",
-       " <a list of 10 Patch objects>)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "BvB2sFTs_6uD",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "555a43d1-bee1-467f-8154-11a0b9efea82"
+      },
+      "source": [
+        "data.describe()"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>A</th>\n",
+              "      <th>B</th>\n",
+              "      <th>C</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>1000000.000000</td>\n",
+              "      <td>1000000.000000</td>\n",
+              "      <td>1000000.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.002164</td>\n",
+              "      <td>1.499644</td>\n",
+              "      <td>18.508191</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.999902</td>\n",
+              "      <td>2.501756</td>\n",
+              "      <td>7.794876</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-4.614127</td>\n",
+              "      <td>-11.344276</td>\n",
+              "      <td>5.000005</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-0.673665</td>\n",
+              "      <td>-0.187840</td>\n",
+              "      <td>11.749997</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.002129</td>\n",
+              "      <td>1.502898</td>\n",
+              "      <td>18.506363</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>0.678266</td>\n",
+              "      <td>3.187172</td>\n",
+              "      <td>25.261393</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>4.553337</td>\n",
+              "      <td>13.301662</td>\n",
+              "      <td>31.999969</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                    A               B               C\n",
+              "count  1000000.000000  1000000.000000  1000000.000000\n",
+              "mean         0.002164        1.499644       18.508191\n",
+              "std          0.999902        2.501756        7.794876\n",
+              "min         -4.614127      -11.344276        5.000005\n",
+              "25%         -0.673665       -0.187840       11.749997\n",
+              "50%          0.002129        1.502898       18.506363\n",
+              "75%          0.678266        3.187172       25.261393\n",
+              "max          4.553337       13.301662       31.999969"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
       ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x126d45748>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "1GCmP9H6_6uE",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 367
+        },
+        "outputId": "389e26c7-7030-4d80-b48a-a360080b878c"
+      },
+      "source": [
+        "plt.hist(data[\"A\"])"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([1.22000e+02, 2.50800e+03, 2.81180e+04, 1.40963e+05, 3.15165e+05,\n",
+              "        3.24594e+05, 1.52739e+05, 3.25730e+04, 3.09000e+03, 1.28000e+02]),\n",
+              " array([-4.61412738, -3.69738095, -2.78063452, -1.86388809, -0.94714166,\n",
+              "        -0.03039522,  0.88635121,  1.80309764,  2.71984407,  3.6365905 ,\n",
+              "         4.55333693]),\n",
+              " <a list of 10 Patch objects>)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(data[\"B\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(array([100314., 100992., 100276.,  99833.,  99972., 100306.,  99547.,\n",
-       "         99831., 100141.,  98788.]),\n",
-       " array([ 5.00001823,  7.7000164 , 10.40001457, 13.10001273, 15.8000109 ,\n",
-       "        18.50000907, 21.20000724, 23.9000054 , 26.60000357, 29.30000174,\n",
-       "        31.99999991]),\n",
-       " <a list of 10 Patch objects>)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "N9nUkcxA_6uF",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 367
+        },
+        "outputId": "e9fae6ec-8936-4406-e6c4-c36075e781f3"
+      },
+      "source": [
+        "plt.hist(data[\"B\"])"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([1.20000e+01, 8.29000e+02, 1.39770e+04, 1.01506e+05, 3.00478e+05,\n",
+              "        3.64624e+05, 1.79885e+05, 3.57100e+04, 2.90300e+03, 7.60000e+01]),\n",
+              " array([-11.34427637,  -8.87968257,  -6.41508878,  -3.95049498,\n",
+              "         -1.48590119,   0.9786926 ,   3.4432864 ,   5.90788019,\n",
+              "          8.37247399,  10.83706778,  13.30166157]),\n",
+              " <a list of 10 Patch objects>)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x126f7e588>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "W1Y5s-qY_6uG",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 367
+        },
+        "outputId": "8078d044-b46c-4183-b68e-0a75194160b3"
+      },
+      "source": [
+        "plt.hist(data[\"C\"])"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([ 99574., 100661.,  99456.,  99788., 100310.,  99741.,  99869.,\n",
+              "        100111., 100293., 100197.]),\n",
+              " array([ 5.00000455,  7.70000101, 10.39999746, 13.09999392, 15.79999037,\n",
+              "        18.49998682, 21.19998328, 23.89997973, 26.59997619, 29.29997264,\n",
+              "        31.99996909]),\n",
+              " <a list of 10 Patch objects>)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(data[\"C\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv(\"../datasets/customer-churn-model/Customer Churn Model.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KPDL3dga_6uH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/customer-churn-model/Customer Churn Model.txt\")"
+      ],
+      "execution_count": 0,
+      "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>State</th>\n",
-       "      <th>Account Length</th>\n",
-       "      <th>Area Code</th>\n",
-       "      <th>Phone</th>\n",
-       "      <th>Int'l Plan</th>\n",
-       "      <th>VMail Plan</th>\n",
-       "      <th>VMail Message</th>\n",
-       "      <th>Day Mins</th>\n",
-       "      <th>Day Calls</th>\n",
-       "      <th>Day Charge</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Eve Calls</th>\n",
-       "      <th>Eve Charge</th>\n",
-       "      <th>Night Mins</th>\n",
-       "      <th>Night Calls</th>\n",
-       "      <th>Night Charge</th>\n",
-       "      <th>Intl Mins</th>\n",
-       "      <th>Intl Calls</th>\n",
-       "      <th>Intl Charge</th>\n",
-       "      <th>CustServ Calls</th>\n",
-       "      <th>Churn?</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>KS</td>\n",
-       "      <td>128</td>\n",
-       "      <td>415</td>\n",
-       "      <td>382-4657</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>25</td>\n",
-       "      <td>265.1</td>\n",
-       "      <td>110</td>\n",
-       "      <td>45.07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>99</td>\n",
-       "      <td>16.78</td>\n",
-       "      <td>244.7</td>\n",
-       "      <td>91</td>\n",
-       "      <td>11.01</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.70</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>OH</td>\n",
-       "      <td>107</td>\n",
-       "      <td>415</td>\n",
-       "      <td>371-7191</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>26</td>\n",
-       "      <td>161.6</td>\n",
-       "      <td>123</td>\n",
-       "      <td>27.47</td>\n",
-       "      <td>...</td>\n",
-       "      <td>103</td>\n",
-       "      <td>16.62</td>\n",
-       "      <td>254.4</td>\n",
-       "      <td>103</td>\n",
-       "      <td>11.45</td>\n",
-       "      <td>13.7</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3.70</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>NJ</td>\n",
-       "      <td>137</td>\n",
-       "      <td>415</td>\n",
-       "      <td>358-1921</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>243.4</td>\n",
-       "      <td>114</td>\n",
-       "      <td>41.38</td>\n",
-       "      <td>...</td>\n",
-       "      <td>110</td>\n",
-       "      <td>10.30</td>\n",
-       "      <td>162.6</td>\n",
-       "      <td>104</td>\n",
-       "      <td>7.32</td>\n",
-       "      <td>12.2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3.29</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>OH</td>\n",
-       "      <td>84</td>\n",
-       "      <td>408</td>\n",
-       "      <td>375-9999</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>299.4</td>\n",
-       "      <td>71</td>\n",
-       "      <td>50.90</td>\n",
-       "      <td>...</td>\n",
-       "      <td>88</td>\n",
-       "      <td>5.26</td>\n",
-       "      <td>196.9</td>\n",
-       "      <td>89</td>\n",
-       "      <td>8.86</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1.78</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>OK</td>\n",
-       "      <td>75</td>\n",
-       "      <td>415</td>\n",
-       "      <td>330-6626</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>166.7</td>\n",
-       "      <td>113</td>\n",
-       "      <td>28.34</td>\n",
-       "      <td>...</td>\n",
-       "      <td>122</td>\n",
-       "      <td>12.61</td>\n",
-       "      <td>186.9</td>\n",
-       "      <td>121</td>\n",
-       "      <td>8.41</td>\n",
-       "      <td>10.1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.73</td>\n",
-       "      <td>3</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "HA94Q1BK_6uI",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "404b0219-bace-4f83-ee13-d83c9207d107"
+      },
+      "source": [
+        "data.head()"
       ],
-      "text/plain": [
-       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
-       "0    KS             128        415  382-4657         no        yes   \n",
-       "1    OH             107        415  371-7191         no        yes   \n",
-       "2    NJ             137        415  358-1921         no         no   \n",
-       "3    OH              84        408  375-9999        yes         no   \n",
-       "4    OK              75        415  330-6626        yes         no   \n",
-       "\n",
-       "   VMail Message  Day Mins  Day Calls  Day Charge   ...    Eve Calls  \\\n",
-       "0             25     265.1        110       45.07   ...           99   \n",
-       "1             26     161.6        123       27.47   ...          103   \n",
-       "2              0     243.4        114       41.38   ...          110   \n",
-       "3              0     299.4         71       50.90   ...           88   \n",
-       "4              0     166.7        113       28.34   ...          122   \n",
-       "\n",
-       "   Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  \\\n",
-       "0       16.78       244.7           91         11.01       10.0           3   \n",
-       "1       16.62       254.4          103         11.45       13.7           3   \n",
-       "2       10.30       162.6          104          7.32       12.2           5   \n",
-       "3        5.26       196.9           89          8.86        6.6           7   \n",
-       "4       12.61       186.9          121          8.41       10.1           3   \n",
-       "\n",
-       "   Intl Charge  CustServ Calls  Churn?  \n",
-       "0         2.70               1  False.  \n",
-       "1         3.70               1  False.  \n",
-       "2         3.29               0  False.  \n",
-       "3         1.78               2  False.  \n",
-       "4         2.73               3  False.  \n",
-       "\n",
-       "[5 rows x 21 columns]"
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>State</th>\n",
+              "      <th>Account Length</th>\n",
+              "      <th>Area Code</th>\n",
+              "      <th>Phone</th>\n",
+              "      <th>Int'l Plan</th>\n",
+              "      <th>VMail Plan</th>\n",
+              "      <th>VMail Message</th>\n",
+              "      <th>Day Mins</th>\n",
+              "      <th>Day Calls</th>\n",
+              "      <th>Day Charge</th>\n",
+              "      <th>Eve Mins</th>\n",
+              "      <th>Eve Calls</th>\n",
+              "      <th>Eve Charge</th>\n",
+              "      <th>Night Mins</th>\n",
+              "      <th>Night Calls</th>\n",
+              "      <th>Night Charge</th>\n",
+              "      <th>Intl Mins</th>\n",
+              "      <th>Intl Calls</th>\n",
+              "      <th>Intl Charge</th>\n",
+              "      <th>CustServ Calls</th>\n",
+              "      <th>Churn?</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>KS</td>\n",
+              "      <td>128</td>\n",
+              "      <td>415</td>\n",
+              "      <td>382-4657</td>\n",
+              "      <td>no</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>25</td>\n",
+              "      <td>265.1</td>\n",
+              "      <td>110</td>\n",
+              "      <td>45.07</td>\n",
+              "      <td>197.4</td>\n",
+              "      <td>99</td>\n",
+              "      <td>16.78</td>\n",
+              "      <td>244.7</td>\n",
+              "      <td>91</td>\n",
+              "      <td>11.01</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2.70</td>\n",
+              "      <td>1</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>OH</td>\n",
+              "      <td>107</td>\n",
+              "      <td>415</td>\n",
+              "      <td>371-7191</td>\n",
+              "      <td>no</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>26</td>\n",
+              "      <td>161.6</td>\n",
+              "      <td>123</td>\n",
+              "      <td>27.47</td>\n",
+              "      <td>195.5</td>\n",
+              "      <td>103</td>\n",
+              "      <td>16.62</td>\n",
+              "      <td>254.4</td>\n",
+              "      <td>103</td>\n",
+              "      <td>11.45</td>\n",
+              "      <td>13.7</td>\n",
+              "      <td>3</td>\n",
+              "      <td>3.70</td>\n",
+              "      <td>1</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>NJ</td>\n",
+              "      <td>137</td>\n",
+              "      <td>415</td>\n",
+              "      <td>358-1921</td>\n",
+              "      <td>no</td>\n",
+              "      <td>no</td>\n",
+              "      <td>0</td>\n",
+              "      <td>243.4</td>\n",
+              "      <td>114</td>\n",
+              "      <td>41.38</td>\n",
+              "      <td>121.2</td>\n",
+              "      <td>110</td>\n",
+              "      <td>10.30</td>\n",
+              "      <td>162.6</td>\n",
+              "      <td>104</td>\n",
+              "      <td>7.32</td>\n",
+              "      <td>12.2</td>\n",
+              "      <td>5</td>\n",
+              "      <td>3.29</td>\n",
+              "      <td>0</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>OH</td>\n",
+              "      <td>84</td>\n",
+              "      <td>408</td>\n",
+              "      <td>375-9999</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>no</td>\n",
+              "      <td>0</td>\n",
+              "      <td>299.4</td>\n",
+              "      <td>71</td>\n",
+              "      <td>50.90</td>\n",
+              "      <td>61.9</td>\n",
+              "      <td>88</td>\n",
+              "      <td>5.26</td>\n",
+              "      <td>196.9</td>\n",
+              "      <td>89</td>\n",
+              "      <td>8.86</td>\n",
+              "      <td>6.6</td>\n",
+              "      <td>7</td>\n",
+              "      <td>1.78</td>\n",
+              "      <td>2</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>OK</td>\n",
+              "      <td>75</td>\n",
+              "      <td>415</td>\n",
+              "      <td>330-6626</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>no</td>\n",
+              "      <td>0</td>\n",
+              "      <td>166.7</td>\n",
+              "      <td>113</td>\n",
+              "      <td>28.34</td>\n",
+              "      <td>148.3</td>\n",
+              "      <td>122</td>\n",
+              "      <td>12.61</td>\n",
+              "      <td>186.9</td>\n",
+              "      <td>121</td>\n",
+              "      <td>8.41</td>\n",
+              "      <td>10.1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>2.73</td>\n",
+              "      <td>3</td>\n",
+              "      <td>False.</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "  State  Account Length  Area Code  ... Intl Charge CustServ Calls  Churn?\n",
+              "0    KS             128        415  ...        2.70              1  False.\n",
+              "1    OH             107        415  ...        3.70              1  False.\n",
+              "2    NJ             137        415  ...        3.29              0  False.\n",
+              "3    OH              84        408  ...        1.78              2  False.\n",
+              "4    OK              75        415  ...        2.73              3  False.\n",
+              "\n",
+              "[5 rows x 21 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 19
+        }
       ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "colum_names = data.columns.values.tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hN3pktK9_6uJ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "colum_names = data.columns.values.tolist()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "21"
+      "cell_type": "code",
+      "metadata": {
+        "id": "321RtjMG_6uK",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "153b3032-42cb-42da-ee53-df9aa52d72a4"
+      },
+      "source": [
+        "a = len(colum_names)\n",
+        "a"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "21"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 21
+        }
       ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = len(colum_names)\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_data = pd.DataFrame(\n",
-    "    {\n",
-    "        'Column Name': colum_names,\n",
-    "        'A' : np.random.randn(a),\n",
-    "        'B' : np.random.uniform(0,1,a)\n",
-    "    }, index = range(42, 42 + a)\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "gFJSTVfp_6uL",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "new_data = pd.DataFrame(\n",
+        "    {\n",
+        "        'Column Name': colum_names,\n",
+        "        'A' : np.random.randn(a),\n",
+        "        'B' : np.random.uniform(0,1,a)\n",
+        "    }, index = range(42, 42 + a)\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "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>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>Column Name</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>0.745216</td>\n",
-       "      <td>0.666901</td>\n",
-       "      <td>State</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>0.978430</td>\n",
-       "      <td>0.445809</td>\n",
-       "      <td>Account Length</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>1.731687</td>\n",
-       "      <td>0.099869</td>\n",
-       "      <td>Area Code</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>-1.045452</td>\n",
-       "      <td>0.774833</td>\n",
-       "      <td>Phone</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>-1.990031</td>\n",
-       "      <td>0.104624</td>\n",
-       "      <td>Int'l Plan</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>1.402830</td>\n",
-       "      <td>0.956325</td>\n",
-       "      <td>VMail Plan</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48</th>\n",
-       "      <td>-0.469498</td>\n",
-       "      <td>0.604139</td>\n",
-       "      <td>VMail Message</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>-0.100384</td>\n",
-       "      <td>0.058382</td>\n",
-       "      <td>Day Mins</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>0.176315</td>\n",
-       "      <td>0.864266</td>\n",
-       "      <td>Day Calls</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>51</th>\n",
-       "      <td>-1.062312</td>\n",
-       "      <td>0.013515</td>\n",
-       "      <td>Day Charge</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>-0.043491</td>\n",
-       "      <td>0.346297</td>\n",
-       "      <td>Eve Mins</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>-1.714240</td>\n",
-       "      <td>0.165366</td>\n",
-       "      <td>Eve Calls</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>-0.078776</td>\n",
-       "      <td>0.343870</td>\n",
-       "      <td>Eve Charge</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>-0.798323</td>\n",
-       "      <td>0.784142</td>\n",
-       "      <td>Night Mins</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>-0.217450</td>\n",
-       "      <td>0.809578</td>\n",
-       "      <td>Night Calls</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>1.941108</td>\n",
-       "      <td>0.400151</td>\n",
-       "      <td>Night Charge</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>-0.911327</td>\n",
-       "      <td>0.984113</td>\n",
-       "      <td>Intl Mins</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>59</th>\n",
-       "      <td>0.939746</td>\n",
-       "      <td>0.255986</td>\n",
-       "      <td>Intl Calls</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>0.161009</td>\n",
-       "      <td>0.194346</td>\n",
-       "      <td>Intl Charge</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>61</th>\n",
-       "      <td>-1.537414</td>\n",
-       "      <td>0.689040</td>\n",
-       "      <td>CustServ Calls</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>-1.165811</td>\n",
-       "      <td>0.917208</td>\n",
-       "      <td>Churn?</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "ovmUIoXZ_6uM",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 700
+        },
+        "outputId": "8baf6056-944f-404d-94e0-b02247d348e3"
+      },
+      "source": [
+        "new_data"
       ],
-      "text/plain": [
-       "           A         B     Column Name\n",
-       "42  0.745216  0.666901           State\n",
-       "43  0.978430  0.445809  Account Length\n",
-       "44  1.731687  0.099869       Area Code\n",
-       "45 -1.045452  0.774833           Phone\n",
-       "46 -1.990031  0.104624      Int'l Plan\n",
-       "47  1.402830  0.956325      VMail Plan\n",
-       "48 -0.469498  0.604139   VMail Message\n",
-       "49 -0.100384  0.058382        Day Mins\n",
-       "50  0.176315  0.864266       Day Calls\n",
-       "51 -1.062312  0.013515      Day Charge\n",
-       "52 -0.043491  0.346297        Eve Mins\n",
-       "53 -1.714240  0.165366       Eve Calls\n",
-       "54 -0.078776  0.343870      Eve Charge\n",
-       "55 -0.798323  0.784142      Night Mins\n",
-       "56 -0.217450  0.809578     Night Calls\n",
-       "57  1.941108  0.400151    Night Charge\n",
-       "58 -0.911327  0.984113       Intl Mins\n",
-       "59  0.939746  0.255986      Intl Calls\n",
-       "60  0.161009  0.194346     Intl Charge\n",
-       "61 -1.537414  0.689040  CustServ Calls\n",
-       "62 -1.165811  0.917208          Churn?"
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Column Name</th>\n",
+              "      <th>A</th>\n",
+              "      <th>B</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>42</th>\n",
+              "      <td>State</td>\n",
+              "      <td>0.009601</td>\n",
+              "      <td>0.571675</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>43</th>\n",
+              "      <td>Account Length</td>\n",
+              "      <td>1.127091</td>\n",
+              "      <td>0.203394</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>44</th>\n",
+              "      <td>Area Code</td>\n",
+              "      <td>1.413309</td>\n",
+              "      <td>0.426407</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>45</th>\n",
+              "      <td>Phone</td>\n",
+              "      <td>1.335081</td>\n",
+              "      <td>0.190180</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>46</th>\n",
+              "      <td>Int'l Plan</td>\n",
+              "      <td>-0.828382</td>\n",
+              "      <td>0.156801</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>47</th>\n",
+              "      <td>VMail Plan</td>\n",
+              "      <td>0.608314</td>\n",
+              "      <td>0.255236</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>48</th>\n",
+              "      <td>VMail Message</td>\n",
+              "      <td>-1.529998</td>\n",
+              "      <td>0.688072</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49</th>\n",
+              "      <td>Day Mins</td>\n",
+              "      <td>0.463638</td>\n",
+              "      <td>0.638551</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50</th>\n",
+              "      <td>Day Calls</td>\n",
+              "      <td>-0.419050</td>\n",
+              "      <td>0.900912</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>51</th>\n",
+              "      <td>Day Charge</td>\n",
+              "      <td>0.180764</td>\n",
+              "      <td>0.204704</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>52</th>\n",
+              "      <td>Eve Mins</td>\n",
+              "      <td>-0.569117</td>\n",
+              "      <td>0.213367</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>53</th>\n",
+              "      <td>Eve Calls</td>\n",
+              "      <td>-1.260274</td>\n",
+              "      <td>0.206531</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>54</th>\n",
+              "      <td>Eve Charge</td>\n",
+              "      <td>0.241916</td>\n",
+              "      <td>0.431233</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>55</th>\n",
+              "      <td>Night Mins</td>\n",
+              "      <td>-0.105272</td>\n",
+              "      <td>0.562286</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>56</th>\n",
+              "      <td>Night Calls</td>\n",
+              "      <td>-0.788214</td>\n",
+              "      <td>0.735034</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>57</th>\n",
+              "      <td>Night Charge</td>\n",
+              "      <td>1.190481</td>\n",
+              "      <td>0.578185</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>58</th>\n",
+              "      <td>Intl Mins</td>\n",
+              "      <td>-1.857890</td>\n",
+              "      <td>0.029463</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>59</th>\n",
+              "      <td>Intl Calls</td>\n",
+              "      <td>-0.085863</td>\n",
+              "      <td>0.238753</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>60</th>\n",
+              "      <td>Intl Charge</td>\n",
+              "      <td>-0.622193</td>\n",
+              "      <td>0.324741</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>61</th>\n",
+              "      <td>CustServ Calls</td>\n",
+              "      <td>0.839831</td>\n",
+              "      <td>0.839604</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>62</th>\n",
+              "      <td>Churn?</td>\n",
+              "      <td>0.570857</td>\n",
+              "      <td>0.642823</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "       Column Name         A         B\n",
+              "42           State  0.009601  0.571675\n",
+              "43  Account Length  1.127091  0.203394\n",
+              "44       Area Code  1.413309  0.426407\n",
+              "45           Phone  1.335081  0.190180\n",
+              "46      Int'l Plan -0.828382  0.156801\n",
+              "47      VMail Plan  0.608314  0.255236\n",
+              "48   VMail Message -1.529998  0.688072\n",
+              "49        Day Mins  0.463638  0.638551\n",
+              "50       Day Calls -0.419050  0.900912\n",
+              "51      Day Charge  0.180764  0.204704\n",
+              "52        Eve Mins -0.569117  0.213367\n",
+              "53       Eve Calls -1.260274  0.206531\n",
+              "54      Eve Charge  0.241916  0.431233\n",
+              "55      Night Mins -0.105272  0.562286\n",
+              "56     Night Calls -0.788214  0.735034\n",
+              "57    Night Charge  1.190481  0.578185\n",
+              "58       Intl Mins -1.857890  0.029463\n",
+              "59      Intl Calls -0.085863  0.238753\n",
+              "60     Intl Charge -0.622193  0.324741\n",
+              "61  CustServ Calls  0.839831  0.839604\n",
+              "62          Churn?  0.570857  0.642823"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 23
+        }
       ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "97ZGkX3z_6uN",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
     }
-   ],
-   "source": [
-    "new_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  ]
+}
\ No newline at end of file
diff --git "a/notebooks/T2 - 3 - Data Cleaning - Agrupaci\303\263n de datos-Colab.ipynb" "b/notebooks/T2 - 3 - Data Cleaning - Agrupaci\303\263n de datos-Colab.ipynb"
new file mode 100644
index 00000000..1a1583a3
--- /dev/null
+++ "b/notebooks/T2 - 3 - Data Cleaning - Agrupaci\303\263n de datos-Colab.ipynb"	
@@ -0,0 +1,3979 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/Collab---v-3.8/notebooks/T2%20-%203%20-%20Data%20Cleaning%20-%20Agrupaci%C3%B3n%20de%20datos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "K2teubACAkRb"
+   },
+   "source": [
+    "# Agregación de datos por categoría"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "VDV8LPSJA61J"
+   },
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "zwuY-TMgAkRc"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "0r6-BV-FAkRi"
+   },
+   "outputs": [],
+   "source": [
+    "gender = [\"Male\", \"Female\"]\n",
+    "income = [\"Poor\", \"Middle Class\", \"Rich\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "dUOu8paRAkRl"
+   },
+   "outputs": [],
+   "source": [
+    "n = 500\n",
+    "\n",
+    "gender_data = []\n",
+    "income_data = []\n",
+    "\n",
+    "for i in range(0,500):\n",
+    "    gender_data.append(np.random.choice(gender))\n",
+    "    income_data.append(np.random.choice(income))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 170
+    },
+    "colab_type": "code",
+    "id": "wz2m3fpuAkRo",
+    "outputId": "fe626221-e112-4cdd-cfb3-6d68a36045b8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Male',\n",
+       " 'Female',\n",
+       " 'Male',\n",
+       " 'Male',\n",
+       " 'Female',\n",
+       " 'Female',\n",
+       " 'Female',\n",
+       " 'Female',\n",
+       " 'Male']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gender_data[1:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 170
+    },
+    "colab_type": "code",
+    "id": "CafW2UzYAkRr",
+    "outputId": "748ee054-8376-4ff8-c445-9d4734231046"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Middle Class',\n",
+       " 'Rich',\n",
+       " 'Rich',\n",
+       " 'Poor',\n",
+       " 'Poor',\n",
+       " 'Rich',\n",
+       " 'Middle Class',\n",
+       " 'Middle Class',\n",
+       " 'Middle Class']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "income_data[1:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Q53rIw03AkRu"
+   },
+   "outputs": [],
+   "source": [
+    "#Z -> N(0,1)\n",
+    "#N(m, s) -> m + s * Z\n",
+    "height = 160 + 30 * np.random.randn(n)\n",
+    "weight = 65 + 25 * np.random.randn(n)\n",
+    "age = 30 + 12 * np.random.randn(n)\n",
+    "income = 18000 + 3500 * np.random.rand(n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "tUaeOrqrAkRv"
+   },
+   "outputs": [],
+   "source": [
+    "data = pd.DataFrame(\n",
+    "    {\n",
+    "        \"Gender\" : gender_data,\n",
+    "        \"Economic Status\" : income_data,\n",
+    "        \"Height\" : height,\n",
+    "        \"Weight\" : weight,\n",
+    "        \"Age\" : age,\n",
+    "        \"Income\" : income\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "MXeeNF_YAkRx",
+    "outputId": "885503b1-4c71-4dea-f6cf-0aefe3c893e6"
+   },
+   "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>Economic Status</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>196.343940</td>\n",
+       "      <td>60.909064</td>\n",
+       "      <td>40.451221</td>\n",
+       "      <td>21385.958772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>153.697110</td>\n",
+       "      <td>48.005164</td>\n",
+       "      <td>48.163870</td>\n",
+       "      <td>19757.653998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>146.713926</td>\n",
+       "      <td>53.476714</td>\n",
+       "      <td>31.144568</td>\n",
+       "      <td>20279.476735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>183.988501</td>\n",
+       "      <td>71.785466</td>\n",
+       "      <td>34.192437</td>\n",
+       "      <td>19778.532678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>179.805582</td>\n",
+       "      <td>58.547234</td>\n",
+       "      <td>32.501684</td>\n",
+       "      <td>19346.337986</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender Economic Status      Height     Weight        Age        Income\n",
+       "0  Female            Rich  196.343940  60.909064  40.451221  21385.958772\n",
+       "1    Male    Middle Class  153.697110  48.005164  48.163870  19757.653998\n",
+       "2  Female            Rich  146.713926  53.476714  31.144568  20279.476735\n",
+       "3    Male            Rich  183.988501  71.785466  34.192437  19778.532678\n",
+       "4    Male            Poor  179.805582  58.547234  32.501684  19346.337986"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "lcIDvGCfAkRy"
+   },
+   "source": [
+    "## Agrupación de datos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "b0ejXt6rAkRz"
+   },
+   "outputs": [],
+   "source": [
+    "grouped_gender = data.groupby(\"Gender\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 153
+    },
+    "colab_type": "code",
+    "id": "aEiG5AFrAkR0",
+    "outputId": "474eeb1c-4efa-4ecf-ba8e-1622a786f9cc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Female': Int64Index([  0,   2,   5,   6,   7,   8,  11,  12,  14,  15,\n",
+       "             ...\n",
+       "             480, 481, 484, 485, 488, 490, 491, 492, 493, 495],\n",
+       "            dtype='int64', length=234),\n",
+       " 'Male': Int64Index([  1,   3,   4,   9,  10,  13,  16,  17,  18,  19,\n",
+       "             ...\n",
+       "             482, 483, 486, 487, 489, 494, 496, 497, 498, 499],\n",
+       "            dtype='int64', length=266)}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped_gender.groups"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 527
+    },
+    "colab_type": "code",
+    "id": "gSVmrowTAkR2",
+    "outputId": "95eea914-8b1e-4c3f-ba12-3d28e821ff4b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Female\n",
+      "     Gender Economic Status      Height     Weight        Age        Income\n",
+      "0    Female            Rich  196.343940  60.909064  40.451221  21385.958772\n",
+      "2    Female            Rich  146.713926  53.476714  31.144568  20279.476735\n",
+      "5    Female            Poor  172.074560  28.192775  42.997942  21277.967078\n",
+      "6    Female            Rich  182.532298  63.768421  57.426784  18952.149135\n",
+      "7    Female    Middle Class  166.360472  33.332115  19.958657  19611.989916\n",
+      "..      ...             ...         ...        ...        ...           ...\n",
+      "490  Female            Rich  150.092007  67.726508  23.798001  19656.450662\n",
+      "491  Female    Middle Class  156.545084  55.503196  26.584817  19038.000602\n",
+      "492  Female    Middle Class  173.475638  52.248953  64.993034  20516.979828\n",
+      "493  Female            Rich  169.567790  63.484671  29.763336  20821.406697\n",
+      "495  Female    Middle Class  180.672252  51.029436  17.900334  20043.401932\n",
+      "\n",
+      "[234 rows x 6 columns]\n",
+      "Male\n",
+      "    Gender Economic Status      Height      Weight        Age        Income\n",
+      "1     Male    Middle Class  153.697110   48.005164  48.163870  19757.653998\n",
+      "3     Male            Rich  183.988501   71.785466  34.192437  19778.532678\n",
+      "4     Male            Poor  179.805582   58.547234  32.501684  19346.337986\n",
+      "9     Male    Middle Class  208.784072   72.931162  30.261495  18603.361929\n",
+      "10    Male            Poor  166.139408  109.438657  38.757674  19853.349989\n",
+      "..     ...             ...         ...         ...        ...           ...\n",
+      "494   Male            Rich  185.358932   70.843868  31.240249  19538.632703\n",
+      "496   Male            Rich  151.602650   43.765061  27.617103  20241.297735\n",
+      "497   Male    Middle Class  149.973149   67.190925  36.479521  19335.750899\n",
+      "498   Male            Rich  163.669365   98.571932  35.257657  18888.280373\n",
+      "499   Male    Middle Class  148.236420   90.327894  13.374767  19467.309309\n",
+      "\n",
+      "[266 rows x 6 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for names, groups in grouped_gender:\n",
+    "    print(names)\n",
+    "    print(groups)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 419
+    },
+    "colab_type": "code",
+    "id": "-eV8DVorAkR3",
+    "outputId": "19c4888f-1ffc-47aa-9550-99fc166a5a93"
+   },
+   "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>Economic Status</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>196.343940</td>\n",
+       "      <td>60.909064</td>\n",
+       "      <td>40.451221</td>\n",
+       "      <td>21385.958772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>146.713926</td>\n",
+       "      <td>53.476714</td>\n",
+       "      <td>31.144568</td>\n",
+       "      <td>20279.476735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>172.074560</td>\n",
+       "      <td>28.192775</td>\n",
+       "      <td>42.997942</td>\n",
+       "      <td>21277.967078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>182.532298</td>\n",
+       "      <td>63.768421</td>\n",
+       "      <td>57.426784</td>\n",
+       "      <td>18952.149135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>166.360472</td>\n",
+       "      <td>33.332115</td>\n",
+       "      <td>19.958657</td>\n",
+       "      <td>19611.989916</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>150.092007</td>\n",
+       "      <td>67.726508</td>\n",
+       "      <td>23.798001</td>\n",
+       "      <td>19656.450662</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>491</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>156.545084</td>\n",
+       "      <td>55.503196</td>\n",
+       "      <td>26.584817</td>\n",
+       "      <td>19038.000602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>173.475638</td>\n",
+       "      <td>52.248953</td>\n",
+       "      <td>64.993034</td>\n",
+       "      <td>20516.979828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>169.567790</td>\n",
+       "      <td>63.484671</td>\n",
+       "      <td>29.763336</td>\n",
+       "      <td>20821.406697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>180.672252</td>\n",
+       "      <td>51.029436</td>\n",
+       "      <td>17.900334</td>\n",
+       "      <td>20043.401932</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>234 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Gender Economic Status      Height     Weight        Age        Income\n",
+       "0    Female            Rich  196.343940  60.909064  40.451221  21385.958772\n",
+       "2    Female            Rich  146.713926  53.476714  31.144568  20279.476735\n",
+       "5    Female            Poor  172.074560  28.192775  42.997942  21277.967078\n",
+       "6    Female            Rich  182.532298  63.768421  57.426784  18952.149135\n",
+       "7    Female    Middle Class  166.360472  33.332115  19.958657  19611.989916\n",
+       "..      ...             ...         ...        ...        ...           ...\n",
+       "490  Female            Rich  150.092007  67.726508  23.798001  19656.450662\n",
+       "491  Female    Middle Class  156.545084  55.503196  26.584817  19038.000602\n",
+       "492  Female    Middle Class  173.475638  52.248953  64.993034  20516.979828\n",
+       "493  Female            Rich  169.567790  63.484671  29.763336  20821.406697\n",
+       "495  Female    Middle Class  180.672252  51.029436  17.900334  20043.401932\n",
+       "\n",
+       "[234 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped_gender.get_group(\"Female\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "cSzGIkkTAkR4"
+   },
+   "outputs": [],
+   "source": [
+    "double_group = data.groupby([\"Gender\", \"Economic Status\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "ZQLuFUejAkR6",
+    "outputId": "d42b8dad-d4aa-4be4-ee8c-71e6f15568f2"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(double_group)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "colab_type": "code",
+    "id": "Eho6KDzUAkR7",
+    "outputId": "a641a83c-cdcd-49d2-8c9b-c7f333a246d1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('Female', 'Middle Class')\n",
+      "     Gender Economic Status      Height     Weight        Age        Income\n",
+      "7    Female    Middle Class  166.360472  33.332115  19.958657  19611.989916\n",
+      "8    Female    Middle Class  128.918824  76.401772  31.228420  21412.474051\n",
+      "20   Female    Middle Class  178.792011  60.561514  42.988117  18128.069658\n",
+      "30   Female    Middle Class  189.867726  54.644142  41.009437  19217.893656\n",
+      "32   Female    Middle Class  175.316787  94.572508  31.077942  20033.912034\n",
+      "..      ...             ...         ...        ...        ...           ...\n",
+      "481  Female    Middle Class  198.371944  58.787997  30.735950  19162.782868\n",
+      "488  Female    Middle Class  206.483490  77.263171  40.110498  20595.400103\n",
+      "491  Female    Middle Class  156.545084  55.503196  26.584817  19038.000602\n",
+      "492  Female    Middle Class  173.475638  52.248953  64.993034  20516.979828\n",
+      "495  Female    Middle Class  180.672252  51.029436  17.900334  20043.401932\n",
+      "\n",
+      "[82 rows x 6 columns]\n",
+      "('Female', 'Poor')\n",
+      "     Gender Economic Status      Height      Weight        Age        Income\n",
+      "5    Female            Poor  172.074560   28.192775  42.997942  21277.967078\n",
+      "12   Female            Poor  153.654025   53.081185  15.043235  21227.230619\n",
+      "15   Female            Poor  150.637828   59.962686  48.190578  20117.515158\n",
+      "23   Female            Poor  169.697662  102.217818  20.978407  20540.140935\n",
+      "24   Female            Poor  190.981807   65.731874  27.070750  19301.011334\n",
+      "..      ...             ...         ...         ...        ...           ...\n",
+      "440  Female            Poor  148.429338   98.029599  41.745566  19370.053905\n",
+      "453  Female            Poor  115.551046   49.196529  32.176829  19887.500027\n",
+      "456  Female            Poor  139.328070   58.870144  19.706834  19362.401595\n",
+      "470  Female            Poor  215.296695   50.900935  45.546388  20787.032943\n",
+      "484  Female            Poor  237.863120   19.951759  49.066417  19810.325445\n",
+      "\n",
+      "[75 rows x 6 columns]\n",
+      "('Female', 'Rich')\n",
+      "     Gender Economic Status      Height     Weight        Age        Income\n",
+      "0    Female            Rich  196.343940  60.909064  40.451221  21385.958772\n",
+      "2    Female            Rich  146.713926  53.476714  31.144568  20279.476735\n",
+      "6    Female            Rich  182.532298  63.768421  57.426784  18952.149135\n",
+      "11   Female            Rich  135.604574  43.813854  37.439586  20665.291519\n",
+      "14   Female            Rich  165.719126  82.115136  19.543581  20145.928402\n",
+      "..      ...             ...         ...        ...        ...           ...\n",
+      "464  Female            Rich  240.729697  78.607448  49.416987  20748.672727\n",
+      "474  Female            Rich  198.195999  80.450540  44.223839  19283.285635\n",
+      "485  Female            Rich  192.922204  49.686179  19.355819  19586.770041\n",
+      "490  Female            Rich  150.092007  67.726508  23.798001  19656.450662\n",
+      "493  Female            Rich  169.567790  63.484671  29.763336  20821.406697\n",
+      "\n",
+      "[77 rows x 6 columns]\n",
+      "('Male', 'Middle Class')\n",
+      "    Gender Economic Status      Height      Weight        Age        Income\n",
+      "1     Male    Middle Class  153.697110   48.005164  48.163870  19757.653998\n",
+      "9     Male    Middle Class  208.784072   72.931162  30.261495  18603.361929\n",
+      "16    Male    Middle Class  161.070008   68.680160  30.838949  18293.220540\n",
+      "31    Male    Middle Class  114.204244   41.165313  31.326135  20729.504794\n",
+      "36    Male    Middle Class  161.539602   68.489234  20.509221  19416.287680\n",
+      "..     ...             ...         ...         ...        ...           ...\n",
+      "461   Male    Middle Class  157.840594   66.737952  11.238893  18514.092443\n",
+      "468   Male    Middle Class  149.264876  103.354641  36.840802  21094.214950\n",
+      "477   Male    Middle Class  135.497506   36.109609  29.088379  19663.439572\n",
+      "497   Male    Middle Class  149.973149   67.190925  36.479521  19335.750899\n",
+      "499   Male    Middle Class  148.236420   90.327894  13.374767  19467.309309\n",
+      "\n",
+      "[77 rows x 6 columns]\n",
+      "('Male', 'Poor')\n",
+      "    Gender Economic Status      Height      Weight        Age        Income\n",
+      "4     Male            Poor  179.805582   58.547234  32.501684  19346.337986\n",
+      "10    Male            Poor  166.139408  109.438657  38.757674  19853.349989\n",
+      "18    Male            Poor  201.007832   21.432816  29.880663  20623.543032\n",
+      "43    Male            Poor  208.985044   51.847661  32.130139  18763.461212\n",
+      "45    Male            Poor  188.868631   72.095984  27.091806  18039.870897\n",
+      "..     ...             ...         ...         ...        ...           ...\n",
+      "473   Male            Poor  129.216300   50.957336  20.444673  21010.592763\n",
+      "479   Male            Poor  170.120335   44.637531  21.618054  20447.343548\n",
+      "482   Male            Poor  187.008562   73.201562  41.575046  19288.194315\n",
+      "486   Male            Poor  177.674228   79.068131  23.654781  20300.329345\n",
+      "487   Male            Poor  227.539516   57.374036  25.997146  19157.948395\n",
+      "\n",
+      "[93 rows x 6 columns]\n",
+      "('Male', 'Rich')\n",
+      "    Gender Economic Status      Height     Weight        Age        Income\n",
+      "3     Male            Rich  183.988501  71.785466  34.192437  19778.532678\n",
+      "13    Male            Rich  173.761763  61.055417  47.740688  20994.127333\n",
+      "17    Male            Rich  142.929235  52.884703  14.840861  20594.052895\n",
+      "19    Male            Rich  105.096782  55.340929  12.126035  20594.083296\n",
+      "21    Male            Rich  176.677934  99.600551  34.836404  19629.864461\n",
+      "..     ...             ...         ...        ...        ...           ...\n",
+      "483   Male            Rich  125.811314  47.194725  22.807730  18006.577638\n",
+      "489   Male            Rich  123.332415  74.980695  28.021002  20640.865665\n",
+      "494   Male            Rich  185.358932  70.843868  31.240249  19538.632703\n",
+      "496   Male            Rich  151.602650  43.765061  27.617103  20241.297735\n",
+      "498   Male            Rich  163.669365  98.571932  35.257657  18888.280373\n",
+      "\n",
+      "[96 rows x 6 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "for names, groups in double_group:\n",
+    "    print(names)\n",
+    "    print(groups)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "bt-jzYaOAkR8"
+   },
+   "source": [
+    "## Operaciones sobre datos agrupados"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 266
+    },
+    "colab_type": "code",
+    "id": "IALClhg8AkR8",
+    "outputId": "6f13982b-30f8-46c1-84c0-f7d82500c562"
+   },
+   "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></th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Economic Status</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>13109.758475</td>\n",
+       "      <td>5114.396788</td>\n",
+       "      <td>2443.013144</td>\n",
+       "      <td>1.609475e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>12105.710834</td>\n",
+       "      <td>5027.709669</td>\n",
+       "      <td>2197.896111</td>\n",
+       "      <td>1.477014e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>12213.117719</td>\n",
+       "      <td>5000.741333</td>\n",
+       "      <td>2327.382814</td>\n",
+       "      <td>1.521338e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>12761.733997</td>\n",
+       "      <td>4959.605170</td>\n",
+       "      <td>2198.240962</td>\n",
+       "      <td>1.512304e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>15301.173067</td>\n",
+       "      <td>5726.156969</td>\n",
+       "      <td>2669.090332</td>\n",
+       "      <td>1.847747e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>15092.221858</td>\n",
+       "      <td>6539.546423</td>\n",
+       "      <td>2925.118149</td>\n",
+       "      <td>1.906379e+06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              Height       Weight          Age        Income\n",
+       "Gender Economic Status                                                      \n",
+       "Female Middle Class     13109.758475  5114.396788  2443.013144  1.609475e+06\n",
+       "       Poor             12105.710834  5027.709669  2197.896111  1.477014e+06\n",
+       "       Rich             12213.117719  5000.741333  2327.382814  1.521338e+06\n",
+       "Male   Middle Class     12761.733997  4959.605170  2198.240962  1.512304e+06\n",
+       "       Poor             15301.173067  5726.156969  2669.090332  1.847747e+06\n",
+       "       Rich             15092.221858  6539.546423  2925.118149  1.906379e+06"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 266
+    },
+    "colab_type": "code",
+    "id": "XIG-ViIKAkR-",
+    "outputId": "daa9cbd7-3175-49a8-ff93-92d29ca6f55b"
+   },
+   "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></th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Economic Status</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>159.875103</td>\n",
+       "      <td>62.370693</td>\n",
+       "      <td>29.792843</td>\n",
+       "      <td>19627.745015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>161.409478</td>\n",
+       "      <td>67.036129</td>\n",
+       "      <td>29.305281</td>\n",
+       "      <td>19693.513975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>158.611918</td>\n",
+       "      <td>64.944693</td>\n",
+       "      <td>30.225751</td>\n",
+       "      <td>19757.636531</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>165.736805</td>\n",
+       "      <td>64.410457</td>\n",
+       "      <td>28.548584</td>\n",
+       "      <td>19640.313086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>164.528743</td>\n",
+       "      <td>61.571580</td>\n",
+       "      <td>28.699896</td>\n",
+       "      <td>19868.250726</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>157.210644</td>\n",
+       "      <td>68.120275</td>\n",
+       "      <td>30.469981</td>\n",
+       "      <td>19858.115811</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            Height     Weight        Age        Income\n",
+       "Gender Economic Status                                                \n",
+       "Female Middle Class     159.875103  62.370693  29.792843  19627.745015\n",
+       "       Poor             161.409478  67.036129  29.305281  19693.513975\n",
+       "       Rich             158.611918  64.944693  30.225751  19757.636531\n",
+       "Male   Middle Class     165.736805  64.410457  28.548584  19640.313086\n",
+       "       Poor             164.528743  61.571580  28.699896  19868.250726\n",
+       "       Rich             157.210644  68.120275  30.469981  19858.115811"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 153
+    },
+    "colab_type": "code",
+    "id": "Dp6clyJuAkR_",
+    "outputId": "c43dbc59-2358-4e5e-a662-7ffad3e3b1a0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender  Economic Status\n",
+       "Female  Middle Class       82\n",
+       "        Poor               75\n",
+       "        Rich               77\n",
+       "Male    Middle Class       77\n",
+       "        Poor               93\n",
+       "        Rich               96\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group.size()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 368
+    },
+    "colab_type": "code",
+    "id": "MVRQed0CAkSA",
+    "outputId": "9d2c6adf-a3c6-41d6-b728-3a9fbcc5902a"
+   },
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+    {
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+       "      <th>Economic Status</th>\n",
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+       "      <th></th>\n",
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+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
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+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>82.0</td>\n",
+       "      <td>159.875103</td>\n",
+       "      <td>30.373012</td>\n",
+       "      <td>80.236149</td>\n",
+       "      <td>135.913928</td>\n",
+       "      <td>160.514160</td>\n",
+       "      <td>180.560591</td>\n",
+       "      <td>228.409064</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>62.370693</td>\n",
+       "      <td>23.546737</td>\n",
+       "      <td>6.792281</td>\n",
+       "      <td>47.414308</td>\n",
+       "      <td>62.017350</td>\n",
+       "      <td>77.201058</td>\n",
+       "      <td>120.122674</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>29.792843</td>\n",
+       "      <td>12.587559</td>\n",
+       "      <td>-3.010737</td>\n",
+       "      <td>22.525387</td>\n",
+       "      <td>28.594280</td>\n",
+       "      <td>40.046598</td>\n",
+       "      <td>64.993034</td>\n",
+       "      <td>82.0</td>\n",
+       "      <td>19627.745015</td>\n",
+       "      <td>994.118644</td>\n",
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+       "      <td>18793.937121</td>\n",
+       "      <td>19615.513639</td>\n",
+       "      <td>20359.835287</td>\n",
+       "      <td>21488.883196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>75.0</td>\n",
+       "      <td>161.409478</td>\n",
+       "      <td>31.477731</td>\n",
+       "      <td>97.008895</td>\n",
+       "      <td>140.289352</td>\n",
+       "      <td>158.853184</td>\n",
+       "      <td>178.470317</td>\n",
+       "      <td>237.863120</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>67.036129</td>\n",
+       "      <td>25.069293</td>\n",
+       "      <td>1.907216</td>\n",
+       "      <td>51.231780</td>\n",
+       "      <td>66.089235</td>\n",
+       "      <td>83.931587</td>\n",
+       "      <td>119.492809</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>29.305281</td>\n",
+       "      <td>12.185900</td>\n",
+       "      <td>-3.837400</td>\n",
+       "      <td>20.324882</td>\n",
+       "      <td>28.187599</td>\n",
+       "      <td>38.071412</td>\n",
+       "      <td>58.123572</td>\n",
+       "      <td>75.0</td>\n",
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+       "      <td>940.946942</td>\n",
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+       "      <td>19049.037827</td>\n",
+       "      <td>19617.861260</td>\n",
+       "      <td>20303.299142</td>\n",
+       "      <td>21489.193581</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>77.0</td>\n",
+       "      <td>158.611918</td>\n",
+       "      <td>28.634846</td>\n",
+       "      <td>90.772317</td>\n",
+       "      <td>142.867855</td>\n",
+       "      <td>157.166704</td>\n",
+       "      <td>172.490364</td>\n",
+       "      <td>240.729697</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>64.944693</td>\n",
+       "      <td>26.263248</td>\n",
+       "      <td>23.419552</td>\n",
+       "      <td>44.661807</td>\n",
+       "      <td>61.909200</td>\n",
+       "      <td>79.491705</td>\n",
+       "      <td>155.874851</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>30.225751</td>\n",
+       "      <td>11.323115</td>\n",
+       "      <td>-2.860013</td>\n",
+       "      <td>22.581370</td>\n",
+       "      <td>30.800982</td>\n",
+       "      <td>37.451463</td>\n",
+       "      <td>60.056761</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>19757.636531</td>\n",
+       "      <td>1019.800743</td>\n",
+       "      <td>18023.726084</td>\n",
+       "      <td>18952.149135</td>\n",
+       "      <td>19633.547998</td>\n",
+       "      <td>20665.291519</td>\n",
+       "      <td>21492.614880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>77.0</td>\n",
+       "      <td>165.736805</td>\n",
+       "      <td>29.776044</td>\n",
+       "      <td>112.820013</td>\n",
+       "      <td>146.685426</td>\n",
+       "      <td>161.936648</td>\n",
+       "      <td>184.058166</td>\n",
+       "      <td>256.194530</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>64.410457</td>\n",
+       "      <td>28.036595</td>\n",
+       "      <td>-7.328773</td>\n",
+       "      <td>46.696287</td>\n",
+       "      <td>65.738645</td>\n",
+       "      <td>83.679865</td>\n",
+       "      <td>139.494219</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>28.548584</td>\n",
+       "      <td>10.917398</td>\n",
+       "      <td>4.067120</td>\n",
+       "      <td>21.499777</td>\n",
+       "      <td>29.746838</td>\n",
+       "      <td>35.230100</td>\n",
+       "      <td>55.429845</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>19640.313086</td>\n",
+       "      <td>995.486333</td>\n",
+       "      <td>18019.519401</td>\n",
+       "      <td>18745.929082</td>\n",
+       "      <td>19643.106107</td>\n",
+       "      <td>20363.599172</td>\n",
+       "      <td>21453.931293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>93.0</td>\n",
+       "      <td>164.528743</td>\n",
+       "      <td>27.822572</td>\n",
+       "      <td>94.173624</td>\n",
+       "      <td>142.346484</td>\n",
+       "      <td>166.335510</td>\n",
+       "      <td>184.756847</td>\n",
+       "      <td>227.539516</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>61.571580</td>\n",
+       "      <td>24.315991</td>\n",
+       "      <td>-2.040686</td>\n",
+       "      <td>48.078668</td>\n",
+       "      <td>61.613466</td>\n",
+       "      <td>75.239205</td>\n",
+       "      <td>116.038660</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>28.699896</td>\n",
+       "      <td>11.980531</td>\n",
+       "      <td>-1.181704</td>\n",
+       "      <td>21.141014</td>\n",
+       "      <td>27.091806</td>\n",
+       "      <td>38.711063</td>\n",
+       "      <td>59.082634</td>\n",
+       "      <td>93.0</td>\n",
+       "      <td>19868.250726</td>\n",
+       "      <td>1052.295893</td>\n",
+       "      <td>18015.391664</td>\n",
+       "      <td>18998.283089</td>\n",
+       "      <td>19853.349989</td>\n",
+       "      <td>20851.226702</td>\n",
+       "      <td>21492.894772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>96.0</td>\n",
+       "      <td>157.210644</td>\n",
+       "      <td>30.459835</td>\n",
+       "      <td>71.472304</td>\n",
+       "      <td>133.824171</td>\n",
+       "      <td>160.216203</td>\n",
+       "      <td>179.105247</td>\n",
+       "      <td>214.449386</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>68.120275</td>\n",
+       "      <td>25.263629</td>\n",
+       "      <td>-2.789199</td>\n",
+       "      <td>48.383348</td>\n",
+       "      <td>65.760364</td>\n",
+       "      <td>86.934608</td>\n",
+       "      <td>130.364826</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>30.469981</td>\n",
+       "      <td>11.385701</td>\n",
+       "      <td>3.368029</td>\n",
+       "      <td>22.619632</td>\n",
+       "      <td>30.073195</td>\n",
+       "      <td>38.002390</td>\n",
+       "      <td>58.030748</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>19858.115811</td>\n",
+       "      <td>978.823389</td>\n",
+       "      <td>18006.577638</td>\n",
+       "      <td>19040.921083</td>\n",
+       "      <td>19804.216243</td>\n",
+       "      <td>20686.405907</td>\n",
+       "      <td>21497.428517</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Height              ...        Income              \n",
+       "                        count        mean  ...           75%           max\n",
+       "Gender Economic Status                     ...                            \n",
+       "Female Middle Class      82.0  159.875103  ...  20359.835287  21488.883196\n",
+       "       Poor              75.0  161.409478  ...  20303.299142  21489.193581\n",
+       "       Rich              77.0  158.611918  ...  20665.291519  21492.614880\n",
+       "Male   Middle Class      77.0  165.736805  ...  20363.599172  21453.931293\n",
+       "       Poor              93.0  164.528743  ...  20851.226702  21492.894772\n",
+       "       Rich              96.0  157.210644  ...  20686.405907  21497.428517\n",
+       "\n",
+       "[6 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "m0tze5dCAkSB"
+   },
+   "outputs": [],
+   "source": [
+    "grouped_income = double_group[\"Income\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 266
+    },
+    "colab_type": "code",
+    "id": "GnO4smZWAkSC",
+    "outputId": "d6a8c7d5-a0fd-4dbd-c1a3-252531f0dde2"
+   },
+   "outputs": [
+    {
+     "data": {
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+       "      <th></th>\n",
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+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Economic Status</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></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 rowspan=\"3\" valign=\"top\">Female</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>82.0</td>\n",
+       "      <td>19627.745015</td>\n",
+       "      <td>994.118644</td>\n",
+       "      <td>18040.895955</td>\n",
+       "      <td>18793.937121</td>\n",
+       "      <td>19615.513639</td>\n",
+       "      <td>20359.835287</td>\n",
+       "      <td>21488.883196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>75.0</td>\n",
+       "      <td>19693.513975</td>\n",
+       "      <td>940.946942</td>\n",
+       "      <td>18034.166483</td>\n",
+       "      <td>19049.037827</td>\n",
+       "      <td>19617.861260</td>\n",
+       "      <td>20303.299142</td>\n",
+       "      <td>21489.193581</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>77.0</td>\n",
+       "      <td>19757.636531</td>\n",
+       "      <td>1019.800743</td>\n",
+       "      <td>18023.726084</td>\n",
+       "      <td>18952.149135</td>\n",
+       "      <td>19633.547998</td>\n",
+       "      <td>20665.291519</td>\n",
+       "      <td>21492.614880</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>77.0</td>\n",
+       "      <td>19640.313086</td>\n",
+       "      <td>995.486333</td>\n",
+       "      <td>18019.519401</td>\n",
+       "      <td>18745.929082</td>\n",
+       "      <td>19643.106107</td>\n",
+       "      <td>20363.599172</td>\n",
+       "      <td>21453.931293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>93.0</td>\n",
+       "      <td>19868.250726</td>\n",
+       "      <td>1052.295893</td>\n",
+       "      <td>18015.391664</td>\n",
+       "      <td>18998.283089</td>\n",
+       "      <td>19853.349989</td>\n",
+       "      <td>20851.226702</td>\n",
+       "      <td>21492.894772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>96.0</td>\n",
+       "      <td>19858.115811</td>\n",
+       "      <td>978.823389</td>\n",
+       "      <td>18006.577638</td>\n",
+       "      <td>19040.921083</td>\n",
+       "      <td>19804.216243</td>\n",
+       "      <td>20686.405907</td>\n",
+       "      <td>21497.428517</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        count          mean  ...           75%           max\n",
+       "Gender Economic Status                       ...                            \n",
+       "Female Middle Class      82.0  19627.745015  ...  20359.835287  21488.883196\n",
+       "       Poor              75.0  19693.513975  ...  20303.299142  21489.193581\n",
+       "       Rich              77.0  19757.636531  ...  20665.291519  21492.614880\n",
+       "Male   Middle Class      77.0  19640.313086  ...  20363.599172  21453.931293\n",
+       "       Poor              93.0  19868.250726  ...  20851.226702  21492.894772\n",
+       "       Rich              96.0  19858.115811  ...  20686.405907  21497.428517\n",
+       "\n",
+       "[6 rows x 8 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "grouped_income.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 266
+    },
+    "colab_type": "code",
+    "id": "kc8V5PfaAkSD",
+    "outputId": "82aef21c-a5f8-4c6b-f7a8-fb80786af5bd"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Income</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Height</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Economic Status</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>1.609475e+06</td>\n",
+       "      <td>29.792843</td>\n",
+       "      <td>30.373012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>1.477014e+06</td>\n",
+       "      <td>29.305281</td>\n",
+       "      <td>31.477731</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>1.521338e+06</td>\n",
+       "      <td>30.225751</td>\n",
+       "      <td>28.634846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>1.512304e+06</td>\n",
+       "      <td>28.548584</td>\n",
+       "      <td>29.776044</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>1.847747e+06</td>\n",
+       "      <td>28.699896</td>\n",
+       "      <td>27.822572</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>1.906379e+06</td>\n",
+       "      <td>30.469981</td>\n",
+       "      <td>30.459835</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              Income        Age     Height\n",
+       "Gender Economic Status                                    \n",
+       "Female Middle Class     1.609475e+06  29.792843  30.373012\n",
+       "       Poor             1.477014e+06  29.305281  31.477731\n",
+       "       Rich             1.521338e+06  30.225751  28.634846\n",
+       "Male   Middle Class     1.512304e+06  28.548584  29.776044\n",
+       "       Poor             1.847747e+06  28.699896  27.822572\n",
+       "       Rich             1.906379e+06  30.469981  30.459835"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group.aggregate(\n",
+    "    {\n",
+    "        \"Income\": np.sum,\n",
+    "        \"Age\" : np.mean,\n",
+    "        \"Height\" : np.std\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 266
+    },
+    "colab_type": "code",
+    "id": "hjThnV4yAkSE",
+    "outputId": "c70ed120-8fc1-4f01-ca77-fe33e8518a11"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Height</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Economic Status</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>29.792843</td>\n",
+       "      <td>5.296115</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>29.305281</td>\n",
+       "      <td>5.162266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>30.225751</td>\n",
+       "      <td>5.575445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>28.548584</td>\n",
+       "      <td>5.602612</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>28.699896</td>\n",
+       "      <td>5.945550</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>30.469981</td>\n",
+       "      <td>5.188337</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              Age    Height\n",
+       "Gender Economic Status                     \n",
+       "Female Middle Class     29.792843  5.296115\n",
+       "       Poor             29.305281  5.162266\n",
+       "       Rich             30.225751  5.575445\n",
+       "Male   Middle Class     28.548584  5.602612\n",
+       "       Poor             28.699896  5.945550\n",
+       "       Rich             30.469981  5.188337"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group.aggregate(\n",
+    "    {\n",
+    "        \"Age\" : np.mean,\n",
+    "        \"Height\" : lambda h:(np.mean(h))/np.std(h)\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 297
+    },
+    "colab_type": "code",
+    "id": "SGx9EOlFAkSF",
+    "outputId": "46b278df-a199-4cd5-bb95-aff120b6ac43"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">Height</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">Weight</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">Age</th>\n",
+       "      <th colspan=\"3\" halign=\"left\">Income</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>sum</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Economic Status</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></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 rowspan=\"3\" valign=\"top\">Female</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>13109.758475</td>\n",
+       "      <td>159.875103</td>\n",
+       "      <td>30.373012</td>\n",
+       "      <td>5114.396788</td>\n",
+       "      <td>62.370693</td>\n",
+       "      <td>23.546737</td>\n",
+       "      <td>2443.013144</td>\n",
+       "      <td>29.792843</td>\n",
+       "      <td>12.587559</td>\n",
+       "      <td>1.609475e+06</td>\n",
+       "      <td>19627.745015</td>\n",
+       "      <td>994.118644</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>12105.710834</td>\n",
+       "      <td>161.409478</td>\n",
+       "      <td>31.477731</td>\n",
+       "      <td>5027.709669</td>\n",
+       "      <td>67.036129</td>\n",
+       "      <td>25.069293</td>\n",
+       "      <td>2197.896111</td>\n",
+       "      <td>29.305281</td>\n",
+       "      <td>12.185900</td>\n",
+       "      <td>1.477014e+06</td>\n",
+       "      <td>19693.513975</td>\n",
+       "      <td>940.946942</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>12213.117719</td>\n",
+       "      <td>158.611918</td>\n",
+       "      <td>28.634846</td>\n",
+       "      <td>5000.741333</td>\n",
+       "      <td>64.944693</td>\n",
+       "      <td>26.263248</td>\n",
+       "      <td>2327.382814</td>\n",
+       "      <td>30.225751</td>\n",
+       "      <td>11.323115</td>\n",
+       "      <td>1.521338e+06</td>\n",
+       "      <td>19757.636531</td>\n",
+       "      <td>1019.800743</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>12761.733997</td>\n",
+       "      <td>165.736805</td>\n",
+       "      <td>29.776044</td>\n",
+       "      <td>4959.605170</td>\n",
+       "      <td>64.410457</td>\n",
+       "      <td>28.036595</td>\n",
+       "      <td>2198.240962</td>\n",
+       "      <td>28.548584</td>\n",
+       "      <td>10.917398</td>\n",
+       "      <td>1.512304e+06</td>\n",
+       "      <td>19640.313086</td>\n",
+       "      <td>995.486333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>15301.173067</td>\n",
+       "      <td>164.528743</td>\n",
+       "      <td>27.822572</td>\n",
+       "      <td>5726.156969</td>\n",
+       "      <td>61.571580</td>\n",
+       "      <td>24.315991</td>\n",
+       "      <td>2669.090332</td>\n",
+       "      <td>28.699896</td>\n",
+       "      <td>11.980531</td>\n",
+       "      <td>1.847747e+06</td>\n",
+       "      <td>19868.250726</td>\n",
+       "      <td>1052.295893</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>15092.221858</td>\n",
+       "      <td>157.210644</td>\n",
+       "      <td>30.459835</td>\n",
+       "      <td>6539.546423</td>\n",
+       "      <td>68.120275</td>\n",
+       "      <td>25.263629</td>\n",
+       "      <td>2925.118149</td>\n",
+       "      <td>30.469981</td>\n",
+       "      <td>11.385701</td>\n",
+       "      <td>1.906379e+06</td>\n",
+       "      <td>19858.115811</td>\n",
+       "      <td>978.823389</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              Height              ...        Income             \n",
+       "                                 sum        mean  ...          mean          std\n",
+       "Gender Economic Status                            ...                           \n",
+       "Female Middle Class     13109.758475  159.875103  ...  19627.745015   994.118644\n",
+       "       Poor             12105.710834  161.409478  ...  19693.513975   940.946942\n",
+       "       Rich             12213.117719  158.611918  ...  19757.636531  1019.800743\n",
+       "Male   Middle Class     12761.733997  165.736805  ...  19640.313086   995.486333\n",
+       "       Poor             15301.173067  164.528743  ...  19868.250726  1052.295893\n",
+       "       Rich             15092.221858  157.210644  ...  19858.115811   978.823389\n",
+       "\n",
+       "[6 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group.aggregate([np.sum, np.mean, np.std])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 297
+    },
+    "colab_type": "code",
+    "id": "UkktjSkgAkSG",
+    "outputId": "649b4150-ed5f-434c-aba1-fd463463fa25"
+   },
+   "outputs": [
+    {
+     "data": {
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+       "<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></th>\n",
+       "      <th></th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>&lt;lambda&gt;</th>\n",
+       "      <th>&lt;lambda&gt;</th>\n",
+       "      <th>&lt;lambda&gt;</th>\n",
+       "      <th>&lt;lambda&gt;</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Economic Status</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>5.296115</td>\n",
+       "      <td>2.665104</td>\n",
+       "      <td>2.381414</td>\n",
+       "      <td>19.865368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>5.162266</td>\n",
+       "      <td>2.692041</td>\n",
+       "      <td>2.421046</td>\n",
+       "      <td>21.070403</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>5.575445</td>\n",
+       "      <td>2.489051</td>\n",
+       "      <td>2.686889</td>\n",
+       "      <td>19.501061</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>5.602612</td>\n",
+       "      <td>2.312436</td>\n",
+       "      <td>2.632110</td>\n",
+       "      <td>19.858739</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>5.945550</td>\n",
+       "      <td>2.545868</td>\n",
+       "      <td>2.408529</td>\n",
+       "      <td>18.983195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>5.188337</td>\n",
+       "      <td>2.710532</td>\n",
+       "      <td>2.690210</td>\n",
+       "      <td>20.394239</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          Height    Weight       Age     Income\n",
+       "                        <lambda>  <lambda>  <lambda>   <lambda>\n",
+       "Gender Economic Status                                         \n",
+       "Female Middle Class     5.296115  2.665104  2.381414  19.865368\n",
+       "       Poor             5.162266  2.692041  2.421046  21.070403\n",
+       "       Rich             5.575445  2.489051  2.686889  19.501061\n",
+       "Male   Middle Class     5.602612  2.312436  2.632110  19.858739\n",
+       "       Poor             5.945550  2.545868  2.408529  18.983195\n",
+       "       Rich             5.188337  2.710532  2.690210  20.394239"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group.aggregate([lambda x: np.mean(x) / np.std(x)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "YqDrlXlFAkSH"
+   },
+   "source": [
+    "## Filtrado de datos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 221
+    },
+    "colab_type": "code",
+    "id": "dD7rfKxSAkSH",
+    "outputId": "324de0e9-e7f3-4d0a-f627-4502bf988198"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3      34.192437\n",
+       "4      32.501684\n",
+       "7      19.958657\n",
+       "8      31.228420\n",
+       "10     38.757674\n",
+       "         ...    \n",
+       "492    64.993034\n",
+       "494    31.240249\n",
+       "495    17.900334\n",
+       "496    27.617103\n",
+       "498    35.257657\n",
+       "Name: Age, Length: 271, dtype: float64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group[\"Age\"].filter(lambda x: x.sum()>2400)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "K8WuegM0AkSI"
+   },
+   "source": [
+    "## Transformación de variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "_3lvmdbVAkSI"
+   },
+   "outputs": [],
+   "source": [
+    "zscore = lambda x : (x - x.mean())/x.std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "fLCs7OEOAkSJ"
+   },
+   "outputs": [],
+   "source": [
+    "z_group = double_group.transform(zscore)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "1_3gMroqAkSK"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 350
+    },
+    "colab_type": "code",
+    "id": "CRgESBjmAkSL",
+    "outputId": "3f2055ac-0d7e-4787-ab2a-aa6045c585db"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  6.,  10.,  38.,  80., 111., 104.,  80.,  47.,  15.,   9.]),\n",
+       " array([-2.92196658, -2.35012721, -1.77828785, -1.20644848, -0.63460912,\n",
+       "        -0.06276976,  0.50906961,  1.08090897,  1.65274833,  2.2245877 ,\n",
+       "         2.79642706]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(z_group[\"Age\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "V5OjiQoAAkSM"
+   },
+   "outputs": [],
+   "source": [
+    "fill_na_mean = lambda x : x.fillna(x.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 419
+    },
+    "colab_type": "code",
+    "id": "Jt30Dhi7AkSN",
+    "outputId": "ca1ea0dc-e6fe-4fbc-a412-9629628408b4"
+   },
+   "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>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>196.343940</td>\n",
+       "      <td>60.909064</td>\n",
+       "      <td>40.451221</td>\n",
+       "      <td>21385.958772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>153.697110</td>\n",
+       "      <td>48.005164</td>\n",
+       "      <td>48.163870</td>\n",
+       "      <td>19757.653998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>146.713926</td>\n",
+       "      <td>53.476714</td>\n",
+       "      <td>31.144568</td>\n",
+       "      <td>20279.476735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>183.988501</td>\n",
+       "      <td>71.785466</td>\n",
+       "      <td>34.192437</td>\n",
+       "      <td>19778.532678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>179.805582</td>\n",
+       "      <td>58.547234</td>\n",
+       "      <td>32.501684</td>\n",
+       "      <td>19346.337986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>180.672252</td>\n",
+       "      <td>51.029436</td>\n",
+       "      <td>17.900334</td>\n",
+       "      <td>20043.401932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>496</th>\n",
+       "      <td>151.602650</td>\n",
+       "      <td>43.765061</td>\n",
+       "      <td>27.617103</td>\n",
+       "      <td>20241.297735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>497</th>\n",
+       "      <td>149.973149</td>\n",
+       "      <td>67.190925</td>\n",
+       "      <td>36.479521</td>\n",
+       "      <td>19335.750899</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>163.669365</td>\n",
+       "      <td>98.571932</td>\n",
+       "      <td>35.257657</td>\n",
+       "      <td>18888.280373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>148.236420</td>\n",
+       "      <td>90.327894</td>\n",
+       "      <td>13.374767</td>\n",
+       "      <td>19467.309309</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>500 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Height     Weight        Age        Income\n",
+       "0    196.343940  60.909064  40.451221  21385.958772\n",
+       "1    153.697110  48.005164  48.163870  19757.653998\n",
+       "2    146.713926  53.476714  31.144568  20279.476735\n",
+       "3    183.988501  71.785466  34.192437  19778.532678\n",
+       "4    179.805582  58.547234  32.501684  19346.337986\n",
+       "..          ...        ...        ...           ...\n",
+       "495  180.672252  51.029436  17.900334  20043.401932\n",
+       "496  151.602650  43.765061  27.617103  20241.297735\n",
+       "497  149.973149  67.190925  36.479521  19335.750899\n",
+       "498  163.669365  98.571932  35.257657  18888.280373\n",
+       "499  148.236420  90.327894  13.374767  19467.309309\n",
+       "\n",
+       "[500 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group.transform(fill_na_mean)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "REgLbwFpAkSO"
+   },
+   "source": [
+    "## Operaciones diversas muy útiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 235
+    },
+    "colab_type": "code",
+    "id": "Onxbpf_5AkSO",
+    "outputId": "ec3f40b7-5e5e-4674-b0a8-01963860af19"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Economic Status</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>196.343940</td>\n",
+       "      <td>60.909064</td>\n",
+       "      <td>40.451221</td>\n",
+       "      <td>21385.958772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>153.697110</td>\n",
+       "      <td>48.005164</td>\n",
+       "      <td>48.163870</td>\n",
+       "      <td>19757.653998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>183.988501</td>\n",
+       "      <td>71.785466</td>\n",
+       "      <td>34.192437</td>\n",
+       "      <td>19778.532678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>179.805582</td>\n",
+       "      <td>58.547234</td>\n",
+       "      <td>32.501684</td>\n",
+       "      <td>19346.337986</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>172.074560</td>\n",
+       "      <td>28.192775</td>\n",
+       "      <td>42.997942</td>\n",
+       "      <td>21277.967078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>166.360472</td>\n",
+       "      <td>33.332115</td>\n",
+       "      <td>19.958657</td>\n",
+       "      <td>19611.989916</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender Economic Status      Height     Weight        Age        Income\n",
+       "0  Female            Rich  196.343940  60.909064  40.451221  21385.958772\n",
+       "1    Male    Middle Class  153.697110  48.005164  48.163870  19757.653998\n",
+       "3    Male            Rich  183.988501  71.785466  34.192437  19778.532678\n",
+       "4    Male            Poor  179.805582  58.547234  32.501684  19346.337986\n",
+       "5  Female            Poor  172.074560  28.192775  42.997942  21277.967078\n",
+       "7  Female    Middle Class  166.360472  33.332115  19.958657  19611.989916"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 235
+    },
+    "colab_type": "code",
+    "id": "srDlzkTjAkSP",
+    "outputId": "7c3e78ed-0525-4c24-d3e5-ffd788897744"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "    }\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>Economic Status</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>484</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>237.863120</td>\n",
+       "      <td>19.951759</td>\n",
+       "      <td>49.066417</td>\n",
+       "      <td>19810.325445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>487</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>227.539516</td>\n",
+       "      <td>57.374036</td>\n",
+       "      <td>25.997146</td>\n",
+       "      <td>19157.948395</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>493</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>169.567790</td>\n",
+       "      <td>63.484671</td>\n",
+       "      <td>29.763336</td>\n",
+       "      <td>20821.406697</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>495</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>180.672252</td>\n",
+       "      <td>51.029436</td>\n",
+       "      <td>17.900334</td>\n",
+       "      <td>20043.401932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>498</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>163.669365</td>\n",
+       "      <td>98.571932</td>\n",
+       "      <td>35.257657</td>\n",
+       "      <td>18888.280373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>499</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>148.236420</td>\n",
+       "      <td>90.327894</td>\n",
+       "      <td>13.374767</td>\n",
+       "      <td>19467.309309</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Gender Economic Status      Height     Weight        Age        Income\n",
+       "484  Female            Poor  237.863120  19.951759  49.066417  19810.325445\n",
+       "487    Male            Poor  227.539516  57.374036  25.997146  19157.948395\n",
+       "493  Female            Rich  169.567790  63.484671  29.763336  20821.406697\n",
+       "495  Female    Middle Class  180.672252  51.029436  17.900334  20043.401932\n",
+       "498    Male            Rich  163.669365  98.571932  35.257657  18888.280373\n",
+       "499    Male    Middle Class  148.236420  90.327894  13.374767  19467.309309"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group.tail(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 266
+    },
+    "colab_type": "code",
+    "id": "PUzcau-IAkSQ",
+    "outputId": "b6414924-5c52-4957-ca75-e482fa6bb218"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Economic Status</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>110.180551</td>\n",
+       "      <td>68.190065</td>\n",
+       "      <td>49.949398</td>\n",
+       "      <td>19487.156361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>158.853184</td>\n",
+       "      <td>63.085928</td>\n",
+       "      <td>25.005120</td>\n",
+       "      <td>20216.239204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>162.750233</td>\n",
+       "      <td>65.951638</td>\n",
+       "      <td>40.062883</td>\n",
+       "      <td>18023.726084</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+       "      <th>Middle Class</th>\n",
+       "      <td>161.936648</td>\n",
+       "      <td>70.117613</td>\n",
+       "      <td>4.067120</td>\n",
+       "      <td>19697.088560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Poor</th>\n",
+       "      <td>158.631750</td>\n",
+       "      <td>52.472696</td>\n",
+       "      <td>24.187545</td>\n",
+       "      <td>19964.779021</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>171.473068</td>\n",
+       "      <td>45.510527</td>\n",
+       "      <td>47.831414</td>\n",
+       "      <td>20692.308396</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            Height     Weight        Age        Income\n",
+       "Gender Economic Status                                                \n",
+       "Female Middle Class     110.180551  68.190065  49.949398  19487.156361\n",
+       "       Poor             158.853184  63.085928  25.005120  20216.239204\n",
+       "       Rich             162.750233  65.951638  40.062883  18023.726084\n",
+       "Male   Middle Class     161.936648  70.117613   4.067120  19697.088560\n",
+       "       Poor             158.631750  52.472696  24.187545  19964.779021\n",
+       "       Rich             171.473068  45.510527  47.831414  20692.308396"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group.nth(32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 142
+    },
+    "colab_type": "code",
+    "id": "TLvuUCwlAkSR",
+    "outputId": "f4db7972-2a76-4a61-9040-8b0ea79773f0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Economic Status</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
+       "      <th>Poor</th>\n",
+       "      <td>179.93339</td>\n",
+       "      <td>76.802942</td>\n",
+       "      <td>21.885402</td>\n",
+       "      <td>20715.359892</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Rich</th>\n",
+       "      <td>164.70270</td>\n",
+       "      <td>86.880958</td>\n",
+       "      <td>26.744570</td>\n",
+       "      <td>19193.082787</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           Height     Weight        Age        Income\n",
+       "Gender Economic Status                                               \n",
+       "Male   Poor             179.93339  76.802942  21.885402  20715.359892\n",
+       "       Rich             164.70270  86.880958  26.744570  19193.082787"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "double_group.nth(82)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Lm1IS7UeAkSS"
+   },
+   "outputs": [],
+   "source": [
+    "data_sorted = data.sort_values([\"Age\", \"Income\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 359
+    },
+    "colab_type": "code",
+    "id": "_E618lUBAkST",
+    "outputId": "bb9cd800-a233-4f86-b033-cfb085b09f2d"
+   },
+   "outputs": [
+    {
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+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
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+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>219.118989</td>\n",
+       "      <td>106.348681</td>\n",
+       "      <td>-3.837400</td>\n",
+       "      <td>21298.802624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>148</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>140.433589</td>\n",
+       "      <td>88.161987</td>\n",
+       "      <td>-3.010737</td>\n",
+       "      <td>19892.145392</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>220.042892</td>\n",
+       "      <td>66.970892</td>\n",
+       "      <td>-2.860013</td>\n",
+       "      <td>18189.632418</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>154</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>101.836328</td>\n",
+       "      <td>78.121454</td>\n",
+       "      <td>-2.139509</td>\n",
+       "      <td>19793.479229</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>193.682572</td>\n",
+       "      <td>79.271949</td>\n",
+       "      <td>-1.181704</td>\n",
+       "      <td>21008.166187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>142.594132</td>\n",
+       "      <td>67.007006</td>\n",
+       "      <td>3.368029</td>\n",
+       "      <td>20693.817605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>209</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>161.936648</td>\n",
+       "      <td>70.117613</td>\n",
+       "      <td>4.067120</td>\n",
+       "      <td>19697.088560</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>299</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>194.034042</td>\n",
+       "      <td>52.824096</td>\n",
+       "      <td>5.591800</td>\n",
+       "      <td>21409.162676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>402</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>149.062605</td>\n",
+       "      <td>52.035421</td>\n",
+       "      <td>6.173861</td>\n",
+       "      <td>21435.420795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>354</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>189.448738</td>\n",
+       "      <td>78.337713</td>\n",
+       "      <td>6.195454</td>\n",
+       "      <td>21369.689456</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Gender Economic Status      Height      Weight       Age        Income\n",
+       "59   Female            Poor  219.118989  106.348681 -3.837400  21298.802624\n",
+       "148  Female    Middle Class  140.433589   88.161987 -3.010737  19892.145392\n",
+       "22   Female            Rich  220.042892   66.970892 -2.860013  18189.632418\n",
+       "154  Female    Middle Class  101.836328   78.121454 -2.139509  19793.479229\n",
+       "89     Male            Poor  193.682572   79.271949 -1.181704  21008.166187\n",
+       "259    Male            Rich  142.594132   67.007006  3.368029  20693.817605\n",
+       "209    Male    Middle Class  161.936648   70.117613  4.067120  19697.088560\n",
+       "299    Male            Poor  194.034042   52.824096  5.591800  21409.162676\n",
+       "402    Male    Middle Class  149.062605   52.035421  6.173861  21435.420795\n",
+       "354    Male            Poor  189.448738   78.337713  6.195454  21369.689456"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sorted.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "p7TM9sRqAkSU"
+   },
+   "outputs": [],
+   "source": [
+    "age_grouped = data_sorted.groupby(\"Gender\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 111
+    },
+    "colab_type": "code",
+    "id": "xilQDYDuAkSU",
+    "outputId": "9d8dba61-abc3-470f-d137-38706f7ddd56"
+   },
+   "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>Economic Status</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>59</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>219.118989</td>\n",
+       "      <td>106.348681</td>\n",
+       "      <td>-3.837400</td>\n",
+       "      <td>21298.802624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>193.682572</td>\n",
+       "      <td>79.271949</td>\n",
+       "      <td>-1.181704</td>\n",
+       "      <td>21008.166187</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Gender Economic Status      Height      Weight       Age        Income\n",
+       "59  Female            Poor  219.118989  106.348681 -3.837400  21298.802624\n",
+       "89    Male            Poor  193.682572   79.271949 -1.181704  21008.166187"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_grouped.head(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 111
+    },
+    "colab_type": "code",
+    "id": "-f2hp-tVAkSV",
+    "outputId": "12df5949-b1ab-444b-dfbe-5e86694ce039"
+   },
+   "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>Economic Status</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>423</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>165.152608</td>\n",
+       "      <td>61.613466</td>\n",
+       "      <td>59.082634</td>\n",
+       "      <td>20577.920332</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>492</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>173.475638</td>\n",
+       "      <td>52.248953</td>\n",
+       "      <td>64.993034</td>\n",
+       "      <td>20516.979828</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Gender Economic Status      Height     Weight        Age        Income\n",
+       "423    Male            Poor  165.152608  61.613466  59.082634  20577.920332\n",
+       "492  Female    Middle Class  173.475638  52.248953  64.993034  20516.979828"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "age_grouped.tail(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "m6-5J5PaAkSW"
+   },
+   "source": [
+    "# Conjunto de entrenamiento y conjunto de testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "TsLsKm4RAkSW"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "nUwo03aqAkSX"
+   },
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/customer-churn-model/Customer Churn Model.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "4JGk0tOCAkSY",
+    "outputId": "b20a3f6f-635d-47d9-9218-1ee8391ba6bf"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3333"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "xyYlgyXNAkSZ"
+   },
+   "source": [
+    "## Dividir utilizando la distribución normal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "0USP9bsAAkSZ"
+   },
+   "outputs": [],
+   "source": [
+    "a = np.random.randn(len(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 350
+    },
+    "colab_type": "code",
+    "id": "t3Tdw4SAAkSa",
+    "outputId": "68053f0c-2a9a-4b87-905d-7eef21808580"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([  1.,  14.,  29.,  84., 111., 123.,  85.,  36.,   9.,   8.]),\n",
+       " array([-3.19536083, -2.56704791, -1.93873498, -1.31042206, -0.68210914,\n",
+       "        -0.05379622,  0.5745167 ,  1.20282963,  1.83114255,  2.45945547,\n",
+       "         3.08776839]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ZOjx59etAkSa"
+   },
+   "outputs": [],
+   "source": [
+    "check = (a<0.75) # No es el 75% de los datos, son los números que son < 0.75!!! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 969
+    },
+    "colab_type": "code",
+    "id": "LAVp0N0JAkSb",
+    "outputId": "ea86ea28-0907-405d-c41e-292975e12ad2"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([False,  True,  True,  True,  True,  True,  True, False,  True,\n",
+       "        True, False,  True,  True, False,  True,  True,  True,  True,\n",
+       "       False, False,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True, False, False,  True,  True,\n",
+       "        True,  True,  True,  True, False, False,  True,  True,  True,\n",
+       "       False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True, False,  True, False,  True, False,  True,  True,\n",
+       "        True,  True, False,  True,  True,  True,  True,  True,  True,\n",
+       "       False,  True, False,  True,  True,  True,  True,  True, False,\n",
+       "        True,  True,  True, False, False,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True, False,  True,  True,  True,  True,  True, False,\n",
+       "       False, False,  True, False,  True,  True, False, False,  True,\n",
+       "       False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True, False,  True,  True,  True,  True,  True,  True,\n",
+       "        True, False, False,  True,  True,  True,  True, False,  True,\n",
+       "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True, False,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True, False,  True, False,  True,  True,\n",
+       "        True,  True, False,  True, False,  True,  True,  True,  True,\n",
+       "       False,  True,  True, False,  True,  True,  True,  True,  True,\n",
+       "        True, False,  True, False,  True,  True,  True, False, False,\n",
+       "        True,  True,  True,  True,  True,  True, False,  True, False,\n",
+       "        True,  True,  True, False,  True,  True, False, False,  True,\n",
+       "       False,  True,  True,  True,  True,  True, False,  True,  True,\n",
+       "        True,  True,  True,  True,  True, False, False,  True, False,\n",
+       "        True,  True,  True, False,  True,  True,  True,  True,  True,\n",
+       "        True, False,  True,  True,  True,  True,  True,  True, False,\n",
+       "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True, False, False,  True,  True,\n",
+       "        True,  True,  True,  True,  True, False,  True,  True, False,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True, False,\n",
+       "        True,  True,  True,  True, False,  True,  True,  True,  True,\n",
+       "        True, False,  True,  True,  True,  True, False,  True,  True,\n",
+       "        True, False, False, False,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True, False,  True,  True, False,  True,\n",
+       "        True, False,  True, False,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True, False,  True,  True,  True,  True, False,\n",
+       "       False,  True,  True, False,  True,  True,  True, False,  True,\n",
+       "        True,  True, False,  True, False,  True,  True,  True,  True,\n",
+       "        True, False,  True,  True,  True,  True,  True, False,  True,\n",
+       "        True, False, False, False,  True,  True,  True,  True,  True,\n",
+       "        True, False,  True,  True,  True, False,  True,  True,  True,\n",
+       "        True, False, False,  True,  True, False,  True,  True,  True,\n",
+       "       False,  True,  True,  True,  True,  True,  True, False,  True,\n",
+       "        True,  True, False,  True, False, False, False,  True,  True,\n",
+       "        True,  True,  True,  True, False,  True,  True, False,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "       False,  True,  True,  True,  True,  True,  True,  True, False,\n",
+       "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True, False, False,  True,\n",
+       "        True,  True, False,  True,  True, False,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+       "        True,  True,  True,  True,  True,  True, False, False,  True,\n",
+       "        True,  True, False,  True,  True])"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "check"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 316
+    },
+    "colab_type": "code",
+    "id": "iWhUObc0AkSc",
+    "outputId": "241296c4-8812-4e44-b70f-f01e4b7deb9b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([108.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 392.]),\n",
+       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
+       " <a list of 10 Patch objects>)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light",
+      "tags": []
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(check.astype(int))#Ha cambiado en la versión 3.7 de python y necesita hacer un cast de bool a entero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "GRqOOoG2AkSc"
+   },
+   "outputs": [],
+   "source": [
+    "training = data[check]\n",
+    "testing = data[~check]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "Ribx2PC1AkSd",
+    "outputId": "6233c63b-3442-463d-9e2b-b9d68bd000a0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "392"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(training)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "0UnqiUWOAkSe",
+    "outputId": "b08d645a-01c5-40fc-9bcd-e13cff80bb71"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "108"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(testing)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "1f2ToslqAkSf"
+   },
+   "source": [
+    "## Con la libreria sklearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "26zoqG7RAkSf"
+   },
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split# Ha cambiado en la 3.7 de Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "aVojsIWeAkSg"
+   },
+   "outputs": [],
+   "source": [
+    "train, test = train_test_split(data, test_size = 0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "60Cjk1ifAkSg",
+    "outputId": "127bd786-c53b-4382-973a-90f1b3bf0244"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "400"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "xWfmYLTNAkSn",
+    "outputId": "ed19bc2b-5dbc-40fd-dcf3-0c4a816838ba"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "9l8-cogRAkSo"
+   },
+   "source": [
+    "## Usando una función de shuffle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "QLvByZ3PAkSo"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "hAwmSuggAkSp",
+    "outputId": "68556d22-1833-44a7-8093-a88587991b76"
+   },
+   "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>Economic Status</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Income</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>196.343940</td>\n",
+       "      <td>60.909064</td>\n",
+       "      <td>40.451221</td>\n",
+       "      <td>21385.958772</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Middle Class</td>\n",
+       "      <td>153.697110</td>\n",
+       "      <td>48.005164</td>\n",
+       "      <td>48.163870</td>\n",
+       "      <td>19757.653998</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>146.713926</td>\n",
+       "      <td>53.476714</td>\n",
+       "      <td>31.144568</td>\n",
+       "      <td>20279.476735</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Rich</td>\n",
+       "      <td>183.988501</td>\n",
+       "      <td>71.785466</td>\n",
+       "      <td>34.192437</td>\n",
+       "      <td>19778.532678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Poor</td>\n",
+       "      <td>179.805582</td>\n",
+       "      <td>58.547234</td>\n",
+       "      <td>32.501684</td>\n",
+       "      <td>19346.337986</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender Economic Status      Height     Weight        Age        Income\n",
+       "0  Female            Rich  196.343940  60.909064  40.451221  21385.958772\n",
+       "1    Male    Middle Class  153.697110  48.005164  48.163870  19757.653998\n",
+       "2  Female            Rich  146.713926  53.476714  31.144568  20279.476735\n",
+       "3    Male            Rich  183.988501  71.785466  34.192437  19778.532678\n",
+       "4    Male            Poor  179.805582  58.547234  32.501684  19346.337986"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "qa0z_ZJ6AkSp"
+   },
+   "outputs": [],
+   "source": [
+    "import sklearn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "yBX-tSSCAkSq"
+   },
+   "outputs": [],
+   "source": [
+    "data = sklearn.utils.shuffle(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "lkwfCbmSAkSr"
+   },
+   "outputs": [],
+   "source": [
+    "cut_id = int(0.75*len(data))\n",
+    "train_data = data[:cut_id]\n",
+    "test_data = data[cut_id+1:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "NuJVmGovAkSr",
+    "outputId": "6fb2bfaf-e9c0-4de0-a154-f135f4823089"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "375"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(train_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "ztoaBbOeAkSs",
+    "outputId": "b3c609f2-fedc-4670-add3-51ca09418772"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "124"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(test_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "wgEx951WBsB-"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "T2 - 3 - Data Cleaning - Agrupación de datos.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git "a/notebooks/T2 - 3 - Data Cleaning - Agrupaci\303\263n de datos.ipynb" "b/notebooks/T2 - 3 - Data Cleaning - Agrupaci\303\263n de datos.ipynb"
index 8fcd5a33..3f0aab4b 100644
--- "a/notebooks/T2 - 3 - Data Cleaning - Agrupaci\303\263n de datos.ipynb"	
+++ "b/notebooks/T2 - 3 - Data Cleaning - Agrupaci\303\263n de datos.ipynb"	
@@ -1,4833 +1,3981 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Agregación de datos por categoría"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gender = [\"Male\", \"Female\"]\n",
-    "income = [\"Poor\", \"Middle Class\", \"Rich\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "n = 500\n",
-    "\n",
-    "gender_data = []\n",
-    "income_data = []\n",
-    "\n",
-    "for i in range(0,500):\n",
-    "    gender_data.append(np.random.choice(gender))\n",
-    "    income_data.append(np.random.choice(income))"
-   ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.6.4"
+    },
+    "colab": {
+      "name": "T2 - 3 - Data Cleaning - Agrupación de datos.ipynb",
+      "provenance": [],
+      "include_colab_link": true
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "['Female',\n",
-       " 'Male',\n",
-       " 'Male',\n",
-       " 'Female',\n",
-       " 'Male',\n",
-       " 'Female',\n",
-       " 'Female',\n",
-       " 'Male',\n",
-       " 'Male']"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/Collab---v-3.8/notebooks/T2%20-%203%20-%20Data%20Cleaning%20-%20Agrupaci%C3%B3n%20de%20datos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
       ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gender_data[1:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "['Middle Class',\n",
-       " 'Poor',\n",
-       " 'Middle Class',\n",
-       " 'Rich',\n",
-       " 'Poor',\n",
-       " 'Middle Class',\n",
-       " 'Rich',\n",
-       " 'Rich',\n",
-       " 'Middle Class']"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K2teubACAkRb",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Agregación de datos por categoría"
       ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "income_data[1:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#Z -> N(0,1)\n",
-    "#N(m, s) -> m + s * Z\n",
-    "height = 160 + 30 * np.random.randn(n)\n",
-    "weight = 65 + 25 * np.random.randn(n)\n",
-    "age = 30 + 12 * np.random.randn(n)\n",
-    "income = 18000 + 3500 * np.random.rand(n)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.DataFrame(\n",
-    "    {\n",
-    "        \"Gender\" : gender_data,\n",
-    "        \"Economic Status\" : income_data,\n",
-    "        \"Height\" : height,\n",
-    "        \"Weight\" : weight,\n",
-    "        \"Age\" : age,\n",
-    "        \"Income\" : income\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "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>Age</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Weight</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>29.406063</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>93.455149</td>\n",
-       "      <td>20717.514475</td>\n",
-       "      <td>50.868452</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10.465520</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>139.545894</td>\n",
-       "      <td>19863.957892</td>\n",
-       "      <td>46.368551</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>41.632979</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>183.857248</td>\n",
-       "      <td>18549.640778</td>\n",
-       "      <td>60.016080</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>17.706009</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>155.885685</td>\n",
-       "      <td>18688.042862</td>\n",
-       "      <td>73.035787</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>43.779882</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>127.650202</td>\n",
-       "      <td>18274.892352</td>\n",
-       "      <td>109.119629</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "VDV8LPSJA61J",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
       ],
-      "text/plain": [
-       "         Age Economic Status  Gender      Height        Income      Weight\n",
-       "0  29.406063    Middle Class    Male   93.455149  20717.514475   50.868452\n",
-       "1  10.465520    Middle Class  Female  139.545894  19863.957892   46.368551\n",
-       "2  41.632979            Poor    Male  183.857248  18549.640778   60.016080\n",
-       "3  17.706009    Middle Class    Male  155.885685  18688.042862   73.035787\n",
-       "4  43.779882            Rich  Female  127.650202  18274.892352  109.119629"
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zwuY-TMgAkRc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "0r6-BV-FAkRi",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "gender = [\"Male\", \"Female\"]\n",
+        "income = [\"Poor\", \"Middle Class\", \"Rich\"]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dUOu8paRAkRl",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "n = 500\n",
+        "\n",
+        "gender_data = []\n",
+        "income_data = []\n",
+        "\n",
+        "for i in range(0,500):\n",
+        "    gender_data.append(np.random.choice(gender))\n",
+        "    income_data.append(np.random.choice(income))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wz2m3fpuAkRo",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 170
+        },
+        "outputId": "fe626221-e112-4cdd-cfb3-6d68a36045b8"
+      },
+      "source": [
+        "gender_data[1:10]"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['Male',\n",
+              " 'Female',\n",
+              " 'Male',\n",
+              " 'Male',\n",
+              " 'Female',\n",
+              " 'Female',\n",
+              " 'Female',\n",
+              " 'Female',\n",
+              " 'Male']"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
       ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Agrupación de datos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grouped_gender = data.groupby(\"Gender\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "{'Female': Int64Index([  1,   4,   6,   7,  10,  11,  12,  16,  17,  19,\n",
-       "             ...\n",
-       "             485, 486, 488, 492, 494, 495, 496, 497, 498, 499],\n",
-       "            dtype='int64', length=250),\n",
-       " 'Male': Int64Index([  0,   2,   3,   5,   8,   9,  13,  14,  15,  18,\n",
-       "             ...\n",
-       "             476, 477, 478, 480, 481, 487, 489, 490, 491, 493],\n",
-       "            dtype='int64', length=250)}"
+      "cell_type": "code",
+      "metadata": {
+        "id": "CafW2UzYAkRr",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 170
+        },
+        "outputId": "748ee054-8376-4ff8-c445-9d4734231046"
+      },
+      "source": [
+        "income_data[1:10]"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['Middle Class',\n",
+              " 'Rich',\n",
+              " 'Rich',\n",
+              " 'Poor',\n",
+              " 'Poor',\n",
+              " 'Rich',\n",
+              " 'Middle Class',\n",
+              " 'Middle Class',\n",
+              " 'Middle Class']"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
       ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grouped_gender.groups"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Female\n",
-      "           Age Economic Status  Gender      Height        Income      Weight\n",
-      "1    10.465520    Middle Class  Female  139.545894  19863.957892   46.368551\n",
-      "4    43.779882            Rich  Female  127.650202  18274.892352  109.119629\n",
-      "6    35.830860    Middle Class  Female  165.501662  20558.353093   52.728741\n",
-      "7    26.818594            Rich  Female  167.299202  19408.300487   -2.328660\n",
-      "10   31.084558    Middle Class  Female  165.983138  18415.623498   48.371730\n",
-      "11   29.470434            Poor  Female  134.003543  21134.226884   60.585127\n",
-      "12   34.334233            Poor  Female  149.824673  19546.903872  119.229619\n",
-      "16   36.066909            Rich  Female  191.962499  18835.366092   64.921943\n",
-      "17   45.601175    Middle Class  Female  157.798020  18342.649900   32.215115\n",
-      "19   32.509053            Poor  Female  192.663994  20910.754143   52.377219\n",
-      "21   31.443146            Rich  Female  143.035623  19194.070439  100.464745\n",
-      "23   26.854852            Rich  Female  164.025603  18111.940350  110.328107\n",
-      "25   38.453075            Poor  Female  156.155689  19575.471899   74.823251\n",
-      "26   21.995241            Poor  Female  159.708864  20840.925825   68.275110\n",
-      "28   19.002201    Middle Class  Female  206.620612  21459.148423   62.110834\n",
-      "31   42.708940            Poor  Female  139.053295  20593.078250   74.091507\n",
-      "33   42.420073    Middle Class  Female  139.301025  18413.619743   99.256675\n",
-      "35   32.305077    Middle Class  Female  183.851098  18846.217429   44.541702\n",
-      "37   22.565455            Rich  Female  190.719719  20567.335892  116.274632\n",
-      "42   29.022916            Poor  Female  161.530479  18587.678108  118.456984\n",
-      "44   46.209037    Middle Class  Female  140.556280  18824.365426   84.158165\n",
-      "45   37.767718    Middle Class  Female  186.985968  18427.698762  108.811298\n",
-      "46   35.251354    Middle Class  Female  179.185782  21041.110538   56.537286\n",
-      "47   35.762436    Middle Class  Female  129.901881  18044.251672   32.875208\n",
-      "52   33.259760            Rich  Female  183.368037  19735.672120   65.929228\n",
-      "53   17.918446    Middle Class  Female  154.389267  18817.649577   53.819634\n",
-      "54   29.244240            Poor  Female  186.656195  18215.623097   47.813661\n",
-      "55   30.241688            Poor  Female  145.659830  18519.259271   42.386419\n",
-      "60   11.941200    Middle Class  Female  162.770127  19418.652760   75.663445\n",
-      "62   20.875818            Rich  Female  191.300631  21248.793542   49.133164\n",
-      "..         ...             ...     ...         ...           ...         ...\n",
-      "441  25.486046    Middle Class  Female  140.831967  18507.627206   48.954062\n",
-      "443  21.402705            Poor  Female  194.015719  21359.952680   66.119103\n",
-      "446  38.096547            Rich  Female  163.545792  19471.045671   80.834042\n",
-      "448  27.068309            Rich  Female  118.118576  18998.793414   80.668010\n",
-      "450  39.809820            Poor  Female  153.686268  20254.676744   47.026061\n",
-      "452  12.965592            Rich  Female  113.115562  18370.400143   87.303529\n",
-      "456  33.693360            Poor  Female  161.024662  20929.338278  118.306944\n",
-      "458   4.577901            Rich  Female  160.383189  19365.473543  105.775523\n",
-      "460  32.993292            Poor  Female  199.659643  20690.693122   89.968323\n",
-      "464  10.639621            Poor  Female  121.984598  19518.403749  110.970896\n",
-      "465  38.493342            Poor  Female  153.518025  21089.370671   38.769568\n",
-      "466  44.073311            Rich  Female  177.297004  20183.673288   49.884070\n",
-      "470  30.877809            Poor  Female  179.549889  20534.403603   57.170932\n",
-      "471  22.482854            Poor  Female  148.114383  21307.143204   94.685959\n",
-      "472  14.041959            Poor  Female  128.786411  19236.856980   65.910370\n",
-      "475  33.212556    Middle Class  Female  186.366218  18995.818308   76.798489\n",
-      "479  33.570631    Middle Class  Female  170.489521  20610.140991   58.052908\n",
-      "482  42.230758            Rich  Female  162.665803  21442.183439  111.199150\n",
-      "483  30.346778            Poor  Female  135.671820  19005.401603   79.888811\n",
-      "484  51.116700            Poor  Female  117.127682  20523.981614   93.502202\n",
-      "485  12.241652            Rich  Female  133.348820  19868.743729   56.394180\n",
-      "486  38.130804            Poor  Female  164.438850  20503.218832   34.314401\n",
-      "488  37.119624            Rich  Female  133.874339  21287.749837   74.270308\n",
-      "492  17.864297    Middle Class  Female  131.060369  21378.622189   84.522898\n",
-      "494  31.182845            Poor  Female  123.483560  19086.528784   85.384170\n",
-      "495  38.516231            Poor  Female  143.732437  20176.033630   78.402899\n",
-      "496  25.569081    Middle Class  Female  160.674814  20224.021932   54.473354\n",
-      "497  36.608103            Poor  Female  149.632378  18324.310236   71.910584\n",
-      "498  30.132207            Poor  Female  169.712073  18243.266976   85.853052\n",
-      "499  36.961529            Poor  Female  199.681593  20542.715644   31.275503\n",
-      "\n",
-      "[250 rows x 6 columns]\n",
-      "Male\n",
-      "           Age Economic Status Gender      Height        Income      Weight\n",
-      "0    29.406063    Middle Class   Male   93.455149  20717.514475   50.868452\n",
-      "2    41.632979            Poor   Male  183.857248  18549.640778   60.016080\n",
-      "3    17.706009    Middle Class   Male  155.885685  18688.042862   73.035787\n",
-      "5    31.511689            Poor   Male  192.882388  20826.579612   88.442163\n",
-      "8    45.239503            Rich   Male  132.588194  20660.630399   51.229028\n",
-      "9    32.535617    Middle Class   Male  138.481238  20343.973756   47.240539\n",
-      "13   28.286581            Poor   Male  117.798582  21226.742907   60.712632\n",
-      "14   42.280704    Middle Class   Male  131.584579  18592.858002   96.606513\n",
-      "15   15.307572            Poor   Male  158.743542  21243.899231   61.155687\n",
-      "18   42.720713    Middle Class   Male   89.417396  20565.126795   77.516021\n",
-      "20   13.282330            Rich   Male  162.059657  19087.166254   81.284012\n",
-      "22   24.546463            Poor   Male  148.232867  18529.475088   47.840715\n",
-      "24   36.759814            Poor   Male  183.562680  19798.790037  103.294236\n",
-      "27   28.041821            Rich   Male  120.856916  20666.441965   62.811662\n",
-      "29   33.865350    Middle Class   Male  159.237730  21176.630376   88.431003\n",
-      "30   15.223665            Poor   Male  143.687508  20848.134261   53.244928\n",
-      "32   38.673025            Poor   Male  113.591964  20146.827332   96.256478\n",
-      "34   25.671351            Poor   Male  134.270156  20189.839410   65.590964\n",
-      "36   37.689419            Rich   Male  207.849203  18638.205028   84.178977\n",
-      "38   43.565371            Poor   Male  184.011414  19314.357756   94.718841\n",
-      "39   27.119116            Rich   Male  172.585129  20830.428222   54.949156\n",
-      "40   33.373076            Poor   Male  166.749750  18185.495702   68.957596\n",
-      "41   23.081560            Rich   Male  231.008073  20518.979096   37.124301\n",
-      "43   45.449231            Poor   Male  199.616249  18486.665013  102.880845\n",
-      "48   37.065833            Poor   Male  157.584210  21231.990757   96.470241\n",
-      "49   33.062813            Rich   Male  130.577611  20769.464752   74.465052\n",
-      "50   24.265501    Middle Class   Male  159.705113  18944.972440   17.862102\n",
-      "51   46.097030    Middle Class   Male  115.178818  18447.026740   39.840291\n",
-      "56   34.069823            Poor   Male  126.234905  19130.604647   36.524401\n",
-      "57   45.994656    Middle Class   Male  244.699568  21044.868558   27.923058\n",
-      "..         ...             ...    ...         ...           ...         ...\n",
-      "440  33.332393            Poor   Male  193.855290  19714.998843   34.648921\n",
-      "442  41.148815            Rich   Male  185.548730  20557.584791   54.320334\n",
-      "444  28.199086            Poor   Male  139.317685  20204.378925   87.835392\n",
-      "445  17.350872            Rich   Male  165.863704  20043.161040   19.577812\n",
-      "447  31.720338            Rich   Male  206.314374  20684.425538  100.824455\n",
-      "449  53.213463            Rich   Male  158.853890  18260.835182   57.657574\n",
-      "451  22.539506            Poor   Male  171.225075  21477.593830   23.523673\n",
-      "453  45.605468            Poor   Male  161.464070  20044.642117   54.322807\n",
-      "454  27.073896            Rich   Male  206.038807  21435.289439  126.426263\n",
-      "455   9.504070            Poor   Male  175.821391  19821.661878   78.242555\n",
-      "457  35.166985    Middle Class   Male  185.930859  20400.600364   51.111471\n",
-      "459  48.143951            Rich   Male  164.569602  18774.690305   57.373974\n",
-      "461  33.710923            Poor   Male  174.128934  20703.893829   60.151746\n",
-      "462  26.702841            Poor   Male  152.275518  18705.322248   68.213105\n",
-      "463  36.244812    Middle Class   Male  167.894078  20827.426451   49.354226\n",
-      "467  46.494498            Poor   Male  156.431611  21227.527975   84.116289\n",
-      "468  28.763444            Poor   Male  176.934490  18339.844982   85.636147\n",
-      "469  30.849618    Middle Class   Male  193.036240  21389.535960   23.796108\n",
-      "473  57.927458            Rich   Male  158.264063  19773.326950  102.165694\n",
-      "474  28.871436            Rich   Male  129.211463  18119.001636   54.231350\n",
-      "476  25.910841    Middle Class   Male  140.929009  19953.551284   94.894692\n",
-      "477  10.643144            Rich   Male  143.849606  18803.688821  109.599023\n",
-      "478  10.190133            Rich   Male  171.248419  18281.357516   95.809197\n",
-      "480  38.163563            Rich   Male  152.699267  18162.912374   18.406700\n",
-      "481  19.399664            Poor   Male  122.253378  21418.957595   34.334860\n",
-      "487  22.489102            Rich   Male  121.876538  20504.227101   60.994486\n",
-      "489  36.219497    Middle Class   Male  156.398762  19019.898289   77.351999\n",
-      "490  23.325366            Poor   Male  154.859406  20178.525442   77.059452\n",
-      "491  28.190877            Poor   Male  113.399151  20883.753347   47.601578\n",
-      "493  11.569556            Rich   Male  180.690792  18827.025459   48.867474\n",
-      "\n",
-      "[250 rows x 6 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "for names, groups in grouped_gender:\n",
-    "    print(names)\n",
-    "    print(groups)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "id": "Q53rIw03AkRu",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "#Z -> N(0,1)\n",
+        "#N(m, s) -> m + s * Z\n",
+        "height = 160 + 30 * np.random.randn(n)\n",
+        "weight = 65 + 25 * np.random.randn(n)\n",
+        "age = 30 + 12 * np.random.randn(n)\n",
+        "income = 18000 + 3500 * np.random.rand(n)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Weight</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10.465520</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>139.545894</td>\n",
-       "      <td>19863.957892</td>\n",
-       "      <td>46.368551</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>43.779882</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>127.650202</td>\n",
-       "      <td>18274.892352</td>\n",
-       "      <td>109.119629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>35.830860</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>165.501662</td>\n",
-       "      <td>20558.353093</td>\n",
-       "      <td>52.728741</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>26.818594</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>167.299202</td>\n",
-       "      <td>19408.300487</td>\n",
-       "      <td>-2.328660</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>31.084558</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>165.983138</td>\n",
-       "      <td>18415.623498</td>\n",
-       "      <td>48.371730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>29.470434</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>134.003543</td>\n",
-       "      <td>21134.226884</td>\n",
-       "      <td>60.585127</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>34.334233</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>149.824673</td>\n",
-       "      <td>19546.903872</td>\n",
-       "      <td>119.229619</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>36.066909</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>191.962499</td>\n",
-       "      <td>18835.366092</td>\n",
-       "      <td>64.921943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>45.601175</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>157.798020</td>\n",
-       "      <td>18342.649900</td>\n",
-       "      <td>32.215115</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>32.509053</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>192.663994</td>\n",
-       "      <td>20910.754143</td>\n",
-       "      <td>52.377219</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>31.443146</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>143.035623</td>\n",
-       "      <td>19194.070439</td>\n",
-       "      <td>100.464745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>26.854852</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>164.025603</td>\n",
-       "      <td>18111.940350</td>\n",
-       "      <td>110.328107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>38.453075</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>156.155689</td>\n",
-       "      <td>19575.471899</td>\n",
-       "      <td>74.823251</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>21.995241</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>159.708864</td>\n",
-       "      <td>20840.925825</td>\n",
-       "      <td>68.275110</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>19.002201</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>206.620612</td>\n",
-       "      <td>21459.148423</td>\n",
-       "      <td>62.110834</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>42.708940</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>139.053295</td>\n",
-       "      <td>20593.078250</td>\n",
-       "      <td>74.091507</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>42.420073</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>139.301025</td>\n",
-       "      <td>18413.619743</td>\n",
-       "      <td>99.256675</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>32.305077</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>183.851098</td>\n",
-       "      <td>18846.217429</td>\n",
-       "      <td>44.541702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>22.565455</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>190.719719</td>\n",
-       "      <td>20567.335892</td>\n",
-       "      <td>116.274632</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>29.022916</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>161.530479</td>\n",
-       "      <td>18587.678108</td>\n",
-       "      <td>118.456984</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>46.209037</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>140.556280</td>\n",
-       "      <td>18824.365426</td>\n",
-       "      <td>84.158165</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>37.767718</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>186.985968</td>\n",
-       "      <td>18427.698762</td>\n",
-       "      <td>108.811298</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>35.251354</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>179.185782</td>\n",
-       "      <td>21041.110538</td>\n",
-       "      <td>56.537286</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>35.762436</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>129.901881</td>\n",
-       "      <td>18044.251672</td>\n",
-       "      <td>32.875208</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>33.259760</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>183.368037</td>\n",
-       "      <td>19735.672120</td>\n",
-       "      <td>65.929228</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>17.918446</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>154.389267</td>\n",
-       "      <td>18817.649577</td>\n",
-       "      <td>53.819634</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>29.244240</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>186.656195</td>\n",
-       "      <td>18215.623097</td>\n",
-       "      <td>47.813661</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>30.241688</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>145.659830</td>\n",
-       "      <td>18519.259271</td>\n",
-       "      <td>42.386419</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>11.941200</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>162.770127</td>\n",
-       "      <td>19418.652760</td>\n",
-       "      <td>75.663445</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>20.875818</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>191.300631</td>\n",
-       "      <td>21248.793542</td>\n",
-       "      <td>49.133164</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>441</th>\n",
-       "      <td>25.486046</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>140.831967</td>\n",
-       "      <td>18507.627206</td>\n",
-       "      <td>48.954062</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>443</th>\n",
-       "      <td>21.402705</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>194.015719</td>\n",
-       "      <td>21359.952680</td>\n",
-       "      <td>66.119103</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>446</th>\n",
-       "      <td>38.096547</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>163.545792</td>\n",
-       "      <td>19471.045671</td>\n",
-       "      <td>80.834042</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>448</th>\n",
-       "      <td>27.068309</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>118.118576</td>\n",
-       "      <td>18998.793414</td>\n",
-       "      <td>80.668010</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>450</th>\n",
-       "      <td>39.809820</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>153.686268</td>\n",
-       "      <td>20254.676744</td>\n",
-       "      <td>47.026061</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>452</th>\n",
-       "      <td>12.965592</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>113.115562</td>\n",
-       "      <td>18370.400143</td>\n",
-       "      <td>87.303529</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>456</th>\n",
-       "      <td>33.693360</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>161.024662</td>\n",
-       "      <td>20929.338278</td>\n",
-       "      <td>118.306944</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>458</th>\n",
-       "      <td>4.577901</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>160.383189</td>\n",
-       "      <td>19365.473543</td>\n",
-       "      <td>105.775523</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>460</th>\n",
-       "      <td>32.993292</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>199.659643</td>\n",
-       "      <td>20690.693122</td>\n",
-       "      <td>89.968323</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>464</th>\n",
-       "      <td>10.639621</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>121.984598</td>\n",
-       "      <td>19518.403749</td>\n",
-       "      <td>110.970896</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>465</th>\n",
-       "      <td>38.493342</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>153.518025</td>\n",
-       "      <td>21089.370671</td>\n",
-       "      <td>38.769568</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>466</th>\n",
-       "      <td>44.073311</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>177.297004</td>\n",
-       "      <td>20183.673288</td>\n",
-       "      <td>49.884070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>470</th>\n",
-       "      <td>30.877809</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>179.549889</td>\n",
-       "      <td>20534.403603</td>\n",
-       "      <td>57.170932</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>22.482854</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>148.114383</td>\n",
-       "      <td>21307.143204</td>\n",
-       "      <td>94.685959</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>472</th>\n",
-       "      <td>14.041959</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>128.786411</td>\n",
-       "      <td>19236.856980</td>\n",
-       "      <td>65.910370</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>475</th>\n",
-       "      <td>33.212556</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>186.366218</td>\n",
-       "      <td>18995.818308</td>\n",
-       "      <td>76.798489</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>479</th>\n",
-       "      <td>33.570631</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>170.489521</td>\n",
-       "      <td>20610.140991</td>\n",
-       "      <td>58.052908</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>482</th>\n",
-       "      <td>42.230758</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>162.665803</td>\n",
-       "      <td>21442.183439</td>\n",
-       "      <td>111.199150</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>483</th>\n",
-       "      <td>30.346778</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>135.671820</td>\n",
-       "      <td>19005.401603</td>\n",
-       "      <td>79.888811</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>484</th>\n",
-       "      <td>51.116700</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>117.127682</td>\n",
-       "      <td>20523.981614</td>\n",
-       "      <td>93.502202</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>485</th>\n",
-       "      <td>12.241652</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>133.348820</td>\n",
-       "      <td>19868.743729</td>\n",
-       "      <td>56.394180</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>486</th>\n",
-       "      <td>38.130804</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>164.438850</td>\n",
-       "      <td>20503.218832</td>\n",
-       "      <td>34.314401</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>488</th>\n",
-       "      <td>37.119624</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>133.874339</td>\n",
-       "      <td>21287.749837</td>\n",
-       "      <td>74.270308</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>492</th>\n",
-       "      <td>17.864297</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>131.060369</td>\n",
-       "      <td>21378.622189</td>\n",
-       "      <td>84.522898</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>494</th>\n",
-       "      <td>31.182845</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>123.483560</td>\n",
-       "      <td>19086.528784</td>\n",
-       "      <td>85.384170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>495</th>\n",
-       "      <td>38.516231</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>143.732437</td>\n",
-       "      <td>20176.033630</td>\n",
-       "      <td>78.402899</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>496</th>\n",
-       "      <td>25.569081</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>160.674814</td>\n",
-       "      <td>20224.021932</td>\n",
-       "      <td>54.473354</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>497</th>\n",
-       "      <td>36.608103</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>149.632378</td>\n",
-       "      <td>18324.310236</td>\n",
-       "      <td>71.910584</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>498</th>\n",
-       "      <td>30.132207</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>169.712073</td>\n",
-       "      <td>18243.266976</td>\n",
-       "      <td>85.853052</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>499</th>\n",
-       "      <td>36.961529</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>199.681593</td>\n",
-       "      <td>20542.715644</td>\n",
-       "      <td>31.275503</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>250 rows × 6 columns</p>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "tUaeOrqrAkRv",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data = pd.DataFrame(\n",
+        "    {\n",
+        "        \"Gender\" : gender_data,\n",
+        "        \"Economic Status\" : income_data,\n",
+        "        \"Height\" : height,\n",
+        "        \"Weight\" : weight,\n",
+        "        \"Age\" : age,\n",
+        "        \"Income\" : income\n",
+        "    }\n",
+        ")"
       ],
-      "text/plain": [
-       "           Age Economic Status  Gender      Height        Income      Weight\n",
-       "1    10.465520    Middle Class  Female  139.545894  19863.957892   46.368551\n",
-       "4    43.779882            Rich  Female  127.650202  18274.892352  109.119629\n",
-       "6    35.830860    Middle Class  Female  165.501662  20558.353093   52.728741\n",
-       "7    26.818594            Rich  Female  167.299202  19408.300487   -2.328660\n",
-       "10   31.084558    Middle Class  Female  165.983138  18415.623498   48.371730\n",
-       "11   29.470434            Poor  Female  134.003543  21134.226884   60.585127\n",
-       "12   34.334233            Poor  Female  149.824673  19546.903872  119.229619\n",
-       "16   36.066909            Rich  Female  191.962499  18835.366092   64.921943\n",
-       "17   45.601175    Middle Class  Female  157.798020  18342.649900   32.215115\n",
-       "19   32.509053            Poor  Female  192.663994  20910.754143   52.377219\n",
-       "21   31.443146            Rich  Female  143.035623  19194.070439  100.464745\n",
-       "23   26.854852            Rich  Female  164.025603  18111.940350  110.328107\n",
-       "25   38.453075            Poor  Female  156.155689  19575.471899   74.823251\n",
-       "26   21.995241            Poor  Female  159.708864  20840.925825   68.275110\n",
-       "28   19.002201    Middle Class  Female  206.620612  21459.148423   62.110834\n",
-       "31   42.708940            Poor  Female  139.053295  20593.078250   74.091507\n",
-       "33   42.420073    Middle Class  Female  139.301025  18413.619743   99.256675\n",
-       "35   32.305077    Middle Class  Female  183.851098  18846.217429   44.541702\n",
-       "37   22.565455            Rich  Female  190.719719  20567.335892  116.274632\n",
-       "42   29.022916            Poor  Female  161.530479  18587.678108  118.456984\n",
-       "44   46.209037    Middle Class  Female  140.556280  18824.365426   84.158165\n",
-       "45   37.767718    Middle Class  Female  186.985968  18427.698762  108.811298\n",
-       "46   35.251354    Middle Class  Female  179.185782  21041.110538   56.537286\n",
-       "47   35.762436    Middle Class  Female  129.901881  18044.251672   32.875208\n",
-       "52   33.259760            Rich  Female  183.368037  19735.672120   65.929228\n",
-       "53   17.918446    Middle Class  Female  154.389267  18817.649577   53.819634\n",
-       "54   29.244240            Poor  Female  186.656195  18215.623097   47.813661\n",
-       "55   30.241688            Poor  Female  145.659830  18519.259271   42.386419\n",
-       "60   11.941200    Middle Class  Female  162.770127  19418.652760   75.663445\n",
-       "62   20.875818            Rich  Female  191.300631  21248.793542   49.133164\n",
-       "..         ...             ...     ...         ...           ...         ...\n",
-       "441  25.486046    Middle Class  Female  140.831967  18507.627206   48.954062\n",
-       "443  21.402705            Poor  Female  194.015719  21359.952680   66.119103\n",
-       "446  38.096547            Rich  Female  163.545792  19471.045671   80.834042\n",
-       "448  27.068309            Rich  Female  118.118576  18998.793414   80.668010\n",
-       "450  39.809820            Poor  Female  153.686268  20254.676744   47.026061\n",
-       "452  12.965592            Rich  Female  113.115562  18370.400143   87.303529\n",
-       "456  33.693360            Poor  Female  161.024662  20929.338278  118.306944\n",
-       "458   4.577901            Rich  Female  160.383189  19365.473543  105.775523\n",
-       "460  32.993292            Poor  Female  199.659643  20690.693122   89.968323\n",
-       "464  10.639621            Poor  Female  121.984598  19518.403749  110.970896\n",
-       "465  38.493342            Poor  Female  153.518025  21089.370671   38.769568\n",
-       "466  44.073311            Rich  Female  177.297004  20183.673288   49.884070\n",
-       "470  30.877809            Poor  Female  179.549889  20534.403603   57.170932\n",
-       "471  22.482854            Poor  Female  148.114383  21307.143204   94.685959\n",
-       "472  14.041959            Poor  Female  128.786411  19236.856980   65.910370\n",
-       "475  33.212556    Middle Class  Female  186.366218  18995.818308   76.798489\n",
-       "479  33.570631    Middle Class  Female  170.489521  20610.140991   58.052908\n",
-       "482  42.230758            Rich  Female  162.665803  21442.183439  111.199150\n",
-       "483  30.346778            Poor  Female  135.671820  19005.401603   79.888811\n",
-       "484  51.116700            Poor  Female  117.127682  20523.981614   93.502202\n",
-       "485  12.241652            Rich  Female  133.348820  19868.743729   56.394180\n",
-       "486  38.130804            Poor  Female  164.438850  20503.218832   34.314401\n",
-       "488  37.119624            Rich  Female  133.874339  21287.749837   74.270308\n",
-       "492  17.864297    Middle Class  Female  131.060369  21378.622189   84.522898\n",
-       "494  31.182845            Poor  Female  123.483560  19086.528784   85.384170\n",
-       "495  38.516231            Poor  Female  143.732437  20176.033630   78.402899\n",
-       "496  25.569081    Middle Class  Female  160.674814  20224.021932   54.473354\n",
-       "497  36.608103            Poor  Female  149.632378  18324.310236   71.910584\n",
-       "498  30.132207            Poor  Female  169.712073  18243.266976   85.853052\n",
-       "499  36.961529            Poor  Female  199.681593  20542.715644   31.275503\n",
-       "\n",
-       "[250 rows x 6 columns]"
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "MXeeNF_YAkRx",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "885503b1-4c71-4dea-f6cf-0aefe3c893e6"
+      },
+      "source": [
+        "data.head()"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Economic Status</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>196.343940</td>\n",
+              "      <td>60.909064</td>\n",
+              "      <td>40.451221</td>\n",
+              "      <td>21385.958772</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>153.697110</td>\n",
+              "      <td>48.005164</td>\n",
+              "      <td>48.163870</td>\n",
+              "      <td>19757.653998</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>146.713926</td>\n",
+              "      <td>53.476714</td>\n",
+              "      <td>31.144568</td>\n",
+              "      <td>20279.476735</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>183.988501</td>\n",
+              "      <td>71.785466</td>\n",
+              "      <td>34.192437</td>\n",
+              "      <td>19778.532678</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>179.805582</td>\n",
+              "      <td>58.547234</td>\n",
+              "      <td>32.501684</td>\n",
+              "      <td>19346.337986</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Gender Economic Status      Height     Weight        Age        Income\n",
+              "0  Female            Rich  196.343940  60.909064  40.451221  21385.958772\n",
+              "1    Male    Middle Class  153.697110  48.005164  48.163870  19757.653998\n",
+              "2  Female            Rich  146.713926  53.476714  31.144568  20279.476735\n",
+              "3    Male            Rich  183.988501  71.785466  34.192437  19778.532678\n",
+              "4    Male            Poor  179.805582  58.547234  32.501684  19346.337986"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
       ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grouped_gender.get_group(\"Female\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "double_group = data.groupby([\"Gender\", \"Economic Status\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "6"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lcIDvGCfAkRy",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Agrupación de datos"
       ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(double_group)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "('Female', 'Middle Class')\n",
-      "           Age Economic Status  Gender      Height        Income      Weight\n",
-      "1    10.465520    Middle Class  Female  139.545894  19863.957892   46.368551\n",
-      "6    35.830860    Middle Class  Female  165.501662  20558.353093   52.728741\n",
-      "10   31.084558    Middle Class  Female  165.983138  18415.623498   48.371730\n",
-      "17   45.601175    Middle Class  Female  157.798020  18342.649900   32.215115\n",
-      "28   19.002201    Middle Class  Female  206.620612  21459.148423   62.110834\n",
-      "33   42.420073    Middle Class  Female  139.301025  18413.619743   99.256675\n",
-      "35   32.305077    Middle Class  Female  183.851098  18846.217429   44.541702\n",
-      "44   46.209037    Middle Class  Female  140.556280  18824.365426   84.158165\n",
-      "45   37.767718    Middle Class  Female  186.985968  18427.698762  108.811298\n",
-      "46   35.251354    Middle Class  Female  179.185782  21041.110538   56.537286\n",
-      "47   35.762436    Middle Class  Female  129.901881  18044.251672   32.875208\n",
-      "53   17.918446    Middle Class  Female  154.389267  18817.649577   53.819634\n",
-      "60   11.941200    Middle Class  Female  162.770127  19418.652760   75.663445\n",
-      "67   34.239703    Middle Class  Female  165.942968  19153.486274   83.038882\n",
-      "68   22.978827    Middle Class  Female  196.183982  21163.250556   46.662068\n",
-      "76   35.591469    Middle Class  Female  130.310404  18023.693683  101.422139\n",
-      "89   14.675221    Middle Class  Female  109.297125  20889.499199   72.247374\n",
-      "102  44.532749    Middle Class  Female  109.939092  20369.757206   64.098746\n",
-      "104  30.281122    Middle Class  Female  162.712084  18532.298931   25.685071\n",
-      "112  42.009819    Middle Class  Female  155.268233  20307.063465  106.800905\n",
-      "124   7.515108    Middle Class  Female  180.152982  19079.806490   64.554424\n",
-      "137  35.924990    Middle Class  Female   93.751828  21235.202185   50.407616\n",
-      "138  21.517235    Middle Class  Female  146.608031  20511.378445   80.296198\n",
-      "154  28.412110    Middle Class  Female  153.341035  20897.365355   36.234998\n",
-      "158  29.623728    Middle Class  Female  190.037299  19820.442277   70.673584\n",
-      "160  29.282062    Middle Class  Female  174.113409  18541.693714  106.172370\n",
-      "162  21.312926    Middle Class  Female  164.143401  19375.292879   73.646797\n",
-      "174  37.997441    Middle Class  Female  143.001599  18292.559195   60.192681\n",
-      "175  26.034914    Middle Class  Female  138.494037  18027.012079   66.706823\n",
-      "176  13.113990    Middle Class  Female  172.763998  19519.611522   65.692884\n",
-      "..         ...             ...     ...         ...           ...         ...\n",
-      "313  26.321725    Middle Class  Female  154.667349  20458.539827   63.942898\n",
-      "314  12.370079    Middle Class  Female  134.727028  20903.469763   38.207083\n",
-      "316  27.095457    Middle Class  Female  154.754570  21289.478424   59.029566\n",
-      "330  55.547160    Middle Class  Female  231.075627  18845.450715   42.438716\n",
-      "332  37.383502    Middle Class  Female  161.171743  21038.498794   92.083739\n",
-      "334  42.338630    Middle Class  Female  154.176486  19172.451861   28.243444\n",
-      "339  51.381003    Middle Class  Female  177.576159  20527.286121   70.203899\n",
-      "345  42.124983    Middle Class  Female  175.712654  20995.409023   83.487523\n",
-      "350  34.944027    Middle Class  Female  125.158477  20171.693906   43.785036\n",
-      "352  36.089125    Middle Class  Female  181.090026  18091.668422   48.764704\n",
-      "353  16.972686    Middle Class  Female  221.543447  18297.099584   52.317087\n",
-      "360  34.605841    Middle Class  Female  104.102651  19157.585958   82.823005\n",
-      "363  18.768257    Middle Class  Female  148.080280  19853.122509    2.000947\n",
-      "377  27.382050    Middle Class  Female  150.296194  20980.127868   83.708274\n",
-      "389  22.720132    Middle Class  Female  155.259976  20707.846562   88.968395\n",
-      "394  33.738940    Middle Class  Female  153.801854  19229.585532   43.308021\n",
-      "402  11.631507    Middle Class  Female  181.244032  19645.844431   10.968214\n",
-      "408  40.703678    Middle Class  Female  186.469132  21400.264072   29.709409\n",
-      "413  56.718107    Middle Class  Female  124.652053  21179.343419   78.173161\n",
-      "414  48.407836    Middle Class  Female  142.595708  18892.969776   21.209439\n",
-      "415  36.601422    Middle Class  Female  123.112372  18272.597349   49.189433\n",
-      "421   8.833892    Middle Class  Female  188.031018  18052.148891   50.459220\n",
-      "431  20.503130    Middle Class  Female  168.058999  18686.258973  103.427061\n",
-      "437  21.955197    Middle Class  Female  208.871462  20223.417068   81.373172\n",
-      "439   9.354293    Middle Class  Female  136.794719  18697.905890   70.031706\n",
-      "441  25.486046    Middle Class  Female  140.831967  18507.627206   48.954062\n",
-      "475  33.212556    Middle Class  Female  186.366218  18995.818308   76.798489\n",
-      "479  33.570631    Middle Class  Female  170.489521  20610.140991   58.052908\n",
-      "492  17.864297    Middle Class  Female  131.060369  21378.622189   84.522898\n",
-      "496  25.569081    Middle Class  Female  160.674814  20224.021932   54.473354\n",
-      "\n",
-      "[77 rows x 6 columns]\n",
-      "('Female', 'Poor')\n",
-      "           Age Economic Status  Gender      Height        Income      Weight\n",
-      "11   29.470434            Poor  Female  134.003543  21134.226884   60.585127\n",
-      "12   34.334233            Poor  Female  149.824673  19546.903872  119.229619\n",
-      "19   32.509053            Poor  Female  192.663994  20910.754143   52.377219\n",
-      "25   38.453075            Poor  Female  156.155689  19575.471899   74.823251\n",
-      "26   21.995241            Poor  Female  159.708864  20840.925825   68.275110\n",
-      "31   42.708940            Poor  Female  139.053295  20593.078250   74.091507\n",
-      "42   29.022916            Poor  Female  161.530479  18587.678108  118.456984\n",
-      "54   29.244240            Poor  Female  186.656195  18215.623097   47.813661\n",
-      "55   30.241688            Poor  Female  145.659830  18519.259271   42.386419\n",
-      "64   23.466625            Poor  Female  191.100354  18955.137348   94.902226\n",
-      "78   23.636457            Poor  Female  152.202727  20834.228251   92.784338\n",
-      "80   36.051093            Poor  Female  142.741723  19701.660426   63.609961\n",
-      "88   11.860977            Poor  Female  149.995543  20610.278601   35.891591\n",
-      "94   30.591980            Poor  Female  173.009403  20784.880341   86.641239\n",
-      "99   22.656404            Poor  Female  157.349953  20509.171228  116.890809\n",
-      "101  35.356566            Poor  Female  177.918821  18750.151429  103.511777\n",
-      "103  31.407046            Poor  Female  153.203288  20311.631354   74.423703\n",
-      "106  33.305150            Poor  Female  159.601843  19032.828862   70.893241\n",
-      "110  19.822668            Poor  Female  132.982739  20341.393540   84.126544\n",
-      "113  36.294769            Poor  Female  182.136652  18066.417864   89.821388\n",
-      "115  18.587315            Poor  Female  193.919347  18118.164011   55.023324\n",
-      "136  10.853776            Poor  Female  199.886480  20250.007076   89.482575\n",
-      "140  15.934916            Poor  Female  165.098002  19904.069486   16.958700\n",
-      "146  19.224866            Poor  Female  188.187461  20609.988261   38.611482\n",
-      "152  29.367794            Poor  Female  138.373978  19261.629641   81.329486\n",
-      "159  35.167965            Poor  Female  176.298045  19058.018484   44.393104\n",
-      "165  37.456781            Poor  Female  125.484152  18045.017713   59.214254\n",
-      "166  32.584963            Poor  Female  109.210656  18318.983746   46.074303\n",
-      "167  41.055663            Poor  Female  196.537707  19043.222255  103.650602\n",
-      "171  46.037510            Poor  Female  169.692363  18015.327084   83.367880\n",
-      "..         ...             ...     ...         ...           ...         ...\n",
-      "337  43.511005            Poor  Female  115.122250  21332.919410   44.137732\n",
-      "347  36.039852            Poor  Female  132.444658  21141.994524   48.523165\n",
-      "351  35.600430            Poor  Female  164.192497  19454.897941  107.803302\n",
-      "358  15.931304            Poor  Female  185.067064  20889.835538   92.030014\n",
-      "361  39.037580            Poor  Female  161.785552  20494.925738   80.113919\n",
-      "364  27.741825            Poor  Female  160.508187  19169.491851  107.472361\n",
-      "371  54.003620            Poor  Female  168.929494  19032.797063   63.997895\n",
-      "378  32.177457            Poor  Female  185.200344  19325.450798   85.933347\n",
-      "395   9.923737            Poor  Female  154.984912  21447.522531  107.846993\n",
-      "419  19.068888            Poor  Female  101.916169  18979.671074   31.070209\n",
-      "422  14.547271            Poor  Female  184.611712  18300.239856   61.701594\n",
-      "425  22.071058            Poor  Female  182.931072  19652.125188   84.110847\n",
-      "430  31.433328            Poor  Female  137.691092  19367.808588   17.089364\n",
-      "443  21.402705            Poor  Female  194.015719  21359.952680   66.119103\n",
-      "450  39.809820            Poor  Female  153.686268  20254.676744   47.026061\n",
-      "456  33.693360            Poor  Female  161.024662  20929.338278  118.306944\n",
-      "460  32.993292            Poor  Female  199.659643  20690.693122   89.968323\n",
-      "464  10.639621            Poor  Female  121.984598  19518.403749  110.970896\n",
-      "465  38.493342            Poor  Female  153.518025  21089.370671   38.769568\n",
-      "470  30.877809            Poor  Female  179.549889  20534.403603   57.170932\n",
-      "471  22.482854            Poor  Female  148.114383  21307.143204   94.685959\n",
-      "472  14.041959            Poor  Female  128.786411  19236.856980   65.910370\n",
-      "483  30.346778            Poor  Female  135.671820  19005.401603   79.888811\n",
-      "484  51.116700            Poor  Female  117.127682  20523.981614   93.502202\n",
-      "486  38.130804            Poor  Female  164.438850  20503.218832   34.314401\n",
-      "494  31.182845            Poor  Female  123.483560  19086.528784   85.384170\n",
-      "495  38.516231            Poor  Female  143.732437  20176.033630   78.402899\n",
-      "497  36.608103            Poor  Female  149.632378  18324.310236   71.910584\n",
-      "498  30.132207            Poor  Female  169.712073  18243.266976   85.853052\n",
-      "499  36.961529            Poor  Female  199.681593  20542.715644   31.275503\n",
-      "\n",
-      "[87 rows x 6 columns]\n",
-      "('Female', 'Rich')\n",
-      "           Age Economic Status  Gender      Height        Income      Weight\n",
-      "4    43.779882            Rich  Female  127.650202  18274.892352  109.119629\n",
-      "7    26.818594            Rich  Female  167.299202  19408.300487   -2.328660\n",
-      "16   36.066909            Rich  Female  191.962499  18835.366092   64.921943\n",
-      "21   31.443146            Rich  Female  143.035623  19194.070439  100.464745\n",
-      "23   26.854852            Rich  Female  164.025603  18111.940350  110.328107\n",
-      "37   22.565455            Rich  Female  190.719719  20567.335892  116.274632\n",
-      "52   33.259760            Rich  Female  183.368037  19735.672120   65.929228\n",
-      "62   20.875818            Rich  Female  191.300631  21248.793542   49.133164\n",
-      "65   28.373658            Rich  Female  176.515775  19982.937493    9.550299\n",
-      "69   32.894766            Rich  Female  143.978191  19206.123557   53.082303\n",
-      "73   20.071017            Rich  Female  177.148689  19512.599053   34.287714\n",
-      "75   42.561143            Rich  Female  223.399999  18605.488244   35.004081\n",
-      "86   14.896808            Rich  Female  128.313999  19417.445671  106.606747\n",
-      "96   29.793803            Rich  Female  139.903821  18551.940301   36.933923\n",
-      "98   22.032406            Rich  Female  161.681760  18262.007675   82.573998\n",
-      "108  20.181688            Rich  Female  139.925942  18352.432775   46.013173\n",
-      "109  16.716083            Rich  Female  146.082378  19658.619799   59.150848\n",
-      "111  34.659522            Rich  Female  150.867746  18697.152638   74.362798\n",
-      "120  34.808701            Rich  Female  156.609818  18181.239464   59.754818\n",
-      "126  51.005973            Rich  Female  136.608623  18745.345754   79.701414\n",
-      "127  17.292591            Rich  Female  196.265543  18541.704743   90.914597\n",
-      "128  36.918219            Rich  Female  182.736977  18525.599111   61.089055\n",
-      "129  17.137054            Rich  Female  186.211612  19495.611434   20.061619\n",
-      "131  27.280349            Rich  Female  133.555419  19876.329434   32.514515\n",
-      "132  36.358718            Rich  Female  176.354722  18908.348984   78.735163\n",
-      "141  39.535039            Rich  Female  162.256604  19214.649452   72.990665\n",
-      "153  45.276044            Rich  Female  153.880078  19482.262872   80.760571\n",
-      "155  41.902799            Rich  Female  139.842208  20979.493799   64.182375\n",
-      "170   8.391689            Rich  Female  146.800695  20217.077308   78.304128\n",
-      "184  31.275101            Rich  Female  198.888959  21461.855436   70.305338\n",
-      "..         ...             ...     ...         ...           ...         ...\n",
-      "310  32.999300            Rich  Female  150.161249  19283.259073   43.161064\n",
-      "312  24.744754            Rich  Female  196.031669  19320.864803   42.341257\n",
-      "319  33.240946            Rich  Female  195.148576  21412.915994   68.317325\n",
-      "322  30.418095            Rich  Female  154.534759  20083.693050   48.748141\n",
-      "340  29.921051            Rich  Female  188.392694  19274.961282   35.112111\n",
-      "342  39.799259            Rich  Female  157.008730  18587.063286   63.032913\n",
-      "349  42.204923            Rich  Female  156.151986  18392.583393   52.845353\n",
-      "356  34.749251            Rich  Female  185.955017  20559.456898   58.594144\n",
-      "374  14.317516            Rich  Female  197.542059  20042.721961   62.629975\n",
-      "375  19.042991            Rich  Female  183.235152  19755.813643   78.507077\n",
-      "379  22.974709            Rich  Female  116.919084  18469.896206   32.397063\n",
-      "383  38.971515            Rich  Female  144.633188  18520.642748   56.818949\n",
-      "384  30.507904            Rich  Female  166.297204  18561.094435   87.840004\n",
-      "390  24.308848            Rich  Female  140.223951  20764.430602   36.630581\n",
-      "400   3.320607            Rich  Female  159.825693  19241.168745   75.400689\n",
-      "404  43.796608            Rich  Female  122.951302  18825.128606   65.843569\n",
-      "405  16.082126            Rich  Female  211.955130  21295.627733   97.833787\n",
-      "410   4.609084            Rich  Female  194.698403  20142.495812   44.781309\n",
-      "429  17.346571            Rich  Female  151.459352  20085.603111   58.821286\n",
-      "432  34.507677            Rich  Female  134.040678  18181.781447   78.830063\n",
-      "434  47.003148            Rich  Female  171.034776  18535.786339   46.689208\n",
-      "436  24.264547            Rich  Female  162.132851  20684.657862   89.599026\n",
-      "446  38.096547            Rich  Female  163.545792  19471.045671   80.834042\n",
-      "448  27.068309            Rich  Female  118.118576  18998.793414   80.668010\n",
-      "452  12.965592            Rich  Female  113.115562  18370.400143   87.303529\n",
-      "458   4.577901            Rich  Female  160.383189  19365.473543  105.775523\n",
-      "466  44.073311            Rich  Female  177.297004  20183.673288   49.884070\n",
-      "482  42.230758            Rich  Female  162.665803  21442.183439  111.199150\n",
-      "485  12.241652            Rich  Female  133.348820  19868.743729   56.394180\n",
-      "488  37.119624            Rich  Female  133.874339  21287.749837   74.270308\n",
-      "\n",
-      "[86 rows x 6 columns]\n",
-      "('Male', 'Middle Class')\n",
-      "           Age Economic Status Gender      Height        Income      Weight\n",
-      "0    29.406063    Middle Class   Male   93.455149  20717.514475   50.868452\n",
-      "3    17.706009    Middle Class   Male  155.885685  18688.042862   73.035787\n",
-      "9    32.535617    Middle Class   Male  138.481238  20343.973756   47.240539\n",
-      "14   42.280704    Middle Class   Male  131.584579  18592.858002   96.606513\n",
-      "18   42.720713    Middle Class   Male   89.417396  20565.126795   77.516021\n",
-      "29   33.865350    Middle Class   Male  159.237730  21176.630376   88.431003\n",
-      "50   24.265501    Middle Class   Male  159.705113  18944.972440   17.862102\n",
-      "51   46.097030    Middle Class   Male  115.178818  18447.026740   39.840291\n",
-      "57   45.994656    Middle Class   Male  244.699568  21044.868558   27.923058\n",
-      "59   31.365961    Middle Class   Male  176.796080  19518.000316   58.637704\n",
-      "72   32.597468    Middle Class   Male  145.593945  20553.221304   53.641066\n",
-      "83   37.329715    Middle Class   Male   84.001149  19842.068188   81.380182\n",
-      "90   43.651921    Middle Class   Male  143.157383  20613.551532   60.242478\n",
-      "91   48.304883    Middle Class   Male  188.992938  20942.550465  104.365391\n",
-      "97   23.081158    Middle Class   Male  154.169306  20963.704747   39.773336\n",
-      "100  45.744495    Middle Class   Male  112.091058  18786.304713   72.114936\n",
-      "119  36.328509    Middle Class   Male  160.684414  18582.130149   78.724031\n",
-      "123  28.780553    Middle Class   Male  221.912962  20769.341950   85.475042\n",
-      "125  29.360929    Middle Class   Male  163.859265  18930.605346   88.148614\n",
-      "133  25.551263    Middle Class   Male  151.219139  18387.565437   40.518630\n",
-      "135  45.608902    Middle Class   Male  182.429889  20710.121829   56.898551\n",
-      "142  49.476191    Middle Class   Male  141.049137  21071.892401   84.717805\n",
-      "143  22.434504    Middle Class   Male  140.409244  20195.339396   82.818686\n",
-      "144  57.574830    Middle Class   Male  152.771777  20438.022285   84.393689\n",
-      "147  25.971870    Middle Class   Male  125.737602  21080.867284   73.104074\n",
-      "161  22.912570    Middle Class   Male  143.134816  18138.478907   72.865152\n",
-      "163  37.503477    Middle Class   Male  131.549445  18174.313122   54.449399\n",
-      "177  28.390797    Middle Class   Male  164.045877  18701.223201   25.876219\n",
-      "179  28.340490    Middle Class   Male  137.996434  20632.887129   85.244800\n",
-      "198  40.992991    Middle Class   Male  196.475725  19540.421026   66.714022\n",
-      "..         ...             ...    ...         ...           ...         ...\n",
-      "287  25.838930    Middle Class   Male  136.304892  18029.109009   33.117664\n",
-      "309  23.037245    Middle Class   Male  114.572611  20278.290292   94.547393\n",
-      "324  24.847973    Middle Class   Male  183.077180  20961.081523   93.777785\n",
-      "326  36.716919    Middle Class   Male  180.726250  19125.093642   66.706796\n",
-      "327  34.418445    Middle Class   Male  148.934081  19713.104448    9.970570\n",
-      "333  26.935167    Middle Class   Male  183.543450  18208.053913   63.038337\n",
-      "335  12.406766    Middle Class   Male  189.676380  20594.954435   82.356134\n",
-      "338  39.633638    Middle Class   Male  162.549429  20183.822821   26.485267\n",
-      "341  20.625766    Middle Class   Male  105.798830  20686.452196   87.240609\n",
-      "355  44.489018    Middle Class   Male  129.316887  19946.881813   74.518745\n",
-      "362  15.715172    Middle Class   Male  177.486605  18735.826952   44.614176\n",
-      "365  23.068501    Middle Class   Male  126.300616  19449.078257   62.495935\n",
-      "370  47.348873    Middle Class   Male  175.658291  20234.164075   18.925658\n",
-      "372  28.420381    Middle Class   Male  180.575340  20829.494696   58.478809\n",
-      "381  23.034165    Middle Class   Male  117.715470  19520.510446   63.970489\n",
-      "382  35.482051    Middle Class   Male  111.123269  20685.722472   24.627329\n",
-      "387  35.909676    Middle Class   Male  170.687040  20095.253178   51.699464\n",
-      "388  24.980599    Middle Class   Male  113.223448  18591.699009   76.400091\n",
-      "392  41.562551    Middle Class   Male  155.330436  19499.810276   55.064608\n",
-      "396  29.069226    Middle Class   Male  149.879194  20747.160594   33.575816\n",
-      "397  31.904712    Middle Class   Male  194.337976  19644.107468   74.472010\n",
-      "409  35.227496    Middle Class   Male  194.457642  19442.561671   26.878912\n",
-      "417  28.196659    Middle Class   Male  200.110935  20153.324396   -1.899898\n",
-      "426  36.468719    Middle Class   Male  193.507393  18626.798765   52.845389\n",
-      "433  35.956098    Middle Class   Male  171.600128  19676.722807   79.879927\n",
-      "457  35.166985    Middle Class   Male  185.930859  20400.600364   51.111471\n",
-      "463  36.244812    Middle Class   Male  167.894078  20827.426451   49.354226\n",
-      "469  30.849618    Middle Class   Male  193.036240  21389.535960   23.796108\n",
-      "476  25.910841    Middle Class   Male  140.929009  19953.551284   94.894692\n",
-      "489  36.219497    Middle Class   Male  156.398762  19019.898289   77.351999\n",
-      "\n",
-      "[74 rows x 6 columns]\n",
-      "('Male', 'Poor')\n",
-      "           Age Economic Status Gender      Height        Income      Weight\n",
-      "2    41.632979            Poor   Male  183.857248  18549.640778   60.016080\n",
-      "5    31.511689            Poor   Male  192.882388  20826.579612   88.442163\n",
-      "13   28.286581            Poor   Male  117.798582  21226.742907   60.712632\n",
-      "15   15.307572            Poor   Male  158.743542  21243.899231   61.155687\n",
-      "22   24.546463            Poor   Male  148.232867  18529.475088   47.840715\n",
-      "24   36.759814            Poor   Male  183.562680  19798.790037  103.294236\n",
-      "30   15.223665            Poor   Male  143.687508  20848.134261   53.244928\n",
-      "32   38.673025            Poor   Male  113.591964  20146.827332   96.256478\n",
-      "34   25.671351            Poor   Male  134.270156  20189.839410   65.590964\n",
-      "38   43.565371            Poor   Male  184.011414  19314.357756   94.718841\n",
-      "40   33.373076            Poor   Male  166.749750  18185.495702   68.957596\n",
-      "43   45.449231            Poor   Male  199.616249  18486.665013  102.880845\n",
-      "48   37.065833            Poor   Male  157.584210  21231.990757   96.470241\n",
-      "56   34.069823            Poor   Male  126.234905  19130.604647   36.524401\n",
-      "58   37.324568            Poor   Male  173.166895  20991.144696   13.617431\n",
-      "74   11.801954            Poor   Male  202.320958  18192.352393   84.807687\n",
-      "77   39.344115            Poor   Male  203.457857  19026.472646   83.975649\n",
-      "79   22.770096            Poor   Male  208.335598  18741.216970   44.834961\n",
-      "81   39.179715            Poor   Male  148.931545  19850.893612   85.428130\n",
-      "87   25.427953            Poor   Male  213.403044  19861.208578   57.407962\n",
-      "107  41.729838            Poor   Male  155.374414  18569.901009   78.693388\n",
-      "114  37.331814            Poor   Male  150.402682  21068.641423   65.907611\n",
-      "118  22.901843            Poor   Male  184.512025  19827.340073   14.368717\n",
-      "122   1.782875            Poor   Male  160.128232  20835.263414   46.569934\n",
-      "139  14.773057            Poor   Male  167.258116  20243.376717   95.815977\n",
-      "145  19.820231            Poor   Male  164.808128  19759.543924   40.826737\n",
-      "148  15.765982            Poor   Male  188.187126  20976.909024   72.143658\n",
-      "150  44.799451            Poor   Male   96.605848  19294.067131   88.013071\n",
-      "151  30.272075            Poor   Male  120.909765  19152.949293   59.053484\n",
-      "157  46.594793            Poor   Male  151.670523  20505.346241   63.728044\n",
-      "..         ...             ...    ...         ...           ...         ...\n",
-      "323  40.039539            Poor   Male  144.013814  20828.551965   50.589190\n",
-      "331  48.408080            Poor   Male  143.798219  18659.671575   72.617937\n",
-      "348  47.547840            Poor   Male  195.394467  20128.033581   67.114319\n",
-      "368  24.917287            Poor   Male  188.322353  18220.451552   56.413736\n",
-      "373  27.034639            Poor   Male  149.769336  19934.409040   43.842051\n",
-      "380  26.267274            Poor   Male  109.392640  20855.312779   59.869248\n",
-      "385  22.287703            Poor   Male  181.586613  20349.120153   37.956876\n",
-      "386   8.928554            Poor   Male  129.324812  19331.616552   45.731853\n",
-      "391  31.058355            Poor   Male  149.217547  18375.661771  115.185166\n",
-      "398  36.530889            Poor   Male  158.750235  18678.210648  101.732496\n",
-      "403  35.436958            Poor   Male  188.242766  21199.707862  117.696619\n",
-      "406  41.844743            Poor   Male  125.377894  19164.120668   76.581747\n",
-      "411  50.877259            Poor   Male  143.335974  18167.619910   56.974097\n",
-      "416  52.874494            Poor   Male  151.818227  19539.236681   56.334119\n",
-      "418  31.731745            Poor   Male  152.959519  20526.558125   38.443125\n",
-      "424  27.025589            Poor   Male  117.422523  20616.671220   60.026339\n",
-      "427  18.101627            Poor   Male  146.991507  18581.304827   42.603240\n",
-      "435  30.121219            Poor   Male  200.261483  20296.081880   34.129741\n",
-      "440  33.332393            Poor   Male  193.855290  19714.998843   34.648921\n",
-      "444  28.199086            Poor   Male  139.317685  20204.378925   87.835392\n",
-      "451  22.539506            Poor   Male  171.225075  21477.593830   23.523673\n",
-      "453  45.605468            Poor   Male  161.464070  20044.642117   54.322807\n",
-      "455   9.504070            Poor   Male  175.821391  19821.661878   78.242555\n",
-      "461  33.710923            Poor   Male  174.128934  20703.893829   60.151746\n",
-      "462  26.702841            Poor   Male  152.275518  18705.322248   68.213105\n",
-      "467  46.494498            Poor   Male  156.431611  21227.527975   84.116289\n",
-      "468  28.763444            Poor   Male  176.934490  18339.844982   85.636147\n",
-      "481  19.399664            Poor   Male  122.253378  21418.957595   34.334860\n",
-      "490  23.325366            Poor   Male  154.859406  20178.525442   77.059452\n",
-      "491  28.190877            Poor   Male  113.399151  20883.753347   47.601578\n",
-      "\n",
-      "[93 rows x 6 columns]\n",
-      "('Male', 'Rich')\n",
-      "           Age Economic Status Gender      Height        Income      Weight\n",
-      "8    45.239503            Rich   Male  132.588194  20660.630399   51.229028\n",
-      "20   13.282330            Rich   Male  162.059657  19087.166254   81.284012\n",
-      "27   28.041821            Rich   Male  120.856916  20666.441965   62.811662\n",
-      "36   37.689419            Rich   Male  207.849203  18638.205028   84.178977\n",
-      "39   27.119116            Rich   Male  172.585129  20830.428222   54.949156\n",
-      "41   23.081560            Rich   Male  231.008073  20518.979096   37.124301\n",
-      "49   33.062813            Rich   Male  130.577611  20769.464752   74.465052\n",
-      "61   32.725306            Rich   Male  125.746098  20564.983521   45.953373\n",
-      "63   22.759912            Rich   Male  176.200693  20086.131030   30.223298\n",
-      "66   -1.287638            Rich   Male  186.858361  18135.675395   60.246966\n",
-      "70    8.425703            Rich   Male  205.962436  20632.502907   15.650330\n",
-      "71   24.559319            Rich   Male  164.159871  19309.557185  109.634307\n",
-      "82   37.087278            Rich   Male  160.676134  18604.781222   54.386087\n",
-      "84   50.708650            Rich   Male  221.084203  21491.877113   89.646159\n",
-      "85   18.441596            Rich   Male  147.514925  21103.919891   33.181814\n",
-      "92   14.633881            Rich   Male  151.164891  20443.769729   72.489576\n",
-      "93   24.781854            Rich   Male  121.866383  20386.472049   66.424455\n",
-      "95   17.879890            Rich   Male  152.919255  18044.114358   66.111337\n",
-      "105  28.295432            Rich   Male  182.892151  18267.682833   70.933853\n",
-      "116  27.013229            Rich   Male  161.959130  18277.445475   49.373486\n",
-      "117  44.043199            Rich   Male  116.283881  21097.187235   88.765325\n",
-      "121  20.118993            Rich   Male  149.669959  20536.563261  110.820505\n",
-      "130  23.025330            Rich   Male  205.508099  18411.240001   74.841211\n",
-      "134  24.344526            Rich   Male  173.755999  20375.866627   46.857964\n",
-      "149  33.326156            Rich   Male  144.538108  18812.545239   41.004157\n",
-      "156  14.560534            Rich   Male  150.887380  20520.991821   61.620005\n",
-      "181  17.188428            Rich   Male  163.699916  20933.920308   70.701092\n",
-      "182  31.133442            Rich   Male  195.701996  21384.503820   69.737551\n",
-      "203  24.393831            Rich   Male  174.563517  20725.963410   38.461386\n",
-      "205  43.446736            Rich   Male  155.733264  20891.314273   62.630459\n",
-      "..         ...             ...    ...         ...           ...         ...\n",
-      "346  18.974193            Rich   Male  119.266116  20912.736997   14.432132\n",
-      "354   8.592472            Rich   Male  157.740834  20256.412988   53.195259\n",
-      "357  23.216386            Rich   Male  150.761258  19537.555780   39.613128\n",
-      "359  31.798265            Rich   Male  161.754975  19708.897142   56.313363\n",
-      "366  18.648546            Rich   Male  185.342298  20100.211018   64.377264\n",
-      "367  24.907147            Rich   Male  171.637710  21099.477647   81.170733\n",
-      "369   6.129743            Rich   Male  140.551730  19506.743428   69.004383\n",
-      "376  51.618422            Rich   Male  139.590044  21134.598903   48.683568\n",
-      "393  51.517283            Rich   Male  179.963471  19479.133045  107.388716\n",
-      "399  56.495331            Rich   Male  114.939922  21220.134146   14.319345\n",
-      "401  20.370103            Rich   Male  116.772157  18073.499806   81.243316\n",
-      "407  32.923019            Rich   Male  171.081189  18908.240054   33.074603\n",
-      "412  20.064681            Rich   Male  150.708547  21344.908197   22.053410\n",
-      "420  28.825230            Rich   Male  126.268030  18915.122663   80.141051\n",
-      "423  19.783875            Rich   Male  153.599616  20025.352758   56.535391\n",
-      "428  21.650390            Rich   Male  145.480398  21113.029624   75.397092\n",
-      "438  17.518248            Rich   Male  153.194625  18592.128306   57.328636\n",
-      "442  41.148815            Rich   Male  185.548730  20557.584791   54.320334\n",
-      "445  17.350872            Rich   Male  165.863704  20043.161040   19.577812\n",
-      "447  31.720338            Rich   Male  206.314374  20684.425538  100.824455\n",
-      "449  53.213463            Rich   Male  158.853890  18260.835182   57.657574\n",
-      "454  27.073896            Rich   Male  206.038807  21435.289439  126.426263\n",
-      "459  48.143951            Rich   Male  164.569602  18774.690305   57.373974\n",
-      "473  57.927458            Rich   Male  158.264063  19773.326950  102.165694\n",
-      "474  28.871436            Rich   Male  129.211463  18119.001636   54.231350\n",
-      "477  10.643144            Rich   Male  143.849606  18803.688821  109.599023\n",
-      "478  10.190133            Rich   Male  171.248419  18281.357516   95.809197\n",
-      "480  38.163563            Rich   Male  152.699267  18162.912374   18.406700\n",
-      "487  22.489102            Rich   Male  121.876538  20504.227101   60.994486\n",
-      "493  11.569556            Rich   Male  180.690792  18827.025459   48.867474\n",
-      "\n",
-      "[83 rows x 6 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "for names, groups in double_group:\n",
-    "    print(names)\n",
-    "    print(groups)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Operaciones sobre datos agrupados"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "id": "b0ejXt6rAkRz",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "grouped_gender = data.groupby(\"Gender\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
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-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Weight</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>2294.526981</td>\n",
-       "      <td>12239.885454</td>\n",
-       "      <td>1.507522e+06</td>\n",
-       "      <td>4674.662643</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>2588.220559</td>\n",
-       "      <td>13496.953076</td>\n",
-       "      <td>1.714421e+06</td>\n",
-       "      <td>6024.342814</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>2456.855354</td>\n",
-       "      <td>13824.517313</td>\n",
-       "      <td>1.684557e+06</td>\n",
-       "      <td>5707.343913</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>2334.089034</td>\n",
-       "      <td>11567.602209</td>\n",
-       "      <td>1.467777e+06</td>\n",
-       "      <td>4538.049069</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>2820.881118</td>\n",
-       "      <td>14962.758180</td>\n",
-       "      <td>1.842958e+06</td>\n",
-       "      <td>6114.923912</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>2291.241542</td>\n",
-       "      <td>13282.930501</td>\n",
-       "      <td>1.643102e+06</td>\n",
-       "      <td>5168.087114</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "aEiG5AFrAkR0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "474eeb1c-4efa-4ecf-ba8e-1622a786f9cc"
+      },
+      "source": [
+        "grouped_gender.groups"
       ],
-      "text/plain": [
-       "                                Age        Height        Income       Weight\n",
-       "Gender Economic Status                                                      \n",
-       "Female Middle Class     2294.526981  12239.885454  1.507522e+06  4674.662643\n",
-       "       Poor             2588.220559  13496.953076  1.714421e+06  6024.342814\n",
-       "       Rich             2456.855354  13824.517313  1.684557e+06  5707.343913\n",
-       "Male   Middle Class     2334.089034  11567.602209  1.467777e+06  4538.049069\n",
-       "       Poor             2820.881118  14962.758180  1.842958e+06  6114.923912\n",
-       "       Rich             2291.241542  13282.930501  1.643102e+06  5168.087114"
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'Female': Int64Index([  0,   2,   5,   6,   7,   8,  11,  12,  14,  15,\n",
+              "             ...\n",
+              "             480, 481, 484, 485, 488, 490, 491, 492, 493, 495],\n",
+              "            dtype='int64', length=234),\n",
+              " 'Male': Int64Index([  1,   3,   4,   9,  10,  13,  16,  17,  18,  19,\n",
+              "             ...\n",
+              "             482, 483, 486, 487, 489, 494, 496, 497, 498, 499],\n",
+              "            dtype='int64', length=266)}"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        }
       ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group.sum()"
-   ]
-  },
-  {
-   "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></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Weight</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>29.799052</td>\n",
-       "      <td>158.959551</td>\n",
-       "      <td>19578.211437</td>\n",
-       "      <td>60.709904</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>29.749662</td>\n",
-       "      <td>155.137392</td>\n",
-       "      <td>19705.993905</td>\n",
-       "      <td>69.245320</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>28.568086</td>\n",
-       "      <td>160.750201</td>\n",
-       "      <td>19587.872781</td>\n",
-       "      <td>66.364464</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>31.541744</td>\n",
-       "      <td>156.318949</td>\n",
-       "      <td>19834.825063</td>\n",
-       "      <td>61.324987</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>30.332055</td>\n",
-       "      <td>160.889873</td>\n",
-       "      <td>19816.748316</td>\n",
-       "      <td>65.751870</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>27.605320</td>\n",
-       "      <td>160.035307</td>\n",
-       "      <td>19796.412646</td>\n",
-       "      <td>62.266110</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "gSVmrowTAkR2",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 527
+        },
+        "outputId": "95eea914-8b1e-4c3f-ba12-3d28e821ff4b"
+      },
+      "source": [
+        "for names, groups in grouped_gender:\n",
+        "    print(names)\n",
+        "    print(groups)"
       ],
-      "text/plain": [
-       "                              Age      Height        Income     Weight\n",
-       "Gender Economic Status                                                \n",
-       "Female Middle Class     29.799052  158.959551  19578.211437  60.709904\n",
-       "       Poor             29.749662  155.137392  19705.993905  69.245320\n",
-       "       Rich             28.568086  160.750201  19587.872781  66.364464\n",
-       "Male   Middle Class     31.541744  156.318949  19834.825063  61.324987\n",
-       "       Poor             30.332055  160.889873  19816.748316  65.751870\n",
-       "       Rich             27.605320  160.035307  19796.412646  62.266110"
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Female\n",
+            "     Gender Economic Status      Height     Weight        Age        Income\n",
+            "0    Female            Rich  196.343940  60.909064  40.451221  21385.958772\n",
+            "2    Female            Rich  146.713926  53.476714  31.144568  20279.476735\n",
+            "5    Female            Poor  172.074560  28.192775  42.997942  21277.967078\n",
+            "6    Female            Rich  182.532298  63.768421  57.426784  18952.149135\n",
+            "7    Female    Middle Class  166.360472  33.332115  19.958657  19611.989916\n",
+            "..      ...             ...         ...        ...        ...           ...\n",
+            "490  Female            Rich  150.092007  67.726508  23.798001  19656.450662\n",
+            "491  Female    Middle Class  156.545084  55.503196  26.584817  19038.000602\n",
+            "492  Female    Middle Class  173.475638  52.248953  64.993034  20516.979828\n",
+            "493  Female            Rich  169.567790  63.484671  29.763336  20821.406697\n",
+            "495  Female    Middle Class  180.672252  51.029436  17.900334  20043.401932\n",
+            "\n",
+            "[234 rows x 6 columns]\n",
+            "Male\n",
+            "    Gender Economic Status      Height      Weight        Age        Income\n",
+            "1     Male    Middle Class  153.697110   48.005164  48.163870  19757.653998\n",
+            "3     Male            Rich  183.988501   71.785466  34.192437  19778.532678\n",
+            "4     Male            Poor  179.805582   58.547234  32.501684  19346.337986\n",
+            "9     Male    Middle Class  208.784072   72.931162  30.261495  18603.361929\n",
+            "10    Male            Poor  166.139408  109.438657  38.757674  19853.349989\n",
+            "..     ...             ...         ...         ...        ...           ...\n",
+            "494   Male            Rich  185.358932   70.843868  31.240249  19538.632703\n",
+            "496   Male            Rich  151.602650   43.765061  27.617103  20241.297735\n",
+            "497   Male    Middle Class  149.973149   67.190925  36.479521  19335.750899\n",
+            "498   Male            Rich  163.669365   98.571932  35.257657  18888.280373\n",
+            "499   Male    Middle Class  148.236420   90.327894  13.374767  19467.309309\n",
+            "\n",
+            "[266 rows x 6 columns]\n"
+          ],
+          "name": "stdout"
+        }
       ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group.mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Gender  Economic Status\n",
-       "Female  Middle Class       77\n",
-       "        Poor               87\n",
-       "        Rich               86\n",
-       "Male    Middle Class       74\n",
-       "        Poor               93\n",
-       "        Rich               83\n",
-       "dtype: int64"
+      "cell_type": "code",
+      "metadata": {
+        "id": "-eV8DVorAkR3",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        },
+        "outputId": "19c4888f-1ffc-47aa-9550-99fc166a5a93"
+      },
+      "source": [
+        "grouped_gender.get_group(\"Female\")"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Economic Status</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>196.343940</td>\n",
+              "      <td>60.909064</td>\n",
+              "      <td>40.451221</td>\n",
+              "      <td>21385.958772</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>146.713926</td>\n",
+              "      <td>53.476714</td>\n",
+              "      <td>31.144568</td>\n",
+              "      <td>20279.476735</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>172.074560</td>\n",
+              "      <td>28.192775</td>\n",
+              "      <td>42.997942</td>\n",
+              "      <td>21277.967078</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>182.532298</td>\n",
+              "      <td>63.768421</td>\n",
+              "      <td>57.426784</td>\n",
+              "      <td>18952.149135</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>166.360472</td>\n",
+              "      <td>33.332115</td>\n",
+              "      <td>19.958657</td>\n",
+              "      <td>19611.989916</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>490</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>150.092007</td>\n",
+              "      <td>67.726508</td>\n",
+              "      <td>23.798001</td>\n",
+              "      <td>19656.450662</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>491</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>156.545084</td>\n",
+              "      <td>55.503196</td>\n",
+              "      <td>26.584817</td>\n",
+              "      <td>19038.000602</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>492</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>173.475638</td>\n",
+              "      <td>52.248953</td>\n",
+              "      <td>64.993034</td>\n",
+              "      <td>20516.979828</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>493</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>169.567790</td>\n",
+              "      <td>63.484671</td>\n",
+              "      <td>29.763336</td>\n",
+              "      <td>20821.406697</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>495</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>180.672252</td>\n",
+              "      <td>51.029436</td>\n",
+              "      <td>17.900334</td>\n",
+              "      <td>20043.401932</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>234 rows × 6 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     Gender Economic Status      Height     Weight        Age        Income\n",
+              "0    Female            Rich  196.343940  60.909064  40.451221  21385.958772\n",
+              "2    Female            Rich  146.713926  53.476714  31.144568  20279.476735\n",
+              "5    Female            Poor  172.074560  28.192775  42.997942  21277.967078\n",
+              "6    Female            Rich  182.532298  63.768421  57.426784  18952.149135\n",
+              "7    Female    Middle Class  166.360472  33.332115  19.958657  19611.989916\n",
+              "..      ...             ...         ...        ...        ...           ...\n",
+              "490  Female            Rich  150.092007  67.726508  23.798001  19656.450662\n",
+              "491  Female    Middle Class  156.545084  55.503196  26.584817  19038.000602\n",
+              "492  Female    Middle Class  173.475638  52.248953  64.993034  20516.979828\n",
+              "493  Female            Rich  169.567790  63.484671  29.763336  20821.406697\n",
+              "495  Female    Middle Class  180.672252  51.029436  17.900334  20043.401932\n",
+              "\n",
+              "[234 rows x 6 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
       ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group.size()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cSzGIkkTAkR4",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "double_group = data.groupby([\"Gender\", \"Economic Status\"])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead tr th {\n",
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-       "\n",
-       "    .dataframe thead tr:last-of-type th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th colspan=\"8\" halign=\"left\">Age</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Height</th>\n",
-       "      <th>...</th>\n",
-       "      <th colspan=\"2\" halign=\"left\">Income</th>\n",
-       "      <th colspan=\"8\" halign=\"left\">Weight</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>min</th>\n",
-       "      <th>25%</th>\n",
-       "      <th>50%</th>\n",
-       "      <th>75%</th>\n",
-       "      <th>max</th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>...</th>\n",
-       "      <th>75%</th>\n",
-       "      <th>max</th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>min</th>\n",
-       "      <th>25%</th>\n",
-       "      <th>50%</th>\n",
-       "      <th>75%</th>\n",
-       "      <th>max</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></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 rowspan=\"3\" valign=\"top\">Female</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>77.0</td>\n",
-       "      <td>29.799052</td>\n",
-       "      <td>11.707560</td>\n",
-       "      <td>7.515108</td>\n",
-       "      <td>21.312926</td>\n",
-       "      <td>29.623728</td>\n",
-       "      <td>36.601422</td>\n",
-       "      <td>56.718107</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>158.959551</td>\n",
-       "      <td>...</td>\n",
-       "      <td>20610.140991</td>\n",
-       "      <td>21459.148423</td>\n",
-       "      <td>77.0</td>\n",
-       "      <td>60.709904</td>\n",
-       "      <td>26.494907</td>\n",
-       "      <td>-2.040668</td>\n",
-       "      <td>43.785036</td>\n",
-       "      <td>60.192681</td>\n",
-       "      <td>80.296198</td>\n",
-       "      <td>112.784576</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>87.0</td>\n",
-       "      <td>29.749662</td>\n",
-       "      <td>10.738025</td>\n",
-       "      <td>4.947131</td>\n",
-       "      <td>22.070886</td>\n",
-       "      <td>31.367809</td>\n",
-       "      <td>36.598902</td>\n",
-       "      <td>54.003620</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>155.137392</td>\n",
-       "      <td>...</td>\n",
-       "      <td>20582.041603</td>\n",
-       "      <td>21447.522531</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>69.245320</td>\n",
-       "      <td>28.913155</td>\n",
-       "      <td>8.508767</td>\n",
-       "      <td>47.419861</td>\n",
-       "      <td>70.893241</td>\n",
-       "      <td>89.894855</td>\n",
-       "      <td>146.921184</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>86.0</td>\n",
-       "      <td>28.568086</td>\n",
-       "      <td>11.203262</td>\n",
-       "      <td>-4.199944</td>\n",
-       "      <td>21.164965</td>\n",
-       "      <td>29.857427</td>\n",
-       "      <td>36.285765</td>\n",
-       "      <td>51.005973</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>160.750201</td>\n",
-       "      <td>...</td>\n",
-       "      <td>20272.772853</td>\n",
-       "      <td>21461.855436</td>\n",
-       "      <td>86.0</td>\n",
-       "      <td>66.364464</td>\n",
-       "      <td>26.548457</td>\n",
-       "      <td>-2.328660</td>\n",
-       "      <td>49.320891</td>\n",
-       "      <td>65.618511</td>\n",
-       "      <td>80.737431</td>\n",
-       "      <td>146.729743</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>74.0</td>\n",
-       "      <td>31.541744</td>\n",
-       "      <td>9.769168</td>\n",
-       "      <td>9.176985</td>\n",
-       "      <td>24.411119</td>\n",
-       "      <td>31.635337</td>\n",
-       "      <td>36.654869</td>\n",
-       "      <td>57.574830</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>156.318949</td>\n",
-       "      <td>...</td>\n",
-       "      <td>20686.269765</td>\n",
-       "      <td>21389.535960</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>61.324987</td>\n",
-       "      <td>24.246727</td>\n",
-       "      <td>-1.899898</td>\n",
-       "      <td>47.453537</td>\n",
-       "      <td>63.504413</td>\n",
-       "      <td>81.005119</td>\n",
-       "      <td>104.365391</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>93.0</td>\n",
-       "      <td>30.332055</td>\n",
-       "      <td>11.808723</td>\n",
-       "      <td>1.782875</td>\n",
-       "      <td>22.287703</td>\n",
-       "      <td>30.121219</td>\n",
-       "      <td>38.673025</td>\n",
-       "      <td>57.937628</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>160.889873</td>\n",
-       "      <td>...</td>\n",
-       "      <td>20616.671220</td>\n",
-       "      <td>21477.593830</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>65.751870</td>\n",
-       "      <td>23.467479</td>\n",
-       "      <td>13.617431</td>\n",
-       "      <td>50.589190</td>\n",
-       "      <td>63.728044</td>\n",
-       "      <td>84.177703</td>\n",
-       "      <td>117.696619</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>83.0</td>\n",
-       "      <td>27.605320</td>\n",
-       "      <td>12.817177</td>\n",
-       "      <td>-1.287638</td>\n",
-       "      <td>18.586982</td>\n",
-       "      <td>24.781854</td>\n",
-       "      <td>34.900324</td>\n",
-       "      <td>57.927458</td>\n",
-       "      <td>83.0</td>\n",
-       "      <td>160.035307</td>\n",
-       "      <td>...</td>\n",
-       "      <td>20675.433751</td>\n",
-       "      <td>21491.877113</td>\n",
-       "      <td>83.0</td>\n",
-       "      <td>62.266110</td>\n",
-       "      <td>25.616222</td>\n",
-       "      <td>9.759498</td>\n",
-       "      <td>46.439896</td>\n",
-       "      <td>60.246966</td>\n",
-       "      <td>77.492265</td>\n",
-       "      <td>126.426263</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>6 rows × 32 columns</p>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZQLuFUejAkR6",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "d42b8dad-d4aa-4be4-ee8c-71e6f15568f2"
+      },
+      "source": [
+        "len(double_group)"
       ],
-      "text/plain": [
-       "                         Age                                             \\\n",
-       "                       count       mean        std       min        25%   \n",
-       "Gender Economic Status                                                    \n",
-       "Female Middle Class     77.0  29.799052  11.707560  7.515108  21.312926   \n",
-       "       Poor             87.0  29.749662  10.738025  4.947131  22.070886   \n",
-       "       Rich             86.0  28.568086  11.203262 -4.199944  21.164965   \n",
-       "Male   Middle Class     74.0  31.541744   9.769168  9.176985  24.411119   \n",
-       "       Poor             93.0  30.332055  11.808723  1.782875  22.287703   \n",
-       "       Rich             83.0  27.605320  12.817177 -1.287638  18.586982   \n",
-       "\n",
-       "                                                        Height              \\\n",
-       "                              50%        75%        max  count        mean   \n",
-       "Gender Economic Status                                                       \n",
-       "Female Middle Class     29.623728  36.601422  56.718107   77.0  158.959551   \n",
-       "       Poor             31.367809  36.598902  54.003620   87.0  155.137392   \n",
-       "       Rich             29.857427  36.285765  51.005973   86.0  160.750201   \n",
-       "Male   Middle Class     31.635337  36.654869  57.574830   74.0  156.318949   \n",
-       "       Poor             30.121219  38.673025  57.937628   93.0  160.889873   \n",
-       "       Rich             24.781854  34.900324  57.927458   83.0  160.035307   \n",
-       "\n",
-       "                           ...            Income               Weight  \\\n",
-       "                           ...               75%           max  count   \n",
-       "Gender Economic Status     ...                                          \n",
-       "Female Middle Class        ...      20610.140991  21459.148423   77.0   \n",
-       "       Poor                ...      20582.041603  21447.522531   87.0   \n",
-       "       Rich                ...      20272.772853  21461.855436   86.0   \n",
-       "Male   Middle Class        ...      20686.269765  21389.535960   74.0   \n",
-       "       Poor                ...      20616.671220  21477.593830   93.0   \n",
-       "       Rich                ...      20675.433751  21491.877113   83.0   \n",
-       "\n",
-       "                                                                               \\\n",
-       "                             mean        std        min        25%        50%   \n",
-       "Gender Economic Status                                                          \n",
-       "Female Middle Class     60.709904  26.494907  -2.040668  43.785036  60.192681   \n",
-       "       Poor             69.245320  28.913155   8.508767  47.419861  70.893241   \n",
-       "       Rich             66.364464  26.548457  -2.328660  49.320891  65.618511   \n",
-       "Male   Middle Class     61.324987  24.246727  -1.899898  47.453537  63.504413   \n",
-       "       Poor             65.751870  23.467479  13.617431  50.589190  63.728044   \n",
-       "       Rich             62.266110  25.616222   9.759498  46.439896  60.246966   \n",
-       "\n",
-       "                                               \n",
-       "                              75%         max  \n",
-       "Gender Economic Status                         \n",
-       "Female Middle Class     80.296198  112.784576  \n",
-       "       Poor             89.894855  146.921184  \n",
-       "       Rich             80.737431  146.729743  \n",
-       "Male   Middle Class     81.005119  104.365391  \n",
-       "       Poor             84.177703  117.696619  \n",
-       "       Rich             77.492265  126.426263  \n",
-       "\n",
-       "[6 rows x 32 columns]"
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "6"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
       ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grouped_income = double_group[\"Income\"]"
-   ]
-  },
-  {
-   "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></th>\n",
-       "      <th></th>\n",
-       "      <th>count</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>min</th>\n",
-       "      <th>25%</th>\n",
-       "      <th>50%</th>\n",
-       "      <th>75%</th>\n",
-       "      <th>max</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></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 rowspan=\"3\" valign=\"top\">Female</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>77.0</td>\n",
-       "      <td>19578.211437</td>\n",
-       "      <td>1127.846281</td>\n",
-       "      <td>18003.363289</td>\n",
-       "      <td>18541.693714</td>\n",
-       "      <td>19418.652760</td>\n",
-       "      <td>20610.140991</td>\n",
-       "      <td>21459.148423</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>87.0</td>\n",
-       "      <td>19705.993905</td>\n",
-       "      <td>1030.772015</td>\n",
-       "      <td>18015.327084</td>\n",
-       "      <td>18967.404211</td>\n",
-       "      <td>19645.006375</td>\n",
-       "      <td>20582.041603</td>\n",
-       "      <td>21447.522531</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>86.0</td>\n",
-       "      <td>19587.872781</td>\n",
-       "      <td>990.586930</td>\n",
-       "      <td>18083.476934</td>\n",
-       "      <td>18649.486076</td>\n",
-       "      <td>19488.937153</td>\n",
-       "      <td>20272.772853</td>\n",
-       "      <td>21461.855436</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>74.0</td>\n",
-       "      <td>19834.825063</td>\n",
-       "      <td>986.224763</td>\n",
-       "      <td>18029.109009</td>\n",
-       "      <td>18934.197119</td>\n",
-       "      <td>19950.216548</td>\n",
-       "      <td>20686.269765</td>\n",
-       "      <td>21389.535960</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>93.0</td>\n",
-       "      <td>19816.748316</td>\n",
-       "      <td>986.345024</td>\n",
-       "      <td>18083.842216</td>\n",
-       "      <td>19049.977207</td>\n",
-       "      <td>19867.839331</td>\n",
-       "      <td>20616.671220</td>\n",
-       "      <td>21477.593830</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>83.0</td>\n",
-       "      <td>19796.412646</td>\n",
-       "      <td>1083.902818</td>\n",
-       "      <td>18044.114358</td>\n",
-       "      <td>18808.117030</td>\n",
-       "      <td>20043.161040</td>\n",
-       "      <td>20675.433751</td>\n",
-       "      <td>21491.877113</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "Eho6KDzUAkR7",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "a641a83c-cdcd-49d2-8c9b-c7f333a246d1"
+      },
+      "source": [
+        "for names, groups in double_group:\n",
+        "    print(names)\n",
+        "    print(groups)"
       ],
-      "text/plain": [
-       "                        count          mean          std           min  \\\n",
-       "Gender Economic Status                                                   \n",
-       "Female Middle Class      77.0  19578.211437  1127.846281  18003.363289   \n",
-       "       Poor              87.0  19705.993905  1030.772015  18015.327084   \n",
-       "       Rich              86.0  19587.872781   990.586930  18083.476934   \n",
-       "Male   Middle Class      74.0  19834.825063   986.224763  18029.109009   \n",
-       "       Poor              93.0  19816.748316   986.345024  18083.842216   \n",
-       "       Rich              83.0  19796.412646  1083.902818  18044.114358   \n",
-       "\n",
-       "                                 25%           50%           75%           max  \n",
-       "Gender Economic Status                                                          \n",
-       "Female Middle Class     18541.693714  19418.652760  20610.140991  21459.148423  \n",
-       "       Poor             18967.404211  19645.006375  20582.041603  21447.522531  \n",
-       "       Rich             18649.486076  19488.937153  20272.772853  21461.855436  \n",
-       "Male   Middle Class     18934.197119  19950.216548  20686.269765  21389.535960  \n",
-       "       Poor             19049.977207  19867.839331  20616.671220  21477.593830  \n",
-       "       Rich             18808.117030  20043.161040  20675.433751  21491.877113  "
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "('Female', 'Middle Class')\n",
+            "     Gender Economic Status      Height     Weight        Age        Income\n",
+            "7    Female    Middle Class  166.360472  33.332115  19.958657  19611.989916\n",
+            "8    Female    Middle Class  128.918824  76.401772  31.228420  21412.474051\n",
+            "20   Female    Middle Class  178.792011  60.561514  42.988117  18128.069658\n",
+            "30   Female    Middle Class  189.867726  54.644142  41.009437  19217.893656\n",
+            "32   Female    Middle Class  175.316787  94.572508  31.077942  20033.912034\n",
+            "..      ...             ...         ...        ...        ...           ...\n",
+            "481  Female    Middle Class  198.371944  58.787997  30.735950  19162.782868\n",
+            "488  Female    Middle Class  206.483490  77.263171  40.110498  20595.400103\n",
+            "491  Female    Middle Class  156.545084  55.503196  26.584817  19038.000602\n",
+            "492  Female    Middle Class  173.475638  52.248953  64.993034  20516.979828\n",
+            "495  Female    Middle Class  180.672252  51.029436  17.900334  20043.401932\n",
+            "\n",
+            "[82 rows x 6 columns]\n",
+            "('Female', 'Poor')\n",
+            "     Gender Economic Status      Height      Weight        Age        Income\n",
+            "5    Female            Poor  172.074560   28.192775  42.997942  21277.967078\n",
+            "12   Female            Poor  153.654025   53.081185  15.043235  21227.230619\n",
+            "15   Female            Poor  150.637828   59.962686  48.190578  20117.515158\n",
+            "23   Female            Poor  169.697662  102.217818  20.978407  20540.140935\n",
+            "24   Female            Poor  190.981807   65.731874  27.070750  19301.011334\n",
+            "..      ...             ...         ...         ...        ...           ...\n",
+            "440  Female            Poor  148.429338   98.029599  41.745566  19370.053905\n",
+            "453  Female            Poor  115.551046   49.196529  32.176829  19887.500027\n",
+            "456  Female            Poor  139.328070   58.870144  19.706834  19362.401595\n",
+            "470  Female            Poor  215.296695   50.900935  45.546388  20787.032943\n",
+            "484  Female            Poor  237.863120   19.951759  49.066417  19810.325445\n",
+            "\n",
+            "[75 rows x 6 columns]\n",
+            "('Female', 'Rich')\n",
+            "     Gender Economic Status      Height     Weight        Age        Income\n",
+            "0    Female            Rich  196.343940  60.909064  40.451221  21385.958772\n",
+            "2    Female            Rich  146.713926  53.476714  31.144568  20279.476735\n",
+            "6    Female            Rich  182.532298  63.768421  57.426784  18952.149135\n",
+            "11   Female            Rich  135.604574  43.813854  37.439586  20665.291519\n",
+            "14   Female            Rich  165.719126  82.115136  19.543581  20145.928402\n",
+            "..      ...             ...         ...        ...        ...           ...\n",
+            "464  Female            Rich  240.729697  78.607448  49.416987  20748.672727\n",
+            "474  Female            Rich  198.195999  80.450540  44.223839  19283.285635\n",
+            "485  Female            Rich  192.922204  49.686179  19.355819  19586.770041\n",
+            "490  Female            Rich  150.092007  67.726508  23.798001  19656.450662\n",
+            "493  Female            Rich  169.567790  63.484671  29.763336  20821.406697\n",
+            "\n",
+            "[77 rows x 6 columns]\n",
+            "('Male', 'Middle Class')\n",
+            "    Gender Economic Status      Height      Weight        Age        Income\n",
+            "1     Male    Middle Class  153.697110   48.005164  48.163870  19757.653998\n",
+            "9     Male    Middle Class  208.784072   72.931162  30.261495  18603.361929\n",
+            "16    Male    Middle Class  161.070008   68.680160  30.838949  18293.220540\n",
+            "31    Male    Middle Class  114.204244   41.165313  31.326135  20729.504794\n",
+            "36    Male    Middle Class  161.539602   68.489234  20.509221  19416.287680\n",
+            "..     ...             ...         ...         ...        ...           ...\n",
+            "461   Male    Middle Class  157.840594   66.737952  11.238893  18514.092443\n",
+            "468   Male    Middle Class  149.264876  103.354641  36.840802  21094.214950\n",
+            "477   Male    Middle Class  135.497506   36.109609  29.088379  19663.439572\n",
+            "497   Male    Middle Class  149.973149   67.190925  36.479521  19335.750899\n",
+            "499   Male    Middle Class  148.236420   90.327894  13.374767  19467.309309\n",
+            "\n",
+            "[77 rows x 6 columns]\n",
+            "('Male', 'Poor')\n",
+            "    Gender Economic Status      Height      Weight        Age        Income\n",
+            "4     Male            Poor  179.805582   58.547234  32.501684  19346.337986\n",
+            "10    Male            Poor  166.139408  109.438657  38.757674  19853.349989\n",
+            "18    Male            Poor  201.007832   21.432816  29.880663  20623.543032\n",
+            "43    Male            Poor  208.985044   51.847661  32.130139  18763.461212\n",
+            "45    Male            Poor  188.868631   72.095984  27.091806  18039.870897\n",
+            "..     ...             ...         ...         ...        ...           ...\n",
+            "473   Male            Poor  129.216300   50.957336  20.444673  21010.592763\n",
+            "479   Male            Poor  170.120335   44.637531  21.618054  20447.343548\n",
+            "482   Male            Poor  187.008562   73.201562  41.575046  19288.194315\n",
+            "486   Male            Poor  177.674228   79.068131  23.654781  20300.329345\n",
+            "487   Male            Poor  227.539516   57.374036  25.997146  19157.948395\n",
+            "\n",
+            "[93 rows x 6 columns]\n",
+            "('Male', 'Rich')\n",
+            "    Gender Economic Status      Height     Weight        Age        Income\n",
+            "3     Male            Rich  183.988501  71.785466  34.192437  19778.532678\n",
+            "13    Male            Rich  173.761763  61.055417  47.740688  20994.127333\n",
+            "17    Male            Rich  142.929235  52.884703  14.840861  20594.052895\n",
+            "19    Male            Rich  105.096782  55.340929  12.126035  20594.083296\n",
+            "21    Male            Rich  176.677934  99.600551  34.836404  19629.864461\n",
+            "..     ...             ...         ...        ...        ...           ...\n",
+            "483   Male            Rich  125.811314  47.194725  22.807730  18006.577638\n",
+            "489   Male            Rich  123.332415  74.980695  28.021002  20640.865665\n",
+            "494   Male            Rich  185.358932  70.843868  31.240249  19538.632703\n",
+            "496   Male            Rich  151.602650  43.765061  27.617103  20241.297735\n",
+            "498   Male            Rich  163.669365  98.571932  35.257657  18888.280373\n",
+            "\n",
+            "[96 rows x 6 columns]\n"
+          ],
+          "name": "stdout"
+        }
       ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "grouped_income.describe()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Height</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th></th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>1.507522e+06</td>\n",
-       "      <td>29.799052</td>\n",
-       "      <td>28.083879</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>1.714421e+06</td>\n",
-       "      <td>29.749662</td>\n",
-       "      <td>25.274224</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>1.684557e+06</td>\n",
-       "      <td>28.568086</td>\n",
-       "      <td>28.074655</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>1.467777e+06</td>\n",
-       "      <td>31.541744</td>\n",
-       "      <td>32.394973</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>1.842958e+06</td>\n",
-       "      <td>30.332055</td>\n",
-       "      <td>31.131787</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>1.643102e+06</td>\n",
-       "      <td>27.605320</td>\n",
-       "      <td>28.051617</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bt-jzYaOAkR8",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Operaciones sobre datos agrupados"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IALClhg8AkR8",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "outputId": "6f13982b-30f8-46c1-84c0-f7d82500c562"
+      },
+      "source": [
+        "double_group.sum()"
       ],
-      "text/plain": [
-       "                              Income        Age     Height\n",
-       "Gender Economic Status                                    \n",
-       "Female Middle Class     1.507522e+06  29.799052  28.083879\n",
-       "       Poor             1.714421e+06  29.749662  25.274224\n",
-       "       Rich             1.684557e+06  28.568086  28.074655\n",
-       "Male   Middle Class     1.467777e+06  31.541744  32.394973\n",
-       "       Poor             1.842958e+06  30.332055  31.131787\n",
-       "       Rich             1.643102e+06  27.605320  28.051617"
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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",
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+              "\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></th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Economic Status</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>13109.758475</td>\n",
+              "      <td>5114.396788</td>\n",
+              "      <td>2443.013144</td>\n",
+              "      <td>1.609475e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>12105.710834</td>\n",
+              "      <td>5027.709669</td>\n",
+              "      <td>2197.896111</td>\n",
+              "      <td>1.477014e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>12213.117719</td>\n",
+              "      <td>5000.741333</td>\n",
+              "      <td>2327.382814</td>\n",
+              "      <td>1.521338e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>12761.733997</td>\n",
+              "      <td>4959.605170</td>\n",
+              "      <td>2198.240962</td>\n",
+              "      <td>1.512304e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>15301.173067</td>\n",
+              "      <td>5726.156969</td>\n",
+              "      <td>2669.090332</td>\n",
+              "      <td>1.847747e+06</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>15092.221858</td>\n",
+              "      <td>6539.546423</td>\n",
+              "      <td>2925.118149</td>\n",
+              "      <td>1.906379e+06</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                              Height       Weight          Age        Income\n",
+              "Gender Economic Status                                                      \n",
+              "Female Middle Class     13109.758475  5114.396788  2443.013144  1.609475e+06\n",
+              "       Poor             12105.710834  5027.709669  2197.896111  1.477014e+06\n",
+              "       Rich             12213.117719  5000.741333  2327.382814  1.521338e+06\n",
+              "Male   Middle Class     12761.733997  4959.605170  2198.240962  1.512304e+06\n",
+              "       Poor             15301.173067  5726.156969  2669.090332  1.847747e+06\n",
+              "       Rich             15092.221858  6539.546423  2925.118149  1.906379e+06"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
       ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group.aggregate(\n",
-    "    {\n",
-    "        \"Income\": np.sum,\n",
-    "        \"Age\" : np.mean,\n",
-    "        \"Height\" : np.std\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Height</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>29.799052</td>\n",
-       "      <td>5.697287</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>29.749662</td>\n",
-       "      <td>6.173750</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>28.568086</td>\n",
-       "      <td>5.759395</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>31.541744</td>\n",
-       "      <td>4.858346</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>30.332055</td>\n",
-       "      <td>5.196037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>27.605320</td>\n",
-       "      <td>5.739711</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "XIG-ViIKAkR-",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "outputId": "daa9cbd7-3175-49a8-ff93-92d29ca6f55b"
+      },
+      "source": [
+        "double_group.mean()"
       ],
-      "text/plain": [
-       "                              Age    Height\n",
-       "Gender Economic Status                     \n",
-       "Female Middle Class     29.799052  5.697287\n",
-       "       Poor             29.749662  6.173750\n",
-       "       Rich             28.568086  5.759395\n",
-       "Male   Middle Class     31.541744  4.858346\n",
-       "       Poor             30.332055  5.196037\n",
-       "       Rich             27.605320  5.739711"
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "\n",
+              "    .dataframe tbody tr th {\n",
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+              "    }\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></th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Economic Status</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>159.875103</td>\n",
+              "      <td>62.370693</td>\n",
+              "      <td>29.792843</td>\n",
+              "      <td>19627.745015</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>161.409478</td>\n",
+              "      <td>67.036129</td>\n",
+              "      <td>29.305281</td>\n",
+              "      <td>19693.513975</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>158.611918</td>\n",
+              "      <td>64.944693</td>\n",
+              "      <td>30.225751</td>\n",
+              "      <td>19757.636531</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>165.736805</td>\n",
+              "      <td>64.410457</td>\n",
+              "      <td>28.548584</td>\n",
+              "      <td>19640.313086</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>164.528743</td>\n",
+              "      <td>61.571580</td>\n",
+              "      <td>28.699896</td>\n",
+              "      <td>19868.250726</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>157.210644</td>\n",
+              "      <td>68.120275</td>\n",
+              "      <td>30.469981</td>\n",
+              "      <td>19858.115811</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                            Height     Weight        Age        Income\n",
+              "Gender Economic Status                                                \n",
+              "Female Middle Class     159.875103  62.370693  29.792843  19627.745015\n",
+              "       Poor             161.409478  67.036129  29.305281  19693.513975\n",
+              "       Rich             158.611918  64.944693  30.225751  19757.636531\n",
+              "Male   Middle Class     165.736805  64.410457  28.548584  19640.313086\n",
+              "       Poor             164.528743  61.571580  28.699896  19868.250726\n",
+              "       Rich             157.210644  68.120275  30.469981  19858.115811"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
       ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group.aggregate(\n",
-    "    {\n",
-    "        \"Age\" : np.mean,\n",
-    "        \"Height\" : lambda h:(np.mean(h))/np.std(h)\n",
-    "    }\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
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-       "      <th colspan=\"3\" halign=\"left\">Age</th>\n",
-       "      <th colspan=\"3\" halign=\"left\">Height</th>\n",
-       "      <th colspan=\"3\" halign=\"left\">Income</th>\n",
-       "      <th colspan=\"3\" halign=\"left\">Weight</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "      <th>sum</th>\n",
-       "      <th>mean</th>\n",
-       "      <th>std</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></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 rowspan=\"3\" valign=\"top\">Female</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>2294.526981</td>\n",
-       "      <td>29.799052</td>\n",
-       "      <td>11.707560</td>\n",
-       "      <td>12239.885454</td>\n",
-       "      <td>158.959551</td>\n",
-       "      <td>28.083879</td>\n",
-       "      <td>1.507522e+06</td>\n",
-       "      <td>19578.211437</td>\n",
-       "      <td>1127.846281</td>\n",
-       "      <td>4674.662643</td>\n",
-       "      <td>60.709904</td>\n",
-       "      <td>26.494907</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>2588.220559</td>\n",
-       "      <td>29.749662</td>\n",
-       "      <td>10.738025</td>\n",
-       "      <td>13496.953076</td>\n",
-       "      <td>155.137392</td>\n",
-       "      <td>25.274224</td>\n",
-       "      <td>1.714421e+06</td>\n",
-       "      <td>19705.993905</td>\n",
-       "      <td>1030.772015</td>\n",
-       "      <td>6024.342814</td>\n",
-       "      <td>69.245320</td>\n",
-       "      <td>28.913155</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>2456.855354</td>\n",
-       "      <td>28.568086</td>\n",
-       "      <td>11.203262</td>\n",
-       "      <td>13824.517313</td>\n",
-       "      <td>160.750201</td>\n",
-       "      <td>28.074655</td>\n",
-       "      <td>1.684557e+06</td>\n",
-       "      <td>19587.872781</td>\n",
-       "      <td>990.586930</td>\n",
-       "      <td>5707.343913</td>\n",
-       "      <td>66.364464</td>\n",
-       "      <td>26.548457</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>2334.089034</td>\n",
-       "      <td>31.541744</td>\n",
-       "      <td>9.769168</td>\n",
-       "      <td>11567.602209</td>\n",
-       "      <td>156.318949</td>\n",
-       "      <td>32.394973</td>\n",
-       "      <td>1.467777e+06</td>\n",
-       "      <td>19834.825063</td>\n",
-       "      <td>986.224763</td>\n",
-       "      <td>4538.049069</td>\n",
-       "      <td>61.324987</td>\n",
-       "      <td>24.246727</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>2820.881118</td>\n",
-       "      <td>30.332055</td>\n",
-       "      <td>11.808723</td>\n",
-       "      <td>14962.758180</td>\n",
-       "      <td>160.889873</td>\n",
-       "      <td>31.131787</td>\n",
-       "      <td>1.842958e+06</td>\n",
-       "      <td>19816.748316</td>\n",
-       "      <td>986.345024</td>\n",
-       "      <td>6114.923912</td>\n",
-       "      <td>65.751870</td>\n",
-       "      <td>23.467479</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>2291.241542</td>\n",
-       "      <td>27.605320</td>\n",
-       "      <td>12.817177</td>\n",
-       "      <td>13282.930501</td>\n",
-       "      <td>160.035307</td>\n",
-       "      <td>28.051617</td>\n",
-       "      <td>1.643102e+06</td>\n",
-       "      <td>19796.412646</td>\n",
-       "      <td>1083.902818</td>\n",
-       "      <td>5168.087114</td>\n",
-       "      <td>62.266110</td>\n",
-       "      <td>25.616222</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "Dp6clyJuAkR_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 153
+        },
+        "outputId": "c43dbc59-2358-4e5e-a662-7ffad3e3b1a0"
+      },
+      "source": [
+        "double_group.size()"
       ],
-      "text/plain": [
-       "                                Age                              Height  \\\n",
-       "                                sum       mean        std           sum   \n",
-       "Gender Economic Status                                                    \n",
-       "Female Middle Class     2294.526981  29.799052  11.707560  12239.885454   \n",
-       "       Poor             2588.220559  29.749662  10.738025  13496.953076   \n",
-       "       Rich             2456.855354  28.568086  11.203262  13824.517313   \n",
-       "Male   Middle Class     2334.089034  31.541744   9.769168  11567.602209   \n",
-       "       Poor             2820.881118  30.332055  11.808723  14962.758180   \n",
-       "       Rich             2291.241542  27.605320  12.817177  13282.930501   \n",
-       "\n",
-       "                                                     Income                \\\n",
-       "                              mean        std           sum          mean   \n",
-       "Gender Economic Status                                                      \n",
-       "Female Middle Class     158.959551  28.083879  1.507522e+06  19578.211437   \n",
-       "       Poor             155.137392  25.274224  1.714421e+06  19705.993905   \n",
-       "       Rich             160.750201  28.074655  1.684557e+06  19587.872781   \n",
-       "Male   Middle Class     156.318949  32.394973  1.467777e+06  19834.825063   \n",
-       "       Poor             160.889873  31.131787  1.842958e+06  19816.748316   \n",
-       "       Rich             160.035307  28.051617  1.643102e+06  19796.412646   \n",
-       "\n",
-       "                                          Weight                        \n",
-       "                                std          sum       mean        std  \n",
-       "Gender Economic Status                                                  \n",
-       "Female Middle Class     1127.846281  4674.662643  60.709904  26.494907  \n",
-       "       Poor             1030.772015  6024.342814  69.245320  28.913155  \n",
-       "       Rich              990.586930  5707.343913  66.364464  26.548457  \n",
-       "Male   Middle Class      986.224763  4538.049069  61.324987  24.246727  \n",
-       "       Poor              986.345024  6114.923912  65.751870  23.467479  \n",
-       "       Rich             1083.902818  5168.087114  62.266110  25.616222  "
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Gender  Economic Status\n",
+              "Female  Middle Class       82\n",
+              "        Poor               75\n",
+              "        Rich               77\n",
+              "Male    Middle Class       77\n",
+              "        Poor               93\n",
+              "        Rich               96\n",
+              "dtype: int64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
       ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group.aggregate([np.sum, np.mean, np.std])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/html": [
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-       "  <thead>\n",
-       "    <tr>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Weight</th>\n",
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-       "      <th></th>\n",
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-       "      <th>&lt;lambda&gt;</th>\n",
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-       "      <th>&lt;lambda&gt;</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th></th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>2.561973</td>\n",
-       "      <td>5.697287</td>\n",
-       "      <td>17.472766</td>\n",
-       "      <td>2.306406</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>2.786557</td>\n",
-       "      <td>6.173750</td>\n",
-       "      <td>19.228532</td>\n",
-       "      <td>2.408826</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>2.564935</td>\n",
-       "      <td>5.759395</td>\n",
-       "      <td>19.889984</td>\n",
-       "      <td>2.514410</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>3.250742</td>\n",
-       "      <td>4.858346</td>\n",
-       "      <td>20.249155</td>\n",
-       "      <td>2.546471</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>2.582536</td>\n",
-       "      <td>5.196037</td>\n",
-       "      <td>20.199987</td>\n",
-       "      <td>2.817015</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>2.166868</td>\n",
-       "      <td>5.739711</td>\n",
-       "      <td>18.375039</td>\n",
-       "      <td>2.445506</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "MVRQed0CAkSA",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 368
+        },
+        "outputId": "9d2c6adf-a3c6-41d6-b728-3a9fbcc5902a"
+      },
+      "source": [
+        "double_group.describe()"
       ],
-      "text/plain": [
-       "                             Age    Height     Income    Weight\n",
-       "                        <lambda>  <lambda>   <lambda>  <lambda>\n",
-       "Gender Economic Status                                         \n",
-       "Female Middle Class     2.561973  5.697287  17.472766  2.306406\n",
-       "       Poor             2.786557  6.173750  19.228532  2.408826\n",
-       "       Rich             2.564935  5.759395  19.889984  2.514410\n",
-       "Male   Middle Class     3.250742  4.858346  20.249155  2.546471\n",
-       "       Poor             2.582536  5.196037  20.199987  2.817015\n",
-       "       Rich             2.166868  5.739711  18.375039  2.445506"
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/html": [
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+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "  <thead>\n",
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+              "      <th colspan=\"8\" halign=\"left\">Height</th>\n",
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+              "      <th>min</th>\n",
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+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Economic Status</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></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 rowspan=\"3\" valign=\"top\">Female</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>82.0</td>\n",
+              "      <td>159.875103</td>\n",
+              "      <td>30.373012</td>\n",
+              "      <td>80.236149</td>\n",
+              "      <td>135.913928</td>\n",
+              "      <td>160.514160</td>\n",
+              "      <td>180.560591</td>\n",
+              "      <td>228.409064</td>\n",
+              "      <td>82.0</td>\n",
+              "      <td>62.370693</td>\n",
+              "      <td>23.546737</td>\n",
+              "      <td>6.792281</td>\n",
+              "      <td>47.414308</td>\n",
+              "      <td>62.017350</td>\n",
+              "      <td>77.201058</td>\n",
+              "      <td>120.122674</td>\n",
+              "      <td>82.0</td>\n",
+              "      <td>29.792843</td>\n",
+              "      <td>12.587559</td>\n",
+              "      <td>-3.010737</td>\n",
+              "      <td>22.525387</td>\n",
+              "      <td>28.594280</td>\n",
+              "      <td>40.046598</td>\n",
+              "      <td>64.993034</td>\n",
+              "      <td>82.0</td>\n",
+              "      <td>19627.745015</td>\n",
+              "      <td>994.118644</td>\n",
+              "      <td>18040.895955</td>\n",
+              "      <td>18793.937121</td>\n",
+              "      <td>19615.513639</td>\n",
+              "      <td>20359.835287</td>\n",
+              "      <td>21488.883196</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>75.0</td>\n",
+              "      <td>161.409478</td>\n",
+              "      <td>31.477731</td>\n",
+              "      <td>97.008895</td>\n",
+              "      <td>140.289352</td>\n",
+              "      <td>158.853184</td>\n",
+              "      <td>178.470317</td>\n",
+              "      <td>237.863120</td>\n",
+              "      <td>75.0</td>\n",
+              "      <td>67.036129</td>\n",
+              "      <td>25.069293</td>\n",
+              "      <td>1.907216</td>\n",
+              "      <td>51.231780</td>\n",
+              "      <td>66.089235</td>\n",
+              "      <td>83.931587</td>\n",
+              "      <td>119.492809</td>\n",
+              "      <td>75.0</td>\n",
+              "      <td>29.305281</td>\n",
+              "      <td>12.185900</td>\n",
+              "      <td>-3.837400</td>\n",
+              "      <td>20.324882</td>\n",
+              "      <td>28.187599</td>\n",
+              "      <td>38.071412</td>\n",
+              "      <td>58.123572</td>\n",
+              "      <td>75.0</td>\n",
+              "      <td>19693.513975</td>\n",
+              "      <td>940.946942</td>\n",
+              "      <td>18034.166483</td>\n",
+              "      <td>19049.037827</td>\n",
+              "      <td>19617.861260</td>\n",
+              "      <td>20303.299142</td>\n",
+              "      <td>21489.193581</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>77.0</td>\n",
+              "      <td>158.611918</td>\n",
+              "      <td>28.634846</td>\n",
+              "      <td>90.772317</td>\n",
+              "      <td>142.867855</td>\n",
+              "      <td>157.166704</td>\n",
+              "      <td>172.490364</td>\n",
+              "      <td>240.729697</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>64.944693</td>\n",
+              "      <td>26.263248</td>\n",
+              "      <td>23.419552</td>\n",
+              "      <td>44.661807</td>\n",
+              "      <td>61.909200</td>\n",
+              "      <td>79.491705</td>\n",
+              "      <td>155.874851</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>30.225751</td>\n",
+              "      <td>11.323115</td>\n",
+              "      <td>-2.860013</td>\n",
+              "      <td>22.581370</td>\n",
+              "      <td>30.800982</td>\n",
+              "      <td>37.451463</td>\n",
+              "      <td>60.056761</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>19757.636531</td>\n",
+              "      <td>1019.800743</td>\n",
+              "      <td>18023.726084</td>\n",
+              "      <td>18952.149135</td>\n",
+              "      <td>19633.547998</td>\n",
+              "      <td>20665.291519</td>\n",
+              "      <td>21492.614880</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>77.0</td>\n",
+              "      <td>165.736805</td>\n",
+              "      <td>29.776044</td>\n",
+              "      <td>112.820013</td>\n",
+              "      <td>146.685426</td>\n",
+              "      <td>161.936648</td>\n",
+              "      <td>184.058166</td>\n",
+              "      <td>256.194530</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>64.410457</td>\n",
+              "      <td>28.036595</td>\n",
+              "      <td>-7.328773</td>\n",
+              "      <td>46.696287</td>\n",
+              "      <td>65.738645</td>\n",
+              "      <td>83.679865</td>\n",
+              "      <td>139.494219</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>28.548584</td>\n",
+              "      <td>10.917398</td>\n",
+              "      <td>4.067120</td>\n",
+              "      <td>21.499777</td>\n",
+              "      <td>29.746838</td>\n",
+              "      <td>35.230100</td>\n",
+              "      <td>55.429845</td>\n",
+              "      <td>77.0</td>\n",
+              "      <td>19640.313086</td>\n",
+              "      <td>995.486333</td>\n",
+              "      <td>18019.519401</td>\n",
+              "      <td>18745.929082</td>\n",
+              "      <td>19643.106107</td>\n",
+              "      <td>20363.599172</td>\n",
+              "      <td>21453.931293</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>93.0</td>\n",
+              "      <td>164.528743</td>\n",
+              "      <td>27.822572</td>\n",
+              "      <td>94.173624</td>\n",
+              "      <td>142.346484</td>\n",
+              "      <td>166.335510</td>\n",
+              "      <td>184.756847</td>\n",
+              "      <td>227.539516</td>\n",
+              "      <td>93.0</td>\n",
+              "      <td>61.571580</td>\n",
+              "      <td>24.315991</td>\n",
+              "      <td>-2.040686</td>\n",
+              "      <td>48.078668</td>\n",
+              "      <td>61.613466</td>\n",
+              "      <td>75.239205</td>\n",
+              "      <td>116.038660</td>\n",
+              "      <td>93.0</td>\n",
+              "      <td>28.699896</td>\n",
+              "      <td>11.980531</td>\n",
+              "      <td>-1.181704</td>\n",
+              "      <td>21.141014</td>\n",
+              "      <td>27.091806</td>\n",
+              "      <td>38.711063</td>\n",
+              "      <td>59.082634</td>\n",
+              "      <td>93.0</td>\n",
+              "      <td>19868.250726</td>\n",
+              "      <td>1052.295893</td>\n",
+              "      <td>18015.391664</td>\n",
+              "      <td>18998.283089</td>\n",
+              "      <td>19853.349989</td>\n",
+              "      <td>20851.226702</td>\n",
+              "      <td>21492.894772</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>96.0</td>\n",
+              "      <td>157.210644</td>\n",
+              "      <td>30.459835</td>\n",
+              "      <td>71.472304</td>\n",
+              "      <td>133.824171</td>\n",
+              "      <td>160.216203</td>\n",
+              "      <td>179.105247</td>\n",
+              "      <td>214.449386</td>\n",
+              "      <td>96.0</td>\n",
+              "      <td>68.120275</td>\n",
+              "      <td>25.263629</td>\n",
+              "      <td>-2.789199</td>\n",
+              "      <td>48.383348</td>\n",
+              "      <td>65.760364</td>\n",
+              "      <td>86.934608</td>\n",
+              "      <td>130.364826</td>\n",
+              "      <td>96.0</td>\n",
+              "      <td>30.469981</td>\n",
+              "      <td>11.385701</td>\n",
+              "      <td>3.368029</td>\n",
+              "      <td>22.619632</td>\n",
+              "      <td>30.073195</td>\n",
+              "      <td>38.002390</td>\n",
+              "      <td>58.030748</td>\n",
+              "      <td>96.0</td>\n",
+              "      <td>19858.115811</td>\n",
+              "      <td>978.823389</td>\n",
+              "      <td>18006.577638</td>\n",
+              "      <td>19040.921083</td>\n",
+              "      <td>19804.216243</td>\n",
+              "      <td>20686.405907</td>\n",
+              "      <td>21497.428517</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                       Height              ...        Income              \n",
+              "                        count        mean  ...           75%           max\n",
+              "Gender Economic Status                     ...                            \n",
+              "Female Middle Class      82.0  159.875103  ...  20359.835287  21488.883196\n",
+              "       Poor              75.0  161.409478  ...  20303.299142  21489.193581\n",
+              "       Rich              77.0  158.611918  ...  20665.291519  21492.614880\n",
+              "Male   Middle Class      77.0  165.736805  ...  20363.599172  21453.931293\n",
+              "       Poor              93.0  164.528743  ...  20851.226702  21492.894772\n",
+              "       Rich              96.0  157.210644  ...  20686.405907  21497.428517\n",
+              "\n",
+              "[6 rows x 32 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 19
+        }
       ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group.aggregate([lambda x: np.mean(x) / np.std(x)])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Filtrado de datos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "2      41.632979\n",
-       "4      43.779882\n",
-       "5      31.511689\n",
-       "7      26.818594\n",
-       "11     29.470434\n",
-       "12     34.334233\n",
-       "13     28.286581\n",
-       "15     15.307572\n",
-       "16     36.066909\n",
-       "19     32.509053\n",
-       "21     31.443146\n",
-       "22     24.546463\n",
-       "23     26.854852\n",
-       "24     36.759814\n",
-       "25     38.453075\n",
-       "26     21.995241\n",
-       "30     15.223665\n",
-       "31     42.708940\n",
-       "32     38.673025\n",
-       "34     25.671351\n",
-       "37     22.565455\n",
-       "38     43.565371\n",
-       "40     33.373076\n",
-       "42     29.022916\n",
-       "43     45.449231\n",
-       "48     37.065833\n",
-       "52     33.259760\n",
-       "54     29.244240\n",
-       "55     30.241688\n",
-       "56     34.069823\n",
-       "         ...    \n",
-       "452    12.965592\n",
-       "453    45.605468\n",
-       "455     9.504070\n",
-       "456    33.693360\n",
-       "458     4.577901\n",
-       "460    32.993292\n",
-       "461    33.710923\n",
-       "462    26.702841\n",
-       "464    10.639621\n",
-       "465    38.493342\n",
-       "466    44.073311\n",
-       "467    46.494498\n",
-       "468    28.763444\n",
-       "470    30.877809\n",
-       "471    22.482854\n",
-       "472    14.041959\n",
-       "481    19.399664\n",
-       "482    42.230758\n",
-       "483    30.346778\n",
-       "484    51.116700\n",
-       "485    12.241652\n",
-       "486    38.130804\n",
-       "488    37.119624\n",
-       "490    23.325366\n",
-       "491    28.190877\n",
-       "494    31.182845\n",
-       "495    38.516231\n",
-       "497    36.608103\n",
-       "498    30.132207\n",
-       "499    36.961529\n",
-       "Name: Age, Length: 266, dtype: float64"
+      "cell_type": "code",
+      "metadata": {
+        "id": "m0tze5dCAkSB",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "grouped_income = double_group[\"Income\"]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GnO4smZWAkSC",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "outputId": "d6a8c7d5-a0fd-4dbd-c1a3-252531f0dde2"
+      },
+      "source": [
+        "grouped_income.describe()"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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></th>\n",
+              "      <th>count</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>min</th>\n",
+              "      <th>25%</th>\n",
+              "      <th>50%</th>\n",
+              "      <th>75%</th>\n",
+              "      <th>max</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Economic Status</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></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 rowspan=\"3\" valign=\"top\">Female</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>82.0</td>\n",
+              "      <td>19627.745015</td>\n",
+              "      <td>994.118644</td>\n",
+              "      <td>18040.895955</td>\n",
+              "      <td>18793.937121</td>\n",
+              "      <td>19615.513639</td>\n",
+              "      <td>20359.835287</td>\n",
+              "      <td>21488.883196</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>75.0</td>\n",
+              "      <td>19693.513975</td>\n",
+              "      <td>940.946942</td>\n",
+              "      <td>18034.166483</td>\n",
+              "      <td>19049.037827</td>\n",
+              "      <td>19617.861260</td>\n",
+              "      <td>20303.299142</td>\n",
+              "      <td>21489.193581</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>77.0</td>\n",
+              "      <td>19757.636531</td>\n",
+              "      <td>1019.800743</td>\n",
+              "      <td>18023.726084</td>\n",
+              "      <td>18952.149135</td>\n",
+              "      <td>19633.547998</td>\n",
+              "      <td>20665.291519</td>\n",
+              "      <td>21492.614880</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>77.0</td>\n",
+              "      <td>19640.313086</td>\n",
+              "      <td>995.486333</td>\n",
+              "      <td>18019.519401</td>\n",
+              "      <td>18745.929082</td>\n",
+              "      <td>19643.106107</td>\n",
+              "      <td>20363.599172</td>\n",
+              "      <td>21453.931293</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>93.0</td>\n",
+              "      <td>19868.250726</td>\n",
+              "      <td>1052.295893</td>\n",
+              "      <td>18015.391664</td>\n",
+              "      <td>18998.283089</td>\n",
+              "      <td>19853.349989</td>\n",
+              "      <td>20851.226702</td>\n",
+              "      <td>21492.894772</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>96.0</td>\n",
+              "      <td>19858.115811</td>\n",
+              "      <td>978.823389</td>\n",
+              "      <td>18006.577638</td>\n",
+              "      <td>19040.921083</td>\n",
+              "      <td>19804.216243</td>\n",
+              "      <td>20686.405907</td>\n",
+              "      <td>21497.428517</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                        count          mean  ...           75%           max\n",
+              "Gender Economic Status                       ...                            \n",
+              "Female Middle Class      82.0  19627.745015  ...  20359.835287  21488.883196\n",
+              "       Poor              75.0  19693.513975  ...  20303.299142  21489.193581\n",
+              "       Rich              77.0  19757.636531  ...  20665.291519  21492.614880\n",
+              "Male   Middle Class      77.0  19640.313086  ...  20363.599172  21453.931293\n",
+              "       Poor              93.0  19868.250726  ...  20851.226702  21492.894772\n",
+              "       Rich              96.0  19858.115811  ...  20686.405907  21497.428517\n",
+              "\n",
+              "[6 rows x 8 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 21
+        }
       ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group[\"Age\"].filter(lambda x: x.sum()>2400)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Transformación de variables"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "zscore = lambda x : (x - x.mean())/x.std()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z_group = double_group.transform(zscore)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(array([ 2., 13., 41., 74., 99., 97., 93., 50., 22.,  9.]),\n",
-       " array([-2.924865  , -2.36589637, -1.80692773, -1.2479591 , -0.68899047,\n",
-       "        -0.13002183,  0.4289468 ,  0.98791543,  1.54688406,  2.1058527 ,\n",
-       "         2.66482133]),\n",
-       " <a list of 10 Patch objects>)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "kc8V5PfaAkSD",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "outputId": "82aef21c-a5f8-4c6b-f7a8-fb80786af5bd"
+      },
+      "source": [
+        "double_group.aggregate(\n",
+        "    {\n",
+        "        \"Income\": np.sum,\n",
+        "        \"Age\" : np.mean,\n",
+        "        \"Height\" : np.std\n",
+        "    }\n",
+        ")"
+      ],
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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></th>\n",
+              "      <th>Income</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Height</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Economic Status</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>1.609475e+06</td>\n",
+              "      <td>29.792843</td>\n",
+              "      <td>30.373012</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>1.477014e+06</td>\n",
+              "      <td>29.305281</td>\n",
+              "      <td>31.477731</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>1.521338e+06</td>\n",
+              "      <td>30.225751</td>\n",
+              "      <td>28.634846</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>1.512304e+06</td>\n",
+              "      <td>28.548584</td>\n",
+              "      <td>29.776044</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>1.847747e+06</td>\n",
+              "      <td>28.699896</td>\n",
+              "      <td>27.822572</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>1.906379e+06</td>\n",
+              "      <td>30.469981</td>\n",
+              "      <td>30.459835</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                              Income        Age     Height\n",
+              "Gender Economic Status                                    \n",
+              "Female Middle Class     1.609475e+06  29.792843  30.373012\n",
+              "       Poor             1.477014e+06  29.305281  31.477731\n",
+              "       Rich             1.521338e+06  30.225751  28.634846\n",
+              "Male   Middle Class     1.512304e+06  28.548584  29.776044\n",
+              "       Poor             1.847747e+06  28.699896  27.822572\n",
+              "       Rich             1.906379e+06  30.469981  30.459835"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 22
+        }
       ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1173a4358>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "hjThnV4yAkSE",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "outputId": "c70ed120-8fc1-4f01-ca77-fe33e8518a11"
+      },
+      "source": [
+        "double_group.aggregate(\n",
+        "    {\n",
+        "        \"Age\" : np.mean,\n",
+        "        \"Height\" : lambda h:(np.mean(h))/np.std(h)\n",
+        "    }\n",
+        ")"
+      ],
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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></th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Height</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Economic Status</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>29.792843</td>\n",
+              "      <td>5.296115</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>29.305281</td>\n",
+              "      <td>5.162266</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>30.225751</td>\n",
+              "      <td>5.575445</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>28.548584</td>\n",
+              "      <td>5.602612</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>28.699896</td>\n",
+              "      <td>5.945550</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>30.469981</td>\n",
+              "      <td>5.188337</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                              Age    Height\n",
+              "Gender Economic Status                     \n",
+              "Female Middle Class     29.792843  5.296115\n",
+              "       Poor             29.305281  5.162266\n",
+              "       Rich             30.225751  5.575445\n",
+              "Male   Middle Class     28.548584  5.602612\n",
+              "       Poor             28.699896  5.945550\n",
+              "       Rich             30.469981  5.188337"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 23
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(z_group[\"Age\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fill_na_mean = lambda x : x.fillna(x.mean())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "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>Age</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Weight</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>29.406063</td>\n",
-       "      <td>93.455149</td>\n",
-       "      <td>20717.514475</td>\n",
-       "      <td>50.868452</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10.465520</td>\n",
-       "      <td>139.545894</td>\n",
-       "      <td>19863.957892</td>\n",
-       "      <td>46.368551</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>41.632979</td>\n",
-       "      <td>183.857248</td>\n",
-       "      <td>18549.640778</td>\n",
-       "      <td>60.016080</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>17.706009</td>\n",
-       "      <td>155.885685</td>\n",
-       "      <td>18688.042862</td>\n",
-       "      <td>73.035787</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>43.779882</td>\n",
-       "      <td>127.650202</td>\n",
-       "      <td>18274.892352</td>\n",
-       "      <td>109.119629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>31.511689</td>\n",
-       "      <td>192.882388</td>\n",
-       "      <td>20826.579612</td>\n",
-       "      <td>88.442163</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>35.830860</td>\n",
-       "      <td>165.501662</td>\n",
-       "      <td>20558.353093</td>\n",
-       "      <td>52.728741</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>26.818594</td>\n",
-       "      <td>167.299202</td>\n",
-       "      <td>19408.300487</td>\n",
-       "      <td>-2.328660</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>45.239503</td>\n",
-       "      <td>132.588194</td>\n",
-       "      <td>20660.630399</td>\n",
-       "      <td>51.229028</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>32.535617</td>\n",
-       "      <td>138.481238</td>\n",
-       "      <td>20343.973756</td>\n",
-       "      <td>47.240539</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>31.084558</td>\n",
-       "      <td>165.983138</td>\n",
-       "      <td>18415.623498</td>\n",
-       "      <td>48.371730</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>29.470434</td>\n",
-       "      <td>134.003543</td>\n",
-       "      <td>21134.226884</td>\n",
-       "      <td>60.585127</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>34.334233</td>\n",
-       "      <td>149.824673</td>\n",
-       "      <td>19546.903872</td>\n",
-       "      <td>119.229619</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>28.286581</td>\n",
-       "      <td>117.798582</td>\n",
-       "      <td>21226.742907</td>\n",
-       "      <td>60.712632</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>42.280704</td>\n",
-       "      <td>131.584579</td>\n",
-       "      <td>18592.858002</td>\n",
-       "      <td>96.606513</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>15.307572</td>\n",
-       "      <td>158.743542</td>\n",
-       "      <td>21243.899231</td>\n",
-       "      <td>61.155687</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>36.066909</td>\n",
-       "      <td>191.962499</td>\n",
-       "      <td>18835.366092</td>\n",
-       "      <td>64.921943</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>45.601175</td>\n",
-       "      <td>157.798020</td>\n",
-       "      <td>18342.649900</td>\n",
-       "      <td>32.215115</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>42.720713</td>\n",
-       "      <td>89.417396</td>\n",
-       "      <td>20565.126795</td>\n",
-       "      <td>77.516021</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>32.509053</td>\n",
-       "      <td>192.663994</td>\n",
-       "      <td>20910.754143</td>\n",
-       "      <td>52.377219</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>13.282330</td>\n",
-       "      <td>162.059657</td>\n",
-       "      <td>19087.166254</td>\n",
-       "      <td>81.284012</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>31.443146</td>\n",
-       "      <td>143.035623</td>\n",
-       "      <td>19194.070439</td>\n",
-       "      <td>100.464745</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>24.546463</td>\n",
-       "      <td>148.232867</td>\n",
-       "      <td>18529.475088</td>\n",
-       "      <td>47.840715</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>26.854852</td>\n",
-       "      <td>164.025603</td>\n",
-       "      <td>18111.940350</td>\n",
-       "      <td>110.328107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>36.759814</td>\n",
-       "      <td>183.562680</td>\n",
-       "      <td>19798.790037</td>\n",
-       "      <td>103.294236</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>38.453075</td>\n",
-       "      <td>156.155689</td>\n",
-       "      <td>19575.471899</td>\n",
-       "      <td>74.823251</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>21.995241</td>\n",
-       "      <td>159.708864</td>\n",
-       "      <td>20840.925825</td>\n",
-       "      <td>68.275110</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>28.041821</td>\n",
-       "      <td>120.856916</td>\n",
-       "      <td>20666.441965</td>\n",
-       "      <td>62.811662</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>19.002201</td>\n",
-       "      <td>206.620612</td>\n",
-       "      <td>21459.148423</td>\n",
-       "      <td>62.110834</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>33.865350</td>\n",
-       "      <td>159.237730</td>\n",
-       "      <td>21176.630376</td>\n",
-       "      <td>88.431003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>470</th>\n",
-       "      <td>30.877809</td>\n",
-       "      <td>179.549889</td>\n",
-       "      <td>20534.403603</td>\n",
-       "      <td>57.170932</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>471</th>\n",
-       "      <td>22.482854</td>\n",
-       "      <td>148.114383</td>\n",
-       "      <td>21307.143204</td>\n",
-       "      <td>94.685959</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>472</th>\n",
-       "      <td>14.041959</td>\n",
-       "      <td>128.786411</td>\n",
-       "      <td>19236.856980</td>\n",
-       "      <td>65.910370</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>473</th>\n",
-       "      <td>57.927458</td>\n",
-       "      <td>158.264063</td>\n",
-       "      <td>19773.326950</td>\n",
-       "      <td>102.165694</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>474</th>\n",
-       "      <td>28.871436</td>\n",
-       "      <td>129.211463</td>\n",
-       "      <td>18119.001636</td>\n",
-       "      <td>54.231350</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>475</th>\n",
-       "      <td>33.212556</td>\n",
-       "      <td>186.366218</td>\n",
-       "      <td>18995.818308</td>\n",
-       "      <td>76.798489</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>476</th>\n",
-       "      <td>25.910841</td>\n",
-       "      <td>140.929009</td>\n",
-       "      <td>19953.551284</td>\n",
-       "      <td>94.894692</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>477</th>\n",
-       "      <td>10.643144</td>\n",
-       "      <td>143.849606</td>\n",
-       "      <td>18803.688821</td>\n",
-       "      <td>109.599023</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>478</th>\n",
-       "      <td>10.190133</td>\n",
-       "      <td>171.248419</td>\n",
-       "      <td>18281.357516</td>\n",
-       "      <td>95.809197</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>479</th>\n",
-       "      <td>33.570631</td>\n",
-       "      <td>170.489521</td>\n",
-       "      <td>20610.140991</td>\n",
-       "      <td>58.052908</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>480</th>\n",
-       "      <td>38.163563</td>\n",
-       "      <td>152.699267</td>\n",
-       "      <td>18162.912374</td>\n",
-       "      <td>18.406700</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>481</th>\n",
-       "      <td>19.399664</td>\n",
-       "      <td>122.253378</td>\n",
-       "      <td>21418.957595</td>\n",
-       "      <td>34.334860</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>482</th>\n",
-       "      <td>42.230758</td>\n",
-       "      <td>162.665803</td>\n",
-       "      <td>21442.183439</td>\n",
-       "      <td>111.199150</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>483</th>\n",
-       "      <td>30.346778</td>\n",
-       "      <td>135.671820</td>\n",
-       "      <td>19005.401603</td>\n",
-       "      <td>79.888811</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>484</th>\n",
-       "      <td>51.116700</td>\n",
-       "      <td>117.127682</td>\n",
-       "      <td>20523.981614</td>\n",
-       "      <td>93.502202</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>485</th>\n",
-       "      <td>12.241652</td>\n",
-       "      <td>133.348820</td>\n",
-       "      <td>19868.743729</td>\n",
-       "      <td>56.394180</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>486</th>\n",
-       "      <td>38.130804</td>\n",
-       "      <td>164.438850</td>\n",
-       "      <td>20503.218832</td>\n",
-       "      <td>34.314401</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>487</th>\n",
-       "      <td>22.489102</td>\n",
-       "      <td>121.876538</td>\n",
-       "      <td>20504.227101</td>\n",
-       "      <td>60.994486</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>488</th>\n",
-       "      <td>37.119624</td>\n",
-       "      <td>133.874339</td>\n",
-       "      <td>21287.749837</td>\n",
-       "      <td>74.270308</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>489</th>\n",
-       "      <td>36.219497</td>\n",
-       "      <td>156.398762</td>\n",
-       "      <td>19019.898289</td>\n",
-       "      <td>77.351999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>490</th>\n",
-       "      <td>23.325366</td>\n",
-       "      <td>154.859406</td>\n",
-       "      <td>20178.525442</td>\n",
-       "      <td>77.059452</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>491</th>\n",
-       "      <td>28.190877</td>\n",
-       "      <td>113.399151</td>\n",
-       "      <td>20883.753347</td>\n",
-       "      <td>47.601578</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>492</th>\n",
-       "      <td>17.864297</td>\n",
-       "      <td>131.060369</td>\n",
-       "      <td>21378.622189</td>\n",
-       "      <td>84.522898</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>493</th>\n",
-       "      <td>11.569556</td>\n",
-       "      <td>180.690792</td>\n",
-       "      <td>18827.025459</td>\n",
-       "      <td>48.867474</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>494</th>\n",
-       "      <td>31.182845</td>\n",
-       "      <td>123.483560</td>\n",
-       "      <td>19086.528784</td>\n",
-       "      <td>85.384170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>495</th>\n",
-       "      <td>38.516231</td>\n",
-       "      <td>143.732437</td>\n",
-       "      <td>20176.033630</td>\n",
-       "      <td>78.402899</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>496</th>\n",
-       "      <td>25.569081</td>\n",
-       "      <td>160.674814</td>\n",
-       "      <td>20224.021932</td>\n",
-       "      <td>54.473354</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>497</th>\n",
-       "      <td>36.608103</td>\n",
-       "      <td>149.632378</td>\n",
-       "      <td>18324.310236</td>\n",
-       "      <td>71.910584</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>498</th>\n",
-       "      <td>30.132207</td>\n",
-       "      <td>169.712073</td>\n",
-       "      <td>18243.266976</td>\n",
-       "      <td>85.853052</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>499</th>\n",
-       "      <td>36.961529</td>\n",
-       "      <td>199.681593</td>\n",
-       "      <td>20542.715644</td>\n",
-       "      <td>31.275503</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>500 rows × 4 columns</p>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "SGx9EOlFAkSF",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "46b278df-a199-4cd5-bb95-aff120b6ac43"
+      },
+      "source": [
+        "double_group.aggregate([np.sum, np.mean, np.std])"
       ],
-      "text/plain": [
-       "           Age      Height        Income      Weight\n",
-       "0    29.406063   93.455149  20717.514475   50.868452\n",
-       "1    10.465520  139.545894  19863.957892   46.368551\n",
-       "2    41.632979  183.857248  18549.640778   60.016080\n",
-       "3    17.706009  155.885685  18688.042862   73.035787\n",
-       "4    43.779882  127.650202  18274.892352  109.119629\n",
-       "5    31.511689  192.882388  20826.579612   88.442163\n",
-       "6    35.830860  165.501662  20558.353093   52.728741\n",
-       "7    26.818594  167.299202  19408.300487   -2.328660\n",
-       "8    45.239503  132.588194  20660.630399   51.229028\n",
-       "9    32.535617  138.481238  20343.973756   47.240539\n",
-       "10   31.084558  165.983138  18415.623498   48.371730\n",
-       "11   29.470434  134.003543  21134.226884   60.585127\n",
-       "12   34.334233  149.824673  19546.903872  119.229619\n",
-       "13   28.286581  117.798582  21226.742907   60.712632\n",
-       "14   42.280704  131.584579  18592.858002   96.606513\n",
-       "15   15.307572  158.743542  21243.899231   61.155687\n",
-       "16   36.066909  191.962499  18835.366092   64.921943\n",
-       "17   45.601175  157.798020  18342.649900   32.215115\n",
-       "18   42.720713   89.417396  20565.126795   77.516021\n",
-       "19   32.509053  192.663994  20910.754143   52.377219\n",
-       "20   13.282330  162.059657  19087.166254   81.284012\n",
-       "21   31.443146  143.035623  19194.070439  100.464745\n",
-       "22   24.546463  148.232867  18529.475088   47.840715\n",
-       "23   26.854852  164.025603  18111.940350  110.328107\n",
-       "24   36.759814  183.562680  19798.790037  103.294236\n",
-       "25   38.453075  156.155689  19575.471899   74.823251\n",
-       "26   21.995241  159.708864  20840.925825   68.275110\n",
-       "27   28.041821  120.856916  20666.441965   62.811662\n",
-       "28   19.002201  206.620612  21459.148423   62.110834\n",
-       "29   33.865350  159.237730  21176.630376   88.431003\n",
-       "..         ...         ...           ...         ...\n",
-       "470  30.877809  179.549889  20534.403603   57.170932\n",
-       "471  22.482854  148.114383  21307.143204   94.685959\n",
-       "472  14.041959  128.786411  19236.856980   65.910370\n",
-       "473  57.927458  158.264063  19773.326950  102.165694\n",
-       "474  28.871436  129.211463  18119.001636   54.231350\n",
-       "475  33.212556  186.366218  18995.818308   76.798489\n",
-       "476  25.910841  140.929009  19953.551284   94.894692\n",
-       "477  10.643144  143.849606  18803.688821  109.599023\n",
-       "478  10.190133  171.248419  18281.357516   95.809197\n",
-       "479  33.570631  170.489521  20610.140991   58.052908\n",
-       "480  38.163563  152.699267  18162.912374   18.406700\n",
-       "481  19.399664  122.253378  21418.957595   34.334860\n",
-       "482  42.230758  162.665803  21442.183439  111.199150\n",
-       "483  30.346778  135.671820  19005.401603   79.888811\n",
-       "484  51.116700  117.127682  20523.981614   93.502202\n",
-       "485  12.241652  133.348820  19868.743729   56.394180\n",
-       "486  38.130804  164.438850  20503.218832   34.314401\n",
-       "487  22.489102  121.876538  20504.227101   60.994486\n",
-       "488  37.119624  133.874339  21287.749837   74.270308\n",
-       "489  36.219497  156.398762  19019.898289   77.351999\n",
-       "490  23.325366  154.859406  20178.525442   77.059452\n",
-       "491  28.190877  113.399151  20883.753347   47.601578\n",
-       "492  17.864297  131.060369  21378.622189   84.522898\n",
-       "493  11.569556  180.690792  18827.025459   48.867474\n",
-       "494  31.182845  123.483560  19086.528784   85.384170\n",
-       "495  38.516231  143.732437  20176.033630   78.402899\n",
-       "496  25.569081  160.674814  20224.021932   54.473354\n",
-       "497  36.608103  149.632378  18324.310236   71.910584\n",
-       "498  30.132207  169.712073  18243.266976   85.853052\n",
-       "499  36.961529  199.681593  20542.715644   31.275503\n",
-       "\n",
-       "[500 rows x 4 columns]"
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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></th>\n",
+              "      <th></th>\n",
+              "      <th colspan=\"3\" halign=\"left\">Height</th>\n",
+              "      <th colspan=\"3\" halign=\"left\">Weight</th>\n",
+              "      <th colspan=\"3\" halign=\"left\">Age</th>\n",
+              "      <th colspan=\"3\" halign=\"left\">Income</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>sum</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>sum</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>sum</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "      <th>sum</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>std</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Economic Status</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></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 rowspan=\"3\" valign=\"top\">Female</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>13109.758475</td>\n",
+              "      <td>159.875103</td>\n",
+              "      <td>30.373012</td>\n",
+              "      <td>5114.396788</td>\n",
+              "      <td>62.370693</td>\n",
+              "      <td>23.546737</td>\n",
+              "      <td>2443.013144</td>\n",
+              "      <td>29.792843</td>\n",
+              "      <td>12.587559</td>\n",
+              "      <td>1.609475e+06</td>\n",
+              "      <td>19627.745015</td>\n",
+              "      <td>994.118644</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>12105.710834</td>\n",
+              "      <td>161.409478</td>\n",
+              "      <td>31.477731</td>\n",
+              "      <td>5027.709669</td>\n",
+              "      <td>67.036129</td>\n",
+              "      <td>25.069293</td>\n",
+              "      <td>2197.896111</td>\n",
+              "      <td>29.305281</td>\n",
+              "      <td>12.185900</td>\n",
+              "      <td>1.477014e+06</td>\n",
+              "      <td>19693.513975</td>\n",
+              "      <td>940.946942</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>12213.117719</td>\n",
+              "      <td>158.611918</td>\n",
+              "      <td>28.634846</td>\n",
+              "      <td>5000.741333</td>\n",
+              "      <td>64.944693</td>\n",
+              "      <td>26.263248</td>\n",
+              "      <td>2327.382814</td>\n",
+              "      <td>30.225751</td>\n",
+              "      <td>11.323115</td>\n",
+              "      <td>1.521338e+06</td>\n",
+              "      <td>19757.636531</td>\n",
+              "      <td>1019.800743</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>12761.733997</td>\n",
+              "      <td>165.736805</td>\n",
+              "      <td>29.776044</td>\n",
+              "      <td>4959.605170</td>\n",
+              "      <td>64.410457</td>\n",
+              "      <td>28.036595</td>\n",
+              "      <td>2198.240962</td>\n",
+              "      <td>28.548584</td>\n",
+              "      <td>10.917398</td>\n",
+              "      <td>1.512304e+06</td>\n",
+              "      <td>19640.313086</td>\n",
+              "      <td>995.486333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>15301.173067</td>\n",
+              "      <td>164.528743</td>\n",
+              "      <td>27.822572</td>\n",
+              "      <td>5726.156969</td>\n",
+              "      <td>61.571580</td>\n",
+              "      <td>24.315991</td>\n",
+              "      <td>2669.090332</td>\n",
+              "      <td>28.699896</td>\n",
+              "      <td>11.980531</td>\n",
+              "      <td>1.847747e+06</td>\n",
+              "      <td>19868.250726</td>\n",
+              "      <td>1052.295893</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>15092.221858</td>\n",
+              "      <td>157.210644</td>\n",
+              "      <td>30.459835</td>\n",
+              "      <td>6539.546423</td>\n",
+              "      <td>68.120275</td>\n",
+              "      <td>25.263629</td>\n",
+              "      <td>2925.118149</td>\n",
+              "      <td>30.469981</td>\n",
+              "      <td>11.385701</td>\n",
+              "      <td>1.906379e+06</td>\n",
+              "      <td>19858.115811</td>\n",
+              "      <td>978.823389</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                              Height              ...        Income             \n",
+              "                                 sum        mean  ...          mean          std\n",
+              "Gender Economic Status                            ...                           \n",
+              "Female Middle Class     13109.758475  159.875103  ...  19627.745015   994.118644\n",
+              "       Poor             12105.710834  161.409478  ...  19693.513975   940.946942\n",
+              "       Rich             12213.117719  158.611918  ...  19757.636531  1019.800743\n",
+              "Male   Middle Class     12761.733997  165.736805  ...  19640.313086   995.486333\n",
+              "       Poor             15301.173067  164.528743  ...  19868.250726  1052.295893\n",
+              "       Rich             15092.221858  157.210644  ...  19858.115811   978.823389\n",
+              "\n",
+              "[6 rows x 12 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 24
+        }
       ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group.transform(fill_na_mean)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Operaciones diversas muy útiles"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "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>Age</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Weight</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>29.406063</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>93.455149</td>\n",
-       "      <td>20717.514475</td>\n",
-       "      <td>50.868452</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>10.465520</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>139.545894</td>\n",
-       "      <td>19863.957892</td>\n",
-       "      <td>46.368551</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>41.632979</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>183.857248</td>\n",
-       "      <td>18549.640778</td>\n",
-       "      <td>60.016080</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>43.779882</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>127.650202</td>\n",
-       "      <td>18274.892352</td>\n",
-       "      <td>109.119629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>45.239503</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>132.588194</td>\n",
-       "      <td>20660.630399</td>\n",
-       "      <td>51.229028</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>29.470434</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>134.003543</td>\n",
-       "      <td>21134.226884</td>\n",
-       "      <td>60.585127</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "UkktjSkgAkSG",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 297
+        },
+        "outputId": "649b4150-ed5f-434c-aba1-fd463463fa25"
+      },
+      "source": [
+        "double_group.aggregate([lambda x: np.mean(x) / np.std(x)])"
       ],
-      "text/plain": [
-       "          Age Economic Status  Gender      Height        Income      Weight\n",
-       "0   29.406063    Middle Class    Male   93.455149  20717.514475   50.868452\n",
-       "1   10.465520    Middle Class  Female  139.545894  19863.957892   46.368551\n",
-       "2   41.632979            Poor    Male  183.857248  18549.640778   60.016080\n",
-       "4   43.779882            Rich  Female  127.650202  18274.892352  109.119629\n",
-       "8   45.239503            Rich    Male  132.588194  20660.630399   51.229028\n",
-       "11  29.470434            Poor  Female  134.003543  21134.226884   60.585127"
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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></th>\n",
+              "      <th></th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th>&lt;lambda&gt;</th>\n",
+              "      <th>&lt;lambda&gt;</th>\n",
+              "      <th>&lt;lambda&gt;</th>\n",
+              "      <th>&lt;lambda&gt;</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Economic Status</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>5.296115</td>\n",
+              "      <td>2.665104</td>\n",
+              "      <td>2.381414</td>\n",
+              "      <td>19.865368</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>5.162266</td>\n",
+              "      <td>2.692041</td>\n",
+              "      <td>2.421046</td>\n",
+              "      <td>21.070403</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>5.575445</td>\n",
+              "      <td>2.489051</td>\n",
+              "      <td>2.686889</td>\n",
+              "      <td>19.501061</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>5.602612</td>\n",
+              "      <td>2.312436</td>\n",
+              "      <td>2.632110</td>\n",
+              "      <td>19.858739</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>5.945550</td>\n",
+              "      <td>2.545868</td>\n",
+              "      <td>2.408529</td>\n",
+              "      <td>18.983195</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>5.188337</td>\n",
+              "      <td>2.710532</td>\n",
+              "      <td>2.690210</td>\n",
+              "      <td>20.394239</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                          Height    Weight       Age     Income\n",
+              "                        <lambda>  <lambda>  <lambda>   <lambda>\n",
+              "Gender Economic Status                                         \n",
+              "Female Middle Class     5.296115  2.665104  2.381414  19.865368\n",
+              "       Poor             5.162266  2.692041  2.421046  21.070403\n",
+              "       Rich             5.575445  2.489051  2.686889  19.501061\n",
+              "Male   Middle Class     5.602612  2.312436  2.632110  19.858739\n",
+              "       Poor             5.945550  2.545868  2.408529  18.983195\n",
+              "       Rich             5.188337  2.710532  2.690210  20.394239"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 25
+        }
       ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group.head(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YqDrlXlFAkSH",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Filtrado de datos"
+      ]
+    },
     {
-     "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>Age</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Weight</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>488</th>\n",
-       "      <td>37.119624</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>133.874339</td>\n",
-       "      <td>21287.749837</td>\n",
-       "      <td>74.270308</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>489</th>\n",
-       "      <td>36.219497</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>156.398762</td>\n",
-       "      <td>19019.898289</td>\n",
-       "      <td>77.351999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>491</th>\n",
-       "      <td>28.190877</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>113.399151</td>\n",
-       "      <td>20883.753347</td>\n",
-       "      <td>47.601578</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>493</th>\n",
-       "      <td>11.569556</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>180.690792</td>\n",
-       "      <td>18827.025459</td>\n",
-       "      <td>48.867474</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>496</th>\n",
-       "      <td>25.569081</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>160.674814</td>\n",
-       "      <td>20224.021932</td>\n",
-       "      <td>54.473354</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>499</th>\n",
-       "      <td>36.961529</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>199.681593</td>\n",
-       "      <td>20542.715644</td>\n",
-       "      <td>31.275503</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "dD7rfKxSAkSH",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 221
+        },
+        "outputId": "324de0e9-e7f3-4d0a-f627-4502bf988198"
+      },
+      "source": [
+        "double_group[\"Age\"].filter(lambda x: x.sum()>2400)"
       ],
-      "text/plain": [
-       "           Age Economic Status  Gender      Height        Income     Weight\n",
-       "488  37.119624            Rich  Female  133.874339  21287.749837  74.270308\n",
-       "489  36.219497    Middle Class    Male  156.398762  19019.898289  77.351999\n",
-       "491  28.190877            Poor    Male  113.399151  20883.753347  47.601578\n",
-       "493  11.569556            Rich    Male  180.690792  18827.025459  48.867474\n",
-       "496  25.569081    Middle Class  Female  160.674814  20224.021932  54.473354\n",
-       "499  36.961529            Poor  Female  199.681593  20542.715644  31.275503"
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "3      34.192437\n",
+              "4      32.501684\n",
+              "7      19.958657\n",
+              "8      31.228420\n",
+              "10     38.757674\n",
+              "         ...    \n",
+              "492    64.993034\n",
+              "494    31.240249\n",
+              "495    17.900334\n",
+              "496    27.617103\n",
+              "498    35.257657\n",
+              "Name: Age, Length: 271, dtype: float64"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 26
+        }
       ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group.tail(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K8WuegM0AkSI",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Transformación de variables"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_3lvmdbVAkSI",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "zscore = lambda x : (x - x.mean())/x.std()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\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></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Weight</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>28.797237</td>\n",
-       "      <td>148.563883</td>\n",
-       "      <td>18003.363289</td>\n",
-       "      <td>9.388164</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>36.725378</td>\n",
-       "      <td>156.805495</td>\n",
-       "      <td>21170.784989</td>\n",
-       "      <td>48.676902</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>26.870549</td>\n",
-       "      <td>212.348298</td>\n",
-       "      <td>18635.339072</td>\n",
-       "      <td>79.424370</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
-       "      <th>Middle Class</th>\n",
-       "      <td>27.517429</td>\n",
-       "      <td>202.972438</td>\n",
-       "      <td>20266.648284</td>\n",
-       "      <td>67.224546</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poor</th>\n",
-       "      <td>51.466574</td>\n",
-       "      <td>124.519397</td>\n",
-       "      <td>18815.374032</td>\n",
-       "      <td>72.618161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>46.078186</td>\n",
-       "      <td>177.958348</td>\n",
-       "      <td>18824.751753</td>\n",
-       "      <td>27.362584</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "fLCs7OEOAkSJ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "z_group = double_group.transform(zscore)"
       ],
-      "text/plain": [
-       "                              Age      Height        Income     Weight\n",
-       "Gender Economic Status                                                \n",
-       "Female Middle Class     28.797237  148.563883  18003.363289   9.388164\n",
-       "       Poor             36.725378  156.805495  21170.784989  48.676902\n",
-       "       Rich             26.870549  212.348298  18635.339072  79.424370\n",
-       "Male   Middle Class     27.517429  202.972438  20266.648284  67.224546\n",
-       "       Poor             51.466574  124.519397  18815.374032  72.618161\n",
-       "       Rich             46.078186  177.958348  18824.751753  27.362584"
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1_3gMroqAkSK",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import matplotlib.pyplot as plt"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CRgESBjmAkSL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 350
+        },
+        "outputId": "3f2055ac-0d7e-4787-ab2a-aa6045c585db"
+      },
+      "source": [
+        "plt.hist(z_group[\"Age\"])"
+      ],
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([  6.,  10.,  38.,  80., 111., 104.,  80.,  47.,  15.,   9.]),\n",
+              " array([-2.92196658, -2.35012721, -1.77828785, -1.20644848, -0.63460912,\n",
+              "        -0.06276976,  0.50906961,  1.08090897,  1.65274833,  2.2245877 ,\n",
+              "         2.79642706]),\n",
+              " <a list of 10 Patch objects>)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 30
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group.nth(32)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "V5OjiQoAAkSM",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "fill_na_mean = lambda x : x.fillna(x.mean())"
+      ],
+      "execution_count": 0,
+      "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></th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Weight</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
-       "      <th>Poor</th>\n",
-       "      <td>31.182845</td>\n",
-       "      <td>123.483560</td>\n",
-       "      <td>19086.528784</td>\n",
-       "      <td>85.384170</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>44.073311</td>\n",
-       "      <td>177.297004</td>\n",
-       "      <td>20183.673288</td>\n",
-       "      <td>49.884070</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
-       "      <th>Poor</th>\n",
-       "      <td>28.199086</td>\n",
-       "      <td>139.317685</td>\n",
-       "      <td>20204.378925</td>\n",
-       "      <td>87.835392</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Rich</th>\n",
-       "      <td>11.569556</td>\n",
-       "      <td>180.690792</td>\n",
-       "      <td>18827.025459</td>\n",
-       "      <td>48.867474</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "Jt30Dhi7AkSN",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 419
+        },
+        "outputId": "ca1ea0dc-e6fe-4fbc-a412-9629628408b4"
+      },
+      "source": [
+        "double_group.transform(fill_na_mean)"
       ],
-      "text/plain": [
-       "                              Age      Height        Income     Weight\n",
-       "Gender Economic Status                                                \n",
-       "Female Poor             31.182845  123.483560  19086.528784  85.384170\n",
-       "       Rich             44.073311  177.297004  20183.673288  49.884070\n",
-       "Male   Poor             28.199086  139.317685  20204.378925  87.835392\n",
-       "       Rich             11.569556  180.690792  18827.025459  48.867474"
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>196.343940</td>\n",
+              "      <td>60.909064</td>\n",
+              "      <td>40.451221</td>\n",
+              "      <td>21385.958772</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>153.697110</td>\n",
+              "      <td>48.005164</td>\n",
+              "      <td>48.163870</td>\n",
+              "      <td>19757.653998</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>146.713926</td>\n",
+              "      <td>53.476714</td>\n",
+              "      <td>31.144568</td>\n",
+              "      <td>20279.476735</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>183.988501</td>\n",
+              "      <td>71.785466</td>\n",
+              "      <td>34.192437</td>\n",
+              "      <td>19778.532678</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>179.805582</td>\n",
+              "      <td>58.547234</td>\n",
+              "      <td>32.501684</td>\n",
+              "      <td>19346.337986</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>495</th>\n",
+              "      <td>180.672252</td>\n",
+              "      <td>51.029436</td>\n",
+              "      <td>17.900334</td>\n",
+              "      <td>20043.401932</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>496</th>\n",
+              "      <td>151.602650</td>\n",
+              "      <td>43.765061</td>\n",
+              "      <td>27.617103</td>\n",
+              "      <td>20241.297735</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>497</th>\n",
+              "      <td>149.973149</td>\n",
+              "      <td>67.190925</td>\n",
+              "      <td>36.479521</td>\n",
+              "      <td>19335.750899</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>498</th>\n",
+              "      <td>163.669365</td>\n",
+              "      <td>98.571932</td>\n",
+              "      <td>35.257657</td>\n",
+              "      <td>18888.280373</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>499</th>\n",
+              "      <td>148.236420</td>\n",
+              "      <td>90.327894</td>\n",
+              "      <td>13.374767</td>\n",
+              "      <td>19467.309309</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>500 rows × 4 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         Height     Weight        Age        Income\n",
+              "0    196.343940  60.909064  40.451221  21385.958772\n",
+              "1    153.697110  48.005164  48.163870  19757.653998\n",
+              "2    146.713926  53.476714  31.144568  20279.476735\n",
+              "3    183.988501  71.785466  34.192437  19778.532678\n",
+              "4    179.805582  58.547234  32.501684  19346.337986\n",
+              "..          ...        ...        ...           ...\n",
+              "495  180.672252  51.029436  17.900334  20043.401932\n",
+              "496  151.602650  43.765061  27.617103  20241.297735\n",
+              "497  149.973149  67.190925  36.479521  19335.750899\n",
+              "498  163.669365  98.571932  35.257657  18888.280373\n",
+              "499  148.236420  90.327894  13.374767  19467.309309\n",
+              "\n",
+              "[500 rows x 4 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 32
+        }
       ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "double_group.nth(82)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_sorted = data.sort_values([\"Age\", \"Income\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "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>Age</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Weight</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>187</th>\n",
-       "      <td>-4.199944</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>119.510928</td>\n",
-       "      <td>20856.440474</td>\n",
-       "      <td>66.666195</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66</th>\n",
-       "      <td>-1.287638</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>186.858361</td>\n",
-       "      <td>18135.675395</td>\n",
-       "      <td>60.246966</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>122</th>\n",
-       "      <td>1.782875</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>160.128232</td>\n",
-       "      <td>20835.263414</td>\n",
-       "      <td>46.569934</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>400</th>\n",
-       "      <td>3.320607</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>159.825693</td>\n",
-       "      <td>19241.168745</td>\n",
-       "      <td>75.400689</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>297</th>\n",
-       "      <td>4.185659</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>114.267174</td>\n",
-       "      <td>21318.896046</td>\n",
-       "      <td>61.859226</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>458</th>\n",
-       "      <td>4.577901</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>160.383189</td>\n",
-       "      <td>19365.473543</td>\n",
-       "      <td>105.775523</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>410</th>\n",
-       "      <td>4.609084</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>194.698403</td>\n",
-       "      <td>20142.495812</td>\n",
-       "      <td>44.781309</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>269</th>\n",
-       "      <td>4.947131</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>185.792630</td>\n",
-       "      <td>21386.306656</td>\n",
-       "      <td>101.335995</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>369</th>\n",
-       "      <td>6.129743</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>140.551730</td>\n",
-       "      <td>19506.743428</td>\n",
-       "      <td>69.004383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124</th>\n",
-       "      <td>7.515108</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>180.152982</td>\n",
-       "      <td>19079.806490</td>\n",
-       "      <td>64.554424</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "REgLbwFpAkSO",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Operaciones diversas muy útiles"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Onxbpf_5AkSO",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 235
+        },
+        "outputId": "ec3f40b7-5e5e-4674-b0a8-01963860af19"
+      },
+      "source": [
+        "double_group.head(1)"
       ],
-      "text/plain": [
-       "          Age Economic Status  Gender      Height        Income      Weight\n",
-       "187 -4.199944            Rich  Female  119.510928  20856.440474   66.666195\n",
-       "66  -1.287638            Rich    Male  186.858361  18135.675395   60.246966\n",
-       "122  1.782875            Poor    Male  160.128232  20835.263414   46.569934\n",
-       "400  3.320607            Rich  Female  159.825693  19241.168745   75.400689\n",
-       "297  4.185659            Poor    Male  114.267174  21318.896046   61.859226\n",
-       "458  4.577901            Rich  Female  160.383189  19365.473543  105.775523\n",
-       "410  4.609084            Rich  Female  194.698403  20142.495812   44.781309\n",
-       "269  4.947131            Poor  Female  185.792630  21386.306656  101.335995\n",
-       "369  6.129743            Rich    Male  140.551730  19506.743428   69.004383\n",
-       "124  7.515108    Middle Class  Female  180.152982  19079.806490   64.554424"
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Economic Status</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>196.343940</td>\n",
+              "      <td>60.909064</td>\n",
+              "      <td>40.451221</td>\n",
+              "      <td>21385.958772</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>153.697110</td>\n",
+              "      <td>48.005164</td>\n",
+              "      <td>48.163870</td>\n",
+              "      <td>19757.653998</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>183.988501</td>\n",
+              "      <td>71.785466</td>\n",
+              "      <td>34.192437</td>\n",
+              "      <td>19778.532678</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>179.805582</td>\n",
+              "      <td>58.547234</td>\n",
+              "      <td>32.501684</td>\n",
+              "      <td>19346.337986</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>172.074560</td>\n",
+              "      <td>28.192775</td>\n",
+              "      <td>42.997942</td>\n",
+              "      <td>21277.967078</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>166.360472</td>\n",
+              "      <td>33.332115</td>\n",
+              "      <td>19.958657</td>\n",
+              "      <td>19611.989916</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Gender Economic Status      Height     Weight        Age        Income\n",
+              "0  Female            Rich  196.343940  60.909064  40.451221  21385.958772\n",
+              "1    Male    Middle Class  153.697110  48.005164  48.163870  19757.653998\n",
+              "3    Male            Rich  183.988501  71.785466  34.192437  19778.532678\n",
+              "4    Male            Poor  179.805582  58.547234  32.501684  19346.337986\n",
+              "5  Female            Poor  172.074560  28.192775  42.997942  21277.967078\n",
+              "7  Female    Middle Class  166.360472  33.332115  19.958657  19611.989916"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 33
+        }
       ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_sorted.head(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "age_grouped = data_sorted.groupby(\"Gender\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "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>Age</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Weight</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>187</th>\n",
-       "      <td>-4.199944</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>119.510928</td>\n",
-       "      <td>20856.440474</td>\n",
-       "      <td>66.666195</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66</th>\n",
-       "      <td>-1.287638</td>\n",
-       "      <td>Rich</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>186.858361</td>\n",
-       "      <td>18135.675395</td>\n",
-       "      <td>60.246966</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "srDlzkTjAkSP",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 235
+        },
+        "outputId": "7c3e78ed-0525-4c24-d3e5-ffd788897744"
+      },
+      "source": [
+        "double_group.tail(1)"
       ],
-      "text/plain": [
-       "          Age Economic Status  Gender      Height        Income     Weight\n",
-       "187 -4.199944            Rich  Female  119.510928  20856.440474  66.666195\n",
-       "66  -1.287638            Rich    Male  186.858361  18135.675395  60.246966"
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Economic Status</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>484</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>237.863120</td>\n",
+              "      <td>19.951759</td>\n",
+              "      <td>49.066417</td>\n",
+              "      <td>19810.325445</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>487</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>227.539516</td>\n",
+              "      <td>57.374036</td>\n",
+              "      <td>25.997146</td>\n",
+              "      <td>19157.948395</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>493</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>169.567790</td>\n",
+              "      <td>63.484671</td>\n",
+              "      <td>29.763336</td>\n",
+              "      <td>20821.406697</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>495</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>180.672252</td>\n",
+              "      <td>51.029436</td>\n",
+              "      <td>17.900334</td>\n",
+              "      <td>20043.401932</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>498</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>163.669365</td>\n",
+              "      <td>98.571932</td>\n",
+              "      <td>35.257657</td>\n",
+              "      <td>18888.280373</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>499</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>148.236420</td>\n",
+              "      <td>90.327894</td>\n",
+              "      <td>13.374767</td>\n",
+              "      <td>19467.309309</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     Gender Economic Status      Height     Weight        Age        Income\n",
+              "484  Female            Poor  237.863120  19.951759  49.066417  19810.325445\n",
+              "487    Male            Poor  227.539516  57.374036  25.997146  19157.948395\n",
+              "493  Female            Rich  169.567790  63.484671  29.763336  20821.406697\n",
+              "495  Female    Middle Class  180.672252  51.029436  17.900334  20043.401932\n",
+              "498    Male            Rich  163.669365  98.571932  35.257657  18888.280373\n",
+              "499    Male    Middle Class  148.236420  90.327894  13.374767  19467.309309"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 34
+        }
       ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "age_grouped.head(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "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>Age</th>\n",
-       "      <th>Economic Status</th>\n",
-       "      <th>Gender</th>\n",
-       "      <th>Height</th>\n",
-       "      <th>Income</th>\n",
-       "      <th>Weight</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>413</th>\n",
-       "      <td>56.718107</td>\n",
-       "      <td>Middle Class</td>\n",
-       "      <td>Female</td>\n",
-       "      <td>124.652053</td>\n",
-       "      <td>21179.343419</td>\n",
-       "      <td>78.173161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>320</th>\n",
-       "      <td>57.937628</td>\n",
-       "      <td>Poor</td>\n",
-       "      <td>Male</td>\n",
-       "      <td>243.511007</td>\n",
-       "      <td>20008.992767</td>\n",
-       "      <td>17.393495</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "PUzcau-IAkSQ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 266
+        },
+        "outputId": "b6414924-5c52-4957-ca75-e482fa6bb218"
+      },
+      "source": [
+        "double_group.nth(32)"
       ],
-      "text/plain": [
-       "           Age Economic Status  Gender      Height        Income     Weight\n",
-       "413  56.718107    Middle Class  Female  124.652053  21179.343419  78.173161\n",
-       "320  57.937628            Poor    Male  243.511007  20008.992767  17.393495"
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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></th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Economic Status</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Female</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>110.180551</td>\n",
+              "      <td>68.190065</td>\n",
+              "      <td>49.949398</td>\n",
+              "      <td>19487.156361</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>158.853184</td>\n",
+              "      <td>63.085928</td>\n",
+              "      <td>25.005120</td>\n",
+              "      <td>20216.239204</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>162.750233</td>\n",
+              "      <td>65.951638</td>\n",
+              "      <td>40.062883</td>\n",
+              "      <td>18023.726084</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"3\" valign=\"top\">Male</th>\n",
+              "      <th>Middle Class</th>\n",
+              "      <td>161.936648</td>\n",
+              "      <td>70.117613</td>\n",
+              "      <td>4.067120</td>\n",
+              "      <td>19697.088560</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Poor</th>\n",
+              "      <td>158.631750</td>\n",
+              "      <td>52.472696</td>\n",
+              "      <td>24.187545</td>\n",
+              "      <td>19964.779021</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>171.473068</td>\n",
+              "      <td>45.510527</td>\n",
+              "      <td>47.831414</td>\n",
+              "      <td>20692.308396</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                            Height     Weight        Age        Income\n",
+              "Gender Economic Status                                                \n",
+              "Female Middle Class     110.180551  68.190065  49.949398  19487.156361\n",
+              "       Poor             158.853184  63.085928  25.005120  20216.239204\n",
+              "       Rich             162.750233  65.951638  40.062883  18023.726084\n",
+              "Male   Middle Class     161.936648  70.117613   4.067120  19697.088560\n",
+              "       Poor             158.631750  52.472696  24.187545  19964.779021\n",
+              "       Rich             171.473068  45.510527  47.831414  20692.308396"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 35
+        }
       ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "age_grouped.tail(1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Conjunto de entrenamiento y conjunto de testing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = pd.read_csv(\"../datasets/customer-churn-model/Customer Churn Model.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "3333"
+      "cell_type": "code",
+      "metadata": {
+        "id": "TLvuUCwlAkSR",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 142
+        },
+        "outputId": "f4db7972-2a76-4a61-9040-8b0ea79773f0"
+      },
+      "source": [
+        "double_group.nth(82)"
+      ],
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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></th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Gender</th>\n",
+              "      <th>Economic Status</th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
+              "      <th>Poor</th>\n",
+              "      <td>179.93339</td>\n",
+              "      <td>76.802942</td>\n",
+              "      <td>21.885402</td>\n",
+              "      <td>20715.359892</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Rich</th>\n",
+              "      <td>164.70270</td>\n",
+              "      <td>86.880958</td>\n",
+              "      <td>26.744570</td>\n",
+              "      <td>19193.082787</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                           Height     Weight        Age        Income\n",
+              "Gender Economic Status                                               \n",
+              "Male   Poor             179.93339  76.802942  21.885402  20715.359892\n",
+              "       Rich             164.70270  86.880958  26.744570  19193.082787"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 36
+        }
       ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Dividir utilizando la distribución normal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.random.randn(len(data))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(array([ 18.,  88., 209., 543., 785., 785., 558., 253.,  72.,  22.]),\n",
-       " array([-3.22055853, -2.57969788, -1.93883724, -1.2979766 , -0.65711596,\n",
-       "        -0.01625532,  0.62460533,  1.26546597,  1.90632661,  2.54718725,\n",
-       "         3.1880479 ]),\n",
-       " <a list of 10 Patch objects>)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "Lm1IS7UeAkSS",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data_sorted = data.sort_values([\"Age\", \"Income\"])"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_E618lUBAkST",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 359
+        },
+        "outputId": "bb9cd800-a233-4f86-b033-cfb085b09f2d"
+      },
+      "source": [
+        "data_sorted.head(10)"
+      ],
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Economic Status</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>59</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>219.118989</td>\n",
+              "      <td>106.348681</td>\n",
+              "      <td>-3.837400</td>\n",
+              "      <td>21298.802624</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>148</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>140.433589</td>\n",
+              "      <td>88.161987</td>\n",
+              "      <td>-3.010737</td>\n",
+              "      <td>19892.145392</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>220.042892</td>\n",
+              "      <td>66.970892</td>\n",
+              "      <td>-2.860013</td>\n",
+              "      <td>18189.632418</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>154</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>101.836328</td>\n",
+              "      <td>78.121454</td>\n",
+              "      <td>-2.139509</td>\n",
+              "      <td>19793.479229</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>89</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>193.682572</td>\n",
+              "      <td>79.271949</td>\n",
+              "      <td>-1.181704</td>\n",
+              "      <td>21008.166187</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>259</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>142.594132</td>\n",
+              "      <td>67.007006</td>\n",
+              "      <td>3.368029</td>\n",
+              "      <td>20693.817605</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>209</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>161.936648</td>\n",
+              "      <td>70.117613</td>\n",
+              "      <td>4.067120</td>\n",
+              "      <td>19697.088560</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>299</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>194.034042</td>\n",
+              "      <td>52.824096</td>\n",
+              "      <td>5.591800</td>\n",
+              "      <td>21409.162676</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>402</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>149.062605</td>\n",
+              "      <td>52.035421</td>\n",
+              "      <td>6.173861</td>\n",
+              "      <td>21435.420795</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>354</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>189.448738</td>\n",
+              "      <td>78.337713</td>\n",
+              "      <td>6.195454</td>\n",
+              "      <td>21369.689456</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     Gender Economic Status      Height      Weight       Age        Income\n",
+              "59   Female            Poor  219.118989  106.348681 -3.837400  21298.802624\n",
+              "148  Female    Middle Class  140.433589   88.161987 -3.010737  19892.145392\n",
+              "22   Female            Rich  220.042892   66.970892 -2.860013  18189.632418\n",
+              "154  Female    Middle Class  101.836328   78.121454 -2.139509  19793.479229\n",
+              "89     Male            Poor  193.682572   79.271949 -1.181704  21008.166187\n",
+              "259    Male            Rich  142.594132   67.007006  3.368029  20693.817605\n",
+              "209    Male    Middle Class  161.936648   70.117613  4.067120  19697.088560\n",
+              "299    Male            Poor  194.034042   52.824096  5.591800  21409.162676\n",
+              "402    Male    Middle Class  149.062605   52.035421  6.173861  21435.420795\n",
+              "354    Male            Poor  189.448738   78.337713  6.195454  21369.689456"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 38
+        }
       ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11b77a320>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "p7TM9sRqAkSU",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "age_grouped = data_sorted.groupby(\"Gender\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xilQDYDuAkSU",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 111
+        },
+        "outputId": "9d8dba61-abc3-470f-d137-38706f7ddd56"
+      },
+      "source": [
+        "age_grouped.head(1)"
+      ],
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Economic Status</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>59</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>219.118989</td>\n",
+              "      <td>106.348681</td>\n",
+              "      <td>-3.837400</td>\n",
+              "      <td>21298.802624</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>89</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>193.682572</td>\n",
+              "      <td>79.271949</td>\n",
+              "      <td>-1.181704</td>\n",
+              "      <td>21008.166187</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "    Gender Economic Status      Height      Weight       Age        Income\n",
+              "59  Female            Poor  219.118989  106.348681 -3.837400  21298.802624\n",
+              "89    Male            Poor  193.682572   79.271949 -1.181704  21008.166187"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 40
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "check = (a<0.75)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array([False,  True, False, ...,  True,  True,  True])"
+      "cell_type": "code",
+      "metadata": {
+        "id": "-f2hp-tVAkSV",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 111
+        },
+        "outputId": "12df5949-b1ab-444b-dfbe-5e86694ce039"
+      },
+      "source": [
+        "age_grouped.tail(1)"
+      ],
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Economic Status</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>423</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>165.152608</td>\n",
+              "      <td>61.613466</td>\n",
+              "      <td>59.082634</td>\n",
+              "      <td>20577.920332</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>492</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>173.475638</td>\n",
+              "      <td>52.248953</td>\n",
+              "      <td>64.993034</td>\n",
+              "      <td>20516.979828</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "     Gender Economic Status      Height     Weight        Age        Income\n",
+              "423    Male            Poor  165.152608  61.613466  59.082634  20577.920332\n",
+              "492  Female    Middle Class  173.475638  52.248953  64.993034  20516.979828"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 41
+        }
       ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "check"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(array([ 767.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
-       "        2566.]),\n",
-       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
-       " <a list of 10 Patch objects>)"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m6-5J5PaAkSW",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Conjunto de entrenamiento y conjunto de testing"
       ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
     },
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x11259db70>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "TsLsKm4RAkSW",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nUwo03aqAkSX",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/customer-churn-model/Customer Churn Model.txt\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "4JGk0tOCAkSY",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "b20a3f6f-635d-47d9-9218-1ee8391ba6bf"
+      },
+      "source": [
+        "len(data)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "3333"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 59
+        }
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(check)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "training = data[check]\n",
-    "testing = data[~check]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "2566"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xyYlgyXNAkSZ",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Dividir utilizando la distribución normal"
       ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(training)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "767"
+      "cell_type": "code",
+      "metadata": {
+        "id": "0USP9bsAAkSZ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "a = np.random.randn(len(data))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "t3Tdw4SAAkSa",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 350
+        },
+        "outputId": "68053f0c-2a9a-4b87-905d-7eef21808580"
+      },
+      "source": [
+        "plt.hist(a)"
+      ],
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([  1.,  14.,  29.,  84., 111., 123.,  85.,  36.,   9.,   8.]),\n",
+              " array([-3.19536083, -2.56704791, -1.93873498, -1.31042206, -0.68210914,\n",
+              "        -0.05379622,  0.5745167 ,  1.20282963,  1.83114255,  2.45945547,\n",
+              "         3.08776839]),\n",
+              " <a list of 10 Patch objects>)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 43
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(testing)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Con la libreria sklearn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.cross_validation import train_test_split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train, test = train_test_split(data, test_size = 0.2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZOjx59etAkSa",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "check = (a<0.75) # No es el 75% de los datos, son los números que son < 0.75!!! "
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "2666"
+      "cell_type": "code",
+      "metadata": {
+        "id": "LAVp0N0JAkSb",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 969
+        },
+        "outputId": "ea86ea28-0907-405d-c41e-292975e12ad2"
+      },
+      "source": [
+        "check"
+      ],
+      "execution_count": 45,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([False,  True,  True,  True,  True,  True,  True, False,  True,\n",
+              "        True, False,  True,  True, False,  True,  True,  True,  True,\n",
+              "       False, False,  True,  True,  True,  True,  True,  True,  True,\n",
+              "        True,  True,  True,  True,  True, False, False,  True,  True,\n",
+              "        True,  True,  True,  True, False, False,  True,  True,  True,\n",
+              "       False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+              "        True,  True, False,  True, False,  True, False,  True,  True,\n",
+              "        True,  True, False,  True,  True,  True,  True,  True,  True,\n",
+              "       False,  True, False,  True,  True,  True,  True,  True, False,\n",
+              "        True,  True,  True, False, False,  True,  True,  True,  True,\n",
+              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+              "        True,  True, False,  True,  True,  True,  True,  True, False,\n",
+              "       False, False,  True, False,  True,  True, False, False,  True,\n",
+              "       False,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+              "        True,  True, False,  True,  True,  True,  True,  True,  True,\n",
+              "        True, False, False,  True,  True,  True,  True, False,  True,\n",
+              "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
+              "        True,  True,  True, False,  True,  True,  True,  True,  True,\n",
+              "        True,  True,  True,  True, False,  True, False,  True,  True,\n",
+              "        True,  True, False,  True, False,  True,  True,  True,  True,\n",
+              "       False,  True,  True, False,  True,  True,  True,  True,  True,\n",
+              "        True, False,  True, False,  True,  True,  True, False, False,\n",
+              "        True,  True,  True,  True,  True,  True, False,  True, False,\n",
+              "        True,  True,  True, False,  True,  True, False, False,  True,\n",
+              "       False,  True,  True,  True,  True,  True, False,  True,  True,\n",
+              "        True,  True,  True,  True,  True, False, False,  True, False,\n",
+              "        True,  True,  True, False,  True,  True,  True,  True,  True,\n",
+              "        True, False,  True,  True,  True,  True,  True,  True, False,\n",
+              "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
+              "        True,  True,  True,  True,  True, False, False,  True,  True,\n",
+              "        True,  True,  True,  True,  True, False,  True,  True, False,\n",
+              "        True,  True,  True,  True,  True,  True,  True,  True, False,\n",
+              "        True,  True,  True,  True, False,  True,  True,  True,  True,\n",
+              "        True, False,  True,  True,  True,  True, False,  True,  True,\n",
+              "        True, False, False, False,  True,  True,  True,  True,  True,\n",
+              "        True,  True,  True,  True, False,  True,  True, False,  True,\n",
+              "        True, False,  True, False,  True,  True,  True,  True,  True,\n",
+              "        True,  True,  True, False,  True,  True,  True,  True, False,\n",
+              "       False,  True,  True, False,  True,  True,  True, False,  True,\n",
+              "        True,  True, False,  True, False,  True,  True,  True,  True,\n",
+              "        True, False,  True,  True,  True,  True,  True, False,  True,\n",
+              "        True, False, False, False,  True,  True,  True,  True,  True,\n",
+              "        True, False,  True,  True,  True, False,  True,  True,  True,\n",
+              "        True, False, False,  True,  True, False,  True,  True,  True,\n",
+              "       False,  True,  True,  True,  True,  True,  True, False,  True,\n",
+              "        True,  True, False,  True, False, False, False,  True,  True,\n",
+              "        True,  True,  True,  True, False,  True,  True, False,  True,\n",
+              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+              "       False,  True,  True,  True,  True,  True,  True,  True, False,\n",
+              "        True, False,  True,  True,  True,  True,  True,  True,  True,\n",
+              "        True,  True,  True,  True,  True,  True, False, False,  True,\n",
+              "        True,  True, False,  True,  True, False,  True,  True,  True,\n",
+              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
+              "        True,  True,  True,  True,  True,  True, False, False,  True,\n",
+              "        True,  True, False,  True,  True])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 45
+        }
       ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "667"
+      "cell_type": "code",
+      "metadata": {
+        "id": "iWhUObc0AkSc",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 316
+        },
+        "outputId": "241296c4-8812-4e44-b70f-f01e4b7deb9b"
+      },
+      "source": [
+        "plt.hist(check.astype(int))#Ha cambiado en la versión 3.7 de python y necesita hacer un cast de bool a entero"
+      ],
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(array([108.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 392.]),\n",
+              " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
+              " <a list of 10 Patch objects>)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 47
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
       ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Usando una función de shuffle"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GRqOOoG2AkSc",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "training = data[check]\n",
+        "testing = data[~check]"
+      ],
+      "execution_count": 0,
+      "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>State</th>\n",
-       "      <th>Account Length</th>\n",
-       "      <th>Area Code</th>\n",
-       "      <th>Phone</th>\n",
-       "      <th>Int'l Plan</th>\n",
-       "      <th>VMail Plan</th>\n",
-       "      <th>VMail Message</th>\n",
-       "      <th>Day Mins</th>\n",
-       "      <th>Day Calls</th>\n",
-       "      <th>Day Charge</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Eve Calls</th>\n",
-       "      <th>Eve Charge</th>\n",
-       "      <th>Night Mins</th>\n",
-       "      <th>Night Calls</th>\n",
-       "      <th>Night Charge</th>\n",
-       "      <th>Intl Mins</th>\n",
-       "      <th>Intl Calls</th>\n",
-       "      <th>Intl Charge</th>\n",
-       "      <th>CustServ Calls</th>\n",
-       "      <th>Churn?</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>KS</td>\n",
-       "      <td>128</td>\n",
-       "      <td>415</td>\n",
-       "      <td>382-4657</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>25</td>\n",
-       "      <td>265.1</td>\n",
-       "      <td>110</td>\n",
-       "      <td>45.07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>99</td>\n",
-       "      <td>16.78</td>\n",
-       "      <td>244.7</td>\n",
-       "      <td>91</td>\n",
-       "      <td>11.01</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.70</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>OH</td>\n",
-       "      <td>107</td>\n",
-       "      <td>415</td>\n",
-       "      <td>371-7191</td>\n",
-       "      <td>no</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>26</td>\n",
-       "      <td>161.6</td>\n",
-       "      <td>123</td>\n",
-       "      <td>27.47</td>\n",
-       "      <td>...</td>\n",
-       "      <td>103</td>\n",
-       "      <td>16.62</td>\n",
-       "      <td>254.4</td>\n",
-       "      <td>103</td>\n",
-       "      <td>11.45</td>\n",
-       "      <td>13.7</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3.70</td>\n",
-       "      <td>1</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>NJ</td>\n",
-       "      <td>137</td>\n",
-       "      <td>415</td>\n",
-       "      <td>358-1921</td>\n",
-       "      <td>no</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>243.4</td>\n",
-       "      <td>114</td>\n",
-       "      <td>41.38</td>\n",
-       "      <td>...</td>\n",
-       "      <td>110</td>\n",
-       "      <td>10.30</td>\n",
-       "      <td>162.6</td>\n",
-       "      <td>104</td>\n",
-       "      <td>7.32</td>\n",
-       "      <td>12.2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>3.29</td>\n",
-       "      <td>0</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>OH</td>\n",
-       "      <td>84</td>\n",
-       "      <td>408</td>\n",
-       "      <td>375-9999</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>299.4</td>\n",
-       "      <td>71</td>\n",
-       "      <td>50.90</td>\n",
-       "      <td>...</td>\n",
-       "      <td>88</td>\n",
-       "      <td>5.26</td>\n",
-       "      <td>196.9</td>\n",
-       "      <td>89</td>\n",
-       "      <td>8.86</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>7</td>\n",
-       "      <td>1.78</td>\n",
-       "      <td>2</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>OK</td>\n",
-       "      <td>75</td>\n",
-       "      <td>415</td>\n",
-       "      <td>330-6626</td>\n",
-       "      <td>yes</td>\n",
-       "      <td>no</td>\n",
-       "      <td>0</td>\n",
-       "      <td>166.7</td>\n",
-       "      <td>113</td>\n",
-       "      <td>28.34</td>\n",
-       "      <td>...</td>\n",
-       "      <td>122</td>\n",
-       "      <td>12.61</td>\n",
-       "      <td>186.9</td>\n",
-       "      <td>121</td>\n",
-       "      <td>8.41</td>\n",
-       "      <td>10.1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>2.73</td>\n",
-       "      <td>3</td>\n",
-       "      <td>False.</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 21 columns</p>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ribx2PC1AkSd",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "6233c63b-3442-463d-9e2b-b9d68bd000a0"
+      },
+      "source": [
+        "len(training)"
       ],
-      "text/plain": [
-       "  State  Account Length  Area Code     Phone Int'l Plan VMail Plan  \\\n",
-       "0    KS             128        415  382-4657         no        yes   \n",
-       "1    OH             107        415  371-7191         no        yes   \n",
-       "2    NJ             137        415  358-1921         no         no   \n",
-       "3    OH              84        408  375-9999        yes         no   \n",
-       "4    OK              75        415  330-6626        yes         no   \n",
-       "\n",
-       "   VMail Message  Day Mins  Day Calls  Day Charge   ...    Eve Calls  \\\n",
-       "0             25     265.1        110       45.07   ...           99   \n",
-       "1             26     161.6        123       27.47   ...          103   \n",
-       "2              0     243.4        114       41.38   ...          110   \n",
-       "3              0     299.4         71       50.90   ...           88   \n",
-       "4              0     166.7        113       28.34   ...          122   \n",
-       "\n",
-       "   Eve Charge  Night Mins  Night Calls  Night Charge  Intl Mins  Intl Calls  \\\n",
-       "0       16.78       244.7           91         11.01       10.0           3   \n",
-       "1       16.62       254.4          103         11.45       13.7           3   \n",
-       "2       10.30       162.6          104          7.32       12.2           5   \n",
-       "3        5.26       196.9           89          8.86        6.6           7   \n",
-       "4       12.61       186.9          121          8.41       10.1           3   \n",
-       "\n",
-       "   Intl Charge  CustServ Calls  Churn?  \n",
-       "0         2.70               1  False.  \n",
-       "1         3.70               1  False.  \n",
-       "2         3.29               0  False.  \n",
-       "3         1.78               2  False.  \n",
-       "4         2.73               3  False.  \n",
-       "\n",
-       "[5 rows x 21 columns]"
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "392"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 49
+        }
       ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sklearn"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data = sklearn.utils.shuffle(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cut_id = int(0.75*len(data))\n",
-    "train_data = data[:cut_id]\n",
-    "test_data = data[cut_id+1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "2499"
+      "cell_type": "code",
+      "metadata": {
+        "id": "0UnqiUWOAkSe",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "b08d645a-01c5-40fc-9bcd-e13cff80bb71"
+      },
+      "source": [
+        "len(testing)"
+      ],
+      "execution_count": 50,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "108"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 50
+        }
       ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(train_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1f2ToslqAkSf",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Con la libreria sklearn"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "26zoqG7RAkSf",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "from sklearn.model_selection import train_test_split# Ha cambiado en la 3.7 de Python"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aVojsIWeAkSg",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "train, test = train_test_split(data, test_size = 0.2)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "60Cjk1ifAkSg",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "127bd786-c53b-4382-973a-90f1b3bf0244"
+      },
+      "source": [
+        "len(train)"
+      ],
+      "execution_count": 54,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "400"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 54
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "xWfmYLTNAkSn",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "ed19bc2b-5dbc-40fd-dcf3-0c4a816838ba"
+      },
+      "source": [
+        "len(test)"
+      ],
+      "execution_count": 55,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "100"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 55
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9l8-cogRAkSo",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Usando una función de shuffle"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QLvByZ3PAkSo",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import numpy as np"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hAwmSuggAkSp",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "68556d22-1833-44a7-8093-a88587991b76"
+      },
+      "source": [
+        "data.head()"
+      ],
+      "execution_count": 58,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Economic Status</th>\n",
+              "      <th>Height</th>\n",
+              "      <th>Weight</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Income</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>196.343940</td>\n",
+              "      <td>60.909064</td>\n",
+              "      <td>40.451221</td>\n",
+              "      <td>21385.958772</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Middle Class</td>\n",
+              "      <td>153.697110</td>\n",
+              "      <td>48.005164</td>\n",
+              "      <td>48.163870</td>\n",
+              "      <td>19757.653998</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Female</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>146.713926</td>\n",
+              "      <td>53.476714</td>\n",
+              "      <td>31.144568</td>\n",
+              "      <td>20279.476735</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Rich</td>\n",
+              "      <td>183.988501</td>\n",
+              "      <td>71.785466</td>\n",
+              "      <td>34.192437</td>\n",
+              "      <td>19778.532678</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Male</td>\n",
+              "      <td>Poor</td>\n",
+              "      <td>179.805582</td>\n",
+              "      <td>58.547234</td>\n",
+              "      <td>32.501684</td>\n",
+              "      <td>19346.337986</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   Gender Economic Status      Height     Weight        Age        Income\n",
+              "0  Female            Rich  196.343940  60.909064  40.451221  21385.958772\n",
+              "1    Male    Middle Class  153.697110  48.005164  48.163870  19757.653998\n",
+              "2  Female            Rich  146.713926  53.476714  31.144568  20279.476735\n",
+              "3    Male            Rich  183.988501  71.785466  34.192437  19778.532678\n",
+              "4    Male            Poor  179.805582  58.547234  32.501684  19346.337986"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 58
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qa0z_ZJ6AkSp",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import sklearn"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yBX-tSSCAkSq",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data = sklearn.utils.shuffle(data)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "833"
+      "cell_type": "code",
+      "metadata": {
+        "id": "lkwfCbmSAkSr",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "cut_id = int(0.75*len(data))\n",
+        "train_data = data[:cut_id]\n",
+        "test_data = data[cut_id+1:]"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NuJVmGovAkSr",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "6fb2bfaf-e9c0-4de0-a154-f135f4823089"
+      },
+      "source": [
+        "len(train_data)"
+      ],
+      "execution_count": 62,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "375"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 62
+        }
       ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ztoaBbOeAkSs",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "b3c609f2-fedc-4670-add3-51ca09418772"
+      },
+      "source": [
+        "len(test_data)"
+      ],
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "124"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 63
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "wgEx951WBsB-",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
     }
-   ],
-   "source": [
-    "len(test_data)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  ]
+}
\ No newline at end of file
diff --git "a/notebooks/T2 - 4 - Data Cleaning - Concatenaci\303\263n de datos-Colab.ipynb" "b/notebooks/T2 - 4 - Data Cleaning - Concatenaci\303\263n de datos-Colab.ipynb"
new file mode 100644
index 00000000..ca2618ef
--- /dev/null
+++ "b/notebooks/T2 - 4 - Data Cleaning - Concatenaci\303\263n de datos-Colab.ipynb"	
@@ -0,0 +1,4991 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/Collab---v-3.8/notebooks/T2%20-%204%20-%20Data%20Cleaning%20-%20Concatenaci%C3%B3n%20de%20datos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "l4rY9ejJB7mV"
+   },
+   "source": [
+    "# Concatenar y apendizar data sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 122
+    },
+    "colab_type": "code",
+    "id": "tg49jgbGB-P2",
+    "outputId": "42f076f4-2db4-4619-89ee-5643f3dab4b6"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+      "\n",
+      "Enter your authorization code:\n",
+      "··········\n",
+      "Mounted at /content/drive\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "GglD0HFUB7mX"
+   },
+   "source": [
+    "## El ejemplo del vino blanco y el vino tinto"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "mR7XsaHYB7mX"
+   },
+   "source": [
+    "Data Set Information:\n",
+    "\n",
+    "These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. \n",
+    "\n",
+    "I think that the initial data set had around 30 variables, but for some reason I only have the 13 dimensional version. I had a list of what the 30 or so variables were, but a.) I lost it, and b.), I would not know which 13 variables are included in the set. \n",
+    "\n",
+    "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it ) \n",
+    "\n",
+    "1. Alcohol \n",
+    "2. Malic acid \n",
+    "3. Ash \n",
+    "4. Alcalinity of ash \n",
+    "5. Magnesium \n",
+    "6. Total phenols \n",
+    "7. Flavanoids \n",
+    "8. Nonflavanoid phenols \n",
+    "9. Proanthocyanins \n",
+    "10. Color intensity \n",
+    "11. Hue \n",
+    "12. OD280/OD315 of diluted wines \n",
+    "13. Proline \n",
+    "\n",
+    "In a classification context, this is a well posed problem with \"well behaved\" class structures. A good data set for first testing of a new classifier, but not very challenging.\n",
+    "\n",
+    "\n",
+    "Attribute Information:\n",
+    "\n",
+    "All attributes are continuous \n",
+    "\n",
+    "No statistics available, but suggest to standardise variables for certain uses (e.g. for us with classifiers which are NOT scale invariant) \n",
+    "\n",
+    "NOTE: 1st attribute is class identifier (1-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ZA6UH67uB7mY"
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "h9QWV_oGB7me",
+    "outputId": "49b5bdf7-f5b1-4fc9-ec32-a6722379cc76"
+   },
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0.9968</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.9970</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.9980</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
+       "0            7.4              0.70         0.00  ...       0.56      9.4        5\n",
+       "1            7.8              0.88         0.00  ...       0.68      9.8        5\n",
+       "2            7.8              0.76         0.04  ...       0.65      9.8        5\n",
+       "3           11.2              0.28         0.56  ...       0.58      9.8        6\n",
+       "4            7.4              0.70         0.00  ...       0.56      9.4        5\n",
+       "\n",
+       "[5 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "red_wine = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/wine/winequality-red.csv\", sep=\";\")\n",
+    "red_wine.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 85
+    },
+    "colab_type": "code",
+    "id": "jRrVyKgmB7mi",
+    "outputId": "7d0af16d-f23a-4f03-f08b-0a7c823ad926"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['fixed acidity', 'volatile acidity', 'citric acid',\n",
+       "       'residual sugar', 'chlorides', 'free sulfur dioxide',\n",
+       "       'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol',\n",
+       "       'quality'], dtype=object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "red_wine.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "3RVTL50JB7ml",
+    "outputId": "84523ae6-48b3-43a7-d9e5-de06c7788567"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1599, 12)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "red_wine.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "zmKgDkZxB7mn",
+    "outputId": "c168bdd5-2a93-4185-d463-6fcaaeb9952d"
+   },
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>20.7</td>\n",
+       "      <td>0.045</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>170.0</td>\n",
+       "      <td>1.0010</td>\n",
+       "      <td>3.00</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6.3</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>0.34</td>\n",
+       "      <td>1.6</td>\n",
+       "      <td>0.049</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>132.0</td>\n",
+       "      <td>0.9940</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>0.49</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>8.1</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>0.050</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>97.0</td>\n",
+       "      <td>0.9951</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.44</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>186.0</td>\n",
+       "      <td>0.9956</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.2</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>0.058</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>186.0</td>\n",
+       "      <td>0.9956</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
+       "0            7.0              0.27         0.36  ...       0.45      8.8        6\n",
+       "1            6.3              0.30         0.34  ...       0.49      9.5        6\n",
+       "2            8.1              0.28         0.40  ...       0.44     10.1        6\n",
+       "3            7.2              0.23         0.32  ...       0.40      9.9        6\n",
+       "4            7.2              0.23         0.32  ...       0.40      9.9        6\n",
+       "\n",
+       "[5 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "white_wine = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/wine/winequality-white.csv\", sep = \";\")\n",
+    "white_wine.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 85
+    },
+    "colab_type": "code",
+    "id": "fX16n8MVB7mp",
+    "outputId": "0a0e3f49-e3e7-4239-b73a-29e114c6f115"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['fixed acidity', 'volatile acidity', 'citric acid',\n",
+       "       'residual sugar', 'chlorides', 'free sulfur dioxide',\n",
+       "       'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol',\n",
+       "       'quality'], dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "white_wine.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "V9A6kxFaB7mr",
+    "outputId": "32d554d4-d056-4ac4-f509-8a8e3211d386"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4898, 12)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "white_wine.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "2QZDLzxwB7ms"
+   },
+   "source": [
+    "En python, tenemos dos tipos de ejes, \n",
+    "* axis = 0 denota el eje horizontal\n",
+    "* axis = 1 denota el eje vertical"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "JbNRUeIkB7mt"
+   },
+   "outputs": [],
+   "source": [
+    "wine_data = pd.concat([red_wine, white_wine], axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "Qwtl3GfQB7mu",
+    "outputId": "6441ce9a-9dd6-4ecf-cd3a-647b6ffa5806"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6497, 12)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "rcEXhdlgB7mw",
+    "outputId": "673ba977-7dee-48ba-9883-1be87b31892a"
+   },
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0.9968</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.9970</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.9980</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
+       "0            7.4              0.70         0.00  ...       0.56      9.4        5\n",
+       "1            7.8              0.88         0.00  ...       0.68      9.8        5\n",
+       "2            7.8              0.76         0.04  ...       0.65      9.8        5\n",
+       "3           11.2              0.28         0.56  ...       0.58      9.8        6\n",
+       "4            7.4              0.70         0.00  ...       0.56      9.4        5\n",
+       "\n",
+       "[5 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine_data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "25TexLrLB7mx"
+   },
+   "outputs": [],
+   "source": [
+    "data1 = wine_data.head(10)\n",
+    "data2 = wine_data[300:310]\n",
+    "data3 = wine_data.tail(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "-ETslsuHB7my"
+   },
+   "outputs": [],
+   "source": [
+    "wine_scramble = pd.concat([data1, data2, data3], axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 979
+    },
+    "colab_type": "code",
+    "id": "QJRAV3kZB7m0",
+    "outputId": "727adb33-3de4-4ada-ef17-bf6d78ccddaa"
+   },
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.700</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.880</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0.99680</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.760</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>2.30</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.99700</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>0.280</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.99800</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.700</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.660</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>0.99640</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.650</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>0.065</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.99460</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>0.47</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.580</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.073</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.99680</td>\n",
+       "      <td>3.36</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>6.10</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.35</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0.530</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>0.086</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>0.99650</td>\n",
+       "      <td>3.38</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>11.1</td>\n",
+       "      <td>0.180</td>\n",
+       "      <td>0.48</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>0.068</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.99730</td>\n",
+       "      <td>3.22</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>8.3</td>\n",
+       "      <td>0.705</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0.99940</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.670</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>0.186</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.99600</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.650</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>2.10</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>0.99730</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>10.3</td>\n",
+       "      <td>0.530</td>\n",
+       "      <td>0.48</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>0.063</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.99980</td>\n",
+       "      <td>3.12</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306</th>\n",
+       "      <td>7.6</td>\n",
+       "      <td>0.620</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.99660</td>\n",
+       "      <td>3.36</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>10.3</td>\n",
+       "      <td>0.410</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>0.213</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.99940</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>0.62</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>10.3</td>\n",
+       "      <td>0.430</td>\n",
+       "      <td>0.44</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>0.214</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.99940</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>0.63</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.290</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.99680</td>\n",
+       "      <td>3.41</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4888</th>\n",
+       "      <td>6.8</td>\n",
+       "      <td>0.220</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>0.99330</td>\n",
+       "      <td>3.04</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4889</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.235</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>11.75</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>0.99540</td>\n",
+       "      <td>3.07</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4890</th>\n",
+       "      <td>6.1</td>\n",
+       "      <td>0.340</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>0.036</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.98938</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.44</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4891</th>\n",
+       "      <td>5.7</td>\n",
+       "      <td>0.210</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>0.99074</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4892</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>0.230</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>1.30</td>\n",
+       "      <td>0.032</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>0.99298</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4893</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.210</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>0.99114</td>\n",
+       "      <td>3.27</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4894</th>\n",
+       "      <td>6.6</td>\n",
+       "      <td>0.320</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>0.047</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>168.0</td>\n",
+       "      <td>0.99490</td>\n",
+       "      <td>3.15</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4895</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>0.240</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>0.041</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>111.0</td>\n",
+       "      <td>0.99254</td>\n",
+       "      <td>2.99</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4896</th>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.290</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>0.022</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>0.98869</td>\n",
+       "      <td>3.34</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4897</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.210</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.020</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>0.98941</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
+       "0               7.4             0.700         0.00  ...       0.56      9.4        5\n",
+       "1               7.8             0.880         0.00  ...       0.68      9.8        5\n",
+       "2               7.8             0.760         0.04  ...       0.65      9.8        5\n",
+       "3              11.2             0.280         0.56  ...       0.58      9.8        6\n",
+       "4               7.4             0.700         0.00  ...       0.56      9.4        5\n",
+       "5               7.4             0.660         0.00  ...       0.56      9.4        5\n",
+       "6               7.9             0.600         0.06  ...       0.46      9.4        5\n",
+       "7               7.3             0.650         0.00  ...       0.47     10.0        7\n",
+       "8               7.8             0.580         0.02  ...       0.57      9.5        7\n",
+       "9               7.5             0.500         0.36  ...       0.80     10.5        5\n",
+       "300             7.5             0.530         0.06  ...       0.59     10.7        6\n",
+       "301            11.1             0.180         0.48  ...       0.64     10.1        6\n",
+       "302             8.3             0.705         0.12  ...       0.72     10.0        5\n",
+       "303             7.4             0.670         0.12  ...       0.54      9.5        5\n",
+       "304             8.4             0.650         0.60  ...       0.52      9.2        5\n",
+       "305            10.3             0.530         0.48  ...       0.59      9.3        6\n",
+       "306             7.6             0.620         0.32  ...       0.52      9.4        5\n",
+       "307            10.3             0.410         0.42  ...       0.62      9.5        6\n",
+       "308            10.3             0.430         0.44  ...       0.63      9.5        6\n",
+       "309             7.4             0.290         0.38  ...       0.53      9.5        6\n",
+       "4888            6.8             0.220         0.36  ...       0.54      9.2        5\n",
+       "4889            4.9             0.235         0.27  ...       0.50      9.4        6\n",
+       "4890            6.1             0.340         0.29  ...       0.44     11.8        6\n",
+       "4891            5.7             0.210         0.32  ...       0.46     10.6        6\n",
+       "4892            6.5             0.230         0.38  ...       0.54      9.7        5\n",
+       "4893            6.2             0.210         0.29  ...       0.50     11.2        6\n",
+       "4894            6.6             0.320         0.36  ...       0.46      9.6        5\n",
+       "4895            6.5             0.240         0.19  ...       0.46      9.4        6\n",
+       "4896            5.5             0.290         0.30  ...       0.38     12.8        7\n",
+       "4897            6.0             0.210         0.38  ...       0.32     11.8        6\n",
+       "\n",
+       "[30 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine_scramble"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 979
+    },
+    "colab_type": "code",
+    "id": "nD28Z5ATB7m1",
+    "outputId": "65a7db3f-82da-484a-edd8-fd638ff7e6a3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0.530</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>0.086</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>44.0</td>\n",
+       "      <td>0.99650</td>\n",
+       "      <td>3.38</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>10.7</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>301</th>\n",
+       "      <td>11.1</td>\n",
+       "      <td>0.180</td>\n",
+       "      <td>0.48</td>\n",
+       "      <td>1.50</td>\n",
+       "      <td>0.068</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>0.99730</td>\n",
+       "      <td>3.22</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>10.1</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>302</th>\n",
+       "      <td>8.3</td>\n",
+       "      <td>0.705</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0.99940</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.72</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>303</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.670</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>0.186</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.99600</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>304</th>\n",
+       "      <td>8.4</td>\n",
+       "      <td>0.650</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>2.10</td>\n",
+       "      <td>0.112</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>90.0</td>\n",
+       "      <td>0.99730</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>10.3</td>\n",
+       "      <td>0.530</td>\n",
+       "      <td>0.48</td>\n",
+       "      <td>2.50</td>\n",
+       "      <td>0.063</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.99980</td>\n",
+       "      <td>3.12</td>\n",
+       "      <td>0.59</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>306</th>\n",
+       "      <td>7.6</td>\n",
+       "      <td>0.620</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>0.082</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.99660</td>\n",
+       "      <td>3.36</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>307</th>\n",
+       "      <td>10.3</td>\n",
+       "      <td>0.410</td>\n",
+       "      <td>0.42</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>0.213</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>0.99940</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>0.62</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>308</th>\n",
+       "      <td>10.3</td>\n",
+       "      <td>0.430</td>\n",
+       "      <td>0.44</td>\n",
+       "      <td>2.40</td>\n",
+       "      <td>0.214</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>0.99940</td>\n",
+       "      <td>3.19</td>\n",
+       "      <td>0.63</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>309</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.290</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>0.062</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.99680</td>\n",
+       "      <td>3.41</td>\n",
+       "      <td>0.53</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.700</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.880</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.60</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0.99680</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.760</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>2.30</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.99700</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>0.280</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.99800</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.700</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.90</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.660</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.80</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7.9</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>0.069</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>0.99640</td>\n",
+       "      <td>3.30</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7.3</td>\n",
+       "      <td>0.650</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>0.065</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>0.99460</td>\n",
+       "      <td>3.39</td>\n",
+       "      <td>0.47</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.580</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>0.073</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.99680</td>\n",
+       "      <td>3.36</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>7.5</td>\n",
+       "      <td>0.500</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>6.10</td>\n",
+       "      <td>0.071</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>102.0</td>\n",
+       "      <td>0.99780</td>\n",
+       "      <td>3.35</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4888</th>\n",
+       "      <td>6.8</td>\n",
+       "      <td>0.220</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>0.052</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>127.0</td>\n",
+       "      <td>0.99330</td>\n",
+       "      <td>3.04</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>9.2</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4889</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.235</td>\n",
+       "      <td>0.27</td>\n",
+       "      <td>11.75</td>\n",
+       "      <td>0.030</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>118.0</td>\n",
+       "      <td>0.99540</td>\n",
+       "      <td>3.07</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4890</th>\n",
+       "      <td>6.1</td>\n",
+       "      <td>0.340</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>2.20</td>\n",
+       "      <td>0.036</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>0.98938</td>\n",
+       "      <td>3.06</td>\n",
+       "      <td>0.44</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4891</th>\n",
+       "      <td>5.7</td>\n",
+       "      <td>0.210</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.038</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>121.0</td>\n",
+       "      <td>0.99074</td>\n",
+       "      <td>3.24</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>10.6</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4892</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>0.230</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>1.30</td>\n",
+       "      <td>0.032</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>0.99298</td>\n",
+       "      <td>3.29</td>\n",
+       "      <td>0.54</td>\n",
+       "      <td>9.7</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4893</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.210</td>\n",
+       "      <td>0.29</td>\n",
+       "      <td>1.60</td>\n",
+       "      <td>0.039</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>92.0</td>\n",
+       "      <td>0.99114</td>\n",
+       "      <td>3.27</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>11.2</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4894</th>\n",
+       "      <td>6.6</td>\n",
+       "      <td>0.320</td>\n",
+       "      <td>0.36</td>\n",
+       "      <td>8.00</td>\n",
+       "      <td>0.047</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>168.0</td>\n",
+       "      <td>0.99490</td>\n",
+       "      <td>3.15</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4895</th>\n",
+       "      <td>6.5</td>\n",
+       "      <td>0.240</td>\n",
+       "      <td>0.19</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>0.041</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>111.0</td>\n",
+       "      <td>0.99254</td>\n",
+       "      <td>2.99</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4896</th>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.290</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>1.10</td>\n",
+       "      <td>0.022</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>0.98869</td>\n",
+       "      <td>3.34</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4897</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.210</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>0.80</td>\n",
+       "      <td>0.020</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>98.0</td>\n",
+       "      <td>0.98941</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>11.8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
+       "300             7.5             0.530         0.06  ...       0.59     10.7        6\n",
+       "301            11.1             0.180         0.48  ...       0.64     10.1        6\n",
+       "302             8.3             0.705         0.12  ...       0.72     10.0        5\n",
+       "303             7.4             0.670         0.12  ...       0.54      9.5        5\n",
+       "304             8.4             0.650         0.60  ...       0.52      9.2        5\n",
+       "305            10.3             0.530         0.48  ...       0.59      9.3        6\n",
+       "306             7.6             0.620         0.32  ...       0.52      9.4        5\n",
+       "307            10.3             0.410         0.42  ...       0.62      9.5        6\n",
+       "308            10.3             0.430         0.44  ...       0.63      9.5        6\n",
+       "309             7.4             0.290         0.38  ...       0.53      9.5        6\n",
+       "0               7.4             0.700         0.00  ...       0.56      9.4        5\n",
+       "1               7.8             0.880         0.00  ...       0.68      9.8        5\n",
+       "2               7.8             0.760         0.04  ...       0.65      9.8        5\n",
+       "3              11.2             0.280         0.56  ...       0.58      9.8        6\n",
+       "4               7.4             0.700         0.00  ...       0.56      9.4        5\n",
+       "5               7.4             0.660         0.00  ...       0.56      9.4        5\n",
+       "6               7.9             0.600         0.06  ...       0.46      9.4        5\n",
+       "7               7.3             0.650         0.00  ...       0.47     10.0        7\n",
+       "8               7.8             0.580         0.02  ...       0.57      9.5        7\n",
+       "9               7.5             0.500         0.36  ...       0.80     10.5        5\n",
+       "4888            6.8             0.220         0.36  ...       0.54      9.2        5\n",
+       "4889            4.9             0.235         0.27  ...       0.50      9.4        6\n",
+       "4890            6.1             0.340         0.29  ...       0.44     11.8        6\n",
+       "4891            5.7             0.210         0.32  ...       0.46     10.6        6\n",
+       "4892            6.5             0.230         0.38  ...       0.54      9.7        5\n",
+       "4893            6.2             0.210         0.29  ...       0.50     11.2        6\n",
+       "4894            6.6             0.320         0.36  ...       0.46      9.6        5\n",
+       "4895            6.5             0.240         0.19  ...       0.46      9.4        6\n",
+       "4896            5.5             0.290         0.30  ...       0.38     12.8        7\n",
+       "4897            6.0             0.210         0.38  ...       0.32     11.8        6\n",
+       "\n",
+       "[30 rows x 12 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "wine_scramble = pd.concat([data2, data1, data3], axis = 0)\n",
+    "wine_scramble"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "kcf3YilvB7m2"
+   },
+   "source": [
+    "## Datos distribuidos "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "hI5_SxMoB7m2",
+    "outputId": "1035185c-2f1e-48d7-8084-bab587795d5a"
+   },
+   "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>Date</th>\n",
+       "      <th>sulfate</th>\n",
+       "      <th>nitrate</th>\n",
+       "      <th>ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2003-01-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2003-01-02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2003-01-03</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2003-01-04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2003-01-05</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date  sulfate  nitrate  ID\n",
+       "0  2003-01-01      NaN      NaN   1\n",
+       "1  2003-01-02      NaN      NaN   1\n",
+       "2  2003-01-03      NaN      NaN   1\n",
+       "3  2003-01-04      NaN      NaN   1\n",
+       "4  2003-01-05      NaN      NaN   1"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "data = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/distributed-data/001.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "Dwu7TFSoB7m3",
+    "outputId": "aba3f868-e575-45fd-aae9-141c429463d5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1461, 4)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "RsJnj3eEB7m5"
+   },
+   "source": [
+    "* Importar el primer fichero\n",
+    "* Hacemos un bucle para ir recorriendo todos y cada uno de los ficheros. \n",
+    "    * Importante tener una consistencia en el nombre de los ficheros \n",
+    "    * Importamos los ficheros uno a uno\n",
+    "    * Cada uno de ellos debe apendizarse (añadirse al final) del primer fichero que ya habíamos cargado\n",
+    "* Repetimos el bucle hasta que no queden ficheros"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ptOP14vxB7m5"
+   },
+   "outputs": [],
+   "source": [
+    "filepath = \"/content/drive/My Drive/Curso Machine Learning con Python/datasets/distributed-data/\"\n",
+    "\n",
+    "data = pd.read_csv(filepath+\"001.csv\")\n",
+    "final_length = len(data)\n",
+    "\n",
+    "for i in range(2,333):\n",
+    "    if i < 10:\n",
+    "        filename = \"00\" + str(i)\n",
+    "    if 10 <= i < 100:\n",
+    "        filename = \"0\" + str(i)\n",
+    "    if i >= 100:\n",
+    "        filename = str(i)\n",
+    "    file = filepath + filename + \".csv\"\n",
+    "    \n",
+    "    temp_data = pd.read_csv(file)\n",
+    "    final_length += len(temp_data)\n",
+    "    \n",
+    "    data = pd.concat([data, temp_data], axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "2cbX3CYeB7m6",
+    "outputId": "f557cf3f-c056-4317-8196-acfb166c28ba"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(155220, 4)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "QRDf9HLmB7m8",
+    "outputId": "e89e5640-29ba-465b-efbc-eee6facea2c7"
+   },
+   "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>Date</th>\n",
+       "      <th>sulfate</th>\n",
+       "      <th>nitrate</th>\n",
+       "      <th>ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3282</th>\n",
+       "      <td>2010-12-27</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3283</th>\n",
+       "      <td>2010-12-28</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3284</th>\n",
+       "      <td>2010-12-29</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3285</th>\n",
+       "      <td>2010-12-30</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3286</th>\n",
+       "      <td>2010-12-31</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Date  sulfate  nitrate  ID\n",
+       "3282  2010-12-27      NaN      NaN  63\n",
+       "3283  2010-12-28      NaN      NaN  63\n",
+       "3284  2010-12-29      NaN      NaN  63\n",
+       "3285  2010-12-30      NaN      NaN  63\n",
+       "3286  2010-12-31      NaN      NaN  63"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "HC4Kws0cB7m9",
+    "outputId": "615bdec3-11c8-4596-efd5-598aa62412d4"
+   },
+   "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>Date</th>\n",
+       "      <th>sulfate</th>\n",
+       "      <th>nitrate</th>\n",
+       "      <th>ID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2003-01-01</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2003-01-02</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2003-01-03</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2003-01-04</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2003-01-05</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Date  sulfate  nitrate  ID\n",
+       "0  2003-01-01      NaN      NaN   1\n",
+       "1  2003-01-02      NaN      NaN   1\n",
+       "2  2003-01-03      NaN      NaN   1\n",
+       "3  2003-01-04      NaN      NaN   1\n",
+       "4  2003-01-05      NaN      NaN   1"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 34
+    },
+    "colab_type": "code",
+    "id": "tX3bm7wgB7m-",
+    "outputId": "931389b3-c922-4ca2-8543-d67683efc134"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_length == data.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "0YdhyaxtB7m_"
+   },
+   "source": [
+    "# Joins de datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "6tRBwQDDB7m_"
+   },
+   "outputs": [],
+   "source": [
+    "filepath = \"/content/drive/My Drive/Curso Machine Learning con Python/datasets/athletes/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Omj7JIrHB7nA"
+   },
+   "outputs": [],
+   "source": [
+    "data_main = pd.read_csv(filepath + \"Medals.csv\", encoding= \"ISO-8859-1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "gqKemyCTB7nB",
+    "outputId": "2c96f639-7496-4327-9227-dabdedefeed9"
+   },
+   "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>Athlete</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Closing Ceremony Date</th>\n",
+       "      <th>Gold Medals</th>\n",
+       "      <th>Silver Medals</th>\n",
+       "      <th>Bronze Medals</th>\n",
+       "      <th>Total Medals</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>08/24/2008</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>08/29/2004</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>08/12/2012</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Natalie Coughlin</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>08/24/2008</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Aleksey Nemov</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>10/01/2000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
+       "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
+       "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
+       "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
+       "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
+       "4     Aleksey Nemov  24.0  2000            10/01/2000            2   \n",
+       "\n",
+       "   Silver Medals  Bronze Medals  Total Medals  \n",
+       "0              0              0             8  \n",
+       "1              0              2             8  \n",
+       "2              2              0             6  \n",
+       "3              2              3             6  \n",
+       "4              1              3             6  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_main.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "LQoGvZNtB7nE",
+    "outputId": "380e0105-a751-4112-f49b-484cf687a36d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6956"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = data_main[\"Athlete\"].unique().tolist()\n",
+    "len(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "jb8YCne0B7nP",
+    "outputId": "0616b8cc-5bb3-4549-efd6-9335e674eee0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8618, 8)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_main.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "SmKLs0RUB7nR"
+   },
+   "outputs": [],
+   "source": [
+    "data_country = pd.read_csv(filepath + \"Athelete_Country_Map.csv\", encoding = \"ISO-8859-1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "xgrqoxzTB7nS",
+    "outputId": "d4a18a48-ea13-46ce-fc74-80b93f678627"
+   },
+   "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>Athlete</th>\n",
+       "      <th>Country</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Natalie Coughlin</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Aleksey Nemov</td>\n",
+       "      <td>Russia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alicia Coutts</td>\n",
+       "      <td>Australia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Missy Franklin</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Athlete        Country\n",
+       "0    Michael Phelps  United States\n",
+       "1  Natalie Coughlin  United States\n",
+       "2     Aleksey Nemov         Russia\n",
+       "3     Alicia Coutts      Australia\n",
+       "4    Missy Franklin  United States"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_country.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "9pfZXk3dB7nT",
+    "outputId": "55cde6ba-6791-4bca-f197-e4ca45e7dffd"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6970"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_country)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "h7OiBAsnB7nU",
+    "outputId": "48e8687e-4842-43a0-b047-5615db9b7da9"
+   },
+   "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>Athlete</th>\n",
+       "      <th>Country</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1029</th>\n",
+       "      <td>Aleksandar Ciric</td>\n",
+       "      <td>Serbia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1086</th>\n",
+       "      <td>Aleksandar Ciric</td>\n",
+       "      <td>Serbia and Montenegro</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Athlete                Country\n",
+       "1029  Aleksandar Ciric                 Serbia\n",
+       "1086  Aleksandar Ciric  Serbia and Montenegro"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_country[data_country[\"Athlete\"] == \"Aleksandar Ciric\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "vs4X4VVbB7nU"
+   },
+   "outputs": [],
+   "source": [
+    "data_sports = pd.read_csv(filepath + \"Athelete_Sports_Map.csv\", encoding=\"ISO-8859-1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 204
+    },
+    "colab_type": "code",
+    "id": "dPctj5BTB7nV",
+    "outputId": "b9e0e0fe-f652-4eae-ff5e-67824286d2b6"
+   },
+   "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>Athlete</th>\n",
+       "      <th>Sport</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>Swimming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Natalie Coughlin</td>\n",
+       "      <td>Swimming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Aleksey Nemov</td>\n",
+       "      <td>Gymnastics</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Alicia Coutts</td>\n",
+       "      <td>Swimming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Missy Franklin</td>\n",
+       "      <td>Swimming</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Athlete       Sport\n",
+       "0    Michael Phelps    Swimming\n",
+       "1  Natalie Coughlin    Swimming\n",
+       "2     Aleksey Nemov  Gymnastics\n",
+       "3     Alicia Coutts    Swimming\n",
+       "4    Missy Franklin    Swimming"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sports.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "9GICMbXfB7nW",
+    "outputId": "e23d7aa6-d38f-4379-8818-ffc9ef434abd"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6975"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_sports)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "4dMxAIv0B7nX",
+    "outputId": "629a3230-7f6a-4854-986e-3e7f12e81c68"
+   },
+   "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>Athlete</th>\n",
+       "      <th>Sport</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>528</th>\n",
+       "      <td>Richard Thompson</td>\n",
+       "      <td>Athletics</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>Chen Jing</td>\n",
+       "      <td>Volleyball</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1419</th>\n",
+       "      <td>Chen Jing</td>\n",
+       "      <td>Table Tennis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2727</th>\n",
+       "      <td>Matt Ryan</td>\n",
+       "      <td>Rowing</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5003</th>\n",
+       "      <td>Matt Ryan</td>\n",
+       "      <td>Equestrian</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5691</th>\n",
+       "      <td>Richard Thompson</td>\n",
+       "      <td>Baseball</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Athlete         Sport\n",
+       "528   Richard Thompson     Athletics\n",
+       "1308         Chen Jing    Volleyball\n",
+       "1419         Chen Jing  Table Tennis\n",
+       "2727         Matt Ryan        Rowing\n",
+       "5003         Matt Ryan    Equestrian\n",
+       "5691  Richard Thompson      Baseball"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_sports[(data_sports[\"Athlete\"]==\"Chen Jing\") | \n",
+    "            (data_sports[\"Athlete\"]==\"Richard Thompson\") | \n",
+    "            (data_sports[\"Athlete\"]==\"Matt Ryan\")\n",
+    "           ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "MMmp-rayB7nY"
+   },
+   "outputs": [],
+   "source": [
+    "data_country_dp = data_country.drop_duplicates(subset=\"Athlete\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "3Y66TIxXB7nZ",
+    "outputId": "6a28a049-ad43-4200-c742-e1da40674fe5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_country_dp)==len(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "BAFBL-h6B7na"
+   },
+   "outputs": [],
+   "source": [
+    "data_main_country = pd.merge(left = data_main, right = data_country_dp,\n",
+    "                            left_on=\"Athlete\", right_on = \"Athlete\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "IAiBbUO5B7nb",
+    "outputId": "0832067f-c794-4771-a385-b3047ddcea9a"
+   },
+   "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>Athlete</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Closing Ceremony Date</th>\n",
+       "      <th>Gold Medals</th>\n",
+       "      <th>Silver Medals</th>\n",
+       "      <th>Bronze Medals</th>\n",
+       "      <th>Total Medals</th>\n",
+       "      <th>Country</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>08/24/2008</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>08/29/2004</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>08/12/2012</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Natalie Coughlin</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>08/24/2008</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Natalie Coughlin</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>08/29/2004</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
+       "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
+       "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
+       "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
+       "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
+       "4  Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
+       "\n",
+       "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
+       "0              0              0             8  United States  \n",
+       "1              0              2             8  United States  \n",
+       "2              2              0             6  United States  \n",
+       "3              2              3             6  United States  \n",
+       "4              2              1             5  United States  "
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_main_country.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "oFopEFmEB7nb",
+    "outputId": "affd8011-d29e-4c14-8ec1-ee6546b3f3a0"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8618, 9)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_main_country.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "By-SS0-CB7nc",
+    "outputId": "7988d1e8-50c3-46d2-a43e-edef4729e029"
+   },
+   "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>Athlete</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Closing Ceremony Date</th>\n",
+       "      <th>Gold Medals</th>\n",
+       "      <th>Silver Medals</th>\n",
+       "      <th>Bronze Medals</th>\n",
+       "      <th>Total Medals</th>\n",
+       "      <th>Country</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1491</th>\n",
+       "      <td>Aleksandar Ciric</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>08/24/2008</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Serbia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1492</th>\n",
+       "      <td>Aleksandar Ciric</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>08/29/2004</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Serbia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1493</th>\n",
+       "      <td>Aleksandar Ciric</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>10/01/2000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Serbia</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
+       "1491  Aleksandar Ciric  30.0  2008            08/24/2008            0   \n",
+       "1492  Aleksandar Ciric  26.0  2004            08/29/2004            0   \n",
+       "1493  Aleksandar Ciric  22.0  2000            10/01/2000            0   \n",
+       "\n",
+       "      Silver Medals  Bronze Medals  Total Medals Country  \n",
+       "1491              0              1             1  Serbia  \n",
+       "1492              1              0             1  Serbia  \n",
+       "1493              0              1             1  Serbia  "
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_main_country[data_main_country[\"Athlete\"] == \"Aleksandar Ciric\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "hX6CjiLMB7nd"
+   },
+   "outputs": [],
+   "source": [
+    "data_sports_dp = data_sports.drop_duplicates(subset=\"Athlete\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "9mwanl1DB7ne",
+    "outputId": "b31df516-19b7-481a-bbf3-ca59d22786cd"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_sports_dp)==len(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "mwflQZ47B7ne"
+   },
+   "outputs": [],
+   "source": [
+    "data_final = pd.merge(left=data_main_country, right=data_sports_dp,\n",
+    "                     left_on=\"Athlete\", right_on=\"Athlete\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "e1EOw4atB7nf",
+    "outputId": "a8557b3b-03f8-4eca-a6f6-99abf589caaf"
+   },
+   "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>Athlete</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Closing Ceremony Date</th>\n",
+       "      <th>Gold Medals</th>\n",
+       "      <th>Silver Medals</th>\n",
+       "      <th>Bronze Medals</th>\n",
+       "      <th>Total Medals</th>\n",
+       "      <th>Country</th>\n",
+       "      <th>Sport</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>08/24/2008</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Swimming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>08/29/2004</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Swimming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>08/12/2012</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Swimming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Natalie Coughlin</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>08/24/2008</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Swimming</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Natalie Coughlin</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>08/29/2004</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>Swimming</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
+       "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
+       "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
+       "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
+       "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
+       "4  Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
+       "\n",
+       "   Silver Medals  Bronze Medals  Total Medals        Country     Sport  \n",
+       "0              0              0             8  United States  Swimming  \n",
+       "1              0              2             8  United States  Swimming  \n",
+       "2              2              0             6  United States  Swimming  \n",
+       "3              2              3             6  United States  Swimming  \n",
+       "4              2              1             5  United States  Swimming  "
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_final.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "NdZzwRR-B7ng",
+    "outputId": "0a914d2c-b530-43da-8a29-c95b58cce2c9"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(8618, 10)"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_final.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "kZLPMM6nB7nh"
+   },
+   "source": [
+    "## Tipos de Joins"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "rsCKw6IHB7nh"
+   },
+   "outputs": [],
+   "source": [
+    "from IPython.display import Image\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "um7CuUxfB7nh"
+   },
+   "source": [
+    "**Inner Join <= A (Left Join), B (Right Join) <= Outer Join**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "znEBQUqzB7ni"
+   },
+   "outputs": [],
+   "source": [
+    "out_athletes = np.random.choice(data_main[\"Athlete\"], size = 6, replace = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "rAIJNBDSB7ni",
+    "outputId": "fc883de7-249c-424c-a605-97ead798a2d3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Oscar Braison', 'Erik Vendt', 'Yuliya Zaripova',\n",
+       "       'Erzsébet Márkus-Peresztegi', 'Belinda Snell', 'Alfredo Despaigne'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "out_athletes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "WoHqCm29B7nj"
+   },
+   "outputs": [],
+   "source": [
+    "data_country_dlt = data_country_dp[(~data_country_dp[\"Athlete\"].isin(out_athletes)) & \n",
+    "                                   (data_country_dp[\"Athlete\"] != \"Michael Phelps\")]\n",
+    "\n",
+    "data_sports_dlt = data_sports_dp[(~data_sports_dp[\"Athlete\"].isin(out_athletes)) &\n",
+    "                                (data_sports_dp[\"Athlete\"] != \"Michael Phelps\")]\n",
+    "\n",
+    "data_main_dlt = data_main[(~data_main[\"Athlete\"].isin(out_athletes)) & \n",
+    "                         (data_main[\"Athlete\"] != \"Michael Phelps\")]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "rBn_2GgHB7nk",
+    "outputId": "6d46ca2f-5fcb-401f-9ff4-6b8c1365efba"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6949"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_country_dlt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "F0cyll4mB7nl",
+    "outputId": "0f5c0954-5b4f-47dd-a03d-da7f07df4670"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6949"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_sports_dlt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "lTngjikNB7nl",
+    "outputId": "a7e76fc4-c967-4442-c5bb-be5fb89d1170"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8605"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_main_dlt)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "mix7obxVB7nm"
+   },
+   "source": [
+    "## Inner Join\n",
+    "* Devuelve un data frame con las filas que tienen valor tanto en el primero como en el segundo data frame que estamos uniendo\n",
+    "* El número de filas será igual al número de filas **comunes** que tengas ambos data sets\n",
+    "    * Data Set A tiene 60 filas\n",
+    "    * Data Set B tiene 50 filas\n",
+    "    * Ambos comparten 30 filas\n",
+    "    * Entonces A Inner Join B tendrá 30 filas\n",
+    "* En términos de teoría de conjuntos, se trata de la intersección de los dos conjuntos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "9NcbGr8oB7nm",
+    "outputId": "d523a395-cff4-4cee-b91a-81e4ead08644"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename=\"resources/inner-join.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ARX5xGM8B7nn"
+   },
+   "outputs": [],
+   "source": [
+    "# data_main contiene toda la info\n",
+    "# data_country_dlt le falta la info de 7 atletas\n",
+    "merged_inner = pd.merge(left = data_main, right = data_country_dlt,\n",
+    "                       how = \"inner\", left_on = \"Athlete\", right_on = \"Athlete\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "AU1UP-AMB7nn",
+    "outputId": "d9f165a0-3c34-497a-f1e0-e0df64d77f3f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8605"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(merged_inner)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "6PjIGzGeB7no",
+    "outputId": "6855a776-2c16-4901-eef1-04e916417f04"
+   },
+   "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>Athlete</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Closing Ceremony Date</th>\n",
+       "      <th>Gold Medals</th>\n",
+       "      <th>Silver Medals</th>\n",
+       "      <th>Bronze Medals</th>\n",
+       "      <th>Total Medals</th>\n",
+       "      <th>Country</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Natalie Coughlin</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>08/24/2008</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Natalie Coughlin</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>08/29/2004</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Natalie Coughlin</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>08/12/2012</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Aleksey Nemov</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>10/01/2000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Russia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Alicia Coutts</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>08/12/2012</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Australia</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
+       "0  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
+       "1  Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
+       "2  Natalie Coughlin  29.0  2012            08/12/2012            0   \n",
+       "3     Aleksey Nemov  24.0  2000            10/01/2000            2   \n",
+       "4     Alicia Coutts  24.0  2012            08/12/2012            1   \n",
+       "\n",
+       "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
+       "0              2              3             6  United States  \n",
+       "1              2              1             5  United States  \n",
+       "2              0              1             1  United States  \n",
+       "3              1              3             6         Russia  \n",
+       "4              3              1             5      Australia  "
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_inner.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "i7mkMH5bB7np"
+   },
+   "source": [
+    "## Left Join\n",
+    "* Devuelve un data frame con las filas que tuvieran valor en el dataset de la izquierda, sin importar si tienen correspondencia en el de la derecha o no.\n",
+    "* Las filas del data frame final que no correspondan a ninguna fila del data frame derecho, tendrán NAs en las columnas del data frame derecho.\n",
+    "* El número de filas será igual al número de filas del data frame izquierdo\n",
+    "    * Data Set A tiene 60 filas\n",
+    "    * Data Set B tiene 50 filas\n",
+    "    * Entonces A Left Join B tendrá 60 filas\n",
+    "* En términos de teoría de conjuntos, se trata del propio data set de la izquierda quien, además tiene la intersección en su interior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ZzD8AKuOB7np",
+    "outputId": "916eacb0-59c6-4c5a-d899-4a03d7e76c3a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename=\"resources/left-join.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "0rCjZZvNB7nq",
+    "outputId": "9337b43b-9843-4141-f7b1-a9d28aed3a2e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8618"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_left = pd.merge(left = data_main, right = data_country_dlt, \n",
+    "                      how = \"left\", left_on = \"Athlete\", right_on = \"Athlete\")\n",
+    "len(merged_left)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "P5JPJxy3B7nr",
+    "outputId": "acc64683-99ca-4e0e-eddc-4d315a2aa1a2"
+   },
+   "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>Athlete</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Closing Ceremony Date</th>\n",
+       "      <th>Gold Medals</th>\n",
+       "      <th>Silver Medals</th>\n",
+       "      <th>Bronze Medals</th>\n",
+       "      <th>Total Medals</th>\n",
+       "      <th>Country</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>08/24/2008</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>2004</td>\n",
+       "      <td>08/29/2004</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>08/12/2012</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Natalie Coughlin</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>08/24/2008</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Aleksey Nemov</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>2000</td>\n",
+       "      <td>10/01/2000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>Russia</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
+       "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
+       "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
+       "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
+       "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
+       "4     Aleksey Nemov  24.0  2000            10/01/2000            2   \n",
+       "\n",
+       "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
+       "0              0              0             8            NaN  \n",
+       "1              0              2             8            NaN  \n",
+       "2              2              0             6            NaN  \n",
+       "3              2              3             6  United States  \n",
+       "4              1              3             6         Russia  "
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_left.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "WmiHHgLtB7ns"
+   },
+   "source": [
+    "## Right Join\n",
+    "* Devuelve un data frame con las filas que tuvieran valor en el dataset de la derecha, sin importar si tienen correspondencia en el de la izquierda o no.\n",
+    "* Las filas del data frame final que no correspondan a ninguna fila del data frame izquierdo, tendrán NAs en las columnas del data frame izquierdo.\n",
+    "* El número de filas será igual al número de filas del data frame derecho\n",
+    "    * Data Set A tiene 60 filas\n",
+    "    * Data Set B tiene 50 filas\n",
+    "    * Entonces A Right Join B tendrá 50 filas\n",
+    "* En términos de teoría de conjuntos, se trata del propio data set de la derecha quien, además tiene la intersección en su interior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "C-QZVmbPB7ns",
+    "outputId": "eadae510-9912-4177-be66-07a951dce98a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename=\"resources/right-join.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "hilGXlhsB7ns",
+    "outputId": "2eabd046-bf18-4f3b-cfea-d103828a093c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8612"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_right = pd.merge(left = data_main_dlt, right = data_country_dp,\n",
+    "                       how = \"right\", left_on = \"Athlete\", right_on = \"Athlete\")\n",
+    "len(merged_right)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "VwxuaheBB7nt",
+    "outputId": "8c34cce6-f608-4317-bcc2-16d75fa3466f"
+   },
+   "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>Athlete</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Closing Ceremony Date</th>\n",
+       "      <th>Gold Medals</th>\n",
+       "      <th>Silver Medals</th>\n",
+       "      <th>Bronze Medals</th>\n",
+       "      <th>Total Medals</th>\n",
+       "      <th>Country</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8602</th>\n",
+       "      <td>Wietse van Alten</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>10/01/2000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Netherlands</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8603</th>\n",
+       "      <td>Sandra Wagner-Sachse</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>10/01/2000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Germany</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8604</th>\n",
+       "      <td>Rod White</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>10/01/2000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8605</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8606</th>\n",
+       "      <td>Erzsébet Márkus-Peresztegi</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Hungary</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8607</th>\n",
+       "      <td>Erik Vendt</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8608</th>\n",
+       "      <td>Oscar Braison</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Cuba</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8609</th>\n",
+       "      <td>Alfredo Despaigne</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Cuba</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8610</th>\n",
+       "      <td>Belinda Snell</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Australia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8611</th>\n",
+       "      <td>Yuliya Zaripova</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Russia</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                         Athlete   Age    Year Closing Ceremony Date  \\\n",
+       "8602            Wietse van Alten  21.0  2000.0            10/01/2000   \n",
+       "8603        Sandra Wagner-Sachse  31.0  2000.0            10/01/2000   \n",
+       "8604                   Rod White  23.0  2000.0            10/01/2000   \n",
+       "8605              Michael Phelps   NaN     NaN                   NaN   \n",
+       "8606  Erzsébet Márkus-Peresztegi   NaN     NaN                   NaN   \n",
+       "8607                  Erik Vendt   NaN     NaN                   NaN   \n",
+       "8608               Oscar Braison   NaN     NaN                   NaN   \n",
+       "8609           Alfredo Despaigne   NaN     NaN                   NaN   \n",
+       "8610               Belinda Snell   NaN     NaN                   NaN   \n",
+       "8611             Yuliya Zaripova   NaN     NaN                   NaN   \n",
+       "\n",
+       "      Gold Medals  Silver Medals  Bronze Medals  Total Medals        Country  \n",
+       "8602          0.0            0.0            1.0           1.0    Netherlands  \n",
+       "8603          0.0            0.0            1.0           1.0        Germany  \n",
+       "8604          0.0            0.0            1.0           1.0  United States  \n",
+       "8605          NaN            NaN            NaN           NaN  United States  \n",
+       "8606          NaN            NaN            NaN           NaN        Hungary  \n",
+       "8607          NaN            NaN            NaN           NaN  United States  \n",
+       "8608          NaN            NaN            NaN           NaN           Cuba  \n",
+       "8609          NaN            NaN            NaN           NaN           Cuba  \n",
+       "8610          NaN            NaN            NaN           NaN      Australia  \n",
+       "8611          NaN            NaN            NaN           NaN         Russia  "
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_right.tail(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "085GEBDCB7nu"
+   },
+   "source": [
+    "## Outer Join\n",
+    "* Devuelve un data frame con todas las filas de ambos, reemplazando las ausencias de uno o de otro con NAs en la región específica..\n",
+    "* Las filas del data frame final que no correspondan a ninguna fila del data frame derecho (o izquierdo), tendrán NAs en las columnas del data frame derecho (o izquierdo).\n",
+    "* El número de filas será igual al máximo número de filas de ambos data frames\n",
+    "    * Data Set A tiene 60 filas\n",
+    "    * Data Set B tiene 50 filas\n",
+    "    * Ambos comparten 30 filas\n",
+    "    * Entonces A Outer Join B tendrá 60 + 50 - 30 = 80 filas\n",
+    "* En términos de teoría de conjuntos, se trata de la unión de conjuntos."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "CCCnyLZHB7nu",
+    "outputId": "d56db534-dd3d-4e3f-82c7-a92b8a5f7896"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Image(filename=\"resources/outer-join.png\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "eYbTeseVB7nv"
+   },
+   "outputs": [],
+   "source": [
+    "data_country_jb = data_country_dlt.append(\n",
+    "    {\n",
+    "        \"Athlete\": \"Juan Gabriel Gomila\",\n",
+    "        \"Country\": \"España\"\n",
+    "    },ignore_index = True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "ggvPoPn9B7nv",
+    "outputId": "933f0eab-70be-4fc1-ee5a-ef14e4ec712c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8619"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_outer = pd.merge(left = data_main, right=data_country_jb,\n",
+    "                       how = \"outer\", left_on = \"Athlete\", right_on=\"Athlete\")\n",
+    "len(merged_outer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "MPNxMoI7B7nw",
+    "outputId": "cc02d613-1b33-477d-85cb-1cfaa5d3f0da"
+   },
+   "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>Athlete</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Closing Ceremony Date</th>\n",
+       "      <th>Gold Medals</th>\n",
+       "      <th>Silver Medals</th>\n",
+       "      <th>Bronze Medals</th>\n",
+       "      <th>Total Medals</th>\n",
+       "      <th>Country</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>2008.0</td>\n",
+       "      <td>08/24/2008</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>2004.0</td>\n",
+       "      <td>08/29/2004</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Michael Phelps</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>2012.0</td>\n",
+       "      <td>08/12/2012</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Natalie Coughlin</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>2008.0</td>\n",
+       "      <td>08/24/2008</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Natalie Coughlin</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>2004.0</td>\n",
+       "      <td>08/29/2004</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Athlete   Age    Year Closing Ceremony Date  Gold Medals  \\\n",
+       "0    Michael Phelps  23.0  2008.0            08/24/2008          8.0   \n",
+       "1    Michael Phelps  19.0  2004.0            08/29/2004          6.0   \n",
+       "2    Michael Phelps  27.0  2012.0            08/12/2012          4.0   \n",
+       "3  Natalie Coughlin  25.0  2008.0            08/24/2008          1.0   \n",
+       "4  Natalie Coughlin  21.0  2004.0            08/29/2004          2.0   \n",
+       "\n",
+       "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
+       "0            0.0            0.0           8.0            NaN  \n",
+       "1            0.0            2.0           8.0            NaN  \n",
+       "2            2.0            0.0           6.0            NaN  \n",
+       "3            2.0            3.0           6.0  United States  \n",
+       "4            2.0            1.0           5.0  United States  "
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_outer.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "eLVu22y7B7nw",
+    "outputId": "1c5e82e7-ea35-42b0-dcaf-a3cac78e39c0"
+   },
+   "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>Athlete</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Closing Ceremony Date</th>\n",
+       "      <th>Gold Medals</th>\n",
+       "      <th>Silver Medals</th>\n",
+       "      <th>Bronze Medals</th>\n",
+       "      <th>Total Medals</th>\n",
+       "      <th>Country</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>8614</th>\n",
+       "      <td>Kateryna Serdiuk</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>10/01/2000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Ukraine</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8615</th>\n",
+       "      <td>Wietse van Alten</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>10/01/2000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Netherlands</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8616</th>\n",
+       "      <td>Sandra Wagner-Sachse</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>10/01/2000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Germany</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8617</th>\n",
+       "      <td>Rod White</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>2000.0</td>\n",
+       "      <td>10/01/2000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>United States</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8618</th>\n",
+       "      <td>Juan Gabriel Gomila</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>España</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Athlete   Age    Year Closing Ceremony Date  Gold Medals  \\\n",
+       "8614      Kateryna Serdiuk  17.0  2000.0            10/01/2000          0.0   \n",
+       "8615      Wietse van Alten  21.0  2000.0            10/01/2000          0.0   \n",
+       "8616  Sandra Wagner-Sachse  31.0  2000.0            10/01/2000          0.0   \n",
+       "8617             Rod White  23.0  2000.0            10/01/2000          0.0   \n",
+       "8618   Juan Gabriel Gomila   NaN     NaN                   NaN          NaN   \n",
+       "\n",
+       "      Silver Medals  Bronze Medals  Total Medals        Country  \n",
+       "8614            1.0            0.0           1.0        Ukraine  \n",
+       "8615            0.0            1.0           1.0    Netherlands  \n",
+       "8616            0.0            1.0           1.0        Germany  \n",
+       "8617            0.0            1.0           1.0  United States  \n",
+       "8618            NaN            NaN           NaN         España  "
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "merged_outer.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "p2OX_U-uB7nx",
+    "outputId": "62542f35-e2f2-4891-8005-58dc2cad099c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8618"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_main)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "jNWhMGyrB7ny",
+    "outputId": "0fbb71e5-6a5c-4982-eddf-e35b9fd9f0fb"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8605"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_main_dlt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "pVVKSDe8B7ny",
+    "outputId": "5f8177f6-1463-4810-86c6-ff1eb4920675"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6956"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_country_dp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "SbLPsMXmB7nz",
+    "outputId": "ae7ca8d1-5cd6-4e84-9ba9-ead709569724"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6949"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_country_dlt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "JM2b7R_DB7n0",
+    "outputId": "61d34041-00cd-44c8-c0e7-971443baa512"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8605"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(merged_inner)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "CUYARPzMB7n1",
+    "outputId": "9624c61b-de77-4df9-b8b0-429da532787f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8618"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(merged_left)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Jg7o_yuUB7n1",
+    "outputId": "abee5bfc-a9e6-4841-9378-d0fef3d305ec"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8612"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(merged_right)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 0,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "3MlUiCAzB7n2",
+    "outputId": "1b45798c-450f-47ca-9fe4-37491598b4a5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8619"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {
+      "tags": []
+     },
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(merged_outer)"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "include_colab_link": true,
+   "name": "T2 - 4 - Data Cleaning - Concatenación de datos.ipynb",
+   "provenance": [],
+   "toc_visible": true
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git "a/notebooks/T2 - 4 - Data Cleaning - Concatenaci\303\263n de datos.ipynb" "b/notebooks/T2 - 4 - Data Cleaning - Concatenaci\303\263n de datos.ipynb"
index 2d633501..c4a8c095 100644
--- "a/notebooks/T2 - 4 - Data Cleaning - Concatenaci\303\263n de datos.ipynb"	
+++ "b/notebooks/T2 - 4 - Data Cleaning - Concatenaci\303\263n de datos.ipynb"	
@@ -1,4551 +1,4991 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Concatenar y apendizar data sets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## El ejemplo del vino blanco y el vino tinto"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Data Set Information:\n",
-    "\n",
-    "These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. \n",
-    "\n",
-    "I think that the initial data set had around 30 variables, but for some reason I only have the 13 dimensional version. I had a list of what the 30 or so variables were, but a.) I lost it, and b.), I would not know which 13 variables are included in the set. \n",
-    "\n",
-    "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it ) \n",
-    "\n",
-    "1. Alcohol \n",
-    "2. Malic acid \n",
-    "3. Ash \n",
-    "4. Alcalinity of ash \n",
-    "5. Magnesium \n",
-    "6. Total phenols \n",
-    "7. Flavanoids \n",
-    "8. Nonflavanoid phenols \n",
-    "9. Proanthocyanins \n",
-    "10. Color intensity \n",
-    "11. Hue \n",
-    "12. OD280/OD315 of diluted wines \n",
-    "13. Proline \n",
-    "\n",
-    "In a classification context, this is a well posed problem with \"well behaved\" class structures. A good data set for first testing of a new classifier, but not very challenging.\n",
-    "\n",
-    "\n",
-    "Attribute Information:\n",
-    "\n",
-    "All attributes are continuous \n",
-    "\n",
-    "No statistics available, but suggest to standardise variables for certain uses (e.g. for us with classifiers which are NOT scale invariant) \n",
-    "\n",
-    "NOTE: 1st attribute is class identifier (1-3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "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": "3.6.4"
+    },
+    "colab": {
+      "name": "T2 - 4 - Data Cleaning - Concatenación de datos.ipynb",
+      "provenance": [],
+      "toc_visible": true,
+      "include_colab_link": true
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/Collab---v-3.8/notebooks/T2%20-%204%20-%20Data%20Cleaning%20-%20Concatenaci%C3%B3n%20de%20datos.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l4rY9ejJB7mV",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Concatenar y apendizar data sets"
+      ]
+    },
     {
-     "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>fixed acidity</th>\n",
-       "      <th>volatile acidity</th>\n",
-       "      <th>citric acid</th>\n",
-       "      <th>residual sugar</th>\n",
-       "      <th>chlorides</th>\n",
-       "      <th>free sulfur dioxide</th>\n",
-       "      <th>total sulfur dioxide</th>\n",
-       "      <th>density</th>\n",
-       "      <th>pH</th>\n",
-       "      <th>sulphates</th>\n",
-       "      <th>alcohol</th>\n",
-       "      <th>quality</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.9</td>\n",
-       "      <td>0.076</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.9978</td>\n",
-       "      <td>3.51</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>7.8</td>\n",
-       "      <td>0.88</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>0.098</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>0.9968</td>\n",
-       "      <td>3.20</td>\n",
-       "      <td>0.68</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>7.8</td>\n",
-       "      <td>0.76</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>0.092</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>0.9970</td>\n",
-       "      <td>3.26</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11.2</td>\n",
-       "      <td>0.28</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>1.9</td>\n",
-       "      <td>0.075</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>0.9980</td>\n",
-       "      <td>3.16</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.9</td>\n",
-       "      <td>0.076</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.9978</td>\n",
-       "      <td>3.51</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "tg49jgbGB-P2",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 122
+        },
+        "outputId": "42f076f4-2db4-4619-89ee-5643f3dab4b6"
+      },
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
       ],
-      "text/plain": [
-       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
-       "0            7.4              0.70         0.00             1.9      0.076   \n",
-       "1            7.8              0.88         0.00             2.6      0.098   \n",
-       "2            7.8              0.76         0.04             2.3      0.092   \n",
-       "3           11.2              0.28         0.56             1.9      0.075   \n",
-       "4            7.4              0.70         0.00             1.9      0.076   \n",
-       "\n",
-       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
-       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
-       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
-       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
-       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
-       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
-       "\n",
-       "   alcohol  quality  \n",
-       "0      9.4        5  \n",
-       "1      9.8        5  \n",
-       "2      9.8        5  \n",
-       "3      9.8        6  \n",
-       "4      9.4        5  "
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+            "\n",
+            "Enter your authorization code:\n",
+            "··········\n",
+            "Mounted at /content/drive\n"
+          ],
+          "name": "stdout"
+        }
       ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "red_wine = pd.read_csv(\"../datasets/wine/winequality-red.csv\", sep=\";\")\n",
-    "red_wine.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array(['fixed acidity', 'volatile acidity', 'citric acid',\n",
-       "       'residual sugar', 'chlorides', 'free sulfur dioxide',\n",
-       "       'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol',\n",
-       "       'quality'], dtype=object)"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GglD0HFUB7mX",
+        "colab_type": "text"
+      },
+      "source": [
+        "## El ejemplo del vino blanco y el vino tinto"
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "red_wine.columns.values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(1599, 12)"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mR7XsaHYB7mX",
+        "colab_type": "text"
+      },
+      "source": [
+        "Data Set Information:\n",
+        "\n",
+        "These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines. \n",
+        "\n",
+        "I think that the initial data set had around 30 variables, but for some reason I only have the 13 dimensional version. I had a list of what the 30 or so variables were, but a.) I lost it, and b.), I would not know which 13 variables are included in the set. \n",
+        "\n",
+        "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it ) \n",
+        "\n",
+        "1. Alcohol \n",
+        "2. Malic acid \n",
+        "3. Ash \n",
+        "4. Alcalinity of ash \n",
+        "5. Magnesium \n",
+        "6. Total phenols \n",
+        "7. Flavanoids \n",
+        "8. Nonflavanoid phenols \n",
+        "9. Proanthocyanins \n",
+        "10. Color intensity \n",
+        "11. Hue \n",
+        "12. OD280/OD315 of diluted wines \n",
+        "13. Proline \n",
+        "\n",
+        "In a classification context, this is a well posed problem with \"well behaved\" class structures. A good data set for first testing of a new classifier, but not very challenging.\n",
+        "\n",
+        "\n",
+        "Attribute Information:\n",
+        "\n",
+        "All attributes are continuous \n",
+        "\n",
+        "No statistics available, but suggest to standardise variables for certain uses (e.g. for us with classifiers which are NOT scale invariant) \n",
+        "\n",
+        "NOTE: 1st attribute is class identifier (1-3)"
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "red_wine.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZA6UH67uB7mY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import pandas as pd"
+      ],
+      "execution_count": 0,
+      "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>fixed acidity</th>\n",
-       "      <th>volatile acidity</th>\n",
-       "      <th>citric acid</th>\n",
-       "      <th>residual sugar</th>\n",
-       "      <th>chlorides</th>\n",
-       "      <th>free sulfur dioxide</th>\n",
-       "      <th>total sulfur dioxide</th>\n",
-       "      <th>density</th>\n",
-       "      <th>pH</th>\n",
-       "      <th>sulphates</th>\n",
-       "      <th>alcohol</th>\n",
-       "      <th>quality</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>20.7</td>\n",
-       "      <td>0.045</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>170.0</td>\n",
-       "      <td>1.0010</td>\n",
-       "      <td>3.00</td>\n",
-       "      <td>0.45</td>\n",
-       "      <td>8.8</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>6.3</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>0.34</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>0.049</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>132.0</td>\n",
-       "      <td>0.9940</td>\n",
-       "      <td>3.30</td>\n",
-       "      <td>0.49</td>\n",
-       "      <td>9.5</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>8.1</td>\n",
-       "      <td>0.28</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>6.9</td>\n",
-       "      <td>0.050</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>97.0</td>\n",
-       "      <td>0.9951</td>\n",
-       "      <td>3.26</td>\n",
-       "      <td>0.44</td>\n",
-       "      <td>10.1</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>7.2</td>\n",
-       "      <td>0.23</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>8.5</td>\n",
-       "      <td>0.058</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>186.0</td>\n",
-       "      <td>0.9956</td>\n",
-       "      <td>3.19</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>9.9</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.2</td>\n",
-       "      <td>0.23</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>8.5</td>\n",
-       "      <td>0.058</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>186.0</td>\n",
-       "      <td>0.9956</td>\n",
-       "      <td>3.19</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>9.9</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "h9QWV_oGB7me",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "49b5bdf7-f5b1-4fc9-ec32-a6722379cc76"
+      },
+      "source": [
+        "red_wine = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/wine/winequality-red.csv\", sep=\";\")\n",
+        "red_wine.head()"
       ],
-      "text/plain": [
-       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
-       "0            7.0              0.27         0.36            20.7      0.045   \n",
-       "1            6.3              0.30         0.34             1.6      0.049   \n",
-       "2            8.1              0.28         0.40             6.9      0.050   \n",
-       "3            7.2              0.23         0.32             8.5      0.058   \n",
-       "4            7.2              0.23         0.32             8.5      0.058   \n",
-       "\n",
-       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
-       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
-       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
-       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
-       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
-       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
-       "\n",
-       "   alcohol  quality  \n",
-       "0      8.8        6  \n",
-       "1      9.5        6  \n",
-       "2     10.1        6  \n",
-       "3      9.9        6  \n",
-       "4      9.9        6  "
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>fixed acidity</th>\n",
+              "      <th>volatile acidity</th>\n",
+              "      <th>citric acid</th>\n",
+              "      <th>residual sugar</th>\n",
+              "      <th>chlorides</th>\n",
+              "      <th>free sulfur dioxide</th>\n",
+              "      <th>total sulfur dioxide</th>\n",
+              "      <th>density</th>\n",
+              "      <th>pH</th>\n",
+              "      <th>sulphates</th>\n",
+              "      <th>alcohol</th>\n",
+              "      <th>quality</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.70</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.076</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.9978</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.88</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>0.098</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>67.0</td>\n",
+              "      <td>0.9968</td>\n",
+              "      <td>3.20</td>\n",
+              "      <td>0.68</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.76</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>0.092</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>54.0</td>\n",
+              "      <td>0.9970</td>\n",
+              "      <td>3.26</td>\n",
+              "      <td>0.65</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>11.2</td>\n",
+              "      <td>0.28</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.075</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>0.9980</td>\n",
+              "      <td>3.16</td>\n",
+              "      <td>0.58</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.70</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.076</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.9978</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
+              "0            7.4              0.70         0.00  ...       0.56      9.4        5\n",
+              "1            7.8              0.88         0.00  ...       0.68      9.8        5\n",
+              "2            7.8              0.76         0.04  ...       0.65      9.8        5\n",
+              "3           11.2              0.28         0.56  ...       0.58      9.8        6\n",
+              "4            7.4              0.70         0.00  ...       0.56      9.4        5\n",
+              "\n",
+              "[5 rows x 12 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
       ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "white_wine = pd.read_csv(\"../datasets/wine/winequality-white.csv\", sep = \";\")\n",
-    "white_wine.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array(['fixed acidity', 'volatile acidity', 'citric acid',\n",
-       "       'residual sugar', 'chlorides', 'free sulfur dioxide',\n",
-       "       'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol',\n",
-       "       'quality'], dtype=object)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "jRrVyKgmB7mi",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "7d0af16d-f23a-4f03-f08b-0a7c823ad926"
+      },
+      "source": [
+        "red_wine.columns.values"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['fixed acidity', 'volatile acidity', 'citric acid',\n",
+              "       'residual sugar', 'chlorides', 'free sulfur dioxide',\n",
+              "       'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol',\n",
+              "       'quality'], dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 7
+        }
       ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "white_wine.columns.values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(4898, 12)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "3RVTL50JB7ml",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "84523ae6-48b3-43a7-d9e5-de06c7788567"
+      },
+      "source": [
+        "red_wine.shape"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1599, 12)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 8
+        }
       ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "white_wine.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "En python, tenemos dos tipos de ejes, \n",
-    "* axis = 0 denota el eje horizontal\n",
-    "* axis = 1 denota el eje vertical"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wine_data = pd.concat([red_wine, white_wine], axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(6497, 12)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "zmKgDkZxB7mn",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "c168bdd5-2a93-4185-d463-6fcaaeb9952d"
+      },
+      "source": [
+        "white_wine = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/wine/winequality-white.csv\", sep = \";\")\n",
+        "white_wine.head()"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>fixed acidity</th>\n",
+              "      <th>volatile acidity</th>\n",
+              "      <th>citric acid</th>\n",
+              "      <th>residual sugar</th>\n",
+              "      <th>chlorides</th>\n",
+              "      <th>free sulfur dioxide</th>\n",
+              "      <th>total sulfur dioxide</th>\n",
+              "      <th>density</th>\n",
+              "      <th>pH</th>\n",
+              "      <th>sulphates</th>\n",
+              "      <th>alcohol</th>\n",
+              "      <th>quality</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7.0</td>\n",
+              "      <td>0.27</td>\n",
+              "      <td>0.36</td>\n",
+              "      <td>20.7</td>\n",
+              "      <td>0.045</td>\n",
+              "      <td>45.0</td>\n",
+              "      <td>170.0</td>\n",
+              "      <td>1.0010</td>\n",
+              "      <td>3.00</td>\n",
+              "      <td>0.45</td>\n",
+              "      <td>8.8</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>6.3</td>\n",
+              "      <td>0.30</td>\n",
+              "      <td>0.34</td>\n",
+              "      <td>1.6</td>\n",
+              "      <td>0.049</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>132.0</td>\n",
+              "      <td>0.9940</td>\n",
+              "      <td>3.30</td>\n",
+              "      <td>0.49</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>8.1</td>\n",
+              "      <td>0.28</td>\n",
+              "      <td>0.40</td>\n",
+              "      <td>6.9</td>\n",
+              "      <td>0.050</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>97.0</td>\n",
+              "      <td>0.9951</td>\n",
+              "      <td>3.26</td>\n",
+              "      <td>0.44</td>\n",
+              "      <td>10.1</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>7.2</td>\n",
+              "      <td>0.23</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>0.058</td>\n",
+              "      <td>47.0</td>\n",
+              "      <td>186.0</td>\n",
+              "      <td>0.9956</td>\n",
+              "      <td>3.19</td>\n",
+              "      <td>0.40</td>\n",
+              "      <td>9.9</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>7.2</td>\n",
+              "      <td>0.23</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>8.5</td>\n",
+              "      <td>0.058</td>\n",
+              "      <td>47.0</td>\n",
+              "      <td>186.0</td>\n",
+              "      <td>0.9956</td>\n",
+              "      <td>3.19</td>\n",
+              "      <td>0.40</td>\n",
+              "      <td>9.9</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
+              "0            7.0              0.27         0.36  ...       0.45      8.8        6\n",
+              "1            6.3              0.30         0.34  ...       0.49      9.5        6\n",
+              "2            8.1              0.28         0.40  ...       0.44     10.1        6\n",
+              "3            7.2              0.23         0.32  ...       0.40      9.9        6\n",
+              "4            7.2              0.23         0.32  ...       0.40      9.9        6\n",
+              "\n",
+              "[5 rows x 12 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
       ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wine_data.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "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>fixed acidity</th>\n",
-       "      <th>volatile acidity</th>\n",
-       "      <th>citric acid</th>\n",
-       "      <th>residual sugar</th>\n",
-       "      <th>chlorides</th>\n",
-       "      <th>free sulfur dioxide</th>\n",
-       "      <th>total sulfur dioxide</th>\n",
-       "      <th>density</th>\n",
-       "      <th>pH</th>\n",
-       "      <th>sulphates</th>\n",
-       "      <th>alcohol</th>\n",
-       "      <th>quality</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.9</td>\n",
-       "      <td>0.076</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.9978</td>\n",
-       "      <td>3.51</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>7.8</td>\n",
-       "      <td>0.88</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>0.098</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>0.9968</td>\n",
-       "      <td>3.20</td>\n",
-       "      <td>0.68</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>7.8</td>\n",
-       "      <td>0.76</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>2.3</td>\n",
-       "      <td>0.092</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>0.9970</td>\n",
-       "      <td>3.26</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11.2</td>\n",
-       "      <td>0.28</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>1.9</td>\n",
-       "      <td>0.075</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>0.9980</td>\n",
-       "      <td>3.16</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.9</td>\n",
-       "      <td>0.076</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.9978</td>\n",
-       "      <td>3.51</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "fX16n8MVB7mp",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "0a0e3f49-e3e7-4239-b73a-29e114c6f115"
+      },
+      "source": [
+        "white_wine.columns.values"
       ],
-      "text/plain": [
-       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
-       "0            7.4              0.70         0.00             1.9      0.076   \n",
-       "1            7.8              0.88         0.00             2.6      0.098   \n",
-       "2            7.8              0.76         0.04             2.3      0.092   \n",
-       "3           11.2              0.28         0.56             1.9      0.075   \n",
-       "4            7.4              0.70         0.00             1.9      0.076   \n",
-       "\n",
-       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
-       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
-       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
-       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
-       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
-       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
-       "\n",
-       "   alcohol  quality  \n",
-       "0      9.4        5  \n",
-       "1      9.8        5  \n",
-       "2      9.8        5  \n",
-       "3      9.8        6  \n",
-       "4      9.4        5  "
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['fixed acidity', 'volatile acidity', 'citric acid',\n",
+              "       'residual sugar', 'chlorides', 'free sulfur dioxide',\n",
+              "       'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol',\n",
+              "       'quality'], dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        }
       ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wine_data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data1 = wine_data.head(10)\n",
-    "data2 = wine_data[300:310]\n",
-    "data3 = wine_data.tail(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wine_scramble = pd.concat([data1, data2, data3], axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "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>fixed acidity</th>\n",
-       "      <th>volatile acidity</th>\n",
-       "      <th>citric acid</th>\n",
-       "      <th>residual sugar</th>\n",
-       "      <th>chlorides</th>\n",
-       "      <th>free sulfur dioxide</th>\n",
-       "      <th>total sulfur dioxide</th>\n",
-       "      <th>density</th>\n",
-       "      <th>pH</th>\n",
-       "      <th>sulphates</th>\n",
-       "      <th>alcohol</th>\n",
-       "      <th>quality</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.700</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.90</td>\n",
-       "      <td>0.076</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.99780</td>\n",
-       "      <td>3.51</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>7.8</td>\n",
-       "      <td>0.880</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>2.60</td>\n",
-       "      <td>0.098</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>0.99680</td>\n",
-       "      <td>3.20</td>\n",
-       "      <td>0.68</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>7.8</td>\n",
-       "      <td>0.760</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>2.30</td>\n",
-       "      <td>0.092</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>0.99700</td>\n",
-       "      <td>3.26</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11.2</td>\n",
-       "      <td>0.280</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>1.90</td>\n",
-       "      <td>0.075</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>0.99800</td>\n",
-       "      <td>3.16</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.700</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.90</td>\n",
-       "      <td>0.076</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.99780</td>\n",
-       "      <td>3.51</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.660</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>0.075</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>0.99780</td>\n",
-       "      <td>3.51</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>7.9</td>\n",
-       "      <td>0.600</td>\n",
-       "      <td>0.06</td>\n",
-       "      <td>1.60</td>\n",
-       "      <td>0.069</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>0.99640</td>\n",
-       "      <td>3.30</td>\n",
-       "      <td>0.46</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>7.3</td>\n",
-       "      <td>0.650</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.20</td>\n",
-       "      <td>0.065</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>0.99460</td>\n",
-       "      <td>3.39</td>\n",
-       "      <td>0.47</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>7.8</td>\n",
-       "      <td>0.580</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>0.073</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.99680</td>\n",
-       "      <td>3.36</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>9.5</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>7.5</td>\n",
-       "      <td>0.500</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>6.10</td>\n",
-       "      <td>0.071</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>102.0</td>\n",
-       "      <td>0.99780</td>\n",
-       "      <td>3.35</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>10.5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>7.5</td>\n",
-       "      <td>0.530</td>\n",
-       "      <td>0.06</td>\n",
-       "      <td>2.60</td>\n",
-       "      <td>0.086</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>0.99650</td>\n",
-       "      <td>3.38</td>\n",
-       "      <td>0.59</td>\n",
-       "      <td>10.7</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>301</th>\n",
-       "      <td>11.1</td>\n",
-       "      <td>0.180</td>\n",
-       "      <td>0.48</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>0.068</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>0.99730</td>\n",
-       "      <td>3.22</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>10.1</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>302</th>\n",
-       "      <td>8.3</td>\n",
-       "      <td>0.705</td>\n",
-       "      <td>0.12</td>\n",
-       "      <td>2.60</td>\n",
-       "      <td>0.092</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>0.99940</td>\n",
-       "      <td>3.51</td>\n",
-       "      <td>0.72</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>303</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.670</td>\n",
-       "      <td>0.12</td>\n",
-       "      <td>1.60</td>\n",
-       "      <td>0.186</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>0.99600</td>\n",
-       "      <td>3.39</td>\n",
-       "      <td>0.54</td>\n",
-       "      <td>9.5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>304</th>\n",
-       "      <td>8.4</td>\n",
-       "      <td>0.650</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>2.10</td>\n",
-       "      <td>0.112</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>0.99730</td>\n",
-       "      <td>3.20</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>9.2</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>305</th>\n",
-       "      <td>10.3</td>\n",
-       "      <td>0.530</td>\n",
-       "      <td>0.48</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>0.063</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>0.99980</td>\n",
-       "      <td>3.12</td>\n",
-       "      <td>0.59</td>\n",
-       "      <td>9.3</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>306</th>\n",
-       "      <td>7.6</td>\n",
-       "      <td>0.620</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>0.082</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>0.99660</td>\n",
-       "      <td>3.36</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>307</th>\n",
-       "      <td>10.3</td>\n",
-       "      <td>0.410</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>2.40</td>\n",
-       "      <td>0.213</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>0.99940</td>\n",
-       "      <td>3.19</td>\n",
-       "      <td>0.62</td>\n",
-       "      <td>9.5</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>308</th>\n",
-       "      <td>10.3</td>\n",
-       "      <td>0.430</td>\n",
-       "      <td>0.44</td>\n",
-       "      <td>2.40</td>\n",
-       "      <td>0.214</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.99940</td>\n",
-       "      <td>3.19</td>\n",
-       "      <td>0.63</td>\n",
-       "      <td>9.5</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>309</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.290</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>1.70</td>\n",
-       "      <td>0.062</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>0.99680</td>\n",
-       "      <td>3.41</td>\n",
-       "      <td>0.53</td>\n",
-       "      <td>9.5</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4888</th>\n",
-       "      <td>6.8</td>\n",
-       "      <td>0.220</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>1.20</td>\n",
-       "      <td>0.052</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>0.99330</td>\n",
-       "      <td>3.04</td>\n",
-       "      <td>0.54</td>\n",
-       "      <td>9.2</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4889</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>0.235</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>11.75</td>\n",
-       "      <td>0.030</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>118.0</td>\n",
-       "      <td>0.99540</td>\n",
-       "      <td>3.07</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4890</th>\n",
-       "      <td>6.1</td>\n",
-       "      <td>0.340</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>0.036</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>0.98938</td>\n",
-       "      <td>3.06</td>\n",
-       "      <td>0.44</td>\n",
-       "      <td>11.8</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4891</th>\n",
-       "      <td>5.7</td>\n",
-       "      <td>0.210</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>0.90</td>\n",
-       "      <td>0.038</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>121.0</td>\n",
-       "      <td>0.99074</td>\n",
-       "      <td>3.24</td>\n",
-       "      <td>0.46</td>\n",
-       "      <td>10.6</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4892</th>\n",
-       "      <td>6.5</td>\n",
-       "      <td>0.230</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>1.30</td>\n",
-       "      <td>0.032</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>0.99298</td>\n",
-       "      <td>3.29</td>\n",
-       "      <td>0.54</td>\n",
-       "      <td>9.7</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4893</th>\n",
-       "      <td>6.2</td>\n",
-       "      <td>0.210</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>1.60</td>\n",
-       "      <td>0.039</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>0.99114</td>\n",
-       "      <td>3.27</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>11.2</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4894</th>\n",
-       "      <td>6.6</td>\n",
-       "      <td>0.320</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>8.00</td>\n",
-       "      <td>0.047</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>168.0</td>\n",
-       "      <td>0.99490</td>\n",
-       "      <td>3.15</td>\n",
-       "      <td>0.46</td>\n",
-       "      <td>9.6</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4895</th>\n",
-       "      <td>6.5</td>\n",
-       "      <td>0.240</td>\n",
-       "      <td>0.19</td>\n",
-       "      <td>1.20</td>\n",
-       "      <td>0.041</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>111.0</td>\n",
-       "      <td>0.99254</td>\n",
-       "      <td>2.99</td>\n",
-       "      <td>0.46</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4896</th>\n",
-       "      <td>5.5</td>\n",
-       "      <td>0.290</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>1.10</td>\n",
-       "      <td>0.022</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>110.0</td>\n",
-       "      <td>0.98869</td>\n",
-       "      <td>3.34</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>12.8</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4897</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.210</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>0.020</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>0.98941</td>\n",
-       "      <td>3.26</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>11.8</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "V9A6kxFaB7mr",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "32d554d4-d056-4ac4-f509-8a8e3211d386"
+      },
+      "source": [
+        "white_wine.shape"
       ],
-      "text/plain": [
-       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
-       "0               7.4             0.700         0.00            1.90      0.076   \n",
-       "1               7.8             0.880         0.00            2.60      0.098   \n",
-       "2               7.8             0.760         0.04            2.30      0.092   \n",
-       "3              11.2             0.280         0.56            1.90      0.075   \n",
-       "4               7.4             0.700         0.00            1.90      0.076   \n",
-       "5               7.4             0.660         0.00            1.80      0.075   \n",
-       "6               7.9             0.600         0.06            1.60      0.069   \n",
-       "7               7.3             0.650         0.00            1.20      0.065   \n",
-       "8               7.8             0.580         0.02            2.00      0.073   \n",
-       "9               7.5             0.500         0.36            6.10      0.071   \n",
-       "300             7.5             0.530         0.06            2.60      0.086   \n",
-       "301            11.1             0.180         0.48            1.50      0.068   \n",
-       "302             8.3             0.705         0.12            2.60      0.092   \n",
-       "303             7.4             0.670         0.12            1.60      0.186   \n",
-       "304             8.4             0.650         0.60            2.10      0.112   \n",
-       "305            10.3             0.530         0.48            2.50      0.063   \n",
-       "306             7.6             0.620         0.32            2.20      0.082   \n",
-       "307            10.3             0.410         0.42            2.40      0.213   \n",
-       "308            10.3             0.430         0.44            2.40      0.214   \n",
-       "309             7.4             0.290         0.38            1.70      0.062   \n",
-       "4888            6.8             0.220         0.36            1.20      0.052   \n",
-       "4889            4.9             0.235         0.27           11.75      0.030   \n",
-       "4890            6.1             0.340         0.29            2.20      0.036   \n",
-       "4891            5.7             0.210         0.32            0.90      0.038   \n",
-       "4892            6.5             0.230         0.38            1.30      0.032   \n",
-       "4893            6.2             0.210         0.29            1.60      0.039   \n",
-       "4894            6.6             0.320         0.36            8.00      0.047   \n",
-       "4895            6.5             0.240         0.19            1.20      0.041   \n",
-       "4896            5.5             0.290         0.30            1.10      0.022   \n",
-       "4897            6.0             0.210         0.38            0.80      0.020   \n",
-       "\n",
-       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
-       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
-       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
-       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
-       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
-       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
-       "5                    13.0                  40.0  0.99780  3.51       0.56   \n",
-       "6                    15.0                  59.0  0.99640  3.30       0.46   \n",
-       "7                    15.0                  21.0  0.99460  3.39       0.47   \n",
-       "8                     9.0                  18.0  0.99680  3.36       0.57   \n",
-       "9                    17.0                 102.0  0.99780  3.35       0.80   \n",
-       "300                  20.0                  44.0  0.99650  3.38       0.59   \n",
-       "301                   7.0                  15.0  0.99730  3.22       0.64   \n",
-       "302                  12.0                  28.0  0.99940  3.51       0.72   \n",
-       "303                   5.0                  21.0  0.99600  3.39       0.54   \n",
-       "304                  12.0                  90.0  0.99730  3.20       0.52   \n",
-       "305                   6.0                  25.0  0.99980  3.12       0.59   \n",
-       "306                   7.0                  54.0  0.99660  3.36       0.52   \n",
-       "307                   6.0                  14.0  0.99940  3.19       0.62   \n",
-       "308                   5.0                  12.0  0.99940  3.19       0.63   \n",
-       "309                   9.0                  30.0  0.99680  3.41       0.53   \n",
-       "4888                 38.0                 127.0  0.99330  3.04       0.54   \n",
-       "4889                 34.0                 118.0  0.99540  3.07       0.50   \n",
-       "4890                 25.0                 100.0  0.98938  3.06       0.44   \n",
-       "4891                 38.0                 121.0  0.99074  3.24       0.46   \n",
-       "4892                 29.0                 112.0  0.99298  3.29       0.54   \n",
-       "4893                 24.0                  92.0  0.99114  3.27       0.50   \n",
-       "4894                 57.0                 168.0  0.99490  3.15       0.46   \n",
-       "4895                 30.0                 111.0  0.99254  2.99       0.46   \n",
-       "4896                 20.0                 110.0  0.98869  3.34       0.38   \n",
-       "4897                 22.0                  98.0  0.98941  3.26       0.32   \n",
-       "\n",
-       "      alcohol  quality  \n",
-       "0         9.4        5  \n",
-       "1         9.8        5  \n",
-       "2         9.8        5  \n",
-       "3         9.8        6  \n",
-       "4         9.4        5  \n",
-       "5         9.4        5  \n",
-       "6         9.4        5  \n",
-       "7        10.0        7  \n",
-       "8         9.5        7  \n",
-       "9        10.5        5  \n",
-       "300      10.7        6  \n",
-       "301      10.1        6  \n",
-       "302      10.0        5  \n",
-       "303       9.5        5  \n",
-       "304       9.2        5  \n",
-       "305       9.3        6  \n",
-       "306       9.4        5  \n",
-       "307       9.5        6  \n",
-       "308       9.5        6  \n",
-       "309       9.5        6  \n",
-       "4888      9.2        5  \n",
-       "4889      9.4        6  \n",
-       "4890     11.8        6  \n",
-       "4891     10.6        6  \n",
-       "4892      9.7        5  \n",
-       "4893     11.2        6  \n",
-       "4894      9.6        5  \n",
-       "4895      9.4        6  \n",
-       "4896     12.8        7  \n",
-       "4897     11.8        6  "
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(4898, 12)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
       ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wine_scramble"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2QZDLzxwB7ms",
+        "colab_type": "text"
+      },
+      "source": [
+        "En python, tenemos dos tipos de ejes, \n",
+        "* axis = 0 denota el eje horizontal\n",
+        "* axis = 1 denota el eje vertical"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JbNRUeIkB7mt",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "wine_data = pd.concat([red_wine, white_wine], axis = 0)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/html": [
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-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>fixed acidity</th>\n",
-       "      <th>volatile acidity</th>\n",
-       "      <th>citric acid</th>\n",
-       "      <th>residual sugar</th>\n",
-       "      <th>chlorides</th>\n",
-       "      <th>free sulfur dioxide</th>\n",
-       "      <th>total sulfur dioxide</th>\n",
-       "      <th>density</th>\n",
-       "      <th>pH</th>\n",
-       "      <th>sulphates</th>\n",
-       "      <th>alcohol</th>\n",
-       "      <th>quality</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>300</th>\n",
-       "      <td>7.5</td>\n",
-       "      <td>0.530</td>\n",
-       "      <td>0.06</td>\n",
-       "      <td>2.60</td>\n",
-       "      <td>0.086</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>0.99650</td>\n",
-       "      <td>3.38</td>\n",
-       "      <td>0.59</td>\n",
-       "      <td>10.7</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>301</th>\n",
-       "      <td>11.1</td>\n",
-       "      <td>0.180</td>\n",
-       "      <td>0.48</td>\n",
-       "      <td>1.50</td>\n",
-       "      <td>0.068</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>0.99730</td>\n",
-       "      <td>3.22</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>10.1</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>302</th>\n",
-       "      <td>8.3</td>\n",
-       "      <td>0.705</td>\n",
-       "      <td>0.12</td>\n",
-       "      <td>2.60</td>\n",
-       "      <td>0.092</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>0.99940</td>\n",
-       "      <td>3.51</td>\n",
-       "      <td>0.72</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>303</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.670</td>\n",
-       "      <td>0.12</td>\n",
-       "      <td>1.60</td>\n",
-       "      <td>0.186</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>0.99600</td>\n",
-       "      <td>3.39</td>\n",
-       "      <td>0.54</td>\n",
-       "      <td>9.5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>304</th>\n",
-       "      <td>8.4</td>\n",
-       "      <td>0.650</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>2.10</td>\n",
-       "      <td>0.112</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>90.0</td>\n",
-       "      <td>0.99730</td>\n",
-       "      <td>3.20</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>9.2</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>305</th>\n",
-       "      <td>10.3</td>\n",
-       "      <td>0.530</td>\n",
-       "      <td>0.48</td>\n",
-       "      <td>2.50</td>\n",
-       "      <td>0.063</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>0.99980</td>\n",
-       "      <td>3.12</td>\n",
-       "      <td>0.59</td>\n",
-       "      <td>9.3</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>306</th>\n",
-       "      <td>7.6</td>\n",
-       "      <td>0.620</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>0.082</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>0.99660</td>\n",
-       "      <td>3.36</td>\n",
-       "      <td>0.52</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>307</th>\n",
-       "      <td>10.3</td>\n",
-       "      <td>0.410</td>\n",
-       "      <td>0.42</td>\n",
-       "      <td>2.40</td>\n",
-       "      <td>0.213</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>0.99940</td>\n",
-       "      <td>3.19</td>\n",
-       "      <td>0.62</td>\n",
-       "      <td>9.5</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>308</th>\n",
-       "      <td>10.3</td>\n",
-       "      <td>0.430</td>\n",
-       "      <td>0.44</td>\n",
-       "      <td>2.40</td>\n",
-       "      <td>0.214</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.99940</td>\n",
-       "      <td>3.19</td>\n",
-       "      <td>0.63</td>\n",
-       "      <td>9.5</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>309</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.290</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>1.70</td>\n",
-       "      <td>0.062</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>0.99680</td>\n",
-       "      <td>3.41</td>\n",
-       "      <td>0.53</td>\n",
-       "      <td>9.5</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.700</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.90</td>\n",
-       "      <td>0.076</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.99780</td>\n",
-       "      <td>3.51</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>7.8</td>\n",
-       "      <td>0.880</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>2.60</td>\n",
-       "      <td>0.098</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>0.99680</td>\n",
-       "      <td>3.20</td>\n",
-       "      <td>0.68</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>7.8</td>\n",
-       "      <td>0.760</td>\n",
-       "      <td>0.04</td>\n",
-       "      <td>2.30</td>\n",
-       "      <td>0.092</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>54.0</td>\n",
-       "      <td>0.99700</td>\n",
-       "      <td>3.26</td>\n",
-       "      <td>0.65</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>11.2</td>\n",
-       "      <td>0.280</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>1.90</td>\n",
-       "      <td>0.075</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>0.99800</td>\n",
-       "      <td>3.16</td>\n",
-       "      <td>0.58</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.700</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.90</td>\n",
-       "      <td>0.076</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.99780</td>\n",
-       "      <td>3.51</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>7.4</td>\n",
-       "      <td>0.660</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.80</td>\n",
-       "      <td>0.075</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>0.99780</td>\n",
-       "      <td>3.51</td>\n",
-       "      <td>0.56</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>7.9</td>\n",
-       "      <td>0.600</td>\n",
-       "      <td>0.06</td>\n",
-       "      <td>1.60</td>\n",
-       "      <td>0.069</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>0.99640</td>\n",
-       "      <td>3.30</td>\n",
-       "      <td>0.46</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>7.3</td>\n",
-       "      <td>0.650</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>1.20</td>\n",
-       "      <td>0.065</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>0.99460</td>\n",
-       "      <td>3.39</td>\n",
-       "      <td>0.47</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>7.8</td>\n",
-       "      <td>0.580</td>\n",
-       "      <td>0.02</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>0.073</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.99680</td>\n",
-       "      <td>3.36</td>\n",
-       "      <td>0.57</td>\n",
-       "      <td>9.5</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>7.5</td>\n",
-       "      <td>0.500</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>6.10</td>\n",
-       "      <td>0.071</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>102.0</td>\n",
-       "      <td>0.99780</td>\n",
-       "      <td>3.35</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>10.5</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4888</th>\n",
-       "      <td>6.8</td>\n",
-       "      <td>0.220</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>1.20</td>\n",
-       "      <td>0.052</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>127.0</td>\n",
-       "      <td>0.99330</td>\n",
-       "      <td>3.04</td>\n",
-       "      <td>0.54</td>\n",
-       "      <td>9.2</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4889</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>0.235</td>\n",
-       "      <td>0.27</td>\n",
-       "      <td>11.75</td>\n",
-       "      <td>0.030</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>118.0</td>\n",
-       "      <td>0.99540</td>\n",
-       "      <td>3.07</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4890</th>\n",
-       "      <td>6.1</td>\n",
-       "      <td>0.340</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>2.20</td>\n",
-       "      <td>0.036</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>100.0</td>\n",
-       "      <td>0.98938</td>\n",
-       "      <td>3.06</td>\n",
-       "      <td>0.44</td>\n",
-       "      <td>11.8</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4891</th>\n",
-       "      <td>5.7</td>\n",
-       "      <td>0.210</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>0.90</td>\n",
-       "      <td>0.038</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>121.0</td>\n",
-       "      <td>0.99074</td>\n",
-       "      <td>3.24</td>\n",
-       "      <td>0.46</td>\n",
-       "      <td>10.6</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4892</th>\n",
-       "      <td>6.5</td>\n",
-       "      <td>0.230</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>1.30</td>\n",
-       "      <td>0.032</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>112.0</td>\n",
-       "      <td>0.99298</td>\n",
-       "      <td>3.29</td>\n",
-       "      <td>0.54</td>\n",
-       "      <td>9.7</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4893</th>\n",
-       "      <td>6.2</td>\n",
-       "      <td>0.210</td>\n",
-       "      <td>0.29</td>\n",
-       "      <td>1.60</td>\n",
-       "      <td>0.039</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>92.0</td>\n",
-       "      <td>0.99114</td>\n",
-       "      <td>3.27</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>11.2</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4894</th>\n",
-       "      <td>6.6</td>\n",
-       "      <td>0.320</td>\n",
-       "      <td>0.36</td>\n",
-       "      <td>8.00</td>\n",
-       "      <td>0.047</td>\n",
-       "      <td>57.0</td>\n",
-       "      <td>168.0</td>\n",
-       "      <td>0.99490</td>\n",
-       "      <td>3.15</td>\n",
-       "      <td>0.46</td>\n",
-       "      <td>9.6</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4895</th>\n",
-       "      <td>6.5</td>\n",
-       "      <td>0.240</td>\n",
-       "      <td>0.19</td>\n",
-       "      <td>1.20</td>\n",
-       "      <td>0.041</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>111.0</td>\n",
-       "      <td>0.99254</td>\n",
-       "      <td>2.99</td>\n",
-       "      <td>0.46</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4896</th>\n",
-       "      <td>5.5</td>\n",
-       "      <td>0.290</td>\n",
-       "      <td>0.30</td>\n",
-       "      <td>1.10</td>\n",
-       "      <td>0.022</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>110.0</td>\n",
-       "      <td>0.98869</td>\n",
-       "      <td>3.34</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>12.8</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4897</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.210</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>0.80</td>\n",
-       "      <td>0.020</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>98.0</td>\n",
-       "      <td>0.98941</td>\n",
-       "      <td>3.26</td>\n",
-       "      <td>0.32</td>\n",
-       "      <td>11.8</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "Qwtl3GfQB7mu",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "6441ce9a-9dd6-4ecf-cd3a-647b6ffa5806"
+      },
+      "source": [
+        "wine_data.shape"
       ],
-      "text/plain": [
-       "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
-       "300             7.5             0.530         0.06            2.60      0.086   \n",
-       "301            11.1             0.180         0.48            1.50      0.068   \n",
-       "302             8.3             0.705         0.12            2.60      0.092   \n",
-       "303             7.4             0.670         0.12            1.60      0.186   \n",
-       "304             8.4             0.650         0.60            2.10      0.112   \n",
-       "305            10.3             0.530         0.48            2.50      0.063   \n",
-       "306             7.6             0.620         0.32            2.20      0.082   \n",
-       "307            10.3             0.410         0.42            2.40      0.213   \n",
-       "308            10.3             0.430         0.44            2.40      0.214   \n",
-       "309             7.4             0.290         0.38            1.70      0.062   \n",
-       "0               7.4             0.700         0.00            1.90      0.076   \n",
-       "1               7.8             0.880         0.00            2.60      0.098   \n",
-       "2               7.8             0.760         0.04            2.30      0.092   \n",
-       "3              11.2             0.280         0.56            1.90      0.075   \n",
-       "4               7.4             0.700         0.00            1.90      0.076   \n",
-       "5               7.4             0.660         0.00            1.80      0.075   \n",
-       "6               7.9             0.600         0.06            1.60      0.069   \n",
-       "7               7.3             0.650         0.00            1.20      0.065   \n",
-       "8               7.8             0.580         0.02            2.00      0.073   \n",
-       "9               7.5             0.500         0.36            6.10      0.071   \n",
-       "4888            6.8             0.220         0.36            1.20      0.052   \n",
-       "4889            4.9             0.235         0.27           11.75      0.030   \n",
-       "4890            6.1             0.340         0.29            2.20      0.036   \n",
-       "4891            5.7             0.210         0.32            0.90      0.038   \n",
-       "4892            6.5             0.230         0.38            1.30      0.032   \n",
-       "4893            6.2             0.210         0.29            1.60      0.039   \n",
-       "4894            6.6             0.320         0.36            8.00      0.047   \n",
-       "4895            6.5             0.240         0.19            1.20      0.041   \n",
-       "4896            5.5             0.290         0.30            1.10      0.022   \n",
-       "4897            6.0             0.210         0.38            0.80      0.020   \n",
-       "\n",
-       "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
-       "300                  20.0                  44.0  0.99650  3.38       0.59   \n",
-       "301                   7.0                  15.0  0.99730  3.22       0.64   \n",
-       "302                  12.0                  28.0  0.99940  3.51       0.72   \n",
-       "303                   5.0                  21.0  0.99600  3.39       0.54   \n",
-       "304                  12.0                  90.0  0.99730  3.20       0.52   \n",
-       "305                   6.0                  25.0  0.99980  3.12       0.59   \n",
-       "306                   7.0                  54.0  0.99660  3.36       0.52   \n",
-       "307                   6.0                  14.0  0.99940  3.19       0.62   \n",
-       "308                   5.0                  12.0  0.99940  3.19       0.63   \n",
-       "309                   9.0                  30.0  0.99680  3.41       0.53   \n",
-       "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
-       "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
-       "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
-       "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
-       "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
-       "5                    13.0                  40.0  0.99780  3.51       0.56   \n",
-       "6                    15.0                  59.0  0.99640  3.30       0.46   \n",
-       "7                    15.0                  21.0  0.99460  3.39       0.47   \n",
-       "8                     9.0                  18.0  0.99680  3.36       0.57   \n",
-       "9                    17.0                 102.0  0.99780  3.35       0.80   \n",
-       "4888                 38.0                 127.0  0.99330  3.04       0.54   \n",
-       "4889                 34.0                 118.0  0.99540  3.07       0.50   \n",
-       "4890                 25.0                 100.0  0.98938  3.06       0.44   \n",
-       "4891                 38.0                 121.0  0.99074  3.24       0.46   \n",
-       "4892                 29.0                 112.0  0.99298  3.29       0.54   \n",
-       "4893                 24.0                  92.0  0.99114  3.27       0.50   \n",
-       "4894                 57.0                 168.0  0.99490  3.15       0.46   \n",
-       "4895                 30.0                 111.0  0.99254  2.99       0.46   \n",
-       "4896                 20.0                 110.0  0.98869  3.34       0.38   \n",
-       "4897                 22.0                  98.0  0.98941  3.26       0.32   \n",
-       "\n",
-       "      alcohol  quality  \n",
-       "300      10.7        6  \n",
-       "301      10.1        6  \n",
-       "302      10.0        5  \n",
-       "303       9.5        5  \n",
-       "304       9.2        5  \n",
-       "305       9.3        6  \n",
-       "306       9.4        5  \n",
-       "307       9.5        6  \n",
-       "308       9.5        6  \n",
-       "309       9.5        6  \n",
-       "0         9.4        5  \n",
-       "1         9.8        5  \n",
-       "2         9.8        5  \n",
-       "3         9.8        6  \n",
-       "4         9.4        5  \n",
-       "5         9.4        5  \n",
-       "6         9.4        5  \n",
-       "7        10.0        7  \n",
-       "8         9.5        7  \n",
-       "9        10.5        5  \n",
-       "4888      9.2        5  \n",
-       "4889      9.4        6  \n",
-       "4890     11.8        6  \n",
-       "4891     10.6        6  \n",
-       "4892      9.7        5  \n",
-       "4893     11.2        6  \n",
-       "4894      9.6        5  \n",
-       "4895      9.4        6  \n",
-       "4896     12.8        7  \n",
-       "4897     11.8        6  "
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(6497, 12)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 13
+        }
       ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wine_scramble = pd.concat([data2, data1, data3], axis = 0)\n",
-    "wine_scramble"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Datos distribuidos "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "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>Date</th>\n",
-       "      <th>sulfate</th>\n",
-       "      <th>nitrate</th>\n",
-       "      <th>ID</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2003-01-01</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2003-01-02</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2003-01-03</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2003-01-04</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2003-01-05</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "rcEXhdlgB7mw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "673ba977-7dee-48ba-9883-1be87b31892a"
+      },
+      "source": [
+        "wine_data.head()"
       ],
-      "text/plain": [
-       "         Date  sulfate  nitrate  ID\n",
-       "0  2003-01-01      NaN      NaN   1\n",
-       "1  2003-01-02      NaN      NaN   1\n",
-       "2  2003-01-03      NaN      NaN   1\n",
-       "3  2003-01-04      NaN      NaN   1\n",
-       "4  2003-01-05      NaN      NaN   1"
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>fixed acidity</th>\n",
+              "      <th>volatile acidity</th>\n",
+              "      <th>citric acid</th>\n",
+              "      <th>residual sugar</th>\n",
+              "      <th>chlorides</th>\n",
+              "      <th>free sulfur dioxide</th>\n",
+              "      <th>total sulfur dioxide</th>\n",
+              "      <th>density</th>\n",
+              "      <th>pH</th>\n",
+              "      <th>sulphates</th>\n",
+              "      <th>alcohol</th>\n",
+              "      <th>quality</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.70</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.076</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.9978</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.88</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>2.6</td>\n",
+              "      <td>0.098</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>67.0</td>\n",
+              "      <td>0.9968</td>\n",
+              "      <td>3.20</td>\n",
+              "      <td>0.68</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.76</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>2.3</td>\n",
+              "      <td>0.092</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>54.0</td>\n",
+              "      <td>0.9970</td>\n",
+              "      <td>3.26</td>\n",
+              "      <td>0.65</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>11.2</td>\n",
+              "      <td>0.28</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.075</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>0.9980</td>\n",
+              "      <td>3.16</td>\n",
+              "      <td>0.58</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.70</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.9</td>\n",
+              "      <td>0.076</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.9978</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "   fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
+              "0            7.4              0.70         0.00  ...       0.56      9.4        5\n",
+              "1            7.8              0.88         0.00  ...       0.68      9.8        5\n",
+              "2            7.8              0.76         0.04  ...       0.65      9.8        5\n",
+              "3           11.2              0.28         0.56  ...       0.58      9.8        6\n",
+              "4            7.4              0.70         0.00  ...       0.56      9.4        5\n",
+              "\n",
+              "[5 rows x 12 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "data = pd.read_csv(\"../datasets/distributed-data/001.csv\")\n",
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "25TexLrLB7mx",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data1 = wine_data.head(10)\n",
+        "data2 = wine_data[300:310]\n",
+        "data3 = wine_data.tail(10)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "(1461, 4)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "-ETslsuHB7my",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "wine_scramble = pd.concat([data1, data2, data3], axis = 0)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QJRAV3kZB7m0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 979
+        },
+        "outputId": "727adb33-3de4-4ada-ef17-bf6d78ccddaa"
+      },
+      "source": [
+        "wine_scramble"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>fixed acidity</th>\n",
+              "      <th>volatile acidity</th>\n",
+              "      <th>citric acid</th>\n",
+              "      <th>residual sugar</th>\n",
+              "      <th>chlorides</th>\n",
+              "      <th>free sulfur dioxide</th>\n",
+              "      <th>total sulfur dioxide</th>\n",
+              "      <th>density</th>\n",
+              "      <th>pH</th>\n",
+              "      <th>sulphates</th>\n",
+              "      <th>alcohol</th>\n",
+              "      <th>quality</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.700</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.90</td>\n",
+              "      <td>0.076</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.99780</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.880</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>2.60</td>\n",
+              "      <td>0.098</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>67.0</td>\n",
+              "      <td>0.99680</td>\n",
+              "      <td>3.20</td>\n",
+              "      <td>0.68</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.760</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>2.30</td>\n",
+              "      <td>0.092</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>54.0</td>\n",
+              "      <td>0.99700</td>\n",
+              "      <td>3.26</td>\n",
+              "      <td>0.65</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>11.2</td>\n",
+              "      <td>0.280</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>1.90</td>\n",
+              "      <td>0.075</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>0.99800</td>\n",
+              "      <td>3.16</td>\n",
+              "      <td>0.58</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.700</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.90</td>\n",
+              "      <td>0.076</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.99780</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.660</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.80</td>\n",
+              "      <td>0.075</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>0.99780</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>7.9</td>\n",
+              "      <td>0.600</td>\n",
+              "      <td>0.06</td>\n",
+              "      <td>1.60</td>\n",
+              "      <td>0.069</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>59.0</td>\n",
+              "      <td>0.99640</td>\n",
+              "      <td>3.30</td>\n",
+              "      <td>0.46</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>7.3</td>\n",
+              "      <td>0.650</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.20</td>\n",
+              "      <td>0.065</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>0.99460</td>\n",
+              "      <td>3.39</td>\n",
+              "      <td>0.47</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.580</td>\n",
+              "      <td>0.02</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>0.073</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>0.99680</td>\n",
+              "      <td>3.36</td>\n",
+              "      <td>0.57</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>7.5</td>\n",
+              "      <td>0.500</td>\n",
+              "      <td>0.36</td>\n",
+              "      <td>6.10</td>\n",
+              "      <td>0.071</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>102.0</td>\n",
+              "      <td>0.99780</td>\n",
+              "      <td>3.35</td>\n",
+              "      <td>0.80</td>\n",
+              "      <td>10.5</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>300</th>\n",
+              "      <td>7.5</td>\n",
+              "      <td>0.530</td>\n",
+              "      <td>0.06</td>\n",
+              "      <td>2.60</td>\n",
+              "      <td>0.086</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>0.99650</td>\n",
+              "      <td>3.38</td>\n",
+              "      <td>0.59</td>\n",
+              "      <td>10.7</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>301</th>\n",
+              "      <td>11.1</td>\n",
+              "      <td>0.180</td>\n",
+              "      <td>0.48</td>\n",
+              "      <td>1.50</td>\n",
+              "      <td>0.068</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>0.99730</td>\n",
+              "      <td>3.22</td>\n",
+              "      <td>0.64</td>\n",
+              "      <td>10.1</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>302</th>\n",
+              "      <td>8.3</td>\n",
+              "      <td>0.705</td>\n",
+              "      <td>0.12</td>\n",
+              "      <td>2.60</td>\n",
+              "      <td>0.092</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>0.99940</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.72</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>303</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.670</td>\n",
+              "      <td>0.12</td>\n",
+              "      <td>1.60</td>\n",
+              "      <td>0.186</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>0.99600</td>\n",
+              "      <td>3.39</td>\n",
+              "      <td>0.54</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>304</th>\n",
+              "      <td>8.4</td>\n",
+              "      <td>0.650</td>\n",
+              "      <td>0.60</td>\n",
+              "      <td>2.10</td>\n",
+              "      <td>0.112</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>90.0</td>\n",
+              "      <td>0.99730</td>\n",
+              "      <td>3.20</td>\n",
+              "      <td>0.52</td>\n",
+              "      <td>9.2</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>305</th>\n",
+              "      <td>10.3</td>\n",
+              "      <td>0.530</td>\n",
+              "      <td>0.48</td>\n",
+              "      <td>2.50</td>\n",
+              "      <td>0.063</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>0.99980</td>\n",
+              "      <td>3.12</td>\n",
+              "      <td>0.59</td>\n",
+              "      <td>9.3</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>306</th>\n",
+              "      <td>7.6</td>\n",
+              "      <td>0.620</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>2.20</td>\n",
+              "      <td>0.082</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>54.0</td>\n",
+              "      <td>0.99660</td>\n",
+              "      <td>3.36</td>\n",
+              "      <td>0.52</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307</th>\n",
+              "      <td>10.3</td>\n",
+              "      <td>0.410</td>\n",
+              "      <td>0.42</td>\n",
+              "      <td>2.40</td>\n",
+              "      <td>0.213</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>0.99940</td>\n",
+              "      <td>3.19</td>\n",
+              "      <td>0.62</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>308</th>\n",
+              "      <td>10.3</td>\n",
+              "      <td>0.430</td>\n",
+              "      <td>0.44</td>\n",
+              "      <td>2.40</td>\n",
+              "      <td>0.214</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>0.99940</td>\n",
+              "      <td>3.19</td>\n",
+              "      <td>0.63</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>309</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.290</td>\n",
+              "      <td>0.38</td>\n",
+              "      <td>1.70</td>\n",
+              "      <td>0.062</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>0.99680</td>\n",
+              "      <td>3.41</td>\n",
+              "      <td>0.53</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4888</th>\n",
+              "      <td>6.8</td>\n",
+              "      <td>0.220</td>\n",
+              "      <td>0.36</td>\n",
+              "      <td>1.20</td>\n",
+              "      <td>0.052</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>127.0</td>\n",
+              "      <td>0.99330</td>\n",
+              "      <td>3.04</td>\n",
+              "      <td>0.54</td>\n",
+              "      <td>9.2</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4889</th>\n",
+              "      <td>4.9</td>\n",
+              "      <td>0.235</td>\n",
+              "      <td>0.27</td>\n",
+              "      <td>11.75</td>\n",
+              "      <td>0.030</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>118.0</td>\n",
+              "      <td>0.99540</td>\n",
+              "      <td>3.07</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4890</th>\n",
+              "      <td>6.1</td>\n",
+              "      <td>0.340</td>\n",
+              "      <td>0.29</td>\n",
+              "      <td>2.20</td>\n",
+              "      <td>0.036</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>0.98938</td>\n",
+              "      <td>3.06</td>\n",
+              "      <td>0.44</td>\n",
+              "      <td>11.8</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4891</th>\n",
+              "      <td>5.7</td>\n",
+              "      <td>0.210</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>0.90</td>\n",
+              "      <td>0.038</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>0.99074</td>\n",
+              "      <td>3.24</td>\n",
+              "      <td>0.46</td>\n",
+              "      <td>10.6</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4892</th>\n",
+              "      <td>6.5</td>\n",
+              "      <td>0.230</td>\n",
+              "      <td>0.38</td>\n",
+              "      <td>1.30</td>\n",
+              "      <td>0.032</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>112.0</td>\n",
+              "      <td>0.99298</td>\n",
+              "      <td>3.29</td>\n",
+              "      <td>0.54</td>\n",
+              "      <td>9.7</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4893</th>\n",
+              "      <td>6.2</td>\n",
+              "      <td>0.210</td>\n",
+              "      <td>0.29</td>\n",
+              "      <td>1.60</td>\n",
+              "      <td>0.039</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>92.0</td>\n",
+              "      <td>0.99114</td>\n",
+              "      <td>3.27</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>11.2</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4894</th>\n",
+              "      <td>6.6</td>\n",
+              "      <td>0.320</td>\n",
+              "      <td>0.36</td>\n",
+              "      <td>8.00</td>\n",
+              "      <td>0.047</td>\n",
+              "      <td>57.0</td>\n",
+              "      <td>168.0</td>\n",
+              "      <td>0.99490</td>\n",
+              "      <td>3.15</td>\n",
+              "      <td>0.46</td>\n",
+              "      <td>9.6</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4895</th>\n",
+              "      <td>6.5</td>\n",
+              "      <td>0.240</td>\n",
+              "      <td>0.19</td>\n",
+              "      <td>1.20</td>\n",
+              "      <td>0.041</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>111.0</td>\n",
+              "      <td>0.99254</td>\n",
+              "      <td>2.99</td>\n",
+              "      <td>0.46</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4896</th>\n",
+              "      <td>5.5</td>\n",
+              "      <td>0.290</td>\n",
+              "      <td>0.30</td>\n",
+              "      <td>1.10</td>\n",
+              "      <td>0.022</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>0.98869</td>\n",
+              "      <td>3.34</td>\n",
+              "      <td>0.38</td>\n",
+              "      <td>12.8</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4897</th>\n",
+              "      <td>6.0</td>\n",
+              "      <td>0.210</td>\n",
+              "      <td>0.38</td>\n",
+              "      <td>0.80</td>\n",
+              "      <td>0.020</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>98.0</td>\n",
+              "      <td>0.98941</td>\n",
+              "      <td>3.26</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>11.8</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
+              "0               7.4             0.700         0.00  ...       0.56      9.4        5\n",
+              "1               7.8             0.880         0.00  ...       0.68      9.8        5\n",
+              "2               7.8             0.760         0.04  ...       0.65      9.8        5\n",
+              "3              11.2             0.280         0.56  ...       0.58      9.8        6\n",
+              "4               7.4             0.700         0.00  ...       0.56      9.4        5\n",
+              "5               7.4             0.660         0.00  ...       0.56      9.4        5\n",
+              "6               7.9             0.600         0.06  ...       0.46      9.4        5\n",
+              "7               7.3             0.650         0.00  ...       0.47     10.0        7\n",
+              "8               7.8             0.580         0.02  ...       0.57      9.5        7\n",
+              "9               7.5             0.500         0.36  ...       0.80     10.5        5\n",
+              "300             7.5             0.530         0.06  ...       0.59     10.7        6\n",
+              "301            11.1             0.180         0.48  ...       0.64     10.1        6\n",
+              "302             8.3             0.705         0.12  ...       0.72     10.0        5\n",
+              "303             7.4             0.670         0.12  ...       0.54      9.5        5\n",
+              "304             8.4             0.650         0.60  ...       0.52      9.2        5\n",
+              "305            10.3             0.530         0.48  ...       0.59      9.3        6\n",
+              "306             7.6             0.620         0.32  ...       0.52      9.4        5\n",
+              "307            10.3             0.410         0.42  ...       0.62      9.5        6\n",
+              "308            10.3             0.430         0.44  ...       0.63      9.5        6\n",
+              "309             7.4             0.290         0.38  ...       0.53      9.5        6\n",
+              "4888            6.8             0.220         0.36  ...       0.54      9.2        5\n",
+              "4889            4.9             0.235         0.27  ...       0.50      9.4        6\n",
+              "4890            6.1             0.340         0.29  ...       0.44     11.8        6\n",
+              "4891            5.7             0.210         0.32  ...       0.46     10.6        6\n",
+              "4892            6.5             0.230         0.38  ...       0.54      9.7        5\n",
+              "4893            6.2             0.210         0.29  ...       0.50     11.2        6\n",
+              "4894            6.6             0.320         0.36  ...       0.46      9.6        5\n",
+              "4895            6.5             0.240         0.19  ...       0.46      9.4        6\n",
+              "4896            5.5             0.290         0.30  ...       0.38     12.8        7\n",
+              "4897            6.0             0.210         0.38  ...       0.32     11.8        6\n",
+              "\n",
+              "[30 rows x 12 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
+        }
       ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "* Importar el primer fichero\n",
-    "* Hacemos un bucle para ir recorriendo todos y cada uno de los ficheros. \n",
-    "    * Importante tener una consistencia en el nombre de los ficheros \n",
-    "    * Importamos los ficheros uno a uno\n",
-    "    * Cada uno de ellos debe apendizarse (añadirse al final) del primer fichero que ya habíamos cargado\n",
-    "* Repetimos el bucle hasta que no queden ficheros"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filepath = \"../datasets/distributed-data/\"\n",
-    "\n",
-    "data = pd.read_csv(\"../datasets/distributed-data/001.csv\")\n",
-    "final_length = len(data)\n",
-    "\n",
-    "for i in range(2,333):\n",
-    "    if i < 10:\n",
-    "        filename = \"00\" + str(i)\n",
-    "    if 10 <= i < 100:\n",
-    "        filename = \"0\" + str(i)\n",
-    "    if i >= 100:\n",
-    "        filename = str(i)\n",
-    "    file = filepath + filename + \".csv\"\n",
-    "    \n",
-    "    temp_data = pd.read_csv(file)\n",
-    "    final_length += len(temp_data)\n",
-    "    \n",
-    "    data = pd.concat([data, temp_data], axis = 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(772087, 4)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "nD28Z5ATB7m1",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 979
+        },
+        "outputId": "65a7db3f-82da-484a-edd8-fd638ff7e6a3"
+      },
+      "source": [
+        "wine_scramble = pd.concat([data2, data1, data3], axis = 0)\n",
+        "wine_scramble"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>fixed acidity</th>\n",
+              "      <th>volatile acidity</th>\n",
+              "      <th>citric acid</th>\n",
+              "      <th>residual sugar</th>\n",
+              "      <th>chlorides</th>\n",
+              "      <th>free sulfur dioxide</th>\n",
+              "      <th>total sulfur dioxide</th>\n",
+              "      <th>density</th>\n",
+              "      <th>pH</th>\n",
+              "      <th>sulphates</th>\n",
+              "      <th>alcohol</th>\n",
+              "      <th>quality</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>300</th>\n",
+              "      <td>7.5</td>\n",
+              "      <td>0.530</td>\n",
+              "      <td>0.06</td>\n",
+              "      <td>2.60</td>\n",
+              "      <td>0.086</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>44.0</td>\n",
+              "      <td>0.99650</td>\n",
+              "      <td>3.38</td>\n",
+              "      <td>0.59</td>\n",
+              "      <td>10.7</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>301</th>\n",
+              "      <td>11.1</td>\n",
+              "      <td>0.180</td>\n",
+              "      <td>0.48</td>\n",
+              "      <td>1.50</td>\n",
+              "      <td>0.068</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>0.99730</td>\n",
+              "      <td>3.22</td>\n",
+              "      <td>0.64</td>\n",
+              "      <td>10.1</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>302</th>\n",
+              "      <td>8.3</td>\n",
+              "      <td>0.705</td>\n",
+              "      <td>0.12</td>\n",
+              "      <td>2.60</td>\n",
+              "      <td>0.092</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>0.99940</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.72</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>303</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.670</td>\n",
+              "      <td>0.12</td>\n",
+              "      <td>1.60</td>\n",
+              "      <td>0.186</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>0.99600</td>\n",
+              "      <td>3.39</td>\n",
+              "      <td>0.54</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>304</th>\n",
+              "      <td>8.4</td>\n",
+              "      <td>0.650</td>\n",
+              "      <td>0.60</td>\n",
+              "      <td>2.10</td>\n",
+              "      <td>0.112</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>90.0</td>\n",
+              "      <td>0.99730</td>\n",
+              "      <td>3.20</td>\n",
+              "      <td>0.52</td>\n",
+              "      <td>9.2</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>305</th>\n",
+              "      <td>10.3</td>\n",
+              "      <td>0.530</td>\n",
+              "      <td>0.48</td>\n",
+              "      <td>2.50</td>\n",
+              "      <td>0.063</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>0.99980</td>\n",
+              "      <td>3.12</td>\n",
+              "      <td>0.59</td>\n",
+              "      <td>9.3</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>306</th>\n",
+              "      <td>7.6</td>\n",
+              "      <td>0.620</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>2.20</td>\n",
+              "      <td>0.082</td>\n",
+              "      <td>7.0</td>\n",
+              "      <td>54.0</td>\n",
+              "      <td>0.99660</td>\n",
+              "      <td>3.36</td>\n",
+              "      <td>0.52</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>307</th>\n",
+              "      <td>10.3</td>\n",
+              "      <td>0.410</td>\n",
+              "      <td>0.42</td>\n",
+              "      <td>2.40</td>\n",
+              "      <td>0.213</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>0.99940</td>\n",
+              "      <td>3.19</td>\n",
+              "      <td>0.62</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>308</th>\n",
+              "      <td>10.3</td>\n",
+              "      <td>0.430</td>\n",
+              "      <td>0.44</td>\n",
+              "      <td>2.40</td>\n",
+              "      <td>0.214</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>12.0</td>\n",
+              "      <td>0.99940</td>\n",
+              "      <td>3.19</td>\n",
+              "      <td>0.63</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>309</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.290</td>\n",
+              "      <td>0.38</td>\n",
+              "      <td>1.70</td>\n",
+              "      <td>0.062</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>0.99680</td>\n",
+              "      <td>3.41</td>\n",
+              "      <td>0.53</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.700</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.90</td>\n",
+              "      <td>0.076</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.99780</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.880</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>2.60</td>\n",
+              "      <td>0.098</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>67.0</td>\n",
+              "      <td>0.99680</td>\n",
+              "      <td>3.20</td>\n",
+              "      <td>0.68</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.760</td>\n",
+              "      <td>0.04</td>\n",
+              "      <td>2.30</td>\n",
+              "      <td>0.092</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>54.0</td>\n",
+              "      <td>0.99700</td>\n",
+              "      <td>3.26</td>\n",
+              "      <td>0.65</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>11.2</td>\n",
+              "      <td>0.280</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>1.90</td>\n",
+              "      <td>0.075</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>60.0</td>\n",
+              "      <td>0.99800</td>\n",
+              "      <td>3.16</td>\n",
+              "      <td>0.58</td>\n",
+              "      <td>9.8</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.700</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.90</td>\n",
+              "      <td>0.076</td>\n",
+              "      <td>11.0</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>0.99780</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>7.4</td>\n",
+              "      <td>0.660</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.80</td>\n",
+              "      <td>0.075</td>\n",
+              "      <td>13.0</td>\n",
+              "      <td>40.0</td>\n",
+              "      <td>0.99780</td>\n",
+              "      <td>3.51</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>7.9</td>\n",
+              "      <td>0.600</td>\n",
+              "      <td>0.06</td>\n",
+              "      <td>1.60</td>\n",
+              "      <td>0.069</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>59.0</td>\n",
+              "      <td>0.99640</td>\n",
+              "      <td>3.30</td>\n",
+              "      <td>0.46</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>7.3</td>\n",
+              "      <td>0.650</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>1.20</td>\n",
+              "      <td>0.065</td>\n",
+              "      <td>15.0</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>0.99460</td>\n",
+              "      <td>3.39</td>\n",
+              "      <td>0.47</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>7.8</td>\n",
+              "      <td>0.580</td>\n",
+              "      <td>0.02</td>\n",
+              "      <td>2.00</td>\n",
+              "      <td>0.073</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>18.0</td>\n",
+              "      <td>0.99680</td>\n",
+              "      <td>3.36</td>\n",
+              "      <td>0.57</td>\n",
+              "      <td>9.5</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>7.5</td>\n",
+              "      <td>0.500</td>\n",
+              "      <td>0.36</td>\n",
+              "      <td>6.10</td>\n",
+              "      <td>0.071</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>102.0</td>\n",
+              "      <td>0.99780</td>\n",
+              "      <td>3.35</td>\n",
+              "      <td>0.80</td>\n",
+              "      <td>10.5</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4888</th>\n",
+              "      <td>6.8</td>\n",
+              "      <td>0.220</td>\n",
+              "      <td>0.36</td>\n",
+              "      <td>1.20</td>\n",
+              "      <td>0.052</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>127.0</td>\n",
+              "      <td>0.99330</td>\n",
+              "      <td>3.04</td>\n",
+              "      <td>0.54</td>\n",
+              "      <td>9.2</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4889</th>\n",
+              "      <td>4.9</td>\n",
+              "      <td>0.235</td>\n",
+              "      <td>0.27</td>\n",
+              "      <td>11.75</td>\n",
+              "      <td>0.030</td>\n",
+              "      <td>34.0</td>\n",
+              "      <td>118.0</td>\n",
+              "      <td>0.99540</td>\n",
+              "      <td>3.07</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4890</th>\n",
+              "      <td>6.1</td>\n",
+              "      <td>0.340</td>\n",
+              "      <td>0.29</td>\n",
+              "      <td>2.20</td>\n",
+              "      <td>0.036</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>100.0</td>\n",
+              "      <td>0.98938</td>\n",
+              "      <td>3.06</td>\n",
+              "      <td>0.44</td>\n",
+              "      <td>11.8</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4891</th>\n",
+              "      <td>5.7</td>\n",
+              "      <td>0.210</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>0.90</td>\n",
+              "      <td>0.038</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>121.0</td>\n",
+              "      <td>0.99074</td>\n",
+              "      <td>3.24</td>\n",
+              "      <td>0.46</td>\n",
+              "      <td>10.6</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4892</th>\n",
+              "      <td>6.5</td>\n",
+              "      <td>0.230</td>\n",
+              "      <td>0.38</td>\n",
+              "      <td>1.30</td>\n",
+              "      <td>0.032</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>112.0</td>\n",
+              "      <td>0.99298</td>\n",
+              "      <td>3.29</td>\n",
+              "      <td>0.54</td>\n",
+              "      <td>9.7</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4893</th>\n",
+              "      <td>6.2</td>\n",
+              "      <td>0.210</td>\n",
+              "      <td>0.29</td>\n",
+              "      <td>1.60</td>\n",
+              "      <td>0.039</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>92.0</td>\n",
+              "      <td>0.99114</td>\n",
+              "      <td>3.27</td>\n",
+              "      <td>0.50</td>\n",
+              "      <td>11.2</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4894</th>\n",
+              "      <td>6.6</td>\n",
+              "      <td>0.320</td>\n",
+              "      <td>0.36</td>\n",
+              "      <td>8.00</td>\n",
+              "      <td>0.047</td>\n",
+              "      <td>57.0</td>\n",
+              "      <td>168.0</td>\n",
+              "      <td>0.99490</td>\n",
+              "      <td>3.15</td>\n",
+              "      <td>0.46</td>\n",
+              "      <td>9.6</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4895</th>\n",
+              "      <td>6.5</td>\n",
+              "      <td>0.240</td>\n",
+              "      <td>0.19</td>\n",
+              "      <td>1.20</td>\n",
+              "      <td>0.041</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>111.0</td>\n",
+              "      <td>0.99254</td>\n",
+              "      <td>2.99</td>\n",
+              "      <td>0.46</td>\n",
+              "      <td>9.4</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4896</th>\n",
+              "      <td>5.5</td>\n",
+              "      <td>0.290</td>\n",
+              "      <td>0.30</td>\n",
+              "      <td>1.10</td>\n",
+              "      <td>0.022</td>\n",
+              "      <td>20.0</td>\n",
+              "      <td>110.0</td>\n",
+              "      <td>0.98869</td>\n",
+              "      <td>3.34</td>\n",
+              "      <td>0.38</td>\n",
+              "      <td>12.8</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4897</th>\n",
+              "      <td>6.0</td>\n",
+              "      <td>0.210</td>\n",
+              "      <td>0.38</td>\n",
+              "      <td>0.80</td>\n",
+              "      <td>0.020</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>98.0</td>\n",
+              "      <td>0.98941</td>\n",
+              "      <td>3.26</td>\n",
+              "      <td>0.32</td>\n",
+              "      <td>11.8</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "      fixed acidity  volatile acidity  citric acid  ...  sulphates  alcohol  quality\n",
+              "300             7.5             0.530         0.06  ...       0.59     10.7        6\n",
+              "301            11.1             0.180         0.48  ...       0.64     10.1        6\n",
+              "302             8.3             0.705         0.12  ...       0.72     10.0        5\n",
+              "303             7.4             0.670         0.12  ...       0.54      9.5        5\n",
+              "304             8.4             0.650         0.60  ...       0.52      9.2        5\n",
+              "305            10.3             0.530         0.48  ...       0.59      9.3        6\n",
+              "306             7.6             0.620         0.32  ...       0.52      9.4        5\n",
+              "307            10.3             0.410         0.42  ...       0.62      9.5        6\n",
+              "308            10.3             0.430         0.44  ...       0.63      9.5        6\n",
+              "309             7.4             0.290         0.38  ...       0.53      9.5        6\n",
+              "0               7.4             0.700         0.00  ...       0.56      9.4        5\n",
+              "1               7.8             0.880         0.00  ...       0.68      9.8        5\n",
+              "2               7.8             0.760         0.04  ...       0.65      9.8        5\n",
+              "3              11.2             0.280         0.56  ...       0.58      9.8        6\n",
+              "4               7.4             0.700         0.00  ...       0.56      9.4        5\n",
+              "5               7.4             0.660         0.00  ...       0.56      9.4        5\n",
+              "6               7.9             0.600         0.06  ...       0.46      9.4        5\n",
+              "7               7.3             0.650         0.00  ...       0.47     10.0        7\n",
+              "8               7.8             0.580         0.02  ...       0.57      9.5        7\n",
+              "9               7.5             0.500         0.36  ...       0.80     10.5        5\n",
+              "4888            6.8             0.220         0.36  ...       0.54      9.2        5\n",
+              "4889            4.9             0.235         0.27  ...       0.50      9.4        6\n",
+              "4890            6.1             0.340         0.29  ...       0.44     11.8        6\n",
+              "4891            5.7             0.210         0.32  ...       0.46     10.6        6\n",
+              "4892            6.5             0.230         0.38  ...       0.54      9.7        5\n",
+              "4893            6.2             0.210         0.29  ...       0.50     11.2        6\n",
+              "4894            6.6             0.320         0.36  ...       0.46      9.6        5\n",
+              "4895            6.5             0.240         0.19  ...       0.46      9.4        6\n",
+              "4896            5.5             0.290         0.30  ...       0.38     12.8        7\n",
+              "4897            6.0             0.210         0.38  ...       0.32     11.8        6\n",
+              "\n",
+              "[30 rows x 12 columns]"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
       ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kcf3YilvB7m2",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Datos distribuidos "
+      ]
+    },
     {
-     "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>Date</th>\n",
-       "      <th>sulfate</th>\n",
-       "      <th>nitrate</th>\n",
-       "      <th>ID</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>726</th>\n",
-       "      <td>2004-12-27</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>727</th>\n",
-       "      <td>2004-12-28</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>728</th>\n",
-       "      <td>2004-12-29</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>729</th>\n",
-       "      <td>2004-12-30</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>332</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>730</th>\n",
-       "      <td>2004-12-31</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>332</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "hI5_SxMoB7m2",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "1035185c-2f1e-48d7-8084-bab587795d5a"
+      },
+      "source": [
+        "import pandas as pd\n",
+        "data = pd.read_csv(\"/content/drive/My Drive/Curso Machine Learning con Python/datasets/distributed-data/001.csv\")\n",
+        "data.head()"
       ],
-      "text/plain": [
-       "           Date  sulfate  nitrate   ID\n",
-       "726  2004-12-27      NaN      NaN  332\n",
-       "727  2004-12-28      NaN      NaN  332\n",
-       "728  2004-12-29      NaN      NaN  332\n",
-       "729  2004-12-30      NaN      NaN  332\n",
-       "730  2004-12-31      NaN      NaN  332"
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Date</th>\n",
+              "      <th>sulfate</th>\n",
+              "      <th>nitrate</th>\n",
+              "      <th>ID</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2003-01-01</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2003-01-02</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2003-01-03</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2003-01-04</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2003-01-05</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         Date  sulfate  nitrate  ID\n",
+              "0  2003-01-01      NaN      NaN   1\n",
+              "1  2003-01-02      NaN      NaN   1\n",
+              "2  2003-01-03      NaN      NaN   1\n",
+              "3  2003-01-04      NaN      NaN   1\n",
+              "4  2003-01-05      NaN      NaN   1"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 21
+        }
       ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "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>Date</th>\n",
-       "      <th>sulfate</th>\n",
-       "      <th>nitrate</th>\n",
-       "      <th>ID</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>2003-01-01</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2003-01-02</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2003-01-03</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2003-01-04</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2003-01-05</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "Dwu7TFSoB7m3",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "aba3f868-e575-45fd-aae9-141c429463d5"
+      },
+      "source": [
+        "data.shape"
       ],
-      "text/plain": [
-       "         Date  sulfate  nitrate  ID\n",
-       "0  2003-01-01      NaN      NaN   1\n",
-       "1  2003-01-02      NaN      NaN   1\n",
-       "2  2003-01-03      NaN      NaN   1\n",
-       "3  2003-01-04      NaN      NaN   1\n",
-       "4  2003-01-05      NaN      NaN   1"
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1461, 4)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 22
+        }
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "True"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RsJnj3eEB7m5",
+        "colab_type": "text"
+      },
+      "source": [
+        "* Importar el primer fichero\n",
+        "* Hacemos un bucle para ir recorriendo todos y cada uno de los ficheros. \n",
+        "    * Importante tener una consistencia en el nombre de los ficheros \n",
+        "    * Importamos los ficheros uno a uno\n",
+        "    * Cada uno de ellos debe apendizarse (añadirse al final) del primer fichero que ya habíamos cargado\n",
+        "* Repetimos el bucle hasta que no queden ficheros"
       ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "final_length == data.shape[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Joins de datasets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filepath = \"../datasets/athletes/\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_main = pd.read_csv(filepath + \"Medals.csv\", encoding= \"ISO-8859-1\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ptOP14vxB7m5",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "filepath = \"/content/drive/My Drive/Curso Machine Learning con Python/datasets/distributed-data/\"\n",
+        "\n",
+        "data = pd.read_csv(filepath+\"001.csv\")\n",
+        "final_length = len(data)\n",
+        "\n",
+        "for i in range(2,333):\n",
+        "    if i < 10:\n",
+        "        filename = \"00\" + str(i)\n",
+        "    if 10 <= i < 100:\n",
+        "        filename = \"0\" + str(i)\n",
+        "    if i >= 100:\n",
+        "        filename = str(i)\n",
+        "    file = filepath + filename + \".csv\"\n",
+        "    \n",
+        "    temp_data = pd.read_csv(file)\n",
+        "    final_length += len(temp_data)\n",
+        "    \n",
+        "    data = pd.concat([data, temp_data], axis = 0)"
+      ],
+      "execution_count": 0,
+      "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>Athlete</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Closing Ceremony Date</th>\n",
-       "      <th>Gold Medals</th>\n",
-       "      <th>Silver Medals</th>\n",
-       "      <th>Bronze Medals</th>\n",
-       "      <th>Total Medals</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>08/24/2008</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>2004</td>\n",
-       "      <td>08/29/2004</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>08/12/2012</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Natalie Coughlin</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>08/24/2008</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Aleksey Nemov</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>10/01/2000</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "2cbX3CYeB7m6",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "f557cf3f-c056-4317-8196-acfb166c28ba"
+      },
+      "source": [
+        "data.shape"
       ],
-      "text/plain": [
-       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
-       "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
-       "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
-       "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
-       "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
-       "4     Aleksey Nemov  24.0  2000            10/01/2000            2   \n",
-       "\n",
-       "   Silver Medals  Bronze Medals  Total Medals  \n",
-       "0              0              0             8  \n",
-       "1              0              2             8  \n",
-       "2              2              0             6  \n",
-       "3              2              3             6  \n",
-       "4              1              3             6  "
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(155220, 4)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 28
+        }
       ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_main.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "6956"
+      "cell_type": "code",
+      "metadata": {
+        "id": "QRDf9HLmB7m8",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "e89e5640-29ba-465b-efbc-eee6facea2c7"
+      },
+      "source": [
+        "data.tail()"
+      ],
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Date</th>\n",
+              "      <th>sulfate</th>\n",
+              "      <th>nitrate</th>\n",
+              "      <th>ID</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>3282</th>\n",
+              "      <td>2010-12-27</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>63</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3283</th>\n",
+              "      <td>2010-12-28</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>63</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3284</th>\n",
+              "      <td>2010-12-29</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>63</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3285</th>\n",
+              "      <td>2010-12-30</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>63</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3286</th>\n",
+              "      <td>2010-12-31</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>63</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Date  sulfate  nitrate  ID\n",
+              "3282  2010-12-27      NaN      NaN  63\n",
+              "3283  2010-12-28      NaN      NaN  63\n",
+              "3284  2010-12-29      NaN      NaN  63\n",
+              "3285  2010-12-30      NaN      NaN  63\n",
+              "3286  2010-12-31      NaN      NaN  63"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 29
+        }
       ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a = data_main[\"Athlete\"].unique().tolist()\n",
-    "len(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(8618, 8)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "HC4Kws0cB7m9",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "615bdec3-11c8-4596-efd5-598aa62412d4"
+      },
+      "source": [
+        "data.head()"
+      ],
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Date</th>\n",
+              "      <th>sulfate</th>\n",
+              "      <th>nitrate</th>\n",
+              "      <th>ID</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2003-01-01</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2003-01-02</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2003-01-03</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2003-01-04</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2003-01-05</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         Date  sulfate  nitrate  ID\n",
+              "0  2003-01-01      NaN      NaN   1\n",
+              "1  2003-01-02      NaN      NaN   1\n",
+              "2  2003-01-03      NaN      NaN   1\n",
+              "3  2003-01-04      NaN      NaN   1\n",
+              "4  2003-01-05      NaN      NaN   1"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 30
+        }
       ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_main.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_country = pd.read_csv(filepath + \"Athelete_Country_Map.csv\", encoding = \"ISO-8859-1\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "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>Athlete</th>\n",
-       "      <th>Country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Natalie Coughlin</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Aleksey Nemov</td>\n",
-       "      <td>Russia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Alicia Coutts</td>\n",
-       "      <td>Australia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Missy Franklin</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "tX3bm7wgB7m-",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "931389b3-c922-4ca2-8543-d67683efc134"
+      },
+      "source": [
+        "final_length == data.shape[0]"
       ],
-      "text/plain": [
-       "            Athlete        Country\n",
-       "0    Michael Phelps  United States\n",
-       "1  Natalie Coughlin  United States\n",
-       "2     Aleksey Nemov         Russia\n",
-       "3     Alicia Coutts      Australia\n",
-       "4    Missy Franklin  United States"
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 31
+        }
       ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_country.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "6970"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0YdhyaxtB7m_",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Joins de datasets"
       ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data_country)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6tRBwQDDB7m_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "filepath = \"/content/drive/My Drive/Curso Machine Learning con Python/datasets/athletes/\""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Omj7JIrHB7nA",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data_main = pd.read_csv(filepath + \"Medals.csv\", encoding= \"ISO-8859-1\")"
+      ],
+      "execution_count": 0,
+      "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>Athlete</th>\n",
-       "      <th>Country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1029</th>\n",
-       "      <td>Aleksandar Ciric</td>\n",
-       "      <td>Serbia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1086</th>\n",
-       "      <td>Aleksandar Ciric</td>\n",
-       "      <td>Serbia and Montenegro</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "gqKemyCTB7nB",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "2c96f639-7496-4327-9227-dabdedefeed9"
+      },
+      "source": [
+        "data_main.head()"
       ],
-      "text/plain": [
-       "               Athlete                Country\n",
-       "1029  Aleksandar Ciric                 Serbia\n",
-       "1086  Aleksandar Ciric  Serbia and Montenegro"
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Athlete</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Closing Ceremony Date</th>\n",
+              "      <th>Gold Medals</th>\n",
+              "      <th>Silver Medals</th>\n",
+              "      <th>Bronze Medals</th>\n",
+              "      <th>Total Medals</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>08/24/2008</td>\n",
+              "      <td>8</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>2004</td>\n",
+              "      <td>08/29/2004</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>8</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>2012</td>\n",
+              "      <td>08/12/2012</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Natalie Coughlin</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>08/24/2008</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Aleksey Nemov</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>10/01/2000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
+              "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
+              "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
+              "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
+              "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
+              "4     Aleksey Nemov  24.0  2000            10/01/2000            2   \n",
+              "\n",
+              "   Silver Medals  Bronze Medals  Total Medals  \n",
+              "0              0              0             8  \n",
+              "1              0              2             8  \n",
+              "2              2              0             6  \n",
+              "3              2              3             6  \n",
+              "4              1              3             6  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 29
+        }
       ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_country[data_country[\"Athlete\"] == \"Aleksandar Ciric\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_sports = pd.read_csv(filepath + \"Athelete_Sports_Map.csv\", encoding=\"ISO-8859-1\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "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>Athlete</th>\n",
-       "      <th>Sport</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>Swimming</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Natalie Coughlin</td>\n",
-       "      <td>Swimming</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Aleksey Nemov</td>\n",
-       "      <td>Gymnastics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Alicia Coutts</td>\n",
-       "      <td>Swimming</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Missy Franklin</td>\n",
-       "      <td>Swimming</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "LQoGvZNtB7nE",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "380e0105-a751-4112-f49b-484cf687a36d"
+      },
+      "source": [
+        "a = data_main[\"Athlete\"].unique().tolist()\n",
+        "len(a)"
       ],
-      "text/plain": [
-       "            Athlete       Sport\n",
-       "0    Michael Phelps    Swimming\n",
-       "1  Natalie Coughlin    Swimming\n",
-       "2     Aleksey Nemov  Gymnastics\n",
-       "3     Alicia Coutts    Swimming\n",
-       "4    Missy Franklin    Swimming"
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "6956"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 30
+        }
       ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_sports.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "6975"
+      "cell_type": "code",
+      "metadata": {
+        "id": "jb8YCne0B7nP",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "0616b8cc-5bb3-4549-efd6-9335e674eee0"
+      },
+      "source": [
+        "data_main.shape"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(8618, 8)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 31
+        }
       ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data_sports)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SmKLs0RUB7nR",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data_country = pd.read_csv(filepath + \"Athelete_Country_Map.csv\", encoding = \"ISO-8859-1\")"
+      ],
+      "execution_count": 0,
+      "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",
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-       "\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>Athlete</th>\n",
-       "      <th>Sport</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>528</th>\n",
-       "      <td>Richard Thompson</td>\n",
-       "      <td>Athletics</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1308</th>\n",
-       "      <td>Chen Jing</td>\n",
-       "      <td>Volleyball</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1419</th>\n",
-       "      <td>Chen Jing</td>\n",
-       "      <td>Table Tennis</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2727</th>\n",
-       "      <td>Matt Ryan</td>\n",
-       "      <td>Rowing</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5003</th>\n",
-       "      <td>Matt Ryan</td>\n",
-       "      <td>Equestrian</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5691</th>\n",
-       "      <td>Richard Thompson</td>\n",
-       "      <td>Baseball</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "xgrqoxzTB7nS",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "d4a18a48-ea13-46ce-fc74-80b93f678627"
+      },
+      "source": [
+        "data_country.head()"
       ],
-      "text/plain": [
-       "               Athlete         Sport\n",
-       "528   Richard Thompson     Athletics\n",
-       "1308         Chen Jing    Volleyball\n",
-       "1419         Chen Jing  Table Tennis\n",
-       "2727         Matt Ryan        Rowing\n",
-       "5003         Matt Ryan    Equestrian\n",
-       "5691  Richard Thompson      Baseball"
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Athlete</th>\n",
+              "      <th>Country</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Natalie Coughlin</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Aleksey Nemov</td>\n",
+              "      <td>Russia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Alicia Coutts</td>\n",
+              "      <td>Australia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Missy Franklin</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Athlete        Country\n",
+              "0    Michael Phelps  United States\n",
+              "1  Natalie Coughlin  United States\n",
+              "2     Aleksey Nemov         Russia\n",
+              "3     Alicia Coutts      Australia\n",
+              "4    Missy Franklin  United States"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 37
+        }
       ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_sports[(data_sports[\"Athlete\"]==\"Chen Jing\") | \n",
-    "            (data_sports[\"Athlete\"]==\"Richard Thompson\") | \n",
-    "            (data_sports[\"Athlete\"]==\"Matt Ryan\")\n",
-    "           ]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_country_dp = data_country.drop_duplicates(subset=\"Athlete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "True"
+      "cell_type": "code",
+      "metadata": {
+        "id": "9pfZXk3dB7nT",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "55cde6ba-6791-4bca-f197-e4ca45e7dffd"
+      },
+      "source": [
+        "len(data_country)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "6970"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 38
+        }
       ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data_country_dp)==len(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_main_country = pd.merge(left = data_main, right = data_country_dp,\n",
-    "                            left_on=\"Athlete\", right_on = \"Athlete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "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",
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-       "\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>Athlete</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Closing Ceremony Date</th>\n",
-       "      <th>Gold Medals</th>\n",
-       "      <th>Silver Medals</th>\n",
-       "      <th>Bronze Medals</th>\n",
-       "      <th>Total Medals</th>\n",
-       "      <th>Country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>08/24/2008</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>2004</td>\n",
-       "      <td>08/29/2004</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>8</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>08/12/2012</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Natalie Coughlin</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>08/24/2008</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Natalie Coughlin</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>2004</td>\n",
-       "      <td>08/29/2004</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "h7OiBAsnB7nU",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "48e8687e-4842-43a0-b047-5615db9b7da9"
+      },
+      "source": [
+        "data_country[data_country[\"Athlete\"] == \"Aleksandar Ciric\"]"
       ],
-      "text/plain": [
-       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
-       "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
-       "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
-       "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
-       "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
-       "4  Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
-       "\n",
-       "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
-       "0              0              0             8  United States  \n",
-       "1              0              2             8  United States  \n",
-       "2              2              0             6  United States  \n",
-       "3              2              3             6  United States  \n",
-       "4              2              1             5  United States  "
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Athlete</th>\n",
+              "      <th>Country</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1029</th>\n",
+              "      <td>Aleksandar Ciric</td>\n",
+              "      <td>Serbia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1086</th>\n",
+              "      <td>Aleksandar Ciric</td>\n",
+              "      <td>Serbia and Montenegro</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "               Athlete                Country\n",
+              "1029  Aleksandar Ciric                 Serbia\n",
+              "1086  Aleksandar Ciric  Serbia and Montenegro"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 40
+        }
       ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_main_country.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "(8618, 9)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "vs4X4VVbB7nU",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data_sports = pd.read_csv(filepath + \"Athelete_Sports_Map.csv\", encoding=\"ISO-8859-1\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "dPctj5BTB7nV",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "b9e0e0fe-f652-4eae-ff5e-67824286d2b6"
+      },
+      "source": [
+        "data_sports.head()"
+      ],
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Athlete</th>\n",
+              "      <th>Sport</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>Swimming</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Natalie Coughlin</td>\n",
+              "      <td>Swimming</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Aleksey Nemov</td>\n",
+              "      <td>Gymnastics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Alicia Coutts</td>\n",
+              "      <td>Swimming</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Missy Franklin</td>\n",
+              "      <td>Swimming</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Athlete       Sport\n",
+              "0    Michael Phelps    Swimming\n",
+              "1  Natalie Coughlin    Swimming\n",
+              "2     Aleksey Nemov  Gymnastics\n",
+              "3     Alicia Coutts    Swimming\n",
+              "4    Missy Franklin    Swimming"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 36
+        }
       ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_main_country.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "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>Athlete</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Closing Ceremony Date</th>\n",
-       "      <th>Gold Medals</th>\n",
-       "      <th>Silver Medals</th>\n",
-       "      <th>Bronze Medals</th>\n",
-       "      <th>Total Medals</th>\n",
-       "      <th>Country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1491</th>\n",
-       "      <td>Aleksandar Ciric</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>08/24/2008</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Serbia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1492</th>\n",
-       "      <td>Aleksandar Ciric</td>\n",
-       "      <td>26.0</td>\n",
-       "      <td>2004</td>\n",
-       "      <td>08/29/2004</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Serbia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1493</th>\n",
-       "      <td>Aleksandar Ciric</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>10/01/2000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Serbia</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "9GICMbXfB7nW",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "e23d7aa6-d38f-4379-8818-ffc9ef434abd"
+      },
+      "source": [
+        "len(data_sports)"
       ],
-      "text/plain": [
-       "               Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
-       "1491  Aleksandar Ciric  30.0  2008            08/24/2008            0   \n",
-       "1492  Aleksandar Ciric  26.0  2004            08/29/2004            0   \n",
-       "1493  Aleksandar Ciric  22.0  2000            10/01/2000            0   \n",
-       "\n",
-       "      Silver Medals  Bronze Medals  Total Medals Country  \n",
-       "1491              0              1             1  Serbia  \n",
-       "1492              1              0             1  Serbia  \n",
-       "1493              0              1             1  Serbia  "
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "6975"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 62
+        }
       ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_main_country[data_main_country[\"Athlete\"] == \"Aleksandar Ciric\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_sports_dp = data_sports.drop_duplicates(subset=\"Athlete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "True"
+      "cell_type": "code",
+      "metadata": {
+        "id": "4dMxAIv0B7nX",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "629a3230-7f6a-4854-986e-3e7f12e81c68"
+      },
+      "source": [
+        "data_sports[(data_sports[\"Athlete\"]==\"Chen Jing\") | \n",
+        "            (data_sports[\"Athlete\"]==\"Richard Thompson\") | \n",
+        "            (data_sports[\"Athlete\"]==\"Matt Ryan\")\n",
+        "           ]"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Athlete</th>\n",
+              "      <th>Sport</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>528</th>\n",
+              "      <td>Richard Thompson</td>\n",
+              "      <td>Athletics</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1308</th>\n",
+              "      <td>Chen Jing</td>\n",
+              "      <td>Volleyball</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1419</th>\n",
+              "      <td>Chen Jing</td>\n",
+              "      <td>Table Tennis</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2727</th>\n",
+              "      <td>Matt Ryan</td>\n",
+              "      <td>Rowing</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5003</th>\n",
+              "      <td>Matt Ryan</td>\n",
+              "      <td>Equestrian</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5691</th>\n",
+              "      <td>Richard Thompson</td>\n",
+              "      <td>Baseball</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "               Athlete         Sport\n",
+              "528   Richard Thompson     Athletics\n",
+              "1308         Chen Jing    Volleyball\n",
+              "1419         Chen Jing  Table Tennis\n",
+              "2727         Matt Ryan        Rowing\n",
+              "5003         Matt Ryan    Equestrian\n",
+              "5691  Richard Thompson      Baseball"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 46
+        }
       ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data_sports_dp)==len(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_final = pd.merge(left=data_main_country, right=data_sports_dp,\n",
-    "                     left_on=\"Athlete\", right_on=\"Athlete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "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>Athlete</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Closing Ceremony Date</th>\n",
-       "      <th>Gold Medals</th>\n",
-       "      <th>Silver Medals</th>\n",
-       "      <th>Bronze Medals</th>\n",
-       "      <th>Total Medals</th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Sport</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>08/24/2008</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Swimming</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>2004</td>\n",
-       "      <td>08/29/2004</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>8</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Swimming</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>08/12/2012</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Swimming</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Natalie Coughlin</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>08/24/2008</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Swimming</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Natalie Coughlin</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>2004</td>\n",
-       "      <td>08/29/2004</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>United States</td>\n",
-       "      <td>Swimming</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "MMmp-rayB7nY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data_country_dp = data_country.drop_duplicates(subset=\"Athlete\")"
       ],
-      "text/plain": [
-       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
-       "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
-       "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
-       "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
-       "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
-       "4  Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
-       "\n",
-       "   Silver Medals  Bronze Medals  Total Medals        Country     Sport  \n",
-       "0              0              0             8  United States  Swimming  \n",
-       "1              0              2             8  United States  Swimming  \n",
-       "2              2              0             6  United States  Swimming  \n",
-       "3              2              3             6  United States  Swimming  \n",
-       "4              2              1             5  United States  Swimming  "
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3Y66TIxXB7nZ",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "6a28a049-ad43-4200-c742-e1da40674fe5"
+      },
+      "source": [
+        "len(data_country_dp)==len(a)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 63
+        }
       ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_final.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BAFBL-h6B7na",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data_main_country = pd.merge(left = data_main, right = data_country_dp,\n",
+        "                            left_on=\"Athlete\", right_on = \"Athlete\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "IAiBbUO5B7nb",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "0832067f-c794-4771-a385-b3047ddcea9a"
+      },
+      "source": [
+        "data_main_country.head()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Athlete</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Closing Ceremony Date</th>\n",
+              "      <th>Gold Medals</th>\n",
+              "      <th>Silver Medals</th>\n",
+              "      <th>Bronze Medals</th>\n",
+              "      <th>Total Medals</th>\n",
+              "      <th>Country</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>08/24/2008</td>\n",
+              "      <td>8</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>2004</td>\n",
+              "      <td>08/29/2004</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>8</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>2012</td>\n",
+              "      <td>08/12/2012</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Natalie Coughlin</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>08/24/2008</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Natalie Coughlin</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>2004</td>\n",
+              "      <td>08/29/2004</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
+              "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
+              "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
+              "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
+              "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
+              "4  Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
+              "\n",
+              "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
+              "0              0              0             8  United States  \n",
+              "1              0              2             8  United States  \n",
+              "2              2              0             6  United States  \n",
+              "3              2              3             6  United States  \n",
+              "4              2              1             5  United States  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 65
+        }
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "(8618, 10)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "oFopEFmEB7nb",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "affd8011-d29e-4c14-8ec1-ee6546b3f3a0"
+      },
+      "source": [
+        "data_main_country.shape"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(8618, 9)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 66
+        }
       ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_final.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Tipos de Joins"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import Image\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Inner Join <= A (Left Join), B (Right Join) <= Outer Join**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "out_athletes = np.random.choice(data_main[\"Athlete\"], size = 6, replace = False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "array(['Oscar Braison', 'Erik Vendt', 'Yuliya Zaripova',\n",
-       "       'Erzsébet Márkus-Peresztegi', 'Belinda Snell', 'Alfredo Despaigne'],\n",
-       "      dtype=object)"
+      "cell_type": "code",
+      "metadata": {
+        "id": "By-SS0-CB7nc",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "7988d1e8-50c3-46d2-a43e-edef4729e029"
+      },
+      "source": [
+        "data_main_country[data_main_country[\"Athlete\"] == \"Aleksandar Ciric\"]"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Athlete</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Closing Ceremony Date</th>\n",
+              "      <th>Gold Medals</th>\n",
+              "      <th>Silver Medals</th>\n",
+              "      <th>Bronze Medals</th>\n",
+              "      <th>Total Medals</th>\n",
+              "      <th>Country</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1491</th>\n",
+              "      <td>Aleksandar Ciric</td>\n",
+              "      <td>30.0</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>08/24/2008</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Serbia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1492</th>\n",
+              "      <td>Aleksandar Ciric</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>2004</td>\n",
+              "      <td>08/29/2004</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Serbia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1493</th>\n",
+              "      <td>Aleksandar Ciric</td>\n",
+              "      <td>22.0</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>10/01/2000</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>Serbia</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "               Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
+              "1491  Aleksandar Ciric  30.0  2008            08/24/2008            0   \n",
+              "1492  Aleksandar Ciric  26.0  2004            08/29/2004            0   \n",
+              "1493  Aleksandar Ciric  22.0  2000            10/01/2000            0   \n",
+              "\n",
+              "      Silver Medals  Bronze Medals  Total Medals Country  \n",
+              "1491              0              1             1  Serbia  \n",
+              "1492              1              0             1  Serbia  \n",
+              "1493              0              1             1  Serbia  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 67
+        }
       ]
-     },
-     "execution_count": 125,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out_athletes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_country_dlt = data_country_dp[(~data_country_dp[\"Athlete\"].isin(out_athletes)) & \n",
-    "                                   (data_country_dp[\"Athlete\"] != \"Michael Phelps\")]\n",
-    "\n",
-    "data_sports_dlt = data_sports_dp[(~data_sports_dp[\"Athlete\"].isin(out_athletes)) &\n",
-    "                                (data_sports_dp[\"Athlete\"] != \"Michael Phelps\")]\n",
-    "\n",
-    "data_main_dlt = data_main[(~data_main[\"Athlete\"].isin(out_athletes)) & \n",
-    "                         (data_main[\"Athlete\"] != \"Michael Phelps\")]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hX6CjiLMB7nd",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data_sports_dp = data_sports.drop_duplicates(subset=\"Athlete\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "6949"
+      "cell_type": "code",
+      "metadata": {
+        "id": "9mwanl1DB7ne",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "b31df516-19b7-481a-bbf3-ca59d22786cd"
+      },
+      "source": [
+        "len(data_sports_dp)==len(a)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "True"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 70
+        }
       ]
-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data_country_dlt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "mwflQZ47B7ne",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data_final = pd.merge(left=data_main_country, right=data_sports_dp,\n",
+        "                     left_on=\"Athlete\", right_on=\"Athlete\")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "6949"
+      "cell_type": "code",
+      "metadata": {
+        "id": "e1EOw4atB7nf",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "a8557b3b-03f8-4eca-a6f6-99abf589caaf"
+      },
+      "source": [
+        "data_final.head()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Athlete</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Closing Ceremony Date</th>\n",
+              "      <th>Gold Medals</th>\n",
+              "      <th>Silver Medals</th>\n",
+              "      <th>Bronze Medals</th>\n",
+              "      <th>Total Medals</th>\n",
+              "      <th>Country</th>\n",
+              "      <th>Sport</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>08/24/2008</td>\n",
+              "      <td>8</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>Swimming</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>2004</td>\n",
+              "      <td>08/29/2004</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>8</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>Swimming</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>2012</td>\n",
+              "      <td>08/12/2012</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>Swimming</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Natalie Coughlin</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>08/24/2008</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>Swimming</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Natalie Coughlin</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>2004</td>\n",
+              "      <td>08/29/2004</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "      <td>United States</td>\n",
+              "      <td>Swimming</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
+              "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
+              "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
+              "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
+              "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
+              "4  Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
+              "\n",
+              "   Silver Medals  Bronze Medals  Total Medals        Country     Sport  \n",
+              "0              0              0             8  United States  Swimming  \n",
+              "1              0              2             8  United States  Swimming  \n",
+              "2              2              0             6  United States  Swimming  \n",
+              "3              2              3             6  United States  Swimming  \n",
+              "4              2              1             5  United States  Swimming  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 72
+        }
       ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data_sports_dlt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "8605"
+      "cell_type": "code",
+      "metadata": {
+        "id": "NdZzwRR-B7ng",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "0a914d2c-b530-43da-8a29-c95b58cce2c9"
+      },
+      "source": [
+        "data_final.shape"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(8618, 10)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 73
+        }
       ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data_main_dlt)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Inner Join\n",
-    "* Devuelve un data frame con las filas que tienen valor tanto en el primero como en el segundo data frame que estamos uniendo\n",
-    "* El número de filas será igual al número de filas **comunes** que tengas ambos data sets\n",
-    "    * Data Set A tiene 60 filas\n",
-    "    * Data Set B tiene 50 filas\n",
-    "    * Ambos comparten 30 filas\n",
-    "    * Entonces A Inner Join B tendrá 30 filas\n",
-    "* En términos de teoría de conjuntos, se trata de la intersección de los dos conjuntos"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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9nD/e4S4fPvhHXkNwDGy8nHe/k2JeRaqdp0ox2bsJNdwz3O29fT4VKvv/m+BSJxUr+icRGC9rjv8HwF+kfWOZ5ENa59vSuufpffrlUZlfzmP72OrxA1avZBwApnskcP1f3n9VGwTAnYXYFQ8a4tF4l/Ym3UjXt97Vpf2xvkzAZ4v/P2eZE7dH0zc5nH+Wt/l1GSARx6GqvMyxnAT4DgK9EoCY9Jp5xA0CwgQgJsJAYQ4EeiWwWUz8a6leoeWK278HkGsM2AWBXASCxCT0BkwuJ1u2C7Zx2fWFFwzjGC718k8YtrANEpOlQbEdBEAABCYCEJOJBD5BAASSCESJiX8alDQ6Ou8R8E/f93ZiAwgoJhAsJluunRTHq9I1sN2WFl9wwW4bu7XW/onCVrbBYrLmBPaDAAj0TSBaTHwF6xuhfPT+kVfeOiyCQB4Cm8Rk62lPHpfbtAq2YXn1hRbMwpiFtvJPEGLYbhKTUKfQDgRAoD8CSWLiK1l/6PJG7B+B845kxzqY5MuVxFzeLCYxpz/5ELRlGWzD8wlW4ay2toxlu1lMtjqG9iAAAn0QiBITX7kkTo/6QB0Wpc8Wp/U7ZmCxYyG9JDWHo8REOhjYA4EtBHzB3dIPbdcJpLAVERMpZVsPtb8WOCIPAxjkq3vJuRstJikKlg9NG5bBdjmPYLPMJnVPKttoMaGOSyoctd37es9H5p5jz1330nM2SUxSlSw3LMv2wXY/e2Cyz0RqiwTbJDGhgUgrHbXf83qPR+geYy5V4znmarKYSChaKYDWxgHbXcbAYsdCekmKbbKY0MByKB4do9f1no7UPcVaup5zzVERMZFSttJQLYwHtsMABvkqVZKtiJjQUHMpHx2nx/Uejtg9xFirdnPOTTExoQqX0+laiag1LmXb8mSjsdHYa+WghXHpnJRmKyYmDra0cy0kUCqGHtn2GLNUvazZycFWVExoAFQJ6X6sxxOgR/B4S3p6thiTFrol5qK4mORQPC0Jqe1HT2x7irV0XeViKy4mDozvbAlFLJ2MmuP5bFs6krcUS8364MYuNQeziAkNqFQwdNwe1luYhDQGXzB7yGHOGOncy8k2m5hQp2lQOQG2bpuypZPRUvzUdxqbpVi0+UrnXG622cTEgaXO0+C0wbfkD2VLJ6WlWCZfaUzTdnymEyjBNquYOAQlgkhHbdOCdbYtCKDWyqlx4M4uJhR2jSCpD62uW5qc1FfrwqippugcK8W2iJjQYGiwmhJhzRfKlk5SjfFQH2kMGn224hOdWyXZFhETlwgaFA3aSrI0+knZ0smqyWfqG/Vdk6/WfKFzqjTbYmLiEkODo8FbS54mfylbOmk1+Ep9oj5r8NGqD3Qu1WBbVExcomiQFILVZGrwm7Klk7emj5p8qckhx9ha5lBxMckBEzbtEaDCZy8CvR7XYltFTGiwWpRVb3mEe0bZajgjoD5QH8OjQ0tKgM6dmmyriIkDQoOmUCg0rIcToGzpZA63lN6Sjk19Sx+hXwt0ztRmezQ6cLNmOigQ50tll2riEB2bY3vlyhXRMZaMURFx7ZDXJVrbtnN51cC22pnJhI+DwMGa2uMznADHlpvk4RbDWnJjcL6EWUMrnwA3N7SwrS4mDhQHg4PmQ8VyGAGOLTfZw6ytt+Jscz6sW0ILSoCbE5rYqhATB42DwsGjgLG+ToBjy036dUuHW3A2ubEPW8FejgA3F7SxVSMmDiAHh4PIwca2wwQ4ttzkP2xleS9nixtz2QL2LBHg5oBGttVvwIYC1AhvyXft22lxpt6UpUKCXMlVAM2VZrZqxcSlg4J02zTDdP5Z+ePYbhUVKiIuduRHpgK4/Ghnq+oyh6aBg8dBpv2wvk6AY8uJw5Ilri1nc6k/ti8T4GrcAlvVYuJwcxA52MupwZ4lAhxbTiRof64NZ4v2w/o6Aa62rbBVfZlD0VsGTWPRts6xpZc9EJF8WeP4WxGRiYopMXFOc9Dddmvgnc/a/pbYLvkJ5ktkwrcvMbfI1pyYTGnikmAxAVM8mj45ttQ/sKZEtq8vcbbK1qyYuNS1lozt5ZivB9iC7VYCpsVkChaFP5FI/1xiSS1bPXrSOEquL7FthWUTYjIVROvJmuLM8bnEzo3lin1pfysTIQfTyeYSu4nt1M76Z1Ni4pLRS+KkCm8Lry1tpfyzbKc3Xs2JyVR8vSVyijv0M4VPSt9Q/yy365VPs2IyFeOhxLo2PZ2mS7OQtjflzOInWIxXBeNkqvqmtVKFs5Zs50erKNZiT417zT7YlqryuuN0IyY+5h6Kfy3GVAHxefrLa+O6trnG9v3IubwWo/X4Ytl1KSYTrLWimNpZKA5tsWjzZ8plzGdLscTEH9qnazHxIYUWzNSnpsBY8tXxsuSvJV+nWtTyCTFhMrG1oKgJKaFJ8UPKBxpb6npKTG5sqbhS/JDyIZWltv4Qk4CMpBRegHmRJlYLHGxF0q/CCMQkMg01J4FV4QhFDbahpHS1g5gI50NyIrQuGlvRg+1WYmXbQ0zK8sZoINAsAfWvbWyWPAIDgcYIQEwaSyjCAYFaBCAmtchjXBBojMD/AD+F4H1PWChBAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kZLPMM6nB7nh",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Tipos de Joins"
       ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename=\"resources/inner-join.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# data_main contiene toda la info\n",
-    "# data_country_dlt le falta la info de 7 atletas\n",
-    "merged_inner = pd.merge(left = data_main, right = data_country_dlt,\n",
-    "                       how = \"inner\", left_on = \"Athlete\", right_on = \"Athlete\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rsCKw6IHB7nh",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "from IPython.display import Image\n",
+        "import numpy as np"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "data": {
-      "text/plain": [
-       "8605"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "um7CuUxfB7nh",
+        "colab_type": "text"
+      },
+      "source": [
+        "**Inner Join <= A (Left Join), B (Right Join) <= Outer Join**"
       ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(merged_inner)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "znEBQUqzB7ni",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "out_athletes = np.random.choice(data_main[\"Athlete\"], size = 6, replace = False)"
+      ],
+      "execution_count": 0,
+      "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>Athlete</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Closing Ceremony Date</th>\n",
-       "      <th>Gold Medals</th>\n",
-       "      <th>Silver Medals</th>\n",
-       "      <th>Bronze Medals</th>\n",
-       "      <th>Total Medals</th>\n",
-       "      <th>Country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Natalie Coughlin</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>08/24/2008</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Natalie Coughlin</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>2004</td>\n",
-       "      <td>08/29/2004</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Natalie Coughlin</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>08/12/2012</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Aleksey Nemov</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>10/01/2000</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>Russia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Alicia Coutts</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>08/12/2012</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Australia</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "rAIJNBDSB7ni",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "fc883de7-249c-424c-a605-97ead798a2d3"
+      },
+      "source": [
+        "out_athletes"
       ],
-      "text/plain": [
-       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
-       "0  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
-       "1  Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
-       "2  Natalie Coughlin  29.0  2012            08/12/2012            0   \n",
-       "3     Aleksey Nemov  24.0  2000            10/01/2000            2   \n",
-       "4     Alicia Coutts  24.0  2012            08/12/2012            1   \n",
-       "\n",
-       "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
-       "0              2              3             6  United States  \n",
-       "1              2              1             5  United States  \n",
-       "2              0              1             1  United States  \n",
-       "3              1              3             6         Russia  \n",
-       "4              3              1             5      Australia  "
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['Oscar Braison', 'Erik Vendt', 'Yuliya Zaripova',\n",
+              "       'Erzsébet Márkus-Peresztegi', 'Belinda Snell', 'Alfredo Despaigne'],\n",
+              "      dtype=object)"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 125
+        }
       ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merged_inner.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Left Join\n",
-    "* Devuelve un data frame con las filas que tuvieran valor en el dataset de la izquierda, sin importar si tienen correspondencia en el de la derecha o no.\n",
-    "* Las filas del data frame final que no correspondan a ninguna fila del data frame derecho, tendrán NAs en las columnas del data frame derecho.\n",
-    "* El número de filas será igual al número de filas del data frame izquierdo\n",
-    "    * Data Set A tiene 60 filas\n",
-    "    * Data Set B tiene 50 filas\n",
-    "    * Entonces A Left Join B tendrá 60 filas\n",
-    "* En términos de teoría de conjuntos, se trata del propio data set de la izquierda quien, además tiene la intersección en su interior."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "WoHqCm29B7nj",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data_country_dlt = data_country_dp[(~data_country_dp[\"Athlete\"].isin(out_athletes)) & \n",
+        "                                   (data_country_dp[\"Athlete\"] != \"Michael Phelps\")]\n",
+        "\n",
+        "data_sports_dlt = data_sports_dp[(~data_sports_dp[\"Athlete\"].isin(out_athletes)) &\n",
+        "                                (data_sports_dp[\"Athlete\"] != \"Michael Phelps\")]\n",
+        "\n",
+        "data_main_dlt = data_main[(~data_main[\"Athlete\"].isin(out_athletes)) & \n",
+        "                         (data_main[\"Athlete\"] != \"Michael Phelps\")]\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "rBn_2GgHB7nk",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "6d46ca2f-5fcb-401f-9ff4-6b8c1365efba"
+      },
+      "source": [
+        "len(data_country_dlt)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "6949"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 124
+        }
       ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename=\"resources/left-join.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "8618"
+      "cell_type": "code",
+      "metadata": {
+        "id": "F0cyll4mB7nl",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "0f5c0954-5b4f-47dd-a03d-da7f07df4670"
+      },
+      "source": [
+        "len(data_sports_dlt)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "6949"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 127
+        }
       ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merged_left = pd.merge(left = data_main, right = data_country_dlt, \n",
-    "                      how = \"left\", left_on = \"Athlete\", right_on = \"Athlete\")\n",
-    "len(merged_left)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "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>Athlete</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Closing Ceremony Date</th>\n",
-       "      <th>Gold Medals</th>\n",
-       "      <th>Silver Medals</th>\n",
-       "      <th>Bronze Medals</th>\n",
-       "      <th>Total Medals</th>\n",
-       "      <th>Country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>08/24/2008</td>\n",
-       "      <td>8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>2004</td>\n",
-       "      <td>08/29/2004</td>\n",
-       "      <td>6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>08/12/2012</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Natalie Coughlin</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>08/24/2008</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Aleksey Nemov</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>2000</td>\n",
-       "      <td>10/01/2000</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>6</td>\n",
-       "      <td>Russia</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "lTngjikNB7nl",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "a7e76fc4-c967-4442-c5bb-be5fb89d1170"
+      },
+      "source": [
+        "len(data_main_dlt)"
       ],
-      "text/plain": [
-       "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
-       "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
-       "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
-       "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
-       "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
-       "4     Aleksey Nemov  24.0  2000            10/01/2000            2   \n",
-       "\n",
-       "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
-       "0              0              0             8            NaN  \n",
-       "1              0              2             8            NaN  \n",
-       "2              2              0             6            NaN  \n",
-       "3              2              3             6  United States  \n",
-       "4              1              3             6         Russia  "
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "8605"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 128
+        }
       ]
-     },
-     "execution_count": 134,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merged_left.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Right Join\n",
-    "* Devuelve un data frame con las filas que tuvieran valor en el dataset de la derecha, sin importar si tienen correspondencia en el de la izquierda o no.\n",
-    "* Las filas del data frame final que no correspondan a ninguna fila del data frame izquierdo, tendrán NAs en las columnas del data frame izquierdo.\n",
-    "* El número de filas será igual al número de filas del data frame derecho\n",
-    "    * Data Set A tiene 60 filas\n",
-    "    * Data Set B tiene 50 filas\n",
-    "    * Entonces A Right Join B tendrá 50 filas\n",
-    "* En términos de teoría de conjuntos, se trata del propio data set de la derecha quien, además tiene la intersección en su interior."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mix7obxVB7nm",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Inner Join\n",
+        "* Devuelve un data frame con las filas que tienen valor tanto en el primero como en el segundo data frame que estamos uniendo\n",
+        "* El número de filas será igual al número de filas **comunes** que tengas ambos data sets\n",
+        "    * Data Set A tiene 60 filas\n",
+        "    * Data Set B tiene 50 filas\n",
+        "    * Ambos comparten 30 filas\n",
+        "    * Entonces A Inner Join B tendrá 30 filas\n",
+        "* En términos de teoría de conjuntos, se trata de la intersección de los dos conjuntos"
       ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename=\"resources/right-join.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "8612"
+      "cell_type": "code",
+      "metadata": {
+        "id": "9NcbGr8oB7nm",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "d523a395-cff4-4cee-b91a-81e4ead08644"
+      },
+      "source": [
+        "Image(filename=\"resources/inner-join.png\")"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 80
+        }
       ]
-     },
-     "execution_count": 135,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merged_right = pd.merge(left = data_main_dlt, right = data_country_dp,\n",
-    "                       how = \"right\", left_on = \"Athlete\", right_on = \"Athlete\")\n",
-    "len(merged_right)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "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>Athlete</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Closing Ceremony Date</th>\n",
-       "      <th>Gold Medals</th>\n",
-       "      <th>Silver Medals</th>\n",
-       "      <th>Bronze Medals</th>\n",
-       "      <th>Total Medals</th>\n",
-       "      <th>Country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>8602</th>\n",
-       "      <td>Wietse van Alten</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>10/01/2000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Netherlands</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8603</th>\n",
-       "      <td>Sandra Wagner-Sachse</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>10/01/2000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Germany</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8604</th>\n",
-       "      <td>Rod White</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>10/01/2000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8605</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8606</th>\n",
-       "      <td>Erzsébet Márkus-Peresztegi</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Hungary</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8607</th>\n",
-       "      <td>Erik Vendt</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8608</th>\n",
-       "      <td>Oscar Braison</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Cuba</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8609</th>\n",
-       "      <td>Alfredo Despaigne</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Cuba</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8610</th>\n",
-       "      <td>Belinda Snell</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Australia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8611</th>\n",
-       "      <td>Yuliya Zaripova</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Russia</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "ARX5xGM8B7nn",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# data_main contiene toda la info\n",
+        "# data_country_dlt le falta la info de 7 atletas\n",
+        "merged_inner = pd.merge(left = data_main, right = data_country_dlt,\n",
+        "                       how = \"inner\", left_on = \"Athlete\", right_on = \"Athlete\")"
       ],
-      "text/plain": [
-       "                         Athlete   Age    Year Closing Ceremony Date  \\\n",
-       "8602            Wietse van Alten  21.0  2000.0            10/01/2000   \n",
-       "8603        Sandra Wagner-Sachse  31.0  2000.0            10/01/2000   \n",
-       "8604                   Rod White  23.0  2000.0            10/01/2000   \n",
-       "8605              Michael Phelps   NaN     NaN                   NaN   \n",
-       "8606  Erzsébet Márkus-Peresztegi   NaN     NaN                   NaN   \n",
-       "8607                  Erik Vendt   NaN     NaN                   NaN   \n",
-       "8608               Oscar Braison   NaN     NaN                   NaN   \n",
-       "8609           Alfredo Despaigne   NaN     NaN                   NaN   \n",
-       "8610               Belinda Snell   NaN     NaN                   NaN   \n",
-       "8611             Yuliya Zaripova   NaN     NaN                   NaN   \n",
-       "\n",
-       "      Gold Medals  Silver Medals  Bronze Medals  Total Medals        Country  \n",
-       "8602          0.0            0.0            1.0           1.0    Netherlands  \n",
-       "8603          0.0            0.0            1.0           1.0        Germany  \n",
-       "8604          0.0            0.0            1.0           1.0  United States  \n",
-       "8605          NaN            NaN            NaN           NaN  United States  \n",
-       "8606          NaN            NaN            NaN           NaN        Hungary  \n",
-       "8607          NaN            NaN            NaN           NaN  United States  \n",
-       "8608          NaN            NaN            NaN           NaN           Cuba  \n",
-       "8609          NaN            NaN            NaN           NaN           Cuba  \n",
-       "8610          NaN            NaN            NaN           NaN      Australia  \n",
-       "8611          NaN            NaN            NaN           NaN         Russia  "
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AU1UP-AMB7nn",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "d9f165a0-3c34-497a-f1e0-e0df64d77f3f"
+      },
+      "source": [
+        "len(merged_inner)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "8605"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 132
+        }
       ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merged_right.tail(10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Outer Join\n",
-    "* Devuelve un data frame con todas las filas de ambos, reemplazando las ausencias de uno o de otro con NAs en la región específica..\n",
-    "* Las filas del data frame final que no correspondan a ninguna fila del data frame derecho (o izquierdo), tendrán NAs en las columnas del data frame derecho (o izquierdo).\n",
-    "* El número de filas será igual al máximo número de filas de ambos data frames\n",
-    "    * Data Set A tiene 60 filas\n",
-    "    * Data Set B tiene 50 filas\n",
-    "    * Ambos comparten 30 filas\n",
-    "    * Entonces A Outer Join B tendrá 60 + 50 - 30 = 80 filas\n",
-    "* En términos de teoría de conjuntos, se trata de la unión de conjuntos."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "6PjIGzGeB7no",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "6855a776-2c16-4901-eef1-04e916417f04"
+      },
+      "source": [
+        "merged_inner.head()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Athlete</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Closing Ceremony Date</th>\n",
+              "      <th>Gold Medals</th>\n",
+              "      <th>Silver Medals</th>\n",
+              "      <th>Bronze Medals</th>\n",
+              "      <th>Total Medals</th>\n",
+              "      <th>Country</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Natalie Coughlin</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>08/24/2008</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Natalie Coughlin</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>2004</td>\n",
+              "      <td>08/29/2004</td>\n",
+              "      <td>2</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Natalie Coughlin</td>\n",
+              "      <td>29.0</td>\n",
+              "      <td>2012</td>\n",
+              "      <td>08/12/2012</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Aleksey Nemov</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>10/01/2000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Russia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Alicia Coutts</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>2012</td>\n",
+              "      <td>08/12/2012</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>1</td>\n",
+              "      <td>5</td>\n",
+              "      <td>Australia</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
+              "0  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
+              "1  Natalie Coughlin  21.0  2004            08/29/2004            2   \n",
+              "2  Natalie Coughlin  29.0  2012            08/12/2012            0   \n",
+              "3     Aleksey Nemov  24.0  2000            10/01/2000            2   \n",
+              "4     Alicia Coutts  24.0  2012            08/12/2012            1   \n",
+              "\n",
+              "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
+              "0              2              3             6  United States  \n",
+              "1              2              1             5  United States  \n",
+              "2              0              1             1  United States  \n",
+              "3              1              3             6         Russia  \n",
+              "4              3              1             5      Australia  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 131
+        }
       ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Image(filename=\"resources/outer-join.png\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_country_jb = data_country_dlt.append(\n",
-    "    {\n",
-    "        \"Athlete\": \"Juan Gabriel Gomila\",\n",
-    "        \"Country\": \"España\"\n",
-    "    },ignore_index = True\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "8619"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i7mkMH5bB7np",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Left Join\n",
+        "* Devuelve un data frame con las filas que tuvieran valor en el dataset de la izquierda, sin importar si tienen correspondencia en el de la derecha o no.\n",
+        "* Las filas del data frame final que no correspondan a ninguna fila del data frame derecho, tendrán NAs en las columnas del data frame derecho.\n",
+        "* El número de filas será igual al número de filas del data frame izquierdo\n",
+        "    * Data Set A tiene 60 filas\n",
+        "    * Data Set B tiene 50 filas\n",
+        "    * Entonces A Left Join B tendrá 60 filas\n",
+        "* En términos de teoría de conjuntos, se trata del propio data set de la izquierda quien, además tiene la intersección en su interior."
       ]
-     },
-     "execution_count": 144,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merged_outer = pd.merge(left = data_main, right=data_country_jb,\n",
-    "                       how = \"outer\", left_on = \"Athlete\", right_on=\"Athlete\")\n",
-    "len(merged_outer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "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>Athlete</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Closing Ceremony Date</th>\n",
-       "      <th>Gold Medals</th>\n",
-       "      <th>Silver Medals</th>\n",
-       "      <th>Bronze Medals</th>\n",
-       "      <th>Total Medals</th>\n",
-       "      <th>Country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>2008.0</td>\n",
-       "      <td>08/24/2008</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>2004.0</td>\n",
-       "      <td>08/29/2004</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Michael Phelps</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>2012.0</td>\n",
-       "      <td>08/12/2012</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Natalie Coughlin</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>2008.0</td>\n",
-       "      <td>08/24/2008</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Natalie Coughlin</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>2004.0</td>\n",
-       "      <td>08/29/2004</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZzD8AKuOB7np",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "916eacb0-59c6-4c5a-d899-4a03d7e76c3a"
+      },
+      "source": [
+        "Image(filename=\"resources/left-join.png\")"
       ],
-      "text/plain": [
-       "            Athlete   Age    Year Closing Ceremony Date  Gold Medals  \\\n",
-       "0    Michael Phelps  23.0  2008.0            08/24/2008          8.0   \n",
-       "1    Michael Phelps  19.0  2004.0            08/29/2004          6.0   \n",
-       "2    Michael Phelps  27.0  2012.0            08/12/2012          4.0   \n",
-       "3  Natalie Coughlin  25.0  2008.0            08/24/2008          1.0   \n",
-       "4  Natalie Coughlin  21.0  2004.0            08/29/2004          2.0   \n",
-       "\n",
-       "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
-       "0            0.0            0.0           8.0            NaN  \n",
-       "1            0.0            2.0           8.0            NaN  \n",
-       "2            2.0            0.0           6.0            NaN  \n",
-       "3            2.0            3.0           6.0  United States  \n",
-       "4            2.0            1.0           5.0  United States  "
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 81
+        }
       ]
-     },
-     "execution_count": 145,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merged_outer.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "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>Athlete</th>\n",
-       "      <th>Age</th>\n",
-       "      <th>Year</th>\n",
-       "      <th>Closing Ceremony Date</th>\n",
-       "      <th>Gold Medals</th>\n",
-       "      <th>Silver Medals</th>\n",
-       "      <th>Bronze Medals</th>\n",
-       "      <th>Total Medals</th>\n",
-       "      <th>Country</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>8614</th>\n",
-       "      <td>Kateryna Serdiuk</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>10/01/2000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Ukraine</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8615</th>\n",
-       "      <td>Wietse van Alten</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>10/01/2000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Netherlands</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8616</th>\n",
-       "      <td>Sandra Wagner-Sachse</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>10/01/2000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>Germany</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8617</th>\n",
-       "      <td>Rod White</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>2000.0</td>\n",
-       "      <td>10/01/2000</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>United States</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8618</th>\n",
-       "      <td>Juan Gabriel Gomila</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>España</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
+      "cell_type": "code",
+      "metadata": {
+        "id": "0rCjZZvNB7nq",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "9337b43b-9843-4141-f7b1-a9d28aed3a2e"
+      },
+      "source": [
+        "merged_left = pd.merge(left = data_main, right = data_country_dlt, \n",
+        "                      how = \"left\", left_on = \"Athlete\", right_on = \"Athlete\")\n",
+        "len(merged_left)"
       ],
-      "text/plain": [
-       "                   Athlete   Age    Year Closing Ceremony Date  Gold Medals  \\\n",
-       "8614      Kateryna Serdiuk  17.0  2000.0            10/01/2000          0.0   \n",
-       "8615      Wietse van Alten  21.0  2000.0            10/01/2000          0.0   \n",
-       "8616  Sandra Wagner-Sachse  31.0  2000.0            10/01/2000          0.0   \n",
-       "8617             Rod White  23.0  2000.0            10/01/2000          0.0   \n",
-       "8618   Juan Gabriel Gomila   NaN     NaN                   NaN          NaN   \n",
-       "\n",
-       "      Silver Medals  Bronze Medals  Total Medals        Country  \n",
-       "8614            1.0            0.0           1.0        Ukraine  \n",
-       "8615            0.0            1.0           1.0    Netherlands  \n",
-       "8616            0.0            1.0           1.0        Germany  \n",
-       "8617            0.0            1.0           1.0  United States  \n",
-       "8618            NaN            NaN           NaN         España  "
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "8618"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 133
+        }
       ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "merged_outer.tail()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "8618"
+      "cell_type": "code",
+      "metadata": {
+        "id": "P5JPJxy3B7nr",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "acc64683-99ca-4e0e-eddc-4d315a2aa1a2"
+      },
+      "source": [
+        "merged_left.head()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Athlete</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Closing Ceremony Date</th>\n",
+              "      <th>Gold Medals</th>\n",
+              "      <th>Silver Medals</th>\n",
+              "      <th>Bronze Medals</th>\n",
+              "      <th>Total Medals</th>\n",
+              "      <th>Country</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>08/24/2008</td>\n",
+              "      <td>8</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>8</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>2004</td>\n",
+              "      <td>08/29/2004</td>\n",
+              "      <td>6</td>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>8</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>2012</td>\n",
+              "      <td>08/12/2012</td>\n",
+              "      <td>4</td>\n",
+              "      <td>2</td>\n",
+              "      <td>0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Natalie Coughlin</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>2008</td>\n",
+              "      <td>08/24/2008</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Aleksey Nemov</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>2000</td>\n",
+              "      <td>10/01/2000</td>\n",
+              "      <td>2</td>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>6</td>\n",
+              "      <td>Russia</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Athlete   Age  Year Closing Ceremony Date  Gold Medals  \\\n",
+              "0    Michael Phelps  23.0  2008            08/24/2008            8   \n",
+              "1    Michael Phelps  19.0  2004            08/29/2004            6   \n",
+              "2    Michael Phelps  27.0  2012            08/12/2012            4   \n",
+              "3  Natalie Coughlin  25.0  2008            08/24/2008            1   \n",
+              "4     Aleksey Nemov  24.0  2000            10/01/2000            2   \n",
+              "\n",
+              "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
+              "0              0              0             8            NaN  \n",
+              "1              0              2             8            NaN  \n",
+              "2              2              0             6            NaN  \n",
+              "3              2              3             6  United States  \n",
+              "4              1              3             6         Russia  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 134
+        }
       ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data_main)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "8605"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WmiHHgLtB7ns",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Right Join\n",
+        "* Devuelve un data frame con las filas que tuvieran valor en el dataset de la derecha, sin importar si tienen correspondencia en el de la izquierda o no.\n",
+        "* Las filas del data frame final que no correspondan a ninguna fila del data frame izquierdo, tendrán NAs en las columnas del data frame izquierdo.\n",
+        "* El número de filas será igual al número de filas del data frame derecho\n",
+        "    * Data Set A tiene 60 filas\n",
+        "    * Data Set B tiene 50 filas\n",
+        "    * Entonces A Right Join B tendrá 50 filas\n",
+        "* En términos de teoría de conjuntos, se trata del propio data set de la derecha quien, además tiene la intersección en su interior."
       ]
-     },
-     "execution_count": 148,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data_main_dlt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "6956"
+      "cell_type": "code",
+      "metadata": {
+        "id": "C-QZVmbPB7ns",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "eadae510-9912-4177-be66-07a951dce98a"
+      },
+      "source": [
+        "Image(filename=\"resources/right-join.png\")"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 82
+        }
       ]
-     },
-     "execution_count": 149,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data_country_dp)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "6949"
+      "cell_type": "code",
+      "metadata": {
+        "id": "hilGXlhsB7ns",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "2eabd046-bf18-4f3b-cfea-d103828a093c"
+      },
+      "source": [
+        "merged_right = pd.merge(left = data_main_dlt, right = data_country_dp,\n",
+        "                       how = \"right\", left_on = \"Athlete\", right_on = \"Athlete\")\n",
+        "len(merged_right)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "8612"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 135
+        }
       ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(data_country_dlt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "8605"
+      "cell_type": "code",
+      "metadata": {
+        "id": "VwxuaheBB7nt",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "8c34cce6-f608-4317-bcc2-16d75fa3466f"
+      },
+      "source": [
+        "merged_right.tail(10)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Athlete</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Closing Ceremony Date</th>\n",
+              "      <th>Gold Medals</th>\n",
+              "      <th>Silver Medals</th>\n",
+              "      <th>Bronze Medals</th>\n",
+              "      <th>Total Medals</th>\n",
+              "      <th>Country</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>8602</th>\n",
+              "      <td>Wietse van Alten</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>2000.0</td>\n",
+              "      <td>10/01/2000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Netherlands</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8603</th>\n",
+              "      <td>Sandra Wagner-Sachse</td>\n",
+              "      <td>31.0</td>\n",
+              "      <td>2000.0</td>\n",
+              "      <td>10/01/2000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Germany</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8604</th>\n",
+              "      <td>Rod White</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>2000.0</td>\n",
+              "      <td>10/01/2000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8605</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8606</th>\n",
+              "      <td>Erzsébet Márkus-Peresztegi</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Hungary</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8607</th>\n",
+              "      <td>Erik Vendt</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8608</th>\n",
+              "      <td>Oscar Braison</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Cuba</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8609</th>\n",
+              "      <td>Alfredo Despaigne</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Cuba</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8610</th>\n",
+              "      <td>Belinda Snell</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Australia</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8611</th>\n",
+              "      <td>Yuliya Zaripova</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Russia</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                         Athlete   Age    Year Closing Ceremony Date  \\\n",
+              "8602            Wietse van Alten  21.0  2000.0            10/01/2000   \n",
+              "8603        Sandra Wagner-Sachse  31.0  2000.0            10/01/2000   \n",
+              "8604                   Rod White  23.0  2000.0            10/01/2000   \n",
+              "8605              Michael Phelps   NaN     NaN                   NaN   \n",
+              "8606  Erzsébet Márkus-Peresztegi   NaN     NaN                   NaN   \n",
+              "8607                  Erik Vendt   NaN     NaN                   NaN   \n",
+              "8608               Oscar Braison   NaN     NaN                   NaN   \n",
+              "8609           Alfredo Despaigne   NaN     NaN                   NaN   \n",
+              "8610               Belinda Snell   NaN     NaN                   NaN   \n",
+              "8611             Yuliya Zaripova   NaN     NaN                   NaN   \n",
+              "\n",
+              "      Gold Medals  Silver Medals  Bronze Medals  Total Medals        Country  \n",
+              "8602          0.0            0.0            1.0           1.0    Netherlands  \n",
+              "8603          0.0            0.0            1.0           1.0        Germany  \n",
+              "8604          0.0            0.0            1.0           1.0  United States  \n",
+              "8605          NaN            NaN            NaN           NaN  United States  \n",
+              "8606          NaN            NaN            NaN           NaN        Hungary  \n",
+              "8607          NaN            NaN            NaN           NaN  United States  \n",
+              "8608          NaN            NaN            NaN           NaN           Cuba  \n",
+              "8609          NaN            NaN            NaN           NaN           Cuba  \n",
+              "8610          NaN            NaN            NaN           NaN      Australia  \n",
+              "8611          NaN            NaN            NaN           NaN         Russia  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 138
+        }
       ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(merged_inner)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "8618"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "085GEBDCB7nu",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Outer Join\n",
+        "* Devuelve un data frame con todas las filas de ambos, reemplazando las ausencias de uno o de otro con NAs en la región específica..\n",
+        "* Las filas del data frame final que no correspondan a ninguna fila del data frame derecho (o izquierdo), tendrán NAs en las columnas del data frame derecho (o izquierdo).\n",
+        "* El número de filas será igual al máximo número de filas de ambos data frames\n",
+        "    * Data Set A tiene 60 filas\n",
+        "    * Data Set B tiene 50 filas\n",
+        "    * Ambos comparten 30 filas\n",
+        "    * Entonces A Outer Join B tendrá 60 + 50 - 30 = 80 filas\n",
+        "* En términos de teoría de conjuntos, se trata de la unión de conjuntos."
       ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(merged_left)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "8612"
+      "cell_type": "code",
+      "metadata": {
+        "id": "CCCnyLZHB7nu",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "d56db534-dd3d-4e3f-82c7-a92b8a5f7896"
+      },
+      "source": [
+        "Image(filename=\"resources/outer-join.png\")"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "image/png": "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\n",
+            "text/plain": [
+              "<IPython.core.display.Image object>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 83
+        }
       ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(merged_right)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eYbTeseVB7nv",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "data_country_jb = data_country_dlt.append(\n",
+        "    {\n",
+        "        \"Athlete\": \"Juan Gabriel Gomila\",\n",
+        "        \"Country\": \"España\"\n",
+        "    },ignore_index = True\n",
+        ")"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ggvPoPn9B7nv",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "933f0eab-70be-4fc1-ee5a-ef14e4ec712c"
+      },
+      "source": [
+        "merged_outer = pd.merge(left = data_main, right=data_country_jb,\n",
+        "                       how = \"outer\", left_on = \"Athlete\", right_on=\"Athlete\")\n",
+        "len(merged_outer)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "8619"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 144
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "MPNxMoI7B7nw",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "cc02d613-1b33-477d-85cb-1cfaa5d3f0da"
+      },
+      "source": [
+        "merged_outer.head()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Athlete</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Closing Ceremony Date</th>\n",
+              "      <th>Gold Medals</th>\n",
+              "      <th>Silver Medals</th>\n",
+              "      <th>Bronze Medals</th>\n",
+              "      <th>Total Medals</th>\n",
+              "      <th>Country</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>2008.0</td>\n",
+              "      <td>08/24/2008</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>2004.0</td>\n",
+              "      <td>08/29/2004</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Michael Phelps</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>2012.0</td>\n",
+              "      <td>08/12/2012</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Natalie Coughlin</td>\n",
+              "      <td>25.0</td>\n",
+              "      <td>2008.0</td>\n",
+              "      <td>08/24/2008</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>6.0</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Natalie Coughlin</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>2004.0</td>\n",
+              "      <td>08/29/2004</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>5.0</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "            Athlete   Age    Year Closing Ceremony Date  Gold Medals  \\\n",
+              "0    Michael Phelps  23.0  2008.0            08/24/2008          8.0   \n",
+              "1    Michael Phelps  19.0  2004.0            08/29/2004          6.0   \n",
+              "2    Michael Phelps  27.0  2012.0            08/12/2012          4.0   \n",
+              "3  Natalie Coughlin  25.0  2008.0            08/24/2008          1.0   \n",
+              "4  Natalie Coughlin  21.0  2004.0            08/29/2004          2.0   \n",
+              "\n",
+              "   Silver Medals  Bronze Medals  Total Medals        Country  \n",
+              "0            0.0            0.0           8.0            NaN  \n",
+              "1            0.0            2.0           8.0            NaN  \n",
+              "2            2.0            0.0           6.0            NaN  \n",
+              "3            2.0            3.0           6.0  United States  \n",
+              "4            2.0            1.0           5.0  United States  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 145
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eLVu22y7B7nw",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "1c5e82e7-ea35-42b0-dcaf-a3cac78e39c0"
+      },
+      "source": [
+        "merged_outer.tail()"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "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>Athlete</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Year</th>\n",
+              "      <th>Closing Ceremony Date</th>\n",
+              "      <th>Gold Medals</th>\n",
+              "      <th>Silver Medals</th>\n",
+              "      <th>Bronze Medals</th>\n",
+              "      <th>Total Medals</th>\n",
+              "      <th>Country</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>8614</th>\n",
+              "      <td>Kateryna Serdiuk</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>2000.0</td>\n",
+              "      <td>10/01/2000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Ukraine</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8615</th>\n",
+              "      <td>Wietse van Alten</td>\n",
+              "      <td>21.0</td>\n",
+              "      <td>2000.0</td>\n",
+              "      <td>10/01/2000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Netherlands</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8616</th>\n",
+              "      <td>Sandra Wagner-Sachse</td>\n",
+              "      <td>31.0</td>\n",
+              "      <td>2000.0</td>\n",
+              "      <td>10/01/2000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Germany</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8617</th>\n",
+              "      <td>Rod White</td>\n",
+              "      <td>23.0</td>\n",
+              "      <td>2000.0</td>\n",
+              "      <td>10/01/2000</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>United States</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8618</th>\n",
+              "      <td>Juan Gabriel Gomila</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>España</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                   Athlete   Age    Year Closing Ceremony Date  Gold Medals  \\\n",
+              "8614      Kateryna Serdiuk  17.0  2000.0            10/01/2000          0.0   \n",
+              "8615      Wietse van Alten  21.0  2000.0            10/01/2000          0.0   \n",
+              "8616  Sandra Wagner-Sachse  31.0  2000.0            10/01/2000          0.0   \n",
+              "8617             Rod White  23.0  2000.0            10/01/2000          0.0   \n",
+              "8618   Juan Gabriel Gomila   NaN     NaN                   NaN          NaN   \n",
+              "\n",
+              "      Silver Medals  Bronze Medals  Total Medals        Country  \n",
+              "8614            1.0            0.0           1.0        Ukraine  \n",
+              "8615            0.0            1.0           1.0    Netherlands  \n",
+              "8616            0.0            1.0           1.0        Germany  \n",
+              "8617            0.0            1.0           1.0  United States  \n",
+              "8618            NaN            NaN           NaN         España  "
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 146
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "p2OX_U-uB7nx",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "62542f35-e2f2-4891-8005-58dc2cad099c"
+      },
+      "source": [
+        "len(data_main)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "8618"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 147
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jNWhMGyrB7ny",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "0fbb71e5-6a5c-4982-eddf-e35b9fd9f0fb"
+      },
+      "source": [
+        "len(data_main_dlt)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "8605"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 148
+        }
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "8619"
+      "cell_type": "code",
+      "metadata": {
+        "id": "pVVKSDe8B7ny",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "5f8177f6-1463-4810-86c6-ff1eb4920675"
+      },
+      "source": [
+        "len(data_country_dp)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "6956"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 149
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "SbLPsMXmB7nz",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "ae7ca8d1-5cd6-4e84-9ba9-ead709569724"
+      },
+      "source": [
+        "len(data_country_dlt)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "6949"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 150
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "JM2b7R_DB7n0",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "61d34041-00cd-44c8-c0e7-971443baa512"
+      },
+      "source": [
+        "len(merged_inner)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "8605"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 151
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CUYARPzMB7n1",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "9624c61b-de77-4df9-b8b0-429da532787f"
+      },
+      "source": [
+        "len(merged_left)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "8618"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 152
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Jg7o_yuUB7n1",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "abee5bfc-a9e6-4841-9378-d0fef3d305ec"
+      },
+      "source": [
+        "len(merged_right)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "8612"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 153
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3MlUiCAzB7n2",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "1b45798c-450f-47ca-9fe4-37491598b4a5"
+      },
+      "source": [
+        "len(merged_outer)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "8619"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 154
+        }
       ]
-     },
-     "execution_count": 154,
-     "metadata": {},
-     "output_type": "execute_result"
     }
-   ],
-   "source": [
-    "len(merged_outer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  ]
+}
\ No newline at end of file
diff --git "a/notebooks/T3 - 1 - Statistics - Correlaci\303\263n-Colab.ipynb" "b/notebooks/T3 - 1 - Statistics - Correlaci\303\263n-Colab.ipynb"
new file mode 100644
index 00000000..3e59111a
--- /dev/null
+++ "b/notebooks/T3 - 1 - Statistics - Correlaci\303\263n-Colab.ipynb"	
@@ -0,0 +1,869 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T3%20-%201%20-%20Statistics%20-%20Correlación-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_ads = pd.read_csv(\"/content/python-ml-course/datasets/ads/Advertising.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>TV</th>\n",
+       "      <th>Radio</th>\n",
+       "      <th>Newspaper</th>\n",
+       "      <th>Sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>230.1</td>\n",
+       "      <td>37.8</td>\n",
+       "      <td>69.2</td>\n",
+       "      <td>22.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>44.5</td>\n",
+       "      <td>39.3</td>\n",
+       "      <td>45.1</td>\n",
+       "      <td>10.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17.2</td>\n",
+       "      <td>45.9</td>\n",
+       "      <td>69.3</td>\n",
+       "      <td>9.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>151.5</td>\n",
+       "      <td>41.3</td>\n",
+       "      <td>58.5</td>\n",
+       "      <td>18.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>180.8</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>58.4</td>\n",
+       "      <td>12.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      TV  Radio  Newspaper  Sales\n",
+       "0  230.1   37.8       69.2   22.1\n",
+       "1   44.5   39.3       45.1   10.4\n",
+       "2   17.2   45.9       69.3    9.3\n",
+       "3  151.5   41.3       58.5   18.5\n",
+       "4  180.8   10.8       58.4   12.9"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_ads.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "200"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data_ads)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_ads[\"corrn\"] = (data_ads[\"TV\"] - np.mean(data_ads[\"TV\"]))* (data_ads[\"Sales\"] - np.mean(data_ads[\"Sales\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>TV</th>\n",
+       "      <th>Radio</th>\n",
+       "      <th>Newspaper</th>\n",
+       "      <th>Sales</th>\n",
+       "      <th>corrn</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>230.1</td>\n",
+       "      <td>37.8</td>\n",
+       "      <td>69.2</td>\n",
+       "      <td>22.1</td>\n",
+       "      <td>670.896956</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>44.5</td>\n",
+       "      <td>39.3</td>\n",
+       "      <td>45.1</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>371.460206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17.2</td>\n",
+       "      <td>45.9</td>\n",
+       "      <td>69.3</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>613.181206</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>151.5</td>\n",
+       "      <td>41.3</td>\n",
+       "      <td>58.5</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>19.958456</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>180.8</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>58.4</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>-37.892794</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      TV  Radio  Newspaper  Sales       corrn\n",
+       "0  230.1   37.8       69.2   22.1  670.896956\n",
+       "1   44.5   39.3       45.1   10.4  371.460206\n",
+       "2   17.2   45.9       69.3    9.3  613.181206\n",
+       "3  151.5   41.3       58.5   18.5   19.958456\n",
+       "4  180.8   10.8       58.4   12.9  -37.892794"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_ads.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_ads[\"corr1\"] = (data_ads[\"TV\"] - np.mean(data_ads[\"TV\"]))**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>TV</th>\n",
+       "      <th>Radio</th>\n",
+       "      <th>Newspaper</th>\n",
+       "      <th>Sales</th>\n",
+       "      <th>corrn</th>\n",
+       "      <th>corr1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>230.1</td>\n",
+       "      <td>37.8</td>\n",
+       "      <td>69.2</td>\n",
+       "      <td>22.1</td>\n",
+       "      <td>670.896956</td>\n",
+       "      <td>6898.548306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>44.5</td>\n",
+       "      <td>39.3</td>\n",
+       "      <td>45.1</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>371.460206</td>\n",
+       "      <td>10514.964306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17.2</td>\n",
+       "      <td>45.9</td>\n",
+       "      <td>69.3</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>613.181206</td>\n",
+       "      <td>16859.074806</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>151.5</td>\n",
+       "      <td>41.3</td>\n",
+       "      <td>58.5</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>19.958456</td>\n",
+       "      <td>19.869306</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>180.8</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>58.4</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>-37.892794</td>\n",
+       "      <td>1139.568806</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      TV  Radio  Newspaper  Sales       corrn         corr1\n",
+       "0  230.1   37.8       69.2   22.1  670.896956   6898.548306\n",
+       "1   44.5   39.3       45.1   10.4  371.460206  10514.964306\n",
+       "2   17.2   45.9       69.3    9.3  613.181206  16859.074806\n",
+       "3  151.5   41.3       58.5   18.5   19.958456     19.869306\n",
+       "4  180.8   10.8       58.4   12.9  -37.892794   1139.568806"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_ads.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_ads[\"corr2\"] = (data_ads[\"Sales\"] - np.mean(data_ads[\"Sales\"]))**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>TV</th>\n",
+       "      <th>Radio</th>\n",
+       "      <th>Newspaper</th>\n",
+       "      <th>Sales</th>\n",
+       "      <th>corrn</th>\n",
+       "      <th>corr1</th>\n",
+       "      <th>corr2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>230.1</td>\n",
+       "      <td>37.8</td>\n",
+       "      <td>69.2</td>\n",
+       "      <td>22.1</td>\n",
+       "      <td>670.896956</td>\n",
+       "      <td>6898.548306</td>\n",
+       "      <td>65.246006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>44.5</td>\n",
+       "      <td>39.3</td>\n",
+       "      <td>45.1</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>371.460206</td>\n",
+       "      <td>10514.964306</td>\n",
+       "      <td>13.122506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17.2</td>\n",
+       "      <td>45.9</td>\n",
+       "      <td>69.3</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>613.181206</td>\n",
+       "      <td>16859.074806</td>\n",
+       "      <td>22.302006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>151.5</td>\n",
+       "      <td>41.3</td>\n",
+       "      <td>58.5</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>19.958456</td>\n",
+       "      <td>19.869306</td>\n",
+       "      <td>20.048006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>180.8</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>58.4</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>-37.892794</td>\n",
+       "      <td>1139.568806</td>\n",
+       "      <td>1.260006</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      TV  Radio  Newspaper  Sales       corrn         corr1      corr2\n",
+       "0  230.1   37.8       69.2   22.1  670.896956   6898.548306  65.246006\n",
+       "1   44.5   39.3       45.1   10.4  371.460206  10514.964306  13.122506\n",
+       "2   17.2   45.9       69.3    9.3  613.181206  16859.074806  22.302006\n",
+       "3  151.5   41.3       58.5   18.5   19.958456     19.869306  20.048006\n",
+       "4  180.8   10.8       58.4   12.9  -37.892794   1139.568806   1.260006"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_ads.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "corrn = sum(data_ads[\"corrn\"])/np.sqrt(sum(data_ads[\"corr1\"]) * sum(data_ads[\"corr2\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.782224424861606"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corrn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def corr_coeff(df, var1, var2):\n",
+    "    df[\"corrn\"] = (df[var1] - np.mean(df[var1]))* (df[var2] - np.mean(df[var2]))\n",
+    "    df[\"corr1\"] = (df[var1] - np.mean(df[var1]))**2\n",
+    "    df[\"corr2\"] = (df[var2] - np.mean(df[var2]))**2\n",
+    "    corr_p = sum(df[\"corrn\"])/np.sqrt(sum(df[\"corr1\"]) * sum(df[\"corr2\"]))\n",
+    "    return corr_p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.782224424861606"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "corr_coeff(data_ads, \"TV\", \"Sales\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = data_ads.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TV, TV : 1.0\n",
+      "TV, Radio : 0.05480866446583009\n",
+      "TV, Newspaper : 0.056647874965056993\n",
+      "TV, Sales : 0.782224424861606\n",
+      "Radio, TV : 0.05480866446583009\n",
+      "Radio, Radio : 1.0\n",
+      "Radio, Newspaper : 0.3541037507611752\n",
+      "Radio, Sales : 0.5762225745710553\n",
+      "Newspaper, TV : 0.056647874965056993\n",
+      "Newspaper, Radio : 0.3541037507611752\n",
+      "Newspaper, Newspaper : 1.0\n",
+      "Newspaper, Sales : 0.22829902637616525\n",
+      "Sales, TV : 0.782224424861606\n",
+      "Sales, Radio : 0.5762225745710553\n",
+      "Sales, Newspaper : 0.22829902637616525\n",
+      "Sales, Sales : 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for x in cols:\n",
+    "    for y in cols:\n",
+    "        print(x + \", \"+ y + \" : \" + str(corr_coeff(data_ads, x, y)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,'Gasto en TV vs Ventas del Producto')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c798ac8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ads[\"TV\"], data_ads[\"Sales\"], \"ro\")\n",
+    "plt.title(\"Gasto en TV vs Ventas del Producto\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,'Gasto en Radio vs Ventas del Producto')"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c601828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ads[\"Radio\"], data_ads[\"Sales\"], \"go\")\n",
+    "plt.title(\"Gasto en Radio vs Ventas del Producto\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,'Gasto en Periódico vs Ventas del Producto')"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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stt4YxCwjQW+QquOauyBeXYvV7uIbQxFFD+W2H9qiR8SMU1fVdOyxx1Y91F6vyBoDddJxMBcXl29rNC0uVlnqfKqu07SYpR8Ts3bKU0TR8eva8RXtQOSYohL0hqlyUOOuiFeXBmruwkOuDEXl7Vt9RD3ECrpMLg0Tk7899hW7SnPHNK/1XcpJ37fenUU+iK75KES3kaB3jDJ28arEqwu2+KroW9fvoody13wUouPENOOrmmRyKabsK/Yk5o7x/yws6LW+LWRDFzEgG3o/qdsuniYQWVPVtvgu2dq7RNFx0XETsYKuXC4do+5kXlnbT6PKBGJ9Gf1HybhEF1Eul55St1Mv1plWtSOxD/HhQ/IliNlEgt4x6nbqZTnTFhbqdST2IVqjDw8dIfKQoHeQOsMAs94Azjqr3tDDPkRrZD1cduxQD03RD3on6G94A6xY4VuSK1b432mou3Q6bYX19SE+PO/hIvOL6AUxntOqpmmjXDZtSo/G2LRp+XoK9eomXY/WiIkAUiinaAOGGOWyYgU88MDe8+fn4f779/zWsG9iUkZRLlmRQBppSrTBIKNc0sQ8bX4fHHCim4z8F1n567tk8xdinF4J+vx83Pw2HHBlbfay8XebPtj8hRinV4K+cWPc/KZvxrLxy32Pd56Fh1HfcsIIARQ7RYEjgK8A1wDfAU4L89cAXwauC58HFm2riq7/mzY5Nz/vHVTz83s7REc06YArm3+lzylR5XAWonmoMH3u/cBbnHOPA44D3mhmRwNnAJc4544CLgm/a+fss70D1Dn/efbZTew1n7I2+z7b+NX5RojuUijozrlbnHPfDN/vxbfUDwNOBs4Lq50HvKiuQpalaZNGWZt9HzrZZNHnh9GQmAWzlyhPKRu6ma0DngxcBhzsnLsFvOgDj8j4z0Yz225m2++4447pSpvB+MV92mnNtiLL2uz77HCrY1xUCVM5+u6DETUSY5fxJhz2A64AXhJ+3z22/K6ibdSRPrfNdLDj5Shjs+96J5ssqrShd9Ue3/Vz02cfjJgMqsyHDqwE/gZ4c2LetcAh4fshwLVF26lD0LMubl3w9VGV4HVRmLr6kEnSlbFkRXPECnqhycXMDPgEcI1z7kOJRRcBp4bvpwJ/Nd27wmS0lQ52lqkqeVgX7fF9cPpmmbfm5mS6mnVibOjHA6cAzzazK8N0EvA+4Hlmdh3wvPC7cdpKByump4vO4awu/7GDgjRBmg8GfI9p2dRnm5gol68558w590Tn3DFhutg5t8s59xzn3FHh88dNFHicOtLBtu2oa3v/eVRZtrRzZ9ZuutrY3shtMt7pKa1sXXurEA0RY5epaqprTNEqnVht21Db3n/TZRudu5ENuO165/lguops6sOHIWZbbIK2MzW2vf886ixbV+rdlXKUoY9lFuUYZLbFJmjbUdf2/vOos2xdqfdJJ3kzRpKuO9T73K9BVMvMC/q4TXjNmvT1mnLUddFRWFSGKsrWhXovLcF553mDxQgzOPXUbjvUlUhMjJhpQU/rcXfPPbBq1fL1mmztdLm1VacTswv1TgtZdA4uvri5MkxKnePQih4RY2ivaqrLKTopWR1bFhba7SnY5Z6KdTox2663nIuiq1BlT9GqprYFfVww2kgR0ARNCGMXe3kmmeQYdL1OYnaJFfSZMbmkmVfGnV8jumCvnpSmEjd1xYmZxqTHoAtmHyGmYWYEPcs+2reIhiKa6rreBSdmFpMeAzkXRd+ZGUHPajk6N6wbOKueVfe+7HJrdpq3BzkXRZ+ZGUHPajmOOl8M5QbOayFXaX7pcmu2y28PQtRJ5wU9NndI0XpdblFWSVbiphFVml+62pqdlXMtxF7EeE6rmspGucTmDslab9Om5ZEO47+bCItrIxQvGVo4xCieGNoOgRSiShhC2GJsGFnWem0ne2o70VbdYXhVJ0VT7L8Q6QxC0GM7emStV6WY9TGuuc4HypCGomt7/0IUMQhBn7aFXpW5YdIbvgs9D+tqeVb5sKr6wVe2zm0/eIUoYhCCPo0NPUtMmxScIQtFlQ+rabeVFPCFBedWrSr38O3Cg1eIPGIFvdNRLrGhcWnrvf711UU6TBrb3cVoi6pGHKoyNHCabY33Ct21C37xi+XrFEX2KMxRDIYY1a9qajqXS92j08e0ALvkbOuq3XuabcWa2/Ja27Khi67DEEwuXSHthu+jGaVtW3Ud24p1iBfVsUsPXiHGiRV0DUEXydKSf23PGv3dzHew6TJzc17exulD2bPIGn4tyerV3enFKsQkaAi6ihn1ilxcTF/eB3vrtLbqKmzvVZPmp1i5EhYWupeSQIi6kaCXpIuOzlgmLXtTKXknIc0h/slPwp13NpeSoKsPOzGDxNhlqprqsqE3bcut295a5/b72EGqy/TJoSo/QX9hVpyiXYm2qIqyZWjiJlWcdjZ9edh14doWkzMzgl7FDVWUzKrJm7NMfZq6SfsiWm3Ql4edzmG/iRX03tvQpx0KLWkfLruPKhnZYbPKkSzDaN1XvKKZ0Yma8hv00Rbdl05JXR4yUFRIjOpXNXWxhR7TMaXuVkyZOPeYdetoHTbhNyj7ttEFm3BfTBlqofcbZsXkMu0NVdQxJS2velMmjbT6dOEBVAdlBadLQpr1YOnCAydZxq4cL1GeygQdOAe4Hbg6Me9M4GbgyjCdFLOzLka5FNnON22q/0bIe6iM1yfmAdTHm7SsLbrrLc4uCmiXHjCiHFUK+gnAU1IE/T/G7CA5tdH1v+giLrrxmhCOMvtYWIgX/zS6elOXPc5dd0Z2/YEj+kWlJhdgXR8FvUz63SyRy2sNV8WmTXGjK23btndqWHBu5co4Ye5iq3GSsm3b5tz8fLcFs+sPHNEvmhD0G4CrgknmwJjtNC3oVbSSsoRjfr74v7GdlNJyuW/aFF+fhYW4unS51bht2/K3j4WF+OPVtYeTc90+1qJ/1C3oBwPz+NQBW4Bzcv67EdgObF+7dm1T9XfOVdNKmrSFnjfoRlLcy9z409Yn5v9tmGTKtM6zjtf8fHfE3Lluvw2J/lGroMcuG5/62EKverSi8Zu7jEhPU58YM0VbIpQn0uMPlj6ZMrrqrxD9o+4W+iGJ76cDF8Zsp6s29Dq2EZOne3GxmZ6hsWaKtswEMcdqVE6ZMsQsUmWUywXALcB9wE3Aa4DzgW8HG/pFSYHPm7oY5TLJNmLi0mPixc2ayd0Sa6Zoq/Ubc6xGoi1ThphFBt+xaFqhnvT/ZSJnyvT+rPPVPFao22r9xhyrZHllyhCzxqAFfdpW2jT/L2siGa0fE5ZYF7FlbrP1mxTprockCtE0gxb0uvK3xPx/UrNEm63KsjHebbd+ZVYRYjmxgt7LMUWnHRtzmv9nZURcXPSj43SV0ZioO3f6TIBbtnR7WLa+lVeIOhn0mKLTpiyd5v99HYJuNCZqmWHZ2kxnO0l5hZh1eino04pq2v/N4MgjiwUsbQzLIQ5C3OVxRKuij/nXhcglxi5T1dSlKJe0/Cnj0yzbbbsY712lfV92etEnGLJTtApiY5+T8c8xdMGpWAVd65FZtQB38YElRBaxgt5Lp2gVmJVbf/XqYtPKyEyRHBYu5n9dpGvO36rLM61jXYgmGbRTtArm58utHzNW5+bNzYzxCfXZf5Njm44/9Np0/lY9JmaWA3zNmm7b1WX3F7nENOOrmrrU9T/W3FLG3NCUmaIu+29shsg2qNpEklbXVat8bvmu2tVl959dkA09+wbIc4hm9VKMEY+mUrvWZf+t065cRaqGaQcrKSpT1mhQXbGry+4/u0jQXb7AZrWkt22rP6thDHnCVNebQF3braplWSTW0+6na47gcbpePpFOFYESEnQXl5Z1fBoxTfKuaXORFAnTtHnRs+rVx5Z/lfvpegu46+UTe1NVY0aC7sq30Ku6MfIeJDEPibwbd3yotmQrLblOGkUXV97yaVoZTbUsp91P123UXS+f2JuqHsISdJdvQ6/zxsg6ibEZF/MeCDFpZssO35a8uNKEe1oh6UsL3bnu9yPoevnEcqpqzEjQA1k3QNU3RnJ7Cws+YiJPzEdT2gDPZd8sYkVs0otrWqFsqmWpFqzoGmqhV0STLZk0IVm50ov1aP954hvr3IsV8yyRnvTimtaENKpTE+dDLdh60HGdDNnQKyArnnrTpum2OY0zMU/UR+sl97HffsvX2W+/7LC6WJGeNC96XlRQG61hiUuz6M1nOhTlMiV5NuxJTC5FF3RMK3nbtvz18kIeR9OKFXt3fFm5cm/zzrRjksaUJavVXlfExajcbT5IZhVF17TPTAt6kYkgJtojtsPJtm3x4pa3ndhkYQsL6U7LKluseTb8IhNSHTHRMQ8YiUt9KP69fWZa0IsEpygssIy9uuhtoMhZOnqQxMbMV3UT5TmLY/bdZKst5mEncakPtdDbZ6YFvajVnCeeZezUoxZr1vIYZ2lRZ6E6bqIy4ZxZ+960KX2dafwUWcQ87CQu9SEbevvMtKA7l56vZXQRxornNC30PDNKrMNyfFq1qpqbaJKwyPEbuEstdIlL/cgR3S4zL+jO5ZsVyoYBZolXXuulrO0x+bCZm1v+n4WF6mLlJ6nr+L6btKtmRS0lz4EQQ0aC7vJbFXm24pgp2VrO2s8krdi6Xm9jHmJlUiI0bVftYwuxj2UW3WSmBH3S7upZorSwUGxLX7my+AadRJzrEsoYs0WZlAiyq+aj4yOqZGYEPevGicltnXfTxZgmYkS2qJU2vjxrX+Mx7WVbfjGhnOPbHj3YYssusdqDIkNElcyMoJe1CY+EMSnaI1NDUpRiIivqyBNeFNM+acsvRmDGxXzS0XvKdtoa4kNBsduiSmZG0MvmPM9yZI6nBqiqhZ5HXgx7lpDmRahM09s11lFcVOey6QWGapZQC11UycwIep4dvKw5ZTw1QJ7AVSE8kyS9KkqtO2lrOPZNp8rMjEMWvSE/rETzVCbowDnA7cDViXlrgC8D14XPA2N21qQNPa9LfJGQJrcda0+ehFg7f0wagmnFMPZNp2j7ZUwNQzdLDNWcJJonVtDnKOZc4MSxeWcAlzjnjgIuCb9bYcMG2LoVFhfBzH9u3ernZ7F2bfaynTuXb/uGG+DBB+HOO/304IN+Xt72Y1hagnvv3Xv+ypWwZcuedTZuhB07vNTt2OH/s3JlXPnLkHdMRqxevadsZbeTNr/Mun0kef1Ucc0IUUiM6gPrWN5CvxY4JHw/BLg2ZjtN50PPa7k3nS1wnDxTUcw6VQ+jV2RiKrLR521nFm3oQlQJVdrQUwT97rHld8Vsp0lBL+renibodY2iU8bskzQ35K1ThxgWdbaaNnIlq7+AzBJC5NMZQQc2AtuB7WvXrq2kcjEiUDb6ZdTyrVJQ8kQ3xn5e5DSMOQ5lBXOSyJtpj4UQIp+6Bb01k0usMEyas6SMyBSJZZ7JZDzGG/ZOvjWtCE7y/0li42MYckSLEHVTt6B/ADgjfD8DeH/MdqYR9KLem+PCEBtXPanIxIhl2beEtAGjpzFJTCqik/ReLWLoES3TILOTKKIyQQcuAG4B7gNuAl4DLOCjW64Ln2tidjapoMeIc5owJG+UMsIaIzIxYjlpL9aqqEpEq2hdz2oLPSb1g0xRoohKW+hVTZMKehW9NqcdYHmcGLGcJs9MFVQlolWIziwK1zQJ4ob+oBPlGJSgF7WwY4Rh27a9h3+bdFvOxd+IWZEdTYhblfupwiwwa6aFmGtEpigRw6AEvch2Po0Tc9OmPaGM8/PxQ6hN6nCss+dp0T5nQUS7RIxYq4UuYhiUoNfVoq0iiqRMbHaZDjdVCL/EvF1iM1wO1RSl6686BiXoztVzcTTZOipjoqkiKVgXhWLWbvDYczDE49LF66/PDE7Q66Ap+2VeD8zxfVXhAM7bTluv8rN6gw9RrGPo2vXXd2IF3fy6zbB+/Xq3ffv2xvZXxLp1PuHVOIuLPplSFYwSbO3enb58fF9zc/7Sz8PMJ3zKI2s7Mf+tgyaOtegOXbv++o6ZXeGcW1+0Xky2xcGyZYvPIJgkJqNgGTZvzhbz0b6Wlrzgzc35qYiYbIRdy2SYlQVy0uyQott07fqbFWZa0CdJvVuWPMHautV/vupVe1LkPvBA/vZiHzige0DTAAAIj0lEQVRNPKzKoBt8tuja9TczxNhlqpq6ZkNvgiJbYlYnI7NhRbnMqg19lunS9dd3kA29G6TZ0Fev3vMmYJb93wZPTSMsLXkT1M6dvmW+ZYsGfRAiBtnQayJp7163zv/OW2fzZjj11HrNOn1BI/gIUS8r2i5Anxhvbe/Y4X/DHnFKW+e887JFfGEBdu1Kny+EEGVQC70EaREru3f7+WXWSXLWWbBq1fJ5q1b5+UIIUQYJegliQu/Khudt2ADnnLPcJHPOOdWYI8qah7LWqZO29993dPzEMmI8p1VNfY9ymSYHetM95GKiStqOPGl7/31Hx292QF3/q6cPIjmiDw+fafavkLj2z59oDgl6TVQ5MHOdohSTp6btXNyT7r8rD822afv8ieaQoHecIlGaVuyH3EJvu9xdQcdhdogVdDlFWyIrGubUU71z9JRT9qQDGIVHlnF4xXS9brt79qT7z3IwpyX/GjJtnz/RQWJUv6pJLfQ9lB24epKWV5XmobqYZP9ZLVOz2TO7tH3+RDOgrv/dJiudbB5KPepZWvJvMGmXrtLxiiGirv8dJ+11uQhlJvRs2JCd50bpeMUsI0FvifHUvfPz+evLNrqcxcX0+XroiVlGgt4iyWRV5523d4t9lImxyoReQ+lZKIegEHsjQe8IaYNtnH++Ny1UlZlwlDhsmuiZrtDE4CRC9I1eOkWVV3syNK6nEP1ksE7RIbUym2YI43oOxWQkRB30TtDLpqcVe2hyXM86hFcPcyHy6Z2g19HK7FOrb5qyNuVIrEt49TAXooCY3kdVTVX0FK06f0WfEj1VUdYmehbWlWNEyajErEITPUXN7AbgXuAB4H5XYLSvwilaNOhyWfrkKOxLWefm0jv+TNvTtS/1F6JqmnSKPss5d0zMzqqg6nC1PjkK+1LWumz1ij0XIp/e2NCTtuPNm/1NXMXo8U06CqelL2WtS3gVey5EATF2mawJ+CHwTeAKYGPGOhuB7cD2tWvXTmQ/qtPOPWs29KZQFkAhqoMmBrgADg2fjwC+BZyQt/6kTtG6E/n3SXz6VFYhRDXECnplPUXN7Ezgp865D2atM6lTtC4nmxBC9IHanaJmtq+Z7T/6Dvw2cPWk28ujL7ZjIYRok2mcogcDXzOzbwGXA//bOffX1RRrOYpuEEKIYlZM+kfn3A+AJ1VYlkxGUQxKyCWEENlMLOhNs2GDBFwIIfLoTRy6EEKIfCToQggxECToQggxECToQggxEAYt6H3Kcy6EENPSmyiXsoyn2R0NsgCKlhFCDJPBttA1uo0QYtYYrKD3JXe4EEJUxWAFXflfhBCzxmAFXflfhBCzxmAFXaPbCCFmjcFGuYDyvwghZovBttCFEGLWkKALIcRAkKALIcRAkKALIcRAkKALIcRAmGlBV/IuIcSQGHTYYh5K3iWEGBoz20JX8i4hxNCYWUFX8i4hxNCYWUFX8i4hxNCYWUFX8i4hxNCYWUFX8i4hxNCY2SgXUPIuIcSwmNkWuhBCDA0JuhBCDAQJuhBCDAQJuhBCDAQJuhBCDARzzjW3M7M7gB0T/PUg4M6Ki9MFhlivIdYJVK8+McQ6LTrnHl60UqOCPilmtt05t77tclTNEOs1xDqB6tUnhlinWGRyEUKIgSBBF0KIgdAXQd/adgFqYoj1GmKdQPXqE0OsUxS9sKELIYQopi8tdCGEEAVI0IUQYiB0XtDN7EQzu9bMrjezM9ouzySY2RFm9hUzu8bMvmNmp4X5a8zsy2Z2Xfg8sO2yToKZzZvZP5nZl8LvR5nZZaFenzazVW2XsQxmdoCZfdbMvhfO2dOGcK7M7PRw/V1tZheY2T59PFdmdo6Z3W5mVyfmpZ4f83w06MdVZvaU9kpeP50WdDObB/4UeD5wNPAyMzu63VJNxP3AW5xzjwOOA94Y6nEGcIlz7ijgkvC7j5wGXJP4/d+BD4d63QW8ppVSTc5ZwF875x4LPAlft16fKzM7DHgTsN459wRgHvhD+nmuzgVOHJuXdX6eDxwVpo3AnzVUxlbotKADTwWud879wDn3C+BC4OSWy1Qa59wtzrlvhu/34gXiMHxdzgurnQe8qJ0STo6ZHQ68APh4+G3As4HPhlV6VS8z+xXgBOATAM65Xzjn7mYA5wo//sFDzWwFsBq4hR6eK+fc3wE/HpuddX5OBj7lPN8ADjCzQ5opafN0XdAPA25M/L4pzOstZrYOeDJwGXCwc+4W8KIPPKK9kk3MR4C3Ag+G3wvA3c65+8Pvvp2zRwN3AJ8MZqSPm9m+9PxcOeduBj4I7MQL+U+AK+j3uUqSdX4GpyF5dF3QLWVeb+MszWw/4HPAHzvn7mm7PNNiZi8EbnfOXZGcnbJqn87ZCuApwJ85554M/IyemVfSCDblk4FHAYcC++LNEeP06VzF0PfrsRRdF/SbgCMSvw8HftRSWabCzFbixXzJOff5MPu20etf+Ly9rfJNyPHA75nZDXhz2LPxLfYDwms99O+c3QTc5Jy7LPz+LF7g+36ungv80Dl3h3PuPuDzwNPp97lKknV+BqMhMXRd0P8ROCp44lfhnTgXtVym0gS78ieAa5xzH0osugg4NXw/Ffirpss2Dc65tznnDnfOrcOfm//rnNsAfAV4aVitV/Vyzt0K3GhmjwmzngN8l56fK7yp5TgzWx2ux1G9enuuxsg6PxcBrwzRLscBPxmZZgaJc67TE3AS8M/A94HNbZdnwjo8A/+adxVwZZhOwtubLwGuC59r2i7rFHV8JvCl8P3RwOXA9cBfAg9pu3wl63IMsD2cry8ABw7hXAHvAL4HXA2cDzykj+cKuADvB7gP3wJ/Tdb5wZtc/jTox7fxUT6t16GuSV3/hRBiIHTd5CKEECISCboQQgwECboQQgwECboQQgwECboQQgwECboQQgwECboQQgyE/w8RnrkeMVQvhgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c094e80>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_ads[\"Newspaper\"], data_ads[\"Sales\"], \"bo\")\n",
+    "plt.title(\"Gasto en Periódico vs Ventas del Producto\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "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>TV</th>\n",
+       "      <th>Radio</th>\n",
+       "      <th>Newspaper</th>\n",
+       "      <th>Sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>TV</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.054809</td>\n",
+       "      <td>0.056648</td>\n",
+       "      <td>0.782224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Radio</th>\n",
+       "      <td>0.054809</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.354104</td>\n",
+       "      <td>0.576223</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Newspaper</th>\n",
+       "      <td>0.056648</td>\n",
+       "      <td>0.354104</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.228299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Sales</th>\n",
+       "      <td>0.782224</td>\n",
+       "      <td>0.576223</td>\n",
+       "      <td>0.228299</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 TV     Radio  Newspaper     Sales\n",
+       "TV         1.000000  0.054809   0.056648  0.782224\n",
+       "Radio      0.054809  1.000000   0.354104  0.576223\n",
+       "Newspaper  0.056648  0.354104   1.000000  0.228299\n",
+       "Sales      0.782224  0.576223   0.228299  1.000000"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_ads = pd.read_csv(\"/content/python-ml-course/datasets/ads/Advertising.csv\")\n",
+    "data_ads.corr()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x120180cf8>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c187ba8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.matshow(data_ads.corr())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T4 - 1 - Linear Regression - Datos ficticios-Colab.ipynb b/notebooks/T4 - 1 - Linear Regression - Datos ficticios-Colab.ipynb
new file mode 100644
index 00000000..dbc8b16a
--- /dev/null
+++ b/notebooks/T4 - 1 - Linear Regression - Datos ficticios-Colab.ipynb	
@@ -0,0 +1,966 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T4%20-%201%20-%20Linear%20Regression%20-%20Datos%20ficticios-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Modelos de Regresión Lineal\n",
+    "## Modelo con datos simulados\n",
+    "* y = a + b * x\n",
+    "* X : 100 valores distribuídos según una N(1.5, 2.5)\n",
+    "* Ye = 5 + 1.9 * x + e\n",
+    "* e estará distribuído según una N(0, 0.8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = 1.5 + 2.5 * np.random.randn(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "res = 0 + 0.8 * np.random.randn(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = 5 + 0.3 * x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_act = 5 + 0.3 * x + res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_list = x.tolist()\n",
+    "y_pred_list = y_pred.tolist()\n",
+    "y_act_list = y_act.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.DataFrame(\n",
+    "    {\n",
+    "        \"x\":x_list,\n",
+    "        \"y_actual\":y_act_list,\n",
+    "        \"y_prediccion\":y_pred_list\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>x</th>\n",
+       "      <th>y_actual</th>\n",
+       "      <th>y_prediccion</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.445763</td>\n",
+       "      <td>5.181348</td>\n",
+       "      <td>5.133729</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.922469</td>\n",
+       "      <td>6.190295</td>\n",
+       "      <td>5.576741</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.756439</td>\n",
+       "      <td>6.325118</td>\n",
+       "      <td>5.226932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.758071</td>\n",
+       "      <td>5.847538</td>\n",
+       "      <td>5.527421</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.579013</td>\n",
+       "      <td>3.990324</td>\n",
+       "      <td>4.526296</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          x  y_actual  y_prediccion\n",
+       "0  0.445763  5.181348      5.133729\n",
+       "1  1.922469  6.190295      5.576741\n",
+       "2  0.756439  6.325118      5.226932\n",
+       "3  1.758071  5.847538      5.527421\n",
+       "4 -1.579013  3.990324      4.526296"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_mean = [np.mean(y_act) for i in range(1, len(x_list) + 1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Valor Actual vs Predicción')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.plot(data[\"x\"],data[\"y_prediccion\"])\n",
+    "plt.plot(data[\"x\"], data[\"y_actual\"], \"ro\")\n",
+    "plt.plot(data[\"x\"],y_mean, \"g\")\n",
+    "plt.title(\"Valor Actual vs Predicción\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## ¿Como es la predicción de buena?\n",
+    "* SST = SSD + SSR\n",
+    "* SST : Variabilidad de los datos con respecto de su media\n",
+    "* SSD : Diferencia entre los datos originales y las predicciones que el modelo no es capaz de explicar (errores que deberían seguir una distribución normal)\n",
+    "* SSR : Diferencia entre la regresión y el valor medio que el modelo busca explicar\n",
+    "* R2 = SSR / SST, coeficiente de determinación entre 0 y 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_m = np.mean(y_act)\n",
+    "data[\"SSR\"]=(data[\"y_prediccion\"]-y_m)**2\n",
+    "data[\"SSD\"]=(data[\"y_prediccion\"]-data[\"y_actual\"])**2\n",
+    "data[\"SST\"]=(data[\"y_actual\"]-y_m)**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>x</th>\n",
+       "      <th>y_actual</th>\n",
+       "      <th>y_prediccion</th>\n",
+       "      <th>SSR</th>\n",
+       "      <th>SSD</th>\n",
+       "      <th>SST</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.445763</td>\n",
+       "      <td>5.181348</td>\n",
+       "      <td>5.133729</td>\n",
+       "      <td>0.087369</td>\n",
+       "      <td>0.002268</td>\n",
+       "      <td>0.061486</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.922469</td>\n",
+       "      <td>6.190295</td>\n",
+       "      <td>5.576741</td>\n",
+       "      <td>0.021735</td>\n",
+       "      <td>0.376449</td>\n",
+       "      <td>0.579095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.756439</td>\n",
+       "      <td>6.325118</td>\n",
+       "      <td>5.226932</td>\n",
+       "      <td>0.040958</td>\n",
+       "      <td>1.206014</td>\n",
+       "      <td>0.802469</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.758071</td>\n",
+       "      <td>5.847538</td>\n",
+       "      <td>5.527421</td>\n",
+       "      <td>0.009625</td>\n",
+       "      <td>0.102475</td>\n",
+       "      <td>0.174913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.579013</td>\n",
+       "      <td>3.990324</td>\n",
+       "      <td>4.526296</td>\n",
+       "      <td>0.815438</td>\n",
+       "      <td>0.287266</td>\n",
+       "      <td>2.070686</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          x  y_actual  y_prediccion       SSR       SSD       SST\n",
+       "0  0.445763  5.181348      5.133729  0.087369  0.002268  0.061486\n",
+       "1  1.922469  6.190295      5.576741  0.021735  0.376449  0.579095\n",
+       "2  0.756439  6.325118      5.226932  0.040958  1.206014  0.802469\n",
+       "3  1.758071  5.847538      5.527421  0.009625  0.102475  0.174913\n",
+       "4 -1.579013  3.990324      4.526296  0.815438  0.287266  2.070686"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SSR = sum(data[\"SSR\"])\n",
+    "SSD = sum(data[\"SSD\"])\n",
+    "SST = sum(data[\"SST\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "54.71008384758909"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SSR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "51.43951939725788"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SSD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101.21039999385397"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "106.14960324484697"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SSR+SSD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "R2 = SSR/SST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5405579253803104"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "R2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 3.,  9.,  9., 13., 22., 10., 15., 10.,  3.,  6.]),\n",
+       " array([-1.57615518, -1.25722405, -0.93829292, -0.61936179, -0.30043067,\n",
+       "         0.01850046,  0.33743159,  0.65636272,  0.97529385,  1.29422497,\n",
+       "         1.6131561 ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(data[\"y_prediccion\"]-data[\"y_actual\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Obteniendo la recta de regresión \n",
+    "\n",
+    "* y = a + b * x\n",
+    "* b = sum((xi - x_m)*(y_i-y_m))/sum((xi-x_m)^2)\n",
+    "* a = y_m - b * x_m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1.4295087271088212, 5.4293119653299815)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_mean = np.mean(data[\"x\"])\n",
+    "y_mean = np.mean(data[\"y_actual\"])\n",
+    "x_mean, y_mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"beta_n\"] = (data[\"x\"]-x_mean)*(data[\"y_actual\"]-y_mean)\n",
+    "data[\"beta_d\"] = (data[\"x\"]-x_mean)**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "beta = sum(data[\"beta_n\"])/sum(data[\"beta_d\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alpha = y_mean - beta * x_mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5.019817505840673, 0.28645817386334554)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "alpha, beta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "El modelo lineal obtenido por regresión es:\n",
+    "y = 5.042341442370516 + 1.9044490309709992 * x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"y_model\"] = alpha + beta * data[\"x\"]"
+   ]
+  },
+  {
+   "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></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y_actual</th>\n",
+       "      <th>y_prediccion</th>\n",
+       "      <th>SSR</th>\n",
+       "      <th>SSD</th>\n",
+       "      <th>SST</th>\n",
+       "      <th>beta_n</th>\n",
+       "      <th>beta_d</th>\n",
+       "      <th>y_model</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.445763</td>\n",
+       "      <td>5.181348</td>\n",
+       "      <td>5.133729</td>\n",
+       "      <td>0.087369</td>\n",
+       "      <td>0.002268</td>\n",
+       "      <td>0.061486</td>\n",
+       "      <td>0.243934</td>\n",
+       "      <td>0.967756</td>\n",
+       "      <td>5.147510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.922469</td>\n",
+       "      <td>6.190295</td>\n",
+       "      <td>5.576741</td>\n",
+       "      <td>0.021735</td>\n",
+       "      <td>0.376449</td>\n",
+       "      <td>0.579095</td>\n",
+       "      <td>0.375135</td>\n",
+       "      <td>0.243010</td>\n",
+       "      <td>5.570525</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.756439</td>\n",
+       "      <td>6.325118</td>\n",
+       "      <td>5.226932</td>\n",
+       "      <td>0.040958</td>\n",
+       "      <td>1.206014</td>\n",
+       "      <td>0.802469</td>\n",
+       "      <td>-0.602940</td>\n",
+       "      <td>0.453023</td>\n",
+       "      <td>5.236506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.758071</td>\n",
+       "      <td>5.847538</td>\n",
+       "      <td>5.527421</td>\n",
+       "      <td>0.009625</td>\n",
+       "      <td>0.102475</td>\n",
+       "      <td>0.174913</td>\n",
+       "      <td>0.137413</td>\n",
+       "      <td>0.107953</td>\n",
+       "      <td>5.523431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-1.579013</td>\n",
+       "      <td>3.990324</td>\n",
+       "      <td>4.526296</td>\n",
+       "      <td>0.815438</td>\n",
+       "      <td>0.287266</td>\n",
+       "      <td>2.070686</td>\n",
+       "      <td>4.329227</td>\n",
+       "      <td>9.051206</td>\n",
+       "      <td>4.567496</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          x  y_actual  y_prediccion       SSR       SSD       SST    beta_n  \\\n",
+       "0  0.445763  5.181348      5.133729  0.087369  0.002268  0.061486  0.243934   \n",
+       "1  1.922469  6.190295      5.576741  0.021735  0.376449  0.579095  0.375135   \n",
+       "2  0.756439  6.325118      5.226932  0.040958  1.206014  0.802469 -0.602940   \n",
+       "3  1.758071  5.847538      5.527421  0.009625  0.102475  0.174913  0.137413   \n",
+       "4 -1.579013  3.990324      4.526296  0.815438  0.287266  2.070686  4.329227   \n",
+       "\n",
+       "     beta_d   y_model  \n",
+       "0  0.967756  5.147510  \n",
+       "1  0.243010  5.570525  \n",
+       "2  0.453023  5.236506  \n",
+       "3  0.107953  5.523431  \n",
+       "4  9.051206  4.567496  "
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SSR = sum((data[\"y_model\"]-y_mean)**2)\n",
+    "SSD = sum((data[\"y_model\"]-data[\"y_actual\"])**2)\n",
+    "SST = sum((data[\"y_actual\"]-y_mean)**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(49.88237713027072, 51.328022863583286, 101.210399993854)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SSR, SSD, SST"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.49285821549267494"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "R2 = SSR / SST\n",
+    "R2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Valor Actual vs Predicción')"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_mean = [np.mean(y_act) for i in range(1, len(x_list) + 1)]\n",
+    "\n",
+    "%matplotlib inline\n",
+    "plt.plot(data[\"x\"],data[\"y_prediccion\"])\n",
+    "plt.plot(data[\"x\"], data[\"y_actual\"], \"ro\")\n",
+    "plt.plot(data[\"x\"],y_mean, \"g\")\n",
+    "plt.plot(data[\"x\"], data[\"y_model\"])\n",
+    "plt.title(\"Valor Actual vs Predicción\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Error estándar de los residuos (RSE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7237094274242161"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RSE = np.sqrt(SSD/(len(data)-2))\n",
+    "RSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.4293119653299815"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.mean(data[\"y_actual\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.13329671089921072"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RSE / np.mean(data[\"y_actual\"])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T4 - 1 - Linear Regression - Datos ficticios.ipynb b/notebooks/T4 - 1 - Linear Regression - Datos ficticios.ipynb
index e007e419..27c0d864 100644
--- a/notebooks/T4 - 1 - Linear Regression - Datos ficticios.ipynb	
+++ b/notebooks/T4 - 1 - Linear Regression - Datos ficticios.ipynb	
@@ -24,7 +24,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -33,7 +33,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -42,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -118,33 +118,33 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>4.170127</td>\n",
-       "      <td>8.774666</td>\n",
-       "      <td>6.251038</td>\n",
+       "      <td>0.445763</td>\n",
+       "      <td>5.181348</td>\n",
+       "      <td>5.133729</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>3.191858</td>\n",
-       "      <td>4.866337</td>\n",
-       "      <td>5.957557</td>\n",
+       "      <td>1.922469</td>\n",
+       "      <td>6.190295</td>\n",
+       "      <td>5.576741</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.242036</td>\n",
-       "      <td>4.622468</td>\n",
-       "      <td>4.927389</td>\n",
+       "      <td>0.756439</td>\n",
+       "      <td>6.325118</td>\n",
+       "      <td>5.226932</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>3.355509</td>\n",
-       "      <td>5.792907</td>\n",
-       "      <td>6.006653</td>\n",
+       "      <td>1.758071</td>\n",
+       "      <td>5.847538</td>\n",
+       "      <td>5.527421</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2.053049</td>\n",
-       "      <td>6.914448</td>\n",
-       "      <td>5.615915</td>\n",
+       "      <td>-1.579013</td>\n",
+       "      <td>3.990324</td>\n",
+       "      <td>4.526296</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -152,14 +152,14 @@
       ],
       "text/plain": [
        "          x  y_actual  y_prediccion\n",
-       "0  4.170127  8.774666      6.251038\n",
-       "1  3.191858  4.866337      5.957557\n",
-       "2 -0.242036  4.622468      4.927389\n",
-       "3  3.355509  5.792907      6.006653\n",
-       "4  2.053049  6.914448      5.615915"
+       "0  0.445763  5.181348      5.133729\n",
+       "1  1.922469  6.190295      5.576741\n",
+       "2  0.756439  6.325118      5.226932\n",
+       "3  1.758071  5.847538      5.527421\n",
+       "4 -1.579013  3.990324      4.526296"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -170,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -179,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -188,27 +188,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0.5,1,'Valor Actual vs Predicción')"
+       "Text(0.5, 1.0, 'Valor Actual vs Predicción')"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117711358>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -234,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -281,63 +283,63 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>4.170127</td>\n",
-       "      <td>8.774666</td>\n",
-       "      <td>6.251038</td>\n",
-       "      <td>0.483482</td>\n",
-       "      <td>6.368697</td>\n",
-       "      <td>10.361682</td>\n",
+       "      <td>0.445763</td>\n",
+       "      <td>5.181348</td>\n",
+       "      <td>5.133729</td>\n",
+       "      <td>0.087369</td>\n",
+       "      <td>0.002268</td>\n",
+       "      <td>0.061486</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>3.191858</td>\n",
-       "      <td>4.866337</td>\n",
-       "      <td>5.957557</td>\n",
-       "      <td>0.161482</td>\n",
-       "      <td>1.190762</td>\n",
-       "      <td>0.475235</td>\n",
+       "      <td>1.922469</td>\n",
+       "      <td>6.190295</td>\n",
+       "      <td>5.576741</td>\n",
+       "      <td>0.021735</td>\n",
+       "      <td>0.376449</td>\n",
+       "      <td>0.579095</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.242036</td>\n",
-       "      <td>4.622468</td>\n",
-       "      <td>4.927389</td>\n",
-       "      <td>0.394786</td>\n",
-       "      <td>0.092977</td>\n",
-       "      <td>0.870939</td>\n",
+       "      <td>0.756439</td>\n",
+       "      <td>6.325118</td>\n",
+       "      <td>5.226932</td>\n",
+       "      <td>0.040958</td>\n",
+       "      <td>1.206014</td>\n",
+       "      <td>0.802469</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>3.355509</td>\n",
-       "      <td>5.792907</td>\n",
-       "      <td>6.006653</td>\n",
-       "      <td>0.203350</td>\n",
-       "      <td>0.045687</td>\n",
-       "      <td>0.056263</td>\n",
+       "      <td>1.758071</td>\n",
+       "      <td>5.847538</td>\n",
+       "      <td>5.527421</td>\n",
+       "      <td>0.009625</td>\n",
+       "      <td>0.102475</td>\n",
+       "      <td>0.174913</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2.053049</td>\n",
-       "      <td>6.914448</td>\n",
-       "      <td>5.615915</td>\n",
-       "      <td>0.003625</td>\n",
-       "      <td>1.686189</td>\n",
-       "      <td>1.846171</td>\n",
+       "      <td>-1.579013</td>\n",
+       "      <td>3.990324</td>\n",
+       "      <td>4.526296</td>\n",
+       "      <td>0.815438</td>\n",
+       "      <td>0.287266</td>\n",
+       "      <td>2.070686</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          x  y_actual  y_prediccion       SSR       SSD        SST\n",
-       "0  4.170127  8.774666      6.251038  0.483482  6.368697  10.361682\n",
-       "1  3.191858  4.866337      5.957557  0.161482  1.190762   0.475235\n",
-       "2 -0.242036  4.622468      4.927389  0.394786  0.092977   0.870939\n",
-       "3  3.355509  5.792907      6.006653  0.203350  0.045687   0.056263\n",
-       "4  2.053049  6.914448      5.615915  0.003625  1.686189   1.846171"
+       "          x  y_actual  y_prediccion       SSR       SSD       SST\n",
+       "0  0.445763  5.181348      5.133729  0.087369  0.002268  0.061486\n",
+       "1  1.922469  6.190295      5.576741  0.021735  0.376449  0.579095\n",
+       "2  0.756439  6.325118      5.226932  0.040958  1.206014  0.802469\n",
+       "3  1.758071  5.847538      5.527421  0.009625  0.102475  0.174913\n",
+       "4 -1.579013  3.990324      4.526296  0.815438  0.287266  2.070686"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -348,7 +350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -359,16 +361,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "57.061592495357665"
+       "54.71008384758909"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -379,16 +381,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "64.62912938200402"
+       "51.43951939725788"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -399,16 +401,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "118.76326975838883"
+       "101.21039999385397"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -419,16 +421,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "121.69072187736168"
+       "106.14960324484697"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -439,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -448,16 +450,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.48046498392510895"
+       "0.5405579253803104"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -468,31 +470,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([ 2.,  2.,  4., 12., 12., 22., 19., 16.,  7.,  4.]),\n",
-       " array([-2.52362778, -2.10919908, -1.69477037, -1.28034166, -0.86591295,\n",
-       "        -0.45148424, -0.03705553,  0.37737318,  0.79180189,  1.2062306 ,\n",
-       "         1.62065931]),\n",
-       " <a list of 10 Patch objects>)"
+       "(array([ 3.,  9.,  9., 13., 22., 10., 15., 10.,  3.,  6.]),\n",
+       " array([-1.57615518, -1.25722405, -0.93829292, -0.61936179, -0.30043067,\n",
+       "         0.01850046,  0.33743159,  0.65636272,  0.97529385,  1.29422497,\n",
+       "         1.6131561 ]),\n",
+       " <BarContainer object of 10 artists>)"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1177c1390>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -513,16 +517,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1.3766343542248387, 5.555709335564895)"
+       "(1.4295087271088212, 5.4293119653299815)"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -535,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -545,7 +549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -554,7 +558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -563,16 +567,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(5.138417257827989, 0.3031248468093598)"
+       "(5.019817505840673, 0.28645817386334554)"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -591,7 +595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -600,7 +604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -638,85 +642,85 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>4.170127</td>\n",
-       "      <td>8.774666</td>\n",
-       "      <td>6.251038</td>\n",
-       "      <td>0.483482</td>\n",
-       "      <td>6.368697</td>\n",
-       "      <td>10.361682</td>\n",
-       "      <td>8.992132</td>\n",
-       "      <td>7.803602</td>\n",
-       "      <td>6.402486</td>\n",
+       "      <td>0.445763</td>\n",
+       "      <td>5.181348</td>\n",
+       "      <td>5.133729</td>\n",
+       "      <td>0.087369</td>\n",
+       "      <td>0.002268</td>\n",
+       "      <td>0.061486</td>\n",
+       "      <td>0.243934</td>\n",
+       "      <td>0.967756</td>\n",
+       "      <td>5.147510</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>3.191858</td>\n",
-       "      <td>4.866337</td>\n",
-       "      <td>5.957557</td>\n",
-       "      <td>0.161482</td>\n",
-       "      <td>1.190762</td>\n",
-       "      <td>0.475235</td>\n",
-       "      <td>-1.251365</td>\n",
-       "      <td>3.295037</td>\n",
-       "      <td>6.105949</td>\n",
+       "      <td>1.922469</td>\n",
+       "      <td>6.190295</td>\n",
+       "      <td>5.576741</td>\n",
+       "      <td>0.021735</td>\n",
+       "      <td>0.376449</td>\n",
+       "      <td>0.579095</td>\n",
+       "      <td>0.375135</td>\n",
+       "      <td>0.243010</td>\n",
+       "      <td>5.570525</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>-0.242036</td>\n",
-       "      <td>4.622468</td>\n",
-       "      <td>4.927389</td>\n",
-       "      <td>0.394786</td>\n",
-       "      <td>0.092977</td>\n",
-       "      <td>0.870939</td>\n",
-       "      <td>1.510609</td>\n",
-       "      <td>2.620093</td>\n",
-       "      <td>5.065050</td>\n",
+       "      <td>0.756439</td>\n",
+       "      <td>6.325118</td>\n",
+       "      <td>5.226932</td>\n",
+       "      <td>0.040958</td>\n",
+       "      <td>1.206014</td>\n",
+       "      <td>0.802469</td>\n",
+       "      <td>-0.602940</td>\n",
+       "      <td>0.453023</td>\n",
+       "      <td>5.236506</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>3.355509</td>\n",
-       "      <td>5.792907</td>\n",
-       "      <td>6.006653</td>\n",
-       "      <td>0.203350</td>\n",
-       "      <td>0.045687</td>\n",
-       "      <td>0.056263</td>\n",
-       "      <td>0.469384</td>\n",
-       "      <td>3.915943</td>\n",
-       "      <td>6.155555</td>\n",
+       "      <td>1.758071</td>\n",
+       "      <td>5.847538</td>\n",
+       "      <td>5.527421</td>\n",
+       "      <td>0.009625</td>\n",
+       "      <td>0.102475</td>\n",
+       "      <td>0.174913</td>\n",
+       "      <td>0.137413</td>\n",
+       "      <td>0.107953</td>\n",
+       "      <td>5.523431</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>2.053049</td>\n",
-       "      <td>6.914448</td>\n",
-       "      <td>5.615915</td>\n",
-       "      <td>0.003625</td>\n",
-       "      <td>1.686189</td>\n",
-       "      <td>1.846171</td>\n",
-       "      <td>0.919071</td>\n",
-       "      <td>0.457537</td>\n",
-       "      <td>5.760747</td>\n",
+       "      <td>-1.579013</td>\n",
+       "      <td>3.990324</td>\n",
+       "      <td>4.526296</td>\n",
+       "      <td>0.815438</td>\n",
+       "      <td>0.287266</td>\n",
+       "      <td>2.070686</td>\n",
+       "      <td>4.329227</td>\n",
+       "      <td>9.051206</td>\n",
+       "      <td>4.567496</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "          x  y_actual  y_prediccion       SSR       SSD        SST    beta_n  \\\n",
-       "0  4.170127  8.774666      6.251038  0.483482  6.368697  10.361682  8.992132   \n",
-       "1  3.191858  4.866337      5.957557  0.161482  1.190762   0.475235 -1.251365   \n",
-       "2 -0.242036  4.622468      4.927389  0.394786  0.092977   0.870939  1.510609   \n",
-       "3  3.355509  5.792907      6.006653  0.203350  0.045687   0.056263  0.469384   \n",
-       "4  2.053049  6.914448      5.615915  0.003625  1.686189   1.846171  0.919071   \n",
+       "          x  y_actual  y_prediccion       SSR       SSD       SST    beta_n  \\\n",
+       "0  0.445763  5.181348      5.133729  0.087369  0.002268  0.061486  0.243934   \n",
+       "1  1.922469  6.190295      5.576741  0.021735  0.376449  0.579095  0.375135   \n",
+       "2  0.756439  6.325118      5.226932  0.040958  1.206014  0.802469 -0.602940   \n",
+       "3  1.758071  5.847538      5.527421  0.009625  0.102475  0.174913  0.137413   \n",
+       "4 -1.579013  3.990324      4.526296  0.815438  0.287266  2.070686  4.329227   \n",
        "\n",
        "     beta_d   y_model  \n",
-       "0  7.803602  6.402486  \n",
-       "1  3.295037  6.105949  \n",
-       "2  2.620093  5.065050  \n",
-       "3  3.915943  6.155555  \n",
-       "4  0.457537  5.760747  "
+       "0  0.967756  5.147510  \n",
+       "1  0.243010  5.570525  \n",
+       "2  0.453023  5.236506  \n",
+       "3  0.107953  5.523431  \n",
+       "4  9.051206  4.567496  "
       ]
      },
-     "execution_count": 73,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -727,7 +731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -738,16 +742,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(56.1769824876691, 62.58628727071979, 118.76326975838883)"
+       "(49.88237713027072, 51.328022863583286, 101.210399993854)"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -758,16 +762,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.4730164688287478"
+       "0.49285821549267494"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -779,27 +783,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0.5,1,'Valor Actual vs Predicción')"
+       "Text(0.5, 1.0, 'Valor Actual vs Predicción')"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Op8Bu4fd9/GDJMrY+Z1rousdP353112587bouOW4z+z1wj7tfU2pbBW5pWmmYUzoNZWiEYsH+pdvgpoPDX3fmfOjTL3TVxNte4LrHun/o/WDXTTj565+Lq+RViT3HbWYtwL8BR9RSMJHMa+SQ60rL4B7UxtOc765EWH/tqNr1nhfAdkeFrnph7kLG/vyR0HXPT9qDtVbvW0spE1FW4Hb3pcB6dS6LSPo1Ysh1NWVol/Z8dzUeuwLuCW1ag7M+CL6BhBh22p2hyy/YbwT7b5vtDhQaOSlSiTTMy51fhrCa99KlwbosB+5Vq+DsdcPX7XomfO2HoaumvfQOh18fnqZ9+Zw9Wb1v77hKmCjNxy2SZc2W7/7DYfDiLeHrJn4YnFeBVauc4WfcFfqSs8Zuzvd23DjOEtaN5uMWaaQk+1SnIedeqxXL4NzwSZz42qmw6xmhq6K68QG8cf6YugySSQsFbpFaJN2nOg0592pdOAw+/iB83aSFoYuXLlvB5mfdE7ru14eMYvcvfDqmwqWbUiUitUhDn+osjaJcugAuikhdFMld7/XzR3h+bngwb8Q0qo1Qt7lKyqXALT1G1nLMRx8dDGJZuRJ69w5q61dcUf/jRnXjg8ja9az3l/C1Hz8Quu6Gw7/CVz87KIaCpYdy3CKNkqUc89FHw5VXdj5fubLzeVjwrrUmP/9V+MWXw9f92zmww3Ghq6K68UHz1K5rpRq39CxxpxXqMf1ovfTpEwTrQr17w4oVXZfVcl5V1K7vfuFtjpz6VOi6+0/ehY0Hhc/i10yUKhEJU68gm0SOuZpjFutlURgHKs3dz/wbXDsmfN+7nA67nBa6SrXrTgrcEi1LDVlxS0NDYph6TsCUr5Iad7m5+ypq15PvfIlfPfxG6LonJ4xm8FqrRe+ziSnHLeGS7rqWX44kPjxqneO5Hqp5TyZM6D7cvZzRkuPHd81x5y8vVCx3/9wf4Jbvhx9j36tgq/27LXZ3Nj49fJBMn17Ga+dF1NYllGrcPUkaapxJ5oTTcP6FqilTJSmPQuX2Kgl7nyYOiN5vRO26WCrk1XO/Qb8+4fOM9ERKlUi4NHRda3TwzK/dDxwIixbB8uWd65NuSKzmPakk5VGLtja45UQY8a/w9Qf/GYZ/rdviRZ8sZ8tJ94a+ZO3+fXl2Yskp/XskpUokXBq6rjUyXVFYa3z/fejXD9ZbDxYsSEeOv5z3pDC1FBa0IXp5Ndpz1yPC1pVfu977xfs55aHr2Wjxe53XW2qm7yk9yeTJQQ0zX6OHR0d9SJT74dHWFtTae/UKfra1RW8blgtetgzWXDOozc6cmXzDbKn3pP3DZ9asoGY+a1Z0qqS1tbLrU+jq0UHADmtwPPKRIGAXBO0X5i5k2Gl3hgbtE999ksv+eiUbLZrfWfbx4ysrk4RSqqSnSbpXSS057kpfG5WGgCD4paHGDcXfk6jUklnXc2tpgUMOgeuuq/zaVtEzpKxufGlsU0gx5bgl3ar98Kg0EERtny/pHHcpxT58Wlu7XsOo+bnDrk+xYH3CC7DOkG6Lr39sJmfd9mLoSyaM+QKH7zy8vLKndTqAhClwS3kKA+iYMXDXXenr491ezqggHBUIwmroYdJcA6zkw6qcQFmv2nXoC4epxl0BNU5KaWH9h/P7+KblFljlBN+o/Hjh3WqiKilJ9uMupZJpW6MaOs9aKzpgX7AIeue+deTZ/vy/8NbCT0JfcuvRX2XroRF3p6m27FIR1bh7qnLSCJB87ahUOStJdWS1BlhuaqnwQ65Yv+sfLer6PHcNYh+CnnSbSoYoVSKlFcud5ks6H1kqx1tJIMjShFDVKpYK+Z/3oO9qoddzFcbwU2/vtvypM0ez3po9cwh6o1USuNUdsKcqt/td0tOTRh2/vZZcScAdNy4I0q2twQdSa2tzBO3lH0d344PObny9+0Zez3kDus5tPfOCbzLzgm/GH7Rr6a4oHZTj7qnC8o+F0pCPjDtPOm5c9gN1u2K164gb6x434ltcMPdyWlZ0joZc2mc1Ltr5YF4/bwy9etXxPo1pmSunCShV0pNlrVdJ2sqVhIVz4eLNo9eH9Ax5Ye5Cxv78kY7n7aMZN1j0HvMGDGKjKy5uzPXMahtDgyjHLdJsGtmNr17Ur7uo2LsDmtk6wNXAFoAD33P3x6ovoohEav+GsWoOfK/InV9CAvYl973KJff9M3TzL24wgDuP2ymuUlYuDXPlNIlyc9yXAne7+7fMrB/QUuoFIlKFtjb459FwKEBI0M5K7TqM+nXHpmTgNrMBwM7k/pTcfRmwrL7FEqlBFnPiT10Ht4ffPBeAa9ftlgcuFqwn7bU5h+6wcUyFi0nhgKisvDcpVDLHbWZfAqYALwFbAU8Bx7v7koLtxgPjAYYOHTpyVjmDO0TilrW+2sVy1/mDZPLywJmoXUvF4u7H3QfYBrjS3bcGlgDd7vzp7lPcfZS7jxo8eHBFBRaJTbHbekVpdN/i234Q3e96Ya8gYBeMbJyz1qDI6VNvO2aHjn7XRakPddMoJ8c9B5jj7n/PPb+ZkMAtkgqV3qihkX2Li9WuN70iOF5bG7R0/cbQ3s+6UEW1a/WhbipldQc0s4eB77v7K2Y2CVjD3X8Ytb26A0pi4pr6Na6+xedtCMs+Cl/34nK4+ePg97x0znF7ndyln/VFOx/Mn7+4KwAzzt6T/v16V16OtPehzmK7RMxi78edy3NfDfQDXgcOc/cPorZX4JbExHWzhVr7FherXV+7bmgQnTNgMDsedU3oS0Jr15UEuzT3oc5au0SdxN6P292fAcraoUiiKu25EGff4mLBepfTYZdchvHs8KalDRfN7/K8aCqk0tRHmvtQF2uX6EGBuxIaOSk9Wxy1vQpHNS769IYMeHdet+WrgBPGnsRlt/+k9DErTX2kuVab5m8DDaQbKYiUq9q+xcWC9f6/g8+P6ba4vUfI3tt+l0vu+Gm3Ll29gMuevxkoI3BX2gib5j7Uaf42kFKqcYtUosLadVSf69cvHBveF7fcWmajGhsb0WiY5m8DDaQat0icigXrY56EwZt1W1xykMzvW2urZTZi+HijuhCm+dtASqnGLRIlpto1wDn7fJGDth/WuSCOWma9a8Np70LYZFTjFqlWsWB96izov07nCMTZs/EhQzh+y2939LMuFNkzJI5aZr1vClFpHl0aRoFbkpWGgRcrl8M5g6LX59euC2rKNns2F8y7HKAjeD92+m58Zu3+pY+b9rvxqNEwtZQqkeQk3ShVrHZ91gLo1XWE4juLPmH5kKFsVNDfGoLBMxstfDfuEpZWzw++pN+fHkapEsmGJAZeXHclvFFkqp0SuevXF70X+rKNFocvr6t6Nx6q0TC1UlXjth/V8Ual0qO5D4hcZ7Yocp1IJUYPH820g6ZV9dq4p3UVyaQvey/cB0QGbbNFCtqSSalKlfjE+Gv/kiJhOdNCUTnUSnK5Zd6coP2vbRXG8FNvj3xJt54h1eSV65GLVne9HitVgVuaXFhOG6B372C0YFRAKyeX+8Sv4K6To489aWEQ6Ohew543oHuPkqITPBXmfttv0hAViOuVi45jEE4aevVI5dw99sfIkSNdUmzqVPfWVnez4OfUqY05rpl7MJ1Q14dZ8de1toa/rrXVfeKA6EehqVN9SZ/VuuxjSZ/V/NixJ3nrqXf4ib//R3nnMXWqe0tL17K0tERfx2LlL9xvpe9LLe9lpechdQVM9zJjbKoaJ6UBkuziVe1X+8LZ4/brDyP6Rm9fpGfI3i/e3+0mBWXNxtdlZ8MqO49yZr9L4n1RqiVVYr+RQqUUuFMsyX/WaoNTe5knRvcMqXQI+h+P2p6RrQPLKHSISqchHTQI3n+/+/L8ax71vqy3HrxXp66Gmk41VdSrRKJVM4w5rpvMjhsXBOnW1iA4tLaWDto/2QwO/SA6aE9a2CVoL/nXisib6gIdN9WtOmhD9MjBsOVtbbB4cfflfft2zUVHXf/336/fTX0rOQ9JFdW4q5XVRp04JuCHoCZ46aX1O+dSt/4quN7FatevnvsN+vWJsY5SyTeHcmvSUdtB/b4NaWRkqihVUm9Z/oOvtOzFAkrc51wsWH92dzjoli6Lpr30DodfH/13VtFd0CtV7gd3uemItjY48MDwY9UzdZHVCkgTUuCut1ryxGn4R4njJrPt4qgNxjh9aslg3ejrX8nfSjm5cGlamquk3qqd7rJRE9OXUu6sdG1tQeBeuTJ6m2qn+CwWrHefCDv9d5dFPz3kLL7zp1+ywaL3eCTXGyR/KtWyAvYRR8CSJZ3LGnH9K+lrfeml9b85gjSHcvsNVvJo+n7c5fbLjet1xdSrT3ZYH984yl5Jv2t3bz31Dj927Emh/a/LPtepU9379q3sHOK8rpXsK6k+9pI41I+7zqrNccfd/aqeufZiue1Kj1Wsdn39Uli1YZeURWEq5JErDwudSrXsFEKpcwnLN2e1DUMySznuRqgmVxp3H+p69skults2K++ciwXsH6/oFhiP2+2o0DvJvH7hXvSihg+8SvP0GpgiCVDgTqu4a3L1HEBRbfAqFqxPeAHWGRK57zkDBrPjUdd0PL9gvxHsv+3Q2gNpsRq3Gfz2t12vvwamSAJib5w0s5nAYmAlsKLcnUuBuCemr+etpSppVHOHH60Tva+CniE+ezZhM69vkLtJQbeGxlonU5o8GQ47DJYv777uyCO7X3/dskvSrpxEODATGFRu4rzpGyfTot6TBJVqKCvW0LhsabfdtZ56h7eeeoe/OWBw5Q2dtTbaTZ3qvt56ncdab73ofWjyJUkAcTdO5mrco9y9rEkTlCppoEb3S17+MUxeP3p9Qe36/+Z/xO4/fbDLsr1fvJ8L7r6clhX/6lyYtsa/NPS3lx4l9hy3mb0BfEAw9/xV7j6l2PYK3E2oWO564odB/jdPyUEyRx8dBOqVK4P5uMePhyuuqL2cCriSUfUYgLODu88zs08B08zsZXd/qOCg44HxAEOVC2wOi96Cn30+en1B7fqKB17jortfidy8I3fd1gbXXdc5sGflyuD5Djt0Btlq7zKThgFOInVWca8SM5sEfOTukZMYq8adcfUegl6ql0it079G7Tfr9G2iqcWaKjGzNYBe7r449/s04Gx3vzvqNQrcGfTWs3DVztHrCwL2V867j3cW/Sti4xJD0Et1t4vrhguF+80yDQpqenGnSj4N3GpBDrMPcEOxoF1XqnHEI/86nrVW9HYV1K73fvF+Lnv+5s73ZsSH0e9Nqe52UfOflBrJ2czd+MLu17l0abBc/wM9TsnA7e6vA1s1oCzFKX8Zj7Y2OOcIOLQ3EBG0CwJ2sVTIWWM353uzHoWfX1n+e1OqX3ZUADYLyh/1fsdx89y0qnZiM2lK2Rk52ez5y0YodXOCgutYdu66mvem2LentjY46KDwtEep97tZv5Xp77/pNeeQ92bNX9Y70Dx7I9w6PnzdrBVwba52mruOxYL1w6fsypCBLd1X1OO9KeheGMs+s0w57qbXnPNxN2P+sp7pn2K16x8t6rZozlqD2LHIfRqLqsd709rafO93LeKeLkEyLTs3C548Oahh5Mt6/rJYg1M17j8/CNhhQfsbFwW5602v6HYdl/ZZjYt2PrjLstfPG9NxY92S6vHepOH9jusmyXEZNy5Ii6xaFfxU0O6xslPjbsYaR1wNThX0u35p17345W5HccpD17PBoveYV3A3marv09i/f+eHUC03Es5PHQ0cGOx3wYLGv99qDJcUy06OuxnV0uB01ynwxFXh6w64CTb7etdD1XKfxmLizL2mKY+rxkBpsOZsnGxG1QSqCmrXVz/8OufeOSN00203HshNR2xfaYm7KzfAldMIm6Zg2ayN4ZJazdk42YzKTf/ceBDM+HP4Po55EgZv1mVR3WrXYcpJ95SbdkhTX+VmbAyXpqEad5pVULve6+eP8Pzc7iMdAX7y7a341siNOhfE2QWxnFpyuTXpNNW405S2kR5BNe4su3xbeC9ihr0fvg5rrNdlUcW167gb3coZrVhuTbrSkY/17APfjI3h0jRU406LCmrXxYL1AyfvwrBBa0Tvqx612lIBtJJjlhuMVSOWJqPGyaz4xXYwP7zxkAlvQ9/+HU9XrnLmI1+1AAAKKElEQVQ+e8ZdkbsqO3edRKNbPYJsmtIqIjFQqiTNKryxbrHa9T8nf4O+vSscQ5VEo1tY2mHMmOD5QQdVl4YoN/3SrHOXSI+mwN0oF4+AhRHB5qwPgppwzvzF/+LLk++L3FVNPUOSmkFv3Liuk0jVmmcv5wNIg2ikSSlVUk8rlsG5g8PXDfoc/OCJLosa1o2vHrXQSvYZR5qjnPSL0imSIcpxJ62Chsbn5nzI3pf/LXTTb275GX5xwDZxlqw+Ks1hx5VnL/VhoUE0kiGVBO7sTDLVLm0T/7RbtjR6gqeRhwYBOy9oDzvtToaddmdo0G6f3KkjaKf1nNtVOllWVD69V6/Kzq3UpEtRx6k2n5/290F6jGzluNOYs7x0K/hgZvi6gtr1bc/M5fjfPxO66a8PGcXuX/h09xXlnHPSDXCVjngMy7NDcLf3ON/POPP5afzbk57L3WN/jBw50is2dap7a6u7WfBz6tTu27S2ugdffrs+WlsrP14tPl7oPnFA+GNk36BMLS0d59B66h2Rj5JKnfPUqcGx8tflHbshqnlfpk517907+nXl/D2UI679pOVvT5oWMN3LjLHpyHGXmyNNOmd51c7B3dDDhN2cYMBgdjzqmm7L/3babmy4Tv9uy0PV647ocaq2n3bUubW/Pk2Da5L+25Oml73GyTTPZfHRu/CTTcPXff+vsNHIyH/qVRjDT72943lVPUNKnXNaAko16Zqoc+vdO0ibFEqyN0gaPiClqVUSuNORKjEL/xpq1nW7RqYFLhwenQ4psGDQZ0LL/+aAwf7xshWlj1Xs63ypc87yV/iocws7n7C/hzSUtZEpKWlqVJAqSUfgriT4xJWzDLPgjehg/e7LXTZdtmJlR5762LEn+ZI+q1X3T11OQKglsKdd2Lml9cOonn970uNVErjTkSpJesKgqH7X1gsmftBl0eHXT2faS+9023TvF+/nsudvrrxnR1yDUZppWHfSfw8iCchejhsaH3zefgF+uUP4uhNfgrU37Hj6wZJlbH3OtNBNz9t3BAd8pYZ5PtKSo06bZvswEikhm4G7UaJq1+u0wgnPdVm0zTnTWLBkWejmsQ1BV6OXiFCn2QHNrDcwHZjr7mOrLVwiZj0G1+wZvu6UN6BlYMfT1979iNE/ezB00zuO3ZEtNiwynL0aSU361JOpNi8ZV8nIyeOBGcCAOpUlflG169Yd4LCuc1s39D6N+XSnlcbSCEhpAmWlSsxsI+A6YDLw36Vq3InmuF+5G373nfB1p8+F1dbsePrQq/M5+DdPhG76xBm786kBq1d+fOkqbbVbpaYkpeqRKrkEOAVYq8hBxwPjAYZWOolPHLWgqNr1iG/Df1zdZVFU7XrbYQO56cjtyzuelJbG2m2a7iQvUqWSNW4zGwuMcfejzWwX4OTYa9zV1oL+0Qa3HR2+7sz50Kdfx9Nr//YGk25/KXTTl8/Zk9X79i6/vFKeNNZu01gmEeKvce8A7G1mY4DVgQFmNtXdD6ylkF1UUgvyIrf+2u4Y2PO8jqfF7tP4/R035syxm1daUqlEGmu3agyWJlAycLv76cDpAHk17viCNpR3G6rZj8Nvvh7++oJbf532x+f4/ZNvhm76xvljMLNaSivlSuL+lqWoMViaQDrm446qBZ17Lvz0C7B4XvfXjJ4EO57Y8fSjf61gi4n3hO7+om9tyX+OGhJvmaW0uOfDriXYpq2RVKQGFQVud38AeCD2UhTWgrb7DOzxEbx2TNft1h8BRzwcjCrM2ePiB3n1nY9Cd1vXbnxSWly121obOdPYSCpSg3SNnLz7dHj8iu7Lj3gIPrNVx9M5HyxlxwvvD93FTUdsz7YbDwxdJxlVa4OiGiQlA+oycrIh8oP2xjvDIbd3WX3+/87gqgdfD32patdNrNZGzjQ2korUIF2B+9RZsHI5rDm4Y9G8Dz/mqxf8NXTzh0/ZlSEDWxpVOklKrY2caWwkFalBugJ3/85ufkf8djr3vNh9+tRJe23OoTts3MhSSdJqbeRUF0BpMqkK3K+9u5jRP3sodN0r5+7Jan00SKZHqrWRU10ApcmkqnGycCj6Lw8cyZ5brB9XsUREUiuzjZMXf2crrn10Frce9VV69dIgGRGRMKkK3PtuvRH7br1R0sUQEUm1XqU3ERGRNFHgFhHJGAXuuLS1BSP0evUKfra1JV0iEWlSqcpxZ5bmwhCRBlKNOw4TJnQd3AHB8wkTkimPiDQ1Be44aC4MEWkgBe44RM15obkwRKQOFLjjMHlyMPdFPs2FISJ1osAdh3HjYMqUYH5ns+DnlClqmBSRulCvkriMG6dALSINoRq3iEjGKHCLiGSMAreISMYocIuIZIwCt4hIxtTlDjhmNh8IuTtr3QwC3mvg8epN55N+zXZOOp/ktbr74NKb1SlwN5qZTS/3lj9ZoPNJv2Y7J51PtihVIiKSMQrcIiIZ0yyBe0rSBYiZzif9mu2cdD4Z0hQ5bhGRnqRZatwiIj2GAreISMY0XeA2s5PNzM1sUNJlqYWZ/djMXjaz58zsVjNbJ+kyVcPM9jSzV8zsNTM7Leny1MLMhpjZ/WY2w8xeNLPjky5THMyst5n9w8zuSLoscTCzdczs5tz/zwwz2z7pMsWtqQK3mQ0B/g1ohnuGTQO2cPctgVeB0xMuT8XMrDfwC+AbwObAd81s82RLVZMVwEnu/gVgO+CYjJ9Pu+OBGUkXIkaXAne7++eBrWiucwOaLHADFwOnAJlvcXX3e919Re7p48BGSZanStsCr7n76+6+DPg9sE/CZaqau7/l7k/nfl9MEBA2TLZUtTGzjYBvAlcnXZY4mNkAYGfg1wDuvszdP0y2VPFrmsBtZnsDc9392aTLUgffA/436UJUYUPgzbznc8h4oGtnZsOArYG/J1uSml1CUNlZlXRBYjIcmA9ck0v/XG1mayRdqLhl6g44ZnYfsH7IqgnAGcAejS1RbYqdj7vflttmAsFX9LZGli0mFrIs89+GzGxN4I/ACe6+KOnyVMvMxgLvuvtTZrZL0uWJSR9gG+BYd/+7mV0KnAb8T7LFilemAre7jw5bbmYjgI2BZ80MgrTC02a2rbu/3cAiViTqfNqZ2SHAWGB3z2aH+znAkLznGwHzEipLLMysL0HQbnP3W5IuT412APY2szHA6sAAM5vq7gcmXK5azAHmuHv7N6GbCQJ3U2nKAThmNhMY5e5Zmx2sg5ntCfwM+Jq7z0+6PNUwsz4EDau7A3OBJ4ED3P3FRAtWJQtqBdcBC9z9hKTLE6dcjftkdx+bdFlqZWYPA99391fMbBKwhrv/MOFixSpTNe4e5nJgNWBa7lvE4+5+ZLJFqoy7rzCzHwD3AL2B32Q1aOfsABwEPG9mz+SWneHudyVYJunuWKDNzPoBrwOHJVye2DVljVtEpJk1Ta8SEZGeQoFbRCRjFLhFRDJGgVtEJGMUuEVEMkaBW0QkYxS4RUQy5v8BX86qo8YQDiQAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x117477e48>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -823,16 +829,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.7991467852623195"
+       "0.7237094274242161"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -844,16 +850,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "5.555709335564895"
+       "5.4293119653299815"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -864,16 +870,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.14384243973070698"
+       "0.13329671089921072"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -881,13 +887,6 @@
    "source": [
     "RSE / np.mean(data[\"y_actual\"])"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -906,7 +905,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T4 - 2 - Linear Regression - Regresi\303\263n lineal con statsmodel-Colab.ipynb" "b/notebooks/T4 - 2 - Linear Regression - Regresi\303\263n lineal con statsmodel-Colab.ipynb"
new file mode 100644
index 00000000..2faa8391
--- /dev/null
+++ "b/notebooks/T4 - 2 - Linear Regression - Regresi\303\263n lineal con statsmodel-Colab.ipynb"	
@@ -0,0 +1,1524 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T4%20-%202%20-%20Linear%20Regression%20-%20Regresión%20lineal%20con%20statsmodel-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Regresión lineal simple en Python\n",
+    "## El paquete statsmodel para regresión lineal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"/content/python-ml-course/datasets/ads/Advertising.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>TV</th>\n",
+       "      <th>Radio</th>\n",
+       "      <th>Newspaper</th>\n",
+       "      <th>Sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>230.1</td>\n",
+       "      <td>37.8</td>\n",
+       "      <td>69.2</td>\n",
+       "      <td>22.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>44.5</td>\n",
+       "      <td>39.3</td>\n",
+       "      <td>45.1</td>\n",
+       "      <td>10.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>17.2</td>\n",
+       "      <td>45.9</td>\n",
+       "      <td>69.3</td>\n",
+       "      <td>9.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>151.5</td>\n",
+       "      <td>41.3</td>\n",
+       "      <td>58.5</td>\n",
+       "      <td>18.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>180.8</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>58.4</td>\n",
+       "      <td>12.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      TV  Radio  Newspaper  Sales\n",
+       "0  230.1   37.8       69.2   22.1\n",
+       "1   44.5   39.3       45.1   10.4\n",
+       "2   17.2   45.9       69.3    9.3\n",
+       "3  151.5   41.3       58.5   18.5\n",
+       "4  180.8   10.8       58.4   12.9"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import statsmodels.formula.api as smf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lm = smf.ols(formula=\"Sales~TV\", data = data).fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intercept    7.032594\n",
+       "TV           0.047537\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.params"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "El modelo lineal predictivo sería \n",
+    "Sales = 7.032594 + 0.047537 * TV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intercept    1.406300e-35\n",
+       "TV           1.467390e-42\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.pvalues"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.611875050850071"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.rsquared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6099148238341623"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.rsquared_adj"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.612</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.610</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   312.1</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sat, 19 Sep 2020</td> <th>  Prob (F-statistic):</th> <td>1.47e-42</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>16:00:18</td>     <th>  Log-Likelihood:    </th> <td> -519.05</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   1042.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   198</td>      <th>  BIC:               </th> <td>   1049.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th> <td>    7.0326</td> <td>    0.458</td> <td>   15.360</td> <td> 0.000</td> <td>    6.130</td> <td>    7.935</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>TV</th>        <td>    0.0475</td> <td>    0.003</td> <td>   17.668</td> <td> 0.000</td> <td>    0.042</td> <td>    0.053</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td> 0.531</td> <th>  Durbin-Watson:     </th> <td>   1.935</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.767</td> <th>  Jarque-Bera (JB):  </th> <td>   0.669</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-0.089</td> <th>  Prob(JB):          </th> <td>   0.716</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 2.779</td> <th>  Cond. No.          </th> <td>    338.</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  Sales   R-squared:                       0.612\n",
+       "Model:                            OLS   Adj. R-squared:                  0.610\n",
+       "Method:                 Least Squares   F-statistic:                     312.1\n",
+       "Date:                Sat, 19 Sep 2020   Prob (F-statistic):           1.47e-42\n",
+       "Time:                        16:00:18   Log-Likelihood:                -519.05\n",
+       "No. Observations:                 200   AIC:                             1042.\n",
+       "Df Residuals:                     198   BIC:                             1049.\n",
+       "Df Model:                           1                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "Intercept      7.0326      0.458     15.360      0.000       6.130       7.935\n",
+       "TV             0.0475      0.003     17.668      0.000       0.042       0.053\n",
+       "==============================================================================\n",
+       "Omnibus:                        0.531   Durbin-Watson:                   1.935\n",
+       "Prob(Omnibus):                  0.767   Jarque-Bera (JB):                0.669\n",
+       "Skew:                          -0.089   Prob(JB):                        0.716\n",
+       "Kurtosis:                       2.779   Cond. No.                         338.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      17.970775\n",
+       "1       9.147974\n",
+       "2       7.850224\n",
+       "3      14.234395\n",
+       "4      15.627218\n",
+       "         ...    \n",
+       "195     8.848493\n",
+       "196    11.510545\n",
+       "197    15.446579\n",
+       "198    20.513985\n",
+       "199    18.065848\n",
+       "Length: 200, dtype: float64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sales_pred = lm.predict(pd.DataFrame(data[\"TV\"]))\n",
+    "sales_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f8894446a60>]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "data.plot(kind = \"scatter\", x = \"TV\", y =\"Sales\")\n",
+    "plt.plot(pd.DataFrame(data[\"TV\"]), sales_pred, c=\"red\", linewidth = 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"sales_pred\"] = 7.032594 + 0.047537*data[\"TV\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"RSE\"] = (data[\"Sales\"]-data[\"sales_pred\"])**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2102.5305838896525"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SSD = sum(data[\"RSE\"])\n",
+    "SSD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.258656369238098"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RSE = np.sqrt(SSD/(len(data)-2))\n",
+    "RSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sales_m = np.mean(data[\"Sales\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.022500000000003"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sales_m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error = RSE/sales_m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.2323876890168014"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 4., 10., 13., 17., 40., 42., 33., 16., 17.,  8.]),\n",
+       " array([-8.3860819 , -6.82624404, -5.26640618, -3.70656832, -2.14673046,\n",
+       "        -0.5868926 ,  0.97294526,  2.53278312,  4.09262098,  5.65245884,\n",
+       "         7.2122967 ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAPX0lEQVR4nO3dbYxcZ32G8euuEwripUmUTbDipFuiQKEUHLS4QRE0xIS6GJHwgYpIRFaLulARlFRU1AlqGz7h8haQWiEZErDaFBQ10ETh1TWkCImYrsEkTg01RS4kuPYCAhKhgpL8+2GPxXYz65ndndnZx75+0mjOeeacPfeH0e3jM+clVYUkqT2/Nu4AkqTlscAlqVEWuCQ1ygKXpEZZ4JLUqNNWc2Nnn312TU5OruYmJal5+/bt+2FVTSwcX9UCn5ycZGZmZjU3KUnNS/LfvcY9hCJJjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY1a1SsxpbVqcvunx7btwzu2jm3bapt74JLUKAtckhplgUtSoyxwSWqUBS5JjRq4wJOsS/KNJHd382cl2Z3kUPd+5uhiSpIWWsoe+HXAwXnz24E9VXURsKeblyStkoEKPMkGYCvwkXnDVwK7uuldwFVDTSZJOqFB98A/ALwdeHze2LlVdQSgez9nuNEkSSfS90rMJK8GjlXVviSXLXUDSaaBaYALLrhgqavrFDPOKyKl1gyyB34p8Jokh4FPAJcn+UfgaJL1AN37sV4rV9XOqpqqqqmJiSc8VFmStEx9C7yqbqiqDVU1Cbwe+GJVvQG4C9jWLbYNuHNkKSVJT7CS88B3AFckOQRc0c1LklbJku5GWFX3APd00z8CNg8/kiRpEF6JKUmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqVN8CT/LkJF9L8s0kDyR5Zzd+U5KHkuzvXq8afVxJ0nGDPJHnF8DlVfVIktOBryT5bPfZzVX13tHFkyQtpm+BV1UBj3Szp3evGmUoSVJ/Ax0DT7IuyX7gGLC7qvZ2H12b5L4ktyY5c5F1p5PMJJmZnZ0dTmpJ0mAFXlWPVdVGYAOwKcnzgQ8BFwIbgSPA+xZZd2dVTVXV1MTExFBCS5KWeBZKVf2EuafSb6mqo12xPw58GNg0/HiSpMUMchbKRJIzuumnAK8AvpVk/bzFXgscGElCSVJPg5yFsh7YlWQdc4V/e1XdneQfkmxk7gfNw8CbRpZSkvQEg5yFch9wcY/xa0aSSJI0EK/ElKRGWeCS1CgLXJIaZYFLUqMGOQtF0ghNbv/0WLZ7eMfWsWxXw+MeuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1KhBHqn25CRfS/LNJA8keWc3flaS3UkOde89n0ovSRqNQfbAfwFcXlUvZO4J9FuSXAJsB/ZU1UXAnm5ekrRK+hZ4zXmkmz29exVwJbCrG98FXDWKgJKk3gY6Bp5kXZL9wDFgd1XtBc6tqiMA3fs5i6w7nWQmyczs7OyQYkuSBirwqnqsqjYCG4BNSZ4/6AaqamdVTVXV1MTExDJjSpIWWtJZKFX1E+AeYAtwNMl6gO792LDDSZIWN8hZKBNJzuimnwK8AvgWcBewrVtsG3DniDJKknoY5JFq64FdSdYxV/i3V9XdSb4K3J7kjcD3gNeNMKckaYG+BV5V9wEX9xj/EbB5FKEkSf15JaYkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJapQFLkmNGuSJPOcn+VKSg0keSHJdN35TkoeS7O9erxp9XEnScYM8kedR4G1V9fUkTwf2JdndfXZzVb13dPEkSYsZ5Ik8R4Aj3fTDSQ4C5406mCTpxJZ0DDzJJHOPV9vbDV2b5L4ktyY5c9jhJEmLG7jAkzwNuAO4vqp+BnwIuBDYyNwe+vsWWW86yUySmdnZ2ZUnliQBAxZ4ktOZK+/bquqTAFV1tKoeq6rHgQ8Dm3qtW1U7q2qqqqYmJiaGlVuSTnmDnIUS4BbgYFW9f974+nmLvRY4MPx4kqTFDHIWyqXANcD9SfZ3YzcCVyfZCBRwGHjTCPJJkhYxyFkoXwHS46PPDD+OJGlQXokpSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSoyxwSWrUII9UOz/Jl5IcTPJAkuu68bOS7E5yqHv3qfSStIoG2QN/FHhbVT0XuAR4S5LnAduBPVV1EbCnm5ckrZK+BV5VR6rq6930w8BB4DzgSmBXt9gu4KoRZZQk9bCkY+BJJoGLgb3AuVV1BOZKHjhnkXWmk8wkmZmdnV1hXEnScQMXeJKnAXcA11fVzwZdr6p2VtVUVU1NTEwsJ6MkqYeBCjzJ6cyV921V9clu+GiS9d3n64Fjo4koSeplkLNQAtwCHKyq98/76C5gWze9Dbhz+PEkSYs5bYBlLgWuAe5Psr8buxHYAdye5I3A94DXjSShJKmnvgVeVV8BssjHm4cbR5I0KK/ElKRGWeCS1CgLXJIaZYFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRg1yKb1OMZPbPz3uCDrJjes7dnjH1rFsd1TcA5ekRlngktQoC1ySGmWBS1Kj/BFTOkX5Y3X73AOXpEYN8ki1W5McS3Jg3thNSR5Ksr97vWq0MSVJCw2yB/4xYEuP8ZuramP3+sxwY0mS+ulb4FX1ZeDHq5BFkrQEKzkGfm2S+7pDLGcutlCS6SQzSWZmZ2dXsDlJ0nzLLfAPARcCG4EjwPsWW7CqdlbVVFVNTUxMLHNzkqSFllXgVXW0qh6rqseBDwObhhtLktTPsgo8yfp5s68FDiy2rCRpNPpeyJPk48BlwNlJHgT+BrgsyUaggMPAm0YXUZLUS98Cr6qrewzfMoIskqQl8EpMSWqUBS5JjbLAJalRFrgkNcrbya5h3u5T0om4By5JjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY2ywCWpUX0LvHto8bEkB+aNnZVkd5JD3fuiDzWWJI3GIHvgHwO2LBjbDuypqouAPd28JGkV9S3wqvoy8OMFw1cCu7rpXcBVw40lSepnuXcjPLeqjgBU1ZEk5yy2YJJpYBrgggsuWObmxsu7Akpai0b+I2ZV7ayqqaqampiYGPXmJOmUsdwCP5pkPUD3fmx4kSRJg1hugd8FbOumtwF3DieOJGlQg5xG+HHgq8BzkjyY5I3ADuCKJIeAK7p5SdIq6vsjZlVdvchHm4ecRZK0BF6JKUmNssAlqVEWuCQ1ygKXpEYt90pMSWrOOK+qPrxj69D/pnvgktQoC1ySGmWBS1KjLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUKAtckhplgUtSo1Z0L5Qkh4GHgceAR6tqahihJEn9DeNmVi+vqh8O4e9IkpbAQyiS1KiVFngBX0iyL8l0rwWSTCeZSTIzOzu7ws1Jko5baYFfWlUvAv4QeEuSly1coKp2VtVUVU1NTEyscHOSpONWVOBV9YPu/RjwKWDTMEJJkvpbdoEneWqSpx+fBl4JHBhWMEnSia3kLJRzgU8lOf53/qmqPjeUVJKkvpZd4FX1XeCFQ8xyQuN8lp0krUWeRihJjbLAJalRFrgkNcoCl6RGWeCS1CgLXJIaZYFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRlngktQoC1ySGmWBS1KjLHBJatSKCjzJliTfTvKdJNuHFUqS1N9Knom5Dvh75p5I/zzg6iTPG1YwSdKJrWQPfBPwnar6blX9EvgEcOVwYkmS+lnJQ43PA74/b/5B4PcWLpRkGpjuZh9J8u0VbPNEzgZ+OKK/vVJmWx6zLY/Zlmek2fK3K1r9N3sNrqTA02OsnjBQtRPYuYLtDBYmmamqqVFvZznMtjxmWx6zLc9azraYlRxCeRA4f978BuAHK4sjSRrUSgr834GLkvxWkicBrwfuGk4sSVI/yz6EUlWPJrkW+DywDri1qh4YWrKlG/lhmhUw2/KYbXnMtjxrOVtPqXrCYWtJUgO8ElOSGmWBS1KjTpoCT7Ixyb1J9ieZSbJp3JnmS/LW7rYDDyR597jzLJTkL5JUkrPHnWW+JO9J8q0k9yX5VJIzxpxnTd4+Isn5Sb6U5GD3Hbtu3JkWSrIuyTeS3D3uLPMlOSPJP3ffs4NJXjLuTIM6aQoceDfwzqraCPx1N78mJHk5c1epvqCqfgd475gj/T9JzgeuAL437iw97AaeX1UvAP4TuGFcQdb47SMeBd5WVc8FLgHesoayHXcdcHDcIXr4IPC5qvpt4IWszYw9nUwFXsAzuunfYG2dk/5nwI6q+gVAVR0bc56FbgbeTo8Lscatqr5QVY92s/cyd73BuKzZ20dU1ZGq+no3/TBzJXTeeFP9SpINwFbgI+POMl+SZwAvA24BqKpfVtVPxhpqCU6mAr8eeE+S7zO3hzu2PbUeng28NMneJP+W5MXjDnRcktcAD1XVN8edZQB/Anx2jNvvdfuINVOSxyWZBC4G9o45ynwfYG4n4fEx51joWcAs8NHu8M5Hkjx13KEGtZJL6Vddkn8Fntnjo3cAm4E/r6o7kvwRc/+ivmKNZDsNOJO5/9q+GLg9ybNqlc7h7JPtRuCVq5FjMSfKV1V3dsu8g7nDBLetZrYFBrp9xDgleRpwB3B9Vf1s3HkAkrwaOFZV+5JcNuY4C50GvAh4a1XtTfJBYDvwV+ONNZiT5jzwJD8FzqiqShLgp1X1jH7rrYYkn2PuEMo93fx/AZdU1eyYc/0usAf4eTd0/HYIm6rqf8YWbIEk24A3A5ur6uf9lh9hjpcAN1XVH3TzNwBU1bvGlWm+JKcDdwOfr6r3jzvPcUneBVzD3D/AT2buUOcnq+oNYw0GJHkmcG9VTXbzLwW2V9XWsQYb0Ml0COUHwO9305cDh8aYZaF/YS4TSZ4NPIk1cEe2qrq/qs6pqsnuC/wg8KI1Vt5bgL8EXjPO8u6s2dtHdDsttwAH11J5A1TVDVW1ofuOvR744loob4Duu/79JM/phjYD/zHGSEvS1CGUPv4U+GCS04D/5Ve3sF0LbgVuTXIA+CWwbbUOn5wE/g74dWD3XEdxb1W9eRxB1uDtI+a7lLm93PuT7O/Gbqyqz4wvUjPeCtzW/aP8XeCPx5xnYCfNIRRJOtWcTIdQJOmUYoFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRv0foeEH05RG2AwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist((data[\"Sales\"]-data[\"sales_pred\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Regresión lineal múltiple en Python\n",
+    "## El paquete statsmodel para regresión múltiple\n",
+    "* Sales ~TV\n",
+    "* Sales ~Newspaper\n",
+    "* Sales ~Radio\n",
+    "* Sales ~TV+Newspaper\n",
+    "* Sales ~TV+Radio\n",
+    "* Sales ~Newspaper+Radio\n",
+    "* Sales ~TV+Newspaper+Radio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Añadir el Newspaper al modelo existente\n",
+    "lm2 = smf.ols(formula=\"Sales~TV+Newspaper\", data = data).fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intercept    5.774948\n",
+       "TV           0.046901\n",
+       "Newspaper    0.044219\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm2.params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Intercept    3.145860e-22\n",
+       "TV           5.507584e-44\n",
+       "Newspaper    2.217084e-05\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm2.pvalues"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sales = 5.774948+0.046901*TV + 0.044219*Newspaper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6458354938293271"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm2.rsquared"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6422399150864777"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm2.rsquared_adj"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sales_pred = lm2.predict(data[[\"TV\", \"Newspaper\"]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      19.626901\n",
+       "1       9.856348\n",
+       "2       9.646055\n",
+       "3      15.467318\n",
+       "4      16.837102\n",
+       "         ...    \n",
+       "195     8.176802\n",
+       "196    10.551220\n",
+       "197    14.359467\n",
+       "198    22.003458\n",
+       "199    17.045429\n",
+       "Length: 200, dtype: float64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sales_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SSD = sum((data[\"Sales\"]-sales_pred)**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1918.5618118968268"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SSD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RSE = np.sqrt(SSD/(len(data)-2-1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.120719860252885"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error = RSE / sales_m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.22255089037282116"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.646</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.642</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   179.6</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sat, 19 Sep 2020</td> <th>  Prob (F-statistic):</th> <td>3.95e-45</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>16:00:22</td>     <th>  Log-Likelihood:    </th> <td> -509.89</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   1026.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   197</td>      <th>  BIC:               </th> <td>   1036.</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th> <td>    5.7749</td> <td>    0.525</td> <td>   10.993</td> <td> 0.000</td> <td>    4.739</td> <td>    6.811</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>TV</th>        <td>    0.0469</td> <td>    0.003</td> <td>   18.173</td> <td> 0.000</td> <td>    0.042</td> <td>    0.052</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Newspaper</th> <td>    0.0442</td> <td>    0.010</td> <td>    4.346</td> <td> 0.000</td> <td>    0.024</td> <td>    0.064</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td> 0.658</td> <th>  Durbin-Watson:     </th> <td>   1.969</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.720</td> <th>  Jarque-Bera (JB):  </th> <td>   0.415</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-0.093</td> <th>  Prob(JB):          </th> <td>   0.813</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 3.122</td> <th>  Cond. No.          </th> <td>    410.</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  Sales   R-squared:                       0.646\n",
+       "Model:                            OLS   Adj. R-squared:                  0.642\n",
+       "Method:                 Least Squares   F-statistic:                     179.6\n",
+       "Date:                Sat, 19 Sep 2020   Prob (F-statistic):           3.95e-45\n",
+       "Time:                        16:00:22   Log-Likelihood:                -509.89\n",
+       "No. Observations:                 200   AIC:                             1026.\n",
+       "Df Residuals:                     197   BIC:                             1036.\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "Intercept      5.7749      0.525     10.993      0.000       4.739       6.811\n",
+       "TV             0.0469      0.003     18.173      0.000       0.042       0.052\n",
+       "Newspaper      0.0442      0.010      4.346      0.000       0.024       0.064\n",
+       "==============================================================================\n",
+       "Omnibus:                        0.658   Durbin-Watson:                   1.969\n",
+       "Prob(Omnibus):                  0.720   Jarque-Bera (JB):                0.415\n",
+       "Skew:                          -0.093   Prob(JB):                        0.813\n",
+       "Kurtosis:                       3.122   Cond. No.                         410.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm2.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Añadir la Radio al modelo existente\n",
+    "lm3 = smf.ols(formula=\"Sales~TV+Radio\", data = data).fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.897</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.896</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   859.6</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sat, 19 Sep 2020</td> <th>  Prob (F-statistic):</th> <td>4.83e-98</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>16:00:22</td>     <th>  Log-Likelihood:    </th> <td> -386.20</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   778.4</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   197</td>      <th>  BIC:               </th> <td>   788.3</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th> <td>    2.9211</td> <td>    0.294</td> <td>    9.919</td> <td> 0.000</td> <td>    2.340</td> <td>    3.502</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>TV</th>        <td>    0.0458</td> <td>    0.001</td> <td>   32.909</td> <td> 0.000</td> <td>    0.043</td> <td>    0.048</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Radio</th>     <td>    0.1880</td> <td>    0.008</td> <td>   23.382</td> <td> 0.000</td> <td>    0.172</td> <td>    0.204</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>60.022</td> <th>  Durbin-Watson:     </th> <td>   2.081</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 148.679</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-1.323</td> <th>  Prob(JB):          </th> <td>5.19e-33</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 6.292</td> <th>  Cond. No.          </th> <td>    425.</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  Sales   R-squared:                       0.897\n",
+       "Model:                            OLS   Adj. R-squared:                  0.896\n",
+       "Method:                 Least Squares   F-statistic:                     859.6\n",
+       "Date:                Sat, 19 Sep 2020   Prob (F-statistic):           4.83e-98\n",
+       "Time:                        16:00:22   Log-Likelihood:                -386.20\n",
+       "No. Observations:                 200   AIC:                             778.4\n",
+       "Df Residuals:                     197   BIC:                             788.3\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "Intercept      2.9211      0.294      9.919      0.000       2.340       3.502\n",
+       "TV             0.0458      0.001     32.909      0.000       0.043       0.048\n",
+       "Radio          0.1880      0.008     23.382      0.000       0.172       0.204\n",
+       "==============================================================================\n",
+       "Omnibus:                       60.022   Durbin-Watson:                   2.081\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              148.679\n",
+       "Skew:                          -1.323   Prob(JB):                     5.19e-33\n",
+       "Kurtosis:                       6.292   Cond. No.                         425.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm3.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sales_pred = lm3.predict(data[[\"TV\", \"Radio\"]])\n",
+    "SSD = sum((data[\"Sales\"]-sales_pred)**2)\n",
+    "RSE = np.sqrt(SSD/(len(data)-2-1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.681360912508001"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.11990450436855059"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RSE/sales_m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Añadir la Radio al modelo existente\n",
+    "lm4 = smf.ols(formula=\"Sales~TV+Radio+Newspaper\", data = data).fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.897</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.896</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   570.3</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sat, 19 Sep 2020</td> <th>  Prob (F-statistic):</th> <td>1.58e-96</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>16:00:23</td>     <th>  Log-Likelihood:    </th> <td> -386.18</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   780.4</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   196</td>      <th>  BIC:               </th> <td>   793.6</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th> <td>    2.9389</td> <td>    0.312</td> <td>    9.422</td> <td> 0.000</td> <td>    2.324</td> <td>    3.554</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>TV</th>        <td>    0.0458</td> <td>    0.001</td> <td>   32.809</td> <td> 0.000</td> <td>    0.043</td> <td>    0.049</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Radio</th>     <td>    0.1885</td> <td>    0.009</td> <td>   21.893</td> <td> 0.000</td> <td>    0.172</td> <td>    0.206</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Newspaper</th> <td>   -0.0010</td> <td>    0.006</td> <td>   -0.177</td> <td> 0.860</td> <td>   -0.013</td> <td>    0.011</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>60.414</td> <th>  Durbin-Watson:     </th> <td>   2.084</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 151.241</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-1.327</td> <th>  Prob(JB):          </th> <td>1.44e-33</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 6.332</td> <th>  Cond. No.          </th> <td>    454.</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  Sales   R-squared:                       0.897\n",
+       "Model:                            OLS   Adj. R-squared:                  0.896\n",
+       "Method:                 Least Squares   F-statistic:                     570.3\n",
+       "Date:                Sat, 19 Sep 2020   Prob (F-statistic):           1.58e-96\n",
+       "Time:                        16:00:23   Log-Likelihood:                -386.18\n",
+       "No. Observations:                 200   AIC:                             780.4\n",
+       "Df Residuals:                     196   BIC:                             793.6\n",
+       "Df Model:                           3                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "Intercept      2.9389      0.312      9.422      0.000       2.324       3.554\n",
+       "TV             0.0458      0.001     32.809      0.000       0.043       0.049\n",
+       "Radio          0.1885      0.009     21.893      0.000       0.172       0.206\n",
+       "Newspaper     -0.0010      0.006     -0.177      0.860      -0.013       0.011\n",
+       "==============================================================================\n",
+       "Omnibus:                       60.414   Durbin-Watson:                   2.084\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              151.241\n",
+       "Skew:                          -1.327   Prob(JB):                     1.44e-33\n",
+       "Kurtosis:                       6.332   Cond. No.                         454.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm4.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sales_pred = lm4.predict(data[[\"TV\", \"Radio\",\"Newspaper\"]])\n",
+    "SSD = sum((data[\"Sales\"]-sales_pred)**2)\n",
+    "RSE = np.sqrt(SSD/(len(data)-3-1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.6855103734147443"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1202004188564624"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RSE/sales_m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Multicolinealidad \n",
+    "#### Factor Inflación de la Varianza\n",
+    "* VIF = 1 : Las variables no están correlacionadas\n",
+    "* VIF < 5 : Las variables tienen una correlación moderada y se pueden quedar en el modelo\n",
+    "* VIF >5 : Las variables están altamente correlacionadas y deben desaparecer del modelo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.1451873787239288"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Newspaper ~ TV + Radio -> R^2 VIF = 1/(1-R^2)\n",
+    "lm_n = smf.ols(formula=\"Newspaper~TV+Radio\", data = data).fit()\n",
+    "rsquared_n = lm_n.rsquared\n",
+    "VIF = 1/(1-rsquared_n)\n",
+    "VIF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0046107849396502"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# TV ~ Newspaper + Radio -> R^2 VIF = 1/(1-R^2)\n",
+    "lm_tv = smf.ols(formula=\"TV~Newspaper+Radio\", data=data).fit()\n",
+    "rsquared_tv = lm_tv.rsquared\n",
+    "VIF = 1/(1-rsquared_tv)\n",
+    "VIF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.1449519171055353"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Radio ~ TV + Newspaper -> R^2 VIF = 1/(1-R^2)\n",
+    "lm_r = smf.ols(formula=\"Radio~Newspaper+TV\", data=data).fit()\n",
+    "rsquared_r = lm_r.rsquared\n",
+    "VIF = 1/(1-rsquared_r)\n",
+    "VIF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.897</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.896</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   859.6</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sat, 19 Sep 2020</td> <th>  Prob (F-statistic):</th> <td>4.83e-98</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>16:00:23</td>     <th>  Log-Likelihood:    </th> <td> -386.20</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   778.4</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   197</td>      <th>  BIC:               </th> <td>   788.3</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th> <td>    2.9211</td> <td>    0.294</td> <td>    9.919</td> <td> 0.000</td> <td>    2.340</td> <td>    3.502</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>TV</th>        <td>    0.0458</td> <td>    0.001</td> <td>   32.909</td> <td> 0.000</td> <td>    0.043</td> <td>    0.048</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Radio</th>     <td>    0.1880</td> <td>    0.008</td> <td>   23.382</td> <td> 0.000</td> <td>    0.172</td> <td>    0.204</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>60.022</td> <th>  Durbin-Watson:     </th> <td>   2.081</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 148.679</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-1.323</td> <th>  Prob(JB):          </th> <td>5.19e-33</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 6.292</td> <th>  Cond. No.          </th> <td>    425.</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  Sales   R-squared:                       0.897\n",
+       "Model:                            OLS   Adj. R-squared:                  0.896\n",
+       "Method:                 Least Squares   F-statistic:                     859.6\n",
+       "Date:                Sat, 19 Sep 2020   Prob (F-statistic):           4.83e-98\n",
+       "Time:                        16:00:23   Log-Likelihood:                -386.20\n",
+       "No. Observations:                 200   AIC:                             778.4\n",
+       "Df Residuals:                     197   BIC:                             788.3\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "Intercept      2.9211      0.294      9.919      0.000       2.340       3.502\n",
+       "TV             0.0458      0.001     32.909      0.000       0.043       0.048\n",
+       "Radio          0.1880      0.008     23.382      0.000       0.172       0.204\n",
+       "==============================================================================\n",
+       "Omnibus:                       60.022   Durbin-Watson:                   2.081\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              148.679\n",
+       "Skew:                          -1.323   Prob(JB):                     5.19e-33\n",
+       "Kurtosis:                       6.292   Cond. No.                         425.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm3.summary()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T4 - 2 - Linear Regression - Regresi\303\263n lineal con statsmodel.ipynb" "b/notebooks/T4 - 2 - Linear Regression - Regresi\303\263n lineal con statsmodel.ipynb"
index f2cde5f2..2afea203 100644
--- "a/notebooks/T4 - 2 - Linear Regression - Regresi\303\263n lineal con statsmodel.ipynb"	
+++ "b/notebooks/T4 - 2 - Linear Regression - Regresi\303\263n lineal con statsmodel.ipynb"	
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T4%20-%202%20-%20Linear%20Regression%20-%20Regresión%20lineal%20con%20statsmodel-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -10,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -137,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -148,7 +155,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -167,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -178,7 +185,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -189,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -198,7 +205,7 @@
        "0.611875050850071"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -209,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -218,7 +225,7 @@
        "0.6099148238341623"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -229,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -247,10 +254,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   312.1</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Mon, 02 Apr 2018</td> <th>  Prob (F-statistic):</th> <td>1.47e-42</td>\n",
+       "  <th>Date:</th>             <td>Sat, 19 Sep 2020</td> <th>  Prob (F-statistic):</th> <td>1.47e-42</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>16:23:52</td>     <th>  Log-Likelihood:    </th> <td> -519.05</td>\n",
+       "  <th>Time:</th>                 <td>16:00:18</td>     <th>  Log-Likelihood:    </th> <td> -519.05</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   1042.</td>\n",
@@ -289,7 +296,7 @@
        "<tr>\n",
        "  <th>Kurtosis:</th>      <td> 2.779</td> <th>  Cond. No.          </th> <td>    338.</td>\n",
        "</tr>\n",
-       "</table>"
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
@@ -299,8 +306,8 @@
        "Dep. Variable:                  Sales   R-squared:                       0.612\n",
        "Model:                            OLS   Adj. R-squared:                  0.610\n",
        "Method:                 Least Squares   F-statistic:                     312.1\n",
-       "Date:                Mon, 02 Apr 2018   Prob (F-statistic):           1.47e-42\n",
-       "Time:                        16:23:52   Log-Likelihood:                -519.05\n",
+       "Date:                Sat, 19 Sep 2020   Prob (F-statistic):           1.47e-42\n",
+       "Time:                        16:00:18   Log-Likelihood:                -519.05\n",
        "No. Observations:                 200   AIC:                             1042.\n",
        "Df Residuals:                     198   BIC:                             1049.\n",
        "Df Model:                           1                                         \n",
@@ -317,12 +324,12 @@
        "Kurtosis:                       2.779   Cond. No.                         338.\n",
        "==============================================================================\n",
        "\n",
-       "Warnings:\n",
+       "Notes:\n",
        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
        "\"\"\""
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -333,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -344,57 +351,7 @@
        "2       7.850224\n",
        "3      14.234395\n",
        "4      15.627218\n",
-       "5       7.446162\n",
-       "6       9.765950\n",
-       "7      12.746498\n",
-       "8       7.441409\n",
-       "9      16.530414\n",
-       "10     10.174765\n",
-       "11     17.238710\n",
-       "12      8.163966\n",
-       "13     11.667416\n",
-       "14     16.734822\n",
-       "15     16.321253\n",
-       "16     10.255578\n",
-       "17     20.409404\n",
-       "18     10.322129\n",
-       "19     14.034741\n",
-       "20     17.414596\n",
-       "21     18.317792\n",
-       "22      7.660077\n",
-       "23     17.885209\n",
-       "24      9.994126\n",
-       "25     19.529976\n",
-       "26     13.825579\n",
-       "27     18.446141\n",
-       "28     18.859710\n",
-       "29     10.388680\n",
        "         ...    \n",
-       "170     9.409426\n",
-       "171    14.852371\n",
-       "172     7.964312\n",
-       "173    15.037764\n",
-       "174    17.604742\n",
-       "175    20.195489\n",
-       "176    18.840695\n",
-       "177    15.123330\n",
-       "178    20.185982\n",
-       "179    14.904661\n",
-       "180    14.476831\n",
-       "181    17.419349\n",
-       "182     9.704153\n",
-       "183    20.704131\n",
-       "184    19.097393\n",
-       "185    16.777605\n",
-       "186    13.663955\n",
-       "187    16.116846\n",
-       "188    20.628073\n",
-       "189     7.921529\n",
-       "190     8.910291\n",
-       "191    10.621610\n",
-       "192     7.850224\n",
-       "193    14.961705\n",
-       "194    14.148829\n",
        "195     8.848493\n",
        "196    11.510545\n",
        "197    15.446579\n",
@@ -403,7 +360,7 @@
        "Length: 200, dtype: float64"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -415,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -424,27 +381,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a21403978>]"
+       "[<matplotlib.lines.Line2D at 0x7f8894446a60>]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a214039e8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -456,7 +415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -465,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -474,7 +433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -483,7 +442,7 @@
        "2102.5305838896525"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -495,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -504,7 +463,7 @@
        "3.258656369238098"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -516,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -525,7 +484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -534,7 +493,7 @@
        "14.022500000000003"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -545,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -554,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -563,7 +522,7 @@
        "0.2323876890168014"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -574,7 +533,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -584,21 +543,23 @@
        " array([-8.3860819 , -6.82624404, -5.26640618, -3.70656832, -2.14673046,\n",
        "        -0.5868926 ,  0.97294526,  2.53278312,  4.09262098,  5.65245884,\n",
        "         7.2122967 ]),\n",
-       " <a list of 10 Patch objects>)"
+       " <BarContainer object of 10 artists>)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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k24A3A5ur6uf9lh9hjpcAN1XVH3TzNwBU1bvGlWm+JKcDdwOfr6r3jzvPcUneBVzD3D/AT2buUOcnq+oNYw0GJHkmcG9VTXbzLwW2V9XWsQYb0Ml0COUHwO9305cDh8aYZaF/YS4TSZ4NPIk1cEe2qrq/qs6pqsnuC/wg8KI1Vt5bgL8EXjPO8u6s2dtHdDsttwAH11J5A1TVDVW1ofuOvR744loob4Duu/79JM/phjYD/zHGSEvS1CGUPv4U+GCS04D/5Ve3sF0LbgVuTXIA+CWwbbUOn5wE/g74dWD3XEdxb1W9eRxB1uDtI+a7lLm93PuT7O/Gbqyqz4wvUjPeCtzW/aP8XeCPx5xnYCfNIRRJOtWcTIdQJOmUYoFLUqMscElqlAUuSY2ywCWpURa4JDXKApekRv0foeEH05RG2AwAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a211302b0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -623,7 +584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -633,7 +594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -645,7 +606,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -656,7 +617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -668,7 +629,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -686,16 +647,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.6458354938293273"
+       "0.6458354938293271"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -706,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -715,7 +676,7 @@
        "0.6422399150864777"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -726,7 +687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -735,7 +696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -746,57 +707,7 @@
        "2       9.646055\n",
        "3      15.467318\n",
        "4      16.837102\n",
-       "5       9.499445\n",
-       "6       9.510924\n",
-       "7      11.925419\n",
-       "8       6.222518\n",
-       "9      16.083262\n",
-       "10      9.945228\n",
-       "11     16.021516\n",
-       "12      9.805257\n",
-       "13     10.666196\n",
-       "14     17.381579\n",
-       "15     17.278653\n",
-       "16     13.995865\n",
-       "17     21.440393\n",
-       "18      9.829727\n",
-       "19     13.528088\n",
-       "20     18.379490\n",
-       "21     17.948453\n",
-       "22      8.587327\n",
-       "23     17.641044\n",
-       "24      9.506109\n",
-       "25     18.967556\n",
-       "26     13.034296\n",
-       "27     18.048554\n",
-       "28     18.456595\n",
-       "29     10.890326\n",
        "         ...    \n",
-       "170     8.933646\n",
-       "171    15.586198\n",
-       "172     7.445942\n",
-       "173    14.239121\n",
-       "174    16.785052\n",
-       "175    20.610266\n",
-       "176    18.322864\n",
-       "177    15.314058\n",
-       "178    19.800514\n",
-       "179    14.320051\n",
-       "180    13.486699\n",
-       "181    17.234475\n",
-       "182     9.724113\n",
-       "183    22.438692\n",
-       "184    19.005059\n",
-       "185    16.256398\n",
-       "186    13.493904\n",
-       "187    15.542563\n",
-       "188    19.352307\n",
-       "189     7.686735\n",
-       "190     7.884019\n",
-       "191     9.581306\n",
-       "192     7.978983\n",
-       "193    13.757260\n",
-       "194    13.061376\n",
        "195     8.176802\n",
        "196    10.551220\n",
        "197    14.359467\n",
@@ -805,7 +716,7 @@
        "Length: 200, dtype: float64"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -816,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -825,16 +736,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1918.5618118968275"
+       "1918.5618118968268"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -845,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -854,16 +765,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3.1207198602528856"
+       "3.120719860252885"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -874,7 +785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -883,16 +794,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.2225508903728212"
+       "0.22255089037282116"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -903,7 +814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -921,10 +832,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   179.6</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Mon, 02 Apr 2018</td> <th>  Prob (F-statistic):</th> <td>3.95e-45</td>\n",
+       "  <th>Date:</th>             <td>Sat, 19 Sep 2020</td> <th>  Prob (F-statistic):</th> <td>3.95e-45</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>16:52:42</td>     <th>  Log-Likelihood:    </th> <td> -509.89</td>\n",
+       "  <th>Time:</th>                 <td>16:00:22</td>     <th>  Log-Likelihood:    </th> <td> -509.89</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   1026.</td>\n",
@@ -966,7 +877,7 @@
        "<tr>\n",
        "  <th>Kurtosis:</th>      <td> 3.122</td> <th>  Cond. No.          </th> <td>    410.</td>\n",
        "</tr>\n",
-       "</table>"
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
@@ -976,8 +887,8 @@
        "Dep. Variable:                  Sales   R-squared:                       0.646\n",
        "Model:                            OLS   Adj. R-squared:                  0.642\n",
        "Method:                 Least Squares   F-statistic:                     179.6\n",
-       "Date:                Mon, 02 Apr 2018   Prob (F-statistic):           3.95e-45\n",
-       "Time:                        16:52:42   Log-Likelihood:                -509.89\n",
+       "Date:                Sat, 19 Sep 2020   Prob (F-statistic):           3.95e-45\n",
+       "Time:                        16:00:22   Log-Likelihood:                -509.89\n",
        "No. Observations:                 200   AIC:                             1026.\n",
        "Df Residuals:                     197   BIC:                             1036.\n",
        "Df Model:                           2                                         \n",
@@ -995,12 +906,12 @@
        "Kurtosis:                       3.122   Cond. No.                         410.\n",
        "==============================================================================\n",
        "\n",
-       "Warnings:\n",
+       "Notes:\n",
        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
        "\"\"\""
       ]
      },
-     "execution_count": 41,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1011,7 +922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1021,7 +932,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -1039,10 +950,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   859.6</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Mon, 02 Apr 2018</td> <th>  Prob (F-statistic):</th> <td>4.83e-98</td>\n",
+       "  <th>Date:</th>             <td>Sat, 19 Sep 2020</td> <th>  Prob (F-statistic):</th> <td>4.83e-98</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>16:56:34</td>     <th>  Log-Likelihood:    </th> <td> -386.20</td>\n",
+       "  <th>Time:</th>                 <td>16:00:22</td>     <th>  Log-Likelihood:    </th> <td> -386.20</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   778.4</td>\n",
@@ -1084,7 +995,7 @@
        "<tr>\n",
        "  <th>Kurtosis:</th>      <td> 6.292</td> <th>  Cond. No.          </th> <td>    425.</td>\n",
        "</tr>\n",
-       "</table>"
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
@@ -1094,8 +1005,8 @@
        "Dep. Variable:                  Sales   R-squared:                       0.897\n",
        "Model:                            OLS   Adj. R-squared:                  0.896\n",
        "Method:                 Least Squares   F-statistic:                     859.6\n",
-       "Date:                Mon, 02 Apr 2018   Prob (F-statistic):           4.83e-98\n",
-       "Time:                        16:56:34   Log-Likelihood:                -386.20\n",
+       "Date:                Sat, 19 Sep 2020   Prob (F-statistic):           4.83e-98\n",
+       "Time:                        16:00:22   Log-Likelihood:                -386.20\n",
        "No. Observations:                 200   AIC:                             778.4\n",
        "Df Residuals:                     197   BIC:                             788.3\n",
        "Df Model:                           2                                         \n",
@@ -1113,12 +1024,12 @@
        "Kurtosis:                       6.292   Cond. No.                         425.\n",
        "==============================================================================\n",
        "\n",
-       "Warnings:\n",
+       "Notes:\n",
        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
        "\"\"\""
       ]
      },
-     "execution_count": 46,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1129,7 +1040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1140,16 +1051,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.6813609125080013"
+       "1.681360912508001"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1160,16 +1071,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.11990450436855062"
+       "0.11990450436855059"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1180,7 +1091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1190,7 +1101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -1208,10 +1119,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   570.3</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Mon, 02 Apr 2018</td> <th>  Prob (F-statistic):</th> <td>1.58e-96</td>\n",
+       "  <th>Date:</th>             <td>Sat, 19 Sep 2020</td> <th>  Prob (F-statistic):</th> <td>1.58e-96</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>16:57:51</td>     <th>  Log-Likelihood:    </th> <td> -386.18</td>\n",
+       "  <th>Time:</th>                 <td>16:00:23</td>     <th>  Log-Likelihood:    </th> <td> -386.18</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   780.4</td>\n",
@@ -1256,7 +1167,7 @@
        "<tr>\n",
        "  <th>Kurtosis:</th>      <td> 6.332</td> <th>  Cond. No.          </th> <td>    454.</td>\n",
        "</tr>\n",
-       "</table>"
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
@@ -1266,8 +1177,8 @@
        "Dep. Variable:                  Sales   R-squared:                       0.897\n",
        "Model:                            OLS   Adj. R-squared:                  0.896\n",
        "Method:                 Least Squares   F-statistic:                     570.3\n",
-       "Date:                Mon, 02 Apr 2018   Prob (F-statistic):           1.58e-96\n",
-       "Time:                        16:57:51   Log-Likelihood:                -386.18\n",
+       "Date:                Sat, 19 Sep 2020   Prob (F-statistic):           1.58e-96\n",
+       "Time:                        16:00:23   Log-Likelihood:                -386.18\n",
        "No. Observations:                 200   AIC:                             780.4\n",
        "Df Residuals:                     196   BIC:                             793.6\n",
        "Df Model:                           3                                         \n",
@@ -1286,12 +1197,12 @@
        "Kurtosis:                       6.332   Cond. No.                         454.\n",
        "==============================================================================\n",
        "\n",
-       "Warnings:\n",
+       "Notes:\n",
        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
        "\"\"\""
       ]
      },
-     "execution_count": 51,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1302,7 +1213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1313,16 +1224,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.685510373414744"
+       "1.6855103734147443"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1333,16 +1244,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.12020041885646238"
+       "0.1202004188564624"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1364,7 +1275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1373,7 +1284,7 @@
        "1.1451873787239288"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1388,7 +1299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -1397,7 +1308,7 @@
        "1.0046107849396502"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1412,7 +1323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -1421,7 +1332,7 @@
        "1.1449519171055353"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1436,7 +1347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1454,10 +1365,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   859.6</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Mon, 02 Apr 2018</td> <th>  Prob (F-statistic):</th> <td>4.83e-98</td>\n",
+       "  <th>Date:</th>             <td>Sat, 19 Sep 2020</td> <th>  Prob (F-statistic):</th> <td>4.83e-98</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>17:15:41</td>     <th>  Log-Likelihood:    </th> <td> -386.20</td>\n",
+       "  <th>Time:</th>                 <td>16:00:23</td>     <th>  Log-Likelihood:    </th> <td> -386.20</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   778.4</td>\n",
@@ -1499,7 +1410,7 @@
        "<tr>\n",
        "  <th>Kurtosis:</th>      <td> 6.292</td> <th>  Cond. No.          </th> <td>    425.</td>\n",
        "</tr>\n",
-       "</table>"
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
@@ -1509,8 +1420,8 @@
        "Dep. Variable:                  Sales   R-squared:                       0.897\n",
        "Model:                            OLS   Adj. R-squared:                  0.896\n",
        "Method:                 Least Squares   F-statistic:                     859.6\n",
-       "Date:                Mon, 02 Apr 2018   Prob (F-statistic):           4.83e-98\n",
-       "Time:                        17:15:41   Log-Likelihood:                -386.20\n",
+       "Date:                Sat, 19 Sep 2020   Prob (F-statistic):           4.83e-98\n",
+       "Time:                        16:00:23   Log-Likelihood:                -386.20\n",
        "No. Observations:                 200   AIC:                             778.4\n",
        "Df Residuals:                     197   BIC:                             788.3\n",
        "Df Model:                           2                                         \n",
@@ -1528,12 +1439,12 @@
        "Kurtosis:                       6.292   Cond. No.                         425.\n",
        "==============================================================================\n",
        "\n",
-       "Warnings:\n",
+       "Notes:\n",
        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
        "\"\"\""
       ]
      },
-     "execution_count": 59,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1541,13 +1452,6 @@
    "source": [
     "lm3.summary()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1566,7 +1470,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T4 - 3 - Linear Regression - Validaci\303\263n del modelo-Colab.ipynb" "b/notebooks/T4 - 3 - Linear Regression - Validaci\303\263n del modelo-Colab.ipynb"
new file mode 100644
index 00000000..4ad61978
--- /dev/null
+++ "b/notebooks/T4 - 3 - Linear Regression - Validaci\303\263n del modelo-Colab.ipynb"	
@@ -0,0 +1,501 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T4%20-%203%20-%20Linear%20Regression%20-%20Validación%20del%20modelo-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dividir el dataset en conjunto de entrenamiento y de testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"/content/python-ml-course/datasets/ads/Advertising.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.random.randn(len(data))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 2.,  6., 19., 37., 36., 33., 38., 19.,  7.,  3.]),\n",
+       " array([-2.70270911, -2.15896235, -1.61521558, -1.07146882, -0.52772206,\n",
+       "         0.0160247 ,  0.55977146,  1.10351822,  1.64726498,  2.19101175,\n",
+       "         2.73475851]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "check = (a<0.8)\n",
+    "training = data[check]\n",
+    "testing = data[~check]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(153, 47)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(training), len(testing)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import statsmodels.formula.api as smf\n",
+    "lm = smf.ols(formula=\"Sales~TV+Radio\", data=training).fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>OLS Regression Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.898</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.897</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   662.2</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Sat, 19 Sep 2020</td> <th>  Prob (F-statistic):</th> <td>3.63e-75</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>16:00:31</td>     <th>  Log-Likelihood:    </th> <td> -299.31</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   153</td>      <th>  AIC:               </th> <td>   604.6</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   150</td>      <th>  BIC:               </th> <td>   613.7</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Intercept</th> <td>    2.7915</td> <td>    0.339</td> <td>    8.244</td> <td> 0.000</td> <td>    2.122</td> <td>    3.460</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>TV</th>        <td>    0.0473</td> <td>    0.002</td> <td>   29.287</td> <td> 0.000</td> <td>    0.044</td> <td>    0.050</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Radio</th>     <td>    0.1823</td> <td>    0.009</td> <td>   19.250</td> <td> 0.000</td> <td>    0.164</td> <td>    0.201</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td>47.013</td> <th>  Durbin-Watson:     </th> <td>   2.109</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 105.046</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-1.323</td> <th>  Prob(JB):          </th> <td>1.55e-23</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 6.078</td> <th>  Cond. No.          </th> <td>    413.</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  Sales   R-squared:                       0.898\n",
+       "Model:                            OLS   Adj. R-squared:                  0.897\n",
+       "Method:                 Least Squares   F-statistic:                     662.2\n",
+       "Date:                Sat, 19 Sep 2020   Prob (F-statistic):           3.63e-75\n",
+       "Time:                        16:00:31   Log-Likelihood:                -299.31\n",
+       "No. Observations:                 153   AIC:                             604.6\n",
+       "Df Residuals:                     150   BIC:                             613.7\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "Intercept      2.7915      0.339      8.244      0.000       2.122       3.460\n",
+       "TV             0.0473      0.002     29.287      0.000       0.044       0.050\n",
+       "Radio          0.1823      0.009     19.250      0.000       0.164       0.201\n",
+       "==============================================================================\n",
+       "Omnibus:                       47.013   Durbin-Watson:                   2.109\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              105.046\n",
+       "Skew:                          -1.323   Prob(JB):                     1.55e-23\n",
+       "Kurtosis:                       6.078   Cond. No.                         413.\n",
+       "==============================================================================\n",
+       "\n",
+       "Notes:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sales = 2.9336 + 0.0465 * TV + 0.1807 * Radio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Validación del modelo con el conjunto de testing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2      11.974545\n",
+       "3      17.486930\n",
+       "14     18.442728\n",
+       "16     12.671650\n",
+       "20     18.170789\n",
+       "23     16.669633\n",
+       "27     17.191197\n",
+       "30     21.803374\n",
+       "35     17.286561\n",
+       "36     23.400172\n",
+       "38      9.698338\n",
+       "42     21.727070\n",
+       "46      8.838684\n",
+       "50     12.805466\n",
+       "51      9.289993\n",
+       "53     19.851174\n",
+       "55     21.205524\n",
+       "60      5.686218\n",
+       "69     21.049129\n",
+       "71     10.591555\n",
+       "77     13.686881\n",
+       "81     14.879451\n",
+       "88     11.617074\n",
+       "94     10.423352\n",
+       "99     16.789025\n",
+       "100    14.093057\n",
+       "104    20.310634\n",
+       "109    19.774678\n",
+       "116    11.981909\n",
+       "120    14.360545\n",
+       "123    14.922148\n",
+       "131    15.861826\n",
+       "134    11.575051\n",
+       "143     8.777466\n",
+       "144    10.039570\n",
+       "149     9.609892\n",
+       "153    18.131537\n",
+       "161    13.372281\n",
+       "172     7.383516\n",
+       "176    20.045383\n",
+       "180    10.671323\n",
+       "181    14.109203\n",
+       "182     6.488583\n",
+       "190    12.153873\n",
+       "193    18.338123\n",
+       "195     5.272654\n",
+       "197    12.857777\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sales_pred = lm.predict(testing)\n",
+    "sales_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "113.61483512299858"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SSD = sum((testing[\"Sales\"]-sales_pred)**2)\n",
+    "SSD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.6069086295444783"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RSE = np.sqrt(SSD/(len(testing)-2-1))\n",
+    "RSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.11100044913079142"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sales_mean = np.mean(testing[\"Sales\"])\n",
+    "error = RSE/sales_mean\n",
+    "error"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='TV', ylabel='Sales'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "data.plot(kind = \"scatter\", x = \"TV\", y =\"Sales\")\n",
+    "#plt.plot(pd.DataFrame(data[\"TV\"]), sales_pred, c=\"red\", linewidth = 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import Image\n",
+    "Image(filename=\"/content/python-ml-course/notebooks/resources/summary-lm.png\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T4 - 3 - Linear Regression - Validaci\303\263n del modelo.ipynb" "b/notebooks/T4 - 3 - Linear Regression - Validaci\303\263n del modelo.ipynb"
index de72275e..82fa960c 100644
--- "a/notebooks/T4 - 3 - Linear Regression - Validaci\303\263n del modelo.ipynb"	
+++ "b/notebooks/T4 - 3 - Linear Regression - Validaci\303\263n del modelo.ipynb"	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -38,31 +38,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([ 4.,  9., 20., 26., 36., 51., 27., 19.,  6.,  2.]),\n",
-       " array([-2.73897754, -2.19590058, -1.65282362, -1.10974666, -0.5666697 ,\n",
-       "        -0.02359273,  0.51948423,  1.06256119,  1.60563815,  2.14871511,\n",
-       "         2.69179207]),\n",
-       " <a list of 10 Patch objects>)"
+       "(array([ 2.,  6., 19., 37., 36., 33., 38., 19.,  7.,  3.]),\n",
+       " array([-2.70270911, -2.15896235, -1.61521558, -1.07146882, -0.52772206,\n",
+       "         0.0160247 ,  0.55977146,  1.10351822,  1.64726498,  2.19101175,\n",
+       "         2.73475851]),\n",
+       " <BarContainer object of 10 artists>)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x107f91390>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -72,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,16 +85,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(163, 37)"
+       "(153, 47)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -103,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -122,25 +124,25 @@
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.895</td>\n",
+       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.898</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.894</td>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.897</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   681.8</td>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   662.2</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Mon, 02 Apr 2018</td> <th>  Prob (F-statistic):</th> <td>5.03e-79</td>\n",
+       "  <th>Date:</th>             <td>Sat, 19 Sep 2020</td> <th>  Prob (F-statistic):</th> <td>3.63e-75</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>22:24:09</td>     <th>  Log-Likelihood:    </th> <td> -315.14</td>\n",
+       "  <th>Time:</th>                 <td>16:00:31</td>     <th>  Log-Likelihood:    </th> <td> -299.31</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>No. Observations:</th>      <td>   163</td>      <th>  AIC:               </th> <td>   636.3</td>\n",
+       "  <th>No. Observations:</th>      <td>   153</td>      <th>  AIC:               </th> <td>   604.6</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Df Residuals:</th>          <td>   160</td>      <th>  BIC:               </th> <td>   645.6</td>\n",
+       "  <th>Df Residuals:</th>          <td>   150</td>      <th>  BIC:               </th> <td>   613.7</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
@@ -154,63 +156,63 @@
        "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Intercept</th> <td>    2.9336</td> <td>    0.329</td> <td>    8.905</td> <td> 0.000</td> <td>    2.283</td> <td>    3.584</td>\n",
+       "  <th>Intercept</th> <td>    2.7915</td> <td>    0.339</td> <td>    8.244</td> <td> 0.000</td> <td>    2.122</td> <td>    3.460</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>TV</th>        <td>    0.0465</td> <td>    0.002</td> <td>   30.530</td> <td> 0.000</td> <td>    0.043</td> <td>    0.049</td>\n",
+       "  <th>TV</th>        <td>    0.0473</td> <td>    0.002</td> <td>   29.287</td> <td> 0.000</td> <td>    0.044</td> <td>    0.050</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Radio</th>     <td>    0.1807</td> <td>    0.009</td> <td>   19.483</td> <td> 0.000</td> <td>    0.162</td> <td>    0.199</td>\n",
+       "  <th>Radio</th>     <td>    0.1823</td> <td>    0.009</td> <td>   19.250</td> <td> 0.000</td> <td>    0.164</td> <td>    0.201</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "  <th>Omnibus:</th>       <td>54.879</td> <th>  Durbin-Watson:     </th> <td>   1.916</td>\n",
+       "  <th>Omnibus:</th>       <td>47.013</td> <th>  Durbin-Watson:     </th> <td>   2.109</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 139.270</td>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 105.046</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Skew:</th>          <td>-1.413</td> <th>  Prob(JB):          </th> <td>5.73e-31</td>\n",
+       "  <th>Skew:</th>          <td>-1.323</td> <th>  Prob(JB):          </th> <td>1.55e-23</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Kurtosis:</th>      <td> 6.538</td> <th>  Cond. No.          </th> <td>    423.</td>\n",
+       "  <th>Kurtosis:</th>      <td> 6.078</td> <th>  Cond. No.          </th> <td>    413.</td>\n",
        "</tr>\n",
-       "</table>"
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
-       "Dep. Variable:                  Sales   R-squared:                       0.895\n",
-       "Model:                            OLS   Adj. R-squared:                  0.894\n",
-       "Method:                 Least Squares   F-statistic:                     681.8\n",
-       "Date:                Mon, 02 Apr 2018   Prob (F-statistic):           5.03e-79\n",
-       "Time:                        22:24:09   Log-Likelihood:                -315.14\n",
-       "No. Observations:                 163   AIC:                             636.3\n",
-       "Df Residuals:                     160   BIC:                             645.6\n",
+       "Dep. Variable:                  Sales   R-squared:                       0.898\n",
+       "Model:                            OLS   Adj. R-squared:                  0.897\n",
+       "Method:                 Least Squares   F-statistic:                     662.2\n",
+       "Date:                Sat, 19 Sep 2020   Prob (F-statistic):           3.63e-75\n",
+       "Time:                        16:00:31   Log-Likelihood:                -299.31\n",
+       "No. Observations:                 153   AIC:                             604.6\n",
+       "Df Residuals:                     150   BIC:                             613.7\n",
        "Df Model:                           2                                         \n",
        "Covariance Type:            nonrobust                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
        "------------------------------------------------------------------------------\n",
-       "Intercept      2.9336      0.329      8.905      0.000       2.283       3.584\n",
-       "TV             0.0465      0.002     30.530      0.000       0.043       0.049\n",
-       "Radio          0.1807      0.009     19.483      0.000       0.162       0.199\n",
+       "Intercept      2.7915      0.339      8.244      0.000       2.122       3.460\n",
+       "TV             0.0473      0.002     29.287      0.000       0.044       0.050\n",
+       "Radio          0.1823      0.009     19.250      0.000       0.164       0.201\n",
        "==============================================================================\n",
-       "Omnibus:                       54.879   Durbin-Watson:                   1.916\n",
-       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              139.270\n",
-       "Skew:                          -1.413   Prob(JB):                     5.73e-31\n",
-       "Kurtosis:                       6.538   Cond. No.                         423.\n",
+       "Omnibus:                       47.013   Durbin-Watson:                   2.109\n",
+       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              105.046\n",
+       "Skew:                          -1.323   Prob(JB):                     1.55e-23\n",
+       "Kurtosis:                       6.078   Cond. No.                         413.\n",
        "==============================================================================\n",
        "\n",
-       "Warnings:\n",
+       "Notes:\n",
        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
        "\"\"\""
       ]
      },
-     "execution_count": 13,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -235,53 +237,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "9      12.689881\n",
-       "18      9.854104\n",
-       "25     15.785321\n",
-       "26     14.869678\n",
-       "29      9.106066\n",
-       "43     14.067888\n",
-       "48     16.348522\n",
-       "53     19.768565\n",
-       "55     21.104381\n",
-       "58     21.693618\n",
-       "60      5.781606\n",
-       "64     16.760553\n",
-       "65      7.821071\n",
-       "67     12.028130\n",
-       "69     20.942554\n",
-       "72     10.142035\n",
-       "74     17.297193\n",
-       "75     11.615286\n",
-       "79      9.716474\n",
-       "81     14.820073\n",
-       "86     11.448940\n",
-       "91      4.533936\n",
-       "98     24.041760\n",
-       "101    23.269023\n",
-       "105    17.727097\n",
-       "107     7.189502\n",
-       "109    19.664895\n",
-       "111    21.033802\n",
-       "122    13.778532\n",
-       "125     9.118714\n",
-       "141    18.333020\n",
-       "148    11.981642\n",
-       "160    14.221716\n",
-       "167    13.485030\n",
-       "181    14.064971\n",
-       "185    20.610932\n",
-       "186     9.796859\n",
+       "2      11.974545\n",
+       "3      17.486930\n",
+       "14     18.442728\n",
+       "16     12.671650\n",
+       "20     18.170789\n",
+       "23     16.669633\n",
+       "27     17.191197\n",
+       "30     21.803374\n",
+       "35     17.286561\n",
+       "36     23.400172\n",
+       "38      9.698338\n",
+       "42     21.727070\n",
+       "46      8.838684\n",
+       "50     12.805466\n",
+       "51      9.289993\n",
+       "53     19.851174\n",
+       "55     21.205524\n",
+       "60      5.686218\n",
+       "69     21.049129\n",
+       "71     10.591555\n",
+       "77     13.686881\n",
+       "81     14.879451\n",
+       "88     11.617074\n",
+       "94     10.423352\n",
+       "99     16.789025\n",
+       "100    14.093057\n",
+       "104    20.310634\n",
+       "109    19.774678\n",
+       "116    11.981909\n",
+       "120    14.360545\n",
+       "123    14.922148\n",
+       "131    15.861826\n",
+       "134    11.575051\n",
+       "143     8.777466\n",
+       "144    10.039570\n",
+       "149     9.609892\n",
+       "153    18.131537\n",
+       "161    13.372281\n",
+       "172     7.383516\n",
+       "176    20.045383\n",
+       "180    10.671323\n",
+       "181    14.109203\n",
+       "182     6.488583\n",
+       "190    12.153873\n",
+       "193    18.338123\n",
+       "195     5.272654\n",
+       "197    12.857777\n",
        "dtype: float64"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -293,16 +305,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "104.31191120382178"
+       "113.61483512299858"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -314,16 +326,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.7515699781475187"
+       "1.6069086295444783"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -335,16 +347,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.11854415436520611"
+       "0.11100044913079142"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -357,18 +369,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='TV', ylabel='Sales'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%matplotlib inline\n",
     "data.plot(kind = \"scatter\", x = \"TV\", y =\"Sales\")\n",
-    "plt.plot(pd.DataFrame(data[\"TV\"]), sales_pred, c=\"red\", linewidth = 2)"
+    "#plt.plot(pd.DataFrame(data[\"TV\"]), sales_pred, c=\"red\", linewidth = 2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -378,7 +413,7 @@
        "<IPython.core.display.Image object>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -387,13 +422,6 @@
     "from IPython.display import Image\n",
     "Image(filename=\"resources/summary-lm.png\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -412,7 +440,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T4 - 4 - Linear Regression - SciKit-Learn-Colab.ipynb b/notebooks/T4 - 4 - Linear Regression - SciKit-Learn-Colab.ipynb
new file mode 100644
index 00000000..ca9b83f3
--- /dev/null
+++ b/notebooks/T4 - 4 - Linear Regression - SciKit-Learn-Colab.ipynb	
@@ -0,0 +1,276 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T4%20-%204%20-%20Linear%20Regression%20-%20SciKit-Learn-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Regresión lineal en Python\n",
+    "## El paquete scikit-learn para regresión lineal y la selección de rasgos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_selection import RFE\n",
+    "from sklearn.svm import SVR\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"/content/python-ml-course/datasets/ads/Advertising.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_cols = [\"TV\", \"Radio\", \"Newspaper\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = data[feature_cols]\n",
+    "Y = data[\"Sales\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "estimator = SVR(kernel=\"linear\")\n",
+    "selector = RFE(estimator, n_features_to_select=2, step=1)\n",
+    "selector = selector.fit(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ True,  True, False])"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selector.support_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 1, 2])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "selector.ranking_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_pred = X[[\"TV\", \"Radio\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm = LinearRegression()\n",
+    "lm.fit(X_pred, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.921099912405138"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.04575482, 0.18799423])"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.coef_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8971942610828956"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.score(X_pred, Y)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T4 - 4 - Linear Regression - SciKit-Learn.ipynb b/notebooks/T4 - 4 - Linear Regression - SciKit-Learn.ipynb
index f6752dbd..c4571c1b 100644
--- a/notebooks/T4 - 4 - Linear Regression - SciKit-Learn.ipynb	
+++ b/notebooks/T4 - 4 - Linear Regression - SciKit-Learn.ipynb	
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,18 +50,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
     "estimator = SVR(kernel=\"linear\")\n",
-    "selector = RFE(estimator, 2, step=1)\n",
+    "selector = RFE(estimator, n_features_to_select=2, step=1)\n",
     "selector = selector.fit(X,Y)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -70,7 +70,7 @@
        "array([ True,  True, False])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -81,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -90,7 +90,7 @@
        "array([1, 1, 2])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -110,7 +110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,16 +119,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+       "LinearRegression()"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -140,16 +140,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2.9210999124051362"
+       "2.921099912405138"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -160,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -169,7 +169,7 @@
        "array([0.04575482, 0.18799423])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -180,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -189,7 +189,7 @@
        "0.8971942610828956"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -197,13 +197,6 @@
    "source": [
     "lm.score(X_pred, Y)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -222,7 +215,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T4 - 5 - Linear Regression - Problemas con la regresi\303\263n lineal-Colab.ipynb" "b/notebooks/T4 - 5 - Linear Regression - Problemas con la regresi\303\263n lineal-Colab.ipynb"
new file mode 100644
index 00000000..e392f062
--- /dev/null
+++ "b/notebooks/T4 - 5 - Linear Regression - Problemas con la regresi\303\263n lineal-Colab.ipynb"	
@@ -0,0 +1,2667 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T4%20-%205%20-%20Linear%20Regression%20-%20Problemas%20con%20la%20regresión%20lineal-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# El tratamiento de las variables categóricas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"/content/python-ml-course/datasets/ecom-expense/Ecom Expense.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>Transaction ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Items</th>\n",
+       "      <th>Monthly Income</th>\n",
+       "      <th>Transaction Time</th>\n",
+       "      <th>Record</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>City Tier</th>\n",
+       "      <th>Total Spend</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TXN001</td>\n",
+       "      <td>42</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7313</td>\n",
+       "      <td>627.668127</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 1</td>\n",
+       "      <td>4198.385084</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TXN002</td>\n",
+       "      <td>24</td>\n",
+       "      <td>8</td>\n",
+       "      <td>17747</td>\n",
+       "      <td>126.904567</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>4134.976648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TXN003</td>\n",
+       "      <td>47</td>\n",
+       "      <td>11</td>\n",
+       "      <td>22845</td>\n",
+       "      <td>873.469701</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>5166.614455</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TXN004</td>\n",
+       "      <td>50</td>\n",
+       "      <td>11</td>\n",
+       "      <td>18552</td>\n",
+       "      <td>380.219428</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 1</td>\n",
+       "      <td>7784.447676</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TXN005</td>\n",
+       "      <td>60</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14439</td>\n",
+       "      <td>403.374223</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>3254.160485</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Transaction ID  Age    Items   Monthly Income  Transaction Time  Record  \\\n",
+       "0         TXN001    42       10            7313        627.668127       5   \n",
+       "1         TXN002    24        8           17747        126.904567       3   \n",
+       "2         TXN003    47       11           22845        873.469701       2   \n",
+       "3         TXN004    50       11           18552        380.219428       7   \n",
+       "4         TXN005    60        2           14439        403.374223       2   \n",
+       "\n",
+       "   Gender City Tier  Total Spend  \n",
+       "0  Female    Tier 1  4198.385084  \n",
+       "1  Female    Tier 2  4134.976648  \n",
+       "2    Male    Tier 2  5166.614455  \n",
+       "3  Female    Tier 1  7784.447676  \n",
+       "4  Female    Tier 2  3254.160485  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dummy_gender = pd.get_dummies(df[\"Gender\"], prefix = \"Gender\")\n",
+    "dummy_city_tier = pd.get_dummies(df[\"City Tier\"], prefix = \"City\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "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_Female</th>\n",
+       "      <th>Gender_Male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender_Female  Gender_Male\n",
+       "0              1            0\n",
+       "1              1            0\n",
+       "2              0            1\n",
+       "3              1            0\n",
+       "4              1            0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy_gender.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>City_Tier 1</th>\n",
+       "      <th>City_Tier 2</th>\n",
+       "      <th>City_Tier 3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City_Tier 1  City_Tier 2  City_Tier 3\n",
+       "0            1            0            0\n",
+       "1            0            1            0\n",
+       "2            0            1            0\n",
+       "3            1            0            0\n",
+       "4            0            1            0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy_city_tier.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Transaction ID',\n",
+       " 'Age ',\n",
+       " ' Items ',\n",
+       " 'Monthly Income',\n",
+       " 'Transaction Time',\n",
+       " 'Record',\n",
+       " 'Gender',\n",
+       " 'City Tier',\n",
+       " 'Total Spend']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "column_names = df.columns.values.tolist()\n",
+    "column_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>Transaction ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Items</th>\n",
+       "      <th>Monthly Income</th>\n",
+       "      <th>Transaction Time</th>\n",
+       "      <th>Record</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>City Tier</th>\n",
+       "      <th>Total Spend</th>\n",
+       "      <th>Gender_Female</th>\n",
+       "      <th>Gender_Male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TXN001</td>\n",
+       "      <td>42</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7313</td>\n",
+       "      <td>627.668127</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 1</td>\n",
+       "      <td>4198.385084</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TXN002</td>\n",
+       "      <td>24</td>\n",
+       "      <td>8</td>\n",
+       "      <td>17747</td>\n",
+       "      <td>126.904567</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>4134.976648</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TXN003</td>\n",
+       "      <td>47</td>\n",
+       "      <td>11</td>\n",
+       "      <td>22845</td>\n",
+       "      <td>873.469701</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>5166.614455</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TXN004</td>\n",
+       "      <td>50</td>\n",
+       "      <td>11</td>\n",
+       "      <td>18552</td>\n",
+       "      <td>380.219428</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 1</td>\n",
+       "      <td>7784.447676</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TXN005</td>\n",
+       "      <td>60</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14439</td>\n",
+       "      <td>403.374223</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>3254.160485</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Transaction ID  Age    Items   Monthly Income  Transaction Time  Record  \\\n",
+       "0         TXN001    42       10            7313        627.668127       5   \n",
+       "1         TXN002    24        8           17747        126.904567       3   \n",
+       "2         TXN003    47       11           22845        873.469701       2   \n",
+       "3         TXN004    50       11           18552        380.219428       7   \n",
+       "4         TXN005    60        2           14439        403.374223       2   \n",
+       "\n",
+       "   Gender City Tier  Total Spend  Gender_Female  Gender_Male  \n",
+       "0  Female    Tier 1  4198.385084              1            0  \n",
+       "1  Female    Tier 2  4134.976648              1            0  \n",
+       "2    Male    Tier 2  5166.614455              0            1  \n",
+       "3  Female    Tier 1  7784.447676              1            0  \n",
+       "4  Female    Tier 2  3254.160485              1            0  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new = df[column_names].join(dummy_gender)\n",
+    "column_names = df_new.columns.values.tolist()\n",
+    "df_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>Transaction ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Items</th>\n",
+       "      <th>Monthly Income</th>\n",
+       "      <th>Transaction Time</th>\n",
+       "      <th>Record</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>City Tier</th>\n",
+       "      <th>Total Spend</th>\n",
+       "      <th>Gender_Female</th>\n",
+       "      <th>Gender_Male</th>\n",
+       "      <th>City_Tier 1</th>\n",
+       "      <th>City_Tier 2</th>\n",
+       "      <th>City_Tier 3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TXN001</td>\n",
+       "      <td>42</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7313</td>\n",
+       "      <td>627.668127</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 1</td>\n",
+       "      <td>4198.385084</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TXN002</td>\n",
+       "      <td>24</td>\n",
+       "      <td>8</td>\n",
+       "      <td>17747</td>\n",
+       "      <td>126.904567</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>4134.976648</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TXN003</td>\n",
+       "      <td>47</td>\n",
+       "      <td>11</td>\n",
+       "      <td>22845</td>\n",
+       "      <td>873.469701</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>5166.614455</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TXN004</td>\n",
+       "      <td>50</td>\n",
+       "      <td>11</td>\n",
+       "      <td>18552</td>\n",
+       "      <td>380.219428</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 1</td>\n",
+       "      <td>7784.447676</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TXN005</td>\n",
+       "      <td>60</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14439</td>\n",
+       "      <td>403.374223</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>3254.160485</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Transaction ID  Age    Items   Monthly Income  Transaction Time  Record  \\\n",
+       "0         TXN001    42       10            7313        627.668127       5   \n",
+       "1         TXN002    24        8           17747        126.904567       3   \n",
+       "2         TXN003    47       11           22845        873.469701       2   \n",
+       "3         TXN004    50       11           18552        380.219428       7   \n",
+       "4         TXN005    60        2           14439        403.374223       2   \n",
+       "\n",
+       "   Gender City Tier  Total Spend  Gender_Female  Gender_Male  City_Tier 1  \\\n",
+       "0  Female    Tier 1  4198.385084              1            0            1   \n",
+       "1  Female    Tier 2  4134.976648              1            0            0   \n",
+       "2    Male    Tier 2  5166.614455              0            1            0   \n",
+       "3  Female    Tier 1  7784.447676              1            0            1   \n",
+       "4  Female    Tier 2  3254.160485              1            0            0   \n",
+       "\n",
+       "   City_Tier 2  City_Tier 3  \n",
+       "0            0            0  \n",
+       "1            1            0  \n",
+       "2            1            0  \n",
+       "3            0            0  \n",
+       "4            1            0  "
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new = df_new[column_names].join(dummy_city_tier)\n",
+    "df_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_cols = [\"Monthly Income\", \"Transaction Time\", \n",
+    "                \"Gender_Female\", \"Gender_Male\", \n",
+    "                \"City_Tier 1\", \"City_Tier 2\", \"City_Tier 3\",\n",
+    "                \"Record\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df_new[feature_cols]\n",
+    "Y = df_new[\"Total Spend\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm = LinearRegression()\n",
+    "lm.fit(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-79.41713030137271\n",
+      "[ 1.47538980e-01  1.54946125e-01 -1.31025013e+02  1.31025013e+02\n",
+      "  7.67643260e+01  5.51389743e+01 -1.31903300e+02  7.72233446e+02]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lm.intercept_)\n",
+    "print(lm.coef_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Monthly Income', 0.14753898049205746),\n",
+       " ('Transaction Time', 0.15494612549589526),\n",
+       " ('Gender_Female', -131.02501325554653),\n",
+       " ('Gender_Male', 131.02501325554647),\n",
+       " ('City_Tier 1', 76.76432601049476),\n",
+       " ('City_Tier 2', 55.138974309232474),\n",
+       " ('City_Tier 3', -131.9033003197273),\n",
+       " ('Record', 772.2334457445645)]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(zip(feature_cols, lm.coef_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9179923586131016"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.score(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "El modelo puede ser escrito como:\n",
+    "* Total_Spend = -79.41713030137362 + 'Monthly Income'* 0.14753898049205738 + 'Transaction Time'* 0.15494612549589545+'Gender_Female'* -131.02501325554567 + 'Gender_Male'* 131.0250132555456+'City_Tier 1'* 76.76432601049527 + 'City_Tier 2'* 55.138974309232474 + 'City_Tier 3'* -131.9033003197278+'Record'* 772.2334457445648\n",
+    "    * Si es hombre y vive en CT1: Total_Spend = 128.37220896466724 + 'Monthly Income'* 0.14753898049205738 + 'Transaction Time'* 0.15494612549589545+'Record'* 772.2334457445648\n",
+    "    * Si es hombre y vive en CT2: Total_Spend = 106.74685726340445 + 'Monthly Income'* 0.14753898049205738 + 'Transaction Time'* 0.15494612549589545 +'Record'* 772.2334457445648\n",
+    "    * Si es hombre y vive en CT3: Total_Spend = -80.29541736555583 + 'Monthly Income'* 0.14753898049205738 + 'Transaction Time'* 0.15494612549589545+'Record'* 772.2334457445648\n",
+    "    * Si es mujer y vive en CT1: Total_Spend = -79.41713030137362 + 'Monthly Income'* 0.14753898049205738 + 'Transaction Time'* 0.15494612549589545 - 131.0250132555456+ 76.76432601049527 +'Record'* 772.2334457445648\n",
+    "    * Si es mujer y vive en CT2: Total_Spend = -79.41713030137362 + 'Monthly Income'* 0.14753898049205738 + 'Transaction Time'* 0.15494612549589545 - 131.0250132555456+ 55.138974309232474  +'Record'* 772.2334457445648\n",
+    "    * Si es mujer y vive en CT3: Total_Spend = -79.41713030137362 + 'Monthly Income'* 0.14753898049205738 + 'Transaction Time'* 0.15494612549589545 - 131.0250132555456-131.9033003197278 +'Record'* 772.2334457445648"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-80.29541736555583"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    " -79.41713030137362 + 131.0250132555456-131.9033003197278"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_new[\"prediction\"] = -79.41713030137362 + df_new['Monthly Income']*0.14753898049205738 + df_new['Transaction Time']* 0.15494612549589545+ df_new['Gender_Female'] * (-131.02501325554567) + df_new['Gender_Male'] * 131.0250132555456+ df_new['City_Tier 1']* 76.76432601049527 +  df_new['City_Tier 2']* 55.138974309232474 + df_new['City_Tier 3']* (-131.9033003197278)+ df_new['Record']* 772.2334457445648"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>Transaction ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Items</th>\n",
+       "      <th>Monthly Income</th>\n",
+       "      <th>Transaction Time</th>\n",
+       "      <th>Record</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>City Tier</th>\n",
+       "      <th>Total Spend</th>\n",
+       "      <th>Gender_Female</th>\n",
+       "      <th>Gender_Male</th>\n",
+       "      <th>City_Tier 1</th>\n",
+       "      <th>City_Tier 2</th>\n",
+       "      <th>City_Tier 3</th>\n",
+       "      <th>prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TXN001</td>\n",
+       "      <td>42</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7313</td>\n",
+       "      <td>627.668127</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 1</td>\n",
+       "      <td>4198.385084</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4903.696720</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TXN002</td>\n",
+       "      <td>24</td>\n",
+       "      <td>8</td>\n",
+       "      <td>17747</td>\n",
+       "      <td>126.904567</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>4134.976648</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4799.434826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TXN003</td>\n",
+       "      <td>47</td>\n",
+       "      <td>11</td>\n",
+       "      <td>22845</td>\n",
+       "      <td>873.469701</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>5166.614455</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5157.082504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TXN004</td>\n",
+       "      <td>50</td>\n",
+       "      <td>11</td>\n",
+       "      <td>18552</td>\n",
+       "      <td>380.219428</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 1</td>\n",
+       "      <td>7784.447676</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8068.012996</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TXN005</td>\n",
+       "      <td>60</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14439</td>\n",
+       "      <td>403.374223</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>3254.160485</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3581.980335</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Transaction ID  Age    Items   Monthly Income  Transaction Time  Record  \\\n",
+       "0         TXN001    42       10            7313        627.668127       5   \n",
+       "1         TXN002    24        8           17747        126.904567       3   \n",
+       "2         TXN003    47       11           22845        873.469701       2   \n",
+       "3         TXN004    50       11           18552        380.219428       7   \n",
+       "4         TXN005    60        2           14439        403.374223       2   \n",
+       "\n",
+       "   Gender City Tier  Total Spend  Gender_Female  Gender_Male  City_Tier 1  \\\n",
+       "0  Female    Tier 1  4198.385084              1            0            1   \n",
+       "1  Female    Tier 2  4134.976648              1            0            0   \n",
+       "2    Male    Tier 2  5166.614455              0            1            0   \n",
+       "3  Female    Tier 1  7784.447676              1            0            1   \n",
+       "4  Female    Tier 2  3254.160485              1            0            0   \n",
+       "\n",
+       "   City_Tier 2  City_Tier 3   prediction  \n",
+       "0            0            0  4903.696720  \n",
+       "1            1            0  4799.434826  \n",
+       "2            1            0  5157.082504  \n",
+       "3            0            0  8068.012996  \n",
+       "4            1            0  3581.980335  "
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SSD = np.sum((df_new[\"prediction\"] - df_new[\"Total Spend\"])**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1517733985.3408163"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SSD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RSE = np.sqrt(SSD/(len(df_new)-len(feature_cols)-1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "803.1318809818165"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RSE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sales_mean=np.mean(df_new[\"Total Spend\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6163.176415976714"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sales_mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "error = RSE/sales_mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13.031135680294161"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "error*100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Eliminar variables dummy redundantes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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_Male</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender_Male\n",
+       "0            0\n",
+       "1            0\n",
+       "2            1\n",
+       "3            0\n",
+       "4            0"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy_gender = pd.get_dummies(df[\"Gender\"], prefix=\"Gender\").iloc[:,1:]\n",
+    "dummy_gender.head()"
+   ]
+  },
+  {
+   "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></th>\n",
+       "      <th>City_Tier 2</th>\n",
+       "      <th>City_Tier 3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   City_Tier 2  City_Tier 3\n",
+       "0            0            0\n",
+       "1            1            0\n",
+       "2            1            0\n",
+       "3            0            0\n",
+       "4            1            0"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dummy_city_tier = pd.get_dummies(df[\"City Tier\"], prefix=\"City\").iloc[:,1:]\n",
+    "dummy_city_tier.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "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>Transaction ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Items</th>\n",
+       "      <th>Monthly Income</th>\n",
+       "      <th>Transaction Time</th>\n",
+       "      <th>Record</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>City Tier</th>\n",
+       "      <th>Total Spend</th>\n",
+       "      <th>Gender_Male</th>\n",
+       "      <th>City_Tier 2</th>\n",
+       "      <th>City_Tier 3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>TXN001</td>\n",
+       "      <td>42</td>\n",
+       "      <td>10</td>\n",
+       "      <td>7313</td>\n",
+       "      <td>627.668127</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 1</td>\n",
+       "      <td>4198.385084</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>TXN002</td>\n",
+       "      <td>24</td>\n",
+       "      <td>8</td>\n",
+       "      <td>17747</td>\n",
+       "      <td>126.904567</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>4134.976648</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>TXN003</td>\n",
+       "      <td>47</td>\n",
+       "      <td>11</td>\n",
+       "      <td>22845</td>\n",
+       "      <td>873.469701</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>5166.614455</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>TXN004</td>\n",
+       "      <td>50</td>\n",
+       "      <td>11</td>\n",
+       "      <td>18552</td>\n",
+       "      <td>380.219428</td>\n",
+       "      <td>7</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 1</td>\n",
+       "      <td>7784.447676</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>TXN005</td>\n",
+       "      <td>60</td>\n",
+       "      <td>2</td>\n",
+       "      <td>14439</td>\n",
+       "      <td>403.374223</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>Tier 2</td>\n",
+       "      <td>3254.160485</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Transaction ID  Age    Items   Monthly Income  Transaction Time  Record  \\\n",
+       "0         TXN001    42       10            7313        627.668127       5   \n",
+       "1         TXN002    24        8           17747        126.904567       3   \n",
+       "2         TXN003    47       11           22845        873.469701       2   \n",
+       "3         TXN004    50       11           18552        380.219428       7   \n",
+       "4         TXN005    60        2           14439        403.374223       2   \n",
+       "\n",
+       "   Gender City Tier  Total Spend  Gender_Male  City_Tier 2  City_Tier 3  \n",
+       "0  Female    Tier 1  4198.385084            0            0            0  \n",
+       "1  Female    Tier 2  4134.976648            0            1            0  \n",
+       "2    Male    Tier 2  5166.614455            1            1            0  \n",
+       "3  Female    Tier 1  7784.447676            0            0            0  \n",
+       "4  Female    Tier 2  3254.160485            0            1            0  "
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "column_names = df.columns.values.tolist()\n",
+    "df_new = df[column_names].join(dummy_gender)\n",
+    "column_names = df_new.columns.values.tolist()\n",
+    "df_new = df_new[column_names].join(dummy_city_tier)\n",
+    "df_new.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "feature_cols = [\"Monthly Income\", \"Transaction Time\", \"Gender_Male\", \"City_Tier 2\", \"City_Tier 3\", \"Record\"]\n",
+    "X = df_new[feature_cols]\n",
+    "Y = df_new[\"Total Spend\"]\n",
+    "lm = LinearRegression()\n",
+    "lm.fit(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-133.67781754642238\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(lm.intercept_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Monthly Income', 0.1475389804920575),\n",
+       " ('Transaction Time', 0.15494612549589615),\n",
+       " ('Gender_Male', 262.05002651109584),\n",
+       " ('City_Tier 2', -21.62535170126288),\n",
+       " ('City_Tier 3', -208.66762633022262),\n",
+       " ('Record', 772.2334457445636)]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(zip(feature_cols, lm.coef_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9179923586131016"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.score(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Coeficientes con todas las variables en el modelo\n",
+    "* ('Monthly Income', 0.14753898049205738),\n",
+    "* ('Transaction Time', 0.15494612549589545),\n",
+    "* ('Gender_Female', -131.02501325554567),\n",
+    "* ('Gender_Male', 131.0250132555456),\n",
+    "* ('City_Tier 1', 76.76432601049527),\n",
+    "* ('City_Tier 2', 55.138974309232474),\n",
+    "* ('City_Tier 3', -131.9033003197278),\n",
+    "* ('Record', 772.2334457445648)\n",
+    " \n",
+    " Coeficientes tras enmascarar las variables dummy pertinentes\n",
+    "* 'Monthly Income', 0.14753898049205744),\n",
+    "* ('Transaction Time', 0.15494612549589631),\n",
+    "* ('Gender_Male', 262.05002651109595),\n",
+    "* ('City_Tier 2', -21.62535170126296),\n",
+    "* ('City_Tier 3', -208.66762633022324),\n",
+    "* ('Record', 772.2334457445635)]\n",
+    "\n",
+    "Los cambios se reflejan en\n",
+    "* Gender_Male: \n",
+    "    * antes -> 131.02, \n",
+    "    * después -> 262.05 = ( 131.02 - (-131.02))\n",
+    "* Gender_Female: \n",
+    "    * antes -> -131.02,\n",
+    "    * después -> 0\n",
+    "* CT1: \n",
+    "    * antes -> 76.76,\n",
+    "    * después -> 0\n",
+    "* CT2: \n",
+    "    * antes -> 55.13, \n",
+    "    * después -> -21.62 = (55.13 - 76.76)\n",
+    "* CT3: \n",
+    "    * antes -> -131.90, \n",
+    "    * después -> -208.66 = (-131.90 - 76.76)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Transformación de variables para conseguir una relación no lineal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>car name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>307.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>3504</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>chevrolet chevelle malibu</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>165.0</td>\n",
+       "      <td>3693</td>\n",
+       "      <td>11.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>buick skylark 320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>318.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3436</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>plymouth satellite</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>304.0</td>\n",
+       "      <td>150.0</td>\n",
+       "      <td>3433</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>amc rebel sst</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>140.0</td>\n",
+       "      <td>3449</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>70</td>\n",
+       "      <td>1</td>\n",
+       "      <td>ford torino</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+       "0  18.0          8         307.0       130.0    3504          12.0   \n",
+       "1  15.0          8         350.0       165.0    3693          11.5   \n",
+       "2  18.0          8         318.0       150.0    3436          11.0   \n",
+       "3  16.0          8         304.0       150.0    3433          12.0   \n",
+       "4  17.0          8         302.0       140.0    3449          10.5   \n",
+       "\n",
+       "   model year  origin                   car name  \n",
+       "0          70       1  chevrolet chevelle malibu  \n",
+       "1          70       1          buick skylark 320  \n",
+       "2          70       1         plymouth satellite  \n",
+       "3          70       1              amc rebel sst  \n",
+       "4          70       1                ford torino  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_auto = pd.read_csv(\"/content/python-ml-course/datasets/auto/auto-mpg.csv\")\n",
+    "data_auto.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(406, 9)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_auto.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'CV vs MPG')"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "data_auto[\"mpg\"] = data_auto[\"mpg\"].dropna()\n",
+    "data_auto[\"horsepower\"] = data_auto[\"horsepower\"].dropna()\n",
+    "plt.plot(data_auto[\"horsepower\"], data_auto[\"mpg\"], \"ro\")\n",
+    "plt.xlabel(\"Caballos de Potencia\")\n",
+    "plt.ylabel(\"Consumo (millas por galeón)\")\n",
+    "plt.title(\"CV vs MPG\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Modelo de regresión lineal\n",
+    "* mpg = a + b * horsepower"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = data_auto[\"horsepower\"].fillna(data_auto[\"horsepower\"].mean()).to_numpy()\n",
+    "Y = data_auto[\"mpg\"].fillna(data_auto[\"mpg\"].mean())\n",
+    "X_data = X[:,np.newaxis]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm = LinearRegression()\n",
+    "lm.fit(X_data,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(X_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f95552e6f10>]"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.plot(X,Y, \"ro\")\n",
+    "plt.plot(X, lm.predict(X_data), color=\"blue\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.574653340645025"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.score(X_data, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(10315.75196006092, 5.046879480825511, 23.51457286432162, 21.46277336163346)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SSD = np.sum((Y - lm.predict(X_data))**2)\n",
+    "RSE = np.sqrt(SSD/(len(X_data)-1))\n",
+    "y_mean = np.mean(Y)\n",
+    "error = RSE/y_mean\n",
+    "SSD, RSE, y_mean, error*100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Modelo de regresión cuadrático\n",
+    "* mpg = a + b * horsepower^2 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_data = X**2\n",
+    "X_data = np.asarray(X_data)\n",
+    "X_data = X_data[:,np.newaxis]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm = LinearRegression()\n",
+    "lm.fit(X_data, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4849887034823205"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.score(X_data, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12490.350340501926, 5.553410772769817, 23.51457286432162, 23.6168898529981)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "SSD = np.sum((Y - lm.predict(X_data))**2)\n",
+    "RSE = np.sqrt(SSD/(len(X_data)-1))\n",
+    "y_mean = np.mean(Y)\n",
+    "error = RSE/y_mean\n",
+    "SSD, RSE, y_mean, error*100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Modelo de regresión  lineal y cuadrático\n",
+    "* mpg = a + b * horsepower + c * horsepower^2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import PolynomialFeatures\n",
+    "from sklearn import linear_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "poly = PolynomialFeatures(degree=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = np.asarray(X)\n",
+    "X_data = poly.fit_transform(X[:,np.newaxis])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm = linear_model.LinearRegression()\n",
+    "lm.fit(X_data, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6439066584257469"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.score(X_data, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55.026192447081144"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.intercept_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 0.        , -0.43404318,  0.00112615])"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.coef_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "mpg = 55.026 -0.434 * hp + 0.00112615 * hp^2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def regresion_validation(X_data, Y, Y_pred):\n",
+    "    SSD = np.sum((Y - Y_pred)**2)\n",
+    "    RSE = np.sqrt(SSD/(len(X_data)-1))\n",
+    "    y_mean = np.mean(Y)\n",
+    "    error = RSE/y_mean\n",
+    "    print(\"SSD: \"+str(SSD)+\", RSE: \" +str(RSE) + \", Y_mean: \" +str(y_mean) +\", error: \" + str(error*100)+ \"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Regresión de grado 2\n",
+      "R2:0.6439066584257469\n",
+      "55.026192447081144\n",
+      "[ 0.         -0.43404318  0.00112615]\n",
+      "SSD: 8636.180643524502, RSE: 4.61778115803654, Y_mean: 23.51457286432162, error: 19.63795466190689%\n",
+      "Regresión de grado 3\n",
+      "R2:0.6444678885560744\n",
+      "58.44877411148498\n",
+      "[ 0.00000000e+00 -5.27113167e-01  1.89736722e-03 -1.95723195e-06]\n",
+      "SSD: 8622.569367428549, RSE: 4.614140736300907, Y_mean: 23.51457286432162, error: 19.622473106036672%\n",
+      "Regresión de grado 4\n",
+      "R2:0.6467674189704502\n",
+      "40.09664385577267\n",
+      "[ 0.00000000e+00  1.62563541e-01 -7.10892744e-03  4.65580255e-05\n",
+      " -9.15840093e-08]\n",
+      "SSD: 8566.799832491353, RSE: 4.5991947315797574, Y_mean: 23.51457286432162, error: 19.55891250126878%\n",
+      "Regresión de grado 5\n",
+      "R2:0.654751249136743\n",
+      "-40.694029173495565\n",
+      "[ 0.00000000e+00  4.00021894e+00 -7.54802468e-02  6.19621641e-04\n",
+      " -2.36220984e-06  3.41983153e-09]\n",
+      "SSD: 8373.171388784698, RSE: 4.546921734125024, Y_mean: 23.51457286432162, error: 19.336612067591556%\n",
+      "Regresión de grado 6\n",
+      "R2:0.6572468033307997\n",
+      "-156.9980060652282\n",
+      "[ 0.00000000e+00  1.07622444e+01 -2.30165519e-01  2.40584080e-03\n",
+      " -1.33797054e-05  3.79780900e-08 -4.32525127e-11]\n",
+      "SSD: 8312.647772335427, RSE: 4.530458721585344, Y_mean: 23.51457286432162, error: 19.266600111028826%\n",
+      "Regresión de grado 7\n",
+      "R2:0.6538574808932471\n",
+      "14.784276957787014\n",
+      "[ 0.00000000e+00  1.26690941e-03  4.31482761e-02 -1.25395193e-03\n",
+      "  1.46292439e-05 -8.50453243e-08  2.45090912e-10 -2.79305875e-13]\n",
+      "SSD: 8394.847570569373, RSE: 4.552803388327227, Y_mean: 23.51457286432162, error: 19.361624872357947%\n",
+      "Regresión de grado 8\n",
+      "R2:0.6512432177866146\n",
+      "36.40675411486028\n",
+      "[ 0.00000000e+00  1.62872847e-07  3.03202377e-06  8.84870688e-05\n",
+      " -3.97062301e-06  5.47387207e-08 -3.43821862e-10  1.02688173e-12\n",
+      " -1.18560107e-15]\n",
+      "SSD: 8458.250183880726, RSE: 4.5699637148735714, Y_mean: 23.51457286432162, error: 19.43460228362269%\n",
+      "Regresión de grado 9\n",
+      "R2:0.6510536224193297\n",
+      "39.46391227417244\n",
+      "[ 0.00000000e+00  2.93874448e-08 -1.25861585e-09 -6.48221251e-08\n",
+      " -1.77070344e-06  3.55390671e-08 -3.00071067e-10  1.32187215e-12\n",
+      " -3.03799240e-15  2.92177398e-18]\n",
+      "SSD: 8462.848359835967, RSE: 4.5712057354857905, Y_mean: 23.51457286432162, error: 19.439884202283878%\n",
+      "Regresión de grado 10\n",
+      "R2:0.6523571001357847\n",
+      "38.44329678404357\n",
+      "[ 0.00000000e+00 -9.08873846e-10  2.52067924e-13 -2.72214177e-11\n",
+      " -1.29903098e-09 -3.51782808e-08  8.70646682e-10 -8.87293169e-12\n",
+      "  4.60549152e-14 -1.20911079e-16  1.27849759e-19]\n",
+      "SSD: 8431.235668134548, RSE: 4.56265995305872, Y_mean: 23.51457286432162, error: 19.40354170745576%\n",
+      "Regresión de grado 11\n",
+      "R2:0.651111197346774\n",
+      "36.3470091267187\n",
+      "[ 0.00000000e+00  3.38646890e-11  1.31264454e-14 -4.88015707e-15\n",
+      " -3.12630666e-13 -1.48183561e-11 -4.06544503e-10  1.03832301e-11\n",
+      " -1.07585926e-13  5.61915382e-16 -1.47344508e-18  1.54764846e-21]\n",
+      "SSD: 8461.45201956252, RSE: 4.570828603478431, Y_mean: 23.51457286432162, error: 19.43828037979671%\n"
+     ]
+    }
+   ],
+   "source": [
+    "for d in range(2,12):\n",
+    "    poly = PolynomialFeatures(degree=d)\n",
+    "    X_data = poly.fit_transform(X[:,np.newaxis])\n",
+    "    lm = linear_model.LinearRegression()\n",
+    "    lm.fit(X_data, Y)\n",
+    "    print(\"Regresión de grado \"+str(d))\n",
+    "    print(\"R2:\" +str(lm.score(X_data, Y)))\n",
+    "    print(lm.intercept_)\n",
+    "    print(lm.coef_)\n",
+    "    regresion_validation(X_data, Y, lm.predict(X_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# El problema de los outliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f95560fe910>]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(data_auto[\"displacement\"], data_auto[\"mpg\"], \"ro\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = data_auto[\"displacement\"].fillna(data_auto[\"displacement\"].mean()).to_numpy()\n",
+    "X = X[:,np.newaxis]\n",
+    "Y = data_auto[\"mpg\"].fillna(data_auto[\"mpg\"].mean())\n",
+    "\n",
+    "lm = LinearRegression()\n",
+    "lm.fit(X, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6261049762826918"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.score(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f9556184280>]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.plot(X,Y, \"ro\")\n",
+    "plt.plot(X, lm.predict(X), color=\"blue\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>car name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>395</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>262.0</td>\n",
+       "      <td>85.0</td>\n",
+       "      <td>3015</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>82</td>\n",
+       "      <td>1</td>\n",
+       "      <td>oldsmobile cutlass ciera (diesel)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+       "395  38.0          6         262.0        85.0    3015          17.0   \n",
+       "\n",
+       "     model year  origin                           car name  \n",
+       "395          82       1  oldsmobile cutlass ciera (diesel)  "
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_auto[(data_auto[\"displacement\"]>250)&(data_auto[\"mpg\"]>35)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "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>mpg</th>\n",
+       "      <th>cylinders</th>\n",
+       "      <th>displacement</th>\n",
+       "      <th>horsepower</th>\n",
+       "      <th>weight</th>\n",
+       "      <th>acceleration</th>\n",
+       "      <th>model year</th>\n",
+       "      <th>origin</th>\n",
+       "      <th>car name</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>20.2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>302.0</td>\n",
+       "      <td>139.0</td>\n",
+       "      <td>3570</td>\n",
+       "      <td>12.8</td>\n",
+       "      <td>78</td>\n",
+       "      <td>1</td>\n",
+       "      <td>mercury monarch ghia</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>125.0</td>\n",
+       "      <td>3900</td>\n",
+       "      <td>17.4</td>\n",
+       "      <td>79</td>\n",
+       "      <td>1</td>\n",
+       "      <td>cadillac eldorado</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>372</th>\n",
+       "      <td>26.6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>350.0</td>\n",
+       "      <td>105.0</td>\n",
+       "      <td>3725</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>oldsmobile cutlass ls</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
+       "258  20.2          8         302.0       139.0    3570          12.8   \n",
+       "305  23.0          8         350.0       125.0    3900          17.4   \n",
+       "372  26.6          8         350.0       105.0    3725          19.0   \n",
+       "\n",
+       "     model year  origin               car name  \n",
+       "258          78       1   mercury monarch ghia  \n",
+       "305          79       1      cadillac eldorado  \n",
+       "372          81       1  oldsmobile cutlass ls  "
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data_auto[(data_auto[\"displacement\"]>300)&(data_auto[\"mpg\"]>20)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_auto_clean = data_auto.drop([395, 258, 305, 372])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LinearRegression()"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = data_auto_clean[\"displacement\"].fillna(data_auto_clean[\"displacement\"].mean()).to_numpy()\n",
+    "X = X[:,np.newaxis]\n",
+    "Y = data_auto_clean[\"mpg\"].fillna(data_auto_clean[\"mpg\"].mean())\n",
+    "\n",
+    "lm = LinearRegression()\n",
+    "lm.fit(X, Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6466514317531822"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm.score(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f9556612130>]"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.plot(X,Y, \"ro\")\n",
+    "plt.plot(X, lm.predict(X), color=\"blue\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T4 - 5 - Linear Regression - Problemas con la regresi\303\263n lineal.ipynb" "b/notebooks/T4 - 5 - Linear Regression - Problemas con la regresi\303\263n lineal.ipynb"
index e239aa2d..7f0e4837 100644
--- "a/notebooks/T4 - 5 - Linear Regression - Problemas con la regresi\303\263n lineal.ipynb"	
+++ "b/notebooks/T4 - 5 - Linear Regression - Problemas con la regresi\303\263n lineal.ipynb"	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,16 +20,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.read_csv(\"../datasets/ecom-expense/Ecom Expense.csv\")"
+    "df = pd.read_csv(\"/content/python-ml-course/datasets/ecom-expense/Ecom Expense.csv\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -145,7 +145,7 @@
        "4  Female    Tier 2  3254.160485  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -233,7 +233,7 @@
        "4              1            0"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -244,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -317,7 +317,7 @@
        "4            0            1            0"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -328,7 +328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -345,7 +345,7 @@
        " 'Total Spend']"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -357,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -485,7 +485,7 @@
        "4  Female    Tier 2  3254.160485              1            0  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -498,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -651,7 +651,7 @@
        "4            1            0  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -663,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -675,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -685,16 +685,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+       "LinearRegression()"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -706,14 +706,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-79.41713030137362\n",
+      "-79.41713030137271\n",
       "[ 1.47538980e-01  1.54946125e-01 -1.31025013e+02  1.31025013e+02\n",
       "  7.67643260e+01  5.51389743e+01 -1.31903300e+02  7.72233446e+02]\n"
      ]
@@ -726,23 +726,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[('Monthly Income', 0.14753898049205738),\n",
-       " ('Transaction Time', 0.15494612549589545),\n",
-       " ('Gender_Female', -131.02501325554567),\n",
-       " ('Gender_Male', 131.0250132555456),\n",
-       " ('City_Tier 1', 76.76432601049527),\n",
+       "[('Monthly Income', 0.14753898049205746),\n",
+       " ('Transaction Time', 0.15494612549589526),\n",
+       " ('Gender_Female', -131.02501325554653),\n",
+       " ('Gender_Male', 131.02501325554647),\n",
+       " ('City_Tier 1', 76.76432601049476),\n",
        " ('City_Tier 2', 55.138974309232474),\n",
-       " ('City_Tier 3', -131.9033003197278),\n",
-       " ('Record', 772.2334457445648)]"
+       " ('City_Tier 3', -131.9033003197273),\n",
+       " ('Record', 772.2334457445645)]"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -753,7 +753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -762,7 +762,7 @@
        "0.9179923586131016"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -787,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -796,7 +796,7 @@
        "-80.29541736555583"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -807,7 +807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -816,7 +816,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -975,7 +975,7 @@
        "4            1            0  3581.980335  "
       ]
      },
-     "execution_count": 49,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -986,7 +986,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -995,7 +995,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1004,7 +1004,7 @@
        "1517733985.3408163"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1015,7 +1015,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1024,7 +1024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1033,7 +1033,7 @@
        "803.1318809818165"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1044,7 +1044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1053,7 +1053,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1062,7 +1062,7 @@
        "6163.176415976714"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1073,7 +1073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1082,7 +1082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1091,7 +1091,7 @@
        "13.031135680294161"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1109,7 +1109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -1170,7 +1170,7 @@
        "4            0"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1182,7 +1182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -1249,7 +1249,7 @@
        "4            1            0"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1261,7 +1261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -1395,7 +1395,7 @@
        "4  Female    Tier 2  3254.160485            0            1            0  "
       ]
      },
-     "execution_count": 76,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1410,16 +1410,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+       "LinearRegression()"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1434,14 +1434,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-133.67781754642056\n"
+      "-133.67781754642238\n"
      ]
     }
    ],
@@ -1451,21 +1451,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[('Monthly Income', 0.14753898049205744),\n",
-       " ('Transaction Time', 0.15494612549589631),\n",
-       " ('Gender_Male', 262.05002651109595),\n",
-       " ('City_Tier 2', -21.62535170126296),\n",
-       " ('City_Tier 3', -208.66762633022324),\n",
-       " ('Record', 772.2334457445635)]"
+       "[('Monthly Income', 0.1475389804920575),\n",
+       " ('Transaction Time', 0.15494612549589615),\n",
+       " ('Gender_Male', 262.05002651109584),\n",
+       " ('City_Tier 2', -21.62535170126288),\n",
+       " ('City_Tier 3', -208.66762633022262),\n",
+       " ('Record', 772.2334457445636)]"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1476,7 +1476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -1485,7 +1485,7 @@
        "0.9179923586131016"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1543,7 +1543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1552,7 +1552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1668,19 +1668,19 @@
        "4          70       1                ford torino  "
       ]
      },
-     "execution_count": 82,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "data_auto = pd.read_csv(\"../datasets/auto/auto-mpg.csv\")\n",
+    "data_auto = pd.read_csv(\"/content/python-ml-course/datasets/auto/auto-mpg.csv\")\n",
     "data_auto.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -1689,7 +1689,7 @@
        "(406, 9)"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1700,7 +1700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1709,27 +1709,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0.5,1,'CV vs MPG')"
+       "Text(0.5, 1.0, 'CV vs MPG')"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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l//Nmdkv8xsy+Q7gZ/ChrgKRFkZOYKB30y4DbJC1ObHYG4SYyXKSZXMrk89m7N7S8z/OoIvNme5RRp0noHMeZFZRV/t+VdImkl0ZtI8Hk8ySiAi8pLAaukbQD+CbB5v954CJJN0X9JwFv6/UkBkJ7FM/FF1eT1z9Jex6eLL9AWZv9+vXw6KPT+x591NM2O84IUlb5vx74AfBWgrL+UdT3GEGBz8DMdpjZ88zsOWZ2jJn9cdR/rpn9QtR/upnt6vEcmkHZ6l5lSTPxZM3Skzb7vGyjec5mr9zlOCNFx6Geg6BxoZ5FlMkDlMb8+fDAA/n7kKbH5ifDO4vCP4vkakJdAcdxKqPnUE+nQ7pxCAM8KZExI2uWbpYd3lnkEyiSyyt3Oc7I4Mo/Zu3aqdz5Y2Phfbd0awK6997wunVrMNukES/ISgvvLIrjLyNXpzH/XvTdcYaTrFzPcQNawPuKtquz1Z7Pf+XK9Pz5k5O97zstt76UXQ+gl3z+S5cW1xnoZttOzq1szYEm43UNnFkCOfn8Sylf4Goi/8AgWq3Kf8uWdCUIoRhLVcdIKpO8oipZSrnVKlZCnSjjKhR3FTeQpjFbb2jOSJKn/Muafb4NfFbSuZJeE7c6nkT6Tp6NOyv/fa+ccMLMdQKrVwdZshyyZWTpJOVDN+kh2pmN6SKqWEvhOENAqWgfSZemdJuZvaF6kWZSa7RPXrWtVgv27+9t/1kROKtXwxe+EBTlggUhyqc9Br+duXNh8+bmROPMxkRxWb+H9iptjjME9BztY2bnpbS+KP7ayctrE1fGyiLP2Rl/ds456TPJSy6ZiuHfu7dY8UPYpigFRBnZOmWUir57/iNnVMiyByUbITHbFYTsnHcDnwaOLDO2ila7zT/NwbpyZefjkkXYx8ezfQm9tm7OqVu7ddG+Zptz1G3+ziyCChy+VwHnEWr+jhFW915VZmwVrfZon24UWJ6zc2KiPsVfRvlX6YidjU7dImbbDc0ZWfKUf1mb/w0WUjPn9tVFI1f45tmGS1zTrpmYgHvuyd+mSru128AdZ2ipYoXvPZLOiYqztCSdAxSkoJzlDMI23GqFBHJFVCmb28AdZ1ZSVvm/ATgLuCtqZ0Z9s4NunKN5zs4y6Z27IWvVL0w/h337Zubtl+DUUzs/5mx06jqOU87mP+jWd4dvWQdflm14crI+e3+arT3tHMbGZo7txenrNnDHGTqowOb/TOBi4IWAEco4vs1yirlUSa02/zpi1bvN6hlT5Ddo/6yT4w1zDL7jOB1Rhc3/o8AnCQVangp8CvhYNeINmCpXqcaml14U/7x58KY35W+zcOF0E1Unsg7z6lvHcSqjrPKXmV1uZvujtoXwBDD8VOXQTBZf6YSJiZkpFjZuzB+zd2+Y/cc1fBcsKH88d9Q6jkN55X+NpAskLZO0VNL5wP+TtEBSquaRdJCkb0i6UdItkv4o6l8g6SpJ349eD6/qZLqiKodmWk6YIubNC9E7aSmay6aDjo/Zfg5z5850+rqj1nGciLLK/3XA7wDXANcCk4Ron+uBLGP8I8DJZvZc4FjgFEkvBC4AtpnZs4Bt0fvBUUWCM+jcnDJnTjjeueemRxht2FC+uPrevXDwweEpIj6HN74RDj10apuJCa/S5TjOE5TN7fOMnPbMjDFmZvuit+NRM+BVwGVR/2XAq3s7hQpoL8beqYLMK76SxeOPw4MPTjffJG8Aq1bBySeX39/evfDww3D55eHGcdlloS/m4YfT5fZCLI4zktRaw1dSi/B0cDTwv83sDyTdZ2aHJbb5iZnlmn4aucI3Ji1rZ7e0WkFpxzefbpzHsbmoKIKpqN6v4zhDT160T18KuEs6jJAY7i3AV8sof0lrgDUAS5YsOX5nLxE0ddJrdE87SQWcl246i9hUVJSSYTamY3YcZxoDL+BuZvcRfAWnAHdLWhwJtpiQKTRtzCYzW2FmKxYtWtQPMbuj6tDJZOGQrMicpUuzHcJLlpSLYJqNhVgcxylNKeUv6QRJh0R/nyPpA5Jyw1EkLYpm/Eg6GHgZcBtwJbA62mw18NkuZe+ONDt3L7bvLEVb1lmbRqyATz115n7Gx0P6hp07Z342b14Ys28fMxgfnx7p4zl7HGe0yVr6m2zADkDAc6O/1wH/UjDmOYTyjzuAm4F3R/0ThCif70evC4qOX1l6h7Q0COPjZnPndp8GISs9xOTkVEqE+fM7T+GQVWegPW1DXAw+qzZw3ObOnX5Ow5S33tNLOE5XUEE+/29Fr+8G3pjs60erTPln5abvNV99GeWUpcyz8u90I2vRmPZzGgalOkw3KcdpGHnKv6zN/wFJ7wTOISzuahFCN4eLutIglAkVTa4nyCN29nYja9GYYbTne0F1x6mFThZ5PUKY9d8FPA14X21S1UUn9uw6bN/xTaLVSv+81Zq6cXQja9GY5OfJdBSWsdagCbhj2nFqoewir7vM7ANm9pXo/R1m9pF6RauBtFQO4+MhFUKSutMgZBWGT/Zv2DDzJiHNlBWC4h4bg6OPnnl+ybE7d4akcAsXZheWX726WYu+3DHtOLVQNtrnhZK+KWmfpEclHZD0X3ULVzlpqRwuvRQ2b+49vUMnbNwIk5NTyr3VCu+TCd2uuw4OHJg+ziwo+LRIogMHYNs2eNGLpkxL8f6TKaL37p2+8jdtP1U+CfS6itiLyThOPWQ5A5KNkL/naEL0TotQzP1Py4ytotVewL2JtFrlHb7J1mpN308njuNeHd/tVOWsHQbHtOM0ECoo5rLdzFZI2mFmz4n6/tXMfqm2u1KCRqd3qIte1gkkv9NuVgm3s3RpsLEvWRJm3GWfinwVseMMlCpW+D4kaS5wg6SLJL0NOKQyCUeZLLNIllO4iDjBXLzfXhV/7CuITUHnnhv6yphw3FnrOI2lrPI/l2DueTPwIPB04LV1CTUUVJERMy/iJsspvHJltlMXggM7duj2mnMorZxk/L6MT8CdtY7TXLLsQU1qjbP5V2XLzrLHx3b2yckp23+rFd7Hx+/Fjp/XDjlkyrbeq0/Abf6OM1Do1uYv6SbILtdokf2/bhpn86/Klp1lj09m3+xUhl5ptWD//vLHKJJ17doQPXXgQNj3mjXFZSqTeOppx+maXmz+pwGvzGmjSVW27Czzx5w5xWaktBDIKkiGl5Y5Rp4JZ+vWUJ8g3ueBA+F9JyYyX+HrOLWQq/zNbGde65eQjaMqW3aWcj1woNienrZmYWKis+OnkXQ0t6ekSMsimhdvX4Xidqex49RCrvKX9ICk+1PaA5Lu75eQjaOqhUexck2L7CmjJNtzCl18ce9PA+2O5vgYZqFEZCeL4apQ3O40dpxaKJr5P9nMDk1pTzazQ/PGNoK1a0PaAym8rl1bzX47LfreHhm0dm2IyJFCVE77St6YOG1DUv6tW6fGSuHGEYdeQvbNpAzLl8MnPzm174ULZ9YV7qTWcbeKO3m99u3rf/oNxxkFsjzBkSP40Oh1QVrLG1tl6yraZ3IyPToljpjpF2VTOVfR4kiarCibiYnO9zk+3n10TTfRPlk1FyYmPNrHcTqEbvP5A5+PXv8d+FH0Grcf5Y2tsnWl/LPSI7SnP6ibXtMrdNri0Mu08Mhub0QTE92HWnYaplkU/uo4TmnylH9fCrj3SlehnnnpEfp5zp2mV5CCWaTbMM6i0MutW2HduvzkbkXUGWqZd726TTPhOCNKJQXcJT1H0umSXhO36kSsgbyc+f2kE8fk0qVT9vRu5SxzvPvu627fMXWGWubVRG567QHHGSLKpnTeDGwmpHSIY/xPKxjzdEnXSLpV0i2S1kX9F0r6saQbonZqj+eQTpmc+f2gk3j8ffumFFq3cib3kSR2FOc5mDuhylDLdgfveEqRuPanAY/1d5zeyLIHJRvwnTLbtY1ZDBwX/f1k4HvAcuBC4B2d7Kvr9A5Z6RH6Tbvde3Iy2/madNrOmdOd3b/dqVqH07kqG3yabHPnTjl485zUUjUyOM4shQoKuP8fYHmZbXP28VngV/qq/Oukinwzec7NKvPwl9mX1Nn+q4q4KXLw5snuTmDHySVP+ZfN5/8S4HPAXYRavgoPDeVy+0haBnwZOAZ4O/B64H5CkZjfM7OfpIxZA6wBWLJkyfE768hj0y1V5ZvJy+0DvTmmk47fKnL6t1PV/oryG+XJvmWLO30dJ4cqHL6bCWmdT2HK3l8qt4+k+cCngbea2f3AJcBRwLHALuD9aePMbJOZrTCzFYsWLSopZp+oKt9M3iKoPMdtmVQOyfFVr4at0mletBAs6/OJiSnFX0V6bccZNbIeCZINuLrMdinjxoEvAm/P+HwZcHPRfhpn9skykXRqg85bBLVly8y1Cq3WlLklz47fvjCrW5t/nimoKh/Kli3Bxt9u8887z6RPo6q00Y4zCyHH7FN25n+bpI9KOrtsqKckEXwFt5rZBxL9ixObnQHcXFKG5lBVvpm8NBFpBdwPHAj9ybFpTwCx2SieEZ97Lhx8cOelIfO2P3AALrmkmpQZ7Wad5PuiVBqe9dNxuqKszf/SlG4zszfkjHkx8BXgJiBedfQu4GyCyceA24HfMbNdecdvXD7/fuSYHxtLD8lM5tuH7Jz7ExPw8MPTZZw7NyjWxx6rRsaYXhZf9VoboZeaCI4zy8mz+c/eFb51s3VrmF3WteK07ArlTp25ExMwf349hWCg85tgr8rbi8Q7TiZdO3wl/aGkBTmfnywpd7HXrKXTDJedUnaFcqempnvvrVcptptcipyxCzJ+Xln97VSVXttxRowim/9NwOckbZP0PknnS3q3pMujEo+vBL5ev5h9pCmRI1krfE88cbp8p546U/nlPTXEqZrLMDZWbrt24tW/eQXqq6JMeu2mfKeO0ySyPMHJBjyLEJv/TuCtwK8CB5cZW0XrW7RP0yJH2lcor1yZLt/k5NRiqE4Xa7VH8CRTJ69c2d1+ihZoJRdnVRU5lUXTvlPH6SOMZFbPbmi6/bhIvl6Kui9dOtNvkeV0TiJNt9knbf5l7Pl1X/Omf6eOUyOVZPUcCZpeL7ZIvqrlLJMAzizb5FImJHbDhpmJ3MbHq7PZN/07dZwB4co/SdPrxXa7GrYMafb4Mit54xl0muO7rDO23UfR6XqEPJr+nTrOgHDln6TpkSNF8qV9PndueorkNNojdYrSShddmzLO2PXr4dFHp4979NHqFmk1/Tt1nAFRNp//kZKukLRH0t2SPi3pyLqF6zudFmavkqyIlGT/+vWwenW2fGnyb94Mv/Vb5fPxJCN1vvCF/G0feihUBVu4sPtImizzy86d1UTmDPI7dZwmk+UJTjbgKuA8YCxqrweuKjO2ita43D5VkxWRMjnZe6RKp3l94vTU3eb/7zTvTlG6aY/McZyuoYJ8/jeU6aurzXrln6UAs4rQd5LHvpO6ALGiraqWQJlQzy1bzMbG8vc3f34zivI4zpCRp/zL2vzvkXSOpFbUzgF6qADuTCPL9JEVbdNJpErRtrFzNWkO6TUSpij6KNl/3XXTcxWlsW/f1LWoMqGc44wwZZX/G4CzCMVcdgFnRn1OFWRFnmTZ6TuJVCmqC3D55WF+nYzU6TUSpij6KNm/aVN3x+h2nOM4QEnlb2Z3mNnpZrbIzJ5iZq82swaV1hpysmLd16zpLVJl69Ywa05jcjLsZ/36mc7aTorOt1Mm+mjfvqljdltMvooi9I4zwpRK3iLpGcBbCMVXnhhjZqfXI9YIkhbrfsIJoXWTPXTrVjjvvOz0zR/+MPzd3019Hsf5w9T+V6/OVrKtVvgsridw770z5Uvm3L/jjpCs7f77Ye/eqWPmEaeKtpRVwlVWE3OcEaSs2ecfCLn3P0Qouxg3pwryYt27zR66bl1+3v79+2d+nozzX7UqO6WyFMabwcUXhxTRWVx3Hdx5Z9h2797ytQQOOSSc75velP550RoEx3FyKZu28adm9le1SjLK1JGCYG+X/vjkMRcsSN9PnG65vahN+9PD2rXBOdsNDz4YXjduDK+bNoUnjVYrHCPudxynK8rO/C+W9B5JL5J0XNxqlWyUaFIKgk6OmVVCcd26YM/vVvHDdLPOxo1TTxr79zdP8XvKaGcIKav8fwH4beC9TJl8/iJvgKSnS7pG0q2SbpG0LupfIOkqSd+PXg/v5QRmBXWkIMgzxeRxzz1Tyuvee9O3ifuznkz27u29Uli3Dt1+K+J+1CxwnDrIWgCQbMBtwNwy2ybGLAaOi/5+MvA9YDlwEXBB1H8B8OdF+5r1i7zMphZXxbn0e13VOjHR/SKteLFX1j4mJsIx5s/v/hjxeWbtIz5Gp9ew37n7yyxkc5wBQa/5/CV9AniLme3u9iYj6bPAX0ftRDPbJWkxcK2ZPTtvbCNr+DadXjNjLl0aQjLTbP4TE+EJodP6wTGTk1Omm/nzp+z7SQ45JDtMNYtB5O73AvJOg8nL51/W4XsEcJukbwKPxJ1WMtRT0jLgeYSSj0eY2a5o/C5JT8kYswZYA7DE0+92ThyK2S15zubY7NOp4k9z1qYp/rz+PAaRu3/JkvQbTtkaxI4zIMra/N8DnAH8KR2GekqaD3waeKuZ3V9WMDPbZGYrzGzFokWLyg5zYnpdBLVkSbEjumysffwUcuSRYd1CXWTJu2BBfX6ADRvCwrV27r9/+nHcKew0jSx7UBUNGAe+CLw90fddYLFN+QW+W7SfkbD5V00vydliO/mWLWbj49k27W5q/CZrDktmc+Zkb9up7yPN5j8+bjZ3br1+gCzfSGz39zrCzoCggqyeDwD3R+2nwAHg/oIxAj4CfLCt/31Md/heVHR8V/5dMDnZvfJPpmRuV5xl29Kl2UqxkyLz3aSwTjrOixRzFRQVoXensDMg8pR/VwXcJb0aeIGZvStnmxcDXwFuAmLP17sIdv9PAkuAO4BfM7OMmMKAO3y7oJdi7vFvotd9dOsQbqcXh20/HLJFjmZ3CjsDovIC7mb2D8DJBdt81cxkZs8xs2Oj9gUz22tmK83sWdFrruJ3uqQKJ2ev+6jKUd+LHP1YQFe0TqOsDO4XcPpI2TKOr0m0MyW9F6hgSufURrfKLenE7VVBpinFbkJQe5GjHzV8i0pFlpHBF4s5/SbLHpRswKWJ9mFgPfCUMmOraG7z74I0J2NWZbBkS1bJ6rac4/Ll0/eRtMGnlabs1eZftECuigV0efsos/+ibdwv4NQAvTp8B91c+SfoRJGlbbt8ebaiXbly5j4mJ8vdNOJ28MHTx7dHBK1cOV2uvH2VKdlYVSRNkXLPOkZVxy9yGjtOF/Ss/AkpGQ4lhG5uA+4Bzikztormyj+iV0VTFAHUvq9uZ/7xTSQrFDR5kznkkM5kaqeKGXPRdc07RlUz9qJUGlVRdRoRp9FUofxviF7PAC4DFgA3lhlbRXPlH9Gpomn/Ry8TYpncVy9rBcyKPzcrl4MoT5H2OmPesiX7ySY+bt4x8q5p3nfRrnT7ofx9vcHIUYXyvyV6/TBwSvS3K/9+04mi63bWntxXt4q/E+XfqUzt9DLzLrpGZeL0826QscmqjNLth9nH/QojR57yLxvq+TlJtwErgG2SFhEWezn9pJOwxbRc+50eox+lEsscIy/ap5donqJrFB837xh5x4mLzGfVPYirpiWPlSVDFQwi95HTXLLuCu0NOBxoRX/PA3627Nhem8/8Izp5bO9kFW3Wvrqd9Xdi8+9Upqzr0k20T941Gh8vH9FT9IRTZlaflkqjExnK4DP/kYMqon2AXwJ+A/jNuJUd22tz5Z+grALI+kefP3/Kxi2F952GH0LYR5ZyT+4nLdonSV5NgCocknk3zLzzmzu3/LGz8hPNmZN/HZNKNy2VRlKGKuz1bvMfOXpW/sDlwL8CGwlF3D8E/FWZsVU0V/5d0A9lUcVMsgpbd94NMU/GIpt/0XkU3UAOOWRqu6LvouhaVjVrrzvax6OJGkUVyv9WCHmABtFc+XdJ3YubqlDcRSaTMvLlKdYiGbdsyT5+3nmUcai3m3XyvosiOYdhHYA/WTSOKpT/p4jSMA+iufJvKFXMRrPCLFutamQoI2PWNhMTnT9RdHsd+jXzr5NhkHHEyFP+ZaN9FgLfkfRFSVfGrTdXszP0bNgQkpAlmTNnegTM2rUwNhZy3oyNhfdJ1qxJ33dWfztFESxlooHStpk7NxRkycq1UxQhMz5ePn/Q1q3pJSulqX30I0dRLEu3yeU8mqha6k70l3VXSDbgpWmtzNgqms/8G0rWiuE4vr3oc7Mwm253mM6ZU95UUNaZ2mnunaI6AEUz/7IO4yLzUdJB3g97fS9mm36tUh4FKjKhUVG0zxHAaVHrW1I3c+XfXIpMNmVMOr2aCuqyM5fxFRTZ/MucQxnzUb/o9btw5V8dFZnQ8pR/2ZTOZwHfAH4NOAv4uqQzq30GcYaOrDrBcX/R59C7qWDVKli9emqxWKsV3sfplLulaNFVMo1zFjt3Tj2uZz3CN8kk0ut3cW9GaY6s/iy8rkF/TGhZd4VkA24kMdsHFuHpHZzZPPPvZL9lTEDtC7jKrDXo98y/15l7P5LsjQp9mPmXVf43tb2f096XMW4zsBu4OdF3IfBj4IaonVq0n5FQ/snUyWVSGTeBqmz+RStb86gz/r2sjb3bPEpl1hqkpdnuhbxz6lX5V6G4u/0+Z9v6gqbY/AlF178IvD5q/0iJwuvAS4DjUpT/O8ocN26zXvmXUZJNJC0jZqs1/QdadFMrWtlaRFWLxKpYENdNFtR4bNZT0lOfWl6GXs+ziqePXpVwNzLM1qeFCm5oecq/dAF3Sa8BXgwI+LKZXVFy3DLg82Z2TPT+QmCfmf1FqQMzAgXcx8bS7eOtFuzf3395ylJUuLwf+2iCDGX2lUX8/5dX3rLk/2ghRefZhN9hNzJU+f3NMrou4C7paEknAJjZZ8zs7Wb2NmCvpKN6kOnNknZI2izp8Ixjr5G0XdL2PXv29HCoIaCMY7SJVOGU6nUfVcS/V+lcS5OnV6pyehadZxN+h93I4OsLuqIo2ueDwAMp/Q9Fn3XDJcBRwLHALuD9aRuZ2SYzW2FmKxYtWtTloYaErLTG/Uip3AtVpCHudR9FxdP7IUORPFmz+rLfb1XF3IvOMytyKS+iqWq6kaEf6bBnI1n2oMgcdHPOZ4UO32i7ZVn7yfss2dzm31BmS6bJumUo8/0+9anF/oFe0yQUneewfhdNkLuh0K3DF/hBN5+1bTdNwZPIEQS8Dfh40T5mvfI3G85oH7P6k8f1i05k6CYyKO37bR9TxkGc97socw5lHPCdnkfVdPN76PdvqOh4VXwXFdCL8v8Y8Nsp/W8EPpE3NjF+F/AYcGc07nLgJmAHcCUlEsaNhPJ3hoO0Web4+MyIpW5mq2VbmpIoM/vtdYY8rE+oVVPFE1SfrmWe8s+N9pF0BHAF8ChwfdS9ApgLnGFmd3VjauqUWR/t4wwPnUTz5EWbdBoVlCQt8qVMxEuvUTFNiAZqAkXXscx17tO1zIv2KRXqKekk4Jjo7S1mdnVl0pXAlb/TGObMKR96KcHjj/e+nzTax2btLylDmW3y6Ec46jBQdB3LXOc+XcuuQz2nZLFrzOxDUeur4necRlFFJFOn+2knLUqoTMRLr1ExwxqVVjVF17HMdW7AtSybz99xZidxEjFpqqXVHYhJi+MfHw9jksydm1/X4Oiju18PcODAzIRxO3fOnE2m1S0YH58pe9n6C73WXihL0xO7FV3HMmtP+nUt88hyBjSpucPXqYUip2uW8609kmNyMj8/UZZzb+XKqf1kpXc47LDsz9IczXG6i6wIlLxUGkVOyJUrs8+jzu+kaWGbZVKSDEG0T+n0DoPEbf5OLRQ5Xcs636pIm1BkA67C0dyrnE1IQdEEhkHGiJ4dvoPGlb9TC2WcrmX+P4ocfGWUZtE2VTiae5WzH8q/V6d0PxgGGSN6dvg6zqykyNFZ1vlW5OCrwrlXZ8qMKuXslWFI1TAMMpbAlb8zuhQlYSvrfCty8JVx7i1fnr5N3J/laJ47d3qfNL2CWJGcye1PPDFfzpUr0z9P9uc5jMvQbaK+fjqJq0gmCIN3bGc5A5rU3OHr1EZaHv5unG9VpEVYvny6HMuXFx8jKX97bYM0R2nR9itX5svZ7vRNOnurWrXaaaqGQTiJe00n0SeZcYev48xyOnVC1uG0HNQK4CFywD5Bn2R2h6/jzHY6dULW4bQc1ArgIXLAPkGfZHaHr+PMdrp18pbtL8OgHMbD6IBtgMyu/B2nKfTiAOzUCVmV0zLJoFat1uGAXbgwtLqcsXVc/07JcgY0qbnD15n1VFlEvhNHaZU58LdsMRsbm34OY2P9WZ1bhwO2bgdyH2oQ4A5fx2k4w+i0bGeYz6HMCuphOI823ObvOE1nNhQhH+ZzKCPjMJxHB7jyd5wm0AAHYM8M8zmUkXEYzqMDalX+kjZL2i3p5kTfAklXSfp+9Hp4nTI4zlDQBAdgrwzzORSt9h6W8+iAumf+fw+c0tZ3AbDNzJ4FbIveO85os2oVbNoU7MpSeN20KfQPC005h26iptpln5gILes8Bp2aoQJqd/hKWgZ83syOid5/FzjRzHZJWgxca2bPztuHO3wdxynF1q0htPShh6b65s2r9ibUj2NURNMcvkeY2S6A6PUpA5DBcZzZyPr105UyhPfr1w/XMfpAYx2+ktZI2i5p+549ewYtjuM4w0A/Io6GOaopwSCU/92RuYfodXfaRma2ycxWmNmKRYsW9VVAx3GGlH5EHA1zVFOCQSj/K4HV0d+rgc8OQAbHcYaJsg7WfkQcDXNUU4K6Qz0/BnwNeLakOyW9EXgv8CuSvg/8SvTecRwnndjBunNnSLawc2d4n3YD6EfEUVOimnrE0zs4jtNshjltxIBpWrSP4zhOeWaJg7VpuPJ3HKfZzBIHa9Nw5e84TrOZJQ7WpuHK33GcZjNLHKxNY2zQAjiO4xSyapUr+4rxmb/jOM4I4srfcRxnBHHl7ziOM4K48nccxxlBXPk7juOMIEOR3kHSHiBlfXfPLATuqWG/VTMMcrqM1TEMcrqM1VGnnEvNLDUt8lAo/7qQtD0r70WTGAY5XcbqGAY5XcbqGJScbvZxHMcZQVz5O47jjCCjrvw3DVqAkgyDnC5jdQyDnC5jdQxEzpG2+TuO44wqoz7zdxzHGUlc+TuO44wgI6X8Jd0u6SZJN0jaHvUtkHSVpO9Hr4cPUL5nR7LF7X5Jb5V0oaQfJ/pPHYBsmyXtlnRzoi/z2kl6p6QfSPqupF8doIzvk3SbpB2SrpB0WNS/TNLDiWv6NwOUMfP7HcR1zJHzEwkZb5d0Q9Q/qGv5dEnXSLpV0i2S1kX9jfld5sg4+N+lmY1MA24HFrb1XQRcEP19AfDng5YzkqUF3AUsBS4E3jFgeV4CHAfcXHTtgOXAjcCTgGcAPwRaA5Lx5cBY9PefJ2RcltxuwNcx9fsd1HXMkrPt8/cD7x7wtVwMHBf9/WTge9E1a8zvMkfGgf8uR2rmn8GrgMuivy8DXj04UaaxEvihmdWxsrljzOzLwL1t3VnX7lXAx83sETP7d+AHwAsGIaOZfcnM9kdv/w04sm458si4jlkM5DpCvpySBJwFfKwfsmRhZrvM7FvR3w8AtwJPo0G/yywZm/C7HDXlb8CXJF0vaU3Ud4SZ7YLwRQFPGZh00/l1pv9zvTl6RNw8SNNUG1nX7mnAfyS2uzPqGzRvAP4x8f4Zkr4t6V8k/fKghIpI+36beh1/GbjbzL6f6BvotZS0DHge8HUa+rtskzHJQH6Xo6b8TzCz44BXAL8r6SWDFigNSXOB04FPRV2XAEcBxwK7CI/cTUYpfQONKZa0HtgPbI26dgFLzOx5wNuBj0o6dEDiZX2/jbuOEWczfWIy0GspaT7waeCtZnZ/3qYpfX25nlkyDvJ3OVLK38z+M3rdDVxBeOS7W9JigOh19+AkfIJXAN8ys7sBzOxuMztgZo8DH6ZPj/4lyLp2dwJPT2x3JPCffZbtCSStBk4DVllkWI0e/fdGf19PsP/+3CDky/l+G3UdASSNAa8BPhH3DfJaShonKNWtZvaZqLtRv8sMGQf+uxwZ5S/pEElPjv8mOFxuBq4EVkebrQY+OxgJpzFtZhX/kCPOIMjdBLKu3ZXAr0t6kqRnAM8CvjEA+ZB0CvAHwOlm9lCif5GkVvT3MyMZfzQgGbO+38ZcxwQvA24zszvjjkFdy8j38H+AW83sA4mPGvO7zJKxEb/LfniVm9CAZxI8/TcCtwDro/4JYBvw/eh1wYDlnAfsBX4m0Xc5cBOwg/ADXjwAuT5GeCR9jDCDemPetQPWE2Yt3wVeMUAZf0Cw894Qtb+Jtn1t9Du4EfgW8MoBypj5/Q7iOmbJGfX/PfCmtm0HdS1fTDDb7Eh8v6c26XeZI+PAf5ee3sFxHGcEGRmzj+M4jjOFK3/HcZwRxJW/4zjOCOLK33EcZwRx5e84jjOCuPJ3BoKkn5X0cUk/lPQdSV+QlLmYJcp22NH6Bkl/L+nM6O9rJVVaJFvSvg63j7PK3ijpS5J+NmfbY1VD9lZJp0u6oOr9OsOHK3+n70QLX64ArjWzo8xsOfAu4IjBStYXTjKz5wLbCeecxbGEePBKMbMrzey9Ve/XGT5c+TuD4CTgMTN7Ile5md1gZl+RNF/SNknfimbJr0qMG5N0WZQA7f9Kmgcg6d2SvinpZkmboptLJpLOjvZ9s6Q/j/pa0ZPCzdFnb0sZ9wxJX4uO9Sdtn/1+1L9D0h+VuAZfBo6WdJCkS6NjflvSSVFupz8GXqeQ0/110Qr1zdExvh1fF0mvl/QZSf+kkL/+ooRMp0TX8UZJ2xLb/3X09yslfT3a3z9LGoWbrxPhyt8ZBMcA12d89lPgDAsJ+E4C3p9Q5s8GNpnZc4D7gbVR/1+b2fPN7BjgYEK+lFQkPZWQP/1kwuz6+ZJeHf39NDM7xsx+Abg0ZfjFwCVm9nxCrYV4ny8nLMN/QbSf41WcNPA0wqre3wWIjnk2IQXxHODdwCfM7Fgz+wRhZerV0bFPAt4XpSkhOubrgF8g3DCeLmkRIU/Qa6MnjV9LkeGrwAstJBH7OHB+gczOLMKVv9M0BPyppB3APxNS7sYz0v8ws+uiv7cQls4DnBTNYG8iKPX/lrP/5xPMTXss5FPfSihc8iPgmZI+FOVdScsOeQJTOZcuT/S/PGrfJizJ/3nCzSCNaxQqYB0K/Fl0DpcDmNltwE7SE3m9HLggGnstcBCwJPpsm5n9l5n9FPgOoQDQC4EvW8hbj5ml5eY/EvhidN1+n/zr5swyxgYtgDOS3AKcmfHZKmARcLyZPSbpdoKig5npd03SQcBGYIWZ/YekCxPbp5FqEjKzn0h6LvCrhNn4WYQ86zM2zdjnn5nZ3+YcN+YkM7vniYEFJqq2Y7zWzL47rVP6ReCRRNcBwv+1MmRN8iHgA2Z2paQTCRXFnBHBZ/7OILgaeJKk3447JD1f0kuBnwF2R4r/JMIsNmaJpBdFf59NMFvEiv4ehZzpWTeVmK8DL5W0MMqeeDbwL5IWAnPM7NPA/ySUMGznOkKRHQg3qZgvAm+Ijo+kp0kqWxToy/G+ominJYSkYw8Qyv4lj/GW+GYh6XkF+/1adJ7PiLZfkLLNzwA/jv5enfK5M4tx5e/0HQvZBM8AfkUh1PMWwqzzPwlmmBWSthOU4m2JobcCqyOT0AKC/f0+gm37JuAfgG8WHHsX8E7gGqLMiWb2WYJ56drIrPL30TbtrCMUAfomQXHG+/wS8FHga5EJ5f8yXXHnsRFoReM+AbzezB6J5FseO3yBPwHGgR0KIa9/krnHINMeYA3wGUk3ksi/n+BC4FOSvgLck/K5M4vxrJ6O4zgjiM/8HcdxRhBX/o7jOCOIK3/HcZwRxJW/4zjOCOLK33EcZwRx5e84jjOCuPJ3HMcZQf4/YbP0Fng83tAAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a19a7b668>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1753,27 +1755,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
-    "X = data_auto[\"horsepower\"].fillna(data_auto[\"horsepower\"].mean())\n",
+    "X = data_auto[\"horsepower\"].fillna(data_auto[\"horsepower\"].mean()).to_numpy()\n",
     "Y = data_auto[\"mpg\"].fillna(data_auto[\"mpg\"].mean())\n",
     "X_data = X[:,np.newaxis]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+       "LinearRegression()"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1785,16 +1787,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "pandas.core.series.Series"
+       "numpy.ndarray"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1805,7 +1807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -1814,7 +1816,7 @@
        "numpy.ndarray"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1825,27 +1827,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a1b7ee978>]"
+       "[<matplotlib.lines.Line2D at 0x7f95552e6f10>]"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a1e7135f8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1857,7 +1861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1866,7 +1870,7 @@
        "0.574653340645025"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1877,7 +1881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1886,7 +1890,7 @@
        "(10315.75196006092, 5.046879480825511, 23.51457286432162, 21.46277336163346)"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1909,26 +1913,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
     "X_data = X**2\n",
+    "X_data = np.asarray(X_data)\n",
     "X_data = X_data[:,np.newaxis]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+       "LinearRegression()"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1940,7 +1945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -1949,7 +1954,7 @@
        "0.4849887034823205"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1960,7 +1965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -1969,7 +1974,7 @@
        "(12490.350340501926, 5.553410772769817, 23.51457286432162, 23.6168898529981)"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1992,7 +1997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2002,7 +2007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2011,25 +2016,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
+    "X = np.asarray(X)\n",
     "X_data = poly.fit_transform(X[:,np.newaxis])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+       "LinearRegression()"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2041,7 +2047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -2050,7 +2056,7 @@
        "0.6439066584257469"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2061,16 +2067,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "55.026192447080355"
+       "55.026192447081144"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2081,7 +2087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -2090,7 +2096,7 @@
        "array([ 0.        , -0.43404318,  0.00112615])"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2108,7 +2114,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def regresion_validation(X_data, Y, Y_pred):\n",
+    "    SSD = np.sum((Y - Y_pred)**2)\n",
+    "    RSE = np.sqrt(SSD/(len(X_data)-1))\n",
+    "    y_mean = np.mean(Y)\n",
+    "    error = RSE/y_mean\n",
+    "    print(\"SSD: \"+str(SSD)+\", RSE: \" +str(RSE) + \", Y_mean: \" +str(y_mean) +\", error: \" + str(error*100)+ \"%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -2117,66 +2137,66 @@
      "text": [
       "Regresión de grado 2\n",
       "R2:0.6439066584257469\n",
-      "55.026192447080355\n",
+      "55.026192447081144\n",
       "[ 0.         -0.43404318  0.00112615]\n",
       "SSD: 8636.180643524502, RSE: 4.61778115803654, Y_mean: 23.51457286432162, error: 19.63795466190689%\n",
       "Regresión de grado 3\n",
-      "R2:0.6444678885560742\n",
-      "58.44877411191809\n",
+      "R2:0.6444678885560744\n",
+      "58.44877411148498\n",
       "[ 0.00000000e+00 -5.27113167e-01  1.89736722e-03 -1.95723195e-06]\n",
-      "SSD: 8622.569367428552, RSE: 4.614140736300908, Y_mean: 23.51457286432162, error: 19.622473106036676%\n",
+      "SSD: 8622.569367428549, RSE: 4.614140736300907, Y_mean: 23.51457286432162, error: 19.622473106036672%\n",
       "Regresión de grado 4\n",
       "R2:0.6467674189704502\n",
-      "40.096643850138875\n",
+      "40.09664385577267\n",
       "[ 0.00000000e+00  1.62563541e-01 -7.10892744e-03  4.65580255e-05\n",
       " -9.15840093e-08]\n",
       "SSD: 8566.799832491353, RSE: 4.5991947315797574, Y_mean: 23.51457286432162, error: 19.55891250126878%\n",
       "Regresión de grado 5\n",
-      "R2:0.6547512490679683\n",
-      "-40.693829800815294\n",
-      "[ 0.00000000e+00  4.00021432e+00 -7.54801920e-02  6.19621368e-04\n",
-      " -2.36220932e-06  3.41982976e-09]\n",
-      "SSD: 8373.171390452659, RSE: 4.546921734577904, Y_mean: 23.51457286432162, error: 19.336612069517507%\n",
+      "R2:0.654751249136743\n",
+      "-40.694029173495565\n",
+      "[ 0.00000000e+00  4.00021894e+00 -7.54802468e-02  6.19621641e-04\n",
+      " -2.36220984e-06  3.41983153e-09]\n",
+      "SSD: 8373.171388784698, RSE: 4.546921734125024, Y_mean: 23.51457286432162, error: 19.336612067591556%\n",
       "Regresión de grado 6\n",
-      "R2:0.6572895595307906\n",
-      "-157.06800537233534\n",
-      "[ 0.00000000e+00  1.07623362e+01 -2.30128490e-01  2.40537374e-03\n",
-      " -1.33773684e-05  3.79725580e-08 -4.32494610e-11]\n",
-      "SSD: 8311.610824368023, RSE: 4.530176140354489, Y_mean: 23.51457286432162, error: 19.265398382923937%\n",
+      "R2:0.6572468033307997\n",
+      "-156.9980060652282\n",
+      "[ 0.00000000e+00  1.07622444e+01 -2.30165519e-01  2.40584080e-03\n",
+      " -1.33797054e-05  3.79780900e-08 -4.32525127e-11]\n",
+      "SSD: 8312.647772335427, RSE: 4.530458721585344, Y_mean: 23.51457286432162, error: 19.266600111028826%\n",
       "Regresión de grado 7\n",
-      "R2:0.6538574297764823\n",
-      "14.78101556705461\n",
-      "[ 0.00000000e+00  1.14612741e-03  4.31542691e-02 -1.25404186e-03\n",
-      "  1.46299381e-05 -8.50482922e-08  2.45097435e-10 -2.79311454e-13]\n",
-      "SSD: 8394.848810282569, RSE: 4.552803724495941, Y_mean: 23.51457286432162, error: 19.361626301976575%\n",
+      "R2:0.6538574808932471\n",
+      "14.784276957787014\n",
+      "[ 0.00000000e+00  1.26690941e-03  4.31482761e-02 -1.25395193e-03\n",
+      "  1.46292439e-05 -8.50453243e-08  2.45090912e-10 -2.79305875e-13]\n",
+      "SSD: 8394.847570569373, RSE: 4.552803388327227, Y_mean: 23.51457286432162, error: 19.361624872357947%\n",
       "Regresión de grado 8\n",
-      "R2:0.651243218495551\n",
-      "36.40672952320243\n",
-      "[ 0.00000000e+00  4.43777164e-07  3.03135040e-06  8.84885484e-05\n",
-      " -3.97067205e-06  5.47393601e-08 -3.43825957e-10  1.02689459e-12\n",
-      " -1.18561693e-15]\n",
-      "SSD: 8458.250166687194, RSE: 4.569963710228768, Y_mean: 23.51457286432162, error: 19.43460226386982%\n",
+      "R2:0.6512432177866146\n",
+      "36.40675411486028\n",
+      "[ 0.00000000e+00  1.62872847e-07  3.03202377e-06  8.84870688e-05\n",
+      " -3.97062301e-06  5.47387207e-08 -3.43821862e-10  1.02688173e-12\n",
+      " -1.18560107e-15]\n",
+      "SSD: 8458.250183880726, RSE: 4.5699637148735714, Y_mean: 23.51457286432162, error: 19.43460228362269%\n",
       "Regresión de grado 9\n",
-      "R2:0.651053631916962\n",
-      "39.46643472093898\n",
-      "[ 0.00000000e+00 -7.79795260e-09 -1.27929387e-09 -6.48447884e-08\n",
-      " -1.77132827e-06  3.55551206e-08 -3.00241201e-10  1.32278884e-12\n",
-      " -3.04048282e-15  2.92448674e-18]\n",
-      "SSD: 8462.848129493923, RSE: 4.571205673276188, Y_mean: 23.51457286432162, error: 19.439883937726222%\n",
+      "R2:0.6510536224193297\n",
+      "39.46391227417244\n",
+      "[ 0.00000000e+00  2.93874448e-08 -1.25861585e-09 -6.48221251e-08\n",
+      " -1.77070344e-06  3.55390671e-08 -3.00071067e-10  1.32187215e-12\n",
+      " -3.03799240e-15  2.92177398e-18]\n",
+      "SSD: 8462.848359835967, RSE: 4.5712057354857905, Y_mean: 23.51457286432162, error: 19.439884202283878%\n",
       "Regresión de grado 10\n",
-      "R2:0.6523570737818101\n",
-      "38.44031944400909\n",
-      "[ 0.00000000e+00 -1.18966016e-09 -9.07249391e-13 -2.72052174e-11\n",
-      " -1.29822393e-09 -3.51557922e-08  8.70014514e-10 -8.86586743e-12\n",
-      "  4.60157706e-14 -1.20803538e-16  1.27732473e-19]\n",
-      "SSD: 8431.236307286306, RSE: 4.562660126000871, Y_mean: 23.51457286432162, error: 19.403542442923726%\n",
+      "R2:0.6523571001357847\n",
+      "38.44329678404357\n",
+      "[ 0.00000000e+00 -9.08873846e-10  2.52067924e-13 -2.72214177e-11\n",
+      " -1.29903098e-09 -3.51782808e-08  8.70646682e-10 -8.87293169e-12\n",
+      "  4.60549152e-14 -1.20911079e-16  1.27849759e-19]\n",
+      "SSD: 8431.235668134548, RSE: 4.56265995305872, Y_mean: 23.51457286432162, error: 19.40354170745576%\n",
       "Regresión de grado 11\n",
-      "R2:0.6511663023950045\n",
-      "36.41004334298087\n",
-      "[ 0.00000000e+00 -6.36130810e-12 -7.34513859e-15 -4.92520665e-15\n",
-      " -3.14729648e-13 -1.49252137e-11 -4.09650002e-10  1.04700269e-11\n",
-      " -1.08552700e-13  5.67263121e-16 -1.48811921e-18  1.56362003e-21]\n",
-      "SSD: 8460.115580221123, RSE: 4.570467620731048, Y_mean: 23.51457286432162, error: 19.436745234976236%\n"
+      "R2:0.651111197346774\n",
+      "36.3470091267187\n",
+      "[ 0.00000000e+00  3.38646890e-11  1.31264454e-14 -4.88015707e-15\n",
+      " -3.12630666e-13 -1.48183561e-11 -4.06544503e-10  1.03832301e-11\n",
+      " -1.07585926e-13  5.61915382e-16 -1.47344508e-18  1.54764846e-21]\n",
+      "SSD: 8461.45201956252, RSE: 4.570828603478431, Y_mean: 23.51457286432162, error: 19.43828037979671%\n"
      ]
     }
    ],
@@ -2193,37 +2213,6 @@
     "    regresion_validation(X_data, Y, lm.predict(X_data))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def regresion_validation(X_data, Y, Y_pred):\n",
-    "    SSD = np.sum((Y - Y_pred)**2)\n",
-    "    RSE = np.sqrt(SSD/(len(X_data)-1))\n",
-    "    y_mean = np.mean(Y)\n",
-    "    error = RSE/y_mean\n",
-    "    print(\"SSD: \"+str(SSD)+\", RSE: \" +str(RSE) + \", Y_mean: \" +str(y_mean) +\", error: \" + str(error*100)+ \"%\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "None\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(regresion_validation(lm, X_data, Y))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2233,27 +2222,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a1f7e97b8>]"
+       "[<matplotlib.lines.Line2D at 0x7f95560fe910>]"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a1f05e5c0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2263,22 +2254,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+       "LinearRegression()"
       ]
      },
-     "execution_count": 148,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "X = data_auto[\"displacement\"].fillna(data_auto[\"displacement\"].mean())\n",
+    "X = data_auto[\"displacement\"].fillna(data_auto[\"displacement\"].mean()).to_numpy()\n",
     "X = X[:,np.newaxis]\n",
     "Y = data_auto[\"mpg\"].fillna(data_auto[\"mpg\"].mean())\n",
     "\n",
@@ -2288,7 +2279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -2297,7 +2288,7 @@
        "0.6261049762826918"
       ]
      },
-     "execution_count": 153,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2308,27 +2299,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a1e713080>]"
+       "[<matplotlib.lines.Line2D at 0x7f9556184280>]"
       ]
      },
-     "execution_count": 150,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a2090bdd8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2340,7 +2333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -2400,7 +2393,7 @@
        "395          82       1  oldsmobile cutlass ciera (diesel)  "
       ]
      },
-     "execution_count": 158,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2411,7 +2404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -2499,7 +2492,7 @@
        "372          81       1  oldsmobile cutlass ls  "
       ]
      },
-     "execution_count": 163,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2510,7 +2503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2519,22 +2512,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
+       "LinearRegression()"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "X = data_auto_clean[\"displacement\"].fillna(data_auto_clean[\"displacement\"].mean())\n",
+    "X = data_auto_clean[\"displacement\"].fillna(data_auto_clean[\"displacement\"].mean()).to_numpy()\n",
     "X = X[:,np.newaxis]\n",
     "Y = data_auto_clean[\"mpg\"].fillna(data_auto_clean[\"mpg\"].mean())\n",
     "\n",
@@ -2544,7 +2537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
@@ -2553,7 +2546,7 @@
        "0.6466514317531822"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2564,27 +2557,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a20a61e48>]"
+       "[<matplotlib.lines.Line2D at 0x7f9556612130>]"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a2081b128>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2593,13 +2588,6 @@
     "plt.plot(X,Y, \"ro\")\n",
     "plt.plot(X, lm.predict(X), color=\"blue\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -2618,7 +2606,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T5 - 1 - Logistic Regression - Matem\303\241ticas-Colab.ipynb" "b/notebooks/T5 - 1 - Logistic Regression - Matem\303\241ticas-Colab.ipynb"
new file mode 100644
index 00000000..9379d9cb
--- /dev/null
+++ "b/notebooks/T5 - 1 - Logistic Regression - Matem\303\241ticas-Colab.ipynb"	
@@ -0,0 +1,1170 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T5%20-%201%20-%20Logistic%20Regression%20-%20Matemáticas-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Las matemáticas tras la regresión logística"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Las tablas de contingencia"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>Purchase</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Female</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>No</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Male</td>\n",
+       "      <td>Yes</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Gender Purchase\n",
+       "0  Female      Yes\n",
+       "1  Female      Yes\n",
+       "2  Female       No\n",
+       "3    Male       No\n",
+       "4    Male      Yes"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"/content/python-ml-course/datasets/gender-purchase/Gender Purchase.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(511, 2)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>Purchase</th>\n",
+       "      <th>No</th>\n",
+       "      <th>Yes</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>106</td>\n",
+       "      <td>159</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>125</td>\n",
+       "      <td>121</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Purchase   No  Yes\n",
+       "Gender            \n",
+       "Female    106  159\n",
+       "Male      125  121"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "contingency_table = pd.crosstab(df[\"Gender\"], df[\"Purchase\"])\n",
+    "contingency_table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Gender\n",
+       "Female    265\n",
+       "Male      246\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "contingency_table.sum(axis = 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Purchase\n",
+       "No     231\n",
+       "Yes    280\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "contingency_table.sum(axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>Purchase</th>\n",
+       "      <th>No</th>\n",
+       "      <th>Yes</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>0.40000</td>\n",
+       "      <td>0.60000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Male</th>\n",
+       "      <td>0.50813</td>\n",
+       "      <td>0.49187</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Purchase       No      Yes\n",
+       "Gender                    \n",
+       "Female    0.40000  0.60000\n",
+       "Male      0.50813  0.49187"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "contingency_table.astype(\"float\").div(contingency_table.sum(axis=1), axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### La probabilidad condicional"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import display, Math, Latex"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* ¿Cuál es la probabilidad de que un cliente compre un producto sabiendo que es un hombre?\n",
+    "* ¿Cuál es la probabilidad de que sabiendo que un cliente compra un producto sea mujer?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle P(Purchase|Male) = \\frac{Numero\\ total\\ de\\ compras\\ hechas\\ por\\ hombres}{Numero\\ total\\ de\\ hombres\\ del\\ grupo} = \\frac{Purchase\\cap Male}{Male}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.491869918699187"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display(Math(r'P(Purchase|Male) = \\frac{Numero\\ total\\ de\\ compras\\ hechas\\ por\\ hombres}{Numero\\ total\\ de\\ hombres\\ del\\ grupo} = \\frac{Purchase\\cap Male}{Male}'))\n",
+    "121/246"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle P(No\\ Purchase|Male) = 1-P(Purchase|Male)$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.508130081300813"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display(Math(r'P(No\\ Purchase|Male) = 1-P(Purchase|Male)'))\n",
+    "125/246"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle P(Female|Purchase) = \\frac{Numero\\ total\\ de\\ compras\\ hechas\\ por\\ mujeres}{Numero\\ total\\ de\\ compras} = \\frac{Female\\cap Purchase}{Purchase}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.5678571428571428"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display(Math(r'P(Female|Purchase) = \\frac{Numero\\ total\\ de\\ compras\\ hechas\\ por\\ mujeres}{Numero\\ total\\ de\\ compras} = \\frac{Female\\cap Purchase}{Purchase}'))\n",
+    "159/280"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle P(Male|Purchase)$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.43214285714285716"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "display(Math(r'P(Male|Purchase)'))\n",
+    "121/280"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle P(Purchase|Male)$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.491869918699187\n"
+     ]
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle P(NO\\ Purchase|Male)$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.508130081300813\n"
+     ]
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle P(Purchase|Female)$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6\n"
+     ]
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle P(NO\\ Purchase|Female)$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "display(Math(r'P(Purchase|Male)'))\n",
+    "print(121/246)\n",
+    "display(Math(r'P(NO\\ Purchase|Male)'))\n",
+    "print(125/246)\n",
+    "display(Math(r'P(Purchase|Female)'))\n",
+    "print(159/265)\n",
+    "display(Math(r'P(NO\\ Purchase|Female)'))\n",
+    "print(106/265)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Ratio de probabilidades\n",
+    "Cociente entre los casos de éxito sobre los de fracaso en el suceso estudiado y para cada grupo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle P_m = \\ probabilidad\\ de\\ hacer\\ compra\\ sabiendo\\ que\\ es \\ un \\ hombre$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle P_f = \\ probabilidad\\ de\\ hacer\\ compra\\ sabiendo\\ que\\ es \\ una\\ mujer$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle odds\\in[0,+\\infty]$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle odds_{purchase,male} = \\frac{P_m}{1-P_m} = \\frac{N_{p,m}}{N_{\\bar p, m}}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle odds_{purchase,female} = \\frac{P_F}{1-P_F} = \\frac{N_{p,f}}{N_{\\bar p, f}}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r'P_m = \\ probabilidad\\ de\\ hacer\\ compra\\ sabiendo\\ que\\ es \\ un \\ hombre'))\n",
+    "\n",
+    "display(Math(r'P_f = \\ probabilidad\\ de\\ hacer\\ compra\\ sabiendo\\ que\\ es \\ una\\ mujer'))\n",
+    "\n",
+    "display(Math(r'odds\\in[0,+\\infty]'))\n",
+    "\n",
+    "display(Math(r'odds_{purchase,male} = \\frac{P_m}{1-P_m} = \\frac{N_{p,m}}{N_{\\bar p, m}}'))\n",
+    "\n",
+    "display(Math(r'odds_{purchase,female} = \\frac{P_F}{1-P_F} = \\frac{N_{p,f}}{N_{\\bar p, f}}'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pm = 121/246\n",
+    "pf = 159/265\n",
+    "odds_m = pm/(1-pm)# 121/125\n",
+    "odds_f = pf/(1-pf)# 159/106"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9680000000000002"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "odds_m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.4999999999999998"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "odds_f"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Si el ratio es superior a 1, es más probable el éxito que el fracas. Cuanto mayor es el ratio, más probabilidad de éxito en nuestro suceso.\n",
+    "* Si el ratio es exactamente igual a 1, éxito y fracaso son equiprobables (p=0.5)\n",
+    "* Si el ratio es menor que 1, el fracaso es más probable que el éxito. Cuanto menor es el ratio, menor es la probabilidad de éxito del suceso."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle odds_{ratio} = \\frac{odds_{purchase,male}}{odds_{purchase,female}}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r'odds_{ratio} = \\frac{odds_{purchase,male}}{odds_{purchase,female}}'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "odds_r = odds_m/odds_f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6453333333333335"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "odds_r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.5495867768595037"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1/odds_r# odds_f/odds_m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### La regresión logística desde la regresión lineal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle y = \\alpha + \\beta \\cdot x$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle (x,y)\\in[-\\infty, +\\infty]^2$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r'y = \\alpha + \\beta \\cdot x'))\n",
+    "display(Math(r'(x,y)\\in[-\\infty, +\\infty]^2'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle Y\\in\\{0,1\\}??$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle P\\in [0,1]$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle X\\in [-\\infty,\\infty]$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle P = \\alpha + \\beta\\cdot X$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r'Y\\in\\{0,1\\}??'))\n",
+    "display(Math(r'P\\in [0,1]'))\n",
+    "display(Math(r'X\\in [-\\infty,\\infty]'))\n",
+    "\n",
+    "display(Math(r'P = \\alpha + \\beta\\cdot X'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "P es la probabilidad condicionada de éxito o de fracaso condicionada a la presencia de la variable X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\frac{P}{1-P} = \\alpha + \\beta\\cdot X\\in [0,+\\infty]$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r'\\frac{P}{1-P} = \\alpha + \\beta\\cdot X\\in [0,+\\infty]'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle  ln(\\frac{P}{1-P}) = \\alpha + \\beta\\cdot X$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r' ln(\\frac{P}{1-P}) = \\alpha + \\beta\\cdot X'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\begin{cases}\\frac{P}{1-P}\\in[0,1]\\Rightarrow ln(\\frac{P}{1-P})\\in[-\\infty,0]\\\\ \\frac{P}{1-P}\\in[1,+\\infty]\\Rightarrow ln(\\frac{P}{1-P})\\in[0, \\infty]\\end{cases}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r'\\begin{cases}\\frac{P}{1-P}\\in[0,1]\\Rightarrow ln(\\frac{P}{1-P})\\in[-\\infty,0]\\\\ \\frac{P}{1-P}\\in[1,+\\infty]\\Rightarrow ln(\\frac{P}{1-P})\\in[0, \\infty]\\end{cases}'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle  ln(\\frac{P}{1-P}) = \\alpha + \\beta\\cdot X$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle  \\frac{P}{1-P} = e^{\\alpha + \\beta\\cdot X}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle  P = \\frac{e^{\\alpha+\\beta\\cdot X}}{1+e^{\\alpha+\\beta\\cdot X}}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle  P = \\frac{1}{1+e^{-(\\alpha+\\beta\\cdot X)}}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r' ln(\\frac{P}{1-P}) = \\alpha + \\beta\\cdot X'))\n",
+    "display(Math(r' \\frac{P}{1-P} = e^{\\alpha + \\beta\\cdot X}'))\n",
+    "display(Math(r' P = \\frac{e^{\\alpha+\\beta\\cdot X}}{1+e^{\\alpha+\\beta\\cdot X}}'))\n",
+    "display(Math(r' P = \\frac{1}{1+e^{-(\\alpha+\\beta\\cdot X)}}'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Si a+bX es muy pequeño (negativo), entonces P tiende a 0\n",
+    "* Si a+bX = 0, P = 0.5\n",
+    "* Si a+bX es muy grande (positivo), entonces P tiende a 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regresión logística múltiple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle  P = \\frac{1}{1+e^{-(\\alpha+\\sum_{i=1}^n\\beta_i\\cdot x_i)}}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r' P = \\frac{1}{1+e^{-(\\alpha+\\sum_{i=1}^n\\beta_i\\cdot x_i)}}'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle  \\vec{\\beta} = (\\beta_1,\\beta_2,\\cdots,\\beta_n)$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle  \\vec{X} = (x_1,x_2,\\cdots,x_n)$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle  P = \\frac{1}{1+e^{-(\\alpha+\\vec{\\beta_i}\\cdot \\vec{X})}}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r' \\vec{\\beta} = (\\beta_1,\\beta_2,\\cdots,\\beta_n)'))\n",
+    "display(Math(r' \\vec{X} = (x_1,x_2,\\cdots,x_n)'))\n",
+    "display(Math(r' P = \\frac{1}{1+e^{-(\\alpha+\\vec{\\beta_i}\\cdot \\vec{X})}}'))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T5 - 1 - Logistic Regression - Matem\303\241ticas.ipynb" "b/notebooks/T5 - 1 - Logistic Regression - Matem\303\241ticas.ipynb"
index 79ed875f..558dc793 100644
--- "a/notebooks/T5 - 1 - Logistic Regression - Matem\303\241ticas.ipynb"	
+++ "b/notebooks/T5 - 1 - Logistic Regression - Matem\303\241ticas.ipynb"	
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -92,7 +92,7 @@
        "4    Male      Yes"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -104,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -113,7 +113,7 @@
        "(511, 2)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -124,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -179,7 +179,7 @@
        "Male      125  121"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -203,7 +203,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -226,7 +226,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -237,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -292,7 +292,7 @@
        "Male      0.50813  0.49187"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -310,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -327,13 +327,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$P(Purchase|Male) = \\frac{Numero\\ total\\ de\\ compras\\ hechas\\ por\\ hombres}{Numero\\ total\\ de\\ hombres\\ del\\ grupo} = \\frac{Purchase\\cap Male}{Male}$$"
+       "$\\displaystyle P(Purchase|Male) = \\frac{Numero\\ total\\ de\\ compras\\ hechas\\ por\\ hombres}{Numero\\ total\\ de\\ hombres\\ del\\ grupo} = \\frac{Purchase\\cap Male}{Male}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -348,7 +348,7 @@
        "0.491869918699187"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -360,13 +360,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$P(No\\ Purchase|Male) = 1-P(Purchase|Male)$$"
+       "$\\displaystyle P(No\\ Purchase|Male) = 1-P(Purchase|Male)$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -381,7 +381,7 @@
        "0.508130081300813"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -393,13 +393,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$P(Female|Purchase) = \\frac{Numero\\ total\\ de\\ compras\\ hechas\\ por\\ mujeres}{Numero\\ total\\ de\\ compras} = \\frac{Female\\cap Purchase}{Purchase}$$"
+       "$\\displaystyle P(Female|Purchase) = \\frac{Numero\\ total\\ de\\ compras\\ hechas\\ por\\ mujeres}{Numero\\ total\\ de\\ compras} = \\frac{Female\\cap Purchase}{Purchase}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -414,7 +414,7 @@
        "0.5678571428571428"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -426,13 +426,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$P(Male|Purchase)$$"
+       "$\\displaystyle P(Male|Purchase)$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -447,7 +447,7 @@
        "0.43214285714285716"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -459,13 +459,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$P(Purchase|Male)$$"
+       "$\\displaystyle P(Purchase|Male)$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -484,7 +484,7 @@
     {
      "data": {
       "text/latex": [
-       "$$P(NO\\ Purchase|Male)$$"
+       "$\\displaystyle P(NO\\ Purchase|Male)$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -503,7 +503,7 @@
     {
      "data": {
       "text/latex": [
-       "$$P(Purchase|Female)$$"
+       "$\\displaystyle P(Purchase|Female)$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -522,7 +522,7 @@
     {
      "data": {
       "text/latex": [
-       "$$P(NO\\ Purchase|Female)$$"
+       "$\\displaystyle P(NO\\ Purchase|Female)$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -560,13 +560,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$P_m = \\ probabilidad\\ de\\ hacer\\ compra\\ sabiendo\\ que\\ es \\ un \\ hombre$$"
+       "$\\displaystyle P_m = \\ probabilidad\\ de\\ hacer\\ compra\\ sabiendo\\ que\\ es \\ un \\ hombre$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -578,7 +578,7 @@
     {
      "data": {
       "text/latex": [
-       "$$P_f = \\ probabilidad\\ de\\ hacer\\ compra\\ sabiendo\\ que\\ es \\ una\\ mujer$$"
+       "$\\displaystyle P_f = \\ probabilidad\\ de\\ hacer\\ compra\\ sabiendo\\ que\\ es \\ una\\ mujer$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -590,7 +590,7 @@
     {
      "data": {
       "text/latex": [
-       "$$odds\\in[0,+\\infty]$$"
+       "$\\displaystyle odds\\in[0,+\\infty]$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -602,7 +602,7 @@
     {
      "data": {
       "text/latex": [
-       "$$odds_{purchase,male} = \\frac{P_m}{1-P_m} = \\frac{N_{p,m}}{N_{\\bar p, m}}$$"
+       "$\\displaystyle odds_{purchase,male} = \\frac{P_m}{1-P_m} = \\frac{N_{p,m}}{N_{\\bar p, m}}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -614,7 +614,7 @@
     {
      "data": {
       "text/latex": [
-       "$$odds_{purchase,female} = \\frac{P_F}{1-P_F} = \\frac{N_{p,f}}{N_{\\bar p, f}}$$"
+       "$\\displaystyle odds_{purchase,female} = \\frac{P_F}{1-P_F} = \\frac{N_{p,f}}{N_{\\bar p, f}}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -638,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -650,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -659,7 +659,7 @@
        "0.9680000000000002"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -670,7 +670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -679,7 +679,7 @@
        "1.4999999999999998"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -699,13 +699,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$odds_{ratio} = \\frac{odds_{purchase,male}}{odds_{purchase,female}}$$"
+       "$\\displaystyle odds_{ratio} = \\frac{odds_{purchase,male}}{odds_{purchase,female}}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -721,7 +721,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -730,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -739,7 +739,7 @@
        "0.6453333333333335"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -750,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -759,7 +759,7 @@
        "1.5495867768595037"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -777,13 +777,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$y = \\alpha + \\beta \\cdot x$$"
+       "$\\displaystyle y = \\alpha + \\beta \\cdot x$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -795,7 +795,7 @@
     {
      "data": {
       "text/latex": [
-       "$$(x,y)\\in[-\\infty, +\\infty]^2$$"
+       "$\\displaystyle (x,y)\\in[-\\infty, +\\infty]^2$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -812,13 +812,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$Y\\in\\{0,1\\}??$$"
+       "$\\displaystyle Y\\in\\{0,1\\}??$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -830,7 +830,7 @@
     {
      "data": {
       "text/latex": [
-       "$$P\\in [0,1]$$"
+       "$\\displaystyle P\\in [0,1]$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -842,7 +842,7 @@
     {
      "data": {
       "text/latex": [
-       "$$X\\in [-\\infty,\\infty]$$"
+       "$\\displaystyle X\\in [-\\infty,\\infty]$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -854,7 +854,7 @@
     {
      "data": {
       "text/latex": [
-       "$$P = \\alpha + \\beta\\cdot X$$"
+       "$\\displaystyle P = \\alpha + \\beta\\cdot X$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -881,13 +881,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$\\frac{P}{1-P} = \\alpha + \\beta\\cdot X\\in [0,+\\infty]$$"
+       "$\\displaystyle \\frac{P}{1-P} = \\alpha + \\beta\\cdot X\\in [0,+\\infty]$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -903,13 +903,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$ ln(\\frac{P}{1-P}) = \\alpha + \\beta\\cdot X$$"
+       "$\\displaystyle  ln(\\frac{P}{1-P}) = \\alpha + \\beta\\cdot X$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -925,13 +925,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$\\begin{cases}\\frac{P}{1-P}\\in[0,1]\\Rightarrow ln(\\frac{P}{1-P})\\in[-\\infty,0]\\\\ \\frac{P}{1-P}\\in[1,+\\infty]\\Rightarrow ln(\\frac{P}{1-P})\\in[0, \\infty]\\end{cases}$$"
+       "$\\displaystyle \\begin{cases}\\frac{P}{1-P}\\in[0,1]\\Rightarrow ln(\\frac{P}{1-P})\\in[-\\infty,0]\\\\ \\frac{P}{1-P}\\in[1,+\\infty]\\Rightarrow ln(\\frac{P}{1-P})\\in[0, \\infty]\\end{cases}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -947,13 +947,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$ ln(\\frac{P}{1-P}) = \\alpha + \\beta\\cdot X$$"
+       "$\\displaystyle  ln(\\frac{P}{1-P}) = \\alpha + \\beta\\cdot X$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -965,7 +965,7 @@
     {
      "data": {
       "text/latex": [
-       "$$ \\frac{P}{1-P} = e^{\\alpha + \\beta\\cdot X}$$"
+       "$\\displaystyle  \\frac{P}{1-P} = e^{\\alpha + \\beta\\cdot X}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -977,7 +977,7 @@
     {
      "data": {
       "text/latex": [
-       "$$ P = \\frac{e^{\\alpha+\\beta\\cdot X}}{1+e^{\\alpha+\\beta\\cdot X}}$$"
+       "$\\displaystyle  P = \\frac{e^{\\alpha+\\beta\\cdot X}}{1+e^{\\alpha+\\beta\\cdot X}}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -989,7 +989,7 @@
     {
      "data": {
       "text/latex": [
-       "$$ P = \\frac{1}{1+e^{-(\\alpha+\\beta\\cdot X)}}$$"
+       "$\\displaystyle  P = \\frac{1}{1+e^{-(\\alpha+\\beta\\cdot X)}}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -1024,13 +1024,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$ P = \\frac{1}{1+e^{-(\\alpha+\\sum_{i=1}^n\\beta_i\\cdot x_i)}}$$"
+       "$\\displaystyle  P = \\frac{1}{1+e^{-(\\alpha+\\sum_{i=1}^n\\beta_i\\cdot x_i)}}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -1046,13 +1046,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$ \\vec{\\beta} = (\\beta_1,\\beta_2,\\cdots,\\beta_n)$$"
+       "$\\displaystyle  \\vec{\\beta} = (\\beta_1,\\beta_2,\\cdots,\\beta_n)$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -1064,7 +1064,7 @@
     {
      "data": {
       "text/latex": [
-       "$$ \\vec{X} = (x_1,x_2,\\cdots,x_n)$$"
+       "$\\displaystyle  \\vec{X} = (x_1,x_2,\\cdots,x_n)$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -1076,7 +1076,7 @@
     {
      "data": {
       "text/latex": [
-       "$$ P = \\frac{1}{1+e^{-(\\alpha+\\vec{\\beta_i}\\cdot \\vec{X})}}$$"
+       "$\\displaystyle  P = \\frac{1}{1+e^{-(\\alpha+\\vec{\\beta_i}\\cdot \\vec{X})}}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -1091,13 +1091,6 @@
     "display(Math(r' \\vec{X} = (x_1,x_2,\\cdots,x_n)'))\n",
     "display(Math(r' P = \\frac{1}{1+e^{-(\\alpha+\\vec{\\beta_i}\\cdot \\vec{X})}}'))"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1116,7 +1109,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T5 - 2 - Logistic Regression - Implementaci\303\263n-Colab.ipynb" "b/notebooks/T5 - 2 - Logistic Regression - Implementaci\303\263n-Colab.ipynb"
new file mode 100644
index 00000000..91a07bb8
--- /dev/null
+++ "b/notebooks/T5 - 2 - Logistic Regression - Implementaci\303\263n-Colab.ipynb"	
@@ -0,0 +1,700 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T5%20-%202%20-%20Logistic%20Regression%20-%20Implementación-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Implementación el método de la máxima verosimilitud para la regresión logística"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Definir la función de entorno L(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle L(\\beta)=\\sum_{i=1}^n P_i^{y_i}(1-Pi)^{y_i}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from IPython.display import display, Math, Latex\n",
+    "display(Math(r'L(\\beta)=\\sum_{i=1}^n P_i^{y_i}(1-Pi)^{y_i}'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def likelihood(y, pi):\n",
+    "    import numpy as np\n",
+    "    total_sum = 1\n",
+    "    sum_in = list(range(1, len(y)+1))\n",
+    "    for i in range(len(y)):\n",
+    "        sum_in[i] = np.where(y[i]==1, pi[i], 1-pi[i])\n",
+    "        total_sum = total_sum * sum_in[i]\n",
+    "    return total_sum"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calcular las probabilidades para cada observación"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle P_i = P(x_i) = \\frac{1}{1+e^{-\\sum_{j=0}^k\\beta_j\\cdot x_{ij}}} $"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r'P_i = P(x_i) = \\frac{1}{1+e^{-\\sum_{j=0}^k\\beta_j\\cdot x_{ij}}} '))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def logitprobs(X,beta):\n",
+    "    import numpy as np\n",
+    "    n_rows = np.shape(X)[0]\n",
+    "    n_cols = np.shape(X)[1]\n",
+    "    pi=list(range(1,n_rows+1))\n",
+    "    expon=list(range(1,n_rows+1))\n",
+    "    for i in range(n_rows):\n",
+    "        expon[i] = 0\n",
+    "        for j in range(n_cols):\n",
+    "            ex=X[i][j] * beta[j]\n",
+    "            expon[i] = ex + expon[i]\n",
+    "        with np.errstate(divide=\"ignore\", invalid=\"ignore\"):\n",
+    "            pi[i]=1/(1+np.exp(-expon[i]))\n",
+    "    return pi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calcular la matriz diagonal W"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle W= diag(P_i \\cdot (1-P_i))_{i=1}^n$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r'W= diag(P_i \\cdot (1-P_i))_{i=1}^n'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def findW(pi):\n",
+    "    import numpy as np\n",
+    "    n = len(pi)\n",
+    "    W = np.zeros(n*n).reshape(n,n)\n",
+    "    for i in range(n):\n",
+    "        print(i)\n",
+    "        W[i,i]=pi[i]*(1-pi[i])\n",
+    "        W[i,i].astype(float)\n",
+    "    return W"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Obtener la solución de la función logística"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\beta_{n+1} = \\beta_n -\\frac{f(\\beta_n)}{f'(\\beta_n)}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle f(\\beta) = X(Y-P)$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle f'(\\beta) = XWX^T$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r\"\\beta_{n+1} = \\beta_n -\\frac{f(\\beta_n)}{f'(\\beta_n)}\"))\n",
+    "display(Math(r\"f(\\beta) = X(Y-P)\"))\n",
+    "display(Math(r\"f'(\\beta) = XWX^T\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def logistics(X, Y, limit):\n",
+    "    import numpy as np\n",
+    "    from numpy import linalg\n",
+    "    nrow = np.shape(X)[0]\n",
+    "    bias = np.ones(nrow).reshape(nrow,1)\n",
+    "    X_new = np.append(X, bias, axis = 1)\n",
+    "    ncol = np.shape(X_new)[1]\n",
+    "    beta = np.zeros(ncol).reshape(ncol,1)\n",
+    "    root_dif = np.array(range(1,ncol+1)).reshape(ncol,1)\n",
+    "    iter_i = 10000\n",
+    "    while(iter_i>limit):\n",
+    "        print(\"Iter:i\"+str(iter_i) + \", limit:\" + str(limit))\n",
+    "        pi = logitprobs(X_new, beta)\n",
+    "        print(\"Pi:\"+str(pi))\n",
+    "        W = findW(pi)\n",
+    "        print(\"W:\"+str(W))\n",
+    "        num = (np.transpose(np.matrix(X_new))*np.matrix(Y - np.transpose(pi)).transpose())\n",
+    "        den = (np.matrix(np.transpose(X_new))*np.matrix(W)*np.matrix(X_new))\n",
+    "        root_dif = np.array(linalg.inv(den)*num)\n",
+    "        beta = beta + root_dif\n",
+    "        print(\"Beta: \"+str(beta))\n",
+    "        iter_i = np.sum(root_dif*root_dif)\n",
+    "        ll = likelihood(Y, pi)\n",
+    "    return beta"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Comprobación experimental"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = np.array(range(10)).reshape(10,1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0],\n",
+       "       [1],\n",
+       "       [2],\n",
+       "       [3],\n",
+       "       [4],\n",
+       "       [5],\n",
+       "       [6],\n",
+       "       [7],\n",
+       "       [8],\n",
+       "       [9]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y = [0,0,0,0,1,0,1,0,1,1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bias = np.ones(10).reshape(10,1)\n",
+    "X_new = np.append(X,bias,axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 1.],\n",
+       "       [1., 1.],\n",
+       "       [2., 1.],\n",
+       "       [3., 1.],\n",
+       "       [4., 1.],\n",
+       "       [5., 1.],\n",
+       "       [6., 1.],\n",
+       "       [7., 1.],\n",
+       "       [8., 1.],\n",
+       "       [9., 1.]])"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter:i10000, limit:1e-05\n",
+      "Pi:[array([0.5]), array([0.5]), array([0.5]), array([0.5]), array([0.5]), array([0.5]), array([0.5]), array([0.5]), array([0.5]), array([0.5])]\n",
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "W:[[0.25 0.   0.   0.   0.   0.   0.   0.   0.   0.  ]\n",
+      " [0.   0.25 0.   0.   0.   0.   0.   0.   0.   0.  ]\n",
+      " [0.   0.   0.25 0.   0.   0.   0.   0.   0.   0.  ]\n",
+      " [0.   0.   0.   0.25 0.   0.   0.   0.   0.   0.  ]\n",
+      " [0.   0.   0.   0.   0.25 0.   0.   0.   0.   0.  ]\n",
+      " [0.   0.   0.   0.   0.   0.25 0.   0.   0.   0.  ]\n",
+      " [0.   0.   0.   0.   0.   0.   0.25 0.   0.   0.  ]\n",
+      " [0.   0.   0.   0.   0.   0.   0.   0.25 0.   0.  ]\n",
+      " [0.   0.   0.   0.   0.   0.   0.   0.   0.25 0.  ]\n",
+      " [0.   0.   0.   0.   0.   0.   0.   0.   0.   0.25]]\n",
+      "Beta: [[ 0.43636364]\n",
+      " [-2.36363636]]\n",
+      "Iter:i5.777190082644626, limit:1e-05\n",
+      "Pi:[array([0.08598797]), array([0.12705276]), array([0.18378532]), array([0.2583532]), array([0.35019508]), array([0.45467026]), array([0.56329497]), array([0.66616913]), array([0.75533524]), array([0.82687453])]\n",
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "W:[[0.07859404 0.         0.         0.         0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.11091035 0.         0.         0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.15000827 0.         0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.19160683 0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.22755849 0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.24794521\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.24599375 0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.         0.22238782 0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.         0.         0.18480392 0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.         0.         0.         0.14315304]]\n",
+      "Beta: [[ 0.60426056]\n",
+      " [-3.34641372]]\n",
+      "Iter:i0.9940407075349087, limit:1e-05\n",
+      "Pi:[array([0.0340128]), array([0.06053134]), array([0.10546805]), array([0.1774629]), array([0.28305225]), array([0.41943069]), array([0.56933774]), array([0.7075284]), array([0.81572841]), array([0.89011647])]\n",
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "W:[[0.03285593 0.         0.         0.         0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.0568673  0.         0.         0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.09434454 0.         0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.14596982 0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.20293367 0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.24350859\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.24519228 0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.         0.20693196 0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.         0.         0.15031557 0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.         0.         0.         0.09780914]]\n",
+      "Beta: [[ 0.65761412]\n",
+      " [-3.66759924]]\n",
+      "Iter:i0.10600674406802064, limit:1e-05\n",
+      "Pi:[array([0.02490177]), array([0.04697681]), array([0.0868775]), array([0.15515129]), array([0.26170168]), array([0.40624059]), array([0.56907679]), array([0.71823018]), array([0.83108181]), array([0.90473054])]\n",
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "W:[[0.02428167 0.         0.         0.         0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.04476999 0.         0.         0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.0793298  0.         0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.13107937 0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.19321391 0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.24120917\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.2452284  0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.         0.20237559 0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.         0.         0.14038483 0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.         0.         0.         0.08619319]]\n",
+      "Beta: [[ 0.66217766]\n",
+      " [-3.6953843 ]]\n",
+      "Iter:i0.000792835124600839, limit:1e-05\n",
+      "Pi:[array([0.02423594]), array([0.04594805]), array([0.08540873]), array([0.15331276]), array([0.25986436]), array([0.40504298]), array([0.56897776]), array([0.71907124]), array([0.83230289]), array([0.90586963])]\n",
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n",
+      "6\n",
+      "7\n",
+      "8\n",
+      "9\n",
+      "W:[[0.02364856 0.         0.         0.         0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.04383683 0.         0.         0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.07811408 0.         0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.12980796 0.         0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.19233487 0.\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.24098316\n",
+      "  0.         0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.24524207 0.         0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.         0.20200779 0.         0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.         0.         0.13957479 0.        ]\n",
+      " [0.         0.         0.         0.         0.         0.\n",
+      "  0.         0.         0.         0.08526985]]\n",
+      "Beta: [[ 0.66220827]\n",
+      " [-3.69557172]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = logistics(X,Y,0.00001)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ll = likelihood(Y, logitprobs(X,a))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.32622426e-06])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ll"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y = 0.66220827 * X -3.69557172"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Con el paquete statsmodel de python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import statsmodels.api as sm\n",
+    "import pandas as pd\n",
+    "from pandas import Timestamp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y = (Y - np.min(Y))/np.ptp(Y)\n",
+    "logit_model = sm.Logit(Y,X_new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.359693\n",
+      "         Iterations 6\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = logit_model.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                         Results: Logit\n",
+      "================================================================\n",
+      "Model:              Logit            Pseudo R-squared: 0.481    \n",
+      "Dependent Variable: y                AIC:              11.1939  \n",
+      "Date:               2020-09-19 17:21 BIC:              11.7990  \n",
+      "No. Observations:   10               Log-Likelihood:   -3.5969  \n",
+      "Df Model:           1                LL-Null:          -6.9315  \n",
+      "Df Residuals:       8                LLR p-value:      0.0098099\n",
+      "Converged:          1.0000           Scale:            1.0000   \n",
+      "No. Iterations:     6.0000                                      \n",
+      "------------------------------------------------------------------\n",
+      "           Coef.    Std.Err.      z      P>|z|     [0.025   0.975]\n",
+      "------------------------------------------------------------------\n",
+      "x1         0.6272     0.3735    1.6793   0.0931   -0.1048   1.3592\n",
+      "const     -2.8224     1.8730   -1.5069   0.1318   -6.4934   0.8485\n",
+      "================================================================\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(result.summary2())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T5 - 2 - Logistic Regression - Implementaci\303\263n.ipynb" "b/notebooks/T5 - 2 - Logistic Regression - Implementaci\303\263n.ipynb"
index f074afb5..2820a835 100644
--- "a/notebooks/T5 - 2 - Logistic Regression - Implementaci\303\263n.ipynb"	
+++ "b/notebooks/T5 - 2 - Logistic Regression - Implementaci\303\263n.ipynb"	
@@ -22,7 +22,7 @@
     {
      "data": {
       "text/latex": [
-       "$$L(\\beta)=\\sum_{i=1}^n P_i^{y_i}(1-Pi)^{y_i}$$"
+       "$\\displaystyle L(\\beta)=\\sum_{i=1}^n P_i^{y_i}(1-Pi)^{y_i}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -62,13 +62,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$P_i = P(x_i) = \\frac{1}{1+e^{-\\sum_{j=0}^k\\beta_j\\cdot x_{ij}}} $$"
+       "$\\displaystyle P_i = P(x_i) = \\frac{1}{1+e^{-\\sum_{j=0}^k\\beta_j\\cdot x_{ij}}} $"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -84,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,13 +113,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$W= diag(P_i \\cdot (1-P_i))_{i=1}^n$$"
+       "$\\displaystyle W= diag(P_i \\cdot (1-P_i))_{i=1}^n$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -159,13 +159,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$\\beta_{n+1} = \\beta_n -\\frac{f(\\beta_n)}{f'(\\beta_n)}$$"
+       "$\\displaystyle \\beta_{n+1} = \\beta_n -\\frac{f(\\beta_n)}{f'(\\beta_n)}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -177,7 +177,7 @@
     {
      "data": {
       "text/latex": [
-       "$$f(\\beta) = X(Y-P)$$"
+       "$\\displaystyle f(\\beta) = X(Y-P)$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -189,7 +189,7 @@
     {
      "data": {
       "text/latex": [
-       "$$f'(\\beta) = XWX^T$$"
+       "$\\displaystyle f'(\\beta) = XWX^T$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -207,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -282,7 +282,7 @@
        "       [9]])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -312,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -330,7 +330,7 @@
        "       [9., 1.]])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -341,7 +341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -517,7 +517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -526,7 +526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -535,7 +535,7 @@
        "array([1.32622426e-06])"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -546,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -562,25 +562,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import statsmodels.api as sm"
+    "import statsmodels.api as sm\n",
+    "import pandas as pd\n",
+    "from pandas import Timestamp"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
+    "Y = (Y - np.min(Y))/np.ptp(Y)\n",
     "logit_model = sm.Logit(Y,X_new)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -588,7 +591,7 @@
      "output_type": "stream",
      "text": [
       "Optimization terminated successfully.\n",
-      "         Current function value: 0.431012\n",
+      "         Current function value: 0.359693\n",
       "         Iterations 6\n"
      ]
     }
@@ -599,28 +602,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                        Results: Logit\n",
-      "==============================================================\n",
-      "Model:              Logit            No. Iterations:   6.0000 \n",
-      "Dependent Variable: y                Pseudo R-squared: 0.360  \n",
-      "Date:               2018-04-05 18:54 AIC:              12.6202\n",
-      "No. Observations:   10               BIC:              13.2254\n",
-      "Df Model:           1                Log-Likelihood:   -4.3101\n",
-      "Df Residuals:       8                LL-Null:          -6.7301\n",
-      "Converged:          1.0000           Scale:            1.0000 \n",
-      "----------------------------------------------------------------\n",
-      "         Coef.    Std.Err.      z      P>|z|     [0.025   0.975]\n",
-      "----------------------------------------------------------------\n",
-      "x1       0.6622     0.4001    1.6551   0.0979   -0.1220   1.4464\n",
-      "const   -3.6956     2.2889   -1.6145   0.1064   -8.1818   0.7906\n",
-      "==============================================================\n",
+      "                         Results: Logit\n",
+      "================================================================\n",
+      "Model:              Logit            Pseudo R-squared: 0.481    \n",
+      "Dependent Variable: y                AIC:              11.1939  \n",
+      "Date:               2020-09-19 17:21 BIC:              11.7990  \n",
+      "No. Observations:   10               Log-Likelihood:   -3.5969  \n",
+      "Df Model:           1                LL-Null:          -6.9315  \n",
+      "Df Residuals:       8                LLR p-value:      0.0098099\n",
+      "Converged:          1.0000           Scale:            1.0000   \n",
+      "No. Iterations:     6.0000                                      \n",
+      "------------------------------------------------------------------\n",
+      "           Coef.    Std.Err.      z      P>|z|     [0.025   0.975]\n",
+      "------------------------------------------------------------------\n",
+      "x1         0.6272     0.3735    1.6793   0.0931   -0.1048   1.3592\n",
+      "const     -2.8224     1.8730   -1.5069   0.1318   -6.4934   0.8485\n",
+      "================================================================\n",
       "\n"
      ]
     }
@@ -628,13 +632,6 @@
    "source": [
     "print(result.summary2())"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -653,7 +650,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T5 - 3 - Logistic Regression - Implementaci\303\263n con Python-Colab.ipynb" "b/notebooks/T5 - 3 - Logistic Regression - Implementaci\303\263n con Python-Colab.ipynb"
new file mode 100644
index 00000000..c84328a9
--- /dev/null
+++ "b/notebooks/T5 - 3 - Logistic Regression - Implementaci\303\263n con Python-Colab.ipynb"	
@@ -0,0 +1,3211 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T5%20-%203%20-%20Logistic%20Regression%20-%20Implementación%20con%20Python-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Regresión logística para predicciones bancarias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"/content/python-ml-course/datasets/bank/bank.csv\", sep=\";\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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>age</th>\n",
+       "      <th>job</th>\n",
+       "      <th>marital</th>\n",
+       "      <th>education</th>\n",
+       "      <th>default</th>\n",
+       "      <th>housing</th>\n",
+       "      <th>loan</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day_of_week</th>\n",
+       "      <th>...</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>poutcome</th>\n",
+       "      <th>emp.var.rate</th>\n",
+       "      <th>cons.price.idx</th>\n",
+       "      <th>cons.conf.idx</th>\n",
+       "      <th>euribor3m</th>\n",
+       "      <th>nr.employed</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30</td>\n",
+       "      <td>blue-collar</td>\n",
+       "      <td>married</td>\n",
+       "      <td>basic.9y</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>may</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>-1.8</td>\n",
+       "      <td>92.893</td>\n",
+       "      <td>-46.2</td>\n",
+       "      <td>1.313</td>\n",
+       "      <td>5099.1</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>39</td>\n",
+       "      <td>services</td>\n",
+       "      <td>single</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>may</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.1</td>\n",
+       "      <td>93.994</td>\n",
+       "      <td>-36.4</td>\n",
+       "      <td>4.855</td>\n",
+       "      <td>5191.0</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>25</td>\n",
+       "      <td>services</td>\n",
+       "      <td>married</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>jun</td>\n",
+       "      <td>wed</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>94.465</td>\n",
+       "      <td>-41.8</td>\n",
+       "      <td>4.962</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38</td>\n",
+       "      <td>services</td>\n",
+       "      <td>married</td>\n",
+       "      <td>basic.9y</td>\n",
+       "      <td>no</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>jun</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>94.465</td>\n",
+       "      <td>-41.8</td>\n",
+       "      <td>4.959</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>47</td>\n",
+       "      <td>admin.</td>\n",
+       "      <td>married</td>\n",
+       "      <td>university.degree</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>nov</td>\n",
+       "      <td>mon</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>93.200</td>\n",
+       "      <td>-42.0</td>\n",
+       "      <td>4.191</td>\n",
+       "      <td>5195.8</td>\n",
+       "      <td>no</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   age          job  marital          education default  housing     loan  \\\n",
+       "0   30  blue-collar  married           basic.9y      no      yes       no   \n",
+       "1   39     services   single        high.school      no       no       no   \n",
+       "2   25     services  married        high.school      no      yes       no   \n",
+       "3   38     services  married           basic.9y      no  unknown  unknown   \n",
+       "4   47       admin.  married  university.degree      no      yes       no   \n",
+       "\n",
+       "     contact month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
+       "0   cellular   may         fri  ...         2    999         0  nonexistent   \n",
+       "1  telephone   may         fri  ...         4    999         0  nonexistent   \n",
+       "2  telephone   jun         wed  ...         1    999         0  nonexistent   \n",
+       "3  telephone   jun         fri  ...         3    999         0  nonexistent   \n",
+       "4   cellular   nov         mon  ...         1    999         0  nonexistent   \n",
+       "\n",
+       "  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
+       "0         -1.8          92.893          -46.2      1.313       5099.1  no  \n",
+       "1          1.1          93.994          -36.4      4.855       5191.0  no  \n",
+       "2          1.4          94.465          -41.8      4.962       5228.1  no  \n",
+       "3          1.4          94.465          -41.8      4.959       5228.1  no  \n",
+       "4         -0.1          93.200          -42.0      4.191       5195.8  no  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4119, 21)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['age', 'job', 'marital', 'education', 'default', 'housing', 'loan',\n",
+       "       'contact', 'month', 'day_of_week', 'duration', 'campaign', 'pdays',\n",
+       "       'previous', 'poutcome', 'emp.var.rate', 'cons.price.idx',\n",
+       "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'], dtype=object)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"y\"] = (data[\"y\"]==\"yes\").astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>age</th>\n",
+       "      <th>job</th>\n",
+       "      <th>marital</th>\n",
+       "      <th>education</th>\n",
+       "      <th>default</th>\n",
+       "      <th>housing</th>\n",
+       "      <th>loan</th>\n",
+       "      <th>contact</th>\n",
+       "      <th>month</th>\n",
+       "      <th>day_of_week</th>\n",
+       "      <th>...</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>poutcome</th>\n",
+       "      <th>emp.var.rate</th>\n",
+       "      <th>cons.price.idx</th>\n",
+       "      <th>cons.conf.idx</th>\n",
+       "      <th>euribor3m</th>\n",
+       "      <th>nr.employed</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4114</th>\n",
+       "      <td>30</td>\n",
+       "      <td>admin.</td>\n",
+       "      <td>married</td>\n",
+       "      <td>basic.6y</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>jul</td>\n",
+       "      <td>thu</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>93.918</td>\n",
+       "      <td>-42.7</td>\n",
+       "      <td>4.958</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4115</th>\n",
+       "      <td>39</td>\n",
+       "      <td>admin.</td>\n",
+       "      <td>married</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>telephone</td>\n",
+       "      <td>jul</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>93.918</td>\n",
+       "      <td>-42.7</td>\n",
+       "      <td>4.959</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4116</th>\n",
+       "      <td>27</td>\n",
+       "      <td>student</td>\n",
+       "      <td>single</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>may</td>\n",
+       "      <td>mon</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>999</td>\n",
+       "      <td>1</td>\n",
+       "      <td>failure</td>\n",
+       "      <td>-1.8</td>\n",
+       "      <td>92.893</td>\n",
+       "      <td>-46.2</td>\n",
+       "      <td>1.354</td>\n",
+       "      <td>5099.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4117</th>\n",
+       "      <td>58</td>\n",
+       "      <td>admin.</td>\n",
+       "      <td>married</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>aug</td>\n",
+       "      <td>fri</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>1.4</td>\n",
+       "      <td>93.444</td>\n",
+       "      <td>-36.1</td>\n",
+       "      <td>4.966</td>\n",
+       "      <td>5228.1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4118</th>\n",
+       "      <td>34</td>\n",
+       "      <td>management</td>\n",
+       "      <td>single</td>\n",
+       "      <td>high.school</td>\n",
+       "      <td>no</td>\n",
+       "      <td>yes</td>\n",
+       "      <td>no</td>\n",
+       "      <td>cellular</td>\n",
+       "      <td>nov</td>\n",
+       "      <td>wed</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>999</td>\n",
+       "      <td>0</td>\n",
+       "      <td>nonexistent</td>\n",
+       "      <td>-0.1</td>\n",
+       "      <td>93.200</td>\n",
+       "      <td>-42.0</td>\n",
+       "      <td>4.120</td>\n",
+       "      <td>5195.8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      age         job  marital    education default housing loan    contact  \\\n",
+       "4114   30      admin.  married     basic.6y      no     yes  yes   cellular   \n",
+       "4115   39      admin.  married  high.school      no     yes   no  telephone   \n",
+       "4116   27     student   single  high.school      no      no   no   cellular   \n",
+       "4117   58      admin.  married  high.school      no      no   no   cellular   \n",
+       "4118   34  management   single  high.school      no     yes   no   cellular   \n",
+       "\n",
+       "     month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
+       "4114   jul         thu  ...         1    999         0  nonexistent   \n",
+       "4115   jul         fri  ...         1    999         0  nonexistent   \n",
+       "4116   may         mon  ...         2    999         1      failure   \n",
+       "4117   aug         fri  ...         1    999         0  nonexistent   \n",
+       "4118   nov         wed  ...         1    999         0  nonexistent   \n",
+       "\n",
+       "     emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
+       "4114          1.4          93.918          -42.7      4.958       5228.1  0  \n",
+       "4115          1.4          93.918          -42.7      4.959       5228.1  0  \n",
+       "4116         -1.8          92.893          -46.2      1.354       5099.1  0  \n",
+       "4117          1.4          93.444          -36.1      4.966       5228.1  0  \n",
+       "4118         -0.1          93.200          -42.0      4.120       5195.8  0  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['basic.9y', 'high.school', 'university.degree',\n",
+       "       'professional.course', 'basic.6y', 'basic.4y', 'unknown',\n",
+       "       'illiterate'], dtype=object)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"education\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"education\"] = np.where(data[\"education\"]==\"basic.4y\", \"Basic\", data[\"education\"])\n",
+    "data[\"education\"] = np.where(data[\"education\"]==\"basic.6y\", \"Basic\", data[\"education\"])\n",
+    "data[\"education\"] = np.where(data[\"education\"]==\"basic.9y\", \"Basic\", data[\"education\"])\n",
+    "\n",
+    "data[\"education\"] = np.where(data[\"education\"]==\"high.school\", \"High School\", data[\"education\"])\n",
+    "data[\"education\"] = np.where(data[\"education\"]==\"professional.course\", \"Professional Course\", data[\"education\"])\n",
+    "data[\"education\"] = np.where(data[\"education\"]==\"university.degree\", \"University Degree\", data[\"education\"])\n",
+    "\n",
+    "data[\"education\"] = np.where(data[\"education\"]==\"illiterate\", \"Illiterate\", data[\"education\"])\n",
+    "data[\"education\"] = np.where(data[\"education\"]==\"unknown\", \"Unknown\", data[\"education\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Basic', 'High School', 'University Degree', 'Professional Course',\n",
+       "       'Unknown', 'Illiterate'], dtype=object)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"education\"].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    3668\n",
+       "1     451\n",
+       "Name: y, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[\"y\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>age</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>emp.var.rate</th>\n",
+       "      <th>cons.price.idx</th>\n",
+       "      <th>cons.conf.idx</th>\n",
+       "      <th>euribor3m</th>\n",
+       "      <th>nr.employed</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>y</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></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>0</th>\n",
+       "      <td>39.895311</td>\n",
+       "      <td>219.40976</td>\n",
+       "      <td>2.605780</td>\n",
+       "      <td>982.763086</td>\n",
+       "      <td>0.141767</td>\n",
+       "      <td>0.240185</td>\n",
+       "      <td>93.599677</td>\n",
+       "      <td>-40.586723</td>\n",
+       "      <td>3.802826</td>\n",
+       "      <td>5175.502072</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>41.889135</td>\n",
+       "      <td>560.78714</td>\n",
+       "      <td>1.980044</td>\n",
+       "      <td>778.722838</td>\n",
+       "      <td>0.585366</td>\n",
+       "      <td>-1.177384</td>\n",
+       "      <td>93.417268</td>\n",
+       "      <td>-39.786475</td>\n",
+       "      <td>2.145448</td>\n",
+       "      <td>5093.118625</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         age   duration  campaign       pdays  previous  emp.var.rate  \\\n",
+       "y                                                                       \n",
+       "0  39.895311  219.40976  2.605780  982.763086  0.141767      0.240185   \n",
+       "1  41.889135  560.78714  1.980044  778.722838  0.585366     -1.177384   \n",
+       "\n",
+       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed  \n",
+       "y                                                         \n",
+       "0       93.599677     -40.586723   3.802826  5175.502072  \n",
+       "1       93.417268     -39.786475   2.145448  5093.118625  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.groupby(\"y\").mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>age</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>campaign</th>\n",
+       "      <th>pdays</th>\n",
+       "      <th>previous</th>\n",
+       "      <th>emp.var.rate</th>\n",
+       "      <th>cons.price.idx</th>\n",
+       "      <th>cons.conf.idx</th>\n",
+       "      <th>euribor3m</th>\n",
+       "      <th>nr.employed</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>education</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></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>Basic</th>\n",
+       "      <td>42.337124</td>\n",
+       "      <td>253.898457</td>\n",
+       "      <td>2.429732</td>\n",
+       "      <td>978.815597</td>\n",
+       "      <td>0.149472</td>\n",
+       "      <td>0.237368</td>\n",
+       "      <td>93.658600</td>\n",
+       "      <td>-41.120552</td>\n",
+       "      <td>3.775701</td>\n",
+       "      <td>5174.133144</td>\n",
+       "      <td>0.079610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>High School</th>\n",
+       "      <td>38.097720</td>\n",
+       "      <td>258.534202</td>\n",
+       "      <td>2.630836</td>\n",
+       "      <td>958.022801</td>\n",
+       "      <td>0.206298</td>\n",
+       "      <td>-0.002497</td>\n",
+       "      <td>93.564314</td>\n",
+       "      <td>-40.995765</td>\n",
+       "      <td>3.511732</td>\n",
+       "      <td>5163.212595</td>\n",
+       "      <td>0.105320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Illiterate</th>\n",
+       "      <td>42.000000</td>\n",
+       "      <td>146.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>999.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-2.900000</td>\n",
+       "      <td>92.201000</td>\n",
+       "      <td>-31.400000</td>\n",
+       "      <td>0.834000</td>\n",
+       "      <td>5076.200000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Professional Course</th>\n",
+       "      <td>40.207477</td>\n",
+       "      <td>278.816822</td>\n",
+       "      <td>2.512150</td>\n",
+       "      <td>958.211215</td>\n",
+       "      <td>0.194393</td>\n",
+       "      <td>0.163925</td>\n",
+       "      <td>93.599630</td>\n",
+       "      <td>-40.127664</td>\n",
+       "      <td>3.701426</td>\n",
+       "      <td>5167.595140</td>\n",
+       "      <td>0.121495</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>University Degree</th>\n",
+       "      <td>39.017405</td>\n",
+       "      <td>247.707278</td>\n",
+       "      <td>2.583070</td>\n",
+       "      <td>947.900316</td>\n",
+       "      <td>0.207278</td>\n",
+       "      <td>-0.009731</td>\n",
+       "      <td>93.499109</td>\n",
+       "      <td>-39.830063</td>\n",
+       "      <td>3.547132</td>\n",
+       "      <td>5163.023180</td>\n",
+       "      <td>0.130538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Unknown</th>\n",
+       "      <td>42.826347</td>\n",
+       "      <td>267.281437</td>\n",
+       "      <td>2.538922</td>\n",
+       "      <td>939.700599</td>\n",
+       "      <td>0.263473</td>\n",
+       "      <td>-0.074251</td>\n",
+       "      <td>93.637455</td>\n",
+       "      <td>-39.487425</td>\n",
+       "      <td>3.410174</td>\n",
+       "      <td>5151.260479</td>\n",
+       "      <td>0.155689</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                           age    duration  campaign       pdays  previous  \\\n",
+       "education                                                                    \n",
+       "Basic                42.337124  253.898457  2.429732  978.815597  0.149472   \n",
+       "High School          38.097720  258.534202  2.630836  958.022801  0.206298   \n",
+       "Illiterate           42.000000  146.000000  4.000000  999.000000  0.000000   \n",
+       "Professional Course  40.207477  278.816822  2.512150  958.211215  0.194393   \n",
+       "University Degree    39.017405  247.707278  2.583070  947.900316  0.207278   \n",
+       "Unknown              42.826347  267.281437  2.538922  939.700599  0.263473   \n",
+       "\n",
+       "                     emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  \\\n",
+       "education                                                                     \n",
+       "Basic                    0.237368       93.658600     -41.120552   3.775701   \n",
+       "High School             -0.002497       93.564314     -40.995765   3.511732   \n",
+       "Illiterate              -2.900000       92.201000     -31.400000   0.834000   \n",
+       "Professional Course      0.163925       93.599630     -40.127664   3.701426   \n",
+       "University Degree       -0.009731       93.499109     -39.830063   3.547132   \n",
+       "Unknown                 -0.074251       93.637455     -39.487425   3.410174   \n",
+       "\n",
+       "                     nr.employed         y  \n",
+       "education                                   \n",
+       "Basic                5174.133144  0.079610  \n",
+       "High School          5163.212595  0.105320  \n",
+       "Illiterate           5076.200000  0.000000  \n",
+       "Professional Course  5167.595140  0.121495  \n",
+       "University Degree    5163.023180  0.130538  \n",
+       "Unknown              5151.260479  0.155689  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.groupby(\"education\").mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Frecuencia de compra del producto')"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "pd.crosstab(data.education, data.y).plot(kind=\"bar\")\n",
+    "plt.title(\"Frecuencia de compra en función del nivel de educación\")\n",
+    "plt.xlabel(\"Nivel de educación\")\n",
+    "plt.ylabel(\"Frecuencia de compra del producto\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Proporción de clientes')"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "table=pd.crosstab(data.marital, data.y)\n",
+    "table.div(table.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
+    "plt.title(\"Diagrama apilado de estado civil contra el nivel de compras\")\n",
+    "plt.xlabel(\"Estado civil\")\n",
+    "plt.ylabel(\"Proporción de clientes\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Frecuencia de compra del producto')"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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BzpbLmqs+D9gTaAPukzQpIh7NzXYM8GhE7CNpGDBN0s8iYkGdIs3MrARFzggAFgGLc68itgWmR8SM7Iv9KmC/mnkCGJT1d7Aa8G/SswpmZtYknSYCSV8EfgasBawNXCHpuAJljwBm5YbbsnF55wLvBp4BHga+mLV2WhvDOEmTJU1+/vnnC6zazMyKKnJG8Flgu4g4JSJOBrYHjiqwnOqMq23F9D+AB4HhwHuBcyWt/raFIi6MiDERMWbYsGEFVm1mZkUVSQQiVQ21W0T9L/labcB6ueGRpF/+eUcC10YyHXgC2LRA2WZm1k2KtDV0MXCPpF9nw/sDPymw3H3AxpI2AJ4GDgI+XTPPU8AewF8krUN6eG1GgbLNzKybdJgIJPUh3S56G7Aj6Uyg0F1DEbFQ0rGkPo/7AhdHxFRJ47PpE4FvApdKejgr+8SIeGF5NsjMzJZNh4kgIhZL+l5EfAC4f1kLj4jrgetrxk3MvX8G2GtZyzUzs+5T5BrBTZI+lt3iaWZmvUyRawQnAKsCiyS9no2LiHjb3T1mZrbiKfJksXsjMzPrxYqcESDpQNLF4gD+EhHXlRmUmZk1T5Eni88HxpOe/H0EGC/pvLIDMzOz5ihyRrALsEVEBICky0hJwczMeoEidw1NI/VJ0G494KFywjEzs2YrckYwFPi7pHuz4W2AuyRNAoiIfcsKzszMylckEZxcehRmZlaZIreP3taMQMzMrBpFO6YxM7NeyonAzKzFORGYmbW4htcIsqaha3sUg9RcdETElqVFZWZmTdPRxeKPNi0KMzOrTMNEEBEz299LWh/YOCL+JGlgR8uZmdmKpUhbQ0cBvwR+lI0aCVxXYkxmZtZERS4WHwPsAMwHiIh/AmuXGZSZmTVPkUTwRkQsaB+Q1I/6F5HNzGwFVCQR3Cbpv4GBkvYErgF+W25YZmbWLEUSwUnA86Smpz9P6oz+62UGZWZmzVOkraHFwEXZy8zMepmuPFAGgB8oMzPrHYo8UHZM9vfy7O8hwKulRWRmZk3V6QNlknaIiB1yk06SdAfwv2UHZ2Zm5StysXhVSTu2D0j6ILBqeSGZmVkzFWkq4rPAxZLWIF0zeBH4TKlRmZlZ0xS5a2gKsJWk1QFFxIvlh2VmZs1SuPG4iJhfZiBmZlYNd0xjZtbinAjMzFpcoaohSVsAmwErt4+LiJ+WFZSZmTVPkf4ITgHOyV67Ad8G9i1SuKS9JU2TNF3SSQ3m2VXSg5KmSrptGWI3M7NuUKRq6OPAHsCzEXEksBWwUmcLSeoLnAeMJZ1NHCxps5p5BgPnA/tGxObAJ5YpejMzW25FEsFrWcNzC7NbSJ8DNiyw3LbA9IiYkfVncBWwX808nwaujYinACLiueKhm5lZdyiSCCZnv9wvAqYA9wP3FlhuBDArN9yWjct7F7CmpFslTZF0eIFyzcysG3V4sViSgNMjYh4wUdINwOoR8VCBslVnXG1rpv2A95OqngYCd0m6OyL+URPHOGAcwKhRowqs2szMiurwjCAiglxH9RHxZMEkAOkMYL3c8EjgmTrz3BARr0TEC8DtpGsQtXFcGBFjImLMsGHDCq7ezMyKKFI1dLekbbpQ9n3AxpI2kDQAOAiYVDPPb4CdJPWTtAqwHfD3LqzLzMy6qMhzBLsBn5c0E3iFVOUTnXVMExELJR0L3Aj0BS6OiKmSxmfTJ0bE37PqpoeAxcCPI+KR5dgeMzNbRkUSwdiuFh4R15P6OM6Pm1gz/B3gO11dh5mZLZ8irY/OlLQ1sCPpYu8dEXF/6ZGZmVlTFHmy+GTgMmAosBZwiaSvlx2YmZk1R5GqoYOB90XE6wCSziA9S/CtMgMzM7PmKHLX0JPkGpsjNS/xeCnRmJlZ0xU5I3gDmCrpj6RrBHsCf5X0Q4CI+EKJ8ZmZWcmKJIJfZ692t5YTipmZVaHIXUOXNSMQMzOrRpG7hj4q6QFJ/5Y0X9JLktx/sZlZL1Gkauhs4EDg4aztITMz60WK3DU0C3jEScDMrHcqckbwVeD6rBvJN9pHRsRZpUVlZmZNUyQRnAa8THqWYEC54ZiZWbMVSQRDImKv0iMxM7NKFLlG8CdJTgRmZr1UkURwDHCDpNezW0d9+6iZWS9S5IGyQc0IxMzMqlHkGgGS9gV2zgZvjYjflReSmZk1U5Eni88Avgg8mr2+mI0zM7NeoMgZwYeB90bEYgBJlwEPACeVGZiZmTVHkYvFAINz79coIQ4zM6tIkTOC04EHJN0CiHSt4GulRmVmZk1T5K6hKyXdCmxDSgQnRsSzZQdmZmbNUeRi8QHAqxExKSJ+A7wuaf/SIzMzs6Yoco3glIh4sX0gIuYBp5QWkZmZNVWRRFBvnkLPH5iZWc9XJBFMlnSWpI0kbSjp+8CUsgMzM7PmKJIIjgMWAL8ArgZeI7U/ZGZmvUCRu4ZewQ+PmZn1WkUfKDMzs17KicDMrMU5EZiZtbgiD5S9S9LNkh7JhreU9PXyQzMzs2YockZwEaltoTcBIuIh4KAygzIzs+YpkghWiYh7a8YtLFK4pL0lTZM0XVLDO48kbSNpkaSPFynXzMy6T5FE8IKkjYAAyL6sZ3e2kKS+wHnAWGAz4GBJmzWY70zgxmWI28zMukmRpiKOAS4ENpX0NPAEcGiB5bYFpkfEDABJVwH7kXo5yzsO+BWpdVMzM2uyIg+UzQA+JGlVoE9EvFSw7BHArNxwG7BdfgZJI4ADgN3pIBFIGgeMAxg1alTB1ZuZWRENE4GkExqMByAizuqkbNUZFzXDZ5P6N1jUXm49EXEh6ayEMWPG1JZhZmbLoaMzgkHZ301Iv9YnZcP7ALcXKLsNWC83PBJ4pmaeMcBVWRJYC/iwpIURcV2B8s3MrBs0TAQRcSqApJuArdurhCRNAK4pUPZ9wMaSNgCeJt1y+umadWzQ/l7SpcDvnATMzJqryMXiUaTWR9stAEZ3tlBELJR0LOluoL7AxRExVdL4bPrEZQ/XzMy6W5FEcDlwr6Rfk+r4DwAuK1J4RFwPXF8zrm4CiIgjipRpZmbdq8hdQ6dJ+gOwUzbqyIh4oNywzMysWQp1ORkR9wP3lxyLmZlVwK2Pmpm1OCcCM7MW50RgZtbiivRHsL2k+yS9LGlB1kro/GYEZ2Zm5StyRnAucDDwT2Ag8DngnDKDMjOz5il619B0SX0jYhFwiaQ7S47LzMyapEgieFXSAOBBSd8m9UWwarlhmZlZsxSpGjqM1ETEscArpIbkPlZmUGZm1jxFniyemb19DTi13HDMzKzZOuqP4OqI+KSkh3l7PwJExJalRmZmZk3R0RnBF7O/H21GIGZmVo2O+iNo76C+DzA7Il4HkDQQWKcJsZmZWRMUuVh8DbA4N7yIYh3TmJnZCqBIIugXEW91TJO9H1BeSGZm1kxFEsHzkvZtH5C0H/BCeSGZmVkzFXmgbDzwM0nnAgJmAYeXGpWZmTVNkecIHge2l7QaoPZO7M3MrHfoNBFIWon0JPFooJ8kACLif0uNzMzMmqJI1dBvgBeBKcAb5YZjZmbNViQRjIyIvUuPxMzMKlHkrqE7Jb2n9EjMzKwSRc4IdgSOkPQEqWpIQLitITOz3qFIIhhbehTL6c0336StrY3XX3+90PwX7btuyRG9XRDMnPcm59wzl/lvLO58ATOzJinUDLWkHYGNI+ISScOA1coPrbi2tjYGDRrE6NGjab+rqSNvts0rP6gaEcHQofM5Djjt9jlNX7+ZWSNFOq8/BTgR+Fo2qj9wRZlBLavXX3+doUOHFkoCVZFEv1VWZ/3B/asOxcxsKUUuFh8A7EvqnYyIeAYYVGZQXdGTk0A7SYieH6eZtZYiiWBBRARZ5zSS3F+xmVkvUiQRXC3pR8BgSUcBfwIuKjcsMzNrliIXi78raU9gPrAJcHJE/LH0yMzMrCmK3D5K9sXfEl/+537nNNYcMpRDPjsegHPO/CZDhq3NIZ/5fMWRmZmVo8hdQy9Jmp+9Xpe0SNL8IoVL2lvSNEnTJZ1UZ/ohkh7KXndK2qorG9GdDjjoMCb98koAFi9ezA2TruUjB3yi4qjMzMpTpGpoqTuEJO0PbNvZcpL6AucBewJtwH2SJkXEo7nZngB2iYi5ksYCFwLbFQ+/+41YbxSD1xzC3x95iH8//xybbrElg9ccUmVIZmalKlQ1lBcR19X7dV/HtsD0iJgBIOkqYD/grUQQEXfm5r8bGLms8ZThgIMOY9I1P+eF555j/08dUnU4ZmalKtIfwYG5wT7AGLJbSTsxgtSbWbs2Ov61/1ngDw1iGAeMAxg1alSBVS+fPfb+KOd/73QWLnyTM871DVJm1rsVOSPYJ/d+IfAk6Zd9Z+o9OVU3gUjajZQIdqw3PSIuJFUbMWbMmCJJaLn0HzCAbT64I4NWX4O+ffuWvTozs0oVuUZwZBfLbgPWyw2PBJ6pnUnSlsCPgbER0SMa4Vm8eDEP3z+Z70y8tOpQzMxKV+SuocskDc4Nrynp4gJl3wdsLGkDSQOAg4BJNWWPAq4FDouIfyxT5CV5/B+P8dGdtmbbHXZh/Q02qjocM7PSFaka2jIi5rUPZHf4vK+zhSJioaRjgRuBvsDFETFV0vhs+kTgZGAocH7WVtDCiBiz7JvRfTZ616Zcf8eDVYZgZtZURRJBH0lrRsRcAElDCi5HRFwPXF8zbmLu/eeAzxUP18zMuluRL/Tvkbqr/CXpYu8ngdNKjcrMzJqmyMXin0qaDOxOuhPowJqHwszMbAVWpPVRgCHAKxFxDvC8pA1KjMnMzJqoyANlp5AeItsEuIQlPZTtUG5oXTf6pN93a3mTji22qXfc8ifOnPA1Fi9axAEHH8Znj/mvbo3DzKwMvaaHsqotWrSI//v6Vzj/p9fw6z/fzQ2/+RWP/+OxqsMyM+uUeyjrJo88OIX1Rm/IyPVH03/AAPbe90Buven6zhc0M6uYeyjrJs89O5t3DB/x1vDa6w7nX8/OrjAiM7NiOrxGoPSU1y+ATXEPZR1KJ01Lyx6SMzPr0TpMBBERkq6LiPfTIj2UddU66w7n2Weefmv4udnPsPY676gwIjOzYopUDd0taZvSI1nBbb7V1jz15OO0PTWTNxcs4IZJ17LLnmOrDsvMrFNFnizeDRgv6UnSnUMinSxsWWZgy+PJMz7S4fSH2uZ1+zr79evH1775bY4+9GMsXrSI/T91CO/c5N3dvh4zs+7WMBFIGhURTwH+WVvQTrvvxU6771V1GGZmy6SjM4LrgK0jYqakX0XEx5oUk5mZNVFH1wjyt7xsWHYgZmZWjY4SQTR4b2ZmvUhHVUNbSZpPOjMYmL2HJReLVy89OjMzK13DRBAR7rXdzKwFFG2G2szMeqlCXU6ucCas0eHkZX0A4qHPzex0npO/dCy333wjQ4auxbU337WMazAzq47PCLrJfp84mAsu/2XVYZiZLTMngm7y/u13YPXBa1YdhpnZMnMiMDNrcU4EZmYtzonAzKzFORGYmbW4Xnr76IsdTi6jGeoTj/ksk+++g3n/nsOe22zO0V86iQMPOqzb12Nm1t16ZyKowJnn/aTqEMzMusRVQ2ZmLc6JwMysxfWaRBDR81vKjgjCLXqbWQ/TKxLByiuvzJw5c3p0MogIFr46n5nz3qw6FDOzpfSKi8UjR46kra2N559/vtD8/5r7WskRvV0QzJz3JufcM7fp6zYz60ivSAT9+/dngw02KDz/2JN+X2I0ZmYrllKrhiTtLWmapOmSTqozXZJ+mE1/SNLWZcZjZmZvV1oikNQXOA8YC2wGHCxps5rZxgIbZ69xwAVlxWNmZvWVeUawLTA9ImZExALgKmC/mnn2A34ayd3AYEnrlhiTmZnVKPMawQhgVm64DdiuwDwjgNn5mSSNI50xALwsaVr3htolawEvdHVhndmNkVTP+2IJ74slvC+W6An7Yv1GE8pMBKozrvb+ziLzEBEXAhd2R1DdRdLkiBhTdRw9gffFEt4XS3hfLNHT90WZVUNtwHq54ZHAM12Yx8zMSlRmIrgP2FjSBpIGAAcBk2rmmQQcnt09tD3wYkTMri3IzMzKU1rVUEQslHQscCPQF7g4IqZKGp9NnwhcD3wYmA68ChxZVjwl6FFVVRXzvljC+2IJ74slevS+UE9ulsHMzMrXK9oaMjOzrnMiMDNrcU4EZmYtzonAzKzF9YrWR8sk6eyIOF7Sb6n/sNu+FYTVI2TtSa1D7jiKiKeqi6j5JN1C/eNi9wrCqZSkdYD/A4ZHxNisbbEPRETLdOjd6HuiXU/9vnAi6Nzl2d/vVhpFDyPpOOAU4F/A4mx0AFtWFlQ1vpx7vzLwMWBhRbFU7VLgEuB/suF/AL8AWiYRsOR74kDgHcAV2fDBwJNVBFSEbx8tIPvle1lEHFp1LD2FpOnAdhExp+pYehpJt0XELlXH0WyS7ouIbSQ9EBHvy8Y9GBHvrTi0ppN0e0Ts3Nm4nsJnBAVExCJJwyQNyFpStdRY4ItVB1E1SUNyg32A95N+CbaiVyQNJasaaW8toNqQKjNM0oYRMQNA0gbAsIpjasiJoLgngTskTQJeaR8ZEWdVFlG1ZgC3Svo98Eb7yBbcH1NIX3wiVQk9AXy20oiqcwKp2ZiNJN1B+uL7eLUhVea/SP8fM7Lh0cDnqwunY64a6oSkyyPiMEnzgO/XTo+IU5sfVfUknVJvfKvuD0sk9QM2ISXGaRHxZsUhVUbSSsCm2eBjEfFGR/NXyYmgE5IeJfWk9ltg19rpEfHvZsfUk0gaBEREvFx1LFWR9EHSL7783VM/rSygikg6vN74Ft0Xq5DOkNaPiKMkbQxsEhG/qzi0ulw11LmJwA3ABsDk3HiRqgQ2rCKoqknagnRH1ZBs+AXg8IiYWmlgTSbpcmAj4EFgUTY6gJb78gO2yb1fGdgDuJ/W3BeXkKoNP5ANtwHXAD0yEfiMoCBJF0TE0VXH0VNIuhP4n4i4JRveFfi/iPhglXE1m6S/A5uF/5HeRtIawOU99d75MrV3RFNzB9XfImKrqmOrx08WF+Qk8DarticBgIi4FVi1unAq8wite5dQZ14F3lV1EBVZIGkgS+6g2ojcTRU9jauGrKtmSPoGSx64O5R0x0xLyD1BOgh4VNK9LH33VCv+Cs53PNUH2Ay4uqJwqnYKqUp5PUk/A3YAjqg0og64asi6RNKawKmkA1zA7cCEiJhXZVzNImkX0nafCXw1Pwk4MyK2qySwCmXJ8CvZ4ELgKeDYiDixuqiqkV07ehh4jXSr9T0R0eXO68vmRGBdImkMqSmB0Sw5s4yIaKkmJiTdHxFb14x7qNX2A3hf5EnaHdgR2Il0Q8mDwO0R8YMq42rEicC6RNI0Ujs7j7CkrSEiYmZlQTWRpKOB/0f6J388N2kQcEcrNUfifVFf1jTNNsBuwHjgtYjYtOOlquFEYF0i6a8RsWPVcVQluyNmTeB04KTcpJda7dkS74u3k3Qz6eaJu4C/AH+NiOeqjaoxJwLrEkl7kFpUvJmlL5JeW1lQZj2EpO+T2p16A7iDdA3troh4rdLAGnAisC6RdAXp8fmp5JqhjojPVBeVWc8iaTXgSFI16jsiYqWKQ6rLt49aV20VEe+pOgiznkjSsaQLxe8HZgIXk6qIeiQnAuuquyVtFhGPVh2IWQ80EDgLmBIRPb6jIlcNWZdkTStsRHqI7A2ytpda8VZBsxWdE4F1iaT1641vldtHzXoTJwIzsxbnRufMzFqcE4Gt8CSNlnRw1XGYraicCKzHk7RI0oOSpkr6m6QTJPXJpvUFziN1gNKVsi+VVLhfXUkTJH25K+sy66l8+6itCF6LiPcCSFob+DmwBqmp33cCZ0TEtOrCM1ux+YzAVihZey3jgGMlCVgXOBFA0raS7pT0QPZ3k9rllZwr6VFJvwfWzk17v6TbJE2RdKOkdTuKRdJRku7LzlJ+lfVTWzvPLtnZzINZXIOy8V/Jln1I0qnZuNGSHpP0Y0mPSPqZpA9JukPSPyVt29F2SjpC0rWSbsjm/3YujgskTc7Oqk5d5h1vvVtE+OVXj34BL9cZNxdYB9gV+F02bnWgX/b+Q8Cv6ix3IPBHoC8wHJgHfBzoD9wJDMvm+xRwcZ3lJwBfzt4PzY3/FnBcnfl/C+yQvV+NdBa+F3Ah6dmLPqR+bHcmNem9EHhPNn4K6YlUAfsB13W0naSOT2aQzpZWJj3Rul42bUj2ty9wK7Bl1Z+rXz3n5aohW1Gpzrg1gMskbUzqPax/nXl2Bq6MiEXAM5L+nI3fBNgC+GM60aAvMLuTGLaQ9C1gMOlL/sY689wBnJX1UnVtRLRJ2ouUDB7I5lkN2JjUkcsTEfEwgKSpwM0REZIeJiWKzrbz5oh4MVv+UWB9YBbwSUnjSIloXVLvYQ91sn3WIpwIbIUjaUNgEfAc8O7cpG8Ct0TEAZJGk3751lPv4RkBUyPiA8sQyqXA/hHxN0lHkM5Oll5RxBlZFdSHSc1yfChb1+kR8aOlAkgx5/u1XZwbXsyS/9eOtjO//CKgn6QNSI2ebRMRcyVdSjpjMAN8jcBWMJKGAROBcyOi9gt9DeDp7P0RDYq4HThIUt/sGsBu2fhpwDBJH8jW01/S5p2EMwiYLak/cEiDeDeKiIcj4kxgMqnF1huBz2QtUyJpRHYRvKgi25m3OvAK8KKkdYCxy7AuawE+I7AVwUBJD5KqQBYCl5Ma9Kr1bVKVyQnAn+tMB/g1sDupP9l/ALcBRMSC7DbSH2YdrfQDziY1s93IN4B7SHXxD5MSQ63jJe1G+nX+KPCHiHhD0ruBu7JqqJeBQ7N5iiiynW/JzlgeyLZlBqm6yuwtbmLCzKzFuWrIzKzFORGYmbU4JwIzsxbnRGBm1uKcCMzMWpwTgZlZi3MiMDNrcf8f+6uCV7kyDogAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "table= pd.crosstab(data.day_of_week, data.y)\n",
+    "table.div(table.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
+    "plt.title(\"Frecuencia de compra en función del día de la semana\")\n",
+    "plt.xlabel(\"Día de la semana\")\n",
+    "plt.ylabel(\"Frecuencia de compra del producto\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Frecuencia de compra del producto')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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8OGZmVrbOzggq7QAbkEYnG5dN7w7cXmRQ3dXda8afaUwYZmbLhA77GoqIEyPiRGA1YMuI+E5EfAfYChhcVoBmZrbIvt86lo+MOoAn//ksg7falfMvu67bZeZpLB4CvFU1/RYwtNt7NjNb1o0ZX3o31JedfVL391cjT++jFwP3SjpB0gnAPcDYPIVL2lXSk5KmSDqmzvKVJf1J0sOSHpN04BJFb2Zm3ZbnqqGfSfoL8HFSVxMHRkSX6SvrrvosYBdgOnCfpHERMblqtUOByRGxu6TVgScl/SEi3qpTpJmZFSDPGQHAAmBh1SOPrYEpETE1+2G/HBhds04A/bPxDt4L/Id0r4KZmZWky0Qg6UjgD6RG4zWASyQdnqPstYFpVdPTs3nVzgQ+BMwAHgGOzHo7NTNrQUHEu/rgbClLE1+eM4KvAdtExPERcRywLfmGqlSdebURfhp4CBgEbA6cKWmldxUkjZF0v6T7Z82alWPXZmaN1+/lqcyeO79lk0FEMHv2bPr167dE2+W5akikqqGKBdT/ka81HVinanow6ci/2oHAyZHe1SlZl9cbAvdWrxQR5wLnAgwfPrw1PwEz6/EGP/hzpnM0s1Zen3d+Bl9+vPsFz3mh+2VkcfTr14/Bg5fsCv88ieAC4B5J12bTewDn59juPmCYpPWA54B9gC/VrPMvYGfgH5LWJN28NjVH2WZmpevz1hzWm3Ds4jNPeLn7BZ+wbQPKWPo4Ok0EknqRLhe9DdiOlAJzXTUUEfMlHUYa87g3cEFEPCbpkGz5OcBPgIskPZKVfXREvLjUr8bMzJZYp4kgIhZKOjUiPgI8uKSFR8QNwA01886pej4D+NSSlmtmZo2Tp7H4Jkl7ZZd4mplZD5OnjeDbwIrAAknzsnkREe+6usfMzJY9ee4s9mhkZmY9WJ4zAiR9jtRYHMA/IuK6IoMyM7Py5Lmz+GzgENKdv48Ch0g6q+jAzMysHHnOCHYANslu+kLSWFJSMDOzHiDPVUNPksYkqFgHmFRMOGZmVrY8ZwQDgcclVbp9GAHcLWkcQESMKio4MzMrXp5EcFzhUZiZWdPkuXz0tjICMTOz5sg7MI2ZmfVQTgRmZm3OicDMrM112EaQdQ1dbxAYkfoa2rSwqMzMrDSdNRZ/trQozMysaTpMBBHxbOW5pHWBYRFxi6TlO9vOzMyWLXn6Gvo6cBXw22zWYOC6AmMyM7MS5WksPhT4GPAKQEQ8BaxRZFBmZlaePIngzYh4qzIhaTnqNyKbmdkyKE8iuE3SD4DlJe0CXAn8qdiwzMysLHkSwTHALFLX098gDUb/X0UGZWZm5cnT19BC4LzsYWZmPczS3FAGgG8oMzPrGfLcUHZo9vfi7O+XgdcLi8jMzErV5Q1lkj4WER+rWnSMpDuBHxcdnJmZFS9PY/GKkrarTEj6KLBicSGZmVmZ8nQV8TXgAkkrk9oMXgYOKjQqMzMrTZ6rhh4ANpO0EqCIeLn4sMzMrCy5O4+LiFeKDMTMrGxD513a7TKe6X4YTeeBaczM2pwTgZlZm8tVNSRpE2AjoF9lXkT8vqigzMysPHnGIzgeOCN77AT8AhiVp3BJu0p6UtIUScd0sM6Okh6S9Jik25YgdjMza4A8VUOfB3YGno+IA4HNgPd0tZGk3sBZwEjS2cS+kjaqWWcAcDYwKiI2Br6wRNGbmVm35UkEb2Qdz83PLiF9AVg/x3ZbA1MiYmo2nsHlwOiadb4EXBMR/wKIiBfyh25mZo2QJxHcnx25nwc8ADwI3Jtju7WBaVXT07N51T4IrCJpvKQHJO2fo1wzM2ugThuLJQk4KSLmAOdI+iuwUkRMylG26syr7c10OWArUtXT8sDdkiZExP/VxDEGGAMwZMiQHLs2M7O8Ok0EERGSriP9WBMRzyxB2dOBdaqmBwMz6qzzYkTMBeZKup3UBrFYIoiIc4FzAYYPH+5hMs2sR2n2jW15qoYmSBqxFGXfBwyTtJ6kvsA+wLiada4HPi5pOUkrANsAjy/FvszMbCnluY9gJ+Abkp4F5pKqfKKrgWkiYr6kw4Abgd7ABRHxmKRDsuXnRMTjWXXTJGAh8LuIeLQbr8fMzJZQnkQwcmkLj4gbSGMcV887p2b6l8Avl3YfZmbWPXl6H31W0pbAdqTG3jsj4sHCIzMzs1LkubP4OGAsMBBYDbhQ0n8VHZiZmZUjT9XQvsAWETEPQNLJpHsJflpkYGZmVo48Vw09Q1Vnc6TuJf5ZSDRmZla6PGcEbwKPSbqZ1EawC3CHpF8DRMQRBcZnZmYFy5MIrs0eFeOLCcXMzJohz1VDY8sIxMzMmiPPVUOflTRR0n8kvSLpVUkev9jMrIfIUzX0P8DngEciwv38mJn1MHmuGpoGPOokYGbWM+U5I/g+cEM2jOSblZkRcVphUZmZWWnyJIKfAa+R7iXoW2w4ZmZWtjyJYNWI+FThkZiZWVPkaSO4RZITgZlZD5UnERwK/FXSvOzSUV8+ambWg+S5oax/GYGYmVlz5GkjQNIoYPtscnxE/Lm4kMzMrEx57iw+GTgSmJw9jszmmZlZD5DnjGA3YPOIWAggaSwwETimyMDMzKwceRqLAQZUPV+5gDjMzKxJ8pwRnARMlHQrIFJbwbGFRmVmZqXJc9XQZZLGAyNIieDoiHi+6MDMzKwceRqL9wRej4hxEXE9ME/SHoVHZmZmpcjTRnB8RLxcmYiIOcDxhUVkZmalypMI6q2T6/4DMzNrfXl+0O+XdBpwFmnw+sOBBwqNyhpm6LxLu7X9M40Jw8xaWJ4zgsOBt4A/AlcAb5D6HzIzsx4gz1VDc/HNY2ZmPVbeG8rMzKyHciIwM2tzTgRmZm0uzw1lH5T0N0mPZtObSvqv4kMzM7My5DkjOI/Ut9DbABExCdinyKDMzKw8ee4jWCEi7pVUPW9+nsIl7QqcDvQGfhcRdccxkDQCmADsHRFX5SnbbFnU3fs6wPd2WOPlOSN4UdL7STeTIenzwMyuNpLUm3QT2khgI2BfSRt1sN7PgRuXIG4zM2uQPGcEhwLnAhtKeg54GvhKju22BqZExFQASZcDo0mjnFU7HLia1LupmZmVLM8NZVOBT0paEegVEa/mLHttYFrV9HRgm+oVJK0N7Al8gk4SgaQxwBiAIUOG5Ny9mZnl0WEikPTtDuYDEBGndVG26syLmun/IY1vsKCmDWLxjSLOJZ2VMHz48NoyzMysGzo7I+if/d2AdLQ+LpveHbg9R9nTgXWqpgcDM2rWGQ5cniWB1YDdJM2PiOtylG9mZg3QYSKIiBMBJN0EbFmpEpJ0AnBljrLvA4ZJWg94jnTJ6Zdq9rFe5bmki4A/OwmYmZUrT2PxEFLvoxVvAUO72igi5ks6jHQ1UG/ggoh4TNIh2fJzljxcMzNrtDyJ4GLgXknXkur49wTG5ik8Im4AbqiZVzcBRMQBeco0M7PGynPV0M8k/QX4eDbrwIiYWGxYZmZWllxDTkbEg8CDBcdiZmZN4N5HzczanBOBmVmbcyIwM2tzecYj2FbSfZJek/SWpAWSXikjODMzK16eM4IzgX2Bp4DlgYOBM4oMyszMypP3qqEpknpHxALgQkl3FRyXmZmVJE8ieF1SX+AhSb8gjUWwYrFhmZlZWfJUDe1H6iLiMGAuqSO5vYoMyszMypPnzuJns6dvACcWG46ZmZWts/EIroiIL0p6hHePI0BEbFpoZGZmVorOzgiOzP5+toxAzMysOTobj6AyQH0vYGZEzAOQtDywZgmxmZlZCfI0Fl8JLKyaXkC+gWnMzGwZkCcRLBcR7wxMkz3vW1xIZmZWpjyJYJakUZUJSaOBF4sLyczMypTnhrJDgD9IOhMQMA3Yv9CozMysNHnuI/gnsK2k9wKqDGJvZmY9Q5eJQNJ7SHcSDwWWkwRARPy40MjMzKwUeaqGrgdeBh4A3iw2HDMzK1ueRDA4InYtPBIzM2uKPFcN3SXpw4VHYmZmTZHnjGA74ABJT5OqhgSE+xoyM+sZ8iSCkYVHYWZmTdNl1VDWDfU6wCey56/n2c7MzJYNeQavPx44Gjg2m9UHuKTIoMzMrDx5juz3BEaRRicjImYA/YsMyszMypMnEbwVEUE2OI0kj1dsZtaD5EkEV0j6LTBA0teBW4Dzig3LzMzKkqevoVMk7QK8AmwAHBcRNxcemZmZlSLP5aNkP/z+8Tcz64HyXDX0qqRXssc8SQskvZKncEm7SnpS0hRJx9RZ/mVJk7LHXZI2W5oXYWZmSy9P1dBiVwhJ2gPYuqvtJPUGzgJ2AaYD90kaFxGTq1Z7GtghIl6SNBI4F9gmf/hmZtZdS3xjWERcB3wix6pbA1MiYmo2vOXlwOiasu6KiJeyyQnA4CWNx8zMuifPeASfq5rsBQwnu5S0C2uTRjOrmE7nR/tfA/6So1wzM2ugPI3Fu1c9nw88Q82RfQdUZ17dBCJpJ1Ii2K6D5WOAMQBDhgzJsWszM8srTxvBgUtZ9nRSH0UVg4EZtStJ2hT4HTAyImZ3EMO5pPYDhg8fnudsxMzMcspz1dBYSQOqpleRdEGOsu8DhklaT1JfYB9gXE3ZQ4BrgP0i4v+WKHIzM2uIPFVDm0bEnMpEdoXPFl1tFBHzJR0G3Aj0Bi6IiMckHZItPwc4DhgInJ2NhTw/IoYv+cswM7OllScR9JK0SuXqHkmr5tyOiLgBuKFm3jlVzw8GDs4frpmZNVqeH/RTScNVXkVq7P0i8LNCozIzs9LkaSz+vaT7SfcOCPhczU1hZma2DMt7Q9mqwNyIOAOYJWm9AmMyM7MSeYQyM7M25xHKzMzanEcoMzNrcx6hzMyszXV61ZDSXV5/BDbEI5SZmfVInSaCiAhJ10XEVniEMjOzHilP1dAESSMKj8TMzJoiz53FOwGHSHqGdOWQSCcLmxYZmJmZlaPDRCBpSET8CxhZYjxmZlayzs4IrgO2jIhnJV0dEXuVFJOZmZWoszaC6hHG1i86EDMza47OEkF08NzMzHqQzqqGNpP0CunMYPnsOSxqLF6p8OjMzKxwHSaCiOhdZiBmZtYcebuhNjOzHsqJwMyszTkRmJm1OScCM7M250RgZtbmnAjMzNqcE4GZWZtzIjAza3NOBGZmbc6JwMyszTkRmJm1OScCM7M250RgZtbmnAjMzNqcE4GZWZtzIjAza3OFJgJJu0p6UtIUScfUWS5Jv86WT5K0ZZHxmJnZuxWWCCT1Bs4CRgIbAftK2qhmtZHAsOwxBvhNUfGYmVl9RZ4RbA1MiYipEfEWcDkwumad0cDvI5kADJC0VoExmZlZDUVEMQVLnwd2jYiDs+n9gG0i4rCqdf4MnBwRd2TTfwOOjoj7a8oaQzpjANgAeLKb4a0GvNjNMrqrFWKA1oijFWKA1oijFWKA1oijFWKA1oijETGsGxGr11vQ4eD1DaA682qzTp51iIhzgXMbERSApPsjYnijyltWY2iVOFohhlaJoxViaJU4WiGGVomj6BiKrBqaDqxTNT0YmLEU65iZWYGKTAT3AcMkrSepL7APMK5mnXHA/tnVQ9sCL0fEzAJjMjOzGoVVDUXEfEmHATcCvYELIuIxSYdky88BbgB2A6YArwMHFhVPjYZVM3VDK8QArRFHK8QArRFHK8QArRFHK8QArRFHoTEU1lhsZmbLBt9ZbGbW5pwIzMzanBOBmVmbK/I+gpaRdXdxRET8qtmxtLOu+pKKiAfLigVA0geB7wHrUvW/EBGfKDOOViFpk4h4tNlxNJuk9SLi6a7mlRjPSkBExKuF7aNdGosljY+IHZsdR7Nll+k+VvlSSeoPbBQR95Sw71s7WRxl/wBLehg4B3gAWFAVyAMlxtAbuDEiPlnWPjuJ5Q6gL3ARcGlEzGlCDA8DfwT+GBH/LHv/WQwPRsSWNfMeiIitSo5jOHAh0J908+0c4KAivp9tcUaQuVPSmaQv2dzKzDKPQiW9yrvvnH4ZuB/4TkRMLSGM3wDVX/K5deYVIiJ2KnofS2h+RDS1o8OIWCDpdUkrR8TLTY5lO0nDgIOA+yXdC1wYETeXGMYoYG/gCkkLSf+vV0TEv4resaQNgY2BlSV9rmrRSkC/ovdfxwXAtyLiH1l825ESw6aN3lE7nRHUOxot9ShU0omkO6cvJWX4fYD3kfpO+mYZZyySHoqIzWvmTYqIhn+5Oolh/3rzI+L3ZcWQxXEC8AJwLfBmVRz/KTmOK4BtgZtZ/CDliDLjqIqnN7AH8GvgFdJ39QcRcU3JcQwDfgR8OSJ6l7C/0aTXPYrFb359Fbg8Iu4qOoaaeO6MiI91Na8h+2qXRNAKJN0TEdvUzJsQEdtKejgiNishhmuA8Szq8vtbwE4RsUfR+66K4YyqyX7AzsCDEfH5smLI4qhX5xsRsX7JcXy13vyIGFtyHJuSbur8DCkpnR8RD0oaBNwdEeuWFMdQ4IukM4MFpGqiU8vYd7b/j0TE3WXtr5M4fgWsAFxGqknYG3gJuBoaW5vRNolA0kDgeGA70pt6B/DjiJhdYgx3A78CrspmfR74dpYI3nWkXlAMa5CO9D5Beh/+BhwVES8Uve9OYloZuDgiRjUrBgNJtwPnAVdFxBs1y/aLiItLiOEeoA9wJSkBlFFdWhvDWODIShuJpFWAUyPioJLjKK1NrZ0Swc3A7cAl2awvAzuW2UgnaX3gdOAjpB/hCcD/A54Dtqp0x91uJPUBJkXEh0reb6tUUQ0DTiIN4PROXXTZZyatQNKGEfFEk2OYGBFbdDWvJ2mnxuJVI+InVdM/lbRHmQFkRze7d7C4lCSQXTL5G2DNiNgkqw4YFRE/LWP/WQx/YlGjeW/SD+AVZe2/yoiq5+9UUQGlJgJSA+DxpLPFnUjVM/W6aC9UiySkmZJOA7bPpm8jnbmX2ZDeS9IqEfESgKRVacJvpaQ1gf8GBkXEyGyEx49ExPkN31cbnRGcQro6p/KD83lg44g4vsQYLqT+eAulnXJKuo107fxvK0c4kh6NiE1KjGEHFr0P84FnI+K5svbfkWZVUVUuTZT0SER8OJv3j4j4eMlx3MGihLQ7WUIq+X/kauBRoNI+sh+wWUR8ruOtGh7D/sAPSNVTAF8AflZG1VhNHH8hHST8MCI2k7QcMLHyHWnovtooEbwKrMii68V7s+gKjYiIlUqIYa+qyX7AnsCMMq8OkXRfRIyoPtUtsX3ijuwSxcpltJWj3sge/wF+GRFnFx1LB/E1q4rqTuDjpLajv5OqCk+OiA1KjqPpCamDq9pK+X7W7POjwHBgIfBAMxqPy/xfbZuqoYjon53iDWPx097bSozh6uppSZcBt5S1/8yLkt5PdkSuNKRoKWNARMR22d/+9ZZnDfp3AaUkghaqojqKdHXIEcBPSNVDddsvCjZPUi/gqawL+eeANUqO4Q1J28Wi4Ws/BrzRxTYNJelI4GDgGtLBym8lnRcRZ3S+ZcPNzf4nKv+r25LuO2q4djojOBg4kjQK2kOk67bvioidmxjTBsD/RsQHStzn+qS+zT9KuhTtadJ12s+WFUNnJK0VJQ1O1CpVVNkdpD8kdXXRJ5sdZd7bkcUxAngcGEBKSCsBv4gS7jqvimFzUrXQytmsl4CvRsSkEmOYRKqLn5tNr0i6fLbsz2NL4AxgE1J12erA54t4L9rmjICUBEYAEyJip+wuwhPLDKDmzuIA/g18v6R9f7tq8gbgVlKng3OBvYDTyoijK2UkgUoVFfBnaqqoJDWjiuoPpHabR0hVEc0SwMUsnpDOo4A7WTvxOPAL4P2khPQy6Sav0hIB6fuwoGp6AU1ovCe9ByNJw/nuBWxDQb/Z7ZQI5kXEPElIek9EPJEdkZemg+qpsk7JKtUxG5AS4vWkL/d+pMtq20arVVEBsyKidhjXZmiFhHQ9qU+dB0lVU81wIXCPpGuz6T2Ahl+pk8OPIuLK7D6GTwKnkq7426bzzZZcO1UNXUu6CuIo0s1ULwF9ImK3EmOoVz11dyNvDMkRw03AXrF4p3NXRsSuZcWwLCi5impnYF/SzX3VXV2U3aVD5Uypacq+gq2TOLYk3Xwq4PaImNiEGCZGxBaSTgIeiYhLi7qfoW0SQbWsbnhl4K8R8VaJ+32ERdVTm1eqpyJi7xJjeIJ0Od6b2fR7gIcjYsOyYrDFSboE2BB4jEVH4tGEO1mbnpAknQucERGPlLXPViXpz6Szok8CW5Eaze+NArqiaaeqoXeUeaVQjaZXT5HqgO/NzpCCdAlrqX3a2LtsVsS14UvhQFJC6kNVQiJdPVOW7YADlPqBepN0RF56w3mL+CKwK3BKRMyRtBap6q7h2vKMoFlaoXoqi2NL0nXr0KTTXltE0nnAryJicpPjeKTZCUlS3Y7tWuWqtp7KiaBJmlU9Za1H0uOkK0SaehTcKgnJyudEYNZkrXIU3CoJycrnRGBmQOskJCufE4GZWZvr1ewAzMysuZwIzMzanBOB9ViSQtLFVdPLSZqV3ajTyP0cIOnM7q5TZ5vPSZog6RpJpV5ibO2lLW8os7YxF9hE0vKRxuDdheb1X7PEsjt6S+1mwtqTzwisp/sL8Jns+b7AZZUFklaUdIGk+yRNlDQ6m7+xpHslPSRpktIQjouRdKCk/1Ma8e1jVfNXl3R1VuZ9WX/6HZK0taS7sv3fVbnTPDuDuEbSXyU9JekXVdvsK+kRSY9K+nl33hwzcCKwnu9yYB9J/UjdKVf3rf9D4O8RMYI0GMwvs77nDwFOz0aCGg5Mry4wu9X/RFIC2IU0oE3F6aSbskaQug7+XRfxPQFsn3UkdhxpjNqKzYG9gQ8De0taR9Ig4OekO9M3B0ao5LG3redx1ZD1aBExSdJQ0tnADTWLPwWMkvTdbLofMAS4G/ihpMHANRHxVM122wDjI2IWgKQ/Ah/Mln0S2Eh6p/v6lbIeXjuyMjA2O+sIFo0DAPC3yAZtlzSZNE7AwJp9/4E00Pt1nb0PZp1xIrB2MA44BdiR9ENaIVKX3E/WrP+4pHtIVUo3Sjo4Iv5es05HN+D0Io1utdjwilWJodZPgFsjYs8sYY2vWvZm1fMFpP/XZgyQYj2cq4asHVwA/LhO18Y3Aocr+5WWVBkgfH1gakT8mpREartYuAfYUdJApQHvv1C17CbgsMqE0tCLnVmZRQ3YB+R4LfcAO0haTVJv0plOs3rTtR7CicB6vIiYHhGn11n0E1JVzCRJj2bTkOrlH5X0EKlb5t/XlDcTOIFUhXQLaTStiiOA4Vkj82RSe0NnfgGcJOlOoHeO1zITOJY01OjDwIMRcX1X25l1xl1MmJm1OZ8RmJm1OScCM7M250RgZtbmnAjMzNqcE4GZWZtzIjAza3NOBGZmbc6JwMyszf1/XyIR9BMCVd4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "table= pd.crosstab(data.month, data.y)\n",
+    "table.div(table.sum(1).astype(float), axis=0).plot(kind=\"bar\", stacked=True)\n",
+    "plt.title(\"Frecuencia de compra en función del mes\")\n",
+    "plt.xlabel(\"Mes del año\")\n",
+    "plt.ylabel(\"Frecuencia de compra del producto\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Frecuencia de compra del producto')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "table.plot(kind=\"bar\", stacked=False)\n",
+    "plt.title(\"Frecuencia de compra en función del mes\")\n",
+    "plt.xlabel(\"Mes del año\")\n",
+    "plt.ylabel(\"Frecuencia de compra del producto\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Cliente')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "data.age.hist()\n",
+    "plt.title(\"Histograma de la Edad\")\n",
+    "plt.xlabel(\"Edad\")\n",
+    "plt.ylabel(\"Cliente\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='age'>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.crosstab(data.age, data.y).plot(kind=\"bar\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='poutcome'>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pd.crosstab(data.poutcome, data.y).plot(kind=\"bar\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Conversión de las variables categóricas a dummies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "categories = [\"job\", \"marital\", \"education\", \"housing\", \"loan\", \"contact\", \n",
+    "              \"month\", \"day_of_week\", \"poutcome\"]\n",
+    "for category in categories:\n",
+    "    cat_list = \"cat\"+ \"_\"+category\n",
+    "    cat_dummies = pd.get_dummies(data[category], prefix=category)\n",
+    "    data_new = data.join(cat_dummies)\n",
+    "    data = data_new"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_vars = data.columns.values.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "to_keep = [v for v in data_vars if v not in categories]\n",
+    "to_keep = [v for v in to_keep if v not in [\"default\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['age', 'duration', 'campaign', 'pdays', 'previous', 'emp.var.rate',\n",
+       "       'cons.price.idx', 'cons.conf.idx', 'euribor3m', 'nr.employed', 'y',\n",
+       "       'job_admin.', 'job_blue-collar', 'job_entrepreneur',\n",
+       "       'job_housemaid', 'job_management', 'job_retired',\n",
+       "       'job_self-employed', 'job_services', 'job_student',\n",
+       "       'job_technician', 'job_unemployed', 'job_unknown',\n",
+       "       'marital_divorced', 'marital_married', 'marital_single',\n",
+       "       'marital_unknown', 'education_Basic', 'education_High School',\n",
+       "       'education_Illiterate', 'education_Professional Course',\n",
+       "       'education_University Degree', 'education_Unknown', 'housing_no',\n",
+       "       'housing_unknown', 'housing_yes', 'loan_no', 'loan_unknown',\n",
+       "       'loan_yes', 'contact_cellular', 'contact_telephone', 'month_apr',\n",
+       "       'month_aug', 'month_dec', 'month_jul', 'month_jun', 'month_mar',\n",
+       "       'month_may', 'month_nov', 'month_oct', 'month_sep',\n",
+       "       'day_of_week_fri', 'day_of_week_mon', 'day_of_week_thu',\n",
+       "       'day_of_week_tue', 'day_of_week_wed', 'poutcome_failure',\n",
+       "       'poutcome_nonexistent', 'poutcome_success'], dtype=object)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bank_data = data[to_keep]\n",
+    "bank_data.columns.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bank_data_vars = bank_data.columns.values.tolist()\n",
+    "Y = ['y']\n",
+    "X = [v for v in bank_data_vars if v not in Y]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Selección de rasgos para el modelo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = 12"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import datasets\n",
+    "from sklearn.feature_selection import RFE\n",
+    "from sklearn.linear_model import LogisticRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr = LogisticRegression(solver='lbfgs',class_weight='balanced', max_iter=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    }
+   ],
+   "source": [
+    "rfe = RFE(lr, n_features_to_select=12)\n",
+    "rfe = rfe.fit(bank_data[X], bank_data[Y].values.ravel())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[False False False False  True False False False  True False False False\n",
+      " False False False  True False False False  True  True False False False\n",
+      " False False False False False False False False False False False False\n",
+      " False False False False False False  True  True  True  True  True False\n",
+      " False False False False False False False  True False  True]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(rfe.support_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[38 37 17 45  1 19 24 22  1 34  2  8  5 39  7  1  6 23 43  1  1 42 16 27\n",
+      " 25 47 33 14 46 18 28  4  9 15 44 13 20 26 11  3 36 21  1  1  1  1  1 12\n",
+      " 40 41 31 30 29 35 10  1 32  1]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(rfe.ranking_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z=zip(bank_data_vars,rfe.support_, rfe.ranking_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('age', False, 38),\n",
+       " ('duration', False, 37),\n",
+       " ('campaign', False, 17),\n",
+       " ('pdays', False, 45),\n",
+       " ('previous', True, 1),\n",
+       " ('emp.var.rate', False, 19),\n",
+       " ('cons.price.idx', False, 24),\n",
+       " ('cons.conf.idx', False, 22),\n",
+       " ('euribor3m', True, 1),\n",
+       " ('nr.employed', False, 34),\n",
+       " ('y', False, 2),\n",
+       " ('job_admin.', False, 8),\n",
+       " ('job_blue-collar', False, 5),\n",
+       " ('job_entrepreneur', False, 39),\n",
+       " ('job_housemaid', False, 7),\n",
+       " ('job_management', True, 1),\n",
+       " ('job_retired', False, 6),\n",
+       " ('job_self-employed', False, 23),\n",
+       " ('job_services', False, 43),\n",
+       " ('job_student', True, 1),\n",
+       " ('job_technician', True, 1),\n",
+       " ('job_unemployed', False, 42),\n",
+       " ('job_unknown', False, 16),\n",
+       " ('marital_divorced', False, 27),\n",
+       " ('marital_married', False, 25),\n",
+       " ('marital_single', False, 47),\n",
+       " ('marital_unknown', False, 33),\n",
+       " ('education_Basic', False, 14),\n",
+       " ('education_High School', False, 46),\n",
+       " ('education_Illiterate', False, 18),\n",
+       " ('education_Professional Course', False, 28),\n",
+       " ('education_University Degree', False, 4),\n",
+       " ('education_Unknown', False, 9),\n",
+       " ('housing_no', False, 15),\n",
+       " ('housing_unknown', False, 44),\n",
+       " ('housing_yes', False, 13),\n",
+       " ('loan_no', False, 20),\n",
+       " ('loan_unknown', False, 26),\n",
+       " ('loan_yes', False, 11),\n",
+       " ('contact_cellular', False, 3),\n",
+       " ('contact_telephone', False, 36),\n",
+       " ('month_apr', False, 21),\n",
+       " ('month_aug', True, 1),\n",
+       " ('month_dec', True, 1),\n",
+       " ('month_jul', True, 1),\n",
+       " ('month_jun', True, 1),\n",
+       " ('month_mar', True, 1),\n",
+       " ('month_may', False, 12),\n",
+       " ('month_nov', False, 40),\n",
+       " ('month_oct', False, 41),\n",
+       " ('month_sep', False, 31),\n",
+       " ('day_of_week_fri', False, 30),\n",
+       " ('day_of_week_mon', False, 29),\n",
+       " ('day_of_week_thu', False, 35),\n",
+       " ('day_of_week_tue', False, 10),\n",
+       " ('day_of_week_wed', True, 1),\n",
+       " ('poutcome_failure', False, 32),\n",
+       " ('poutcome_nonexistent', True, 1)]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cols = [\"previous\", \"euribor3m\", \"job_blue-collar\", \"job_retired\", \"month_aug\", \"month_dec\", \n",
+    "        \"month_jul\", \"month_jun\", \"month_mar\", \"month_nov\", \"day_of_week_wed\", \"poutcome_nonexistent\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = bank_data[cols]\n",
+    "Y = bank_data[\"y\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementación del modelo en Python con statsmodel.api"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import statsmodels.api as sm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "logit_model = sm.Logit(Y, X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Optimization terminated successfully.\n",
+      "         Current function value: 0.291770\n",
+      "         Iterations 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = logit_model.fit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "        <td>Model:</td>              <td>Logit</td>      <td>Pseudo R-squared:</td>    <td>0.155</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <td>Dependent Variable:</td>         <td>y</td>              <td>AIC:</td>         <td>2427.6025</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "         <td>Date:</td>        <td>2020-09-19 17:22</td>       <td>BIC:</td>         <td>2503.4828</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "   <td>No. Observations:</td>        <td>4119</td>        <td>Log-Likelihood:</td>    <td>-1201.8</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "       <td>Df Model:</td>             <td>11</td>            <td>LL-Null:</td>        <td>-1422.9</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "     <td>Df Residuals:</td>          <td>4107</td>         <td>LLR p-value:</td>    <td>6.4492e-88</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "      <td>Converged:</td>           <td>1.0000</td>           <td>Scale:</td>         <td>1.0000</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "    <td>No. Iterations:</td>        <td>7.0000</td>              <td></td>               <td></td>     \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "            <td></td>            <th>Coef.</th>  <th>Std.Err.</th>     <th>z</th>     <th>P>|z|</th> <th>[0.025</th>  <th>0.975]</th> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>previous</th>             <td>-0.1229</td>  <td>0.0700</td>   <td>-1.7545</td> <td>0.0793</td> <td>-0.2601</td> <td>0.0144</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>euribor3m</th>            <td>-0.6049</td>  <td>0.0383</td>  <td>-15.7882</td> <td>0.0000</td> <td>-0.6800</td> <td>-0.5298</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>job_blue-collar</th>      <td>-0.5032</td>  <td>0.1519</td>   <td>-3.3136</td> <td>0.0009</td> <td>-0.8009</td> <td>-0.2056</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>job_retired</th>          <td>0.2235</td>   <td>0.2191</td>   <td>1.0205</td>  <td>0.3075</td> <td>-0.2058</td> <td>0.6529</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>month_aug</th>            <td>0.6048</td>   <td>0.1759</td>   <td>3.4374</td>  <td>0.0006</td> <td>0.2600</td>  <td>0.9497</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>month_dec</th>            <td>1.1358</td>   <td>0.4493</td>   <td>2.5281</td>  <td>0.0115</td> <td>0.2552</td>  <td>2.0163</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>month_jul</th>            <td>1.0327</td>   <td>0.1910</td>   <td>5.4071</td>  <td>0.0000</td> <td>0.6584</td>  <td>1.4070</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>month_jun</th>            <td>1.0775</td>   <td>0.1752</td>   <td>6.1493</td>  <td>0.0000</td> <td>0.7341</td>  <td>1.4210</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>month_mar</th>            <td>1.6448</td>   <td>0.3139</td>   <td>5.2407</td>  <td>0.0000</td> <td>1.0297</td>  <td>2.2600</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>month_nov</th>            <td>0.3828</td>   <td>0.1950</td>   <td>1.9634</td>  <td>0.0496</td> <td>0.0007</td>  <td>0.7649</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>day_of_week_wed</th>      <td>-0.0649</td>  <td>0.1391</td>   <td>-0.4665</td> <td>0.6409</td> <td>-0.3375</td> <td>0.2077</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>poutcome_nonexistent</th> <td>-0.7753</td>  <td>0.1221</td>   <td>-6.3492</td> <td>0.0000</td> <td>-1.0147</td> <td>-0.5360</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary2.Summary'>\n",
+       "\"\"\"\n",
+       "                           Results: Logit\n",
+       "=====================================================================\n",
+       "Model:                Logit             Pseudo R-squared:  0.155     \n",
+       "Dependent Variable:   y                 AIC:               2427.6025 \n",
+       "Date:                 2020-09-19 17:22  BIC:               2503.4828 \n",
+       "No. Observations:     4119              Log-Likelihood:    -1201.8   \n",
+       "Df Model:             11                LL-Null:           -1422.9   \n",
+       "Df Residuals:         4107              LLR p-value:       6.4492e-88\n",
+       "Converged:            1.0000            Scale:             1.0000    \n",
+       "No. Iterations:       7.0000                                         \n",
+       "---------------------------------------------------------------------\n",
+       "                      Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
+       "---------------------------------------------------------------------\n",
+       "previous             -0.1229   0.0700  -1.7545 0.0793 -0.2601  0.0144\n",
+       "euribor3m            -0.6049   0.0383 -15.7882 0.0000 -0.6800 -0.5298\n",
+       "job_blue-collar      -0.5032   0.1519  -3.3136 0.0009 -0.8009 -0.2056\n",
+       "job_retired           0.2235   0.2191   1.0205 0.3075 -0.2058  0.6529\n",
+       "month_aug             0.6048   0.1759   3.4374 0.0006  0.2600  0.9497\n",
+       "month_dec             1.1358   0.4493   2.5281 0.0115  0.2552  2.0163\n",
+       "month_jul             1.0327   0.1910   5.4071 0.0000  0.6584  1.4070\n",
+       "month_jun             1.0775   0.1752   6.1493 0.0000  0.7341  1.4210\n",
+       "month_mar             1.6448   0.3139   5.2407 0.0000  1.0297  2.2600\n",
+       "month_nov             0.3828   0.1950   1.9634 0.0496  0.0007  0.7649\n",
+       "day_of_week_wed      -0.0649   0.1391  -0.4665 0.6409 -0.3375  0.2077\n",
+       "poutcome_nonexistent -0.7753   0.1221  -6.3492 0.0000 -1.0147 -0.5360\n",
+       "=====================================================================\n",
+       "\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result.summary2()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Implementación del modelo en Python con scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import linear_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logit_model = linear_model.LogisticRegression()\n",
+    "logit_model.fit(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8958485069191552"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "logit_model.score(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8905074047098811"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1-Y.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "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>0</th>\n",
+       "      <th>1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>previous</td>\n",
+       "      <td>[0.5362541179381843]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>euribor3m</td>\n",
+       "      <td>[-0.5443670397995866]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>job_blue-collar</td>\n",
+       "      <td>[-0.3533138743513389]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>job_retired</td>\n",
+       "      <td>[0.36165159081170917]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>month_aug</td>\n",
+       "      <td>[0.6292588895072758]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>month_dec</td>\n",
+       "      <td>[1.1943974572117282]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>month_jul</td>\n",
+       "      <td>[0.9630554140662402]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>month_jun</td>\n",
+       "      <td>[1.0566897056470022]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>month_mar</td>\n",
+       "      <td>[1.6386680234582125]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>month_nov</td>\n",
+       "      <td>[0.4577084191059241]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>day_of_week_wed</td>\n",
+       "      <td>[0.046113127259597986]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>poutcome_nonexistent</td>\n",
+       "      <td>[0.35494520625376097]</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       0                       1\n",
+       "0               previous    [0.5362541179381843]\n",
+       "1              euribor3m   [-0.5443670397995866]\n",
+       "2        job_blue-collar   [-0.3533138743513389]\n",
+       "3            job_retired   [0.36165159081170917]\n",
+       "4              month_aug    [0.6292588895072758]\n",
+       "5              month_dec    [1.1943974572117282]\n",
+       "6              month_jul    [0.9630554140662402]\n",
+       "7              month_jun    [1.0566897056470022]\n",
+       "8              month_mar    [1.6386680234582125]\n",
+       "9              month_nov    [0.4577084191059241]\n",
+       "10       day_of_week_wed  [0.046113127259597986]\n",
+       "11  poutcome_nonexistent   [0.35494520625376097]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.DataFrame(list(zip(X.columns, np.transpose(logit_model.coef_))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Validación del modelo logístico"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm = linear_model.LogisticRegression()\n",
+    "lm.fit(X_train, Y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import display, Math, Latex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle Y_p=\\begin{cases}0& si\\ p\\leq0.5\\\\1&si\\ p >0.5\\end{cases}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r'Y_p=\\begin{cases}0& si\\ p\\leq0.5\\\\1&si\\ p >0.5\\end{cases}'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "probs = lm.predict_proba(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.95409621, 0.04590379],\n",
+       "       [0.83896646, 0.16103354],\n",
+       "       [0.93216535, 0.06783465],\n",
+       "       ...,\n",
+       "       [0.65098445, 0.34901555],\n",
+       "       [0.97381005, 0.02618995],\n",
+       "       [0.57635748, 0.42364252]])"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "probs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prediction = lm.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 0, 0, ..., 0, 0, 0])"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\varepsilon\\in (0,1), Y_p=\\begin{cases}0& si\\ p\\leq \\varepsilon\\\\1&si\\ p >\\varepsilon\\end{cases}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "display(Math(r'\\varepsilon\\in (0,1), Y_p=\\begin{cases}0& si\\ p\\leq \\varepsilon\\\\1&si\\ p >\\varepsilon\\end{cases}'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "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>0</th>\n",
+       "      <th>prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.045904</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.161034</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.067835</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.062222</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.042308</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0  prediction\n",
+       "0  0.045904           0\n",
+       "1  0.161034           1\n",
+       "2  0.067835           0\n",
+       "3  0.062222           0\n",
+       "4  0.042308           0"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prob = probs[:,1]\n",
+    "prob_df = pd.DataFrame(prob)\n",
+    "threshold = 0.1\n",
+    "prob_df[\"prediction\"] = np.where(prob_df[0]>threshold, 1, 0)\n",
+    "prob_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "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>col_0</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prediction</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>390</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "col_0       count\n",
+       "prediction       \n",
+       "0             846\n",
+       "1             390"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pd.crosstab(prob_df.prediction, columns=\"count\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "31.55339805825243"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "390/len(prob_df)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "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>col_0</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prediction</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>915</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>321</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "col_0       count\n",
+       "prediction       \n",
+       "0             915\n",
+       "1             321"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threshold = 0.15\n",
+    "prob_df[\"prediction\"] = np.where(prob_df[0]>threshold, 1, 0)\n",
+    "pd.crosstab(prob_df.prediction, columns=\"count\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26.779935275080906"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "331/len(prob_df)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "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>col_0</th>\n",
+       "      <th>count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>prediction</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>732</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "col_0       count\n",
+       "prediction       \n",
+       "0             504\n",
+       "1             732"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threshold = 0.05\n",
+    "prob_df[\"prediction\"] = np.where(prob_df[0]>threshold, 1, 0)\n",
+    "pd.crosstab(prob_df.prediction, columns=\"count\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "59.22330097087378"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "732/len(prob_df)*100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn import metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.901294498381877"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics.accuracy_score(Y_test, prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Validación cruzada"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores = cross_val_score(linear_model.LogisticRegression(), X, Y, scoring=\"accuracy\", cv=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.90533981, 0.88834951, 0.90533981, 0.89563107, 0.90048544,\n",
+       "       0.8907767 , 0.88349515, 0.89320388, 0.89320388, 0.88807786])"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8943903101599225"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Matrices de Confusión y curvas ROC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,test_size=0.3, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lm = linear_model.LogisticRegression()\n",
+    "lm.fit(X_train, Y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "probs = lm.predict_proba(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "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>0</th>\n",
+       "      <th>prediction</th>\n",
+       "      <th>actual</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.045904</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.161034</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.067835</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.062222</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.042308</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0  prediction  actual\n",
+       "0  0.045904           0       0\n",
+       "1  0.161034           1       0\n",
+       "2  0.067835           0       0\n",
+       "3  0.062222           0       0\n",
+       "4  0.042308           0       0"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prob=probs[:,1]\n",
+    "prob_df = pd.DataFrame(prob)\n",
+    "threshold = 0.1\n",
+    "prob_df[\"prediction\"] = np.where(prob_df[0]>=threshold, 1, 0)\n",
+    "prob_df[\"actual\"] = list(Y_test)\n",
+    "prob_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "confusion_matrix = pd.crosstab(prob_df.prediction, prob_df.actual)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "TN=confusion_matrix[0][0]\n",
+    "TP=confusion_matrix[1][1]\n",
+    "FN=confusion_matrix[0][1]\n",
+    "FP=confusion_matrix[1][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.21025641025641026"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sens = TP/(TP+FN)\n",
+    "sens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.047281323877068515"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "espc_1 = 1-TN/(TN+FP)\n",
+    "espc_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "thresholds = [0.04, 0.05, 0.07, 0.10, 0.12, 0.15, 0.18, 0.20, 0.25, 0.3, 0.4, 0.5]\n",
+    "sensitivities = [1]\n",
+    "especifities_1 = [1]\n",
+    "\n",
+    "for t in thresholds:\n",
+    "    prob_df[\"prediction\"] = np.where(prob_df[0]>=t, 1, 0)\n",
+    "    prob_df[\"actual\"] = list(Y_test)\n",
+    "    prob_df.head()\n",
+    "\n",
+    "    confusion_matrix = pd.crosstab(prob_df.prediction, prob_df.actual)\n",
+    "    TN=confusion_matrix[0][0]\n",
+    "    TP=confusion_matrix[1][1]\n",
+    "    FP=confusion_matrix[0][1]\n",
+    "    FN=confusion_matrix[1][0]\n",
+    "    \n",
+    "    sens = TP/(TP+FN)\n",
+    "    sensitivities.append(sens)\n",
+    "    espc_1 = 1-TN/(TN+FP)\n",
+    "    especifities_1.append(espc_1)\n",
+    "\n",
+    "sensitivities.append(0)\n",
+    "especifities_1.append(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1,\n",
+       " 0.9344262295081968,\n",
+       " 0.8442622950819673,\n",
+       " 0.680327868852459,\n",
+       " 0.6721311475409836,\n",
+       " 0.6639344262295082,\n",
+       " 0.6229508196721312,\n",
+       " 0.5163934426229508,\n",
+       " 0.45081967213114754,\n",
+       " 0.4016393442622951,\n",
+       " 0.36065573770491804,\n",
+       " 0.1721311475409836,\n",
+       " 0.12295081967213115,\n",
+       " 0]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sensitivities"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1,\n",
+       " 0.7800718132854578,\n",
+       " 0.5646319569120287,\n",
+       " 0.2989228007181328,\n",
+       " 0.2764811490125674,\n",
+       " 0.24596050269299818,\n",
+       " 0.21992818671454217,\n",
+       " 0.12387791741472176,\n",
+       " 0.08617594254937166,\n",
+       " 0.07181328545780974,\n",
+       " 0.06463195691202872,\n",
+       " 0.022441651705565557,\n",
+       " 0.013464991023339312,\n",
+       " 0]"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "especifities_1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Curva ROC')"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.plot(especifities_1, sensitivities, marker=\"o\", linestyle=\"--\", color=\"r\")\n",
+    "x=[i*0.01 for i in range(100)]\n",
+    "y=[i*0.01 for i in range(100)]\n",
+    "plt.plot(x,y)\n",
+    "plt.xlabel(\"1-Especifidad\")\n",
+    "plt.ylabel(\"Sensibilidad\")\n",
+    "plt.title(\"Curva ROC\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#HAY QUE ESPERAR QUE ACTUALICE GGPLOT LAS LIBRERIAS, SINO HAY QUE MODIFICAR ARCHIVOS INTERNOS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting plotnine[all]\n",
+      "  Downloading plotnine-0.7.1-py3-none-any.whl (4.4 MB)\n",
+      "\u001b[K     |████████████████████████████████| 4.4 MB 782 kB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: matplotlib>=3.1.1 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from plotnine[all]) (3.3.1)\n",
+      "Requirement already satisfied: scipy>=1.2.0 in /Users/johnnynunez/.local/lib/python3.8/site-packages (from plotnine[all]) (1.4.1)\n",
+      "Collecting mizani>=0.7.1\n",
+      "  Downloading mizani-0.7.1-py3-none-any.whl (62 kB)\n",
+      "\u001b[K     |████████████████████████████████| 62 kB 1.4 MB/s eta 0:00:011\n",
+      "\u001b[?25hRequirement already satisfied: pandas>=1.1.0 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from plotnine[all]) (1.1.2)\n",
+      "Requirement already satisfied: numpy>=1.16.0 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from plotnine[all]) (1.19.1)\n",
+      "Requirement already satisfied: patsy>=0.5.1 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from plotnine[all]) (0.5.1)\n",
+      "Requirement already satisfied: statsmodels>=0.11.1 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from plotnine[all]) (0.11.1)\n",
+      "Collecting descartes>=1.1.0\n",
+      "  Downloading descartes-1.1.0-py3-none-any.whl (5.8 kB)\n",
+      "Collecting scikit-misc; extra == \"all\"\n",
+      "  Downloading scikit_misc-0.1.3-cp38-cp38-macosx_10_9_x86_64.whl (10.8 MB)\n",
+      "\u001b[K     |████████████████████████████████| 10.8 MB 1.1 MB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: scikit-learn; extra == \"all\" in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from plotnine[all]) (0.23.2)\n",
+      "Requirement already satisfied: cycler>=0.10 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from matplotlib>=3.1.1->plotnine[all]) (0.10.0)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from matplotlib>=3.1.1->plotnine[all]) (7.2.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from matplotlib>=3.1.1->plotnine[all]) (1.2.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from matplotlib>=3.1.1->plotnine[all]) (2.4.7)\n",
+      "Requirement already satisfied: python-dateutil>=2.1 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from matplotlib>=3.1.1->plotnine[all]) (2.8.1)\n",
+      "Requirement already satisfied: certifi>=2020.06.20 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from matplotlib>=3.1.1->plotnine[all]) (2020.6.20)\n",
+      "Collecting palettable\n",
+      "  Downloading palettable-3.3.0-py2.py3-none-any.whl (111 kB)\n",
+      "\u001b[K     |████████████████████████████████| 111 kB 879 kB/s eta 0:00:01\n",
+      "\u001b[?25hRequirement already satisfied: pytz>=2017.2 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from pandas>=1.1.0->plotnine[all]) (2020.1)\n",
+      "Requirement already satisfied: six in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from patsy>=0.5.1->plotnine[all]) (1.15.0)\n",
+      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from scikit-learn; extra == \"all\"->plotnine[all]) (2.1.0)\n",
+      "Requirement already satisfied: joblib>=0.11 in /Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages (from scikit-learn; extra == \"all\"->plotnine[all]) (0.16.0)\n",
+      "Installing collected packages: palettable, mizani, descartes, scikit-misc, plotnine\n",
+      "Successfully installed descartes-1.1.0 mizani-0.7.1 palettable-3.3.0 plotnine-0.7.1 scikit-misc-0.1.3\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install 'plotnine[all]'\n",
+    "from sklearn import metrics\n",
+    "from pandas import Timestamp\n",
+    "from plotnine import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "espc_1, sensit, _ = metrics.roc_curve(Y_test, prob)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'sensit' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-83-c003ab85484b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m df = pd.DataFrame({\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"esp\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mespc_1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;34m\"sens\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0msensit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m })\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'sensit' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame({\n",
+    "    \"esp\":espc_1,\n",
+    "    \"sens\":sensit\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-84-c42a15b2c7cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'ggplot' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-85-e7bec62addb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mggplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"esp\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sens\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0mgeom_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mgeom_abline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinetype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"dashed\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mxlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mxlab\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1-Especifidad\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mylab\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Sensibilidad\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'ggplot' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "ggplot(df, aes(x=\"esp\", y=\"sens\")) +geom_line() + geom_abline(linetype=\"dashed\")+xlim(-0.01,1.01)+ylim(-0.01,1.01)+xlab(\"1-Especifidad\")+ylab(\"Sensibilidad\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'sensit' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-86-6dcb6a0c7485>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mauc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mespc_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msensit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'sensit' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "auc = metrics.auc(espc_1, sensit)\n",
+    "auc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'ggplot' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-87-7259c767e7f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mggplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"esp\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sens\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mgeom_area\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mgeom_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sens\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mggtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Curva ROC y AUC=%s\"\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'ggplot' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "ggplot(df, aes(x=\"esp\", y=\"sens\")) + geom_area(alpha=0.25)+geom_line(aes(y=\"sens\"))+ggtitle(\"Curva ROC y AUC=%s\"%str(auc))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T5 - 3 - Logistic Regression - Implementaci\303\263n con Python.ipynb" "b/notebooks/T5 - 3 - Logistic Regression - Implementaci\303\263n con Python.ipynb"
index aaae00b5..37b80e21 100644
--- "a/notebooks/T5 - 3 - Logistic Regression - Implementaci\303\263n con Python.ipynb"	
+++ "b/notebooks/T5 - 3 - Logistic Regression - Implementaci\303\263n con Python.ipynb"	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -210,12 +210,12 @@
        "3   38     services  married           basic.9y      no  unknown  unknown   \n",
        "4   47       admin.  married  university.degree      no      yes       no   \n",
        "\n",
-       "     contact month day_of_week ...  campaign  pdays  previous     poutcome  \\\n",
-       "0   cellular   may         fri ...         2    999         0  nonexistent   \n",
-       "1  telephone   may         fri ...         4    999         0  nonexistent   \n",
-       "2  telephone   jun         wed ...         1    999         0  nonexistent   \n",
-       "3  telephone   jun         fri ...         3    999         0  nonexistent   \n",
-       "4   cellular   nov         mon ...         1    999         0  nonexistent   \n",
+       "     contact month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
+       "0   cellular   may         fri  ...         2    999         0  nonexistent   \n",
+       "1  telephone   may         fri  ...         4    999         0  nonexistent   \n",
+       "2  telephone   jun         wed  ...         1    999         0  nonexistent   \n",
+       "3  telephone   jun         fri  ...         3    999         0  nonexistent   \n",
+       "4   cellular   nov         mon  ...         1    999         0  nonexistent   \n",
        "\n",
        "  emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
        "0         -1.8          92.893          -46.2      1.313       5099.1  no  \n",
@@ -227,7 +227,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -238,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -247,7 +247,7 @@
        "(4119, 21)"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -258,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -270,7 +270,7 @@
        "       'cons.conf.idx', 'euribor3m', 'nr.employed', 'y'], dtype=object)"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -281,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,7 +290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -471,12 +471,12 @@
        "4117   58      admin.  married  high.school      no      no   no   cellular   \n",
        "4118   34  management   single  high.school      no     yes   no   cellular   \n",
        "\n",
-       "     month day_of_week ...  campaign  pdays  previous     poutcome  \\\n",
-       "4114   jul         thu ...         1    999         0  nonexistent   \n",
-       "4115   jul         fri ...         1    999         0  nonexistent   \n",
-       "4116   may         mon ...         2    999         1      failure   \n",
-       "4117   aug         fri ...         1    999         0  nonexistent   \n",
-       "4118   nov         wed ...         1    999         0  nonexistent   \n",
+       "     month day_of_week  ...  campaign  pdays  previous     poutcome  \\\n",
+       "4114   jul         thu  ...         1    999         0  nonexistent   \n",
+       "4115   jul         fri  ...         1    999         0  nonexistent   \n",
+       "4116   may         mon  ...         2    999         1      failure   \n",
+       "4117   aug         fri  ...         1    999         0  nonexistent   \n",
+       "4118   nov         wed  ...         1    999         0  nonexistent   \n",
        "\n",
        "     emp.var.rate  cons.price.idx  cons.conf.idx  euribor3m  nr.employed  y  \n",
        "4114          1.4          93.918          -42.7      4.958       5228.1  0  \n",
@@ -488,7 +488,7 @@
        "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -499,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -510,7 +510,7 @@
        "       'illiterate'], dtype=object)"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -521,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -539,7 +539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -549,7 +549,7 @@
        "       'Unknown', 'Illiterate'], dtype=object)"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -560,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -571,7 +571,7 @@
        "Name: y, dtype: int64"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -582,7 +582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -674,7 +674,7 @@
        "1       93.417268     -39.786475   2.145448  5093.118625  "
       ]
      },
-     "execution_count": 125,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -685,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -854,7 +854,7 @@
        "Unknown              5151.260479  0.155689  "
       ]
      },
-     "execution_count": 126,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -865,27 +865,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0,0.5,'Frecuencia de compra del producto')"
+       "Text(0, 0.5, 'Frecuencia de compra del producto')"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a2ae10a58>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -899,27 +901,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0,0.5,'Proporción de clientes')"
+       "Text(0, 0.5, 'Proporción de clientes')"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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AauQkkrXDgTW17ADMzCo4iVjDtUNCBidls0Zwm4iZmdXNScTMzOrmJGJmZnVzEjEzs7o5iZiZWd2cRMzMrG7u4mvW4txl2lqZSyJmZlY3JxEzM6ubk4iZmdXNScTMzOrmJGJmZnVzEjEzs7o5iZiZWd2cRMzMrG5OImZmVremJhFJ20t6RNJkSUdWmb6MpD9KulfSA5L2a2Y8ZmbWWE1LIpIGAKcAY4F1gD0lrVMx20HAgxGxAbANcJKkxZoVk5mZNVYzSyKbApMjYkpEvA1cBOxSMU8AgyUJWAp4BZjTxJjMzKyBmplEVgaeLgxPy+OKfg18EHgWmAQcGhHzmhiTmZk1UDOTiKqMi4rhTwP3ACsBGwK/lrR01ZVJB0i6U9KdL774YiPjNDOzOjUziUwDVikMjyCVOIr2Ay6PZDLwBLB2tZVFxGkRMSYixgwfPrwpAZuZWc80M4ncAawhaXRuLN8DGF8xz1PAJwAkvR9YC5jSxJjMzKyBakoikg6VtLSSMyXdJelTXS0TEXOAg4FrgYeASyLiAUkHSjowz3YM8FFJk4DrgSMi4qX6346ZmfWmWp9s+NWIOFnSp4HhpGqos4DrulooIq4Grq4YN67w+lmgy2RkZmatq9bqrI5G8h2AsyLiXqo3nJuZWT9SaxKZKOk6UhK5VtJgwF1xzcz6uVqrs/YndcGdEhFvShpGqtIyM7N+rNaSSJBuXXJIHn4fsERTIjIzs7ZRaxI5FfgIsGcefo10XywzM+vHaq3O2iwiNpJ0N0BEzPCNEs3MrNaSyDv5rrwBIGk4blg3M+v3ak0ivwSuAJaXdBzwT+AnTYvKzMzaQk3VWRFxvqSJpFuUCPhsRDzU1MjMzKzl1ZREJJ0bEXsDD1cZZ2Zm/VSt1VnrFgdy+8jGjQ/HzMzaSZdJRNJRkl4D1pc0S9JrefgF4KpeidDMzFpWl0kkIn4SEYOBEyNi6YgYnP+GRcRRvRSjmZm1qFob1o+StDKwanGZiJjQrMDMzKz11dqwfjzpoVIPAnPz6ACcRMzM+rFar1jfFVgrIt5qZjBmZtZeau2dNQVYtJmBmJlZ+6m1JPImcI+k64H5pZGIOKTzRczMrK+rNYmMz39mZmbz1do76/eSBgEjI+KRJsdkZmZtoqY2EUk7AfcA1+ThDSW5ZGJm1s/V2rB+NLApMBMgIu4BRjclIjMzaxu1JpE5EfFqxbhodDBmZtZeam1Yv1/Sl4ABktYgPWv9luaFZWZm7aDWksi3SXfyfQu4EJgFfKdJMZmZWZuotXfWm8D38p+ZmRnQTRKR9IuI+I6kP1KlDSQidm5aZGZm1vK6K4mcm///tNmBmJlZ++kyiUTExPz/H70TjpmZtZPuqrMmUb0rr4CIiPWbEpWZmbWF7qqzduyVKMzMrC11V531JICk0cD0iJidhwcB729+eGZm1spqvU7kUmBeYXhuHtclSdtLekTSZElHdjLPNpLukfSAJLe9mJm1kVqvWB8YEW93DETE25IW62oBSQOAU4DtgGnAHZLGR8SDhXmGAKcC20fEU5KW7+kbMDOz8tRaEnlR0vxrQiTtArzUzTKbApMjYkpOQBcBu1TM8yXg8oh4CiAiXqgxHjMzawG1JpEDgf+W9JSkp4AjgAO6WWZl4OnC8LQ8rmhNYFlJN0qaKGmfGuMxM7MWUOttTx4HNpe0FKCIeK2GxVRtVVW2vzHwCWAQ8C9Jt0bEo+9ZmXQAOXGNHDmylrDNzKzJai2JABARr9eYQCCVPFYpDI8Anq0yzzUR8UZEvARMADboZNunRcSYiBgzfPjwnoRtZmZN0qMk0kN3AGtIGp0b4ffgvc9pvwrYStJASUsCmwEPNTEmMzNroFp7Z/VYRMyRdDBwLTAA+F1EPCDpwDx9XEQ8JOka4D5SF+IzIuL+ZsVkZmaNVVMSyaWEw4CREfH1/GCqtSLiT10tFxFXA1dXjBtXMXwicGKPojYzs5ZQa3XWWaQHUn0kD08Djm1KRGZm1jZqTSKrRcT/Ae8ARMS/qd77yszM+pFak8jb+X5ZASBpNVLJxMzM+rFaG9Z/CFwDrCLpfGALYN9mBWVmZu2h1osN/yrpLmBzUjXWofm6DjMz68e6eyjVRhWjpuf/IyWNjIi7mhOWmZm1g+5KIifl/0sAY4B7SSWR9YHbgC2bF5qZmbW6LhvWI+LjEfFx4Elgo3zbkY2BDwOTeyNAMzNrXbX2zlo7IiZ1DOSryjdsSkRmZtY2au2d9ZCkM4DzSN18v4zvcWVm1u/VmkT2A74JHJqHJwC/aUpEZmbWNmrt4jsb+Hn+MzMzA5p7K3gzM+vjnETMzKxuTiJmZla3Wp8nsiZwOLBqcZmI2LZJcZmZWRuotXfWpcA44HRgbvPCMTOzdlJrEpkTEe7Sa2ZmC6i1TeSPkr4laUVJQzv+mhqZmZm1vFpLIl/J/w8vjAvgA40Nx8zM2kmtFxuObnYgZmbWfmrtnbUo6bYnH8ujbgR+GxHvNCkuMzNrA522iUjaWdLSefA3wMbAqflvY3zvLDOzfq+rksjDpESxF7BJRGxQmHaDpHubGpmZmbW8TksiEfEo8N08OFfSah3TJH0AXy9iZtbvddkmEhEdz1Q/HPi7pCmkx+OuSro9vJmZ9WO19s66XtIawFqkJPJwRLzV1MjMzKzldZlEJG0bETdI+lzFpNUkERGXNzE2MzNrcd2VRLYGbgB2qjItACcRM7N+rLs2kR/m/27/MDOz96jp3lmS/lfSkMLwspKObVpUZmbWFmq9AePYiJjZMRARM4AdmhKRmZm1jVqTyABJi3cMSBoELN7F/GZm1g/UmkTOA66XtL+krwJ/BX7f3UKStpf0iKTJko7sYr5NJM2V9IUa4zEzsxbQ7XUikgRcCNwHfJJ0ncgxEXFtN8sNAE4BtgOmAXdIGh8RD1aZ7wSgy/WZmVnr6TaJRERIujIiNgau6cG6NwUmR8QUAEkXAbsAD1bM923gD8AmPVi3mZm1gFqrs26V1NOT/MrA04XhaXncfJJWBnYlPb/dzMzaTK1PNvw4cKCkqcAbpCqtiIj1u1hGVcZFxfAvgCMiYm6qNetiZdIBwAEAI0eOrC1qMzNrqlqTyNg61j0NWKUwPAJ4tmKeMcBFOYEsB+wgaU5EXFm5sog4DTgNYMyYMZXJyMzMSlDrDRiflLQBsFUedVNEdPc8kTuANSSNBp4B9gC+VLHe+Y/dlXQ28KdqCcTMzFpTrVesHwqcDyyf/86T9O2ulomIOcDBpF5XDwGXRMQDkg6UdODChW1mZq2g1uqs/YHNIuINAEknAP8CftXVQhFxNXB1xbiqjegRsW+NsZiZWYuotXeWWPBJhnOp3nBuZmb9SK0lkbOA2yRdQUoeuwBnNi0qMzNrC7U2rP9M0o3AlnnUfhFxd9OiMjOztlBrdVYHka71cFWWmZnV3DvrB6QbLi5Lup7jLEnfb2ZgZmbW+mptE9kT+HBEzAaQdDxwF+AHU5mZ9WO1VmdNBZYoDC8OPN7waMzMrK3UWhJ5C3hA0l9JbSLbAf+U9EuAiDikSfGZmVkLqzWJXJH/OtzY+FDMzKzd1NrF9/eSFgPWzKMeiYh3mheWmZm1g5qSiKRtSL2zppK6964i6SsRMaFpkZmZWcurtTrrJOBTEfEIgKQ1SY/M3bhZgZmZWeurtXfWoh0JBCAiHgUWbU5IZmbWLmotiUyUdCZwbh7eC5jYnJDMzKxd1JpEDgQOAg4htYlMAE5tVlBmZtYeuk0ikhYBJkbEesDPmh+SmZm1i27bRCJiHnCvpJG9EI+ZmbWRWquzViRdsX478EbHyIjYuSlRmZlZW6g1ifyoqVGYmVlb6jKJSFqC1Ki+OjAJODMi5vRGYGZm1vq6axP5PTCGlEDGki46NDMzA7qvzlonIj4EkK8Tub35IZmZWbvoriQy/yaLrsYyM7NK3ZVENpA0K78WMCgPC4iIWLqp0ZmZWUvrMolExIDeCmRhvfPOO0ybNo3Zs2fXtfzpO6/Y4IgWFARPznyHX902g1lvzWvqtszMekutXXxb3rRp0xg8eDCjRo1CUo+Xf2fazMYHVRARDBs2i28Dx014uanbMjPrLbXexbflzZ49m2HDhtWVQHqDJAYuuTSrDvHNj82s7+gzSQRo2QTSQRKitWM0M+uJPpVEzMysdzmJmJlZ3ZxEzMysbk4iFX594nGcf+a4+cO/OuEYzv/db0uMyMysdTU1iUjaXtIjkiZLOrLK9L0k3Zf/bpG0QTPjqcWue+zN+MsuBGDevHlcM/5yPrPrF0uOysysNTXtOhFJA4BTgO2AacAdksZHxIOF2Z4Ato6IGZLGAqcBmzUrplqsvMpIhiw7lIfuv49XXnyBtddbnyHLDi0zJDOzltXMiw03BSZHxBQASRcBuwDzk0hE3FKY/1ZgRBPjqdmue+zN+Esv4KUXXuCzu+9VdjhmZi2rmdVZKwNPF4an5XGd2R/4SxPjqdkntt+Rm2+8ngfuu4uPbv2JssMxM2tZzSyJVLuqLqrOKH2clES27HRl0gHAAQAjRzb3ce+LLrYYm3x0SwYvvQwDBrTN7cPMzHpdM0si04BVCsMjgGcrZ5K0PnAGsEtEdHpTqYg4LSLGRMSY4cOHNzzYonnz5jHprjvZdY+9m7odM7N218wkcgewhqTRkhYD9gDGF2eQNBK4HNg7Ih5tYiw1e/zRh9lxq43YdIutWXX0amWHY2bW0ppWnRURcyQdDFwLDAB+FxEPSDowTx8H/AAYBpya73s1JyLGNCumWqy25tpcffM9ZYZgZtY2mnor+Ii4Gri6Yty4wuuvAV9rZgxmZtY8vmLdzMzq5iRiZmZ1cxIxM7O69ZnH41YadeSfG7q+8Qdv0e08N//9b5xw9FHMmzuXXffcm/0P+o+GxmBm1mpcEmmQuXPn8r/fP5xTz7mUK264lWuu+gOPP/pw2WGZmTWVk0iD3H/PRFYZ9QFGrDqKRRdbjO13/hw3Xnd19wuambUxJ5EGeeG56ayw0ru3Blt+xZV4/rnpJUZkZtZ8TiINEvHe24LlCyjNzPosJ5EGef+KK/Hcs8/MH35h+rMs//4VSozIzKz5nEQaZN0NNuKpqY8z7akneeftt7lm/OVsvd3YssMyM2uqPtvFd+rxn+nR/PdNm7lQ2xs4cCBHHfN/fPPLn2fe3Ll8dve9WH2tDy7UOs3MWl2fTSJl2GrbT7HVtp8qOwwzs17j6iwzM6ubk4iZmdXNScTMzOrmJGJmZnVzEjEzs7o5iZiZWd36bhffo5fp0ezrdzP9vq892e06fnDYwUy4/lqGDluOy6//V4+2b2bWjlwSaaBdvrgnvzn3srLDMDPrNU4iDbTx5luw9JBlyw7DzKzXOImYmVndnETMzKxuTiJmZlY3JxEzM6tbH+7i+2qPZl/YW8EDHHHQ/tx5683MfOVltttkXb552JF8bo+9F3q9Zmatqu8mkRKccMqZZYdgZtarXJ1lZmZ1cxIxM7O69akkEhFlh9CliCBo7RjNzHqizySRJZZYgpdffrllE0lEMOfNWTw5852yQzEza5g+07A+YsQIpk2bxosvvljX8s/P+HeDI1pQEDw58x1+dduMpm7HzKw39ZkksuiiizJ69Oi6lx975J8bGI2ZWf/Q1OosSdtLekTSZElHVpkuSb/M0++TtFEz4zEzs8ZqWhKRNAA4BRgLrAPsKWmditnGAmvkvwOA3zQrHjMza7xmlkQ2BSZHxJSIeBu4CNilYp5dgHMiuRUYImnFJsZkZmYN1Mw2kZWBpwvD04DNaphnZWB65cokHUAqrQC8LumRxoXaNMsBLzVqZTqhUWtqSw3dl+D9ifdnI7XL/ly10StsZhJRlXGV/W9rmSeNjDgNOG1hg+pNku6MiDFlx9EXeF82lvdnY/Xn/dnM6qxpwCqF4RHAs3XMY2ZmLaqZSeQOYA1JoyUtBuwBjK+YZzywT+6ltTnwakS8pyrLzMxaU9OqsyJijqSDgWuBAcDvIuIBSQfm6eOAq4EdgMnAm8B+zYqnJG1V/dbivC8by/uzsfrt/lSr3ibEzMxaX5+5d5aZmfU+JxEzM6ubk4iZmdXNScTMzOrWZ+7iWyZJQ7uaHhGv9FYsfYGkz3U1PSIu761Y+hpJqwJrRMTfJA0CBkbEa2XH1Y4kLQ58HhhF4VwaET8uK6YyOIk0xkTSlfYCRgIz8ushwFNA/feo7592yv+XBz4K3JCHPw7cCDiJ1EHS10m3DhoKrEa6uHcc8Iky42pjVwGvkr7/b5UcS2mcRBogIkYDSBoHjI+Iq/PwWOCTZcbWjiJiPwBJfwLW6bgANd+c85QyY2tzB5FujHobQEQ8Jmn5ckNqayMiYvuygyib20Qaa5OOBAIQEX8Bti4xnnY3quIOBs8Da5YVTB/wVr6jNgCSBtLJveqsJrdI+lDZQZTNJZHGeknS94HzSF/OLwMvlxtSW7tR0rXAhaT9uQfw93JDamv/kPTfwCBJ2wHfAv5YckztbEtgX0lPkKqzBERErF9uWL3LV6w3UG5g/yHwMdJJbwLwYzes10/SrqT9CTAhIq4oM552JmkRYH/gU6QT3rXAGeGTQF1yJ4X3iIgnezuWMjmJNIGkpSLi9bLj6AsqehMtCQxwbyJrBZJ+DNwE3BIRb5QdT1lcndVAkj4KnAEsBYyUtAHwjYj4VrmRtacqvYlWxr2JekzSJLpo++hv1S8NNBXYE/ilpNdICWVCRFxValS9zCWRBpJ0G/AFUg+tD+dx90fEeuVG1p4k3UPuTVTYn5Miot83ZvZEZ9UuHfpb9UujSVoB2A34LrBsRAwuOaRe5ZJIg0XE09ICD2ycW1YsfcBbEfF2x/50b6L6OEk0h6QzgHVIvQZvIv2AvKvUoErgLr6N9XSu0gpJi0n6LvBQ2UG1screRJfi3kR1k/SapFkVf09LukLSB8qOrw0NIz0raSbwCvBSRMwpNaISuDqrgSQtB5xMusBQwHXAoRHhbr51cG+ixpL0I9Ljpy8g7c89gBWAR4BvRsQ25UXXviR9EPg08B+kjh8jSg6pVzmJmPUTkm6LiM0qxt0aEZtLujciNigrtnYkaUdgK1IX9GWBfwE3RcTvSg2sl7k6q4Ek/V7SkMLwspL61QHVCJIuyf8nSbqv8q/s+NrYPEm7SVok/+1WmOZfkz03ltQG8vmIWDsi9utvCQRcEmkoSXd39CLqapx1TdKKETHdF3M1Vm73OBn4CClp3EqqgnkG2Dgi/llieG1J0vuBTfLg7RHxQpnxlMFJpIEk3QtsExEz8vBQ4B/uktpzkgYA10aEb2BpLUnSF4Gfku4sLVLV1uERcVmZcfU2d/FtrJNIN2W7jPRLbzfguHJDak8RMVfSm5KWiYhXy46nL5A0HPg6733+xVfLiqnNfZ9009UXYP7+/RvgJGI9l3sSTSY9pGZb0i+Tz0XEg6UG1t5mA5Mk/RWYf1uJiDikvJDa2lWk6xn+hq9faoRFKqqvXqYftjM7iTRIRMyTdFJEfARw4miMP+c/a4wlI+KIsoPoQ64p3GUaYHfg6i7m75PcJtJAuR/+fcDlvpZh4bhNpPEkHUu6WWC/O9E1i6TPA1uQah765V2mnUQaKN+E7X2kqoLZeXRExNLlRdW+JI0H9nabSGMUjs+3gHd49/kXPj6tbq7OaqD+duO1XuA2kQby8dlYkj4HnAAsT0rI/TIpuyTSYJJ25t2HKN0YEX8qM552Jukr1cZHxO97O5Z2JmntiHhY0kbVpkdEv7tpYCNImgzsFBH9+v54TiINJOl40oVH5+dRewITI+LI8qKy/k7SaRFxgKTio4Xnf/EjYtsSwmp7km6OiC3KjqNsTiINlG/JsWFEzMvDA4C7/dCf+khaA/gJ6XbbS3SMjwjfcbYO+TYn10TELEn/A2wEHOOSSH0knUy6geWVpHYmACLi8rJiKkO/69PcC4YUXi9TVhB9xFnAb4A5wMeBc4BzS42ovX0/J5Atge2As0n71+qzNPAm6S7TO+W/HUuNqARuWG+snwB352oDkdpGjio3pLY2KCKul6R8v6yjJd0E/LDswNpUxwWGnwHGRcRVko4uMZ52d1hEvFIcIWl0WcGUxUmkgSLiQkk3ktpFBBwREc+VG1Vbm53vBPCYpINJNwpcvuSY2tkzkn5Let7NCZIWx7URC+OPksZGxCyY/1yRS4F+9Thst4k0UL6u4ULSM9bf6G5+65qkTUhPhhwCHEOqPvi/iLitzLjalaQlge2BSRHxmKQVgQ9FxHUlh9aWJH0G+C9SyW4tUnXrXhFxT5lx9TYnkQaStDXp1gefAW4HLgb+FBGzu1zQqpI0BvgesCqwaB4d7qhgrULSZ0mJZDDpXnmPlRtR73MSaYLcK2tb0h1Tt+9vFx81iqRHgMOBScC8jvF+noiVSdKvWPAhXtsCU4Cp0P8uhnWbSINJGkTqpbE7qQulL4yr34sRMb7sIMwq3FkxPLGUKFqESyINJOliYDPgGuAS0hXr87peyjoj6ROkCzavpx/3wzdrZS6JNNZZwJciws9qaIz9gLVJ7SEdyTgAJxErnaQtgKNJbXYDeffeWf3qYliXRBpA0rYRcUO+Idt7+JdzfSRN8qOFrVVJepj0jPqJFB7yFREvlxZUCVwSaYyPATeQ2kKC/Iuk8N9JpD63SlrHT4e0FvVqRPyl7CDK5pJIA0g6jPcmD/JrIuJnJYXW1iQ9BKwGPEFqE+moLnAXXytdvuHqANKPxGKbXb+6F5lLIo2xVP6/Fulq9atIJ7ydgAllBdUHbF92AGZd2Cz/3zj/7/gR2a/uiuwk0gAR8SMASdcBG0XEa3n4aNJtEKwOvh7EWtyNVcb1u6odJ5HGGgm8XRh+GxhVTihm1mSvF14vQbqDb797QJWTSGOdC9wu6QrSL5Jd8cWGZn1SRJxUHJb0U6DfXRzrhvUGy48g3SoPToiIu8uMx8x6h6RlgdsjYo2yY+lNLok0WO6Z0a96Z5j1R5Im8W4byABgOPDj8iIqh0siZmZ1kLRqYXAO8HxEzCkrnrI4iZiZWd38VDMzM6ubk4iZmdXNScT6PElzJd1T+Duyi3k/K2mdOrbxevdzdbuOAyXt0808Z3TEJ2mqpOUWdrtmC8NtItbnSXo9Ipbqfk6QdDbpkcaXNWsbjSJpKjAmIl7qze2aFbkkYv2WpOMlPSjpPkk/lfRRYGfgxFxiWU3S1yXdIeleSX+QtGRedrSkf+VpxxTWKUknSrpf0iRJu3ey7X3ydu+VdG4ed7Sk70r6oKTbC/OOknRffn1jfva8WUvwdSLWHwySdE9h+CfAX0l3FFg7IkLSkIiYKWk8hZKIpJkRcXp+fSywP/Ar4GTgNxFxjqSDCuv+HLAhsAGwHHCHpAkRMb1jBknrAt8DtoiIlyQNLQYbEQ9JWkzSByJiCulRy5c0bneYNY5LItYf/DsiNiz8XQzMAmYDZ+SHib3ZybLrSbopX1i2F7BuHr8FcGF+fW5h/i2BCyNibkQ8D/yDdGfnom2ByzqqoSLilSrbvQTYLb/eHbi41jdr1pucRKxfyheFbQr8AfgscE0ns54NHJyfsPgj0o325q+myvyqMq7aPN01Rl4M7CZpzRRuPFbDes16nZOI9UuSlgKWiYirge+QqqAAXgMGF2YdDEyXtCipJNLhZmCP/Lo4fgKwu6QBkoaTnnp5Owu6npQghuVYhlZMJyIeJz1y9X9wKcRamNtErD+obBO5htSmcZWkJUglg//I0y4CTpd0CPAF0kn8NuBJYBLvJphDgQskHUoqzXS4AvgIcC+ptPFfEfFcMZiIeEDSccA/JM0F7gb2rRL3xcCJwOg63rNZr3AXXzMzq5urs8zMrG5OImZmVjcnETMzq5uTiJmZ1c1JxMzM6uYkYmZmdXMSMTOzujmJmJlZ3f4fhj9wDS0hgbsAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a2acfd8d0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -933,27 +937,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0,0.5,'Frecuencia de compra del producto')"
+       "Text(0, 0.5, 'Frecuencia de compra del producto')"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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tDVwJTOzkPpiZWRcp8vDaYLJmLdrvK8yQdFZEvNhi0Z2AeRExP63nYuAAIJ8UAhiWXg8HHu1E7GZm1sWKPLz2Q2Aw8IM0fHga95EWy20OLMgNtwE7180zDfijpE8CGwJ7NlqRpKOBowHGjx9fIGQzM1sbRZLCjhGxfW74ekl3FVhODcbVP/dwKHBeRJwuaRfgQknbRsSqNRaKOBs4G2Dq1KkdPjthZmbrpsiN5pWSJrcPpJpIKwss1waMyw2P5aXFQ0cBlwBExC3ABsDoAus2M7MSFLlS+AJwg6T5ZL/+JwBHFljuNmBKqsL6CHAI8P66eR4G3gacJ+nVZEnBje2ZmVWkaVKQNABYBkwBtiRLCoVqH0XECknHAlcDA4FzIuI+SScDsyJiOvA54MeSPktWtHRERLh4yMysIk2TQkSsknR6ROwC3N3ZlUfElWTVTPPjTsi9ngPs2tn1mplZOYrcU/ijpHdJanTj2MzM+pAi9xSOI6suulLSMrIipIiIYc0XMzOz3qbIE81DW81jZmZ9Q5ErBSQdDOxGdjP4TxFxWalRmZlZJVreU5D0A+AY4B7gXuAYSWeWHZiZmXW/IlcKuwPbtlcVlXQ+WYIwM7M+pkjto7lAvsGhcaxF9VQzM+v5ilwpjAL+JumvaXhH4BZJ08F9NZuZ9SVFksIJrWcxM7O+oEiV1Bu7IxAzM6tekXsKZmbWTzgpmJlZjZOCmZnVdHhPQdI9vLSnNFjd9tF2pUVlZmaVaHajeb9ui8LMzHqEDpNCRDzU/lrSBGBKRFwraUiz5czMrPcq0vbRR4FfAWelUWMBN4hnZtYHFbnR/Amy3tGWAkTEP4BNygzKzMyqUSQpPB8RL7QPSBpE4xvQZmbWyxVJCjdK+m9giKS9gEuBy8sNy8zMqlAkKRwPPE7WXPbHgCuBr5QZlJmZVaNI20ergB+nPzMz68PW5uE1APzwmplZ31Pk4bVPpP8Xpv+HAc+VFpGZmVWm5cNrknaNiF1zk46XNBM4uezgzMysexW50byhpN3aByS9EdiwvJDMzKwqRZqrOAo4R9JwsnsMTwEfLjUqMzOrRJHaR7OB7SUNAxQRT5UflpmZVaFww3YRsbTMQMzMrHruZMfMzGqcFMzMrKZQ8ZGkbYGtgQ3ax0XEBWUFZWZm1SjSn8KJwPfS31uAbwL7F1m5pH0kzZU0T9LxHczzXklzJN0n6eediN3MzLpYkSuFdwPbA3dExJGSNgV+0mohSQOBM4G9gDbgNknTI2JObp4pwJeAXSNisST302BmVqEi9xSWpUbxVqRqqY8BWxRYbidgXkTMT/0xXAwcUDfPR4EzI2IxQEQ8Vjx0MzPrakWSwixJI8haSZ0N3A78tcBymwMLcsNtaVzeq4BXSZop6VZJ+xRYr5mZlaRp8ZEkAadExBLgR5KuAoZFxN0F1q0G4+pbXR0ETAH2IOv7+U+Stk3by8dxNHA0wPjx4wts2szM1kbTK4WICOCy3PCDBRMCZFcG43LDY4FHG8zzu4h4MSL+CcwlSxL1cZwdEVMjYuqYMWMKbt7MzDqrSPHRrZJ2XIt13wZMkTRJ0nrAIcD0unkuI6vRhKTRZMVJ89diW2Zm1gWK1D56C/AxSQ8Bz5IVC0WrTnYiYoWkY4GrgYHAORFxn6STgVkRMT1N21vSHGAl8IWIeGId9sfMzNZBkaSw79quPCKuJOvTOT/uhNzrAI5Lf2ZmVrEiraQ+JGkHYDeyG8UzI+L20iMzM7NuV+SJ5hOA84FRwGjgXElfKTswMzPrfkWKjw4FXhcRywEknUr2rMLXygzMzMy6X5HaRw+SawgPWB94oJRozMysUkWuFJ4H7pN0Ddk9hb2AP0v6P4CI+FSJ8ZmZWTcqkhR+m/7azSgnFDMzq1qR2kfnd0cgZmZWvSK1j/aTdIekJyUtlfS0JPfXbGbWBxUpPvoucDBwT3rYzMzM+qgitY8WAPc6IZiZ9X1FrhT+C7hS0o1kNZEAiIgzSovKzMwqUSQpfB14huxZhfXKDcfMzKpUJCmMjIi9S4/EzMwqV+SewrWSnBTMzPqBIknhE8BVkpan6qiukmpm1kcVeXhtaHcEYmZm1StyTwFJ+wNvToMzIuL35YVkZmZVKfJE86nAp4E56e/TaZyZmfUxRa4U3g68NiJWAUg6H7gDOL7MwMzMrPsVudEMMCL3engZgZiZWfWKXCmcAtwh6QZAZPcWvlRqVGZmVokitY9+IWkGsCNZUvhiRPyr7MDMzKz7FbnRfBDwXERMj4jfAcslHVh+aGZm1t2K3FM4MSKeah+IiCXAieWFZGZmVSmSFBrNU+j5BjMz612KJIVZks6QNFnSFpK+A8wuOzAzM+t+RZLCJ4EXgF8ClwDLyNpDMjOzPqZI7aNn8YNqZmb9QtGH18zMrB9wUjAzsxonBTMzqyny8NqrJF0n6d40vJ2kr5QfmpmZdbciVwo/Jmvr6EWAiLgbOKTMoMzMrBpFksLLIuKvdeNWFFm5pH0kzZU0T1KHNZgkvVtSSJpaZL1mZlaOIklhkaTJQED2BQ4sbLWQpIHAmcC+wNbAoZK2bjDfUOBTwF86EbeZmZWgSFL4BHAWsJWkR4DPAB8vsNxOwLyImB8RLwAXAwc0mO+rwDeB5cVCNjOzsrRMCulLfU9gDLBVROwWEQ8WWPfmwILccFsaVyPpdcC4Vn0+Szpa0ixJsx5//PECmzYzs7XR4RPNko7rYDwAEXFGi3WrwbjIrWcA8B3giFZBRsTZwNkAU6dOjRazm5nZWmrWzMXQ9H9Lsg52pqfhdwI3FVh3GzAuNzwWeLRu/dsCM1KieTkwXdL+ETGrwPrNzKyLdZgUIuIkAEl/BHaIiKfT8DTg0gLrvg2YImkS8AhZNdb359b/FDC6fTj17vZ5JwQzs+oUudE8nqyV1HYvABNbLRQRK4BjgauBvwGXRMR9kk6WtP9axGpmZiUr0lnOhcBfJf2W7J7AQcD5RVYeEVcCV9aNO6GDefcosk4zMytPkaazvy7pD8Cb0qgjI+KOcsMyM7MqFOpWMyJuB24vORYzM6uYW0k1M7MaJwUzM6txUjAzs5oi/Sm8QdJtkp6R9IKklZKWdkdwZmbWvYpcKXwfOBT4BzAE+AjwvTKDMjOzahStfTRP0sCIWAmcK+nmkuMyM7MKFEkKz0laD7hT0jfJ+lLYsNywzMysCkWKjw4HBpI1WfEsWSN37yozKDMzq0aRJ5ofSi+XASeVG46ZmVWpWX8Kl0TEeyXdQ64fhHYRsV2pkZmZWbdrdqXw6fR/v+4IxMzMqtesP4WF6eUAYGFELAeQNATYtBtiMzOzblbkRvOlwKrc8EqKdbJjZma9TJGkMCgiap3spNfrlReSmZlVpUhSeDzfU5qkA4BF5YVkZmZVKfLw2jHAzyR9HxCwAPhgqVGZmVklijyn8ADwBkkbAYqIp8sPy8zMqtAyKUhan+wJ5onAIEkARMTJpUZmZmbdrkjx0e+Ap4DZwPPlhmNmZlUqkhTGRsQ+pUdiZmaVK1L76GZJryk9EjMzq1yRK4XdgCMk/ZOs+EhAuO0jM7O+p0hS2Lf0KNbRiy++SFtbG8uXLy80/4/3f0XJEa0pCB5a8iLf+8tilj6/qvUCZmYVKdR0tqTdgCkRca6kMcBG5YdWXFtbG0OHDmXixIm0145q5sW2Jd0Q1WoRwahRS/kk8PWbnujWbZuZdUbLewqSTgS+CHwpjRoMXFRmUJ21fPlyRo0aVSghVEESg142jAkjBlcdiplZU0VuNB8E7E/W6xoR8SgwtMyg1kZPTQjtJCF6doxmZkWSwgsREaSOdiS5f2Yzsz6qSFK4RNJZwAhJHwWuBX5cblhmZlaFIjeavy1pL2ApsCVwQkRcU3pkZmbW7YpUSSUlgX6RCL7/ra+z8chRHHbUMQB877SvMnLMJhz24Y9VHJmZWfmK1D56WtLS9Ldc0kpJS4usXNI+kuZKmifp+AbTj5M0R9Ldkq6TNGFtdqIrHXTI4Uz/1S8AWLVqFVdN/w3vOOg9FUdlZtY9ihQfrVHTSNKBwE6tlpM0EDgT2AtoA26TND0i5uRmuwOYGhHPSfo48E3gfZ2Iv8ttPm48IzYeyd/uvZsnH3+MrbbdjhEbj6wyJDOzblOo+CgvIi5r9Ku/gZ2AeRExH0DSxcABQC0pRMQNuflvBT7Q2XjKcNAhhzP90p+z6LHHOPB9h1UdjplZtynSn8LBucEBwFRS9dQWNifrpa1dG7Bzk/mPAv7QQQxHA0cDjB8/vsCm183b9tmPH5x+CitWvMip33dFKzPrP4pcKbwz93oF8CDZL/5WGj2p1TCZSPoAWbLZvdH0iDgbOBtg6tSpRRLSOhm83nrs+MbdGDpsOAMHDix7c2ZmPUaRewpHruW624BxueGxwKP1M0naE/gysHtE9IhOfFatWsU9t8/iWz86r+pQzMy6VZHaR+dLGpEb3ljSOQXWfRswRdIkSesBhwDT69b9OuAsYP+IeKxzoZfjgb/fz35v2oGddt2dCZMmVx2OmVm3KlJ8tF1E1JoVjYjF6cu8qYhYIelY4GpgIHBORNwn6WRgVkRMB75F1uLqpantoocjYv+12ZGuMvlVW3HlzDurDMHMrDJFksIASRtHxGIASSMLLkdEXAlcWTfuhNzrPTsRq5mZlazIl/vpZF1y/orsRvF7ga+XGpWZmVWiyI3mCyTNAt5KVqPo4LoH0MzMrI8o0koqwEjg2Yj4HvC4pEklxmRmZhUp8vDaiWTPEGwJnMvqntd2LTe0tTfx+Cu6dH3Tjy22qzNvuJbTpn2JVStXctChh3PUJz7bpXGYmZWtz/S8VrWVK1fyja98gR9ccCm/vf5Wrvrdr3ng7/dXHZaZWae457Uucu+dsxk3cQvGTpjI4PXWY5/9D2bGH69svaCZWQ/inte6yGP/WsjLN9u8NrzJKzbj3/9aWGFEZmad557Xukh2MbWm9ECemVmv0TQppD4Rrk4PmTkRNLHpKzbjX48+Uht+bOGjbLLpyyuMyMys85oWH0XESuA5ScO7KZ5ea5vtd+DhBx+g7eGHePGFF7hq+m/Yfa99qw7LzKxTijzRvBy4R9I1pBpIABHxqdKiWkcPnvqOptPvblvSdPraGDRoEF/66jf5+AfexaqVKznwfYfxyi1f3eXbMTMrU5GkcEX6sxbe9Na9edNb9646DDOztdZhUpA0PiIejojzuzMgMzOrTrN7Cpe1v5D0626IxczMKtYsKeTrU25RdiBmZla9ZkkhOnhtZmZ9VLMbzdtLWkp2xTAkvSYNR0QMKz06MzPrVh0mhYgY2J2BmJlZ9Qp1q9nrTGv+rN12nVzd3R95qOU8J3zuWG667mpGjhrNb667pZNbMDPrGYp2smMtHPCeQ/nhhb+qOgwzs3XipNBFXv+GXRk2YuOqwzAzWydOCmZmVuOkYGZmNU4KZmZW46RgZmY1fbRK6lNNJ5fRdPYXP3EUs26dyZInn2CvHbfh4587noMPObzLt2NmVqa+mRQqcNqZP606BDOzdebiIzMzq3FSMDOzmj6TFCJ6dkOuEUG4sVkz6+H6RFLYYIMNeOKJJ3psYogIVjy3lIeWvFh1KGZmTfWJG81jx46lra2Nxx9/vND8/168rOSI1hQEDy15ke/9ZXG3btfMrLP6RFIYPHgwkyZNKjz/vsdfUWI0Zma9V6nFR5L2kTRX0jxJxzeYvr6kX6bpf5E0scx4zMysudKSgqSBwJnAvsDWwKGStq6b7ShgcUS8EvgOcFpZ8ZiZWWtlXinsBMyLiPkR8QJwMXBA3TwHAOen178C3iZJJcZkZmZNqKwaO5LeDewTER9Jw4cDO0fEsbl57k3ztKXhB9I8i+rWdTRwdBrcEphbStCdMxpY1HKu/sHHIuPjsJqPxWo95VhMiIgxrWYq80Zzo1/89RmoyDxExNnA2V0RVFeRNCsiplYdR0/gY5HxcVjNx2K13nYsyiw+agPG5YbHAo92NI+kQcBw4MkSYzIzsybKTAq3AVMkTZK0HnAIML1ununAh9LrdwPXR099As3MrB8orfgoIlZIOha4GhgInBMR90k6GZgVEdOBnwIXSppHdoVwSFnxlKBHFWdVzMci4+Owmo/Far2Dh1ssAAAIDklEQVTqWJR2o9nMzHqfPtH2kZmZdQ0nBTMzq3FSMDOzGicFMzOr6ROtpJZJ0ncj4jOSLqfxg3X7VxBW5VLbVpuSO4ci4uHqIqqGpBtofF68tYJwKiVpU+AbwGYRsW9q62yXiOg3HZh39D3Rrjd8XzgptHZh+v/tSqPoQSR9EjgR+DewKo0OYLvKgqrO53OvNwDeBayoKJaqnQecC3w5Df8d+CVZ1fP+ov174mDg5cBFafhQ4MEqAuosV0ktIP0qPj8iPlB1LD1Beq5k54h4oupYeiJJN0bE7lXH0d0k3RYRO0q6IyJel8bdGRGvrTq27ibppoh4c6txPZGvFAqIiJWSxkhaL7X42t8tAJ6qOoieQNLI3OAA4PVkvxD7o2cljSIVn0h6A/33PBkjaYuImA8gaRLQsjG6nsBJobgHgZmSpgPPto+MiDMqi6g684EZkq4Anm8f2U+PxWyyL0GRFRv9k6yfkP7oOLKmayZLmkn2JfjuakOqzGfJPiPz0/BE4GPVhVOci49akHRhRBwuaQlZR0BriIiTKgirUpJObDS+Px4LW1Nq2HJLsiQ5NyJerDikykhaH9gqDd4fEc83m7+ncFJoQdIcst7jLgf2qJ8eEf22VVdJQ4GIiGeqjqVKkt5I9kswXxPrgsoCqoikDzYa30+PxcvIrpwmRMRHJU0BtoyI31ccWksuPmrtR8BVwCRgVm68yIoNtqgiqCpJ2pasVtbINLwI+GBE3FdpYBWQdCEwGbgTWJlGB9DvvgiBHXOvNwDeBtxO/zwW55IVLe6ShtuAS4EenxR8pVCQpB9GxMerjqMnkHQz8OWIuCEN7wF8IyLeWGlgFZD0N2BrN/n+UpKGAxf2hrr5Xa29Y526mlh3RcT2VcfWip9oLsgJYQ0bticEgIiYAWxYXTiVupf+W9uoleeAV1UdREVekDSE1TWxJpOrlNGTufjI1sZ8Sf/D6gf7PkBW66bfyD25OhSYI+mvrFkTqz/+Os53ojUA2Bq4pKJwqnYiWbHzOEk/A3YFjqg0ooJcfGSdJmlj4CSyE13ATcC0iFhSaWDdSNLuZPt+GvBf+UnAaRGxcyWBVSglxi+kwRXAw8CxEfHF6qKqRrrXdA+wjKwK918iYlG1URXjpGCdJmkqWVMGE1l9tRkR0e+auZB0e0TsUDfubh+L2rj+eizeCuwGvImsMsqdwE0R8b+VBlaAk4J1mqS5ZG3+3Mvqto+IiIcqC6qbSfo48J9kH/gHcpOGAjP7U5MoPhaNpeZxdgTeAhwDLIuIrZovVT0nBes0SX+OiN2qjqNKqWbNxsApwPG5SU/3t2dXfCxeStJ1ZJUvbgH+BPw5Ih6rNqpinBSs0yS9jazVx+tY8+bqbyoLyqwHkfQdsnawngdmkt13uyUillUaWAFOCtZpki4ie3z/PnJNZ0fEh6uLyqznkbQRcCRZcevLI2L9ikNqyVVSbW1sHxGvqToIs55K0rFkN5lfDzwEnENWjNTjOSnY2rhV0tYRMafqQMx6qCHAGcDsiOhVnS65+Mg6LTXtMJnsgbXnSe1A9ceqh2Z9jZOCdZqkCY3G96cqqWZ9lZOCmZnVuEE8MzOrcVKwXk/SREmHVh2HWV/gpGA9nqSVku6UdJ+kuyQdJ2lAmjYQOJOsM5e1Wfd5kgr3IyxpmqTPr822zHoDV0m13mBZRLwWQNImwM+B4WTNE78SODUi5lYYn1mf4SsF61VS+zFHA8dKEvAK4IsAknaSdLOkO9L/LeuXV+b7kuZIugLYJDft9ZJulDRb0tWSXtEsFkkflXRbunr5deqXt36e3dNVzp0prqFp/BfSsndLOimNmyjpfkk/kXSvpJ9J2lPSTEn/kLRTs/2UdISk30i6Ks3/zVwcP5Q0K11tndTpA2/9R0T4z389+g94psG4xcCmwB7A79O4YcCg9HpP4NcNljsYuAYYCGwGLAHeDQwGbgbGpPneB5zTYPlpwOfT61G58V8DPtlg/suBXdPrjciuzvcGziZ7vmMAWb+9byZrinwF8Jo0fjbZk7ACDgAua7afZJ24zCe7itqA7EnacWnayPR/IDAD2K7q99V/PfPPxUfWW6nBuOHA+ZKmkPWKNrjBPG8GfhERK4FHJV2fxm8JbAtck12AMBBY2CKGbSV9DRhB9oV/dYN5ZgJnpN63fhMRbZL2JksMd6R5NgKmkHVK88+IuAdA0n3AdRERku4hSxqt9vO6iHgqLT8HmAAsAN4r6WiypPQKsl7R7m6xf9YPOSlYryNpC2Al8Bjw6tykrwI3RMRBkiaS/SJupNHDOQLui4hdOhHKecCBEXGXpCPIrlrW3FDEqamY6u1kzYPsmbZ1SkSctUYAWcz5fnxX5YZXsfrz2mw/88uvBAZJmkTWINuOEbFY0nlkVxJmL+F7CtarSBoD/Aj4fkTUf7kPBx5Jr4/oYBU3AYdIGpjuGbwljZ8LjJG0S9rOYEnbtAhnKLBQ0mDgsA7inRwR90TEacAsstZlrwY+nFrQRNLm6QZ6UUX2M28Y8CzwlKRNgX07sS3rZ3ylYL3BEEl3khWTrAAuJGtsrN43yYpVjgOubzAd4LfAW8n6z/07cCNARLyQqqb+X+o0ZhDwXbLmwTvyP8BfyMru7yFLEvU+I+ktZL/a5wB/iIjnJb0auCUVVT0DfCDNU0SR/axJVzJ3pH2ZT1akZdaQm7kwM7MaFx+ZmVmNk4KZmdU4KZiZWY2TgpmZ1TgpmJlZjZOCmZnVOCmYmVnN/wNogJce98jvhQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a2accbda0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -968,27 +974,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0,0.5,'Frecuencia de compra del producto')"
+       "Text(0, 0.5, 'Frecuencia de compra del producto')"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a2b89b828>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1003,27 +1011,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0,0.5,'Frecuencia de compra del producto')"
+       "Text(0, 0.5, 'Frecuencia de compra del producto')"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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RRx3FgQceOLB1Z2XGOL5cWF0K2BTwnZLMzGpqq6224qmnnmra8cu0OFYoLC8gjXlc1pxwzMys7spMx/1WFYGYmdng0N1l1X9Lg9u0AkTEbk2JyMzMaq27FsdJ+edewN8Av8zr+wFPNTEmMzOrsYbTcSPipoi4CdgkIr4QEb/Nj38AtqwuRDMz64399tuPzTffnMcee4y2tjbOOqvhPfT6pMzg+BhJa0XEEwD5nuBjBjQKM7MlVYnpswPtwgsvbOrxyySOfwWmS3oir48HjmhaRGZmVmtlZlVdI2kCsF4uejQi3m5uWGZmVldlTgAcDnwR2CoXTZf084j4a1MjMzOzWirTVfVTYDjwk7x+QC47vFlBmZkNZhGBpFaH0VBEwzMtSimTOCZFxMaF9Rsk3devWs3MllAjRozgpZdeYvTo0bVMHhHBSy+9xIgRI/p8jDKJY6GktSPiTwCS1gIW9vQkSWcDnwVeiIgNc9nKwK9JA+xPAZ+PiHlKr+6pwM7AG8DBEXF3fs5BwDfyYb8bEVPK/3pmZtVqa2ujvb2duXPre0m/ESNG0NbW1ufnl0kc/wHcmGdVCVgDOKTE884FTgfOK5QdC1wfEZMlHZvXvwrsBEzIj4+TusI+nhPN8cBE0lnsMyVNjYh5Jeo3M6vc8OHDWXPNNVsdRlN1mzgkLQW8SfqHvi4pcZSaVRURf5Q0vlPx7sDWeXkKMJ2UOHYHzovU8Xa7pBUlfTjvOy0iXs7xTAN2BJo7SdnMzBrq9kZOEfEucHJEvB0R90fEff2civuhiJiTjz0HWDWXjwVmF/Zrz2WNyt9H0hGSZkiaUecmopnZYNfjHQCB6yR9Ts0d5enq2NFN+fsLI86IiIkRMXHMGJ/YbmbWLGUSx5eBS4B3JM2X9Jqk+X2s7/ncBUX++UIubwfGFfZrA57rptzMzFqkzD3HV4iIpSJieESMzOsj+1jfVOCgvHwQcGWh/EAlmwGv5q6sa4HPSFpJ0krAZ3KZmZm1SKl7jkvai3RF3AD+JyKuKPGcC0mD26tIaifNjpoMXCzpMOAZYJ+8+9WkqbizSNNxDwGIiJclfQe4K+/37Y6BcjMza40ylxz5CbAOi2YyfUnS9hFxZHfPi4j9Gmzatot9A+jyeBFxNnB2T3GamVk1yrQ4PgVsmP+5I2kK8EBTozIzs9oqMzj+GLB6YX0ccH9zwjEzs7or0+IYDTwi6c68Pgm4TdJU8L3HzcyGmjKJ4z+bHoWZmQ0aZW7kdFMVgZiZ2eBQZozDzMzsPU4cZmbWK04cZmbWKw3HOCQ9QNcXFBTpnL2NmhaVmZnVVneD45+tLAozMxs0GiaOiHi6Y1nSGsCEiPiDpOW6e56ZmS3ZehzjkPSPwKXAz3NRG9DjRQ7NzGzJVGZw/EhgC2A+QEQ8zqI795mZ2RBTJnG8HRHvdKxIWpoGd+EzM7MlX5nEcZOkrwPLSdqedDfA3zY3LDMzq6syieNYYC7pUupfJN106RvNDMrMzOqrzLWq3gXOzA8zMxvi+nICIAA+AdDMbGgqcwJgxy1dz88/9yfdF9zMzIagHk8AlLRFRGxR2HSspFuAbzc7ODMzq58yg+PLS9qyY0XSJ4DlmxeSmZnVWZlLhxwGnC1pFGnM41Xg0KZGZWZmtVVmVtVMYGNJIwFFxKvND8vMzOqq9P04ImL+QCUNSf8q6SFJD0q6UNIISWtKukPS45J+LWmZvO+yeX1W3j5+IGIwM7O+qfxGTpLGAkcDEyNiQ2AYsC/wfeCUiJgAzCN1kZF/zouIdYBT8n5mZtYirboD4NKkS5gsDXwAmAN8mnQVXoApwB55efe8Tt6+rSRVGKuZmRWUuq+GpA2B9YERHWURcV5fKoyIZyWdBDwDvAlcB8wEXomIBXm3dmBsXh4LzM7PXSDpVWA08GJf6jczs/4pcz+O44Ef58c2wA+A3fpaoaSVSK2INYHVSFN7d+pi146z1rtqXbzvjHZJR0iaIWnG3Llz+xqemZn1oExX1d7AtsCfI+IQYGNg2X7UuR3wZETMjYi/ApcDnwBWzF1XkG4W9VxebgfGwXuXdB8FvNz5oBFxRkRMjIiJY8aM6Ud4ZmbWnTKJ4818ocMFeUruC8Ba/ajzGWAzSR/IYxXbAg8DN5KSFMBBwJV5eWpeJ2+/ISJ8PxAzsxYpM8YxQ9KKpKvjzgT+AtzZ1woj4g5JlwJ3AwuAe4AzgKuAiyR9N5edlZ9yFnC+pFmklsa+fa3bzMz6r9vEkVsEJ0bEK8DPJF0DjIyI+/tTaUQcDxzfqfgJ4GNd7PsWsE9/6jMzs4HTbVdV7hK6orD+VH+ThpmZDW5lxjhulzSp6ZGYmdmgUGaMYxvgi5KeBl4nTY8N38jJzGxoKpM4ujrHwszMhqgyV8d9WtKmwJakE+9uiYi7mx6ZmZnVUpkzx/+TdK2o0cAqwDmSvtHswMzMrJ7KdFXtB2ySp8UiaTLpHIzvNjMwMzOrpzKzqp6icHFD0uVG/tSUaMzMrPbKtDjeBh6SNI00xrE9cLOk0wAi4ugmxmdmZjVTJnH8Jj86TG9OKGZmNhiUmVU1pad9zMxs6Cgzq+qzku6R9LKk+ZJekzS/iuDMzKx+ynRV/QjYC3jAlzM3M7Mys6pmAw86aZiZGZRrcXwFuFrSTaQZVgBExA+bFpWZmdVWmcTxPdLNm0YAyzQ3HDMzq7syiWPliPhM0yMxM7NBocwYxx8kOXGYmRlQrsVxJPAVSe8Af81lEREjmxeWDTXjj72qx32emrxLBZGYWU/KnAC4QhWBmJnZ4FCmxYGk3YCt8ur0iPhd80IyM7M6K3Pm+GTgGODh/Dgml5mZ2RBUpsWxM/DRiHgXQNIU4B7g2GYGZmZm9VRmVhXAioXlUf2tVNKKki6V9KikRyRtLmllSdMkPZ5/rpT3laTTJM2SdH++ja2ZmbVImcRxInCPpHNza2Mm8P/6We+pwDURsR6wMfAIqQVzfURMAK5nUYtmJ2BCfhwB/LSfdZuZWT+UmVV1oaTpwCRAwFcj4s99rVDSSNJA+8H5+O8A70jaHdg67zaFdN+PrwK7A+fla2XdnlsrH46IOX2NwczM+q7M4PiewBsRMTUirgTekrRHP+pcC5gLnJMv1/4LScsDH+pIBvnnqnn/saQLLXZoz2VmZtYCZbqqjo+IVztWIuIV4Ph+1Lk0sCnw04jYBHid7gfa1UXZ+67UK+kISTMkzZg7d24/wjMzs+6USRxd7VPq/I8G2oH2iLgjr19KSiTPS/owQP75QmH/cYXntwHPdT5oRJwRERMjYuKYMWP6EZ6ZmXWnTOKYIemHktaWtJakU0gD5H2Sx0dmS1o3F21LOj9kKnBQLjsIuDIvTwUOzLOrNgNe9fiGmVnrlGk5/DPwTeDXef064Bv9rPefgQskLQM8ARxCSmIXSzoMeAbYJ+97NelcklnAG3lfMzNrkTKzqnoag+i1iLgXmNjFpm272DdIF1pc4vjCfmY2GJU9AdDMzAxw4jAzs15y4jAzs14pcwLgRyRdL+nBvL6RpP4OjpuZ2SBVpsVxJvA18t3/IuJ+YN9mBmVmZvVVJnF8ICLu7FS2oBnBmJlZ/ZVJHC9KWpt8mQ9JewM+Ac/MbIgqcwLgkcAZwHqSngWeBP5PU6MyM7PaKnMC4BPAdvkKtktFxGvND8vMzOqqYeKQ9OUG5QBExA+bFJOZmdVYdy2OFfLPdUk3cZqa13cF/tjMoMzMrL4aJo6I+BaApOuATTu6qCSdAFxSSXRmZlY7ZWZVrQ68U1h/BxjflGjMzKz2ysyqOh+4U9JvSFNy9yTdE9zMzIagMrOqvifp98Anc9EhEXFPc8MyM7O6KnUL2Ii4G7i7ybGYmdkg4KvjmplZr5RqcZgNFT3dldF3ZDRzi8PMzHqpzP04NpN0l6S/SHpH0kJJ86sIzszM6qdMi+N0YD/gcWA54HDgx80MyszM6qvsrKpZkoZFxELgHEm3NjkuMzOrqTKJ4w1JywD3SvoB6V4cyzc3LDMzq6syXVUHAMOAo4DXgXHA5/pbsaRhku6R9Lu8vqakOyQ9LunXOVkhadm8PitvH9/fus3MrO96TBwR8XREvBkR8yPiWxHx5YiYNQB1HwM8Ulj/PnBKREwA5gGH5fLDgHkRsQ5wSt7PzMxapGHikHRx/vmApPs7P/pTqaQ2YBfgF3ldwKeBS/MuU4A98vLuLLo21qXAtuq4KYiZmVWuuzGOY/LPzzah3h8BX2HRPT9GA69ExIK83g6MzctjgdkAEbFA0qt5/xebEJeZ1UhPJ2SCT8pshYYtjoiYU9jn+dxl9TTwAtDnb/ySPgu8EBEzi8VdhVBiW/G4R0iaIWnG3Llz+xqemZn1oMzg+CXAu4X1hfTvRk5bALtJegq4iNRF9SNgRUkdLaA24Lm83E4akCdvHwW83PmgEXFGREyMiIljxozpR3hmZtadMolj6Yh470ZOeXmZvlYYEV+LiLaIGA/sC9wQEfsDNwJ7590OAq7My1PzOnn7DRHxvhaHmZlVo0zimCtpt44VSbvTnPGFrwJfljSLNIZxVi4/Cxidy78MHNuEus3MrKQyJwB+CbhA0umk8YbZwIEDUXlETAem5+UngI91sc9bwD4DUZ+ZmfVfmTsA/gnYTNIHAUXEa80Py8zM6qrHxCFpWdKZ4uOBpTtOoYiIbzc1MjMzq6UyXVVXAq8CM4G3mxuOmZnVXZnE0RYROzY9EjMzGxTKzKq6VdLfNT0SMzMbFMq0OLYEDpb0JKmrSkBExEZNjczMzGqpTOLYqelRmJnZoFHqsuqkS358Oi+/UeZ5Zma2ZOoxAUg6nnRW99dy0XDgl80MyszM6qtMy2FPYDfS3f+IiOdYdDl0MzMbYsokjnfyRQUDQJLvN25mNoSVSRwXS/o56bLn/wj8ATizuWGZmVldlblW1UmStgfmA+sC/xkR05oemZmZ1VKZ6bjkROFkYWZmpS5y+BqLbtW6DGlW1esRMbKZgZmZWT2V6apabAaVpD3o4r4ZZmY2NPT6RL6IuIJ0n3AzMxuCynRV7VVYXQqYyKKuKzMzG2LKDI7vWlheADwF7N6UaMzMrPbKjHEcUkUgZmY2OJS5VtUUSSsW1leSdHZzwzIzs7oqMzi+UUS80rESEfOATZoXkpmZ1VmZxLGUpJU6ViStTMkTB83MbMlTJnGcTLp97HckfRu4FfhBXyuUNE7SjZIekfSQpGNy+cqSpkl6PP9cKZdL0mmSZkm6X9Kmfa3bzMz6r8yNnM4DPgc8D8wF9oqI8/tR5wLg3yLib4HNgCMlrQ8cC1wfEROA6/M6pDsQTsiPI4Cf9qNuMzPrp7InAK5MuszIj4G5ktbsa4URMSci7s7LrwGPAGNJU3yn5N2mAHvk5d2B8yK5nXSV3g/3tX4zM+uflt4BUNJ40kD7HcCHImIOpOQCrJp3GwvMLjytPZeZmVkLtOwOgJI+CFwG/EtEzO9u1y7K3nfmuqQjJM2QNGPu3Ln9Dc/MzBpoyR0AJQ0nJY0LIuLyXPx8RxdU/vlCLm8HxhWe3gY81/mYEXFGREyMiIljxozpb4hmZtZA5XcAlCTgLOCRiPhhYdNU4KC8fBBwZaH8wDy7ajPg1Y4uLTMzq14r7gC4BXAA8ICke3PZ14HJpCR1GPAMsE/edjWwMzALeAMYkEugjD/2qm63PzV5l4GoxsxsidNt4pA0DLg2IrZjgO4AGBE30/W4BcC2XewfwJEDUbeZmfVft11VEbEQeEPSqIriMTOzmitz6ZC3SN1K08gzqwAi4uimRWVmZrVVJnFclR9mZmaNE4ek1SPimYiY0mgfMzMberob47iiY0HSZRXEYmZmg0B3iaM482mtZgdiZmaDQ3eJIxosm5nZENbd4PjGkuaTWh7L5WXyekTEyKZHZ2ZmtdMwcUTEsCoDMTOzwaHs/TjMzMwAJw4zM+slJw4zM+sVJw4zM+uVMpccMTN7vxN6uPbpCa9WE4dVzi0OMzPrFScOMzPrFXdVmfWGu2fMnDhqz/+ozKxmnDhs8HASNasFj3GYmVmvuMVhZoNbBS3R8cd2fxPUpybv0u86BhMnjkZ6+jCCu0bMrDI9JS+oLoG5q8rMzHrFLQ6zwcgTBeqlLu9HRXFwxDygAAAMCUlEQVQMmsQhaUfgVGAY8IuImNzikMyaolSXxIgKAjFrYFAkDknDgP8GtgfagbskTY2Ih1sb2RDh8R4zKxgUiQP4GDArIp4AkHQRsDvgxDEAepwx4m+3ZlagiGh1DD2StDewY0QcntcPAD4eEUcV9jkCOCKvrgs81s9qVwFe7OcxBkId4qhDDFCPOOoQA9QjjjrEAPWIow4xQP/jWCMixvS002BpcaiLssUyXkScAZwxYBVKMyJi4kAdbzDHUYcY6hJHHWKoSxx1iKEucdQhhirjGCzTcduBcYX1NuC5FsViZjakDZbEcRcwQdKakpYB9gWmtjgmM7MhaVB0VUXEAklHAdeSpuOeHREPNbnaAev26qc6xFGHGKAecdQhBqhHHHWIAeoRRx1igIriGBSD42ZmVh+DpavKzMxqwonDzMx6xYnDzMx6ZVAMjlchX9bk6Ig4pdWxDHWSNu1ue0TcXWEsHwH+A1iDwt9LRHy6qhjqRNKGEfFgq+OoA0lrRsSTPZVVFMtIICLitUrq8+D4IpKmR8TWrY6jDiRtBjzU8UGUtAKwfkTcUUHdN3azOar8py3pPuBnwExgYSGImRXGMAy4NiK2q6rObmK5GVgGOBf4VUS80oIY7gN+Dfw6Iv5Udf2FOO6OiE07lc2MiL+vMIaJwDnACqQTpV8BDm3259MtjsXdIul00ofy9Y7Cir/hvkans+KBV4EZwL91XK+rAj8Fin8Ur3dR1hQRsU2z6+iFBRHx01YGEBELJb0haVREtPRqkhGxpaQJwKHADEl3AudExLQKw9gN+AJwsaR3SX+vF0fEM1VULmk9YANglKS9CptGAlVf2e1s4J8i4n9ybFuSEslGzazULY6CBt90q/6G+y3SWfG/In2D2Bf4G9K1t/5vVS0iSfdGxEc7ld0fEU39QHaq78CuyiPivApjOAF4AfgN8HYhhperiiHHcTGwGTCNxb/UHF1lHIV4hgF7AKcB80mf1a9HxOUVxzEB+Cawf0QMq6jO3Um/+24sfiLya8BFEXFrFXHkWG6JiC16Khvwep046kXSHRHx8U5lt0fEZpLui4iNK4rjcmA6qZUB8E/ANhGxRxX15xh+XFgdAWwL3B0Re1cYQ1f91RERa1UVQ47joK7KI2JKxXFsBBwC7EJKYmdFxN2SVgNui4g1KopjPPB5UstjIanb6uQq6i7EsHlE3FZlnV3EcArwAeBCUk/FF4B5wGXQvN4SJ44CSaOB44EtSW/CzcC3I+KlCmO4DTgFuDQX7Q18OSeO97UCmhjHqqRvk58mvRbXA/8SES9UUX+DmEYB50fEbq2KYaiT9EfgTODSiHiz07YDIuL8CmK4AxgOXEJKGFV133aOYwpwTMc4j6SVgJMj4tAKY2jJeKATR4GkacAfgV/mov2BrasclJS0FulOh5uT/mHfDvwr8Czw9xFxc1Wx1I2k4cD9EfG3FdbZ8u6yHMcE4ERgfQr96FW3fOpA0noR8WgN4rgnIjbpqWxJ5MHxxa0cEd8prH9XUmVdMwD529OuDTZXljTyNNSfAh+KiA1zF8VuEfHdCmP4LYsmCgwj/dO8uKr6s0mF5fe6y4BKEwdpwPN4Umt0G1J3UVe3G2iqmiSwOZJ+CGyV128i9QxUPXFgKUkrRcQ8AEkrU/H/VEkfAv4fsFpE7CRpfWDziDirqfW6xbGIpJNIs5c6/jntDWwQEcdXGMM5vH9WFVU2f3McN5HOX/h5xzcoSQ9GxIYVxvApFr0WC4CnI+LZqurvSqu6yzqmeUp6ICL+Lpf9T0R8suI4bmZRAtuVnMAq/hu5DHgQ6BjfOQDYOCL2avyspsRxIPB1UpcZwD7A96rorivE8HvSl4rjImJjSUsD93R8RppWrxPHInkq7PIsmq8/jEUzWCIiRlYQw+cKqyOAPYHnqp49I+muiJhUbHpXNcYi6eY87bNjanLHN+vIj5eB/4qInzQ7li5iq7y7LNd7C/BJ0tjXDaSuy8kRsW7FcbQ8gTWY8VfZ+F+nej8BTATeBWZWPVjeqr9Td1UVRMQKubk5gcWb4TdVGMNlxXVJFwJ/qKr+ghclrU3+xq90+945VVQcEVvmnyt0tT1PYrgVaHriqEl3GcC/kGbPHA18h9Rd1eX4S5O9JWkp4PF8q4NngVUrjuFNSVt2jPdJ2gJ4s4fnDDhJxwCHA5eTvtz8XNKZEfHj7p85oF7Pfw8df6ebkc77aiq3OAokHQ4cQ7rD4L2kefO3RsS2LYxpXeCqiFin4nrXIl3b/xOk6X1PkubKP11lHI1I+nBEND2R1aW7LJ8hfBzp0ifDc3FUeV5NjmMS8AiwIimBjQR+EBVcUaAQw0dJ3VSjctE84KCIuL+qGHIc95PGE17P68uTpiRXea7TpsCPgQ1J3XdjgL2b/Vq4xbG4Y0iDobdHxDb5DNFvVRlApzPHA3ge+EqF9X+5sHo1cCPpYpivA58DflhVLN1pdtLo6C4Dfken7jJJreguu4A05vQAqVukVQI4n8UT2Jk0+UzlTh4BfgCsTUpgr5JOyKs0cZA+EwsL6wupfsLC2sBOpFtrfw74OBX8X3fiWNxbEfGWJCQtGxGP5m/8lWnQXVZls7Cje2hdUhK9kvTHcABpqvKQUKfusmxuRNThdsl1SGBXkq7JdDepq6xVzgHukPSbvL4H0NTZTF34ZkRcks8h2Q44mTQb8uPdP61/3FVVkD8Ah5D6kz9NagIPj4idK4yhq+6y26q87EmO4zrgc7H4RQ4viYgdq4yjzqrqLst1bQvsRzoRs3jpk6ov8dHREmuZqmf3dSd3FW1J+nL1x4i4p+L674mITSSdCDwQEb+q4lwSJ44Gct/2KOCaiHinwnofYFF32Uc7ussi4gtVxZDjeJQ0xfHtvL4scF9ErFdlHJZI+iWwHvAQi77pRwumabc8gUk6A/hxRDxQVZ11Jel3pFbXdsDfkyYJ3BlNvjSRu6oaqHImVSct7y7LzgfuzK2wIE0LrvS6SLaYjZs9N7+kQ0gJbDiFBEaaWVSVLYGDla4j9jbp237lEwVq4vPAjsBJEfGKpA+TuhKbyi2OmqlDd1khlk1J5w5AC5rhtoikM4FTIuLhFsfxQKsTmKQuL6RYlxl/Q4ETR421qrvM6kfSI6QZNC39ll2XBGat5cRhNgjU5Vt2XRKYtZYTh5mVVpcEZq3lxGFmZr2yVKsDMDOzwcWJw8zMesWJw4Y0SSHp/ML60pLm5hOrBrKegyWd3t99unjOXpJul3S5pMqnbNvQ5BMAbah7HdhQ0nKR7qG9Pa29/lGv5DO2K73siJlbHGbwe2CXvLwfcGHHBknLSzpb0l2S7pG0ey7fQNKdku6VdL/SLVUXI+kQSf+rdDfFLQrlYyRdlo95V76fREOSPibp1lz/rR1XEsgtlMslXSPpcUk/KDxnP0kPSHpQ0vf78+KYdebEYQYXAftKGkG6PHjx3hLHATdExCTSzZP+K9934UvAqflOaxOB9uIB86UfvkVKGNuTbgDV4VTSSXSTSJfC/kUP8T0KbJUvXPefpHtMd/go8AXg74AvSBonaTXg+6QrD3wUmCRpj1KvhFkJ7qqyIS8i7pc0ntTauLrT5s8Au0n697w+AlgduA04TlIbcHlEPN7peR8HpkfEXABJvwY+krdtB6wvvXfrhpH56sONjAKm5FZNsOg+GADXR8SruY6HSffJGN2p7guArYArunsdzMpy4jBLpgInAVuT/vF2EOny8o912v8RSXeQuriulXR4RNzQaZ9GJ0ktRbpz3GK3Oy0kks6+A9wYEXvmBDe9sO3twvJC0t901TcTsiHGXVVmydnAt7u4VPe1wD8r/1eXtEn+uRbwREScRko6nS+5cQewtaTRkoYD+xS2XQcc1bGSb4XanVEsGrA/uMTvcgfwKUmrSBpGakm16mrPtgRy4jADIqI9Ik7tYtN3SF1D90t6MK9DGld4UNK9pMuMn9fpeHOAE0hdWn8g3a2uw9HAxDyo/jBpvKQ7PwBOlHQLMKzE7zIH+Brptr/3AXdHxJU9Pc+sLF9yxMzMesUtDjMz6xUnDjMz6xUnDjMz6xUnDjMz6xUnDjMz6xUnDjMz6xUnDjMz6xUnDjMz65X/D4CofyK4MhKmAAAAAElFTkSuQmCC\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x129512160>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1037,27 +1047,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0,0.5,'Cliente')"
+       "Text(0, 0.5, 'Cliente')"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a2bdd2710>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1071,27 +1083,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x12967b0b8>"
+       "<AxesSubplot:xlabel='age'>"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x129588828>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1101,27 +1115,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2b410b38>"
+       "<AxesSubplot:xlabel='poutcome'>"
       ]
      },
-     "execution_count": 134,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a2b2c7be0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -1138,7 +1154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1153,7 +1169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1162,7 +1178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1172,7 +1188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1197,7 +1213,7 @@
        "       'poutcome_nonexistent', 'poutcome_success'], dtype=object)"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1209,7 +1225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1227,7 +1243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1236,7 +1252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1247,7 +1263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1256,17 +1272,254 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n",
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:762: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
+      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
+      "\n",
+      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
+      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
+      "Please also refer to the documentation for alternative solver options:\n",
+      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
+      "  n_iter_i = _check_optimize_result(\n"
+     ]
+    }
+   ],
    "source": [
-    "rfe = RFE(lr, n)\n",
+    "rfe = RFE(lr, n_features_to_select=12)\n",
     "rfe = rfe.fit(bank_data[X], bank_data[Y].values.ravel())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -1274,10 +1527,10 @@
      "output_type": "stream",
      "text": [
       "[False False False False  True False False False  True False False False\n",
-      "  True False False False  True False False False False False False False\n",
+      " False False False  True False False False  True  True False False False\n",
       " False False False False False False False False False False False False\n",
       " False False False False False False  True  True  True  True  True False\n",
-      "  True False False False False False False  True False  True]\n"
+      " False False False False False False False  True False  True]\n"
      ]
     }
    ],
@@ -1287,16 +1540,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[33 38 19 43  1 12 26 25  1 37 23  3  1 36  2 39  1  5 32 30 11 45 20 42\n",
-      " 29 47 40 34 46 13 14  8  9  6 21 22 15 16 18  4 31 24  1  1  1  1  1 17\n",
-      "  1 44 35 27 41 28 10  1  7  1]\n"
+      "[38 37 17 45  1 19 24 22  1 34  2  8  5 39  7  1  6 23 43  1  1 42 16 27\n",
+      " 25 47 33 14 46 18 28  4  9 15 44 13 20 26 11  3 36 21  1  1  1  1  1 12\n",
+      " 40 41 31 30 29 35 10  1 32  1]\n"
      ]
     }
    ],
@@ -1306,7 +1559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1315,73 +1568,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[('age', False, 33),\n",
-       " ('duration', False, 38),\n",
-       " ('campaign', False, 19),\n",
-       " ('pdays', False, 43),\n",
+       "[('age', False, 38),\n",
+       " ('duration', False, 37),\n",
+       " ('campaign', False, 17),\n",
+       " ('pdays', False, 45),\n",
        " ('previous', True, 1),\n",
-       " ('emp.var.rate', False, 12),\n",
-       " ('cons.price.idx', False, 26),\n",
-       " ('cons.conf.idx', False, 25),\n",
+       " ('emp.var.rate', False, 19),\n",
+       " ('cons.price.idx', False, 24),\n",
+       " ('cons.conf.idx', False, 22),\n",
        " ('euribor3m', True, 1),\n",
-       " ('nr.employed', False, 37),\n",
-       " ('y', False, 23),\n",
-       " ('job_admin.', False, 3),\n",
-       " ('job_blue-collar', True, 1),\n",
-       " ('job_entrepreneur', False, 36),\n",
-       " ('job_housemaid', False, 2),\n",
-       " ('job_management', False, 39),\n",
-       " ('job_retired', True, 1),\n",
-       " ('job_self-employed', False, 5),\n",
-       " ('job_services', False, 32),\n",
-       " ('job_student', False, 30),\n",
-       " ('job_technician', False, 11),\n",
-       " ('job_unemployed', False, 45),\n",
-       " ('job_unknown', False, 20),\n",
-       " ('marital_divorced', False, 42),\n",
-       " ('marital_married', False, 29),\n",
+       " ('nr.employed', False, 34),\n",
+       " ('y', False, 2),\n",
+       " ('job_admin.', False, 8),\n",
+       " ('job_blue-collar', False, 5),\n",
+       " ('job_entrepreneur', False, 39),\n",
+       " ('job_housemaid', False, 7),\n",
+       " ('job_management', True, 1),\n",
+       " ('job_retired', False, 6),\n",
+       " ('job_self-employed', False, 23),\n",
+       " ('job_services', False, 43),\n",
+       " ('job_student', True, 1),\n",
+       " ('job_technician', True, 1),\n",
+       " ('job_unemployed', False, 42),\n",
+       " ('job_unknown', False, 16),\n",
+       " ('marital_divorced', False, 27),\n",
+       " ('marital_married', False, 25),\n",
        " ('marital_single', False, 47),\n",
-       " ('marital_unknown', False, 40),\n",
-       " ('education_Basic', False, 34),\n",
+       " ('marital_unknown', False, 33),\n",
+       " ('education_Basic', False, 14),\n",
        " ('education_High School', False, 46),\n",
-       " ('education_Illiterate', False, 13),\n",
-       " ('education_Professional Course', False, 14),\n",
-       " ('education_University Degree', False, 8),\n",
+       " ('education_Illiterate', False, 18),\n",
+       " ('education_Professional Course', False, 28),\n",
+       " ('education_University Degree', False, 4),\n",
        " ('education_Unknown', False, 9),\n",
-       " ('housing_no', False, 6),\n",
-       " ('housing_unknown', False, 21),\n",
-       " ('housing_yes', False, 22),\n",
-       " ('loan_no', False, 15),\n",
-       " ('loan_unknown', False, 16),\n",
-       " ('loan_yes', False, 18),\n",
-       " ('contact_cellular', False, 4),\n",
-       " ('contact_telephone', False, 31),\n",
-       " ('month_apr', False, 24),\n",
+       " ('housing_no', False, 15),\n",
+       " ('housing_unknown', False, 44),\n",
+       " ('housing_yes', False, 13),\n",
+       " ('loan_no', False, 20),\n",
+       " ('loan_unknown', False, 26),\n",
+       " ('loan_yes', False, 11),\n",
+       " ('contact_cellular', False, 3),\n",
+       " ('contact_telephone', False, 36),\n",
+       " ('month_apr', False, 21),\n",
        " ('month_aug', True, 1),\n",
        " ('month_dec', True, 1),\n",
        " ('month_jul', True, 1),\n",
        " ('month_jun', True, 1),\n",
        " ('month_mar', True, 1),\n",
-       " ('month_may', False, 17),\n",
-       " ('month_nov', True, 1),\n",
-       " ('month_oct', False, 44),\n",
-       " ('month_sep', False, 35),\n",
-       " ('day_of_week_fri', False, 27),\n",
-       " ('day_of_week_mon', False, 41),\n",
-       " ('day_of_week_thu', False, 28),\n",
+       " ('month_may', False, 12),\n",
+       " ('month_nov', False, 40),\n",
+       " ('month_oct', False, 41),\n",
+       " ('month_sep', False, 31),\n",
+       " ('day_of_week_fri', False, 30),\n",
+       " ('day_of_week_mon', False, 29),\n",
+       " ('day_of_week_thu', False, 35),\n",
        " ('day_of_week_tue', False, 10),\n",
        " ('day_of_week_wed', True, 1),\n",
-       " ('poutcome_failure', False, 7),\n",
+       " ('poutcome_failure', False, 32),\n",
        " ('poutcome_nonexistent', True, 1)]"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1392,7 +1645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1402,7 +1655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1419,7 +1672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1428,7 +1681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1437,7 +1690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -1456,7 +1709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -1464,25 +1717,28 @@
       "text/html": [
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "        <td>Model:</td>              <td>Logit</td>       <td>No. Iterations:</td>   <td>7.0000</td>  \n",
+       "        <td>Model:</td>              <td>Logit</td>      <td>Pseudo R-squared:</td>    <td>0.155</td>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <td>Dependent Variable:</td>         <td>y</td>        <td>Pseudo R-squared:</td>   <td>0.155</td>  \n",
+       "  <td>Dependent Variable:</td>         <td>y</td>              <td>AIC:</td>         <td>2427.6025</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "         <td>Date:</td>        <td>2018-04-06 17:44</td>       <td>AIC:</td>        <td>2427.6025</td>\n",
+       "         <td>Date:</td>        <td>2020-09-19 17:22</td>       <td>BIC:</td>         <td>2503.4828</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "   <td>No. Observations:</td>        <td>4119</td>             <td>BIC:</td>        <td>2503.4828</td>\n",
+       "   <td>No. Observations:</td>        <td>4119</td>        <td>Log-Likelihood:</td>    <td>-1201.8</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "       <td>Df Model:</td>             <td>11</td>         <td>Log-Likelihood:</td>   <td>-1201.8</td> \n",
+       "       <td>Df Model:</td>             <td>11</td>            <td>LL-Null:</td>        <td>-1422.9</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "     <td>Df Residuals:</td>          <td>4107</td>           <td>LL-Null:</td>       <td>-1422.9</td> \n",
+       "     <td>Df Residuals:</td>          <td>4107</td>         <td>LLR p-value:</td>    <td>6.4492e-88</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "      <td>Converged:</td>           <td>1.0000</td>           <td>Scale:</td>        <td>1.0000</td>  \n",
+       "      <td>Converged:</td>           <td>1.0000</td>           <td>Scale:</td>         <td>1.0000</td>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "    <td>No. Iterations:</td>        <td>7.0000</td>              <td></td>               <td></td>     \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
@@ -1532,13 +1788,14 @@
        "\"\"\"\n",
        "                           Results: Logit\n",
        "=====================================================================\n",
-       "Model:                 Logit             No. Iterations:    7.0000   \n",
-       "Dependent Variable:    y                 Pseudo R-squared:  0.155    \n",
-       "Date:                  2018-04-06 17:44  AIC:               2427.6025\n",
-       "No. Observations:      4119              BIC:               2503.4828\n",
-       "Df Model:              11                Log-Likelihood:    -1201.8  \n",
-       "Df Residuals:          4107              LL-Null:           -1422.9  \n",
-       "Converged:             1.0000            Scale:             1.0000   \n",
+       "Model:                Logit             Pseudo R-squared:  0.155     \n",
+       "Dependent Variable:   y                 AIC:               2427.6025 \n",
+       "Date:                 2020-09-19 17:22  BIC:               2503.4828 \n",
+       "No. Observations:     4119              Log-Likelihood:    -1201.8   \n",
+       "Df Model:             11                LL-Null:           -1422.9   \n",
+       "Df Residuals:         4107              LLR p-value:       6.4492e-88\n",
+       "Converged:            1.0000            Scale:             1.0000    \n",
+       "No. Iterations:       7.0000                                         \n",
        "---------------------------------------------------------------------\n",
        "                      Coef.  Std.Err.    z     P>|z|   [0.025  0.975]\n",
        "---------------------------------------------------------------------\n",
@@ -1559,7 +1816,7 @@
        "\"\"\""
       ]
      },
-     "execution_count": 172,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1577,7 +1834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1586,19 +1843,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
-       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
-       "          verbose=0, warm_start=False)"
+       "LogisticRegression()"
       ]
      },
-     "execution_count": 174,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1610,16 +1864,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.8963340616654528"
+       "0.8958485069191552"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1630,7 +1884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
@@ -1639,7 +1893,7 @@
        "0.8905074047098811"
       ]
      },
-     "execution_count": 180,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1650,7 +1904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -1682,84 +1936,84 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>previous</td>\n",
-       "      <td>[0.5076571353667045]</td>\n",
+       "      <td>[0.5362541179381843]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>euribor3m</td>\n",
-       "      <td>[-0.5464961339893047]</td>\n",
+       "      <td>[-0.5443670397995866]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>job_blue-collar</td>\n",
-       "      <td>[-0.3591553622555765]</td>\n",
+       "      <td>[-0.3533138743513389]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>job_retired</td>\n",
-       "      <td>[0.3560383887648494]</td>\n",
+       "      <td>[0.36165159081170917]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>month_aug</td>\n",
-       "      <td>[0.625398308593638]</td>\n",
+       "      <td>[0.6292588895072758]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>month_dec</td>\n",
-       "      <td>[1.1822172985981176]</td>\n",
+       "      <td>[1.1943974572117282]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>month_jul</td>\n",
-       "      <td>[0.9622633626926286]</td>\n",
+       "      <td>[0.9630554140662402]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>month_jun</td>\n",
-       "      <td>[1.0543179248161756]</td>\n",
+       "      <td>[1.0566897056470022]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>month_mar</td>\n",
-       "      <td>[1.630636629778435]</td>\n",
+       "      <td>[1.6386680234582125]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>month_nov</td>\n",
-       "      <td>[0.45195768178610307]</td>\n",
+       "      <td>[0.4577084191059241]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>day_of_week_wed</td>\n",
-       "      <td>[0.04171433846647722]</td>\n",
+       "      <td>[0.046113127259597986]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>poutcome_nonexistent</td>\n",
-       "      <td>[0.3056987709299087]</td>\n",
+       "      <td>[0.35494520625376097]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                       0                      1\n",
-       "0               previous   [0.5076571353667045]\n",
-       "1              euribor3m  [-0.5464961339893047]\n",
-       "2        job_blue-collar  [-0.3591553622555765]\n",
-       "3            job_retired   [0.3560383887648494]\n",
-       "4              month_aug    [0.625398308593638]\n",
-       "5              month_dec   [1.1822172985981176]\n",
-       "6              month_jul   [0.9622633626926286]\n",
-       "7              month_jun   [1.0543179248161756]\n",
-       "8              month_mar    [1.630636629778435]\n",
-       "9              month_nov  [0.45195768178610307]\n",
-       "10       day_of_week_wed  [0.04171433846647722]\n",
-       "11  poutcome_nonexistent   [0.3056987709299087]"
+       "                       0                       1\n",
+       "0               previous    [0.5362541179381843]\n",
+       "1              euribor3m   [-0.5443670397995866]\n",
+       "2        job_blue-collar   [-0.3533138743513389]\n",
+       "3            job_retired   [0.36165159081170917]\n",
+       "4              month_aug    [0.6292588895072758]\n",
+       "5              month_dec    [1.1943974572117282]\n",
+       "6              month_jul    [0.9630554140662402]\n",
+       "7              month_jun    [1.0566897056470022]\n",
+       "8              month_mar    [1.6386680234582125]\n",
+       "9              month_nov    [0.4577084191059241]\n",
+       "10       day_of_week_wed  [0.046113127259597986]\n",
+       "11  poutcome_nonexistent   [0.35494520625376097]"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1777,16 +2031,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.cross_validation import train_test_split"
+    "from sklearn.model_selection import train_test_split"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1795,19 +2049,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
-       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
-       "          verbose=0, warm_start=False)"
+       "LogisticRegression()"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1819,7 +2070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1828,13 +2079,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$Y_p=\\begin{cases}0& si\\ p\\leq0.5\\\\1&si\\ p >0.5\\end{cases}$$"
+       "$\\displaystyle Y_p=\\begin{cases}0& si\\ p\\leq0.5\\\\1&si\\ p >0.5\\end{cases}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -1850,7 +2101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1859,22 +2110,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.95462912, 0.04537088],\n",
-       "       [0.83762689, 0.16237311],\n",
-       "       [0.93244632, 0.06755368],\n",
+       "array([[0.95409621, 0.04590379],\n",
+       "       [0.83896646, 0.16103354],\n",
+       "       [0.93216535, 0.06783465],\n",
        "       ...,\n",
-       "       [0.65044409, 0.34955591],\n",
-       "       [0.97383524, 0.02616476],\n",
-       "       [0.57021896, 0.42978104]])"
+       "       [0.65098445, 0.34901555],\n",
+       "       [0.97381005, 0.02618995],\n",
+       "       [0.57635748, 0.42364252]])"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1885,7 +2136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1894,7 +2145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -1903,7 +2154,7 @@
        "array([0, 0, 0, ..., 0, 0, 0])"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1914,13 +2165,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/latex": [
-       "$$\\varepsilon\\in (0,1), Y_p=\\begin{cases}0& si\\ p\\leq \\varepsilon\\\\1&si\\ p >\\varepsilon\\end{cases}$$"
+       "$\\displaystyle \\varepsilon\\in (0,1), Y_p=\\begin{cases}0& si\\ p\\leq \\varepsilon\\\\1&si\\ p >\\varepsilon\\end{cases}$"
       ],
       "text/plain": [
        "<IPython.core.display.Math object>"
@@ -1936,7 +2187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -1967,27 +2218,27 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.045371</td>\n",
+       "      <td>0.045904</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.162373</td>\n",
+       "      <td>0.161034</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.067554</td>\n",
+       "      <td>0.067835</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.062144</td>\n",
+       "      <td>0.062222</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.041582</td>\n",
+       "      <td>0.042308</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1996,14 +2247,14 @@
       ],
       "text/plain": [
        "          0  prediction\n",
-       "0  0.045371           0\n",
-       "1  0.162373           1\n",
-       "2  0.067554           0\n",
-       "3  0.062144           0\n",
-       "4  0.041582           0"
+       "0  0.045904           0\n",
+       "1  0.161034           1\n",
+       "2  0.067835           0\n",
+       "3  0.062222           0\n",
+       "4  0.042308           0"
       ]
      },
-     "execution_count": 197,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2018,7 +2269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
@@ -2069,7 +2320,7 @@
        "1             390"
       ]
      },
-     "execution_count": 201,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2080,7 +2331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -2089,7 +2340,7 @@
        "31.55339805825243"
       ]
      },
-     "execution_count": 218,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2100,7 +2351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -2134,11 +2385,11 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>905</td>\n",
+       "      <td>915</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>331</td>\n",
+       "      <td>321</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -2147,11 +2398,11 @@
       "text/plain": [
        "col_0       count\n",
        "prediction       \n",
-       "0             905\n",
-       "1             331"
+       "0             915\n",
+       "1             321"
       ]
      },
-     "execution_count": 219,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2164,7 +2415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -2173,7 +2424,7 @@
        "26.779935275080906"
       ]
      },
-     "execution_count": 220,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2184,7 +2435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -2235,7 +2486,7 @@
        "1             732"
       ]
      },
-     "execution_count": 221,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2248,7 +2499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -2257,7 +2508,7 @@
        "59.22330097087378"
       ]
      },
-     "execution_count": 222,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2268,7 +2519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2277,16 +2528,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9004854368932039"
+       "0.901294498381877"
       ]
      },
-     "execution_count": 224,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2304,16 +2555,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.cross_validation import cross_val_score"
+    "from sklearn.model_selection import cross_val_score"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 241,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2322,17 +2573,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([0.9031477 , 0.88834951, 0.90533981, 0.89563107, 0.90048544,\n",
-       "       0.8907767 , 0.88349515, 0.89320388, 0.89537713, 0.88807786])"
+       "array([0.90533981, 0.88834951, 0.90533981, 0.89563107, 0.90048544,\n",
+       "       0.8907767 , 0.88349515, 0.89320388, 0.89320388, 0.88807786])"
       ]
      },
-     "execution_count": 242,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2343,16 +2594,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.8943884240990478"
+       "0.8943903101599225"
       ]
      },
-     "execution_count": 243,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2370,7 +2621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 259,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2379,19 +2630,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 260,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
-       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
-       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
-       "          verbose=0, warm_start=False)"
+       "LogisticRegression()"
       ]
      },
-     "execution_count": 260,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2403,7 +2651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 261,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2412,7 +2660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 268,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
@@ -2444,31 +2692,31 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.045371</td>\n",
+       "      <td>0.045904</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.162373</td>\n",
+       "      <td>0.161034</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.067554</td>\n",
+       "      <td>0.067835</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.062144</td>\n",
+       "      <td>0.062222</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.041582</td>\n",
+       "      <td>0.042308</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
@@ -2478,14 +2726,14 @@
       ],
       "text/plain": [
        "          0  prediction  actual\n",
-       "0  0.045371           0       0\n",
-       "1  0.162373           1       0\n",
-       "2  0.067554           0       0\n",
-       "3  0.062144           0       0\n",
-       "4  0.041582           0       0"
+       "0  0.045904           0       0\n",
+       "1  0.161034           1       0\n",
+       "2  0.067835           0       0\n",
+       "3  0.062222           0       0\n",
+       "4  0.042308           0       0"
       ]
      },
-     "execution_count": 268,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2501,7 +2749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 271,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2510,7 +2758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 274,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2522,7 +2770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 275,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -2531,7 +2779,7 @@
        "0.21025641025641026"
       ]
      },
-     "execution_count": 275,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2543,7 +2791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 276,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
@@ -2552,7 +2800,7 @@
        "0.047281323877068515"
       ]
      },
-     "execution_count": 276,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2564,7 +2812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 301,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2594,29 +2842,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 298,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0,\n",
+       "[1,\n",
        " 0.9344262295081968,\n",
        " 0.8442622950819673,\n",
        " 0.680327868852459,\n",
        " 0.6721311475409836,\n",
        " 0.6639344262295082,\n",
-       " 0.6475409836065574,\n",
+       " 0.6229508196721312,\n",
        " 0.5163934426229508,\n",
-       " 0.45901639344262296,\n",
+       " 0.45081967213114754,\n",
        " 0.4016393442622951,\n",
        " 0.36065573770491804,\n",
        " 0.1721311475409836,\n",
-       " 0.11475409836065574,\n",
-       " 1]"
+       " 0.12295081967213115,\n",
+       " 0]"
       ]
      },
-     "execution_count": 298,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2627,29 +2875,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 299,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0,\n",
-       " 0.7782764811490126,\n",
+       "[1,\n",
+       " 0.7800718132854578,\n",
        " 0.5646319569120287,\n",
        " 0.2989228007181328,\n",
        " 0.2764811490125674,\n",
        " 0.24596050269299818,\n",
-       " 0.22621184919210058,\n",
+       " 0.21992818671454217,\n",
        " 0.12387791741472176,\n",
-       " 0.1077199281867145,\n",
+       " 0.08617594254937166,\n",
        " 0.07181328545780974,\n",
        " 0.06463195691202872,\n",
-       " 0.02333931777378817,\n",
+       " 0.022441651705565557,\n",
        " 0.013464991023339312,\n",
-       " 1]"
+       " 0]"
       ]
      },
-     "execution_count": 299,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2660,7 +2908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 295,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2669,27 +2917,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 302,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0.5,1,'Curva ROC')"
+       "Text(0.5, 1.0, 'Curva ROC')"
       ]
      },
-     "execution_count": 302,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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OZkzGBE12thsr+M9/YMoUt61/f6hRwz1uYBeqQPsjeQejJi5g9fb9DOvelHv6t6d6xbJehxXSgpYURCQaeBXoA6QAs0QkUVUX59ntPuBTVX1dRDoAk4HYYMVkTFCkpbnBYFU3RqDqag3deKO1CoJkb3omT01Zygcz19G0VkUSburJ6a3qeB1WWAhmS6EHsFJVkwFE5GNgEJA3KSiQW6u3OrAxiPEYEzhZWfD1124GUVKSW5+gbFl313GLFlZ0Loh+XLqV0ZMWsGlPOjee0Zx/9m1DpXL25x0owfyTbAysz/M8BeiZb5+HgGki8negMnBeQW8kIiOAEQAx9s3LeGnjRnjjDTdWsHGjW6D+xhtdUbqyZaFNG68jDFs79x/k0a8WM2neBlrXq8KEW0+jW0xNr8MKO8FMCgXNAdN8z68A3lXV50TkVOB9EemkqjlHHKQ6FhgLEB8fn/89jAmurCy3WH3Vqm4R+8cec6uRvfoqDBhgrYIgU1W+XrCJB79YROqBTG7v3Zq/ntOS8mWsgF0wBPNfcwrQNM/zJhzdPXQj0A9AVX8XkQpAHWBrEOMyxj/r1x+eQXTZZfD883DuubB6NTRr5nV0EWHLnnTu+3wh3y7eQlyT6nxwU0/aN4zs1eGCLZhJYRbQWkSaAxuAYcCV+fZZB/QG3hWR9kAFYFsQYzKmeFOmuFbA5Mlu0Pj88w+vWRwVZQmhBKgqn8xaz5jJSziYlcPo/u25/vRYK2BXAoKWFFQ1S0RuA6bippu+raqLROQRYLaqJgL/BN4UkTtwXUvXqap1D5mSt3nz4SmjH33k1i245x646Sa3aI0pMet2pDFqYhK/rdpBz+a1eGpIHLF1KnsdVsSQULsGx8fH6+zZs70Ow4SD7Gz45hs3g+jrr10i6NoVduxwC9iUtfnuJSk7R3nn19U8N2050VHCvf3bM6x7UytgFyAiMkdV44vbz0bITORJTYUXX3TjBSkproUwahTUr+9et7UKStyyzXsZOSGJP9fv5tx29RgzuBMNq1sBOy9YUjCRITsbNmxwN5NFR8MLL0DPni45DBxorQKPHMzK4fXpq3jlxxVUrVCWl4Z1YeBJjayAnYcsKZjwtmGDmz00bpzrElqwAKpUcRVLc8tPGE/MX7+bu8cnsWzLXgae1IgHL+pAbStg5zlLCiY8/f47PPmkq0WUkwN9+rjKpKpujWNLCJ45cDCb579dxlu/rKZu1fK8eU08fTrU9zos42NJwYSPjRuhYkWoWdPdSzBzJtx9t5tB1LKl19EZ4PdVOxg1MYm1O9K4okcM9/RvR7UK1nVXmtikXxPacmcQDR7sxgvGjnXbL73U3Xz2xBOWEEqBPemZ3DNxAVe8ORNV+PCmnjxxSWdLCKWQtRRMaFJ1F/yxY93aBfXqwb/+5ZIB2MBxKfL9ki2MnrSQrXvTufnM5tzZpy0Vy1mJitLKkoIJHdnZMG8exMe7cYFff4VWreCZZ2DQIChXzusITR479mXw8JeLSZy/kbb1q/LG1SfTpamN5ZR2lhRM6bdxo1u97M03XZfQmjXQtClMmmSJoBRSVRLnb+ThLxezN90VsPvbOa0oV8Z6q0OBJQVTeq1c6QaKExNdK6F3b3j66cM3mVlCKHU2p6Zz3+cL+G7JVk5qWoOnh8TRtkFVr8Myx8CSgildNm92ZSY6dnT3E8yc6dY2vvlmaN3a6+hMIVSVj2et5/Gvl5CZk8N9F7bn+tObE20lKkKOJQXjvZwc+PZbN2icmAhnnulWMGvQwHUXRdugZGm2Zvt+Rk1MYmbyTk5tUZsnh3SmWW0rYBeqLCkYb739Njz6qBsnqFMH7rjD3VeQyxJCqZWVncM7v67huW+XUTYqiicu6cyw7k2tREWIs6RgSlZODnz/PZx2GlSuDHv2QPPmbnrp4MFQ3sochIKlm/cwcnwS81NSOa99PR67uDMNqlfwOiwTADYdwARWQoJbfyAqyv1OSHDbN292F/5WrdyCNePHu+233+66ioYNs4QQAjKysnn+2+UMePkXUnYd4OUruvLmNfGWEMKItRRM4CQkwIgRkJbmnq9d6waIX34Z5s51ax336gWPP+5aBeDuNzAhYd66XYyckMTyLfsY3LUx9w/oQK3KNgMs3FhLwRy7wloDo0cfTgi5DhyApCTXIli6FH780VoFISbtYBaPfrWYS17/jb3pWbxzXXdeuLyLJYQwZS0Fc2wKag3cdJNbz3jt2oKPyciAZ58tuRhNwPy2cjujJi5g3c40rjolhpH92lHV6hWFNWspmCMV1goA963/X/86ujWQng4ffghlCvmOERMTrGhNkKQeyGTUhCSuHPcH0VHCxyNO4bGLO1tCiADWUjCHFdQKGDECFi2CCRPcHcY5OQUfKwLvvnvk8QCVKsGYMUEP3QTOtEWbue/zhWzfl8Ffzm7BHee1oUJZmxocKaylYA67996jWwFpae5i37Ej3Hefu5egIDExMHy4uwGtWTOXJJo1c8+HDw966ObEbd+XwW0fzmXE+3OoVbkcn//tdO65oL0lhAhjLYVIlZDgBobXrXMX9Ouvd48LsnkzTJzoHrdpU3RrYPhwSwIhRlX5/M8NPPzlYtIysvlnnzbc0qslZaPtO2MksqQQiQrqJnrkkcL3zzsmkHvBz5tQxoyxRBCiNu4+wOhJC/hx2Ta6xrgCdq3rWwG7SGZJIRIVNHU0Jwdq13aDycWNCVhrIOTl5CgJ/1vHU98sJTtHeWBAB649LdYK2BkbU4hIhXUT7dxpYwIRIHnbPoa9OZP7P19Il6Y1mHbHWdxwhlU0NY61FCLN2rVu6mhm5tGv5Q4WWxIIS1nZOYz7ZTUvfLuc8mWieHpoHJee3MQK2JkjFJkURORLQAt7XVUHBjwiEzxz5sAFF7ikEBXlbirLZVNHw9rijXu4e8J8Fm7YQ98O9Xn04k7Ur2b1iszRimsp5N6GegnQAPjA9/wKYE2QYjLB0rgxxMXBK6+4BGGDxWEvIyubV35YyevTV1GjUlleG96NCzo1sNaBKZSoFtoQOLyTyAxVPau4bSUhPj5eZ8+eXdIfG7qys936xtddV/gdxyYszVm7i7vHz2fVtv0M6daE+we0p0Ylq1cUqURkjqrGF7efv1eJuiLSQlWTfW/eHKh7IgGaErB3r/v2/+WXUK0aXHaZ1xGZErA/I4tnpi7jv7+voVH1irx7fXd6ta3ndVgmRPibFO4ApotIsu95LPCX4g4SkX7AS0A0ME5Vnyxgn8uAh3BjF/NV9Uo/YzJFWbMGLroIliyBf/8bLr3U64hMCfh5xTbumbiAlF0HuPbUZtzVrx1VylsL0fjPr38tqjpFRFoD7XyblqpqRlHHiEg08CrQB0gBZolIoqouzrNPa+Ae4HRV3SUi9nUmEH77DS6+GA4ehG++gT59vI7IBFlqWiaPfb2Yz+ak0KJOZT675VS6x9byOiwTgo7lK0RroC1QAThJRFDV94rYvwewMk+X08fAIGBxnn1uBl5V1V0Aqrr1WII3hahQwQ0qf/wxtG3rdTQmyKYs3Mz9Xyxk5/6D/LVXS/7Ru7XVKzLHza+kICIPAr2ADsBk4ALgF6CopNAYWJ/neQrQM98+bXzv/yuui+khVZ1SwOePAEYAxFgZ5oJlZ7s1DS66CLp1c7OLouzexHC2dW86DyUuYvKCzXRoWI13rutOp8bVvQ7LhDh/rxpDgd7AZlW9HjgJKG7prILmvOWf6lQG1wLphZvmOk5Eahx1kOpYVY1X1fi6dW18+yh79rjuooED4Zdf3DZLCGFLVZkwJ4U+z8/guyVbuev8tnxx2+mWEExA+Nt9dEBVc0QkS0SqAVuBFsUckwI0zfO8CbCxgH1mqmomsFpEluGSxCw/4zKrV7tksGQJvPoqnHGG1xGZIErZlca9kxYyY/k24pvV5MkhcbSqV8XrsEwY8TcpzPZ9g38TmAPsA/5XzDGzgNa+6asbgGFA/plFn+NaCO+KSB1cd1Iyxj8//wyXXAJZWTBlCpx3ntcRmSDJyVHen7mWp6YsBeDhgR25+pRmRFm9IhNg/s4++qvv4RsiMgWopqpJxRyTJSK3AVNx4wVvq+oiEXkEmK2qib7X+orIYiAbuEtVdxzvyUScjRvdojdffOHWOTBhaeXWfYyakMTstbs4s3UdnrikM01qVvI6LBOmiryjWUS6FXWwqs4NeETFiPg7mrOz3SByjx7ueUYGlC9ueMeEoszsHMbOSOal71dQsWw0DwzowCXdGluJCnNcAnVH83O+3xWAeGA+bgA5DvgDsA7skrRnD1x5JUyb5sYQWra0hBCmFm5I5e7xSSzetIf+nRvw8MBO1K1qf9cm+IpMCqp6Dhy6x2CEqi7wPe8E/Cv44ZlDkpPdgPLSpa6gXcuWXkdkgiA9M5uXv1/Bf2YkU6tyOd64qhv9OjX0OiwTQfwdaG6XmxAAVHWhiHQJUkwmvxkz3IByTo5rJZx7rtcRmSCYtWYnI8cnkbx9P5ee3IT7LuxA9UplvQ7LRBh/k8ISERmHK52twFXAkqBFZY707bduQPnLL6F1a6+jMQG2LyOLZ6Ys5b2Za2lcoyIf3NiTM1rX8TosE6H8LZ1dAbgVyC2VPQN4XVXTgxhbgSJmoDk72xW1a9nStRD27XOVTk1Ymb5sK6MnLWRj6gGuPTWWu85vS2UrYGeCIKCls30X/xd8PybYUlNh2DCYO9eNIdSsaQkhzOxOO8gjXy1m4twNtKpXhfG3nMbJzWp6HZYxxS7H+amqXiYiCyhgWU5VjQtaZJFq1SpXv2jFCneHck27UIQTVeWbhZt54IuF7E7L5LZzWvH33q0oX8YK2JnSobiWwu2+3wOCHYgBpk+HIUPc42nT4JxzPA3HBNbWPenc/8VCpi7aQufG1Xnvhp50aGQtQFO6FDcldZPv99qSCSfCJCQcuU5yvXru58svoVUrr6MzAaKqfDY7hce+XkxGVg4j+7Xj5jObUybaihaa0qe47qO9FNBthLuBTVXVvuYcr4QEGDEC0tLc87VrYetWePllSwhhZP3ONO6ZuIBfVm6nR2wtnhzSmRZ1rYCdKb38mn1UmoTN7KPYWJcI8mvWzM06MiEtO0f5729reGbqMqIERvVvz/AeMVbAzngmILOPRKSaqu4RkQLX9VPVnccbYMRbt+7YtpuQsWLLXkZOSGLuut30aluXxwd3plGNil6HZYxfihto/hA3yDwH142U92uOUvyaCqYwMTEFtxRsZbmQlZmdwxvTV/HvH1ZSuXw0L17ehUFdGlkBOxNSihtoHuD73bxkwokgDzwAN9545LZKlWDMGG/iMSdkQUoqd42fz9LNexkQ15CHBnakThUrYGdCj9+3TorIJbiqqAr8rKqfBy2qSJC7XGb9+m6AOSbGJYThw72NyxyT9MxsXvhuOW/OSKZOlfKMvfpk+nZs4HVYxhw3v5KCiLwGtAI+8m26RUT6qOrfghZZuLvmGjfYfPbZYN0LIemP5B2MmriA1dv3M6x7U+7p357qFa2AnQlt/rYUzgY6qW+qkoj8F1hQ9CGmSFFR0KuX11GY47A3PZOnpyzj/ZlraVqrIgk39eT0VlbAzoQHf5PCMiAGyB0ZbQoUuRynKcLVV8NJJ8G/bEmKUPPjsq2MnriATXvSufGM5vyzbxsqlbMCdiZ8FDcl9UvcGEJ1XPns//me9wR+C354YWjZMvjgAyuBHWJ27j/Io18tZtK8DbSuV4UJt55GtxirS2XCT3FfcZ4tkSgiQW5Ji9xpqHWsuyEUqCpfJW3iocRFpB7I5B+9W/O3c1paATsTtoqbkvpTSQUS1vKXtAC46y6oXt1mG5ViW/akM3rSQr5bsoW4JtX54KaetG9olV1MeCuyzIWI/KKqZxRQA8mz2kchWebCSlqEFFXlk1nrGTN5CQezcvhX37Zcf3qsFbAzIS0gZS5U9Qzf76qBCiwiWUmLkLFuRxr3TEri15U76Nm8Fk8NiSO2TmWvwzKmxPh7n0JLIEVVM0SkFxAHvKequ4MZXNho0AA2bTp6u5W0KDWyc5R3fl3Nc9OWEx0lPD64M8O6N7UCdibi+DuXbgIQLyKtgLeARFxdpP7BCixszJgB27dD2bKQmXl4u5W0KDWWb9kFbQflAAAZ10lEQVTL3eOT+HP9bs5tV48xgzvRsLoVsDORyd9O0hxVzQIGAy+q6h1Aw+CFFSZmzoQLL3TrI7z0khtDEHG/x461QWaPHczK4aXvVnDhyz+zbmcaLw3rwlvXxltCMBHN35ZCpohcAVwLXOTbZvfzF2XuXOjXz9U2+u47aNQIbr3V66iMz/z1u7l7fBLLtuxl4EmNePCiDtS2AnbG+J0UrgduAcao6moRaQ58ELywQlxGBgwa5Kac/vCDSwimVDhw0BWwG/dzMvWqVmDcNfGc16G+12EZU2r4lRRUdTHwjzzPVwNPBiuokFe+PLz/PjRtaoPJpcjvq3YwamISa3ekcWXPGEZd0I5qFazBa0xe/s4+Oh14CGjmOyb3PgVbZCev5GQ3jnDllVbsrhTZk57JE5OX8tH/1tGsdiU+vLknp7W0O8qNKYi/3UdvAXfgVmDLDl44IWz9eujdG/buhf79oUYNryMywHeLt3Df5wvZujedm89szp192lKxnJWoMKYw/s4+SlXVb1R1q6ruyP0p7iAR6Sciy0RkpYiMKmK/oSKiIlLs3XalSkKCu1s5KgpatIDNm2HqVEsIpcCOfRn846N53PTebKpXLMvEv57O6As7WEIwphj+thR+FJFngIlARu5GVZ1b2AEiEg28CvQBUoBZIpLoG5/Iu19V3HjFH8cYu7fy1zPKyoLoaFi6FE4+2dvYIpiqkjh/Iw8lLmJfRhb/d15r/tqrFeXKWIkKY/zhb1Lo6fud95u8AucWcUwPYKWqJgOIyMfAIGBxvv0eBZ4GQmtxgdGjjyxwB27W0ejRdv+BRzalHuC+SQv5fulWujStwdND42hT3yq0GHMs/J19dM5xvHdjYH2e5ykcTi4AiEhXoKmqfiUihSYFERkBjACIKS2zeayeUamRk6N8NGsdT0xeSnaOct+F7bn+9OZEW4kKY46Zv7OP6gOPA41U9QIR6QCcqqpvFXVYAdsOVVoVkSjgBeC64j5fVccCY8FVSfUn5qCLiSm48mlpSVoRYs32/YyamMTM5J2c3qo2TwyOI6Z2Ja/DMiZk+dvR+i4wFci9C2s58H/FHJOCW7YzVxNgY57nVYFOwHQRWQOcAiSGzGDzmDFQrtyR26yeUYnJys5h7IxVnP/iDBZt3MNTQzrzwY09LSEYc4L8TQp1VPVTIAfAVwepuKmps4DWItJcRMoBw3CF9PC9R6qq1lHVWFWNBWYCA1U1NBZLGD4cOnSAMmWsnlEJW7JpD5e8/huPT17KWW3q8t2dZ3N59xhErLvImBPl70DzfhGpja/7R0ROAVKLOkBVs0TkNlwLIxp4W1UXicgjwGxVTSzq+FLv4EFYsQJuvhlee83raCJCRlY2r/64itd+XEn1imX59xVdGRDX0JKBMQHkb1K4E/ctv6WI/ArUBYYWd5CqTgYm59v2QCH79vIzFu8lJMA//wn798P48XD66dZCCLJ563YxckISy7fs4+IujXjgoo7Uqlyu+AONMcekyKQgIt2B9ao6V0TOBv4CDAGm4cYMIk/++xO2bXPPwRJDEKQdzOK5act5+9fVNKxWgXeu68457ep5HZYxYau4NZrnAuep6k4ROQv4GPg70AVor6rFthYCzfM1mm295RLz68rtjJqYxPqdB7j6lGbc3a8tVa2AnTHHJSBrNAPRqrrT9/hyYKyqTgAmiMifJxpkSLL7E4Iu9UAmj3+9hE9mr6d5ncp8MuIUerao7XVYxkSEYpOCiJTxzTbqje8GMj+PDU92f0JQTVu0mfs+X8iO/Qe55eyW/N95ralQ1uoVGVNSiruwfwT8JCLbgQPAzwC+tZqLnH0UtsaMgauvhrzdbnZ/wgnbtjeDh75cxNdJm2jfsBpvXdudzk2qex2WMRGnyKSgqmNE5HvceszT9PAARBRubCHy9OzpEkLNmrB7t2shjBljg8zHSVWZNG8Dj3y1mLSMbP7Vtw1/ObslZaOtgJ0xXii2C0hVZxawbXlwwgkBn37qfv/5p3UZnaANuw8wetICpi/bRrcYV8CuVT0rYGeMlyJzXOBEfPopnHqqJYQTkJOjJPyxlie/WUqOwoMXdeCaU2OtgJ0xpYAlhWORnAzz58OLL3odSchatW0foyYkMWvNLs5sXYfHB3emaS2rV2RMaWFJ4Vi0aAGLFkH9+l5HEnKysnN48+fVvPDdciqUieKZoXEMPbmJlagwppSxpHCsOnTwOoKQs3jjHu6eMJ+FG/Zwfsf6PDqoE/WqVfA6LGNMAWyKh7+WLnUzjFat8jqSkJGemc2zU5cx8JVf2JyawWvDu/Gfq+MtIRhTillLwV+ffAIffQTPPut1JCFhztqd3D0+iVXb9jOkWxPuH9CeGpWsgJ0xpZ0lBX+ouqRw1lnQsKHX0ZRq+zOyeGbqMv77+xoaVa/If2/owdlt6nodljHGT5YU/LFoESxZArfd5nUkpdqM5du4Z+ICNqYe4NpTY/nX+W2pUt7+iRkTSux/rD8+/RSiomDIEK8jKZVS0zJ59OvFjJ+TQou6lfnsL6cSH1vL67CMMcfBkoI/atd29Y5sKupRpizczP1fLGTn/oP8tVdL/tHbCtgZE8osKfjj9tu9jqDU2bY3gwcTFzJ5wWY6NqrGu9d3p2MjK2BnTKizpFCc5GRX0qKM/VGBK2A3Ye4GHv1qMQcys7m7X1tuPrOFFbAzJkzYla4oqnD++RAXBxMmeB2N59bvTOPeSQv4ecV2usfW5MkhcbSsW8XrsIwxAWRJoSh//gkrV8LIkV5H4qmcHOW939fw9NRlCPDIoI5c1bMZUVbAzpiwY0mhKJ984rqNBg/2OhLPrNzqCtjNXruLs9rU5fHBnWhS0wrYGROuLCkURtVNRT3vPDf7KMJkZucwdkYyL32/goplo3n20pMY0q2xFbAzJsxZUijMnDmwejU88IDXkZS4hRtSGTkhiUUb99C/cwMeHtiJulXLex2WMaYEWFIoTNeu8MMP0K2b15GUmPTMbF76fgVjZyRTq3I53riqG/06WVkPYyKJJYXCREfDOed4HUWJmbVmJyPHJ5G8fT+XxTdhdP8OVK9U1uuwjDElzJJCQebOhYQEN+uoXj2vowmqfRlZPD1lKe/9vpYmNSvywY09OaN1Ha/DMsZ4xJJCQd5/H157LezHE6Yv28roSQvZmHqA606L5a7z21LZCtgZE9HsCpBfTg589hn06wfVw7Nsw+60gzzy1WImzt1Aq3pVGH/LqZzczArYGWMsKRzt999hwwZ46imvIwk4VeWbhZt54IuF7E7L5O/ntuK2c1tRvowVsDPGOEFNCiLSD3gJiAbGqeqT+V6/E7gJyAK2ATeo6tpgxlSsTz6B8uVh4EBPwwi0rXvSuf+LhUxdtIXOjavz3g096dComtdhGWNKmaAlBRGJBl4F+gApwCwRSVTVxXl2mwfEq2qaiNwKPA1cHqyY/FKuHAwbBlWrehpGoKgqn81J4bGvFpORlcM9F7TjxjOaU8YK2BljChDMlkIPYKWqJgOIyMfAIOBQUlDVH/PsPxO4Kojx+CeM1mBevzONeyYu4JeV2+nRvBZPXtKZFlbAzhhThGAmhcbA+jzPU4CeRex/I/BNQS+IyAhgBEBMTEyg4jva1q1Qty6EeCmH7Bzlv7+t4Zmpy4iOEh67uBNX9oixAnbGmGIFMykUdAXSAncUuQqIB84u6HVVHQuMBYiPjy/wPU5YdrYrkX355fDSS0H5iJKwYste7p6QxLx1u+nVti5jBnemcY2KXodljAkRwUwKKUDTPM+bABvz7yQi5wGjgbNVNSOI8RRtxgzYsgXOOMOzEE5EZnYOr09fxSs/rKRy+WhevLwLg7o0sgJ2xphjEsykMAtoLSLNgQ3AMODKvDuISFfgP0A/Vd0axFgKl5AAo0fD2rWu22jfPk/COBELUlK5a/x8lm7ey0UnNeLBizpQp4oVsDPGHLugJQVVzRKR24CpuCmpb6vqIhF5BJitqonAM0AV4DPfN9p1qlpyc0ETEmDECEhLyw0abrvNzUAaPrzEwjhe6ZnZvPDdct6ckUzdquUZe/XJ9O3YwOuwjDEhTFSD00UfLPHx8Tp79uzAvFlsrGsh5NesGaxZE5jPCJKZyTu4Z+ICVm/fzxU9mjLqgvZUr2gF7IwxBROROaoaX9x+kX1H87p1x7a9FNibnsmT3ywl4Y91NK1VkQ9v6slprayAnTEmMCI7KcTEFNxSCOa01xPw49Kt3DtpAVv2pHPTGc25s28bKpWL7L9CY0xgRfYVZcwYuPlmOHDg8LZKldz2UmTn/oM88uUiPv9zI23qV+G14afRNaam12EZY8JQZCeF4cNh5kx45RU38ygmxiWEUjLIrKp8lbSJhxIXsSc9k9t7t+Zv57SiXBkrUWGMCY7ITgoA6elQrRrs2AFlSs8fx+bUdO77fCHfLdnCSU2q89TQnrRrYAXsjDHBVXqugl757ju37GYpSQiqysez1vP410vIzMlhdP/23HBGc6KtRIUxpgSUjiuhV5KT3dTTf/7T60gAWLtjP6MmLOD35B2c0qIWT14SR2ydyl6HZYyJIJGdFJKSXAvhvPM8DSM7R3nn19U8O20ZZaOieHxwZ4Z1b2oF7IwxJS4yk0JuaYt166BJE5gzB9q18ySUZZtdAbv563fTu109HhvciYbVrYCdMcYbkZcU8pe2WL/ePYcSnXV0MCuH16av5NUfV1K1QllevqIrF8U1tAJ2xhhPRV6Zi1JQ2uLP9bsZOT6JZVv2MqhLIx68qCO1Kpcrkc82xkQmK3NRGA9LWxw4mM3z3y7jrV9WU69qBd66Np7e7esH/XONMcZfkZcUPCpt8duq7YyasIB1O9O4smcMoy5oR7UKVsDOGFO6RN6tsWPGuFIWeQWxtMWe9EzumZjElW/+QZTARzefwuODO1tCMMaUSpHXUhg+HHJy4Npr3foJzZoFrbTFd4u3MPrzBWzbm8GIs1pwx3ltqFguOuCfY4wxgRJ5SQFgwADo1w9uuAGGDg342+/Yl8FDXy7my/kbadegKm9eE09ckxoB/xxjjAm0yEwKNWvC5MkBf1tVJXH+Rh5KXMS+jCzu7NOGW85uaQXsjDEhIzKTQk4ORAX2Qr1x9wHu+3whPyzdSpemNXh6aBxt6lcN6GcYY0ywRWZSuPRSt4ZCAFoLOTnKR7PW8cTkpWTnKPcP6MB1p8VaATtjTEiKzKQwbx706HHCb7N6+35GTUjij9U7Ob1VbZ4YHEdM7UrFH2iMMaVU5CWFPXtg9Wq46abjfous7Bze+mU1z3+7nHJlonhqSGcui29qJSqMMSEv8pLCwoXud1zccR2+ZNMeRk5IIikllT4d6vPYxZ2oX61CAAM0xhjvRF5SSEpyv0866ZgOy8jK5tUfVvLa9FVUr1iWV67syoWdrYCdMSa8RF5SaNsWbr3Vlcz209x1uxg5PokVW/dxSdfG3D+gAzWtgJ0xJgxFXlI45xz344e0g1k8M3UZ7/62hobVKvDO9d05p229IAdojDHeiaykkJPjymPHxhZ7n8IvK7YzamISKbsOcPUpzbi7X1uqWr0iY0yYi6xbbdesgZYt4e23C90l9UAmd4+fz1Vv/UHZ6Cg+GXEKj17cyRKCMSYiRFZLIXeQuXPnAl+eumgz93++kB37D3Jrr5bc3rs1FcpaATtjTOSIvKQgAp06HbF5294MHkpcxNcLNtGhYTXevq47nRpX9yhIY4zxTuQlhVatoHJlwBWwmzRvA498tZi0jGzuOr8tI85qQdnoyOpVM8aYXJGXFHw3rW3YfYB7Jy7gp+XbOLlZTZ4aEkerelU8DtAYY7wV1KQgIv2Al4BoYJyqPpnv9fLAe8DJwA7gclVdE/BAEhJg9GhYt46c1D188PJnPLW9Kgo8dFEHrj7VCtgZYwwEMSmISDTwKtAHSAFmiUiiqi7Os9uNwC5VbSUiw4CngMsDGkhCAowYAWlprKrVmFHn/YNZGytxZpV0Hv9rX5rWsgJ2xhiTK5gthR7ASlVNBhCRj4FBQN6kMAh4yPd4PPCKiIiqasCiGD0a0tL4tHMf7ut7KxWyDvLM1y8wdO9K5L41AfsYY4wJB8FMCo2B9XmepwA9C9tHVbNEJBWoDWzPu5OIjABGAMTExBxbFOvWAdB85wZ6r5rFw9++Qb39u9wsJGOMMUcIZlIo6KqbvwXgzz6o6lhgLEB8fPyxtSJiYmDtWrpvWEz3DYuP3G6MMeYIwZx7mQI0zfO8CbCxsH1EpAxQHdgZ0CjGjIFK+cYNKlVy240xxhwhmElhFtBaRJqLSDlgGJCYb59E4Frf46HADwEdTwAYPhzGjoVmzVyXUbNm7vnw4QH9GGOMCQdB6z7yjRHcBkzFTUl9W1UXicgjwGxVTQTeAt4XkZW4FsKwoAQzfLglAWOM8UNQ71NQ1cnA5HzbHsjzOB24NJgxGGOM8Z/VczDGGHOIJQVjjDGHWFIwxhhziCUFY4wxh0igZ4AGm4hsA9Ye5+F1yHe3dASwc44Mds6R4UTOuZmq1i1up5BLCidCRGararzXcZQkO+fIYOccGUrinK37yBhjzCGWFIwxxhwSaUlhrNcBeMDOOTLYOUeGoJ9zRI0pGGOMKVqktRSMMcYUwZKCMcaYQ8IyKYhIPxFZJiIrRWRUAa+XF5FPfK//ISKxJR9lYPlxzneKyGIRSRKR70WkmRdxBlJx55xnv6EioiIS8tMX/TlnEbnM93e9SEQ+LOkYA82Pf9sxIvKjiMzz/fvu70WcgSIib4vIVhFZWMjrIiIv+/48kkSkW0ADUNWw+sGV6V4FtADKAfOBDvn2+Svwhu/xMOATr+MugXM+B6jke3xrJJyzb7+qwAxgJhDvddwl8PfcGpgH1PQ9r+d13CVwzmOBW32POwBrvI77BM/5LKAbsLCQ1/sD3+BWrjwF+COQnx+OLYUewEpVTVbVg8DHwKB8+wwC/ut7PB7oLRLSizYXe86q+qOqpvmezsSthBfK/Pl7BngUeBpIL8nggsSfc74ZeFVVdwGo6tYSjjHQ/DlnBar5Hlfn6BUeQ4qqzqDoFSgHAe+pMxOoISINA/X54ZgUGgPr8zxP8W0rcB9VzQJSgdolEl1w+HPOed2I+6YRyoo9ZxHpCjRV1a9KMrAg8ufvuQ3QRkR+FZGZItKvxKILDn/O+SHgKhFJwa3f8veSCc0zx/r//ZgEdZEdjxT0jT//vFt/9gklfp+PiFwFxANnBzWi4CvynEUkCngBuK6kAioB/vw9l8F1IfXCtQZ/FpFOqro7yLEFiz/nfAXwrqo+JyKn4lZz7KSqOcEPzxNBvX6FY0shBWia53kTjm5OHtpHRMrgmpxFNddKO3/OGRE5DxgNDFTVjBKKLViKO+eqQCdguoiswfW9Job4YLO//7a/UNVMVV0NLMMliVDlzznfCHwKoKq/AxVwhePClV//349XOCaFWUBrEWkuIuVwA8mJ+fZJBK71PR4K/KC+EZwQVew5+7pS/oNLCKHezwzFnLOqpqpqHVWNVdVY3DjKQFWd7U24AeHPv+3PcZMKEJE6uO6k5BKNMrD8Oed1QG8AEWmPSwrbSjTKkpUIXOObhXQKkKqqmwL15mHXfaSqWSJyGzAVN3PhbVVdJCKPALNVNRF4C9fEXIlrIQzzLuIT5+c5PwNUAT7zjamvU9WBngV9gvw857Di5zlPBfqKyGIgG7hLVXd4F/WJ8fOc/wm8KSJ34LpRrgvlL3ki8hGu+6+Ob5zkQaAsgKq+gRs36Q+sBNKA6wP6+SH8Z2eMMSbAwrH7yBhjzHGypGCMMeYQSwrGGGMOsaRgjDHmEEsKxhhjDrGkYMJGcdUlfftki8ifeX4Kra4awLge8d04iIic6ate+qeINBaR8YUcM/1YbrQTkV4iEi7lPIyHwu4+BRPR3gVeAd4rYp8DqtqlZMJxVPWBPE+HA8+q6ju+50NLMhZjimMtBRM2/KguWSgReTLPehPP+ra9KyJviMjPIrJcRAb4tkeLyDMiMsu3/1/yvM/dIrJAROaLyJN53meoiNwEXAY8ICIJIhKb26oRkYoi8rHv/T4BKuZ5z9dFZLavhfFwnu39RGSpiPwCXHI8521MftZSMJGmooj8mef5E8C3wGCgnaqqiNTI83osrnhgS+BHEWkFXIMrLdBdRMoDv4rINKAdcDHQU1XTRKRW3g9W1XEicgbwlaqOlyMXd7oVSFPVOBGJA+bmeW20qu4UkWjge9/ry4E3gXNxd7Z+ciJ/KMbksqRgIs1R3Ue+oojpwDgR+RrI2zf/qa/a5goRScZd+PsCcSKS2/VTHVd07jzgndx1K1T1WFotZwEv+45LEpGkPK9dJiIjcP9fG+IWkokCVqvqCt85fACMOIbPM6ZA1n1kwpaINM0zoHxLYfv51tToAUzAfdOfkvfl/LvjShf/XVW7+H6aq+o03/YTqRtz1LEi0hz4F9BbVeOAr3EF3wrc35gTZUnBhC1VXZ/nwv1GYfuJSBWguqpOBv4PyNuSuFREokSkJW5JyGW44my3ikhZ3/FtRKQyMA24QUQq+bYf0X1UjBm4QWhEpBMQ59teDdgPpIpIfeAC3/alQHNfXODWFDDmhFn3kQkbBVWXVNW38u2Wf0xhCvAS8IWIVMB9278jz+vLgJ+A+sAtqpouIuNwYw1zxZWc3QZcrKpTRKQLMFtEDuKqWd7rZ/ivA+/4uo3+BP4HoKrzRWQesAhXAvtX3/Z0X5fS1yKyHfgFt36EMSfEqqQaUwgReRffoLDXsRhTUqz7yBhjzCHWUjDGGHOItRSMMcYcYknBGGPMIZYUjDHGHGJJwRhjzCGWFIwxxhzy/6Q700hQZvT2AAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c2f238c18>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2706,17 +2956,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 305,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "#HAY QUE ESPERAR QUE ACTUALICE GGPLOT LAS LIBRERIAS, SINO HAY QUE MODIFICAR ARCHIVOS INTERNOS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "module 'pandas' has no attribute 'tslib'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-82-20fefc971645>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTimestamp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mggplot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/ggplot/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mgeoms\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgeom_area\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_blank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_boxplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_line\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_point\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_jitter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_histogram\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_density\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_hline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_vline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_bar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_abline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_tile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_rect\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_bin2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_step\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_text\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_ribbon\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_now_its_art\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_violin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_errorbar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeom_polygon\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mstat_smooth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstat_density\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/ggplot/geoms/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mgeom_abline\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgeom_abline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mgeom_area\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgeom_area\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mgeom_bar\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgeom_bar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mgeom_bin2d\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgeom_bin2d\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mgeom_blank\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgeom_blank\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/ggplot/geoms/geom_abline.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mgeom\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mgeom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mgeom_abline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgeom\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \"\"\"\n\u001b[1;32m      5\u001b[0m     \u001b[0mLine\u001b[0m \u001b[0mspecified\u001b[0m \u001b[0mby\u001b[0m \u001b[0mslope\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mintercept\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/ggplot/geoms/geom.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m from __future__ import (absolute_import, division, print_function,\n\u001b[1;32m      2\u001b[0m                         unicode_literals)\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mggplot\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mggplot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maes\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/ggplot/ggplot.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0maes\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0maes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mlegend\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmake_legend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mthemes\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtheme_gray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/ggplot/aes.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpatsy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mEvalEnvironment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/ggplot/utils.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     79\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m date_types = (\n\u001b[0;32m---> 81\u001b[0;31m     \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtslib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTimestamp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m     \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDatetimeIndex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m     \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPeriod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(name)\u001b[0m\n\u001b[1;32m    256\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0m_SparseArray\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 258\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"module 'pandas' has no attribute '{name}'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    259\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    260\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: module 'pandas' has no attribute 'tslib'"
+     ]
+    }
+   ],
    "source": [
     "from sklearn import metrics\n",
+    "from pandas import Timestamp\n",
     "from ggplot import *"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 307,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2725,9 +3005,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 332,
+   "execution_count": 83,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'sensit' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-83-c003ab85484b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m df = pd.DataFrame({\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"esp\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mespc_1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0;34m\"sens\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0msensit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m })\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'sensit' is not defined"
+     ]
+    }
+   ],
    "source": [
     "df = pd.DataFrame({\n",
     "    \"esp\":espc_1,\n",
@@ -2737,76 +3029,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 333,
+   "execution_count": 84,
    "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>esp</th>\n",
-       "      <th>sens</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.008197</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.024590</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.032787</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.002693</td>\n",
-       "      <td>0.032787</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.002693</td>\n",
-       "      <td>0.049180</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        esp      sens\n",
-       "0  0.000000  0.008197\n",
-       "1  0.000000  0.024590\n",
-       "2  0.000000  0.032787\n",
-       "3  0.002693  0.032787\n",
-       "4  0.002693  0.049180"
-      ]
-     },
-     "execution_count": 333,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'df' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-84-c42a15b2c7cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'df' is not defined"
+     ]
     }
    ],
    "source": [
@@ -2815,28 +3050,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 337,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c335fc860>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<ggplot: (-9223372029284709828)>"
-      ]
-     },
-     "execution_count": 337,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'ggplot' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-85-e7bec62addb9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mggplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"esp\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sens\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0mgeom_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mgeom_abline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinetype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"dashed\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mxlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mylim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m0.01\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1.01\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mxlab\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1-Especifidad\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mylab\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Sensibilidad\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'ggplot' is not defined"
+     ]
     }
    ],
    "source": [
@@ -2845,18 +3071,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 316,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.7575712982311564"
-      ]
-     },
-     "execution_count": 316,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'sensit' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-86-6dcb6a0c7485>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mauc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mespc_1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msensit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mauc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'sensit' is not defined"
+     ]
     }
    ],
    "source": [
@@ -2866,28 +3093,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 335,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1c3307bc88>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<ggplot: (-9223372029284968921)>"
-      ]
-     },
-     "execution_count": 335,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "NameError",
+     "evalue": "name 'ggplot' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-87-7259c767e7f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mggplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"esp\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sens\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mgeom_area\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.25\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mgeom_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"sens\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mggtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Curva ROC y AUC=%s\"\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'ggplot' is not defined"
+     ]
     }
    ],
    "source": [
@@ -2911,7 +3129,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T6 - 1 - Distancias-Colab.ipynb b/notebooks/T6 - 1 - Distancias-Colab.ipynb
new file mode 100644
index 00000000..492c886d
--- /dev/null
+++ b/notebooks/T6 - 1 - Distancias-Colab.ipynb	
@@ -0,0 +1,3017 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T6%20-%201%20-%20Distancias-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Distancias"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.spatial import distance_matrix\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>user_id</th>\n",
+       "      <th>star_wars</th>\n",
+       "      <th>lord_of_the_rings</th>\n",
+       "      <th>harry_potter</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.2</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>2.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.1</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>7.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>2.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>3.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   user_id  star_wars  lord_of_the_rings  harry_potter\n",
+       "0        1        1.2                4.9           2.1\n",
+       "1        2        2.1                8.1           7.9\n",
+       "2        3        7.4                3.0           9.9\n",
+       "3        4        5.6                0.5           1.8\n",
+       "4        5        1.5                8.3           2.6\n",
+       "5        6        2.5                3.7           6.5\n",
+       "6        7        2.0                8.2           8.5\n",
+       "7        8        1.8                9.3           4.5\n",
+       "8        9        2.6                1.7           3.1\n",
+       "9       10        1.5                4.7           2.3"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = pd.read_csv(\"/content/python-ml-course/datasets/movies/movies.csv\", sep=\";\")\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['star_wars', 'lord_of_the_rings', 'harry_potter']"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "movies = data.columns.values.tolist()[1:]\n",
+    "movies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dd1 = distance_matrix(data[movies], data[movies], p=1)\n",
+    "dd2 = distance_matrix(data[movies], data[movies], p=2)\n",
+    "dd10 = distance_matrix(data[movies], data[movies], p=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dm_to_df(dd, col_name):\n",
+    "    import pandas as pd\n",
+    "    return pd.DataFrame(dd, index=col_name, columns=col_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15.9</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>15.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.1</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4.2</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>3.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6.9</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10.5</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>10.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>7.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user_id    1     2     3     4     5     6     7     8     9     10\n",
+       "user_id                                                            \n",
+       "1         0.0   9.9  15.9   9.1   4.2   6.9  10.5   7.4   5.6   0.7\n",
+       "2         9.9   0.0  12.4  17.2   6.1   6.2   0.8   4.9  11.7   9.6\n",
+       "3        15.9  12.4   0.0  12.4  18.5   9.0  12.0  17.3  12.9  15.2\n",
+       "4         9.1  17.2  12.4   0.0  12.7  11.0  18.0  15.3   5.5   8.8\n",
+       "5         4.2   6.1  18.5  12.7   0.0   9.5   6.5   3.2   8.2   3.9\n",
+       "6         6.9   6.2   9.0  11.0   9.5   0.0   7.0   8.3   5.5   6.2\n",
+       "7        10.5   0.8  12.0  18.0   6.5   7.0   0.0   5.3  12.5  10.2\n",
+       "8         7.4   4.9  17.3  15.3   3.2   8.3   5.3   0.0   9.8   7.1\n",
+       "9         5.6  11.7  12.9   5.5   8.2   5.5  12.5   9.8   0.0   4.9\n",
+       "10        0.7   9.6  15.2   8.8   3.9   6.2  10.2   7.1   4.9   0.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dm_to_df(dd1, data[\"user_id\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "      <td>5.890671</td>\n",
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+       "      <td>5.337602</td>\n",
+       "      <td>10.779147</td>\n",
+       "      <td>8.848164</td>\n",
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+       "      <td>6.709694</td>\n",
+       "      <td>3.612478</td>\n",
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+       "      <td>8.471718</td>\n",
+       "      <td>7.769170</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.293934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.412311</td>\n",
+       "      <td>6.578754</td>\n",
+       "      <td>9.770363</td>\n",
+       "      <td>5.890671</td>\n",
+       "      <td>3.612478</td>\n",
+       "      <td>4.431704</td>\n",
+       "      <td>7.137226</td>\n",
+       "      <td>5.107837</td>\n",
+       "      <td>3.293934</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user_id         1          2          3          4          5         6   \\\n",
+       "user_id                                                                    \n",
+       "1         0.000000   6.685058  10.143471   6.229767   3.449638  4.742362   \n",
+       "2         6.685058   0.000000   7.622336  10.354709   5.337602  4.634652   \n",
+       "3        10.143471   7.622336   0.000000   8.666026  10.779147  6.004998   \n",
+       "4         6.229767  10.354709   8.666026   0.000000   8.848164  6.476110   \n",
+       "5         3.449638   5.337602  10.779147   8.848164   0.000000  6.113101   \n",
+       "6         4.742362   4.634652   6.004998   6.476110   6.113101  0.000000   \n",
+       "7         7.244998   0.616441   7.626270  10.823123   5.921993  4.949747   \n",
+       "8         5.047772   3.618011  10.010494   9.958414   2.167948  5.987487   \n",
+       "9         3.633180   8.015610   8.424369   3.482815   6.709694  3.945884   \n",
+       "10        0.412311   6.578754   9.770363   5.890671   3.612478  4.431704   \n",
+       "\n",
+       "user_id         7          8         9         10  \n",
+       "user_id                                            \n",
+       "1         7.244998   5.047772  3.633180  0.412311  \n",
+       "2         0.616441   3.618011  8.015610  6.578754  \n",
+       "3         7.626270  10.010494  8.424369  9.770363  \n",
+       "4        10.823123   9.958414  3.482815  5.890671  \n",
+       "5         5.921993   2.167948  6.709694  3.612478  \n",
+       "6         4.949747   5.987487  3.945884  4.431704  \n",
+       "7         0.000000   4.153312  8.471718  7.137226  \n",
+       "8         4.153312   0.000000  7.769170  5.107837  \n",
+       "9         8.471718   7.769170  0.000000  3.293934  \n",
+       "10        7.137226   5.107837  3.293934  0.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dm_to_df(dd2, data[\"user_id\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\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>user_id</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></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>0.000000</td>\n",
+       "      <td>5.801514</td>\n",
+       "      <td>7.875189</td>\n",
+       "      <td>4.715803</td>\n",
+       "      <td>3.400000</td>\n",
+       "      <td>4.400003</td>\n",
+       "      <td>6.400850</td>\n",
+       "      <td>4.401025</td>\n",
+       "      <td>3.200085</td>\n",
+       "      <td>0.301025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.801514</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.582463</td>\n",
+       "      <td>7.680689</td>\n",
+       "      <td>5.300000</td>\n",
+       "      <td>4.400005</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>3.400010</td>\n",
+       "      <td>6.435159</td>\n",
+       "      <td>5.603800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>7.875189</td>\n",
+       "      <td>5.582463</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>8.100007</td>\n",
+       "      <td>7.408914</td>\n",
+       "      <td>4.912532</td>\n",
+       "      <td>5.689450</td>\n",
+       "      <td>6.570254</td>\n",
+       "      <td>6.820602</td>\n",
+       "      <td>7.658364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.715803</td>\n",
+       "      <td>7.680689</td>\n",
+       "      <td>8.100007</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.801255</td>\n",
+       "      <td>4.717102</td>\n",
+       "      <td>7.873307</td>\n",
+       "      <td>8.800205</td>\n",
+       "      <td>3.000101</td>\n",
+       "      <td>4.450759</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>3.400000</td>\n",
+       "      <td>5.300000</td>\n",
+       "      <td>7.408914</td>\n",
+       "      <td>7.801255</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.681464</td>\n",
+       "      <td>5.900000</td>\n",
+       "      <td>1.900310</td>\n",
+       "      <td>6.600000</td>\n",
+       "      <td>3.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4.400003</td>\n",
+       "      <td>4.400005</td>\n",
+       "      <td>4.912532</td>\n",
+       "      <td>4.717102</td>\n",
+       "      <td>4.681464</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.500135</td>\n",
+       "      <td>5.600019</td>\n",
+       "      <td>3.401683</td>\n",
+       "      <td>4.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>6.400850</td>\n",
+       "      <td>0.600000</td>\n",
+       "      <td>5.689450</td>\n",
+       "      <td>7.873307</td>\n",
+       "      <td>5.900000</td>\n",
+       "      <td>4.500135</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.000001</td>\n",
+       "      <td>6.595259</td>\n",
+       "      <td>6.202035</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4.401025</td>\n",
+       "      <td>3.400010</td>\n",
+       "      <td>6.570254</td>\n",
+       "      <td>8.800205</td>\n",
+       "      <td>1.900310</td>\n",
+       "      <td>5.600019</td>\n",
+       "      <td>4.000001</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.600000</td>\n",
+       "      <td>4.600288</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>3.200085</td>\n",
+       "      <td>6.435159</td>\n",
+       "      <td>6.820602</td>\n",
+       "      <td>3.000101</td>\n",
+       "      <td>6.600000</td>\n",
+       "      <td>3.401683</td>\n",
+       "      <td>6.595259</td>\n",
+       "      <td>7.600000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000014</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.301025</td>\n",
+       "      <td>5.603800</td>\n",
+       "      <td>7.658364</td>\n",
+       "      <td>4.450759</td>\n",
+       "      <td>3.600000</td>\n",
+       "      <td>4.200000</td>\n",
+       "      <td>6.202035</td>\n",
+       "      <td>4.600288</td>\n",
+       "      <td>3.000014</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user_id        1         2         3         4         5         6         7   \\\n",
+       "user_id                                                                         \n",
+       "1        0.000000  5.801514  7.875189  4.715803  3.400000  4.400003  6.400850   \n",
+       "2        5.801514  0.000000  5.582463  7.680689  5.300000  4.400005  0.600000   \n",
+       "3        7.875189  5.582463  0.000000  8.100007  7.408914  4.912532  5.689450   \n",
+       "4        4.715803  7.680689  8.100007  0.000000  7.801255  4.717102  7.873307   \n",
+       "5        3.400000  5.300000  7.408914  7.801255  0.000000  4.681464  5.900000   \n",
+       "6        4.400003  4.400005  4.912532  4.717102  4.681464  0.000000  4.500135   \n",
+       "7        6.400850  0.600000  5.689450  7.873307  5.900000  4.500135  0.000000   \n",
+       "8        4.401025  3.400010  6.570254  8.800205  1.900310  5.600019  4.000001   \n",
+       "9        3.200085  6.435159  6.820602  3.000101  6.600000  3.401683  6.595259   \n",
+       "10       0.301025  5.603800  7.658364  4.450759  3.600000  4.200000  6.202035   \n",
+       "\n",
+       "user_id        8         9         10  \n",
+       "user_id                                \n",
+       "1        4.401025  3.200085  0.301025  \n",
+       "2        3.400010  6.435159  5.603800  \n",
+       "3        6.570254  6.820602  7.658364  \n",
+       "4        8.800205  3.000101  4.450759  \n",
+       "5        1.900310  6.600000  3.600000  \n",
+       "6        5.600019  3.401683  4.200000  \n",
+       "7        4.000001  6.595259  6.202035  \n",
+       "8        0.000000  7.600000  4.600288  \n",
+       "9        7.600000  0.000000  3.000014  \n",
+       "10       4.600288  3.000014  0.000000  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dm_to_df(dd10, data[\"user_id\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from mpl_toolkits.mplot3d import Axes3D"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f806dd235e0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111, projection=\"3d\")\n",
+    "ax.scatter(xs = data[\"star_wars\"], ys = data[\"lord_of_the_rings\"], zs=data[\"harry_potter\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Enlaces"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "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>user_id</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></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>0.0</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>15.9</td>\n",
+       "      <td>9.1</td>\n",
+       "      <td>4.2</td>\n",
+       "      <td>6.9</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>7.4</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15.9</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>15.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.1</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4.2</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>3.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6.9</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10.5</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>10.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>7.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user_id    1     2     3     4     5     6     7     8     9     10\n",
+       "user_id                                                            \n",
+       "1         0.0   9.9  15.9   9.1   4.2   6.9  10.5   7.4   5.6   0.7\n",
+       "2         9.9   0.0  12.4  17.2   6.1   6.2   0.8   4.9  11.7   9.6\n",
+       "3        15.9  12.4   0.0  12.4  18.5   9.0  12.0  17.3  12.9  15.2\n",
+       "4         9.1  17.2  12.4   0.0  12.7  11.0  18.0  15.3   5.5   8.8\n",
+       "5         4.2   6.1  18.5  12.7   0.0   9.5   6.5   3.2   8.2   3.9\n",
+       "6         6.9   6.2   9.0  11.0   9.5   0.0   7.0   8.3   5.5   6.2\n",
+       "7        10.5   0.8  12.0  18.0   6.5   7.0   0.0   5.3  12.5  10.2\n",
+       "8         7.4   4.9  17.3  15.3   3.2   8.3   5.3   0.0   9.8   7.1\n",
+       "9         5.6  11.7  12.9   5.5   8.2   5.5  12.5   9.8   0.0   4.9\n",
+       "10        0.7   9.6  15.2   8.8   3.9   6.2  10.2   7.1   4.9   0.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = dm_to_df(dd1, data[\"user_id\"])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Z=[]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "<div>\n",
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+       "      <th>user_id</th>\n",
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+       "      <td>6.1</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>19.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15.9</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>31.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.1</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>17.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4.2</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>8.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6.9</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>13.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10.5</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>20.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>14.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>10.5</td>\n",
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+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.7</td>\n",
+       "      <td>19.5</td>\n",
+       "      <td>31.1</td>\n",
+       "      <td>17.9</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>13.1</td>\n",
+       "      <td>20.7</td>\n",
+       "      <td>14.5</td>\n",
+       "      <td>10.5</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>1.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user_id    1     2     3     4     5     6     7     8     9     10    11\n",
+       "user_id                                                                  \n",
+       "1         0.0   9.9  15.9   9.1   4.2   6.9  10.5   7.4   5.6   0.7   0.7\n",
+       "2         9.9   0.0  12.4  17.2   6.1   6.2   0.8   4.9  11.7   9.6  19.5\n",
+       "3        15.9  12.4   0.0  12.4  18.5   9.0  12.0  17.3  12.9  15.2  31.1\n",
+       "4         9.1  17.2  12.4   0.0  12.7  11.0  18.0  15.3   5.5   8.8  17.9\n",
+       "5         4.2   6.1  18.5  12.7   0.0   9.5   6.5   3.2   8.2   3.9   8.1\n",
+       "6         6.9   6.2   9.0  11.0   9.5   0.0   7.0   8.3   5.5   6.2  13.1\n",
+       "7        10.5   0.8  12.0  18.0   6.5   7.0   0.0   5.3  12.5  10.2  20.7\n",
+       "8         7.4   4.9  17.3  15.3   3.2   8.3   5.3   0.0   9.8   7.1  14.5\n",
+       "9         5.6  11.7  12.9   5.5   8.2   5.5  12.5   9.8   0.0   4.9  10.5\n",
+       "10        0.7   9.6  15.2   8.8   3.9   6.2  10.2   7.1   4.9   0.0   0.7\n",
+       "11        0.7  19.5  31.1  17.9   8.1  13.1  20.7  14.5  10.5   0.7   1.4"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[11]=df[1]+df[10]\n",
+    "df.loc[11]=df.loc[1]+df.loc[10]\n",
+    "Z.append([1,10,0.7,2])#id1, id2, d, n_elementos_en_cluster -> 11.\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
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+       "      <th>user_id</th>\n",
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+       "      <th>user_id</th>\n",
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+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></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",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>9.9</td>\n",
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+       "      <td>12.4</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15.9</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>15.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>9.1</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>8.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>4.2</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>3.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6.9</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>10.5</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>10.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>7.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>4.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user_id    1     2     3     4     5     6     7     8     9     10    11\n",
+       "user_id                                                                  \n",
+       "1         0.0   9.9  15.9   9.1   4.2   6.9  10.5   7.4   5.6   0.7   0.0\n",
+       "2         9.9   0.0  12.4  17.2   6.1   6.2   0.8   4.9  11.7   9.6   9.6\n",
+       "3        15.9  12.4   0.0  12.4  18.5   9.0  12.0  17.3  12.9  15.2  15.2\n",
+       "4         9.1  17.2  12.4   0.0  12.7  11.0  18.0  15.3   5.5   8.8   8.8\n",
+       "5         4.2   6.1  18.5  12.7   0.0   9.5   6.5   3.2   8.2   3.9   3.9\n",
+       "6         6.9   6.2   9.0  11.0   9.5   0.0   7.0   8.3   5.5   6.2   6.2\n",
+       "7        10.5   0.8  12.0  18.0   6.5   7.0   0.0   5.3  12.5  10.2  10.2\n",
+       "8         7.4   4.9  17.3  15.3   3.2   8.3   5.3   0.0   9.8   7.1   7.1\n",
+       "9         5.6  11.7  12.9   5.5   8.2   5.5  12.5   9.8   0.0   4.9   4.9\n",
+       "10        0.7   9.6  15.2   8.8   3.9   6.2  10.2   7.1   4.9   0.0   0.0\n",
+       "11        0.0   9.6  15.2   8.8   3.9   6.2  10.2   7.1   4.9   0.0   0.0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "for i in df.columns.values.tolist():\n",
+    "    df.loc[11][i] = min(df.loc[1][i], df.loc[10][i])\n",
+    "    df.loc[i][11] = min(df.loc[i][1], df.loc[i][10])\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    }\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>user_id</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>11</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></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>2</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>15.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.2</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6.1</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>3.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>6.2</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.8</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>10.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>4.9</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>5.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>7.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>11.7</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>12.5</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>9.6</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user_id    2     3     4     5     6     7     8     9     11\n",
+       "user_id                                                      \n",
+       "2         0.0  12.4  17.2   6.1   6.2   0.8   4.9  11.7   9.6\n",
+       "3        12.4   0.0  12.4  18.5   9.0  12.0  17.3  12.9  15.2\n",
+       "4        17.2  12.4   0.0  12.7  11.0  18.0  15.3   5.5   8.8\n",
+       "5         6.1  18.5  12.7   0.0   9.5   6.5   3.2   8.2   3.9\n",
+       "6         6.2   9.0  11.0   9.5   0.0   7.0   8.3   5.5   6.2\n",
+       "7         0.8  12.0  18.0   6.5   7.0   0.0   5.3  12.5  10.2\n",
+       "8         4.9  17.3  15.3   3.2   8.3   5.3   0.0   9.8   7.1\n",
+       "9        11.7  12.9   5.5   8.2   5.5  12.5   9.8   0.0   4.9\n",
+       "11        9.6  15.2   8.8   3.9   6.2  10.2   7.1   4.9   0.0"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.drop([1,10])\n",
+    "df = df.drop([1,10], axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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>user_id</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>11</th>\n",
+       "      <th>12</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></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>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>18.5</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>17.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>18.5</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>6.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>9.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>17.3</td>\n",
+       "      <td>15.3</td>\n",
+       "      <td>3.2</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>4.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>12.9</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>11.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>15.2</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>7.1</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>6.1</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user_id    3     4     5     6     8     9     11    12\n",
+       "user_id                                                \n",
+       "3         0.0  12.4  18.5   9.0  17.3  12.9  15.2  12.0\n",
+       "4        12.4   0.0  12.7  11.0  15.3   5.5   8.8  17.2\n",
+       "5        18.5  12.7   0.0   9.5   3.2   8.2   3.9   6.1\n",
+       "6         9.0  11.0   9.5   0.0   8.3   5.5   6.2   6.2\n",
+       "8        17.3  15.3   3.2   8.3   0.0   9.8   7.1   4.9\n",
+       "9        12.9   5.5   8.2   5.5   9.8   0.0   4.9  11.7\n",
+       "11       15.2   8.8   3.9   6.2   7.1   4.9   0.0   9.6\n",
+       "12       12.0  17.2   6.1   6.2   4.9  11.7   9.6   0.0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 2\n",
+    "y = 7\n",
+    "\n",
+    "n = 12\n",
+    "\n",
+    "df[n]=df[x]+df[y]\n",
+    "df.loc[n]=df.loc[x]+df.loc[y]\n",
+    "Z.append([x,y,df.loc[x][y],2])#id1, id2, d, n_elementos_en_cluster -> 11.\n",
+    "\n",
+    "for i in df.columns.values.tolist():\n",
+    "    df.loc[n][i] = min(df.loc[x][i], df.loc[y][i])\n",
+    "    df.loc[i][n] = min(df.loc[i][x], df.loc[i][y])\n",
+    "\n",
+    "df = df.drop([x,y])\n",
+    "df = df.drop([x,y], axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>user_id</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>6</th>\n",
+       "      <th>9</th>\n",
+       "      <th>11</th>\n",
+       "      <th>12</th>\n",
+       "      <th>13</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user_id</th>\n",
+       "      <th></th>\n",
+       "      <th></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>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>15.2</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>17.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>12.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>8.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>12.9</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>8.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>15.2</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>3.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>9.6</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>17.3</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user_id    3     4     6     9     11    12    13\n",
+       "user_id                                          \n",
+       "3         0.0  12.4   9.0  12.9  15.2  12.0  17.3\n",
+       "4        12.4   0.0  11.0   5.5   8.8  17.2  12.7\n",
+       "6         9.0  11.0   0.0   5.5   6.2   6.2   8.3\n",
+       "9        12.9   5.5   5.5   0.0   4.9  11.7   8.2\n",
+       "11       15.2   8.8   6.2   4.9   0.0   9.6   3.9\n",
+       "12       12.0  17.2   6.2  11.7   9.6   0.0   4.9\n",
+       "13       17.3  12.7   8.3   8.2   3.9   4.9   0.0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 5\n",
+    "y = 8\n",
+    "\n",
+    "n = 13\n",
+    "\n",
+    "df[n]=df[x]+df[y]\n",
+    "df.loc[n]=df.loc[x]+df.loc[y]\n",
+    "Z.append([x,y,df.loc[x][y],2])#id1, id2, d, n_elementos_en_cluster -> 11.\n",
+    "\n",
+    "for i in df.columns.values.tolist():\n",
+    "    df.loc[n][i] = min(df.loc[x][i], df.loc[y][i])\n",
+    "    df.loc[i][n] = min(df.loc[i][x], df.loc[i][y])\n",
+    "\n",
+    "df = df.drop([x,y])\n",
+    "df = df.drop([x,y], axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>user_id</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>6</th>\n",
+       "      <th>9</th>\n",
+       "      <th>12</th>\n",
+       "      <th>14</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user_id</th>\n",
+       "      <th></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>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>12.9</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>15.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>8.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>6.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>12.9</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>4.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>17.2</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15.2</td>\n",
+       "      <td>8.8</td>\n",
+       "      <td>6.2</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user_id    3     4     6     9     12    14\n",
+       "user_id                                    \n",
+       "3         0.0  12.4   9.0  12.9  12.0  15.2\n",
+       "4        12.4   0.0  11.0   5.5  17.2   8.8\n",
+       "6         9.0  11.0   0.0   5.5   6.2   6.2\n",
+       "9        12.9   5.5   5.5   0.0  11.7   4.9\n",
+       "12       12.0  17.2   6.2  11.7   0.0   4.9\n",
+       "14       15.2   8.8   6.2   4.9   4.9   0.0"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 11\n",
+    "y = 13\n",
+    "\n",
+    "n = 14\n",
+    "\n",
+    "df[n]=df[x]+df[y]\n",
+    "df.loc[n]=df.loc[x]+df.loc[y]\n",
+    "Z.append([x,y,df.loc[x][y],2])#id1, id2, d, n_elementos_en_cluster -> 11.\n",
+    "\n",
+    "for i in df.columns.values.tolist():\n",
+    "    df.loc[n][i] = min(df.loc[x][i], df.loc[y][i])\n",
+    "    df.loc[i][n] = min(df.loc[i][x], df.loc[i][y])\n",
+    "\n",
+    "df = df.drop([x,y])\n",
+    "df = df.drop([x,y], axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "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>user_id</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>6</th>\n",
+       "      <th>15</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.4</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>12.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>12.4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>5.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>12.0</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user_id    3     4     6     15\n",
+       "user_id                        \n",
+       "3         0.0  12.4   9.0  12.0\n",
+       "4        12.4   0.0  11.0   5.5\n",
+       "6         9.0  11.0   0.0   5.5\n",
+       "15       12.0   5.5   5.5   0.0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 9\n",
+    "y = 12\n",
+    "z = 14\n",
+    "\n",
+    "n = 15\n",
+    "\n",
+    "df[n]=df[x]+df[y]\n",
+    "df.loc[n]=df.loc[x]+df.loc[y]\n",
+    "Z.append([x,y,df.loc[x][y],3])#id1, id2, d, n_elementos_en_cluster -> 11.\n",
+    "\n",
+    "for i in df.columns.values.tolist():\n",
+    "    df.loc[n][i] = min(df.loc[x][i], df.loc[y][i], df.loc[z][i])\n",
+    "    df.loc[i][n] = min(df.loc[i][x], df.loc[i][y], df.loc[i][z])\n",
+    "\n",
+    "df = df.drop([x,y,z])\n",
+    "df = df.drop([x,y,z], axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>user_id</th>\n",
+       "      <th>3</th>\n",
+       "      <th>16</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user_id   3    16\n",
+       "user_id          \n",
+       "3        0.0  9.0\n",
+       "16       9.0  0.0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 4\n",
+    "y = 6\n",
+    "z = 15\n",
+    "\n",
+    "n = 16\n",
+    "\n",
+    "df[n]=df[x]+df[y]\n",
+    "df.loc[n]=df.loc[x]+df.loc[y]\n",
+    "Z.append([x,y,df.loc[x][y],3])#id1, id2, d, n_elementos_en_cluster -> 11.\n",
+    "\n",
+    "for i in df.columns.values.tolist():\n",
+    "    df.loc[n][i] = min(df.loc[x][i], df.loc[y][i], df.loc[z][i])\n",
+    "    df.loc[i][n] = min(df.loc[i][x], df.loc[i][y], df.loc[i][z])\n",
+    "\n",
+    "df = df.drop([x,y,z])\n",
+    "df = df.drop([x,y,z], axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "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>user_id</th>\n",
+       "      <th>17</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>user_id</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "user_id   17\n",
+       "user_id     \n",
+       "17       0.0"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 3\n",
+    "y = 16\n",
+    "\n",
+    "n = 17\n",
+    "\n",
+    "df[n]=df[x]+df[y]\n",
+    "df.loc[n]=df.loc[x]+df.loc[y]\n",
+    "Z.append([x,y,df.loc[x][y],2])#id1, id2, d, n_elementos_en_cluster -> 11.\n",
+    "\n",
+    "for i in df.columns.values.tolist():\n",
+    "    df.loc[n][i] = min(df.loc[x][i], df.loc[y][i])\n",
+    "    df.loc[i][n] = min(df.loc[i][x], df.loc[i][y])\n",
+    "\n",
+    "df = df.drop([x,y])\n",
+    "df = df.drop([x,y], axis=1)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[1, 10, 0.7, 2],\n",
+       " [2, 7, 0.7999999999999994, 2],\n",
+       " [5, 8, 3.2, 2],\n",
+       " [11, 13, 3.900000000000001, 2],\n",
+       " [9, 12, 11.7, 3],\n",
+       " [4, 6, 11.0, 3],\n",
+       " [3, 16, 9.0, 2]]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clustering jerárquico"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.cluster.hierarchy import dendrogram, linkage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['star_wars', 'lord_of_the_rings', 'harry_potter']"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "movies"
+   ]
+  },
+  {
+   "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>star_wars</th>\n",
+       "      <th>lord_of_the_rings</th>\n",
+       "      <th>harry_potter</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>2.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.1</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>7.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>2.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.8</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2.6</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>3.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   star_wars  lord_of_the_rings  harry_potter\n",
+       "0        1.2                4.9           2.1\n",
+       "1        2.1                8.1           7.9\n",
+       "2        7.4                3.0           9.9\n",
+       "3        5.6                0.5           1.8\n",
+       "4        1.5                8.3           2.6\n",
+       "5        2.5                3.7           6.5\n",
+       "6        2.0                8.2           8.5\n",
+       "7        1.8                9.3           4.5\n",
+       "8        2.6                1.7           3.1\n",
+       "9        1.5                4.7           2.3"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[movies]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Z = linkage(data[movies], \"ward\")\n",
+    "Z\n",
+    "plt.figure(figsize=(25,10))\n",
+    "plt.title(\"Dendrograma jerárquico para el Clustering\")\n",
+    "plt.xlabel(\"ID de los usuarios de Netflix\")\n",
+    "plt.ylabel(\"Distancia\")\n",
+    "dendrogram(Z, leaf_rotation=90., leaf_font_size=10.0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Z = linkage(data[movies], \"average\")\n",
+    "Z\n",
+    "plt.figure(figsize=(25,10))\n",
+    "plt.title(\"Dendrograma jerárquico para el Clustering\")\n",
+    "plt.xlabel(\"ID de los usuarios de Netflix\")\n",
+    "plt.ylabel(\"Distancia\")\n",
+    "dendrogram(Z, leaf_rotation=90., leaf_font_size=10.0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "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></th>\n",
+       "      <th>star_wars</th>\n",
+       "      <th>lord_of_the_rings</th>\n",
+       "      <th>harry_potter</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.2</td>\n",
+       "      <td>4.9</td>\n",
+       "      <td>2.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2.1</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>7.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>5.6</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>1.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>2.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.5</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>6.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>8.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.8</td>\n",
+       "      <td>9.3</td>\n",
+       "      <td>4.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2.6</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>3.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>4.7</td>\n",
+       "      <td>2.3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   star_wars  lord_of_the_rings  harry_potter\n",
+       "0        1.2                4.9           2.1\n",
+       "1        2.1                8.1           7.9\n",
+       "2        7.4                3.0           9.9\n",
+       "3        5.6                0.5           1.8\n",
+       "4        1.5                8.3           2.6\n",
+       "5        2.5                3.7           6.5\n",
+       "6        2.0                8.2           8.5\n",
+       "7        1.8                9.3           4.5\n",
+       "8        2.6                1.7           3.1\n",
+       "9        1.5                4.7           2.3"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[movies]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Z = linkage(data[movies], \"complete\")\n",
+    "Z\n",
+    "plt.figure(figsize=(25,10))\n",
+    "plt.title(\"Dendrograma jerárquico para el Clustering\")\n",
+    "plt.xlabel(\"ID de los usuarios de Netflix\")\n",
+    "plt.ylabel(\"Distancia\")\n",
+    "dendrogram(Z, leaf_rotation=90., leaf_font_size=10.0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Z = linkage(data[movies], method=\"single\", metric=\"cosine\")\n",
+    "Z\n",
+    "plt.figure(figsize=(25,10))\n",
+    "plt.title(\"Dendrograma jerárquico para el Clustering\")\n",
+    "plt.xlabel(\"ID de los usuarios de Netflix\")\n",
+    "plt.ylabel(\"Distancia\")\n",
+    "dendrogram(Z, leaf_rotation=90., leaf_font_size=10.0)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T6 - 1 - Distancias.ipynb b/notebooks/T6 - 1 - Distancias.ipynb
index 7b24324b..ecb19a79 100644
--- a/notebooks/T6 - 1 - Distancias.ipynb	
+++ b/notebooks/T6 - 1 - Distancias.ipynb	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -138,7 +138,7 @@
        "9       10        1.5                4.7           2.3"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -159,7 +159,7 @@
        "['star_wars', 'lord_of_the_rings', 'harry_potter']"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -193,7 +193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -392,7 +392,7 @@
        "10        0.7   9.6  15.2   8.8   3.9   6.2  10.2   7.1   4.9   0.0"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -403,7 +403,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -615,7 +615,7 @@
        "10        7.137226   5.107837  3.293934  0.000000  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -626,7 +626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -838,7 +838,7 @@
        "10       4.600288  3.000014  0.000000  "
       ]
      },
-     "execution_count": 13,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -849,7 +849,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -859,27 +859,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x6105409e8>"
+       "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x7f806dd235e0>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x610800978>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -898,7 +900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -1097,7 +1099,7 @@
        "10        0.7   9.6  15.2   8.8   3.9   6.2  10.2   7.1   4.9   0.0"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1109,7 +1111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1118,7 +1120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1344,7 +1346,7 @@
        "11        0.7  19.5  31.1  17.9   8.1  13.1  20.7  14.5  10.5   0.7   1.4"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1358,7 +1360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -1584,7 +1586,7 @@
        "11        0.0   9.6  15.2   8.8   3.9   6.2  10.2   7.1   4.9   0.0   0.0"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1598,7 +1600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1772,7 +1774,7 @@
        "11        9.6  15.2   8.8   3.9   6.2  10.2   7.1   4.9   0.0"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1785,7 +1787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -1936,7 +1938,7 @@
        "12       12.0  17.2   6.1   6.2   4.9  11.7   9.6   0.0"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1962,7 +1964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -2092,7 +2094,7 @@
        "13       17.3  12.7   8.3   8.2   3.9   4.9   0.0"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2118,7 +2120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -2229,7 +2231,7 @@
        "14       15.2   8.8   6.2   4.9   4.9   0.0"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2255,7 +2257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -2334,7 +2336,7 @@
        "15       12.0   5.5   5.5   0.0"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2361,7 +2363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -2416,7 +2418,7 @@
        "16       9.0  0.0"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2443,7 +2445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -2489,7 +2491,7 @@
        "17       0.0"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2515,7 +2517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -2530,7 +2532,7 @@
        " [3, 16, 9.0, 2]]"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2548,7 +2550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2558,7 +2560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -2567,7 +2569,7 @@
        "['star_wars', 'lord_of_the_rings', 'harry_potter']"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2578,7 +2580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -2686,7 +2688,7 @@
        "9        1.5                4.7           2.3"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2697,17 +2699,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x623c2a7f0>"
+       "<Figure size 1800x720 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2724,17 +2728,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x624ae7320>"
+       "<Figure size 1800x720 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2751,7 +2757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -2859,7 +2865,7 @@
        "9        1.5                4.7           2.3"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2870,17 +2876,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x624ab1898>"
+       "<Figure size 1800x720 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2897,17 +2905,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x62682d240>"
+       "<Figure size 1800x720 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -2928,13 +2938,6 @@
    "source": [
     "The distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘yule’."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -2953,7 +2956,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T6 - 2 - Clustering Jer\303\241rquico-Colab.ipynb" "b/notebooks/T6 - 2 - Clustering Jer\303\241rquico-Colab.ipynb"
new file mode 100644
index 00000000..e57cb778
--- /dev/null
+++ "b/notebooks/T6 - 2 - Clustering Jer\303\241rquico-Colab.ipynb"	
@@ -0,0 +1,879 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T6%20-%202%20-%20Clustering%20Jerárquico-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clustering jerárquico y dendrogramas\n",
+    "Antes de empezar, pongamos un poco de notación para hablar todos el mismo idioma\n",
+    "\n",
+    "* X dataset (array de n x m) de puntos a clusterizar\n",
+    "* n número de datos\n",
+    "* m número de rasgos \n",
+    "* Z array de enlace del cluster con la información de las uniones\n",
+    "* k número de clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.cluster.hierarchy import dendrogram,linkage\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(150, 2)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "np.random.seed(4711)\n",
+    "a = np.random.multivariate_normal([10,0],[[3,1],[1,4]], size = [100,])\n",
+    "b = np.random.multivariate_normal([0,20], [[3,1],[1,4]], size = [50,])\n",
+    "X = np.concatenate((a,b))\n",
+    "print(X.shape)\n",
+    "plt.scatter(X[:,0], X[:,1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Z = linkage(X, \"ward\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.cluster.hierarchy import cophenet\n",
+    "from scipy.spatial.distance import pdist"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9800148387574268"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c, coph_dist = cophenet(Z, pdist(X))\n",
+    "c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5.20000000e+01, 5.30000000e+01, 4.15105485e-02, 2.00000000e+00])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Z[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.40000000e+01, 7.90000000e+01, 5.91375926e-02, 2.00000000e+00])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Z[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[5.20000000e+01, 5.30000000e+01, 4.15105485e-02, 2.00000000e+00],\n",
+       "       [1.40000000e+01, 7.90000000e+01, 5.91375926e-02, 2.00000000e+00],\n",
+       "       [3.30000000e+01, 6.80000000e+01, 7.10677929e-02, 2.00000000e+00],\n",
+       "       [1.70000000e+01, 7.30000000e+01, 7.13712071e-02, 2.00000000e+00],\n",
+       "       [1.00000000e+00, 8.00000000e+00, 7.54313099e-02, 2.00000000e+00],\n",
+       "       [8.50000000e+01, 9.50000000e+01, 1.09277896e-01, 2.00000000e+00],\n",
+       "       [1.08000000e+02, 1.31000000e+02, 1.10071548e-01, 2.00000000e+00],\n",
+       "       [9.00000000e+00, 6.60000000e+01, 1.13022407e-01, 2.00000000e+00],\n",
+       "       [1.50000000e+01, 6.90000000e+01, 1.14289714e-01, 2.00000000e+00],\n",
+       "       [6.30000000e+01, 9.80000000e+01, 1.21200766e-01, 2.00000000e+00],\n",
+       "       [1.07000000e+02, 1.15000000e+02, 1.21671017e-01, 2.00000000e+00],\n",
+       "       [6.50000000e+01, 7.40000000e+01, 1.24900190e-01, 2.00000000e+00],\n",
+       "       [5.80000000e+01, 6.10000000e+01, 1.40277358e-01, 2.00000000e+00],\n",
+       "       [6.20000000e+01, 1.52000000e+02, 1.72599535e-01, 3.00000000e+00],\n",
+       "       [4.10000000e+01, 1.58000000e+02, 1.77901377e-01, 3.00000000e+00],\n",
+       "       [1.00000000e+01, 8.30000000e+01, 1.86354938e-01, 2.00000000e+00],\n",
+       "       [1.14000000e+02, 1.39000000e+02, 2.04186147e-01, 2.00000000e+00],\n",
+       "       [3.90000000e+01, 8.80000000e+01, 2.06282849e-01, 2.00000000e+00],\n",
+       "       [7.00000000e+01, 9.60000000e+01, 2.19312547e-01, 2.00000000e+00],\n",
+       "       [4.60000000e+01, 5.00000000e+01, 2.20492804e-01, 2.00000000e+00]])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Z[:20]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[33.         68.          0.07106779  2.        ]\n",
+      "[15.         69.          0.11428971  2.        ]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(Z[152-len(X)])# cluster 152\n",
+    "print(Z[158-len(X)])#cluster 158"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 9.83913054, -0.48729797],\n",
+       "       [ 9.97792822, -0.56383202],\n",
+       "       [ 9.8934927 , -0.44152257]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X[[33,62,68]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "idx = [33,62,68]\n",
+    "idx2 = [15,69,41]\n",
+    "plt.figure(figsize=(10,8))\n",
+    "plt.scatter(X[:,0], X[:,1])##pintar todos los puntos\n",
+    "plt.scatter(X[idx,0], X[idx,1], c='r')##destacamos en rojo los puntos interesantes\n",
+    "plt.scatter(X[idx2,0], X[idx2,1], c='y')##destacamos en amarillo el segundo cluster\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Representación gráfica de un dendrograma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25,10))\n",
+    "plt.title(\"Dendrograma del clustering jerárquico\")\n",
+    "plt.xlabel(\"Índices de la Muestra\")\n",
+    "plt.ylabel(\"Distancias\")\n",
+    "dendrogram(Z, leaf_rotation=90., leaf_font_size=8.0, color_threshold=0.7*180)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[290.        , 294.        ,  15.11533118,  76.        ],\n",
+       "       [287.        , 292.        ,  17.11527362,  50.        ],\n",
+       "       [293.        , 295.        ,  23.12198936, 100.        ],\n",
+       "       [296.        , 297.        , 180.27043021, 150.        ]])"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Z[-4:,]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Truncar el dendrograma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25,10))\n",
+    "plt.title(\"Dendrograma del clustering jerárquico truncado\")\n",
+    "plt.xlabel(\"Índices de la Muestra\")\n",
+    "plt.ylabel(\"Distancias\")\n",
+    "dendrogram(Z, leaf_rotation=90., leaf_font_size=12.0, color_threshold=0.7*180,\n",
+    "           truncate_mode=\"lastp\", p=12, show_leaf_counts=True, show_contracted=True,)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dendrograma tuneado"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def dendrogram_tune(*args, **kwargs):\n",
+    "    \n",
+    "    max_d=kwargs.pop(\"max_d\", None)\n",
+    "    if max_d and 'color_threshold' not in kwargs:\n",
+    "        kwargs['color_threshold'] = max_d\n",
+    "    annotate_above = kwargs.pop('annotate_above', 0)\n",
+    "    \n",
+    "    ddata = dendrogram(*args,**kwargs)\n",
+    "        \n",
+    "    if not kwargs.get('no_plot', False):\n",
+    "        plt.title(\"Clustering jerárquico con Dendrograma truncado\")\n",
+    "        plt.xlabel(\"Índice del Dataset (o tamaño del cluster)\")\n",
+    "        plt.ylabel(\"Distancia\")\n",
+    "        for i, d, c in zip(ddata['icoord'], ddata['dcoord'], ddata['color_list']):\n",
+    "            x = 0.5 * sum(i[1:3])\n",
+    "            y = d[1]\n",
+    "            if y>annotate_above:\n",
+    "                plt.plot(x,y,'o',c=c)\n",
+    "                plt.annotate('%.3g'%y, (x,y), xytext=(0,-5),\n",
+    "                            textcoords=\"offset points\", va=\"top\", ha=\"center\")\n",
+    "                \n",
+    "    if max_d:\n",
+    "        plt.axhline(y=max_d, c='k')\n",
+    "        \n",
+    "    return ddata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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0R7M0Wj179mTq1KkAPP/88/Tt2xeAr3/960yYMIHq6mrmzp3L7NmzOe6444pZVJFmQ88+kkZh0KBBTJkyheXLl9O7d29GjBjBXXfdxaWXXsrWrVtp3749o0ePBuCII47g29/+NocffjglJSXceeedtG7dushrINI8WKb22aaivLzcZ8yYUexi7CTxsDE9tE0aK9XRls3MZrp7eaZpaj4SEZEkBQUREUlSUBARkSQFBRERSVJQEBGRJAUFERFJUlAQEZEkBQUREUlSUBARkSQFBRERSVJQEBGRJAUFERFJUlAQEZEkBQUREUlSUBARkSQFBRERScpbUDCzMWa21MxmpYy7zswWmllF/Ds9ZdrVZvahmb1vZl/JV7lERKR2+TxTGAuclmH8re5eFv8mAZjZ4cB5wBFxmd+bmd6vKCJSYHkLCu7+IrCinrOfCUxw92p3nwt8COhN7CIiBVaMPoVLzOzN2LzUOY7rBcxPmWdBHLcTMxtmZjPMbMayZcvyXVYRkRal0EHhD8BBQBmwGLg5jrcM83qmBNx9tLuXu3t5165d81JIEZGWqqBBwd2r3H2bu9cAd7G9iWgBsF/KrL2BRYUsm4iIFDgomFmPlI/fBBJXJk0EzjOzdmbWB+gLvFrIsomICJTkK2EzewAYCHQxswXAtcBAMysjNA1VAj8AcPe3zewh4B1gK/Bf7r4tX2UTEZHM8hYU3H1QhtF3Z5l/JDAyX+UREZG66Y5mERFJUlAQEZEkBQUREUlSUBARkSQFBRERSVJQEBGRJAUFERFJUlAQEZEkBQUREUlSUBARkSQFBRERSVJQEBGRJAUFERFJUlAQEZEkBQUREUlSUBARkSQFBRERSVJQEBGRJAUFERFJUlAQEZGkvAUFMxtjZkvNbFbKuN+a2Xtm9qaZPWZm+8TxpWa20cwq4t8f81UuERGpXT7PFMYCp6WNexY40t2PBj4Ark6Z9pG7l8W/H+axXCIiUou8BQV3fxFYkTbuGXffGj9OB3rnK38REWm4YvYpXAT8PeVzHzN73cymmtnna1vIzIaZ2Qwzm7Fs2bL8l1JEpAUpSlAws2uArcC4OGoxsL+7HwNcDow3s70zLevuo9293N3Lu3btWpgCi4i0EAUPCmZ2IXAGMNjdHcDdq939kzg8E/gIOKTQZRMRaekKGhTM7DTgF8DX3X1DyviuZtY6Dh8I9AXmFLJsIiICJflK2MweAAYCXcxsAXAt4WqjdsCzZgYwPV5pNAC43sy2AtuAH7r7iowJi4hI3uQtKLj7oAyj765l3keAR/JVFhERqR/d0SwiIkkKCiIikqSgICIiSQoKIiKSpKAgIiJJCgoiIpKkoCAiIkkKCiIikqSgICIiSfW6o9nM+gKjgMOB9onx7n5gnsolIiJFUN8zhb8AfyA87vqLwL3AffkqlIiIFEd9g8Ie7j4ZMHef5+7XASfnr1giIlIM9X0g3iYzawXMNrNLgIVAt/wVS0REiqG+ZwrDgQ7AT4Fjge8CF+apTCIiUiT1OlNw93/HwXXA9/JXHBERKaasQcHMbnP34Wb2BODp093963krmYiIFFxdZwqJK4x+l++CiIhI8WUNCu4+Mw7OADa6ew1AfJ9yuzyXTURECqy+Hc2TCR3NCXsAz+W+OCIiUkz1DQrt3X1d4kMc7pBlfhERaYLqGxTWm9lnEx/M7FhgY7YFzGyMmS01s1kp4/Y1s2fNbHb83zll2tVm9qGZvW9mX2noioiIyO5ryH0KfzWzl8zsJeBB4JI6lhkLnJY27ipgsrv3JTRJXQVgZocD5wFHxGV+H/stRESkgOp9n4KZHQr0Awx4z9231LHMi2ZWmjb6TGBgHL4HmAL8Io6f4O7VwFwz+xA4DnilfqshIiK5UN/HXAB8DiiNyxxjZrj7vQ3Mr7u7LwZw98VmlnhURi9gesp8C+K4nZjZMGAYwP7779/A7EVEJJv6Pjr7PuAgoALYFkc74WmpuWAZxu10sxyAu48GRgOUl5dnnEdERHZNfc8UyoHD3X13D8JVZtYjniX0AJbG8QuA/VLm6w0s2s28RESkgerb0TwL+HQO8pvI9gfpXQg8njL+PDNrZ2Z9gL7AqznIT0REGqC+ZwpdgHfM7FWgOjEy27OPzOwBQqdyFzNbAFwL3AA8ZGZDgY+Bc2I6b5vZQ8A7hBf5/Je7b8uYsIiI5E19g8J1DU3Y3QfVMumUWuYfCYxsaD4iIpI79b0kdWq+CyIiIsVXrz4FM+tvZv82s3VmttnMtpnZmnwXTkRECqu+Hc13AIOA2YSH4V0cx4mISDNS75vX3P1DM2sdO4D/YmbT8lguEREpgvoGhQ1m1haoMLObgMXAnvkrloiIFEN9m4++G+e9BFhPuNHsrHwVSkREiqO+QeEb7r7J3de4+wh3vxw4I58FExGRwqtvULgww7ghOSyHiIg0Aln7FMxsEHA+0MfMJqZM2hv4JJ8FExGRwquro3kaoVO5C3Bzyvi1wJv5KpSIiBRH1qDg7vOAeWZ2KrDR3WvM7BDgUOCtQhRQREQKp759Ci8C7c2sF+E1mt8jvG5TRESakfoGBXP3DYTLUP/P3b8JHJ6/YomISDHUOyiY2QnAYOCpOK4hr/IUEZEmoL5BYThwNfBYfPfBgcALeSuViIgURUMenT015fMc4Kf5KpSIiBRHXfcp3Obuw83sCWCn9zNne/OaiIg0PXWdKdwX//8u3wUREZHiq+s+hZnx/1Qz6xqHlxWiYCIiUnhZO5otuM7MlgPvAR+Y2TIz+59dzdDM+plZRcrfGjMbHvNZmDL+9F3NQ0REdk1dVx8NB04EPufun3L3zsDxwIlmdtmuZOju77t7mbuXAccCG4DH4uRbE9PcfdKupC8iIruurqBwATDI3ecmRsQrj74Tp+2uU4CP4uM0RESkyOoKCm3cfXn6yNiv0CYH+Z8HPJDy+RIze9PMxphZ5xykLyIiDVBXUNi8i9PqFF/v+XXgr3HUH4CDgDLCk1lvrmW5YWY2w8xmLFumPm8RkVyq65LUz5jZmgzjDWi/m3l/FXjN3asAEv8BzOwu4MlMC7n7aGA0QHl5+U73ToiIyK6r65LU1nnMexApTUdm1sPdF8eP3wRm5TFvERHJoCgPtTOzDsCXgB+kjL7JzMoId05Xpk0TEZECKEpQiI/h/lTauO8WoywiIrJdfZ+SKiIiLYCCgoiIJCkoiIhIkoKCiIgkKSiIiEiSgoKIiCQpKIiISJKCgoiIJCkoiIhIkoKCiIgkKSiIiEiSgoKIiCQpKIiISJKCgoiIJCkoiIhIkoKCiIgkKSiIiEiSgoKIiCQpKIiISJKCgoiIJJUUI1MzqwTWAtuAre5ebmb7Ag8CpUAl8G13X1mM8omItFTFPFP4oruXuXt5/HwVMNnd+wKT42cRESmgxtR8dCZwTxy+B/hG8YoiItIyFSsoOPCMmc00s2FxXHd3XwwQ/3fLtKCZDTOzGWY2Y9myZQUqrohIy1CUPgXgRHdfZGbdgGfN7L36Lujuo4HRAOXl5Z6vAoqItERFOVNw90Xx/1LgMeA4oMrMegDE/0uLUTYRkZas4EHBzPY0s46JYeDLwCxgInBhnO1C4PFCl01EpKUrRvNRd+AxM0vkP97dnzazfwMPmdlQ4GPgnCKUTUSkRSt4UHD3OcBnMoz/BDil0OUREZHtGtMlqSIiUmQKCiIikqSgICIiSQoKIiKSpKAgIiJJCgoiIpKkoCAiIkkKCiIikqSgICIiSQoKIiKSpKAgIiJJCgoiIpKkoCAiIkkKCiIikqSgICIiSQoKIiKSpKAgIiJJCgoiIpKkoCAiIkkKCiIiklTwoGBm+5nZC2b2rpm9bWaXxvHXmdlCM6uIf6cXumwiIi1dSRHy3Ar8zN1fM7OOwEwzezZOu9Xdf1eEMomICEU4U3D3xe7+WhxeC7wL9Cp0OURaqnHjYPp0mDoVSkvDZ5GEovYpmFkpcAzwrzjqEjN708zGmFnnWpYZZmYzzGzGsmXLClVUkWZh3DgYNgyqq8PnefPCZwUGSTB3L07GZnsBU4GR7v6omXUHlgMO/C/Qw90vypZGeXm5z5gxI/+FbaCBA8P/KVOKWQqpr9GjYfz4YpeiMKZP3x4QUrVrB/37F748hXb++SEItnRmNtPdyzNNK8qZgpm1AR4Bxrn7owDuXuXu29y9BrgLOK4YZZOWZ/x4qKgodikKI1NAyDa+OamoaDnBf3cUvKPZzAy4G3jX3W9JGd/D3RfHj98EZhW6bNJylZW1jDO70tLQZJTugAOa//onzuAlu2KcKZwIfBc4Oe3y05vM7C0zexP4InBZEcom0qyNHAkdOuw4rkOHMF4EitinkAsdO3b0Y489ttjF2EmiKaKsrJilkPpqafurqgrmzg1NRu3aQZ8+0L17sUuVfy1tP2czderUWvsUinGfQrNWtb6KNXvPxVtVM31BO/p07kP3PVvAN06ajO7dW0YQkF3TpINCv379mNKIGkLHvTWOYU8Mw7eEXrtqqvm4zcf88mu/ZPBRg4tcOqmNrhZrGbSftwtdu5k16aCwK0bPHM34t/JzCcL0BdOp3rbjZRwbtmxg6ONDuWvmXTnN6/yjzmfYsbq2TnY0f/58LrjgApYsWUKrVq0YNmwYl156Kb/61a94/PHHadWqFd26dWPs2LH07Nlzp+UvuuginnzySbp168asWc3nWo9xVVVMHz6H6k7VlL7SjpEHHshgnS5l1OIeiDf+rfFULKnIS9rpAaGu8buqYklF3gKbNG0lJSXcfPPNvPvuu0yfPp0777yTd955hyuvvJI333yTiooKzjjjDK6//vqMyw8ZMoSnn366wKXOr3FVVQx7/32q96kGg3nV1Qx7/33GVVUVu2iNUos7UwAo+3QZU4ZMyXm6pbeVMm/1ztf7HdDpgJzmN3DswJylJc1Ljx496NGjBwAdO3bksMMOY+HChRx++OHJedavX19r88GAAQOorKzcrTKMXrSI8Y3ogDt9zRqq0y6o2VBTw9D33uOuRYuKVKodnd+9O8MynLkVQ4s7U8inkaeMpEObHa/369CmAyNP0fV+UniVlZW8/vrrHH/88QBcc8017LfffowbN67WM4VcGF9VRcW6dXlLv6HSA0Jd4wutYt26RhVEW+SZQr4kOpOvmXwNH6/+mP077c/IU0aqk1kKbt26dZx99tncdttt7L333gCMHDmSkSNHMmrUKO644w5GjBiRt/zL9tqLKccck7f0G6L0lVeYl+GW7QPatSt6GcdVVTH0vfeYuno1pa+80ij6OnSmkGODjxpM5fBKaq6toXJ4pQKCFNyWLVs4++yzGTx4MGedddZO088//3weeeSRIpSsOEYeeCAdWu14qOvQqhUjDzywSCUKkn0d8YylsfR16ExBpBlxd4YOHcphhx3G5Zdfnhw/e/Zs+vbtC8DEiRM59NBDi1XEgkv88r5mzhw+rq5m/3b5ufqooX0puejryEdfhM4UGuiiiy6iW7duHHnkkclx5557LmVlZZSVlVFaWkpZLbdMZlpWJJdefvll7rvvPp5//vlknZw0aRJXXXUVRx55JEcffTTPPPMMt99+OwCLFi3i9NO3v+Rw0KBBnHDCCbz//vv07t2bu+++u1irklODu3en8oQTqBk4kMoTTshLE01D+1J2t68jX30ROlNooCFDhnDJJZdwwQUXJMc9+OCDyeGf/exndOrUqd7LSgPl4znXFbeF/wOH5zbdIjyn+aSTTiLTo2tSD/ypevbsyaRJk5KfH3jggbyVrSVoSF/K7vZ1DHz99QaXrz50ptBAAwYMYN999804zd156KGHGDRoUIOXlXrKw3Oup5QNZ0rZ8Jymqec0F1ams/DrrruOXr167XDGVN9lC6Gx9nXoTCGHXnrpJbp3755su4Wd76DetHwTc1fN3a17DRI33+X6foUmc5d0U3jOtZ7TXFC1nYVfdtllXHHFFbu0bL4Vqq+joRQUcuiBBx7Y6SwhcQd12afLcpZPtrQWr11M1fqGtzOurl69y3dKN5lgIjvJx41miXb1XDZv1NWhujs33eXihr1dNbh796IHgXQKCjmydetWHn30UWbOnLnTtNQ7qCsrKzlj7Bl5uaMawtlD1fqqnAahbBJnLc02KOxqH0aiiWtXzhgK2BeR6Bwt22uvnKVZW1qLN2+mavPmBqe3etu2enWqblq0iLmbNiWDUeXixaybNIl7772X8vJybr75Zjp3zvjq97zJ9CypK6+8kieeeIK2bdty0EEH8Ze//IV99tkn4/Lbtm2jvLycXr168eSTTxakzAoKOfLcc89x6KGH0rt372IXJW+P8cik2T9yI9GH0dCH8O/qQ/sTwaSAHdSFutFs4OuvU7V5c04DUDYrTj+dY77/fV747Gf51a9+xc9+9jPGjBlTkLwTMjVNfelLX2LUqFGUlJTwi1/8glGjRnHjjTdmXP7222/nsMMOY82aNTtNc8h4UcHuUlBooEGDBjFlyhSWL19O7969GTFiBEOHDmXChAk7NR0tWrSIt255i6MuPyrrstLIFbIPo5n3ReQzAFV27swZ7dsn0x8Yx7dq1Yrvf//7nHHGGXnJN5tMTVNf/vKXk8P9+/fn4YcfzrjsggULeOqpp7jmmmu45ZZbdpi2bt06FixYQM22bbzVpg1HHXVUzsrcooLC5s2b+eCDD6iurubRRx/NeLdnXWq7ZG/s2LE7jevZs2cyIGRbVkRyr3rZMtp17QrAY489lveri7Zt28aWrVupqamhVav6Xdg5ZswYzj333IzThg8fzk033cTatWt3mmZmWMpwLrWoS1Krq6vZsGED1dXVvPPOOwXJc8OGDaxevZotW7YUJD9p2mrcaRyPaWtaMt10N+f225nx7W9z9NFH88ILL3DrrbcC+blhr7q6mvnz57NgwQL+8Y9/1GuZkSNHUlJSwuDBOz8KJ9EPUdvrhvfcc0969uxJr169ch7sWtSZQklJCa1bt2brtq10jb8g8qmyspLZs2dTU1PDPffcw8UXX5z3PAtt69atBcvrk08+YevSpbRu1YpOW7bQpk2bguVdCC+99BL7zZtH27Zt+XQDfm3uqldffZWFS5fSoUOHumfOgZqaGqo3b6a6upp27drlNO1MZ+FjjjqK6upqXjz22B3WMR837NXU1LBhwwY2b9nCgpUr65z/nnvu4cknn2Ty5MkZf+m//PLLTJw4kUmTJrFp0ybWrFnDd77zHe6//34A1q5dy0dz5rBlyxb+DXzuc5/b7XVIaHRnCmZ2mpm9b2YfmtlVuUx75cqVrFq1ig3rNzC+ADcWbdu2jfXr17Nu3To+/vjjvOe3ceNGlixZwqJFi1ixYkXe85s9ezYffvQhH330EfPm7fweiVx77rnnmD9/Ph/Pn1+Q/Artww8/ZMXKlRk7FfPhpZdeYvknn/BJAeoKwJKqKqqqqgry3QOYW1nJnDlz+Mtf/lKQ/DZu3MjGDRuoquMqqaeffpobb7yRiRMn1hqQR40axYIFC6isrGTChAmcfPLJyYAA8Nprr7F48WKWLVvGDTfckNP1aFRBwcxaA3cCXwUOBwaZ2eHZl6q/efPmUVNTg7szffr0XCVbq8mTJ7Nly5bkmUK+zZo1iyVVS1i6dCkvvPBC3vObNm0aK1esZMXKFRkvxc21559/PgTZtWuZPXt23vMrtFGjRrFmzRqqli5l06ZNec/vzjvvZNWqVVQtWZL3vAA6dOhAm5IS+vXrV5D8qqqqWLlqVfI5T/n0wQcfsG7dOjZv3rzDlUSZmqYuueQS1q5dy5e+9CXKysr44Q9/COzcrJXNtGnT8JoacK93c1V9Nbbmo+OAD919DoCZTQDOBHLSAbBq1arkcE1NTS6SzGrZsmXJ4Y0bN+Y9v379+tF5n87U1NTQv3//vOfXtWtX2rZtC0CXLl3ynl9VVRW442YsX7487/kV2sKFC4FwmeHKlSvz3qyzdu1acC/IdwFgn06d2KdTJ/6jQO8wSHTGlpTk/zCX2ke5fv365HCmpqnarjhMb9ZKGDhwIAPTrkobO3Ys/OhHO+WXC5aP61x3lZl9CzjN3S+On78LHO/ul6TMMwxIXMTdD3i/4AUVEWnaDnD3jB2rje1MIdO1VTtELXcfDYwuTHFERFqWRtWnACwA9kv53BtoHG/WFhFpARpbUPg30NfM+phZW+A8YGKRyyQi0mI0quYjd99qZpcA/wBaA2Pc/e0iF0tEpMVoVB3NIiJSXI2t+UhERIpIQUFERJIUFEREJElBQUREkhrV1Uf5YmZtgP7AZ4B9gFXAG8B0d8/5M60LnV9zVoxtaWaHAEcAHYG1wNvu/kGe8lLdbMKa4/5r1lcfmVkX4CrgQmAF8B7hS94ROAzoDNwD3ODuu/0wnULnF/MsAb4O/Cc7V5S/A39z95w837rAeRVjW+4PPEhYt4+A1cDewEGEdTzP3XPyuFvVzdzWl0Ln15z3X3MPCu8CdwMPuPvCDNN7AoOB77n7bj+NtQj5/QC4BngXmBr/p1aUL8T/v3H3PzaVvGJ+Bd2WMc3JwEzgOnffkDJ+T+B/gM+5+8k5ykt1M7f1pVnXz4Lm5+7N9g9om8v5GmF+NwOfrmOeHsDvmlJexdiWMa11taUHtAPW5zAv1c3c1pdmXT8LmV+zPlMQaYj4a+wad380w7RvEn5lHlb4kokUTrO/+sjMWpnZcDO7w8yOMrNuZvaImb1uZr+Nz1jKZX5vmNk1ZnZALtPdhXJcaWZ5fSC/mfUzs5/Ev0PylMcwM5tmZqvNbFv8P83Mvp+H7C4BxpjZP83sTjP7Taw3/wTGAP+Vy8wKXTdjnoXcnpny72Jmn8nHuqXk0cfMvmZm55rZiWbWKV95ZSlDGzN7PsdpFqS+NPszBTO7GSgDaoCjgT8SXtrThtBx86S75+y1n2ZWDUwDPg+8CPwFeMRT2qhzycxqa+N+iPDeiVXunpPKaWZTgMvc/XUzO4vQsZV4xdsXgO+6e84eYGhmNwJnEJoG3mB7x28ZcDnwhLtfnav8Yp5dgG8Srj7ai9CkNIvQSZnTN/sUoW4WdHua2WHABEJH/ciY571AB0Jn6WnuPiuH+fUAHgAGxFE1hH6FEuA24H+8QAc8M2sHbHD31jlMszD1JRftXY35j/A47n2AT8WNeVDKtMOBOTnOb038X0ronJwNrAHGAl/Iw/rVxHWcm/a3Ffg4l+sHrGT7D4k3gAEp004EZuV43ZYBPWqZ1hNYXqA6NAPYNw/pFrpuFnR7As8APyGcYW0Ffkh4Z0oJ8P8IB7Fc5jcJ+D2h76AX8CfgF0Bfwo+XX+c4vzlZ/iqBbU2xvrSEM4XV7t4pfThl+lp375jD/Na4+95p404CLgDOIfxy75PD/K4FzgZ+7u5Pp4xfDHzG3ZfmMK9lQD93XxGHe3i8xM/C+7VX5XhbLgeOcvfFGab1BN5y90/lML97a5l0NvAUsMndL8hhfoWum4Xenp8AXQhPPN4AdHL3jXHa3sBsd++ew/zWEIJ3ok7uAcx1909buNx4urv3zGF+q4ArCD/C0rUlBL1cnikUpL60hJvXlpvZ3u6+hvBLJcnMugK5fcFphrfHufs/gX+a2U+Bb+QyM3cfYWb3AXfEy/Iu9RxdS5/BX2M+w4D7gKvN7NeEdb4KeCvH+d0NPB9Pm1ObOz5DaO64K8f5nQO8Ckxmx/1YA8wjNCXlUqHrZqG3p3n41bnVzNYnAkK0Dtgjx/lVAQcTruGH0Gy1BsDdPzaznAXY6DVgo7tPTp8Qm48yvUlydxSmvuTy9KYx/gGXAn1qmTaU8M6GXOY3qYjreg7wAfBLYCnQLcfp7wHcz/YbgmqATfHvA+CwPKzTD4CXY55b4/+XgR/kIa++hHd5TAB6pYxfnOttGdMtaN0swvb8N7B/LdPKgHdznN/FhMDwf8Ad8TvwozjtCOCNHOc3kHDvSqZpRo6biwtVX5p981FLY+FGqxHAKcDJ7r4yD3kcAhxPeF3qRuBN4EXP4d2pxWRm5xG24V2EDsqPgTLPYVNcSxA7mhd6+GWbPu1UYB93fzjHeX6B0JluhB9oz8fx7YD27r46l/k1RwoKBWZm+3v+mndalHxuy9jmfT1wKnAAoVOvoEGh0HVFdbPpiVfL9SKcdW3ORZot4T6FT5vZ31Oux/6PtOk7/YrJY1nakblTalfT+2lMM2uesS+jyeRVz/LkdFumc/c17j4c+A6hOa5g9QTyv375zq/Q9aUY9bPQxxYzO8zCfVDrzOxqMzud0H/yCjDHzI7MRT4toaP5dmARof3vC8ATZvYTdx8fp+e0M8jMBmSZnLXS7oJPAx+a2STC817eZ/vzXg4hrPNXCdeGN6W8gIJvy4zcvQKoyEfahV6/Zlw3i5EfFPjYEvP7M6Ev73bCzZZdCVd33QLcQGg62y3NvvnIzKqAA9x9U/x8NOF65hHuflemS0h3M78aQsdkTS2z9PTcXqbWBRhCqPBHEa5jXklo558E3OvunzS1vGJ+Bd2WhVaEutJs62aR8iv0saUgl/i2hKDwCWHHrUsZdzDwLOEKhWtzvOPmAoPdfVqGae0JD1VrsgeyQmru27LQ69fct2ehFeHYssLd943DK929c8q0VoT7hHY7v2bfp0B4FPJXUke4+4eEU74fAXvmOL8ZQHkt02oIV7JI/TT3bVno9Wvu27PQCn1s+SjehEdqQIiOBnZ6pPauaAlnCscDnT3lbt+Uab2Aoe5+fQ7zawPgeovVbmvu27LQ69fct2ehFeHYUpBLfJt9UBARkfpr1s1HLeGyuOaquW9L1c2mrTnvv+Z+SWpLuCyuuWru21J1s2lrtvuv2TcfNffL4pqz5r4tVTebtua6/5p9UBARkfpr1n0KIiLSMAoKIiKSpKDQDJjZ7fE6ZRGR3aKg0MTFy9Recffnil0WEWn6FBSaOHevJjw5sd7MbKCZPRmHv25mV+WyTKnpN3SeOH61mb1uZu+b2YtmVueTH+Ny/1HXfA1hZvuY2Y+zTN/DzKZaeD/1bqeXS2Z2lplNN7NH4yOWdzWdXd6XWeafYma1PW4j23Jlu7IuZtbVzHa661gyU1Bo4dx9orvfUOxypHnJ3Y9x937ATwnvhT6ljmUGAjkNCoRL/rIdxC8CHnX3bTlKL2fc/VF37+/uZ7n7pELkWQBlQIOCgpmVuPsyYLGZnZiXUjUzCgrNSPzFNsXMHjaz98xsnJlZnHZaHPdP4KyUZYaY2R1xuLuZPWbhRR5vJH55m9l3zOxVM6swsz9l+mWcJf09zWyMmf07/vo/syHrFN9ncD3h2fGY2dfM7F8xredimUsJLzK/LJbx85nmi8t/Ic5TEad1jOOvjGV808xGxOxvAA6K8/42Q/EGA4/H5c3Mfmtms8zsLTM7N8P8O6RnZnuZ2WQzey0uc2ZMqzRuyz/H9MaZ2alm9rKZzTaz4+J8x1l4ucvr8X+/OH5IPEN4Os5/U8r+GBTzmmVmN2ba5rnal2bW2sx+F/N708x+kmGe1CeMfsvMxsbhc2IZ37BwttiWUA/Ojdvv3NrKE9f/r2b2BPBMTP5vcX9JXXLxomf9FfcPWBf/DwRWE96d3IrwRqaTgPbAfMKL6Q14CHgyLjMEuCMOPwgMj8OtgU7AYcATQJs4/vfABWn5Z0v/N8B34vA+wAeEp0cOTMyTltZO40l5yTvQme3311wM3ByHrwOuSFmmtvmeAE6Mw3sR7ur/MjA6lr0V8CQwACgFZtWyzdsCS1I+n014ZHJroDvhiaM90pbZIb2Y995xuAvwYSxDKbCVcINSK8LTOMfEaWcCf4vL7A2UxOFTgUdS9umcuP/aA/OA/YCesVxdY97PA9/I4778EfBIShn3jf+nAOWpdTcOfwsYG4ffAnol8kqvq3WUZwiwIJFfnN4LeKvY39Wm8NfcH3PREr3q7gsAzKyCcIBZB8x199lx/P3AsAzLngxcAOChSWS1mX0XOBb4dzzp2ANIf1fxoVnS/zLwdTO7In5uD+zfwHVKfYNVb+BBM+tBODDX9grJ2uZ7GbjFzMYRmn4WmNmXYzlfj/PsRTgoZnuUdBdgVcrnk4AH4narMrOpwOeAiXWs128svBGthnDgSrwkZa67vwVgZm8Dk93dzewtwj6FcNC/x8z6Ag60SUl7sseX1JvZO4T3TH8KmOKhOYW4DQYQfkUn5HJfngr80d23Arj7iizzpnsZGGtmDwGP1jJPtvI8m5bfUkJQlDooKDQ/1SnD29i+j3f11nUD7nH3q+uYr7b0DTjb3d/fYWRszqmnY4B34/D/Abe4+0QzG0g4Q8gk43zufoOZPUVom55u4VJeA0a5+5/SyliapUwbCQeh5Oz1XpvtBhN+tR/r7lvMrDIlzdT9WJPyuYbt+/R/gRfc/ZuxrFNSlslUD+pbxlztS8uSVqa8ktvT3X9o4dHU/wlUmFlZA8pzPLA+bd72hH0mdVCfQsvwHtDHzA6KnwfVMt9kwil/oj147zjuW2bWLY7f18wOaED6/wB+Ypbs2zimIQW38IrDXwF3xlGd2P4ykQtTZk08HIxs85nZQe7+lrvfSHjpzKGxjBeZ2V5xnl5xfdPTTHL3lUBrC28sA3iR0N7d2sy6En6Bv5q2WKYyLo0B4YuEX/MNkbqOQ+ox/7+AL5hZFwv9QoMID1dLlct9+QzwQzMrifPvm2GeKgsvpG8FfDMxMu6nf7n7/wDLCc1f6duvIeU5BJhVR3kFBYUWwcM7ZIcBT8XOw3m1zHop8MXYRDETOMLd3wF+CTxjZm8S2s17NCD9/yU0a7xpZrPi57p8PnYcvk8IBj9198lx2nXAX83sJcLBIuEJ4JuxE/LzWeYbnujAJPxy/Lu7PwOMB16J6/4w0NHDw8VejvNn6mh+htBsBPAY4cFkbxDa6n/u7kvStlN6euOAcjObQThreK8e2ybVTcAoM3uZ0JeRlbsvBq4GXojlfM3dH0+bJ5f78s+EJrg34/Y+P8M8VxH6cJ4nvD864bexg3oWIeC+Ect9eKKjuYHl+SLwVB3lFfRAPJFdFn+ZXu7u3y12WSQ7M3sRODOe4UkWOlMQ2UXu/jrwgtXz5jUpjticd4sCQv3oTEFERJJ0piAiIkkKCiIikqSgICIiSQoKIiKSpKAgIiJJ/x9TDv3qB3Uq2QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dendrogram_tune(Z,truncate_mode='lastp',p=12, leaf_rotation=90., leaf_font_size=12.,\n",
+    "                show_contracted=True,annotate_above=10, max_d=20)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Corte automático del dendrograma\n",
+    "inconsistency_i = (h_i-avg(h_j))/std(h_j)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.cluster.hierarchy import inconsistent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 3.63777835,  2.5556114 ,  4.        ,  1.35908084],\n",
+       "       [ 3.89767268,  2.57216151,  7.        ,  1.54388156],\n",
+       "       [ 3.05885714,  2.66707272,  6.        ,  1.87115096],\n",
+       "       [ 4.92746418,  2.73259589,  7.        ,  1.39821573],\n",
+       "       [ 4.76943311,  3.16276553,  6.        ,  1.60455941],\n",
+       "       [ 5.27287862,  3.56604844,  7.        ,  2.00627335],\n",
+       "       [ 8.22057081,  4.07583053,  7.        ,  1.69162096],\n",
+       "       [ 7.83287032,  4.46681266,  7.        ,  2.07808207],\n",
+       "       [11.38091435,  6.29430022,  7.        ,  1.86535033],\n",
+       "       [37.25844589, 63.31539362,  7.        ,  2.25872377]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "depth = 3\n",
+    "incons = inconsistent(Z, depth)\n",
+    "incons[-10:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Método del codo\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[180.27043021  23.12198936  17.11527362  15.11533118  12.42734657\n",
+      "   9.84427829   8.74822275   8.04935282   7.86878542   7.11106083]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "El número óptimo de cluster es 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "last = Z[-10:,2]\n",
+    "last_rev = last[::-1]\n",
+    "print(last_rev)\n",
+    "idx = np.arange(1, len(last)+1)\n",
+    "plt.plot(idx, last_rev)\n",
+    "\n",
+    "acc = np.diff(last,2)\n",
+    "acc_rev = acc[::-1]\n",
+    "plt.plot(idx[:-2]+1, acc_rev)\n",
+    "plt.show()\n",
+    "k = acc_rev.argmax() +2\n",
+    "print(\"El número óptimo de cluster es %s\"%str(k))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "c = np.random.multivariate_normal([40,40],[[20,1],[1,30]], size=[200,])\n",
+    "d = np.random.multivariate_normal([80,80],[[30,1],[1,30]], size=[200,])\n",
+    "e = np.random.multivariate_normal([0,100],[[100,1],[1,100]], size=[200,])\n",
+    "X2 = np.concatenate((X,c,d,e),)\n",
+    "plt.scatter(X2[:,0], X2[:,1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Z2 = linkage(X2,\"ward\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,10))\n",
+    "dendrogram_tune(\n",
+    "    Z2,\n",
+    "    truncate_mode=\"lastp\",\n",
+    "    p=30,\n",
+    "    leaf_rotation=90.,\n",
+    "    leaf_font_size=10.,\n",
+    "    show_contracted=True,\n",
+    "    annotate_above = 40,\n",
+    "    max_d = 170\n",
+    ")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1262.52130994 1186.7588235   614.06504667  180.27043021  166.66434658\n",
+      "  141.92437181   92.54599212   90.91214341   80.96733501   74.17015312]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "El número óptimo de cluster es 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "last = Z2[-10:,2]\n",
+    "last_rev = last[::-1]\n",
+    "print(last_rev)\n",
+    "idx = np.arange(1, len(last)+1)\n",
+    "plt.plot(idx, last_rev)\n",
+    "\n",
+    "acc = np.diff(last,2)\n",
+    "acc_rev = acc[::-1]\n",
+    "plt.plot(idx[:-2]+1, acc_rev)\n",
+    "plt.show()\n",
+    "k = acc_rev.argmax() +2\n",
+    "print(\"El número óptimo de cluster es %s\"%str(k))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[ 13.99221995  15.56655759  30.           3.8658472 ]\n",
+      " [ 16.73940735  18.56390061  30.           3.45982932]\n",
+      " [ 19.05945013  20.53210626  31.           3.49952861]\n",
+      " [ 19.25573887  20.8265769   29.           3.51907342]\n",
+      " [ 21.36116189  26.77659523  30.           4.50255938]\n",
+      " [ 36.58100874  37.08602393  31.           3.50761079]\n",
+      " [ 12.12200256  32.15467931  30.           5.22936105]\n",
+      " [ 42.61369802 111.38576865  31.           5.13038026]\n",
+      " [ 81.75198678 208.31582073  31.           5.30447871]\n",
+      " [147.25602023 307.95700562  31.           3.62149673]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(inconsistent(Z2, 5)[-10:])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Recuperar los clusters y sus elementos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.cluster.hierarchy import fcluster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max_d=25\n",
+    "clusters = fcluster(Z, max_d, criterion=\"distance\")\n",
+    "clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "k=2\n",
+    "clusters = fcluster(Z, k, criterion=\"maxclust\")\n",
+    "clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fcluster(Z, 8, depth=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,8))\n",
+    "plt.scatter(X[:,0], X[:,1], c = clusters, cmap=\"prism\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "max_d=170\n",
+    "clusters = fcluster(Z2, max_d, criterion=\"distance\")\n",
+    "clusters\n",
+    "\n",
+    "plt.figure(figsize=(10,8))\n",
+    "plt.scatter(X2[:,0], X2[:,1], c = clusters, cmap=\"prism\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T6 - 2 - Clustering Jer\303\241rquico.ipynb" "b/notebooks/T6 - 2 - Clustering Jer\303\241rquico.ipynb"
index 0e0efea3..08107925 100644
--- "a/notebooks/T6 - 2 - Clustering Jer\303\241rquico.ipynb"	
+++ "b/notebooks/T6 - 2 - Clustering Jer\303\241rquico.ipynb"	
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -39,12 +39,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6190f9cc0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -60,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -88,7 +90,7 @@
        "0.9800148387574268"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -100,7 +102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -109,7 +111,7 @@
        "array([5.20000000e+01, 5.30000000e+01, 4.15105485e-02, 2.00000000e+00])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -120,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -129,7 +131,7 @@
        "array([1.40000000e+01, 7.90000000e+01, 5.91375926e-02, 2.00000000e+00])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -140,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -168,7 +170,7 @@
        "       [4.60000000e+01, 5.00000000e+01, 2.20492804e-01, 2.00000000e+00]])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -179,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -198,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -209,7 +211,7 @@
        "       [ 9.8934927 , -0.44152257]])"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -220,17 +222,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x6191e2b38>"
+       "<Figure size 720x576 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -253,17 +257,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x619c6c908>"
+       "<Figure size 1800x720 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -278,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -290,7 +296,7 @@
        "       [296.        , 297.        , 180.27043021, 150.        ]])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -308,17 +314,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x618d80748>"
+       "<Figure size 1800x720 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -341,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -374,17 +382,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x61bae9e80>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -404,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -413,7 +423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -431,7 +441,7 @@
        "       [37.25844589, 63.31539362,  7.        ,  2.25872377]])"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -451,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -464,12 +474,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x61e22e9b0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -497,17 +509,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x61e3f82e8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -522,7 +536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -531,17 +545,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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a7Oyzz6Z169Zlzv/e975Hq1atAOjfvz/Z2dkA5OTkMGfOHEaOHAlA48aNadmyJQD//e9/GTRoEH379uWss85i2bJlCcXyn//8h4EDB9K6dWtatWrFwIEDi5PII444AgDnHPn5+ZhZpbZXRKS+UaImUk88/fTTnH/++QCsWrWKNm3acO2119KnTx+uv/569u3bB8CoUaN4/PHHWbhwIePHj+fnP/95QuVv2LCBjh07Fj9PT09nw4YNxc+vvfZajjnmGJYtW8Ytt9wS4JaJiNRdStRE6oEPP/yQp59+mgcffBCAgwcP8vnnn3PTTTexaNEiDjvsMB544AFyc3P55JNPuOyyy+jduzc33HADmzZtAmDSpEn07t2b3r17s2DBAi644AJ69+7N4MGDAa+2LFZ0zdmkSZPYuHEjPXr04OWXX66BrRYRqf2UqInUcUuWLOH6669n6tSpHHXUUYBX25Wens7pp58OwJAhQ/j8888pKiqiZcuWZGVlFf99/fXXgFcjFpnWr18/3n77bbKysnjzzTeLy1y/fn3x62ZnZ9O+ffsSsTRs2JArrriC119/vSY2XUSk1lOiJlKHrVu3jksuuYTnn3+e7373u8XTjznmGDp27Mjy5csBmDlzJieccAJHHHEEXbp04dVXXwW8WrLonqLlOe+883j33XfZtWsXu3bt4t133+W8887DOcfKlSuLy5s+fTrdu3cPeEtFROomjaMmUosNGzaMWbNmsX37dtLT0xkzZgwHDhwA4MYbb+QPf/gDO3bsKL7OLC0tjQULFgDw+OOPM3z4cAoLC+natSuTJk0CvCE9brrpJv70pz9x4MABhg4dSq9evSqMpXXr1vzud7/j1FNPBeD3v/89rVu3pqioiGuuuYacnBycc/Tq1YunnnqqOnaHiEidY/GuK6kL+vXr5yJfSJIakfsN6h6RIjVLnz2R2sfMFjrn+sVOV9OniIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQqraEjUze8bMtprZ0qhp/2tmy8xsiZm9aWYto+b9xsxWmtlyMzsvanpfM/vCn/eYRd/lWURERKQOq84atWeBQTHT3gN6OudOBr4BfgNgZicAQ4ET/XX+ZmYN/XWeAkYB3fy/2DJFRERE6qRqS9Scc3OAnTHT3nXOHfSfzgPS/ccXAVOccwXOudXASuA0MzsWOMI5N9d597p6Dri4umIWERERCZNUXqN2HfBv/3EHYH3UvGx/Wgf/cez0uMxslJktMLMF27ZtCzhcERERkZqVkkTNzEYDB4HMyKQ4i7lypsflnJvgnOvnnOvXpk2bqgcqIiIikkJpNf2CZnYN8GPgHL85E7yaso5Ri6UDG/3p6XGmi4iIiNR5NVqjZmaDgLuBnzrn8qJmTQOGmlkTM+uC12lgvnNuE7DXzPr7vT1/BkytyZhFRGqTzEyYNw9mz4aMDO+5iNRe1VajZmYvAQOAo80sG7gPr5dnE+A9f5SNec65G51zX5rZK8BXeE2iv3DOHfKLugmvB2kzvGva/o2IiJSSmQmjRkFBgfd87VrvOcDw4amLS0Qqz75tfaxb+vXr5xYsWJDqMOq1AQO8/7NmpTIKqU4TJsCLL6Y6ComYN+/bJC1akybQv3/NxyMlXXnlt4mzSCwzW+ic6xc7XXcmEJFKe/FFyMpKdRQSES9JK2+61JysLP2okcqp8c4EIlK39O6tWtOwyMjwmjtjde6s9yjVIi0MIslSjZqISB0xdiw0b15yWvPm3nQRqZ2UqImI1BHDh3vXDXbuDGbe/wkT1JFApDZT06eISB0yfLgSM5G6RDVqIiIpcN1119G2bVt69uwZd/6yZcs444wzaNKkCePHjy8x79FHH6Vnz56ceOKJPPLII4HEM3nyZLp160a3bt2YPHly8fSRI0fSq1cvTj75ZIYMGUJubm4gryciiVGiJiKSAiNGjOCdd94pc37r1q157LHHuOOOO0pMX7p0KRMnTmT+/PksXryYGTNmsGLFioRfd8CAAaxZs6bEtJ07dzJmzBg+/fRT5s+fz5gxY9i1axcADz/8MIsXL2bJkiV06tSJJ554IvGNFJEqU6ImIpICZ599Nq1bty5zftu2bTn11FNp1KhRielff/01/fv3p3nz5qSlpfGDH/yAN998E4D//ve/DBo0iL59+3LWWWexbNmyhGL5z3/+w8CBA2ndujWtWrVi4MCBxUnkEUccAYBzjvz8fPzBykWkhihRExGpRXr27MmcOXPYsWMHeXl5vP3226xfvx6AUaNG8fjjj7Nw4ULGjx/Pz3/+84TK3LBhAx07fnu75fT0dDZs2FD8/Nprr+WYY45h2bJl3HLLLcFukIiUS50JRERqkR49enD33XczcOBAWrRoQa9evUhLSyM3N5dPPvmEyy67rHjZAn+k20mTJvHoo48CsHLlSi644AIaN25Mly5dePPNN4l3h5romrNJkyZx6NAhbrnlFl5++WWuvfbaat5KEYlQjZqISC0zcuRIPv/8c+bMmUPr1q3p1q0bRUVFtGzZkqysrOK/r7/+GvBqxCLT+vXrx9tvv01WVlZxk2l6enpxrRxAdnY27du3L/GaDRs25IorruD111+vuQ0VESVqIiK1zdatWwFYt24db7zxBsOGDeOII46gS5cuvPrqq4B3TdnixYsTKu+8887j3XffZdeuXezatYt3332X8847D+ccK1euLC5v+vTpdO/evXo2SkTiUtOniEgKDBs2jFmzZrF9+3bS09MZM2YMBw4cAODGG29k8+bN9OvXj5ycHBo0aMAjjzzCV199xRFHHMGll17Kjh07aNSoEU8++SStWrUCIDMzk5tuuok//elPHDhwgKFDh9KrV68KY2ndujW/+93vOPXUUwH4/e9/T+vWrSkqKuKaa64hJycH5xy9evXiqaeeqr6dIiKlWLxrE+qCfv36uQULFqQ6jHotcm873WOw7tJ7LJIYfVakIma20DnXL3a6mj5FREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQmpakvUzOwZM9tqZkujprU2s/fMbIX/v1XUvN+Y2UozW25m50VN72tmX/jzHjMzq66YRURERMKkOmvUngUGxUy7B5jpnOsGzPSfY2YnAEOBE/11/mZmDf11ngJGAd38v9gyRUREROqkakvUnHNzgJ0xky8CJvuPJwMXR02f4pwrcM6tBlYCp5nZscARzrm5zjkHPBe1joiIiEidVtPXqLVzzm0C8P+39ad3ANZHLZftT+vgP46dHpeZjTKzBWa2YNu2bYEGLiIiIlLTwtKZIN51Z66c6XE55yY45/o55/q1adMmsOBEREREUqGmE7UtfnMm/v+t/vRsoGPUcunARn96epzpIiIiInVeTSdq04Br/MfXAFOjpg81syZm1gWv08B8v3l0r5n193t7/ixqHREREZE6La26Cjazl4ABwNFmlg3cBzwAvGJmI4F1wGUAzrkvzewV4CvgIPAL59whv6ib8HqQNgP+7f+JiIiI1HnVlqg554aVMeucMpYfC4yNM30B0DPA0ERERERqhbB0JhARERGRGErUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUilJ1MzsdjP70syWmtlLZtbUzFqb2XtmtsL/3ypq+d+Y2UozW25m56UiZhEREZGaVuOJmpl1AH4J9HPO9QQaAkOBe4CZzrluwEz/OWZ2gj//RGAQ8Dcza1jTcYuIiIjUtFQ1faYBzcwsDWgObAQuAib78ycDF/uPLwKmOOcKnHOrgZXAaTUbroiIiEjNq/FEzTm3ARgPrAM2AXucc+8C7Zxzm/xlNgFt/VU6AOujisj2p5ViZqPMbIGZLdi2bVt1bYKIiIhIjUhF02crvFqyLkB74DAzu6q8VeJMc/EWdM5NcM71c871a9OmTdWDFREREUmhVDR9/ghY7Zzb5pw7ALwBfA/YYmbHAvj/t/rLZwMdo9ZPx2sqFREREanTUpGorQP6m1lzMzPgHOBrYBpwjb/MNcBU//E0YKiZNTGzLkA3YH4NxywiIiJS49Jq+gWdc5+a2WvA58BBYBEwAWgBvGJmI/GSucv85b80s1eAr/zlf+GcO1TTcYuIiIjUtBpP1ACcc/cB98VMLsCrXYu3/FhgbHXHJSIiIhImujOBiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkEpLZCEz6waMA04AmkamO+e6VlNcIiIiIvVeojVqk4CngIPA/wDPAc9XV1AiIiIiknii1sw5NxMw59xa59z9wA+rLywRERERSajpE9hvZg2AFWZ2M7ABaFt9YYmIiIhIojVqtwHNgV8CfYGrgWuqKSYRERERIcEaNefcZ/7DXODa6gtHRERERCLKTdTM7BHn3G1mNh1wsfOdcz+ttshERERE6rmKatQiPTvHV3cgIiIiIlJSuYmac26h/3ABkO+cKwIws4ZAk2qOTURERKReS7QzwUy8zgQRzYD3gw9HRERERCISTdSaOudyI0/8x83LWV5EREREqijRRG2fmZ0SeWJmfYH86glJRERERCDxAW9vA141s43+82OBK6olIhEREREBkhhHzcy6A8cDBixzzh2o1shERERE6rlEa9QATgUy/HX6mBnOueeqJSoRERERSSxRM7PngeOALOCQP9kBStREREREqkmiNWr9gBOcc6XuTiAiIiIi1SPRXp9LgWOqMxARERERKSnRGrWjga/MbD5QEJmoe32KiIiIVJ9EE7X7qzMIERERESkt0eE5Zld3ICIiIiJSUkLXqJlZfzP7zMxyzazQzA6ZWU51ByciIiJSnyXameAJYBiwAu+G7Nf700RERESkmiQ84K1zbqWZNXTOHQImmdkn1RiXiIiISL2XaKKWZ2aNgSwzewjYBBxWfWGJiIiISKJNn1f7y94M7AM6ApdUV1AiIiIikniidrFzbr9zLsc5N8Y59yvgx9UZmIiIiEh9l2iidk2caSMCjENEREREYpR7jZqZDQOuBLqY2bSoWUcAO6ozMBEREZH6rqLOBJ/gdRw4GvhL1PS9wJLqCkpEREREKkjUnHNrgbVm9iMg3zlXZGbfBboDX9REgCIiIiL1VaLXqM0BmppZB2AmcC3wbHUFJSIiIiKJJ2rmnMvDG5LjcefcYOCE6gtLRERERBJO1MzsDGA48JY/LeG7GoiIiIhI8hJN1G4DfgO86Zz70sy6Ah9WW1QiIiIiklitmHNuNjA76vkq4JfVFZSIiIiIVDyO2iPOudvMbDrgYuc7535abZGJiIiI1HMV1ag97/8fX92BiIiIiEhJFY2jttD/P9vM2viPt9VEYFK7ZWbCvHlQUAAZGTB2LAwfnuqoREREapdyOxOY534z2w4sA74xs21m9vuaCU9qo8xMGDXKS9IA1q71nmdmpjYuERGR2qaips/bgO8DpzrnVgP4PT6fMrPbnXMPV3N89c6ECfDii6mOomoiNWnR8vJg5EiYODE1MQXhyiu9hFNERKSmVDQ8x8+AYZEkDYp7fF7lz5OAvfgiZGWlOoqqiU3SKppeG2Rl1f4EWkREap+KatQaOee2x050zm0zs0bVFFO917s3zJqV6igqLyPDa+6M1blz7d2uAQNSHYGIiNRHFdWoFVZyXrnMrKWZvWZmy8zsazM7w8xam9l7ZrbC/98qavnfmNlKM1tuZudV9nWlZowdC82bl5zWvLk3XURERBJXUaLWy8xy4vztBU6qwus+CrzjnOsO9AK+Bu4BZjrnuuHd+P0eADM7ARgKnAgMAv5mZg2r8NpSzYYP966169wZzLz/Eyao16eIiEiyKhqeI/CEyMyOAM4GRvivUQgUmtlFwAB/scnALOBu4CJginOuAFhtZiuB04C5QccmwRk+XImZiIhIVSV6r88gdQW2AZPMbJGZ/dPMDgPaOec2Afj/2/rLdwDWR62f7U8rxcxGmdkCM1uwbZuGe6sJu3fvZsiQIXTv3p0ePXowd+5cXn31VU488UQaNGjAggULSq2zbt06WrRowfjxGkdZRESkPKlI1NKAU4CnnHN9gH34zZxlsDjTSt3OCsA5N8E51885169NmzZVj1QqdOuttzJo0CCWLVvG4sWL6dGjBz179uSNN97g7LPPjrvO7bffzvnnn1/DkYqIiNQ+Cd2UPWDZQLZz7lP/+Wt4idoWMzvWObfJzI4FtkYt3zFq/XRgY41FK2XKyclhzpw5PPvsswA0btyYxo0b07JlyzLX+de//kXXrl057LDDaiZIERGRWqzGa9Scc5uB9WZ2vD/pHOArYBpwjT/tGmCq/3gaMNTMmphZF6AbML8GQ5YyrFq1ijZt2nDttdfSp08frr/+evbt21fm8vv27ePBBx/kvvvuq8EoRUREaq9UNH0C3AJkmtkSoDfwZ+ABYKCZrQAG+s9xzn0JvIKXzL0D/MI5dygVQUtJBw8e5PPPP+emm25i0aJFHHbYYTzwwANlLn/fffdx++2306JFixqMUkREpPZKRdMnzrksoF+cWeeUsfxYQKNwhUx6ejrp6emcfvrpAAwZMqTcRO3TTz/ltdde46677mL37t00aNCApk2bcvPNN9dUyCIiIrVKShI1qRuOOeYYOnbsyPLlyzn++OOZOXMmJ5xwQpnLf/TRR8WP77//flq0aKEkTUREpBypavqUOuLxxx9n+PDhnHzyyWRlZXHvvffy5ptvkp6ezty5c7nwwgs57zzdTEJERKQyVKMmVdK7d+9SY6UNHjyYwYMHl7ve/fffX41RiYiI1A2qURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElIpS9TMrKGZLTKzGf7z1mb2npmt8P+3ilr2N2a20syWm9l5qYpZREREpCalskbtVuDrqOf3ADOdc92Amf5zzOwEYChwIjAI+JuZNazhWEVERERqXEoSNTNLBy4E/hk1+SJgsv94MnBx1PQpzrkC59xqYCVwWg2FKiIiIpIyqapRewS4CyiKmtbOObcJwP/f1p/eAVgftVy2P60UMxtlZgvMbMG2bdsCD1pERESkJtV4omZmPwa2OucWJrpKnGku3oLOuQnOuX7OuX5t2rSpdIwiIiIiYZCWgtf8PvBTM7sAaAocYWYvAFvM7Fjn3CYzOxbY6i+fDXSMWj8d2FijEYuIiIikQI3XqDnnfuOcS3fOZeB1EvjAOXcVMA24xl/sGmCq/3gaMNTMmphZF6AbML+GwxYRERGpcamoUSvLA8ArZjYSWAdcBuCc+9LMXgG+Ag4Cv3DOHUpdmCIiIiI1I6WJmnNuFjDLf7wDOKeM5cYCY2ssMBEREZEQ0J0JREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREJKiZqIiIhISClRExEREQkpJWoiIiIiIaVETURERCSklKiJiIiIhJQSNREREZGQUqImIiIiElJK1ERERERCSomaiIiISEgpURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkFKiJiIiIhJSStREREREQkqJmoiIiEhIKVETERERCSklaiIiIiIhpURNREREJKSUqImIiIiElBI1ERERkZBSoiYiIiISUkrUREREREKqxhM1M+toZh+a2ddm9qWZ3epPb21m75nZCv9/q6h1fmNmK81suZmdV9Mxi4iIiKRCKmrUDgK/ds71APoDvzCzE4B7gJnOuW7ATP85/ryhwInAIOBvZtYwBXGLiIiI1KgaT9Scc5ucc5/7j/cCXwMdgIuAyf5ik4GL/ccXAVOccwXOudXASuC0Gg1aREREJAVSeo2amWUAfYBPgXbOuU3gJXNAW3+xDsD6qNWy/WnxyhtlZgvMbMG2bduqLW4RERGRmpCyRM3MWgCvA7c553LKWzTONBdvQefcBOdcP+dcvzZt2gQRpoiIiEjKpCRRM7NGeElapnPuDX/yFjM71p9/LLDVn54NdIxaPR3YWFOxioiIiKRKKnp9GvA08LVz7q9Rs6YB1/iPrwGmRk0famZNzKwL0A2YX1PxioiIiKRKWgpe8/vA1cAXZpblT7sXeAB4xcxGAuuAywCcc1+a2SvAV3g9Rn/hnDtU41GLiIiI1LAaT9Scc/9H/OvOAM4pY52xwNhqC0pEREQkhHRnApEKZGbCvHkwezZkZHjPRUREaoISNZFyZGbCqFFQUOA9X7vWe65kTUREakIqrlETCY0JE+DFF8ueP2/et0laRF4ejBwJEyeWXv7KK71ETkREJAiqUZN67cUXISur7PmxSVp507Oyyk/6REREkqUaNan3eveGWbPiz8vI8Jo7Y3XuXHqdAQMCDUtEREQ1aiLlGTsWmjcvOa15c2+6iEgiMrdsYd5tc5l93ywy5s4lc8uWVIcktYg5F/duTLXe4Ycf7vr27ZvqMJIWaYbr3TuVUdQfiezvLVtg9WqvubNJE+jSBdq1q1xZdU193GaRZGwpLOSb/HyKor5rG5jx3WbNaNe4cQojk7CZPXv2Qudcv9jpavoUqUC7dvETMxFJzqbCQrYUFqY6jBqVc/BgqZtTFznH8rw8NtWzfdGucWOOVXKatDqbqB1//PHMKuvCoxCLXOdUC0OvlSL7u2vX65gxYwZt27Zl6dKlAGRlZXHjjTeyf/9+0tLS+Nvf/sZpp50GwLhx43j66adp2LAhjz32GOedd169fO/q4zZL5Q1YtIgtubn0btEi1aHUmNl79sSd7oDeRx5Zs8GkUFZuLse2aMGsPn1SHUpoeXfYLK3OJmoiyRgxYgQ333wzP/vZz4qn3XXXXdx3332cf/75vP3229x1113MmjWLr776iilTpvDll1+yceNGfvSjH/HNN98ADVO3ASK1RO969mWdMXcua+N0E+/cpEm92g8DFi1KdQi1ljoTiABnn302rVu3LjHNzMjJyQFgz549tG/fHoCpU6cydOhQmjRpQpcuXfjOd77D/PnzazxmEQm/sV270rxBya/a5g0aMLZr1xRFJLWNatREyvDII49w3nnncccdd1BUVMQnn3wCwIYNG+jfv3/xcunp6WzYsCFVYYpIiA33L3AdvWoV6woK6NSkCWO7di2eLlIRJWoiZXjqqad4+OGHufTSS3nllVcYOXIk77//PvF6Spd1bYGIyPB27ZSYSaWp6VOkDJMnT+aSSy4B4LLLLitu3kxPT2f9+vXFy2VnZxc3i4pI/XXdddfRtm1bevbsWWL6448/zvHHH8+JJ57IXXfdBUBmZia9e/cu/mvQoAFZ5d0mReotJWoiZWjfvj2zZ88G4IMPPqBbt24A/PSnP2XKlCkUFBSwevVqVqxYUdwbVETqrxEjRvDOO++UmPbhhx8ydepUlixZwpdffskdd9wBwPDhw8nKyiIrK4vnn3+ejIwMemtAQolDTZ8iwLBhw5g1axbbt28nPT2dMWPGMHHiRG699VYOHjxI06ZNmTBhAgAnnngil19+OSeccAJpaWk8+eSTNGyoHp8i9d3ZZ5/NmjVrSkx76qmnuOeee2jSpAkAbdu2LbXeSy+9xLBhw2oixJTI3LKFeTk5FDhHxty5ukYvSUrURPBOlPEsXLgw7vTRo0czevTo6gxJROqAb775ho8++ojRo0fTtGlTxo8fz6mnnlpimZdffpmpU6emKMLqlbllC6OWL6fAv7Z3bUEBo5YvB1CyliAlaiIiItXk4MGD7Nq1i3nz5vHZZ59x+eWXs2rVquIOSJ9++inNmzcvdV1bECZs3MiLKb6vaKQmLVpeUREjly1j4saNKYoKrmzXjlG15NpiXaMmIiJSTdLT07nkkkswM0477TQaNGjA9u3bi+dPmTKl2po9X9yyhazc3GopO1GxSVpF02tCVm5uyhPYZKhGTUREpJpcfPHFfPDBBwwYMIBvvvmGwsJCjj76aACKiop49dVXmTNnTrW9fqrvBBHGOzPUtrskqEZNREQkAMOGDeOMM85g+fLlpKen8/TTT3PdddexatUqevbsydChQ5k8eXJxs+ecOXNIT0+nax2+S4HuzFB1qlETEREJQFmdkl544YW40wcMGMC8efOqM6SU050Zqk6JmoiIiFQb3ZmhatT0KSIiIhJSStRERESkSsq6fRbA+PHjMbPi3q7vvfceffv25aSTTqJv37588MEHNR1uraJETURERKok3u2zANavX897771Hp06diqcdffTRTJ8+nS+++ILJkydz9dVX12SotY4SNREREamSs88+m9atW5eafvvtt/PQQw8V93QF6NOnD+39wWZPPPFE9u/fT0GcITzEo0RNREREAjdt2jQ6dOhAr169ylzm9ddfp0+fPsX3QpXS1OtTREREApWXl8fYsWN59913y1zmyy+/5O677y53GVGNmoiIiATsv//9L6tXr6ZXr15kZGSQnZ3NKaecwubNmwHIzs5m8ODBPPfccxx33HEpjjbcVKMmIiIigTrppJPYunVr8fOMjAwWLFjA0Ucfze7du7nwwgsZN24c3//+91MYZe2gGjURERGpkni3zyrLE088wcqVK/njH/9I79696d27d4mkTkpSjZqIiIhUSVm3z4pYs2ZN8ePf/va3/Pa3v63miOoO1aiJiIiIhJQSNREREZGQUqImIiLVLnPLFubl5DB7zx4y5s4lc8uWVIckUisoURORysnMZMq8DD6Y3QAyMiAzM9URSUhlbtnCqOXLKXAOgLUFBYxavlzJmkgC1JlARJKXmQmjRnFMQZ73fO1aGDXKezx8eOrikhImbNzIiyFIhubl5BQnaRF5RUWMXLaMiRs3pigqz5Xt2jHKv52RSBgpUROprSZMgBdfTM1rz5sHsffmy8uDkSNh4sTUxHTlld8miwLAi1u2kJWbS+8WLVIaR2ySVtH0mpKVmwugRE1CTYmaSG314ouQlQW9e9f8a5d1A+VU3Vg5K8v7r0StlN4tWjCrT5+UxpAxdy5r4xwbnZs0SWlsAxYtStlriyRKiVqIZH6Rybz+oylouo6MRzox9pyxDD9JzUhSjt69Ydasmn/djAyvuTNW586piWfAgJp/TUnY2K5dGbV8OXlFRcXTmjdowNiuXVMYlUjtoM4EIZH5RSajpo+ioNlaMMfaPWsZNX0UmV/oAm0JobFjoXnzktOaN/emi8QY3q4dE44/ns5NmmB4NWkTjj+e4e3apTo0kdBTjVo5JiycwItf1Mw1QPOy51FwqGTTQN6BPEZOHcnEhdV/zc+VJ13JqL5qNpIERToMjB4N69ZBp05ekqaOBFKG4e3aKTETqQTVqJXjxS9eJGtzVo28VmySVtH0IGVtzqqxhFTqkOHDYc0aKCry/qcqScvM9Do3zJ6tYUJEpM5RjVoFeh/Tm1kjZlX762Q8ksHaPaWv+el8ZOdqf/0Bzw6o1vJFqo0/TEhxJwYNE5Iyjz76KBMnTsQ5x//7f/+P2267jTvvvJPp06fTuHFjjjvuOCZNmkTLli1LrZuRkcHhhx9Ow4YNSUtLY8GCBTW/ASIhpUQtJMaeM5ZR00eRdyCveFrzRs0Ze46u+ZHUifflu3PnTq644grWrFlDRkYGr7zyCq1atSp73Z07+X/Nm3NbenrwAaZimBANA1LK0qVLmThxIvPnz6dx48YMGjSICy+8kIEDBzJu3DjS0tK4++67GTduHA8++GDcMj788EOOPvroGo5cJPzU9BkSw08azoSfTKDzkZ0xjM5HdmbCTyao16ekTPSX7+LFi5kxYwYrVqzggQce4JxzzmHFihWcc845PPDAA+Wv260bM9asYUVeXpxXqaKaHiYkKyt1Y9eF2Ndff03//v1p3rw5aWlp/OAHP+DNN9/k3HPPJS3Nqw/o378/2dnZKY5UpPZRjVqIDD9puBIzCY3oL1+g+Mt36tSpzPKH4LjmmmsYMGBAqVqSEuua8YOOHXlzyBDuuuuuYIOs6WFCNAxIXD179mT06NHs2LGDZs2a8fbbb9OvX78SyzzzzDNcccUVcdc3M84991zMjBtuuIFRqrEUKaYatXou84tM5mXPY/ba2WQ8kqHhQKRYz549mTNnDjt27CAvL4+3336b9evXs2XLFo499lgAjj32WLZu3Vr+uocO8faOHaxfvz74IDVMSCj06NGDu+++m4EDBzJo0CB69epVXJMGMHbsWNLS0hhexnWDH3/8MZ9//jn//ve/efLJJ5kzZ061x6ybxEttoUQtBB5++GFOPPFEevbsybBhw9i/fz+vvvoqJ554Ig0aNKi2C2uLx27ze5Zq7DaJVtGXb8LrfvEFvVq0SHjdpAwf7t1Kq3NnMPP+T5igjgTUfCIycuRIPv/8c+bMmUPr1q3p1q0bAJMnT2bGjBlkZmZiZnHXbe/fwqlt27YMHjyY+fPnV2usukm81CZq+kyxDRs28Nhjj/HVV1/RrFkzLr/8cqZMmcLpp5/OG2+8wQ033FBqnaDGd6vusds0NlvtN3LkSEaOHAnAvffeS3p6Ou3atWPTpk0ce+yxbNq0ibZt25a/7oAB3LtqFen+F3fghg9XYhajrEQEqLaxzLZu3Urbtm1Zt24db7zxBnPnzuWdd97hwQcfZPbs2cVN6LH27dtHUVERhx9+OPv27ePdd9/l97//fVKvnezN54O4Sbxu5i41RYlaCBw8eJD8/HwaNWpEXl4e7du3p0ePHmUuHxnfrfcxvav0utU5dltk/DklarVbvC/f1atXM3nyZO655x4mT57MRRddVP66+/fzxvbtzB02rFIxLF++vMS1TatWreIPf/gDt912GwDjx4/nzjvvZNu2baV6Da5fv56f/exnbN68mQYNGjBq1ChuvfXWSsWRCskmIBFBJCIRiSYkl156KTt27KBRo0Y8+eSTtGrViptvvpmCggIGDhwIeB0K/v73v7Nx40auv/563n77bbZs2cLgwYMB71x45ZVXMmjQoKRiTPbm81W9Sbxu5l57RWqaC5wjY+5cxnbtGvqBmJWopViHDh2444476NSpE82aNePcc8/l3HPPrXC9IMZ3q86x2zQ2W90Q78v3nnvu4fLLL+fpp5+mU6dOvPrqqwAlvnxLrLtuHU926xZ3CI9EHH/88WT5N10/dOgQHTp0KP5iX79+Pe+99x6dOnWKu25aWhp/+ctfOOWUU9i7dy99+/Zl4MCBnHDCCZWKpaYlm4BEVDURiUgmIfnoo49KTVu5cmXcZdu3b198nHTt2pXFixcnFVc8ydx8vqo3idfN3GunVNQ0B0GJWort2rWLqVOnsnr1alq2bMlll13GCy+8wFVXXVXtr62x26Qi8b58jzrqKGbOnFlqevSXb4l1A+wpOXPmTI477jg6d+4MwO23385DDz1UZq3escceW9zx4fDDD6dHjx5s2LCh1iRqkFwCElHVRCSiriYkukl87VPZ2uVoQdY0R9REE7g6E6TY+++/T5cuXWjTpg2NGjXikksu4ZNPPqmR166usdvUk1Sqy5QpUxjmN6FOmzaNDh060KtXr4TWXbNmDYsWLeL000+vzhBDYWzXrjRvUPL0rkTkW/XhJvF1rVdrpHa5KoKqaQbYUljInD17uOGbb6p9/6pGLcU6derEvHnzyMvLo1mzZsycObPU+EPVKeix28rqSRp5LZHKKiwsZNq0aYwbN468vDzGjh3Lu+++m9C6ubm5XHrppTzyyCMcccQRlQsgck/RggJv/LYQ34Q+knCMXrWKdQUFdGrSpFZci1OT6vJN4mtrE19FKlO7HC2omubI/o2kd9W9f5Wopdjpp5/OkCFDOOWUU0hLS6NPnz6MGjWKN998k1tuuYVt27Zx4YUX0rt3b/7zn//UeHzJ9jANoiepeosmoBYlDUH597//zSmnnEK7du344osvWL16dXFtWnZ2Nqeccgrz58/nmGOOKbHegQMHuPTSSxk+fDiXXHJJ5V68Ft5TtC4nIvVNTfZq3VJYyOr9+ylwjs51LMFPpsm7vH2e7P6tavOoErUQGDNmDGPGjCkxbfDgwcUXTFfV7t27uf7661m6dClmxjPPPEN2djb3338/X3/9NfPnz49bi7d//37uuOwO9uXvo2mDprTp14aMwRnlvlZVe5Kqt2gCamHSEISXXnqpuNnzpJNOKjHQbkZGBgsWLCjV69M5x8iRI+nRowe/+tWvyi58woTybw2V7D1FdT/QUKjsuQ/CdaP4murVuqWwkG/y84mkMXWlJi4imZrm8vZ5Mvs3iB7CStTqgVtvvZVBgwbx2muvUVhYSF5eHi1btixznLaIJk2a0OuuXjRs2pD3hr/HmWeeyQPdH6B///5lrlPVnqTqLYqShjjy8vJ47733+Mc//lHhstG9Tz/++GOef/55TjrpJHr37g3An//8Zy644IKSK734oncfT3+ZUpK5p6jfQzUV+zxecrF48WJuvPFGcnNzycjIIDMzs/LNv7VMZc99EWG6UXwQvVpbVzDo9Or9+ymKmVadNUWpkExNc1n7PJkm1CA65ChRq+NycnKYM2cOzz77LACNGzemcePGtGzZssJ1zYyGTRsCXvPRgQMHyhxZPEI9SQMQ1qShogSyLJEYKtP7008ymzdvzo4dO8pcbM2aNcWPo3ufnnnmmbhELxTu3bvs+4Mmc0/RFN8PNDa5uP766xk/fjw/+MEPeOaZZ/jf//1f/vjHP6YwwppRlXNfbVdWE1/bRo3KrZmr6Zqi6lBeTWh5Yy7GrvvfwkL6ZsbvCFfTvYaVqNVxq1atok2bNlx77bUsXryYvn378uijj3LYYYcltL4rciy8fyFtb27LL37xiwp7zEU6DIyeOZp1e9bR6chOjD1nrDoSJCuMSUNFCWRZkl0+IoU1U6WMHevFkfftD5Dack/R5cuXc/bZZwMwcOBAzjvvvHqRqFX23Be5NmnLgQN0OessAI699FLaX3pp8TKRJKWi2pJU1TiV1cQ3ceNG2jVuXGbNXE3XFFWXeDWhFY25GLtuedtX05116m2ilshF8pHrpRJpjgvrBfAHDx7k888/5/HHH+f000/n1ltv5YEHHkj4RG0NjH5/6Me/Lv4XgwcPZunSpfTs2bPcdYLuSSoxUpk0lJdABi3FNVMlRK79Gz0a1q2DTp1C2YHDzDj33HMxM2644QZGjRpFz549mTZtGhdddBGvvvoq69evT3WYNaKy577ia5MmTaJJmzYU7tzJkptuonlGBi379gVI6FqxVNc4xWviq6gjQV0eX66iMReTVZOddeptopbIbZgSvUVTmC+AT09PJz09vbgmbMiQITzwwANJl9OyZUsGDBjAO++8U5yoHTp0iH79+tGhQwdmzJhBVlYWN954I/v37yctLY2//e1vnHbaaXHLi11XklBLkoYyJdqEmmiTaU1dg1cL7in68ccf0759e7Zu3crAgQPp3r07zzzzDJfccANX3XMPR/3gB+xv2DDh2pBEa47iSfX1S1U59/Vu0YJZfm0awP1XXkmL3bu5o44PFlwXhnWJ92Ml0TEXo9fde/75HHvxxfTp06f4e+qKK65gud+5Yvfu3bRs2bL4rinRHn74Yf75z39iZmxNT6f7/fdXaZvqbaIGwdyGCcJ9AfwxxxxDx44dWb58OccffzwzZ85MeFT2bdu2cTDvIGnN08jPz+f999/n7rvvLp7/6KOP0qNHD3JycgC46667uO+++zj//PN5++23ueuuu5hVRu1L7LqSpFqQNJQp0SbURJpMA24eLesHREXXtkSfmE/aupVJ3bvTNJCIktPeT4zatm3L4MGDmT9/PnfccQdtH3mEjbm5tN2xg53/938Jl5fsrasiUl2bBFU79x3Kz2fv3r1VulF8TYh3vD7++OM88cQTpKWlceGFF/LQQw+VWq+867hSMaxLIsOPJPqj4abXX+euU04p8WMl0TEXo3/odD3zTHJXrGBA1PfUyy+/XLzsr3/9a4488shSZWzYsIHHHnuMr776imbNmtH23HPZ+p//QDmd8CpSrxO1sEl0zLJEm2QjzbGPP/44w4cPp7CwkK5duzJp0qQyx2mL7jG3adMmFj+4GFfkOHX8qVx++eX8+Mc/Brxxq9566y1Gjx7NX//6V8D7NRI5oPfs2VP8pRG7fQU7C1j2z2V0+nEnst/NLrEddaG5OSixJ+Hf/e53TJ06lQYNGtC2bVueffbZUvsYYoYkWL+eZ777Xc5IQfzlCqoJNeDm0Xg/ICq6tiX2xHx527ZM2bqVEYFGVrF9+/ZRVFRUKrmIDGPSq3lzOk+cyI2/+hXXVWHQ0ETH9MrKzU35NVyVOfcxdiyFO3Zw5plnApW/UXxNiD1eP/zwQ6ZOncqSJUto0qRJiSFs4klPT2fGjBnceeedTJ8+ncaNG3PccccxadKkuJ0uos8t6wsK+O5990EVjqWIRIYfSbi5uUUL7uLbHyuzZ89OeMzF6B86LU87jR2zZ3P95MnF33ERzjleeeUVPvjgg7hxHDx4kPz8fBo1asSh/Hwat2lTYezlUaIWIok0x0JiTbLRzbG9e/cuNQZQWeO0RfeYO/nkk+k7xrsmI7bm8bbbbuOhhx5i7969xdMeeeQRzjvvPO644w6KiopK3Qorsn2NXm9E18u7cmj/oUptW+z21VWxJ+E777yz+Pqaxx57jD/84Q/8/e9/L7VeiSEJzj67xPUmUrbsgoJSPz4gsWtbok/MeYcO0b5x40BiKioqokGDxO70t2XLluLPdHRy8eijjzLf354zhg3j2muvrVJMgX6pUr21bpU59w1YtIhm6el8GsCN4qtTvB/LTz31FPfccw9NmjQBvIQjnpycHPr27UuB33Fg4MCBjBs3jrS0NO6++27GjRvHgw8+WGq96HPL2fPnU7R/f2DbU9W7DgCc9cknHNy3DyDujxUoe8zF2B86W2bMoPOoUXE/fx999BHt2rWjW7dupeZ16NCBO+64g06dOtGsWTPSTj2V1mdU7aeyErWQqQ3NsTNmzKBt27b07du3RNPmU089xcMPP8yll17KK6+8wsiRI3n//fdLrNtxc0fOOvUs/nb/35g1axbjl45nxojkr1ELc3NzEOIlDdFjX+3bty/uUCmlhiRo0IDGCX7R13e3rVzJQ++8U+LHRyLXtsSemM9NS+Pc1q2rHE9hYSHZ2dmlfvWXpWvXriyOk1zceuutvOn3+nwggNoPCOZLtTZewxUm8X4sf/PNN3z00UeMHj2apk2bMn78eE499dQS6xVs2UJ+fj5Llixh69atTJgwgVFRlw7079+f1157rdTrxZ5bGjRqRINGjapn4yqpcMcOvvz1r+nVrFlCNaHRLUjRP3R27dpFs44dSR82DPbsKbVe9ODbsXbt2sXUqVNZvXo1LVu25NhzzyV76tQq1TwqUZOkffzxx0ybNo23336b/fv3k5OTw1VXXcX06dN59NFHAbjsssu8ZoQYe1bsYdri0uu+8MILNb0ZgcvPz6dx48ZAwyqXFS9pABg9ejTPPfccRx55JB9++GGp9WKHJOi1YQPjO3cmHEN2hteMHTto27hxiR8fid5PNPbEfPHRR/OP1aupeDjV8m3YsIH9+/eTn59fxZIkFfLy8thbxZuIl6WsH8sHDx5k165dzJs3j88++4zLL7+cVatWlfhRt3L8eN58802aNm3K2LFjefLJJ+nevXvxEC7PPPMMV1xxRanXjD23bM7I4Dt33lkt21dZzdLT6ffyy+X+iChrzMXoHzq/+c1v+OszzzDvwgsZWlRU4nvq4MGDvPHGGyxcuDBu+e+//z5dunShjd/ceeRZZ7H100/Jy8ujefPmldou/dQOwMGDB1MdQin79u0LLK686GEggHHjxpGdnc2aNWuYMmUKP/zhD3nhhRdo3749s2fPBuCDDz6IWy3c9bKucdetC7Zs2UxOTulfX8masWMHRzVsSF9/KIBoY8eOZf369QwfPpwnnnii1PzIkAQ33XQTixYtIu3AAcauXl3lmILknOPgodLN3qn08Z49TNu+nYyMDIYOHcoHH3zA1VdfXXxtS0ZGRvG1LZs3by6xbvSJuVGjRpzbtCmf7N5d5Zi2bt1KXn4++QE2L9VV+/fvZ3/I9lNeXh778/MpLCwMvOzIj+Xo4/Wqq64iPT2dSy65BDPjtNNOo0GDBmzfvr14vU0zZ7LPjB/+8IeAd/eZSKcT8M4vaWlpDI/TUSn23HKoYUNWJHCnkNpo3Lhx9PnXvzh1+vRS31Pvv/8+3bt3Jz09Pe66nTp1Yt68eeTl5eGcY9enn5KWns7GCoZGKY8StSrav38/GzZsKG7rDwPnHNu2bQukR2V+fj5bt20tVbMTz8SJE/n1r39Nr169uPfee5kwYQLgVS9fcMEFxXc3qItyc3PZs2cPu3dXPVGbvWMH07Zvp3PnziVOwtGuvPJKXn/99VLrxg5JMLBJE7JiEu1Uy83NZWsSN5iuCeO6diX7jDNK/IB4/fXX2bp1K2vWrGHNmjWkp6fz+eefl2qKjD0xf5iTQ3qid0MoR+QzdyiEPwSDEvsjsLJ279nDnjhNVKl0qKgIB4HUiG7ZurXEvirrx/LFF19cfIH7N998Q2FhYYlrsbbPn0/+ggV06tSJoUOHMnPmTJ588kl69uzJ5MmTmTFjBpmZmXEvq4g9tzQ57TT2fvVVlbctrLZt3x73e2/KlCmlmj0j33EAp59+OkOGDOGUU07hpJNOwhUVcfi551ap4kSJWhVt3LiRQ0WHAvk1V1RUFMivr9zcXHJycsjZW/VEbf/+/eTtyytVixAxYMCA4m7hZ555JgsXLmTx4sV8+umnxTVCkerlXbt2sWvXrrjrJiuok/K+fbls2LChyuXk5OSQl5fPvn1Vb+q456ijeOeII3j33XdLnIRXrFhRvMy0adPo3r17qXWjhyQA+GD3bjoFUHt16NAh1q9fz4EAkob8/Hz2FxQE8gW2devWwJoGDyXR6aK8E3PhgQNcEMA+b9OmDQcPHCCtgvszJirhW2nVEO9H4LaEfgRW5MCBAxSG7EegKyriQGFhld+/wsJC9ufnJ5TUXnfddaxatYqePXsydOhQJk+ejJkVH68tr76aw+65h+bNm3PYYYeRlpbGbbfdBsCDDz7ItGnTymyeiz237Pz0UwjZ+Gr79u2rUs1VRG5uLocOHaKwoKDU99Szzz7LjTfeWGL56CZUgDFjxrBs2TKWLl1K53vvZW9+Pk2bVn7Annp7jVp59w1MRk5ODtu3beew5ondkqk8e/fuJTeAaxpyc3PZt29fwr3FKiprz549bNiwIW5TZjJy9uZglH+v0ERs3ryZr7/+usrxAKxfn83u3bs5cKAtjapwYeyBAwcoLCwggO8c9vlNJlvXrSsR0z333MPy5ctp0KABnTt3Lu7xGX1BLJQckqDlgQOMDuCLfs2aNezYvJmmzZpRtY7mkJefz77cXAoLC2nWrFmlyzl48CAbN22i1f79dK5iTPn5+axevZrj/BPzgDjDfpR1bQt4J+YxY8YA8EnjxhBA0uCco6CggEMBJH25+/aRG8TBGaCCggIOFBayefNmDj/88CqVFf0DMCzy8vLYs2dPpa9LisjPz2f3nj2UlWZHH6+NGzeOeylJ5Hjt+Npr5Ddvzl/+8hdatWrF+PHjGT16NN/5zncoKChg4MCBgNeh4O9//3u555aChg1pOmJElbYtYv/+/VVKZCK279jBnj17OHDgQJXO54cOHSJ3714aBvAdui8vz/8hX/na43qbqJVVQ5SspUuXkp+fz86dO6tc1vYd29m5Y2eVLjqEb2vmgvilmrM3h8LCQlasWBH3yysZ0ddKVMW0adMoKChg85aqv4e7du2ksLCQtWvX8p3vfKfS5Wzbtg2AwsKqN4Hn7dvH/oICNm/ezNVXX1283+M1dULppCF6SII5DRtCALVgc+fOpWN+Pjt27Khyopabm8vevXurXMOzbds29ubkUFRUVOVEbefOneTk5LBo0SL6V2FgSiCw5v3PPvuMgwcPBvK52bVzJzt37aKgoKB46IZU279/Pzl79wZSOx62Zk/wKgPy8vPZvXs3rVq1qnQ5hw4dIicnJ5CEfV9eHkWHDrF9+3YGDx5cfG5ZuXJl3OXLO7c0+8c/COJK03379vHf//6XLl26VLms3Nxc8vPy2LZtW9wxJhPVqFEjcnNzA6ns2LN7N4cOHeLjjz9OeMDlWLWm6dPMBpnZcjNbaWb3VLW87du3B3ICjAw/sXNX1RO17PXZXk+hKiZY2dnZAIE0o+7a6f1SjR0TrTIK9hdQsL/qicx7770HwO5du6tcVmQfVfX+h0uXLq1yLBG7du3COccXX3xR5bKKioooCmActZkzZ+KcC+Tasp07dlB44ECVr+vcvn07h/weWVW1fccO9hcUlBpzqzKc/1dVmZmZQDBJyMaNG9m9e3cgNfZBdVTaX1DAgQMHin/kVEXRoUMUhayDSl5+PjhX3FRYWTt37qTo0CH2BfDe5fjHUrwe48kqLCigMIBrsz/++GO2b9/OugDuQbt71y72799f5XPLzp07KSoqCuRSjz05OeAcc+bMqXQZtSJRM7OGwJPA+cAJwDAzq1xqGrC33noLgIMHgrvg95133qnS+tOnTw8oEop/xb3yyiuBlVlVke7oQfa2nTp1apXWnzx5ckCRQIGfPD733HOBlVlVkbGTigK4zilSxkcffVSlchYFOA7XXj/Zq+pxEKTI9gWRaBcWFoJzVf5B4Zxjx86dgQw7YWY0MAvdtXOB8bfrs88+q1IxQR4HkfN5EMd5kXOBnA8ig3gHkbBHvhOq2mK2b98+GjSs+jBL8G0N+5IlSypdRq1I1IDTgJXOuVXOuUJgClD2MOE1aJ8/CnKQJk6cWKX133jjjYAi+VZQvbOCEEStQKyqJkVl3dO0KoJoTg+zqn5ZBDmsSyRZCOra1SAE+UMk8nVa1S+w3Nxcr6YhgNr6RmlpNG7SpMrXcIVdpIWjsiq6DVRlhGlsvsi1n0EkohFV/RF41FFHYUBaQMkaVG37rDb8mjGzIcAg59z1/vOrgdOdczfHLDcKiAyxfDxQtTpnERERkZrR2TlX6jLg2tKZIF5XwVIZpnNuAjCh+sMRERERqX61pekzG+gY9TwdqPpgKSIiIiIhVlsStc+AbmbWxcwaA0OBaSmOSURERKRa1YqmT+fcQTO7GfgP3h2vn3HOfZnisERERESqVa3oTCAiIiJSH9WWpk8RERGRekeJmoiIiEhIKVETERERCSklaiIiIiIhVSt6fQbFzJoCPwbOAtoD+cBS4K1ke5GGsSzFlJqyoso8DNjvnKvU3aG1zxVTyMtqAPSKKudL59yWZMoIsqygP8NBbV9dPqbCGFM1lBWq4wDqUa9PM7sf+AkwC1gIbAWaAt8F/sd//GvnXIV3Tg1jWYopJdvXAG9Mv+HAqUAB0ATYBrwNTHDOragonoBjCqScsJalmFKyfccBdwM/AlbgHd+RcvKAfwCTnXMV3swwqLIC3k9Bbl8gcYX0OAhdTAFvX+iOg2LOuXrxB1xYwfy2QL/aWpZiSsn2zQZ+B5wMNIia3hq4FHgduEr7vG5vXxhjCnj7XgLOxv9hH6eM24BrEowpkLIC3k9Bbl+dPabCGFPA2xe64yDyV29q1ESCZmaNnHMHqrqMiIhIWepNZwIza2Bm15nZW2a22MwWmtkUMxtQibKam9ldZnanmTU1sxFmNs3MHjKzFkmW1dXMJpnZn8yshZlNNLOlZvaqmWUkUc4bZnZVsq+fLDNL+Kb3Id7naWZ2g5m9Y2ZL/Nj+bWY3mlmjRMuJJGBm1s7MTjGzPmbWLt4yCcR0s5kd7T/+jpnNMbPdZvapmZ2UxLYFdhwEvM8b+vv8j2b2/Zh5v02inEA+L35ZgW1fOa+R8OelnDK+qeR6gRznflnHmNkx/uM2ZnaJmZ1Ymbhiyu3il9U9yfUC2za/vCPMa/qKnX5ykuUEds6LU3bKj4NyXiOp4zx6v5pZIzP7rf/Z+7OZNU9FWWbWybzryjDPtWb2uJndZGZJXc8f1Pm8uLz6UqNmZpOAtcD7wBAgB/gIr016qnPu8STKegVYDzQDjge+Bl7Ba5M+xjl3dRJlzcGrcj0SuAqY5Jd1LjDcOffDBMvZAMwFfoi3jS/hXbRYmGgsUWW1LmsWsNg5l55gOWHd5y8Bu4HJQLY/OR24BmjtnLsiwXL6AE/hvXcbosrZDfzcOfd5EjF96Zw70X/8FvBP59yb/gl+rHPu++WtH1VOkMdBkPv8n0BzYD5wNTDbOfcrf97nzrlTEiwnkM+LX1Yg2xfU58Uvay8QOSmb/7853jUyzjl3RBJlBXWc3wDc48fzIDAC+BL4PvCQc+7pJGL6l3PuYv/xRcAjeNfxfA8Y55x7NsFyAtk2v6zL/Ti2Ao2AEc65z/x5CR+b/vKBnPNCehwEeZwX71cz+wtwFN5n+WLgKOfcz2q6LDNbCpzmnMszsweB44B/4Z1Lcc5dl0RMgZzPiyXaRlrb/4AlMc/n+f+bAF8nWVaW/9+AzXyb8Frs6yRQ1qKox+vKmpdoOcDheF+Eb+NdDDkJODfJmA4Bq4DVUX+R54V1YJ8vL2feN8nEBJweZ3p/vBNXpWICPitvP9bgcRDkPl8S9TgNmAC84R8Li5LdPv9xpT8vQW5fUJ8Xv6zHgeeAdlHTVidTRrxjKs68ZI7zL/CShKOAXLwkFqBVZB9W8v37BOjiPz46mc9MUNsWOQ6AY/3HpwHLgEsqeUwFcs4L6XEQ5HEefRxkAY38x1X9Dq10WcBXUY8XUvK645SczyN/9abpEzgQqdo2s1OAQgDnXAHf/nJJivP2+tv+/8jzZMsqMrPvmtmpQHMz6+fH+B28G9AnHI4fw17n3PPOuQvwagk+xfs1nIxVwADnXJeov67OuS5AMt2Uw7rPd5nZZeb12sSPr4GZXQHsSqKcw5xzn8aJcR5wWJIxvWZmz5pZV+BNM7vNr4q/FliXRDlBHgf4ZQWxzxtHlXfQOTcK76T6AZBMM2NQn5diAWxfUJ8XnHO3AI8CL5nZL/1jtLLNHkEd5wecc3nOuR3Af51zm/1Yd1Uitujl05xzq/2ytgMV9qaLEtS2ATR0zm3y45iP1ytvtJn9kuS3L5BzXkiPg8COc+BIMxtsZpcCTZx/iUglzy1BlbXezCI18muAjgBmdlSS8UBw53NPspldbf3Dq75cB3yD9wvgdH96G7zq+2TK+ifQIs7044D/S7Ksc4DleM0uZ+L1FFyJVw1/URLlzAlwX/0C6FXGvFvqwD7PAF7Gq2n6Bq8r9lZ/WpckynkMeAu4Aq/p5nv+47eAJyqx30fgJVTbgb3AV8CfgSNTdBwEuc9fAAbFmX49XiKQaDmBfF6C3L6gPi8x6zUAfonXbLaxkmXEHuffVPI4X8C3tRTpUdObknxNwyG85sC9eElMpHauMcnVfgTyGfbL+gQ4Lmba4cBMoCDJsgI751XjcVDZ811gxzleDX/0Xzt/+jHAzFSUBXQCPgTmANPxktgPgEXAjyqx30dQxfN55K/eXKMG3gWCeG3W26vzNVwVd6p/EeIuV8mBU8Mk7Pvc/7VklY3PzM4HLgI64FW1ZwPTnHNvV6a82iKI4zwo1fF5Ccv2mdmxQJ+qHk9VOc7NrCOwyTl3MGZ6B6CHc+79RPeXmTVwccahMrOWfllzk933AXyGTwFynHMrY6Y3Ai53zmUmE1N1nPPCcBzUdZH32Mx64I13loZ3Pv8scsym6rxQn5o+cZ5SB6iZDUy2LCujlxCQfI+OmLKcc9udc4cs+R5HgfXMCrCsdvh3wAggpsD2eYTzmnMOt0r0PPPX/7dz7kbn3E+ccz/2H1fqZFrW9lXiOAiknDLK/jMUNysku261bF9lPy8V+FFVVo7sp0quW7x9zrlNkeOpKtvnnNsRfe5L8pz3PHCTmXWKmb4NcGY2Ge/C9ER8YGa3xCkrD2iWTFmRc5T/GbYqnFv+CpwfJyYDNie5fYF+z0SVGX0cVOb7Kqh9VVb5ld62EJX1oZndAuxzzk11zr3uvEtb0szsh8keBwHF5Em2Cq4u/hFzUXICy18ObMS7xuZL4NSoeZ+noizgBryq9jXATXhVrs/gNRONTDKmQMoKOKYg9/m/oh5f5Mc4Ca9ZYEQS5aT52/hvYAmw2H98I35TUQqOgyD302Mxf4/j9R57DHistm9fBa+T8DkhjPsp4O1rCvwc+NiP7Su865XWAhOB3jVdVsDnlsC2L6h9HmQ5Qe6r6t62VJYV5uOg3jR9mtm0smYBP3TOJXzxt5llAec75zaZ2Wl4vXPudc69YWaLnHN9arosM/sCOB1vqIG1wHecc5vNrBXwoXOudxIxBVJWwDFlEdw+L17ezD7BG9Zhtd+ENtM51yvBcoIcIiCLYI6DQMrxy8rGGzrhXb4dImA8cAeAc25yEmUFElfA2xfIOSGM+8kvK7BzXlSZjfB6aOY753Ynu35QZQV5bgkqJn/9oI6pIL+vgjqfBxlTKMuKKjMUx0FEfbop+1l44y7lxkw3vC7ZySjRS8jM/geYYWbpJN9jJaiyDjjn8oA8MyvRM8vMko0pqLKCjCnIfR69fImeZ2aWTM+zU5xzx8dMywbmWfKDUwa1fUHupx7AH4FBwJ3OuQ1mdl8yiUc1xBXk9gV1TgjjfoJgz3n4MR0ANlVm3YDLCvLcElRMENw+D/K9C2pfBRlTWMsCQnUcAPUrUZsH5DnnZsfOMLPlSZa118yOc879F7zrB8wbyO5fQLLt/kGVVWTf3q7owshE80ZaTvZaxKDKCjKmIPd5LzPLwfvQNPGv3dhsZo1JboiHXWZ2GfC6+/Zi0wbAZSQ/REBQ2xfYfnLO7QVuM7O+wAvmDdxY2etaQ7d9BHROCOl+gmDPeWET5LklSEHt8yDfu6D2VZAxhbWsoAQbkwuoLTjsf1D6RquVWcZf7hS86uPY6Y3wmtFqvCy8MV/S4kzvgN+1OImYAikr4JiC3OcNypjeEjgjie3LIIDhDwI+DoLcTxb9GK97/gtlLVObty+oZcKyn4LcvjD+BXluCTiuwI+pqr53AZ7Pg4wplGWF7TiI/NWnXp8fWpzeRmbW2JLv0RFkL6GgygqyZ1ZQZQUZU5D7PJCeZ865Nc65K5xzbYAzgO8559r601YnGEtEUNsX5H4q/sw4z5POuatS/Jmplu0rUVDy2xfG/VQirhIFVS6usAny3BKkwI+pKpYDwe2rIGMKa1lBCTSm+tSZoClwHTAc6IJ3EXhTvKaud4EnnXNZtbUsxZSassp5jYHOufeSWF77vJZuXxhjCrqssAnrttXlYyqMMQVdVlCCjqneJGrRLCQ9l6qrLMWUmrJiyl3nnIv9BVujMYV1P9Xl7QtjTEGXFTZh3ba6fEyFMaagywpKEDHVy0RNJAhWDd3CRUREotWnXp8iQQu8W7iIiEg0JWoilRfGbuEiIlKHqOlTpJLMKr5BbyLLiIiIlKU+Dc8hErQwdgsXEZE6RDVqIpUUxm7hIiJStyhREwlAGLuFi4hI7adETURERCSkdI2aiIiISEgpURMREREJKSVqIiFlZo+a2Y9SHYeIiKSOEjWREDKzJsBc59z7qY5FRERSR4maSAg55wqAfyazjpkNMLMZ/uOfmtk9QcYUXX6yy/jT95jZIjNbbmZzzOzHCb7m96oSd5wyW5rZz8uZ38zMZptZwyDKC5KZXWJm88zsDTO7oArlVPq9LGf5WWbWrxKx9K7MtphZGzN7J9n1RGobJWoidZBzbppz7oFUxxHjI+dcH+fc8cAvgSfM7JwK1hkABJqoAS2B8hKr64A3nHOHAiovMM65N5xz/Z1zlzjn3q6J16wBvYGkEjUzS3PObQM2mdn3qyUqkZBQoiYScn7Nxiwze83MlplZppmZP2+QP+3/gEui1hlhZk/4j9uZ2Ztmttj/+54//Sozm29mWWb2j3g1SOWUf5iZPWNmn/m1ZBcls03+QMB/AG72y/uJmX3ql/W+H3MGcCNwux/jWfGW89f/gb9Mlj/vcH/6nX6MS8xsjP/yDwDH+cv+b5zwhgNT/fXNzP7XzJaa2RdmdkWc5UuUZ2YtzGymmX3ur3ORX1aGvy//6ZeXaWY/MrOPzWyFmZ3mL3eamX3ib8cnZna8P32EX5P2jr/8Q1HvxzD/tZaa2YPx9nlQ76WZNTSz8f7rLTGzW+Iskxv1eIiZPes/vsyPcbF5taqN8Y6DK/z9d0VZ8fjb/6qZTccbUBrgX/77JVJ3Oef0pz/9hfAPyPX/DwD2AOl4P67mAmfi3QVhPdANMOAVYIa/zgjgCf/xy8Bt/uOGwJFAD2A60Mif/jfgZzGvX175fwau8h+3BL4BDvNjnRFnW0pNx6tJ+dp/3Ipvx3W8HviL//h+4I6odcpabjrwff9xCyANOBeY4MfeAJgBnA1kAEvL2OeNgc1Rzy8F3vP3WztgHXBszDolyvNf+wj/8dHASj+GDOAgcJIfz0LgGX/eRcC//HWOANL8xz8CXo96T1f5719TYC3QEWjvx9XGf+0PgIur8b28CXg9KsbW/v9ZQL/oY9d/PAR41n/8BdAh8lqxx2oF8YwAsiOv58/vAHyR6s+q/vRXnX9piEhtMN85lw1gZll4X/q5wGrn3Ap/+gvAqDjr/hD4GYDzmvP2mNnVQF/gM79yrhmwNWa97uWUfy7wUzO7w3/eFOhEcizqcTrwspkdi5csrS5jnbKW+xj4q5ll4jVbZpvZuX6ci/xlWuAlKuvKielovFuBRZwJvOTvty1mNhs4FZhWwXb92czOBorwkol2/rzVzrkvAMzsS2Cmc86Z2Rd47yl4idhkM+sGOKBRVNkznXN7/PW/AjoDRwGznNcUiL8PzsarbYoI8r38EfB359xBAOfcznKWjfUx8KyZvQK8UcYy5cXzXszrbcVLVEXqLCVqIrVDQdTjQ3z72a3srUUMmOyc+00Fy5VVvgGXOueWl5joN0UmqA/wtf/4ceCvzrlpZjYAryYtnrjLOeceMLO38K51mmfesCYGjHPO/SMmxoxyYsrHSwyKF094a741HK92q69z7oCZrYkqM/p9LIp6XsS37+kfgQ+dc4P9WGdFrRPvOEg0xqDeSyunrHivVbw/nXM3mtnpwIVAlpn1TiKe04F9Mcs2xXvPROosXaMmUnstA7qY2XH+82FlLDcTr7kqcn3REf60IWbW1p/e2sw6J1H+f4BbzIqvleuTTOBmdjLwO+BJf9KRwAb/8TVRi+4FDo96Hnc5MzvOOfeFc+5BYAFeDdJ/gOvMrIW/TAd/e2PLLOac2wU0NLNIcjEH7/qphmbWBq+man7MavFi3Oonaf+DV+uVjOhtHJHA8p8CPzCzo827znAYMDtmmSDfy3eBG80szV++dZxltphZDzNrAAyOTPTfp0+dc78HtuM13cbuv2Ti+S6wtIJ4RWo1JWoitZRzbj9e89Vb/gXia8tY9Fbgf/zmtYXAic65r4DfAu+a2RK867COTaL8P+I1yS0xs6X+84qc5V8cvhwvQfulc26mP+9+4FUz+wjvCzxiOjDYv9D8rHKWuy1ykTpeDcu/nXPvAi8Cc/1tfw043Dm3A/jYXz5eZ4J38Zo8Ad4ElgCL8a79uss5tzlmP8WWlwn0M7MFeLVryxLYN9EeAsaZ2cd418aVyzm3CfgN8KEf5+fOuakxywT5Xv4Tr/l4ib+/r4yzzD141wR+AGyKmv6/fieEpXhJ8GI/7hMinQmSjOd/gLcqiFekVtNN2UVEovg1OL9yzl2d6likfGY2B7jIrwkVqZNUoyYiEsU5twj40BIc8FZSw2+K/quSNKnrVKMmIiIiElKqURMREREJKSVqIiIiIiGlRE1EREQkpJSoiYiIiISUEjURERGRkPr/ajFcDaK9Qg4AAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x61e61b0b8>"
+       "<Figure size 720x720 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -563,7 +579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -576,12 +592,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x61e6a1a58>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -609,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -642,7 +660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -651,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -666,7 +684,7 @@
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -679,7 +697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -694,7 +712,7 @@
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -707,7 +725,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -722,7 +740,7 @@
        "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -733,17 +751,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x61ea62198>"
+       "<Figure size 720x576 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -755,17 +775,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x61e7d8a58>"
+       "<Figure size 720x576 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -778,13 +800,6 @@
     "plt.scatter(X2[:,0], X2[:,1], c = clusters, cmap=\"prism\")\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -803,7 +818,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T6 - 3 - K-Means-Colab.ipynb b/notebooks/T6 - 3 - K-Means-Colab.ipynb
new file mode 100644
index 00000000..432fabd3
--- /dev/null
+++ b/notebooks/T6 - 3 - K-Means-Colab.ipynb	
@@ -0,0 +1,379 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T6%20-%203%20-%20K-Means-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# El método de k-means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[6.29147980e-01, 6.06404205e-01, 1.88141032e-01],\n",
+       "       [1.26149489e-02, 5.16384408e-01, 2.84904055e-01],\n",
+       "       [7.41433480e-01, 3.38824753e-01, 8.88956404e-01],\n",
+       "       [5.62906682e-01, 3.26042026e-01, 6.63232583e-01],\n",
+       "       [4.27731985e-01, 2.58430836e-01, 2.03435285e-01],\n",
+       "       [8.56002964e-01, 9.74937992e-02, 1.63879425e-01],\n",
+       "       [1.83413083e-01, 7.85274616e-01, 5.22590376e-01],\n",
+       "       [1.08497479e-01, 8.40478007e-01, 5.11016484e-01],\n",
+       "       [6.46451183e-01, 6.19757354e-01, 5.29945925e-01],\n",
+       "       [7.80874043e-01, 5.21973134e-02, 1.71662529e-01],\n",
+       "       [6.59423402e-01, 1.69502919e-01, 8.33304614e-02],\n",
+       "       [3.56848606e-01, 1.16374844e-01, 2.30458097e-01],\n",
+       "       [5.84120109e-01, 8.40197797e-01, 9.72455374e-01],\n",
+       "       [9.16082529e-01, 2.33083741e-01, 6.75067566e-01],\n",
+       "       [9.95222781e-01, 6.29975622e-01, 3.92362489e-01],\n",
+       "       [1.34723208e-01, 5.38025756e-01, 1.15284999e-01],\n",
+       "       [5.83957662e-02, 5.37907566e-01, 1.33294137e-01],\n",
+       "       [9.46400776e-01, 1.64623730e-01, 6.34832584e-01],\n",
+       "       [8.93746931e-01, 2.80637595e-01, 1.65130691e-01],\n",
+       "       [5.39562360e-01, 2.07266311e-02, 7.97995111e-02],\n",
+       "       [8.19704004e-01, 5.95519544e-01, 5.69995785e-01],\n",
+       "       [6.85071290e-01, 6.23518529e-01, 5.90798667e-01],\n",
+       "       [4.06128687e-01, 1.12649853e-02, 3.96032651e-01],\n",
+       "       [8.52166022e-01, 5.94052984e-01, 5.36217408e-01],\n",
+       "       [6.23095629e-01, 6.80364719e-01, 1.60587705e-04],\n",
+       "       [4.32550951e-01, 3.16329561e-01, 2.62452668e-01],\n",
+       "       [6.33852484e-01, 6.59820999e-01, 9.43297565e-01],\n",
+       "       [1.01694869e-01, 4.54377517e-01, 2.53691294e-02],\n",
+       "       [8.05619994e-01, 3.88882031e-01, 6.32589586e-01],\n",
+       "       [6.22258356e-02, 4.05728994e-01, 4.97752949e-01]])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data = np.random.random(90).reshape(30,3)\n",
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.85216602, 0.59405298, 0.53621741],\n",
+       "       [0.35684861, 0.11637484, 0.2304581 ]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c1 = np.random.choice(range(len(data)))\n",
+    "c2 = np.random.choice(range(len(data)))\n",
+    "clust_centers = np.vstack([data[c1], data[c2]])\n",
+    "clust_centers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.cluster.vq import vq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
+       "        1, 0, 0, 1, 0, 1, 0, 1], dtype=int32),\n",
+       " array([0.413578  , 0.53053636, 0.44925265, 0.41428697, 0.1610422 ,\n",
+       "        0.50392885, 0.69568808, 0.78383881, 0.20740936, 0.43286631,\n",
+       "        0.34061803, 0.        , 0.56810161, 0.39199934, 0.20603376,\n",
+       "        0.49029994, 0.52555187, 0.45057144, 0.48750747, 0.25540342,\n",
+       "        0.04687123, 0.17823575, 0.20221657, 0.        , 0.58930457,\n",
+       "        0.216186  , 0.46658385, 0.47054301, 0.23140711, 0.49190948]))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clusters = vq(data, clust_centers)\n",
+    "clusters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
+       "       1, 0, 0, 1, 0, 1, 0, 1], dtype=int32)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels = clusters[0]\n",
+    "labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "\nThe plotly.plotly module is deprecated,\nplease install the chart-studio package and use the\nchart_studio.plotly module instead. \n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-7-56589139b65c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotly\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_objs\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffline\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mply\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/plotly/plotly/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m_plotly_future_\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_chart_studio_error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0m_chart_studio_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"plotly\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/_plotly_future_/__init__.py\u001b[0m in \u001b[0;36m_chart_studio_error\u001b[0;34m(submodule)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_chart_studio_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubmodule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m     raise ImportError(\n\u001b[0m\u001b[1;32m     44\u001b[0m         \"\"\"\n\u001b[1;32m     45\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0msubmodule\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mdeprecated\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mImportError\u001b[0m: \nThe plotly.plotly module is deprecated,\nplease install the chart-studio package and use the\nchart_studio.plotly module instead. \n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install chart_studio\n",
+    "import chart_studio.plotly as py\n",
+    "import plotly.graph_objects as go \n",
+    "import plotly.graph_objects as go\n",
+    "import plotly.offline as ply"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = []\n",
+    "y = []\n",
+    "z = []\n",
+    "x2 = []\n",
+    "y2 = []\n",
+    "z2 = []\n",
+    "\n",
+    "for i in range(0, len(labels)):\n",
+    "    if(labels[i] == 0):\n",
+    "        x.append(data[i,0])\n",
+    "        y.append(data[i,1])\n",
+    "        z.append(data[i,2])\n",
+    "        \n",
+    "    else:\n",
+    "        x2.append(data[i,0])\n",
+    "        y2.append(data[i,1])\n",
+    "        z2.append(data[i,2])\n",
+    "\n",
+    "cluster1 = go.Scatter3d(\n",
+    "    x=x,\n",
+    "    y=y,\n",
+    "    z=z,\n",
+    "    mode='markers',\n",
+    "    marker=dict(\n",
+    "        size=12,\n",
+    "        line=dict(\n",
+    "            color='rgba(217, 217, 217, 0.14)',\n",
+    "            width=0.5\n",
+    "        ),\n",
+    "        opacity=0.9\n",
+    "    ),\n",
+    "    name=\"Cluster 0\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "cluster2 = go.Scatter3d(\n",
+    "    x=x2,\n",
+    "    y=y2,\n",
+    "    z=z2,\n",
+    "    mode='markers',\n",
+    "    marker=dict(\n",
+    "        color='rgb(127, 127, 127)',\n",
+    "        size=12,\n",
+    "        symbol='circle',\n",
+    "        line=dict(\n",
+    "            color='rgb(204, 204, 204)',\n",
+    "            width=1\n",
+    "        ),\n",
+    "        opacity=0.9\n",
+    "    ),\n",
+    "    name=\"Cluster 1\"\n",
+    ")\n",
+    "data2 = [cluster1, cluster2]\n",
+    "layout = go.Layout(\n",
+    "    margin=dict(\n",
+    "        l=0,\n",
+    "        r=0,\n",
+    "        b=0,\n",
+    "        t=30\n",
+    "    )\n",
+    ")\n",
+    "\n",
+    "fig = go.Figure(data=data2, layout=layout)\n",
+    "ply.plot(fig, filename='Clusters.html')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.cluster.vq import kmeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[0.67721014, 0.54571798, 0.64738277],\n",
+       "        [0.43592301, 0.31638448, 0.18756801]]),\n",
+       " 0.3454152706463719)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans(data, clust_centers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[0.76575261, 0.50119159, 0.66914599],\n",
+       "        [0.40370437, 0.37155024, 0.22414973]]),\n",
+       " 0.34445385283461694)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "kmeans(data, 2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T6 - 3 - K-Means.ipynb b/notebooks/T6 - 3 - K-Means.ipynb
index c16aadf2..8a4ca0f1 100644
--- a/notebooks/T6 - 3 - K-Means.ipynb	
+++ b/notebooks/T6 - 3 - K-Means.ipynb	
@@ -24,36 +24,36 @@
     {
      "data": {
       "text/plain": [
-       "array([[0.64794608, 0.58978154, 0.457586  ],\n",
-       "       [0.86418797, 0.93960306, 0.91205676],\n",
-       "       [0.45517333, 0.83186738, 0.85112973],\n",
-       "       [0.90980972, 0.74137858, 0.28493695],\n",
-       "       [0.55135898, 0.02933044, 0.29142465],\n",
-       "       [0.02745073, 0.9058125 , 0.33332778],\n",
-       "       [0.77050918, 0.22592456, 0.79742113],\n",
-       "       [0.32355707, 0.39808319, 0.51058496],\n",
-       "       [0.54893633, 0.46920048, 0.42631236],\n",
-       "       [0.06625477, 0.01840404, 0.12293609],\n",
-       "       [0.18495803, 0.18841901, 0.44726363],\n",
-       "       [0.930496  , 0.25575929, 0.99384862],\n",
-       "       [0.18861802, 0.63469948, 0.59330767],\n",
-       "       [0.93784481, 0.85932239, 0.34059446],\n",
-       "       [0.67945637, 0.80951088, 0.25275286],\n",
-       "       [0.40479666, 0.96152374, 0.15785926],\n",
-       "       [0.87539872, 0.06414777, 0.39982498],\n",
-       "       [0.65101227, 0.65018467, 0.529103  ],\n",
-       "       [0.09670817, 0.56969742, 0.8748495 ],\n",
-       "       [0.26659514, 0.19276489, 0.63348146],\n",
-       "       [0.8400831 , 0.32396245, 0.85072164],\n",
-       "       [0.72074906, 0.66361744, 0.55696167],\n",
-       "       [0.60781852, 0.00821706, 0.9049929 ],\n",
-       "       [0.39145011, 0.16247534, 0.69021987],\n",
-       "       [0.77561671, 0.44300145, 0.58768359],\n",
-       "       [0.37390132, 0.12720886, 0.31267122],\n",
-       "       [0.85189291, 0.02612322, 0.0743608 ],\n",
-       "       [0.99605337, 0.61541593, 0.23039751],\n",
-       "       [0.97521937, 0.23951825, 0.44752608],\n",
-       "       [0.31241785, 0.83913499, 0.421719  ]])"
+       "array([[6.29147980e-01, 6.06404205e-01, 1.88141032e-01],\n",
+       "       [1.26149489e-02, 5.16384408e-01, 2.84904055e-01],\n",
+       "       [7.41433480e-01, 3.38824753e-01, 8.88956404e-01],\n",
+       "       [5.62906682e-01, 3.26042026e-01, 6.63232583e-01],\n",
+       "       [4.27731985e-01, 2.58430836e-01, 2.03435285e-01],\n",
+       "       [8.56002964e-01, 9.74937992e-02, 1.63879425e-01],\n",
+       "       [1.83413083e-01, 7.85274616e-01, 5.22590376e-01],\n",
+       "       [1.08497479e-01, 8.40478007e-01, 5.11016484e-01],\n",
+       "       [6.46451183e-01, 6.19757354e-01, 5.29945925e-01],\n",
+       "       [7.80874043e-01, 5.21973134e-02, 1.71662529e-01],\n",
+       "       [6.59423402e-01, 1.69502919e-01, 8.33304614e-02],\n",
+       "       [3.56848606e-01, 1.16374844e-01, 2.30458097e-01],\n",
+       "       [5.84120109e-01, 8.40197797e-01, 9.72455374e-01],\n",
+       "       [9.16082529e-01, 2.33083741e-01, 6.75067566e-01],\n",
+       "       [9.95222781e-01, 6.29975622e-01, 3.92362489e-01],\n",
+       "       [1.34723208e-01, 5.38025756e-01, 1.15284999e-01],\n",
+       "       [5.83957662e-02, 5.37907566e-01, 1.33294137e-01],\n",
+       "       [9.46400776e-01, 1.64623730e-01, 6.34832584e-01],\n",
+       "       [8.93746931e-01, 2.80637595e-01, 1.65130691e-01],\n",
+       "       [5.39562360e-01, 2.07266311e-02, 7.97995111e-02],\n",
+       "       [8.19704004e-01, 5.95519544e-01, 5.69995785e-01],\n",
+       "       [6.85071290e-01, 6.23518529e-01, 5.90798667e-01],\n",
+       "       [4.06128687e-01, 1.12649853e-02, 3.96032651e-01],\n",
+       "       [8.52166022e-01, 5.94052984e-01, 5.36217408e-01],\n",
+       "       [6.23095629e-01, 6.80364719e-01, 1.60587705e-04],\n",
+       "       [4.32550951e-01, 3.16329561e-01, 2.62452668e-01],\n",
+       "       [6.33852484e-01, 6.59820999e-01, 9.43297565e-01],\n",
+       "       [1.01694869e-01, 4.54377517e-01, 2.53691294e-02],\n",
+       "       [8.05619994e-01, 3.88882031e-01, 6.32589586e-01],\n",
+       "       [6.22258356e-02, 4.05728994e-01, 4.97752949e-01]])"
       ]
      },
      "execution_count": 2,
@@ -74,8 +74,8 @@
     {
      "data": {
       "text/plain": [
-       "array([[0.85189291, 0.02612322, 0.0743608 ],\n",
-       "       [0.97521937, 0.23951825, 0.44752608]])"
+       "array([[0.85216602, 0.59405298, 0.53621741],\n",
+       "       [0.35684861, 0.11637484, 0.2304581 ]])"
       ]
      },
      "execution_count": 3,
@@ -107,14 +107,14 @@
     {
      "data": {
       "text/plain": [
-       "(array([1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "        1, 1, 1, 0, 0, 1, 1, 1]),\n",
-       " array([0.47947198, 0.84748773, 0.88556269, 0.53158014, 0.37073932,\n",
-       "        1.16415406, 0.40560768, 0.67363418, 0.48468668, 0.78717623,\n",
-       "        0.78115209, 0.54839061, 0.8922792 , 0.63007016, 0.67104692,\n",
-       "        0.96466619, 0.20735083, 0.52953937, 1.03121555, 0.73410734,\n",
-       "        0.43354272, 0.50654853, 0.63068178, 0.63688495, 0.31763306,\n",
-       "        0.54358635, 0.        , 0.43460089, 0.        , 0.8941544 ]))"
+       "(array([0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
+       "        1, 0, 0, 1, 0, 1, 0, 1], dtype=int32),\n",
+       " array([0.413578  , 0.53053636, 0.44925265, 0.41428697, 0.1610422 ,\n",
+       "        0.50392885, 0.69568808, 0.78383881, 0.20740936, 0.43286631,\n",
+       "        0.34061803, 0.        , 0.56810161, 0.39199934, 0.20603376,\n",
+       "        0.49029994, 0.52555187, 0.45057144, 0.48750747, 0.25540342,\n",
+       "        0.04687123, 0.17823575, 0.20221657, 0.        , 0.58930457,\n",
+       "        0.216186  , 0.46658385, 0.47054301, 0.23140711, 0.49190948]))"
       ]
      },
      "execution_count": 5,
@@ -135,8 +135,8 @@
     {
      "data": {
       "text/plain": [
-       "array([1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       1, 1, 1, 0, 0, 1, 1, 1])"
+       "array([0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0,\n",
+       "       1, 0, 0, 1, 0, 1, 0, 1], dtype=int32)"
       ]
      },
      "execution_count": 6,
@@ -153,7 +153,21 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "\nThe plotly.plotly module is deprecated,\nplease install the chart-studio package and use the\nchart_studio.plotly module instead. \n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-7-56589139b65c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotly\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph_objs\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moffline\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mply\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/plotly/plotly/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m_plotly_future_\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_chart_studio_error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0m_chart_studio_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"plotly\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/_plotly_future_/__init__.py\u001b[0m in \u001b[0;36m_chart_studio_error\u001b[0;34m(submodule)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_chart_studio_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubmodule\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m     raise ImportError(\n\u001b[0m\u001b[1;32m     44\u001b[0m         \"\"\"\n\u001b[1;32m     45\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0msubmodule\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mdeprecated\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mImportError\u001b[0m: \nThe plotly.plotly module is deprecated,\nplease install the chart-studio package and use the\nchart_studio.plotly module instead. \n"
+     ]
+    }
+   ],
    "source": [
     "import plotly.plotly as py\n",
     "import plotly.graph_objs as go\n",
@@ -249,8 +263,9 @@
     {
      "data": {
       "text/plain": [
-       "(array([[0.55149309, 0.14895199, 0.50973511],\n",
-       "        [0.59158003, 0.69692438, 0.50948822]]), 0.38784900251440213)"
+       "(array([[0.67721014, 0.54571798, 0.64738277],\n",
+       "        [0.43592301, 0.31638448, 0.18756801]]),\n",
+       " 0.3454152706463719)"
       ]
      },
      "execution_count": 9,
@@ -270,8 +285,9 @@
     {
      "data": {
       "text/plain": [
-       "(array([[0.55511363, 0.20456483, 0.5587382 ],\n",
-       "        [0.59603233, 0.75078951, 0.45343179]]), 0.3860151283499985)"
+       "(array([[0.76575261, 0.50119159, 0.66914599],\n",
+       "        [0.40370437, 0.37155024, 0.22414973]]),\n",
+       " 0.34445385283461694)"
       ]
      },
      "execution_count": 10,
@@ -282,13 +298,6 @@
    "source": [
     "kmeans(data, 2)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -307,7 +316,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T6 - 4 - Clustering completo-Colab.ipynb b/notebooks/T6 - 4 - Clustering completo-Colab.ipynb
new file mode 100644
index 00000000..b9c7fcb8
--- /dev/null
+++ b/notebooks/T6 - 4 - Clustering completo-Colab.ipynb	
@@ -0,0 +1,1332 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T6%20-%204%20-%20Clustering%20completo-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clustering con Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Importar el dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.88</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>0.098</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>0.9968</td>\n",
+       "      <td>3.20</td>\n",
+       "      <td>0.68</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>7.8</td>\n",
+       "      <td>0.76</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>0.092</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0.9970</td>\n",
+       "      <td>3.26</td>\n",
+       "      <td>0.65</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.2</td>\n",
+       "      <td>0.28</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.075</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>0.9980</td>\n",
+       "      <td>3.16</td>\n",
+       "      <td>0.58</td>\n",
+       "      <td>9.8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.4</td>\n",
+       "      <td>0.70</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1.9</td>\n",
+       "      <td>0.076</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0.9978</td>\n",
+       "      <td>3.51</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>9.4</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+       "0            7.4              0.70         0.00             1.9      0.076   \n",
+       "1            7.8              0.88         0.00             2.6      0.098   \n",
+       "2            7.8              0.76         0.04             2.3      0.092   \n",
+       "3           11.2              0.28         0.56             1.9      0.075   \n",
+       "4            7.4              0.70         0.00             1.9      0.076   \n",
+       "\n",
+       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
+       "0                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "1                 25.0                  67.0   0.9968  3.20       0.68   \n",
+       "2                 15.0                  54.0   0.9970  3.26       0.65   \n",
+       "3                 17.0                  60.0   0.9980  3.16       0.58   \n",
+       "4                 11.0                  34.0   0.9978  3.51       0.56   \n",
+       "\n",
+       "   alcohol  quality  \n",
+       "0      9.4        5  \n",
+       "1      9.8        5  \n",
+       "2      9.8        5  \n",
+       "3      9.8        6  \n",
+       "4      9.4        5  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"/content/python-ml-course/datasets/wine/winequality-red.csv\", sep = \";\")\n",
+    "df.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1599, 12)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([ 10.,   0.,  53.,   0., 681.,   0., 638.,   0., 199.,  18.]),\n",
+       " array([3. , 3.5, 4. , 4.5, 5. , 5.5, 6. , 6.5, 7. , 7.5, 8. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(df[\"quality\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>quality</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></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>3</th>\n",
+       "      <td>8.360000</td>\n",
+       "      <td>0.884500</td>\n",
+       "      <td>0.171000</td>\n",
+       "      <td>2.635000</td>\n",
+       "      <td>0.122500</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>24.900000</td>\n",
+       "      <td>0.997464</td>\n",
+       "      <td>3.398000</td>\n",
+       "      <td>0.570000</td>\n",
+       "      <td>9.955000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>7.779245</td>\n",
+       "      <td>0.693962</td>\n",
+       "      <td>0.174151</td>\n",
+       "      <td>2.694340</td>\n",
+       "      <td>0.090679</td>\n",
+       "      <td>12.264151</td>\n",
+       "      <td>36.245283</td>\n",
+       "      <td>0.996542</td>\n",
+       "      <td>3.381509</td>\n",
+       "      <td>0.596415</td>\n",
+       "      <td>10.265094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>8.167254</td>\n",
+       "      <td>0.577041</td>\n",
+       "      <td>0.243686</td>\n",
+       "      <td>2.528855</td>\n",
+       "      <td>0.092736</td>\n",
+       "      <td>16.983847</td>\n",
+       "      <td>56.513950</td>\n",
+       "      <td>0.997104</td>\n",
+       "      <td>3.304949</td>\n",
+       "      <td>0.620969</td>\n",
+       "      <td>9.899706</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>8.347179</td>\n",
+       "      <td>0.497484</td>\n",
+       "      <td>0.273824</td>\n",
+       "      <td>2.477194</td>\n",
+       "      <td>0.084956</td>\n",
+       "      <td>15.711599</td>\n",
+       "      <td>40.869906</td>\n",
+       "      <td>0.996615</td>\n",
+       "      <td>3.318072</td>\n",
+       "      <td>0.675329</td>\n",
+       "      <td>10.629519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8.872362</td>\n",
+       "      <td>0.403920</td>\n",
+       "      <td>0.375176</td>\n",
+       "      <td>2.720603</td>\n",
+       "      <td>0.076588</td>\n",
+       "      <td>14.045226</td>\n",
+       "      <td>35.020101</td>\n",
+       "      <td>0.996104</td>\n",
+       "      <td>3.290754</td>\n",
+       "      <td>0.741256</td>\n",
+       "      <td>11.465913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8.566667</td>\n",
+       "      <td>0.423333</td>\n",
+       "      <td>0.391111</td>\n",
+       "      <td>2.577778</td>\n",
+       "      <td>0.068444</td>\n",
+       "      <td>13.277778</td>\n",
+       "      <td>33.444444</td>\n",
+       "      <td>0.995212</td>\n",
+       "      <td>3.267222</td>\n",
+       "      <td>0.767778</td>\n",
+       "      <td>12.094444</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
+       "quality                                                                 \n",
+       "3             8.360000          0.884500     0.171000        2.635000   \n",
+       "4             7.779245          0.693962     0.174151        2.694340   \n",
+       "5             8.167254          0.577041     0.243686        2.528855   \n",
+       "6             8.347179          0.497484     0.273824        2.477194   \n",
+       "7             8.872362          0.403920     0.375176        2.720603   \n",
+       "8             8.566667          0.423333     0.391111        2.577778   \n",
+       "\n",
+       "         chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
+       "quality                                                                   \n",
+       "3         0.122500            11.000000             24.900000  0.997464   \n",
+       "4         0.090679            12.264151             36.245283  0.996542   \n",
+       "5         0.092736            16.983847             56.513950  0.997104   \n",
+       "6         0.084956            15.711599             40.869906  0.996615   \n",
+       "7         0.076588            14.045226             35.020101  0.996104   \n",
+       "8         0.068444            13.277778             33.444444  0.995212   \n",
+       "\n",
+       "               pH  sulphates    alcohol  \n",
+       "quality                                  \n",
+       "3        3.398000   0.570000   9.955000  \n",
+       "4        3.381509   0.596415  10.265094  \n",
+       "5        3.304949   0.620969   9.899706  \n",
+       "6        3.318072   0.675329  10.629519  \n",
+       "7        3.290754   0.741256  11.465913  \n",
+       "8        3.267222   0.767778  12.094444  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.groupby(\"quality\").mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Normalización de los datos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.247788</td>\n",
+       "      <td>0.397260</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.068493</td>\n",
+       "      <td>0.106845</td>\n",
+       "      <td>0.140845</td>\n",
+       "      <td>0.098940</td>\n",
+       "      <td>0.567548</td>\n",
+       "      <td>0.606299</td>\n",
+       "      <td>0.137725</td>\n",
+       "      <td>0.153846</td>\n",
+       "      <td>0.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.283186</td>\n",
+       "      <td>0.520548</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.116438</td>\n",
+       "      <td>0.143573</td>\n",
+       "      <td>0.338028</td>\n",
+       "      <td>0.215548</td>\n",
+       "      <td>0.494126</td>\n",
+       "      <td>0.362205</td>\n",
+       "      <td>0.209581</td>\n",
+       "      <td>0.215385</td>\n",
+       "      <td>0.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.283186</td>\n",
+       "      <td>0.438356</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.095890</td>\n",
+       "      <td>0.133556</td>\n",
+       "      <td>0.197183</td>\n",
+       "      <td>0.169611</td>\n",
+       "      <td>0.508811</td>\n",
+       "      <td>0.409449</td>\n",
+       "      <td>0.191617</td>\n",
+       "      <td>0.215385</td>\n",
+       "      <td>0.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.584071</td>\n",
+       "      <td>0.109589</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>0.068493</td>\n",
+       "      <td>0.105175</td>\n",
+       "      <td>0.225352</td>\n",
+       "      <td>0.190813</td>\n",
+       "      <td>0.582232</td>\n",
+       "      <td>0.330709</td>\n",
+       "      <td>0.149701</td>\n",
+       "      <td>0.215385</td>\n",
+       "      <td>0.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.247788</td>\n",
+       "      <td>0.397260</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.068493</td>\n",
+       "      <td>0.106845</td>\n",
+       "      <td>0.140845</td>\n",
+       "      <td>0.098940</td>\n",
+       "      <td>0.567548</td>\n",
+       "      <td>0.606299</td>\n",
+       "      <td>0.137725</td>\n",
+       "      <td>0.153846</td>\n",
+       "      <td>0.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+       "0       0.247788          0.397260         0.00        0.068493   0.106845   \n",
+       "1       0.283186          0.520548         0.00        0.116438   0.143573   \n",
+       "2       0.283186          0.438356         0.04        0.095890   0.133556   \n",
+       "3       0.584071          0.109589         0.56        0.068493   0.105175   \n",
+       "4       0.247788          0.397260         0.00        0.068493   0.106845   \n",
+       "\n",
+       "   free sulfur dioxide  total sulfur dioxide   density        pH  sulphates  \\\n",
+       "0             0.140845              0.098940  0.567548  0.606299   0.137725   \n",
+       "1             0.338028              0.215548  0.494126  0.362205   0.209581   \n",
+       "2             0.197183              0.169611  0.508811  0.409449   0.191617   \n",
+       "3             0.225352              0.190813  0.582232  0.330709   0.149701   \n",
+       "4             0.140845              0.098940  0.567548  0.606299   0.137725   \n",
+       "\n",
+       "    alcohol  quality  \n",
+       "0  0.153846      0.4  \n",
+       "1  0.215385      0.4  \n",
+       "2  0.215385      0.4  \n",
+       "3  0.215385      0.6  \n",
+       "4  0.153846      0.4  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_norm = (df-df.min())/(df.max()-df.min())\n",
+    "df_norm.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clustering jerárquico con scikit-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import AgglomerativeClustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clus= AgglomerativeClustering(n_clusters=6, linkage=\"ward\").fit(df_norm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "md_h = pd.Series(clus.labels_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'Número de vinos del cluster')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(md_h)\n",
+    "plt.title(\"Histograma de los clusters\")\n",
+    "plt.xlabel(\"Cluster\")\n",
+    "plt.ylabel(\"Número de vinos del cluster\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[   0,    4],\n",
+       "       [ 135,  140],\n",
+       "       [ 750,  751],\n",
+       "       ...,\n",
+       "       [3179, 3191],\n",
+       "       [3192, 3193],\n",
+       "       [3194, 3195]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clus.children_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.cluster.hierarchy import dendrogram, linkage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Z = linkage(df_norm, \"ward\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1800x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(25,10))\n",
+    "plt.title(\"Dendrograma de los vinos\")\n",
+    "plt.xlabel(\"ID del vino\")\n",
+    "plt.ylabel(\"Distancia\")\n",
+    "dendrogram(Z, leaf_rotation=90., leaf_font_size=4.)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## K-means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn import datasets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KMeans(n_clusters=6)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = KMeans(n_clusters=6)\n",
+    "model.fit(df_norm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2, 2, ..., 4, 2, 3], dtype=int32)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.labels_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "md_k = pd.Series(model.labels_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_norm[\"clust_h\"] = md_h\n",
+    "df_norm[\"clust_k\"] = md_k"
+   ]
+  },
+  {
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "      <th>clust_h</th>\n",
+       "      <th>clust_k</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.247788</td>\n",
+       "      <td>0.397260</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.068493</td>\n",
+       "      <td>0.106845</td>\n",
+       "      <td>0.140845</td>\n",
+       "      <td>0.098940</td>\n",
+       "      <td>0.567548</td>\n",
+       "      <td>0.606299</td>\n",
+       "      <td>0.137725</td>\n",
+       "      <td>0.153846</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.283186</td>\n",
+       "      <td>0.520548</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.116438</td>\n",
+       "      <td>0.143573</td>\n",
+       "      <td>0.338028</td>\n",
+       "      <td>0.215548</td>\n",
+       "      <td>0.494126</td>\n",
+       "      <td>0.362205</td>\n",
+       "      <td>0.209581</td>\n",
+       "      <td>0.215385</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.283186</td>\n",
+       "      <td>0.438356</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.095890</td>\n",
+       "      <td>0.133556</td>\n",
+       "      <td>0.197183</td>\n",
+       "      <td>0.169611</td>\n",
+       "      <td>0.508811</td>\n",
+       "      <td>0.409449</td>\n",
+       "      <td>0.191617</td>\n",
+       "      <td>0.215385</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.584071</td>\n",
+       "      <td>0.109589</td>\n",
+       "      <td>0.56</td>\n",
+       "      <td>0.068493</td>\n",
+       "      <td>0.105175</td>\n",
+       "      <td>0.225352</td>\n",
+       "      <td>0.190813</td>\n",
+       "      <td>0.582232</td>\n",
+       "      <td>0.330709</td>\n",
+       "      <td>0.149701</td>\n",
+       "      <td>0.215385</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.247788</td>\n",
+       "      <td>0.397260</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.068493</td>\n",
+       "      <td>0.106845</td>\n",
+       "      <td>0.140845</td>\n",
+       "      <td>0.098940</td>\n",
+       "      <td>0.567548</td>\n",
+       "      <td>0.606299</td>\n",
+       "      <td>0.137725</td>\n",
+       "      <td>0.153846</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
+       "0       0.247788          0.397260         0.00        0.068493   0.106845   \n",
+       "1       0.283186          0.520548         0.00        0.116438   0.143573   \n",
+       "2       0.283186          0.438356         0.04        0.095890   0.133556   \n",
+       "3       0.584071          0.109589         0.56        0.068493   0.105175   \n",
+       "4       0.247788          0.397260         0.00        0.068493   0.106845   \n",
+       "\n",
+       "   free sulfur dioxide  total sulfur dioxide   density        pH  sulphates  \\\n",
+       "0             0.140845              0.098940  0.567548  0.606299   0.137725   \n",
+       "1             0.338028              0.215548  0.494126  0.362205   0.209581   \n",
+       "2             0.197183              0.169611  0.508811  0.409449   0.191617   \n",
+       "3             0.225352              0.190813  0.582232  0.330709   0.149701   \n",
+       "4             0.140845              0.098940  0.567548  0.606299   0.137725   \n",
+       "\n",
+       "    alcohol  quality  clust_h  clust_k  \n",
+       "0  0.153846      0.4        2        2  \n",
+       "1  0.215385      0.4        2        2  \n",
+       "2  0.215385      0.4        2        2  \n",
+       "3  0.215385      0.6        3        0  \n",
+       "4  0.153846      0.4        2        2  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_norm.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([216.,   0., 297.,   0., 358.,   0., 246.,   0., 216., 266.]),\n",
+       " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(md_k)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.59099476, 0.20822235, 0.54398148, 0.13885718, 0.17548538,\n",
+       "        0.14769171, 0.10039589, 0.66266928, 0.31342957, 0.25529497,\n",
+       "        0.28615859, 0.54722222],\n",
+       "       [0.32880427, 0.28006088, 0.27414141, 0.08814169, 0.13219563,\n",
+       "        0.14008631, 0.13014717, 0.5213293 , 0.41978844, 0.18181818,\n",
+       "        0.20717431, 0.48080808],\n",
+       "       [0.23836951, 0.38502525, 0.06706704, 0.09327887, 0.12084853,\n",
+       "        0.16407664, 0.10570109, 0.47994651, 0.52192847, 0.15264443,\n",
+       "        0.23688583, 0.42625698],\n",
+       "       [0.37790489, 0.15696347, 0.43670732, 0.10557969, 0.10683795,\n",
+       "        0.16357495, 0.08302451, 0.41122274, 0.41021702, 0.23944793,\n",
+       "        0.50075047, 0.71056911],\n",
+       "       [0.17551622, 0.31760528, 0.09634259, 0.09370244, 0.09543684,\n",
+       "        0.24771779, 0.12411661, 0.31965859, 0.57706328, 0.18743069,\n",
+       "        0.49824311, 0.61759259],\n",
+       "       [0.31888349, 0.27608405, 0.3087218 , 0.16430631, 0.12829653,\n",
+       "        0.40980091, 0.31292011, 0.54078478, 0.42960748, 0.19179686,\n",
+       "        0.22068633, 0.4556391 ]])"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.cluster_centers_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "188.56101989654408"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.inertia_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpretación final"
+   ]
+  },
+  {
+   "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>fixed acidity</th>\n",
+       "      <th>volatile acidity</th>\n",
+       "      <th>citric acid</th>\n",
+       "      <th>residual sugar</th>\n",
+       "      <th>chlorides</th>\n",
+       "      <th>free sulfur dioxide</th>\n",
+       "      <th>total sulfur dioxide</th>\n",
+       "      <th>density</th>\n",
+       "      <th>pH</th>\n",
+       "      <th>sulphates</th>\n",
+       "      <th>alcohol</th>\n",
+       "      <th>quality</th>\n",
+       "      <th>clust_h</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>clust_k</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></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>0</th>\n",
+       "      <td>0.590995</td>\n",
+       "      <td>0.208222</td>\n",
+       "      <td>0.543981</td>\n",
+       "      <td>0.138857</td>\n",
+       "      <td>0.175485</td>\n",
+       "      <td>0.147692</td>\n",
+       "      <td>0.100396</td>\n",
+       "      <td>0.662669</td>\n",
+       "      <td>0.313430</td>\n",
+       "      <td>0.255295</td>\n",
+       "      <td>0.286159</td>\n",
+       "      <td>0.547222</td>\n",
+       "      <td>2.592593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.328804</td>\n",
+       "      <td>0.280061</td>\n",
+       "      <td>0.274141</td>\n",
+       "      <td>0.088142</td>\n",
+       "      <td>0.132196</td>\n",
+       "      <td>0.140086</td>\n",
+       "      <td>0.130147</td>\n",
+       "      <td>0.521329</td>\n",
+       "      <td>0.419788</td>\n",
+       "      <td>0.181818</td>\n",
+       "      <td>0.207174</td>\n",
+       "      <td>0.480808</td>\n",
+       "      <td>1.531987</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.238370</td>\n",
+       "      <td>0.385025</td>\n",
+       "      <td>0.067067</td>\n",
+       "      <td>0.093279</td>\n",
+       "      <td>0.120849</td>\n",
+       "      <td>0.164077</td>\n",
+       "      <td>0.105701</td>\n",
+       "      <td>0.479947</td>\n",
+       "      <td>0.521928</td>\n",
+       "      <td>0.152644</td>\n",
+       "      <td>0.236886</td>\n",
+       "      <td>0.426257</td>\n",
+       "      <td>2.134078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.377905</td>\n",
+       "      <td>0.156963</td>\n",
+       "      <td>0.436707</td>\n",
+       "      <td>0.105580</td>\n",
+       "      <td>0.106838</td>\n",
+       "      <td>0.163575</td>\n",
+       "      <td>0.083025</td>\n",
+       "      <td>0.411223</td>\n",
+       "      <td>0.410217</td>\n",
+       "      <td>0.239448</td>\n",
+       "      <td>0.500750</td>\n",
+       "      <td>0.710569</td>\n",
+       "      <td>0.077236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.175516</td>\n",
+       "      <td>0.317605</td>\n",
+       "      <td>0.096343</td>\n",
+       "      <td>0.093702</td>\n",
+       "      <td>0.095437</td>\n",
+       "      <td>0.247718</td>\n",
+       "      <td>0.124117</td>\n",
+       "      <td>0.319659</td>\n",
+       "      <td>0.577063</td>\n",
+       "      <td>0.187431</td>\n",
+       "      <td>0.498243</td>\n",
+       "      <td>0.617593</td>\n",
+       "      <td>3.412037</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.318883</td>\n",
+       "      <td>0.276084</td>\n",
+       "      <td>0.308722</td>\n",
+       "      <td>0.164306</td>\n",
+       "      <td>0.128297</td>\n",
+       "      <td>0.409801</td>\n",
+       "      <td>0.312920</td>\n",
+       "      <td>0.540785</td>\n",
+       "      <td>0.429607</td>\n",
+       "      <td>0.191797</td>\n",
+       "      <td>0.220686</td>\n",
+       "      <td>0.455639</td>\n",
+       "      <td>1.056391</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
+       "clust_k                                                                 \n",
+       "0             0.590995          0.208222     0.543981        0.138857   \n",
+       "1             0.328804          0.280061     0.274141        0.088142   \n",
+       "2             0.238370          0.385025     0.067067        0.093279   \n",
+       "3             0.377905          0.156963     0.436707        0.105580   \n",
+       "4             0.175516          0.317605     0.096343        0.093702   \n",
+       "5             0.318883          0.276084     0.308722        0.164306   \n",
+       "\n",
+       "         chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
+       "clust_k                                                                   \n",
+       "0         0.175485             0.147692              0.100396  0.662669   \n",
+       "1         0.132196             0.140086              0.130147  0.521329   \n",
+       "2         0.120849             0.164077              0.105701  0.479947   \n",
+       "3         0.106838             0.163575              0.083025  0.411223   \n",
+       "4         0.095437             0.247718              0.124117  0.319659   \n",
+       "5         0.128297             0.409801              0.312920  0.540785   \n",
+       "\n",
+       "               pH  sulphates   alcohol   quality   clust_h  \n",
+       "clust_k                                                     \n",
+       "0        0.313430   0.255295  0.286159  0.547222  2.592593  \n",
+       "1        0.419788   0.181818  0.207174  0.480808  1.531987  \n",
+       "2        0.521928   0.152644  0.236886  0.426257  2.134078  \n",
+       "3        0.410217   0.239448  0.500750  0.710569  0.077236  \n",
+       "4        0.577063   0.187431  0.498243  0.617593  3.412037  \n",
+       "5        0.429607   0.191797  0.220686  0.455639  1.056391  "
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_norm.groupby(\"clust_k\").mean()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T6 - 4 - Clustering completo.ipynb b/notebooks/T6 - 4 - Clustering completo.ipynb
index 46e8a5d1..e6b3b27a 100644
--- a/notebooks/T6 - 4 - Clustering completo.ipynb	
+++ b/notebooks/T6 - 4 - Clustering completo.ipynb	
@@ -25,7 +25,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -166,7 +166,7 @@
        "4      9.4        5  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -187,7 +187,7 @@
        "(1599, 12)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -207,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -215,21 +215,23 @@
       "text/plain": [
        "(array([ 10.,   0.,  53.,   0., 681.,   0., 638.,   0., 199.,  18.]),\n",
        " array([3. , 3.5, 4. , 4.5, 5. , 5.5, 6. , 6.5, 7. , 7.5, 8. ]),\n",
-       " <a list of 10 Patch objects>)"
+       " <BarContainer object of 10 artists>)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQ3UlEQVR4nO3dX4wdZ3nH8e8PGwJJQSTNxnJtUxvJSusgkdCVC42EWgzENCjORSNtJJCFUpkLg6CthOzeVFxYykVVwUWDZIU/K/HHWlKiWFBRLFPUIpWYzR9KnMSKIcFebOwlFaVAZWrz9GIn7Um86z3rPccnfv39SKuZec47Z55Jot/OvmfmJFWFJKktrxh1A5KkwTPcJalBhrskNchwl6QGGe6S1KCVo24A4Prrr6/169ePug1Juqw88sgjP62qsflee1mE+/r165menh51G5J0WUnyo4VeW3RaJsmNSR7v+fl5ko8muS7JgSTPdMtre/bZneRokiNJbhvUiUiS+rNouFfVkaq6uapuBv4A+BXwILALOFhVG4GD3TZJNgETwE3AVuC+JCuG074kaT5L/UB1C/CDqvoRsA2Y7OqTwJ3d+jZgX1WdqapngaPA5gH0Kknq01LDfQL4Ure+qqpOAnTLG7r6GuB4zz4zXe1FkuxIMp1kenZ2doltSJIupO9wT/Iq4A7gy4sNnad23hfYVNXeqhqvqvGxsXk/7JUkXaSlXLm/B3i0qk5126eSrAbolqe7+gywrme/tcCJ5TYqSerfUsL9bv5/SgZgP7C9W98OPNRTn0hyVZINwEbg0HIblST1r6/73JNcDbwL+GBP+V5gKsk9wDHgLoCqOpxkCngSOAvsrKpzA+1aknRBfYV7Vf0K+O2X1J5n7u6Z+cbvAfYsuztJ0kV5WTyhKi1m/a6vjezYz917+8iOLV0svzhMkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ3yi8Oklym/LE3L4ZW7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkN6ivck7w+yQNJnk7yVJK3JbkuyYEkz3TLa3vG705yNMmRJLcNr31J0nz6vXL/JPD1qvo94M3AU8Au4GBVbQQOdtsk2QRMADcBW4H7kqwYdOOSpIUtGu5JXge8Hfg0QFX9uqp+BmwDJrthk8Cd3fo2YF9VnamqZ4GjwObBti1JupB+rtzfCMwCn03yWJL7k1wDrKqqkwDd8oZu/BrgeM/+M13tRZLsSDKdZHp2dnZZJyFJerF+wn0l8BbgU1V1C/BLuimYBWSeWp1XqNpbVeNVNT42NtZXs5Kk/vQT7jPATFU93G0/wFzYn0qyGqBbnu4Zv65n/7XAicG0K0nqx6LhXlU/AY4nubErbQGeBPYD27vaduChbn0/MJHkqiQbgI3AoYF2LUm6oH6/8vfDwBeSvAr4IfAB5n4xTCW5BzgG3AVQVYeTTDH3C+AssLOqzg28c0nSgvoK96p6HBif56UtC4zfA+y5+LYkScvhE6qS1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBfYV7kueSfD/J40mmu9p1SQ4keaZbXtszfneSo0mOJLltWM1Lkua3lCv3P6mqm6tqvNveBRysqo3AwW6bJJuACeAmYCtwX5IVA+xZkrSI5UzLbAMmu/VJ4M6e+r6qOlNVzwJHgc3LOI4kaYn6DfcCvpHkkSQ7utqqqjoJ0C1v6OprgOM9+850tRdJsiPJdJLp2dnZi+tekjSvlX2Ou7WqTiS5ATiQ5OkLjM08tTqvULUX2AswPj5+3uuSpIvX15V7VZ3olqeBB5mbZjmVZDVAtzzdDZ8B1vXsvhY4MaiGJUmLWzTck1yT5LUvrAPvBp4A9gPbu2HbgYe69f3ARJKrkmwANgKHBt24JGlh/UzLrAIeTPLC+C9W1deTfBeYSnIPcAy4C6CqDieZAp4EzgI7q+rcULqXJM1r0XCvqh8Cb56n/jywZYF99gB7lt2dJOmi+ISqJDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoP6DvckK5I8luSr3fZ1SQ4keaZbXtszdneSo0mOJLltGI1Lkha2lCv3jwBP9WzvAg5W1UbgYLdNkk3ABHATsBW4L8mKwbQrSepHX+GeZC1wO3B/T3kbMNmtTwJ39tT3VdWZqnoWOApsHki3kqS+9Hvl/gngY8BvemqrquokQLe8oauvAY73jJvpai+SZEeS6STTs7OzS+1bknQBi4Z7kvcCp6vqkT7fM/PU6rxC1d6qGq+q8bGxsT7fWpLUj5V9jLkVuCPJnwKvBl6X5PPAqSSrq+pkktXA6W78DLCuZ/+1wIlBNi1JurBFr9yrandVra2q9cx9UPrNqnofsB/Y3g3bDjzUre8HJpJclWQDsBE4NPDOJUkL6ufKfSH3AlNJ7gGOAXcBVNXhJFPAk8BZYGdVnVt2p5Kkvi0p3KvqW8C3uvXngS0LjNsD7Flmb5Kki+QTqpLUIMNdkhpkuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNWjTck7w6yaEk30tyOMnHu/p1SQ4keaZbXtuzz+4kR5McSXLbME9AknS+fq7czwDvqKo3AzcDW5O8FdgFHKyqjcDBbpskm4AJ4CZgK3BfkhVD6F2StIBFw73m/KLbfGX3U8A2YLKrTwJ3duvbgH1VdaaqngWOApsH2bQk6cL6mnNPsiLJ48Bp4EBVPQysqqqTAN3yhm74GuB4z+4zXU2SdIn0Fe5Vda6qbgbWApuTvOkCwzPfW5w3KNmRZDrJ9OzsbF/NSpL6s6S7ZarqZ8C3mJtLP5VkNUC3PN0NmwHW9ey2Fjgxz3vtrarxqhofGxtbeueSpAX1c7fMWJLXd+uvAd4JPA3sB7Z3w7YDD3Xr+4GJJFcl2QBsBA4NuG9J0gWs7GPMamCyu+PlFcBUVX01yb8BU0nuAY4BdwFU1eEkU8CTwFlgZ1WdG077kqT5LBruVfXvwC3z1J8Htiywzx5gz7K7kyRdFJ9QlaQGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUIMNdkhpkuEtSgwx3SWqQ4S5JDVo03JOsS/LPSZ5KcjjJR7r6dUkOJHmmW17bs8/uJEeTHEly2zBPQJJ0vn6u3M8Cf1VVvw+8FdiZZBOwCzhYVRuBg9023WsTwE3AVuC+JCuG0bwkaX6LhntVnayqR7v1/wKeAtYA24DJbtgkcGe3vg3YV1VnqupZ4CiwecB9S5IuYOVSBidZD9wCPAysqqqTMPcLIMkN3bA1wHd6dpvpai99rx3ADoA3vOENS25cUnvW7/raSI773L23j+S4w9T3B6pJfgv4B+CjVfXzCw2dp1bnFar2VtV4VY2PjY3124YkqQ99hXuSVzIX7F+oqq905VNJVnevrwZOd/UZYF3P7muBE4NpV5LUj37ulgnwaeCpqvq7npf2A9u79e3AQz31iSRXJdkAbAQODa5lSdJi+plzvxV4P/D9JI93tb8G7gWmktwDHAPuAqiqw0mmgCeZu9NmZ1WdG3TjkqSFLRruVfVt5p9HB9iywD57gD3L6EuStAw+oSpJDTLcJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQYa7JDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1yHCXpAYZ7pLUoEXDPclnkpxO8kRP7bokB5I80y2v7Xltd5KjSY4kuW1YjUuSFtbPlfvngK0vqe0CDlbVRuBgt02STcAEcFO3z31JVgysW0lSXxYN96r6F+A/XlLeBkx265PAnT31fVV1pqqeBY4CmwfTqiSpXxc7576qqk4CdMsbuvoa4HjPuJmudp4kO5JMJ5menZ29yDYkSfMZ9AeqmadW8w2sqr1VNV5V42NjYwNuQ5KubBcb7qeSrAbolqe7+gywrmfcWuDExbcnSboYFxvu+4Ht3fp24KGe+kSSq5JsADYCh5bXoiRpqVYuNiDJl4A/Bq5PMgP8DXAvMJXkHuAYcBdAVR1OMgU8CZwFdlbVuSH1LklawKLhXlV3L/DSlgXG7wH2LKcpSdLy+ISqJDXIcJekBhnuktQgw12SGmS4S1KDDHdJapDhLkkNMtwlqUGGuyQ1aNEnVPXys37X10Z27OfuvX1kx5bUP6/cJalBhrskNchwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ0y3CWpQT6hKumK1+JT3165S1KDDHdJatDQpmWSbAU+CawA7q+qe4d1rFH9SeWXaEl6uRrKlXuSFcDfA+8BNgF3J9k0jGNJks43rGmZzcDRqvphVf0a2AdsG9KxJEkvkaoa/JsmfwZsrao/77bfD/xhVX2oZ8wOYEe3eSNwZBmHvB746TL2v9xcaecLnvOVwnNemt+tqrH5XhjWnHvmqb3ot0hV7QX2DuRgyXRVjQ/ivS4HV9r5gud8pfCcB2dY0zIzwLqe7bXAiSEdS5L0EsMK9+8CG5NsSPIqYALYP6RjSZJeYijTMlV1NsmHgH9i7lbIz1TV4WEcqzOQ6Z3LyJV2vuA5Xyk85wEZygeqkqTR8glVSWqQ4S5JDbpswz3Jq5McSvK9JIeTfHzUPV0qSVYkeSzJV0fdy6WQ5Lkk30/yeJLpUfdzKSR5fZIHkjyd5Kkkbxt1T8OU5Mbu3+8LPz9P8tFR9zVMSf6iy64nknwpyasH+v6X65x7kgDXVNUvkrwS+Dbwkar6zohbG7okfwmMA6+rqveOup9hS/IcMF5VV8zDLUkmgX+tqvu7O86urqqfjbitS6L7+pIfM/fg449G3c8wJFnDXGZtqqr/TjIF/GNVfW5Qx7hsr9xrzi+6zVd2P5fnb6olSLIWuB24f9S9aDiSvA54O/BpgKr69ZUS7J0twA9aDfYeK4HXJFkJXM2AnwW6bMMd/m964nHgNHCgqh4ecUuXwieAjwG/GXEfl1IB30jySPe1Fa17IzALfLabfrs/yTWjbuoSmgC+NOomhqmqfgz8LXAMOAn8Z1V9Y5DHuKzDvarOVdXNzD0BuznJm0bc0lAleS9wuqoeGXUvl9itVfUW5r5ldGeSt4+6oSFbCbwF+FRV3QL8Etg12pYujW4K6g7gy6PuZZiSXMvclyluAH4HuCbJ+wZ5jMs63F/Q/cn6LWDraDsZuluBO7o56H3AO5J8frQtDV9VneiWp4EHmfvW0ZbNADM9f4k+wFzYXwneAzxaVadG3ciQvRN4tqpmq+p/gK8AfzTIA1y24Z5kLMnru/XXMPcP6+mRNjVkVbW7qtZW1Xrm/nT9ZlUN9Lf9y02Sa5K89oV14N3AE6Ptariq6ifA8SQ3dqUtwJMjbOlSupvGp2Q6x4C3Jrm6uzlkC/DUIA9wOf8PslcDk90n668Apqrqirg18AqzCnhw7r9/VgJfrKqvj7alS+LDwBe6aYofAh8YcT9Dl+Rq4F3AB0fdy7BV1cNJHgAeBc4CjzHgryG4bG+FlCQt7LKdlpEkLcxwl6QGGe6S1CDDXZIaZLhLUoMMd0lqkOEuSQ36X2wmdxVnxtpUAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x110edf1d0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -239,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -408,7 +410,7 @@
        "8        3.267222   0.767778  12.094444  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -426,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -567,7 +569,7 @@
        "4  0.153846      0.4  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -586,7 +588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -595,7 +597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -604,7 +606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -613,27 +615,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Text(0,0.5,'Número de vinos del cluster')"
+       "Text(0, 0.5, 'Número de vinos del cluster')"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a2013d630>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -646,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -661,7 +665,7 @@
        "       [3194, 3195]])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -672,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -681,7 +685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -690,17 +694,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a208cf208>"
+       "<Figure size 1800x720 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -722,7 +728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -732,18 +738,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
-       "    n_clusters=6, n_init=10, n_jobs=1, precompute_distances='auto',\n",
-       "    random_state=None, tol=0.0001, verbose=0)"
+       "KMeans(n_clusters=6)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -755,16 +759,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([3, 3, 3, ..., 0, 0, 4], dtype=int32)"
+       "array([2, 2, 2, ..., 4, 2, 3], dtype=int32)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -775,7 +779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -784,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -794,7 +798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -850,7 +854,7 @@
        "      <td>0.153846</td>\n",
        "      <td>0.4</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -867,7 +871,7 @@
        "      <td>0.215385</td>\n",
        "      <td>0.4</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -884,7 +888,7 @@
        "      <td>0.215385</td>\n",
        "      <td>0.4</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -901,7 +905,7 @@
        "      <td>0.215385</td>\n",
        "      <td>0.6</td>\n",
        "      <td>3</td>\n",
-       "      <td>1</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -918,7 +922,7 @@
        "      <td>0.153846</td>\n",
        "      <td>0.4</td>\n",
        "      <td>2</td>\n",
-       "      <td>3</td>\n",
+       "      <td>2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -940,14 +944,14 @@
        "4             0.140845              0.098940  0.567548  0.606299   0.137725   \n",
        "\n",
        "    alcohol  quality  clust_h  clust_k  \n",
-       "0  0.153846      0.4        2        3  \n",
-       "1  0.215385      0.4        2        3  \n",
-       "2  0.215385      0.4        2        3  \n",
-       "3  0.215385      0.6        3        1  \n",
-       "4  0.153846      0.4        2        3  "
+       "0  0.153846      0.4        2        2  \n",
+       "1  0.215385      0.4        2        2  \n",
+       "2  0.215385      0.4        2        2  \n",
+       "3  0.215385      0.6        3        0  \n",
+       "4  0.153846      0.4        2        2  "
       ]
      },
-     "execution_count": 33,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -958,29 +962,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([229.,   0., 247.,   0.,  32.,   0., 499.,   0., 274., 318.]),\n",
+       "(array([216.,   0., 297.,   0., 358.,   0., 246.,   0., 216., 266.]),\n",
        " array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ]),\n",
-       " <a list of 10 Patch objects>)"
+       " <BarContainer object of 10 artists>)"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a205ac780>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -990,33 +996,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[0.17556131, 0.32434049, 0.08882096, 0.0943052 , 0.09611361,\n",
-       "        0.24952334, 0.12330458, 0.32533392, 0.57751951, 0.18442591,\n",
-       "        0.48708991, 0.60960699],\n",
-       "       [0.58353337, 0.20590927, 0.51210526, 0.13429649, 0.13073071,\n",
-       "        0.13941951, 0.09204446, 0.65638172, 0.33150563, 0.22165386,\n",
-       "        0.2924115 , 0.54817814],\n",
-       "       [0.3340708 , 0.28745719, 0.474375  , 0.07427226, 0.5235288 ,\n",
-       "        0.20202465, 0.20781802, 0.51495962, 0.24187992, 0.57541168,\n",
-       "        0.16394231, 0.4625    ],\n",
-       "       [0.26362459, 0.36105087, 0.12016032, 0.09147748, 0.12277309,\n",
-       "        0.14433092, 0.10790486, 0.49221792, 0.48670569, 0.15529262,\n",
-       "        0.21852423, 0.43927856],\n",
-       "       [0.36202442, 0.16017148, 0.41905109, 0.10378962, 0.10596127,\n",
-       "        0.1598643 , 0.08302597, 0.41531882, 0.42059888, 0.23565278,\n",
-       "        0.47894441, 0.69124088],\n",
-       "       [0.31627428, 0.2775588 , 0.30345912, 0.15174033, 0.12611165,\n",
-       "        0.38092391, 0.29696424, 0.5387656 , 0.43735453, 0.17843558,\n",
-       "        0.21498145, 0.45471698]])"
+       "array([[0.59099476, 0.20822235, 0.54398148, 0.13885718, 0.17548538,\n",
+       "        0.14769171, 0.10039589, 0.66266928, 0.31342957, 0.25529497,\n",
+       "        0.28615859, 0.54722222],\n",
+       "       [0.32880427, 0.28006088, 0.27414141, 0.08814169, 0.13219563,\n",
+       "        0.14008631, 0.13014717, 0.5213293 , 0.41978844, 0.18181818,\n",
+       "        0.20717431, 0.48080808],\n",
+       "       [0.23836951, 0.38502525, 0.06706704, 0.09327887, 0.12084853,\n",
+       "        0.16407664, 0.10570109, 0.47994651, 0.52192847, 0.15264443,\n",
+       "        0.23688583, 0.42625698],\n",
+       "       [0.37790489, 0.15696347, 0.43670732, 0.10557969, 0.10683795,\n",
+       "        0.16357495, 0.08302451, 0.41122274, 0.41021702, 0.23944793,\n",
+       "        0.50075047, 0.71056911],\n",
+       "       [0.17551622, 0.31760528, 0.09634259, 0.09370244, 0.09543684,\n",
+       "        0.24771779, 0.12411661, 0.31965859, 0.57706328, 0.18743069,\n",
+       "        0.49824311, 0.61759259],\n",
+       "       [0.31888349, 0.27608405, 0.3087218 , 0.16430631, 0.12829653,\n",
+       "        0.40980091, 0.31292011, 0.54078478, 0.42960748, 0.19179686,\n",
+       "        0.22068633, 0.4556391 ]])"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1027,16 +1033,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "186.55601831533752"
+       "188.56101989654408"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1054,7 +1060,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1112,99 +1118,99 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>0.175561</td>\n",
-       "      <td>0.324340</td>\n",
-       "      <td>0.088821</td>\n",
-       "      <td>0.094305</td>\n",
-       "      <td>0.096114</td>\n",
-       "      <td>0.249523</td>\n",
-       "      <td>0.123305</td>\n",
-       "      <td>0.325334</td>\n",
-       "      <td>0.577520</td>\n",
-       "      <td>0.184426</td>\n",
-       "      <td>0.487090</td>\n",
-       "      <td>0.609607</td>\n",
-       "      <td>3.423581</td>\n",
+       "      <td>0.590995</td>\n",
+       "      <td>0.208222</td>\n",
+       "      <td>0.543981</td>\n",
+       "      <td>0.138857</td>\n",
+       "      <td>0.175485</td>\n",
+       "      <td>0.147692</td>\n",
+       "      <td>0.100396</td>\n",
+       "      <td>0.662669</td>\n",
+       "      <td>0.313430</td>\n",
+       "      <td>0.255295</td>\n",
+       "      <td>0.286159</td>\n",
+       "      <td>0.547222</td>\n",
+       "      <td>2.592593</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>0.583533</td>\n",
-       "      <td>0.205909</td>\n",
-       "      <td>0.512105</td>\n",
-       "      <td>0.134296</td>\n",
-       "      <td>0.130731</td>\n",
-       "      <td>0.139420</td>\n",
-       "      <td>0.092044</td>\n",
-       "      <td>0.656382</td>\n",
-       "      <td>0.331506</td>\n",
-       "      <td>0.221654</td>\n",
-       "      <td>0.292412</td>\n",
-       "      <td>0.548178</td>\n",
-       "      <td>2.024291</td>\n",
+       "      <td>0.328804</td>\n",
+       "      <td>0.280061</td>\n",
+       "      <td>0.274141</td>\n",
+       "      <td>0.088142</td>\n",
+       "      <td>0.132196</td>\n",
+       "      <td>0.140086</td>\n",
+       "      <td>0.130147</td>\n",
+       "      <td>0.521329</td>\n",
+       "      <td>0.419788</td>\n",
+       "      <td>0.181818</td>\n",
+       "      <td>0.207174</td>\n",
+       "      <td>0.480808</td>\n",
+       "      <td>1.531987</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>0.334071</td>\n",
-       "      <td>0.287457</td>\n",
-       "      <td>0.474375</td>\n",
-       "      <td>0.074272</td>\n",
-       "      <td>0.523529</td>\n",
-       "      <td>0.202025</td>\n",
-       "      <td>0.207818</td>\n",
-       "      <td>0.514960</td>\n",
-       "      <td>0.241880</td>\n",
-       "      <td>0.575412</td>\n",
-       "      <td>0.163942</td>\n",
-       "      <td>0.462500</td>\n",
-       "      <td>4.906250</td>\n",
+       "      <td>0.238370</td>\n",
+       "      <td>0.385025</td>\n",
+       "      <td>0.067067</td>\n",
+       "      <td>0.093279</td>\n",
+       "      <td>0.120849</td>\n",
+       "      <td>0.164077</td>\n",
+       "      <td>0.105701</td>\n",
+       "      <td>0.479947</td>\n",
+       "      <td>0.521928</td>\n",
+       "      <td>0.152644</td>\n",
+       "      <td>0.236886</td>\n",
+       "      <td>0.426257</td>\n",
+       "      <td>2.134078</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>0.263625</td>\n",
-       "      <td>0.361051</td>\n",
-       "      <td>0.120160</td>\n",
-       "      <td>0.091477</td>\n",
-       "      <td>0.122773</td>\n",
-       "      <td>0.144331</td>\n",
-       "      <td>0.107905</td>\n",
-       "      <td>0.492218</td>\n",
-       "      <td>0.486706</td>\n",
-       "      <td>0.155293</td>\n",
-       "      <td>0.218524</td>\n",
-       "      <td>0.439279</td>\n",
-       "      <td>2.022044</td>\n",
+       "      <td>0.377905</td>\n",
+       "      <td>0.156963</td>\n",
+       "      <td>0.436707</td>\n",
+       "      <td>0.105580</td>\n",
+       "      <td>0.106838</td>\n",
+       "      <td>0.163575</td>\n",
+       "      <td>0.083025</td>\n",
+       "      <td>0.411223</td>\n",
+       "      <td>0.410217</td>\n",
+       "      <td>0.239448</td>\n",
+       "      <td>0.500750</td>\n",
+       "      <td>0.710569</td>\n",
+       "      <td>0.077236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>0.362024</td>\n",
-       "      <td>0.160171</td>\n",
-       "      <td>0.419051</td>\n",
-       "      <td>0.103790</td>\n",
-       "      <td>0.105961</td>\n",
-       "      <td>0.159864</td>\n",
-       "      <td>0.083026</td>\n",
-       "      <td>0.415319</td>\n",
-       "      <td>0.420599</td>\n",
-       "      <td>0.235653</td>\n",
-       "      <td>0.478944</td>\n",
-       "      <td>0.691241</td>\n",
-       "      <td>0.135036</td>\n",
+       "      <td>0.175516</td>\n",
+       "      <td>0.317605</td>\n",
+       "      <td>0.096343</td>\n",
+       "      <td>0.093702</td>\n",
+       "      <td>0.095437</td>\n",
+       "      <td>0.247718</td>\n",
+       "      <td>0.124117</td>\n",
+       "      <td>0.319659</td>\n",
+       "      <td>0.577063</td>\n",
+       "      <td>0.187431</td>\n",
+       "      <td>0.498243</td>\n",
+       "      <td>0.617593</td>\n",
+       "      <td>3.412037</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>0.316274</td>\n",
-       "      <td>0.277559</td>\n",
-       "      <td>0.303459</td>\n",
-       "      <td>0.151740</td>\n",
-       "      <td>0.126112</td>\n",
-       "      <td>0.380924</td>\n",
-       "      <td>0.296964</td>\n",
-       "      <td>0.538766</td>\n",
-       "      <td>0.437355</td>\n",
-       "      <td>0.178436</td>\n",
-       "      <td>0.214981</td>\n",
-       "      <td>0.454717</td>\n",
-       "      <td>1.034591</td>\n",
+       "      <td>0.318883</td>\n",
+       "      <td>0.276084</td>\n",
+       "      <td>0.308722</td>\n",
+       "      <td>0.164306</td>\n",
+       "      <td>0.128297</td>\n",
+       "      <td>0.409801</td>\n",
+       "      <td>0.312920</td>\n",
+       "      <td>0.540785</td>\n",
+       "      <td>0.429607</td>\n",
+       "      <td>0.191797</td>\n",
+       "      <td>0.220686</td>\n",
+       "      <td>0.455639</td>\n",
+       "      <td>1.056391</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1213,33 +1219,33 @@
       "text/plain": [
        "         fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
        "clust_k                                                                 \n",
-       "0             0.175561          0.324340     0.088821        0.094305   \n",
-       "1             0.583533          0.205909     0.512105        0.134296   \n",
-       "2             0.334071          0.287457     0.474375        0.074272   \n",
-       "3             0.263625          0.361051     0.120160        0.091477   \n",
-       "4             0.362024          0.160171     0.419051        0.103790   \n",
-       "5             0.316274          0.277559     0.303459        0.151740   \n",
+       "0             0.590995          0.208222     0.543981        0.138857   \n",
+       "1             0.328804          0.280061     0.274141        0.088142   \n",
+       "2             0.238370          0.385025     0.067067        0.093279   \n",
+       "3             0.377905          0.156963     0.436707        0.105580   \n",
+       "4             0.175516          0.317605     0.096343        0.093702   \n",
+       "5             0.318883          0.276084     0.308722        0.164306   \n",
        "\n",
        "         chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
        "clust_k                                                                   \n",
-       "0         0.096114             0.249523              0.123305  0.325334   \n",
-       "1         0.130731             0.139420              0.092044  0.656382   \n",
-       "2         0.523529             0.202025              0.207818  0.514960   \n",
-       "3         0.122773             0.144331              0.107905  0.492218   \n",
-       "4         0.105961             0.159864              0.083026  0.415319   \n",
-       "5         0.126112             0.380924              0.296964  0.538766   \n",
+       "0         0.175485             0.147692              0.100396  0.662669   \n",
+       "1         0.132196             0.140086              0.130147  0.521329   \n",
+       "2         0.120849             0.164077              0.105701  0.479947   \n",
+       "3         0.106838             0.163575              0.083025  0.411223   \n",
+       "4         0.095437             0.247718              0.124117  0.319659   \n",
+       "5         0.128297             0.409801              0.312920  0.540785   \n",
        "\n",
        "               pH  sulphates   alcohol   quality   clust_h  \n",
        "clust_k                                                     \n",
-       "0        0.577520   0.184426  0.487090  0.609607  3.423581  \n",
-       "1        0.331506   0.221654  0.292412  0.548178  2.024291  \n",
-       "2        0.241880   0.575412  0.163942  0.462500  4.906250  \n",
-       "3        0.486706   0.155293  0.218524  0.439279  2.022044  \n",
-       "4        0.420599   0.235653  0.478944  0.691241  0.135036  \n",
-       "5        0.437355   0.178436  0.214981  0.454717  1.034591  "
+       "0        0.313430   0.255295  0.286159  0.547222  2.592593  \n",
+       "1        0.419788   0.181818  0.207174  0.480808  1.531987  \n",
+       "2        0.521928   0.152644  0.236886  0.426257  2.134078  \n",
+       "3        0.410217   0.239448  0.500750  0.710569  0.077236  \n",
+       "4        0.577063   0.187431  0.498243  0.617593  3.412037  \n",
+       "5        0.429607   0.191797  0.220686  0.455639  1.056391  "
       ]
      },
-     "execution_count": 38,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1247,13 +1253,6 @@
    "source": [
     "df_norm.groupby(\"clust_k\").mean()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1272,7 +1271,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T6 - 5 - M\303\251todo del codo y la silueta-Colab.ipynb" "b/notebooks/T6 - 5 - M\303\251todo del codo y la silueta-Colab.ipynb"
new file mode 100644
index 00000000..4f979d44
--- /dev/null
+++ "b/notebooks/T6 - 5 - M\303\251todo del codo y la silueta-Colab.ipynb"	
@@ -0,0 +1,492 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T6%20-%205%20-%20Método%20del%20codo%20y%20la%20silueta-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# El método del codo y el factor de la silueta del clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.cluster import KMeans\n",
+    "from sklearn import metrics\n",
+    "from scipy.spatial.distance import cdist\n",
+    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
+    "import matplotlib.cm as cm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x1 = np.array([3,1,1,2,1,6,6,6,5,6,7,8,9,8,9,9,8])\n",
+    "x2 = np.array([5,4,5,6,5,8,6,7,6,7,1,2,1,2,3,2,3])\n",
+    "X = np.array(list(zip(x1,x2))).reshape(len(x1), 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot()\n",
+    "plt.xlim([0,10])\n",
+    "plt.ylim([0,10])\n",
+    "plt.title(\"Dataset a clasificar\")\n",
+    "plt.xlabel(\"x\")\n",
+    "plt.ylabel(\"y\")\n",
+    "plt.scatter(x1,x2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Para k =  2  el promedio de la silueta es de : 0.6113424368705715\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.7746251901389686\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.49704450958269375\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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Gqupvq+rGqvrGuGV7VNWnquq7w6+795kR6MWpGZzd96YM5rR8fGvtzn4jAbsih7YBAEwjVXVGBpPa/l1r7ZjhstcluaW19tqqekmS3VtrL+4zJwCwa1IkAQBMM8PJ8z86rkj6dpIzW2vXV9W+SS5urXU98QMAwF0c2gYAMP3t01q7PkmGX/fuOQ8AsIsa6ztAF3vttVdbtmxZ3zEAgAly2WWX3dRaW9p3DpKquiDJBUmyYMGCk+93v/v1nAgAmCjbMwbbJYqkZcuW5dJLL+07BgAwQarqB31nmOZuqKp9xx3aduOWNmytXZjkwiRZsWJFMwYDgOlre8ZgDm0DAJj+Lkpy3vD6eUk+0mMWAGAXpkgCAJhGquq9Sb6Q5Miquraqzk/y2iRnV9V3k5w9vA0AcK/tEoe2AQDQTWvtqVtYddakBgEApiV7JAEAAADQiSIJAAAAgE4USQAAAAB0okgCAAAAoBNFEgAAAACdKJIAAAAA6ESRBAAAAEAniiQAAAAAOlEkAQAAANCJIgkAAACAThRJAAAAAHSiSAIAAACgE0USAAAAAJ0okgAAAADoRJEEAAAAQCeKJAAAAAA6USQBAAAA0IkiCQAAAIBOFEkAAAAAdKJIAgAAAKATRRIAAAAAnSiSAAAAAOhEkQQAAABAJ4okAAAAADpRJAEAAADQiSIJAAAAgE4USQAAAAB0okgCAAAAoBNFEgAAAACdKJIAAAAA6ESRBAAAAEAniiQAAAAAOlEkAQAAANCJIgkAAACAThRJAAAAAHSiSAIAAACgE0USAAAAAJ0okgAAAADoRJEEAAAAQCeKJAAAAAA6USQBAAAA0IkiCQAAAIBOFEkAAAAAdKJIAgAAAKATRRIAAAAAnSiSAAAAAOhEkQQAAABAJ4okAAAAADpRJAEAAADQiSIJAAAAgE4USQAAAAB0okgCAAAAoBNFEgAAAACdKJIAAAAA6ESRBAAAAEAniiQAAAAAOpnwIqmqRqvqK1X10eHtParqU1X13eHX3Sc6AwAAAAA7bjL2SHpBkivG3X5Jks+01o5I8pnhbQAAAACmuAktkqrqgCSPTfLWcYvPTfLO4fV3Jnn8RGYAAAAAYOeY6D2S3pjkd5NsGLdsn9ba9Uky/Lr3BGcAAAAAYCeYsCKpqs5JcmNr7bLtvP8FVXVpVV26cuXKnZwOAAAAgHtrIvdIOj3JL1TVVUnel+RhVfXuJDdU1b5JMvx64+bu3Fq7sLW2orW2YunSpRMYEwAAAIAuJqxIaq29tLV2QGttWZKnJPlsa+3pSS5Kct5ws/OSfGSiMgAAAACw80zGWds29dokZ1fVd5OcPbwNAAAAwBQ3NhlP0lq7OMnFw+s3JzlrMp4XAAAAgJ2njz2SAAAAANgFKZIAAAAA6ESRBAAAAEAniiQAAAAAOlEkAQAAANCJIgkAAACAThRJAAAAAHSiSAIAAACgE0USAAAAAJ0okgAAAADoRJEEAAAAQCeKJAAAAAA6USQBAAAA0MlY3wEAAJgcVfVbSZ6bpCX5epJnt9ZW9ZsKYOdZ/4NvZd1/fCrZsD5jJ52ZkcOPT1X1HQumFUUSAMAMUFX7J3l+kuWttTur6gNJnpLkHb0GA9hJ1nzsHVn7r/+QrF2TtGTdf346YyvOypyn/Fbf0WBacWgbAMDMMZZkXlWNJZmf5Lqe8wDsFBtWXpe1n/1AsmZ10lqSlqxZlXWXfjrrf/CtvuPBtKJIAgCYAVprP0zyJ0muTnJ9kltba/+v31QAO8f6y780OGh3U2vXZN3X/33S88B0pkgCAJgBqmr3JOcmOSTJfkkWVNXTN7PdBVV1aVVdunLlysmOCbB9Zs1ORjbz39uR0dTsuZOfB6YxRRIAwMzw8CTfb62tbK2tTfKhJKdtulFr7cLW2orW2oqlS5dOekiA7TF23IOGh7RtYmQkYyedOel5YDpTJAEAzAxXJzmlqubX4BRGZyW5oudMADtFLdwts897WTJrTjJn3uAya3ZmP/n5Gdlrv77jwbTirG0AADNAa+1LVfXBJF9Osi7JV5Jc2G8qgJ1n1nGnZ+w1H8j6y/8j2bAho8tXpBbs1ncsmHYUSQAAM0Rr7RVJXtF3DoCJUvMWZOzkh/YdA6Y1h7YBAAAA0IkiCQAAAIBOFEkAAAAAdKJIAgAAAKATRRIAAAAAnSiSAAAAAOhEkQQAAABAJ4okAAAAADpRJAEAAADQyVjfAaaz9Vd/J2kb+o4BTFejYxnZ79DUiM8EAACAyaFImkBrP/uBZPWqvmMA09DoEcdn7P5nK5EAAIBJpUgC2IWM3OfgjJ366IzsfUDfUQAAgBlIkQSwC6jd9sjYAx6ZkUOWp6r6jgMAAMxQiiSAqWzOvIyddGZGjz4lNTradxoAAGCGUyQBTEUjIxk9+pSMnXRmau78vtMAAAAkUSQBTDkjhyzP2AMfkZHd9uo7CgAAwN0okgCmiFq6X2ad8uiM7HdI31EAAAA2S5EE0LNauFvG7n92Ro443kTaAADAlKZIAujLrNkZO+GMjB57WmrW7L7TAAAAbJMiCaAnIwcdmbGTzuw7BgAAQGcjfQcAAAAAYNegSAIAAACgE0USAAAAAJ0okgAAAADoRJEEAAAAQCeKJAAAAAA6USQBAAAA0IkiCQAAAIBOFEkAPRk9+Mi+IwAAANwriiSAHoydcW5Gjzih7xgAAAD3iiIJYJKNPehxGTvq/n3HAAAAuNcUSQCTaOy0x2bs6Af2HQMAAGC7KJIAJsnYKY/K2LGn9h0DAABguymSACbB2AMekbHjH9R3DAAAgB2iSAKYYGMrzsrYiWf0HQMAYEbYcNP12XDDNX3HgGlrrO8AANPZ2EkPzdjJD+07BgDAtLfuyv/K6r/+vWT1nYMFY7Mz51n/O2PHnd5vMJhm7JEEMEFGTzgjoyse1ncMAIBpb8Oqn2X1n//2z0ukJFm3Jqvf+opsuOWG/oLBNKRIAtjJao99Bmdne8DZqaq+4wAATHtrP/mupLXNrltz0VsnOQ1Mbw5tA9gJasleGT302IwcdkxG9tin7zgAADNKu/HaLa7bcNN1k5gEpj9FUpJrrrkmz3zmM/OjH/0oIyMjueCCC/KCF7yg71jAFFe77ZGRQ4/N6GHHpPa4j72PAAB6MnrfE7P+G1/Y/LrDj5vkNDC9KZKSjI2N5fWvf31OOumk3H777Tn55JNz9tlnZ/ny5X1HA6aYWrQkI4cek9HDjk3ttZ/yCABgChg94/HJx95+9zmSkmR0VmY95rxeMsF0pUhKsu+++2bfffdNkixatChHHXVUfvjDHyqSgCRJLVj88/Jo7wOURwAAU8zIyEjmvfI9g8m1v/fNJC11wOGZe/4rMzJ7bt/xYFpRJG3iqquuyle+8pU88IEP7DsK0KOav3BQHh16bOo+BymPAACmuJEFizPvBW/oOwZMe4qkcX7605/miU98Yt74xjdm8eLFfccBJtu8BRk9ZPlgz6P7LEuNOLElAADAeIqkobVr1+aJT3xinva0p+UJT3hC33GAyTJ3XkaXLc/IYcdmZL9DUiOjfScCAACYshRJSVprOf/883PUUUflhS98Yd9xgIk2Z25GDz5qUB7tf1hqVHkEAADQhSIpySWXXJJ3vetdOfbYY3PCCSckSf7wD/8wj3nMY/oNBuxUIwcdmdHl98/IAYenRv36AwAAuLf8TyrJgx70oLTW+o4BTKSqzHrYk1Jz5vWdBAAAYJdlJllgRhjZ7xAlEgAAwA5SJAEzwsiy5X1HAAAA2OUpkoAZYXTZUX1HAAAA2OUpkoBpr5bul1q4W98xAAAAdnmKJGDaszcSAADAzqFIAqY98yMBAADsHIokYFqr3fZI7b533zEAAACmBUUSMK2NHHxUqqrvGAAAANPCWN8BACZCLd0/Y8eempFDj+07CgAAwLShSAKmj6qMHHJ0xo49NbXPQfZEAgAA2MkUScCub87cjB51/4wtf2Bq0ZK+0wAAAExbiiRgl1VL9srosadl9IgTUrNm9x0HAABg2lMkAbuckQOPyOgxp2bkwCMcvgYAADCJJqxIqqq5ST6fZM7weT7YWntFVe2R5P1JliW5KskvtdZ+PFE5gGlibFZG73vioEDafWnfaQAAAGakidwjaXWSh7XWflpVs5L8W1V9IskTknymtfbaqnpJkpckefEE5gB2YbVwt4wec0pG77ciNWde33EAAABmtAkrklprLclPhzdnDS8tyblJzhwuf2eSi6NIAjYxcp+DBnsfHbI8NTLadxwAAAAywXMkVdVoksuSHJ7kza21L1XVPq2165OktXZ9Ve09kRmAXcjoaEYPPTajx5ySkb0P6DsNAAAAm5jQIqm1tj7JCVW1JMk/VdUxXe9bVRckuSBJDjrooIkJCEwps895Tkbuc3DfMQAAANiCkcl4ktbaTzI4hO1RSW6oqn2TZPj1xi3c58LW2orW2oqlS02sCzPC7Ll9JwAAAGArJqxIqqqlwz2RUlXzkjw8ybeSXJTkvOFm5yX5yERlAAAAAGDnmchD2/ZN8s7hPEkjST7QWvtoVX0hyQeq6vwkVyd58gRmAAAAAGAnmciztn0tyYmbWX5zkrMm6nkBAAAAmBiTMkcSAAAAALs+RRIAAAAAnSiSAAAAAOhEkQQAAABAJ4okYGqoSs2Z23cKAAAAtkKRBEwJo8eellqwW98xAAAA2ApFEtC7WrAoYyc/rO8YAAAAbIMiCejd2KmPSc2e03cMAAAAtmGs7wDAzDZywGEZOfSYvmMAzAhVtSTJW5Mck6QleU5r7Qu9hqKT9Vd/J+u++Ilk1Z0ZPeHBGT3mlNTIaN+xmCY23Hx91l7ysbRbbsjokSdl7OSH7bIf8q3/769n3X98KtmwPqMnPTSj9zs5VdV3LJhWFElAf0ZHM3baOf64A0yeNyX5ZGvtSVU1O8n8vgOxbWsu/lDW/vPbknVrk7Yh6772bxk5/LjMveD3lUnssPXfuiyr3vryZP36ZP26rP/GF7L2M+/PvBf9RWrewr7j3SurL3pr1n3+n5I1a5K0rPvK5zJ24kMy+1d+23gTdiKHtgG9GT329IzsvrTvGAAzQlUtTnJGkrclSWttTWvtJ72GYpva7T/J2ov+Jlm7OmkbBgvXrMqGK7+W9d/4Yr/h2OW1DRuy6l2vTdasTtavGyxcsyrtlhuy9rMf7DfcvbThxmuz7nMfGnwvaYOFa1Zl3Vcuzoarrug1G0w3iiSgF7Vwt4yddGbfMQBmkkOTrEzy9qr6SlW9taoW9B2KrVv/3a8ko5s5iGDNqqz76ucnPxDTSlt5bbL6znuuWLc2677yuckPtAPWX/Gfd/VHd7NmTdYpXWGnUiQBvRg77bGpWbP7jgEwk4wlOSnJX7bWTkxyR5KXbLpRVV1QVZdW1aUrV66c7Ixsava8JJs5JKdGUnMdmbijWmtbvT3tzZrz8z3dNlFz5k5ymB00a04yspn/3o6OpubMm/w8MI0pkoDJNWt2Ru93ckaWHdV3EoCZ5tok17bWvjS8/cEMiqW7aa1d2Fpb0VpbsXSpw4/7NnrkScnIZoqksVkZO+XRkx9oGnnDp76TV3/08rvKo9ZaXv3Ry/OGT32n52STZ2SPfVL7HJzUJv8tnD03Yw8+t59Q22ns+AclmysCR0YydvJDJz8QTGOKJGDiVKV23zuj9zs5Y2c8PrOf/BuZ86zfy6yH/KIJDwEmWWvtR0muqaojh4vOSnJ5j5HooGbNztz/+YfJvAXJnPmDy9iszDrn2Rk96L59x9tltdZy26q1efslV91VJr36o5fn7ZdcldtWrZ1ReybNPf8Vqd2XJnPmDS5jszN24pkZe8Aj+o52r9SCxZnznJcns+cmc4f/VmbNzuynvDAje96n73gwrThrG7DzzJ2fkb0PzMg+B6b2OTAjS/dPzd7FdosGmN5+I8l7hmds+16SZ/echw5GDz0681/zD1n/rcuSNasyet8TU4uW9B1rl1ZVefk5y5Mkb7/kqrz9kquSJM8+fVlefs7yGfWB18ie98m8l78rG678WtqtN2dk2VEZWbpf37G2y9jRD8zoHwz/rWxYn9H7nbzLnXkOdgWKJGD7jI6m9rhPRvY5MCN7D4qjWrT7jBp4AexqWmtfTbKi7xzcezVrdsaOPbXvGNPKxjJpY4mUZMaVSBvVyEhG73tC3zF2ipozb3CYGzBhFElAJ7VoSWrpAYPiaJ8DU3vumxqb1XcsAIDtsvFwtvFe/dHLZ2yZBNCVIgm4p7FZg8PS9jkoI3sfkJG9D0gtWNx3KgCAnWL8nEgbD2fbeDuZuXsmAXShSAJSuy/NyNIDBvMa7XNgave9UyOjfccCAJgQVZXFc2fdbU6kjXMmLZ47S4kEsBWKJJhp5s4bTIi994GpjXsbzZnXdyoAgEn1W2ffN621u0qjjWWSEglg6xRJMJ2NjKT2vM/Pi6N9Dkgt3tMACQAguceYyBgJYNsUSRNpZDQZdXgQk6fmLRzuZTQsjvbaNzVrdt+xAAAAmCa2WiRV1WiSd7bWnj5JeaaVuc98ad8RAAAAAHaaka2tbK2tT7K0quzSAAAAADDDdTm07aokl1TVRUnu2LiwtfanExUKAAAAgKmnS5F03fAykmTRxMYBAAAAYKraZpHUWntVklTVgtbaHdvaHgAAAIDpaatzJCVJVZ1aVZcnuWJ4+/iqesuEJwMAAABgStlmkZTkjUkemeTmJGmt/VeSMyYwEwAAAABTUJciKa21azZZtH4CsgAAAAAwhXWZbPuaqjotSauq2Umen+FhbgAAAADMHF32SPrVJM9Lsn+Sa5OckOTXJjATAAAAAFNQlz2SjmytPW38gqo6PcklExMJAAAAgKmoyx5Jf95xGQAAAADT2Bb3SKqqU5OclmRpVb1w3KrFSUYnOhgAAAAAU8vWDm2bnWThcJtF45bfluRJExkKAAAAgKlni0VSa+1zST5XVe9orf0gSapqJMnC1tptkxUQAAAAgKmhyxxJf1RVi6tqQZLLk3y7qn5ngnMBAAAAMMV0KZKWD/dAenySjyc5KMkzJjIUAAAAAFNPlyJpVlXNyqBI+khrbW2SNqGpAAAAAJhyuhRJf53kqiQLkny+qg7OYMJtAAAAAGaQrZ21LUnSWvuzJH82btEPquqhExcJAAAAgKlom0VSVb18C6tevZOzAAAAADCFbbNISnLHuOtzk5yT5IqJiQMAAADAVNXl0LbXj79dVX+S5KIJSwQAAADAlNRlsu1NzU9y6M4OAgAAAMDU1mWOpK8nacObo0mWxvxIAAAAADNOlzmSzhl3fV2SG1pr6yYoDwAAAABT1BaLpKraY3j19k1WLa6qtNZumbhYAAAAAEw1W9sj6bIMDmmrzaxrMU8SAAAAwIyyxSKptXbIZAYBAICdbcPtP86GG67OyAFHZGTu/L7jkKS1lvaTlanZc1ILdus7DgD30tYObXtkkkWttQ9usvxXkqxsrX1qosMBAMD22LBmVVa9/tfTrr/qrmUjx52eec99VX+hyPorv5bV7/7jtNt/nGzYkJFDjs6cZ/3vjCzeY9t3BmBKGNnKulcl+dxmln82ztoGAMAUtuoNL7hbiZQkG752SVb/45v7CUQ23PyjrPqrl6XdckOydk2yfl02fO8bWfXnv53W2rYfAIApYWtF0vzW2spNF7bWfpRkwcRFAgCA7bfhzjvSfvjfm1237pKPTnIaNlp7yUeT9Zuc/HnD+rSfrMyG73+zn1AA3GtbK5LmVtU9Dn2rqllJ5k1cJAAA2AG33rTldevWTl4O7qat/OE9i6SN6358j8+vAZiitlYkfSjJ31TVXXsfDa//1XAdAABMPXvtn9TmTjycZP6iyc3CXUYOPz6ZPeeeK9avz8hB9538QABsl60VSb+X5IYkP6iqy6rqsiRXJVk5XAcAAFPOyNhYRk973GbXzX7Sr09yGjaa9cBHDM7SNjruoIdZczJ6/IMzsnT//oIBcK9s8axtrbV1SV5SVa9Kcvhw8ZWttTsnJRkAAGynub/8/KxZsmfWfvp9yZpVycIlmf2k52XWiWf2HW3GqrnzM++335I1//KerP/avyWz52bswedm1oM3X/oBMDVtsUjaaFgcfX0SsgAAwE4z+5FPy+xHPq3vGIxTi5ZkzpOelzzpeX1HAWA7be3QNgAAAAC4iyIJAAAAgE62eGhbVZ20tTu21r688+MAAAAAMFVtbY6k129lXUvysJ2cBQAAAIApbGtnbXvoZAYBAAAAYGrb5hxJVTW/qn6vqi4c3j6iqs6Z+GgAAAAATCVdJtt+e5I1SU4b3r42yWsmLBEAAAAAU1KXIumw1trrkqxNktbanUlqQlMBAAAAMOV0KZLWVNW8DCbYTlUdlmT1hKYCAAAAYMrZ2lnbNnpFkk8mObCq3pPk9CTPmshQAAAAAEw92yySWmufqqovJzklg0PaXtBau2nCkwEAAAAwpWyxSKqqkzZZdP3w60FVdVBr7csTFwsAAACAqWZreyS9fvh1bpIVSf4rgz2SjkvypSQPmthoAAAAAEwlW5xsu7X20NbaQ5P8IMlJrbUVrbWTk5yY5MrJCggAAADA1NDlrG33a619feON1to3kpwwYYkAAAAAmJK6nLXtiqp6a5J3J2lJnp7kiglNBQAAAMCU06VIenaS/5XkBcPbn0/ylxOWCAAAAIApaZtFUmttVZI3DC8AAAAAzFBd5kgCAAAAAEUSAAAAAN0okgAAAADoZKtFUlWdV1Vfrqo7hpdLq+qZkxUOAAAAgKlji5NtDwuj30zywiRfTlJJTkryf6sqrbW/m5SEAAAAAEwJW9sj6deS/GJr7V9ba7e21n7SWvtskicO1wEAAAAwg2ytSFrcWrtq04XDZYu39cBVdWBV/WtVXVFV36yqFwyX71FVn6qq7w6/7r694QEAAACYPFsrku7cznUbrUvyotbaUUlOSfK8qlqe5CVJPtNaOyLJZ4a3AQAAAJjitjhHUpKjquprm1leSQ7d1gO31q5Pcv3w+u1VdUWS/ZOcm+TM4WbvTHJxkhd3jwwAAABAH7ZaJO2sJ6mqZUlOTPKlJPsMS6a01q6vqr131vMAAAAAMHG2WCS11n6wM56gqhYm+cckv9lau62qut7vgiQXJMlBBx20M6IAAAAAsAO2OEdSVd1eVbdt5nJ7Vd3W5cGralYGJdJ7WmsfGi6+oar2Ha7fN8mNm7tva+3C1tqK1tqKpUuX3rvvCgAAAICdbotFUmttUWtt8WYui1prXc7aVkneluSK1tqfjlt1UZLzhtfPS/KRHfkGAAAAAJgcW5sjaUednuQZSb5eVV8dLntZktcm+UBVnZ/k6iRPnsAMAAAAAOwkE1Yktdb+LYMzvG3OWRP1vAAAAABMjC0e2gYAAAAA4ymSAAAAAOhkIudIAgBgiqmq0SSXJvlha+2cvvNMpA233JC1//bPaTdcnZFDj8msUx+Tmr+w71gAsEtTJAEAzCwvSHJFkm2ehXdXtv77l2fVW16crFubrF+X9d+6LGs/+w+Z9zt/mZEle/UdDwB2WQ5tAwCYIarqgCSPTfLWvrNMtNV//yfJ6juT9esGC9auTu64LWs/+rf9BgOAXZwiCQBg5nhjkt9NsqHnHBOq3XFb2k3X3XPFhvVZ980vTn4gAJhGFEkAADNAVZ2T5MbW2mXb2O6Cqrq0qi5duXLlJKXbycZmbXFVzZ47iUEAYPpRJAEAzAynJ/mFqroqyfuSPKyq3r3pRq21C1trK1prK5YuXTrZGXeKmjMvo0fdPxndZDrQWXMy9qDH9RMKAKYJRRIAwAzQWntpa+2A1tqyJE9J8tnW2tN7jjVh5jztt1P7Lktmz03mzk9mzc7o0Q/MrIf9Ut/RAGCX5qxtAABMO7Vgt8z7nb/Mhmu+k3bzjzKy/2EZ2fuAvmMBwC5PkQQAMMO01i5OcnHPMSZcVWX0oCOTg47sOwoATBsObQMAAACgE0USAAAAAJ0okgAAAADoRJEEAAAAQCeKJAAAAAA6USQBAAAA0IkiCQAAAIBOFEkAAAAAdKJIAgAAAKATRRIAAAAAnSiSAAAAAOhEkQQAAABAJ4okAAAAADpRJAEAAADQiSIJAAAAgE4USQAAAAB0okgCAAAAoBNFEgAAAACdKJIAAAAA6ESRBAAAAEAnY30HmM5WX3VN2oYNfccAYJLNPfTgviMAAMCEUCRNoBvf8f5sWLWq7xgATKKqkRz0Ry/rOwYAAEwIh7YBAAAA0IkiCQAAAIBOFEkAAAAAdKJIAgAAAKATRRIAAAAAnSiSAAAAAOhEkQQAAABAJ4okAAAAADpRJAHATrTw1JP7jgAAABNGkQQAO8m8+x6W3R/78L5jAADAhFEkAcBOMHufvbPXrzwhNTradxQAAJgwiiQA2EGjCxZk6bN+KSNz5/QdBQAAJpQiCQB2QI2OZekzn5yx3Zf0HQUAYEracOeqrL76h9lw56q+o+ywdT+5NauvuS5t/fq+o/RmrO8AALAr2/PJj8ucgw/oOwYAwJTTNmzIDX/1d7nlnz6eGhlJ27AhezzxnOxzwdNTI7vWfi3rbr0t1776T/Ozr34zGR3JyJw52fd3fi27nXFK39Em3a71zk2Q5zznOdl7771zzDHH9B0FgF3Ibmc9OAtOOLrvGAAAU9JN7/lQbvnwJ9JWr8mGO1elrV6TWz70sdz0vg/3He1eu/olf5A7vvL1tLVr01atzvpbb8sPf/8NufM73+s72qRTJCV51rOelU9+8pN9xwBgF7Lg+KOz28PP6DsGAMCUdfP7P5K2avXdlrVVq3PzLlYkrf7BtVl15VXJursfztbWrMnN7/9IP6F6pEhKcsYZZ2SPPfboOwYAu4g5B+6fPZ50Tqqq7ygAAFNSay3rb//pZtetv/X2SU6zY9bedHNqbDNn5m0ta66/YfID9UyRBAAdVSoLV5yQpef9UkZmzeo7DgDAlFVVmbPswM2um3PowZOcZsfMPfyQtLVr77G8Zs/KghXH95CoX4okAOhg/tFHZt/fuiB7PumcjC5c0HccAIAp7z7Pf25qzuyfL6hKzZmTfV/w3P5CbYex3RZnjyc/LjV3zs8Xjo5mZOGC7PmEx/YXrCfO2gYAWzH30IOz5FEPy5yD9u87CgDALmXhycdl2Ztek5XveF9Wf/+azDnkoOz97Kdk3lFH9B3tXtvngmdk7qEH5+b3X5T1t92ehaeenKXPeHLGlizuO9qkUyQBwGbM3vc+WfLoh2XuEYeYCwkAYDvNX37fHPy6l/cdY4dVVZac/ZAsOfshfUfpnUPbkjz1qU/Nqaeemm9/+9s54IAD8ra3va3vSAD0ZNaeu2evp/5i7vP88zPvvocqkQAAYBx7JCV573vf23cEAHo2umhRdnv4g7Pw5OM3f1YOAABAkQTAzDYyd24Wn3laFp12/4zMdiY2AADYGkUSADNSjY1l8ekPyKKHnJrR+fP6jgMAALsERRIAM0rVSBbc/4TsdtaDMrbbzDvLBgAA7AhFEgAzxrz7HZHdH/vwzFq6Z99RAABgl6RIAmDaG12wILs/7hGZf/xyZ2EDAIAdoEgCYFpbcOKx2f1xjzAPEgAA7ASKJACmpbElu2WPX3x05h15eN9RAABg2lAkATCtVCoLT12RJY86MyNz5vQdBwAAphVFEgDTxqyle2XPJz42c5Yd2HcUAACYlhRJAOzyamQki888LYsfenpGZs3qOw4AAExbiiQAdmlzDtgvezzxsZm97z59RwEAgGlPkQTALqnGxrLkEWdm0en3T42O9h0HAABmBEUSALucOQcfkD2f/AuZtdcefUcBAIAZRZEEwC5lZM6cLH36kzK6aGHfUQAAYMYZ6TsAANwbu531YCUSAAD0RJEEwC5j1l57ZtFp9+87BgAAzFiKJAB2Gbs/7uzUmIm1AQCgL4okAHYJ84+6b+YdeXjfMQAAYEZTJAEw5dXoaJY89uF9xwAAgBlPkQTAlLf4jFMya689+o4BAAAzniIJgCltbPGiLD7z9L5jAAAAUSQBMMUteczDMzJndt8xAACAJGN9BwCATY3MmZ35xy3PwpOPz+yDD+g7DgAAMKRIAmDKmHvYsiw8+fjMO+Z+GZk9q+84AADAJhRJAPRqbMluWbji+Cw4+biM7b6k7zgAAMBWKJIAmHQ1Npb5xx6VhSuOz5xDD05V9R0JAADoQJEEwKSZc/ABWXjy8Zl/3PKMzJ3TdxwAAOBeUiQBMKFGFy3KwpOPy4KTj8uspXv2HQcAANgBE1YkVdXfJjknyY2ttWOGy/ZI8v4ky5JcleSXWms/nqgMAPSjRkczb/mRWbjiuMw94tDUyEjfkQAAgJ1gIvdIekeSv0jyd+OWvSTJZ1prr62qlwxvv3gCMwAwiWbvv28Wrjg+848/OqPz5/UdBwAA2MkmrEhqrX2+qpZtsvjcJGcOr78zycVRJAHs0kYXLMiCE4/JgpOPy+x99+k7DgAAMIEme46kfVpr1ydJa+36qtp7SxtW1QVJLkiSgw46aJLi7VxLn/GktPUb+o4BMGFqbDRzDjogNTbadxQAAGASTNnJtltrFya5MElWrFjReo6zXeYetqzvCAAAAAA7zWQXSTdU1b7DvZH2TXLjJD8/AMCMVFUHZjB35X2SbEhyYWvtTf2mAmC6u/M7/53r33Bh1lx7XWYfuH/2/c3/kXn3PazTfVtrqaot3p5sd37rytzyTx/Puh/fmkUPekCWPPLMjMyZ01uevkz2aXQuSnLe8Pp5ST4yyc8PADBTrUvyotbaUUlOSfK8qlrecyYAprFbP/+FfO+5L8qd3/x21t96e+78xrfyvee+KLdd8h/bvO8bPvWdvPqjl6e1wQFKrbW8+qOX5w2f+s5Ex96sH3/sU/n+b7wsP/nkv+anX7wsP/qLv833fvXF2bBqdS95+jRhRVJVvTfJF5IcWVXXVtX5SV6b5Oyq+m6Ss4e3AQCYYK2161trXx5evz3JFUn27zcVANPZdX/055td/sM/2PoOsa213LZqbd5+yVV3lUmv/ujlefslV+W2VWvvKpcmy4Y7V+X6N701bfWaZGOxtWp11lx7XX78sU9PapapYCLP2vbULaw6a6KeEwCAbRueWffEJF/qOQoA09SGDRuy4Y6fbX7dT+/Y6n2rKi8/Z7DT7NsvuSpvv+SqJMmzT1+Wl5+zfNIPb7vziu+mRkezaX3VVq/JbRdfkj2f+NhJzdO3yT60DQCAHlXVwiT/mOQ3W2u3bWb9BVV1aVVdunLlyskPCMC0MDKyY3XD+DJpoz5KpCQZWTAv2bD5M7KPLlo0yWn6p0gCAJghqmpWBiXSe1prH9rcNq21C1trK1prK5YuXTq5AQGYVuYcevDmlx9+yDbvu/FwtvHGz5k0mebe97CM7r4k2aTEqrlzsscTHjPpefqmSAIAmAFq8BHu25Jc0Vr7077zADD9Lfuz12R0t8V3Wza62+Ise9Pvb/V+4+dEevbpy/L9P3pMnn36srvNmTSZqioH/9+XZ9bee2Vk/ryMLJifmj0rS5/55CxccfykZpkKJmyOJAAAppTTkzwjyder6qvDZS9rrX28v0gATGdjixflfv/8d7n9C5fmjq9dngXHH5NFp5y0zftVVRbPnXW3OZE2Hua2eO6sXg5vm3Pgfjni/X+dO7/57ay79fbMP+Z+GVuyeNt3nIaqj93C7q0VK1a0Sy+9tO8YAMAEqarLWmsr+s7B3RmDAdCn1trdSqNNb7PjtmcM5tA2AAAAYMrZtDRSIk0NiiQAAAAAOlEkAQAAANCJIgkAAACAThRJAAAAAHSiSAIAAACgE0USAAAAAJ0okgAAAADoRJEEAAAAQCeKJAAAAAA6USQBAAAA0IkiCQAAAIBOFEkAAAAAdKJIAgAAAKATRRIAAAAAnSiSAAAAAOhEkQQAAABAJ4okAAAAADpRJAEAAADQiSIJAAAAgE4USQAAAAB0okgCAAAAoBNFEgAAAACdKJIAAAAA6ESRBAAAAEAniiQAAAAAOlEkAQAAANCJIgkAAACAThRJAAAAAHSiSAIAAACgE0USAAAAAJ0okgAAAADoRJEEAAAAQCeKJAAAAAA6USQBAADAFNPWrcuq71+dtTfd0ncUxll70y1Z9f2r09at7ztKb8b6DgAAAAD83E8+/flc/6d/naxfn7Z+feYtPzIHvvp3M7Zkcd/RZqx1P7k11/yf1+XOK76TGh1NRkez74v+V5ac9aC+o006eyQBAADAFPGzy7+T6/74zdnw0zuy4c5VaWvW5mffuCJXv/Q1fUeb0X7wu6/Jz775rbQ1a7PhzlXZ8NM7ct1r/yx3fuvKvqNNOkUSAAAATBE3f+CitDVr7r5w3fqsuvKqrL76h/2EmuFWXXVNVl91dbLJ4Wxtzdrc9IGLekrVH0USAAAATBFrb1iZtHaP5TU2lnU3my+pD+tuvmVwONumWsvaG26c/EA9UyQBAADAFLHg/iekZs26x/K2dl3mHn5ID4mYe8ShaWvX3mN5zZ6Vhfc/sYdE/VIkAQAAwBSx5xMem9FFC5Oxn58bq+bOyV5Pe8JgOZNubPGi7PXUX0zNnTNu4VhGFy/KHk94TH/BeuKsbQAAADBFjC1ZnMPe/oasfPc/5qdfuDSjS3bLXr98bhY/5NS+o81oS5/z1Mw5bFlufv9Hsv7W27PotBXZ62lPyNjiRX1Hm3SKJAAAAJhCxnZfkn1/4/zkN87vOwpDVZXdzjwtu515Wt9ReufQNgAAAAA6USQBAAAA0IkiCQAAAIBOFEkAAAAAdKJIAgAAAKATRRIAAAAAnSiSAAAAAOhEkQQAAABAJ4okAAAAADpRJAEAAADQiSIJAAAAgE4USQAAAAB0okgCAAAAoBNFEgAAAACdKJIAAAAA6ESRBAAAAEAniiQAAAAAOlEkAQAAANCJIgkAAACAThRJAAAAAHSiSAIAAACgE0USAAAAAJ0okgAAAADoRJEEAAAAQCeKJAAAAAA6USQBAAAA0IkiCQAAAIBOFEkAAAAAdKJIAgAAAKATRRIAAAAAnSiSAAAAAOhEkQQAAABAJ4okAAAAADpRJAEAAADQiSIJAAAAgE56KZKq6lFV9e2qurKqXtJHBgCAmcYYDADYUZNeJFXVaJI3J3l0kuVJnlpVyyc7BwDATGIMBgDsDH3skfSAJFe21r7XWluT5H1Jzu0hBwDATGIMBgDssD6KpP2TXDPu9rXDZQAATBxjMABgh4318Jy1mWXtHhtVXZDkguHNn1bVtyc01cTYK8lNfYeYQbzek89rPrm83pPL6z25juw7wAywPWOw1VX1jQlNxb3ld9PU5H2ZerwnU4/3ZGq612OwPoqka5McOO72AUmu23Sj1tqFSS6crFAToaouba2t6DvHTOH1nnxe88nl9Z5cXu/JVVWX9p1hBrjXYzD/DqYe78nU5H2ZerwnU4/3ZGranjFYH4e2/WeSI6rqkKqaneQpSS7qIQcAwExiDAYA7LBJ3yOptbauqn49yb8kGU3yt621b052DgCAmcQYDADYGfo4tC2ttY8n+Xgfzz3JdulD83ZBXu/J5zWfXF7vyeX1nlxe70mwHWMw78vU4z2ZmrwvU4/3ZOrxnkxN9/p9qdbuMcciAAAAANxDH3MkAQAAALALUiTtBFX1qKr6dlVdWVUv2cz6qqo/G67/WlWd1EfO6aLD6/204ev8tar696o6vo+c08W2Xu9x292/qtZX1ZMmM9900+X1rqozq+qrVfXNqvrcZGecbjr8Ttmtqv65qv5r+Jo/u4+c00FV/W1V3bil08n7ezl1dP3dz+SpqgOr6l+r6orh76IX9J2JgaoaraqvVNVH+87CQFUtqaoPVtW3hv9mTu0700xXVb81/N31jap6b1XN7TvTTLS5sVhV7VFVn6qq7w6/7r6tx1Ek7aCqGk3y5iSPTrI8yVOravkmmz06yRHDywVJ/nJSQ04jHV/v7yd5SGvtuCS/H8fibreOr/fG7f44gwlc2U5dXu+qWpLkLUl+obV2dJInT3bO6aTjz/jzklzeWjs+yZlJXj884xX33juSPGor6/29nAK6/u5n0q1L8qLW2lFJTknyPO/LlPGCJFf0HYK7eVOST7bW7pfk+Hh/elVV+yd5fpIVrbVjMjjhw1P6TTVjvSP3HIu9JMlnWmtHJPnM8PZWKZJ23AOSXNla+15rbU2S9yU5d5Ntzk3yd23gi0mWVNW+kx10mtjm691a+/fW2o+HN7+Y5IBJzjiddPn5TpLfSPKPSW6czHDTUJfX+1eSfKi1dnWStNa85jumy2vekiyqqkqyMMktGfyHjnuptfb5DF6/LfH3cmro+rufSdRau7619uXh9dsz+I/x/v2moqoOSPLYJG/tOwsDVbU4yRlJ3pYkrbU1rbWf9BqKZHCir3lVNZZkfpLres4zI21hLHZukncOr78zyeO39TiKpB23f5Jrxt2+Nvf8o95lG7q5t6/l+Uk+MaGJprdtvt7DTxh+MclfTWKu6arLz/d9k+xeVRdX1WVV9cxJSzc9dXnN/yLJURkMeL6e5AWttQ2TE2/G8fdyavA+THFVtSzJiUm+1HMUkjcm+d0k/i5MHYcmWZnk7cNDDt9aVQv6DjWTtdZ+mORPklyd5Pokt7bW/l+/qRhnn9ba9cngQ4ske2/rDoqkHVebWbbpqfC6bEM3nV/LqnpoBkXSiyc00fTW5fV+Y5IXt9bWT3ycaa/L6z2W5OQMPv18ZJL/U1X3nehg01iX1/yRSb6aZL8kJyT5i+Gnnex8/l5ODd6HKayqFmawF/BvttZu6zvPTFZV5yS5sbV2Wd9ZuJuxJCcl+cvW2olJ7kiHQ3WYOMM5d85NckgG46kFVfX0flOxIxRJO+7aJAeOu31A7rmbXpdt6KbTa1lVx2Wwi/G5rbWbJynbdNTl9V6R5H1VdVWSJyV5S1U9flLSTT9df598srV2R2vtpiSfz+DYf7ZPl9f82RkcTthaa1dmMA/b/SYp30zj7+XU4H2YoqpqVgYl0ntaax/qOw85PckvDMdA70vysKp6d7+RyOB32LWttY177H0wg2KJ/jw8yfdbaytba2uTfCjJaT1n4udu2DiVwPDrNqfOUCTtuP9MckRVHTKcfPUpSS7aZJuLkjxzeDaaUzLYle/6yQ46TWzz9a6qgzL45fSM1tp3esg4nWzz9W6tHdJaW9ZaW5bBH+pfa619eNKTTg9dfp98JMmDq2qsquYneWBMILkjurzmVyc5K0mqap8kRyb53qSmnDn8vZwauvy7YJIN52l7W5IrWmt/2ncektbaS1trBwzHQE9J8tnWmr0setZa+1GSa6rqyOGis5Jc3mMkBmOpU6pq/vB32Vkxfp1KLkpy3vD6eRn8f2OrxiY0zgzQWltXVb+ewdmqRpP8bWvtm1X1q8P1f5Xk40kek+TKJD/L4NNttkPH1/vlSfbMYM+YJFnXWlvRV+ZdWcfXm52ky+vdWruiqj6Z5GsZzMfw1tbaZk+lzrZ1/Bn//STvqKqvZ3DIz4uHe4NxL1XVezM4891eVXVtklckmZX4ezmVbOnfRc+xGOz98owkX6+qrw6Xvay19vH+IsGU9RtJ3jMsw78Xf0961Vr7UlV9MMmXMzhhyVfizNq92MJY7LVJPlBV52dQ+m3zrNDVmkPeAQAAANg2h7YBAAAA0IkiCQAAAIBOFEkAAAAAdKJIAgAAAKATRRIAAAAAnSiSAAAAAOhEkQT0pqr+d1V9s6q+VlVfraoHDpd/sKoOHV7/eFUtqarZVfX5qhrrNzUAAMDM5T9kQC+q6tQk5yQ5qbW2uqr2SjK7qo5OMtpa+16StNYeM+4+n0nyy0ne00dmAACAmc4eSUBf9k1yU2ttdZK01m5qrV2X5GlJPrJxo6q6algyJcmHh+sBAADogSIJ6Mv/S3JgVX2nqt5SVQ8ZLj89yWVbuM83ktx/UtIBAABwD4okoBettZ8mOTnJBUlWJnl/VT0rgz2VVm7hPuuTrKmqRZOVEwAAgJ8zRxLQm2ExdHGSi6vq60nOS3JnkrlbuducJKsmPh0AAACbskcS0IuqOrKqjhi36IQkP0hyRZLDt3CfPZOsbK2tnfiEAAAAbMoeSUBfFib586pakmRdkiszOMzt0UnOTPLpzdznoUk+Pkn5AAAA2ES11vrOAHCXqpqX5F+TnD489G38ug8leWlr7du9hAMAAJjhHNoGTCmttTuTvCLJ/uOXV9XsJB9WIgEAAPTHHkkAAAAAdGKPJAAAAAA6USQBAAAA0IkiCQAAAIBOFEkAAAAAdKJIAgAAAKCT/x9fM0XgbS6EjgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Para k =  3  el promedio de la silueta es de : 0.7122079383287169\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.6609508863896014\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.7345257364682265\n",
+      "   - Para i =  3  la silueta del cluster vale :  0.7322200728725188\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Para k =  4  el promedio de la silueta es de : 0.6389948643127632\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.7345257364682265\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.24096929517637128\n",
+      "   - Para i =  3  la silueta del cluster vale :  0.654458796162702\n",
+      "   - Para i =  4  la silueta del cluster vale :  0.655666655624379\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Para k =  5  el promedio de la silueta es de : 0.42883912423017356\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.24096929517637128\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.2698039021743969\n",
+      "   - Para i =  3  la silueta del cluster vale :  0.654458796162702\n",
+      "   - Para i =  4  la silueta del cluster vale :  0.1899052168375926\n",
+      "   - Para i =  5  la silueta del cluster vale :  0.655666655624379\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Para k =  6  el promedio de la silueta es de : 0.37500287671219246\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.5664789734180768\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.2698039021743969\n",
+      "   - Para i =  3  la silueta del cluster vale :  0.655666655624379\n",
+      "   - Para i =  4  la silueta del cluster vale :  0.1899052168375926\n",
+      "   - Para i =  5  la silueta del cluster vale :  0.32882042637679976\n",
+      "   - Para i =  6  la silueta del cluster vale :  0.24096929517637128\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Para k =  7  el promedio de la silueta es de : 0.3273255327190837\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.3503771888434877\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.3028895866899326\n",
+      "   - Para i =  3  la silueta del cluster vale :  0.32882042637679976\n",
+      "   - Para i =  4  la silueta del cluster vale :  0.30004208861569454\n",
+      "   - Para i =  5  la silueta del cluster vale :  0.5664789734180768\n",
+      "   - Para i =  6  la silueta del cluster vale :  0.0\n",
+      "   - Para i =  7  la silueta del cluster vale :  0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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9n2HWCAzFk9O7u+/N6c1peWpr7e7hlgQsRIa2AQAsIlX11PQmtX1fa+2x/WVvSfKj1tqbq+r1SfZprf32MOsEABYmQRIAwCLTnzz//GlB0tVJTmqt3VBVBye5uLXW9cYPAAD3MrQNAGDxO7C1dkOS9L8fMOR6AIAFamLYBXSx3377tcMPP3zYZQAAA3LppZfe3Frbf9h1kFTVy5O8PEl2333344488sghVwQADMqOtMEWRJB0+OGHZ9WqVcMuAwAYkKr67rBrWORurKqDpw1tu2m2DVtrZyc5O0lWrlzZtMEAYPHakTaYoW0AAIvfeUlO6/98WpKPD7EWAGABEyQBACwiVfWhJF9I8uiqur6qXpbkzUlOrqpvJjm5/xgA4AFbEEPbAADoprX24llWPX1eCwEAFiU9kgAAAADoRJAEAAAAQCeCJAAAAAA6ESQBAAAA0IkgCQAAAIBOBEkAAAAAdCJIAgAAAKATQRIAAAAAnQiSAAAAAOhEkAQAAABAJ4IkAAAAADoRJAEAAADQiSAJAAAAgE4ESQAAAAB0IkgCAAAAoBNBEgAAAACdCJIAAAAA6ESQBAAAAEAngiQAAAAAOhEkAQAAANCJIAkAAACATgRJAAAAAHQiSAIAAACgE0ESAAAAAJ0IkgAAAADoRJAEAAAAQCeCJAAAAAA6ESQBAAAA0IkgCQAAAIBOBEkAAAAAdCJIAgAAAKATQRIAAAAAnQiSAAAAAOhEkAQAAABAJ4KkJFdffXWOOeaYe79WrFiRs846a9hlAQAAAIyUiWEXMAoe/ehH5/LLL0+SbNq0KYceemie+9znDrcoAAAAgBGjR9JWLrroojz84Q/PQx/60GGXAgAAADBSBElb+fCHP5wXv/jFwy4DAAAAYOQIkqZZv359zjvvvLzwhS8cdikAAAAAI0eQNM2nPvWpHHvssTnwwAOHXQoAAADAyBEkTfOhD33IsDYAAACAWQiS+u66665ceOGFed7znjfsUgAAAABG0sSwCxgVy5cvzy233DLsMgAAAABGlh5JAAAAAHQiSAIAAACgE0ESAAAAAJ0IkgAAAADoRJAEAAAAQCeCJAAAAAA6ESQBAAAA0IkgCQAAAIBOBEkAAAAAdCJIAgAAAKATQRIAAAAAnQiSAAAAAOhEkAQAAABAJ4IkAAAAADoRJAEAAADQiSAJAAAAgE4ESQAAAAB0IkgCAAAAoBNBEgAAAACdCJIAAAAA6ESQBAAAAEAngiQAAAAAOhEkAQAAANCJIAkAAACATgYaJFXVa6vq61V1RVV9qKqWVdW+VXVhVX2z/32fQdYAAAAAwNwYWJBUVYcmeVWSla21xyYZT/KiJK9PclFr7ZFJLuo/BgAAAGDEDXpo20SS3apqIsnyJD9I8pwk7+2vf2+SUwdcAwAAAABzYGBBUmvt+0nemuR7SW5Icntr7dNJDmyt3dDf5oYkBwyqBgAAAADmziCHtu2TXu+jhyU5JMnuVfXSB/D8l1fVqqpatXr16kGVCQAAAEBHgxza9lNJvtNaW91a25Dko0l+PMmNVXVwkvS/3zTTk1trZ7fWVrbWVu6///4DLBMAAACALgYZJH0vyQlVtbyqKsnTk1yV5Lwkp/W3OS3JxwdYAwAAAABzZGJQO26tfamqzk1yWZKNSb6S5OwkeyT5SFW9LL2w6YWDqgEAAACAuTOwIClJWmtvSPKGrRbfk17vJAAAAAAWkEEObQMAAABgEREkAQAAANCJIAkAYBdRVa+tqq9X1RVV9aGqWjbsmgDm1pok30hydZJbk7ThlgOLkCAJAGAXUFWHJnlVkpWttccmGU/youFWBTCXvp3ePZ6+n+QHSf49vVAJmEuCJACAXcdEkt2qaiLJ8vT+0gJYBO5Ocl2SqWnLppL8ML1eSsBcESQBAOwCWmvfT/LWJN9LckOS21trnx5uVQBz5ZbMPIxtKsnN81wLLG6CJACAXUBV7ZPkOUkeluSQJLtX1Utn2O7lVbWqqlatXr16vssE2EFjSWqG5RV/9sLc8i8KAGDX8FNJvtNaW91a25Dko0l+fOuNWmtnt9ZWttZW7r///vNeJMCO2W+W5ZXkwPksBBY9QVLfbbfdlhe84AU58sgjc9RRR+ULX/jCsEsCAJhL30tyQlUtr6pK8vQkVw25JoA5Mpnk6PT+xB3vf40leVSS3YZYFyw+E8MuYFS8+tWvzrOe9ayce+65Wb9+fe66665hlwQAMGdaa1+qqnOTXJZkY3q3Njp7uFUBzKX9k5yY++ZLelCSJUOtCBYjQVKSNWvW5POf/3ze8573JEkmJyczOTk53KIAAOZYa+0NSd4w7DoABmcihrLBYBnaluTb3/529t9//5x++ul54hOfmF/7tV/L2rVrh10WAAAAwEgRJCXZuHFjLrvssvzGb/xGvvKVr2T33XfPm9/85mGXBQAAADBSBElJDjvssBx22GE5/vjjkyQveMELctlllw25KgAAAIDRIkhKctBBB+XBD35wrr766iTJRRddlKOPPnrIVQEAAACMFpNt9/3Zn/1ZXvKSl2T9+vU54ogjcs455wy7JAAAAICRIkjqO+aYY7Jq1aphlwEAAAAwsgxtAwAAAKATQRIAAAAAnQiSAAAAAOhEkAQAAABAJ4IkAAAAADoRJAEAAADQiSAJAAAAgE4ESQAAAAB0IkgCAAAAoBNBEgAAAACdCJIAAAAA6ESQBAAAAEAngiQAAAAAOhEkAQAAANCJIAkAAIBFobW2zcfAzhMkAQAAsOC948Jv5E3nX3lveNRay5vOvzLvuPAbQ64MFhdBEgAAAAtaay1r1m3IOZdce2+Y9Kbzr8w5l1ybNes26JkEc2hi2AUAAADAzqiqnHHK0UmScy65Nudccm2S5PQTD88ZpxydqhpidbC46JEEAADAgjc9TNpMiARzT5AEAADAgrd5ONt00+dMAuaGIAkAAIAFbfqcSKefeHi+c+azc/qJh28xZxIwN8yRBAAAwIJWVVmxbMkWcyJtHua2YtkSw9tgDgmSAAAAWPBee/Kj0lq7NzTaHCYJkWBuGdoGAADAorB1aCREgrknSAIAAACgE0ESAAAAAJ0IkgAAAADoRJAEAAAAQCeCJAAAABaRu5OsHXYRsGhNDLsAAAAA2Hm3Jvlakk39x2NJjk6y/9AqgsVIjyQAAAAWuI1JLs99IVKSTCW5Ism6YRQEi5YgCQAAgAXu2m2s+9Z8FQG7BEESAAAAC9xd21h397xVAbsCQRIAAAAL3D7bWLf3fBUBuwRBEgAAAAvcoUnGZ1heSQ6f31JgkRMkAQAAsMCNJTkhyV7Tlu3RX+Zm5TCX/IsCAABgEZhMcuywi4BFT48kAAAAADoRJAEAAADQiSAJAAAAgE4ESQAAAAB0IkgCAAAAoBNBEgAAAACdCJIAAAAA6GRi2AWMisMPPzx77rlnxsfHMzExkVWrVg27JAAAAICRIkia5nOf+1z222+/YZcBAAAAMJIMbQMAAACgE0FSX1XlGc94Ro477ricffbZwy4HAAAAYOQY2tZ3ySWX5JBDDslNN92Uk08+OUceeWSe+tSnDrssAAAAgJEhSOo75JBDkiQHHHBAnvvc5+bLX/7yHARJ65O0na4N2JUsic6iAADAqBIkJVm7dm2mpqay5557Zu3atfn0pz+dM844Yw72/M0km+ZgP8Cu4xFJlg+7CAAAgBkJkpLceOONee5zn5sk2bhxY37xF38xz3rWs4ZcFQAAAMBoESQlOeKII/LVr3512GUAAAAAjDQTcQAAAADQiR5JAEM1lmRp/2tZksnhlgMAALANgiSAeVG5Lyxali2DoxpiXQAAAN0JkgDm1PTAaHpwJDACAAAWPkESwA7ZHBht3ctoaQRGAADAYiVIGqilSTYNuwhgp83Uy2gy7lcAAADsagRJA/WIYRcAAAAAMGd8nA4AAABAJ4IkAAAAADoRJAEAAADQiSAJAAAAgE4GGiRV1d5VdW5V/UdVXVVVT66qfavqwqr6Zv/7PoOsAQAAAIC5MegeSe9MckFr7cgkT0hyVZLXJ7motfbIJBf1HwMAAAAw4gYWJFXViiRPTfLuJGmtrW+t3ZbkOUne29/svUlOHVQNAAAAAMydQfZIOiLJ6iTnVNVXqupdVbV7kgNbazckSf/7AQOsAQAAAIA5MsggaSLJsUn+qrX2xCRr8wCGsVXVy6tqVVWtWr169aBqBAAAAKCjQQZJ1ye5vrX2pf7jc9MLlm6sqoOTpP/9ppme3Fo7u7W2srW2cv/99x9gmQAAAAB0MbAgqbX2wyTXVdWj+4uenuTKJOclOa2/7LQkHx9UDQAAAADMnYkB7/+3knywqiaTfDvJ6emFVx+pqpcl+V6SFw64BgAAAADmwECDpNba5UlWzrDq6YM8LgAA91dVeyd5V5LHJmlJfrW19oWhFkUnd199TW79xEWZuuvurPhPT86eP74yNT4+7LJYJNra1WnfuTi565bUAUcnDz4hNT4598dpLVU16+M5OcbN30j73iXJ1FTqwccnBzxmzo8Bu7pB90gCAGB0vDPJBa21F/R7jC8fdkFs381//4+56ez3p23YmExNZc3nv5jdj3lMHnLm7wqT2Gntxq+nffFPk6lNSduUdsPlyTc+lTztD1JL5u6/iHdc+I2sWbchZ5xydKoqrbW86fwrs2LZkrz25EfNyTGmrvj75FufSTatT5K07/9bcuiPJcf9qjAJ5tAgJ9sGAGBEVNWKJE9N8u4kaa2tb63dNtSi2K6Nt92em/76fWn3rE+mppIkbd26rP3q13PHv/7bkKtjoWttKm3V3/SCl7apt3DTPcldN6d98//N4XFa1qzbkHMuuTZvOv/Ke0Okcy65NmvWbUhrbeePcccPk2suvDdEStJ7Ld//cvKjb+30/oH76JEEALBrOCLJ6iTnVNUTklya5NWttbXDLYttWXvp11ITE2nrN2yxvN29Lmsu/kJW/MQJQ6qMReHOG5ON6+6/fGpjcv2Xk6OfOyeHqaqcccrRSZJzLrk251xybZLk9BMPv7eH0k678WvpjdjdyqYNaTd8NfWgR+z8MYAkeiQBAOwqJpIcm+SvWmtPTLI2yeu33qiqXl5Vq6pq1erVq+e7RrYyttvSWVaMZWz33ea3GBaf8cmkTc28bmKW370dND1M2mzOQqSk91pqhqGeY2Nz/lpgVydIAgDYNVyf5PrW2pf6j89NL1jaQmvt7Nbaytbayv33339eC+T+dl95TDJ2/z+0a8mS7PMzPzX/BbGo1PIHJSsOSbLV79j4ZOqIub0/0ubhbNNtHuY2Jw49LjP2SMpY6sF67sFcEiQBAOwCWms/THJdVT26v+jpSa7cxlMYAWOTS/LQ/3VGxvZYnrHlu2Vs+W6pySU54Ndfkt0ebagOO6+Of2WyfN9kYlnva2xJctiTkoeeOGfHmD4n0uknHp7vnPnsnH7i4VvMmbSzanKP1PG/mYwv3fK1HPsrqd33m4NXAWxmjiQAgF3HbyX5YP+Obd9OcvqQ66GD5Y87Mo/+2Huz9t++kql192T34x6fib33GnZZLBK1+37JM9+S3Hx1cvdtyb4PT+1xwNweoyorli3ZYk6kzcPcVixbMmfD2+qgJyQ/887kxit6Q/YOfMyc3nkO6Kk560o4QCtXrmyrVq0adhkAwIBU1aWttZXDroMtaYMBc6m1tkVotPVjYP7tSBvM0DYAAAAGbuvQSIgEC5MgCQAAAIBOBEkAAAAAdCJIAgAAAKATQRIAAAAAnQiSAAAAAOhkm0FSVY1X1QfmqxgAAAAARtc2g6TW2qYk+1fV5DzVAwAAAMCImuiwzbVJLqmq85Ks3bywtfb2QRUFAAAAwOjpEiT9oP81lmTPwZYDAAAAwKjabpDUWntjklTV7q21tdvbHgAAAIDFabt3bauqJ1fVlUmu6j9+QlX95cArAwAAAGCkbDdISnJWkmcmuSVJWmtfTfLUAdYEAAAAwAjqEiSltXbdVos2DaAWAAAAAEZYl8m2r6uqH0/SqmoyyavSH+YGAAAAwK6jS4+k/5LkFUkOTXJ9kmOS/OYAawIAAABgBHXpkfTo1tpLpi+oqhOTXDKYkgAAAAAYRV16JP1Zx2UAAAAALGKz9kiqqicn+fEk+1fV66atWpFkfNCFAQAAADBatjW0bTLJHv1t9py2fE2SFwyyKAAAAABGz6xBUmvtn5L8U1W9p7X23SSpqrEke7TW1sxXgQAAAACMhi5zJJ1ZVSuqavckVya5uqr++4DrAgAAAGDEdAmSju73QDo1ySeTPCTJLw2yKAAAAABGT5cgaUlVLUkvSPp4a21DkjbQqgAAAAAYOV2CpL9Ocm2S3ZN8vqoemt6E24vKpk2b8sQnPjGnnHLKsEsBAAAAGEnbDZJaa3/aWju0tfbs1vPdJE+bh9rm1Tvf+c4cddRRwy4DAAAAYGTNete2zarqjFlWvWmOaxma66+/Pp/4xCfye7/3e3n7298+7HIAAAAARtJ2g6Qka6f9vCzJKUmuGkw5w/Ga17wmb3nLW3LHHXfM6X7bjz6XtI1zuk/YYTWeLD04WXpoamLFsKsBAABgAdpukNRae9v0x1X11iTnDayieXb++efngAMOyHHHHZeLL754bne+6c6kbZjbfcIDUePJ5EHJ0kOTpQelqkt2DAAAADPbkb8qlyc5Yq4LGZZLLrkk5513Xj75yU9m3bp1WbNmTV760pfmAx/4wLBLgx00lkwemCw7LJk8KDW2ZNgFAQAAsEh0mSPpa0la/+F4kv2ziOZHOvPMM3PmmWcmSS6++OK89a1vFSKxAI0lk/snSw9Llh6cGpscdkEAAAAsQl16JJ0y7eeNSW5szcQ/MBLGlia7H92b90h4BAAAwIDNGiRV1b79H7eegXpFVaW19qPBlTUcJ510Uk466aRhlwHdjS1P7fawYVcBAADALmJbPZIuTW9IW82wrmURzZMEAAAAwPbNGiS11nRzAABgQdt4622557vfz7JHPSzjy5dvd/vWWqpq1sfsvKmpqbTclaqJjNUy5xhggdnW0LZnJtmztXbuVst/Mcnq1tqFgy4OAAB2xNS6dfnWf/4fWf+d7927bI+fOD4P/ePfmfU577jwG1mzbkPOOOXo9KdyyJvOvzIrli3Ja09+1HyUvei99dOX5ua1382rnvGjVLUsqYPy5xc+JHvttptzDLBAjG1j3RuT/NMMyz+bRXTXNhgNY72Js8f3SCb2SZYckCw9NFl2eLLbI5Ldj0r2eHyy53HJXicke/9Ess9PJnsdP+zCAWAkffs3X79FiJQkd/7zl3LDO9814/attaxZtyHnXHJt3nT+lfeGSOdccm3WrNuQ1tqMz6O7DZvWZPXab+XDX1qasz69R1qbylsuuCvv+dfrs+Zu5xhgodjWHEnLW2urt17YWvthVe0+wJpggamkJpJakowt6X3f4ueJLZfPtF3GdOkGgDmy8c67cs8118647tbzLsjBr/61+y2vqpxxytFJknMuuTbnXNJ7/uknHn5vDyV2zt2brsqrn3F7WqbykS/tmY98ac8kyS8cvzavf/a+zjHAArGtIGlZVU201jZOX1hVS5LsNtiyYB7V+Czhz+R9AdG2QqJaouEDACNk4y23zLqubdg467rNYdLmECmJEGkObZy6PVVTec0zbr83REqSVz/jzkxl7RArA+CB2FaQ9NEkf1NVr2ytrU2Sfk+kP+2vgxEwdv/QZ8bAZ4ZwqL+ualsjPAGAhWby0IOTqmSGoVJjK/ac4Rk9m4ezTfem868UJs2RyfFDsm7TdTnr01sObjjr03vkj39uvyFVBcADta0g6feT/FGS71bVd/vLHpLk3Un+YNCFLQrLHpq02T/1YhtqbPtDxMaWJBnXsAMAtjA2MZG9n/PM3PaxC+637uBX3X9YW5It5kTaPJxt8+NEz6S5sNv4I/M/P/2NfORLS/Pzx9+R1zzj9pz16X3ykS/tnt0nvp8zTtnLOQZYAGYNkvpD2l5fVW9M8oj+4mtaa3fPS2WLQO35+GGXAACwSzr0df8lk/s/KKs/8NG0desyvveKHPzqX89eP/mUGbevqqxYtmSLOZE2z5m0Yplh7HNhfGxpDtzj0XnJCT/Ibz1jY8Zq7/zBzxyV3cfLOQZYQGoh3B1h5cqVbdWqVcMuAwAYkKq6tLW2cth1sKVdsQ3WWtsi0Nj6MTvPOQYYHTvSBjM5DAAA9G0daAg45p5zDLCwCZIAAAAA6GTWOZKq6thtPbG1dtnclwMAAADAqNrWXdveto11LclPznEtAAAAAIywbd217WnzWQgAAAAAo227cyRV1fKq+v2qOrv/+JFVdcrgSwMAAABglHSZbPucJOuT/Hj/8fVJ/mhgFQEAAAAwkroESQ9vrb0lyYYkaa3dncQ9OgEAAAB2MV2CpPVVtVt6E2ynqh6e5J6BVgUAAADAyNnWXds2e0OSC5I8uKo+mOTEJL8yyKIAAAAAGD3bDZJaaxdW1WVJTkhvSNurW2s3D7wyAAAAAEbKrEFSVR271aIb+t8fUlUPaa1dNriyAAAAABg12+qR9Lb+92VJVib5ano9kh6f5EtJnjLY0gAAAAAYJbNOtt1ae1pr7WlJvpvk2NbaytbacUmemOSa+SoQAAAAgNHQ5a5tR7bWvrb5QWvtiiTHDKwiAAAAAEZSl7u2XVVV70rygSQtyUuTXDXQqgAAAAAYOV2CpNOT/EaSV/cffz7JXw2sIgAAAABG0naDpNbauiTv6H8BAAAAsIvqMkcSAAAAAAiSAAAAAOhGkAQAAABAJ9sMkqrqtKq6rKrW9r9WVdUvz1dxAAAAAIyOWSfb7gdGr0nyuiSXJakkxyb5/6oqrbX3zUuFAAAAAIyEbfVI+s0kz22tfa61dntr7bbW2meTPL+/DgAAAIBdyLaCpBWttWu3XthftmJQBQEAAAAwmrYVJN29g+sAAAAAWIRmnSMpyVFV9e8zLK8kRwyoHgAAAABG1DaDpHmrAgAAAICRN2uQ1Fr77nwWAgAAAMBomzVIqqo7krSZViVprTUTbgMAAADsQrbVI2nPuThAVY0nWZXk+621U6pq3yR/l+TwJNcm+fnW2q1zcSwAAAAABmdbd22bK69OctW0x69PclFr7ZFJLuo/BgAAAGDEDTRIqqrDkvxMkndNW/ycJO/t//zeJKcOsgYAAAAA5sageySdleR/JJmatuzA1toNSdL/fsBMT6yql1fVqqpatXr16gGXCQAAAMD2DCxIqqpTktzUWrt0R57fWju7tbaytbZy//33n+PqAAAAAHigZp1sew6cmOTnqurZSZYlWVFVH0hyY1Ud3Fq7oaoOTnLTAGsAAGCarW+EMux6Bmn9jatz68cuyD3fvS7LH3dU9jnl5IzvucewywKABW1gPZJaa7/TWjustXZ4khcl+Wxr7aVJzktyWn+z05J8fFA1AABwP1vfCGVRuuvrV+dbv/xbufkjH88d//Ll3PS3H8o3f+mV2bD6lmGXBgAL2nzctW1rb05yclV9M8nJ/ccAAAzYLDdCWZS+f+afZurudcmGjUmSds/6bLp9TW78mw8MuTIAWNgGObTtXq21i5Nc3P/5liRPn4/jAgCwhbPSuxHKnkOuY6A2rrkj639w4/1XbJrKnf+6av4LAoBFZBg9kkbOunXr8qQnPSlPeMIT8pjHPCZveMMbhl0SAMCc6nojlMVw59yxJUtmXVfLls5jJQCw+AiSkixdujSf/exn89WvfjWXX355Lrjggnzxi18cdlkAAHNp841Qrk3y4SQ/2b8RyhYWw51zx3Zblj2OPzaZ2LLzfS2dzL6n/vSQqgKAxUGQlKSqsscevTt4bNiwIRs2bEhVDbkqAIC5s40boSxKh77+lVl2xENSy5ZlbPluqcnJ7PnkldnvRacOuzQAWNDmZY6khWDTpk057rjjcs011+QVr3hFjj/++GGXBADADprYa0WO+Ju3Zd3V38r6G27Mskc8LEsffMiwywKABU+PpL7x8fFcfvnluf766/PlL385V1xxxbBLAgAYiNbaxa21U4Zdx6BVVXY78hHZ62knCpEAYI4Ikray995756STTsoFF1ww7FIAAAAARoogKcnq1atz2223JUnuvvvufOYzn8mRRx453KIAAAAARow5kpLccMMNOe2007Jp06ZMTU3l53/+53PKKTvf27ttuDtJ2/kCYdSNT6bG/HcCAACw2PnLL8njH//4fOUrX5nz/bavvCfZeM+c7xfm3MTSZHxp//tkaovH079PJhPLej+PT/aWTyxNlc6NAAAAuwJBEix0VfcLfaofCPUeL7s3ILp/MLQ0GV8iCAIAAKATQRIMW41P6+nTC39qYum08GfZFr2FMjE9CFqWjE2kqob9KgAAANgFCJJgZ41N3K+nT20xFGw7w8RqXBAEAADAgiBIGqA6/KnJ1NSwy2AuVN3XO2hicsvQaGx82NUBAADAvBAkDVAd8JhhlwAAAAAwZ8ywCwAAAEAngiQAAAAAOhEkAQAAANCJIAkAAACATgRJAAAAAHQiSAIAAACgE0ESAAAAAJ0IkgAAAADoRJAEAAAAQCeCJAAAAAA6ESQBAAAA0IkgCQAAAIBOBEkAAAAAdCJIAgAAAKATQRIAAAAAnQiSAAAAAOhEkAQAAABAJ4IkAAAAADoRJAEAAADQiSAJAAAAgE4ESQAAAAB0IkgCAAAAoBNBEgAAAACdCJIAAAAA6ESQBAAAAEAngiQAAABgYNo9d2fqxuvS7rl7cMdobZuP5+w4d9yWqZuuT5vaNJD9LwQTwy4AAAAAWHza1FTWn/c32fjP5yVjY8nUVCaeemomf/ZlqbG569fyjgu/kTXrNuSMU45OVaW1ljedf2VWLFuS1578qDk5Rlt7e9a9508y9a1/T8bGkyVLM/mi12bJE54yJ/tfSPRIAgAAAObchs98OBv/5bxkwz3JPXcnG+7Jxn/+WDZ89u/n7BittaxZtyHnXHJt3nT+lfeGSOdccm3WrNswZz2T1v3172fqmq8mGzck69cla2/P+vedmU3XfXNO9r+Q6JEEAAAAzLkNnz03WX/PlgvX35MNn/1IJn/qF+bkGFWVM045OklyziXX5pxLrk2SnH7i4ff2UNpZUzd+L1Pf/3ayaeOWKzauz4bPnZvxX/6dnT7GQqJHEgAAADCnWmvJXXfMvHLtmjk91vQwabO5CpGSpN12czI+Qz+c1tJu+eGcHGMhESQBAAAAc6qqUgc/dOZ1Bz9sTo+1eTjbdJuHuc2FscMe3hvStrWJyYw/+olzcoyFRJAEAAAAzLmlz39FsmTptCWVLFnaWz5Hps+JdPqJh+c7Zz47p594+BZzJu2s2n2vTJz0vGRy2X0Lx8eT3XbPkqc+d6f3v9CYIwkAAACYc+OPemKWvept2fCp92XqhmszdsjDsuRZv5Txhx45Z8eoqqxYtmSLOZE2D3NbsWzJnA1vm/zZl2X8kIdlw+fOTVu7JuNHH58lz3xJao+95mT/C0nNVVevQVq5cmVbtWrVsMsAAAakqi5tra0cdh1sSRsMgIWitbZFaLT1Y2a2I20wQ9sAAACABW3r0EiINDiCJAAAAAA6ESQBAAAA0IkgCQAAAIBOBEkAAAAAdCJIAgAAAKATQVKS6667Lk972tNy1FFH5TGPeUze+c53DrskAAAAgJEzMewCRsHExETe9ra35dhjj80dd9yR4447LieffHKOPvroYZcGAAAAMDL0SEpy8MEH59hjj02S7LnnnjnqqKPy/e9/f8hVAQAAAIwWQdJWrr322nzlK1/J8ccfP+xSAAAAAEaKIGmaO++8M89//vNz1llnZcWKFcMuBwAAAGCkCJL6NmzYkOc///l5yUtekuc973nDLgcAAABg5AiSkrTW8rKXvSxHHXVUXve61w27HAAAAICR5K5tSS655JK8//3vz+Me97gcc8wxSZI/+ZM/ybOf/eyd2u+m669JpqbmoEJ2RWP77J/ac59hlwEAAAD3EiQlecpTnpLW2pzvd8NnPpzcs27O98viNnbYIzL++BOTPfYedikAAACwBUESjILx8Yw//PEZf/yJGXvQQcOuBgAAAGYkSIJhWrZbxo96UiYec3xqd3cKBAAAYLQJkmAIaq99M/64EzP+qCemlkwOuxwAAADoRJA0QJOn/GoygLmXWOCqUvselBpz00QAAAAWFkHSAI3td8iwSwAAAACYM7pEAAAAANCJIAkAAACATgRJAAAAAHQiSAIAAACgE0ESAAAAAJ0IkgAAAADoRJAEAAAAQCeCJAAAAAA6ESQBAAAA0IkgCQAAAIBOBEkAAAAAdCJIAgAAAKCTgQVJVfXgqvpcVV1VVV+vqlf3l+9bVRdW1Tf73/cZVA0AAAAAzJ1B9kjamOS/ttaOSnJCkldU1dFJXp/kotbaI5Nc1H8MAAAAwIgbWJDUWruhtXZZ/+c7klyV5NAkz0ny3v5m701y6qBqAAAAAGDuTMzHQarq8CRPTPKlJAe21m5IemFTVR0wHzUAAOzKqurBSd6X5KAkU0nObq29c7hVAbDYtXtuTH70mWTjrcnEvsm+T08tPXDYZe2Qds8Pkzu+kmy6K1n+iGT3o1NjS4Zd1rwbeJBUVXsk+Yckr2mtramqrs97eZKXJ8lDHvKQwRUIALBr2DztwGVVtWeSS6vqwtbalcMuDIDFqa39RnLzefctWP+D5IfvT9vv1NTujxheYTug3fG15NaLkrYpSUvuuT654/K0g35xlwuTBnrXtqpakl6I9MHW2kf7i2+sqoP76w9OctNMz22tnd1aW9laW7n//vsPskwAgEVvG9MOAMBg3HLBLMs/Nb917KQ2tb4fIm1M0voLN/R6Wd35taHWNgyDvGtbJXl3kqtaa2+ftuq8JKf1fz4tyccHVQMAAPe31bQDADDn2tRU0tbPsvKe+S1mZ63/YWaMT9rG5K5vzHs5wzbIoW0nJvmlJF+rqsv7y343yZuTfKSqXpbke0leOMAaAACYZutpB2ZYb3oBAHZajY1t7ruz8NVkMturGVs2r6WMgoEFSa21f0ky24RITx/UcQEAmNks0w5sobV2dpKzk2TlypWL5m8AAIZgyX7JhptnWL7Apq+ZPDAZX55svH3L5TWR7PnE4dQ0RAOdIwkAgNGwjWkHAGAwDviFpLbqsVO7JQf9wnDq2UFVlRzwgmR8RVJL+j2UxpO9npza7aHDLm/eDfyubQAAjIQZpx1orX1yeCUBsJjVxG7JQ16Zdte3knu+nyx7cGq3hw27rB1SS/ZJO/TXk3t+kEzdnSw9JDW+fNhlDYUgCQBgF7CdaQcAYGBq+cOT5Q8fdhk7raqSZW54amgbAAAAAJ0IkgAAAADoRJAEAAAALGittW0+Zu4IkgAAAIAF6x0XfiNvOv/Ke8Oj1lredP6VeceF3xhyZYuTIAkAAABYkFprWbNuQ8655Np7w6Q3nX9lzrnk2qxZt0HPpAFw1zYAAABgQaqqnHHK0UmScy65Nudccm2S5PQTD88Zpxzdu9Mac0qPJAAAAGDBmh4mbSZEGhxBEgAAALBgbR7ONt30OZOYW4IkAAAAYEGaPifS6Scenu+c+eycfuLhW8yZxNwyRxIAAACwIFVVVixbssWcSJuHua1YtsTwtgEQJAEAAAAL1mtPflRaa/eGRpvDJCHSYBjaBgAAACxoW4dGQqTBESQBAAAA0IkgCQAAAIBOBEkAAAAAdCJIAgAAAKATQRIAAAAAnQiSAAAAAOhEkAQAAABAJ4IkAAAAADoRJCX51V/91RxwwAF57GMfO+xSAAAAAEaWICnJr/zKr+SCCy4YdhkAAAAAI02QlOSpT31q9t1332GXAQAAADDSJoZdwGK25vNfTNuwcdhlAAvckgP2y/LHHTnsMgAAAARJg3T7Z/8lU+vWDbsMYIFb/tijBEkAAMBIMLQNAAAAgE4ESQAAAAB0IkhK8uIXvzhPfvKTc/XVV+ewww7Lu9/97mGXBAAAADByzJGU5EMf+tCwSwAWuKqx1OSS1ORkxiYnUpOTvcdLlmRsyZJ7H48tnUwtmehv11t/77r+87dY398GAABgFAiSgF1GTUxkbHPAM7lk2s+T/cBnIrV08r51S5Zs9fP0gGf6+iXJ+HiqatgvEQAAYKAEScDIqPHxXjizZFrvnMmJ+wc+m3vqTE72wp/JyS2DoSVLZnws6AEAANg5giSgs0ptGcxsDnWWTm4V/mwezjU98Lnv53uHcW3d42difNgvEQAAgG0QJMGIqVRvmNT4WDI2lhof738f6y8bT42N3bss42OpsfH+9/7j8WnbjI3db3/Tt8v4+CwB0NaBT3/uHr16AAAAdlmCJBasLcKS8X5YMjY9fKneNuPjSVU/NJkWzGwOWMbqfs+9L3SZIcyZfozx6fsZmxb4jM8a4my93/vtT1ADAADAiBIkLXJVY9sIO/q9W8bvH75sDkG2FZZsfrxDYcnY/eu6d/sO+0uVwAUAAFi0Nk1typ0b78jk2GR2m1ie1pq/gUZA23hnMrUuWbJv7+/tXZAgKUlrLdm0KW3TVDI1lbZpU9pU/+eNm3rfp+5bl029x21Tf92mqd6yNtXbz1Tv8Z4nHJep9eu3CE1m7Q2zrfBlYlqPmll639y7rKb1rtG7BQAAYMH5w098Od++9Qc59cn3pNVU9p18UD532UHZZ7dlee3Jjxp2ebuktumuZPV5yT03JDWWZCxt35NTexw57NLm3YIIkjbdfkduPf/CtE3TwpupzaFNuy+82SrouTcU2rQpmWr3f+7m4GiqDaTuw37/NRnbbdlA9g0AAMDic8vdq/OtW7+ff75iMlOZys+dcHf+9uI7889XXJfTTzxcz6RhuemjyfqbkkwlbVNv2Y8uSFuyd2rpQUMtbb4tiCBpfK89s88pJw+7DAAAABiob97+H/nZE+5KS8u/XLE0/3LF0iTJTzx2fV77jMOESEPQ1t+cbLg5ydRWKzYld1yaLP2ZodQ1LLvmgD4AAAAYQXdtvCtVyc+dcPcWy5/75A1ZN7VuSFXt4jatzczxSUs23j7f1QydIAkAAABGxAHLD0q1sZz3xd22WP5/v7Akey3Za0hV7eImD7hvONsWxpNlD5v3coZNkAQAAAAj4hErHpXzv7Q8/3LF0jzlsffkLb92W37isevzz1dM5s2f+lbvZlHMqxrfLVnxY0ktmbZ0LBlflux5zLDKGpoFMUcSAAAA7AqWTeyWx+z3yOxx7I159pM2ZdnEfvmT5xyZv93rzqxYtsQcScOy94nJ5P7JmkuTqbuT3Y5I9npSL2TaxQiSAAAAYIT892cendaO2iI0OuMUd2sbpqpKdn9072sXZ2gbAAAAjJitQyMhEqNCkAQAAABAJ4IkAAAAADoRJAEAAADQiSAJAAAAgE4ESQAAAAB0IkgCAAAAoBNBEgAAAACdCJIAAAAA6ESQBAAAAEAngiQAAAAAOhEkAQAAANCJIAkAAACATgRJAAAAAHQiSAIAAACgE0ESAAAAAJ0IkgAAAADoRJAEAAAAQCeCJAAAAAA6ESQBAAAA0IkgCQAAAIBOBEkAAAAAdCJIAgAAAKATQRIAAAAAnQiSAAAAAOhEkAQAAABAJ4IkAAAAADoRJAEAAADQiSAJAAAAgE4ESQAAAAB0IkgCAAAAoBNBEgAAAACdCJIAAAAA6ESQBAAAAEAngiQAAAAAOhlKkFRVz6qqq6vqmqp6/TBqAADY1WiDAQA7a96DpKoaT/IXSX46ydFJXlxVR893HQAAuxJtMABgLgyjR9KTklzTWvt2a219kg8nec4Q6gAA2JVogwEAO20YQdKhSa6b9vj6/jIAAAZHGwwA2GkTQzhmzbCs3W+jqpcneXn/4Z1VdfVAqxqM/ZLcPOwidiHO9/xzzueX8z2/nO/59ehhF7AL2JE22D1VdcVAq+KB8n/TaHJdRo9rMnpck9H0gNtgwwiSrk/y4GmPD0vyg603aq2dneTs+SpqEKpqVWtt5bDr2FU43/PPOZ9fzvf8cr7nV1WtGnYNu4AH3Abz72D0uCajyXUZPa7J6HFNRtOOtMGGMbTt35I8sqoeVlWTSV6U5Lwh1AEAsCvRBgMAdtq890hqrW2sqlcm+X9JxpP8bWvt6/NdBwDArkQbDACYC8MY2pbW2ieTfHIYx55nC3po3gLkfM8/53x+Od/zy/meX873PNiBNpjrMnpck9Hkuowe12T0uCaj6QFfl2rtfnMsAgAAAMD9DGOOJAAAAAAWIEHSHKiqZ1XV1VV1TVW9fob1VVV/2l//71V17DDqXCw6nO+X9M/zv1fVv1bVE4ZR52KxvfM9bbsfq6pNVfWC+axvselyvqvqpKq6vKq+XlX/NN81LjYd/k/Zq6r+saq+2j/npw+jzsWgqv62qm6a7Xby3i9HR9f/+5k/VfXgqvpcVV3V/7/o1cOuiZ6qGq+qr1TV+cOuhZ6q2ruqzq2q/+j/m3nysGva1VXVa/v/d11RVR+qqmXDrmlXNFNbrKr2raoLq+qb/e/7bG8/gqSdVFXjSf4iyU8nOTrJi6vq6K02++kkj+x/vTzJX81rkYtIx/P9nST/qbX2+CT/M8bi7rCO53vzdv8rvQlc2UFdzndV7Z3kL5P8XGvtMUleON91LiYdf8dfkeTK1toTkpyU5G39O17xwL0nybO2sd775Qjo+n8/825jkv/aWjsqyQlJXuG6jIxXJ7lq2EWwhXcmuaC1dmSSJ8T1GaqqOjTJq5KsbK09Nr0bPrxouFXtst6T+7fFXp/kotbaI5Nc1H+8TYKknfekJNe01r7dWluf5MNJnrPVNs9J8r7W88Uke1fVwfNd6CKx3fPdWvvX1tqt/YdfTHLYPNe4mHT5/U6S30ryD0lums/iFqEu5/sXk3y0tfa9JGmtOec7p8s5b0n2rKpKskeSH6X3Bx0PUGvt8+mdv9l4vxwNXf/vZx611m5orV3W//mO9P4wPnS4VVFVhyX5mSTvGnYt9FTViiRPTfLuJGmtrW+t3TbUokh6N/raraomkixP8oMh17NLmqUt9pwk7+3//N4kp25vP4KknXdokuumPb4+939T77IN3TzQc/myJJ8aaEWL23bPd/8Thucm+d/zWNdi1eX3+1FJ9qmqi6vq0qr65XmrbnHqcs7/PMlR6TV4vpbk1a21qfkpb5fj/XI0uA4jrqoOT/LEJF8acikkZyX5H0m8L4yOI5KsTnJOf8jhu6pq92EXtStrrX0/yVuTfC/JDUlub619erhVMc2BrbUbkt6HFkkO2N4TBEk7r2ZYtvWt8LpsQzedz2VVPS29IOm3B1rR4tblfJ+V5Ldba5sGX86i1+V8TyQ5Lr1PP5+Z5A+q6lGDLmwR63LOn5nk8iSHJDkmyZ/3P+1k7nm/HA2uwwirqj3S6wX8mtbammHXsyurqlOS3NRau3TYtbCFiSTHJvmr1toTk6xNh6E6DE5/zp3nJHlYeu2p3avqpcOtip0hSNp51yd58LTHh+X+3fS6bEM3nc5lVT0+vS7Gz2mt3TJPtS1GXc73yiQfrqprk7wgyV9W1anzUt3i0/X/kwtaa2tbazcn+Xx6Y//ZMV3O+enpDSdsrbVr0puH7ch5qm9X4/1yNLgOI6qqlqQXIn2wtfbRYddDTkzyc/020IeT/GRVfWC4JZHe/2HXt9Y299g7N71gieH5qSTfaa2tbq1tSPLRJD8+5Jq4z42bpxLof9/u1BmCpJ33b0keWVUP60+++qIk5221zXlJfrl/N5oT0uvKd8N8F7pIbPd8V9VD0vvP6Zdaa98YQo2LyXbPd2vtYa21w1trh6f3Rv2brbWPzXuli0OX/08+nuQnqmqiqpYnOT4mkNwZXc7595I8PUmq6sAkj07y7Xmtctfh/XI0dPl3wTzrz9P27iRXtdbePux6SFprv9NaO6zfBnpRks+21vSyGLLW2g+TXFdVj+4venqSK4dYEr221AlVtbz/f9nTo/06Ss5Lclr/59PS+3tjmyYGWs4uoLW2sapemd7dqsaT/G1r7etV9V/66/93kk8meXaSa5Lcld6n2+yAjuf7jCQPSq9nTJJsbK2tHFbNC1nH880c6XK+W2tXVdUFSf49vfkY3tVam/FW6mxfx9/x/5nkPVX1tfSG/Px2vzcYD1BVfSi9O9/tV1XXJ3lDkiWJ98tRMtu/iyGXRa/3yy8l+VpVXd5f9ruttU8OryQYWb+V5IP9MPzb8X4yVK21L1XVuUkuS++GJV+JO2sPxSxtsTcn+UhVvSy90G+7d4Wu1gx5BwAAAGD7DG0DAAAAoBNBEgAAAACdCJIAAAAA6ESQBAAAAEAngiQAAAAAOhEkAQAAANCJIAkYmqr6var6elX9e1VdXlXH95efW1VH9H/+ZFXtXVWTVfX5qpoYbtUAAAC7Ln+QAUNRVU9OckqSY1tr91TVfkkmq+oxScZba99Oktbas6c956Ikv5Dkg8OoGQAAYFenRxIwLAcnubm1dk+StNZubq39IMlLknx880ZVdW0/ZEqSj/XXAwAAMASCJGBYPp3kwVX1jar6y6r6T/3lJya5dJbnXJHkx+alOgAAAO5HkAQMRWvtziTHJXl5ktVJ/q6qfiW9nkqrZ3nOpiTrq2rP+aoTAACA+5gjCRiafjB0cZKLq+prSU5LcneSZdt42tIk6wZfHQAAAFvTIwkYiqp6dFU9ctqiY5J8N8lVSR4xy3MelGR1a23D4CsEAABga3okAcOyR5I/q6q9k2xMck16w9x+OslJST4zw3OeluST81QfAAAAW6nW2rBrALhXVe2W5HNJTuwPfZu+7qNJfqe1dvVQigMAANjFGdoGjJTW2t1J3pDk0OnLq2oyyceESAAAAMOjRxIAAAAAneiRBAAAAEAngiQAAAAAOhEkAQAAANCJIAkAAACATgRJAAAAAHTy/wNvYOW5XReIPQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Para k =  8  el promedio de la silueta es de : 0.35284612510104646\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.31700053499298475\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.6152265411044983\n",
+      "   - Para i =  3  la silueta del cluster vale :  0.5664789734180768\n",
+      "   - Para i =  4  la silueta del cluster vale :  0.0\n",
+      "   - Para i =  5  la silueta del cluster vale :  0.0\n",
+      "   - Para i =  6  la silueta del cluster vale :  0.263812295212263\n",
+      "   - Para i =  7  la silueta del cluster vale :  0.32882042637679976\n",
+      "   - Para i =  8  la silueta del cluster vale :  0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Para k =  9  el promedio de la silueta es de : 0.34011594848992555\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.5664789734180768\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.20382042637679978\n",
+      "   - Para i =  3  la silueta del cluster vale :  0.6152265411044983\n",
+      "   - Para i =  4  la silueta del cluster vale :  0.39052429175126996\n",
+      "   - Para i =  5  la silueta del cluster vale :  0.32882042637679976\n",
+      "   - Para i =  6  la silueta del cluster vale :  0.0\n",
+      "   - Para i =  7  la silueta del cluster vale :  0.0\n",
+      "   - Para i =  8  la silueta del cluster vale :  0.0\n",
+      "   - Para i =  9  la silueta del cluster vale :  0.0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1440x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "max_k = 10## maximo número de clusters que vamos a crear\n",
+    "K = range(1,max_k)\n",
+    "ssw = []\n",
+    "cmap = cm.get_cmap(\"Spectral\")\n",
+    "color_palette = [cmap(float(i)/max_k) for i in K]\n",
+    "centroid = [sum(X)/len(X) for i in K]\n",
+    "sst = sum(np.min(cdist(X, centroid, \"euclidean\"), axis = 1))\n",
+    "\n",
+    "\n",
+    "for k in K:\n",
+    "    kmeanModel = KMeans(n_clusters=k).fit(X)\n",
+    "    \n",
+    "    centers = pd.DataFrame(kmeanModel.cluster_centers_)\n",
+    "    labels = kmeanModel.labels_\n",
+    "    \n",
+    "    ssw_k = sum(np.min(cdist(X, kmeanModel.cluster_centers_, \"euclidean\"), axis = 1))\n",
+    "    ssw.append(ssw_k)\n",
+    "    \n",
+    "    label_color = [color_palette[i] for i in labels]\n",
+    "    \n",
+    "    ##Fabricaremos una silueta para cada cluster\n",
+    "    # Por seguridad, no hacemos silueta si k = 1 o k = len(X)\n",
+    "    if 1<k<len(X):\n",
+    "       ##Crear un subplot de una fila y dos columnas\n",
+    "        fig, (axis1,axis2) = plt.subplots(1,2)\n",
+    "        fig.set_size_inches(20,8)\n",
+    "    \n",
+    "        #El primer subplot contendrá la silueta, que puede tener valores desde -1 a 1\n",
+    "        #En nuestro caso, ya controlamos que los valores están entre -0.1 y 1\n",
+    "        axis1.set_xlim([-0.1, 1.0])\n",
+    "        #El número de clusters a insertar determinará el tamaño de cada barra\n",
+    "        #El coeficiente (n_clusters+1)*10 será el espacio en blanco que dejaremos \n",
+    "        #entre siluetas individuales de cada cluster para separarlas.\n",
+    "        axis1.set_ylim([0, len(X)+ (k+1)*10])\n",
+    "    \n",
+    "        silhouette_avg = silhouette_score(X, labels)\n",
+    "        print(\"* Para k = \",k, \" el promedio de la silueta es de :\",silhouette_avg)\n",
+    "        sample_silhouette_values = silhouette_samples(X, labels)\n",
+    "        \n",
+    "        y_lower = 10\n",
+    "        for i in range(k):\n",
+    "            #Agregamos la silueta del cluster i-ésimo\n",
+    "            ith_cluster_sv = sample_silhouette_values[labels == i]\n",
+    "            print(\"   - Para i = \", i+1, \" la silueta del cluster vale : \", np.mean(ith_cluster_sv))\n",
+    "            #Ordenamos descendientemente las siluetas del cluster i-ésimo\n",
+    "            ith_cluster_sv.sort()\n",
+    "        \n",
+    "            #Calculamos donde colocar la primera silueta en el eje vertical\n",
+    "            ith_cluster_size = ith_cluster_sv.shape[0]\n",
+    "            y_upper = y_lower + ith_cluster_size\n",
+    "            \n",
+    "            #Elegimos el color del cluster\n",
+    "            color = color_palette[i]\n",
+    "            \n",
+    "            #Pintamos la silueta del cluster i-ésimo\n",
+    "            axis1.fill_betweenx(np.arange(y_lower, y_upper),\n",
+    "                               0, ith_cluster_sv, facecolor = color, alpha = 0.7)\n",
+    "            \n",
+    "            \n",
+    "            #Etiquetamos dicho cluster con el número en el centro\n",
+    "            axis1.text(-0.05, y_lower + 0.5 * ith_cluster_size, str(i+1))\n",
+    "            \n",
+    "            #Calculamos el nuevo y_lower para el siguiente cluster del gráfico\n",
+    "            y_lower = y_upper + 10 #dejamos vacías 10 posiciones sin muestra\n",
+    "            \n",
+    "        axis1.set_title(\"Representación de la silueta para k = %s\"%str(k))\n",
+    "        axis1.set_xlabel(\"S(i)\")\n",
+    "        axis1.set_ylabel(\"ID del Cluster\")\n",
+    "        \n",
+    "        ##Fin de la representación de la silueta\n",
+    "        \n",
+    "    ##Plot de los k-means con los puntos respectivos\n",
+    "    plt.plot()\n",
+    "    plt.xlim([0,10])\n",
+    "    plt.ylim([0,10])\n",
+    "    plt.title(\"Clustering para k = %s\"%str(k))\n",
+    "    plt.scatter(x1,x2, c=label_color)\n",
+    "    plt.scatter(centers[0], centers[1], marker = \"x\")\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Representación del codo\n",
+    "plt.plot(K, ssw, \"bx-\")\n",
+    "plt.xlabel(\"k\")\n",
+    "plt.ylabel(\"SSw(k)\")\n",
+    "plt.title(\"La técnica del codo para encontrar el k óptimo\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Representación del codo normalizado\n",
+    "plt.plot(K, 1-ssw/sst, \"bx-\")\n",
+    "plt.xlabel(\"k\")\n",
+    "plt.ylabel(\"1-norm(SSw(k))\")\n",
+    "plt.title(\"La técnica del codo normalizado para encontrar el k óptimo\")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T6 - 5 - M\303\251todo del codo y la silueta.ipynb" "b/notebooks/T6 - 5 - M\303\251todo del codo y la silueta.ipynb"
index a54226e0..4c17091b 100644
--- "a/notebooks/T6 - 5 - M\303\251todo del codo y la silueta.ipynb"	
+++ "b/notebooks/T6 - 5 - M\303\251todo del codo y la silueta.ipynb"	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -20,12 +20,13 @@
     "from sklearn.cluster import KMeans\n",
     "from sklearn import metrics\n",
     "from scipy.spatial.distance import cdist\n",
-    "from sklearn.metrics import silhouette_samples, silhouette_score"
+    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
+    "import matplotlib.cm as cm"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,17 +37,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a0a43ca90>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -63,17 +66,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a16254ef0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -87,12 +92,14 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a0c9a0908>"
+       "<Figure size 1440x576 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -100,19 +107,21 @@
      "output_type": "stream",
      "text": [
       "* Para k =  3  el promedio de la silueta es de : 0.7122079383287169\n",
-      "   - Para i =  1  la silueta del cluster vale :  0.7345257364682265\n",
-      "   - Para i =  2  la silueta del cluster vale :  0.6609508863896014\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.6609508863896014\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.7345257364682265\n",
       "   - Para i =  3  la silueta del cluster vale :  0.7322200728725188\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a166ae5f8>"
+       "<Figure size 1440x576 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
@@ -128,132 +137,144 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a166ae5c0>"
+       "<Figure size 1440x576 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "* Para k =  5  el promedio de la silueta es de : 0.4337431629436393\n",
-      "   - Para i =  1  la silueta del cluster vale :  0.655666655624379\n",
-      "   - Para i =  2  la silueta del cluster vale :  0.1804754830212678\n",
+      "* Para k =  5  el promedio de la silueta es de : 0.42883912423017356\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.24096929517637128\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.2698039021743969\n",
       "   - Para i =  3  la silueta del cluster vale :  0.654458796162702\n",
-      "   - Para i =  4  la silueta del cluster vale :  0.2777436957346689\n",
-      "   - Para i =  5  la silueta del cluster vale :  0.24096929517637128\n"
+      "   - Para i =  4  la silueta del cluster vale :  0.1899052168375926\n",
+      "   - Para i =  5  la silueta del cluster vale :  0.655666655624379\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a165a2668>"
+       "<Figure size 1440x576 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "* Para k =  6  el promedio de la silueta es de : 0.3799069154256582\n",
-      "   - Para i =  1  la silueta del cluster vale :  0.24096929517637128\n",
-      "   - Para i =  2  la silueta del cluster vale :  0.2777436957346689\n",
-      "   - Para i =  3  la silueta del cluster vale :  0.32882042637679976\n",
-      "   - Para i =  4  la silueta del cluster vale :  0.655666655624379\n",
-      "   - Para i =  5  la silueta del cluster vale :  0.5664789734180768\n",
-      "   - Para i =  6  la silueta del cluster vale :  0.1804754830212678\n"
+      "* Para k =  6  el promedio de la silueta es de : 0.37500287671219246\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.5664789734180768\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.2698039021743969\n",
+      "   - Para i =  3  la silueta del cluster vale :  0.655666655624379\n",
+      "   - Para i =  4  la silueta del cluster vale :  0.1899052168375926\n",
+      "   - Para i =  5  la silueta del cluster vale :  0.32882042637679976\n",
+      "   - Para i =  6  la silueta del cluster vale :  0.24096929517637128\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a163a74a8>"
+       "<Figure size 1440x576 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "* Para k =  7  el promedio de la silueta es de : 0.3800485694524957\n",
-      "   - Para i =  1  la silueta del cluster vale :  0.0\n",
-      "   - Para i =  2  la silueta del cluster vale :  0.32882042637679976\n",
-      "   - Para i =  3  la silueta del cluster vale :  0.24096929517637128\n",
-      "   - Para i =  4  la silueta del cluster vale :  0.027847735322324364\n",
-      "   - Para i =  5  la silueta del cluster vale :  0.655666655624379\n",
-      "   - Para i =  6  la silueta del cluster vale :  0.5664789734180768\n",
-      "   - Para i =  7  la silueta del cluster vale :  0.5237553814972481\n"
+      "* Para k =  7  el promedio de la silueta es de : 0.3273255327190837\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.3503771888434877\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.3028895866899326\n",
+      "   - Para i =  3  la silueta del cluster vale :  0.32882042637679976\n",
+      "   - Para i =  4  la silueta del cluster vale :  0.30004208861569454\n",
+      "   - Para i =  5  la silueta del cluster vale :  0.5664789734180768\n",
+      "   - Para i =  6  la silueta del cluster vale :  0.0\n",
+      "   - Para i =  7  la silueta del cluster vale :  0.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a16ab3400>"
+       "<Figure size 1440x576 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "* Para k =  8  el promedio de la silueta es de : 0.35345323083317454\n",
-      "   - Para i =  1  la silueta del cluster vale :  0.30004208861569454\n",
-      "   - Para i =  2  la silueta del cluster vale :  0.0\n",
-      "   - Para i =  3  la silueta del cluster vale :  0.32882042637679976\n",
-      "   - Para i =  4  la silueta del cluster vale :  0.6152265411044983\n",
-      "   - Para i =  5  la silueta del cluster vale :  0.5664789734180768\n",
-      "   - Para i =  6  la silueta del cluster vale :  0.0\n",
-      "   - Para i =  7  la silueta del cluster vale :  0.3028895866899326\n",
+      "* Para k =  8  el promedio de la silueta es de : 0.35284612510104646\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.31700053499298475\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.6152265411044983\n",
+      "   - Para i =  3  la silueta del cluster vale :  0.5664789734180768\n",
+      "   - Para i =  4  la silueta del cluster vale :  0.0\n",
+      "   - Para i =  5  la silueta del cluster vale :  0.0\n",
+      "   - Para i =  6  la silueta del cluster vale :  0.263812295212263\n",
+      "   - Para i =  7  la silueta del cluster vale :  0.32882042637679976\n",
       "   - Para i =  8  la silueta del cluster vale :  0.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a16bbf438>"
+       "<Figure size 1440x576 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "* Para k =  9  el promedio de la silueta es de : 0.39932610787930767\n",
-      "   - Para i =  1  la silueta del cluster vale :  0.0\n",
-      "   - Para i =  2  la silueta del cluster vale :  0.14644660940672627\n",
-      "   - Para i =  3  la silueta del cluster vale :  0.5664789734180768\n",
-      "   - Para i =  4  la silueta del cluster vale :  0.6152265411044983\n",
-      "   - Para i =  5  la silueta del cluster vale :  0.14644660940672627\n",
-      "   - Para i =  6  la silueta del cluster vale :  1.0\n",
+      "* Para k =  9  el promedio de la silueta es de : 0.34011594848992555\n",
+      "   - Para i =  1  la silueta del cluster vale :  0.5664789734180768\n",
+      "   - Para i =  2  la silueta del cluster vale :  0.20382042637679978\n",
+      "   - Para i =  3  la silueta del cluster vale :  0.6152265411044983\n",
+      "   - Para i =  4  la silueta del cluster vale :  0.39052429175126996\n",
+      "   - Para i =  5  la silueta del cluster vale :  0.32882042637679976\n",
+      "   - Para i =  6  la silueta del cluster vale :  0.0\n",
       "   - Para i =  7  la silueta del cluster vale :  0.0\n",
-      "   - Para i =  8  la silueta del cluster vale :  0.32882042637679976\n",
+      "   - Para i =  8  la silueta del cluster vale :  0.0\n",
       "   - Para i =  9  la silueta del cluster vale :  0.0\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a16bbf198>"
+       "<Figure size 1440x576 with 2 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -261,7 +282,8 @@
     "max_k = 10## maximo número de clusters que vamos a crear\n",
     "K = range(1,max_k)\n",
     "ssw = []\n",
-    "color_palette = [plt.cm.spectral(float(i)/max_k) for i in K]\n",
+    "cmap = cm.get_cmap(\"Spectral\")\n",
+    "color_palette = [cmap(float(i)/max_k) for i in K]\n",
     "centroid = [sum(X)/len(X) for i in K]\n",
     "sst = sum(np.min(cdist(X, centroid, \"euclidean\"), axis = 1))\n",
     "\n",
@@ -334,23 +356,25 @@
     "    plt.ylim([0,10])\n",
     "    plt.title(\"Clustering para k = %s\"%str(k))\n",
     "    plt.scatter(x1,x2, c=label_color)\n",
-    "    plt.scatter(centers[0], centers[1], c=color_palette, marker = \"x\")\n",
+    "    plt.scatter(centers[0], centers[1], marker = \"x\")\n",
     "    plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a169afcc0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -365,17 +389,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a16e26518>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -387,13 +413,6 @@
     "plt.title(\"La técnica del codo normalizado para encontrar el k óptimo\")\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -412,7 +431,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T6 - 6 - Propagaci\303\263n de la afinidad-Colab.ipynb" "b/notebooks/T6 - 6 - Propagaci\303\263n de la afinidad-Colab.ipynb"
new file mode 100644
index 00000000..3a3c6ec4
--- /dev/null
+++ "b/notebooks/T6 - 6 - Propagaci\303\263n de la afinidad-Colab.ipynb"	
@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T6%20-%206%20-%20Propagación%20de%20la%20afinidad-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Propagación de la afinidad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import AffinityPropagation\n",
+    "from sklearn import metrics\n",
+    "from sklearn.datasets import make_blobs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "centers = [[1,1], [-1,-1], [1,-1]]\n",
+    "X, labels = make_blobs(n_samples=300, centers=centers, cluster_std=0.5, random_state=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from itertools import cycle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f844488fac0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:,0], X[:,1], c=labels, s = 5, cmap = \"autumn\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/cluster/_affinity_propagation.py:146: FutureWarning: 'random_state' has been introduced in 0.23. It will be set to None starting from 0.25 which means that results will differ at every function call. Set 'random_state' to None to silence this warning, or to 0 to keep the behavior of versions <0.23.\n",
+      "  warnings.warn((\"'random_state' has been introduced in 0.23. \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "af = AffinityPropagation(preference=-50).fit(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cluster_center_ids = af.cluster_centers_indices_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "labels = af.labels_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_clust = len(cluster_center_ids)\n",
+    "n_clust"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def report_affinity_propagation(X):\n",
+    "    af = AffinityPropagation(preference=-50).fit(X)\n",
+    "    cluster_center_ids = af.cluster_centers_indices_\n",
+    "    n_clust = len(cluster_center_ids)\n",
+    "    clust_labels = af.labels_\n",
+    "    \n",
+    "    print(\"Número estimado de clusters: %d\"%n_clust)\n",
+    "    print(\"Homogeneidad: %0.3f\"%metrics.homogeneity_score(labels, clust_labels))\n",
+    "    print(\"Completitud: %0.3f\"%metrics.completeness_score(labels, clust_labels))\n",
+    "    print(\"V-measure: %0.3f\"%metrics.v_measure_score(labels, clust_labels))\n",
+    "    print(\"R2 ajustado: %0.3f\"%metrics.adjusted_rand_score(labels, clust_labels))\n",
+    "    print(\"Información mútua ajustada: %0.3f\"%metrics.adjusted_mutual_info_score(labels, clust_labels))\n",
+    "    print(\"Coeficiente de la silueta: %0.3f\"%metrics.silhouette_score(X, labels, metric=\"sqeuclidean\"))\n",
+    "    \n",
+    "    plt.figure(figsize=(16,9))\n",
+    "    plt.clf()\n",
+    "    \n",
+    "    colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')\n",
+    "    for k, col in zip(range(n_clust), colors):\n",
+    "        class_members = (clust_labels==k)\n",
+    "        clust_center = X[cluster_center_ids[k]]\n",
+    "        plt.plot(X[class_members,0], X[class_members, 1], col +'.')\n",
+    "        plt.plot(clust_center[0], clust_center[1], 'o', markerfacecolor=col, markeredgecolor='k', markersize=14)\n",
+    "        for x in X[class_members]:\n",
+    "            plt.plot([clust_center[0],x[0]], [clust_center[1], x[1]], col)\n",
+    "            \n",
+    "    plt.title(\"Número estimado de clusters %d\"%n_clust)\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/cluster/_affinity_propagation.py:146: FutureWarning: 'random_state' has been introduced in 0.23. It will be set to None starting from 0.25 which means that results will differ at every function call. Set 'random_state' to None to silence this warning, or to 0 to keep the behavior of versions <0.23.\n",
+      "  warnings.warn((\"'random_state' has been introduced in 0.23. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Número estimado de clusters: 3\n",
+      "Homogeneidad: 1.000\n",
+      "Completitud: 1.000\n",
+      "V-measure: 1.000\n",
+      "R2 ajustado: 1.000\n",
+      "Información mútua ajustada: 1.000\n",
+      "Coeficiente de la silueta: 0.761\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "report_affinity_propagation(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([101.,   0.,   0.,   0.,   0.,  98.,   0.,   0.,   0., 101.]),\n",
+       " array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),\n",
+       " <BarContainer object of 10 artists>)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(af.labels_)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T6 - 6 - Propagaci\303\263n de la afinidad.ipynb" "b/notebooks/T6 - 6 - Propagaci\303\263n de la afinidad.ipynb"
index 570109ed..a2f99c22 100644
--- "a/notebooks/T6 - 6 - Propagaci\303\263n de la afinidad.ipynb"	
+++ "b/notebooks/T6 - 6 - Propagaci\303\263n de la afinidad.ipynb"	
@@ -15,22 +15,22 @@
    "source": [
     "from sklearn.cluster import AffinityPropagation\n",
     "from sklearn import metrics\n",
-    "from sklearn.datasets.samples_generator import make_blobs"
+    "from sklearn.datasets import make_blobs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
     "centers = [[1,1], [-1,-1], [1,-1]]\n",
-    "X, labels = make_blobs(n_samples=300, centers=centers, cluster_std=0.5, random_state=0)"
+    "X, labels = make_blobs(n_samples=300, centers=centers, cluster_std=0.5, random_state=None)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,27 +40,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1a15fede10>"
+       "<matplotlib.collections.PathCollection at 0x7f844488fac0>"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a15f73cc0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -70,16 +72,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/cluster/_affinity_propagation.py:146: FutureWarning: 'random_state' has been introduced in 0.23. It will be set to None starting from 0.25 which means that results will differ at every function call. Set 'random_state' to None to silence this warning, or to 0 to keep the behavior of versions <0.23.\n",
+      "  warnings.warn((\"'random_state' has been introduced in 0.23. \"\n"
+     ]
+    }
+   ],
    "source": [
     "af = AffinityPropagation(preference=-50).fit(X)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -97,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -106,7 +117,7 @@
        "3"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -118,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,30 +165,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/johnnynunez/opt/anaconda3/lib/python3.8/site-packages/sklearn/cluster/_affinity_propagation.py:146: FutureWarning: 'random_state' has been introduced in 0.23. It will be set to None starting from 0.25 which means that results will differ at every function call. Set 'random_state' to None to silence this warning, or to 0 to keep the behavior of versions <0.23.\n",
+      "  warnings.warn((\"'random_state' has been introduced in 0.23. \"\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Número estimado de clusters: 3\n",
-      "Homogeneidad: 0.872\n",
-      "Completitud: 0.872\n",
-      "V-measure: 0.872\n",
-      "R2 ajustado: 0.912\n",
-      "Información mútua ajustada: 0.871\n",
-      "Coeficiente de la silueta: 0.735\n"
+      "Homogeneidad: 1.000\n",
+      "Completitud: 1.000\n",
+      "V-measure: 1.000\n",
+      "R2 ajustado: 1.000\n",
+      "Información mútua ajustada: 1.000\n",
+      "Coeficiente de la silueta: 0.761\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1094444e0>"
+       "<Figure size 1152x648 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -187,42 +208,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(array([ 99.,   0.,   0.,   0.,   0., 101.,   0.,   0.,   0., 100.]),\n",
+       "(array([101.,   0.,   0.,   0.,   0.,  98.,   0.,   0.,   0., 101.]),\n",
        " array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),\n",
-       " <a list of 10 Patch objects>)"
+       " <BarContainer object of 10 artists>)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Y1+ah6V8C7qj/v/nzpa6+jd30qatVV9fveQxujGU1xmtoH1tY+ubia/j+m2WfmfZ4jVDTWQzuOV2woP1k4FlD058GLprkWI1Q208c+/0xCMyHu7Eb6RiYVl3d8mMngSevxph1/90fAT5wnD6rdnxN9CCYwkF1CYO71Q8CV3Rtf8TgLBngGcDfdgf+Z4Bzhta9olvvAeDiVa7rX4FHgbu7n5u69guAe7sD/V5gxyrX9cfA/d3+bwOeP7Tur3XjeBB4y2rW1c3/IXDlgvWmPV7XAkeA7zA4i9oBvBV4a7c8DP4RnQe7/c9Oe7xGqOlq4LGhY2uuaz+nG6fPdb/jKyY5ViPW9vah4+sOht6MFjsGVquurs+bGfyBxvB6UxszBpfUCrhn6Hd1yVodX34zVpIat56v0UuSJsCgl6TGGfSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcf8HSNB93xkLZwAAAAAASUVORK5CYII=\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a15ca1c50>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "plt.hist(af.labels_)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -241,7 +257,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T6 - 7 - K Medoides-Colab.ipynb b/notebooks/T6 - 7 - K Medoides-Colab.ipynb
new file mode 100644
index 00000000..cee86e5a
--- /dev/null
+++ b/notebooks/T6 - 7 - K Medoides-Colab.ipynb	
@@ -0,0 +1,346 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T6%20-%207%20-%20K%20Medoides-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Distribuciones en forma de anillo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from math import sin, cos, radians, pi, sqrt\n",
+    "import numpy.random as rnd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def ring(r_min = 0, r_max = 1, n_samples = 360):\n",
+    "    angle = rnd.uniform(0, 2*pi, n_samples)\n",
+    "    distance = rnd.uniform(r_min, r_max, n_samples)\n",
+    "    data = []\n",
+    "    for a, d in zip(angle, distance):\n",
+    "        data.append([d*cos(a), d*sin(a)])\n",
+    "    return np.array(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f84c80c8280>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "data1 = ring(3, 5)\n",
+    "data2 = ring(24, 27)\n",
+    "\n",
+    "data = np.concatenate([data1, data2], axis = 0)\n",
+    "labels = np.concatenate([[0 for i in range(0,len(data1))], [1 for i in range(0,len(data2))]])\n",
+    "plt.scatter(data[:,0], data[:,1], c = labels, s = 5, cmap = \"autumn\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Algoritmo con Kmeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import KMeans"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "km = KMeans(2).fit(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clust = km.predict(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f84c93fa2e0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data[:,0], data[:, 1], c = clust, s = 5, cmap=\"autumn\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Algoritmo de los K Medoides"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install pyclust\n",
+    "!pip install treelib\n",
+    "from pyclust import KMedoids"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmed = KMedoids(2).fit_predict(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f84c9504d60>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data[:,0], data[:,1], c=kmed, s=5, cmap=\"autumn\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Algoritmo del Clustering Espectral"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.cluster import SpectralClustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clust = SpectralClustering(2).fit_predict(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x7f84cb500880>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(data[:,0], data[:,1], c = clust, s = 5, cmap = \"autumn\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Podemos estimar la k:\n",
+    "    * No: Propagación de la afinidad\n",
+    "    * Si: Podemos usar la distancia Euclídea:\n",
+    "        * Si: K-Means\n",
+    "        * No: Buscar valores centrales:\n",
+    "            * Si: K-Medoides \n",
+    "            * No: Los datos son linealmente separables:\n",
+    "                * Si: Clustering aglomerativo\n",
+    "                * No: Clustering Espectral"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T6 - 7 - K Medoides.ipynb b/notebooks/T6 - 7 - K Medoides.ipynb
index ae9373de..2e06f1a6 100644
--- a/notebooks/T6 - 7 - K Medoides.ipynb	
+++ b/notebooks/T6 - 7 - K Medoides.ipynb	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,27 +36,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x112c152e8>"
+       "<matplotlib.collections.PathCollection at 0x7f84c80c8280>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d1eeef0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -78,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -87,7 +89,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -96,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,27 +107,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1a18ee8940>"
+       "<matplotlib.collections.PathCollection at 0x7f84c93fa2e0>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a18bef9b0>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -142,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -151,7 +155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,27 +164,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1a18d57e80>"
+       "<matplotlib.collections.PathCollection at 0x7f84c9504d60>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1a1935a748>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -197,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,7 +212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,27 +221,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x1a18da87b8>"
+       "<matplotlib.collections.PathCollection at 0x7f84cb500880>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x108c98e80>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -257,13 +265,6 @@
     "                * Si: Clustering aglomerativo\n",
     "                * No: Clustering Espectral"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -282,7 +283,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T7 - 1 - Trees - \303\201rboles de Decisi\303\263n-Colab.ipynb" "b/notebooks/T7 - 1 - Trees - \303\201rboles de Decisi\303\263n-Colab.ipynb"
new file mode 100644
index 00000000..331a91b2
--- /dev/null
+++ "b/notebooks/T7 - 1 - Trees - \303\201rboles de Decisi\303\263n-Colab.ipynb"	
@@ -0,0 +1,423 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T7%20-%201%20-%20Trees%20-%20Árboles%20de%20Decisión-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Árbol de decisión para especies de flores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"/content/python-ml-course/datasets/iris/iris.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.hist(data.Species)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.Species.unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colnames = data.columns.values.tolist()\n",
+    "predictors = colnames[:4]\n",
+    "target = colnames[4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"is_train\"] = np.random.uniform(0,1, len(data))<=0.75"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.hist(data[\"is_train\"].astype(np.int))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train, test = data[data[\"is_train\"]==True], data[data[\"is_train\"]==False]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tree = DecisionTreeClassifier(criterion=\"entropy\", min_samples_split=20, random_state=99)\n",
+    "tree.fit(train[predictors], train[target])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds = tree.predict(test[predictors])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.crosstab(test[target], preds, rownames=[\"Actual\"], colnames=[\"Predictions\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualización del árbol de decisión"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import export_graphviz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"/content/python-ml-course/notebooks/resources/iris_dtree.dot\", \"w\") as dotfile:\n",
+    "    export_graphviz(tree, out_file=dotfile, feature_names=predictors)\n",
+    "    dotfile.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from graphviz import Source"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "file = open(\"/content/python-ml-course/notebooks/resources/iris_dtree.dot\", \"r\")\n",
+    "text = file.read()\n",
+    "text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Source(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cross Validation para la poda"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = data[predictors]\n",
+    "Y = data[target]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=5, min_samples_split=20, random_state=99)\n",
+    "tree.fit(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import KFold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv = KFold(n_splits=10, shuffle=True, random_state=1)\n",
+    "cv.get_n_splits(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.metrics import accuracy_score, make_scorer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scores = cross_val_score(tree, X, Y, scoring=make_scorer(accuracy_score), cv = cv, n_jobs=1)\n",
+    "scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score = np.mean(scores)\n",
+    "score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in range(1,11):\n",
+    "    tree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=i, min_samples_split=20, random_state=99)\n",
+    "    tree.fit(X,Y)\n",
+    "    cv = KFold(n_splits=10, shuffle=True, random_state=1)\n",
+    "    cv.get_n_splits(X)\n",
+    "    scores = cross_val_score(tree, X, Y, scoring=\"accuracy\", cv = cv, n_jobs=-1)\n",
+    "    score = np.mean(scores)\n",
+    "    print(\"Score para i = \",i,\" es de \", score)\n",
+    "    print(\"   \",tree.feature_importances_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestClassifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "forest = RandomForestClassifier(n_jobs=-1, oob_score=True, n_estimators=100)\n",
+    "forest.fit(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "forest.oob_decision_function_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "forest.oob_score_"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T7 - 1 - Trees - \303\201rboles de Decisi\303\263n.ipynb" "b/notebooks/T7 - 1 - Trees - \303\201rboles de Decisi\303\263n.ipynb"
index 5968c473..ec046239 100644
--- "a/notebooks/T7 - 1 - Trees - \303\201rboles de Decisi\303\263n.ipynb"	
+++ "b/notebooks/T7 - 1 - Trees - \303\201rboles de Decisi\303\263n.ipynb"	
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -19,96 +19,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "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>Sepal.Length</th>\n",
-       "      <th>Sepal.Width</th>\n",
-       "      <th>Petal.Length</th>\n",
-       "      <th>Petal.Width</th>\n",
-       "      <th>Species</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>setosa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   Sepal.Length  Sepal.Width  Petal.Length  Petal.Width Species\n",
-       "0           5.1          3.5           1.4          0.2  setosa\n",
-       "1           4.9          3.0           1.4          0.2  setosa\n",
-       "2           4.7          3.2           1.3          0.2  setosa\n",
-       "3           4.6          3.1           1.5          0.2  setosa\n",
-       "4           5.0          3.6           1.4          0.2  setosa"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "data = pd.read_csv(\"../datasets/iris/iris.csv\")\n",
     "data.head()"
@@ -116,79 +29,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(150, 5)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([50.,  0.,  0.,  0.,  0., 50.,  0.,  0.,  0., 50.]),\n",
-       " array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),\n",
-       " <a list of 10 Patch objects>)"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x105767160>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.hist(data.Species)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data.Species.unique()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -199,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -208,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -217,39 +85,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([ 39.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0., 111.]),\n",
-       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
-       " <a list of 10 Patch objects>)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x1121f0940>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
+    "data[\"is_train\"] = (data[\"is_train\"].astype(np.float32) - data[\"is_train\"].astype(np.float32)).astype(np.bool)\n",
     "plt.hist(data.is_train)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -258,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -267,25 +113,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
-       "            max_features=None, max_leaf_nodes=None,\n",
-       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
-       "            min_samples_leaf=1, min_samples_split=20,\n",
-       "            min_weight_fraction_leaf=0.0, presort=False, random_state=99,\n",
-       "            splitter='best')"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tree = DecisionTreeClassifier(criterion=\"entropy\", min_samples_split=20, random_state=99)\n",
     "tree.fit(train[predictors], train[target])"
@@ -293,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -302,77 +132,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "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>Predictions</th>\n",
-       "      <th>setosa</th>\n",
-       "      <th>versicolor</th>\n",
-       "      <th>virginica</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Actual</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>setosa</th>\n",
-       "      <td>12</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>versicolor</th>\n",
-       "      <td>0</td>\n",
-       "      <td>15</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>virginica</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>10</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Predictions  setosa  versicolor  virginica\n",
-       "Actual                                    \n",
-       "setosa           12           0          0\n",
-       "versicolor        0          15          1\n",
-       "virginica         0           1         10"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "pd.crosstab(test[target], preds, rownames=[\"Actual\"], colnames=[\"Predictions\"])"
    ]
@@ -386,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -395,8 +157,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [],
    "source": [
     "with open(\"resources/iris_dtree.dot\", \"w\") as dotfile:\n",
@@ -406,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -416,20 +180,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'digraph Tree {\\nnode [shape=box] ;\\n0 [label=\"Petal.Length <= 2.6\\\\nentropy = 1.582\\\\nsamples = 111\\\\nvalue = [38, 34, 39]\"] ;\\n1 [label=\"entropy = 0.0\\\\nsamples = 38\\\\nvalue = [38, 0, 0]\"] ;\\n0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\\n2 [label=\"Petal.Width <= 1.75\\\\nentropy = 0.997\\\\nsamples = 73\\\\nvalue = [0, 34, 39]\"] ;\\n0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\\n3 [label=\"Petal.Length <= 4.95\\\\nentropy = 0.406\\\\nsamples = 37\\\\nvalue = [0, 34, 3]\"] ;\\n2 -> 3 ;\\n4 [label=\"entropy = 0.0\\\\nsamples = 32\\\\nvalue = [0, 32, 0]\"] ;\\n3 -> 4 ;\\n5 [label=\"entropy = 0.971\\\\nsamples = 5\\\\nvalue = [0, 2, 3]\"] ;\\n3 -> 5 ;\\n6 [label=\"entropy = 0.0\\\\nsamples = 36\\\\nvalue = [0, 0, 36]\"] ;\\n2 -> 6 ;\\n}'"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
    "source": [
     "file = open(\"resources/iris_dtree.dot\", \"r\")\n",
     "text = file.read()\n",
@@ -438,132 +193,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
-       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
-       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
-       "<!-- Generated by graphviz version 2.40.1 (20161225.0304)\n",
-       " -->\n",
-       "<!-- Title: Tree Pages: 1 -->\n",
-       "<svg width=\"269pt\" height=\"373pt\"\n",
-       " viewBox=\"0.00 0.00 269.00 373.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
-       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 369)\">\n",
-       "<title>Tree</title>\n",
-       "<polygon fill=\"#ffffff\" stroke=\"transparent\" points=\"-4,4 -4,-369 265,-369 265,4 -4,4\"/>\n",
-       "<!-- 0 -->\n",
-       "<g id=\"node1\" class=\"node\">\n",
-       "<title>0</title>\n",
-       "<polygon fill=\"none\" stroke=\"#000000\" points=\"151.5,-365 51.5,-365 51.5,-297 151.5,-297 151.5,-365\"/>\n",
-       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-349.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Petal.Length &lt;= 2.6</text>\n",
-       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-334.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 1.582</text>\n",
-       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-319.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 111</text>\n",
-       "<text text-anchor=\"middle\" x=\"101.5\" y=\"-304.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [38, 34, 39]</text>\n",
-       "</g>\n",
-       "<!-- 1 -->\n",
-       "<g id=\"node2\" class=\"node\">\n",
-       "<title>1</title>\n",
-       "<polygon fill=\"none\" stroke=\"#000000\" points=\"89,-253.5 0,-253.5 0,-200.5 89,-200.5 89,-253.5\"/>\n",
-       "<text text-anchor=\"middle\" x=\"44.5\" y=\"-238.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.0</text>\n",
-       "<text text-anchor=\"middle\" x=\"44.5\" y=\"-223.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 38</text>\n",
-       "<text text-anchor=\"middle\" x=\"44.5\" y=\"-208.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [38, 0, 0]</text>\n",
-       "</g>\n",
-       "<!-- 0&#45;&gt;1 -->\n",
-       "<g id=\"edge1\" class=\"edge\">\n",
-       "<title>0&#45;&gt;1</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" d=\"M82.8361,-296.9465C76.7991,-285.9316 70.0909,-273.6922 63.9953,-262.5703\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"66.9227,-260.6292 59.0471,-253.5422 60.7842,-263.9936 66.9227,-260.6292\"/>\n",
-       "<text text-anchor=\"middle\" x=\"52.0414\" y=\"-273.8405\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">True</text>\n",
-       "</g>\n",
-       "<!-- 2 -->\n",
-       "<g id=\"node3\" class=\"node\">\n",
-       "<title>2</title>\n",
-       "<polygon fill=\"none\" stroke=\"#000000\" points=\"209.5,-261 107.5,-261 107.5,-193 209.5,-193 209.5,-261\"/>\n",
-       "<text text-anchor=\"middle\" x=\"158.5\" y=\"-245.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">Petal.Width &lt;= 1.75</text>\n",
-       "<text text-anchor=\"middle\" x=\"158.5\" y=\"-230.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">entropy = 0.997</text>\n",
-       "<text text-anchor=\"middle\" x=\"158.5\" y=\"-215.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 73</text>\n",
-       "<text text-anchor=\"middle\" x=\"158.5\" y=\"-200.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = [0, 34, 39]</text>\n",
-       "</g>\n",
-       "<!-- 0&#45;&gt;2 -->\n",
-       "<g id=\"edge2\" class=\"edge\">\n",
-       "<title>0&#45;&gt;2</title>\n",
-       "<path fill=\"none\" stroke=\"#000000\" d=\"M120.1639,-296.9465C124.888,-288.3271 130.0231,-278.9579 134.954,-269.9611\"/>\n",
-       "<polygon fill=\"#000000\" stroke=\"#000000\" points=\"138.0571,-271.5815 139.7941,-261.13 131.9186,-268.2171 138.0571,-271.5815\"/>\n",
-       "<text text-anchor=\"middle\" x=\"146.7999\" y=\"-281.4283\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">False</text>\n",
-       "</g>\n",
-       "<!-- 3 -->\n",
-       "<g id=\"node4\" class=\"node\">\n",
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-       "</svg>\n"
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-      "text/plain": [
-       "<graphviz.files.Source at 0x1a1ffac5f8>"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "Source(text)"
    ]
@@ -577,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -587,25 +219,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=5,\n",
-       "            max_features=None, max_leaf_nodes=None,\n",
-       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
-       "            min_samples_leaf=1, min_samples_split=20,\n",
-       "            min_weight_fraction_leaf=0.0, presort=False, random_state=99,\n",
-       "            splitter='best')"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "tree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=5, min_samples_split=20, random_state=99)\n",
     "tree.fit(X,Y)"
@@ -613,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -622,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -631,7 +247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -640,21 +256,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1.        , 0.93333333, 0.93333333, 0.93333333, 1.        ,\n",
-       "       0.93333333, 1.        , 0.86666667, 0.93333333, 0.8       ])"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "scores = cross_val_score(tree, X, Y, scoring=\"accuracy\", cv = cv, n_jobs=1)\n",
     "scores"
@@ -662,20 +266,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.9333333333333333"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "score = np.mean(scores)\n",
     "score"
@@ -683,36 +276,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Score para i =  1  es de  0.5666666666666667\n",
-      "    [0. 0. 1. 0.]\n",
-      "Score para i =  2  es de  0.9200000000000002\n",
-      "    [0.         0.         0.66620285 0.33379715]\n",
-      "Score para i =  3  es de  0.9400000000000001\n",
-      "    [0.         0.         0.68976981 0.31023019]\n",
-      "Score para i =  4  es de  0.9333333333333333\n",
-      "    [0.         0.         0.66869158 0.33130842]\n",
-      "Score para i =  5  es de  0.9333333333333333\n",
-      "    [0.         0.         0.66869158 0.33130842]\n",
-      "Score para i =  6  es de  0.9333333333333333\n",
-      "    [0.         0.         0.66869158 0.33130842]\n",
-      "Score para i =  7  es de  0.9333333333333333\n",
-      "    [0.         0.         0.66869158 0.33130842]\n",
-      "Score para i =  8  es de  0.9333333333333333\n",
-      "    [0.         0.         0.66869158 0.33130842]\n",
-      "Score para i =  9  es de  0.9333333333333333\n",
-      "    [0.         0.         0.66869158 0.33130842]\n",
-      "Score para i =  10  es de  0.9333333333333333\n",
-      "    [0.         0.         0.66869158 0.33130842]\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "for i in range(1,11):\n",
     "    tree = DecisionTreeClassifier(criterion=\"entropy\", max_depth=i, min_samples_split=20, random_state=99)\n",
@@ -726,20 +292,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width']"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "predictors"
    ]
@@ -753,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -762,25 +317,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
-       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
-       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
-       "            min_samples_leaf=1, min_samples_split=2,\n",
-       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=2,\n",
-       "            oob_score=True, random_state=None, verbose=0, warm_start=False)"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "forest = RandomForestClassifier(n_jobs=2, oob_score=True, n_estimators=100)\n",
     "forest.fit(X,Y)"
@@ -788,189 +327,18 @@
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   {
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-   "metadata": {},
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-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "forest.oob_decision_function_"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.9466666666666667"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "forest.oob_score_"
    ]
@@ -999,7 +367,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T7 - 2 - Trees - \303\201rboles de Regresi\303\263n-Colab.ipynb" "b/notebooks/T7 - 2 - Trees - \303\201rboles de Regresi\303\263n-Colab.ipynb"
new file mode 100644
index 00000000..156971fb
--- /dev/null
+++ "b/notebooks/T7 - 2 - Trees - \303\201rboles de Regresi\303\263n-Colab.ipynb"	
@@ -0,0 +1,288 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T7%20-%202%20-%20Trees%20-%20Árboles%20de%20Regresión-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Árboles de Regresión"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv(\"/content/python-ml-course/datasets/boston/Boston.csv\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "colnames = data.columns.values.tolist()\n",
+    "predictors = colnames[:13]\n",
+    "target = colnames[13]\n",
+    "X = data[predictors]\n",
+    "Y = data[target]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regtree = DecisionTreeRegressor(min_samples_split=30, min_samples_leaf=10, max_depth=5, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "regtree.fit(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds = regtree.predict(data[predictors])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"preds\"] = preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[[\"preds\", \"medv\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import export_graphviz\n",
+    "with open(\"/content/python-ml-course/notebooks/resources/boston_rtree.dot\", \"w\") as dotfile:\n",
+    "    export_graphviz(regtree, out_file=dotfile, feature_names=predictors)\n",
+    "    dotfile.close()\n",
+    "    \n",
+    "import os\n",
+    "from graphviz import Source\n",
+    "file = open(\"/content/python-ml-course/notebooks/resources/boston_rtree.dot\", \"r\")\n",
+    "text = file.read()\n",
+    "Source(text)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import KFold\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "from sklearn.metrics import mean_squared_error, make_scorer\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cv = KFold(n_splits = 10, shuffle=True, random_state=1)\n",
+    "cv.get_n_splits(X)\n",
+    "scores = cross_val_score(regtree, X, Y, scoring=make_scorer(mean_squared_error), cv = cv, n_jobs=1)\n",
+    "print(scores)\n",
+    "score = np.mean(scores)\n",
+    "print(score)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(zip(predictors,regtree.feature_importances_))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Random Forests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "forest = RandomForestRegressor(n_jobs=-1, oob_score=True, n_estimators=10000)\n",
+    "forest.fit(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"rforest_pred\"]= forest.oob_prediction_\n",
+    "data[[\"rforest_pred\", \"medv\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[\"rforest_error2\"] = (data[\"rforest_pred\"]-data[\"medv\"])**2\n",
+    "sum(data[\"rforest_error2\"])/len(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "forest.oob_score_"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T7 - 2 - Trees - \303\201rboles de Regresi\303\263n.ipynb" "b/notebooks/T7 - 2 - Trees - \303\201rboles de Regresi\303\263n.ipynb"
index 8e75f0af..c6ce4d13 100644
--- "a/notebooks/T7 - 2 - Trees - \303\201rboles de Regresi\303\263n.ipynb"	
+++ "b/notebooks/T7 - 2 - Trees - \303\201rboles de Regresi\303\263n.ipynb"	
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -218,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -227,20 +227,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "DecisionTreeRegressor(criterion='mse', max_depth=5, max_features=None,\n",
-       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
-       "           min_impurity_split=None, min_samples_leaf=10,\n",
-       "           min_samples_split=30, min_weight_fraction_leaf=0.0,\n",
-       "           presort=False, random_state=0, splitter='best')"
+       "DecisionTreeRegressor(max_depth=5, min_samples_leaf=10, min_samples_split=30,\n",
+       "                      random_state=0)"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -251,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -260,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -269,7 +266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -324,261 +321,11 @@
        "      <td>36.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>23.787500</td>\n",
-       "      <td>28.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>21.863636</td>\n",
-       "      <td>22.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>20.020833</td>\n",
-       "      <td>27.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>20.020833</td>\n",
-       "      <td>16.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>20.020833</td>\n",
-       "      <td>18.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>20.020833</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>21.863636</td>\n",
-       "      <td>18.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>20.020833</td>\n",
-       "      <td>21.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>23.787500</td>\n",
-       "      <td>20.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>18.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>23.787500</td>\n",
-       "      <td>19.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>23.787500</td>\n",
-       "      <td>23.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>17.233962</td>\n",
-       "      <td>17.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>20.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>18.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>14.041667</td>\n",
-       "      <td>13.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>19.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>17.233962</td>\n",
-       "      <td>15.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>14.041667</td>\n",
-       "      <td>14.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>17.233962</td>\n",
-       "      <td>15.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>17.233962</td>\n",
-       "      <td>13.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>17.233962</td>\n",
-       "      <td>16.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>17.233962</td>\n",
-       "      <td>14.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>18.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>21.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>476</th>\n",
-       "      <td>17.233962</td>\n",
-       "      <td>16.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>477</th>\n",
-       "      <td>9.913636</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>478</th>\n",
-       "      <td>13.922222</td>\n",
-       "      <td>14.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>479</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>21.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>480</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>23.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>481</th>\n",
-       "      <td>23.787500</td>\n",
-       "      <td>23.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>482</th>\n",
-       "      <td>28.978261</td>\n",
-       "      <td>25.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>483</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>21.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>484</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>20.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>485</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>21.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>486</th>\n",
-       "      <td>17.233962</td>\n",
-       "      <td>19.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>487</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>20.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>488</th>\n",
-       "      <td>17.233962</td>\n",
-       "      <td>15.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>489</th>\n",
-       "      <td>14.041667</td>\n",
-       "      <td>7.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>490</th>\n",
-       "      <td>14.041667</td>\n",
-       "      <td>8.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>491</th>\n",
-       "      <td>17.233962</td>\n",
-       "      <td>13.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>492</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>20.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>493</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>21.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>494</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>24.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>495</th>\n",
-       "      <td>17.233962</td>\n",
-       "      <td>23.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>496</th>\n",
-       "      <td>14.041667</td>\n",
-       "      <td>19.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>497</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>18.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>498</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>21.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>499</th>\n",
-       "      <td>17.233962</td>\n",
-       "      <td>17.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>500</th>\n",
-       "      <td>20.318750</td>\n",
-       "      <td>16.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>501</th>\n",
        "      <td>23.787500</td>\n",
        "      <td>22.4</td>\n",
@@ -615,57 +362,7 @@
        "2    35.247826  34.7\n",
        "3    35.247826  33.4\n",
        "4    35.247826  36.2\n",
-       "5    23.787500  28.7\n",
-       "6    21.863636  22.9\n",
-       "7    20.020833  27.1\n",
-       "8    20.020833  16.5\n",
-       "9    20.020833  18.9\n",
-       "10   20.020833  15.0\n",
-       "11   21.863636  18.9\n",
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-       "14   20.318750  18.2\n",
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-       "16   23.787500  23.1\n",
-       "17   17.233962  17.5\n",
-       "18   20.318750  20.2\n",
-       "19   20.318750  18.2\n",
-       "20   14.041667  13.6\n",
-       "21   20.318750  19.6\n",
-       "22   17.233962  15.2\n",
-       "23   14.041667  14.5\n",
-       "24   17.233962  15.6\n",
-       "25   17.233962  13.9\n",
-       "26   17.233962  16.6\n",
-       "27   17.233962  14.8\n",
-       "28   20.318750  18.4\n",
-       "29   20.318750  21.0\n",
        "..         ...   ...\n",
-       "476  17.233962  16.7\n",
-       "477   9.913636  12.0\n",
-       "478  13.922222  14.6\n",
-       "479  20.318750  21.4\n",
-       "480  20.318750  23.0\n",
-       "481  23.787500  23.7\n",
-       "482  28.978261  25.0\n",
-       "483  20.318750  21.8\n",
-       "484  20.318750  20.6\n",
-       "485  20.318750  21.2\n",
-       "486  17.233962  19.1\n",
-       "487  20.318750  20.6\n",
-       "488  17.233962  15.2\n",
-       "489  14.041667   7.0\n",
-       "490  14.041667   8.1\n",
-       "491  17.233962  13.6\n",
-       "492  20.318750  20.1\n",
-       "493  20.318750  21.8\n",
-       "494  20.318750  24.5\n",
-       "495  17.233962  23.1\n",
-       "496  14.041667  19.7\n",
-       "497  20.318750  18.3\n",
-       "498  20.318750  21.2\n",
-       "499  17.233962  17.5\n",
-       "500  20.318750  16.8\n",
        "501  23.787500  22.4\n",
        "502  23.787500  20.6\n",
        "503  28.978261  23.9\n",
@@ -675,7 +372,7 @@
        "[506 rows x 2 columns]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -686,449 +383,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
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-       "<text text-anchor=\"middle\" x=\"527.5\" y=\"-245.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">nox &lt;= 0.605</text>\n",
-       "<text text-anchor=\"middle\" x=\"527.5\" y=\"-230.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 14.674</text>\n",
-       "<text text-anchor=\"middle\" x=\"527.5\" y=\"-215.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 74</text>\n",
-       "<text text-anchor=\"middle\" x=\"527.5\" y=\"-200.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = 11.978</text>\n",
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-       "<title>11&#45;&gt;17</title>\n",
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-       "<text text-anchor=\"middle\" x=\"332.5\" y=\"-134.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 9.016</text>\n",
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-       "<text text-anchor=\"middle\" x=\"430.5\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">lstat &lt;= 18.885</text>\n",
-       "<text text-anchor=\"middle\" x=\"430.5\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 8.733</text>\n",
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-       "<title>12&#45;&gt;14</title>\n",
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-       "<text text-anchor=\"middle\" x=\"428.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 5.952</text>\n",
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-       "<text text-anchor=\"middle\" x=\"525.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 7.862</text>\n",
-       "<text text-anchor=\"middle\" x=\"525.5\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 24</text>\n",
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-       "<title>14&#45;&gt;16</title>\n",
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-       "<text text-anchor=\"middle\" x=\"528.5\" y=\"-134.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 18.606</text>\n",
-       "<text text-anchor=\"middle\" x=\"528.5\" y=\"-119.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 12</text>\n",
-       "<text text-anchor=\"middle\" x=\"528.5\" y=\"-104.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = 16.633</text>\n",
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-       "<title>17&#45;&gt;18</title>\n",
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-       "<text text-anchor=\"middle\" x=\"626.5\" y=\"-141.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">lstat &lt;= 19.645</text>\n",
-       "<text text-anchor=\"middle\" x=\"626.5\" y=\"-126.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 8.908</text>\n",
-       "<text text-anchor=\"middle\" x=\"626.5\" y=\"-111.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 62</text>\n",
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-       "<title>17&#45;&gt;19</title>\n",
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-       "<title>20</title>\n",
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-       "<text text-anchor=\"middle\" x=\"624.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 4.18</text>\n",
-       "<text text-anchor=\"middle\" x=\"624.5\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 18</text>\n",
-       "<text text-anchor=\"middle\" x=\"624.5\" y=\"-7.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">value = 13.922</text>\n",
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-       "<title>19&#45;&gt;20</title>\n",
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-       "<title>21</title>\n",
-       "<polygon fill=\"none\" stroke=\"#000000\" points=\"756.5,-53 682.5,-53 682.5,0 756.5,0 756.5,-53\"/>\n",
-       "<text text-anchor=\"middle\" x=\"719.5\" y=\"-37.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 6.177</text>\n",
-       "<text text-anchor=\"middle\" x=\"719.5\" y=\"-22.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">samples = 44</text>\n",
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-       "<title>19&#45;&gt;21</title>\n",
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-       "<title>23</title>\n",
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-       "<text text-anchor=\"middle\" x=\"672.5\" y=\"-349.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">lstat &lt;= 5.495</text>\n",
-       "<text text-anchor=\"middle\" x=\"672.5\" y=\"-334.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 41.296</text>\n",
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-       "<text text-anchor=\"middle\" x=\"818.5\" y=\"-349.8\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">ptratio &lt;= 15.4</text>\n",
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-       "<text text-anchor=\"middle\" x=\"818.5\" y=\"-238.3\" font-family=\"Times,serif\" font-size=\"14.00\" fill=\"#000000\">mse = 7.774</text>\n",
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-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'graphviz'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-11-bbc8428a56ea>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mgraphviz\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSource\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"resources/boston_rtree.dot\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"r\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'graphviz'"
+     ]
     }
    ],
    "source": [
@@ -1146,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1157,19 +424,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-14.21501779 -16.62018743 -18.48312343 -46.22608214 -10.25202434\n",
-      " -18.39546877 -15.08005674 -32.23929702 -23.04375075 -10.93761052]\n",
-      "-20.54926189262014\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cv = KFold(n=X.shape[0], n_folds = 10, shuffle=True, random_state=1)\n",
     "scores = cross_val_score(regtree, X, Y, scoring=\"mean_squared_error\", cv = cv, n_jobs=1)\n",
@@ -1180,32 +437,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('crim', 0.032184533425691254),\n",
-       " ('zn', 0.0),\n",
-       " ('indus', 0.0),\n",
-       " ('chas', 0.0),\n",
-       " ('nox', 0.016195328299152056),\n",
-       " ('rm', 0.6341876193016562),\n",
-       " ('age', 0.014165271650613091),\n",
-       " ('dis', 0.0),\n",
-       " ('rad', 0.0),\n",
-       " ('tax', 0.0),\n",
-       " ('ptratio', 0.009620458196377114),\n",
-       " ('black', 0.0),\n",
-       " ('lstat', 0.2936467891265104)]"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "list(zip(predictors,regtree.feature_importances_))"
    ]
@@ -1219,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1228,25 +462,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
-       "           max_features='auto', max_leaf_nodes=None,\n",
-       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
-       "           min_samples_leaf=1, min_samples_split=2,\n",
-       "           min_weight_fraction_leaf=0.0, n_estimators=10000, n_jobs=2,\n",
-       "           oob_score=True, random_state=None, verbose=0, warm_start=False)"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "forest = RandomForestRegressor(n_jobs=2, oob_score=True, n_estimators=10000)\n",
     "forest.fit(X,Y)"
@@ -1254,417 +472,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "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></th>\n",
-       "      <th>rforest_pred</th>\n",
-       "      <th>medv</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>28.481279</td>\n",
-       "      <td>24.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>22.624058</td>\n",
-       "      <td>21.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>34.585730</td>\n",
-       "      <td>34.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>34.790003</td>\n",
-       "      <td>33.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>34.323048</td>\n",
-       "      <td>36.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>23.997725</td>\n",
-       "      <td>28.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>20.229325</td>\n",
-       "      <td>22.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>17.294122</td>\n",
-       "      <td>27.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>17.910405</td>\n",
-       "      <td>16.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>19.775659</td>\n",
-       "      <td>18.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>20.693292</td>\n",
-       "      <td>15.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>20.813106</td>\n",
-       "      <td>18.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>21.245221</td>\n",
-       "      <td>21.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>19.944679</td>\n",
-       "      <td>20.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>19.966388</td>\n",
-       "      <td>18.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>20.347713</td>\n",
-       "      <td>19.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>21.397943</td>\n",
-       "      <td>23.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>18.328238</td>\n",
-       "      <td>17.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>18.738948</td>\n",
-       "      <td>20.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>19.772087</td>\n",
-       "      <td>18.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>14.508871</td>\n",
-       "      <td>13.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>18.584422</td>\n",
-       "      <td>19.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>16.068443</td>\n",
-       "      <td>15.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>15.013955</td>\n",
-       "      <td>14.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>16.630460</td>\n",
-       "      <td>15.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>15.760181</td>\n",
-       "      <td>13.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>17.402940</td>\n",
-       "      <td>16.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>15.118416</td>\n",
-       "      <td>14.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>20.057530</td>\n",
-       "      <td>18.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>23.006519</td>\n",
-       "      <td>21.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>476</th>\n",
-       "      <td>16.016040</td>\n",
-       "      <td>16.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>477</th>\n",
-       "      <td>13.579000</td>\n",
-       "      <td>12.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>478</th>\n",
-       "      <td>14.573479</td>\n",
-       "      <td>14.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>479</th>\n",
-       "      <td>20.076588</td>\n",
-       "      <td>21.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>480</th>\n",
-       "      <td>21.017078</td>\n",
-       "      <td>23.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>481</th>\n",
-       "      <td>28.701422</td>\n",
-       "      <td>23.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>482</th>\n",
-       "      <td>32.116689</td>\n",
-       "      <td>25.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>483</th>\n",
-       "      <td>20.267125</td>\n",
-       "      <td>21.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>484</th>\n",
-       "      <td>20.983144</td>\n",
-       "      <td>20.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>485</th>\n",
-       "      <td>22.013684</td>\n",
-       "      <td>21.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>486</th>\n",
-       "      <td>19.040222</td>\n",
-       "      <td>19.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>487</th>\n",
-       "      <td>21.368733</td>\n",
-       "      <td>20.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>488</th>\n",
-       "      <td>14.401306</td>\n",
-       "      <td>15.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>489</th>\n",
-       "      <td>13.073253</td>\n",
-       "      <td>7.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>490</th>\n",
-       "      <td>10.850246</td>\n",
-       "      <td>8.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>491</th>\n",
-       "      <td>16.120535</td>\n",
-       "      <td>13.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>492</th>\n",
-       "      <td>20.220841</td>\n",
-       "      <td>20.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>493</th>\n",
-       "      <td>19.672850</td>\n",
-       "      <td>21.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>494</th>\n",
-       "      <td>20.752433</td>\n",
-       "      <td>24.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>495</th>\n",
-       "      <td>19.425565</td>\n",
-       "      <td>23.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>496</th>\n",
-       "      <td>18.670488</td>\n",
-       "      <td>19.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>497</th>\n",
-       "      <td>20.333564</td>\n",
-       "      <td>18.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>498</th>\n",
-       "      <td>21.284089</td>\n",
-       "      <td>21.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>499</th>\n",
-       "      <td>19.855477</td>\n",
-       "      <td>17.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>500</th>\n",
-       "      <td>20.299728</td>\n",
-       "      <td>16.8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>501</th>\n",
-       "      <td>24.289566</td>\n",
-       "      <td>22.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>502</th>\n",
-       "      <td>18.987793</td>\n",
-       "      <td>20.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>503</th>\n",
-       "      <td>27.636796</td>\n",
-       "      <td>23.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>504</th>\n",
-       "      <td>26.192514</td>\n",
-       "      <td>22.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>505</th>\n",
-       "      <td>20.897088</td>\n",
-       "      <td>11.9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>506 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     rforest_pred  medv\n",
-       "0       28.481279  24.0\n",
-       "1       22.624058  21.6\n",
-       "2       34.585730  34.7\n",
-       "3       34.790003  33.4\n",
-       "4       34.323048  36.2\n",
-       "5       23.997725  28.7\n",
-       "6       20.229325  22.9\n",
-       "7       17.294122  27.1\n",
-       "8       17.910405  16.5\n",
-       "9       19.775659  18.9\n",
-       "10      20.693292  15.0\n",
-       "11      20.813106  18.9\n",
-       "12      21.245221  21.7\n",
-       "13      19.944679  20.4\n",
-       "14      19.966388  18.2\n",
-       "15      20.347713  19.9\n",
-       "16      21.397943  23.1\n",
-       "17      18.328238  17.5\n",
-       "18      18.738948  20.2\n",
-       "19      19.772087  18.2\n",
-       "20      14.508871  13.6\n",
-       "21      18.584422  19.6\n",
-       "22      16.068443  15.2\n",
-       "23      15.013955  14.5\n",
-       "24      16.630460  15.6\n",
-       "25      15.760181  13.9\n",
-       "26      17.402940  16.6\n",
-       "27      15.118416  14.8\n",
-       "28      20.057530  18.4\n",
-       "29      23.006519  21.0\n",
-       "..            ...   ...\n",
-       "476     16.016040  16.7\n",
-       "477     13.579000  12.0\n",
-       "478     14.573479  14.6\n",
-       "479     20.076588  21.4\n",
-       "480     21.017078  23.0\n",
-       "481     28.701422  23.7\n",
-       "482     32.116689  25.0\n",
-       "483     20.267125  21.8\n",
-       "484     20.983144  20.6\n",
-       "485     22.013684  21.2\n",
-       "486     19.040222  19.1\n",
-       "487     21.368733  20.6\n",
-       "488     14.401306  15.2\n",
-       "489     13.073253   7.0\n",
-       "490     10.850246   8.1\n",
-       "491     16.120535  13.6\n",
-       "492     20.220841  20.1\n",
-       "493     19.672850  21.8\n",
-       "494     20.752433  24.5\n",
-       "495     19.425565  23.1\n",
-       "496     18.670488  19.7\n",
-       "497     20.333564  18.3\n",
-       "498     21.284089  21.2\n",
-       "499     19.855477  17.5\n",
-       "500     20.299728  16.8\n",
-       "501     24.289566  22.4\n",
-       "502     18.987793  20.6\n",
-       "503     27.636796  23.9\n",
-       "504     26.192514  22.0\n",
-       "505     20.897088  11.9\n",
-       "\n",
-       "[506 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data[\"rforest_pred\"]= forest.oob_prediction_\n",
     "data[[\"rforest_pred\", \"medv\"]]"
@@ -1672,20 +482,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10.234649905954406"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "data[\"rforest_error2\"] = (data[\"rforest_pred\"]-data[\"medv\"])**2\n",
     "sum(data[\"rforest_error2\"])/len(data)"
@@ -1693,30 +492,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.8787644667661886"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "forest.oob_score_"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -1735,7 +516,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T8 - 1 - SVM - Linear SVC-Colab.ipynb b/notebooks/T8 - 1 - SVM - Linear SVC-Colab.ipynb
new file mode 100644
index 00000000..8d9acaf5
--- /dev/null
+++ b/notebooks/T8 - 1 - SVM - Linear SVC-Colab.ipynb	
@@ -0,0 +1,346 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T8%20-%201%20-%20SVM%20-%20Linear%20SVC-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Linear Support Vector Classifier"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import style\n",
+    "style.use(\"ggplot\")\n",
+    "from sklearn import svm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = [1,5,1.5,8,1,9]\n",
+    "Y = [2,8,1.8,8,0.6,11]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1227ac88>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X,Y)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = np.array(list(zip(X,Y)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1. ,  2. ],\n",
+       "       [ 5. ,  8. ],\n",
+       "       [ 1.5,  1.8],\n",
+       "       [ 8. ,  8. ],\n",
+       "       [ 1. ,  0.6],\n",
+       "       [ 9. , 11. ]])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "target = [0, 1, 0, 1, 0, 1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classifier = svm.SVC(kernel=\"linear\", C = 1.0)\n",
+    "classifier.fit(data, target)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[10.32 12.67]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([1])"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = np.array([10.32, 12.67]).reshape(1,2)\n",
+    "print(p)\n",
+    "classifier.predict(p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Modelo: w0 . x + w1 . y + e = 0\n",
+    "* Ecuación del hiperplano en 2D: y = a . x + b "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.1380943 , 0.24462418])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w = classifier.coef_[0]\n",
+    "w"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.5645161290322581"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = -w[0]/w[1]\n",
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.734677437813051"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b = - classifier.intercept_[0]/w[1]\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xx = np.linspace(0,10)\n",
+    "yy = a * xx + b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a1e1e9160>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(xx, yy, 'k-', label = \"Hiperplano de separación\")\n",
+    "plt.scatter(X, Y, c = target)\n",
+    "plt.legend()\n",
+    "plt.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T8 - 1 - SVM - Linear SVC.ipynb b/notebooks/T8 - 1 - SVM - Linear SVC.ipynb
index c397677a..058169ed 100644
--- a/notebooks/T8 - 1 - SVM - Linear SVC.ipynb	
+++ b/notebooks/T8 - 1 - SVM - Linear SVC.ipynb	
@@ -285,7 +285,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T8 - 2 - SVM - Model-Colab.ipynb b/notebooks/T8 - 2 - SVM - Model-Colab.ipynb
new file mode 100644
index 00000000..bf047256
--- /dev/null
+++ b/notebooks/T8 - 2 - SVM - Model-Colab.ipynb	
@@ -0,0 +1,461 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T8%20-%202%20-%20SVM%20-%20Model-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Support Vector Machines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import stats\n",
+    "\n",
+    "import seaborn as sns; sns.set()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets.samples_generator import make_blobs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.6)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x11826c550>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x117add048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:,0], X[:,1], c = Y, s = 50, cmap=\"autumn\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-1, 3.5)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a24b08550>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xx = np.linspace(-1, 3.5)\n",
+    "plt.scatter(X[:,0], X[:,1], c = Y, s = 50, cmap=\"autumn\")\n",
+    "plt.plot([0.5], [2.1], 'x', color=\"blue\", markeredgewidth=2, markersize=10)\n",
+    "\n",
+    "for a, b in [(1,0.65), (0.5, 1.6), (-0.2, 2.9)]:\n",
+    "    yy = a * xx + b\n",
+    "    plt.plot(xx, yy, \"-k\")\n",
+    "    \n",
+    "plt.xlim(-1,3.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Maximización del margen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-1, 3.5)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a24b33198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "xx = np.linspace(-1, 3.5)\n",
+    "plt.scatter(X[:,0], X[:,1], c = Y, s = 50, cmap=\"autumn\")\n",
+    "plt.plot([0.5], [2.1], 'x', color=\"blue\", markeredgewidth=2, markersize=10)\n",
+    "\n",
+    "for a, b, d in [(1,0.65, 0.33), (0.5, 1.6,0.55), (-0.2, 2.9, 0.2)]:\n",
+    "    yy = a * xx + b\n",
+    "    plt.plot(xx, yy, \"-k\")\n",
+    "    plt.fill_between(xx, yy-d, yy+d, edgecolor='none', color=\"#BBBBBB\", alpha = 0.4)\n",
+    "    \n",
+    "plt.xlim(-1,3.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creación del modelo SVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.svm import SVC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=10000000000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model=SVC(kernel=\"linear\", C = 1E10)\n",
+    "model.fit(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plt_svc(model, ax=None, plot_support=True):\n",
+    "    \"\"\"Plot de la función de decisión para una clasificación en 2D con SVC\"\"\"\n",
+    "    if ax is None:\n",
+    "        ax = plt.gca()\n",
+    "    xlim = ax.get_xlim()\n",
+    "    ylim = ax.get_ylim()\n",
+    "    \n",
+    "    ##Generamos la parrila de puntos para evaluar el modelo\n",
+    "    xx = np.linspace(xlim[0], xlim[1], 30)\n",
+    "    yy = np.linspace(ylim[0], ylim[1], 30)\n",
+    "    Y, X = np.meshgrid(yy,xx)\n",
+    "    \n",
+    "    xy = np.vstack([X.ravel(), Y.ravel()]).T\n",
+    "    P = model.decision_function(xy).reshape(X.shape)\n",
+    "\n",
+    "    \n",
+    "    ##Representamos las fronteras y los márgenes del SVC\n",
+    "    ax.contour(X,Y,P, colors=\"k\", levels=[-1,0,1], alpha = 0.5, linestyles=[\"--\", \"-\", \"--\"])\n",
+    "    \n",
+    "    print(model.support_vectors_)\n",
+    "    \n",
+    "    if plot_support:\n",
+    "        ax.scatter(model.support_vectors_[:,0], \n",
+    "                   model.support_vectors_[:,1], \n",
+    "                   s=300, linewidth=1, facecolors = \"black\");\n",
+    "    \n",
+    "    \n",
+    "    ax.set_xlim(xlim)\n",
+    "    ax.set_ylim(ylim)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.44359863 3.11530945]\n",
+      " [2.33812285 3.43116792]\n",
+      " [2.06156753 1.96918596]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a28667b38>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:,0], X[:,1], c = Y, s = 50, cmap = \"autumn\")\n",
+    "plt_svc(model, plot_support=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_svm(N=10, ax=None):\n",
+    "    X, Y = make_blobs(n_samples=200, centers=2, random_state=0, cluster_std=0.6)\n",
+    "    \n",
+    "    X = X[:N]\n",
+    "    Y = Y[:N]\n",
+    "    model = SVC(kernel=\"linear\", C=1E10)\n",
+    "    model.fit(X,Y)\n",
+    "    \n",
+    "    ax = ax or plt.gca()\n",
+    "    ax.scatter(X[:,0], X[:,1], c=Y, s = 50, cmap=\"autumn\")\n",
+    "    ax.set_xlim(-1,4)\n",
+    "    ax.set_ylim(-1,6)\n",
+    "    plt_svc(model, ax)\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.44359863 3.11530945]\n",
+      " [1.25566754 3.38204112]\n",
+      " [0.83685684 2.13635938]]\n",
+      "[[0.44359863 3.11530945]\n",
+      " [1.25566754 3.38204112]\n",
+      " [0.83685684 2.13635938]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a267989b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,2, figsize=(16,6))\n",
+    "fig.subplots_adjust(left=0.0625, right = 0.95, wspace = 0.1)\n",
+    "for ax_i, N, in zip(ax, [60, 120]):\n",
+    "    plot_svm(N, ax_i)\n",
+    "    ax_i.set_title(\"N={0}\".format(N))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ipywidgets import interact, fixed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d8687d66bf24d54822fc413298c3c04",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "interactive(children=(Dropdown(description='N', options=(10, 200), value=10), Output()), _dom_classes=('widget-interact',))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_svm>"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(plot_svm, N=[10, 200], ax=fixed(None))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T8 - 2 - SVM - Model.ipynb b/notebooks/T8 - 2 - SVM - Model.ipynb
index 1d530de7..339afb1f 100644
--- a/notebooks/T8 - 2 - SVM - Model.ipynb	
+++ b/notebooks/T8 - 2 - SVM - Model.ipynb	
@@ -400,7 +400,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T8 - 3 - SVM - Kernels-Colab.ipynb b/notebooks/T8 - 3 - SVM - Kernels-Colab.ipynb
new file mode 100644
index 00000000..524632aa
--- /dev/null
+++ b/notebooks/T8 - 3 - SVM - Kernels-Colab.ipynb	
@@ -0,0 +1,497 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T8%20-%203%20-%20SVM%20-%20Kernels-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Identificar fronteras no lineales"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import make_circles, make_blobs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = make_circles(100, factor = .1, noise = .1) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.svm import SVC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plt_svc(model, ax=None, plot_support=True):\n",
+    "    \"\"\"Plot de la función de decisión para una clasificación en 2D con SVC\"\"\"\n",
+    "    if ax is None:\n",
+    "        ax = plt.gca()\n",
+    "    xlim = ax.get_xlim()\n",
+    "    ylim = ax.get_ylim()\n",
+    "    \n",
+    "    ##Generamos la parrila de puntos para evaluar el modelo\n",
+    "    xx = np.linspace(xlim[0], xlim[1], 30)\n",
+    "    yy = np.linspace(ylim[0], ylim[1], 30)\n",
+    "    Y, X = np.meshgrid(yy,xx)\n",
+    "    \n",
+    "    xy = np.vstack([X.ravel(), Y.ravel()]).T\n",
+    "    P = model.decision_function(xy).reshape(X.shape)\n",
+    "\n",
+    "    \n",
+    "    ##Representamos las fronteras y los márgenes del SVC\n",
+    "    ax.contour(X,Y,P, colors=\"k\", levels=[-1,0,1], alpha = 0.5, linestyles=[\"--\", \"-\", \"--\"])\n",
+    "        \n",
+    "    if plot_support:\n",
+    "        ax.scatter(model.support_vectors_[:,0], \n",
+    "                   model.support_vectors_[:,1], \n",
+    "                   s=300, linewidth=1, facecolors = \"blue\");\n",
+    "    \n",
+    "    \n",
+    "    ax.set_xlim(xlim)\n",
+    "    ax.set_ylim(ylim)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x11aae6278>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1150c4390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:,0], X[:,1], c=Y, s=50, cmap=\"autumn\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b81b860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:,0], X[:,1], c=Y, s=50, cmap=\"autumn\")\n",
+    "plt_svc(SVC(kernel=\"linear\").fit(X,Y), plot_support=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = np.exp(-(X**2).sum(1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.36818596, 0.99683274, 0.98004106, 0.96359441, 0.45527788,\n",
+       "       0.3319918 , 0.40182683, 0.97431764, 0.99588324, 0.96024516,\n",
+       "       0.99652482, 0.95688224, 0.95229884, 0.99459105, 0.34325532,\n",
+       "       0.99750123, 0.99690068, 0.98038774, 0.92990067, 0.99778036,\n",
+       "       0.39502319, 0.48150218, 0.35830228, 0.38120558, 0.41764426,\n",
+       "       0.96665473, 0.39219701, 0.52198638, 0.99134565, 0.92408127,\n",
+       "       0.98918207, 0.94499936, 0.34842023, 0.97751382, 0.98308532,\n",
+       "       0.99750055, 0.96594058, 0.3706404 , 0.98458359, 0.97418051,\n",
+       "       0.28276771, 0.22185955, 0.47046283, 0.352884  , 0.33848479,\n",
+       "       0.38546437, 0.94911696, 0.99311764, 0.96859857, 0.9947006 ,\n",
+       "       0.98696264, 0.97436558, 0.62154576, 0.35523754, 0.9747215 ,\n",
+       "       0.3767697 , 0.28617381, 0.98720335, 0.42925749, 0.95700044,\n",
+       "       0.97389292, 0.98095638, 0.99025277, 0.41530283, 0.32975922,\n",
+       "       0.27432486, 0.43010132, 0.40576651, 0.31337897, 0.95751945,\n",
+       "       0.30547137, 0.97402227, 0.40798255, 0.99037529, 0.97472555,\n",
+       "       0.92117192, 0.96137008, 0.98060834, 0.94494153, 0.87190453,\n",
+       "       0.50531722, 0.99796355, 0.41404561, 0.98908129, 0.39959505,\n",
+       "       0.37631477, 0.43930779, 0.4761309 , 0.4460329 , 0.38877863,\n",
+       "       0.98799743, 0.29643436, 0.39131145, 0.44033889, 0.39400008,\n",
+       "       0.43590187, 0.42037137, 0.37017225, 0.42398422, 0.35942632])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mpl_toolkits import mplot3d"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_3D(elev=30, azim=30, X=X, Y=Y, r=r):\n",
+    "    ax = plt.subplot(projection=\"3d\")\n",
+    "    ax.scatter3D(X[:,0], X[:,1],r, c=Y, s= 50, cmap=\"autumn\")\n",
+    "    ax.view_init(elev=elev, azim=azim)\n",
+    "    \n",
+    "    ax.set_xlabel(\"x[0]\")\n",
+    "    ax.set_ylabel(\"y[1]\")\n",
+    "    ax.set_zlabel(\"r\")\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ipywidgets import interact, fixed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b93008af555d40298f05f6f0c3c23bb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "interactive(children=(Dropdown(description='elev', index=4, options=(-90, -60, -30, 0, 30, 60, 90), value=30), Dropdown(description='azim', index=7, options=(-180, -150, -120, -90, -60, -30, 0, 30, 60, 90, 120, 150, 180), value=30), Output()), _dom_classes=('widget-interact',))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.plot_3D>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(plot_3D, elev=[-90,-60,-30,0,30,60,90], \n",
+    "         azim=[-180,-150,-120,-90,-60,-30,0,30,60,90,120,150, 180], \n",
+    "         X = fixed(X), Y = fixed(Y), r = fixed(r))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=1000000.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rbf = SVC(kernel=\"rbf\", C=1E6)\n",
+    "rbf.fit(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x11b3f4e48>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11b8dbf60>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:,0], X[:,1], c=Y, s=50, cmap=\"autumn\")\n",
+    "plt_svc(rbf)\n",
+    "plt.scatter(rbf.support_vectors_[:,0], rbf.support_vectors_[:,1], s=300, lw=1, facecolors=\"blue\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Ajustar los parámetros de SVM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = make_blobs(n_samples=100, centers = 2, random_state=0, cluster_std=1.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a231c1780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X[:,0], X[:,1], c = Y, s=50, cmap=\"autumn\")\n",
+    "model = SVC(kernel=\"linear\", C=10)\n",
+    "model.fit(X,Y)\n",
+    "plt_svc(model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x1a21169e48>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11c2f43c8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X, Y = make_blobs(n_samples=100, centers=2, random_state=0, cluster_std=0.8)\n",
+    "plt.scatter(X[:,0], X[:,1], c = Y, s=50, cmap=\"autumn\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a22c75278>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,2, figsize=(16,6))\n",
+    "fig.subplots_adjust(left = 0.05, right = 0.95, wspace=0.1)\n",
+    "\n",
+    "for ax_i, C in zip(ax, [10.0, 0.1]):\n",
+    "    model = SVC(kernel=\"linear\", C=C)\n",
+    "    model.fit(X,Y)\n",
+    "    ax_i.scatter(X[:,0],X[:,1], c = Y, s = 50, cmap=\"autumn\")\n",
+    "    plt_svc(model, ax_i)\n",
+    "    ax_i.set_title(\"C = {0:.1f}\".format(C), size = 15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T8 - 3 - SVM - Kernels.ipynb b/notebooks/T8 - 3 - SVM - Kernels.ipynb
index 6bfcab6a..17c75471 100644
--- a/notebooks/T8 - 3 - SVM - Kernels.ipynb	
+++ b/notebooks/T8 - 3 - SVM - Kernels.ipynb	
@@ -436,7 +436,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T8 - 4 - SVM - Face Recognition-Colab.ipynb b/notebooks/T8 - 4 - SVM - Face Recognition-Colab.ipynb
new file mode 100644
index 00000000..aac90c38
--- /dev/null
+++ b/notebooks/T8 - 4 - SVM - Face Recognition-Colab.ipynb	
@@ -0,0 +1,464 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T8%20-%204%20-%20SVM%20-%20Face%20Recognition-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reconocimiento Facial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import fetch_lfw_people\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Downloading LFW metadata: https://ndownloader.figshare.com/files/5976012\n",
+      "Downloading LFW metadata: https://ndownloader.figshare.com/files/5976009\n",
+      "Downloading LFW metadata: https://ndownloader.figshare.com/files/5976006\n",
+      "Downloading LFW data (~200MB): https://ndownloader.figshare.com/files/5976015\n"
+     ]
+    }
+   ],
+   "source": [
+    "faces = fetch_lfw_people(min_faces_per_person=60)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Ariel Sharon' 'Colin Powell' 'Donald Rumsfeld' 'George W Bush'\n",
+      " 'Gerhard Schroeder' 'Hugo Chavez' 'Junichiro Koizumi' 'Tony Blair']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(faces.target_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1348, 62, 47)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(faces.images.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c5c4f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(5,5, figsize=(16,9))\n",
+    "for i, ax_i in enumerate(ax.flat):\n",
+    "    ax_i.imshow(faces.images[i], cmap=\"bone\")\n",
+    "    ax_i.set(xticks=[], yticks=[],xlabel=faces.target_names[faces.target[i]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2914"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "62*47"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.decomposition import PCA as RandomizedPCA\n",
+    "from sklearn.pipeline import make_pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pca = RandomizedPCA(n_components=150, whiten=True, random_state=42)\n",
+    "svc = SVC(kernel=\"rbf\", class_weight=\"balanced\")\n",
+    "model = make_pipeline(pca, svc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(faces.data, faces.target, random_state = 42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.grid_search import GridSearchCV"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1min 8s, sys: 4.08 s, total: 1min 12s\n",
+      "Wall time: 24.1 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=None, error_score='raise',\n",
+       "       estimator=Pipeline(memory=None,\n",
+       "     steps=[('randomizedpca', RandomizedPCA(copy=True, iterated_power=2, n_components=150, random_state=42,\n",
+       "       whiten=True)), ('svc', SVC(C=1.0, cache_size=200, class_weight='balanced', coef0=0.0,\n",
+       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False))]),\n",
+       "       fit_params={}, iid=True, n_jobs=1,\n",
+       "       param_grid={'svc__C': [0.1, 1, 5, 10, 50], 'svc__gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01]},\n",
+       "       pre_dispatch='2*n_jobs', refit=True, scoring=None, verbose=0)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "param_grid = {\n",
+    "    \"svc__C\":[0.1,1,5,10,50],\n",
+    "    \"svc__gamma\":[0.0001, 0.0005, 0.001, 0.005, 0.01]\n",
+    "}\n",
+    "grid = GridSearchCV(model, param_grid)\n",
+    "\n",
+    "%time grid.fit(Xtrain, Ytrain)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'svc__C': 5, 'svc__gamma': 0.001}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(grid.best_params_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classifier = grid.best_estimator_\n",
+    "yfit = classifier.predict(Xtest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,0.98,'Predicciones de las imágnes (incorrectas en rojo)')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10be972e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(8,6,figsize=(16,9))\n",
+    "\n",
+    "for i, ax_i in enumerate(ax.flat):\n",
+    "    ax_i.imshow(Xtest[i].reshape(62,47), cmap=\"bone\")\n",
+    "    ax_i.set(xticks=[], yticks=[])\n",
+    "    ax_i.set_ylabel(faces.target_names[yfit[i]].split()[-1],\n",
+    "                   color = \"black\" if yfit[i]==Ytest[i] else \"red\")\n",
+    "\n",
+    "fig.suptitle(\"Predicciones de las imágnes (incorrectas en rojo)\", size = 15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import classification_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                   precision    recall  f1-score   support\n",
+      "\n",
+      "     Ariel Sharon       0.62      0.87      0.72        15\n",
+      "     Colin Powell       0.82      0.88      0.85        68\n",
+      "  Donald Rumsfeld       0.72      0.84      0.78        31\n",
+      "    George W Bush       0.98      0.82      0.89       126\n",
+      "Gerhard Schroeder       0.76      0.83      0.79        23\n",
+      "      Hugo Chavez       0.88      0.75      0.81        20\n",
+      "Junichiro Koizumi       0.80      1.00      0.89        12\n",
+      "       Tony Blair       0.89      0.95      0.92        42\n",
+      "\n",
+      "      avg / total       0.87      0.85      0.86       337\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(Ytest, yfit, target_names = faces.target_names))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import confusion_matrix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mat = confusion_matrix(Ytest, yfit)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
+      "  return f(*args, **kwds)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import seaborn as sns; sns.set()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1f7d7a58>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1086385c0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(mat.T, square=True, annot=True, fmt='d', cbar=True, \n",
+    "            xticklabels=faces.target_names, yticklabels=faces.target_names )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T8 - 4 - SVM - Face Recognition.ipynb b/notebooks/T8 - 4 - SVM - Face Recognition.ipynb
index 7768464f..1ef1fe35 100644
--- a/notebooks/T8 - 4 - SVM - Face Recognition.ipynb	
+++ b/notebooks/T8 - 4 - SVM - Face Recognition.ipynb	
@@ -403,7 +403,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T8 - 5 - SVM - Clasificaci\303\263n de Iris-Colab.ipynb" "b/notebooks/T8 - 5 - SVM - Clasificaci\303\263n de Iris-Colab.ipynb"
new file mode 100644
index 00000000..f25ab592
--- /dev/null
+++ "b/notebooks/T8 - 5 - SVM - Clasificaci\303\263n de Iris-Colab.ipynb"	
@@ -0,0 +1,930 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T8%20-%205%20-%20SVM%20-%20Clasificación%20de%20Iris-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clasificación de flores Iris"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn import svm, datasets\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'data': array([[5.1, 3.5, 1.4, 0.2],\n",
+      "       [4.9, 3. , 1.4, 0.2],\n",
+      "       [4.7, 3.2, 1.3, 0.2],\n",
+      "       [4.6, 3.1, 1.5, 0.2],\n",
+      "       [5. , 3.6, 1.4, 0.2],\n",
+      "       [5.4, 3.9, 1.7, 0.4],\n",
+      "       [4.6, 3.4, 1.4, 0.3],\n",
+      "       [5. , 3.4, 1.5, 0.2],\n",
+      "       [4.4, 2.9, 1.4, 0.2],\n",
+      "       [4.9, 3.1, 1.5, 0.1],\n",
+      "       [5.4, 3.7, 1.5, 0.2],\n",
+      "       [4.8, 3.4, 1.6, 0.2],\n",
+      "       [4.8, 3. , 1.4, 0.1],\n",
+      "       [4.3, 3. , 1.1, 0.1],\n",
+      "       [5.8, 4. , 1.2, 0.2],\n",
+      "       [5.7, 4.4, 1.5, 0.4],\n",
+      "       [5.4, 3.9, 1.3, 0.4],\n",
+      "       [5.1, 3.5, 1.4, 0.3],\n",
+      "       [5.7, 3.8, 1.7, 0.3],\n",
+      "       [5.1, 3.8, 1.5, 0.3],\n",
+      "       [5.4, 3.4, 1.7, 0.2],\n",
+      "       [5.1, 3.7, 1.5, 0.4],\n",
+      "       [4.6, 3.6, 1. , 0.2],\n",
+      "       [5.1, 3.3, 1.7, 0.5],\n",
+      "       [4.8, 3.4, 1.9, 0.2],\n",
+      "       [5. , 3. , 1.6, 0.2],\n",
+      "       [5. , 3.4, 1.6, 0.4],\n",
+      "       [5.2, 3.5, 1.5, 0.2],\n",
+      "       [5.2, 3.4, 1.4, 0.2],\n",
+      "       [4.7, 3.2, 1.6, 0.2],\n",
+      "       [4.8, 3.1, 1.6, 0.2],\n",
+      "       [5.4, 3.4, 1.5, 0.4],\n",
+      "       [5.2, 4.1, 1.5, 0.1],\n",
+      "       [5.5, 4.2, 1.4, 0.2],\n",
+      "       [4.9, 3.1, 1.5, 0.1],\n",
+      "       [5. , 3.2, 1.2, 0.2],\n",
+      "       [5.5, 3.5, 1.3, 0.2],\n",
+      "       [4.9, 3.1, 1.5, 0.1],\n",
+      "       [4.4, 3. , 1.3, 0.2],\n",
+      "       [5.1, 3.4, 1.5, 0.2],\n",
+      "       [5. , 3.5, 1.3, 0.3],\n",
+      "       [4.5, 2.3, 1.3, 0.3],\n",
+      "       [4.4, 3.2, 1.3, 0.2],\n",
+      "       [5. , 3.5, 1.6, 0.6],\n",
+      "       [5.1, 3.8, 1.9, 0.4],\n",
+      "       [4.8, 3. , 1.4, 0.3],\n",
+      "       [5.1, 3.8, 1.6, 0.2],\n",
+      "       [4.6, 3.2, 1.4, 0.2],\n",
+      "       [5.3, 3.7, 1.5, 0.2],\n",
+      "       [5. , 3.3, 1.4, 0.2],\n",
+      "       [7. , 3.2, 4.7, 1.4],\n",
+      "       [6.4, 3.2, 4.5, 1.5],\n",
+      "       [6.9, 3.1, 4.9, 1.5],\n",
+      "       [5.5, 2.3, 4. , 1.3],\n",
+      "       [6.5, 2.8, 4.6, 1.5],\n",
+      "       [5.7, 2.8, 4.5, 1.3],\n",
+      "       [6.3, 3.3, 4.7, 1.6],\n",
+      "       [4.9, 2.4, 3.3, 1. ],\n",
+      "       [6.6, 2.9, 4.6, 1.3],\n",
+      "       [5.2, 2.7, 3.9, 1.4],\n",
+      "       [5. , 2. , 3.5, 1. ],\n",
+      "       [5.9, 3. , 4.2, 1.5],\n",
+      "       [6. , 2.2, 4. , 1. ],\n",
+      "       [6.1, 2.9, 4.7, 1.4],\n",
+      "       [5.6, 2.9, 3.6, 1.3],\n",
+      "       [6.7, 3.1, 4.4, 1.4],\n",
+      "       [5.6, 3. , 4.5, 1.5],\n",
+      "       [5.8, 2.7, 4.1, 1. ],\n",
+      "       [6.2, 2.2, 4.5, 1.5],\n",
+      "       [5.6, 2.5, 3.9, 1.1],\n",
+      "       [5.9, 3.2, 4.8, 1.8],\n",
+      "       [6.1, 2.8, 4. , 1.3],\n",
+      "       [6.3, 2.5, 4.9, 1.5],\n",
+      "       [6.1, 2.8, 4.7, 1.2],\n",
+      "       [6.4, 2.9, 4.3, 1.3],\n",
+      "       [6.6, 3. , 4.4, 1.4],\n",
+      "       [6.8, 2.8, 4.8, 1.4],\n",
+      "       [6.7, 3. , 5. , 1.7],\n",
+      "       [6. , 2.9, 4.5, 1.5],\n",
+      "       [5.7, 2.6, 3.5, 1. ],\n",
+      "       [5.5, 2.4, 3.8, 1.1],\n",
+      "       [5.5, 2.4, 3.7, 1. ],\n",
+      "       [5.8, 2.7, 3.9, 1.2],\n",
+      "       [6. , 2.7, 5.1, 1.6],\n",
+      "       [5.4, 3. , 4.5, 1.5],\n",
+      "       [6. , 3.4, 4.5, 1.6],\n",
+      "       [6.7, 3.1, 4.7, 1.5],\n",
+      "       [6.3, 2.3, 4.4, 1.3],\n",
+      "       [5.6, 3. , 4.1, 1.3],\n",
+      "       [5.5, 2.5, 4. , 1.3],\n",
+      "       [5.5, 2.6, 4.4, 1.2],\n",
+      "       [6.1, 3. , 4.6, 1.4],\n",
+      "       [5.8, 2.6, 4. , 1.2],\n",
+      "       [5. , 2.3, 3.3, 1. ],\n",
+      "       [5.6, 2.7, 4.2, 1.3],\n",
+      "       [5.7, 3. , 4.2, 1.2],\n",
+      "       [5.7, 2.9, 4.2, 1.3],\n",
+      "       [6.2, 2.9, 4.3, 1.3],\n",
+      "       [5.1, 2.5, 3. , 1.1],\n",
+      "       [5.7, 2.8, 4.1, 1.3],\n",
+      "       [6.3, 3.3, 6. , 2.5],\n",
+      "       [5.8, 2.7, 5.1, 1.9],\n",
+      "       [7.1, 3. , 5.9, 2.1],\n",
+      "       [6.3, 2.9, 5.6, 1.8],\n",
+      "       [6.5, 3. , 5.8, 2.2],\n",
+      "       [7.6, 3. , 6.6, 2.1],\n",
+      "       [4.9, 2.5, 4.5, 1.7],\n",
+      "       [7.3, 2.9, 6.3, 1.8],\n",
+      "       [6.7, 2.5, 5.8, 1.8],\n",
+      "       [7.2, 3.6, 6.1, 2.5],\n",
+      "       [6.5, 3.2, 5.1, 2. ],\n",
+      "       [6.4, 2.7, 5.3, 1.9],\n",
+      "       [6.8, 3. , 5.5, 2.1],\n",
+      "       [5.7, 2.5, 5. , 2. ],\n",
+      "       [5.8, 2.8, 5.1, 2.4],\n",
+      "       [6.4, 3.2, 5.3, 2.3],\n",
+      "       [6.5, 3. , 5.5, 1.8],\n",
+      "       [7.7, 3.8, 6.7, 2.2],\n",
+      "       [7.7, 2.6, 6.9, 2.3],\n",
+      "       [6. , 2.2, 5. , 1.5],\n",
+      "       [6.9, 3.2, 5.7, 2.3],\n",
+      "       [5.6, 2.8, 4.9, 2. ],\n",
+      "       [7.7, 2.8, 6.7, 2. ],\n",
+      "       [6.3, 2.7, 4.9, 1.8],\n",
+      "       [6.7, 3.3, 5.7, 2.1],\n",
+      "       [7.2, 3.2, 6. , 1.8],\n",
+      "       [6.2, 2.8, 4.8, 1.8],\n",
+      "       [6.1, 3. , 4.9, 1.8],\n",
+      "       [6.4, 2.8, 5.6, 2.1],\n",
+      "       [7.2, 3. , 5.8, 1.6],\n",
+      "       [7.4, 2.8, 6.1, 1.9],\n",
+      "       [7.9, 3.8, 6.4, 2. ],\n",
+      "       [6.4, 2.8, 5.6, 2.2],\n",
+      "       [6.3, 2.8, 5.1, 1.5],\n",
+      "       [6.1, 2.6, 5.6, 1.4],\n",
+      "       [7.7, 3. , 6.1, 2.3],\n",
+      "       [6.3, 3.4, 5.6, 2.4],\n",
+      "       [6.4, 3.1, 5.5, 1.8],\n",
+      "       [6. , 3. , 4.8, 1.8],\n",
+      "       [6.9, 3.1, 5.4, 2.1],\n",
+      "       [6.7, 3.1, 5.6, 2.4],\n",
+      "       [6.9, 3.1, 5.1, 2.3],\n",
+      "       [5.8, 2.7, 5.1, 1.9],\n",
+      "       [6.8, 3.2, 5.9, 2.3],\n",
+      "       [6.7, 3.3, 5.7, 2.5],\n",
+      "       [6.7, 3. , 5.2, 2.3],\n",
+      "       [6.3, 2.5, 5. , 1.9],\n",
+      "       [6.5, 3. , 5.2, 2. ],\n",
+      "       [6.2, 3.4, 5.4, 2.3],\n",
+      "       [5.9, 3. , 5.1, 1.8]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+      "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+      "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+      "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+      "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]), 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'), 'DESCR': 'Iris Plants Database\\n====================\\n\\nNotes\\n-----\\nData Set Characteristics:\\n    :Number of Instances: 150 (50 in each of three classes)\\n    :Number of Attributes: 4 numeric, predictive attributes and the class\\n    :Attribute Information:\\n        - sepal length in cm\\n        - sepal width in cm\\n        - petal length in cm\\n        - petal width in cm\\n        - class:\\n                - Iris-Setosa\\n                - Iris-Versicolour\\n                - Iris-Virginica\\n    :Summary Statistics:\\n\\n    ============== ==== ==== ======= ===== ====================\\n                    Min  Max   Mean    SD   Class Correlation\\n    ============== ==== ==== ======= ===== ====================\\n    sepal length:   4.3  7.9   5.84   0.83    0.7826\\n    sepal width:    2.0  4.4   3.05   0.43   -0.4194\\n    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\\n    petal width:    0.1  2.5   1.20  0.76     0.9565  (high!)\\n    ============== ==== ==== ======= ===== ====================\\n\\n    :Missing Attribute Values: None\\n    :Class Distribution: 33.3% for each of 3 classes.\\n    :Creator: R.A. Fisher\\n    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\\n    :Date: July, 1988\\n\\nThis is a copy of UCI ML iris datasets.\\nhttp://archive.ics.uci.edu/ml/datasets/Iris\\n\\nThe famous Iris database, first used by Sir R.A Fisher\\n\\nThis is perhaps the best known database to be found in the\\npattern recognition literature.  Fisher\\'s paper is a classic in the field and\\nis referenced frequently to this day.  (See Duda & Hart, for example.)  The\\ndata set contains 3 classes of 50 instances each, where each class refers to a\\ntype of iris plant.  One class is linearly separable from the other 2; the\\nlatter are NOT linearly separable from each other.\\n\\nReferences\\n----------\\n   - Fisher,R.A. \"The use of multiple measurements in taxonomic problems\"\\n     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\\n     Mathematical Statistics\" (John Wiley, NY, 1950).\\n   - Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.\\n     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\\n   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\\n     Structure and Classification Rule for Recognition in Partially Exposed\\n     Environments\".  IEEE Transactions on Pattern Analysis and Machine\\n     Intelligence, Vol. PAMI-2, No. 1, 67-71.\\n   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\\n     on Information Theory, May 1972, 431-433.\\n   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\\n     conceptual clustering system finds 3 classes in the data.\\n   - Many, many more ...\\n', 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)']}\n"
+     ]
+    }
+   ],
+   "source": [
+    "iris = datasets.load_iris()\n",
+    "print(iris)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = iris.data[:, :2]\n",
+    "Y = iris.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_min, x_max = X[:,0].min()-1, X[:,0].max()+1\n",
+    "y_min, y_max = X[:,1].min()-1, X[:,1].max()+1\n",
+    "h = (x_max - x_min)/100\n",
+    "\n",
+    "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "X_plot = np.c_[xx.ravel(), yy.ravel()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C = 1.0\n",
+    "svc = svm.SVC(kernel=\"linear\", C=C, decision_function_shape=\"ovr\").fit(X,Y)\n",
+    "Ypred = svc.predict(X_plot)\n",
+    "Ypred = Ypred.reshape(xx.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,'SVC para las flores de Iris con Kernel Lineal')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a21427e48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16,9))\n",
+    "plt.contourf(xx,yy,Ypred, cmap=plt.cm.tab10, alpha = 0.3)\n",
+    "plt.scatter(X[:,0], X[:,1], c=Y, cmap=plt.cm.tab10)\n",
+    "plt.xlabel(\"Longitud de los pétalos\")\n",
+    "plt.ylabel(\"Anchura de los pétalos\")\n",
+    "plt.xlim(xx.min(), xx.max())\n",
+    "plt.title(\"SVC para las flores de Iris con Kernel Lineal\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C = 1.0\n",
+    "svc = svm.SVC(kernel=\"rbf\", C=C, decision_function_shape=\"ovr\").fit(X,Y)\n",
+    "Ypred = svc.predict(X_plot)\n",
+    "Ypred = Ypred.reshape(xx.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,'SVC para las flores de Iris con Kernel Radial')"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a21464438>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(16,9))\n",
+    "plt.contourf(xx,yy,Ypred, cmap=plt.cm.tab10, alpha = 0.3)\n",
+    "plt.scatter(X[:,0], X[:,1], c=Y, cmap=plt.cm.tab10)\n",
+    "plt.xlabel(\"Longitud de los pétalos\")\n",
+    "plt.ylabel(\"Anchura de los pétalos\")\n",
+    "plt.xlim(xx.min(), xx.max())\n",
+    "plt.title(\"SVC para las flores de Iris con Kernel Radial\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5,1,'SVC para las flores de Iris con Kernel Sigmoide')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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aAcOsVEsGWkmSJM1KFZ34qccgK9UDA60kSZJmFZfjkRqHgVaSJEmzhsvxSI3FQCtJkqQZz66s1JgMtJIkSZrRKjXxk8vxSPXPQCtJkqQZyeV4JBloJUmSNOPYlZUEBlpJkiTNIHZlJY1moJUkSdKMMG7ip1XnsaUrAeWF2RHL1nZAV57l69bT3Xc9YJiVZioDrSRJkmpqdNd1Mod1ZbvSlILsRHJr2qHHMCvNRAZaSZIk1cRIkD1ndeekt+29ccOhr6cyxFjS7GaglSRJUtWNHj7cO2rt2CMxyEoay0ArSZKkqhndlT3UdV113qTHHc21spJmPwOtJEmSqmJsV/bQUjvFsDoZw6yksSoeaCNiE9ALDANDKaXzx+wP4F+BZwIHgFellH5V6bokSZJUHce61M50GVmyJ9OVr9o5JVVWtTq0T0kp7Zxg3zOAk4t/LgQ+XPy/JEmSZrixS+0c6spSmzA7sv5sbtV2uvu2k1vTzjU9zVWrQ9L0qochx88FPptSSsDPI6IzIlaklLbVujBJkiQdnXrrysLhYRaKa8+6XI80o1Uj0CbgqohIwEdTSh8bs38VsGXU9w8WtxloJUmSZqCxXVmobZgdG2ShGGYxzEozXTUC7RNSSl0RsRT4YUTcmVK6btT+KHHMuJkBIuK1wGsBlq44rjKVSpIk6ZhMNMS4rrqyGGSl2aLigTal1FX8/8MR8Q3gAmB0oH0QWD3q++OArhL38zHgYwDrzzy7vKnwJEmSVBX12pVdvm493X3XA4ZZaTaqaKCNiLlAJqXUW/z6UuDvxtzsW8CfRMR/UZgMaq/Xz0qSJM0c9dCVhYknfhoJsmCYlWabSndolwHfKKzMQxPwxZTS9yPidQAppY8A/01hyZ57KSzb8+oK1yRJkqRpUG8TP40EWXCIsdQoKhpoU0obgXGvHsUgO/J1Av53JeuQJEnS9Krn5XgAu7JSg6iHZXskSZI0Q9RbV3bZ2g4yXXnArqzUiAy0kiRJKsu4iZ9WnceWrsJcnTXrynblXY5HamAGWkmSJE2q5BDjruRyPJJqykArSZKkCdXrcjx2ZSWBgVaSJEkTqIfleOzKSjoSA60kSZIOU69dWTDMSjqcgVaSJEmHuByPpJnEQCtJkqS6W47HrqykchhoJUmSGly9d2Vza9q5pqcZMMxKOpyBVpIkqUHVW1cWJhhi3GOQlVSagVaSJKkB1evETy7HI2kqDLSSJEkNxuV4JM0WBlpJkqQGUa9d2eXr1tPddz1gmJU0NQZaSZKkBlAPXVkoPcS4u2+7y/FIOioGWkmSpFms3iZ+cjkeSdPJQCtJkjRL1ftyPHZlJR0rA60kSdIsY1dWUqMw0EqSJM0i9d6VBcOspOljoJUkSZol6mHiJ5fjkVRNBlpJkqQZrl6X47ErK6nSDLSSJEkzmF1ZSY3MQCtJkjQD1WtXFgyzkqrHQCtJkjTD1PvETy7HI6laDLSSJEkzhMvxSNLhDLSSJEkzQL13ZXNr2rmmpxkwzEqqHgOtJElSHau3rixMMMS4xyArqfoMtJIkSXWqXid+cjkeSfXCQCtJklSHXI5HkiZnoJUkSaojdmUlqXwGWkmSpDpRD11ZcDkeSTOHgVaSJKnG6m3iJ5fjkTRTGGglSZJqqN6X47ErK6meGWglSZJqwK6sJB07A60kSVKV1XtXFgyzkmYGA60kSVIV1cPETy7HI2m2MNBKkiRVgcvxSNL0M9BKkiRVmF1ZSaoMA60kSVKF1GtXFgyzkmYHA60kSVIF1PvETy7HI2k2MNBKkiRNI5fjkaTqMdBKkiRNE7uyklRdBlpJkqRjVG9dWXDiJ0mNwUArSZJ0DOpl4qcRy9Z2FL7oyrN83Xq6+7aTW9MOPYZZSbOPgVaSJOko1cNyPJLUyAy0kiRJU1RvXVlJalQGWkmSpCmwKytJ9cNAK0mSVIZ6mfhJkvSIigfaiMgCNwFbU0rPHrPvVcA/AyPjdT6YUvqPStckSZI0FfWyHI8k6XDV6ND+GXAH0DHB/i+llP6kCnVIkiRNyUzqyo5esifTlQegu+96AK7paa5JTZJUaRUNtBFxHPAs4J3AGyp5LkmSpHKNDqqTmQld2ZEwu6JlHnTlD609C64/K2l2q3SH9v3Am4D2I9zmBRFxMXA38BcppS0VrkmSJDWwkTB7zurOSW/be+MGYGZ0ZVe0zAM4FGYNspIaQcUCbUQ8G3g4pXRzRDx5gpt9G/jPlFJ/RLwO+Azw1Anu77XAawGWrjiuAhVLkqTZbOx1sL2jltw5knoPs2ODLBhmJTWOSnZonwBcERHPBFqBjoj4fErp5SM3SCntGnX7jwPvnujOUkofAz4GsP7Ms1NlSpYkSbPRkSZ1mky9BVmYOMwaZCU1mooF2pTSm4E3AxQ7tH81OswWt69IKW0rfnsFhcmjJEmSpsXYIAv123Etx9ggC4ZZSY2t6uvQRsTfATellL4FvD4irgCGgG7gVdWuR5IkzU6zbamdybqyYJiV1HiqEmhTStcA1xS//ttR2w91cSVJkqbDTFpqpxx2ZSVpYlXv0EqSJFVKo3Rlc2vaD60ta5iV1MgMtJIkacabrV1ZmGCIcY9BVpLAQCtJmmFSSiQgE1HrUlQnZvvETy7HI0kTM9BKkmaEgaE8X7l5Cz+9dyeDw4m1i+fy8scez5qFc2pdmmpooiHGMznIgsvxSFK5DLSSpBnhw9fey53behnMF5Yi37hzP//3+3fyjivOYNG8lhpXp2qb7V3Z5evW0913PWCYlaQjMdBKkure9p4+7nzokTA7Yiif+J87H+bF56+uUWWqhdnUlYXSQ4y7+7a7HI8klcFAK0mqe9t7+mjKBIPD4wPtA90HalSVqm0kyJ6zupPeGzcAMzvMuhyPJB07A60kqe6tmN/G0JjuLEBTJjhhkdfQNoLRXdnebVtn7XI8dmUlaWoMtJKkurekvYUzV87n9q69h3Vpm7LB005bVsPKVGmzdTmeZWs7yHTlAbuyknQsDLSSpBnhtRefyDdv3cp19+ykfyjP+qXzuPKCNSyYk6t1aaqQia6VhZkdZle0zIOuvMvxSNI0MNBKkmaE5myGF563mhee5wRQjWA2TfzkcjySVDkGWkmSVDdm+3I8dmUlaXoZaCVJUl2wKytJmioDrSRJqqnZ3pUFw6wkVYqBVpIk1cysnvgJl+ORpEoz0EqSpKqbrcvx2JWVpOoy0EqSpKpqlK5sbk071/Q0A4ZZSaoUA60kSaqK2daVhUfC7LK1HdCVZ/m69XT3bT8UZg2yklRZBlpJklRxs23ip9GWre2odQmS1LAMtJIkqaJm03I8kqT6YqCVJEkVMZu7spKk+mCglSRJ086urCSpGgy0kiRp2owE2XNWd9J74wbAMCtJqhwDrSRJmhaju7K927bO+OV4piLTlQegu+96gEPL9UiSKstAK0mSjslsXI5nMqOX6xkJsyPrz97QuRBw7VlJqgYDrSSpLCkl7treyy82dhMBjz1xEeuXtde6LNXYRNfKwuwPsyta5kFX/lCQBcOsJFWbgVaSVJYv3vgAP71vFwNDhW7Uz+/v5snrl/Di81fXuDLVSqNN/DQSZKEYZrErK0m1ZqCVJE3qgV0H+Om9uxgYzh/aNjCU58d3PcxFJy1mZWdbDatTtTXicjyHdWXBrqwk1QkDrSRpUrdt3cPgqDA7Ip/gtgf3GmgbSKN1ZWHiMDsSZMEwK0m1YqCVJE0q15QhmwmG8umw7Zko7NPsZ1e2wCHGklRfDLSSpEmdf/xCvnHL1pL7zjt+QZWrUbXZlbUrK0n1ykArSZrUwrk5XvX4E/j0DZvIRgCF4cavuegE5re53uZs1cjL8diVlaSZwUArSSrLhWsX8ahV87l9aw8RcObK+bTlsrUuSxXS8Mvx8EiQza1p55qewgc3hllJqi8GWklS2ebkmrhg7cLJb6gZq5G7sjDBEOMeg6wk1SsDrSRJAkpM/LTqPLZ0FSYCm+1h1uV4JGlmMtBKkqTSQ4y70qwPsjDxxE8GWUmqfwZaSaqiLbsPsHHHfjrnNHPGyg6aMi55o9pyOR5Yvm493X3XA4ZZSZppDLSSVAXD+cRHr7uP27fuJQHZCFqbs7zp8lNY2t5a6/LUoFyOp9CV7e7b7nI8kjRDGWglqQquvfthbt+6l4HhwvWIgyT6h/J8+Jr7eNtzzqhxdWo0I0H2nNWd9N64AZj9YdbleCRpdjLQSlIVXHv3zkNhdkQCHurpo3v/AAvn5mpTmBrO6K5s77atDb0cj11ZSZr5DLSSVAVDw/mS24NgcIJ90nRq5OV4lq3tINNV+D2zKytJs4uBVpKq4IK1C/n+hocYHNOlndfaxNL2lhpVpUYx0bWyMPvD7IqWedCVdzkeSZqlDLSSVAWXnbGcXz2wm537BugfytOcDTIR/OET1xIRtS5Ps1gjT/y0bG0HdOWLsxhvJ7emnWt6mg2ykjSLGGglqQpam7O89Vmn86sH9nDX9h4WzW3hCSctZn5bc61L0yzViMvxjLZsbUetS5AkVYGBVpKqpCmb4YK1C7lg7cLJbywdg0bsykqSGpOBVpKkWaLRu7KSpMZjoJUkaRawKytJakQVD7QRkQVuAramlJ49Zl8L8FngPGAX8JKU0qZK1yRJalzd+wfo6RtkxfxWWpqytS7nmDXicjySJI2oRof2z4A7gFKzM7wG2J1SOikiXgq8G3hJFWqSJDWY/f1DfPja+7hvxz6ymSCf4PmPXsUlpy2rdWlHrRGX45mKkbVnu/uur3ElkqRKqWigjYjjgGcB7wTeUOImzwXeXvz6q8AHIyJSSqnEbSVJOmofue4+7nl4H8P5dGg94K//aivLO1o5c9X8Glc3NXZlSxtZrgceCbMj68/e0LkQelx7VpJmm0p3aN8PvAlon2D/KmALQEppKCL2AouAnRWuS5LUQPYcGOCe7YUwO9rAcJ7vb3hoRgXacRM/rTqPLV2Fx2WYhRUt84BHgiwUwyyGWUmajSoWaCPi2cDDKaWbI+LJE92sxLaS3dmIeC3wWoClK46blholSY2hp2+IpmwwlB//T8yeA4M1qOjolBxi3JUMskVjw6xBVpJmv0p2aJ8AXBERzwRagY6I+HxK6eWjbvMgsBp4MCKagPlAd6k7Syl9DPgYwPozz3ZIsiSpbCvmt1LqYpZsBs5cWWqKh/ricjylje3KgmFWkhpNxQJtSunNwJsBih3avxoTZgG+BbwS+BnwQuBqr5+VJE235myGF5+3mi/dtIWB4cK1ldlM0Nac5fIzl9e4uiNzOZ7SJhpiPBJkwTArSY2g6uvQRsTfATellL4FfAL4XETcS6Ez+9Jq1yNJagxPOmUJSzta+P6Gh9h9YIAzVnRw2RnL6ZyTq3VpJY0E2XNWd9J74wbAMAuPBNllaztKT/yEQVaSGklVAm1K6RrgmuLXfztqex/womrUIEnSaSs6OG3FzBpi3Lttq8vxjDESZpevWw9Ad992cmvancVYkhpQ1Tu0kiSpNJfjkSRpagy0kiTVgYmulQXDrCRJEzHQSlKDGcrnyQCZTKbWpajIiZ8kSTo6BlpJahC3d+3lY9dt5MDAMABL2nO88dJTWTi3PidFagQuxyNJ0rHx43lJagDb9/bx/h/dcyjMAuzoHeCt37ydfD5fw8oaV6mu7JY4l7azzjLMSpJUJju0ktQA/uuXD5Tc3j+U54aNu7jopCVVrqhx2ZU9eiNL9ows1wPQ3Xc9ANf0NNekJklSbRloJakBdO3tm3DffTv2G2irxGtlj95ImF3RMg8orD3b3bf90Nqz4JI9ktSIDLSS1ABWdbaxa/9AyX0nL5lX5Woaj8vxHL2xQRYKYRY4FGYNspLUuAy0ktQArrxgNbd9Y++47a3NGR5/0uIaVNQ4XI7n6JXqyo4wzEqSwEArSQ1hSXsrb7z0FD5y7X309g8BsKqzlb98+voaVzZ72ZU9eiNBFsaHWYOsJGk0A60kNYhTlrfzLy85p9ZlNIRxEz+tOo8tXQkwzE7GrqwkaSoMtJIkTaOSQ4y7kkF2EnZlJUlHw0ArSdI0cDmeo+fET5Kko2WglSTpGLkcz9GbaIixy/FIksphoJWkMl1953a+cvODDA4nspngklOX8qLjxn72AAAgAElEQVTzV9e6rIaRUuLGTd386LcPs39giHNWd/KMM5fT3tpcs5pGguw5qzvpvXEDYJgtl11Z6ej8qmc//71jL3uHhjlzXhtXLO1kQXP13tJvPNDPNx/ezdb+Qda05vidpZ2saWup2vmlsQy0klSG797WxTdu7Tr0/XA+8YPfbqf7wAB/dPG6GlbWOL5684P8+O4dDAzlAbj6zof55aZu3nHFGczJVf+fs9Fd2d5tW12OZwrsykpH5zsP7+H/PbyHgVSYZG5ndy837t3PO09eRWcVQu1v9x3kfZu2Hzr/joEhbus9yF+vXc7Jc1srfn6plEytC5CkmeCbv+4quf2Xm3YzPDxc5Woaz96Dg/zPnQ8fCrMAQ/nEvv4hrrt7Z1VrWdx19RGHGBtmJ7bt7lsmDbPzO842zEol9A3nDwuzAMPAgeE839s5fp3xSvhc167Dzp+AgZT4wrZdVTm/VIqBVpLKkE8T79veO1C9QhrU5l37ac6O/ydrcDixoas6b+Tg8K7sumJX1iHG5RkdZFe0zGP5uvUOMZamYGv/INkYv30YuH3fwYqfP58SW/sHS+7bfNB/B1U7DjmWpGO0YE6u1iXMep1tOYbT+E8VMgGL5lXn2i0nfjp2y9Z2QFee5evWH9qWW9MOPYZZaTLzm7IMTfDh6qIqDDcOoC0THCzxCe/cJntkqh1/+iSpDCcsaiu5fV5LlrZctsrVNJ7VC9tYOq+FzJjuRFMmw9NOW1rRc48MMR7pyoJhVlL1Lc41cdKclnHdqFwEz1wyv+LnjwguXdRBLg5/Ic5F8MxFlT+/NBEDrSSV4c2Xn8rCOYfPptvalOEfn/eoGlXUWCKCv3j6ek5cMo/mbNDSlGFeSxN/+MS1rF4wp2LnLdWV3RLneq2spJp4/fFLOW1eG80BrZmgLRO8cuUiTp1b+kPX6fa8ZQu4eME8miNozQTNETxtUTuXVyFQSxNxyLEklSGbzfJ/X3g2O3r7+PWDe1m/dB5rFs2tdVkNZX5bM39z+ansPjDAwYFhlne0khnbsp0mY4Ms2JWVVHtzs1neuHY5eweH6R0eZnlLM01RmdfBUjIRvGLVYl64fCHdg0Msam6ircT8BlI1GWglaQqWtLdyyWkuTVBLC+bkqGBT1mtlJdW9+c1Z5jfX7nKXOdkMc7LOH6H6YKCVJIlHgiwYZiVJmikMtJKkhjdRVxYMs9NhZMmeTFdhHeHuvusP7bump7nkMZIklcNAK0llOjgwzA0bd3L/jv2s6GzliSctoaOtem/Gh/J5bnlgD7c9uJf21iYuPnkJy+eXP/w5nxK3b93LzZt309Kc5aJ1i1mzqIJjd2cAu7KVNRJkobD+LODas5KkaWWglaQy7DkwwN9/9w4ODgwxMJxozgbfu/0h/vryUys6y+6IweE877nqLh7cfZD+oTyZgB/f9TC//4S1POaEhZMen0+JD11zH3ds66F/KE8A19+zgxc8+jguOX1ZxeuvR+Mmflp1Hlu6CusrGmaP3UiYHRtkwTArSZo+TksmSWX4ys0P0ts3yMBwIfAMDif6BvN85oZNVTn/DffuZEsxzALkU6GGT9+wicHh/KTH375176EwC5AoHP+1Wx6k5+BgJUuvSyWX4+lKLsczDbbdfcuEYfaGzoXc0LmQ+R1nG2YlSdPCDq0kleG2B/eST+O3P9B9gP7BYVoqPNvkjZu6GRgaH1wj4L4d+zh1eccRj79p8+5DYXa0TAR3bOvhwhMXTVut9czleCprbJAFhxhLkirLQCtJZWjKBpRoZEZExdZCHS3XVDowpwS5pskH27Q2ZYgo3H60iPKOnw1cjqeyjtSVHWGYlSRNt8Z4FyNJx+iJJy2mOXt4cM1m4KxV82muwqLyTzllScngOSeX5YRFcyc9/gknLaY5U7rOM1bOP+b66tnirqtZ3HU156zuNMxWyLa7b2HZ2o5DYXb5uvUA5Na0AzjEWJJUMWW9C4uIF0VEe/Hrt0TE1yNGrWkgSbPcc85eyclL28llM7Q0Ff4s72jjlY87oSrnf9Sq+TzllCU0Z4OWpgytTRnmtTTx+qedTCYm7xAfv2guz3v0ykeOby78ef1TT57VHdrRXdneGzcUrpU1zFbUSJiVJKkayh1y/NaU0lci4iLgMuA9wIeBCytWmSTVkeZshjc8fT0PdB/gwd0HWDKvhZOWziPKCJPTISJ40XmreeopS7l7+z7mtmQ5fWUHTRN0XUt5+unLuWDtIu7Y1kOuKcOZK+fP2jDrcjySJDWGcgPtcPH/zwI+nFL6ZkS8vTIlSVL9WrNwDmsW1m7t1kXzWnjcvJajPn5+WzOPneUTQE10rSwYZiVJmm3KDbRbI+KjwCXAuyOiBa+/lSTVEbuykiQ1nnID7YuBy4H3pJT2RMQK4I2VK0uSpPK5HI8kSY2prECbUjoQEfcBl0XEZcD1KaWrKluapOl2YGCIXz+4l3w+ceaq+cxva651SVWVUuKu7b1s7+lnZWcrJy2p3jWwqhyX46k/3X3XA3BNT2O9xqg82/oHuXP/QdqzWc5un0NzFZY+kzR7lRVoI+LPgD8Evl7c9PmI+FhK6d8qVpmkafWrzbv5j59sPBTg8inx4vNX85RTlta4surY1z/EP//gTnbuGyClRESwsrONv3z6elqbS6/xqvpmV7b2Dlt7tisPPBJmR9afdbkejUgp8amtO/npnv0EkAloiuBv1q5gTVuu1uVJmqHKHXL8GuDClNJ+gIh4N/AzwEArzQC9fYN8/CcbGRxOQDq0/cs3beG0FR0s72itXXFV8vmfb+ahnn6G8yOPP7Gl+wBf+9WDvOzC42tam6bOrmztHRZmgdyq7cAjQRYMszrcjXv387M9+xlMxdfhVPjPv2x+iPedstoRM5KOSrkTOwWPzHRM8WtfdaQZ4pYte0q+UcjnEzdu7K5BRdWVT4lbHtgzKswWDOUTP2+Axz+bLO662jBbY9vuvoVtd9/CsrUdE4bZ+R1nG2Y1zo+7e+lPadz2fcN5NvcN1KAiSbNBuR3aTwG/iIhvFL//HeATlSlJ0nQbGk6kEm8i8gkG8sMljphlUiHUljKUz1e5GB0tl+OpvbFDjEeCLDjEWJMbmOB1OIChCfZJ0mTKnRTqfRFxDXARhdedV6eUbqlkYZKmz1nHzecrN28Zt705m+HcNQtqUFF1ZTLB+mXzuHv7Pka/ZQrgrOM6a1WWyuRyPLU3EmRh4iHGBllN5vHz5/LAwYFxwTZDcELb0a+vLamxHXHIcUQsHPkDbAI+D3wO2FzcJmkGWDyvhWeftZJcNkNQCHK5bIbHr1vEiYvn1bq8qnjF405gTi5LLlt42WtpytDR1sxLzl9d48p0JKO7suu2bYVV5xlmq2x0V3ZFyzyWr1tvmNVRedLCDo5vy9FSnNW4CchF8Merl9Dk9bOSjtJkHdqbKVyyP/IqM/KRWhS/PrFCdUmaZs961AoetXI+P79/F8P5xPknLOCkJY0RZgGWdbTyruc/ihvu28XW3Qc5YdEcLjxxkTMc17GSQ4y7kkG2Bpat7YCuPMvXrT+0LbemHXoMsypfcyb4Pyeu4JaeA/xm30HmN2W5eEE7i3PlXgEnSeMd8RUkpbS2WoVIqrw1i+awZtGcWpdRM3NyTVxy2rJal6FJuByPNHtlIzh//lzOnz+31qVImiXK/kgsIhYAJwOH1vdIKV1XiaIkSY3JGYwlSdJUlBVoI+IPgD8DjgNuBR5LYR3ap1auNElSo7ArK0mSjka569D+GfAYYHNK6SnAo4Edkx0UEa0RcWNE/DoiNkTEO0rc5lURsSMibi3++YMpPQJJmoJtew/ys4072dHbd1TH79rXz8827mTr7gNHdXz/4DCbd+1n94GZueZiSomtew7StedgyaWgjkaprqxhVpIklaPcIcd9KaW+iCAiWlJKd0bEKWUc1w88NaW0LyKagZ9ExPdSSj8fc7svpZT+ZEqVS9IU9A8N845v/5aHe/sPbTtuQRtvedZpNGUm/2wvn8/zj9+7k027HgmyC+c08/YrzmBOmROa/GDDQ3zz1i4ymcLawKcub+ePLl5HW25mTEy1ccc+PnztfRwYGCYB81qa+F9PXscJi47uWjiX45EkSceq3A7tgxHRCfw/4IcR8U2ga7KDUsG+4rfNxT+unC2p6v75B3cdFmYBHtx9kH//8b1lHf/R6+8/LMwCdB8Y5N3fv7Os4295YDffvLWLgeE8fYN5hvKJOx/q5T9+srG8B1Bj+/uHeN+P7mb3gUH6h/IMDOXp3j/Ae6+6m77B4aO+33NWdx5ajodV5wGGWUmSVL6yAm1K6XkppT0ppbcDbwU+ATy3nGMjIhsRtwIPAz9MKf2ixM1eEBG3RcRXI8JFISVNq3w+Py6Mjrh9a09Z93HLA7tLbt+6p4/+ockD3fc3PMTAcP6wbUP5xIauHnr7BsuqoZZ+uambfH789nxK3Ly59HMjSZJUaWUF2oj43MjXKaVrU0rfAj5ZzrEppeGU0jkUJpS6ICLOHHOTbwMnpJTOAn4EfGaCGl4bETdFxE17u7vLObUkATBUIoiNKHfISP4IN9zfP3mg3XuwdGjNZoJ9/UNlVlE7ew8OjgvkAIPD+QkfmyRJUqWVO+T4jNHfREQWOG8qJ0op7QGuAS4fs31XSmlkHODHJ7rflNLHUkrnp5TOn79w4VROLanB5ZoyNGWi5L7W5vJeBudOcJ1rJqCzbfJraE9b3kGpEjIRLGlvKauGWlq/rJ2WpvHPVVM2w8nL5tWgIkmSpEkCbUS8OSJ6gbMioicieovfPwx8c7I7j4glxWtviYg24BLgzjG3WTHq2yuAO6b4GCRpUs8/d1XJ7S+7cE1Zx7/icceX3H7F2SvJlDGp1HPOXklbc5bsqFCby2Z46WNWlzUpVa2durydtYvnkss+UmuuKcP6pfM4aYmBVpIk1cYR2woppXcB74qId6WU3nwU978C+Eyxo5sBvpxS+k5E/B1wU3Ho8usj4gpgCOgGXnUU55GkI7r09OV0tuX4ys1b6O0bonNOM1c+Zg1nr+4s6/jzjl/IX1yS5Qu/eIDu/QPMbWniBY9exeNPWlzW8Qvn5nj7FWfwvdsf4s5tPSycl+PyM5Zz6vKOY3lYVRMR/PnTTubae3Zww727IOCikxZz8clLiCjd/ZYkSaq0KGcdwYjIAL8LrE0p/X1x4qYVKaUbK11gKevPPDt96MtX1eLUkqRpMLJkzzmrO+m9cQOsOo8tXYV/j5zluH5su/sWAJat7SDTlWf5uvV0910PwA2dhct/5necXbP6JEmz12VLOm9OKZ0/2e0mHOcWEU8odlYB/h14HIVQC7CvuE2SpCkZCbPrtm2l98YNbIlzDbN1aCTMrmiZR6YrT27VdsOsJKnuHGnIcQI+DLwWuDCldG5E3AKQUtodEblqFChJmh1GgiwUwizAljgXMMjWk5EgC4UwC5BbtR0wyEqS6s+EgTaldENEjCzcOFjs1iYoTPYEHGEhDEn1auQyg1pe95jP58uaSKlez59SOqbn71iPP1b54oKy1fw7GN2VBRp+iHGtfwYmOv/oriw8EmTh8DBbD68jkiTB5JNC3Vr88gPAN4BlEfFO4IXAWypcm6Rp1Ns3yBdvfIBfPbCHlBJnrJzPyy9cw6J51Vkypn9omPf84C7u31X4nKw5G1z5mDVcvH5JVc4P8B/Xb+QX93eTKCy386T1S3jZhaVnLy7l5s3dfOXmB9m5b4CO1iaeddYKnnrK0rLf1N+xrYf/vPEBuvb2MSeX5emnLeNZj1pBZoIlhabb7V17+dh1GzkwUFg3d2l7C3916SksnFvZATdjw+yWOBe6UkMG2c2/+Sm//fFXOdi7m9b2BZzxpOdz/NlPrNr5fz3Yz3v37+ae4SHmRvCi1nm8pq2DpogJw+xIkAXY23waH7ivi3sP9NMcwRMXzOPKFQvJzYCZuiVJs1NZk0IBRMSpwNOK316dUqrZ8jpOCiVNTT6f+NtvbWDHvj6Gi2MrMgHtrc384/POpKWp9Bqr0+lvvn4bO/cNjNv+p085qeyZho/Fx6/byC82dY/bfunpy3jx+asnPf62B/fw4WvvY3D4kdfMXFOG552zkqefvnzS4+/fuZ9//sGdDIw+Ppvh4vWLeeljyls66Fhs39vH//fN28dtb2nK8G8vPaci3dpxXVkae4jxA7ffwC3f/TTDQ4/8HmSbc5xz+Ss4/qyLKn7+e4cG+MO9O+jjkZ/BFoIn9h3gf+3fdyjIQukhxt2DQ/zN3Q/Sl3/k+OaAU+e28ca1k/8OSJI0Fcc8KVQJc4CR5XfajrYwSdX322097DkwcCjMAuQT9A0O88tNuyt+/i27D5QMswBfumlLxc+fz+e5sUSYBfifOx8u6z6+fsvWw8IswMBQnm/fto18GR8MfuvXXYeFWYCB4TzX3r2DvsHhsmo4Fv/1ywdKbu8fynPDxl3Tfr5SXdktcS5tZ53VkGEWYMOPv3ZYmAUYHhzgt9d+vSrn//TBXvo5/Gewn8S1La3MKYbZ5evWk1u1ndya9nHXy161cy9D+cOPH0xw1/4+tvUPVuERSJI0XlmBNiL+FvgMsBBYDHwqIhxyLM0Q2/b2jXsjCoUws3X3gRJHTK97H+6dcN/uA6WD7nQazCcmipzDJZ6XUnb09pfc3j+ULyuQdu05WHJ7NoLu/ZV/Drr29k24774d+6ftPIu7rmZx19Wcs7rTiZ/GONhb+kOVgz3dlDta6ljcNzRY8vcgF7AtEyxft37cvtGTP23uG2CoxPHZgG39lf8ZliSplHI7tFcCj0kpvT2l9DbgscDLKleWpOm0srOVbInrNFuaMhy3cE7Fz79+afuE+xbOqfyE6S1NWSa6SrXU81LKso7S1xq3NmdobZ58yPbqBaUHtgynVPFrWAFWdU48sObkJfMm3DcVJZfjMcweMmf+ogm3V2NypfVNzSX/0R8EVpbxwc6JbS00lShzKMHKFhc+kCTVRrmBdhPQOur7FuC+aa9GUkWctqKDxfNyh4W3TEBbLstjjl94hCOnx6oFc1jWXjoQXnlh5a8fBXjciaUf56WnLyvr+Oc/+jias4e/m89lMzz37JVkyggjzzl7Jbns4S+5uWzw1FOWlhWIj9WVF5S+Tri1OcPjT1p8TPc90pWF0svxGGYLznjyi8g2HR78sk05Tn/yC6py/le2dZAb89FOS0o8u3mYjjKOv2RRB81jftabA86Y18ryluZprFSSpPKVG2j7gQ0R8emI+BRwO7AvIj4QER+oXHmSpkMmgjdddioXrl1ILpuhKROcs7qTtzzzNHJN1Zmd9G3POYP1Sx/pBLY0ZXjNRSdw5sr5VTn/7190IhefvIiRTJ8NuPyMZbzg3OPKOv7MVfP54yetY3lHC5mABXOaufKC1Tz11PIC8fGL5vIXl5zMmoVzihNyNXHF2St5wXnlnf9YLWlv5Y2XnkJ7yyOT26+c38o//s6Zx3S/o4Psum1b7coeweozLuS85/wBczuXEJFhTucSzn3277PmzMdX5fwnNjXzwY7FnJ5tJgvMjwwvOLCfP28t7xruBc1N/O26lZw+t5Us0JYJLlnYwZ+uKe93QJKkSihrluOIeOWR9qeUPjNtFZXBWY4lqfaO1JXVzLDt7ltYtraDTFf+0DW03X3Xk1vTzjU9zYddQytJUjWVO8vxEdehHVHtwCpJql8uxyNJkupFWYFWkiSwKytJkuqLgVaSNCm7spIkqR5NOdBGRAaYl1LqqUA9klQx9+/cz3du66Jrbx9rFs7hOWet4LgF5S9b1LXnIN+5bRv379rP8o5Wnn3WCtZN05I39cyurEbs2buXr/36N/ymaQ6tw0NcMqeJpz76HDKZ6kwuV2v5fJ4vbd/Nj7t7GcwnjmvN8drjlrC6zWWLJKlWygq0EfFF4HXAMHAzMD8i3pdS+udKFidJ0+W3XT188Mf3MjCcB2Bnbz+/eXAvf3Xpek4sI5Q+0H2Ad3//TgaG86QEO3r7ueuhXl73pBM567jOSpdfEyNBFgyzgn379/GWOzaxv30Zw02Ftw//OTjAxutv4LVPuqjG1VXHu+5/iLsO9B/6fnPfAG+5dyv/dPIqVrQaaiWpFsr9SPX0Ykf2d4D/BtYAv1exqiRpmn3hxs2HwixAAgaG83zppi1lHf+Vm7bQP1QIsyMGhvN84RcPUM5s8TONy/ForO/9+jccyLUeCrMAg805ftaxjJ3d3TWsrDoeHhg8LMyOSMAnt+6sfkGSJKD8IcfNEdFMIdB+MKU0GBGz7x2cpFlpKJ9ne8/4N6IAm3cdKOs+Nu7cX3L77gMD9A3mactlj7q+emJXVhP57WBiqH18FzKbH+KurVtZvHBhDaqqnl/tLf0aALDp4EAVK5EkjVZuh/ajwCZgLnBdRBwPeA2tpBkhG0FLU+mXu7kt5X2u195a+nZNmQy5Ce57phnblWXVeYZZHbIo5Ynh4XHbU2RYNG/2X0u+srV5wn1zs7PjNUCSZqKyXoFTSh9IKa1KKT0zFWwGnlLh2iRpWkQETzt1Kbkxbzpz2QyXnbGsrPt4xpnLxwXXXDbDxesXk83EtNVaK6UmftrSlWg76yzDrAB4xolryKb8YdsiP0znwV7WH398jaqqnrPa59ISpX/Xn79sQZWrkSSNKCvQRsT8iHhfRNxU/PNeCt1aSZoRnnvOKh63bhHN2aC1OUNzNnjqqUt4+mnlBdqLT17CpactO+z4C9Yu4IXnHVfhyitrcdfVLO66+pGuLA4xVmnr1qzh97N9tPUdoHmwn+zQIMft3cXfnLa2YWY5/ruTVjJnzAdYT1/YzsUL22tUkSSp3GtoPwncDry4+P3vAZ8Cnl+JoiRpumUzwe899nhecO4quvcPsHheC63N5V/3GhH8zqNXcfmZy9m1b4DOOc1lD1euVy7Ho6m66KyzeNzwMJu7upjb2sqyJetrXVJVrWjN8ZEzTmDTgX52DQ5xRnsbrQ0S5iWpXpX7bmxdSukFo75/R0TcWomCJKmS5uSamJM7+iDa2pxl1YK2aayo+kaC7DmrO+m9cQNgmFX5stksJ65eXesyauqEOS2cQEuty5AkUX6gPRgRF6WUfgIQEU8ADlauLElSJYzuyvYWl+MZYZiVJEkzTbmB9o+Bz0TEfCCAbuBVlSpKkjS9XI5HkiTNRmUF2pTSrcDZEdFR/N4le6QpyucTt23dyy0P7GZOLstFJy2p+tDVjTv2ccN9uxjOJy5Yu5BTl7cTE8zaWcrNm7v57m+20TeY5zEnLOQ5Z6+gaQZdP7avb4if3LuTB/cc4PiFc3nCSYuOafjxTDHRtbJgmJ2qvn172HTrdfTu2sai405mzaMeT1OutdZlzRj5fJ6b7riDn+/YTXPAk1ev5LQTT5zSfdyzv4+f7tlHnsTj5s/j1LmtU3odq7VdA0Nc093LzsFBTpvbxmM755Kbwutoz9Aw13b3srV/gBPbWnjignbaprBs0PDBA+z++XUc3LyR1pWrWfD4J9M0r3qTWg2lxE179/Pr3oN0NmV48sIOlrVMvCSSJE0mUkoT74x4w5EOTim9b9orKsP6M89OH/ryVbU4tXRU8vnEB66+h3se3kf/UJ5MFCYpuvIxa7h4/ZKq1PDNW7fygw3bGRzOk4BcU4YLTljIKx93fFlvBj9+/UZ+cX/3YdvmtWR5zwvOpmkGrMO6be9B3vW9OxkczjM4nMhlM7Q0Z3jLM09j0bzZey2cEz9Nn93bNnH95/+JfH6Y/NAg2eYWcm1zecqr30brvPm1Lu+obLv7Fpat7SDTlWf5usIET91915Nb0841Pc3M7zh72s6Vz+d533U3cEfHEgabmoiUyA4P8/QDu7jyoseXdR9f2tbND3f1MJgSCWjJBBd1zuOVqxZPW52VdOf+g7z3/u0Mp8QQ0BLBguYsbztpJXOzk09S92DfAH9/XxdDCQZTIhdBWzbDO05aycLmyT+cG9i9i3vf/Rby/X2kgQGiOUc0NbHuL99G64rKz9g+kM/zzo3b6OofpD+fyFJYJ/yP1yzhvA4Xz5B0uMuWdN6cUjp/sttN9i60fZI/ksrwqwd2HwqzAPkEg8OJ//zlAxwYGKr4+Xf09vP9DQ8xUAyzAANDeW7c1M3GnfsnPX7nvr5xYRZgX/8wX7v1wWmutjI++7PNHBgYZnC48AwMDOfZ1zfEl27aUuPKKsPleKbfzd/+D4YG+sgPDQIwPNhP3769/Pbar9e4spnhlrvvKoTZ5hxEhpTJMtSc46q5i9m+Y8ekx2/rH+CqXT0MFMMsQH8+cf3ufWw62F/Z4qdBSomPbNlBfzHMAvSnxM7BIb67Y29Z9/EfD+7gYD4xWGxGDKRE79Aw/7Vt/OtzKdu+9nmG9+0jDQwUahocIH/wAFu/+IkpP56jcU13L1v7CmEWYJjCY/jYlh0M5SdusEjSkRzx47yU0juqVYg0m/1y8+5DYXa0bCa466FeHr1mQUXP/5utpd8sDQzlueWB3axbMu+Ix19z18RvNn+5aTcvOX/NMdVXacP5xL0P7xu3PTHxczOT2ZWdfgMH99G7a9u47Sk/TNfdv+LcZ726BlXNLL/cvpPBzpXjtmdS4qb7N/GsJUcerXJrz0FKjSobTIlbeg5wQlt9j7TYMTjEvhL/DgwluHHvfl68fOERjx/I59l0cGDc9jxwa++Bsmro3fBrSONrOHD/PeSHhsg0VfYSjJ/t2c9Aib/DBNx/sJ+T5zp8X9LUzf6Lx6Q60HKEIbm5KgzXzWUzZCKAw99IZDPQ0jT5MLcjrdeam8K1W7USAZlMMFyiAzCTrgGezNggC4bZ6ZLJTvzPZbbJ6//KkYsgSIz7LUyJljKG2+YyUXgdGxOIssV99a45YvxjL8qVcdlHhmCiWzWXeQ1xpqmJ4YHx3ezIZP5/9u47vq3rPPz/59wLXIB7L1GkBrWsvTwkJ+HTZj4AACAASURBVLZjx3Hi2E1SN3H2/DajzddN0vy+bb/fzrRJm7ZJmrTNaNokjtOmcfay6i1HnrJsWdPWHhSXJG4S4+Lee35/gKQ4QAEiCYAgn/frxZcoAIf3AQgCeO4553lQGXgtDEzye9KXuU4IIZKZO5/khJjFXr2sMmHiZyjFypr0r97f2Fg6/jPgyPGvXXr5WQGA166qnvS6W1dPft1sYSjFlkVl+MZ9YPKbiuubKrIU1cxKNCvbrDaTt369JLMzwGcFqV68esKHfsNnsWTTTdkJKsfctGQRpjdxdhCluG7ViqTjry4pSJgQKqW4tuTyq0xmgzK/j4agNeGDl6UUN5cnfx/wGYpNxfmMT/39Cm4oS+19pPTaG1DjT8CYPoo3Xp2RhPaWimICCZLvYtOkIWil/fhCiLlJElohMmB5TRG3r6vFZygCPoOg3yDPb/IHtyzHl4EZzsKAj4/euBTLNAj64l9+U/GuaxZRXZR8iVfQ8vGe6xZNuPyq2iJes7ImHSHPuHdf28iC0jwCPoOAz8AyDZZUFvCWzfXZDm1ahvfKgiwxTrctd/4vCspq8FlBTH8A02dRvXgVK7bdnu3QcsLShgbeHO3GdGJYdhTLjuCP2Xwk4FBYkDwhLfaZfKyhEkspgkb8y68UH6yvoDJHqpV/vLGacr+PoKEIqHj8m4vzubmiOKXxH6ivZEHQPzLeUorl+UHeUlOa0vja33or+UuWoSwLZQUwAgGCdfXUv/2D07lbKdtanM8N5UX4lSIw9DssNg0+tbgmpypVCyFml8tWOR65kVI1wOeABVrrNyilVgPbtNaZqSIwjlQ5FrmqJ2Tzcls/Qb/B2voS/BlerhuJuRxs6cXVmjULSigMXNmHwJDtsONgO4NRh5tWVNFYkVtVKbXWnLw4SHtvhPqyPBbnWPzjSTuezNNac/HsEUK9FymtWURJTUO2Q5qWTFY5Htbd28sLx0/gNw2uWbmCvLz8Kxofdj3294fQwLqivJSqA88mntYcHozQHXNoyguw4ApnJrXWHAtF6bBjNAStKe0dDp89RaTlLFZNHflLlmc8mbxgxzgyGKHINFlTlIdPklkhRAKpVjlONaHdAXwb+H9a6w1KKR+wV2u9bvqhXjlJaIUQ89lwIgsyKyumJxsJrRBCCJGKmWrbM6xSa30/8WJ6aK0d4tXWhRBCZNDoWdmmthao3yLJrBBCCCHmrVTXGw4qpSoYKpGqlLoOmHu9LoQQYhZLuMS4VUsiK4QQQoh5K9WE9lPAL4AmpdRTQBXwO2mLSgiRFiHbYf+5+B7adQtKKM7LbLsRx/U40NJLf8RhRW0RtcVX1nPQtl3u232G9t4ImxrLuH1dXZoinV2kHY+YS3piDgcGwphKsakon7wcaP01Wjg0yJNP7qLfjrGhaSlNV63J6PFjjsOvj5zgTDjK6uJ8bm5agplj+4iFEGImpZTQaq1fVErdCKwEFHBEax1La2RCiBn14plu/v3JkyPFPzytefvWRm5cWZWR47d0h/mHh47geB6eBxrNdUsqeO+2RSkVJHnhTBdfe+LkyP9PdYb4+b5WvvS2DeTnSIXTqZAKxmIuefBCL/d3dGGoeE/Vb2n4343VbCi+ssJQ2XLwhd18xSvAq1iEpwx+FVFs+Pkv+fgdt2ckqTzb2cWfN3fiGX6w/LwQgfv3HecfVzVQkp8bj6EQQsy0y54WVUr99vAX8FvEE9oVwJ1DlwkhcsBAxOGbT57EdjVRxyPqeMRczX/vOUtHXyTtx9da88+PH2Mg6hCJedhu/Pi7T3fx/OnulH7G10cls8NcT/O5B16e6XBnheF2PBsbSiWZFXPCuYjNDzu6iWmIepqIp7G15p/PnifkJuhPO8s4sRhftS0iwXxsK4jjt3B8fvY3LOexRx/OSAx/d6odzzBBqZGvqM/P5w8ez8jxhRBiNkq2zufOoa8PAf8BvGvo69+Bd6c3NCHETNnb3J1wFtTzNM+d7Er78c/1hOmPOBMujzoeTxy9kHT88Y5+JqvH3t4XnWZ0s8/oWdn+3YdoVpslmRU576nuAZwEnRUMBS/1hbIQ0ZU5uPcFotbEbRIxf4AnvfSvEgnZNgNWMJ7IjqYU54LJ+/gKIcRcddlXYK31BwCUUr8CVmut24b+Xwf8a/rDE0LMhJirSdSiy9Nge+kvWO64esJnsGG2m/z4IWd+FFWXdjxiLotpTaJ5WK3j1812sVgMZSSO0zHTn9C6OTCLLYQQ2ZBqJYbFw8nskA7iS4+FEDlg/cKShJf7TYPNjWVpP35jeT5mgozWMhXXLqlIOn5NbdGk1xUF58b+2fHteGRWVsw1W0vyCRgJVooA64vyMh/QFVq7aXPCy32xKJujfWk/flFeEMuJxc8AjKY1ZdFw2o8vhBCzVaoJ7U6l1INKqfcrpd4H/Bp4PI1xCSFmUGVhgDvWLcAy44VYACyfwfamCpZWpn+pmmkofvfVS7FMA3PoA23AZ1Bfls+NK5IXpTJNkzeuq0143adeu3xGY82GyxV+kmRWzBUr84NcU1xAYOjklgFYSnFXTRll/tl/Yiovv4B39DTji9kYTnwLhd+OUtvVwRtvuTUjMXy4Yqjw03BSqzVKa+5ZXJ2R4wshxGykEi1DTHhDpd4C3DD0399orX+atqiSWLF2g/7q/Q9l6/BC5KyznSGeOXUR19VcvbicZdWFKVUYnimdA1GePH6R3nCMNQtK2NhQOpLgpuKV9j6++8wZ+iMxGsvz+fANSynJs9IYcfoNF37q330ofkH9Fpqlt6zIkLaje6lZUozR6lHbFF941RXZhdVYxM4+PyXFG2b0eFprXhmMsLt3EMtQXF9aRGOO/Q2fPvIKj7x8hH7DYF2exU033YzPn7kWaC3dPXzvxBk6tEGj0rz/qiZKCwoydnwhhMiU26pKX9Bab012u5RPiQ4lsFlLYoUQ09dYkU9jRWPWjl9RGOBNG+unPH5VbTGfe8u6GYxolqnfku0IhEgrpRRXFeZxVeHsX2I8mcUrV/G/Vq7K2vHry0r5o62lWTu+EELMNrnVzVwIIYQQQgghhBgiCa0QQgghhBBCiJyU1oRWKRVUSu1WSu1TSh1SSv1VgtsElFI/UEodV0o9p5RanM6YhJiOmOtxpnOQrkF7SuNdT3O2K8SF/rnXOzVVx8/38+zJi4TsiX1pU9EbjnG6c5BIbGqtfAYiDqc7B6d8/FynPY+ejrMMdLVn5fie59F+4gDnDj+P50ztdzDYc4Ge9jN4bnZ+h20DPexuP01vZGq9U3s9j1ccm15vam1YeiMhdrefprW/e0rjc53rujzS2ctDF3pxU2j7lUj0fDvhc2fQU/wdtHV0cOTUKexYbErju2MOp8NRolM8frZFPY9T4Sg9san9DcY8zelwlIvz9HUY4Lwd40w4ijvFllWddvw5ZOfoc0iImZTSHlql1HLgb4HVwEhXca310iRDo8DNWusBpZQfeFIptUNr/eyo23wI6NZaL1NKvR34PHD3ldwJITLhN0cv8IM9zSgVT0yXVBbwsRubKAqmVgxk79luvvP0aVxP42pNbXGQj79mGRWFgTRHPju09Yb53AMvE45devPdtrScD70q2ctIXDTm8s1dJznY2ofPVHgevHFdHW9cX5fSeMfzuO+ZM+w+1YXPNHA8jxuWV3H31Q0YGSyMlU3tJ/az5xffxHNiaM8jv7SKbW+9h8Lymowd/5n7v4we6X2sWHvz21ix7Q0pjQ/3dfPMj75C34VzGIaJUgabbn8/C1dfk76gRwnZUf7wxEscKK/Gp13c3g5ef/I8f7Lqagwj+flhV2u+MNjDr6OD+FHE0NweyOfTBWUJ21qN53kef/fK8+yoqMbQLm6oh7XnjvLFpo3kW/PjdeQX57v5UUfPyP+/197FHVXFvK02efsvAPvieU5//QvYF8+jDAPl89Pwvo9StGZjSuMvdnfzpQNHaCsqx/A86DrOW3WI27amtv897Hp8tfk8hwcimCreC/y3a0q5vSp39sT+8nwPPz/fg6nA0bC2MMjHGqoJmqnNkfymq5/vtXUC4GpYkmdxz6Iain1mOsOeNS7aDl8+00FbNIahwFSKD9VXsLUktY4D/Y7Lv5w9z/FQFHPoZeMdteW8pqI4jVELMbulOkP7beBrgAO8BvgucF+yQTpuYOi//qGv8aei3gTcO/T9j4BbVCbLrgqRgiPt/fz3881EHY9IzCPmak5cGOBfHz+R0vjWnjDf3HWKQdsl4sTHn+sJ848PHSXVSuO57rO/HpvMAjxzsotHX+5Iafx3njnNodY+HE8TiXnYrsevD7Sx+1RXSuN/ureF5093EfM04ZhLzNXsOnaRhw5lZ6YSLrXrGa25NT3Ph8Hu8zz343/BDvXj2BFcx6a/s5XffO/vpjxLdSUcO8LT//2lUcksgObgYz+gq/Vk0vFaa578/j/Q034az4nh2BFi0RAv/PLf6Wk/k77AR/nT43s5UF6N6/MTtYI4Pj8Plldx74mXUhr/nXA/D0QHsYFBNDawIxrmW+HUepjed2If/1NehePzY1tBXJ+fQ+XV/N8Te6d+p3JIR9Qek8wO+9WFPk6FIknHa8/j5Jf+mmh7Czpm40UjuIP9nPn3LxO9kNrr0D8cPEZLceXQ7yCAbQX5gVHIgWPHUhr/jeYLHBoIE9OaiKexteYnHT3s6R1MaXy27e4d5Ofne7C1JuxpYlpzYCDMN89dSGn8kcEI323tJOLF739Ma06EovzT6dQe/1yntebzp9pojtjYQ8+BQdfj680XORdJbeXXV850cHQwMvIcinia/2zr4vCA9CIW81eqCW2e1vpR4m1+zmit/xK4OZWBSilTKfUScB54WGv93Lib1APNAFprB+gFUjvVKkSGPHi4Hdsd+6Hf9eBM12BKy4d3HrmAMy5p0Br6IjGOXxiYZNTc8XJbLxEncdL06wNtSceHbZe9Z3uIeWOTPdv12HEw+XitNTuPXMB2J45/KMWEeqaN7j3bv/vQSLseIC0te0699ATe+OWZWuNEw5w/fXjGjzfekad/zcTzmXEHH7s/6fie9jOEejsv9d8c4roxTux5ZCZCvKxQLMruihpc39gVGTG/xY99qa3SuD/Sz/hXiyiaH0ZSew34kc8k5h/b4sbx+dlTXsuAnTyhy3X/ce7ipNd9q6Uz6fjBo4dxw6EJzyHtunQ9+WjS8afOneNCQQmeOXYm0TF97GhJ/jrS77jsHwjhjPszsLXm1xd6k46fDX41lMyO5mjY2x9iMIXl3zsu9E4Y7wJnIzbt0akt384lx0NReh2X8e+GjtY80pn8xNYFO8bJsM34R9rWmh058hwSIh1STWgjSikDOKaU+vhQT9qUunhrrV2t9UZgIXCNUmrtuJskmo2d8KlHKfVhpdQepdSe3q7UZmSEmCndk+yZ9RmKvkjyN+GukI2X4LO8AvrCc38PUUff5El/OIW9sCHbmXRZcF8k+ePnabAnSahD0antwZuO0cksQLPaPNJ7Nl39Z8N9XeNmR+O01kQH0/9BKNQ7ecIR6Z846zZedLAXlWhZr9aEetP/ntAfDZP47QoGrWDCy8cbmGQ1xoDWKa3UmPw4mr7o1Pbz5pLeyyRMfSns5Yz1TfI8d11i3ckT4u6BAUyd4HXEMOgxkp/UGHC9SZeW90xxP3mm9TqJfwcGikE3+UqP7knup6km/9lzyWT30QO6UngO9zkevknWMHblyHNIiHRINaH9BJAP3ANsAd4NvO9KDqS17gF2Aq8fd9U5oAFAKeUDSoAJn0601v+mtd6qtd5aUl5+JYcWYtrW1pfgMya+i7ge1Jcm76e4ZkExVoL9RY6nWVpVMCMxzmabGibfH9ZYnp90fFm+heWb+PgpYGVtUdLxpqGoLUmcDCypzNzjX9n6GJWtj9HU1jImmYX0zMqOVr1kLaZ/4j5LrT3KFy5P67GBy+5zrWlKft/L6pYkLCJl+PzUNqW/N3FVfjH5doIlfZ5HU19qxZlWmImTnhWmn1R22izr74YEy8Pz7Cg1BSUpxZDLNhdN/lqx7jLXDStYuhydIClWVoCiq5I/B5fX1+MaE/d5mk6M1Sp5MlZl+TATnBQxgDU50pf3qsJgwg+OlqGo9Ccvy7KuMD9hQuZqaAxaE6+YY5ryAzgJTl5ZSrE+hefAwqAfN8G5Lx+wLkeeQ0KkQ9KEVillAm/TWg9orc9prT+gtb5rXGGnycZWKaVKh77PA14LvDLuZr/gUnL8O8Bjer5sKhQ543Wra8i3zDFJreUzePPGBQT9yQtZbF9aQVm+f8L4m1ZWUZY/99/ES/It1tdP/MCtgPdvW5x0vGEo3nlNw5iTAoaCoN/kLRvrU4rhXdc2YpnGyMdJQ8V/B3dvbUhp/HQlnJVVm9M6KzvawtXXUFBaiTFqeazpD9C4djuFZSktuJmWuuUbyS+tnHC56bNYc9NvJx0fKChm+bW3jUnKDdNHsKCExZtunNFYEzEMg993PHwxeySpVJ6L5cT4RFVqz8FPFZQSRI288SogiOKTBakVBPpEVQOWY6OGkzIvHs/HYg5mgkRrrvmd6lIS3UsDeO+C5Ce6rcpqyq67ATWqgJby+bEqqijZui3p+KKiIm4dvIg/dmnFjuk45NkR7tgwfvHZRD6lePeCcqxRJy9MIGgavLk6N4pC3VVTRtAwxvweLKV4d115SsX1bqssptA0x1QktZTiLdWl5KVYVCqXlfl93FpRTGDUY+VXijK/yavLk5+cDRgGb60pG/Mc8gH5ppFThcWEmGkqldxRKfUYcMuVJppKqfXECz6ZxN9z7tdaf0Yp9Rlgj9b6F0qpIPECU5uIz8y+XWt92QohK9Zu0F+9/6ErCUWIaesLx/ifQ+0cbOmlOM/PbWtqWZcgSZtMyHZ49OXz7DnTRZ7fx82rqrl6cVlKMzNzxS/3tfLwyx3Yjkd9WR4f3L6Y+rLkMyvDjnX088DBdi4ORFlRU8jta+uuqEr02a4Qvz7QRmtPmEXl+dy+ro4FKcywT8dwIruxoTS+V5bMzcqO59gRjj//MOcOPYfPCrB0yy00rN2Wseeg5zm8tOO7NB96Du15VC5axdY3fZhgfvIPchBfHt165AWO736IWGSQBSu3sOya27DyMjfL/nTrCb7V18l5K8iySIiP1i5hxRVUiT7hxLg33McxJ8Yyn5/35RWzLMU9uADHujr4evtJjgULqLYjvL+4glctaJrKXQGg7eheapYUY7R61DatAKArsgursYidfX5KijdM+WenQ9h1+dsTbZyJxtBAg+XjT5YuoDCFE4sQfw717nmazicexo1GKN1yHRU33YYZTP114KkDB3iwa4ABn8VaN8xbNqyjrCT194JXBsL86kIvF2MOVxUEuaOqlAorpaYTs8JF2+FXF3p4ZTBCld/HHdWlrCxIbdk9QJ/j8sCFHvb1hyn2mdxeWcKG4tTfB3Kd1prn+0I8dLGXkOtxdUkBt1WWkH8FCf3+/hAPXOilx3HZUJTH7ZWllKT4NyBELrmtqvQFrfXWZLdLNaH9ArAc+CEwUopPa/2T6QQ5VZLQCiFyQaJZ2WGZTmaFSCTXElohhBDzR6oJbaqnBMuBTsZWNtZAVhJaIYSYzUa348n0XlkhhBBCiPkkpYRWa/2BdAcihBBzgczKCiGEEEJkTkoJrVLq2yRopaO1/uCMRyTEHKe1nlf7ZueTyZJZSWTHGt7qMp2/g1z/O5pu/HNhPEzvOTAd2T6+mD75HQohhqW65PhXo74PAm8BWmc+HCHmrtaeMN977gzHOgbwm4ptSyt429YGAlLIIeeNT2RBktlE7Mgg+x78T1pe3o32PKoWr2bTG95LQYpVll3H5uCj93N63y7cmE35wiY2vv69lNY0pjnymXP2wFMc2vljwn1dBIvKWHPTXSxa/6qUx7cde4n9D3+fwe4OrPwiVm5/I8uuuS3lD/UXm4+y78Hv0dtxFl8gj5qla6la9LqUe/g1R2zubbnIsVAUv1LcUFbI2+vKsRL1CE7Avnielv/+NgNHDqIMg5LN17Lgre/DzM9MYS83EqbtR/fRs+dptONQsPwq6t/+AQI1CzJyfDF9LRGbe1s7OTIYwacU15cW8M66CoLzoEqyECKxlIpCTRiklAE8orW+OemN00CKQolc0xuO8ac/O0g4dqlXoc9QLKsu5NOvW5nFyMR0yaxsarTWPPYff0HfxZZLvUCVwgoWcNvv/T3+YPIqp0//4EucP30Yz4mNXOazgrz2I58lv7giXaHPmLMHn2bvA9/BHd32xW+x8Q3vY9G665OOP3/6MM/84J9wnbHjV26/g1Wv+q2k43s7mtn5nb8eM94wfTSuXcfmdbcnLQrVFXP446PniHiXPjf4FVxVkMenl9QmPb4bDnHkLz+FOzgAw589TB/B2gUs+5PPpX2mTWvNyS/+FeGzp9DDPY2VwszLZ8VffAFfYWrVtkX29Dku/+fIOUKj+jH7FSzNC/D/muSkhBBzTapFoaZ6Oms5kDunxIXIsp1HzhNzvTGXOZ7m5IVBWrrDWYpKTEdl62NUtj5GU1uLJLMp6Gw+ymB3x6VkFkBrXMfm7IGnko4f6OqYkMwCuG6ME7sfnulw0+Lwzh+PSWYB3JjN4Z0/Tn28M3H80WcewHOdpONfefqXuO7Yx89zHc4ePEA0Gko6/uGLfTje2JPgMQ0vD0Zoi8YmGXVJ9/NP4dn2pWQWwHWwL55n8NjLScdPV/jsKcLnzl5KZgG0xovF6Hr68bQfX0zf4519xPTY99KYhlNhm7PhaJaiEkJkW0oJrVKqXynVN/wv8Evgj9IbmhBzx9mu0IQPggCGAW19ktDmmkSzspLMXl7/xTZ0gr8BN2bT09GcfHxnG4Y5cZeMdl16Os7MSIzpFurrSnh5uK+LVFZLDXS1J7zc81yioYGk4/vOnxubTA5RyiA00Ev7iaO0nzgKgH22H4Devn0jtzsdiZIobfYpaIvaCa4ZK9pyFm1PTDq05xHtSP8upmhHa8JZYB2ziTSfTvvxxfSdjtjEEvypGApaUzipIoSYm1JKaLXWRVrr4lH/rtBap3ZKWQjBoop8/ObED1Kup1lQkpeFiMRUDM/KQuIlxpLMTq6oagEkSCZMv0Vp7aLk4ysXJJyFNEyT0rrFMxFi2uWXJF4WnVdckdJy26KKxEsqDcMkkF+YdHxJTWPC34FG02/5aYvGk2K7pQaA7T3xBLy3bx+9fftYmhfAlyBMR8OCgJX0+MGFi1BWYMLlyjAI1tYnHT9dwdp69LjZPQDlt8hrXJr244vpW5xn4U/wHPZSfA4KIeamlJccK6XqlVLblVI3DH+lMzAh5pIbV1TjH1ewwmcollcXsaBUEtpcMDqRbWprkVnZK1SxcDlFlXVjZlmVMvD5AzSu2550fGFZNTVL12L4/GMuN0yLpq23zni86bDmprdi+sd+6DZ9Fmtec1dK41ffdBemb9x4v8XK6+9IOHs93qrr78Qc9/iZPotF619F49ptAGOSWrulhu09XSOJ7bXWuQnJhF/BmsIgtYGxPzeR0quvxwgExiTVyufDqq4lf9mqpOOnK69xCXkNS1C+UY+VUhiWRdn2m9J+fDF9rykvxjIUo5+FfqVYlh+gMU8SWiHmq5SKQimlPg/cDRwGhjdAaa118ioUaSBFoUQuau+N8F+7z/JKex+Wz+D6pkru2rwQyyeVGWez4UQWpPDTdMWiYQ488n2aDz2H5zrUNK1j423vJr+kMqXxrhPj8BM/5tTeJ3BjNhUNy9l423sorkr/7N5MaT70HId3/ohQ70XySipZc+NdNKy9LuXxHScPsv/h7zPQ2UagoJiV19/J0i03p1xQqavlOPse+i962k7jD+bTdM2trNp+J2pUleK2o3sBqAvEZ32t+g4Ani4tp802+FlfJUcGI1iG4sayIt5aW47fSO34dtdFWu+/l4HD+8E0Kd1yHXV3vRszL3lRsJngRSO0/fT79Ox+Es+JUbhyLQve+l4C1cmLWonZoS0a477WTl4eCOM3FK8qLeLuujICKVbaFkLkjlSLQqWa0B4B1mutZ8WOe0lohRCZMKEdT/0Wmlvjr5mSzIq5bLKkFuKJLTCmArIQQggx02a6yvFJIPl6IiGEmCMSFn5q1bJXVswLdSs2AfElyG3RgZElyDB2b60QQgiRbZfddKOU+mdAAyHgJaXUo8DILK3W+p70hieEEJk1YVYWWWIs5qeRpPboXrwFBkarR3nw1XRFdrG9p2uoV+0+makVQgiRVcmqSOwZ+vcF4BdpjkUIIbLqchWMhRBxw0mtEEIIMRtcNqHVWt8LoJQqACJaa3fo/yYwsfa+EELkoOFEdmNDKf27DwGSzKaDqzW/jAzy0+ggNppbrXzenldIvkpt94vneex/+L84s28XnutQUtPI1W/6CEUVqRf0ecYOc1+4n4uey1Z/kPfnFVGdQoXg2aLj5EH27riXcF8XvkAeV736TSy7OvUqz+ddl3vDfTwfi1BpmLwrr4jrrdQrrXd7Lv822M+OyAKKjys2F8S4uUinXJSqz3H46tnzHA1FUcDW4nx+d2EVvhQL+mjXofM3j9D9zE60pym77gYqbrwVw587FW4HXjnI+Qd/Qay7k4Llq6h+/ZuxKqqyHda8YXseD3X28VT3AIZS3FhWyM0VxfhSfA7PBnv7QjxwoYcex2VdYR53VpdS5s+d1zEhZlqqRaGeBV6rtR4Y+n8h8JDWOnmvhTSQolBCiJky2awsSDI70/6sv5Mn7QgR4u87FtBo+vhWSU3C3pLjPf6tv6K77dTYC5XB63//Hybt8TraD8P9fDXUN3J8EyhQivtKanIiqW09updnf/jlCZc3XXMbG259R9LxFzyXd/d0ENIewx19gyg+kl/M2/OKJh3XdnQvNUuKCbd6vK+4kAueiz10naU0N5eX8M4FyR//iOfx+4fPEBv3saPMGxrdEwAAIABJREFUZ/LlqxqTjtdac/pf/57BE6+g7XgEym+R17CIpZ/88zGVmmerrqd30vrDe0fixzAwAkGW//FnsSqrsxrbfOBpzV+faONsxCY29PnXUoqrCoN8alFNyidmsumBCz38tKOHqL70OpZvGnx2eT2lktSKOWami0IFh5NZgKHvM1NjXwgh0qCy9bHLLjGWZHZmnXRi7BqVzALYQIvrstMOJx3f29E8MZkF0B57d9ybdHxEa742KpmFeA+6kNbcF+5P5S5k3d4HvpPw8hO7H8LznITXjXZfuG9MMgsQQfONUB8R7SUd/wu/SafnjSSzALZWPNrVR08s+fF/1N41IZkF6HZcnu8dTDo+dPIooRNHLiWDgI7ZRFqaGXjlYNLx2aZdh7af/OeY+PE8vEiEjh0/zV5g88i+/jAt0UvJLICtNa8MRDgZnhWNPC4r4nr8ZFQyC/HXsbDrseNib/YCEyLLUk1oB5W6NG2hlNoCJP8EIoQQs9DoRLaprSVewViWGKfVASe+xHS8MJo9seQfJFuPvDDpdV0tx5OOP+vGSDT54gDPp3D82SA6ONkHVk3/hdak4/fEoiRKO03gjJs8IX3ONMecEBjmU4pTYTvBiLEODUQmjy2lhPYYnjMxTi8aYfDEkaTjs82+eAE8d+IV2mPw6OHMBzQPHR2MEPEmPoddrTkWmv2vAy3RGOYkr2MHB+RjuZi/Ul2b8Angh0qp4XfMOuDu9IQkhBDpk6i3LEPteET6VBhmwjOoFlBrmEnHF5ZPvk/Wyp98ueywcsPEmWSLTbWZ/PizgTJ96EkSz7yi8qTjqw2TUwnGx9CUqcs/Bh2n+qgrKsPUGnfcmQEPKPUnfwzLfCYt0VjC6yqt5B9HfCWlGH4/XnRsUqj8Fv7SsqTjs80sLEK7CRJawF8y++OfC8r8JpZS2ONeC3yGotQ3+18HSnwGziQ7BStkubGYx1KaodVaPw+sAj4G/B5wldZ68tPlQggxi21sKI1/U78lu4HMI9f5g+QpNWGW1kBxR7Ag6fj61ddgTLLPdc2Nv5N0fKVhssUfmNBQPYji3cHkCfFssHj9qxNeXlBei5VfmHT8u/OKCI77DfiBjf7AZZP64fY9N4b6Jjx+Jpoy06HMfiXp8e+uS5x0K+DO6tKk40s2Xo1KEKcyTUq3bEs6Ptt8BYUUrduM8o19FJUVoOp1d2YpqvllW2khRoIZTp9SbC6e/TvpKi0/y/IDE2ajLKW4vbIkKzEJMRtcSQWFq4H1wCbgHUqp96YnJCGEEHONTym+VlLNUtNHAEUQRaUy+EJxJVUpzNAahsFrPvDnmFZwzOXLrn0DC1dfnVIMnymsYKs/iAXko8hH8QcFJVwz7mfOVhte/x6qFq8Zc1lecQWv+cCfpTR+qz/IJwpKKFDx+24BW/wBPluYvKBT3YpNLHJdPtHfR5Hnkac1ltasMaN8J9iKUtDbt++yP2NRXoD31FWMSal9Cv5wcQ3BFAo6GVaApZ/4U6zqWpTfQlkW/ooqltzzJ5j5yU+KzAYL3/0RCtdsQPl8GIEgRiBAzW+9jeL1cnItE4p8Jn+0pJaKoZlaSylqLT//d2kdVg4UFQO4Z1E1qwqD+BQEDUWeoXjvggpWFaZerVyIuSbVKsf3AU3AS8T3nwNorfU9aYxtUlLlWAgxVZWtj11qzzM0Q9ssS44zqtV1sNE0Gj6MKVQV7Wo9SaS/h+qmtfh8V96upctz6fY8GkwfVg5UNR0vEuqn8+xRiqvqr6hl0TBba5pdhzLDoDyFkwnjnTu6l1bTZLE/j2oNVn0HAFZjETv74rOPJcUbJh3veR77B8IEDWNKH8K11tgXOkDreHKbg79Dp78Pp78Xq6omp1oOzRVaa9ptBwOotnw5+RzqiTkMuB61lh9fomlnIeaAVKscp7rgfiuwWqeS/QohhBCXsWCaLXLKFyyd3njDnFIiN1sE84uoXzX1GT1LKZp84xcPp27hik2YR/fiugMQKMRuqRm6poPtwNOl5fT27Zs0qTUMg43FU59RVUoRqL7yRH428RUV4ysqznYY85ZSirrA1P8GZoNSv4/S3L4LQsyYVNdXHARy+91DCCGEEDOibsUm6lZsoi06QFs03tVvOLHd3tMFxJcgJ1uGLIQQQkxXqqfJK4HDSqndwEhdc631b6UlKiGEyJDmVll4IsRU1a3YRNvRvbRFB6gbNVu7nfgy5GSztUIIIcR0pZrQ/mU6gxBCiHQbbtcDxPfPcimZzdT+WdexOXd4N+dPHaagtIrFm24gvzh5QZ65pLvtNGf3P4kTs1m4+hqql6zJ6P61XS3H+ddQL33+AGsGB/iTJWspT6FC8Eyxw4Oc2f8kPe2nKa1ZxKINr8bKy42CRhDv1/lkLMIuO0yJMrgzUMDiUUktMJLYWvUdbO/pGpPUaq05NBBhd+8APqV4dXkRS/ICWb5XQgghcllKRaEmDFLqeuCdWuvfn/mQkpOiUEKIKzGh9yzQrDYDmUtmY5EQj3/nrwn3deHGohimD2WYbL/7k1QtWpWRGLLt6DMP8PJvfobrxkBrTH+AuuUbufrNH81IUvulV3Zzf2Vd/D9KxWNwHe7PK2FBcfI+rtM12H2ex7/9GdyYjevYmD4L029x0/v/jMLymuQ/IMscrflk30UOOTZhNCbgQ/FHBaW8Yaj1UtvRvUA8qYVLBaMAniop57udQQ6EAkS1RgF+pXhTdWlKbXuEEELML6kWhUq5RrlSaqNS6u+VUqeBvwFenkZ8QgiREeOT2Wa1mWa1mbz16zNa2fjosw8Q6rmAG4vv2vBcBzcWZc/Pv8F8qLcX7uvm8BM/wXVsGLq/bixK27GXuHA6/W8nEdvm/sraeCI7nDwrhWv6+KPW42k/PsBLD96HHRmMPwbEZ+ztyCAv/c99GTn+dD1qhzk4lMxCvOVBFM3nB3sIaQ+41LN2eG+t3VIzsgzZ6gyxP+QnOvT718QrLv/sfA9dMSfj90cIIcTccNmEVim1Qin150qpl4F/AZqJz+q+Rmv9LxmJUAghpqCy9TEqWx+jqa1lTDILmZuVHe3c4efx3Ikf2u3IIIPdHQlGzC0dpw6iElQWdmNRWo7sSfvxHzh3BEgwC6wUp8qq0n58gPOnDo8k8yO05sLpwzlxUuOhaIgIE+P0KdgXGymvMVIwChhTMGpnLI9YgrupgP394bTELIQQYu5Ltof2FWAXcKfW+jiAUuqTaY9KCCGmIdGsLGQnkR1m+hP3V9CexphCL9VcY/r8iZcVKwOfP/17KIsv8xgbGUomDdPE9dwJlyvTzIk+mHmTxKiBQILrxheMsgbyMfzgjrupocCSPppCCCGmKNmS47uAduBxpdQ3lVK3kPAUtxBCZN/wrOzGhtJZlcwCLN18M6Z/XFKlFMVV9eRnYP9mttUu25BwFtI0TRrXXZ/247+2cSWm6yScIb2663zajw/QsGYbxrgevIbpo2HNtowcf7reHCwgmOAjgIVivS/xSYnR7X22hPoTnkX3tMdSTsxwtEIIIeaLyya0Wuufaq3vBlYBO4FPAjVKqa8ppV6XgfiEECIlo2dl+3cfGtkrC9lPZgGWbLqJuhWbMXx+TH8AnxUkr6ica+/KSm29jPMH8tj21ntG7rtpBTB8ftbecjcl1QszEsNfRKIoreNJ7dBXeX8Pf7tic0aOv+61b6e0dhGmPzDyVVLTyPpb35GR40/XVn+Qd+QVYgF5KPKVokgpvlhciS/JDHPdik00uC7vGxzA0pr8oa88PL6Y10GegfSsFUIIMSVXXOVYKVUOvBW4W2t9c1qiSkKqHAshho1uxzPbZmUT6b/YSlfLCYJFZVQvXo0yUq7NNyc4dpSOE/txnRg1S9cSKCjO6PEjts0/n3iJNu1yW0EZt2W4wrTWmu7Wk/RdbKW4so6yBU05sdx4tPOuwx4nSqEyuM4fxLrC+F859hIv+S2qfQG2uR4l9Zd61g6TvrVCCCFSrXI8pbY92SYJrRACJt8rC7MzmRVCxI1v71PbtIKuyC7gUmIrSa0QQsxvM962RwghZpPLFX6SZFaI2W18e5/2E0dH2vts7+kCZAmyEEKI1CSrciyEELPK+EQWZvcSYyFEYiNJ7ahKyHZLDVZ9x0hS+zTxpFZma4UQQkxGElohRM6Yje145ptoqJ+OE/sBRe2y9Vh5hRk9vhuzaT+xj1gkRNXi1RSUXlkPWc916Dh5kMhALxULl1FcVZ+mSNPniGPzimNTZ/jY4g9g5tge3PFGt/cBqBuaqR1ObJ8uLae3b58ktUIMcTzN/oEQfY7HyoIAdYG53/pNiMuRhFYIMevJrOzscGb/U+zd8R2UMuIN3DzN5js+SMOa6zJy/K6Wkzz1/X9Ea2/oS9O09RbW3nx3SoWV+jvb+c19f4sbi6I9D9AsWLmVrW/63fh9muVsrfk//RfZF7OB+J6hMsPgayXVVBlmdoObpmSztVZjETv7ZLZWiJaIzedOthHTGk/H+0BvKy3gg/WVGDl+ckuIqZr97+BCiHkt0aysJLOZF+rtZO+O7+A5MdxYFNeO4jo2L/zqPwj396T9+J7n8vT9XyIWDeHYEdyYjefEOPnCY5w/eTCln/Hsj75CdLAvPt6xcZ0YrUdf4My+J9Mc/cz4Xrifl2JRImgiaEJo2jyXz/R3ZTu0GTN6by2A3VIT/zrbL3trxbynteZLZzrodz0insbWmpjWPNczyLM9g9kOT4iskYRWCDErVbY+JoWfZpFzL++O925NoPWVPWk/fmfzMTwnNuFyN2Zz6qUnko4f6Oog1HOR+HzG2PEnX3x8psJMq19EB4mOu8wDXnKiDHheNkJKi7oVm+LLkIcKRgEJC0ZJYivmm5ZojN6YO+HyqNY81tWXhYiEmB1kybEQYtaRdjyzj+fE8BIlTZ6H605MNGf8+K4Dkyync4eW4E51vOckHz8bxCY5oaAAh9xrwZfM6L21w0uQAbZzqW+t7K0V84mjdfxlLMGfu+3NvdcAIVIlM7RCiFlDZmVnr7rlmzDNBOdADYO65RvTfvyKhuVD+17HMv2BlPbwFlXW4bOCEy43fH4a1m6bkRjT7SYrL+FZ6EbTR2mO76GdzPj2PpB4tlaI+aAhaOFLcGLOUoptpQVZiEiI2UESWiHErDA6kZUqxrNPSU0DS7a8BtNvAQqUwvRbLL/mNooq6tJ+fJ8/wOY3fhDTZ6GGkjfTH6Bi4TIWrrk26XilDK5580cx/QGMocTc9AcoqqijaeutaY19pnw4v5gqwyRI/ANtAMhH8WeF5dkNLM2GlyADCZPa7T1dsgRZzAumUny0oQpLqZGTWwFDUR/0c0tFcVZjEyKblJ5kCdNstmLtBv3V+x/KdhhCiBki7XhyR+e545w79BwoaFizjfL6pRk9/kBXB2f27cIOD1C7fCO1TetRRurnZkN9XZzZt4twXxfVS1azYOWWkQQ3F0S05pFoiAOOzULD5I5gAWVzdHY2kbaje0e+rwvEW0ZZ9ZeWIINUQRZz30XbYVd3P90xh7VF+Wwuzk84cytErrutqvQFrfXWZLeThFYIkXWVrY+xsaGU/t2HoH4Lza1aklkhxKSGE9vxSS1IYiuEEHNFqglt7pyWFkIIIYRgbMGomiXFI0uQh/vWDheMSoUkvkIIkdskoRVCCCFEzhnZVztqttZuqRlJalMxOvGVxFYIIXJT2hJapVQD8F2glnirvH/TWn953G1uAn4OnBq66Cda68+kKyYhRO7r9Fwuei6Npo88lfm6drFomIGuDvKLywkUzL8iHFpr+i+2glIUVdShrnDfltaaga4OPDdGcVU9agq/wzbXoV97LDX9sm8sC2wnxsHONkqsIE1l1dkOZ9L2PrVNK5KO3d6zC7jyFkAXbYdB16U+YOEz5DkohBDZlM4ZWgf4Q631i0qpIuAFpdTDWuvD4263S2t9RxrjEELMAWHt8Zf9XTwbi+BH4QLvyyvi/fmZSSq11hx87Iec2PMwhunDcxzqV21l8x0fxPT5MxJDtnW1nOS5n/wLdngQgEB+Edfe9XHK6hanNL6/s41nf/gVQn2dgMLnD3D1Wz5K9eLVKY0/77r8cf9FTroxTBSGgj8uKOOWQP4U75G4Uj88eYCvBoN4ysCzXWqO7+ULVQ0sKqnMalyJZmvbTxxNYWRNwmXKkyW2vTGXr5zt4HTYxlTxHsDvWVDBq8qKZuJuCCGEmIK0TW9ordu01i8Ofd8PvAzUp+t4Qoi57XMD3Twbi2ADg2giaO4N9/NINJSR45984TFOvvAInhPDiYbx3BitR/Zw4JH/zsjxs80OD/Lkf/094b4u3FgUNxYl1HuRJ//z88Si4aTjPdfhN/f9Lf2dbbgxGzcWJRrq45n7v0yoL/nyUK01n+i7wBE3RhQIoRnQmr8Z6OaoY8/APRTJ7O1o5iv5BUQCedhWAMfnp7W4nI93teIl6BGcDeP71ib7gngLoPLgq0daAMHkvW2/eKadE6EoMa2JeJqwp/lOSyfHQ5HM3EEhhBATZGS9nlJqMbAJeC7B1duUUvuUUjuUUmsyEY8QYvYYbtnTv/sQAM2tEyuvD3oeT9hhxqctETT3hfvTHSIAx57dgRsbG4HrxDizbxee62Qkhmw6d/g5dIKkxfM8Wl/Zk3R8x4kDEx4/AO25nNn3m6Tjj7gx2j2X8RHYaH4YGUg6Xkzf9zrP4ZpjWwRp06Q3r4DdHWeyFNVEw31rk33BpcS3/cTRMb1tYWJS2xKxaYnEJj4HteZ/Lvam/X4JIYRILO1FoZRShcCPgU9orfvGXf0isEhrPaCUuh34GbB8kp/zYeDDANV1C9MYsRAiE8b3noXJ+8/2aQ8DBUxMdrs8N31BjmKHEydNnufiOrGc6mU6FZHBXtwEM6GeEyMymPzDfGSwN3FC7DqE+7qTju/yXIwETwEP6HAz8xyY7y6aPnSCnrdKa85HMrNSYiaNXqY8ev/t6KJST3MpqW2LmBjkE19oPNb5cB9Qk4mwhRBCjJPWGVqllJ94MvufWuufjL9ea92ntR4Y+v4BwK+USrgRR2v9b1rrrVrrrSXl5ekMWwiRZuOT2Wa1mWa1mbz16xP2n602TPISFP8xgM3+QFpjHVZevyzh5XnF5fisYEZiyKbKhpWYCR5rw+ensmFl0vEVDStIdELC9AeoXpJ8cc5VPotYgr7pARTXzYPHfza4GvAlmGV3DZOtlQsyH9AMGT9ba7fUjJmtvak4xk3FMe6uiCZ4BoOlNKvznJTbBAkhhJhZaUtoVbz05X8AL2utvzjJbWqHbodS6pqheDrTFZMQIrsqWx+jsvUxmtpaxiSzMHFWdjRTKT6ZX0Jw1MyICeQrxe/ml6Q15mHrbrkb0x8YU5XX9FlsfP17rrjSby6qWnwVZQuWYPqskctMv0VlwwrKFyZO9kcrrlzAglVXY/ovjTd8fgrLa1mwcnPS8WWGyTvyisY8B/xAmWHwpkDBld0ZMSXvWbSGwmgY04mNXOaP2dza2cGCorIsRjZ945chAyNJrX22H/tsP8GWPj5k9RActejYj0cJLp/2zrO9p4vevn2S2AohRIYpneCM94z8YKVeBewCDsDIq///BRoBtNZfV0p9HPgY8YrIYeBTWuunk/3sFWs36K/e/1Ba4hZCpEeiWdlhl0tmR3spFuW74X7aXIeN/gDvzSuiLoNLffs72zny1C/pbjtFYXktK6+/g/IFSzN2/GzzXIeTLz7OmX1PohQs2nADSzbdmPJya609zu5/ipMvPo7r2DSs2UbT1a/Fl+Isu9aanXaYH0QG6NMeN1p5vCNYRLGR+fZN81VnqJ9vnDnMc4Eg+Y7DXT6L3168BmMO/Q5GV0pOZJdp8H2/jx6luN51eZftUApY9R1AvAUQSF9bIYSYrtuqSl/QWm9Ndru0JbTpJAmtELljOJGFiclsqomsEEJk2nBiW7MkeWswozV+3n58UguS2AohxFSlmtDO7SomQoismolZWSGEyIa6FZtoO7qXjlPj61lOcvuholIA27mU2Pb27ZOkVggh0kgSWiHEjJNZWSHEXDC8rzYVo5cqj66WPJzUziRJkIUQ4hJJaIUQM+pK2vFMldZ6XhRimq28ofY7c2nfpBDTNTyjO1kLIKuxaEaOs7PPL7O+QggxiiS0QogZM9kS45lIZLXWHHt2B0efeQA7PEBRRR3rX/cuapaunfbPFqnpbj/D0z/4ItGBeN/ZYFEZ17/j05RU1Wc5MiFmh9FJLUDd0BJkq74D+2z/jBxjO7KUWQghRpOiUEKIacvErOyhnT/m+O4HcUf1wTR9Fq965/9HRcPyGTmGmJwdCfGrL34ctDfmcmUY3PmHX50XvXiFuBLJqiVP1XDhKZCKykKIuS3VolCyXkwIMS2JZmWb1Wby1q+fsWTWdewJyezw5Yef+MmMHENc3uEnfjIhmQXQnscrT/4iCxEJMbuN7mvbFh3AW2DMyJfdUnOp+NTQcmbpfSuEmM9kybEQYsrSucR4tMhAL5B4z2x/Z+uMHksk1ttxdtLretonv06I+Wwkqb2Caskp/dxxe3StxiJ2DiW1MlsrhJhvJKEVQkzLxoZS+ttaoH4LtOq0VDEOFpZMel1RpezfzITS2kV0Nh9NfF3dogxHI0RuuZJqycmMLzwV1yF7a4UQ85YktEKIWc/0WSy/9vUce27HhD20q2/87SxGNn+suekuTu55FD1hD63Jqle/KUtRCTH/jJ71BWkTJIQQktAKIXLCVTe8GX8wn6PP/Bo71E9RVT3rb30nFQuXZTu0ecFnBbn5Q3/J0z/4J8L98X17eSUVXH/3p/D5rCxHJ8T8k6hNEMB2OpKMvDIy6yuEmO0koRVC5ASlFMuvvY3l196W7VDmrZKaRt5wzxezHYYQYshks7W1TStm5Od3RXZNmPWVxFYIMdtIQiuEEEIIkcPGz9a2n0i83/3KXVrKDDJbK4SYnSShFUIIIYTIcaOT2hn9uUNLmcfv0ZWkVggxW0hCK4S4YqPb9fQPt+xp1dkMSQgh5r2ZrKYMEysqj5mtRZYgCyFmB0lohcgRtuPxyMsdPHuqE1MpblhexQ0rqjCNxP1Z02Wy3rMw8/1nZ1p36yleeeoX9F1opbS2kVWvehMl1QuzHVbOcJ0YJ/c8ypn9T4JSLN7wapZuuRnDlLeS+cIOD3D0mR20Hn0RfyCPZde8joWrr0WpzL4OicwYP+s72WxtLpJEXIi5Q2mde7MqK9Zu0F+9/6FshyFExnie5nM7XqalJ0zMjf/NWqbiqrpi/vfNyzMSw3AiCxOT2dmeyAKcP3WYZ+7/J1xnqO2PUpg+P69+1x9TXr80u8HlAK09fnPf39HTdnrkMTR9FhUNy7n+HZ+WhGYeiEXDPPLNPyU60IvnOgCYfoslm29m/WvfnuXoRLqNLjwF8aQ2lz1dWg5IYivEbHZbVekLWuutyW4np9WFyAH7zvXQ1hsZSWYBbFfzcls/py8OsriyIK3Hz+VZ2WEvPXjfpWQWQGvcmM3+R/6Lm973p9kLLEecP3WYnvYzYx5D17HpPHeczuajVDauzGJ0IhNO730Ce7B/JJkFcGM2J/c8yorrXk+wsDSL0Yl0m6xN0ExVVM6k8dWbJakVIrdJQitEDjh2foCo40243NOa4xcG0pbQ5vqs7DDPdRjobEt4XU/b6YzGkqu6zh3HjUUnXO45MTrPHZOEdh7oOHVw7EmhIYbPR3frqRnfvylmn0RtgmauonIm1SRcNi2JrRC5SRJaIXJAaZ4fv6nGzNAC+AxFaZ4/LcccPysLuZnMAijDxGcFcOyJCZmVl97Z7bkiUFiC6bdwY2MTGtPnl5m5eSK/uAKlDLQee3JNe548B+aZ0bO1NUuKsx3OFTNavZEZ5u3El07LbK0QuUsSWiFywLamCn6+rxUYl9CaBhsaZv6D5GRLjHMtkR2mlGLpltdy4vmHx8wwmX6L5de+IYuR5Y6Fq6/h4GP3T7hcmSb1q5JubxFzQNPVt9J86JmxJzWUQX5JBaV1i7MWl8iO8bO1uWh89WaZrRUiN0lRKCFyxPHzA3zjNycYtF201lQUWPzeTctYUJo3Y8eYS7Oy43mey0v/cx9n9z+FYZp4nsvSLbew7pa3oZSR7fByQnfrKZ77yb8SDfUBECws5dq7Pk5pTWOWIxOZ0nrkBV789bfx3Bie51Fa08i1d32cvKKybIcmxBUZX+SqtmkFXZFdwKWCUUKI7Hpb040pFYWShFaIHKK1pr0vgmkoqgoDM1pZdq7Nyk7GDg8S7usiv7QSf2DmTgbMF1prBro6UAoKymqkuvE85HkuA53t+AJB8osrsh2OEFM2enZ5fPVmq7EoKzEJIS6prnqtVDkWYq5RSlFXMrNJ2Fwp/JQqK69A9s1Og1KKoorabIchssgwTIqr6rMdhhDTNnrZ9OjqzVZ9B/bZ/ixHJ4RIlSS0Qsxjc6EdjxBCCDEdo4tcAdQNFYwSQuQGSWiFmIfm26ysEEIIcTnjZ2tzsXqzEPOVJLRCzFMbG0rp330I6rfQ3BrfSy/JrBDp1Rnu5/tnX+Go67LMMHlX4yoq8jO3V89zHc69vJvzJw+RV1zO4o03UFBalbHjCzHbDc/Wdpzqy3YoQogUSUIrhAAkmRUi3Y51dfCRcC+xkgocv8XemM3Putv4eiTEivL0L3F07Cg77/0bBrvP48aiGKbJ8d0Pct3v3EPN0rVpP74QuWJ4tlYIkRukV4UQQgiRAZ+7cIaIFcTxWwA4fouIFeSzF85m5Pgn9jzMQFc7biwKgOe6uDGb53/+DbTnZSQGIYQQYqZJQiuEEEJkwNHyGrQx9m1XGwbHymvwMpBQNh96Ds+JTbjcc2z6LrQkGCGEEELMfpLQCiGEEBlguG7Cy03PxTDS/3ZsDs0Mj6e1xvT70358IYQQIh0koRVCCCEy4Nqu85jjZkhNx+Hqzo6MHL9py80JklpFfnGpWZ2gAAATAUlEQVQlheXSW1gIIURukoRWiHlmdMseIUTm/NnS9dT1d+OP2SNfdf1d/PnSdRk5fsPabSxcfS2Gz4/pD+CzggQLi9n2tnsycnwhhBAiHaTKsRDzyHAy29TWQn9bS7z37FDLHiFEepUE8/nB0o08236Ko4N9LCsoZvvSjRlZbgyglMGWOz7Eyu1vpLP5GIGCEqqXrsEwzIwcXwghhEgHSWiFmAdGz8o2tcWLvzSrzYC06xEikwzDYPuCJrZnMYbC8lpZYiyEEGLOkIRWiDlu9KzsMElmhRBCCCHEXCAJrRBz2PhkVhJZIYQQQggxl0hCK8QcJLOyQgghhBBiPpCEVog5RmZlhRBCCCHEfCEJrRBzhBR+EkIIIYQQ840ktELMAZPNyoIks0IIIYQQYu6ShFaIHCazskIIIYQQYj6ThFaIHDWh8FP9FppbNSDJrBBCCCGEmB8koRUiByVcYtyqJZEVQgghhBDziiS0QuQQaccjhBBCCCHEJZLQCpFjNjaU0t/WMrLEWBJZIYQQQggxXxnZDkAIIYQQQgghhJgKSWiFEEIIIYQQQuSktCa0SqkGpdTjSqmXlVKHlFJ/kOA2Sin1FaXUcaXUfqVGNdAUQogZZocH6Gk/QywSynYoQgghhBBimtK9h9YB/lBr/aJSqgh4QSn1sNb68KjbvAFYPvR1LfC1oX+FEGLGeK7D3h330nzwGQzTh+e5LN30Gtbd+naUksUqQgghhBC5KK2f4rTWbVrrF4e+///bu/9gze66PuDvD9lQkyAsECBxsyYWLSo0LNkMJgZpKtICRtA2HaFRFEdTHWyGOtSibQeHTqfaMtZBptJMxBCJcQSTaDEDOCCB1CbbkGDIhh8ioPsLg6BhIdhkk0//eM7Gy91791f2ueee+7xeM3fuec75Pud8zpkzu/ve7/ec7/4kH0uyZVmzlyW5pmduTbK5qs6cZ13A4rnn5uuze+dtefihAznwwN/m4QMP5jN3fiB/euu7xy4NAIDjtGbdElV1TpLnJLlt2aYtSXYt+bw7h4ZeWHgHp+zZv2PnyJVMT3fnz25/Xx468MDXrH/owAP509veM1JVAAA8WmsybU9VPS7J7yZ5TXd/afnmFb7SK+zj8iSXJ8lTzzzrhNcI69ny+Wd31XmJKXuOWvfDeejBB1bc9uDffmWNqwEA4ESZe6CtqpMzC7PXdvf1KzTZnWTrks9nJdm7vFF3X5nkyiT5B8969iGBFzai5UE2GcJsIsweg8c85qR8/ZPPyP4v7Dtk2xPP/KYRKgIA4ESY91uOK8mvJ/lYd//yKs1+P8krh7cdX5Dkvu4+9F+dsGBW6pXdVefllHPPFWaPw7YXvTInbXpsHhkUUpWTTn5szn3hvxy1LgAAjt+8e2gvSvLDST5aVR8Z1v18km9Mku5+S5KbkrwkyaeS3J/kVXOuCda1g0E2WTbEOHplH42nnPNt+Uc/8u/z8Vv+V770V3uy+Yyz860XfV8e/xSP7AMATNVcA21335KVn5Fd2qaTvHqedcBUrPis7ECYffQ2n3F2Lrj0p8cuAwCAE2RNXgoFHNlqYVaQBQCAla3ZtD3AkW3bunm2sGV7EmEWAAAOR6AFAABgkgRaAAAAJkmgBQAAYJIEWgAAACZJoAUAAGCSTNsDIzs4XU+S7N+xM0mya2+PVQ4AAEyGQAsjWm3u2cSUPQAAcCQCLYxktTAryAIAwNERaGGNLQ+yiTALAADHQ6CFNaRXFgAAThyBFtaAXlkAADjxTNsDa2Tb1s2zhS3bhVkAADgBBFoYiTALAACPjkALAADAJAm0AAAATJJACwAAwCQJtAAAAEySQAtzdnDKnv07do5cCQAAbCzmoYU5Wj7/7K46L9nb3nAMAAAngEALc3AwyCbLwmxM1wMAACeKQAsn2Iq9sgNhFgAAThyBFk4QvbIAALC2BFo4AfTKAgDA2hNo4VFaLcwKsgAAMF+m7YETYNvWzbOFLduTCLMAALAWBFoAAAAmSaAFAABgkgRaAAAAJkmgBQAAYJIEWgAAACbJtD1wnA5O15Mk+3fsTJLs2ttjlQMAAAtHoIXjsHzu2cT8swAAsNYEWjhGy8OsIAsAAOMQaOEo6ZUFAID1RaCFo6BXFgAA1h+BFg5j6YufhFkAAFhfBFo4gm1bN8/eYrxl+yNvMRZmAQBgfOahhWMkzAIAwPog0AIAADBJAi0AAACTJNACAAAwSQItAAAAkyTQwiqWTtkDAACsP6btgRUcDLNP37cn+/ftmc09O0zZAwAArA8CLSyxtFf26fv2JMkszMZ0PQAAsN7MNdBW1VuTXJLk3u5+1grbL07ye0k+M6y6vrvfMM+aYDVLe2WTvwuyiTALAADr0bx7aK9O8uYk1xymzYe6+5I51wGHtVqYFWQBAGD9mmug7e4PVtU58zwGPBrLg2wizAIAwFSsh2doL6yqP0myN8lru3vn2AWxGPTKAgDAtI09bc8dSc7u7mcn+dUkN67WsKour6rbq+r2+774xTUrkI1t29bNs4Ut25MIswAAMCWjBtru/lJ3f3lYvinJyVV1+iptr+zu87v7/Cc86UlrWicAAADrz6iBtqrOqKoalp871POFMWsCAABgGuY9bc91SS5OcnpV7U7y+iQnJ0l3vyXJpUl+qqoOJPlqkpd3d8+zJgAAADaGeb/l+BVH2P7mzKb1AQAAgGMy9kuhAAAA4LgItCykg1P27N8xmyVq114j3QEAYGrWwzy0sGaWzz2bmH8WAACmSqBlYSwPs4IsAABMm0DLhncwyCbCLAAAbCQCLRvaar2yiTALAABTJ9CyIemVBQCAjU+gZcPatnXz7C3GW7Y/8hZjYRYAADYO0/awMIRZAADYWARaAAAAJkmgBQAAYJIEWgAAACZJoAUAAGCSBFoAAAAmSaAFAABgkgRaAAAAJkmgBQAAYJIEWgAAACZJoAUAAGCSBFoAAAAmadPYBcCJdPre9z+yvH/HziTJrr09VjkAAMAcCbRsGAfD7NP37UmS7KrzHtl2yrnnjlITAAAwPwItk7e0V3Z5mBVkAQBg4xJombTlvbLZsv2RIcbCLAAAbGwCLZO14hDjvS3IAgDAghBomZxDemVjiDEAACwigZZJ2rZ1c/bv2/PIEGNBFgAAFo95aAEAAJgkgRYAAIBJEmgBAACYJIEWAACASRJoAQAAmCSBFgAAgEkSaAEAAJgkgRYAAIBJEmgBAACYJIEWAACASRJoAQAAmCSBlkk5fe/7xy4BAABYJzaNXQAcrYNh9un79mT/vj1Jkl17e8ySAACAEQm0TMLSMJsku+q8JMkp5547Wk0AAMC4BFrWteVBNhFmAQCAGYGWdUuvLAAAcDgCLevOwSC7bevm7N+xM4kwCwAAHEqgZV1Z/uKng0E2EWYBAICvJdCy7mzbunn2FuMt25O9LcgCAAArMg8tAAAAkzTXQFtVb62qe6vq7lW2V1W9qao+VVV3VS0ZXwoAAACHMe8e2quTvOgw21+c5FuGn8uT/Nqc6wEAAGCDmGug7e4PJvniYZq8LMk1PXNrks1VdeY8awIAAGBjGPsZ2i1Jdi35vHtYBwAAAIc19luOa4V1vWLDqsszG5acJF9+4TPP+MTcqmLeTk/yV2MXwUJzD7IeuA8Zm3uQsbkHOZyzj6bR2IF2d5KtSz6flWTvSg27+8okV65FUcxXVd3e3eePXQeLyz3IeuA+ZGzuQcbmHuREGHvI8e8neeXwtuMLktzX3ftGrgkAAIAJmGsPbVVdl+TiJKdX1e4kr09ycpJ091uS3JTkJUk+leT+JK+aZz0AAABsHHMNtN39iiNs7ySvnmcNrEuGjjM29yDrgfuQsbkHGZt7kEetZpkSAAAApmXsZ2gBAADguAi0rLmqOqmq7qyqd41dC4unqj5bVR+tqo9U1e1j18PiqarNVfXOqvp4VX2sqi4cuyYWR1U9Y/jz7+DPl6rqNWPXxWKpqn9TVTur6u6quq6qvm7smpguQ45Zc1X1M0nOT/L47r5k7HpYLFX12STnd7d57xhFVb0tyYe6+6qqemySU7v7b8aui8VTVScl2ZPkO7r7z8euh8VQVVuS3JLk27v7q1X1O0lu6u6rx62MqdJDy5qqqrOSfG+Sq8auBWCtVdXjkzw/ya8nSXc/IMwyohck+TNhlhFsSnJKVW1KcmqSvSPXw4QJtKy1X0nys0keHrsQFlYneW9VfbiqLh+7GBbO30/y+SS/MTx6cVVVnTZ2USyslye5buwiWCzdvSfJG5P8RZJ9Se7r7veOWxVTJtCyZqrqkiT3dveHx66FhXZRd5+X5MVJXl1Vzx+7IBbKpiTnJfm17n5Okq8ked24JbGIhuHuL03yjrFrYbFU1ROTvCzJNyX5hiSnVdUPjVsVUybQspYuSvLS4RnG307y3VX19nFLYtF0997h971Jbkjy3HErYsHsTrK7u28bPr8zs4ALa+3FSe7o7r8cuxAWzvck+Ux3f767H0xyfZLvHLkmJkygZc10989191ndfU5mw5ze393+R441U1WnVdXXH1xO8k+S3D1uVSyS7v5ckl1V9Yxh1QuS3DNiSSyuV8RwY8bxF0kuqKpTq6oy+3PwYyPXxIRtGrsAgDX0tCQ3zP7+zKYkv9Xd7x63JBbQv05y7TDk89NJXjVyPSyYqjo1yQuT/Kuxa2HxdPdtVfXOJHckOZDkziRXjlsVU2baHgAAACbJkGMAAAAmSaAFAABgkgRaAAAAJkmgBQAAYJIEWgAAACZJoAUAVlVVF1XVd41dBwCsRKAFYNKq6stz3v9VVfXtw/LPH8f3z6mqu4+i3dVVdekx7PcXquq1x1rPsaiq52Q2T+6tS9Yd1TWoqs9W1enzqg0AEoEWAA6ru3+8u+8ZPh5zoJ2y7r5zOP8Hl6xeqGsAwPom0AKw4VTV2VX1vqq6a/j9jcP6q6vqTVX1x1X16YM9olX1mKr6H1W1s6reVVU3Ldn2gao6v6p+MckpVfWRqrp2ec9rVb22qn5hWN5eVX9SVf8nyatXqbGq6s1VdU9V/UGSpy7Ztr2qbq6qD1fVe6rqzCOc77aqunU43xuq6onD+iuG/d9VVb+9wvd+tKp+r6reXVWfqKrXL9n2Q1W1Yzjf/1lVJy2/BkO7G4c6d1bV5avU9zNVdffw85ph3WlV9QfDdbq7qn7wcOcIACsRaAHYiN6c5JruPjfJtUnetGTbmUmel+SSJL84rPtnSc5J8g+T/HiSC5fvsLtfl+Sr3b2tuy87wvF/I8kV3X3Ifpb4gSTPGI75E0m+M0mq6uQkv5rk0u7enuStSf7zEY53TZJ/N5zvR5McDKavS/KcYf1PrvLd5ya5LMm2JP9iCO/fluQHk1zU3duSPJTkslWuwY8NdZ6f5IqqevLSnVfV9syGLX9HkguS/MQwlPlFSfZ297O7+1lJ3n2EcwSAQ2wauwAAmIMLMwupSfKbSf7rkm03dvfDSe6pqqcN656X5B3D+s9V1R8d74Gr6glJNnf3zUuO/+IVmj4/yXXd/VCSvVX1/mH9M5I8K8kfVlWSnJRk3zEc721J3jEs35Xk2qq6McmNq+ziD7v7C8O+rs/sWhxIsj3J/x1qOCXJvat8/4qq+oFheWuSb0nyhSXbn5fkhu7+ypJjfFdmAfaNVfVLSd7V3R9a7RwBYDUCLQCLoJcs/78ly7Xs97E4kK8d6fR1S/bVhzY/Yl1La9p5hN7do/W9mQXnlyb5j1X1zO4+cIQaeqjhbd39c4fbeVVdnOR7klzY3fdX1Qfyd9fhkWYrfbe7Pzn03r4kyX+pqvd29xuO4pwA4BGGHAOwEf1xkpcPy5clueUI7W9J8s+HZ2mfluTiVdo9OAwJTpK/TPLUqnpyVf29zIYwp7v/Jsl9VfW8JcdfyQeTvHx4NvXMJP94WP+JJE+pqguT2RDkqnrmaoV3931J/nrJ1Do/nOTmqnpMkq3d/UdJfjbJ5iSPW2EXL6yqJ1XVKUm+P8n/TvK+JJdW1VOHGp5UVWevcA2ekOSvhzD7rZkNKV7pPL+/qk6tqtMyG2r9oar6hiT3d/fbk7wxyXmrnSMArEYPLQBTd2pV7V7y+ZeTXJHkrVX1b5N8PrNnOA/nd5O8IMndST6Z5LYk963Q7sokd1XVHd19WVW9YWj7mSQfX9LuVcPx70/ynlWOeUOS787smddPJrk5Sbr7geGFVG8ahhNvSvIrSXYepv4fSfKWqjo1yaeH45+U5O3DPirJfx/C9nK3ZDYs+puT/FZ3354kVfUfkrx3CMYPZvZyqz9feg2S/FiSn6yquzIL4rcu33l331FVVyfZMay6qrvvrKp/muS/VdXDw/5/6jDnBwArqu6jHRUFABtXVT2uu788vNRoR2YvRPrc2HXNU1X9aJLzu/unx64FAI6HHloAmHlXVW1O8tgk/2mjh1kA2Aj00AIAADBJXgoFAADAJAm0AAAATJJACwAAwCQJtAAAAEySQAsAAMAkCbQAAABM0v8HkwtJhj7m4WoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a21de6240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "C = 1.0\n",
+    "svc = svm.SVC(kernel=\"poly\", C=C, decision_function_shape=\"ovr\").fit(X,Y)\n",
+    "Ypred = svc.predict(X_plot)\n",
+    "Ypred = Ypred.reshape(xx.shape)\n",
+    "plt.figure(figsize=(16,9))\n",
+    "plt.contourf(xx,yy,Ypred, cmap=plt.cm.tab10, alpha = 0.3)\n",
+    "plt.scatter(X[:,0], X[:,1], c=Y, cmap=plt.cm.tab10)\n",
+    "plt.xlabel(\"Longitud de los pétalos\")\n",
+    "plt.ylabel(\"Anchura de los pétalos\")\n",
+    "plt.xlim(xx.min(), xx.max())\n",
+    "plt.title(\"SVC para las flores de Iris con Kernel Sigmoide\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
+    "from sklearn.metrics import classification_report\n",
+    "from sklearn.utils import shuffle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, Y = shuffle(X,Y, random_state = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.25, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameters = [\n",
+    "    {\n",
+    "        'kernel': ['rbf'],\n",
+    "        'gamma' : [1e-4,1e-3,1e-2, 0.1, 0.2, 0.5],\n",
+    "        'C': [1,10,100,1000]\n",
+    "    },\n",
+    "    {\n",
+    "        'kernel':[\"linear\"],\n",
+    "        'C':[1,10,100,1000]\n",
+    "    }\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GridSearchCV(cv=5, error_score='raise',\n",
+       "       estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n",
+       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
+       "  tol=0.001, verbose=False),\n",
+       "       fit_params=None, iid=True, n_jobs=1,\n",
+       "       param_grid=[{'kernel': ['rbf'], 'gamma': [0.0001, 0.001, 0.01, 0.1, 0.2, 0.5], 'C': [1, 10, 100, 1000]}, {'kernel': ['linear'], 'C': [1, 10, 100, 1000]}],\n",
+       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
+       "       scoring=None, verbose=0)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf = GridSearchCV(svm.SVC(decision_function_shape='ovr'), param_grid=parameters, cv=5)\n",
+    "clf.fit(X,Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf.best_params_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
+      "  warnings.warn(*warn_args, **warn_kwargs)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split0_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
+      "  warnings.warn(*warn_args, **warn_kwargs)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split1_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
+      "  warnings.warn(*warn_args, **warn_kwargs)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split2_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
+      "  warnings.warn(*warn_args, **warn_kwargs)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split3_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
+      "  warnings.warn(*warn_args, **warn_kwargs)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('split4_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
+      "  warnings.warn(*warn_args, **warn_kwargs)\n",
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
+      "  warnings.warn(*warn_args, **warn_kwargs)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'mean_fit_time': array([0.00080914, 0.00071759, 0.00071797, 0.00047698, 0.00052872,\n",
+       "        0.00047536, 0.0007504 , 0.00063925, 0.00047584, 0.00048094,\n",
+       "        0.00042958, 0.00047908, 0.00071597, 0.00048261, 0.00046563,\n",
+       "        0.00056596, 0.00056248, 0.00070386, 0.00050192, 0.00043306,\n",
+       "        0.00053606, 0.00124187, 0.00179281, 0.00172877, 0.00035152,\n",
+       "        0.00036664, 0.00078921, 0.00484943]),\n",
+       " 'mean_score_time': array([0.00029712, 0.00026445, 0.00022964, 0.00020909, 0.00021605,\n",
+       "        0.00021515, 0.00039182, 0.00021648, 0.00021491, 0.00022945,\n",
+       "        0.00019264, 0.00019031, 0.0002306 , 0.00020213, 0.00022182,\n",
+       "        0.000214  , 0.00018773, 0.00019073, 0.00019846, 0.0002008 ,\n",
+       "        0.00020046, 0.00024786, 0.0002336 , 0.0002027 , 0.00018096,\n",
+       "        0.00016913, 0.00020161, 0.00020552]),\n",
+       " 'mean_test_score': array([0.74666667, 0.74666667, 0.74666667, 0.80666667, 0.78666667,\n",
+       "        0.78      , 0.74666667, 0.74666667, 0.81333333, 0.77333333,\n",
+       "        0.78      , 0.76666667, 0.74666667, 0.81333333, 0.76      ,\n",
+       "        0.78      , 0.77333333, 0.76666667, 0.81333333, 0.76      ,\n",
+       "        0.76666667, 0.78      , 0.76666667, 0.75333333, 0.77333333,\n",
+       "        0.76666667, 0.76666667, 0.76666667]),\n",
+       " 'mean_train_score': array([0.765     , 0.765     , 0.765     , 0.81333333, 0.81666667,\n",
+       "        0.815     , 0.765     , 0.765     , 0.81833333, 0.815     ,\n",
+       "        0.81      , 0.80833333, 0.765     , 0.82      , 0.81166667,\n",
+       "        0.81      , 0.82166667, 0.81666667, 0.82      , 0.81      ,\n",
+       "        0.81      , 0.81833333, 0.81833333, 0.82166667, 0.81666667,\n",
+       "        0.81333333, 0.81333333, 0.81333333]),\n",
+       " 'param_C': masked_array(data=[1, 1, 1, 1, 1, 1, 10, 10, 10, 10, 10, 10, 100, 100,\n",
+       "                    100, 100, 100, 100, 1000, 1000, 1000, 1000, 1000, 1000,\n",
+       "                    1, 10, 100, 1000],\n",
+       "              mask=[False, False, False, False, False, False, False, False,\n",
+       "                    False, False, False, False, False, False, False, False,\n",
+       "                    False, False, False, False, False, False, False, False,\n",
+       "                    False, False, False, False],\n",
+       "        fill_value='?',\n",
+       "             dtype=object),\n",
+       " 'param_gamma': masked_array(data=[0.0001, 0.001, 0.01, 0.1, 0.2, 0.5, 0.0001, 0.001,\n",
+       "                    0.01, 0.1, 0.2, 0.5, 0.0001, 0.001, 0.01, 0.1, 0.2,\n",
+       "                    0.5, 0.0001, 0.001, 0.01, 0.1, 0.2, 0.5, --, --, --,\n",
+       "                    --],\n",
+       "              mask=[False, False, False, False, False, False, False, False,\n",
+       "                    False, False, False, False, False, False, False, False,\n",
+       "                    False, False, False, False, False, False, False, False,\n",
+       "                     True,  True,  True,  True],\n",
+       "        fill_value='?',\n",
+       "             dtype=object),\n",
+       " 'param_kernel': masked_array(data=['rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf',\n",
+       "                    'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf',\n",
+       "                    'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf', 'rbf',\n",
+       "                    'linear', 'linear', 'linear', 'linear'],\n",
+       "              mask=[False, False, False, False, False, False, False, False,\n",
+       "                    False, False, False, False, False, False, False, False,\n",
+       "                    False, False, False, False, False, False, False, False,\n",
+       "                    False, False, False, False],\n",
+       "        fill_value='?',\n",
+       "             dtype=object),\n",
+       " 'params': [{'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'},\n",
+       "  {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'},\n",
+       "  {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'},\n",
+       "  {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'},\n",
+       "  {'C': 1, 'gamma': 0.2, 'kernel': 'rbf'},\n",
+       "  {'C': 1, 'gamma': 0.5, 'kernel': 'rbf'},\n",
+       "  {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'},\n",
+       "  {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'},\n",
+       "  {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'},\n",
+       "  {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'},\n",
+       "  {'C': 10, 'gamma': 0.2, 'kernel': 'rbf'},\n",
+       "  {'C': 10, 'gamma': 0.5, 'kernel': 'rbf'},\n",
+       "  {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'},\n",
+       "  {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'},\n",
+       "  {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'},\n",
+       "  {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'},\n",
+       "  {'C': 100, 'gamma': 0.2, 'kernel': 'rbf'},\n",
+       "  {'C': 100, 'gamma': 0.5, 'kernel': 'rbf'},\n",
+       "  {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'},\n",
+       "  {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'},\n",
+       "  {'C': 1000, 'gamma': 0.01, 'kernel': 'rbf'},\n",
+       "  {'C': 1000, 'gamma': 0.1, 'kernel': 'rbf'},\n",
+       "  {'C': 1000, 'gamma': 0.2, 'kernel': 'rbf'},\n",
+       "  {'C': 1000, 'gamma': 0.5, 'kernel': 'rbf'},\n",
+       "  {'C': 1, 'kernel': 'linear'},\n",
+       "  {'C': 10, 'kernel': 'linear'},\n",
+       "  {'C': 100, 'kernel': 'linear'},\n",
+       "  {'C': 1000, 'kernel': 'linear'}],\n",
+       " 'rank_test_score': array([23, 23, 23,  4,  5,  6, 23, 23,  1, 10,  6, 13, 23,  1, 20,  6, 10,\n",
+       "        13,  1, 20, 13,  6, 13, 22, 10, 13, 13, 13], dtype=int32),\n",
+       " 'split0_test_score': array([0.63333333, 0.63333333, 0.63333333, 0.7       , 0.7       ,\n",
+       "        0.7       , 0.63333333, 0.63333333, 0.7       , 0.7       ,\n",
+       "        0.73333333, 0.7       , 0.63333333, 0.7       , 0.7       ,\n",
+       "        0.73333333, 0.73333333, 0.7       , 0.7       , 0.7       ,\n",
+       "        0.7       , 0.73333333, 0.7       , 0.66666667, 0.7       ,\n",
+       "        0.7       , 0.7       , 0.7       ]),\n",
+       " 'split0_train_score': array([0.78333333, 0.78333333, 0.78333333, 0.85833333, 0.85833333,\n",
+       "        0.84166667, 0.78333333, 0.78333333, 0.85833333, 0.85833333,\n",
+       "        0.84166667, 0.84166667, 0.78333333, 0.86666667, 0.85      ,\n",
+       "        0.83333333, 0.85      , 0.85      , 0.86666667, 0.84166667,\n",
+       "        0.84166667, 0.84166667, 0.85      , 0.85      , 0.85833333,\n",
+       "        0.84166667, 0.84166667, 0.84166667]),\n",
+       " 'split1_test_score': array([0.73333333, 0.73333333, 0.73333333, 0.83333333, 0.83333333,\n",
+       "        0.8       , 0.73333333, 0.73333333, 0.83333333, 0.8       ,\n",
+       "        0.8       , 0.76666667, 0.73333333, 0.83333333, 0.8       ,\n",
+       "        0.8       , 0.76666667, 0.8       , 0.83333333, 0.8       ,\n",
+       "        0.8       , 0.8       , 0.8       , 0.76666667, 0.8       ,\n",
+       "        0.8       , 0.8       , 0.8       ]),\n",
+       " 'split1_train_score': array([0.775     , 0.775     , 0.775     , 0.775     , 0.775     ,\n",
+       "        0.775     , 0.775     , 0.775     , 0.79166667, 0.775     ,\n",
+       "        0.775     , 0.8       , 0.775     , 0.79166667, 0.775     ,\n",
+       "        0.78333333, 0.8       , 0.8       , 0.79166667, 0.775     ,\n",
+       "        0.775     , 0.8       , 0.80833333, 0.81666667, 0.775     ,\n",
+       "        0.775     , 0.775     , 0.775     ]),\n",
+       " 'split2_test_score': array([0.8       , 0.8       , 0.8       , 0.76666667, 0.73333333,\n",
+       "        0.73333333, 0.8       , 0.8       , 0.8       , 0.73333333,\n",
+       "        0.7       , 0.7       , 0.8       , 0.8       , 0.7       ,\n",
+       "        0.73333333, 0.73333333, 0.73333333, 0.8       , 0.7       ,\n",
+       "        0.73333333, 0.73333333, 0.73333333, 0.7       , 0.73333333,\n",
+       "        0.73333333, 0.73333333, 0.73333333]),\n",
+       " 'split2_train_score': array([0.75833333, 0.75833333, 0.75833333, 0.80833333, 0.80833333,\n",
+       "        0.81666667, 0.75833333, 0.75833333, 0.81666667, 0.80833333,\n",
+       "        0.79166667, 0.8       , 0.75833333, 0.81666667, 0.8       ,\n",
+       "        0.79166667, 0.80833333, 0.825     , 0.81666667, 0.8       ,\n",
+       "        0.8       , 0.80833333, 0.825     , 0.825     , 0.80833333,\n",
+       "        0.81666667, 0.81666667, 0.81666667]),\n",
+       " 'split3_test_score': array([0.76666667, 0.76666667, 0.76666667, 0.86666667, 0.86666667,\n",
+       "        0.86666667, 0.76666667, 0.76666667, 0.86666667, 0.83333333,\n",
+       "        0.86666667, 0.86666667, 0.76666667, 0.86666667, 0.83333333,\n",
+       "        0.83333333, 0.83333333, 0.8       , 0.86666667, 0.83333333,\n",
+       "        0.83333333, 0.83333333, 0.8       , 0.83333333, 0.83333333,\n",
+       "        0.83333333, 0.83333333, 0.83333333]),\n",
+       " 'split3_train_score': array([0.76666667, 0.76666667, 0.76666667, 0.81666667, 0.81666667,\n",
+       "        0.81666667, 0.76666667, 0.76666667, 0.81666667, 0.81666667,\n",
+       "        0.81666667, 0.79166667, 0.76666667, 0.81666667, 0.81666667,\n",
+       "        0.81666667, 0.825     , 0.80833333, 0.81666667, 0.81666667,\n",
+       "        0.81666667, 0.81666667, 0.80833333, 0.80833333, 0.81666667,\n",
+       "        0.81666667, 0.81666667, 0.81666667]),\n",
+       " 'split4_test_score': array([0.8       , 0.8       , 0.8       , 0.86666667, 0.8       ,\n",
+       "        0.8       , 0.8       , 0.8       , 0.86666667, 0.8       ,\n",
+       "        0.8       , 0.8       , 0.8       , 0.86666667, 0.76666667,\n",
+       "        0.8       , 0.8       , 0.8       , 0.86666667, 0.76666667,\n",
+       "        0.76666667, 0.8       , 0.8       , 0.8       , 0.8       ,\n",
+       "        0.76666667, 0.76666667, 0.76666667]),\n",
+       " 'split4_train_score': array([0.74166667, 0.74166667, 0.74166667, 0.80833333, 0.825     ,\n",
+       "        0.825     , 0.74166667, 0.74166667, 0.80833333, 0.81666667,\n",
+       "        0.825     , 0.80833333, 0.74166667, 0.80833333, 0.81666667,\n",
+       "        0.825     , 0.825     , 0.8       , 0.80833333, 0.81666667,\n",
+       "        0.81666667, 0.825     , 0.8       , 0.80833333, 0.825     ,\n",
+       "        0.81666667, 0.81666667, 0.81666667]),\n",
+       " 'std_fit_time': array([7.10019015e-05, 6.26374932e-05, 1.25455539e-04, 1.72115097e-05,\n",
+       "        1.14039233e-04, 4.22337231e-05, 1.99959068e-04, 4.93413341e-05,\n",
+       "        1.99335043e-05, 5.14735497e-05, 1.12592148e-05, 2.73159861e-05,\n",
+       "        2.08046134e-04, 1.53497133e-05, 3.96021800e-05, 8.21945277e-05,\n",
+       "        5.12624119e-05, 1.34972158e-04, 1.05144637e-04, 3.32303159e-05,\n",
+       "        8.54315484e-05, 5.12996041e-04, 9.24814532e-04, 5.80882294e-04,\n",
+       "        9.97444863e-06, 1.82457363e-05, 4.13948415e-04, 2.76602757e-03]),\n",
+       " 'std_score_time': array([7.80023685e-05, 3.75342029e-05, 1.74273381e-05, 7.25670333e-06,\n",
+       "        1.26944405e-05, 1.41858854e-05, 3.23473817e-04, 1.96054890e-05,\n",
+       "        2.95013874e-05, 2.03238939e-05, 6.03721792e-06, 4.93566769e-06,\n",
+       "        2.14255410e-05, 5.60440894e-06, 4.96097676e-05, 1.84534132e-05,\n",
+       "        5.52102481e-06, 3.22702872e-06, 1.07140970e-05, 1.41720945e-05,\n",
+       "        1.47968648e-05, 4.41715959e-05, 1.87557700e-05, 1.49710162e-05,\n",
+       "        8.69492651e-06, 5.69935113e-06, 2.13277913e-05, 1.93709777e-05]),\n",
+       " 'std_test_score': array([0.06182412, 0.06182412, 0.06182412, 0.06463573, 0.06182412,\n",
+       "        0.05811865, 0.06182412, 0.06182412, 0.06182412, 0.04898979,\n",
+       "        0.05811865, 0.06324555, 0.06182412, 0.06182412, 0.05333333,\n",
+       "        0.04      , 0.03887301, 0.0421637 , 0.06182412, 0.05333333,\n",
+       "        0.04714045, 0.04      , 0.0421637 , 0.06182412, 0.04898979,\n",
+       "        0.04714045, 0.04714045, 0.04714045]),\n",
+       " 'std_train_score': array([0.01433721, 0.01433721, 0.01433721, 0.02666667, 0.02687419,\n",
+       "        0.02198484, 0.01433721, 0.01433721, 0.02198484, 0.0265623 ,\n",
+       "        0.02380476, 0.01748015, 0.01433721, 0.02505549, 0.0244949 ,\n",
+       "        0.01929306, 0.01715938, 0.01900292, 0.02505549, 0.02198484,\n",
+       "        0.02198484, 0.01433721, 0.01779513, 0.01545603, 0.02687419,\n",
+       "        0.0214735 , 0.0214735 , 0.0214735 ])}"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf.cv_results_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.747 (+/-0.124) para {'C': 1, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
+      "0.747 (+/-0.124) para {'C': 1, 'gamma': 0.001, 'kernel': 'rbf'}\n",
+      "0.747 (+/-0.124) para {'C': 1, 'gamma': 0.01, 'kernel': 'rbf'}\n",
+      "0.807 (+/-0.129) para {'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}\n",
+      "0.787 (+/-0.124) para {'C': 1, 'gamma': 0.2, 'kernel': 'rbf'}\n",
+      "0.780 (+/-0.116) para {'C': 1, 'gamma': 0.5, 'kernel': 'rbf'}\n",
+      "0.747 (+/-0.124) para {'C': 10, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
+      "0.747 (+/-0.124) para {'C': 10, 'gamma': 0.001, 'kernel': 'rbf'}\n",
+      "0.813 (+/-0.124) para {'C': 10, 'gamma': 0.01, 'kernel': 'rbf'}\n",
+      "0.773 (+/-0.098) para {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}\n",
+      "0.780 (+/-0.116) para {'C': 10, 'gamma': 0.2, 'kernel': 'rbf'}\n",
+      "0.767 (+/-0.126) para {'C': 10, 'gamma': 0.5, 'kernel': 'rbf'}\n",
+      "0.747 (+/-0.124) para {'C': 100, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
+      "0.813 (+/-0.124) para {'C': 100, 'gamma': 0.001, 'kernel': 'rbf'}\n",
+      "0.760 (+/-0.107) para {'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}\n",
+      "0.780 (+/-0.080) para {'C': 100, 'gamma': 0.1, 'kernel': 'rbf'}\n",
+      "0.773 (+/-0.078) para {'C': 100, 'gamma': 0.2, 'kernel': 'rbf'}\n",
+      "0.767 (+/-0.084) para {'C': 100, 'gamma': 0.5, 'kernel': 'rbf'}\n",
+      "0.813 (+/-0.124) para {'C': 1000, 'gamma': 0.0001, 'kernel': 'rbf'}\n",
+      "0.760 (+/-0.107) para {'C': 1000, 'gamma': 0.001, 'kernel': 'rbf'}\n",
+      "0.767 (+/-0.094) para {'C': 1000, 'gamma': 0.01, 'kernel': 'rbf'}\n",
+      "0.780 (+/-0.080) para {'C': 1000, 'gamma': 0.1, 'kernel': 'rbf'}\n",
+      "0.767 (+/-0.084) para {'C': 1000, 'gamma': 0.2, 'kernel': 'rbf'}\n",
+      "0.753 (+/-0.124) para {'C': 1000, 'gamma': 0.5, 'kernel': 'rbf'}\n",
+      "0.773 (+/-0.098) para {'C': 1, 'kernel': 'linear'}\n",
+      "0.767 (+/-0.094) para {'C': 10, 'kernel': 'linear'}\n",
+      "0.767 (+/-0.094) para {'C': 100, 'kernel': 'linear'}\n",
+      "0.767 (+/-0.094) para {'C': 1000, 'kernel': 'linear'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "means = clf.cv_results_['mean_test_score']\n",
+    "stds = clf.cv_results_['std_test_score']\n",
+    "params = clf.cv_results_['params']\n",
+    "for m, s, p in zip(means, stds, params):\n",
+    "    print(\"%0.3f (+/-%0.3f) para %r\"%(m, 2*s, p))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = clf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             precision    recall  f1-score   support\n",
+      "\n",
+      "     setosa       1.00      1.00      1.00        11\n",
+      " versicolor       0.60      0.82      0.69        11\n",
+      "  virginica       0.83      0.62      0.71        16\n",
+      "\n",
+      "avg / total       0.81      0.79      0.79        38\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(classification_report(Y_test, y_pred, target_names=[\"setosa\", \"versicolor\",\"virginica\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Resumen final de la clasificación de Iris"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def svm_iris(C=1.0, gamma = 0.01, kernel = \"rbf\"):\n",
+    "    import pandas as pd\n",
+    "    import numpy as np\n",
+    "    from sklearn import svm, datasets\n",
+    "    import matplotlib.pyplot as plt\n",
+    "\n",
+    "    iris = datasets.load_iris()\n",
+    "    \n",
+    "    \n",
+    "    X = iris.data[:, :2]\n",
+    "    Y = iris.target\n",
+    "\n",
+    "    x_min, x_max = X[:,0].min()-1, X[:,0].max()+1\n",
+    "    y_min, y_max = X[:,1].min()-1, X[:,1].max()+1\n",
+    "    h = (x_max - x_min)/100\n",
+    "\n",
+    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
+    "\n",
+    "    X_plot = np.c_[xx.ravel(), yy.ravel()]\n",
+    "\n",
+    "    svc = svm.SVC(kernel=kernel, C=C, gamma=gamma, decision_function_shape=\"ovr\").fit(X,Y)\n",
+    "    Ypred = svc.predict(X_plot)\n",
+    "    Ypred = Ypred.reshape(xx.shape)\n",
+    "    plt.figure(figsize=(16,9))\n",
+    "    plt.contourf(xx,yy,Ypred, cmap=plt.cm.tab10, alpha = 0.3)\n",
+    "    plt.scatter(X[:,0], X[:,1], c=Y, cmap=plt.cm.tab10)\n",
+    "    plt.xlabel(\"Longitud de los pétalos\")\n",
+    "    plt.ylabel(\"Anchura de los pétalos\")\n",
+    "    plt.xlim(xx.min(), xx.max())\n",
+    "    plt.title(\"SVC para las flores de Iris con Kernel \"+kernel)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from ipywidgets import interact, fixed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ca1d0d4dcf8d4c148ee68defb02c85b5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "interactive(children=(Dropdown(description='C', index=2, options=(0.01, 0.1, 1, 10, 100, 1000, 1000000.0, 10000000000.0), value=1), Dropdown(description='gamma', index=3, options=(1e-05, 0.0001, 0.001, 0.01, 0.1, 0.2, 0.5, 0.99), value=0.01), Dropdown(description='kernel', options=('rbf', 'linear', 'sigmoid', 'poly'), value='rbf'), Output()), _dom_classes=('widget-interact',))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.svm_iris>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "interact(svm_iris, C=[0.01, 0.1, 1,10,100,1000, 1e6, 1e10],\n",
+    "        gamma=[1e-5,1e-4,1e-3,1e-2, 0.1, 0.2, 0.5,0.99],\n",
+    "        kernel=[\"rbf\",\"linear\",\"sigmoid\",\"poly\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T8 - 5 - SVM - Clasificaci\303\263n de Iris.ipynb" "b/notebooks/T8 - 5 - SVM - Clasificaci\303\263n de Iris.ipynb"
index 8aae0437..83765581 100644
--- "a/notebooks/T8 - 5 - SVM - Clasificaci\303\263n de Iris.ipynb"	
+++ "b/notebooks/T8 - 5 - SVM - Clasificaci\303\263n de Iris.ipynb"	
@@ -869,7 +869,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T8 - 6 - SVM - Regresi\303\263n-Colab.ipynb" "b/notebooks/T8 - 6 - SVM - Regresi\303\263n-Colab.ipynb"
new file mode 100644
index 00000000..de872a5d
--- /dev/null
+++ "b/notebooks/T8 - 6 - SVM - Regresi\303\263n-Colab.ipynb"	
@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T8%20-%206%20-%20SVM%20-%20Regresión-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SVM para Regresión"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = np.sort(5*np.random.rand(200,1),axis=0)\n",
+    "Y = np.sin(X).ravel()\n",
+    "Y[::5] += 3*(0.5 - np.random.rand(40))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x10c111cc0>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10bbfd390>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X,Y, color=\"darkorange\", label=\"data\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.svm import SVR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "C=1e3\n",
+    "svr_lin = SVR(kernel=\"linear\", C=C)\n",
+    "svr_rbf = SVR(kernel=\"rbf\", C=C, gamma=0.1)\n",
+    "svr_pol = SVR(kernel=\"poly\", C=C, degree=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_lin = svr_lin.fit(X,Y).predict(X)\n",
+    "y_rbf = svr_rbf.fit(X,Y).predict(X)\n",
+    "y_pol = svr_pol.fit(X,Y).predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a17099828>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lw = 2\n",
+    "plt.figure(figsize=(16,9))\n",
+    "plt.scatter(X,Y,color=\"darkorange\", label =\"data\")\n",
+    "plt.plot(X,y_lin, color=\"navy\", lw = lw, label = \"SVM Lineal\")\n",
+    "plt.plot(X,y_rbf, color=\"c\", lw=lw, label=\"SVM Radial\")\n",
+    "plt.plot(X,y_pol, color=\"cornflowerblue\", label=\"SVM Polinómico\")\n",
+    "plt.xlabel(\"x\")\n",
+    "plt.ylabel(\"y\")\n",
+    "plt.title(\"Support Vector Regression\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git "a/notebooks/T8 - 6 - SVM - Regresi\303\263n.ipynb" "b/notebooks/T8 - 6 - SVM - Regresi\303\263n.ipynb"
index 046badbe..d82f007a 100644
--- "a/notebooks/T8 - 6 - SVM - Regresi\303\263n.ipynb"	
+++ "b/notebooks/T8 - 6 - SVM - Regresi\303\263n.ipynb"	
@@ -144,7 +144,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T9 - 1 - K Nearest Neighbors-Colab.ipynb b/notebooks/T9 - 1 - K Nearest Neighbors-Colab.ipynb
new file mode 100644
index 00000000..88e576bf
--- /dev/null
+++ b/notebooks/T9 - 1 - K Nearest Neighbors-Colab.ipynb	
@@ -0,0 +1,948 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T9%20-%201%20-%20K%20Nearest%20Neighbors-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# K Nearest Neighbors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn import preprocessing, neighbors\n",
+    "from sklearn. model_selection import cross_val_score\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "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>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1000025</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1002945</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1015425</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1016277</td>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1017023</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        0   1   2   3   4   5   6   7   8   9   10\n",
+       "0  1000025   5   1   1   1   2   1   3   1   1   2\n",
+       "1  1002945   5   4   4   5   7  10   3   2   1   2\n",
+       "2  1015425   3   1   1   1   2   2   3   1   1   2\n",
+       "3  1016277   6   8   8   1   3   4   3   7   1   2\n",
+       "4  1017023   4   1   1   3   2   1   3   1   1   2"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"/content/python-ml-course/datasets/cancer/breast-cancer-wisconsin.data.txt\", header=None)\n",
+    "\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>9</th>\n",
+       "      <th>10</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>6.990000e+02</td>\n",
+       "      <td>699.000000</td>\n",
+       "      <td>699.000000</td>\n",
+       "      <td>699.000000</td>\n",
+       "      <td>699.000000</td>\n",
+       "      <td>699.000000</td>\n",
+       "      <td>699.000000</td>\n",
+       "      <td>699.000000</td>\n",
+       "      <td>699.000000</td>\n",
+       "      <td>699.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1.071704e+06</td>\n",
+       "      <td>4.417740</td>\n",
+       "      <td>3.134478</td>\n",
+       "      <td>3.207439</td>\n",
+       "      <td>2.806867</td>\n",
+       "      <td>3.216023</td>\n",
+       "      <td>3.437768</td>\n",
+       "      <td>2.866953</td>\n",
+       "      <td>1.589413</td>\n",
+       "      <td>2.689557</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>6.170957e+05</td>\n",
+       "      <td>2.815741</td>\n",
+       "      <td>3.051459</td>\n",
+       "      <td>2.971913</td>\n",
+       "      <td>2.855379</td>\n",
+       "      <td>2.214300</td>\n",
+       "      <td>2.438364</td>\n",
+       "      <td>3.053634</td>\n",
+       "      <td>1.715078</td>\n",
+       "      <td>0.951273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>6.163400e+04</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>8.706885e+05</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1.171710e+06</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>1.238298e+06</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1.345435e+07</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 0           1           2           3           4   \\\n",
+       "count  6.990000e+02  699.000000  699.000000  699.000000  699.000000   \n",
+       "mean   1.071704e+06    4.417740    3.134478    3.207439    2.806867   \n",
+       "std    6.170957e+05    2.815741    3.051459    2.971913    2.855379   \n",
+       "min    6.163400e+04    1.000000    1.000000    1.000000    1.000000   \n",
+       "25%    8.706885e+05    2.000000    1.000000    1.000000    1.000000   \n",
+       "50%    1.171710e+06    4.000000    1.000000    1.000000    1.000000   \n",
+       "75%    1.238298e+06    6.000000    5.000000    5.000000    4.000000   \n",
+       "max    1.345435e+07   10.000000   10.000000   10.000000   10.000000   \n",
+       "\n",
+       "               5           7           8           9           10  \n",
+       "count  699.000000  699.000000  699.000000  699.000000  699.000000  \n",
+       "mean     3.216023    3.437768    2.866953    1.589413    2.689557  \n",
+       "std      2.214300    2.438364    3.053634    1.715078    0.951273  \n",
+       "min      1.000000    1.000000    1.000000    1.000000    2.000000  \n",
+       "25%      2.000000    2.000000    1.000000    1.000000    2.000000  \n",
+       "50%      2.000000    3.000000    1.000000    1.000000    2.000000  \n",
+       "75%      4.000000    5.000000    4.000000    1.000000    4.000000  \n",
+       "max     10.000000   10.000000   10.000000   10.000000    4.000000  "
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.columns = [\"name\", \"V1\", \"V2\", \"V3\", \"V4\", \"V5\", \"V6\", \"V7\", \"V8\", \"V9\", \"class\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
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+     "data": {
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+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1000025</td>\n",
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+       "      <td>1</td>\n",
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+       "      <th>1</th>\n",
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+       "      <td>5</td>\n",
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+       "      <th>2</th>\n",
+       "      <td>1015425</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1016277</td>\n",
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+       "      <td>8</td>\n",
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+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1017023</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      name  V1  V2  V3  V4  V5  V6  V7  V8  V9  class\n",
+       "0  1000025   5   1   1   1   2   1   3   1   1      2\n",
+       "1  1002945   5   4   4   5   7  10   3   2   1      2\n",
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+       "3  1016277   6   8   8   1   3   4   3   7   1      2\n",
+       "4  1017023   4   1   1   3   2   1   3   1   1      2"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.drop([\"name\"],1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.replace(\"?\", -99999, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Y = df[\"class\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df[[\"V1\", \"V2\", \"V3\", \"V4\", \"V5\", \"V6\", \"V7\", \"V8\", \"V9\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
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+       "  <thead>\n",
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+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   V1  V2  V3  V4  V5  V6  V7  V8  V9\n",
+       "0   5   1   1   1   2   1   3   1   1\n",
+       "1   5   4   4   5   7  10   3   2   1\n",
+       "2   3   1   1   1   2   2   3   1   1\n",
+       "3   6   8   8   1   3   4   3   7   1\n",
+       "4   4   1   1   3   2   1   3   1   1"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    2\n",
+       "1    2\n",
+       "2    2\n",
+       "3    2\n",
+       "4    2\n",
+       "Name: class, dtype: int64"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Y.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clasificador de los K vecinos "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = neighbors.KNeighborsClassifier()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
+       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
+       "           weights='uniform')"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf.fit(X_train, Y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9571428571428572"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "accuracy = clf.score(X_test, Y_test)\n",
+    "accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clasificación sin limpieza"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6428571428571429"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"/content/python-ml-course/datasets/cancer/breast-cancer-wisconsin.data.txt\", header=None)\n",
+    "df.replace(\"?\", -99999, inplace=True)\n",
+    "df.columns = [\"name\", \"V1\", \"V2\", \"V3\", \"V4\", \"V5\", \"V6\", \"V7\", \"V8\", \"V9\", \"class\"]\n",
+    "\n",
+    "Y = df[\"class\"]\n",
+    "X = df[[\"name\", \"V1\", \"V2\", \"V3\", \"V4\", \"V5\", \"V6\", \"V7\", \"V8\", \"V9\"]]\n",
+    "\n",
+    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)\n",
+    "\n",
+    "clf = neighbors.KNeighborsClassifier()\n",
+    "clf.fit(X_train, Y_train)\n",
+    "\n",
+    "accuracy = clf.score(X_test, Y_test)\n",
+    "accuracy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clasificar nuevos datos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_measure = np.array([5,4,2,1,1,1,2,3,2,1])\n",
+    "print(sample_measure)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_measure = sample_measure.reshape(1,-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predict = clf.predict(sample_measure)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2])"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_measure2 = np.array([[5,4,2,1,1,1,2,3,2,1], [2,2,4,4,2,2,6,2,4,5]]).reshape(2,-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predict = clf.predict(sample_measure2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2, 2])"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T9 - 1 - K Nearest Neighbors.ipynb b/notebooks/T9 - 1 - K Nearest Neighbors.ipynb
index 45890071..15263950 100644
--- a/notebooks/T9 - 1 - K Nearest Neighbors.ipynb	
+++ b/notebooks/T9 - 1 - K Nearest Neighbors.ipynb	
@@ -885,7 +885,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/T9 - 2 - K Nearest Neighbors Implementation-Colab.ipynb b/notebooks/T9 - 2 - K Nearest Neighbors Implementation-Colab.ipynb
new file mode 100644
index 00000000..31c82a32
--- /dev/null
+++ b/notebooks/T9 - 2 - K Nearest Neighbors Implementation-Colab.ipynb	
@@ -0,0 +1,1243 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T9%20-%202%20-%20K%20Nearest%20Neighbors%20Implementation-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Creando nuestro propio KNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib import style\n",
+    "import warnings\n",
+    "from math import sqrt\n",
+    "from collections import Counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset = {\n",
+    "    'k':[[1,2],[2,3],[3,1]],\n",
+    "    'r':[[6,5],[7,7],[8,6]]\n",
+    "}\n",
+    "new_point = [5,7]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x1177ef5c0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1177ef6a0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "[[plt.scatter(ii[0],ii[1], s=50, color = i) for ii in dataset[i]] for i in dataset]\n",
+    "plt.scatter(new_point[0],new_point[1], s = 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def k_nearest_neighbors(data, predict, k = 3, verbose = False):\n",
+    "    \n",
+    "    if len(data) >= k:\n",
+    "        warnings.warn(\"K es un valor menor que el número total de elementos a votar!!\")\n",
+    "    \n",
+    "    distances = []\n",
+    "    for group in data:\n",
+    "        for feature in data[group]:\n",
+    "            #d = sqrt((feature[0]-predict[0])**2 + (feature[1]-predict[1])**2)\n",
+    "            #d = np.sqrt(np.sum((np.array(feature) - np.array(predict))**2))\n",
+    "            d = np.linalg.norm(np.array(feature) - np.array(predict))\n",
+    "            distances.append([d, group])\n",
+    "    if verbose:\n",
+    "        print(distances)\n",
+    "    \n",
+    "    votes = [i[1] for i in sorted(distances)[:k]]#sorted ordena por la primera columna\n",
+    "    if verbose:\n",
+    "        print(votes)\n",
+    "    \n",
+    "    vote_result = Counter(votes).most_common(1)\n",
+    "    if verbose:\n",
+    "        print(vote_result)\n",
+    "    \n",
+    "    \n",
+    "    return vote_result[0][0]#[('r',2), ('k', 1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[3.905124837953327, 'k'], [2.5, 'k'], [3.640054944640259, 'k'], [2.0615528128088303, 'r'], [3.905124837953327, 'r'], [4.272001872658765, 'r']]\n",
+      "['r', 'k', 'k']\n",
+      "[('k', 2)]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'k'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_point = [4,4.5]\n",
+    "result = k_nearest_neighbors(dataset, [new_point])\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x11f001a20>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x11e71cc50>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "[[plt.scatter(ii[0],ii[1], s=50, color = i) for ii in dataset[i]] for i in dataset]\n",
+    "plt.scatter(new_point[0],new_point[1], s = 100, color=result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Aplicando nuestro KNN al Dataset del Cancer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/JuanGabriel/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
+      "  return f(*args, **kwds)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"/content/python-ml-course/datasets/cancer/breast-cancer-wisconsin.data.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.replace(\"?\", -99999, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.columns = [\"name\", \"V1\", \"V2\", \"V3\", \"V4\", \"V5\", \"V6\", \"V7\", \"V8\", \"V9\", \"class\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.drop([\"name\"], 1, inplace=True)"
+   ]
+  },
+  {
+   "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>V1</th>\n",
+       "      <th>V2</th>\n",
+       "      <th>V3</th>\n",
+       "      <th>V4</th>\n",
+       "      <th>V5</th>\n",
+       "      <th>V6</th>\n",
+       "      <th>V7</th>\n",
+       "      <th>V8</th>\n",
+       "      <th>V9</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6</td>\n",
+       "      <td>8</td>\n",
+       "      <td>8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8</td>\n",
+       "      <td>10</td>\n",
+       "      <td>10</td>\n",
+       "      <td>8</td>\n",
+       "      <td>7</td>\n",
+       "      <td>10</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   V1  V2  V3  V4  V5  V6  V7  V8  V9  class\n",
+       "0   5   4   4   5   7  10   3   2   1      2\n",
+       "1   3   1   1   1   2   2   3   1   1      2\n",
+       "2   6   8   8   1   3   4   3   7   1      2\n",
+       "3   4   1   1   3   2   1   3   1   1      2\n",
+       "4   8  10  10   8   7  10   9   7   1      4"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "full_data = df.astype(float).values.tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[5.0, 4.0, 4.0, 5.0, 7.0, 10.0, 3.0, 2.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 8.0, 8.0, 1.0, 3.0, 4.0, 3.0, 7.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 3.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 10.0, 10.0, 8.0, 7.0, 10.0, 9.0, 7.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 10.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 5.0, 2.0],\n",
+       " [4.0, 2.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 3.0, 3.0, 3.0, 2.0, 3.0, 4.0, 4.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 7.0, 5.0, 10.0, 7.0, 9.0, 5.0, 5.0, 4.0, 4.0],\n",
+       " [7.0, 4.0, 6.0, 4.0, 6.0, 1.0, 4.0, 3.0, 1.0, 4.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 7.0, 7.0, 6.0, 4.0, 10.0, 4.0, 1.0, 2.0, 4.0],\n",
+       " [6.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [7.0, 3.0, 2.0, 10.0, 5.0, 10.0, 5.0, 4.0, 4.0, 4.0],\n",
+       " [10.0, 5.0, 5.0, 3.0, 6.0, 7.0, 7.0, 10.0, 1.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 4.0, 5.0, 1.0, 2.0, -99999.0, 7.0, 3.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 2.0, 3.0, 4.0, 2.0, 7.0, 3.0, 6.0, 1.0, 4.0],\n",
+       " [3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 3.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 7.0, 7.0, 3.0, 8.0, 5.0, 7.0, 4.0, 3.0, 4.0],\n",
+       " [2.0, 1.0, 1.0, 2.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 10.0, 10.0, 8.0, 6.0, 1.0, 8.0, 9.0, 1.0, 4.0],\n",
+       " [6.0, 2.0, 1.0, 1.0, 1.0, 1.0, 7.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 4.0, 4.0, 9.0, 2.0, 10.0, 5.0, 6.0, 1.0, 4.0],\n",
+       " [2.0, 5.0, 3.0, 3.0, 6.0, 7.0, 7.0, 5.0, 1.0, 4.0],\n",
+       " [6.0, 6.0, 6.0, 9.0, 6.0, -99999.0, 7.0, 8.0, 1.0, 2.0],\n",
+       " [10.0, 4.0, 3.0, 1.0, 3.0, 3.0, 6.0, 5.0, 2.0, 4.0],\n",
+       " [6.0, 10.0, 10.0, 2.0, 8.0, 10.0, 7.0, 3.0, 3.0, 4.0],\n",
+       " [5.0, 6.0, 5.0, 6.0, 10.0, 1.0, 3.0, 1.0, 1.0, 4.0],\n",
+       " [10.0, 10.0, 10.0, 4.0, 8.0, 1.0, 8.0, 10.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 2.0],\n",
+       " [3.0, 7.0, 7.0, 4.0, 4.0, 9.0, 4.0, 8.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 3.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [7.0, 8.0, 7.0, 2.0, 4.0, 8.0, 3.0, 8.0, 2.0, 4.0],\n",
+       " [9.0, 5.0, 8.0, 1.0, 2.0, 3.0, 2.0, 1.0, 5.0, 4.0],\n",
+       " [5.0, 3.0, 3.0, 4.0, 2.0, 4.0, 3.0, 4.0, 1.0, 4.0],\n",
+       " [10.0, 3.0, 6.0, 2.0, 3.0, 5.0, 4.0, 10.0, 2.0, 4.0],\n",
+       " [5.0, 5.0, 5.0, 8.0, 10.0, 8.0, 7.0, 3.0, 7.0, 4.0],\n",
+       " [10.0, 5.0, 5.0, 6.0, 8.0, 8.0, 7.0, 1.0, 1.0, 4.0],\n",
+       " [10.0, 6.0, 6.0, 3.0, 4.0, 5.0, 3.0, 6.0, 1.0, 4.0],\n",
+       " [8.0, 10.0, 10.0, 1.0, 3.0, 6.0, 3.0, 9.0, 1.0, 4.0],\n",
+       " [8.0, 2.0, 4.0, 1.0, 5.0, 1.0, 5.0, 4.0, 4.0, 4.0],\n",
+       " [5.0, 2.0, 3.0, 1.0, 6.0, 10.0, 5.0, 1.0, 1.0, 4.0],\n",
+       " [9.0, 5.0, 5.0, 2.0, 2.0, 2.0, 5.0, 1.0, 1.0, 4.0],\n",
+       " [5.0, 3.0, 5.0, 5.0, 3.0, 3.0, 4.0, 10.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [9.0, 10.0, 10.0, 1.0, 10.0, 8.0, 3.0, 3.0, 1.0, 4.0],\n",
+       " [6.0, 3.0, 4.0, 1.0, 5.0, 2.0, 3.0, 9.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 4.0, 2.0, 1.0, 3.0, 2.0, 4.0, 3.0, 10.0, 4.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 3.0, 4.0, 1.0, 8.0, 10.0, 4.0, 9.0, 1.0, 4.0],\n",
+       " [8.0, 3.0, 8.0, 3.0, 4.0, 9.0, 8.0, 9.0, 8.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 2.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 3.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 10.0, 2.0, 8.0, 10.0, 2.0, 7.0, 8.0, 10.0, 4.0],\n",
+       " [1.0, 3.0, 3.0, 2.0, 2.0, 1.0, 7.0, 2.0, 1.0, 2.0],\n",
+       " [9.0, 4.0, 5.0, 10.0, 6.0, 10.0, 4.0, 8.0, 1.0, 4.0],\n",
+       " [10.0, 6.0, 4.0, 1.0, 3.0, 4.0, 3.0, 2.0, 3.0, 4.0],\n",
+       " [1.0, 1.0, 2.0, 1.0, 2.0, 2.0, 4.0, 2.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 4.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 3.0, 1.0, 2.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 3.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 3.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 7.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 2.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 2.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 2.0, 7.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 5.0, 7.0, 8.0, 8.0, 9.0, 7.0, 10.0, 7.0, 4.0],\n",
+       " [5.0, 10.0, 6.0, 1.0, 10.0, 4.0, 4.0, 10.0, 10.0, 4.0],\n",
+       " [3.0, 3.0, 6.0, 4.0, 5.0, 8.0, 4.0, 4.0, 1.0, 4.0],\n",
+       " [3.0, 6.0, 6.0, 6.0, 5.0, 10.0, 6.0, 8.0, 3.0, 4.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 2.0, 3.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [9.0, 6.0, 9.0, 2.0, 10.0, 6.0, 2.0, 9.0, 10.0, 4.0],\n",
+       " [7.0, 5.0, 6.0, 10.0, 5.0, 10.0, 7.0, 9.0, 4.0, 4.0],\n",
+       " [10.0, 3.0, 5.0, 1.0, 10.0, 5.0, 3.0, 10.0, 2.0, 4.0],\n",
+       " [2.0, 3.0, 4.0, 4.0, 2.0, 5.0, 2.0, 5.0, 1.0, 4.0],\n",
+       " [4.0, 1.0, 2.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 2.0, 3.0, 1.0, 6.0, 3.0, 7.0, 1.0, 1.0, 4.0],\n",
+       " [10.0, 10.0, 10.0, 10.0, 10.0, 1.0, 8.0, 8.0, 8.0, 4.0],\n",
+       " [7.0, 3.0, 4.0, 4.0, 3.0, 3.0, 3.0, 2.0, 7.0, 4.0],\n",
+       " [10.0, 10.0, 10.0, 8.0, 2.0, 10.0, 4.0, 1.0, 1.0, 4.0],\n",
+       " [1.0, 6.0, 8.0, 10.0, 8.0, 10.0, 5.0, 7.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 3.0, 1.0, 2.0],\n",
+       " [6.0, 5.0, 4.0, 4.0, 3.0, 9.0, 7.0, 8.0, 3.0, 4.0],\n",
+       " [1.0, 3.0, 1.0, 2.0, 2.0, 2.0, 5.0, 3.0, 2.0, 2.0],\n",
+       " [8.0, 6.0, 4.0, 3.0, 5.0, 9.0, 3.0, 1.0, 1.0, 4.0],\n",
+       " [10.0, 3.0, 3.0, 10.0, 2.0, 10.0, 7.0, 3.0, 3.0, 4.0],\n",
+       " [10.0, 10.0, 10.0, 3.0, 10.0, 8.0, 8.0, 1.0, 1.0, 4.0],\n",
+       " [3.0, 3.0, 2.0, 1.0, 2.0, 3.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 3.0, 3.0, 1.0, 2.0, 2.0, 3.0, 2.0, 1.0, 2.0],\n",
+       " [4.0, 5.0, 5.0, 10.0, 4.0, 10.0, 7.0, 5.0, 8.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 4.0, 3.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 2.0, 1.0, 1.0, 2.0, 2.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 2.0, 2.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 2.0, 1.0, 1.0, 2.0, 2.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 10.0, 10.0, 2.0, 10.0, 10.0, 5.0, 3.0, 3.0, 4.0],\n",
+       " [5.0, 3.0, 5.0, 1.0, 8.0, 10.0, 5.0, 3.0, 1.0, 4.0],\n",
+       " [5.0, 4.0, 6.0, 7.0, 9.0, 7.0, 8.0, 10.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [7.0, 5.0, 3.0, 7.0, 4.0, 10.0, 7.0, 5.0, 5.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 3.0, 5.0, 4.0, 5.0, 10.0, 1.0, 6.0, 2.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 10.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 3.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 10.0, 8.0, 10.0, 8.0, 10.0, 3.0, 6.0, 3.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 2.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 3.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 3.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, -99999.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [9.0, 5.0, 5.0, 4.0, 4.0, 5.0, 4.0, 3.0, 3.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 3.0, 1.0, 2.0, -99999.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 4.0, 5.0, 2.0, 6.0, 8.0, 4.0, 1.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 3.0, 2.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 3.0, 8.0, 1.0, 5.0, 8.0, 1.0, 2.0],\n",
+       " [8.0, 8.0, 7.0, 4.0, 10.0, 10.0, 7.0, 8.0, 7.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [7.0, 2.0, 4.0, 1.0, 6.0, 10.0, 5.0, 4.0, 3.0, 4.0],\n",
+       " [10.0, 10.0, 8.0, 6.0, 4.0, 5.0, 8.0, 10.0, 1.0, 4.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 3.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 5.0, 5.0, 6.0, 3.0, 10.0, 3.0, 1.0, 1.0, 4.0],\n",
+       " [1.0, 2.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 2.0, 1.0, 3.0, -99999.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [9.0, 9.0, 10.0, 3.0, 6.0, 10.0, 7.0, 10.0, 6.0, 4.0],\n",
+       " [10.0, 7.0, 7.0, 4.0, 5.0, 10.0, 5.0, 7.0, 2.0, 4.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 2.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 7.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, -99999.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 2.0, 3.0, 2.0, 1.0, 2.0],\n",
+       " [5.0, 6.0, 7.0, 8.0, 8.0, 10.0, 3.0, 10.0, 3.0, 4.0],\n",
+       " [10.0, 8.0, 10.0, 10.0, 6.0, 1.0, 3.0, 1.0, 10.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 10.0, 10.0, 10.0, 8.0, 10.0, 10.0, 10.0, 7.0, 4.0],\n",
+       " [8.0, 6.0, 5.0, 4.0, 3.0, 10.0, 6.0, 1.0, 1.0, 4.0],\n",
+       " [5.0, 8.0, 7.0, 7.0, 10.0, 10.0, 5.0, 7.0, 1.0, 4.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 10.0, 10.0, 3.0, 8.0, 1.0, 5.0, 10.0, 3.0, 4.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 3.0, 3.0, 3.0, 6.0, 10.0, 3.0, 1.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 8.0, 8.0, 8.0, 5.0, 10.0, 7.0, 8.0, 1.0, 4.0],\n",
+       " [8.0, 7.0, 6.0, 4.0, 4.0, 10.0, 5.0, 1.0, 1.0, 4.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 5.0, 8.0, 6.0, 5.0, 8.0, 7.0, 10.0, 1.0, 4.0],\n",
+       " [10.0, 5.0, 6.0, 10.0, 6.0, 10.0, 7.0, 7.0, 10.0, 4.0],\n",
+       " [5.0, 8.0, 4.0, 10.0, 5.0, 8.0, 9.0, 10.0, 1.0, 4.0],\n",
+       " [1.0, 2.0, 3.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 10.0, 10.0, 8.0, 6.0, 8.0, 7.0, 10.0, 1.0, 4.0],\n",
+       " [7.0, 5.0, 10.0, 10.0, 10.0, 10.0, 4.0, 10.0, 3.0, 4.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 4.0, 4.0, 5.0, 4.0, 7.0, 7.0, 8.0, 2.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 4.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [9.0, 7.0, 7.0, 5.0, 5.0, 10.0, 7.0, 8.0, 3.0, 4.0],\n",
+       " [10.0, 8.0, 8.0, 4.0, 10.0, 10.0, 8.0, 1.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 10.0, 10.0, 9.0, 6.0, 10.0, 7.0, 10.0, 5.0, 4.0],\n",
+       " [10.0, 10.0, 9.0, 3.0, 7.0, 5.0, 3.0, 5.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 10.0, 10.0, 10.0, 5.0, 10.0, 8.0, 10.0, 6.0, 4.0],\n",
+       " [8.0, 10.0, 8.0, 8.0, 4.0, 8.0, 7.0, 7.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 10.0, 10.0, 10.0, 7.0, 10.0, 7.0, 10.0, 4.0, 4.0],\n",
+       " [10.0, 10.0, 10.0, 10.0, 3.0, 10.0, 10.0, 6.0, 1.0, 4.0],\n",
+       " [8.0, 7.0, 8.0, 7.0, 5.0, 5.0, 5.0, 10.0, 2.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 10.0, 7.0, 7.0, 6.0, 4.0, 8.0, 10.0, 2.0, 4.0],\n",
+       " [6.0, 1.0, 3.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 6.0, 4.0, 3.0, 10.0, 10.0, 9.0, 10.0, 1.0, 4.0],\n",
+       " [4.0, 1.0, 1.0, 3.0, 1.0, 5.0, 2.0, 1.0, 1.0, 4.0],\n",
+       " [7.0, 5.0, 6.0, 3.0, 3.0, 8.0, 7.0, 4.0, 1.0, 4.0],\n",
+       " [10.0, 5.0, 5.0, 6.0, 3.0, 10.0, 7.0, 9.0, 2.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 5.0, 7.0, 4.0, 4.0, 10.0, 8.0, 9.0, 1.0, 4.0],\n",
+       " [8.0, 9.0, 9.0, 5.0, 3.0, 5.0, 7.0, 7.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 10.0, 10.0, 3.0, 10.0, 10.0, 9.0, 10.0, 1.0, 4.0],\n",
+       " [7.0, 4.0, 7.0, 4.0, 3.0, 7.0, 7.0, 6.0, 1.0, 4.0],\n",
+       " [6.0, 8.0, 7.0, 5.0, 6.0, 8.0, 8.0, 9.0, 2.0, 4.0],\n",
+       " [8.0, 4.0, 6.0, 3.0, 3.0, 1.0, 4.0, 3.0, 1.0, 2.0],\n",
+       " [10.0, 4.0, 5.0, 5.0, 5.0, 10.0, 4.0, 1.0, 1.0, 4.0],\n",
+       " [3.0, 3.0, 2.0, 1.0, 3.0, 1.0, 3.0, 6.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 4.0, 1.0, 2.0, -99999.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 8.0, 8.0, 2.0, 8.0, 10.0, 4.0, 8.0, 10.0, 4.0],\n",
+       " [9.0, 8.0, 8.0, 5.0, 6.0, 2.0, 4.0, 10.0, 4.0, 4.0],\n",
+       " [8.0, 10.0, 10.0, 8.0, 6.0, 9.0, 3.0, 10.0, 10.0, 4.0],\n",
+       " [10.0, 4.0, 3.0, 2.0, 3.0, 10.0, 5.0, 3.0, 2.0, 4.0],\n",
+       " [5.0, 1.0, 3.0, 3.0, 2.0, 2.0, 2.0, 3.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 5.0, 5.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 2.0, 2.0, 2.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 10.0, 10.0, 8.0, 5.0, 10.0, 7.0, 8.0, 1.0, 4.0],\n",
+       " [8.0, 4.0, 4.0, 1.0, 2.0, 9.0, 3.0, 3.0, 1.0, 4.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 6.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, -99999.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 2.0, 2.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 4.0, 4.0, 10.0, 2.0, 10.0, 5.0, 3.0, 3.0, 4.0],\n",
+       " [6.0, 3.0, 3.0, 5.0, 3.0, 10.0, 3.0, 5.0, 3.0, 2.0],\n",
+       " [6.0, 10.0, 10.0, 2.0, 8.0, 10.0, 7.0, 3.0, 3.0, 4.0],\n",
+       " [9.0, 10.0, 10.0, 1.0, 10.0, 8.0, 3.0, 3.0, 1.0, 4.0],\n",
+       " [5.0, 6.0, 6.0, 2.0, 4.0, 10.0, 3.0, 6.0, 1.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 7.0, 7.0, 1.0, 5.0, 8.0, 3.0, 4.0, 1.0, 2.0],\n",
+       " [10.0, 5.0, 8.0, 10.0, 3.0, 10.0, 5.0, 1.0, 3.0, 4.0],\n",
+       " [5.0, 10.0, 10.0, 6.0, 10.0, 10.0, 10.0, 6.0, 5.0, 4.0],\n",
+       " [8.0, 8.0, 9.0, 4.0, 5.0, 10.0, 7.0, 8.0, 1.0, 4.0],\n",
+       " [10.0, 4.0, 4.0, 10.0, 6.0, 10.0, 5.0, 5.0, 1.0, 4.0],\n",
+       " [7.0, 9.0, 4.0, 10.0, 10.0, 3.0, 5.0, 3.0, 3.0, 4.0],\n",
+       " [5.0, 1.0, 4.0, 1.0, 2.0, 1.0, 3.0, 2.0, 1.0, 2.0],\n",
+       " [10.0, 10.0, 6.0, 3.0, 3.0, 10.0, 4.0, 3.0, 2.0, 4.0],\n",
+       " [3.0, 3.0, 5.0, 2.0, 3.0, 10.0, 7.0, 1.0, 1.0, 4.0],\n",
+       " [10.0, 8.0, 8.0, 2.0, 3.0, 4.0, 8.0, 7.0, 8.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 4.0, 7.0, 1.0, 3.0, 10.0, 3.0, 9.0, 2.0, 4.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 3.0, 5.0, 2.0, 3.0, 10.0, 7.0, 1.0, 1.0, 4.0],\n",
+       " [7.0, 2.0, 4.0, 1.0, 3.0, 4.0, 3.0, 3.0, 1.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 2.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 3.0, 1.0, 2.0, -99999.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 5.0, 7.0, 3.0, 3.0, 7.0, 3.0, 3.0, 8.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 2.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 4.0, 3.0, 10.0, 4.0, 10.0, 5.0, 6.0, 1.0, 4.0],\n",
+       " [10.0, 4.0, 6.0, 1.0, 2.0, 10.0, 5.0, 3.0, 1.0, 4.0],\n",
+       " [7.0, 4.0, 5.0, 10.0, 2.0, 10.0, 3.0, 8.0, 2.0, 4.0],\n",
+       " [8.0, 10.0, 10.0, 10.0, 8.0, 10.0, 10.0, 7.0, 3.0, 4.0],\n",
+       " [10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 4.0, 10.0, 10.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 3.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 1.0, 3.0, 1.0, 4.0, 5.0, 5.0, 10.0, 1.0, 4.0],\n",
+       " [5.0, 6.0, 6.0, 8.0, 6.0, 10.0, 4.0, 10.0, 4.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 8.0, 8.0, 1.0, 2.0, -99999.0, 6.0, 10.0, 1.0, 4.0],\n",
+       " [10.0, 4.0, 4.0, 6.0, 2.0, 10.0, 2.0, 3.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, -99999.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 5.0, 7.0, 8.0, 6.0, 10.0, 7.0, 4.0, 1.0, 4.0],\n",
+       " [5.0, 3.0, 4.0, 3.0, 4.0, 5.0, 4.0, 7.0, 1.0, 2.0],\n",
+       " [5.0, 4.0, 3.0, 1.0, 2.0, -99999.0, 2.0, 3.0, 1.0, 2.0],\n",
+       " [8.0, 2.0, 1.0, 1.0, 5.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [9.0, 1.0, 2.0, 6.0, 4.0, 10.0, 7.0, 7.0, 2.0, 4.0],\n",
+       " [8.0, 4.0, 10.0, 5.0, 4.0, 4.0, 7.0, 10.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 10.0, 10.0, 7.0, 9.0, 10.0, 7.0, 10.0, 10.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 3.0, 4.0, 9.0, 3.0, 10.0, 3.0, 3.0, 1.0, 4.0],\n",
+       " [10.0, 8.0, 4.0, 4.0, 4.0, 10.0, 3.0, 10.0, 4.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [7.0, 8.0, 7.0, 6.0, 4.0, 3.0, 8.0, 8.0, 4.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 5.0, 5.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 3.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 6.0, 4.0, 10.0, 10.0, 1.0, 3.0, 5.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 6.0, 5.0, 6.0, 7.0, -99999.0, 4.0, 9.0, 1.0, 2.0],\n",
+       " [5.0, 5.0, 5.0, 2.0, 5.0, 10.0, 4.0, 3.0, 1.0, 4.0],\n",
+       " [6.0, 8.0, 7.0, 8.0, 6.0, 8.0, 8.0, 9.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 5.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 4.0, 4.0, 4.0, 6.0, 5.0, 7.0, 3.0, 1.0, 2.0],\n",
+       " [7.0, 6.0, 3.0, 2.0, 5.0, 10.0, 7.0, 4.0, 6.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, -99999.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 4.0, 6.0, 10.0, 2.0, 10.0, 4.0, 1.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 2.0, 2.0, 1.0, 2.0, 1.0, 2.0, 3.0, 1.0, 2.0],\n",
+       " [10.0, 1.0, 1.0, 1.0, 2.0, 10.0, 5.0, 4.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 10.0, 3.0, 2.0, 6.0, 4.0, 3.0, 10.0, 1.0, 4.0],\n",
+       " [10.0, 4.0, 6.0, 4.0, 5.0, 10.0, 7.0, 1.0, 1.0, 4.0],\n",
+       " [10.0, 4.0, 7.0, 2.0, 2.0, 8.0, 6.0, 1.0, 1.0, 4.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 2.0, 2.0],\n",
+       " [5.0, 2.0, 2.0, 2.0, 2.0, 1.0, 2.0, 2.0, 1.0, 2.0],\n",
+       " [5.0, 4.0, 6.0, 6.0, 4.0, 10.0, 4.0, 3.0, 1.0, 4.0],\n",
+       " [8.0, 6.0, 7.0, 3.0, 3.0, 10.0, 3.0, 4.0, 2.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 5.0, 5.0, 8.0, 4.0, 10.0, 3.0, 4.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 5.0, 5.0, 5.0, 2.0, 10.0, 4.0, 3.0, 1.0, 4.0],\n",
+       " [10.0, 3.0, 3.0, 1.0, 2.0, 10.0, 7.0, 6.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [7.0, 6.0, 4.0, 8.0, 10.0, 10.0, 9.0, 5.0, 3.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 2.0, 2.0, 2.0, 3.0, 1.0, 1.0, 3.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 2.0],\n",
+       " [3.0, 4.0, 4.0, 10.0, 5.0, 1.0, 3.0, 3.0, 1.0, 4.0],\n",
+       " [4.0, 2.0, 3.0, 5.0, 3.0, 8.0, 7.0, 6.0, 1.0, 4.0],\n",
+       " [5.0, 1.0, 1.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 4.0, 5.0, 3.0, 7.0, 3.0, 4.0, 6.0, 1.0, 2.0],\n",
+       " [2.0, 7.0, 10.0, 10.0, 7.0, 10.0, 4.0, 9.0, 4.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 3.0, 1.0, 2.0, 2.0, 1.0, 2.0],\n",
+       " [5.0, 3.0, 3.0, 1.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0],\n",
+       " [8.0, 10.0, 10.0, 7.0, 10.0, 10.0, 7.0, 3.0, 8.0, 4.0],\n",
+       " [8.0, 10.0, 5.0, 3.0, 8.0, 4.0, 4.0, 10.0, 3.0, 4.0],\n",
+       " [10.0, 3.0, 5.0, 4.0, 3.0, 7.0, 3.0, 5.0, 3.0, 4.0],\n",
+       " [6.0, 10.0, 10.0, 10.0, 10.0, 10.0, 8.0, 10.0, 10.0, 4.0],\n",
+       " [3.0, 10.0, 3.0, 10.0, 6.0, 10.0, 5.0, 1.0, 4.0, 4.0],\n",
+       " [3.0, 2.0, 2.0, 1.0, 4.0, 3.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 4.0, 4.0, 2.0, 2.0, 3.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 10.0, 10.0, 10.0, 8.0, 10.0, 7.0, 10.0, 7.0, 4.0],\n",
+       " [5.0, 8.0, 8.0, 10.0, 5.0, 10.0, 8.0, 10.0, 3.0, 4.0],\n",
+       " [1.0, 1.0, 3.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 3.0, 2.0, 1.0, 3.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 3.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 2.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 4.0, 3.0, 1.0, 2.0, 2.0, 1.0, 2.0],\n",
+       " [5.0, 3.0, 4.0, 1.0, 4.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 6.0, 3.0, 6.0, 4.0, 10.0, 7.0, 8.0, 4.0, 4.0],\n",
+       " [3.0, 2.0, 2.0, 2.0, 2.0, 1.0, 3.0, 2.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 3.0, 2.0, 2.0, 3.0, 1.0, 1.0, 2.0, 3.0, 2.0],\n",
+       " [7.0, 6.0, 6.0, 3.0, 2.0, 10.0, 7.0, 1.0, 1.0, 4.0],\n",
+       " [5.0, 3.0, 3.0, 2.0, 3.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 2.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 3.0, 2.0, 2.0, 2.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 8.0, 7.0, 4.0, 3.0, 10.0, 7.0, 9.0, 1.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 2.0, 3.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 2.0, 1.0, 1.0, 2.0, 1.0, 2.0, 2.0, 1.0, 2.0],\n",
+       " [1.0, 2.0, 3.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 10.0, 8.0, 7.0, 6.0, 9.0, 9.0, 3.0, 8.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 3.0, 3.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 4.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 2.0, 1.0, 3.0, 2.0, 1.0, 1.0, 2.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 2.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 3.0, 2.0, 2.0, 2.0, 2.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, -99999.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 10.0, 10.0, 6.0, 8.0, 4.0, 8.0, 5.0, 1.0, 4.0],\n",
+       " [5.0, 1.0, 2.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 5.0, 6.0, 2.0, 3.0, 10.0, 6.0, 6.0, 1.0, 4.0],\n",
+       " [3.0, 3.0, 2.0, 6.0, 3.0, 3.0, 3.0, 5.0, 1.0, 2.0],\n",
+       " [8.0, 7.0, 8.0, 5.0, 10.0, 10.0, 7.0, 2.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 2.0, 2.0, 2.0, 2.0, 2.0, 3.0, 2.0, 2.0, 2.0],\n",
+       " [2.0, 3.0, 1.0, 1.0, 5.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 2.0, 2.0, 3.0, 2.0, 3.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 10.0, 10.0, 7.0, 10.0, 10.0, 8.0, 2.0, 1.0, 4.0],\n",
+       " [4.0, 3.0, 3.0, 1.0, 2.0, 1.0, 3.0, 3.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 3.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [9.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 1.0, 4.0],\n",
+       " [5.0, 3.0, 6.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 7.0, 8.0, 2.0, 4.0, 2.0, 5.0, 10.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 3.0, 1.0, 1.0, 2.0, 1.0, 2.0, 2.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 3.0, 4.0, 1.0, 3.0, 2.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 2.0, 1.0, 2.0],\n",
+       " [3.0, 2.0, 2.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 9.0, 7.0, 5.0, 5.0, 8.0, 4.0, 2.0, 1.0, 2.0],\n",
+       " [10.0, 8.0, 10.0, 1.0, 3.0, 10.0, 5.0, 1.0, 1.0, 4.0],\n",
+       " [10.0, 10.0, 10.0, 1.0, 6.0, 1.0, 2.0, 8.0, 1.0, 4.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 4.0, 3.0, 10.0, 4.0, 10.0, 10.0, 1.0, 1.0, 4.0],\n",
+       " [5.0, 2.0, 2.0, 4.0, 2.0, 4.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 3.0, 2.0, 3.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 6.0, 3.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 7.0, 9.0, 8.0, 6.0, 10.0, 8.0, 10.0, 1.0, 4.0],\n",
+       " [4.0, 1.0, 1.0, 3.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 5.0, 5.0, 8.0, 6.0, 10.0, 10.0, 7.0, 1.0, 4.0],\n",
+       " [2.0, 3.0, 1.0, 1.0, 3.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 2.0, 2.0, 1.0, 2.0, 6.0, 1.0, 1.0, 2.0, 4.0],\n",
+       " [10.0, 6.0, 5.0, 8.0, 5.0, 10.0, 8.0, 6.0, 1.0, 4.0],\n",
+       " [8.0, 8.0, 9.0, 6.0, 6.0, 3.0, 10.0, 10.0, 1.0, 4.0],\n",
+       " [5.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 3.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 5.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 1.0, 1.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 9.0, 8.0, 7.0, 6.0, 4.0, 7.0, 10.0, 3.0, 4.0],\n",
+       " [10.0, 6.0, 6.0, 2.0, 4.0, 10.0, 9.0, 7.0, 1.0, 4.0],\n",
+       " [6.0, 6.0, 6.0, 5.0, 4.0, 10.0, 7.0, 6.0, 2.0, 4.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 1.0, 1.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 2.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 8.0, 7.0, 10.0, 4.0, 10.0, 7.0, 5.0, 1.0, 4.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 3.0, 2.0, 4.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [9.0, 10.0, 10.0, 10.0, 10.0, 5.0, 10.0, 10.0, 10.0, 4.0],\n",
+       " [8.0, 7.0, 8.0, 5.0, 5.0, 10.0, 9.0, 10.0, 1.0, 4.0],\n",
+       " [5.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 3.0, 1.0, 3.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 10.0, 10.0, 10.0, 6.0, 10.0, 8.0, 1.0, 5.0, 4.0],\n",
+       " [3.0, 6.0, 4.0, 10.0, 3.0, 3.0, 3.0, 4.0, 1.0, 4.0],\n",
+       " [6.0, 3.0, 2.0, 1.0, 3.0, 4.0, 4.0, 1.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 8.0, 9.0, 4.0, 3.0, 10.0, 7.0, 1.0, 1.0, 4.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 10.0, 10.0, 10.0, 6.0, 10.0, 6.0, 5.0, 2.0, 4.0],\n",
+       " [5.0, 1.0, 2.0, 10.0, 4.0, 5.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 2.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 2.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 3.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 10.0, 10.0, 10.0, 7.0, 5.0, 4.0, 8.0, 7.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 4.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 6.0, 7.0, 10.0, 3.0, 10.0, 8.0, 10.0, 2.0, 4.0],\n",
+       " [4.0, 10.0, 4.0, 7.0, 3.0, 10.0, 9.0, 10.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 7.0, 8.0, 3.0, 4.0, 10.0, 9.0, 1.0, 1.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 4.0, 5.0, 4.0, 3.0, 5.0, 7.0, 3.0, 1.0, 4.0],\n",
+       " [7.0, 5.0, 6.0, 10.0, 4.0, 10.0, 5.0, 3.0, 1.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 1.0, 3.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [7.0, 4.0, 4.0, 3.0, 4.0, 10.0, 6.0, 9.0, 1.0, 4.0],\n",
+       " [4.0, 2.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 3.0, 2.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 2.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 3.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 3.0, 2.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 10.0, 10.0, 10.0, 4.0, 10.0, 7.0, 10.0, 1.0, 4.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [7.0, 8.0, 3.0, 7.0, 4.0, 5.0, 7.0, 8.0, 2.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 2.0, 2.0, 2.0, 2.0, 1.0, 4.0, 2.0, 1.0, 2.0],\n",
+       " [4.0, 4.0, 2.0, 1.0, 2.0, 5.0, 2.0, 1.0, 2.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 3.0, 1.0, 1.0, 2.0, 1.0, 4.0, 8.0, 1.0, 2.0],\n",
+       " [5.0, 2.0, 2.0, 2.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 2.0, 1.0, 2.0],\n",
+       " [5.0, 7.0, 10.0, 10.0, 5.0, 10.0, 10.0, 10.0, 1.0, 4.0],\n",
+       " [3.0, 1.0, 2.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 3.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 4.0, 4.0, 1.0, 6.0, 10.0, 2.0, 5.0, 2.0, 4.0],\n",
+       " [10.0, 10.0, 8.0, 10.0, 6.0, 5.0, 10.0, 3.0, 1.0, 4.0],\n",
+       " [8.0, 10.0, 4.0, 4.0, 8.0, 10.0, 8.0, 2.0, 1.0, 4.0],\n",
+       " [7.0, 6.0, 10.0, 5.0, 3.0, 10.0, 9.0, 10.0, 2.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 9.0, 7.0, 3.0, 4.0, 2.0, 7.0, 7.0, 1.0, 4.0],\n",
+       " [5.0, 1.0, 2.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 7.0, 10.0, 6.0, 5.0, 10.0, 7.0, 5.0, 1.0, 4.0],\n",
+       " [6.0, 10.0, 5.0, 5.0, 4.0, 10.0, 6.0, 10.0, 1.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 6.0, 3.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 10.0, 10.0, 10.0, 6.0, 10.0, 10.0, 10.0, 1.0, 4.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 2.0, 1.0, 2.0],\n",
+       " [9.0, 8.0, 8.0, 9.0, 6.0, 3.0, 4.0, 1.0, 1.0, 4.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 10.0, 8.0, 5.0, 4.0, 1.0, 10.0, 1.0, 1.0, 4.0],\n",
+       " [2.0, 5.0, 7.0, 6.0, 4.0, 10.0, 7.0, 6.0, 1.0, 4.0],\n",
+       " [10.0, 3.0, 4.0, 5.0, 3.0, 10.0, 4.0, 1.0, 1.0, 4.0],\n",
+       " [5.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 8.0, 6.0, 3.0, 4.0, 10.0, 7.0, 1.0, 1.0, 4.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 3.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 2.0, 4.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 4.0, 6.0, 8.0, 4.0, 1.0, 8.0, 10.0, 1.0, 4.0],\n",
+       " [5.0, 3.0, 2.0, 8.0, 5.0, 10.0, 8.0, 1.0, 2.0, 4.0],\n",
+       " [10.0, 5.0, 10.0, 3.0, 5.0, 8.0, 7.0, 8.0, 3.0, 4.0],\n",
+       " [4.0, 1.0, 1.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 1.0, 1.0, 4.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 4.0, 3.0, 10.0, 3.0, 10.0, 7.0, 1.0, 2.0, 4.0],\n",
+       " [5.0, 10.0, 10.0, 10.0, 5.0, 2.0, 8.0, 5.0, 1.0, 4.0],\n",
+       " [8.0, 10.0, 10.0, 10.0, 6.0, 10.0, 10.0, 10.0, 10.0, 4.0],\n",
+       " [2.0, 3.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 3.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 1.0, -99999.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 3.0, 3.0, 3.0, 3.0, 2.0, 6.0, 1.0, 1.0, 2.0],\n",
+       " [7.0, 1.0, 2.0, 3.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 3.0, 1.0, 3.0, 4.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 6.0, 6.0, 5.0, 7.0, 6.0, 7.0, 7.0, 3.0, 4.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 5.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [6.0, 2.0, 3.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [8.0, 7.0, 4.0, 4.0, 5.0, 3.0, 5.0, 10.0, 1.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 4.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 10.0, 7.0, 8.0, 7.0, 1.0, 10.0, 10.0, 3.0, 4.0],\n",
+       " [4.0, 2.0, 4.0, 3.0, 2.0, 2.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 2.0, 2.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 10.0, 10.0, 10.0, 10.0, 2.0, 10.0, 10.0, 10.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 2.0, 3.0, 4.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 2.0, 1.0, 3.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 2.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 4.0, 5.0, 1.0, 8.0, 1.0, 3.0, 6.0, 1.0, 2.0],\n",
+       " [7.0, 8.0, 8.0, 7.0, 3.0, 10.0, 7.0, 2.0, 3.0, 4.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 3.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 3.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 3.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 2.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 7.0, 4.0, 1.0, 6.0, 1.0, 7.0, 10.0, 3.0, 4.0],\n",
+       " [5.0, 10.0, 10.0, 8.0, 5.0, 5.0, 7.0, 10.0, 1.0, 4.0],\n",
+       " [3.0, 10.0, 7.0, 8.0, 5.0, 8.0, 7.0, 4.0, 1.0, 4.0],\n",
+       " [3.0, 2.0, 1.0, 2.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 3.0, 2.0, 1.0, 3.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 4.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [10.0, 10.0, 10.0, 10.0, 5.0, 10.0, 10.0, 10.0, 7.0, 4.0],\n",
+       " [5.0, 10.0, 10.0, 10.0, 4.0, 10.0, 5.0, 6.0, 3.0, 4.0],\n",
+       " [5.0, 1.0, 1.0, 1.0, 2.0, 1.0, 3.0, 2.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 3.0, 1.0, 2.0],\n",
+       " [4.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 8.0, 2.0],\n",
+       " [1.0, 1.0, 1.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 10.0, 10.0, 5.0, 4.0, 5.0, 4.0, 4.0, 1.0, 4.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 2.0],\n",
+       " [3.0, 1.0, 1.0, 1.0, 3.0, 2.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [2.0, 1.0, 1.0, 1.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0],\n",
+       " [5.0, 10.0, 10.0, 3.0, 7.0, 3.0, 8.0, 10.0, 2.0, 4.0],\n",
+       " [4.0, 8.0, 6.0, 4.0, 3.0, 4.0, 10.0, 6.0, 1.0, 4.0],\n",
+       " [4.0, 8.0, 8.0, 5.0, 4.0, 5.0, 10.0, 4.0, 1.0, 4.0]]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "full_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random.shuffle(full_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_size = 0.2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_set = {2:[],4:[]}\n",
+    "test_set = {2:[], 4:[]}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_data= full_data[:-int(test_size*len(full_data))]\n",
+    "test_data = full_data[-int(test_size*len(full_data)):]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i in train_data:\n",
+    "    train_set[i[-1]].append(i[:-1])\n",
+    "    \n",
+    "for i in test_data:\n",
+    "    test_set[i[-1]].append(i[:-1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eficacia del KNN =  0.9424460431654677\n"
+     ]
+    }
+   ],
+   "source": [
+    "correct = 0\n",
+    "total = 0\n",
+    "for group in test_set:\n",
+    "    for data in test_set[group]:\n",
+    "        vote = k_nearest_neighbors(train_set, data, k = 5)\n",
+    "        if group == vote:\n",
+    "            correct += 1\n",
+    "        total +=1\n",
+    "print(\"Eficacia del KNN = \",correct/total)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/T9 - 2 - K Nearest Neighbors Implementation.ipynb b/notebooks/T9 - 2 - K Nearest Neighbors Implementation.ipynb
index 95224ec0..927302b5 100644
--- a/notebooks/T9 - 2 - K Nearest Neighbors Implementation.ipynb	
+++ b/notebooks/T9 - 2 - K Nearest Neighbors Implementation.ipynb	
@@ -1182,7 +1182,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git "a/notebooks/T9 - 3 - Sistemas de recomendaci\303\263n.ipynb" "b/notebooks/T9 - 3 - Sistemas de recomendaci\303\263n-Colab.ipynb"
similarity index 54%
rename from "notebooks/T9 - 3 - Sistemas de recomendaci\303\263n.ipynb"
rename to "notebooks/T9 - 3 - Sistemas de recomendaci\303\263n-Colab.ipynb"
index a337ef04..7eb92746 100644
--- "a/notebooks/T9 - 3 - Sistemas de recomendaci\303\263n.ipynb"	
+++ "b/notebooks/T9 - 3 - Sistemas de recomendaci\303\263n-Colab.ipynb"	
@@ -1,5 +1,58 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/joanby/python-ml-course/blob/master/notebooks/T9%20-%203%20-%20Sistemas%20de%20recomendación-Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Clonamos el repositorio para obtener los dataSet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/joanby/python-ml-course.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Damos acceso a nuestro Drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "# Test it\n",
+    "!ls '/content/drive/My Drive' "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "import glob # Para manejar los archivos y, por ejemplo, exportar a su navegador\n",
+    "from google.colab import drive # Montar tu Google drive"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -19,16 +72,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.read_csv(\"../datasets/ml-100k/u.data.csv\", sep=\"\\t\", header=None)"
+    "df = pd.read_csv(\"/content/python-ml-course/datasets/ml-100k/u.data.csv\", sep=\"\\t\", header=None)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -37,7 +90,7 @@
        "pandas.core.frame.DataFrame"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -48,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -127,7 +180,7 @@
        "4  166  346  1  886397596"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -138,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -147,7 +200,7 @@
        "(100000, 4)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -158,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -167,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -246,7 +299,7 @@
        "4     166     346       1  886397596"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -264,7 +317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -273,7 +326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -285,18 +338,20 @@
        " <a list of 10 Patch objects>)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10a8c0898>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -306,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -320,18 +375,20 @@
        " <a list of 10 Patch objects>)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1104d7860>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -341,7 +398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -356,7 +413,7 @@
        "Name: UserID, dtype: int64"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -367,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -380,18 +437,20 @@
        " <a list of 10 Patch objects>)"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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4OHB56+dxYHMbvxl4vKpeAVzexh2NPgDsGVof934A/ryqXj/08chxfc8BfAr4WlW9Cngdg/9XC9dPVS2rH+As4Oah9UuBS5d6Xkc493XAvUPr9wMnt+WTgfvb8r8D755t3NH6A9wA/MVy6Al4IfAdBt9i/wmwstV/995j8Em3s9ryyjYuSz33Q/pY036BnA3cyOCLmmPbT5vbQ8CJh9TG8j0HvAT40aH/nReyn2V3BMDst5tYvURzGdXLquoRgPZ4UquPVY/tdMEbgNsZ457a6ZK7gP3ATuCHwBNVdaANGZ7z7/pp258ETljcGc/pk8CHgd+29RMY735gcGeBrye5o91VAMb3PXcqMAN8rp2m+2ySF7GA/SzHAJjzdhPLwNj0mOTFwJeAD1bVTw83dJbaUdVTVf2mql7P4G/OZwCvnm1Yezyq+0nyTmB/Vd0xXJ5l6Fj0M+RNVXU6g9MhFyd582HGHu09rQROB66oqjcAP+Op0z2zedb9LMcAmPN2E2Pk0SQnA7TH/a0+Fj0meR6DX/5frKovt/JY9wRQVU8A32BwbWNVkoPfpxme8+/6adtfCjy2uDM9rDcB70ryEIO78p7N4IhgXPsBoKr2tcf9wFcYBPW4vuemgemqur2tX88gEBasn+UYAMvpdhM7gE1teROD8+gH6+9tV/3PBJ48eEh4tEgS4EpgT1V9YmjTWPaUZCLJqrb8AuCtDC7I3Qpc0IYd2s/BPi8Abql2YvZoUFWXVtWaqlrH4M/ILVX1Hsa0H4AkL0ryhweXgbcB9zKm77mq+jGwN8krW2kD8D0Wsp+lvtDxHF08OQ/4AYNztB9d6vkc4ZyvBh4Bfs0gyTczOMe6C3igPR7fxobBJ51+CNwDTC71/Gfp588YHH7eDdzVfs4b156A1wJ3tn7uBf6h1U8FvgVMAf8BHNvqz2/rU237qUvdw2F6ewtw47j30+b+3fZz38E/++P6nmtzfD2wu73v/hM4biH78ZvAktSp5XgKSJJ0BAwASeqUASBJnTIAJKlTBoAkdcoAkKROGQCS1CkDQJI69f85QgmmjCpgqQAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x11390c080>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -408,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -417,7 +476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -426,7 +485,7 @@
        "943"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -438,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -447,7 +506,7 @@
        "1682"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -459,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -468,7 +527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -478,7 +537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -487,7 +546,7 @@
        "numpy.ndarray"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -498,7 +557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -507,7 +566,7 @@
        "(943, 1682)"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -518,7 +577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -533,7 +592,7 @@
        "       [0., 5., 0., ..., 0., 0., 0.]])"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -544,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -571,16 +630,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.cross_validation import train_test_split"
+    "from sklearn.model_selection import train_test_split"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -589,7 +648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -598,7 +657,7 @@
        "(660, 1682)"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -609,7 +668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -618,7 +677,7 @@
        "(283, 1682)"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -639,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -649,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -658,7 +717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -667,7 +726,7 @@
        "numpy.ndarray"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -678,7 +737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -687,7 +746,7 @@
        "(660, 660)"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -698,7 +757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -719,7 +778,7 @@
        "        1.        ]])"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -730,7 +789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -739,7 +798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -760,7 +819,7 @@
        "        0.00000000e+00, 6.39996638e-03, 5.37442746e-03]])"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -771,7 +830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -780,7 +839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -794,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -803,7 +862,7 @@
        "7.878218313143215"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -814,21 +873,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "8.745164067978985"
+       "7.51355110112698"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "sim_matrix = 1 - sklearn.metrics.pairwise.cosine_distances(ratings_test)\n",
+    "users_predictions = sim_matrix.dot(ratings_test) / np.array([np.abs(sim_matrix).sum(axis=1)]).T\n",
     "get_mse(users_predictions, ratings_test)"
    ]
   },
@@ -841,7 +902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -850,7 +911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -859,7 +920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -868,17 +929,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "NearestNeighbors(algorithm='auto', leaf_size=30, metric='minkowski',\n",
-       "         metric_params=None, n_jobs=1, n_neighbors=5, p=2, radius='cosine')"
+       "                 metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
+       "                 radius='cosine')"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -889,7 +951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -898,7 +960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -907,7 +969,7 @@
        "(660, 5)"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -918,7 +980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -927,7 +989,7 @@
        "array([ 0.        , 41.71330723, 43.3474336 , 45.04442252, 45.04442252])"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -938,7 +1000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -947,7 +1009,7 @@
        "(660, 5)"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -958,7 +1020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -967,7 +1029,7 @@
        "array([  0, 211,  16, 583, 428])"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -978,7 +1040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -989,7 +1051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -998,7 +1060,7 @@
        "(660, 1682)"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1009,7 +1071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -1030,7 +1092,7 @@
        "        0.        ]])"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1041,7 +1103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1050,7 +1112,7 @@
        "8.180803170774984"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1061,22 +1123,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "index 583 is out of bounds for axis 0 with size 283",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-54-7df8ee7c964c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0musers_predicts_k\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mratings_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mratings_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;31m# para cada usuario del conjunto de entrenamiento\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0musers_predicts_k\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtop_k_distances\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mratings_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtop_k_users\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtop_k_distances\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mget_mse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0musers_predicts_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mratings_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mIndexError\u001b[0m: index 583 is out of bounds for axis 0 with size 283"
+     ]
+    }
+   ],
+   "source": [
+    "users_predicts_k = np.zeros(ratings_test.shape)\n",
+    "for i in range(ratings_test.shape[0]):# para cada usuario del conjunto de test\n",
+    "    users_predicts_k[i,:] = top_k_distances[i].T.dot(ratings_test[top_k_users][i]) / np.array([np.abs(top_k_distances[i].T).sum(axis=0)]).T\n",
+    "get_mse(users_predicts_k, ratings_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "11.338914012692959"
+       "array([[4., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       ...,\n",
+       "       [2., 4., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.],\n",
+       "       [0., 0., 0., ..., 0., 0., 0.]])"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "get_mse(users_predicts_k, ratings_test)"
+    "ratings_test"
    ]
   },
   {
@@ -1613,7 +1705,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/resources/boston_rtree.dot b/notebooks/resources/boston_rtree.dot
index 9eddd36d..21df5723 100644
--- a/notebooks/resources/boston_rtree.dot
+++ b/notebooks/resources/boston_rtree.dot
@@ -33,7 +33,7 @@ node [shape=box] ;
 14 -> 15 ;
 16 [label="mse = 7.862\nsamples = 24\nvalue = 14.042"] ;
 14 -> 16 ;
-17 [label="nox <= 0.605\nmse = 14.674\nsamples = 74\nvalue = 11.978"] ;
+17 [label="nox <= 0.606\nmse = 14.674\nsamples = 74\nvalue = 11.978"] ;
 11 -> 17 ;
 18 [label="mse = 18.606\nsamples = 12\nvalue = 16.633"] ;
 17 -> 18 ;
diff --git a/notebooks/resources/iris_dtree.dot b/notebooks/resources/iris_dtree.dot
index 0457ef0a..e69de29b 100644
--- a/notebooks/resources/iris_dtree.dot
+++ b/notebooks/resources/iris_dtree.dot
@@ -1,16 +0,0 @@
-digraph Tree {
-node [shape=box] ;
-0 [label="Petal.Length <= 2.6\nentropy = 1.582\nsamples = 111\nvalue = [38, 34, 39]"] ;
-1 [label="entropy = 0.0\nsamples = 38\nvalue = [38, 0, 0]"] ;
-0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
-2 [label="Petal.Width <= 1.75\nentropy = 0.997\nsamples = 73\nvalue = [0, 34, 39]"] ;
-0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
-3 [label="Petal.Length <= 4.95\nentropy = 0.406\nsamples = 37\nvalue = [0, 34, 3]"] ;
-2 -> 3 ;
-4 [label="entropy = 0.0\nsamples = 32\nvalue = [0, 32, 0]"] ;
-3 -> 4 ;
-5 [label="entropy = 0.971\nsamples = 5\nvalue = [0, 2, 3]"] ;
-3 -> 5 ;
-6 [label="entropy = 0.0\nsamples = 36\nvalue = [0, 0, 36]"] ;
-2 -> 6 ;
-}
\ No newline at end of file