practica-4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Práctica 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analizando los datos abiertos del Ayuntamiento de Alcobendas: Población. Empadronados organizados en totales y por grupos de edad y sexo. Histórico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data set (su traducción al español sería \"conjuntos de datos\") significa \"[una colección de datos separados de información que son tratados como una unidad total por el ordenador](https://dictionary.cambridge.org/es/diccionario/ingles/dataset) los cuales son  susceptibles de ser reutilizados (por sí solos o mezclados con otros datos) con el fin de crear servicios de interés para ciudadanos, empresas u organizaciones\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En la siguiente práctica se trabajará con el conjunto de valores obtenidos a partir del [portal de datos abiertos del gobierno](https://datos.gob.es/es/) que nos vincula con la [página web](https://www.alcobendas.org/es/ayuntamiento) del Ayuntamiento de Alcobendas. A través de la misma podemos acceder a un amplio conjunto de bases de datos en relación con su administración. Con esta plataforma el ayuntamiento quiere asegurar una buena política de transparencia ante la ciudadanía. Gracias a la licencia de uso (propiedad abierta) hemos podido acceder y descargar los datos para reutilizarlos en la presente práctica (en formato csv). En concreto a continuación vamos tratar valores relacionados con las personas empadronadas en la localidad.  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para analizar los datos seguiremos los pasos aprendidos en la práctica anterior. Profundizando y explorando nuevas formas de representación."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Empadronados en Alcobendas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para poder trabajar en profundidad con los datos primero vamos a centrarnos en qué consiste un padrón y cuáles son los requisitos necesarios para poder empadronarse en este municipio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El Padrón de Habitantes es el registro por el que se acredita la residencia y el domicilio a efectos administrativos de los vecinos de una localidad. Toda persona que viva en España debe estar inscrita en el padrón de su municipio de residencia habitual. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los menores no emancipados o personas dependientes deben figurar empadronadas en el domicilio de sus padres o representantes legales. La inscripción en el padrón de los recién nacidos se realiza a través del Registro Civil."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para poder aparecer en el padrón se deben presentar los documentos requeridos, los cuales variarán según el solicitante (persona mayor de edad, padres con hijos menores, establecimientos colectivos como conventos...) El plazo máximo para resolver el empadronamiento son los tres meses posteriores a la solicitud."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuración de pandas y folium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lo primero es configurar el lenguaje Pandas usándolo como pd. Como se explicó en la práctica anterior, Pandas es una librería especializada en el manejo y análisis de de datos que nos permite trabajar con diferentes tipos de estructuras "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También tenemos que instalar el folium (un folium es una biblioteca, una herramienta de visualización que permite la concepción de mapas interactivos). En el caso de esta práctica en concreto no nos interesa especialmente la representación por mapas ya que estamos trabajando con datos demográficos de un punto cardila singular "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: folium in ./.local/lib/python3.8/site-packages (0.12.1.post1)\n",
      "Requirement already satisfied: jinja2>=2.9 in /usr/lib/python3/dist-packages (from folium) (2.10.1)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from folium) (1.21.1)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from folium) (2.25.0)\n",
      "Requirement already satisfied: branca>=0.3.0 in ./.local/lib/python3.8/site-packages (from folium) (0.5.0)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/lib/python3/dist-packages (from requests->folium) (2.8)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->folium) (2020.6.20)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/lib/python3/dist-packages (from requests->folium) (3.0.4)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3/dist-packages (from requests->folium) (1.25.8)\n"
     ]
    }
   ],
   "source": [
    "!pip install folium "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.3.1)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.1)\r\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (1.21.1)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas) (2020.4)\r\n",
      "Requirement already satisfied: six>=1.5 in /usr/lib/python3/dist-packages (from python-dateutil>=2.7.3->pandas) (1.14.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import folium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creamos la variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los datos los hemos obtenido a través de una [URL](https://datos.alcobendas.org/dataset/poblacion-empadronados-totales-y-grupos-de-edad-y-sexo-historico) de la que hemos descargado su correspondiente archivo en csv "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las variables se asignan con el símbolo `=` y la url se escribe entre comillas porque es una cadena de caracteres (string). En la función utilizaremos la abreviatura \"alco\" refiriéndonos a alcobendas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "url_alco = 'https://datos.alcobendas.org/dataset/d8710e65-fe6b-449e-a8e7-26c58e6f7326/resource/826375e4-e7f5-4627-9f16-978d0a0ad5dc/download/poblacion-de-alcobendas.-historiconueva-version.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El DataFrame se define como `df`. A lo que añadiremos `pd` porque es como hemos nombrado a Pandas (biblioteca de python que tenemos ya instalada). Seguido de `read_csv` que es la parte de la función que nos permite leer archivos en csv (Excel). Entre paréntesis se encuentra el valor que queremos que lea. En este caso la url con los datos del padrón de Alcobendas que ya ha sido previamente definida como una variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(url_alco)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>Año</th>\n",
       "      <th>Intervalo</th>\n",
       "      <th>Hombres</th>\n",
       "      <th>Mujeres</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1998</td>\n",
       "      <td>0-4 años</td>\n",
       "      <td>2261</td>\n",
       "      <td>2194</td>\n",
       "      <td>4455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1998</td>\n",
       "      <td>5-9 años</td>\n",
       "      <td>2520</td>\n",
       "      <td>2336</td>\n",
       "      <td>4856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1998</td>\n",
       "      <td>10-14 años</td>\n",
       "      <td>2894</td>\n",
       "      <td>2759</td>\n",
       "      <td>5653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1998</td>\n",
       "      <td>15-19 años</td>\n",
       "      <td>3756</td>\n",
       "      <td>3713</td>\n",
       "      <td>7469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998</td>\n",
       "      <td>20-24 años</td>\n",
       "      <td>4977</td>\n",
       "      <td>4672</td>\n",
       "      <td>9649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>2017</td>\n",
       "      <td>85-89 años</td>\n",
       "      <td>417</td>\n",
       "      <td>735</td>\n",
       "      <td>1152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>2017</td>\n",
       "      <td>90-94 años</td>\n",
       "      <td>146</td>\n",
       "      <td>398</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>2017</td>\n",
       "      <td>95-99 años</td>\n",
       "      <td>22</td>\n",
       "      <td>114</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>2017</td>\n",
       "      <td>Más de 100 años</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>2017</td>\n",
       "      <td>Total</td>\n",
       "      <td>55206</td>\n",
       "      <td>59658</td>\n",
       "      <td>114864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Año        Intervalo  Hombres  Mujeres   Total\n",
       "0    1998         0-4 años     2261     2194    4455\n",
       "1    1998         5-9 años     2520     2336    4856\n",
       "2    1998       10-14 años     2894     2759    5653\n",
       "3    1998       15-19 años     3756     3713    7469\n",
       "4    1998       20-24 años     4977     4672    9649\n",
       "..    ...              ...      ...      ...     ...\n",
       "435  2017       85-89 años      417      735    1152\n",
       "436  2017       90-94 años      146      398     544\n",
       "437  2017       95-99 años       22      114     136\n",
       "438  2017  Más de 100 años        5       27      32\n",
       "439  2017          Total      55206    59658  114864\n",
       "\n",
       "[440 rows x 5 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se ha generado como vemos una tabla, un dataframe (una estructura de datos con varias dimensiones en la que se puede guardar valores de distintos tipos organizados en columnas) de Python, que proviene del csv obtenido de la página web del ayuntamiento. La tabla está conformada por cinco columnas de datos más la columna de control de Pandas (columna índice). A simple viste podemos obtener información por grupos de edad del número de mujeres y hombres residentes en acolbendas a lo largo de los últimos veinticuatro años."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explorar la información de la tabla"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como se ha dicho previamente cinco de las seis columnas de la tabla nos aportan información relevante a los datos extraídos de la url. Para poder jugar con estos valores y explorarlos utilizaremos dos funciones: `df_alco.info()` para obtener información general del DataFrame o `df_alco.columns` cuando queramos conocer específica las características de una de las columnas que conforma la tabla."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 5 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   Año        440 non-null    int64 \n",
      " 1   Intervalo  440 non-null    object\n",
      " 2   Hombres    440 non-null    int64 \n",
      " 3   Mujeres    440 non-null    int64 \n",
      " 4   Total      440 non-null    int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 17.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_alco.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A través de esta función conocemos todas las variables de nuestros datos: año, intervalo, género, total... Es una forma muy útil de tener desgranada la vasta cantidad de información que se nos presenta para poder así decidir cuáles utilizaremos y de qué manera"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Año', 'Intervalo', 'Hombres', 'Mujeres', 'Total'], dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_alco.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el caso de querer conocer el valor concreto de una columna tenemos que utilizar la función `df_alco['nombre de la columna']`. Por ejemplo, a continuación exploraremos los valores correspondientes a la columna de mujeres y de hombres en dos apartados diferenciados gracias a esta función (lo que nos permitirá trabajar más fácilmente con ellos)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       2194\n",
       "1       2336\n",
       "2       2759\n",
       "3       3713\n",
       "4       4672\n",
       "       ...  \n",
       "435      735\n",
       "436      398\n",
       "437      114\n",
       "438       27\n",
       "439    59658\n",
       "Name: Mujeres, Length: 440, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_alco['Mujeres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             0-4 años\n",
       "1             5-9 años\n",
       "2           10-14 años\n",
       "3           15-19 años\n",
       "4           20-24 años\n",
       "            ...       \n",
       "435         85-89 años\n",
       "436         90-94 años\n",
       "437         95-99 años\n",
       "438    Más de 100 años\n",
       "439            Total  \n",
       "Name: Intervalo, Length: 440, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_alco['Intervalo']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Acabamos de elegir las columnas con las que vamos a trabajar: mujeres y años. Por medio de set index fijamos una de las columnas del DataFrame como índice, en nuestro caso marcaremos \"años\" como tal, cuyos valores sustirán a la columna índice de Pandas. Lo conseguiremos gracias a la función: `df_alco.set_idex`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>Hombres</th>\n",
       "      <th>Mujeres</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intervalo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-4 años</th>\n",
       "      <td>1998</td>\n",
       "      <td>2261</td>\n",
       "      <td>2194</td>\n",
       "      <td>4455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-9 años</th>\n",
       "      <td>1998</td>\n",
       "      <td>2520</td>\n",
       "      <td>2336</td>\n",
       "      <td>4856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14 años</th>\n",
       "      <td>1998</td>\n",
       "      <td>2894</td>\n",
       "      <td>2759</td>\n",
       "      <td>5653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19 años</th>\n",
       "      <td>1998</td>\n",
       "      <td>3756</td>\n",
       "      <td>3713</td>\n",
       "      <td>7469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-24 años</th>\n",
       "      <td>1998</td>\n",
       "      <td>4977</td>\n",
       "      <td>4672</td>\n",
       "      <td>9649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85-89 años</th>\n",
       "      <td>2017</td>\n",
       "      <td>417</td>\n",
       "      <td>735</td>\n",
       "      <td>1152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90-94 años</th>\n",
       "      <td>2017</td>\n",
       "      <td>146</td>\n",
       "      <td>398</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95-99 años</th>\n",
       "      <td>2017</td>\n",
       "      <td>22</td>\n",
       "      <td>114</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Más de 100 años</th>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>2017</td>\n",
       "      <td>55206</td>\n",
       "      <td>59658</td>\n",
       "      <td>114864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Año  Hombres  Mujeres   Total\n",
       "Intervalo                                      \n",
       " 0-4 años        1998     2261     2194    4455\n",
       " 5-9 años        1998     2520     2336    4856\n",
       " 10-14 años      1998     2894     2759    5653\n",
       " 15-19 años      1998     3756     3713    7469\n",
       " 20-24 años      1998     4977     4672    9649\n",
       "...               ...      ...      ...     ...\n",
       "85-89 años       2017      417      735    1152\n",
       "90-94 años       2017      146      398     544\n",
       "95-99 años       2017       22      114     136\n",
       "Más de 100 años  2017        5       27      32\n",
       "Total            2017    55206    59658  114864\n",
       "\n",
       "[440 rows x 4 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_alco.set_index('Intervalo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nuestro propósito es acabar por crear un gráfico con los valores 'años' y 'mujeres', por lo que precisamos de una tabla que cuente con tan solo dos columnas (años y mujeres). Utilizaremos la función `df_alco.set_index`que sería el comando de agrupando en dos columnas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intervalo\n",
       " 0-4 años           2194\n",
       " 5-9 años           2336\n",
       " 10-14 años         2759\n",
       " 15-19 años         3713\n",
       " 20-24 años         4672\n",
       "                   ...  \n",
       "85-89 años           735\n",
       "90-94 años           398\n",
       "95-99 años           114\n",
       "Más de 100 años       27\n",
       "Total              59658\n",
       "Name: Mujeres, Length: 440, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_alco.set_index('Intervalo')['Mujeres']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Crear tablas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A la fórmula anterior le añadiremos 'plot' para crear un gráfico y un título a la vez: `df.set_index('Año')['Mujeres'].plot(title=...)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Número de mujeres en Alcobendas a lo largo de los años'}, xlabel='Intervalo'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index('Intervalo')['Mujeres'].plot(title='Número de mujeres en Alcobendas a lo largo de los años')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2194,  2336,  2759,  3713,  4672,  3983,  3750,  3382,  3289,\n",
       "        3752,  3205,  1843,  1444,  1098,   893,   627,   443,   218,\n",
       "          80,     9,     1, 43691,  2198,  2300,  2632,  3400,  4709,\n",
       "        4225,  3837,  3594,  3201,  3635,  3538,  2097,  1437,  1196,\n",
       "         949,   673,   461,   293,    95,    22, 44493,  2047,  2344,\n",
       "        2606,  3264,  4676,  4531,  3970,  3820,  3275,  3520,  3642,\n",
       "        2370,  1433,  1243,   989,   713,   474,   334,   118,     3,\n",
       "       45394,  2301,  2427,  2594,  3119,  4505,  4804,  4174,  4080,\n",
       "        3445,  3466,  3762,  2516,  1620,  1300,  1032,   814,   477,\n",
       "         383,   136,    34,     5, 46994,  2395,  2549,  2540,  3034,\n",
       "        4250,  5033,  4373,  4353,  3565,  3547,  3778,  2833,  1696,\n",
       "        1385,  1054,   846,   518,   402,   158,    39,     8, 48356,\n",
       "        2545,  2597,  2613,  3063,  4220,  5119,  4640,  4482,  3817,\n",
       "        3546,  3825,  3207,  1836,  1436,  1122,   866,   574,   393,\n",
       "          36, 50100,  2765,  2675,  2684,  2931,  3943,  5308,  4778,\n",
       "        4581,  4051,  3483,  3646,  3470,  2048,  1419,  1198,   913,\n",
       "         619,   207,     4, 51155,  2814,  2757,  2698,  2847,  3892,\n",
       "        5409,  5173,  4744,  4357,  3555,  3615,  2357,  1430,  1234,\n",
       "         981,   661,   404,   230,    53, 52761,  3086,  2726,  2723,\n",
       "        2801,  3659,  5182,  5374,  4817,  4486,  3629,  3490,  3682,\n",
       "        2421,  1592,  1245,   986,   737,   394,   240,    54, 53327,\n",
       "        3010,  2741,  2820,  2764,  4930,  5708,  5013,  4737,  3689,\n",
       "        3534,  3616,  2718,  1651,  1304,  1010,   752,   423,   237,\n",
       "          55, 54358,  2875,  2841,  2889,  3588,  4775,  5589,  4857,\n",
       "        3866,  3481,  3664,  3031,  1767,  1361,  1052,   769,   467,\n",
       "         222,    69, 55145,  2927,  2909,  2898,  3519,  4589,  5474,\n",
       "        5279,  4956,  4156,  3472,  3537,  3309,  1978,  1359,  1151,\n",
       "         820,   494,   221,    85,    10, 55963,  2976,  3011,  2864,\n",
       "        2853,  3346,  5380,  5441,  4982,  4417,  3527,  3430,  3439,\n",
       "        2235,  1362,  1182,   857,   537,    83,    18, 56659,  2957,\n",
       "        2989,  2832,  2803,  3179,  4264,  5211,  5463,  5006,  4502,\n",
       "        3576,  3392,  3498,  2311,  1541,  1200,   891,   572,   258,\n",
       "          78,    15, 56538,  3009,  3066,  2830,  2860,  3116,  4130,\n",
       "        5021,  5513,  5160,  4637,  3688,  3415,  3492,  2611,  1591,\n",
       "        1270,   907,   608,   275,    88,    17, 57304,  3048,  3123,\n",
       "        2878,  3070,  3949,  4860,  5539,  5290,  4748,  3776,  3365,\n",
       "        3510,  1702,  1284,   965,   635,   313,    99, 57951,  2993,\n",
       "        3225,  2813,  3703,  4668,  5506,  5255,  4818,  3956,  3320,\n",
       "        3367,  3196,  1890,  1280,  1030,   662,   321,   103, 58049,\n",
       "        3226,  2987,  2868,  3030,  3509,  4448,  5410,  5452,  4838,\n",
       "        4194,  3364,  3288,  3298,  2129,  1070,   705,   326,   111,\n",
       "       58508,  3234,  3007,  2910,  2991,  3387,  4197,  5256,  5451,\n",
       "        4887,  3432,  3260,  3371,  2256,  1454,  1075,   746,   382,\n",
       "         107, 58709,  2984,  3128,  2920,  3092,  3351,  4028,  5087,\n",
       "        5600,  5069,  4466,  3524,  3292,  3356,  2536,  1527,  1149,\n",
       "         735,   398,   114,    27, 59658])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('Intervalo')['Mujeres'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Número'}, xlabel='Intervalo'>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.set_index('Intervalo')['Mujeres'].plot(title='Número')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Acabamos de probar no hay cambios significativos en la elaboración del gráfico al abarcar todos los datos o seleccionarlos sin su reiteración."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Otro tipo de gráficos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfico de dispersión"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Intervalo', ylabel='Mujeres'>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('https://datos.alcobendas.org/dataset/d8710e65-fe6b-449e-a8e7-26c58e6f7326/resource/826375e4-e7f5-4627-9f16-978d0a0ad5dc/download/poblacion-de-alcobendas.-historiconueva-version.csv')\n",
    "df.plot(kind = 'scatter', x = 'Intervalo', y = 'Mujeres')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Los dos gráficos que hemos hecho hasta ahora no nos son especialmente útiles porque las variables se acumulan. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfico de barras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Según el [Instituto Nacional de Estadística (INE)](https://www.ine.es/explica/docs/pasos_tipos_graficos.pdf) los gráficos de barras son una \"representación gráfica en un eje cartesiano de las frecuencias de una variable cualitativa o discreta. En uno de los ejes se posicionan las distintas categorías o modalidades de la variable cualitativa o discreta y en el otro el valor o frecuencia de cada categoría en una determinada escala\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para representar datos relacionados con los índices de población la gráfica de barras es interesante ya que nos permite realizar comparativas de forma rápida. A continuación vamos a intentar generar un gráfico de barras con la variable 'Mujeres' en relación a los grupos de edad por 'Intervalos'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "no numeric data to plot",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-63-08e936f4064a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Intervalo\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'hist'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Mujeres'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Intervalo'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/plotting/_core.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    970\u001b[0m                     \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    971\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 972\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    973\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    974\u001b[0m     \u001b[0m__call__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/plotting/_matplotlib/__init__.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(data, kind, **kwargs)\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"ax\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"left_ax\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 71\u001b[0;31m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     72\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36mgenerate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    284\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/plotting/_matplotlib/core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    451\u001b[0m         \u001b[0;31m# no non-numeric frames or series allowed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    452\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_empty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"no numeric data to plot\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumeric_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_ndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: no numeric data to plot"
     ]
    }
   ],
   "source": [
    "df[\"Intervalo\"].plot(kind = 'hist', x = 'Mujeres', y = 'Intervalo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gráfico de barras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al intentar hacer un gráfico de barras con los valores de 'Intervalo' y 'Mujeres' nos da erorr porque los datos relacionados con los grupos de edad no son numéricos y por lo tanto el gráfico no puede leerlos. A continuación intentaremos generar una gráfica de barras poniendo en relación el número de mujeres con los totales de población."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        4455\n",
       "1        4856\n",
       "2        5653\n",
       "3        7469\n",
       "4        9649\n",
       "        ...  \n",
       "435      1152\n",
       "436       544\n",
       "437       136\n",
       "438        32\n",
       "439    114864\n",
       "Name: Total, Length: 440, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_alco['Total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>Año</th>\n",
       "      <th>Hombres</th>\n",
       "      <th>Mujeres</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intervalo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-4 años</th>\n",
       "      <td>1998</td>\n",
       "      <td>2261</td>\n",
       "      <td>2194</td>\n",
       "      <td>4455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-9 años</th>\n",
       "      <td>1998</td>\n",
       "      <td>2520</td>\n",
       "      <td>2336</td>\n",
       "      <td>4856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14 años</th>\n",
       "      <td>1998</td>\n",
       "      <td>2894</td>\n",
       "      <td>2759</td>\n",
       "      <td>5653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19 años</th>\n",
       "      <td>1998</td>\n",
       "      <td>3756</td>\n",
       "      <td>3713</td>\n",
       "      <td>7469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-24 años</th>\n",
       "      <td>1998</td>\n",
       "      <td>4977</td>\n",
       "      <td>4672</td>\n",
       "      <td>9649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85-89 años</th>\n",
       "      <td>2017</td>\n",
       "      <td>417</td>\n",
       "      <td>735</td>\n",
       "      <td>1152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90-94 años</th>\n",
       "      <td>2017</td>\n",
       "      <td>146</td>\n",
       "      <td>398</td>\n",
       "      <td>544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95-99 años</th>\n",
       "      <td>2017</td>\n",
       "      <td>22</td>\n",
       "      <td>114</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Más de 100 años</th>\n",
       "      <td>2017</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total</th>\n",
       "      <td>2017</td>\n",
       "      <td>55206</td>\n",
       "      <td>59658</td>\n",
       "      <td>114864</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>440 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Año  Hombres  Mujeres   Total\n",
       "Intervalo                                      \n",
       " 0-4 años        1998     2261     2194    4455\n",
       " 5-9 años        1998     2520     2336    4856\n",
       " 10-14 años      1998     2894     2759    5653\n",
       " 15-19 años      1998     3756     3713    7469\n",
       " 20-24 años      1998     4977     4672    9649\n",
       "...               ...      ...      ...     ...\n",
       "85-89 años       2017      417      735    1152\n",
       "90-94 años       2017      146      398     544\n",
       "95-99 años       2017       22      114     136\n",
       "Más de 100 años  2017        5       27      32\n",
       "Total            2017    55206    59658  114864\n",
       "\n",
       "[440 rows x 4 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_alco.set_index('Intervalo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intervalo\n",
       " 0-4 años           2194\n",
       " 5-9 años           2336\n",
       " 10-14 años         2759\n",
       " 15-19 años         3713\n",
       " 20-24 años         4672\n",
       "                   ...  \n",
       "85-89 años           735\n",
       "90-94 años           398\n",
       "95-99 años           114\n",
       "Más de 100 años       27\n",
       "Total              59658\n",
       "Name: Mujeres, Length: 440, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_alco.set_index('Intervalo')['Mujeres']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"Total\"].plot(kind = 'hist', x = 'Mujeres' , y='Hombres')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este caso hemos podido extraer un gráfico de barras, sin embargo como no se ha definido de manera concreta el rango y además, los dos valores de datos recogen números muy grandes y amplios, el resultado es cuanto menos esclarecedor. Siguiendo la definición previamente citada del INE, el resultado obtenido no es válido en tanto que no hay una variable de tipo cualitativa o discreta como tal, solo dos frecuencias determinadas a escala. Seguiremos probando."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cambiamos los valores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero acotaremos un rango: el año 1998 que se divide en veintiún subgrupos de edades. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El método plot de Pandas tiene un parámetro kind que nos permite cambiar el tipo de gráfico que queremos dibujar. En este caso, gracias a esta posibilidad para obtener un diagrama de barras solo tenemos que indicar que la gráfica que requerimos es de tipo \"bar\", es decir tenemos que escribir `kind='bar`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antes de la revisión de la práctica estábamos trabajando con cuatro columnas de datos (hombres, mujeres, total y año). Sin embargo no tenía ningún tipo de sentido ya que lo que se quería comparar era el número de hombres y mujeres en Alcobendas clasificados por grupos de edad independientes (no se requería la suma de los mismos, es decir, el valor de los totales) durante la constante del año 1998 (era un error que apareciese en la gráfica porque no aportaba ninguna información últil). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Año', 'Intervalo', 'Hombres', 'Mujeres', 'Total'], dtype='object')"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_alco.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 Hombres  Mujeres\n",
      "Intervalo                        \n",
      " 0-4 años           2261     2194\n",
      " 5-9 años           2520     2336\n",
      " 10-14 años         2894     2759\n",
      " 15-19 años         3756     3713\n",
      " 20-24 años         4977     4672\n",
      "...                  ...      ...\n",
      "85-89 años           417      735\n",
      "90-94 años           146      398\n",
      "95-99 años            22      114\n",
      "Más de 100 años        5       27\n",
      "Total              55206    59658\n",
      "\n",
      "[440 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "print(df[['Hombres','Mujeres']])\n",
    "muestra = df[:21].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación hemos acotado la muestra a nivel del número de personas `df[]`y los valores representados por medio de un doble corchete `[[]]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>Hombres</th>\n",
       "      <th>Mujeres</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Intervalo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-4 años</th>\n",
       "      <td>2261</td>\n",
       "      <td>2194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5-9 años</th>\n",
       "      <td>2520</td>\n",
       "      <td>2336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-14 años</th>\n",
       "      <td>2894</td>\n",
       "      <td>2759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-19 años</th>\n",
       "      <td>3756</td>\n",
       "      <td>3713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-24 años</th>\n",
       "      <td>4977</td>\n",
       "      <td>4672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-29 años</th>\n",
       "      <td>4007</td>\n",
       "      <td>3983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-34 años</th>\n",
       "      <td>3502</td>\n",
       "      <td>3750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35-39 años</th>\n",
       "      <td>3196</td>\n",
       "      <td>3382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-44 años</th>\n",
       "      <td>2749</td>\n",
       "      <td>3289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45-49 años</th>\n",
       "      <td>3303</td>\n",
       "      <td>3752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-54 años</th>\n",
       "      <td>3352</td>\n",
       "      <td>3205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55-59 años</th>\n",
       "      <td>2110</td>\n",
       "      <td>1843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-64 años</th>\n",
       "      <td>1437</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65-69 años</th>\n",
       "      <td>1018</td>\n",
       "      <td>1098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-74 años</th>\n",
       "      <td>664</td>\n",
       "      <td>893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75-79 años</th>\n",
       "      <td>386</td>\n",
       "      <td>627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-84 años</th>\n",
       "      <td>193</td>\n",
       "      <td>443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85-89 años</th>\n",
       "      <td>93</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90-94 años</th>\n",
       "      <td>33</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95-99 años</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>más de 100 años</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Hombres  Mujeres\n",
       "Intervalo                         \n",
       " 0-4 años            2261     2194\n",
       " 5-9 años            2520     2336\n",
       " 10-14 años          2894     2759\n",
       " 15-19 años          3756     3713\n",
       " 20-24 años          4977     4672\n",
       " 25-29 años          4007     3983\n",
       " 30-34 años          3502     3750\n",
       " 35-39 años          3196     3382\n",
       " 40-44 años          2749     3289\n",
       " 45-49 años          3303     3752\n",
       " 50-54 años          3352     3205\n",
       " 55-59 años          2110     1843\n",
       " 60-64 años          1437     1444\n",
       " 65-69 años          1018     1098\n",
       " 70-74 años           664      893\n",
       " 75-79 años           386      627\n",
       " 80-84 años           193      443\n",
       " 85-89 años            93      218\n",
       " 90-94 años            33       80\n",
       " 95-99 años             4        9\n",
       " más de 100 años        0        1"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "muestra = df[:21][['Hombres','Mujeres']]\n",
    "muestra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Una vez acotado los grupos de datos, nos disponemos a representarlos a través de diferentes gráficas. La primera será un gráfico de líneas que nos permite estudiar a simple vista la evolución del número de hombres y mujeres según cada grupo de edad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'1998'}, xlabel='Intervalo'>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Uyh3q1KnDzp07efjhh90disfTRFNKzI0+TEU/b7rXArb+AO2Gg29A4RYOaQoRYyHqc26sl4q/j4MZOnyA8iCefAnfk5WW/aaJphTIzjFEbkvg6uY18N0yq+BGAHnpPR58A6m49GWub1OHHzccJOVMlkviVaoo/P39SUpKKjUfmp7CGENSUhL+/v7uDqVA2uqsFFi77zjHUjIY0LIGLJkCoV2gZquirSSwOlz5OES+yL3XjOKbdYZfNh3i1s71XBKzUoUVGhpKfHw8iYmJ7g6l1PH39yc0NNTdYRRIE00pMDf6ML5eDvoExEDSLhjywaWtqOt9sOYzmm56jSYhE5mxZr8mGuV2Pj4+NGyoXSOVZXrpzMMZY5i39TA9woMJ2PSF1Qig1Y2XtjL7IU45tJHn6m1h/f4T7Dh8qngDVkqpC2ii8XDbD58i7lgaNzTxL3ojgLy0vhnqdODKuA+p6JWlTZ2VUi6nicbDzY0+jAhck7Xo0hoBXMjhgAGv4nXqAK/WXsp36w+QnpldLLEqpVReNNF4uHnRR+hUrwpBm7+A0M5FbwSQl7Ae0PwGBibPwDvtKL9tOXT561RKqXxoovFgccdS2XroJHfWPWA1ArhwOIDL0W8iXtnp/DPoB2asjiu+9Sql1AU00XiwefbYM31Sfru8RgB5qR6ORIxlcNZ8kmI3szvxdPGtWymlnBQ60YiIl4isF5Gf7dcNReRPEYkRkVki4muX+9mvY+z5YU7reNYu3yEi1xT71pQx86IPExFiCNr9C7S99fIaAeSl13jwDeA5nxnMWqNnNUop1yjKGc0jwDan168Dk4wx4cBx4G67/G7guF0+ya6HiLQERgCtgGuBD0Tk/P6z1VnHUjJYE3uMB4PXQPYZiCjGy2a5AoNxXPUkfRzr2B/1GxlZOQUvo5RSRVSoRCMiocD1wKf2awH6AHPsKlOBofb0EPs19vy+dv0hwExjzBljzF4gBuhSDNtQJkVuO0KOMXQ/8XPxNQLIS9f7SA+sy0NZU5kTpU2dlVLFr7BnNG8BTwO5X3mDgRPGmNzOsuKBuvZ0XSAOwJ6fbNc/W57HMmeJyL0iEiUiUeW5S4p50Ue4ruJe/E8UcjiAS+Xjj+81E2ntiKXirw8Qs2eP695LKVUuFZhoROQGIMEYs7YE4sEY87ExJsIYExESElISb+lxUjOyWLorkfuDloBfpeJtBJAHR+ubSOn2BNc6/qTWtJ6cWfE/yNFna5RSxaMwZzQ9gMEiEgvMxLpk9jZQRURy+0oLBQ7Y0weAegD2/MpAknN5HssoJ0t2JlIhK5nWyYuh7XDwDXTtGzocBF77ApsH/8bGnDD85j2N+aQPHCiR7xZKqTKuwERjjHnWGBNqjAnDupm/0BhzB7AIuNmuNhr4wZ7+0X6NPX+hsfr//hEYYbdKawg0AVYX25aUIfOij3C7/0ocORnQaXTBCxSTjh27sL7XFB7OeIi0Ywfgk77w82OQdrzEYlBKlT2X8xzNM8DjIhKDdQ/mM7v8MyDYLn8cGA9gjIkGZgNbgd+BB40xen3mApnZOSzYdoTRvguhbgTUalOi7//A1U1IDh9Cj9Ovc7T1WGs0z3cjYP2XoOOFKKUuQZESjTFmsTHmBnt6jzGmizEm3BhzizHmjF2ebr8Ot+fvcVr+VWNMY2NMM2PMb8W7KWXD6r3HaHpmCzUz9rumSXMBHA7hreHt8Q+qwrA9gzg1egFUawQ/PACfXwdHoks8JqVU6aY9A3iYedGHGemzEONX0eWNAPJTLdCX927vwMETaTy5NAcz9ncY/B4k7oCProS5z8EZHV5AKVU4mmg8iDGGVdExDPT6EymJRgAX0alBNcZf15y50Uf4bPk+6DgKHl4LHUbCyvfgvc4Q/Z1eTlNKFUgTjQfZfCCZHimR+JhM1z47U0h392xI/5Y1ee237azddxwCqsHgd+DuSAgMga/HwBc3wtEYd4eqlPJgmmg8yC+bDnKb10Kyancq8UYAeRER/nNLO2pX8eehr9ZxLCXDmlGvM9yzCK77P6sJ9Ifd4c+P3RtsUR3eDNOGQMK2gusqpS6LJhoPkZaRzY4182niOIB355JvBJCfyhV8+OD2TiSdzuDx2RvIybEvlXl5Q9e/w0NR0Lgv/PYULHnDvcEW1ukE+GoE7FkMc+6GrDPujkipMk0TjYf4Zl08g7Pmke0TBK2HuTuc87QJrcw/B7Vk8Y5EPvxj9/kzK9aE4dOh7QhY+AoseMmz79tknYGZd0BqEvR9ARKiYeHL7o5KqTLNu+AqytVycgxzlm5kttefONqNdmsjgPyM7Fqf1XuP8d95O+hYvyrdGwefm+nlDUM/BB9/WPpfyEyDa/4FIu4LOC/GWA+gxq+GW6ZYrfqS42HFe9BkADS8yt0RKlUm6RmNB1i0I4FOJ+biSyYSMcbd4eRJRPj3sDaEBQcybuZ6Ek6ln1/B4YAb3oKu98OqD6wP9BwPG3Zg5Xuw4Uvo/ey5puMDXoHgxvDd/ZB2wq3hKVVWaaLxAJ8u2cOdvovIcUNPAEUR5OfNByM7cio9k0dmbCA754JLZCJw7b+h5+Ow9nP4/n7Izsp7ZSVt5zyY909oORSuevpcuW8gDPsYTh+GX590W3hKlWWaaNws+mAy2bHLaWAO4HBDTwBF1bxWJV4e0pqVe5J4K3LnXyuIQL8J0Od52DQTvhkLWRklH6izhO0wZ6yVxId+aJ19OavbCXo9A5u/hs1z8l6HUuqSaaJxs8+W7eVO34UYv0rQyrMaAeTnloh63NIplHcXxrB4R0Lela56Cga8Clt/gNmjIDM973qulnoMZgwHnwpw24z8h8Pu+TiEdoGfH7fu2yilio0mGjc6cjKdZRu3c61jNdJuRP4fgh7opSGtaVazIo/N2sDBE2l5V7riIbj+v7Dzd+vDPiOlZIPMzoTZd8LJQzDiK6gcmn9dL28Y9j8w2fDdfZ53f0mpUkwTjRtNWxnLEFmCt4f0BFAUFXy9+GBkRzKycnjoq3VkZufzwdz5b9blqr1LYPrNkH6y5IL87WmIXWr1ZlCvc8H1qzWy7jHFLoVV77s+PqXKCU00bpKakcWXq/Zxd4U/rEs2NVu5O6QiaxwSxGs3tWXd/hM89fVG0jPzGfWh/e1w02dWs+IvhlqXs1xt9ScQNRl6PArtRhR+uQ6joPkN1vNAh7e4LDylyhNNNG7yzboDND+ziVqZ8W4ZDqC4DGpXhyf6N+X7DQe59X8r87+M1noY3PqF1fXL1MFwOtF1Qe1ZDL89A02vsx7KLAoRGPQ2+FeBb+91370lpcoQTTRukJNj+HzZXh4IWoLxr+y24QCKy8N9m/DRyE7sTjjN4PeW8eeepLwrNh8It82EpBiYcr1176S4Je2G2aOhelO46RNweBV9HYHVYcj72muAUsVEE40bLNqRwImjh+iZuRJpd5vVIqqUu7Z1LX54qAeV/H2449M/mbJ8LyavrmjC+8LIOXDygDWQ2on9xRdE2gn4ajiIw2ph5lfx0tfVdIB1f2nle7Dnj2ILUanySBONG3y6dC9jA1fgKIWNAC4mvEZFvn+oB72ahvDiT1t58utNed+3CesJo76HtGMw+To4uuvy3zw7y3pW5vheGP4FVGt4+evs/zIEN7EePE07fvnrU6qc0kRTwrYcSGbVnkRG+S6Cet2gRgt3h1SsKvn78MmdETzStwnfrIvnlo/yuW9TrzOM/gkyU+G9CHivC/z0KGz6GpIPFP2N578AuxdYzanDel72dgBWc/NhH8PpI/DLE8WzTqXKIe1Us4RNXraXq323UzktDiKed3c4LuFwCI/1b0qrOpV4fPZGBr27jPdu73h+R5wAtdvBvYsh+lvYtwK2fGN1XQNQNQwa9IAGV1g/VRvm30nnui+s5shd7yvWM8TTZ7KIc4Tj3fIhmmx5i29S2vAbVxJ/PJUjJ9OZOKQ1g9vVKbb3U6qskjyvo3uIiIgIExUV5e4wis2Rk+n0fH0h34V8Suv0tfDE9jJxf+ZiYhJOc+8XUexLSuX561sw5oowJL+EkZNttUrbtwL2Lbd+p9lNoSvWPpd0GvSAkOZW4tm3EqYOss5i7phjPXhZSOmZ2cQfTyP+eCpxx9OIP5ZK3PFU4o5ZZcdTMwHwIptZvi/TVOJ5oNK7+AU3YGN8Ms1rVWT637pe7i5SqtiJyFpjTIS748ilZzQlaNrKWCrnnKDVySXQ5Z4yn2QAwmsE8f2DPXh81kYm/rSVzfHJ/GtYG/x98mgN5vCCOu2tn+4PWE/nH91xLunELrfOegACgqF+d9i/Cqo2gFs+L1KSWbk7ibFT1pDmdA/J18tBaNUK1K1agTahtalXNYB61SoQWjWABjKditP6MD14Ctz5I/+eu4PPlu4lOS2TyhV8LmsfKVXWaaIpIakZWXz5535eqL0OOVa2GgEUpJK/Dx+P6sS7C2OYFLmTnQmn+N+oCOpWKSDROhzWPawaLawWYMZYN/v3rbATzzLAWE2mK1QtdDzJaZk8MXsDtSr7M65vOKFVA6hXNYAaFf1wOPIbQ6cKXPc6/PAgrHyPAS1H8r8/9rB4RwJD2tct9HsrVR5poikh36w7QHLqGQYGzrUv/TRzd0glyuEQHunXhFZ1KvHYrA32fZsOXNG4euFXImJ1E1OtEXQYaZUZU+QB1ib+GM2RU2f45v4raF+vSuEXbH8H7PgNFr5M+7t7Uz3Il/lbj2iiUaoA2uqsBOTkGCYv28uoGrH4n9pfrs5mLtSvZU2+f6gHVQN8GPXZaj5bls/zNoVVxCTz2+ZDfLv+AA9eHV60JJP7XoPegQpV8fruXq5pWoU/diSSkaUdcCp1MZpoSsCiHQnsPZrCfUFLoUI1aDHY3SG5VeMQ675N3+Y1ePnnrTz77ebLSzaFlHAynX98t5m2oZV5uE/4pa0kMBiGfACJ2xjtM59TZ7JYlV9PCEopQBNNifh06V5aV0qj9uEFVgeTPv7uDsntKvr78NHITjzQuzEz18QxKbIYHtq8CGMMz3yzidSMbN68tT0+Xpdx6DfpB7Xa0PjEcir4eDF/65HiC1SpMkgTjYttOZDMyj1JvBC6HsnJKteXzS7kcAhPXdOMWzqF8s6CXXyz1nUDjs1YHceiHYmMv6454TWCLn+F4f3wiltFv8YBRG47UiJnZEqVVppoXGzysr0E+QoRST9B2JVQvYm7Q/IoIsKrN7ahe6Ngxn+7ySWXofYlpfDKL1vpER7M6O5hxbPS8H6Qk8WI6ns5lJzOlgMlOM6OUqWMJhoXOpyczo8bDzK+6SEcyfv0bCYfvt4OPhrZifrVAvj7F2vZnXi62NadnWN4fPZGvBzCGze3u0jz5SIK7QK+FemUuRaHwPyth4tnvUqVQZpoXGjayliyjWFYznzrAcMWg9wdkseqHODD52O64O0Qxk5Zw7GUjGJZ70d/7GbtvuO8PKQ1dQp6bqcovH2hUS/8YxcRUb8q8/Q+jVL5KjDRiIi/iKwWkY0iEi0iE+3yhiLyp4jEiMgsEfG1y/3s1zH2/DCndT1rl+8QkWtctlUeIDUji69W7+eWpj4E7J1nNQLw9nN3WB6tfnAAH98ZwaHkdO6dFpX/iJ2FFH0wmbcid3J9m9oMae+CPsnC+0Lyfm4OS2P74VPEHUst/vdQqgwozBnNGaCPMaYd0B64VkS6Aa8Dk4wx4cBx4G67/t3Acbt8kl0PEWkJjABaAdcCH4jIJYxKVTp8s+4AJ1IzeaTaKjDZ0Kn0jqJZkjo1qMp/b2lH1L7jPPPNpku+yZ6emc1jszZQNcCXV4a2zr9/tcvRuC8A/Xw2A2jrM6XyUWCiMZbci+Y+9o8B+gBz7PKpwFB7eoj9Gnt+X7H+y4cAM40xZ4wxe4EYoEtxbISnyX1As0NoRersmQ0Nr4Lgxu4Oq9QY1K4OT13TjB82HLzkZs//nbeDnUdO8/rNbaka6FvMEdqqNoDqTal2aAlNagRpolEqH4W6RyMiXiKyAUgA5gO7gRPGmCy7SjyQ2w9HXSAOwJ6fDAQ7l+exjPN73SsiUSISlZjownHlXWjhdusBzfFNDiLJcXo2cwke6N34kps9r9qTxKfL9jKyW32ublbDRRHawvvBvuVc17wyq2OPcSK1eO4tKVWWFCrRGGOyjTHtgVCss5DmrgrIGPOxMSbCGBMREhLiqrdxqU+X7aFOZX86J/0AAdWh+Q3uDqnUudRmz6fSM3li9kbCggP5x8ASGFQuvC9kpTO4yl6ycwyLdiS4/j2VKmWK1OrMGHMCWAR0B6qISG6nnKFA7rCIB4B6APb8ykCSc3key5QZ1giax3ggIgDHrrnQ4Q6rhZIqsktp9jzxp60cSk7jv7e2I8C3BPqMbdADvP1pnLyKGhX99PKZUnkoTKuzEBGpYk9XAPoD27ASzs12tdHAD/b0j/Zr7PkLjXVH90dghN0qrSHQBFhdTNvhEXJyDG8v2EWgrxc3y2KrEUDH0QUup/JXlGbPc6MPM2dtPA9eHU7H+oUfNuCy+FSAsJ5IzAL6tqjJHzsSOZN1ea3llCprCnNGUxtYJCKbgDXAfGPMz8AzwOMiEoN1D+Yzu/5nQLBd/jgwHsAYEw3MBrYCvwMPGmPKzH9kdo7hqTmbmL/1COP6NMJ/03Ro1FsbARSDwjR7Tjx1hme/3UzrupUY17eEe18I7wdJuxhcP4OUjGxW7NZONpVyVphWZ5uMMR2MMW2NMa2NMS/Z5XuMMV2MMeHGmFuMMWfs8nT7dbg9f4/Tul41xjQ2xjQzxvzmus0qWVnZOTw2awPfrIvn8f5N+XudvXAyXhsBFKOLNXs2xvDst5s4fSaLSZfbYealCO8HQETWegJ8tZNNpS6kPQNcpszsHMbNXM+PGw/y9LXNrG/TUZ9DYA1ofr27wytT8mv2PDsqjshtCTxzbXOa1KxY8oEFh0OV+vjsXUivpiFEbj1CTo52sqlULk00l+FMVjYPfLmOXzcf5vnrW/BA73BIjofcRgBeOpZ8cbuw2fP+pFRe+mkrVzQO5q4rwtwTlIh1VrP3DwY0r0rCqTNsOpDsnliU8kA6lPMlSs/M5v7pa1m0I5GJg1sxOvdDbt0XYHK0EYCL5DZ7jj+exvhvN9GweiAOEd64pRg7zLwU4f0gajL9g/bh5RDmbz1c9BE8lSqj9IzmEqRlZHPPtCgW7UjkXze2OZdk9v8Jy9+GJtdAtYZujbEsc272vPPIaSYOaUXd4uww81I0vAoc3gTFLaZzWFW9T6OUE000RZSakcXYKWtYFnOU/7u5Lbd3rW/NSNgOX90KlerAkPfdG2Q5UDnAh6/u6cb7t3fkxg5/6WCi5PlVhPrdIWYB/VvWYueR0+xLSnF3VEp5BE00RXD6TBZjJq/hz71JvHlrO26NsJ8/TY6H6cOs3plHfQtBpbNHg9KmZiV/rm9b2zUdZl6K8L5wZDPX1rcaAuhZjVIWTTSFdDI9kzs/+5O1+4/z9ogO3Ngh1JqRegy+GAZnTsEdc6BqmFvjVG4U3h+AukkraV6roo5Ro5RNE00hJKdmMvLTP9l8IJn3b+/IoHb22CYZqfDVcDi+F0Z8BbXbujdQ5V41W0FQLYiJpH/LmkTFHiu2AdyUKs000RTgWEoGt32yiu2HTvHhHZ24tnUta0Z2Fsy5C+LXwE2fQsMr3Ruocr/cZs67F9K/eXVyjNWTt1LlnSaaizh6+gy3f7KKmMTTfHxnJ/q1rGnNMAZ+egR2/g7X/xdaDnFvoMpzhPeF9BO0kd3UquTP/K2H3R2RUm6niSYfCSfTGfHxKmKTUvh8TGd6O49rsuAl2DAdeo2HznfnvxJV/jTqDeJAYhbQr2UNluw8etlDUitV2mmiycOh5DSGf7yKgyfSmHJXF3qEVz83c9WHsOxNqx+z3uPdF6TyTAHVoG4ExETSr0VN0jKzWR5z1N1RKeVWmmgusCb2GDd/uJLEU2eYNrYL3RoFn5u5eQ78Ph5aDLIumXlKs1rlWcL7wYG1dK8tBPl5azNnVe5porGlZ2bzys9bufV/K/FyCF/+rSsRYdXOVdi9EL67zxroatin4PByX7DKs4X3Awx++/6wOtnclqCdbKpyTRMNsH7/cQa+s9QaZ75rA3575EraOfdTdWAdzBoF1ZtazZh9/N0WqyoF6rSHCtXsXgJqcvT0GdbHnXB3VEq5TblONGeysvm/37dz04crSM/IZvrdXXl5aGsC/Zz6Gk3aDV/eYn1wjPwGKlRxW7yqlHB4QeM+EBPJ1U2r251s6uUzVX6V20Sz5UAyQ95bzgeLd3Nzp1B+f+wqejapfn6lU4fhixsBA6O+g0q13RKrKoXC+0FKApVP7qBrw2pEbtNEo8qvcpdoMrNzeCtyJ0PfX86xlAwmj4ng/25uRyX/C8aOSU+G6TdDylG4/WuoHu6egFXp1LiP9dvuJSAm4TR7j2onm6p8KleJZsfhU9z4wXLeitzFDW1rM++xq+jTvOZfK2amw8w7IHEbDJ8GoZ1KPlhVulWsCbXanr1PA+jDm6rcKheJJis7h/cXxTDo3WUcOpHORyM78daIDlQJ8D2/ojFwcD3MHgWxS2Hoh2fHg1eqyML7QdwqQitk0aJ2Jb1Po8qtMj/C5u7E0zwxeyMb4k5wXetavDK0NcFBfudXOnkINs2CjTOtsxgvX7juDWh7q3uCVmVDeD/r4d69S+jfsinvLdxF0ukzfz3+lCrjymyiyckxTF6+lzfm7qCCrxfv3NaBQc5jl2SmwfZfYMNXsGeRNfxyaBe4YRK0uhEqVHXvBqjSr14X8K0IMZEM6Hgl7yzYxYLtCefGMVKqnCiTiSbuWCpPzN7I6thj9GtRg3/d2IYalfytS2P7VsLGryD6ezhzEiqFQs/Hod1tesNfFS8vH2jUC2IW0Or6N6lT2Z/5W49oolHlTplMNJnZOcQmpfCfW9pxU8e6yIn9sHgmbJxhjR3jEwgtB1vJJexKcJSLW1XKHcL7wfafkaQY+rWsyeyoONIysqngqz1LqPKjTCaaRiFBLH00Ar8dP8GUmbBvmTUj7Ero9TS0GAx+Qe4NUpUP4X2t3zGR9G95C9NW7mNZzNGzLdGUKg/KZKJh30r8vrgRstKgWmPo8zy0HQ5V6rs7MlXeVKkP1ZtBTCRdO99HRT9v5m89rIlGlStlM9HUbgsdRlqtxkI7ay/Lyr3C+0HUZ/iaM/RuXoMF2xLIzjF4OfS4VOVD2bw54RsI1//HavWjSUa5W3hfyEqH2OX0b1mTpJQM1u8/7u6olCoxZTPRKOVJGvQA7woQE0nvZiH4eAlzo7WXAFV+aKJRytV8/CGsJ8REUsnfhz7NazBjdRyJp864OzKlSkSBiUZE6onIIhHZKiLRIvKIXV5NROaLyC77d1W7XETkHRGJEZFNItLRaV2j7fq7RGS06zZLKQ8T3g+SdsHxWJ6+tjnpmdm8OX+nu6NSqkQU5owmC3jCGNMS6AY8KCItgfHAAmNME2CB/RrgOqCJ/XMv8CFYiQmYAHQFugATcpOTUmVebp95MQtoHBLEqO4NmLVmP9sOnXRvXEqVgAITjTHmkDFmnT19CtgG1AWGAFPtalOBofb0EGCasawCqohIbeAaYL4x5pgx5jgwH7i2ODdGKY8V3BiqNICYBQA80rcJlSr48MovWzFGh3lWZVuR7tGISBjQAfgTqGmMOWTPOgzkPhhQF4hzWizeLsuv/ML3uFdEokQkKjExsSjhKeW5RKyzmr1/QFYGVQJ8ebRvE5bHJBG5LcHd0SnlUoVONCISBHwDPGqMOe9831hfyYrla5kx5mNjTIQxJiIkJKQ4VqmUZwjvBxmnIe5PAO7o1oDGIYG8+stWMrJy3BycUq5TqEQjIj5YSeZLY8y3dvER+5IY9u/cr2UHAOdeA0PtsvzKlSofGl4JDh+ImQ+Aj5eD529oSWxSKtNWxro3NqVcqDCtzgT4DNhmjHnTadaPQG7LsdHAD07ld9qtz7oByfYltrnAABGpajcCGGCXKVU++FWE+t3O3qcBuLpZDXo1DeHtBbs4lpLhxuCUcp3CnNH0AEYBfURkg/0zEHgN6C8iu4B+9muAX4E9QAzwCfAAgDHmGPAysMb+eckuU6r8CO8HR7ZYg+3Znr++BakZ2UzS5s6qjCqwrzNjzDIgv35c+uZR3wAP5rOuycDkogSoVJkS3g8iJ8DuBVZ/fECTmhW5o2t9pq/ax6juDWhas6Kbg1SqeGnPAEqVpJqtoGJtiPrcGuXV9mi/pgT5efPyz9rcWZU9mmiUKkkiMOAVOLAWZo2CLKsbmmqBvozr24Slu46yeIc261dliyYapUpam5th0FtW67M5YyE7C4A7u4fRsHogL/+ylcxsbe6syg5NNEq5Q6cxcO3rsP1n+P4+yMnG19vBcwNbsCcxhemr9rk7QqWKjSYapdyl233Q70XY/DX89Ajk5NC3RQ16hlfnrchdnEjV5s6qbNBEo5Q79XwMrnoa1n8Bvz+DAM/f0IJT6Zm8FbnL3dEpVSw00Sjlblf/A7o/BKs/hvkv0LxmRUZ0qc8Xq/YRk3DK3dEpddk00Sjlbrkt0SLuhhXvwB+v83j/pgT4ePHqL9vcHZ1Sl00TjVKeQAQG/gfa3wGL/031jR/xUJ9wFu1I5I+d2txZlW6aaJTyFA4HDH4XWg2D+S8w1i+S+tUCeOXnrWRpc2dVimmiUcqTOLxg2MfQ7Hp8fn+a91tEsyvhNDNW73d3ZEpdMk00SnkaLx+45XNo3JfWa5/n8VobeXP+TpJTM90dmVKXRBONUp7I2w+GT0fCevJw8n/ocmYF7y7U5s6qdNJEo5Sn8g2A22YgdTvxvu+77Fn5PXsST7s7KqWKTBONUp7MryLc8TUmpCUfeL/JN9/McHdEShWZJhqlPF2FKviM+YGUoPo8cOg5Nq3UgWlV6aKJRqnSIKAagX/7mSRHMOFzR5OyfUHByyjlITTRKFVK+Fetw5Eb57A/pzp+M28lY8Nsd4ekVKFoolGqFOncthW7b/iatTnh+H5/D9krP3R3SEoVSBONUqXM9V1asKPfVH7L7ozX3PGY+S+CDv+sPJgmGqVKoTuvas62nu8wPasvsnwS/PAgZOsDncozaaJRqpR6bEALtnZ8kTczb4YNX8LM2yEjxd1hKfUXmmiUKqVEhJeHtmFXiwd4NvNuzK5ImDoYUo+5OzSlzqOJRqlSzMshvDWiPfvCbuX+zEfJPrQJPhsAJ7QTTuU5NNEoVcr5eXvxv1GdiK/VhzsznyXr1BEr2RyJdndoSgGaaJQqEyr6+zDlri4cqNSB4RkTyMrJgcnXQexyd4emlCYapcqK6kF+fHF3V+J8wrg5YyKZASHwxY2w7Sd3h6bKOU00SpUh9aoFMO3uLuzJrMYtGRPIrNEaZt8Jaz5zd2iqHNNEo1QZ07xWJT4b05ltyT7cnvEPshr1g18eh0X/1gc7lVtoolGqDOocVo33b+/IukMZ3J3+KNltb4M/XoOfHtFnbVSJ00SjVBnVr2VNXr+pLX/sPsEjafeQ0+NxWDcV3ukAaz7VngRUiSkw0YjIZBFJEJEtTmXVRGS+iOyyf1e1y0VE3hGRGBHZJCIdnZYZbdffJSKjXbM5SilnN3cK5R8Dm/Pz5sO8mHoTZuxcqNYYfnkC3u8CW76BnBx3h6nKuMKc0UwBrr2gbDywwBjTBFhgvwa4Dmhi/9wLfAhWYgImAF2BLsCE3OSklHKte69qzN97NWLayn28vbMa3PUr3D4bvCvAnLHwSW/YvdDdYaoyrMBEY4xZAlzYp8UQYKo9PRUY6lQ+zVhWAVVEpDZwDTDfGHPMGHMcmM9fk5dSykXGX9ucWzqF8lbkLt6M3IVpMgDuWwo3/g9Sj1vNoKcOhgNr3R2qKoMu9R5NTWPMIXv6MFDTnq4LxDnVi7fL8iv/CxG5V0SiRCQqMTHxEsNTSjkTEf49rA23RoTyzoJdTPgxmhwc0G4EPBwF174GR7bAJ32s5tBHY9wdsipDLrsxgDHGAMXWZtIY87ExJsIYExESElJcq1Wq3PP2cvD6TW259yrrMtqjszaQkZUD3n7Q7X4YtwF6PQO7Iq37Nz89AicPFbhepQpyqYnmiH1JDPt3gl1+AKjnVC/ULsuvXClVgkSEfwxswfjrmvPjxoPc+0UUaRnZ1kz/SnD1P+CRDdD5blj/pdVCLfJFSDvhxqhVaXepieZHILfl2GjgB6fyO+3WZ92AZPsS21xggIhUtRsBDLDLlFJucF+vxrw2rA1LdiYy8rM/SU51auocVAMGvgEPrYEWN8CySfB2O1j+NmSdcV/QqtQqTPPmGcBKoJmIxIvI3cBrQH8R2QX0s18D/ArsAWKAT4AHAIwxx4CXgTX2z0t2mVLKTUZ0qc/7t3dkc3wywz9eScLJ9PMrVGsIN30Kf18KoZ1h/gvWPRztFVoVkRgP7pIiIiLCREVFuTsMpcq0ZbuOcu8XUVQP8mP63V2pHxyQd8Udv8GPD0N6MvT5J3R/EBxeJRusKhQRWWuMiXB3HLm0ZwClyrmeTarz1T3dOJWeyU0frWDboZN5V2x2HTywCpoMgPn/tJpD6wBrqhA00SilaF+vCl/f1x0vEYb/byVRsflc2Q6sDsOnw5AP4NBG+OAK2DBDO+tUF6WJRikFQHiNisy5vzvVg/wY+dmfLNqRkHdFEehwB9y/DGq1ge/vg9mjICWpZANWpYYmGqXUWaFVA5h9X3fCawRxz9QofthwkacQqobBmJ+h30TY8Tt80A12ziuxWFXpoYlGKXWe6kF+zLinG50aVOXRWRv4YmVs/pUdXtDzUbh3EQSGwFe3wM+P6VAE6jyaaJRSf1HR34epY7vQt3lN/vlDNG9H7uKiLVRrtYF7FsIVD0PU5/BRT4hbU3IBK4+miUYplSd/Hy8+GtmRmzqGMilyJxN/2kpOzkWSjY8/DHjFupyWnQmTB8DCV3XcG6WJRimVP28vB2/c3Ja/9WzIlBWxjP58NQmn0i++UFhPuH85tB0BS/4PPu0HiTtKJmDlkTTRKKUuyuEQnru+Bf+6sQ2r9x5j4NtL82+Rlsu/Mtz4Idz6hfWszf+ugsWvQdrxkglaeRRNNEqpAokIt3etz08P96R6kB93fb6GV37eypms7Isv2HKw/ZBnf1j8b5jUBuZPgNMFJCpVpmgXNEqpIknPzOZfv25j2sp9tKpTiXdv60CjkKCCFzy8BZb+F6K/s4Ym6HgnXDEOqtQreFlVJJ7WBY0mGqXUJZkXfZinv9lERlYOEwe34uZOoYhIwQsejbF6hN40ExBoNxx6Pg7BjV0ec3mhiaYINNEo5dkOJafx2KwNrNpzjEHt6vDqja2p5O9TuIVP7Ifl78C6aZCTCa1uhCufgJqtXBt0OaCJpgg00Sjl+bJzDB8ujmFS5C5qV/bnnds60LF+1cKv4NQRWPkeRE2GjNPQbCBc+SSEdnJd0GWcJpoi0ESjVOmxdt9xxs1Yz+GT6Tzevyn39WqMl6MQl9JypR6D1R/Dqg8h/QQ06m0lnLCeVv9qqtA00RSBJhqlSpfktEye+24zP286xBWNg5k0vD01K/kXbSVnTllnNyveg5QEqNcVeo+Hxn1cE3QZpImmCDTRKFX6GGP4OiqeCT9G4+/j4I2b29GvZc2irygzDdZPt4aQTo6DZtfDNa9aI3+qi/K0RKPP0SilipWIcGvnevw8rie1K1fgb9OimPDDFtIzC3jm5kI+FaDLPfDwWuj3IuxZDO93hQUva6edpYye0SilXOZMVjav/7aDycv3UruyPw9cHc6tEaH4eV/CENAnD1oPe26eDZXqwoCXodUwvX+TB087o9FEo5RyuZW7k/jPvB2s3XecOpX9ebBPOLd0qoev9yVcVNm3En57Cg5vhgY94brXoVbr4g+6FNNEUwSaaJQqO4wxLN11lEmRO1m//wR1q1TgoT7h3NwpFB+vIiacnGxYN9W6jJZ+AiLuhqv/AQHVXBJ7aaOJpgg00ShV9hhj+GNnIpMid7Ex7gShVSvwcJ9whnW8hISTegwW/QuiPgP/KtD3n9BxtDUgWzmmiaYINNEoVXYZY1i8I5FJkTvZFJ9M/WoBPNQnnGEd6uJd1IRzeAv89gzsW2YNwnbdG9Cgu2sCLwU00RSBJhqlyj5jDAu3JzApcidbDpykQXAAD/dpwtD2dYqWcIyB6G9h3j/h5AFocyv0nwiV6rgueA+liaYINNEoVX4YY4jclsBbkTuJPniShtUDebhPOIPbFTHhZKTA0jdhxTvg8IEej0DHUeUq4WiiKQJNNEqVP8YY5m09wluRu9h26CSNqgdyV48wejYJISw4oHA9RAMc2wNzn4cdvwACjXpB2+HQYhD4VXTpNribJpoi0ESjVPmVk2OYt/Uwb0XuYvvhUwDUqezPFeHV6REezBWNqxeue5ujMdazN5tmwfFY8K4AzQdaSadxH/AqZG/TpYgmmiLQRKOUMsaw92gKy3cnsSLmKCv3JHEiNROAxiGB9AivzhWNq9O9UTCVAy6SNIyBuNVWwon+1hpWOqA6tL7JSjp1O5aZhz810RSBJhql1IVycgxbD51kxe6jLI9JYvXeY6RlZuMQaF23Mt0bB9OjcXU6h1Wjgm8+zZyzMiAm0ko6O36D7DNQrbGVcNreWur7U9NEUwSaaJRSBcnIymFj/AmWxxxlRUwS6+OOk5lt8PVy0KF+Fbo2rEZ4zYo0DgmkUfWgvyaftBOw7UfYNBtil1pl9bpaCafVsFL5EKgmmiLQRKOUKqrUjCxW7z3Git1JrNh9lOiDJ3H+mKtbpQLhNYJoHBJE4xqB1u+QIKoH+SLJ8bBlDmycBYnbQBwQ3MTq4qZWG6jZxvpd8RJ6oy5B5T7RiMi1wNuAF/CpMea1/OpqolFKXa70zGxik1LYnZDC7sTTxCScZnfiafYkppDm1KN0JX9vGucmoOqBtPeNo9nxxQSd2I5P4hYrCeUKDIGadvLJ/QluAl7ebtjCvyrXiUZEvICdQH8gHlgD3GaM2ZpXfU00SilXyckxHDqZzm478exOPH02GSWcOnNeXYdAqP8ZOvkfoI13HE1NLGFZe6h1JhZvYzVMyHb4kl6lKVk1WuFVpy0V6rbFK7Ca1arNy8d6psfLB7x8weFt/fbycUl3OZ6WaEo6/XYBYowxewBEZCYwBMgz0SillKs4HELdKhWoW6UCVzUNOW/eyfRM9iSmEHs0hWMpGRxPtX9SwohMzeDr1EyOZ2RwMjOVetnxtJD9tHTso0XiPlom/Url7bMKHUcOQibeZONFFt5kiTdZeLE/pDcRD0wu7s12i5JONHWBOKfX8UBX5woici9wL0D9+vVLLjKllLJV8vehfb0qtK9X5aL1jDGkZWZzPDWT43ZCWp6SwZnjB/A+ugNH5mkkJwPJyUJyMpGcLBz2b8nJxGEyceRkIyYTR04mjpwsHCYLL5NJTrVmJbOxJcAzLig6McZ8DHwM1qUzN4ejlFL5EhECfL0J8PWmbpUKTnPqYl3AUVDyQzkfAOo5vQ61y5RSSpVRJZ1o1gBNRKShiPgCI4AfSzgGpZRSJahEL50ZY7JE5CFgLlbz5snGmOiSjEEppVTJKvF7NMaYX4FfS/p9lVJKuUdJXzpTSilVzmiiUUop5VKaaJRSSrmUJhqllFIu5dG9N4tIIrDvMlZRHThaTOEUJ42raDSuotG4iqYsxtXAGBNScLWS4dGJ5nKJSJQndSyXS+MqGo2raDSuotG4XE8vnSmllHIpTTRKKaVcqqwnmo/dHUA+NK6i0biKRuMqGo3Lxcr0PRqllFLuV9bPaJRSSrmZJhqllCoEEblfRCq5O47SyKMSjYh0EpHNIhIjIu+IiFykrpeIrBeRny/hfV4SkZUiMltEml5e1K4lIgEi8ouIbBeRaBF5zWneGBFJFJEN9s/f8lnH4yKyVUQ2icgCEWngNO91Edli/wy/hPg8bl+KyGIR2eG0X2rY5X4iMss+vv4UkbB8ln9RRA44LT/wgvn1ReS0iDxZxLi8ROQHEVkkIlNFxO0DD4pIRaft3CAiR0XkLXteYY+vSU51dorICad5ZeL4EpGbgbrGmJOXuHzvon5WicjvInLiwuXsYVb+tI/jWfaQK4U+vi/yfk1FZIn9efNcUZYtkDHGY36A1UA3QIDfgOsuUvdx4CvgZ3fH7eJ9EgBcbU/7Aktz9wswBnivEOu4Ggiwp+8HZtnT1wPzsXrxDsQaL6iSu7e5GPbZYiAij/IHgI/s6RG5+yGPei8CT15k/XOAry9Wp7T+AGuBq4pyfF2w/MNYw3+UqeMLGAU4LmP53kX9rAL6AoMuXA6YDYywpz8C7renC3V8u+PHY85oRKQ21kG4ylh7ahowNJ+6oVgH8acXWd89IrJGRDaKyDciEmCXT7HPllaIyB77mwpiecP+5rU599uXiNS2s/wGe96VxbvlF2eMSTXGLLKnM4B1WCOTFmUdi4wxqfbLVU7LtwSWGGOyjDEpwCbg2guXLyv7EhgCTLWn5wB9RfI/a86LiAwF9gL5jqMkIh+KSJRYZ6ATncpjRWSiiKyz90tzu7yaiHwv1hnnKhFpa5f3cjpTWC8iFYu2uUVjnzHUwPoyc6luA2bY0x59fIl1VvqG/XeKFJEuYp0N7xGRwXadMBFZCjwGRInIFYV9LxG5VqwrEeuAYU7lgSIyWURW23/XIXnFZ4xZAJy6YJ0C9ME6fsE6nofa0wUe3yISJNZVjdxjcIjTdm4TkU/s/TFPRCrY89rbx+UmEflORKra5ePk3JWSmRfb127PdE5ZOgKIdHp9Jfl8A7B3Yicu8i0BCHaafgV42J6egvVt1IH1jxBjl9+E9e3LC6gJ7AdqA08Az9l1vICKbtxHVYA9QCP79RjgENY/8BygXiHW8R7wvD09AFiOddZU3V73E6V9X2Kd0WwGNgD/5Fzryi1AqFO93UD1PJZ/EYi19+tkoKpdHgSstH+/SD5nNEA1p21cDLS1X8c67bsHgE/t6XeBCfZ0H2CDPf0T0MPpvb1dfHy9APzH6XWRji+ggV3fqzQcX4Dh3NWB74B5gA/QzulvEAD429PNgbX29EXfC/AH4oAmWFdoZmN/VgH/AkY6/U/vBALzibE3Tp9x9n6McXpdD9hS2OMb6+yykvO67PjCgCygvT1vtlOMm4Be9vRLwFv29EHAL3c7LravPeaMprBE5AYgwRiztoCqrUVkqYhsBu4AWjnN+94Yk2OM2Yp1oAL0BGYYY7KNMUeAP4DOWKf7d4nIi0AbY8x53zBKiljX82cA7xhj9tjFPwFhxpi2WP94U/Nb3l7HSKyE/gaAMWYe1iB0K+x1rwSy81i0tO3LO4wxbbC+rFyJddmjKD4EGgPtsT44/2uXvwhMMsacLmD5W+1vseux9lVLp3nf2r/XYv1zg7W/vgAwxiwEgsW66bwceFNExmH9I2cVcTuKagTnzkagiMeXvfwcY0w2lIrjKwP43Z7eDPxhjMm0p8Pscm/gfRFZjnWZqrldXtB7NQf2GmN2GeuTeLrTvAHAeBHZgPVFxB+oX0CsxUWAf4nIJiASqMu5/brXGLPBnl4LhIlIZaxj7w+7fCpwlT29CfjS/ly56LHpSYnmAOdfEgoFDohIPafLB/cBPYDBIhILzAT6iMj0v66OKcBD9gfORKw/Zq4zTtMXvXRijFmCtWMPAFNE5M6ibVax+RjYZYx5yym2JGNM7rZ8inWWh4i8mrvPcuuKSD/gOWCw0zIYY141xrQ3xvTH2hc783jvKZSifWmMOWD/PoV1H6+LPesA1jfA3MRdGUgSkc/t/fWrvdwR+0MsB/jEafmuwP/Zx96jwD/EGpr8LBFpCDwJ9LU/oH8h7/2VTQEj3BpjXgP+BlQAludeanMFEWmHdcZ09gtcUY4v24WJytOPr0w7CQDk5L6X/XfP/ds8BiQaY3oA/QC/S3wvZwLcZO+X9saY+saYbYVcNgmoIucakoTaMUA+x/cFy98BhACdjDHtgSOc29/O+7rA4xPr9sX7QEdgjVykcYvHJBpjzCHgpIh0s68r3gn8YIyJc/qDfGSMedYYE2qMCcM6sBcaY0bmscqKwCER8cHauQVZCgwXq2VQCNZBtFqsFlpHjDGfYP2zdbz8rS0aEXkF66B59ILy2k4vBwPbAIwxz+XuM7teB+B/WEkmwWl5LxEJtqfbAm2xLh9cqNTsSxHxFpHq9rQPcAPWJQWAH4HR9vTNWMeOMcbcZe+vgfZyzvv1xtzljTFXGmPC7GPvLeBfxpj3LgihEpACJItITeC6QoS9FHu/ikhv4Kgx5qSINDbGbDbGvI71DdpliYbz761gx1Ko48uu2xyoinXWkltWFo6vqkCiPT0K6zIZhXiv7VhnBI3t17c5zZsLPJx7/8T+/ywUOzEuwjp+wTqef7Cn8zy+L1hFZawrQpkicjXW5c6LvV8ycNzpHtQo4A8RcWBdSl0EPGOvNyi/9bi9eeUFHsD6dlMBq9XZb5exrn8Cf2IdJH9iHcwX8x3QHdiIde32aWPMYREZDTwlIpnAaawEWGLEavjwHNaBu84+Nt8zxnwKjBPrpmUWcAzrmnpe3sA6CL62l99vjBmMdT16qV12EuuabF6nwKVpX/oBc+0PLS+sywOf2PM+A74QkRis/TUin3X8n4i0t2OPBf5e2Dc3xmwUkfVYf684rMtfBXkRmGxfzkjl3IfFo/aHQQ5W44PL+X8oyK3AwAvKCnt8gbUvZ17wwVYWjq8PgTn2GcvvWF8iwLp3ku97GWPSReRe4BcRScVKjrnb9TLWF5VN9gf2XqwvROcRqxFCcyBIROKBu40xc7E+2GfaX0DXYx3XULjj+0vgJ/syZRTWcVqQ0cBHYjXS2APchfW/Nd2+tCZYl/RP5LcC7YJGKaWUS3nMpTOllFJlkyYapZRSLqWJRimllEtpolFKKeVSmmiUUkq5lCYaVS6ISEFP8yMij9pNOF0dyxSx++1SqjzQRKPUOY9i9W1VaCLi5ZpQlCo7NNGockWscUEWi8gcsXrW/VIs44A6wCIRWWTXHSDWWCjrRORrEQmyy2PFGmdlHdZDe6ud1h9mPwyHiLwgVq/EW0Tk49wnwS+Ip69YPfhuFqtHX78S2RFKlSBNNKo86oB19tISaITVQ/I7WL3RXm2MudruxuZ5oJ8xpiPWU9SPO60jyRjT0e6PzNfu4wxgODDLnn7PGNPZGNMaq7eL857+FhF/rJ4whtv9fHljjRekVJmiiUaVR6uNMfF254kbONdTr7NuWIloud155GjO7xdqltP0bKwEA+cnmqvFGulwM1b3/869EgM0w+oxN7ejSeeecZUqMzytrzOlSkJheqkVYL4x5rY85sG5Pq/ASixfi8i3WP0e7rLPVj7AGukzTqzu5P3zWI9SZZ6e0Sh1zinOdXy4CughIuFwdlTEPMesN8bsxkpY/+Tc2UxuUjlq39vJq5XZDqwefsPt16OwxlZRqkzRMxqlzvkY+F1EDtr3acYAM5xu0D9P3uOpgJVg3gAaAhhjTojIJ1hDDBzG6ub/PHYPv3dhnQ1523U+Ks4NUsoTaO/NSimlXEovnSmllHIpTTRKKaVcShONUkopl9JEo5RSyqU00SillHIpTTRKKaVcShONUkopl/p/3e09AASF/JQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "muestra.plot(title='1998')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Intervalo'>"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "muestra.plot(kind = 'bar')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Se ha generado un gráfico de barras con el total de población por grupos de edad como eje de las x y dos barras (naranja:mujeres, azul:hombres). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "muestra.plot(kind = 'bar');\n",
    "muestra1998 = [('Hombres') , ('Mujeres')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cada vez que dibujamos un gráfico nos aparece un texto que acredita que se ha ejecutado la última instrucción ejecutada. Para que no se reitere continúamente adjuntaremos un punto y una coma `(;)`al final de la instrucción"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El resultado es que cada barra representa el valor de una columna del DataFrame. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos alterar la dirección de las barras de vertical a horizontal sustituyendo bar por barh (h de horizontal). Es decir, la fórmula sería `kind = 'barh'`) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfico de barras horizontales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "muestra.plot(kind = 'barh');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El grosor de las barras se establece de manera automática, podemos modificarlas paras hacerlas más accesibles a nivel visual con el parámetro `width`, que nos permite oscilar entre 0 y 1. Por defecto la función establece un grosor de 0.5. Vamos a mostrar las barrar con su máximo de grosor y con 0.2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Intervalo'>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "muestra.plot(kind = 'barh', width=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Intervalo'>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "muestra.plot(kind = 'barh', width=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfico de barras apiladas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por medio del parámetreo stacked podemos generar gráficos de barras apiladas. Entre sus opciones de diseño podemos establecer cierto nivel de transparencia a los colores de las barras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "muestra.plot(kind = 'bar', \n",
    "             stacked = 'True',\n",
    "             alpha = 0.4,\n",
    "             width = 0.9, \n",
    "             figsize=(9,4));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este tipo de gráfico también es interesante porque es muy visual permitiéndonos discernir los datos y hacer comparativas rápidamente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Gráfico de barras separados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QmcAFANDZovleAIBJWXb0qRNvc9Vxh068TWDHYwsXAEBnAhcAQGcCFwBAZwIXAEBnDpoHunMwO7Cjs4ULAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM1eahwXGVdkBZo8tXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdbTJwVdXiqvpSVX2lqs6rqpeN4/erqv+qqpVVdXJVXW8cf/3x+cpx+rI5bb1oHH9BVT20W68AABaQzdnC9cskD2qt3SXJXZMcUlX3SvLqJK9rrd02yeVJnjHO/4wkl4/jXzfOl6o6IMkRSQ5MckiSN1bVzhPsCwDAgrTJwNUGPx2f7jI+WpIHJXn/OP7EJIePw4eNzzNOP7iqahx/Umvtl621byVZmeSek+gEAMBCtlnHcFXVzlV1VpIfJDktyTeS/Ki1dtU4y+oke4/Deye5OEnG6WuT7Dl3/AZeM7fWUVW1oqpWrFmzZos7BACw0GxW4GqtXd1au2uSpRm2Sv33XgvUWju+tba8tbZ8yZIlvcoAAEzNFp2l2Fr7UZJPJ7l3kj2qatE4aWmSS8bhS5LskyTj9N2TXDp3/AZeAwAwszbnLMUlVbXHOHyDJA9Ocn6G4PWYcbYjk3x4HD5lfJ5x+qdaa20cf8R4FuN+SfZP8qUJ9QMAYMFatOlZcsskJ45nFO6U5L2ttX+rqq8mOamqXpHkzCRvGed/S5J3VNXKJJdlODMxrbXzquq9Sb6a5Kokz2ytXT3Z7gAALDybDFyttbOT3G0D47+ZDZxl2Fq7IsljN9LWK5O8cssXEwBg++VK8wAAnQlcAACdCVwAAJ0JXAAAnW3OWYpMwbKjT514m6uOO3TibQIAW84WLgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzhbN9wIA82PZ0adOvM1Vxx068TYBZoEtXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0tmu8FWOiWHX3qxNtcddyhE28TAFi4bOECAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6GzRfC/A1lp29KkTb3PVcYdOvE0AAFu4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADpbNN8LALC9WXb0qRNvc9Vxh068TWDhsIULAKAzgQsAoDOBCwCgM4ELAKAzgQsAoLNNBq6q2qeqPl1VX62q86rqz8bxN62q06rqwvHnTcbxVVVvqKqVVXV2Vd19TltHjvNfWFVH9usWAMDCsTlbuK5K8vzW2gFJ7pXkmVV1QJKjk3yytbZ/kk+Oz5PkYUn2Hx9HJXlTMgS0JMckOSjJPZMcsy6kAQDMsk0Grtbad1trZ4zDP0lyfpK9kxyW5MRxthOTHD4OH5bk7W3wxSR7VNUtkzw0yWmttctaa5cnOS3JIZPsDADAQrRFx3BV1bIkd0vyX0n2aq19d5z0vSR7jcN7J7l4zstWj+M2Nh4AYKZtduCqqt2SfCDJc1trP547rbXWkrRJLFBVHVVVK6pqxZo1aybRJADAvNqswFVVu2QIW+9qrX1wHP39cVdhxp8/GMdfkmSfOS9fOo7b2PjraK0d31pb3lpbvmTJki3pCwDAgrQ5ZylWkrckOb+19to5k05Jsu5MwyOTfHjO+KeMZyveK8nacdfjx5M8pKpuMh4s/5BxHADATNucm1ffN8mTk5xTVWeN4/4yyXFJ3ltVz0hyUZLHjdM+muThSVYm+XmSpyVJa+2yqvqrJF8e53t5a+2ySXQCAGAh22Tgaq39R5LayOSDNzB/S/LMjbR1QpITtmQBAQC2d640DwDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0Nmi+V4AADZs2dGnTrzNVccdOvE2gU2zhQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoLNNBq6qOqGqflBV584Zd9OqOq2qLhx/3mQcX1X1hqpaWVVnV9Xd57zmyHH+C6vqyD7dAQBYeDZnC9fbkhyy3rijk3yytbZ/kk+Oz5PkYUn2Hx9HJXlTMgS0JMckOSjJPZMcsy6kAQDMukWbmqG19rmqWrbe6MOSPGAcPjHJZ5K8cBz/9tZaS/LFqtqjqm45zntaa+2yJKmq0zKEuPdsexcA2BbLjj514m2uOu7QibcJ27OtPYZrr9bad8fh7yXZaxzeO8nFc+ZbPY7b2HgAgJm3zQfNj1uz2gSWJUlSVUdV1YqqWrFmzZpJNQsAMG+2NnB9f9xVmPHnD8bxlyTZZ858S8dxGxv/G1prx7fWlrfWli9ZsmQrFw8AYOHY2sB1SpJ1ZxoemeTDc8Y/ZTxb8V5J1o67Hj+e5CFVdZPxYPmHjOMAAGbeJg+ar6r3ZDjo/WZVtTrD2YbHJXlvVT0jyUVJHjfO/tEkD0+yMsnPkzwtSVprl1XVXyX58jjfy9cdQA8AMOs25yzFJ2xk0sEbmLcleeZG2jkhyQlbtHQAADPAleYBADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOls03wsAwI5j2dGnTrzNVccdOvE2YdJs4QIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDobNF8LwAATNqyo0+deJurjjt04m2y47CFCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKCzRfO9AACwvVp29KkTb3PVcYdOvE3mny1cAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ258CkALHAusLr9s4ULAKAzgQsAoDOBCwCgM4ELAKCzqQeuqjqkqi6oqpVVdfS06wMATNtUz1Ksqp2T/FOSBydZneTLVXVKa+2r01wOAOA3ORuyn2lv4bpnkpWttW+21n6V5KQkh015GQAApmragWvvJBfPeb56HAcAMLOqtTa9YlWPSXJIa+0Px+dPTnJQa+1Zc+Y5KslR49PbJ7lgG8veLMkPt7GNhVZra+rsnmTtFOpsjVmrM81avgsLu840a/kuLOw606zluzB/dfZtrS3Z0IRpX2n+kiT7zHm+dBx3rdba8UmOn1TBqlrRWls+qfYWQq2tqVNVx7fWjtr0nNtWZ2vMWp1p1vJdWNh1plnLd2Fh15lmLd+FhVln2rsUv5xk/6rar6qul+SIJKdMeRl2VB+Z7wVgwfBdYB3fBdbxXehsqlu4WmtXVdWzknw8yc5JTmitnTfNZdhRtdasTCTxXeDXfBdYx3ehv6nfvLq19tEkH51iyYntnlxAtdRZ2HWmWUudhV1nmrXUWdh1pllLnQVYZ6oHzQMA7Ijc2gcAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELmGlV9dOqus18LwewYxO4gAWrqlZV1a+q6mbrjT+zqlpVLdtUG6213Vpr3+y2kACbQeACFrpvJXnCuidVdackN+xdtKoW9a4B7DgELmChe0eSp8x5fmSSt697UlWfqao/nPP8qVX1H3Oet6q67Th8/ar626r6dlV9v6reXFU3GKc9oKpWV9ULq+p7Sd5aVTtV1dFV9Y2qurSq3ltVNx3nX1xV7xzH/6iqvlxVe/V9K4DtlcAFLHRfTHLjqrpDVe2c5Igk79zKto5Lcrskd01y2yR7J3npnOm3SHLTJPsmOSrJs5McnuT+SW6V5PIk/zTOe2SS3ZPsk2TPJH+c5BdbuVzAjBO4gO3Buq1cD05yfpJLtrSBqqoMIep5rbXLWms/SfKqDAFunWuSHNNa+2Vr7RcZQtSLW2urW2u/THJskseMuxuvzBC0bttau7q1dnpr7cdb30VgljlGAdgevCPJ55Lslzm7E7fQkgzHfp0+ZK8kSSXZec48a1prV8x5vm+SD1XVNXPGXZ1kr3GZ9klyUlXtkWGr24tba1du5fIBM8wWLmDBa61dlOHg+Ycn+eB6k3+W6x5Ef4uNNPPDDLv8Dmyt7TE+dm+t7Ta31HqvuTjJw+bMv0drbXFr7ZLW2pWttZe11g5Icp8kj8h1jzUDuJbABWwvnpHkQa21n603/qwkj6qqG44Hxz9jQy9urV2T5J+TvK6qbp4kVbV3VT30t9R8c5JXVtW+4/xLquqwcfiBVXWn8biyH2fYxXjNxpsCdmQCF7BdaK19o7W2YgOTXpfkV0m+n+TEJO/6Lc28MMnKJF+sqh8n+f+S3P63zP/6JKck+URV/STDAfwHjdNukeT9GcLW+Uk+m2E3I8BvqNbW34IOMBuqaqcMx1zt21r79nwvD7DjsoULmGV3THJFku/N94IAOzaBC5hJVfXoJJ9O8sLW2q/me3mAHZtdigAAndnCBQDQmcAFANDZgr7S/M1udrO2bNmy+V4MAIBNOv3003/YWluyoWkLOnAtW7YsK1Zs6LI7AAALS1VdtLFpdikCAHQmcAEAdCZwAQB0tqCP4QIApufKK6/M6tWrc8UVV8z3oixoixcvztKlS7PLLrts9msELgAgSbJ69erc6EY3yrJly1JV8704C1JrLZdeemlWr16d/fbbb7NfZ5ciAJAkueKKK7LnnnsKW79FVWXPPffc4q2AAhcAcC1ha9O25j0SuACABaOq8qQnPena51dddVWWLFmSRzziEZt87X3uc5+ei7ZNHMMFAGzYsbtPuL21m5xl1113zbnnnptf/OIXucENbpDTTjste++992Y1//nPf36zF6W1ltZadtppOtuebOECABaUhz/84Tn11FOTJO95z3vyhCc84dppxx57bP72b//22ud3vOMds2rVqiTJbrvtdu3417zmNbnHPe6RO9/5zjnmmGOSJKtWrcrtb3/7POUpT8kd73jHXHzxxRuc72c/+1kOPfTQ3OUud8kd73jHnHzyydvcJ4ELAFhQjjjiiJx00km54oorcvbZZ+eggw7aotd/4hOfyIUXXpgvfelLOeuss3L66afnc5/7XJLkwgsvzJ/+6Z/mvPPOywUXXLDB+T72sY/lVre6Vb7yla/k3HPPzSGHHLLNfbJLEQBYUO585ztn1apVec973pOHP/zhW/z6T3ziE/nEJz6Ru93tbkmSn/70p7nwwgtz61vfOvvuu2/uda97/db57ne/++X5z39+XvjCF+YRj3hE7ne/+21znwQuAGDBeeQjH5k///M/z2c+85lceuml145ftGhRrrnmmmufb+jyDK21vOhFL8of/dEfXWf8qlWrsuuuu25yviQ544wz8tGPfjQveclLcvDBB+elL33pNvXHLkUAYMF5+tOfnmOOOSZ3utOdrjN+2bJlOeOMM5IMoehb3/rWb7z2oQ99aE444YT89Kc/TZJccskl+cEPfrDZ833nO9/JDW94wzzpSU/KC17wgmvrbQtbuACABWfp0qV5znOe8xvjH/3oR+ftb397DjzwwBx00EG53e1ud+20ddfHeshDHpLzzz8/9773vZMMB9O/853vzM4773ydtjY238qVK/OCF7wgO+20U3bZZZe86U1v2ub+VGttmxvpZfny5W3FihXzvRgAsEM4//zzc4c73GG+F2OrXHrppbn73e+eiy66aCr1NvReVdXprbXlG5rfLkUAYLv2ne98J/e+973z53/+5/O9KBtllyIAsF271a1ula9//evzvRi/lS1cAACdCVwAwLUW8rHdC8XWvEcCFwCQJFm8eHEuvfRSoeu3aK3l0ksvzeLFi7fodY7hAgCSDJdiWL16ddasWTPfi7KgLV68OEuXLt2i1whc9DPpu8wnm3WneQC2zi677JL99ttvvhdjJtmlCADQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFANDZZgeuqtq5qs6sqn8bn+9XVf9VVSur6uSqut44/vrj85Xj9GVz2njROP6CqnroxHsDALAAbckWrj9Lcv6c569O8rrW2m2TXJ7kGeP4ZyS5fBz/unG+VNUBSY5IcmCSQ5K8sap23rbFBwBY+DYrcFXV0iSHJvmX8XkleVCS94+znJjk8HH4sPF5xukHj/MfluSk1tovW2vfSrIyyT0n0AcAgAVtc7dw/X2Sv0hyzfh8zyQ/aq1dNT5fnWTvcXjvJBcnyTh97Tj/teM38BoAgJm1ycBVVY9I8oPW2ulTWJ5U1VFVtaKqVqxZs2YaJQEAutqcLVz3TfLIqlqV5KQMuxJfn2SPqlo0zrM0ySXj8CVJ9kmScfruSS6dO34Dr7lWa+341try1tryJUuWbHGHAAAWmkWbmqG19qIkL0qSqnpAkj9vrT2xqt6X5DEZQtiRST48vuSU8fkXxumfaq21qjolybur6rVJbpVk/yRfmmhv2DEdu3uHNtdOvk0AdlibDFy/xQuTnFRVr0hyZpK3jOPfkuQdVbUyyWUZzkxMa+28qnpvkq8muSrJM1trV29DfQCA7cIWBa7W2meSfGYc/mY2cJZha+2KJI/dyOtfmeSVW7qQAADbM1eaBwDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhs0XwvAGw3jt29Q5trJ9/mQuS9A3ZwtnABAHQmcAEAdCZwAQB05hiuHY1jaQBg6mzhAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhs0XwvALCeY3fv0ObaybcJwGazhQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzgQsAoDOBCwCgM4ELAKAzV5qHHZUr2gNMjS1cAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACduQ4XMDtcWwxYoGzhAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDobJOBq6oWV9WXquorVXVeVb1sHL9fVf1XVa2sqpOr6nrj+OuPz1eO05fNaetF4/gLquqh3XoFALCAbM4Wrl8meVBr7S5J7prkkKq6V5JXJ3lda+22SS5P8oxx/mckuXwc/7pxvlTVAUmOSHJgkkOSvLGqdp5gXwAAFqRNBq42+On4dJfx0ZI8KMn7x/EnJjl8HD5sfJ5x+sFVVeP4k1prv2ytfSvJyiT3nEQnAAAWss06hquqdq6qs5L8IMlpSb6R5EettavGWVYn2Xsc3jvJxUkyTl+bZM+54zfwGgCAmbVZgau1dnVr7a5JlmbYKvXfey1QVR1VVSuqasWaNWt6lQEAmJotOkuxtfajJJ9Ocu8ke1TVonHS0iSXjMOXJNknScbpuye5dO74Dbxmbo3jW2vLW2vLlyxZsiWLBwCwIG3OWYpLqmqPcfgGSR6c5PwMwesx42xHJvnwOHzK+Dzj9E+11to4/ojxLMb9kuyf5EsT6gcAwIK1aNOz5JZJThzPKNwpyXtba/9WVV9NclJVvSLJmUneMs7/liTvqKqVSS7LcGZiWmvnVdV7k3w1yVVJntlau3qy3QEAWHg2Gbhaa2cnudsGxn8zGzjLsLV2RZLHbqStVyZ55ZYvJgDA9suV5gEAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOls03wvA6NjdO7S5dvJtAgBbzBYuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzhbN9wIseMfu3qHNtZNvEwBYsGzhAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDobNF8L8BWO3b3Dm2unXybAMAOzxYuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzjYZuKpqn6r6dFV9tarOq6o/G8fftKpOq6oLx583GcdXVb2hqlZW1dlVdfc5bR05zn9hVR3Zr1sAAAvH5mzhuirJ81trByS5V5JnVtUBSY5O8snW2v5JPjk+T5KHJdl/fByV5E3JENCSHJPkoCT3THLMupAGADDLNhm4Wmvfba2dMQ7/JMn5SfZOcliSE8fZTkxy+Dh8WJK3t8EXk+xRVbdM8tAkp7XWLmutXZ7ktCSHTLIzAAAL0RYdw1VVy5LcLcl/JdmrtfbdcdL3kuw1Du+d5OI5L1s9jtvYeACAmbbZgauqdkvygSTPba39eO601lpL0iaxQFV1VFWtqKoVa9asmUSTAADzarMCV1XtkiFsvau19sFx9PfHXYUZf/5gHH9Jkn3mvHzpOG5j46+jtXZ8a215a235kiVLtqQvAAAL0uacpVhJ3pLk/Nbaa+dMOiXJujMNj0zy4TnjnzKerXivJGvHXY8fT/KQqrrJeLD8Q8ZxAAAzbdFmzHPfJE9Ock5VnTWO+8skxyV5b1U9I8lFSR43TvtokocnWZnk50meliSttcuq6q+SfHmc7+Wttcsm0QkAgIVsk4GrtfYfSWojkw/ewPwtyTM30tYJSU7YkgUEANjeudI8AEBnAhcAQGcCFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGebc/NqAOY6dvcOba6dfJvAgmELFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGcCFwBAZwIXAEBnAhcAQGeL5nsBANiIY3fv0ObaybcJbJItXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnS2a7wUAYJ4du3uHNtdOvk3YjtnCBQDQmcAFANCZwAUA0JnABQDQmcAFANDZJgNXVZ1QVT+oqnPnjLtpVZ1WVReOP28yjq+qekNVrayqs6vq7nNec+Q4/4VVdWSf7gAALDybs4XrbUkOWW/c0Uk+2VrbP8knx+dJ8rAk+4+Po5K8KRkCWpJjkhyU5J5JjlkX0gAAZt0mA1dr7XNJLltv9GFJThyHT0xy+Jzxb2+DLybZo6pumeShSU5rrV3WWrs8yWn5zRAHADCTtvYYrr1aa98dh7+XZK9xeO8kF8+Zb/U4bmPjf0NVHVVVK6pqxZo1a7Zy8QAAFo5tPmi+tdaStAksy7r2jm+tLW+tLV+yZMmkmgUAmDdbG7i+P+4qzPjzB+P4S5LsM2e+peO4jY0HAJh5Wxu4Tkmy7kzDI5N8eM74p4xnK94rydpx1+PHkzykqm4yHiz/kHEcAMDM2+TNq6vqPUkekORmVbU6w9mGxyV5b1U9I8lFSR43zv7RJA9PsjLJz5M8LUlaa5dV1V8l+fI438tba+sfiA8AMJM2Gbhaa0/YyKSDNzBvS/LMjbRzQpITtmjpAABmgCvNAwB0JnABAHQmcAEAdCZwAQB0JnABAHS2ybMUAWBijt29Q5trJ98mTJgtXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdCVwAAJ0JXAAAnQlcAACdLZrvBQCAiTt29w5trp18m+wwbOECAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDoTOACAOhM4AIA6EzgAgDobNF8LwAAbLeO3b1Dm2sn3ybzzhYuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzgQuAIDOBC4AgM4ELgCAzhbN9wIAAJtw7O4d2lw7+TbZKFu4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6WzTfCwAALBDH7t6hzbWTb3M7ZAsXAEBnAhcAQGcCFwBAZ1MPXFV1SFVdUFUrq+roadcHAJi2qQauqto5yT8leViSA5I8oaoOmOYyAABM27S3cN0zycrW2jdba79KclKSw6a8DAAAUzXty0LsneTiOc9XJzloyssAAMy3HewSFNVam16xqsckOaS19ofj8ycnOai19qw58xyV5Kjx6e2TXLCNZW+W5Ifb2MZCq7U1dXZPsqXfxIXcn4VcZ5q1fBcWdp1p1vJdWNh1plnLd2H+6uzbWluyoQnT3sJ1SZJ95jxfOo67Vmvt+CTHT6pgVa1orS2fVHsLodbW1Kmq41trR216zm2rszVmrc40a/kuLOw606zlu7Cw60yzlu/Cwqwz7WO4vpxk/6rar6qul+SIJKdMeRl2VB+Z7wVgwfBdYB3fBdbxXehsqlu4WmtXVdWzknw8yc5JTmitnTfNZdhRtdasTCTxXeDXfBdYx3ehv6nfS7G19tEkH51iyYntnlxAtdRZ2HWmWUudhV1nmrXUWdh1pllLnQVYZ6oHzQMA7Ijc2gcAoDOBCwCgM4FrO1BVu1bVTuPw7arqkVW1y3wv19aatf6sr6p2qqobd2p76u/dLPRnFr9zs9anWevP+qxHC1vPz2edmQtcVXXfqtp1HH5SVb22qvbtVOvPqurGNXhLVZ1RVQ/pUOpzSRZX1d5JPpHkyUneNukiU3zvZq0/qap3j9+FXZOcm+SrVfWCDqWm9d7NVH+mWMd6tPVmrT/Wo600rc9oip9PkhkMXEnelOTnVXWXJM9P8o0kb+9U6+mttR8neUiSm2T48h3XoU611n6e5FFJ3thae2ySAzvUmdZ7N2v9SZIDxu/C4Un+X5L9MnwfJm1a792s9WdadRLr0daatf4k1qOtNa3PaFqfT5LZDFxXteHUy8OS/GNr7Z+S3KhTrRp/PjzJO8ZritVvmX+r61TVvZM8Mcmp47idO9SZ1ns3a/1Jkl3GTeuHJzmltXZlkh6nAE/rvZu1/kyrTmI92lqz1p/EerS1pvUZTevzSTKbgesnVfWiDCn11HF/c699zKdX1ScyBK6PV9WNklzToc5zk7woyYdaa+dV1W2SfLpDnWm9d8/NbPUnSf5vklVJdk3yuXHz94871HlupvPezVp/plUnsR5tredmtvqTWI+21rQ+o2l9Pklm8DpcVXWLJH+Q5MuttX+vqlsneUBrbeKbI8cvwV2TfLO19qOq2jPJ3q21sydda6y3W5K01n7aqf2pvXdjvZnqzwbqL2qtXdWp7a7v3UZqbvf9mUYd69E215up/mygvvVo0+3P22fU9fOZtcCVJFW1V5J7jE+/1Fr7Qcdaj0zye+PTz/a4PUJV3SnD/uubZthluSbJU3rcFmka792s9Wess3uSYzLnu5Dk5a21tROuM5X3bgb7M7Xv3FjPerTlNWaqP2Md69HW15rGd24qn8+1Wmsz9UjyuCQXJTkxwxfjW0ke06nWcUk+meTp4+O0JK/qUOfzSR445/kDknx+e33vZq0/Y60PJHlZktuMj2OSfHA7fu9mrT9TqTPN792srUez1p+xlvVoAX9G0/p8rq3Xq+H5eiT5SpKbz3m+JMlXOtU6O8lOc57vnOTsHn3anHHby3s3a/0Z2z5rc8ZtR+/drPVnKnXWtWs90p+x7bM2Z9z29N7N0mc0rc9n3WMWD5rfqV130+Ol6XtywB5zhnfvVOObVfV/qmrZ+HhJkm92qDOt927W+pMkv6iq3133pKrum+QXHepM672btf5Mq05iPdpas9afxHq0tab1GU3r80mSLOrV8Dz6WFV9PMl7xuePT/LRTrX+OsmZVfXpDPuzfy/J0R3qPD3DZs8Pjs//fRw3adN672atP0nyx0nePh4TUEkuS/LUDnWm9d7NWn+mVSexHm2tWetPYj3aWtP6jKb1+SSZ3YPmH53kvuPTf2+tfahjrVvmugf2fa9XrWmY5ns3DdPuT423hmjDxfS2e7PWn2mxHi1sfi8sfFP+Oz6Vz2cmA9c01XCLg30zZ2tha+1zE2p7p9baNVV1uyR/nmTZenUeNIk60zJr/Zmrqq6f5NH5zT69fELtT/W9m5X+zOJ3btb6NGv9mct6tLD1/nzWNzO7FKvqtq21lWMqPi7JzTNsIqwkrbU2sZtSVtVhrbUPV9WrM2zqPC+/vuBpy3C/qUl4S5KnJXlfkjcn+ZckV0+o7WtN8b2btf6kql7UWvvrJB9OsjbJ6Ul+Oan255jWezdT/ZliHevR1pu1/liPttK0PqMpfj7X1eto/Gk/kpw0/lyZ5A6da71//Pn1JNfvWOeT48/TZ+G9m7X+jDU+Mv48t3Odab13s9afqdQZa1iP9GddLevRAv6MpvX5rP+YpbMU9xl/fr+1dn7nWnuPP7+RfreESJKfjD8/UlV/WlW3rKqbrntMsM603rtZ60+SrPuP6/PjRQF7mdZ7N2v9mVadxHq0tWatP4n1aGtN6zOa1udzHTNzDFdVvay1dkxVvT7JLZL8a+ZsImytfXBjr92KWv+ztfahqvpAkrtkuPjp3FrPmVCddfvNv7WBya21dpsJ1ZnKezdr/Rlr7d9au7Cqvprkthku0PfL/HoT+J0nVGda792s9WcqdcZa1qOtqzNT/RlrWY+2rs60vnNT+Xx+o+6sBK51quqtGxjdWmsTP3W1qo7c0PjW2omTrjUN03zvpmHK34V9NzS+tXbRpGtNw6z1Z5qsRwub3wsL37Q+o2l/PjMXuGZVVd0xyQFJFq8b16Z0s9UeZq0/61TVzXPdPn27Q42pvXez1J9Z/M7NWp9mrT/rWI8Wtml8PskMBq6qWpzkGUkOzHXfwB7/veyf4eKn63/5JrabYqxzTIZ7Vh2Q4eJvD0vyH621x0y4zlTeu1nrz1jrkUn+Lsmtkvwgw6VCzm+tHTjhOtN672atP1OpM9ayHm1dnZnqz1jLerR1dab1nZvK57POLB00v847Muz7fWiGO38vza8P+Ju0tyZ5U5Krkjwww00239mhzmOSHJzke621p2U4bqzHbYSm9d7NWn+S5K+S3CvJ11tr+2Xo3xc71JnWezdr/ZlWncR6tLVmrT+J9WhrTeszmtbnk2Q2A9dtW2v/J8nPxmOpDk1yUKdaN2itfTLDlsKLWmvHjvUm7RettWuSXFXDFXF/kF+fzTFJ03rvZq0/SXJla+3SJDuNB5h+OsnyDnWm9d7NWn+mVSexHm2tWetPYj3aWtP6jKb1+SSZoQufznHl+PNH477m72W4eFoPv6yqnZJcWFXPSnJJkt061FlRVXsk+ecMF2j7aZIvdKgzrfdu1vqzrsZuGS56+66q+kGSn3WoM633btb6M606ifVoa81af9bVsB5tuWl9RtP6fAZtihf9msYjyR8muUmGG0l/M0MC/6NOte6RIWAtzbB78QNJ7tW5f8uS3Hl7f+9mrT9Jds2wxXhRkiOTPCfJntvxezdT/ZlmHeuR/sypZT1awJ/RtD+fmTtoHgBgoZnFY7gAABYUgQsAoDOBaztTVX8638swSbPWnySpqrt3bHtJVd2tqu48HuzZ3Qz2Zxa/czPVp1nrT2I9Wqiqz31VN2gWz1K8VlW9vbX2lE5tPzDJozOcEnt1kq8n+ZfW2soJ1vjf649K8qLxonBprb12grV2S3JIrtufT7ThFOBJ1Zip/ox11v8lWkk+XFW/n+FyIWdMqM4BSd6Q4UDVWyc5M8nNq+qzSf6stbZ2QnVmrT9T+86N9axHW15jpvoz1rEebX2taXznbp3kbzJcd+tHw6i6cZJPJTm6tbZqUrXmmpnAVVWnrD8qyQPHU1jTWnvkBGv9dYaLsn1y/PmtJN9I8r6qelVr7X0TKvWyDFfzPS9Df5Jk5yQ3mlD7SZKqelySP09ydoYLuH4+wzVP/qaqnthaO2dCpWatP0myIsOF8n45Z9yeSV6bpCV50ITqnJDkyNbaBVV1zyTPbK0dVFX/K8lbMlyQcBJmrT9T+c4l1qNtMGv9SaxHW2WKn9HJSf4+yRNba1ePtXdO8tgkJ2W4GOrk9Tr9cdqPJGdkuMr7A5Lcf/z53XH4/hOudc6c4UVJ/nMcvkmScydY59ZJ3pfk1UluOI77Zof37uw57d8sycfH4Tsn+bz+/NZaj85wJeSHzRn3rQ59+sp6z8+YM3y+/szvd25s13qkP+tqWY8W8GeU5MKtmbatj1k6hmt5hguxvTjJ2tbaZzJcFfezrbXPTrjWNXP2+d4qQ9JPa+3y/Dr9b7PW2rdba4/NkPJPq6qJ3/dtVEl+MQ7/LOMF5lprZye58aSKzFp/xjY/kOEqyA+pqveNm6p7XGvlG1X1f6rqvlX1d0nOSpKq2iUTPBZzBvszre9cYj3aKrPWn7FN69HWmdZndHpVvbGqDqqqW42Pg6rqjRl2y3YxM4GrtXZNa+11SZ6W5MVV9Y/pt8v0VUnOrKrTkvxHhvsxpaqWJPnKpIu11j6c5CEZNq2unnT7GTYVf6yqXpzkExn+k8kYKicWINeZwf78tLX2vAzfixPTYXdVkqeP7b4oyRVJ/mwcf8MMF+ybmFnrTzKV71xiPdomM9ifWV2PHprt/zN6SpJzMuwq/fj4eFmSc5M8eYJ1rmNmL3xaVYcmuW9r7S87tX/TJLdJsrK19qMeNaapqh6e4Q7wX2mtnTaO2ynJLq21X/7WFy9A89WfqqokN2qt/bhXjWmaZn+q6uattR/0rtPTPH7v9mzDPeEm3a7fC5OpW0l2a631ukn2zJi179x19NpXuRAeSY6aYq1jp9y3/zff7+9WLPONk/x1hjvB/8F6094438u3lX26YZK/SPKCJIsz/Fd5SoYzYHabYJ1bJHljkn/KcPDtsRn+Q3tvklt27uPXO7R50/UeeyZZleE4yJtOsM4hc4b3SPIvGY4TeXeSveb7+7OVfTouyc3G4eUZbn1yYZKLMuHjVdere+Mkv5PkJhNu94wkL0ny3+b7vZ1gn26T4YD2V2S4/ds/Z9h68r4kyyZYZ3mST2c4fnmfJKclWZvky0nuNsE6uyV5eYaD5tcmWZPhpICndnwPb5Lkxp3aXpTkj5L8v/H3wdnj8B9nCHZd+jQzuxQ34o+nWGtiZ0GuU1V338jjd5LcddL1NrIMkzxz560ZNgt/IMkRVfWBqrr+OG1iZ4VU1T5VdVJV/XtV/eV4PMO6af86qTqjtyXZK8l+SU7NcH/N12To55smXOf8JBdn+AX7iyQPT/LvSd48qSJV9ZOq+vH4+ElV/STJf1s3flJ1kvwwwzGX6x4rkuyd4Y/vignWedWc4b/NcBPc38/wB+n/TrBOqurpc4aXVtUnq+pHVfX5qrrdBEsd2lr74Tj8miSPb63tn+TBSf5uUkWq6p1VdbNx+KEZAsOrk5xVVY+dVJ0Mf1j3SPLpqvpSVT2vqm41wfaTJFV1WVX9S1UdPG5x6ultGb5jP80QTC5I8rAkH8sQxCbljRn+uTs1w/FV/7e1tnuSo8dpk/KuDMH+oRl2vb0hw663B1bVq37bC7fEeCzV26tqbYbfEedW1ber6ti5v8cn4B0Z/oa+LMPv0YePw3fJEF776J305/OR5MztuVaGa5B8KsMf2PUfv5hgnUdt5PHoJGsmWOes9Z6/OMl/Zti6ccYE65yWIWzfNck/ZPhFtGePz2ldnzIErO/l17vpK8nZPb5fSb79297XbazzhiRvz5ytP+lzdtXzM/zxuVPnOnPP2lr/+zex920Dtd6b5KgMx8n+zySfnGCd85MsGoe/uN60cyZYZ+7Z2J/PuGUmw9ljX5lgnbnv2/0yBIXvjb/nJraXIkPoedb4O+eSJK9Pcq9Jf+fGWmfOGV5/fT1zO6yz/tmQXx5/7pTkaxOs86kkDxiHH5XkdRluMP2KJMdPsM5Gt9r/tmnb+piZ63BtxO9PsdbvdGjz/Ax3SL9w/QlVdfEE65yc4T+YDR3Qt3iCda5fVTu18QJ2rbVXVtUlST6XYZP1pCxpra3b6vPsqnpSks9V1SPT50yhtNZaVX20jWvs+HySteZujX77b5m2TVprzxm3oL5n3Br4j+nwnrXW/q6qTk7yuvG7fEyPOhkuAvm/MwTgG1dVrfuM0vekodu11h43Dn+oql46wbbfmOSjVXVchgOMX5/kgxmu7XTWBOvsVFU3bsPxe9ck+XaStNZ+WFVd/na01v49yb9X1bMzbLF7fJLjJ9T8z1pr/5jkH8ezBo9I8sYartV4Upvs8b7XjFs1d09yw6pa3lpbUVW3zXhW+4RcUVUPGeu0qjq8tfavVXX/DP+wT8rPqup3W2v/Mf4evSwZTlab8NbCPdtwhYG01j5YVS9urf0syUuq6msTrHPZuJX2A+v+Ho3HiT02yeUTrHMdMxW4xs3eh2fYNZEkl1TVh1trH5twnZu1X2/Sz/gH/Z5VdW6Sf57zC31bHZuN/1F49oRqJMP+679trZ27/oSq+h8TrPORDH8U/r91I1prb6uq72XYEjUpu1TV4tbaFWONd441Pp7hv6VJWlFVu7XhjKS5u5T+W5JJHiD74Tl1XjKnzm0zXIl5Ylprp4+f+7MyXEtokqF7bp3VSR47/gI/LcPxcJP2z/n12WEnZtg6s6aqbpHJhpMkWVpVb8gQ7pZU1S6ttSvHaRPbHdJa+4dxV/+fJLldht/jt0vyoQxbAiblZRl28/1Thq1C76vhAtMPzLB1clJ+4/vbhotRfmzCda4NBq21b2fYFfc3VfXfMwS7SfqLDL/vrsnwN+lFVXWXDMfB/a8J1vnjDP24JsPuvj+pqrdl2II36Tr/UlX7ZziO6+nJtWfm/9ME66wZ/55+OsMWrlVjncpk/0E6IsPu8TdW1bqAtcdY94gJ1rmOmTlLsar+PsMvnbfn16erLs1w+ueFrbU/28hLt6bWGa21u4/DL8mwGfzdSR6RZHUbTgXeblTV/ZJcNP4SWn/a8tbaJI+p6a6qnpdhN8Vn1xt/tyR/01p78JSWoyYYvudFVd0yw8G3H+1c5wYZDpr+jdC/vaiq9U/DP6W1dvkY7p4z4S0oUzEG+v+VXwe71Un+tbX28XldsK1QVa9tra1/i5pp1r9ZksvHMMkGjFse/zbDWYpnJXlBa+27VbVnhl2NH+hQc88kaR3O8v2NWtv534NrVdXXW2u/cWDqmIy/3oaDSidV68zW2t3G4TOS3K+19rPxoL4zWmt3mlStDdT+t9baI3q1P22z1p8kqarjW2tHTaHOVN67GezPLH7nZqpPs9afxHq0vaiqB7fxchSTNktnKV5RVffYwPh7ZLgg3CTdoIY7s/9Okp3HfcwZdx/0/u9l703Psu3GIDkNs9afZDhVexqm8t5l9vozrTrWo603a/1JrEdbZcqfUTLcg7KLWTqG66lJ3lRVN8qvdynuk+GaIU+dcK3vZrgJaTIcfHfLOZs9r5pwrfV1u+3AenqfNr3OrPUnSaZ18c5pvXez1p9p1UmsR1tr1vqTWI+21sQ/o/FYxI3V2nPS9a5tfFZ2Ka4zHi9x7UHzrbXvTbH2zkmu31r7eYe2b5okrbXLJt32Ruq9Yu7B2R3an6n+sPBN+zs31rQebVn7M9WfWTQLn9F4oPyTMlwn7TqTkpzcWttrkvWu1et6Ex4TuVbIrZOclOGqvhcmWZnhv6STMsGrFevPNvVp9wxX/v5ahlOlL81wOY/jkuwxwTqHrFfzLelwxfQZ7M8sfudmqk+z1p+xT9ajBfzIcFX5B25k2ud61Z2lY7hm0ckZTvW+RWtt/9babZPcMsm/ZviiT0Rt+ArZl9fkr5A9a/1JhgtcXp7hDJqbttb2zHDa/OXjtEmZezXnv8uwW7vHFdNnrT9T+c4l1qNtMGv9SaxHW2Van1Fr7WGttU9vZNrvTarOhhr3WKCPDJez2OJpW1FnWlfInqn+jO1fsDXTtrFPZ6037awJ1pm1/kzlO7eBPlmPdtD+jO1bjxb4ZzQfD1u4FrbTq+qNVXVQDfeYutU4/Mb0O2Dxdq2141tr17TWPpThxsKTMmv9SZKLquovquraff5VtVdVvTDDfQ8n5eZV9b+r6vkZr5g+Z9ok1+NZ6898fOcS69GWmLX+JNajSej9GU3dLJ2lOIuekuQZGa74vO5EgNUZrmA8yVNXp3KF7Mxef5LhCtVHJ/lsVd18HPf9JKckedxGX7XlpnXF9Fnrz4a+c5dk6M+kT/+ez/WoR5/8Xth6669H6+61aj367ab5GU3dzJ2lyJarGbtC9qz1h+3DrH3v9Idpm4/PqIa7XNy6tXbBpNv+jVoC1/apql7aWnv5fC/HlqrhfpdLk/x/rbWL5ox/emvthPlbsq0zbsJ/bIYbL78/w70iD8twdtKb23hj1O2lzkZqf6q19qBe7fesUxu572mSSd/3dGqq6n8m+Wxr7bIa7mX3t0nunuSrSZ7fhntUbjeq6rUZbiL8n/O9LJNSw6UTnpVhK9AJSV6U5D4ZzlR8VWttIjdInlPnOxm2NP1lkntPus5Y64FJHp3h+pZXZ7gH5r+01lZOqsa0VdXvZ1h/rtda26+q7prk5a21R3aptx3+viFJVX27tXbrKdSZWLCrqlcl+d0kZ2Q4k+bvW2v/ME679v6UE6q1Lth9srW2as74iQa78RiGmye5XpIfJ7l+hs3shyb5fpvQPTynWOfs9UdluI/eBUnSWrvzdlZnavc9nVa4q6qvttYOGIdPTvLFJO9L8j+SPLFN6F6hGwh2f5fkbplwsKuqNUkuSrIkw9lw72mtTfy4oGkGu6r6aJJzMtys+g7j8HuTPDjJXVprh21ndf46yS2SfDLDzbi/lSFw/WmGYPe+SdQZa00t2FXV6Rn+ef1M+/Xt+s5pvW7PN99H7Xts/JHhD+uGHj9JctWUluHbE2zrnCSLxuE9knw0yevG52dOsM5fJ/lckr9P8o0kz54z7YxJ1VnXp/HnLhmutXO98fmiJGdvh3VOSfLOJP89yb5JlmU4yHffJPtuh3XOnDN8RpJd57yP50z4uzD3DKuXJPl4kiMzhKHXTbDOBXOGT19v2lkTrPPVOcMnJ3lehn9inprktEl/RhkC9/9Jcl6GLbfHZDhwelJ11iRZkSHc/U2Gm7JP7PPf0OeQ4R+JSzp+RtOqc86c4UVJ/nMcvkmScydY56+TvDXDRUnfn+Q1GW6efmaSx3b4nL449zs4Dk/s9+n6D2cpLmw/SrJ/a+3G6z1ulOF6KxNRVT/eyOMnSW41qToZwtZVSdJa+1GGrVw3rqr3ZdhyMymPSPKg1tpzk/xOkodV1evGaZO+TcS6/lyZ5MuttV+Nz69KMsndfFOp04ZN6R9IcnyG/5BXJbmytXZRm7MLeHupk+ne93Tud+tRSR7VWjsxyR9k2Po0KZ+pqpePx558ZtwStW7LwNoJ1tl5zvBtW2uva62tbq29LcPWqElpSdJa+3pr7a9aawdmOLB8cYZ/yiZldWtteYatPz9J8s6q+lpVHVOTvw7XTlV1kwxbaXarqmVJUsPt3yb5u25ada4Zd18mw9+EnZOkDbssJ/k79RGttae11t6Z5Igk92mt/XOGrVDHTLDOOudV1R8k2bmq9q+qf0jy+Q51kszWzatn0dsz/Me/Ie+eYJ0fZQrBLsk3qur+65601q5urT0jw26kO0ywzrSCXZJ8r6p2G2sdsm7keJDnr7bDOmnDKdgPS/KAqvpwJv+eTbPOuvue/m3G+54m1/5BmvR9T6cV7p6VIWRfkOG4vg+M/xz9ryRPnmCdaQW73/iD3Vo7u7X2ojZcYHNSphXskmFLzdcyXID06Un+papOy3AV+L/fDuu8KsmZY9v/keSvkmTc1fyVCdaZVrBb59lJDkzyyyTvybAH6bkd6iRxDBcZ7lWV4WyQL21g2qtbay+cUJ0bJElr7RcbmLZ3a+2SCdX5tySvaa19dr3xr0jyl6217v9oVNWuGXZfdb1hbe86VXWXJPdurb25R/vTrjOn3sTve1pVn15v1B+0X9/U/uPj1pWJqqrdM/yDcWmHtndJ8uIMf8iTYXfizzJcruHo1tq3J1Rnt9ba+ve0m7iqOrONx+lMw/gdq9baVVW1KMldM+z2m+Q/sdOsc9Mkt0mycvxHduKq6vEZdvd+Pcntk/xJa+3UMdi9vrX2Bz3qTovAxcyZVrCDzdEj3E1bz2A3LdMKdmybKQW7j2Tc4rkhzVmKAADbZs6hLY/KcPblO8fnT8hw1vfEzl6+Tl2BCwDY0VTVivV39W9o3KQ4aB4A2BHtWlW3WfekqvZLsmuvYu6lCADsiJ6X4Qzcb2Y4C3LfJEf1KmaXIgCwQ6qq62e4AHOSfK219stutQQuAIC+HMMFANCZwAUA0JnABQDscKrqvuPdOlJVT6qq11bVxm6nt80ELgBgR/SmJD8fby32/CTfyHAP4y4ELgBgR3RVG84cPCzJP7bW/inJjXoVcx0uAGBH9JOqelGSJyX5varaKckuvYrZwgUA7Igen+SXSZ7RWvtekqVJXtOrmOtwAQB0ZgsXALBDqKrd5gzfq6pWVNVPqupXVXV1Va3tVVvgAgB2FE+qqpdXVSX5xyRPTLIiyQ2S/GGSN/YqLHABADuE1tqbk3wlQ9BKa+2CJLu01q5urb01ySG9ajtLEQDYYbTWPpAkVXVUVV0vydeq6lVJ1iTZuVddW7gAgB3RkzPkoOcluSLJrZM8plcxZykCAHRmCxcAsMOpqkdU1ZlVdVlV/Xg8W/HH3erZwgUA7GiqamWSRyU5p00hDNnCBQDsiC5Ocu40wlZiCxcAsAOqqnsk+askn81wi58kSWvttT3quSwEALAjemWSnyZZnOR6vYsJXADAjuhWrbU7TquYY7gAgB3RR6vqIdMq5hguAGCHU1U/SbJrhuO3rkxSSVpr7cZd6glcAAB92aUIANCZwAUA0JnABQDQmcAFANCZwAUA0JnABQDQmcAFLGhV9dPNmOe5VXXDKSzL26rqMb3rALNH4AJmwXOTbFHgqqqd+ywKwG8SuIDtQlU9oKo+U1Xvr6qvVdW7avCcJLdK8umq+vQ470Oq6gtVdUZVva+qdhvHr6qqV1fVGUleUFVfmtP+sqo6Zxx+aVV9uarOrarjq6o2sDwHV9WZVXVOVZ1QVdefyhsBbJcELmB7crcMW7MOSHKbJPdtrb0hyXeSPLC19sCqulmSlyT5H621uydZkeR/z2nj0tba3VtrxyW5XlXtN45/fJKTx+F/bK3dY7yx7Q2SPGLuQlTV4iRvS/L41tqdkixK8icT7y0wMwQuYHvypdba6tbaNUnOSrJsA/PcK0Mg+8+qOivJkUn2nTP95DnD780QtJLrBq4HVtV/jVu8HpTkwPVq3D7Jt1prXx+fn5jk97amQ8COYdF8LwDAFvjlnOGrs+HfYZXktNbaEzbSxs/mDJ+c5H1V9cEMN629cNx69cYky1trF1fVsUkWb/uiAzsyW7iAWfCTJDcah7+Y5L5Vddskqapdq+p2G3pRa+0bGYLb/8mvt26tC1c/HI/92tBZiRckWbauRpInJ/nsNvcCmFm2cAGz4PgkH6uq74zHcT01yXvmHMj+kiRf38hrT07ymiT7JUlr7UdV9c9Jzk3yvSRfXv8FrbUrquppGbaOLRrnefMkOwTMlmqtzfcyAADMNLsUAQA6E7gAADoTuAAAOhO4AAA6E7gAADoTuAAAOhO4AAA6E7gAADr7/wHifsyD+pE1RQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "muestra.plot(kind = 'bar',\n",
    "             width=0.8,\n",
    "             subplots=True,\n",
    "             figsize=(10,20));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}