Intro_DataAnalysis.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:176a32bc7ca921799675f84b259e7b13e4c481104191138763368f7370974acc"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Introducci\u00f3n al an\u00e1lisis de datos usando Python"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Cargar el dataset"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd #Cargamos la librer\u00eda de apoyo, Pandas (Python for Data Science)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mortalidad = pd.DataFrame.from_csv(\"mortalidad_materna_2002-2013.csv\",index_col=None, encoding=\"utf-8\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(mortalidad.columns)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Index([u'\ufeff\"A\u00f1o de nacimiento\"', u'Mes de nacimiento',\n",
        "       u'Descripci\u00f3n del mes de nacimiento', u'D\u00eda de nacimiento', u'Genero',\n",
        "       u'Clave de la edad', u'Edad cumplida', u'Estado conyugal',\n",
        "       u'Descripci\u00f3n del estado conyugal', u'Entidad de residencia',\n",
        "       u'Descripci\u00f3n de entidad de residencia', u'Municipio de residencia',\n",
        "       u'Descripci\u00f3n del municipio de residencia', u'Localidad de residencia',\n",
        "       u'Descripi\u00f3n de la localidad de residencia', u'Tama\u00f1o de localidad',\n",
        "       u'Descripci\u00f3n del tama\u00f1o de localidad', u'Ocupaci\u00f3n habitual',\n",
        "       u'Descripci\u00f3n de la ocupaci\u00f3n habitual', u'Escolaridad',\n",
        "       u'Descripci\u00f3n de la escolaridad', u'Derechiahabiencia',\n",
        "       u'Descrici\u00f3n de la derechiahabiencia', u'Entidad de ocurrencia',\n",
        "       u'Descripci\u00f3n de la entidad de ocurrencia', u'Municipio de ocurrencia',\n",
        "       u'Descrici\u00f3n del municipio de ocurrencia', u'Localidad de ocurrencia',\n",
        "       u'Descrici\u00f3n de la localidad de ocurrencia',\n",
        "       u'Sitio donde ocurrio la defunci\u00f3n',\n",
        "       u'Descripci\u00f3n del sitio donde ocurrio la defunci\u00f3n',\n",
        "       u'A\u00f1o de la defunci\u00f3n', u'Mes de la defunci\u00f3n',\n",
        "       u'Descrici\u00f3n del mes de la defunci\u00f3n', u'D\u00eda de la defunci\u00f3n',\n",
        "       u'Hora de la defunci\u00f3n', u'Minutos de la defunci\u00f3n',\n",
        "       u'Asistencia m\u00e9dica', u'Descripci\u00f3n de la asistencia m\u00e9dica',\n",
        "       u'Causa CIE a  cuarto d\u00edgito', u'Descrici\u00f3n de la causa CIE',\n",
        "       u'Quien certific\u00f3', u'Descrici\u00f3n de quien certific\u00f3',\n",
        "       u'Entidad de registro', u'Descrici\u00f3n de la entidad de registro',\n",
        "       u'Municipio de registro', u'Descrici\u00f3n del municipio de registro',\n",
        "       u'A\u00f1o de registro', u'Mes de registro',\n",
        "       u'Descripci\u00f3n del mes de registro', u'D\u00eda de registro',\n",
        "       u'A\u00f1o de la certificaci\u00f3n', u'Mes de la certificaci\u00f3n',\n",
        "       u'Descripci\u00f3 del mes de la certificaci\u00f3n', u'D\u00eda de la certificaci\u00f3n',\n",
        "       u'A\u00f1o de la base de datos', u'Raz\u00f3n de mortalidad materna',\n",
        "       u'Descrici\u00f3n de la raz\u00f3n de mortalidad materna'],\n",
        "      dtype='object')\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mortalidad.head(5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>\ufeff\"A\u00f1o de nacimiento\"</th>\n",
        "      <th>Mes de nacimiento</th>\n",
        "      <th>Descripci\u00f3n del mes de nacimiento</th>\n",
        "      <th>D\u00eda de nacimiento</th>\n",
        "      <th>Genero</th>\n",
        "      <th>Clave de la edad</th>\n",
        "      <th>Edad cumplida</th>\n",
        "      <th>Estado conyugal</th>\n",
        "      <th>Descripci\u00f3n del estado conyugal</th>\n",
        "      <th>Entidad de residencia</th>\n",
        "      <th>...</th>\n",
        "      <th>Mes de registro</th>\n",
        "      <th>Descripci\u00f3n del mes de registro</th>\n",
        "      <th>D\u00eda de registro</th>\n",
        "      <th>A\u00f1o de la certificaci\u00f3n</th>\n",
        "      <th>Mes de la certificaci\u00f3n</th>\n",
        "      <th>Descripci\u00f3 del mes de la certificaci\u00f3n</th>\n",
        "      <th>D\u00eda de la certificaci\u00f3n</th>\n",
        "      <th>A\u00f1o de la base de datos</th>\n",
        "      <th>Raz\u00f3n de mortalidad materna</th>\n",
        "      <th>Descrici\u00f3n de la raz\u00f3n de mortalidad materna</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>1972</td>\n",
        "      <td>6</td>\n",
        "      <td>JUNIO</td>\n",
        "      <td>25</td>\n",
        "      <td>2</td>\n",
        "      <td>A</td>\n",
        "      <td>29</td>\n",
        "      <td>5</td>\n",
        "      <td>CASADO</td>\n",
        "      <td>31</td>\n",
        "      <td>...</td>\n",
        "      <td>1</td>\n",
        "      <td>ENERO</td>\n",
        "      <td>20</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "      <td>NO ESPECIFICADO</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2002</td>\n",
        "      <td>1</td>\n",
        "      <td>Muertes Maternas para la raz\u00f3n de Mortalidad M...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1967</td>\n",
        "      <td>2</td>\n",
        "      <td>FEBRERO</td>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
        "      <td>A</td>\n",
        "      <td>35</td>\n",
        "      <td>5</td>\n",
        "      <td>CASADO</td>\n",
        "      <td>31</td>\n",
        "      <td>...</td>\n",
        "      <td>7</td>\n",
        "      <td>JULIO</td>\n",
        "      <td>28</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "      <td>NO ESPECIFICADO</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2002</td>\n",
        "      <td>1</td>\n",
        "      <td>Muertes Maternas para la raz\u00f3n de Mortalidad M...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>1973</td>\n",
        "      <td>6</td>\n",
        "      <td>JUNIO</td>\n",
        "      <td>21</td>\n",
        "      <td>2</td>\n",
        "      <td>A</td>\n",
        "      <td>29</td>\n",
        "      <td>5</td>\n",
        "      <td>CASADO</td>\n",
        "      <td>31</td>\n",
        "      <td>...</td>\n",
        "      <td>10</td>\n",
        "      <td>OCTUBRE</td>\n",
        "      <td>24</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "      <td>NO ESPECIFICADO</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2002</td>\n",
        "      <td>1</td>\n",
        "      <td>Muertes Maternas para la raz\u00f3n de Mortalidad M...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>1966</td>\n",
        "      <td>1</td>\n",
        "      <td>ENERO</td>\n",
        "      <td>23</td>\n",
        "      <td>2</td>\n",
        "      <td>A</td>\n",
        "      <td>36</td>\n",
        "      <td>5</td>\n",
        "      <td>CASADO</td>\n",
        "      <td>31</td>\n",
        "      <td>...</td>\n",
        "      <td>10</td>\n",
        "      <td>OCTUBRE</td>\n",
        "      <td>20</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0</td>\n",
        "      <td>NO ESPECIFICADO</td>\n",
        "      <td>NaN</td>\n",
        "      <td>2002</td>\n",
        "      <td>1</td>\n",
        "      <td>Muertes Maternas para la raz\u00f3n de Mortalidad M...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>1975</td>\n",
        "      <td>0</td>\n",
        "      <td>NO ESPECIFICADO</td>\n",
        "      <td>0</td>\n",
        "      <td>2</td>\n",
        "      <td>A</td>\n",
        "      <td>30</td>\n",
        "      <td>5</td>\n",
        "      <td>CASADO</td>\n",
        "      <td>9</td>\n",
        "      <td>...</td>\n",
        "      <td>4</td>\n",
        "      <td>ABRIL</td>\n",
        "      <td>8</td>\n",
        "      <td>2005</td>\n",
        "      <td>4</td>\n",
        "      <td>ABRIL</td>\n",
        "      <td>8</td>\n",
        "      <td>2005</td>\n",
        "      <td>1</td>\n",
        "      <td>Muertes Maternas para la raz\u00f3n de Mortalidad M...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 58 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "   \ufeff\"A\u00f1o de nacimiento\"  Mes de nacimiento Descripci\u00f3n del mes de nacimiento  \\\n",
        "0                  1972                  6                             JUNIO   \n",
        "1                  1967                  2                           FEBRERO   \n",
        "2                  1973                  6                             JUNIO   \n",
        "3                  1966                  1                             ENERO   \n",
        "4                  1975                  0                   NO ESPECIFICADO   \n",
        "\n",
        "   D\u00eda de nacimiento  Genero Clave de la edad  Edad cumplida  Estado conyugal  \\\n",
        "0                 25       2                A             29                5   \n",
        "1                  1       2                A             35                5   \n",
        "2                 21       2                A             29                5   \n",
        "3                 23       2                A             36                5   \n",
        "4                  0       2                A             30                5   \n",
        "\n",
        "  Descripci\u00f3n del estado conyugal  Entidad de residencia  \\\n",
        "0                          CASADO                     31   \n",
        "1                          CASADO                     31   \n",
        "2                          CASADO                     31   \n",
        "3                          CASADO                     31   \n",
        "4                          CASADO                      9   \n",
        "\n",
        "                         ...                         Mes de registro  \\\n",
        "0                        ...                                       1   \n",
        "1                        ...                                       7   \n",
        "2                        ...                                      10   \n",
        "3                        ...                                      10   \n",
        "4                        ...                                       4   \n",
        "\n",
        "   Descripci\u00f3n del mes de registro D\u00eda de registro  A\u00f1o de la certificaci\u00f3n  \\\n",
        "0                            ENERO              20                      NaN   \n",
        "1                            JULIO              28                      NaN   \n",
        "2                          OCTUBRE              24                      NaN   \n",
        "3                          OCTUBRE              20                      NaN   \n",
        "4                            ABRIL               8                     2005   \n",
        "\n",
        "  Mes de la certificaci\u00f3n  Descripci\u00f3 del mes de la certificaci\u00f3n  \\\n",
        "0                       0                         NO ESPECIFICADO   \n",
        "1                       0                         NO ESPECIFICADO   \n",
        "2                       0                         NO ESPECIFICADO   \n",
        "3                       0                         NO ESPECIFICADO   \n",
        "4                       4                                   ABRIL   \n",
        "\n",
        "  D\u00eda de la certificaci\u00f3n  A\u00f1o de la base de datos  \\\n",
        "0                     NaN                     2002   \n",
        "1                     NaN                     2002   \n",
        "2                     NaN                     2002   \n",
        "3                     NaN                     2002   \n",
        "4                       8                     2005   \n",
        "\n",
        "  Raz\u00f3n de mortalidad materna  \\\n",
        "0                           1   \n",
        "1                           1   \n",
        "2                           1   \n",
        "3                           1   \n",
        "4                           1   \n",
        "\n",
        "        Descrici\u00f3n de la raz\u00f3n de mortalidad materna  \n",
        "0  Muertes Maternas para la raz\u00f3n de Mortalidad M...  \n",
        "1  Muertes Maternas para la raz\u00f3n de Mortalidad M...  \n",
        "2  Muertes Maternas para la raz\u00f3n de Mortalidad M...  \n",
        "3  Muertes Maternas para la raz\u00f3n de Mortalidad M...  \n",
        "4  Muertes Maternas para la raz\u00f3n de Mortalidad M...  \n",
        "\n",
        "[5 rows x 58 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mortalidad[\"Edad cumplida\"].hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7fb92ff43090>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEACAYAAABCl1qQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb930007cd0>"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mortalidad[\"Edad cumplida\"][mortalidad[\"Edad cumplida\"]<100].hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 86,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7fb92ffaa050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEACAYAAAC08h1NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb92fecbc10>"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mortalidad_infantil = mortalidad[mortalidad[\"Edad cumplida\"]<18]\n",
      "mortalidad_infantil[\"Edad cumplida\"].hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7fb92fc3b1d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXgAAAEACAYAAAC57G0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb92ffba250>"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(mortalidad_infantil)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "862"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mortalidad_infantil[\"Edad cumplida\"].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "count    862.000000\n",
        "mean      16.038283\n",
        "std        1.095730\n",
        "min       11.000000\n",
        "25%       15.000000\n",
        "50%       16.000000\n",
        "75%       17.000000\n",
        "max       17.000000\n",
        "Name: Edad cumplida, dtype: float64"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mortalidad_infantil[u\"Descripci\u00f3n del estado conyugal\"].value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "UNION LIBRE                       451\n",
        "SOLTERO                           235\n",
        "CASADO                            148\n",
        "SE IGNORA                          19\n",
        "DIVORCIADO                          6\n",
        "NO APLICA A MENORES DE 12 A\u00d1OS      1\n",
        "SEPARADO                            1\n",
        "VIUDO                               1\n",
        "Name: Descripci\u00f3n del estado conyugal, dtype: int64"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "## "
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mortalidad_counts_estado = mortalidad_infantil[u\"Descripci\u00f3n de entidad de residencia\"].value_counts().to_dict()\n",
      "print(mortalidad_counts_estado)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "{u'Coahuila de Zaragoza': 10, u'Chihuahua': 49, u'Veracruz de Ignacio de la Llave': 84, u'Guanajuato': 25, u'Michoac\\xe1n de Ocampo': 30, u'Sinaloa': 16, u'Tamaulipas': 21, u'Quer\\xe9taro Arteaga': 9, u'Guerrero': 60, u'Aguascalientes': 6, u'Nuevo Le\\xf3n': 11, u'Tlaxcala': 8, u'Nayarit': 14, u'Chiapas': 82, u'Zacatecas': 1, u'Baja California': 23, u'San Luis Potos\\xed': 10, u'Quintana Roo': 17, u'Colima': 3, u'Sonora': 17, u'Tabasco': 14, u'Baja California Sur': 6, u'Puebla': 46, u'M\\xe9xico': 114, u'Jalisco': 27, u'Distrito Federal': 44, u'Campeche': 3, u'Durango': 12, u'Oaxaca': 48, u'Yucat\\xe1n': 17, u'Morelos': 17, u'Hidalgo': 18}\n"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mortalidad_infantil[\"Descripci\u00f3n de entidad de residencia\"].value_counts().plot(kind=\"bar\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 54,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7fb9301e6790>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAGRCAYAAAB49DglAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naDk2kDMMw2i5/wP2eNU0xr/jpgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb9300df990>"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "PIB_Per_Capita = {u\"Campeche\":815557,\n",
      "u\"Distrito Federal\":334066,\n",
      "u\"Nuevo Le\u00f3n\":257178,\n",
      "u\"Tabasco\":227652,\n",
      "u\"Coahuila de Zaragoza\":203458,\n",
      "u\"Quer\u00e9taro Arteaga\":198129,\n",
      "u\"Quintana Roo\":180365,\n",
      "u\"Sonora\":179774,\n",
      "u\"Baja California Sur\":175278,\n",
      "u\"Aguascalientes\":158083,\n",
      "u\"Baja California\":149751,\n",
      "u\"Tamaulipas\":148131,\n",
      "u\"Jalisco\":146425,\n",
      "u\"Colima\":145436,\n",
      "u\"Chihuahua\":139984,\n",
      "u\"San Luis Potos\u00ed\":128663,\n",
      "u\"Sinaloa\":128505,\n",
      "u\"Guanajuato\":127491,\n",
      "u\"Yucat\u00e1n\":126965,\n",
      "u\"Durango\":125008,\n",
      "u\"Zacatecas\":120440,\n",
      "u\"Veracruz de Ignacio de la Llave\":114668,\n",
      "u\"Morelos\":110317,\n",
      "u\"Hidalgo\":103137,\n",
      "u\"Nayarit\":98582,\n",
      "u\"M\u00e9xico\":98241,\n",
      "u\"Puebla\":93347,\n",
      "u\"Michoac\u00e1n de Ocampo\":87518,\n",
      "u\"Tlaxcala\":81026,\n",
      "u\"Guerrero\":72685,\n",
      "u\"Oaxaca\":70455,\n",
      "u\"Chiapas\":56924}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Estado_PIB_Counts= []\n",
      "for estado in mortalidad_counts_estado:\n",
      "    Estado_PIB_Counts.append([estado,PIB_Per_Capita[estado],mortalidad_counts_estado[estado]])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(Estado_PIB_Counts)\n",
      "cols = [u\"Estado\",u\"PIBperCapita\",u\"Conteo de Fallecimientos\"]\n",
      "df.columns = cols"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Estado</th>\n",
        "      <th>PIB per Capita</th>\n",
        "      <th>Conteo de Fallecimientos</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>Coahuila de Zaragoza</td>\n",
        "      <td>203458</td>\n",
        "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>Chihuahua</td>\n",
        "      <td>139984</td>\n",
        "      <td>49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Veracruz de Ignacio de la Llave</td>\n",
        "      <td>114668</td>\n",
        "      <td>84</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Guanajuato</td>\n",
        "      <td>127491</td>\n",
        "      <td>25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Michoac\u00e1n de Ocampo</td>\n",
        "      <td>87518</td>\n",
        "      <td>30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>Sinaloa</td>\n",
        "      <td>128505</td>\n",
        "      <td>16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>Tamaulipas</td>\n",
        "      <td>148131</td>\n",
        "      <td>21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>Quer\u00e9taro Arteaga</td>\n",
        "      <td>198129</td>\n",
        "      <td>9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
        "      <td>Guerrero</td>\n",
        "      <td>72685</td>\n",
        "      <td>60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
        "      <td>Aguascalientes</td>\n",
        "      <td>158083</td>\n",
        "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>Nuevo Le\u00f3n</td>\n",
        "      <td>257178</td>\n",
        "      <td>11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>Tlaxcala</td>\n",
        "      <td>81026</td>\n",
        "      <td>8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>Nayarit</td>\n",
        "      <td>98582</td>\n",
        "      <td>14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>Chiapas</td>\n",
        "      <td>56924</td>\n",
        "      <td>82</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>Zacatecas</td>\n",
        "      <td>120440</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>Baja California</td>\n",
        "      <td>149751</td>\n",
        "      <td>23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td>San Luis Potos\u00ed</td>\n",
        "      <td>128663</td>\n",
        "      <td>10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>Quintana Roo</td>\n",
        "      <td>180365</td>\n",
        "      <td>17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>Colima</td>\n",
        "      <td>145436</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>Sonora</td>\n",
        "      <td>179774</td>\n",
        "      <td>17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>Tabasco</td>\n",
        "      <td>227652</td>\n",
        "      <td>14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>Baja California Sur</td>\n",
        "      <td>175278</td>\n",
        "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>Puebla</td>\n",
        "      <td>93347</td>\n",
        "      <td>46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>M\u00e9xico</td>\n",
        "      <td>98241</td>\n",
        "      <td>114</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>Jalisco</td>\n",
        "      <td>146425</td>\n",
        "      <td>27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>Distrito Federal</td>\n",
        "      <td>334066</td>\n",
        "      <td>44</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>Campeche</td>\n",
        "      <td>815557</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>Durango</td>\n",
        "      <td>125008</td>\n",
        "      <td>12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>Oaxaca</td>\n",
        "      <td>70455</td>\n",
        "      <td>48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>Yucat\u00e1n</td>\n",
        "      <td>126965</td>\n",
        "      <td>17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>Morelos</td>\n",
        "      <td>110317</td>\n",
        "      <td>17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
        "      <td>Hidalgo</td>\n",
        "      <td>103137</td>\n",
        "      <td>18</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 111,
       "text": [
        "                             Estado  PIB per Capita  Conteo de Fallecimientos\n",
        "0              Coahuila de Zaragoza          203458                        10\n",
        "1                         Chihuahua          139984                        49\n",
        "2   Veracruz de Ignacio de la Llave          114668                        84\n",
        "3                        Guanajuato          127491                        25\n",
        "4               Michoac\u00e1n de Ocampo           87518                        30\n",
        "5                           Sinaloa          128505                        16\n",
        "6                        Tamaulipas          148131                        21\n",
        "7                 Quer\u00e9taro Arteaga          198129                         9\n",
        "8                          Guerrero           72685                        60\n",
        "9                    Aguascalientes          158083                         6\n",
        "10                       Nuevo Le\u00f3n          257178                        11\n",
        "11                         Tlaxcala           81026                         8\n",
        "12                          Nayarit           98582                        14\n",
        "13                          Chiapas           56924                        82\n",
        "14                        Zacatecas          120440                         1\n",
        "15                  Baja California          149751                        23\n",
        "16                  San Luis Potos\u00ed          128663                        10\n",
        "17                     Quintana Roo          180365                        17\n",
        "18                           Colima          145436                         3\n",
        "19                           Sonora          179774                        17\n",
        "20                          Tabasco          227652                        14\n",
        "21              Baja California Sur          175278                         6\n",
        "22                           Puebla           93347                        46\n",
        "23                           M\u00e9xico           98241                       114\n",
        "24                          Jalisco          146425                        27\n",
        "25                 Distrito Federal          334066                        44\n",
        "26                         Campeche          815557                         3\n",
        "27                          Durango          125008                        12\n",
        "28                           Oaxaca           70455                        48\n",
        "29                          Yucat\u00e1n          126965                        17\n",
        "30                          Morelos          110317                        17\n",
        "31                          Hidalgo          103137                        18"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot(kind=\"scatter\", x=u\"PIBperCapita\",y=u\"Conteo de Fallecimientos\")\n",
      "# Campeche, que est\u00e1 hasta la derecha, nos mete un tanto de ruido.."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 113,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7fb92f797dd0>"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/opt/anaconda/envs/np18py27-1.9/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
        "  if self._edgecolors == str('face'):\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEPCAYAAACKplkeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb92f7e65d0>"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Si hacemos zoom para datos menores en PIB per c\u00e1pita menores a 200000 pesos por habitante, se ve una relaci\u00f3n un tanto m\u00e1s clara.\n",
      "df_lower = df[df[\"PIBperCapita\"]<200000]\n",
      "df_lower.plot(kind=\"scatter\", x=u\"PIBperCapita\",y=u\"Conteo de Fallecimientos\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 125,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x7fb924ce2a50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEPCAYAAACKplkeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtYVHX+B/D3EIpk3tIcFDRIQCUQ8Ia1uQ3RME89SgGB\nqSF5SVt/1dqyRNvu/qI2An/++JVaPOsduqxh66pUyoDa2JKZiqitbGUKIRfHC+CNUQS+vz+IsyKM\nA3Jmzgy8X8/DExxm+L4fOJ2P39s5KiGEABERkQyclA5ARETdB4sKERHJhkWFiIhkw6JCRESyYVEh\nIiLZsKgQEZFsrF5U5s2bB7VajYCAAOlYYmIixo4di8DAQERFReHChQvS91JTU+Hj44MxY8YgLy/P\n2vGIiEhGVi8qc+fORW5ubqtj4eHhOHbsGI4cOQJfX1+kpqYCAIqLi5GdnY3i4mLk5uZi8eLFaGpq\nsnZEIiKSidWLytSpUzFo0KBWx7RaLZycmpsOCQlBeXk5AGDbtm2YOXMmevXqBU9PT3h7e2P//v3W\njkhERDJRfE5l/fr1ePzxxwEAlZWV8PDwkL7n4eGBiooKpaIREVEnKVpUUlJS0Lt3b8yaNcvsa1Qq\nlQ0TERFRVzgr1XBmZia2b9+OXbt2Scfc3d1x6tQp6evy8nK4u7u3eS8LDRHR7bH27R4V6ank5uZi\n2bJl2LZtG/r06SMdj4iIwCeffIL6+nqUlJTg+PHjmDx5crs/Qwhhdx+vv/664hmYiZl6Yi5m6tiH\nLVi9pzJz5kzs2bMH586dw4gRI/DGG28gNTUV9fX10Gq1AIAHHngAGRkZ8PPzQ2xsLPz8/ODs7IyM\njAz2SoiIHIjVi8rGjRvbHJs3b57Z17/22mt47bXXrBmJiIisRPHVX92JRqNROkIbzNQxzNRx9piL\nmeyHSthqoE1GKpXKZuODRETdhS2uneypkFXp9XqEh0cjPDwaer1e6ThEZGXsqZDV6PV6REbGw2Ra\nCgBwdU3Cli1Z0Ol0Cicj6plsce1kUSGrCQ+PRn5+BID4X45kQavNQV7eZiVjEfVYHP4iIiKHotiO\neur+EhIWoqAgHiZT89eurklISMhSNhQRWRWHv8iq9Ho90tNXA2guMpxPIVIO51TMYFEhIuo8zqkQ\nEZFDYVEhIiLZsKgQEZFsWFSIiEg2LCpERCQbFhUiIpINiwoREcmGRYWIiGTDokJERLJhUSEiItmw\nqBARkWxYVIiISDYsKkREJBsWFSIiko3Vi8q8efOgVqsREBAgHauuroZWq4Wvry/Cw8NRW1srfS81\nNRU+Pj4YM2YM8vLyrB2PiIhkZPWiMnfuXOTm5rY6lpaWBq1Wix9//BFhYWFIS0sDABQXFyM7OxvF\nxcXIzc3F4sWL0dTUZO2IREQkE6sXlalTp2LQoEGtjuXk5CA+Ph4AEB8fj61btwIAtm3bhpkzZ6JX\nr17w9PSEt7c39u/fb+2IREQkE0XmVIxGI9RqNQBArVbDaDQCACorK+Hh4SG9zsPDAxUVFUpEJCKi\n2+CsdACVSgWVSnXL77cnOTlZ+lyj0UCj0cicjIjIsRkMBhgMBpu2qUhRUavVOH36NNzc3FBVVYWh\nQ4cCANzd3XHq1CnpdeXl5XB3d2/3Z9xYVIiIqK2b/8H9xhtvWL1NRYa/IiIikJWVBQDIysrCk08+\nKR3/5JNPUF9fj5KSEhw/fhyTJ09WIiIREd0Gq/dUZs6ciT179uDcuXMYMWIE3nzzTbz66quIjY3F\nunXr4OnpiU2bNgEA/Pz8EBsbCz8/Pzg7OyMjI+OWQ2NERGRfVEIIoXSIzlKpVHDA2EREirLFtZM7\n6omISDYsKkREJBsWFSIikg2LChERyYZFhYiIZMOiQkREsmFRISIi2bCoEBGRbFhUiIhINiwqREQk\nGxYVIiKSDYsKERHJhkWFiIhkw6JCRESysVhU3n33XVy4cAFCCMyfPx/BwcHQ6/W2yEZERA7GYlFZ\nv349BgwYgLy8PFRXV+PDDz/Eq6++aotsRETkYCwWlZYHunzxxReIi4uDv7+/1UMREZFjslhUJkyY\ngPDwcGzfvh06nQ4XL16EkxOnYoiIqC2LjxNubGzEkSNHcN9992HgwIE4f/48ysvLERgYaKuMbfBx\nwmSJXq9HevpqAEBCwkLodDqFExEpzxbXzg49o37btm346quvAAAajQbTp0+3aihLWFToVvR6PSIj\n42EyLQUAuLomYcuWLBYW6vHsoqi8+uqrOHDgAGbPng0hBD755BNMnDgRqampVg12KywqdCvh4dHI\nz48AEP/LkSxotTnIy9usZCwixdni2uls6QVffPEFDh8+jDvuuAMA8OyzzyIoKEjRoqIUDqkQEd2a\nxaKiUqlQW1uLwYMHAwBqa2uhUqmsHsze3DykUlAQzyEVO5WQsBAFBfEwmZq/dnVNQkJClrKhiHoI\ni0XlD3/4A8aPH4/Q0FAIIbBnzx6kpaXJ0vg777yDdevWQaVSISAgABs2bMCVK1cwY8YM/Pzzz/D0\n9MSmTZswcOBAWdrrivT01b8UlOYhFZOp+RiLiv3R6XTYsiXrhl4liz+RrXRoor6yshIHDhyASqXC\npEmTMGzYsC43XFFRgalTp+Lf//43XFxcMGPGDDz++OM4duwYhgwZgldeeQVLly5FTU1NmyKmxJwK\nx+mJyNHZ4tppccNJWFgYhg8fjieeeAIREREYNmwYwsLCZGm8oaEBdXV10n+HDx+OnJwcxMc3X7jj\n4+OxdetWWdrqqoSEhXB1TQKQBSDrlyGVhUrHIiKyK2aHv0wmE+rq6nD27FlUV1dLxy9evIiKioou\nN+zu7o6EhASMHDkSrq6u0Ol00Gq1MBqNUKvVAAC1Wg2j0djltuTAIRUiIsvMFpVVq1Zh+fLlqKys\nxIQJE6Tj/fr1wwsvvNDlhmtqapCTk4PS0lIMGDAAMTEx+Oijj1q9RqVSmV0UkJycLH2u0Wig0Wi6\nnMkSnU7HQkJEDsNgMMBgMNi0TYtzKitWrMBLL70ke8Offvop9Ho91q5dCwD48MMPsW/fPuzevRtf\nfvkl3NzcUFVVhdDQUHz//fetQ3OfChFRp9nFPpWXXnoJe/fuRWlpKRoaGqTjc+bM6VLD9957L/bt\n2weTyYQ+ffpg586dmDx5Mvr27YusrCwkJSUhKysLTz75ZJfaISIi27HYU3nmmWdw8uRJBAUFSRsg\nAWDlypVdbjw5ORnZ2dlwdnbG+PHjsXbtWly6dAmxsbEoKyszu6SYPRUios6zi9u0jB07FsXFxXa1\n4ZFFhYio8+xiSbG/vz+qqqqsGoKIiLoHi3MqZ8+ehZ+fHyZPngwXFxcAzdUuJyfH6uGIiMixWCwq\nLUt3b+w22dNQGBER2Y8O3aaltLQUP/30Ex599FFpB3z//v1tka9dnFMhIuo8u5hTWb16NWJiYrBo\n0SIAQHl5OSIjI60aioiIHJPFovL++++joKBA6pn4+vrizJkzVg9GRESOx2JRcXFxkSbogeabQHJO\nhYiI2mOxqDz88MNISUlBXV0d8vPzERMTo/gz6omIyD5ZnKhvbGzEunXrkJeXB6D5pooLFixQtLfC\niXoios6zix319ohFhYio8xS9oWRMTAw+/fRTBAQEtBvs6NGjVg1GRESOx2xPpbKyEsOHD0dpaWm7\nb/T09LRirFtjT4WIqPPsavjr4sWLrW59f/fdd1stlCUsKkREnWcXz1NZtWoVXn/9dbi4uMDJyUkK\ndvLkSasGIyIix2Oxp+Lt7Y19+/ZhyJAhtspkEXsqRESdZxe3aRk1ahRcXV2tGoKIiLoHiz2VoqIi\nPPvsswgJCWl16/sVK1bYJGB72FMhIuo8u5hTWbhwIcLCwhAQEAAnJycIIXibFiIiapfFnkpwcDCK\niopsladD2FMhIuo8u5hTeeyxx7Bq1SpUVVWhurpa+iAiIrqZxZ6Kp6dnu8NdJSUlVgtlCXsqRESd\nZ1ebH+0JiwoRUecpOlG/a9cuhIWFYfPmze32VKKioqwajIiIHI/ZovLVV18hLCwMn332mdWKSm1t\nLRYsWIBjx45BpVJhw4YN8PHxwYwZM/Dzzz/D09MTmzZtwsCBA7vcFhERWZ+iw1/x8fF4+OGHMW/e\nPDQ0NODKlStISUnBkCFD8Morr2Dp0qWoqalBWlpaq/f1pOEvvV6P9PTVAICEhIXQ6XQKJyIiR2WT\na6ew4A9/+IOoqamRvq6urhZ//OMfLb3NotraWuHl5dXm+OjRo8Xp06eFEEJUVVWJ0aNHt3lNB2J3\nC7m5ucLVVS2ATAFkCldXtcjNzVU6FhE5KFtcOy0uKd6+fXur4adBgwbhiy++6HIxKykpwT333IO5\nc+di/PjxeO6553DlyhUYjUao1WoAgFqthtFo7HJbjio9fTVMpqUA4gHEw2RaKvVaiIjskcUd9U1N\nTbh69Sr69OkDADCZTKivr+9yww0NDTh06BDee+89TJo0CUuWLGl3mMvc7v3k5GTpc41GA41G0+VM\nRETdicFggMFgsGmbFudUli5dipycHMybNw9CCGzYsAERERFISkrqUsOnT5/GAw88IO13KSgoQGpq\nKk6ePIkvv/wSbm5uqKqqQmhoKL7//vvWoXvInIper0dkZPwvvRXA1TUJW7ZkcV6FiG6L3exT2bFj\nB3bt2gUA0Gq1sl3Ufv3rX2Pt2rXw9fVFcnIy6urqAACDBw9GUlIS0tLSUFtby4l6TtQTkQzspqiU\nlpbi+PHj0Gq1qKurQ2NjI/r169flxo8cOYIFCxagvr4eo0aNwoYNG9DY2IjY2FiUlZWZXVLck4oK\nEZFc7KKorF69GmvWrEF1dTVOnDiBH3/8Eb/5zW+knosSWFSIiDrPLm4o+f7776OgoAD9+/cHAPj6\n+uLMmTNWDUVERI7JYlFxcXGRHs4FNK/a4vNUiIioPRaLysMPP4yUlBTU1dUhPz8fMTExmD59ui2y\nERGRg7E4p9LY2Ih169YhLy8PAKDT6bBgwQJFeyucUyEi6jy7mKi3RywqRESdp+it7wMCAsy+SaVS\n4ejRo1YJREREjstsT6W0tPSWb/T09LRCnI5hT4WIqPM4/GUGiwoRUecpOvx11113mZ2MV6lUuHjx\notVCERGRY2JPhYioh7CLHfUtzpw5g7KyMumDiKxDr9cjPDwa4eHR0Ov1Ssch6hSLRSUnJwc+Pj7w\n8vLCww8/DE9PTzz22GO2yNYj8YLSs7U87iA/PwL5+RGIjIzneUAOxWJR+dOf/oRvvvkGvr6+KCkp\nwa5duxASEmKLbD0OLyjEp32So7NYVHr16oUhQ4agqakJjY2NCA0NxcGDB22RrcfhBYWIHJ3FxwkP\nGjQIly5dwtSpUzF79mwMHToUd911ly2yEfU4CQkLUVAQD5Op+WtX1yQkJGQpG4qoE8yu/iorK8PI\nkSNx5coV9OnTB01NTfj4449x8eJFzJ49G4MHD7Z1Vkl3Xf3laI8P5lMprYO/V7IWRTc/BgcHo6io\nCAAQHR2NzZs3WzVIZ3TXogI4zgXF0QogESm8+fFGJ0+etGoI+g+dTucQF+bW8z+AydR8zBGyE5H1\ndHifChERkSVmeypHjx5Fv379AAAmk0n6HOBtWogTykTUPt6mhW6bo8z/EFEz3qXYDBYVIqLOs6t7\nfxEREVmieFFpbGxEcHAwpk+fDgCorq6GVquFr68vwsPDUVtbq3BCIiLqqA4VldLSUuzcuRMAUFdX\nJ+sk/fLly+Hn5yc9uyUtLQ1arRY//vgjwsLCkJaWJltbRERkXRaLyurVqxETE4NFixYBAMrLyxEZ\nGSlL4+Xl5di+fTsWLFggjfPl5OQgPr5570N8fDy2bt0qS1tERGR9FovK+++/j4KCAvTv3x8A4Ovr\nizNnzsjS+Msvv4xly5bByek/MYxGI9RqNQBArVbDaDTK0hYREVmfxR31Li4ucHFxkb5uaGgw+5jh\nzvj8888xdOhQBAcHw2AwtPsalUpltq3k5GTpc41GA41G0+VMRETdicFgMHt9tRaLS4oTExMxcOBA\nfPDBB3jvvfeQkZEBPz8/pKSkdKnh1157DR9++CGcnZ1x9epVXLx4EVFRUThw4AAMBgPc3NxQVVWF\n0NBQfP/9961Dc0kxEVGn2cU+lcbGRqxbtw55eXkAmu9NtWDBAll6Ky327NmD//3f/8Vnn32GV155\nBYMHD0ZSUhLS0tJQW1vbZrKeRYWIqPPsoqjYwp49e5Ceno6cnBxUV1cjNjYWZWVl8PT0xKZNmzBw\n4MBWr2dRISLqPEWLSkBAgPk3qVQ4evSo1UJZwqJCRNR5it76/rPPPgMAZGRkAADi4uIghMDHH39s\n1UBEROS4LA5/BQUF4fDhw62O3fgALyWwp0JE1Hl2ce8vIQQKCgqkr7/++mte0ImIqF0W96msX78e\nc+fOxYULFwAAAwcOxIYNG6wejIiIHE+HV3+13Njx5pVYSuDwFxFR5/WYJcWdxaJCRNR5djGnQkRE\n1FEsKkREJBuLRaW+vh7Lly9HdHQ0oqOjsXLlSly/ft0W2UgBer0e4eHRCA+Phl6vVzoOETkYi3Mq\n8+fPR0NDA+Lj4yGEkG4CuXbtWltlbINzKtah1+sRGRkPk2kpAMDVNQlbtmRBp9MpnIyI5GAXE/Xj\nxo1rc0uW9o7ZEouKdYSHRyM/PwJA/C9HsqDV5iAvb7OSsYhIJnYxUe/s7IyffvpJ+vrEiRNwdra4\nvYWIiHogi9Vh2bJleOSRR+Dl5QWg+Xn13PzYPSUkLERBQTxMpuavXV2TkJCQpWwoInIoHdqncu3a\nNfzwww8AgNGjR7d6EqQSOPxlPXq9HunpqwE0FxnOp9g//s2oo+xiTuXKlSv4v//7P5SVlWHNmjU4\nfvw4fvjhB0ybNs2qwW6FRYWoGRdXUGfYxZzK3Llz0bt3b+zduxcAMHz4cPzxj3+0aigi6pj09NW/\nFJR4AM3FpaXXQqQEi0XlxIkTSEpKQu/evQEAffv2tXooInvH/TxE7bM4Ue/i4gJTy8wtmouM0nMq\nREq6ecipoCBesSEnLq4ge2NxTiUvLw8pKSkoLi6GVqvF119/jczMTISGhtoqYxucUyEl2dt+Hk7U\nU0cp+jjhFuHh4Rg/fjz27dsHAFi+fDnuueceq4Yioo7T6XRmCwkLDtmaxZ5KWFgYdu3aZfGYLbGn\nQkpylBVXjpKTbEfRnorJZEJdXR3Onj2L6upq6fjFixdRUVFh1VBE9kyn02HLlqwbegD2eaFuvTIM\nMJmaj9ljVuo+zBaVVatWYfny5aisrMSECROk4/369cMLL7xgk3BE9upWQ05KaxnyKiw8AiBC6TjU\nw1gc/lqxYgVeeukl2Rs+deoU5syZgzNnzkClUmHhwoV46aWXUF1djRkzZuDnn3+Gp6cnNm3a1OYR\nxhz+Impf6yGv7wCsAbACAIe/yE521APA3r17UVpaioaGBunYnDlzutTw6dOncfr0aQQFBeHy5cuY\nMGECtm7dig0bNmDIkCF45ZVXsHTpUtTU1CAtLa11aBYVona1XZn2e9x991ZMmBDIiXqyj9Vfzzzz\nDE6ePImgoCDccccd0vGuFhU3Nze4ubkBAO666y6MHTsWFRUVyMnJwZ49ewAA8fHx0Gg0bYoKEXVU\nACZMKOHjC8hmLBaVwsJCFBcXQ6VSWS1EaWkpioqKEBISAqPRCLVaDQBQq9UwGo1Wa5eou+FmSFKa\nxaLi7++PqqoqDB8+3CoBLl++jOjoaCxfvhz9+vVr9T2VSmW2mCUnJ0ufazQaaDQaq+Qj6+NeCvk4\nyso0sg2DwQCDwWDTNi3OqWg0Ghw+fBiTJ0+Wbs+iUqmQk5PT5cavX7+OadOm4bHHHsOSJUsAAGPG\njIHBYICbmxuqqqoQGhqK77//vnVozql0G9xLQWQ7djGn0tIjaOkxCCFkGQoTQmD+/Pnw8/OTCgoA\nREREICsrC0lJScjKysKTTz7Z5bbIft3uXgolezfsWdmHlr/DuXPnATRgyBA1/x72QHRAVVWVyMnJ\nEZ999pkwGo0deYtF//znP4VKpRKBgYEiKChIBAUFiR07dojz58+LsLAw4ePjI7RaraipqWnz3g7G\nJgeg1UYJIFMA4pePTKHVRt3yPbm5ucLVVf3L+zKFq6ta5Obm2iTvzW337n2PCA7+ldBqo2yWgdr+\nHYAhAkiw6bngiGxx7bTYQnZ2thg5cqSIi4sTcXFx4t577xWbNm2yerBbYVHpPm6nQNxOIZJLe20D\nU2xe3Hq69v8OUTY9FxyRLa6dFoe/3nrrLRw4cABDhw4FAJw9exZhYWGIiYmxag+KeobuMbE8HM0P\nyOJtUIgsFhUhRKu7Eg8ePJiT5CSrzt7yRMllsze3DfwewEc2aZv+o/2/QzyXUNsBi6u/EhMTceTI\nEcyaNQtCCGRnZ2PcuHH4n//5H1tlbIOrvxyHtSa17WGi/ty58zh27Ajq698FwJVrtsaJ+s6zm9u0\nbN68GV9//TUAYOrUqYiMjLRqKEtYVBxDT1guzJVg5EgULSrHjx+H0WjEQw891Op4QUEBhg0bhlGj\nRlk12K2wqDgGe3tCIlFPZ4trp5O5byxZsgT9+/dvc7x///6t9pUQ9QR6vR7h4dEID4+GXq9XOg6R\n3TI7UW80GjFu3Lg2x8eNG4eSkhKrhqLuobvch+rmYbyCgvhuN4xHJBezRaW2ttbsm65evWqVMNS9\ndI/lwnyCIlFnmB3+mjhxIlavXt3m+Jo1a1o9CZLoVnQ6HfLyNiMvb3O3vAhzWIyoNbMT9adPn0Zk\nZCR69+4tFZHCwkJcu3YNW7ZswbBhw2wa9EacqCdbMreKDUC3X91G3YviS4qFEPjyyy/xr3/9CyqV\nCvfffz8eeeQRqwbqCBYVsrX2lg5zdRu1cJSl5YrfpVilUuGRRx6xi0JCt8dRTnZ719ld/9RzcCFH\naxZv00KOiye7dXWX1W3UNVzI0RqLSjfGk926usvqNiI5sagQdUF3HhZTeuhU6fY7ij3Wm1j95vpW\n4KCxbU7Jh1mRY1P63FG6/c7Kzc0VWm2U3T+szRbXzg7dUNLecPVXxznKv/bIvii9sk3p9rsrxVd/\nkePrzsMzRGR/WFSIqA2l5wmUbp9uH4e/iByYNYc3lR46Vbr97kjxHfX2ikWFqGc8BI3kxaJiBosK\nESezqfMUfUgXUU/BOw0Tyccui0pubi7GjBkDHx8fLF26VOk41I21DCHl50cgPz8CkZHxDlNYEhIW\nwtU1CUAWgKxfJrMXKh2Leji7G/5qbGzE6NGjsXPnTri7u2PSpEnYuHEjxo4dK72Gw18kF0cfQuJk\nNnVGj9ynsn//fnh7e8PT0xMA8PTTT2Pbtm2tigoRNeM+JLI3dldUKioqMGLECOlrDw8PfPvttwom\nou6M+yGI5GV3RUWlUnXodcnJydLnGo0GGo3GOoGoW+Odhqk7MxgMMBgMNm3T7uZU9u3bh+TkZOTm\n5gIAUlNT4eTkhKSkJOk1nFMhIuq8HrmkeOLEiTh+/DhKS0tRX1+P7OxsREREKB2LiIg6wO6Gv5yd\nnfHee+9Bp9OhsbER8+fP5yQ9EZGDsLvhr47g8BcRUef1yOEvIiJyXCwqREQkGxYVIiKSDYsKERHJ\nhkWFiIhkw6JCRESyYVEhIiLZsKgQEZFsWFSIiEg2LCpERCQbFhUiIpINiwoREcmGRYWIiGTDokJE\nRLJhUSEiItmwqBARkWxYVIiISDYsKkREJBsWFSIikg2LChERyYZFhYiIZMOiQkREslGkqCQmJmLs\n2LEIDAxEVFQULly4IH0vNTUVPj4+GDNmDPLy8pSIR0REt0mRohIeHo5jx47hyJEj8PX1RWpqKgCg\nuLgY2dnZKC4uRm5uLhYvXoympiYlIt4Wg8GgdIQ2mKljmKnj7DEXM9kPRYqKVquFk1Nz0yEhISgv\nLwcAbNu2DTNnzkSvXr3g6ekJb29v7N+/X4mIt8UeTyJm6hhm6jh7zMVM9kPxOZX169fj8ccfBwBU\nVlbCw8ND+p6HhwcqKiqUikZERJ3kbK0frNVqcfr06TbH3377bUyfPh0AkJKSgt69e2PWrFlmf45K\npbJWRCIikptQyIYNG8SDDz4oTCaTdCw1NVWkpqZKX+t0OrFv37427wXAD37wgx/8uI0Pa1P9cpG2\nqdzcXCQkJGDPnj0YMmSIdLy4uBizZs3C/v37UVFRgUcffRQ//fQTeytERA7CasNft/Liiy+ivr4e\nWq0WAPDAAw8gIyMDfn5+iI2NhZ+fH5ydnZGRkcGCQkTkQBTpqRARUfek+OqvxsZGBAcHS5P31dXV\n0Gq18PX1RXh4OGpra6XXmtsYWVhYiICAAPj4+OC3v/2tdPzatWuYMWMGfHx8MGXKFPz8888W89TW\n1uKpp57C2LFj4efnh2+//VbxTADwzjvvwN/fHwEBAZg1axauXbtm81zz5s2DWq1GQECAdMxWGbKy\nsuDr6wtfX1988MEHt8x0O5trrZ2pRXp6OpycnFBdXW0XmVauXImxY8fC398fSUlJNs1kLtfhw4cx\nZcoUBAcHY9KkSThw4IBNc506dQqhoaG4//774e/vjxUrVgBQ9lw3l0npc71dVp+1sSA9PV3MmjVL\nTJ8+XQghRGJioli6dKkQQoi0tDSRlJQkhBDi2LFjIjAwUNTX14uSkhIxatQo0dTUJIQQYtKkSeLb\nb78VQgjx2GOPiR07dgghhHj//ffFb37zGyGEEJ988omYMWOGxTxz5swR69atE0IIcf36dVFbW6t4\npvLycuHl5SWuXr0qhBAiNjZWZGZm2jzXV199JQ4dOiT8/f2lY7bIcP78eXHfffeJmpoaUVNTI31u\nLlNeXp5obGwUQgiRlJRkF5mEEKKsrEzodDrh6ekpzp8/r3im3bt3i0cffVTU19cLIYQ4c+aMTTOZ\ny6XVakVubq4QQojt27cLjUZj01xVVVWiqKhICCHEpUuXhK+vryguLlb0XDeXSelzvT2KFpVTp06J\nsLAwsXv3bjFt2jQhhBCjR48Wp0+fFkI0/3FHjx4thBDi7bffFmlpadJ7dTqd+Oabb0RlZaUYM2aM\ndHzjxo3SLRCAAAAJ8ElEQVRi0aJF0mtaVo9dv35dDBky5JZ5amtrhZeXV5vjSmYSormojBgxQlRX\nV4vr16+LadOmiby8PEVylZSUtLoA2CLD3/72N/H8889L71m0aJHYuHGj2Uw3+sc//iFmz55tF5me\neuopceTIkVZFRclMMTExYteuXW1+Z7bM1F4unU4nsrOzpfcr8fe70RNPPCHy8/Pt4ly/MdPOnTtb\nHVPqXL+ZosNfL7/8MpYtWybtrgcAo9EItVoNAFCr1TAajQDMb4y8+bi7u7u0YbKiogIjRowAADg7\nO2PAgAGthh1uVlJSgnvuuQdz587F+PHj8dxzz+HKlSuKZmp5f0JCAkaOHInhw4dj4MCB0Gq1iucC\nrP/3On/+fJc2xXZkc60tMm3btg0eHh4YN25cq+NKZjp+/Di++uorTJkyBRqNBgcPHlQ8EwC8++67\nSExMxMiRI5GYmCjdxkmJXKWlpSgqKkJISIjdnOs3ZrqRvZzrihWVzz//HEOHDkVwcDCEmbUCKpXK\npqu/GhoacOjQISxevBiHDh1C3759kZaWpmgmAKipqUFOTg5KS0tRWVmJy5cv46OPPlI8183sIcON\nOrK51hbq6urw9ttv44033pCOmTvnbamhoQE1NTXYt28fli1bhtjYWKUjAQAyMjLw7rvvoqysDO+8\n8w7mzZunSI7Lly8jOjoay5cvR79+/Vp9T6lz/fLly3jqqaewfPly3HXXXdJxeznXAQWLyt69e5GT\nkwMvLy/MnDkTu3fvRlxcHNRqtbQTv6qqCkOHDgXQXFFPnTolvb+8vBweHh5wd3eX7h124/GW95SV\nlQFo/h/owoULuPvuu81m8vDwgIeHByZNmgQAeOqpp3Do0CG4ubkplgkAdu7cCS8vLwwePBjOzs6I\niorCN998o3guAFb/ew0ePLjNzzp16lSrfzm1JzMzE9u3b8fHH38sHVMq04kTJ1BaWorAwEB4eXmh\nvLwcEyZMgNFoVPT35OHhgaioKADApEmT4OTkhHPnzin+t/vggw8QGRkJoPn/wZb7/9ky1/Xr1xEd\nHY24uDg8+eSTAJQ/11syPfPMM1ImwL7OdQDKT9QLIYTBYJDmVBITE6WxwNTU1DYTT9euXRMnT54U\n9913nzTxNHnyZLFv3z7R1NTUZuKpZSxw48aNHZoUnzp1qvjhhx+EEEK8/vrrIjExUfFM3377rbj/\n/vtFXV2daGpqEnPmzBHvvfeeIrluHv+2RYbz588LLy8vUVNTI6qrq6XPzWXasWOH8PPzE2fPnm2V\nXclMN2pvol6JTH/961/Ff//3fwshhPjhhx/EiBEjbJ6pvVxjx44VBoNBCCHEzp07xcSJE22aq6mp\nScTFxYklS5a0yqnkuW4ukz2c6zezm6LSsvrr/PnzIiwsTPj4+AitVtsqfEpKihg1apQYPXq0tDpE\nCCEOHjwo/P39xahRo8SLL74oHb969aqIiYkR3t7eIiQkRJSUlFjMcvjwYTFx4kQxbtw4ERkZKWpr\naxXPJERzgRszZozw9/cXc+bMEfX19TbP9fTTT4thw4aJXr16CQ8PD7F+/XqbZVi/fr3w9vYW3t7e\nIjMz02ymdevWCW9vbzFy5EgRFBQkgoKCpBUtts7Uu3dv6fd0Iy8vL6moKJmpvr5ePPPMM8Lf31+M\nHz9efPnllzbN1N7fb/369aKgoEBMmDBBBAYGiilTpohDhw7ZNNc///lPoVKpRGBgoHQO7dixQ9Fz\nvb1M27dvV/xcbw83PxIRkWwU3/xIRETdB4sKERHJhkWFiIhkw6JCRESyYVEhIiLZsKgQEZFsWFSo\nW7rjjjsQHByMgIAAxMbGwmQyAYB0a4vS0lK4uroiODgYQUFB+NWvfoUff/xRtvavX7+OV199Fb6+\nvpgwYQIefPBB5Obm3tbPWrVqFT788EMAzbunq6qqZMtJJDcWFeqW7rzzThQVFeG7775D79698de/\n/hUAWt2vydvbG0VFRTh8+DDi4+Px9ttvy9J2U1MT/vznP8NoNOLYsWMoLCzE1q1bcenSpdv6eYsW\nLUJcXByA5udaVFZWypKTyBpYVKjbe+ihh3DixIlbvubGe51lZmbiiSeeQGhoKHx9ffHmm29Kr/vo\no48QEhKC4OBgPP/882hqagLQ3AP6/e9/j6CgIHz99ddYu3YtVq5ciV69egEAhg4dipiYGADA4sWL\nMWnSJPj7+yM5OVn62Z6enkhKSsK4ceMQEhIiZU5OTkZ6ejo2b96MgwcPYvbs2Rg/fjyuXr2KN998\nE5MnT0ZAQAAWLVok2++M6HaxqFC31tDQgB07drT7FMYTJ04gODgY3t7eeOedd/C73/1O+t6BAwfw\nj3/8A0ePHsWnn36KwsJC/Pvf/8amTZuwd+9eFBUVwcnJSbqJX11dHaZMmYLDhw9jwIABGDlyZKu7\nyN4oJSUFBw4cwJEjR7Bnzx7861//AtDcixo4cCCOHj2KF154AUuWLJGOq1QqREdHY+LEifjb3/6G\nQ4cOoU+fPnjxxRexf/9+fPfddzCZTPj888/l/hUSdYqz0gGIrMFkMiE4OBgA8Otf/xrz589v85pR\no0ahqKgIALBp0yY899xz2LFjBwAgPDwcgwYNAgBERUWhoKAAd9xxBwoLCzFx4kSpDTc3NwDNczjR\n0dEdypadnY01a9agoaEBVVVVKC4uhr+/PwBg5syZAICnn34aL7/8crvvv/HOSrt378ayZctQV1eH\n6upq3H///Zg2bVqHchBZA4sKdUuurq5SweiI6dOnY+7cue1+TwghzcWYm3vp06eP9Bpvb2+UlZXh\n0qVLbZ7DUVJSgvT0dBw8eBADBgzA3LlzcfXq1XbbNfe8jpbjV69exX/913+hsLAQ7u7ueOONN8z+\nLCJb4fAXEYCCggJ4e3tLX+fn56OmpgYmkwnbtm3DQw89hLCwMPz973/H2bNnAQDV1dXS8ydudOed\nd2L+/Pn47W9/i+vXrwMAzp49i7///e+4dOkS+vbti/79+8NoNEo9oxbZ2dnSfx988EEAzUWtpXfS\nr18/XLx4EQCkAjJ48GBcvnwZn376qV09JI16JvZUqFuy9K984D9zKkIIuLi4YO3atdJrJk+ejOjo\naJSXlyMuLg7jx48HALz11lsIDw9HU1MTevXqhYyMDIwcObJNe2+99Rb+9Kc/wc/PD3369EHfvn3x\nl7/8BePGjUNwcDDGjBmDESNG4KGHHmr1vpqaGgQGBqJPnz7YuHGjlKfl5z/77LN4/vnnceedd2Lv\n3r147rnn4O/vDzc3tzaPlyVSAm99T3STzMxMFBYWYuXKlTZt18vLC4WFhR164iaRveLwF9FNlHr+\nOIeuqDtgT4WIiGTDngoREcmGRYWIiGTDokJERLJhUSEiItmwqBARkWxYVIiISDb/D1kNmT3zz/er\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb924c77d10>"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "from sklearn import datasets, linear_model  \n",
      "\n",
      "#Generar una lista con los valores para x y y\n",
      "x_list = list(df_lower[u\"PIBperCapita\"])\n",
      "y_list = list(df_lower[u\"Conteo de Fallecimientos\"])\n",
      "#Imprimamos la regresi\u00f3n lineal\n",
      "x = np.array(x_list).reshape(len(x_list),1)\n",
      "y = np.array(y_list).reshape(len(y_list),1)\n",
      "\n",
      "regr = linear_model.LinearRegression()\n",
      "regr.fit(x,y)\n",
      "\n",
      "# Dibujar la gr\u00e1fica de la correlaci\u00f3n.\n",
      "plt.scatter(x, y,  color='black')\n",
      "plt.plot(x, regr.predict(x), color='blue', linewidth=3)\n",
      "plt.xticks(())\n",
      "plt.yticks(())\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAADtCAYAAAAcNaZ2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nogsAhoguABgiugBg6P8RADtgtP6RjAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb924b47b90>"
       ]
      }
     ],
     "prompt_number": 155
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numpy.corrcoef(x_list, y_list)[0][1]\n",
      "#R2, que es el indice de correlaci\u00f3n, da negativo. A mayor PIBperCapita, menor conteo. Lo cual tiene sentido."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 164,
       "text": [
        "-0.50383550317970494"
       ]
      }
     ],
     "prompt_number": 164
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Nuestro Primer modelo de Predicci\u00f3n Por texto"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vamos a intentar predecir, mediante el texto encontrado en el padecimiento, cual es el Estado en el que pudo ocurrir con mayor probabilidad dicho caso."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mortalidad[u\"Descrici\u00f3n de la causa CIE\"].value_counts().head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 169,
       "text": [
        "O150  Eclampsia en el embarazo                                                                            1264\n",
        "O141  Preeclampsia severa                                                                                 1220\n",
        "O720  Hemorragia del tercer per\u00edodo del parto                                                             1056\n",
        "O721  Otras hemorragias postparto inmediatas                                                              1008\n",
        "O998  Otras enfermedades especificadas y afecciones que complican el embarazo, el parto y el puerperio     781\n",
        "Name: Descrici\u00f3n de la causa CIE, dtype: int64"
       ]
      }
     ],
     "prompt_number": 169
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Sintaxis\n",
      "Python usa sangr\u00eda para separar instrucciones(espacios y tabulaciones) en lugar de llaves, el est\u00e1ndar es de 4 espacios."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = 1\n",
      "if x == 1:\n",
      "    # 4 espacios (1 tab)\n",
      "    print(\"x es 1\")\n",
      "else:\n",
      "    print(\"x no es 1\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "x es 1\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Variables\n",
      " - Python permite guardar nuevos valores en una variable al asign\u00e1rselo.\n",
      " - Cada variable en Python es un objeto."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Tipos de datos\n",
      "#### Tipos Num\u00e9ricos\n",
      "Hay tres tipos num\u00e9ricos: enteros, n\u00fameros de punto flotante, y los n\u00fameros complejos."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mi_int = 1 #Un numero entero\n",
      "mi_float = 7.0 #Un n\u00famero con decimales\n",
      "mi_string = \"Una frase matona\" #un texto \n",
      "\n",
      "type(mi_float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "float"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Tipos secuenciales\n",
      " - Listas: Pueden contener cualquier tipo de variable, y pueden contener tantas variables como se desee."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mi_lista = []\n",
      "mi_lista.append(1)\n",
      "mi_lista.append(\"Monty\")\n",
      "mi_lista.append(True)\n",
      "\n",
      "print(mi_lista)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 'Monty', True]\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      " - Tuplas: A diferencia de las listas, las tuplas no se pueden cambiar y utilizan par\u00e9ntesis."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mi_tupla = (1, \"Monty\", True);\n",
      "print(mi_tupla)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1, 'Monty', True)\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      " - Rangos: Los rangos representan una secuencia de n\u00fameros"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "list(range(-5, 30, 5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "[-5, 0, 5, 10, 15, 20, 25]"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "list(range(10))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Tipo String\n",
      "El texto en Python se maneja con objetos str (strings), o cadenas. Se pueden escribir de distintas maneras:\n",
      "\n",
      "\u00a1Importante!: En python no existe el tipo \"char\", un s\u00f3lo car\u00e1cter es un string de tama\u00f1o 1"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "comillas_simples = 'permite usar \"comillas\" dobles'\n",
      "comillas_dobles = \"permite usar 'comillas' simples\"\n",
      "comillas_triples = '''permite correrse por varias lineas'''\n",
      "\n",
      "print(comillas_simples[1:5])\n",
      "\n",
      "comillas_simples.upper()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ermi\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "'PERMITE USAR \"COMILLAS\" DOBLES'"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Tipo Mapping\n",
      "- Diccionario: Un diccionario funciona con claves y valores en lugar de \u00edndices. Cada valor almacenado en un diccionario se puede acceder mediante una clave, es decir, cualquier tipo de objeto (una cadena, un n\u00famero, una lista, etc.) en lugar de utilizar su \u00edndice de abordarlo."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mi_diccionario = {\n",
      "    \"Nombre\" : \"Ricardo D.\",\n",
      "    \"Edad\" : 28,\n",
      "    \"Apellido\" : \"Alanis\"\n",
      "}\n",
      "\n",
      "mi_diccionario[\"Nombre\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "'Ricardo D.'"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Tipo Booleano"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mi_boolean = True\n",
      "mi_boolean = False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Operadores\n",
      "\n",
      "#### Operaciones aritm\u00e9ticas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "numero = 3 + 4 - 2 * 5 / 2 ** 9 % 1\n",
      "print(numero)\n",
      "\n",
      "import math\n",
      "\n",
      "math.sqrt(numero)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "7\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "2.6457513110645907"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Operaciones con strings"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "texto = comillas_simples + \" \" + comillas_dobles\n",
      "print(texto)\n",
      "\n",
      "texto = comillas_simples * 2\n",
      "print(texto)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "permite usar \"comillas\" dobles permite usar 'comillas' simples\n",
        "permite usar \"comillas\" doblespermite usar \"comillas\" dobles\n"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Operaciones con listas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bajos = [1,2,3,4,5]\n",
      "altos = [6,7,8,9,10]\n",
      "numeros = bajos + altos\n",
      "print(numeros)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Operaciones booleanas"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "variable_or = True or False\n",
      "variable_and = True and False\n",
      "variable_not = not False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Comparadores"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "5 < 6 #Menor que"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "5 <= 6 #Menor o igual a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "6 > 5 #Mayor que"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "6 >= 5 #Mayor o igual a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "6 == 6 #Igual a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "6 != 5 #Diferente a (No igual a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = [1,2,3]\n",
      "y = [1,2,3]\n",
      "print(x == y)\n",
      "print(x is y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "True\n",
        "False\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x is not None"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### El operador \"in\"\n",
      "El operador \"in\" se utiliza para comprobar si existe un objeto especificado dentro de un objeto iterable, tal como una lista:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"Carl\" in [\"Carl\", \"Edo\", \"Rick\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Control de flujo\n",
      "#### If\n",
      "\"If\" se utiliza para la ejecuci\u00f3n condicional, es decir, \"If\" ejecuta alg\u00fan c\u00f3digo espec\u00edfico despu\u00e9s de comprobar si su expresi\u00f3n es verdadera."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = 5\n",
      "\n",
      "if x < 0:\n",
      "    x = 0\n",
      "    print('Negative changed to zero')\n",
      "elif x == 0:\n",
      "    print('Zero')\n",
      "elif x == 1:\n",
      "    print('Single')\n",
      "else:\n",
      "    print('More')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "More\n"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### For\n",
      "For se utiliza para iterar sobre los elementos de una secuencia (como una cadena, tupla o lista) u otro objeto iterable:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for numero in numeros[1:3]:\n",
      "    print(numero)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2\n",
        "3\n"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### While\n",
      "While se utiliza para la ejecuci\u00f3n repetida, siempre y cuando se cumpla alguna condici\u00f3n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "contador = 0\n",
      "while contador < 3:\n",
      "    print(contador)\n",
      "    contador += 1  #contador = contador + 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0\n",
        "1\n",
        "2\n"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Funciones\n",
      "\n",
      "Las funciones son una manera conveniente dividir el c\u00f3digo en bloques \u00fatiles, lo que nos permite pedir nuestro c\u00f3digo, que sea m\u00e1s legible, reutilizar y ahorrar algo de tiempo. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def hola_usuario(name, last_name, greeting=\"Merry Christmas\"):\n",
      "    print \"Hello, %s %s , From My Function!, I wish you %s\" %(name, last_name, greeting)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hola_usuario(\"Ricardo\", \"Alanis\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Hello, Ricardo Alanis , From My Function!, I wish you Merry Christmas\n"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### M\u00e9todos\n",
      "Los m\u00e9todos son funciones que pertenecen a cierto objeto"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mi_lista = [1,2,3,\"hola\",5]\n",
      "mi_lista.count(\"hola\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "1"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Los m\u00e9todos pueden regresar o modificar el objeto\n",
      "mi_lista.append(\"Nuevo\")\n",
      "print(mi_lista)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[1, 2, 3, 'hola', 5, 'Nuevo']\n"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### M\u00f3dulos y paquetes\n",
      "- Los m\u00f3dulos en Python son archivos de Python con la extensi\u00f3n .py, que tienen dentro algunas funciones preparadas. \n",
      "- Los m\u00f3dulos se importan utilizando el comando de 'import'\n",
      "\n",
      "Librer\u00edas est\u00e1ndar de python:\n",
      "https://docs.python.org/2/library/"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "help(math)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Help on module math:\n",
        "\n",
        "NAME\n",
        "    math\n",
        "\n",
        "FILE\n",
        "    /opt/anaconda/envs/np18py27-1.9/lib/python2.7/lib-dynload/math.so\n",
        "\n",
        "MODULE DOCS\n",
        "    http://docs.python.org/library/math\n",
        "\n",
        "DESCRIPTION\n",
        "    This module is always available.  It provides access to the\n",
        "    mathematical functions defined by the C standard.\n",
        "\n",
        "FUNCTIONS\n",
        "    acos(...)\n",
        "        acos(x)\n",
        "        \n",
        "        Return the arc cosine (measured in radians) of x.\n",
        "    \n",
        "    acosh(...)\n",
        "        acosh(x)\n",
        "        \n",
        "        Return the inverse hyperbolic cosine of x.\n",
        "    \n",
        "    asin(...)\n",
        "        asin(x)\n",
        "        \n",
        "        Return the arc sine (measured in radians) of x.\n",
        "    \n",
        "    asinh(...)\n",
        "        asinh(x)\n",
        "        \n",
        "        Return the inverse hyperbolic sine of x.\n",
        "    \n",
        "    atan(...)\n",
        "        atan(x)\n",
        "        \n",
        "        Return the arc tangent (measured in radians) of x.\n",
        "    \n",
        "    atan2(...)\n",
        "        atan2(y, x)\n",
        "        \n",
        "        Return the arc tangent (measured in radians) of y/x.\n",
        "        Unlike atan(y/x), the signs of both x and y are considered.\n",
        "    \n",
        "    atanh(...)\n",
        "        atanh(x)\n",
        "        \n",
        "        Return the inverse hyperbolic tangent of x.\n",
        "    \n",
        "    ceil(...)\n",
        "        ceil(x)\n",
        "        \n",
        "        Return the ceiling of x as a float.\n",
        "        This is the smallest integral value >= x.\n",
        "    \n",
        "    copysign(...)\n",
        "        copysign(x, y)\n",
        "        \n",
        "        Return x with the sign of y.\n",
        "    \n",
        "    cos(...)\n",
        "        cos(x)\n",
        "        \n",
        "        Return the cosine of x (measured in radians).\n",
        "    \n",
        "    cosh(...)\n",
        "        cosh(x)\n",
        "        \n",
        "        Return the hyperbolic cosine of x.\n",
        "    \n",
        "    degrees(...)\n",
        "        degrees(x)\n",
        "        \n",
        "        Convert angle x from radians to degrees.\n",
        "    \n",
        "    erf(...)\n",
        "        erf(x)\n",
        "        \n",
        "        Error function at x.\n",
        "    \n",
        "    erfc(...)\n",
        "        erfc(x)\n",
        "        \n",
        "        Complementary error function at x.\n",
        "    \n",
        "    exp(...)\n",
        "        exp(x)\n",
        "        \n",
        "        Return e raised to the power of x.\n",
        "    \n",
        "    expm1(...)\n",
        "        expm1(x)\n",
        "        \n",
        "        Return exp(x)-1.\n",
        "        This function avoids the loss of precision involved in the direct evaluation of exp(x)-1 for small x.\n",
        "    \n",
        "    fabs(...)\n",
        "        fabs(x)\n",
        "        \n",
        "        Return the absolute value of the float x.\n",
        "    \n",
        "    factorial(...)\n",
        "        factorial(x) -> Integral\n",
        "        \n",
        "        Find x!. Raise a ValueError if x is negative or non-integral.\n",
        "    \n",
        "    floor(...)\n",
        "        floor(x)\n",
        "        \n",
        "        Return the floor of x as a float.\n",
        "        This is the largest integral value <= x.\n",
        "    \n",
        "    fmod(...)\n",
        "        fmod(x, y)\n",
        "        \n",
        "        Return fmod(x, y), according to platform C.  x % y may differ.\n",
        "    \n",
        "    frexp(...)\n",
        "        frexp(x)\n",
        "        \n",
        "        Return the mantissa and exponent of x, as pair (m, e).\n",
        "        m is a float and e is an int, such that x = m * 2.**e.\n",
        "        If x is 0, m and e are both 0.  Else 0.5 <= abs(m) < 1.0.\n",
        "    \n",
        "    fsum(...)\n",
        "        fsum(iterable)\n",
        "        \n",
        "        Return an accurate floating point sum of values in the iterable.\n",
        "        Assumes IEEE-754 floating point arithmetic.\n",
        "    \n",
        "    gamma(...)\n",
        "        gamma(x)\n",
        "        \n",
        "        Gamma function at x.\n",
        "    \n",
        "    hypot(...)\n",
        "        hypot(x, y)\n",
        "        \n",
        "        Return the Euclidean distance, sqrt(x*x + y*y).\n",
        "    \n",
        "    isinf(...)\n",
        "        isinf(x) -> bool\n",
        "        \n",
        "        Check if float x is infinite (positive or negative).\n",
        "    \n",
        "    isnan(...)\n",
        "        isnan(x) -> bool\n",
        "        \n",
        "        Check if float x is not a number (NaN).\n",
        "    \n",
        "    ldexp(...)\n",
        "        ldexp(x, i)\n",
        "        \n",
        "        Return x * (2**i).\n",
        "    \n",
        "    lgamma(...)\n",
        "        lgamma(x)\n",
        "        \n",
        "        Natural logarithm of absolute value of Gamma function at x.\n",
        "    \n",
        "    log(...)\n",
        "        log(x[, base])\n",
        "        \n",
        "        Return the logarithm of x to the given base.\n",
        "        If the base not specified, returns the natural logarithm (base e) of x.\n",
        "    \n",
        "    log10(...)\n",
        "        log10(x)\n",
        "        \n",
        "        Return the base 10 logarithm of x.\n",
        "    \n",
        "    log1p(...)\n",
        "        log1p(x)\n",
        "        \n",
        "        Return the natural logarithm of 1+x (base e).\n",
        "        The result is computed in a way which is accurate for x near zero.\n",
        "    \n",
        "    modf(...)\n",
        "        modf(x)\n",
        "        \n",
        "        Return the fractional and integer parts of x.  Both results carry the sign\n",
        "        of x and are floats.\n",
        "    \n",
        "    pow(...)\n",
        "        pow(x, y)\n",
        "        \n",
        "        Return x**y (x to the power of y).\n",
        "    \n",
        "    radians(...)\n",
        "        radians(x)\n",
        "        \n",
        "        Convert angle x from degrees to radians.\n",
        "    \n",
        "    sin(...)\n",
        "        sin(x)\n",
        "        \n",
        "        Return the sine of x (measured in radians).\n",
        "    \n",
        "    sinh(...)\n",
        "        sinh(x)\n",
        "        \n",
        "        Return the hyperbolic sine of x.\n",
        "    \n",
        "    sqrt(...)\n",
        "        sqrt(x)\n",
        "        \n",
        "        Return the square root of x.\n",
        "    \n",
        "    tan(...)\n",
        "        tan(x)\n",
        "        \n",
        "        Return the tangent of x (measured in radians).\n",
        "    \n",
        "    tanh(...)\n",
        "        tanh(x)\n",
        "        \n",
        "        Return the hyperbolic tangent of x.\n",
        "    \n",
        "    trunc(...)\n",
        "        trunc(x:Real) -> Integral\n",
        "        \n",
        "        Truncates x to the nearest Integral toward 0. Uses the __trunc__ magic method.\n",
        "\n",
        "DATA\n",
        "    e = 2.718281828459045\n",
        "    pi = 3.141592653589793\n",
        "\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Los paquetes son colecciones de m\u00f3dulos (puedes imaginarlos como un directorio)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Python tiene varios paquetes(tambien llamadas librer\u00edas) que ayudan a analizar datos:\n",
      "- NumPy: Para c\u00e1lculo cient\u00edfico\n",
      "- Scipy: Para an\u00e1lisis de datos\n",
      "- Jupyter (Ipython): Esta interfaz para trabajo\n",
      "- matplotlib: Gr\u00e1fica \n",
      "- pandas: An\u00e1lisis de datos"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}