Manipulación y visualización de datos (básicas).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Manipulación y visualización de datos (básicas)\n",
    "\n",
    "A lo largo de este Jupyter Notebook explicaré algunos básicos sobre manipulación y visualización de datos con Python. Los tópicos específicos a cubrir en este cuaderno interactivo son los siguientes:\n",
    "\n",
    "> - Formatos de datos\n",
    "> - Cargar una base de datos\n",
    "> - Extraer datos de una base\n",
    "> - Estadísticos descriptivos\n",
    "> - Visualizaciones básicas\n",
    "\n",
    "## Repaso rápido a formatos de datos...\n",
    "\n",
    "Puedes consultar la siguiente referencia: <https://5stardata.info/es/>\n",
    "\n",
    "\n",
    "## Visualizando archivos de excel o separados por comas\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cargando una base de datos\n",
    "\n",
    "Para cargar una base de datos, utilizaremos un paquete de Python que resulta muy útil y eficiente, llamado [Pandas](https://pandas.pydata.org). Dado que a los datos podemos encontrarlos en la misma ruta que este cuaderno interactivo, bastará sólo usar la función `.read_excel()` de Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "\n",
    "nombre_de_archivo = 'distritos-locales-electorales.xlsx' \n",
    "dataframe = pandas.read_excel(nombre_de_archivo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos imprimir toda la base de datos llamando a la variable `dataframe`, o acceder sólo al inicio (o final) de los datos con `.head()` (o `.tail()`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Seccion</th>\n",
       "      <th>ClaveMPIO</th>\n",
       "      <th>Municipio</th>\n",
       "      <th>Distrito Local</th>\n",
       "      <th>Cabecera Distrital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Seccion  ClaveMPIO Municipio  Distrito Local Cabecera Distrital\n",
       "0        1          1   Abasolo              18            Pénjamo\n",
       "1        2          1   Abasolo              18            Pénjamo\n",
       "2        3          1   Abasolo              18            Pénjamo\n",
       "3        4          1   Abasolo              18            Pénjamo\n",
       "4        5          1   Abasolo              18            Pénjamo"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tanto `.head()` como `.head()` de manera estándar retorna 5 elementos de la tabla de datos, sin embargo pueden recibir como argumento un número entero para devolver esa cantidad de elementos.\n",
    "\n",
    "#### Ejercicio:\n",
    "\n",
    "Haz que las funciones ya mecnionadas devuelvan una cantidad distinta de elementos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Seccion</th>\n",
       "      <th>ClaveMPIO</th>\n",
       "      <th>Municipio</th>\n",
       "      <th>Distrito Local</th>\n",
       "      <th>Cabecera Distrital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "      <td>18</td>\n",
       "      <td>Pénjamo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Seccion  ClaveMPIO Municipio  Distrito Local Cabecera Distrital\n",
       "0        1          1   Abasolo              18            Pénjamo\n",
       "1        2          1   Abasolo              18            Pénjamo\n",
       "2        3          1   Abasolo              18            Pénjamo\n",
       "3        4          1   Abasolo              18            Pénjamo\n",
       "4        5          1   Abasolo              18            Pénjamo\n",
       "5        6          1   Abasolo              18            Pénjamo\n",
       "6        7          1   Abasolo              18            Pénjamo\n",
       "7        8          1   Abasolo              18            Pénjamo\n",
       "8        9          1   Abasolo              18            Pénjamo\n",
       "9       10          1   Abasolo              18            Pénjamo"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO.\n",
    "# Haz que del dataframe .head() devuelva los primeros 10 elementos\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Seccion</th>\n",
       "      <th>ClaveMPIO</th>\n",
       "      <th>Municipio</th>\n",
       "      <th>Distrito Local</th>\n",
       "      <th>Cabecera Distrital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>3139</td>\n",
       "      <td>3153</td>\n",
       "      <td>20</td>\n",
       "      <td>León</td>\n",
       "      <td>4</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3140</td>\n",
       "      <td>3154</td>\n",
       "      <td>20</td>\n",
       "      <td>León</td>\n",
       "      <td>4</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Seccion  ClaveMPIO Municipio  Distrito Local Cabecera Distrital\n",
       "3139     3153         20      León               4               León\n",
       "3140     3154         20      León               4               León"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO.\n",
    "# Ahora haz que del dataframe .tail() devuelva los ultimos 2 elementos\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extraer datos de una base\n",
    "\n",
    "Para extraer las cabeceras de una tabla de datos, podemos convertir a lista una tabla misma:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Seccion', 'ClaveMPIO', 'Municipio', 'Distrito Local', 'Cabecera Distrital']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cabeceras = list(dataframe)\n",
    "cabeceras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Si quisiéramos extraer una columna completa de una tabla de datos, podemos utilizar la cabecera como sigue:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Municipio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Abasolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Abasolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Abasolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Abasolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Abasolo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3136</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3137</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3138</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3139</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3140</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3141 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Municipio\n",
       "0      Abasolo\n",
       "1      Abasolo\n",
       "2      Abasolo\n",
       "3      Abasolo\n",
       "4      Abasolo\n",
       "...        ...\n",
       "3136      León\n",
       "3137      León\n",
       "3138      León\n",
       "3139      León\n",
       "3140      León\n",
       "\n",
       "[3141 rows x 1 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "municipio = dataframe[[ 'Municipio' ]]\n",
    "municipio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De aquí podemos observar que la extracción de objetos conserva la estructura de la misma tabla de datos, por lo que para transofrmar los valores a formato de lista, utilizamos el atributo `.values`.\n",
    "\n",
    "Una vez transformado en lista podemos utilizar indexación como vimos previamente."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['Abasolo'],\n",
       "       ['Abasolo'],\n",
       "       ['Abasolo'],\n",
       "       ...,\n",
       "       ['León'],\n",
       "       ['León'],\n",
       "       ['León']], dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "municipio.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Puedes filtrar datos utilizando condicionales dentro de los corchetes de selección. \n",
    "\n",
    "#### Ejemplo:\n",
    "\n",
    "Si quisiéramos filtrar a las secciones que pertenezcan al municipio de Guanajuato, hacemos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>ClaveMPIO</th>\n",
       "      <th>Municipio</th>\n",
       "      <th>Distrito Local</th>\n",
       "      <th>Cabecera Distrital</th>\n",
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       "      <td>819</td>\n",
       "      <td>823</td>\n",
       "      <td>15</td>\n",
       "      <td>Guanajuato</td>\n",
       "      <td>8</td>\n",
       "      <td>Guanajuato</td>\n",
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       "    <tr>\n",
       "      <td>820</td>\n",
       "      <td>824</td>\n",
       "      <td>15</td>\n",
       "      <td>Guanajuato</td>\n",
       "      <td>8</td>\n",
       "      <td>Guanajuato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>821</td>\n",
       "      <td>825</td>\n",
       "      <td>15</td>\n",
       "      <td>Guanajuato</td>\n",
       "      <td>8</td>\n",
       "      <td>Guanajuato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>822</td>\n",
       "      <td>826</td>\n",
       "      <td>15</td>\n",
       "      <td>Guanajuato</td>\n",
       "      <td>8</td>\n",
       "      <td>Guanajuato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>823</td>\n",
       "      <td>827</td>\n",
       "      <td>15</td>\n",
       "      <td>Guanajuato</td>\n",
       "      <td>8</td>\n",
       "      <td>Guanajuato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>907</td>\n",
       "      <td>911</td>\n",
       "      <td>15</td>\n",
       "      <td>Guanajuato</td>\n",
       "      <td>8</td>\n",
       "      <td>Guanajuato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>908</td>\n",
       "      <td>912</td>\n",
       "      <td>15</td>\n",
       "      <td>Guanajuato</td>\n",
       "      <td>8</td>\n",
       "      <td>Guanajuato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>909</td>\n",
       "      <td>913</td>\n",
       "      <td>15</td>\n",
       "      <td>Guanajuato</td>\n",
       "      <td>8</td>\n",
       "      <td>Guanajuato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>910</td>\n",
       "      <td>914</td>\n",
       "      <td>15</td>\n",
       "      <td>Guanajuato</td>\n",
       "      <td>8</td>\n",
       "      <td>Guanajuato</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>911</td>\n",
       "      <td>915</td>\n",
       "      <td>15</td>\n",
       "      <td>Guanajuato</td>\n",
       "      <td>8</td>\n",
       "      <td>Guanajuato</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Seccion  ClaveMPIO   Municipio  Distrito Local Cabecera Distrital\n",
       "819      823         15  Guanajuato               8         Guanajuato\n",
       "820      824         15  Guanajuato               8         Guanajuato\n",
       "821      825         15  Guanajuato               8         Guanajuato\n",
       "822      826         15  Guanajuato               8         Guanajuato\n",
       "823      827         15  Guanajuato               8         Guanajuato\n",
       "..       ...        ...         ...             ...                ...\n",
       "907      911         15  Guanajuato               8         Guanajuato\n",
       "908      912         15  Guanajuato               8         Guanajuato\n",
       "909      913         15  Guanajuato               8         Guanajuato\n",
       "910      914         15  Guanajuato               8         Guanajuato\n",
       "911      915         15  Guanajuato               8         Guanajuato\n",
       "\n",
       "[93 rows x 5 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe[dataframe['Municipio'] == 'Guanajuato']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ejercicio:\n",
    "\n",
    "Filtra elementos de acuerdo a al distrito, específicamente si es `'1'`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Seccion</th>\n",
       "      <th>ClaveMPIO</th>\n",
       "      <th>Municipio</th>\n",
       "      <th>Distrito Local</th>\n",
       "      <th>Cabecera Distrital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>498</td>\n",
       "      <td>500</td>\n",
       "      <td>7</td>\n",
       "      <td>Celaya</td>\n",
       "      <td>16</td>\n",
       "      <td>Celaya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>499</td>\n",
       "      <td>501</td>\n",
       "      <td>7</td>\n",
       "      <td>Celaya</td>\n",
       "      <td>16</td>\n",
       "      <td>Celaya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>500</td>\n",
       "      <td>502</td>\n",
       "      <td>7</td>\n",
       "      <td>Celaya</td>\n",
       "      <td>16</td>\n",
       "      <td>Celaya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>501</td>\n",
       "      <td>503</td>\n",
       "      <td>7</td>\n",
       "      <td>Celaya</td>\n",
       "      <td>16</td>\n",
       "      <td>Celaya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>502</td>\n",
       "      <td>504</td>\n",
       "      <td>7</td>\n",
       "      <td>Celaya</td>\n",
       "      <td>16</td>\n",
       "      <td>Celaya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3136</td>\n",
       "      <td>3150</td>\n",
       "      <td>20</td>\n",
       "      <td>León</td>\n",
       "      <td>4</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3137</td>\n",
       "      <td>3151</td>\n",
       "      <td>20</td>\n",
       "      <td>León</td>\n",
       "      <td>4</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3138</td>\n",
       "      <td>3152</td>\n",
       "      <td>20</td>\n",
       "      <td>León</td>\n",
       "      <td>4</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3139</td>\n",
       "      <td>3153</td>\n",
       "      <td>20</td>\n",
       "      <td>León</td>\n",
       "      <td>4</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3140</td>\n",
       "      <td>3154</td>\n",
       "      <td>20</td>\n",
       "      <td>León</td>\n",
       "      <td>4</td>\n",
       "      <td>León</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2643 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Seccion  ClaveMPIO Municipio  Distrito Local Cabecera Distrital\n",
       "498       500          7    Celaya              16             Celaya\n",
       "499       501          7    Celaya              16             Celaya\n",
       "500       502          7    Celaya              16             Celaya\n",
       "501       503          7    Celaya              16             Celaya\n",
       "502       504          7    Celaya              16             Celaya\n",
       "...       ...        ...       ...             ...                ...\n",
       "3136     3150         20      León               4               León\n",
       "3137     3151         20      León               4               León\n",
       "3138     3152         20      León               4               León\n",
       "3139     3153         20      León               4               León\n",
       "3140     3154         20      León               4               León\n",
       "\n",
       "[2643 rows x 5 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TODO.\n",
    "# Filtra elementos con la condición 'rural' que pertenezcan a la columna 'ZONA'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estadísticos descriptivos\n",
    "\n",
    "De manera muy general, Pandas ya puede entregarnos un resumen estadístico descriptivo utilizando funciones como `.count()` o `.describe()`. \n",
    "\n",
    "Dichas descripciones contienen elementos estadísticos como la media, mímimos, máximos, [percentiles](https://es.wikipedia.org/wiki/Percentil) y [desviación estándar](https://es.wikipedia.org/wiki/Desviación_típica)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Seccion               3141\n",
       "ClaveMPIO             3141\n",
       "Municipio             3141\n",
       "Distrito Local        3141\n",
       "Cabecera Distrital    3141\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Seccion</th>\n",
       "      <th>ClaveMPIO</th>\n",
       "      <th>Distrito Local</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>3141.000000</td>\n",
       "      <td>3141.000000</td>\n",
       "      <td>3141.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>1577.222222</td>\n",
       "      <td>20.342884</td>\n",
       "      <td>11.980261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>911.064599</td>\n",
       "      <td>11.131892</td>\n",
       "      <td>6.444639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>1578.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>2365.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>3154.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Seccion    ClaveMPIO  Distrito Local\n",
       "count  3141.000000  3141.000000     3141.000000\n",
       "mean   1577.222222    20.342884       11.980261\n",
       "std     911.064599    11.131892        6.444639\n",
       "min       1.000000     1.000000        1.000000\n",
       "25%     789.000000    14.000000        7.000000\n",
       "50%    1578.000000    20.000000       12.000000\n",
       "75%    2365.000000    27.000000       18.000000\n",
       "max    3154.000000    46.000000       22.000000"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agrupando datos\n",
    "\n",
    "Leeremos el mismo excel cambiaremos los nombres de las cabeceras y agruparemos información relevante"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "distrito_local\n",
       "1     153\n",
       "2     158\n",
       "3      88\n",
       "4     115\n",
       "5      99\n",
       "6     126\n",
       "7     206\n",
       "8     128\n",
       "9     146\n",
       "10    148\n",
       "11    109\n",
       "12    140\n",
       "13    128\n",
       "14    154\n",
       "15    171\n",
       "16    111\n",
       "17    107\n",
       "18    185\n",
       "19    156\n",
       "20    212\n",
       "21     89\n",
       "22    212\n",
       "dtype: int64"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Cambiar el nombre de los headers\n",
    "otro_dataframe = pandas.read_excel('distritos-locales-electorales.xlsx', names=['seccion', 'clave', 'municipio', 'distrito_local', 'cabecera_distrital'])\n",
    "data = otro_dataframe.groupby(['distrito_local']).size()\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## JSON DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "objeto_json = \"\"\"\n",
    "{\"name\": \"Wes\",\n",
    " \"places_lived\": [\"United States\", \"Spain\", \"Germany\"],\n",
    " \"pet\": null,\n",
    " \"siblings\": [{\"name\": \"Scott\", \"age\": 30, \"pets\": [\"Zeus\", \"Zuko\"]},\n",
    "              {\"name\": \"Katie\", \"age\": 38,\n",
    "               \"pets\": [\"Sixes\", \"Stache\", \"Cisco\"]}]\n",
    "}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Wes',\n",
       " 'places_lived': ['United States', 'Spain', 'Germany'],\n",
       " 'pet': None,\n",
       " 'siblings': [{'name': 'Scott', 'age': 30, 'pets': ['Zeus', 'Zuko']},\n",
       "  {'name': 'Katie', 'age': 38, 'pets': ['Sixes', 'Stache', 'Cisco']}]}"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "result = json.loads(objeto_json)\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Scott</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Katie</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  age\n",
       "0  Scott   30\n",
       "1  Katie   38"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asjson = json.dumps(result)\n",
    "siblings = pd.DataFrame(result['siblings'], columns=['name', 'age'])\n",
    "siblings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interactuando con Web API's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "url = 'https://www.congresogto.gob.mx/leyes.json'\n",
    "resp = requests.get(url)\n",
    "resp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizaciones básicas\n",
    "\n",
    "Otro paquete que resulta muy útil, para gráficos estadísticos descriptivos, es [Seaborn](https://seaborn.pydata.org)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports necesarios\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set(style=\"darkgrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "visualizacion_dataframe = pandas.read_excel('puntosconectividadleonjun2019.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x150d45748>"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 20))\n",
    "sns.barplot(x=\"POBLACION\", y=\"PUNTO_DE_CONEXION\", data=visualizacion_dataframe,\n",
    "            label=\"No. de seccion por Distrito Local\", color=\"b\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las visualizaciones y su utilidad dependen un poco de los datos, en este caso, valdría la pena explorar los datos utilizando mapas. 🗺👀"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sin embargo, te comparto el siguiente material para que puedas conocer más visualizaciones que resultan útiles:\n",
    "- [La galería de gráficos de Seaborn](https://seaborn.pydata.org/examples/index.html)\n",
    "- [La galería de gráficos en Python](https://python-graph-gallery.com)"
   ]
  }
 ],
 "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": 4
}