evoting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Elecciones Federación de Estudiantes Universidad Mayor 2020-2021"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Introducción\r\n",
    "Este cuaderno contiene código para obtener, procesar y visualizar datos de las elecciones realizadas por estudiantes de la Universidad Mayor para elegir a sus representantes.\r\n",
    "\r\n",
    "Los datos se encuentran disponibles a través de la API pública de evoting.cl, plataforma de voto electrónico en la que se han realizado todas las elecciones federativas a la fecha."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Código"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "import json\n",
    "import requests\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### API Evoting\r\n",
    "La API de evoting contiene dos categorías relevantes para el análisis cuantitativo de las votaciones: ``snapshot``, con los datos de participación de cada carrera, y ``votesByHour``, con la cantidad de votos para cada hora dentro del rango de tiempo en el que fue posible votar."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "def get_votes_by_program(election_code: str):\r\n",
    "    r = requests.get('https://public-api.evoting.cl/api/election/' + election_code + '/snapshot').json()\r\n",
    "    return r\r\n",
    "\r\n",
    "def get_votes_by_hour(election_code: str):\r\n",
    "    r = requests.get('https://public-api.evoting.cl/api/election/' + election_code + '/votesByHour').json()\r\n",
    "    return r\r\n",
    "\r\n",
    "def get_results(election_code: str):\r\n",
    "    r = requests.get('https://public-api.evoting.com/api/election/' + election_code + '/results').json()\r\n",
    "    return r"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Cada votación tiene un código asignado y quedarán almacenados bajo el nombre de la lista de Mesa Directiva que estuvo postulando en ese momento. Este código debe ser ingresado en la solicitud a evoting para obtener los datos correspondientes."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "\r\n",
    "impulsar_2018 = 'beZd3kuB'\r\n",
    "conectemos_2020_1 = 'u1iywcKL'\r\n",
    "conectemos_2020_2 = 'dbh0p9bZ'\r\n",
    "construyendo_2021 = 'xNUQ8wgx'\r\n",
    "\r\n",
    "program_0 = get_votes_by_program(impulsar_2018)\r\n",
    "hour_0 = get_votes_by_hour(impulsar_2018)\r\n",
    "program_1 = get_votes_by_program(conectemos_2020_1)\r\n",
    "hour_1 = get_votes_by_hour(conectemos_2020_1)\r\n",
    "program_2 = get_votes_by_program(conectemos_2020_2)\r\n",
    "hour_2 = get_votes_by_hour(conectemos_2020_2)\r\n",
    "program_3 = get_votes_by_program(construyendo_2021)\r\n",
    "hour_3 = get_votes_by_hour(construyendo_2021)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "results_0 = get_results('beZd3kuB')\r\n",
    "results_1 = get_results('u1iywcKL')\r\n",
    "results_2 = get_results('dbh0p9bZ')\r\n",
    "results_3 = get_results('xNUQ8wgx')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Procesamiento de datos\r\n",
    "Los documentos JSON devueltos por evoting se encuentran separados por distrito o por hora, dependiendo de si es una consulta a ``snapshot`` o a ``votesByHour`` respectivamente, y en el caso de la separación por distritos tenemos que cada distrito corresponde a una carrera de la Universidad Mayor que es impartida en Santiago, con una gran cantidad de columnas no utilizadas para cada distrito. Por lo mismo, solo almaceno en un dataframe aquellas columnas que sean relevantes.\r\n",
    "\r\n",
    "Respecto al documento JSON devuelto por ``votesByHour``, las keys corresponden a [tiempos Unix](https://es.wikipedia.org/wiki/Tiempo_Unix), requiriendo una conversión a otro formato de fecha para ser útil."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "def create_dataframe_program(input_json):\r\n",
    "    districts = []\r\n",
    "    for key in input_json['districts']:\r\n",
    "        districts.append(str(key))\r\n",
    "    df = pd.DataFrame(columns=districts)\r\n",
    "    df = df.T\r\n",
    "    for district in districts:\r\n",
    "        df.loc[district, 'Carrera'] = input_json['districts'][district]['name']\r\n",
    "        df.loc[district, 'Votó'] = input_json['districts'][district]['voters']['electronic_voted']\r\n",
    "        df.loc[district, 'No votó'] = input_json['districts'][district]['voters']['ready']\r\n",
    "    df.style.set_precision(0)\r\n",
    "    return df\r\n",
    "\r\n",
    "def create_dataframe_hour(input_json):\r\n",
    "    df = pd.DataFrame()\r\n",
    "    i = 0\r\n",
    "    for hour in input_json['hourly']:\r\n",
    "        datetime_hour =  datetime.fromtimestamp(int(hour)) + timedelta(hours=1)\r\n",
    "        if (i == 0):\r\n",
    "            day = 1\r\n",
    "            current_date = datetime_hour.strftime(r'%Y-%m-%d')\r\n",
    "        if (current_date != datetime_hour.strftime(r'%Y-%m-%d')):\r\n",
    "            day += 1\r\n",
    "            current_date = datetime_hour.strftime(r'%Y-%m-%d')\r\n",
    "        df.loc[i, 'Día'] = 'Día ' + str(day)\r\n",
    "        df.loc[i, 'Fecha'] = datetime_hour.strftime(r'%Y-%m-%d')\r\n",
    "        df.loc[i, 'Hora'] =datetime_hour.strftime(r'%H:%M')\r\n",
    "        df.loc[i, 'Votos'] = input_json['hourly'][hour]['electronic_voted']\r\n",
    "        i = i + 1\r\n",
    "    df.style.set_precision(0)\r\n",
    "    return df"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "source": [
    "def create_dataframe_results_positive(input_json):\n",
    "    df = pd.DataFrame()\n",
    "    for json in input_json:\n",
    "        for key in json['questions']:\n",
    "            if 'edera' in json['questions'][key]['name']:\n",
    "                df.loc[json['election-id'],'Código Pregunta'] = key\n",
    "                df.loc[json['election-id'],'Pregunta'] = json['questions'][key]['name']\n",
    "                #df = df.T\n",
    "                for district in json['questions'][key]['districts']:\n",
    "                    for box in json['questions'][key]['districts'][district]['ballotboxes']:\n",
    "                        #print(json['questions'][key]['districts'][district]['ballotboxes']['BX01']['choices'][0]['total'])\n",
    "                        name = json['questions'][key]['districts'][district]['name']\n",
    "                        mapping = [ ('í', 'i'), ('í', 'i'), ('é','e'), ('é','e'), ('ó','o'), ('ó','o'), ('Kinesiologial', 'Kinesiologia'),\n",
    "                                    ('Pedagogia Educacion Parvularia', 'Pedagogia en Educacion Parvularia')]\n",
    "                        for k, v in mapping:\n",
    "                            name = name.replace(k, v)\n",
    "                        df.loc[json['election-id'],name] = json['questions'][key]['districts'][district]['ballotboxes'][box]['choices'][0]['total']\n",
    "                        pass\n",
    "    return df\n",
    "results = [results_0, results_1, results_2, results_3]\n",
    "df_results = create_dataframe_results_positive(results)\n",
    "df_results = df_results.T\n",
    "df_results.columns = ['Impulsar 2018','Conectemos 2020-1', 'Conectemos 2020-2', 'Construyendo 2021']\n",
    "\n",
    "def create_dataframe_results_negative(input_json):\n",
    "    df = pd.DataFrame()\n",
    "    for json in input_json:\n",
    "        for key in json['questions']:\n",
    "            if 'edera' in json['questions'][key]['name']:\n",
    "                df.loc[json['election-id'],'Código Pregunta'] = key\n",
    "                df.loc[json['election-id'],'Pregunta'] = json['questions'][key]['name']\n",
    "                #df = df.T\n",
    "                for district in json['questions'][key]['districts']:\n",
    "                    for box in json['questions'][key]['districts'][district]['ballotboxes']:\n",
    "                        #print(json['questions'][key]['districts'][district]['ballotboxes']['BX01']['choices'][0]['total'])\n",
    "                        name = json['questions'][key]['districts'][district]['name']\n",
    "                        mapping = [ ('í', 'i'), ('í', 'i'), ('é','e'), ('é','e'), ('ó','o'), ('ó','o'), ('Kinesiologial', 'Kinesiologia'),\n",
    "                                    ('Pedagogia Educacion Parvularia', 'Pedagogia en Educacion Parvularia')]\n",
    "                        for k, v in mapping:\n",
    "                            name = name.replace(k, v)\n",
    "                        df.loc[json['election-id'],name] = json['questions'][key]['districts'][district]['ballotboxes'][box]['choices'][1]['total']\n",
    "                        pass\n",
    "    return df\n",
    "df_negative = create_dataframe_results_negative(results)\n",
    "df_negative = df_negative.T\n",
    "df_negative.columns = ['Impulsar 2018','Conectemos 2020-1', 'Conectemos 2020-2', 'Construyendo 2021']"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "df_program_0 = create_dataframe_program(program_0)\r\n",
    "df_hour_0 = create_dataframe_hour(hour_0)\r\n",
    "df_program_1 = create_dataframe_program(program_1)\r\n",
    "df_hour_1 = create_dataframe_hour(hour_1)\r\n",
    "df_program_2 = create_dataframe_program(program_2)\r\n",
    "df_hour_2 = create_dataframe_hour(hour_2)\r\n",
    "df_program_3 = create_dataframe_program(program_3)\r\n",
    "df_hour_3 = create_dataframe_hour(hour_3)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "pd.set_option('precision',0) # Configura cada columna numérica para aparecer como un entero"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "df_program_0"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               Carrera  Votó  No votó\n",
       "D01            Alameda   393      549\n",
       "D02      Conservatorio    48      169\n",
       "D03            Oriente    99      514\n",
       "D04        El Claustro   117      797\n",
       "D05         Huechuraba  1798     3059\n",
       "D06       Manuel Montt   727     3675\n",
       "D07          República    62      190\n",
       "D08      Santo Domingo   140      148\n",
       "D09      Estadio Mayor   123      355\n",
       "D10  Alonso de Córdova     0       31"
      ],
      "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>Carrera</th>\n      <th>Votó</th>\n      <th>No votó</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>D01</th>\n      <td>Alameda</td>\n      <td>393</td>\n      <td>549</td>\n    </tr>\n    <tr>\n      <th>D02</th>\n      <td>Conservatorio</td>\n      <td>48</td>\n      <td>169</td>\n    </tr>\n    <tr>\n      <th>D03</th>\n      <td>Oriente</td>\n      <td>99</td>\n      <td>514</td>\n    </tr>\n    <tr>\n      <th>D04</th>\n      <td>El Claustro</td>\n      <td>117</td>\n      <td>797</td>\n    </tr>\n    <tr>\n      <th>D05</th>\n      <td>Huechuraba</td>\n      <td>1798</td>\n      <td>3059</td>\n    </tr>\n    <tr>\n      <th>D06</th>\n      <td>Manuel Montt</td>\n      <td>727</td>\n      <td>3675</td>\n    </tr>\n    <tr>\n      <th>D07</th>\n      <td>República</td>\n      <td>62</td>\n      <td>190</td>\n    </tr>\n    <tr>\n      <th>D08</th>\n      <td>Santo Domingo</td>\n      <td>140</td>\n      <td>148</td>\n    </tr>\n    <tr>\n      <th>D09</th>\n      <td>Estadio Mayor</td>\n      <td>123</td>\n      <td>355</td>\n    </tr>\n    <tr>\n      <th>D10</th>\n      <td>Alonso de Córdova</td>\n      <td>0</td>\n      <td>31</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "df_program_1"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                               Carrera  Votó  No votó\n",
       "D01                             Administración Pública     0       23\n",
       "D02                                          Agronomía   112      491\n",
       "D03                                  Animación Digital    80      171\n",
       "D04                                       Arquitectura   151      289\n",
       "D05                           Bachillerato en Ciencias     1        4\n",
       "D06               Bachillerato en Ciencias de la Salud     8      111\n",
       "D07                        Bachillerato en Humanidades     1        8\n",
       "D08                                      Biotecnología   107       89\n",
       "D09                                               Cine    82      107\n",
       "D10                                      Conservatorio    13      197\n",
       "D11                                 Construcción Civil    67      117\n",
       "D12                           Contador Público Auditor     1       70\n",
       "D13                                       Data Science     0        6\n",
       "D14                      Derecho - Campus Manuel Montt   279      344\n",
       "D15                           Derecho - Campus Oriente    57       43\n",
       "D16                                             Diseño    69       82\n",
       "D17                                         Enfermería   285      172\n",
       "D18                                     Fonoaudiología   174       72\n",
       "D19                                           Geología   174      113\n",
       "D20                       Ingeniería Civil Electrónica    18       85\n",
       "D21      Ingeniería Civil En Computación e Informática    29      174\n",
       "D22                        Ingeniería Civil Industrial   160      329\n",
       "D23         Ingenieria Comercial - Campus Manuel Montt    58      727\n",
       "D24              Ingenieria Comercial - Campus Oriente    39       77\n",
       "D25                             Ingeniería Electrónica     0       19\n",
       "D26                       Ingeniería en Administración   104      401\n",
       "D27                Ingeniería en Administración Online   176      229\n",
       "D28                        Ingeniería en Construcción     23       14\n",
       "D29                          Ingeniería en Informática     2       40\n",
       "D30                            Ingeniería en Logística     1       16\n",
       "D31     Ingeniería en Medio Ambiente y Sustentabilidad    46       59\n",
       "D32                                Ingeniería Forestal    28       25\n",
       "D33                              Ingeniería Industrial     2      154\n",
       "D34                                       Kinesiología   176      183\n",
       "D35                                           Medicina   268      239\n",
       "D36                               Medicina Veterinaria   723      339\n",
       "D37                              Nutrición y Dietética    92      326\n",
       "D38                         Obstetricia y Puericultura   208      187\n",
       "D39                                        Odontología   355      189\n",
       "D40                     Pedagogía Educación Parvularia    71       44\n",
       "D41                       Pedagogía en Artes Musicales   110       75\n",
       "D42                 Pedagogía en Educación Diferencial   112       73\n",
       "D43  Pedagogia en Educación Física Deportes y Recre...   181      245\n",
       "D44                                Pedagogía en Inglés   128       88\n",
       "D45                                         Periodismo    19       93\n",
       "D46                                         Psicología   148      476\n",
       "D47                                     Psicopedagogía    61       38\n",
       "D48                                         Sociología     0       59\n",
       "D49                                             Teatro   102       90\n",
       "D50                                  Tecnología Médica   316      167\n",
       "D51                                Terapia Ocupacional   256      190"
      ],
      "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>Carrera</th>\n      <th>Votó</th>\n      <th>No votó</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>D01</th>\n      <td>Administración Pública</td>\n      <td>0</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>D02</th>\n      <td>Agronomía</td>\n      <td>112</td>\n      <td>491</td>\n    </tr>\n    <tr>\n      <th>D03</th>\n      <td>Animación Digital</td>\n      <td>80</td>\n      <td>171</td>\n    </tr>\n    <tr>\n      <th>D04</th>\n      <td>Arquitectura</td>\n      <td>151</td>\n      <td>289</td>\n    </tr>\n    <tr>\n      <th>D05</th>\n      <td>Bachillerato en Ciencias</td>\n      <td>1</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>D06</th>\n      <td>Bachillerato en Ciencias de la Salud</td>\n      <td>8</td>\n      <td>111</td>\n    </tr>\n    <tr>\n      <th>D07</th>\n      <td>Bachillerato en Humanidades</td>\n      <td>1</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>D08</th>\n      <td>Biotecnología</td>\n      <td>107</td>\n      <td>89</td>\n    </tr>\n    <tr>\n      <th>D09</th>\n      <td>Cine</td>\n      <td>82</td>\n      <td>107</td>\n    </tr>\n    <tr>\n      <th>D10</th>\n      <td>Conservatorio</td>\n      <td>13</td>\n      <td>197</td>\n    </tr>\n    <tr>\n      <th>D11</th>\n      <td>Construcción Civil</td>\n      <td>67</td>\n      <td>117</td>\n    </tr>\n    <tr>\n      <th>D12</th>\n      <td>Contador Público Auditor</td>\n      <td>1</td>\n      <td>70</td>\n    </tr>\n    <tr>\n      <th>D13</th>\n      <td>Data Science</td>\n      <td>0</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>D14</th>\n      <td>Derecho - Campus Manuel Montt</td>\n      <td>279</td>\n      <td>344</td>\n    </tr>\n    <tr>\n      <th>D15</th>\n      <td>Derecho - Campus Oriente</td>\n      <td>57</td>\n      <td>43</td>\n    </tr>\n    <tr>\n      <th>D16</th>\n      <td>Diseño</td>\n      <td>69</td>\n      <td>82</td>\n    </tr>\n    <tr>\n      <th>D17</th>\n      <td>Enfermería</td>\n      <td>285</td>\n      <td>172</td>\n    </tr>\n    <tr>\n      <th>D18</th>\n      <td>Fonoaudiología</td>\n      <td>174</td>\n      <td>72</td>\n    </tr>\n    <tr>\n      <th>D19</th>\n      <td>Geología</td>\n      <td>174</td>\n      <td>113</td>\n    </tr>\n    <tr>\n      <th>D20</th>\n      <td>Ingeniería Civil Electrónica</td>\n      <td>18</td>\n      <td>85</td>\n    </tr>\n    <tr>\n      <th>D21</th>\n      <td>Ingeniería Civil En Computación e Informática</td>\n      <td>29</td>\n      <td>174</td>\n    </tr>\n    <tr>\n      <th>D22</th>\n      <td>Ingeniería Civil Industrial</td>\n      <td>160</td>\n      <td>329</td>\n    </tr>\n    <tr>\n      <th>D23</th>\n      <td>Ingenieria Comercial - Campus Manuel Montt</td>\n      <td>58</td>\n      <td>727</td>\n    </tr>\n    <tr>\n      <th>D24</th>\n      <td>Ingenieria Comercial - Campus Oriente</td>\n      <td>39</td>\n      <td>77</td>\n    </tr>\n    <tr>\n      <th>D25</th>\n      <td>Ingeniería Electrónica</td>\n      <td>0</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>D26</th>\n      <td>Ingeniería en Administración</td>\n      <td>104</td>\n      <td>401</td>\n    </tr>\n    <tr>\n      <th>D27</th>\n      <td>Ingeniería en Administración Online</td>\n      <td>176</td>\n      <td>229</td>\n    </tr>\n    <tr>\n      <th>D28</th>\n      <td>Ingeniería en Construcción</td>\n      <td>23</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>D29</th>\n      <td>Ingeniería en Informática</td>\n      <td>2</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>D30</th>\n      <td>Ingeniería en Logística</td>\n      <td>1</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>D31</th>\n      <td>Ingeniería en Medio Ambiente y Sustentabilidad</td>\n      <td>46</td>\n      <td>59</td>\n    </tr>\n    <tr>\n      <th>D32</th>\n      <td>Ingeniería Forestal</td>\n      <td>28</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>D33</th>\n      <td>Ingeniería Industrial</td>\n      <td>2</td>\n      <td>154</td>\n    </tr>\n    <tr>\n      <th>D34</th>\n      <td>Kinesiología</td>\n      <td>176</td>\n      <td>183</td>\n    </tr>\n    <tr>\n      <th>D35</th>\n      <td>Medicina</td>\n      <td>268</td>\n      <td>239</td>\n    </tr>\n    <tr>\n      <th>D36</th>\n      <td>Medicina Veterinaria</td>\n      <td>723</td>\n      <td>339</td>\n    </tr>\n    <tr>\n      <th>D37</th>\n      <td>Nutrición y Dietética</td>\n      <td>92</td>\n      <td>326</td>\n    </tr>\n    <tr>\n      <th>D38</th>\n      <td>Obstetricia y Puericultura</td>\n      <td>208</td>\n      <td>187</td>\n    </tr>\n    <tr>\n      <th>D39</th>\n      <td>Odontología</td>\n      <td>355</td>\n      <td>189</td>\n    </tr>\n    <tr>\n      <th>D40</th>\n      <td>Pedagogía Educación Parvularia</td>\n      <td>71</td>\n      <td>44</td>\n    </tr>\n    <tr>\n      <th>D41</th>\n      <td>Pedagogía en Artes Musicales</td>\n      <td>110</td>\n      <td>75</td>\n    </tr>\n    <tr>\n      <th>D42</th>\n      <td>Pedagogía en Educación Diferencial</td>\n      <td>112</td>\n      <td>73</td>\n    </tr>\n    <tr>\n      <th>D43</th>\n      <td>Pedagogia en Educación Física Deportes y Recre...</td>\n      <td>181</td>\n      <td>245</td>\n    </tr>\n    <tr>\n      <th>D44</th>\n      <td>Pedagogía en Inglés</td>\n      <td>128</td>\n      <td>88</td>\n    </tr>\n    <tr>\n      <th>D45</th>\n      <td>Periodismo</td>\n      <td>19</td>\n      <td>93</td>\n    </tr>\n    <tr>\n      <th>D46</th>\n      <td>Psicología</td>\n      <td>148</td>\n      <td>476</td>\n    </tr>\n    <tr>\n      <th>D47</th>\n      <td>Psicopedagogía</td>\n      <td>61</td>\n      <td>38</td>\n    </tr>\n    <tr>\n      <th>D48</th>\n      <td>Sociología</td>\n      <td>0</td>\n      <td>59</td>\n    </tr>\n    <tr>\n      <th>D49</th>\n      <td>Teatro</td>\n      <td>102</td>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>D50</th>\n      <td>Tecnología Médica</td>\n      <td>316</td>\n      <td>167</td>\n    </tr>\n    <tr>\n      <th>D51</th>\n      <td>Terapia Ocupacional</td>\n      <td>256</td>\n      <td>190</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "df_program_2"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                               Carrera  Votó  No votó\n",
       "D01                             Administración Pública     0       23\n",
       "D02                                          Agronomía    30      566\n",
       "D03                                  Animación Digital   128      121\n",
       "D04                                       Arquitectura    53      384\n",
       "D05                           Bachillerato en Ciencias     0        5\n",
       "D06               Bachillerato en Ciencias de la Salud     2      114\n",
       "D07                        Bachillerato en Humanidades     1        8\n",
       "D08                                      Biotecnología    44      150\n",
       "D09                                               Cine    79      110\n",
       "D10                                      Conservatorio     4      201\n",
       "D11                                 Construcción Civil    41      146\n",
       "D12                           Contador Público Auditor     1       72\n",
       "D13                                       Data Science     0        6\n",
       "D14                      Derecho - Campus Manuel Montt   203      419\n",
       "D15                           Derecho - Campus Oriente    48       49\n",
       "D16                                             Diseño    37      111\n",
       "D17                                         Enfermería   201      255\n",
       "D18                                     Fonoaudiología   115      130\n",
       "D19                                           Geología   189       92\n",
       "D20                       Ingeniería Civil Electrónica     9       93\n",
       "D21      Ingeniería Civil En Computación e Informática    15      198\n",
       "D22                        Ingeniería Civil Industrial    52      451\n",
       "D23         Ingenieria Comercial - Campus Manuel Montt    23      763\n",
       "D24              Ingenieria Comercial - Campus Oriente     2      114\n",
       "D25                             Ingeniería Electrónica     0       19\n",
       "D26                       Ingeniería en Administración    85      419\n",
       "D27                Ingeniería en Administración Online   192      228\n",
       "D28                        Ingeniería en Construcción     14       23\n",
       "D29                          Ingeniería en Informática     1       41\n",
       "D30                            Ingeniería en Logística     1       16\n",
       "D31     Ingeniería en Medio Ambiente y Sustentabilidad    11       93\n",
       "D32                                Ingeniería Forestal    25       28\n",
       "D33                              Ingeniería Industrial     2      161\n",
       "D34                                       Kinesiología   163      194\n",
       "D35                                           Medicina   147      360\n",
       "D36                               Medicina Veterinaria   435      618\n",
       "D37                              Nutrición y Dietética    35      385\n",
       "D38                         Obstetricia y Puericultura   174      221\n",
       "D39                                        Odontología   279      265\n",
       "D40                  Pedagogía en Educación Parvularia    61       54\n",
       "D41                       Pedagogía en Artes Musicales    55      127\n",
       "D42                 Pedagogía en Educación Diferencial   107       77\n",
       "D43  Pedagogia en Educación Física Deportes y Recre...   179      245\n",
       "D44                                Pedagogía en Inglés    61      152\n",
       "D45                                         Periodismo     6      106\n",
       "D46                                         Psicología   196      428\n",
       "D47                                     Psicopedagogía    55       43\n",
       "D48                                         Sociología     1       67\n",
       "D49                                             Teatro     9      155\n",
       "D50                                  Tecnología Médica   219      258\n",
       "D51                                Terapia Ocupacional   226      216"
      ],
      "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>Carrera</th>\n      <th>Votó</th>\n      <th>No votó</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>D01</th>\n      <td>Administración Pública</td>\n      <td>0</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>D02</th>\n      <td>Agronomía</td>\n      <td>30</td>\n      <td>566</td>\n    </tr>\n    <tr>\n      <th>D03</th>\n      <td>Animación Digital</td>\n      <td>128</td>\n      <td>121</td>\n    </tr>\n    <tr>\n      <th>D04</th>\n      <td>Arquitectura</td>\n      <td>53</td>\n      <td>384</td>\n    </tr>\n    <tr>\n      <th>D05</th>\n      <td>Bachillerato en Ciencias</td>\n      <td>0</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>D06</th>\n      <td>Bachillerato en Ciencias de la Salud</td>\n      <td>2</td>\n      <td>114</td>\n    </tr>\n    <tr>\n      <th>D07</th>\n      <td>Bachillerato en Humanidades</td>\n      <td>1</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>D08</th>\n      <td>Biotecnología</td>\n      <td>44</td>\n      <td>150</td>\n    </tr>\n    <tr>\n      <th>D09</th>\n      <td>Cine</td>\n      <td>79</td>\n      <td>110</td>\n    </tr>\n    <tr>\n      <th>D10</th>\n      <td>Conservatorio</td>\n      <td>4</td>\n      <td>201</td>\n    </tr>\n    <tr>\n      <th>D11</th>\n      <td>Construcción Civil</td>\n      <td>41</td>\n      <td>146</td>\n    </tr>\n    <tr>\n      <th>D12</th>\n      <td>Contador Público Auditor</td>\n      <td>1</td>\n      <td>72</td>\n    </tr>\n    <tr>\n      <th>D13</th>\n      <td>Data Science</td>\n      <td>0</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>D14</th>\n      <td>Derecho - Campus Manuel Montt</td>\n      <td>203</td>\n      <td>419</td>\n    </tr>\n    <tr>\n      <th>D15</th>\n      <td>Derecho - Campus Oriente</td>\n      <td>48</td>\n      <td>49</td>\n    </tr>\n    <tr>\n      <th>D16</th>\n      <td>Diseño</td>\n      <td>37</td>\n      <td>111</td>\n    </tr>\n    <tr>\n      <th>D17</th>\n      <td>Enfermería</td>\n      <td>201</td>\n      <td>255</td>\n    </tr>\n    <tr>\n      <th>D18</th>\n      <td>Fonoaudiología</td>\n      <td>115</td>\n      <td>130</td>\n    </tr>\n    <tr>\n      <th>D19</th>\n      <td>Geología</td>\n      <td>189</td>\n      <td>92</td>\n    </tr>\n    <tr>\n      <th>D20</th>\n      <td>Ingeniería Civil Electrónica</td>\n      <td>9</td>\n      <td>93</td>\n    </tr>\n    <tr>\n      <th>D21</th>\n      <td>Ingeniería Civil En Computación e Informática</td>\n      <td>15</td>\n      <td>198</td>\n    </tr>\n    <tr>\n      <th>D22</th>\n      <td>Ingeniería Civil Industrial</td>\n      <td>52</td>\n      <td>451</td>\n    </tr>\n    <tr>\n      <th>D23</th>\n      <td>Ingenieria Comercial - Campus Manuel Montt</td>\n      <td>23</td>\n      <td>763</td>\n    </tr>\n    <tr>\n      <th>D24</th>\n      <td>Ingenieria Comercial - Campus Oriente</td>\n      <td>2</td>\n      <td>114</td>\n    </tr>\n    <tr>\n      <th>D25</th>\n      <td>Ingeniería Electrónica</td>\n      <td>0</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>D26</th>\n      <td>Ingeniería en Administración</td>\n      <td>85</td>\n      <td>419</td>\n    </tr>\n    <tr>\n      <th>D27</th>\n      <td>Ingeniería en Administración Online</td>\n      <td>192</td>\n      <td>228</td>\n    </tr>\n    <tr>\n      <th>D28</th>\n      <td>Ingeniería en Construcción</td>\n      <td>14</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>D29</th>\n      <td>Ingeniería en Informática</td>\n      <td>1</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>D30</th>\n      <td>Ingeniería en Logística</td>\n      <td>1</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>D31</th>\n      <td>Ingeniería en Medio Ambiente y Sustentabilidad</td>\n      <td>11</td>\n      <td>93</td>\n    </tr>\n    <tr>\n      <th>D32</th>\n      <td>Ingeniería Forestal</td>\n      <td>25</td>\n      <td>28</td>\n    </tr>\n    <tr>\n      <th>D33</th>\n      <td>Ingeniería Industrial</td>\n      <td>2</td>\n      <td>161</td>\n    </tr>\n    <tr>\n      <th>D34</th>\n      <td>Kinesiología</td>\n      <td>163</td>\n      <td>194</td>\n    </tr>\n    <tr>\n      <th>D35</th>\n      <td>Medicina</td>\n      <td>147</td>\n      <td>360</td>\n    </tr>\n    <tr>\n      <th>D36</th>\n      <td>Medicina Veterinaria</td>\n      <td>435</td>\n      <td>618</td>\n    </tr>\n    <tr>\n      <th>D37</th>\n      <td>Nutrición y Dietética</td>\n      <td>35</td>\n      <td>385</td>\n    </tr>\n    <tr>\n      <th>D38</th>\n      <td>Obstetricia y Puericultura</td>\n      <td>174</td>\n      <td>221</td>\n    </tr>\n    <tr>\n      <th>D39</th>\n      <td>Odontología</td>\n      <td>279</td>\n      <td>265</td>\n    </tr>\n    <tr>\n      <th>D40</th>\n      <td>Pedagogía en Educación Parvularia</td>\n      <td>61</td>\n      <td>54</td>\n    </tr>\n    <tr>\n      <th>D41</th>\n      <td>Pedagogía en Artes Musicales</td>\n      <td>55</td>\n      <td>127</td>\n    </tr>\n    <tr>\n      <th>D42</th>\n      <td>Pedagogía en Educación Diferencial</td>\n      <td>107</td>\n      <td>77</td>\n    </tr>\n    <tr>\n      <th>D43</th>\n      <td>Pedagogia en Educación Física Deportes y Recre...</td>\n      <td>179</td>\n      <td>245</td>\n    </tr>\n    <tr>\n      <th>D44</th>\n      <td>Pedagogía en Inglés</td>\n      <td>61</td>\n      <td>152</td>\n    </tr>\n    <tr>\n      <th>D45</th>\n      <td>Periodismo</td>\n      <td>6</td>\n      <td>106</td>\n    </tr>\n    <tr>\n      <th>D46</th>\n      <td>Psicología</td>\n      <td>196</td>\n      <td>428</td>\n    </tr>\n    <tr>\n      <th>D47</th>\n      <td>Psicopedagogía</td>\n      <td>55</td>\n      <td>43</td>\n    </tr>\n    <tr>\n      <th>D48</th>\n      <td>Sociología</td>\n      <td>1</td>\n      <td>67</td>\n    </tr>\n    <tr>\n      <th>D49</th>\n      <td>Teatro</td>\n      <td>9</td>\n      <td>155</td>\n    </tr>\n    <tr>\n      <th>D50</th>\n      <td>Tecnología Médica</td>\n      <td>219</td>\n      <td>258</td>\n    </tr>\n    <tr>\n      <th>D51</th>\n      <td>Terapia Ocupacional</td>\n      <td>226</td>\n      <td>216</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "df_program_3.sort_values(by='Carrera')"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                               Carrera  Votó  No votó\n",
       "D19                                          Agronomía     7      640\n",
       "D07                                 Animación Digital    74      215\n",
       "D20                                       Arquitectura     4      469\n",
       "D21                           Bachillerato en Ciencias     0       15\n",
       "D22               Bachillerato en Ciencias de la Salud     0       70\n",
       "D23                        Bachillerato en Humanidades     0        9\n",
       "D24                                      Biotecnología    21      200\n",
       "D13                                               Cine    71      176\n",
       "D25                                      Conservatorio     0       65\n",
       "D26                                 Construcción Civil     0      190\n",
       "D27                           Contador Público Auditor     0       33\n",
       "D28                                       Data Science     0       18\n",
       "D16                      Derecho - Campus Manuel Montt    82      558\n",
       "D29                           Derecho - Campus Oriente    19       97\n",
       "D30                                             Diseño     3      146\n",
       "D31                                         Enfermería    71      403\n",
       "D17                                    Fonoaudiología    74      205\n",
       "D09                                          Geología   128      212\n",
       "D32                       Ingeniería Civil Electrónica     5      123\n",
       "D01                        Ingeniería Civil Industrial    43      585\n",
       "D08                               Ingeniería Comercial    70      742\n",
       "D41                                Ingeniería Forestal    18       50\n",
       "D36                       Ingeniería en Administración     4      395\n",
       "D11                Ingeniería en Administración Online   107      358\n",
       "D38                          Ingeniería en Computación     4      291\n",
       "D40     Ingeniería en Medio Ambiente y Sustentabilidad     4      134\n",
       "D43                                      Kinesiologíal    52      356\n",
       "D44                                           Medicina    31      503\n",
       "D15                               Medicina Veterinaria   219     1016\n",
       "D02                              Nutrición y Dietética    75      372\n",
       "D45                         Obstetricia y Puericultura    67      356\n",
       "D10                                       Odontología   204      385\n",
       "D06  Pedagogia en Educación Física Deportes y Recre...    88      297\n",
       "D46                     Pedagogía Educación Parvularia    33       68\n",
       "D47                       Pedagogía en Artes Musicales    10      161\n",
       "D48                 Pedagogía en Educación Diferencial    17      170\n",
       "D49                                Pedagogía en Inglés    14      182\n",
       "D50                                         Periodismo     2      127\n",
       "D14                                        Psicología   146      609\n",
       "D05                                    Psicopedagogía    22       84\n",
       "D51                                         Sociología     0       20\n",
       "D04                                             Teatro    50      108\n",
       "D03                                Tecnología Médica   183      348\n",
       "D12                                Terapia Ocupacional    88      418"
      ],
      "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>Carrera</th>\n      <th>Votó</th>\n      <th>No votó</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>D19</th>\n      <td>Agronomía</td>\n      <td>7</td>\n      <td>640</td>\n    </tr>\n    <tr>\n      <th>D07</th>\n      <td>Animación Digital</td>\n      <td>74</td>\n      <td>215</td>\n    </tr>\n    <tr>\n      <th>D20</th>\n      <td>Arquitectura</td>\n      <td>4</td>\n      <td>469</td>\n    </tr>\n    <tr>\n      <th>D21</th>\n      <td>Bachillerato en Ciencias</td>\n      <td>0</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>D22</th>\n      <td>Bachillerato en Ciencias de la Salud</td>\n      <td>0</td>\n      <td>70</td>\n    </tr>\n    <tr>\n      <th>D23</th>\n      <td>Bachillerato en Humanidades</td>\n      <td>0</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>D24</th>\n      <td>Biotecnología</td>\n      <td>21</td>\n      <td>200</td>\n    </tr>\n    <tr>\n      <th>D13</th>\n      <td>Cine</td>\n      <td>71</td>\n      <td>176</td>\n    </tr>\n    <tr>\n      <th>D25</th>\n      <td>Conservatorio</td>\n      <td>0</td>\n      <td>65</td>\n    </tr>\n    <tr>\n      <th>D26</th>\n      <td>Construcción Civil</td>\n      <td>0</td>\n      <td>190</td>\n    </tr>\n    <tr>\n      <th>D27</th>\n      <td>Contador Público Auditor</td>\n      <td>0</td>\n      <td>33</td>\n    </tr>\n    <tr>\n      <th>D28</th>\n      <td>Data Science</td>\n      <td>0</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>D16</th>\n      <td>Derecho - Campus Manuel Montt</td>\n      <td>82</td>\n      <td>558</td>\n    </tr>\n    <tr>\n      <th>D29</th>\n      <td>Derecho - Campus Oriente</td>\n      <td>19</td>\n      <td>97</td>\n    </tr>\n    <tr>\n      <th>D30</th>\n      <td>Diseño</td>\n      <td>3</td>\n      <td>146</td>\n    </tr>\n    <tr>\n      <th>D31</th>\n      <td>Enfermería</td>\n      <td>71</td>\n      <td>403</td>\n    </tr>\n    <tr>\n      <th>D17</th>\n      <td>Fonoaudiología</td>\n      <td>74</td>\n      <td>205</td>\n    </tr>\n    <tr>\n      <th>D09</th>\n      <td>Geología</td>\n      <td>128</td>\n      <td>212</td>\n    </tr>\n    <tr>\n      <th>D32</th>\n      <td>Ingeniería Civil Electrónica</td>\n      <td>5</td>\n      <td>123</td>\n    </tr>\n    <tr>\n      <th>D01</th>\n      <td>Ingeniería Civil Industrial</td>\n      <td>43</td>\n      <td>585</td>\n    </tr>\n    <tr>\n      <th>D08</th>\n      <td>Ingeniería Comercial</td>\n      <td>70</td>\n      <td>742</td>\n    </tr>\n    <tr>\n      <th>D41</th>\n      <td>Ingeniería Forestal</td>\n      <td>18</td>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>D36</th>\n      <td>Ingeniería en Administración</td>\n      <td>4</td>\n      <td>395</td>\n    </tr>\n    <tr>\n      <th>D11</th>\n      <td>Ingeniería en Administración Online</td>\n      <td>107</td>\n      <td>358</td>\n    </tr>\n    <tr>\n      <th>D38</th>\n      <td>Ingeniería en Computación</td>\n      <td>4</td>\n      <td>291</td>\n    </tr>\n    <tr>\n      <th>D40</th>\n      <td>Ingeniería en Medio Ambiente y Sustentabilidad</td>\n      <td>4</td>\n      <td>134</td>\n    </tr>\n    <tr>\n      <th>D43</th>\n      <td>Kinesiologíal</td>\n      <td>52</td>\n      <td>356</td>\n    </tr>\n    <tr>\n      <th>D44</th>\n      <td>Medicina</td>\n      <td>31</td>\n      <td>503</td>\n    </tr>\n    <tr>\n      <th>D15</th>\n      <td>Medicina Veterinaria</td>\n      <td>219</td>\n      <td>1016</td>\n    </tr>\n    <tr>\n      <th>D02</th>\n      <td>Nutrición y Dietética</td>\n      <td>75</td>\n      <td>372</td>\n    </tr>\n    <tr>\n      <th>D45</th>\n      <td>Obstetricia y Puericultura</td>\n      <td>67</td>\n      <td>356</td>\n    </tr>\n    <tr>\n      <th>D10</th>\n      <td>Odontología</td>\n      <td>204</td>\n      <td>385</td>\n    </tr>\n    <tr>\n      <th>D06</th>\n      <td>Pedagogia en Educación Física Deportes y Recre...</td>\n      <td>88</td>\n      <td>297</td>\n    </tr>\n    <tr>\n      <th>D46</th>\n      <td>Pedagogía Educación Parvularia</td>\n      <td>33</td>\n      <td>68</td>\n    </tr>\n    <tr>\n      <th>D47</th>\n      <td>Pedagogía en Artes Musicales</td>\n      <td>10</td>\n      <td>161</td>\n    </tr>\n    <tr>\n      <th>D48</th>\n      <td>Pedagogía en Educación Diferencial</td>\n      <td>17</td>\n      <td>170</td>\n    </tr>\n    <tr>\n      <th>D49</th>\n      <td>Pedagogía en Inglés</td>\n      <td>14</td>\n      <td>182</td>\n    </tr>\n    <tr>\n      <th>D50</th>\n      <td>Periodismo</td>\n      <td>2</td>\n      <td>127</td>\n    </tr>\n    <tr>\n      <th>D14</th>\n      <td>Psicología</td>\n      <td>146</td>\n      <td>609</td>\n    </tr>\n    <tr>\n      <th>D05</th>\n      <td>Psicopedagogía</td>\n      <td>22</td>\n      <td>84</td>\n    </tr>\n    <tr>\n      <th>D51</th>\n      <td>Sociología</td>\n      <td>0</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>D04</th>\n      <td>Teatro</td>\n      <td>50</td>\n      <td>108</td>\n    </tr>\n    <tr>\n      <th>D03</th>\n      <td>Tecnología Médica</td>\n      <td>183</td>\n      <td>348</td>\n    </tr>\n    <tr>\n      <th>D12</th>\n      <td>Terapia Ocupacional</td>\n      <td>88</td>\n      <td>418</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "df_hour_0"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Día       Fecha   Hora  Votos\n",
       "0   Día 1  2018-10-08  11:00    155\n",
       "1   Día 1  2018-10-08  12:00    231\n",
       "2   Día 1  2018-10-08  13:00    220\n",
       "3   Día 1  2018-10-08  14:00    176\n",
       "4   Día 1  2018-10-08  15:00     99\n",
       "5   Día 1  2018-10-08  16:00     98\n",
       "6   Día 1  2018-10-08  17:00    101\n",
       "7   Día 1  2018-10-08  18:00     68\n",
       "8   Día 1  2018-10-08  19:00     79\n",
       "9   Día 1  2018-10-08  20:00     95\n",
       "10  Día 1  2018-10-08  21:00     95\n",
       "11  Día 1  2018-10-08  22:00     80\n",
       "12  Día 1  2018-10-08  23:00     92\n",
       "13  Día 2  2018-10-09  00:00     49\n",
       "14  Día 2  2018-10-09  01:00     15\n",
       "15  Día 2  2018-10-09  02:00     18\n",
       "16  Día 2  2018-10-09  03:00      1\n",
       "17  Día 2  2018-10-09  04:00      2\n",
       "18  Día 2  2018-10-09  05:00      1\n",
       "19  Día 2  2018-10-09  06:00      1\n",
       "20  Día 2  2018-10-09  07:00      6\n",
       "21  Día 2  2018-10-09  08:00     21\n",
       "22  Día 2  2018-10-09  09:00     19\n",
       "23  Día 2  2018-10-09  10:00     26\n",
       "24  Día 2  2018-10-09  11:00    126\n",
       "25  Día 2  2018-10-09  12:00    216\n",
       "26  Día 2  2018-10-09  13:00    238\n",
       "27  Día 2  2018-10-09  14:00    206\n",
       "28  Día 2  2018-10-09  15:00    135\n",
       "29  Día 2  2018-10-09  16:00    229\n",
       "30  Día 2  2018-10-09  17:00    169\n",
       "31  Día 2  2018-10-09  18:00    191\n",
       "32  Día 2  2018-10-09  19:00    148\n",
       "33  Día 2  2018-10-09  20:00    101"
      ],
      "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>Día</th>\n      <th>Fecha</th>\n      <th>Hora</th>\n      <th>Votos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Día 1</td>\n      <td>2018-10-08</td>\n      <td>11:00</td>\n      <td>155</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Día 1</td>\n      <td>2018-10-08</td>\n      <td>12:00</td>\n      <td>231</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Día 1</td>\n      <td>2018-10-08</td>\n      <td>13:00</td>\n      <td>220</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Día 1</td>\n      <td>2018-10-08</td>\n      <td>14:00</td>\n      <td>176</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Día 1</td>\n      <td>2018-10-08</td>\n      <td>15:00</td>\n      <td>99</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Día 1</td>\n      <td>2018-10-08</td>\n      <td>16:00</td>\n      <td>98</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Día 1</td>\n      <td>2018-10-08</td>\n      <td>17:00</td>\n      <td>101</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Día 1</td>\n      <td>2018-10-08</td>\n      <td>18:00</td>\n      <td>68</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Día 1</td>\n      <td>2018-10-08</td>\n      <td>19:00</td>\n      <td>79</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Día 1</td>\n      <td>2018-10-08</td>\n      <td>20:00</td>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Día 1</td>\n      <td>2018-10-08</td>\n      <td>21:00</td>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Día 1</td>\n      <td>2018-10-08</td>\n      <td>22:00</td>\n      <td>80</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Día 1</td>\n      <td>2018-10-08</td>\n      <td>23:00</td>\n      <td>92</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>00:00</td>\n      <td>49</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>01:00</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>02:00</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>03:00</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>04:00</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>05:00</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>06:00</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>07:00</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>08:00</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>09:00</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>10:00</td>\n      <td>26</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>11:00</td>\n      <td>126</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>12:00</td>\n      <td>216</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>13:00</td>\n      <td>238</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>14:00</td>\n      <td>206</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>15:00</td>\n      <td>135</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>16:00</td>\n      <td>229</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>17:00</td>\n      <td>169</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>18:00</td>\n      <td>191</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>19:00</td>\n      <td>148</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Día 2</td>\n      <td>2018-10-09</td>\n      <td>20:00</td>\n      <td>101</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "df_hour_1"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Día       Fecha   Hora  Votos\n",
       "0   Día 1  2020-06-08  10:00    107\n",
       "1   Día 1  2020-06-08  11:00    238\n",
       "2   Día 1  2020-06-08  12:00    309\n",
       "3   Día 1  2020-06-08  13:00    378\n",
       "4   Día 1  2020-06-08  14:00    285\n",
       "5   Día 1  2020-06-08  15:00    188\n",
       "6   Día 1  2020-06-08  16:00    213\n",
       "7   Día 1  2020-06-08  17:00    195\n",
       "8   Día 1  2020-06-08  18:00    158\n",
       "9   Día 1  2020-06-08  19:00    140\n",
       "10  Día 1  2020-06-08  20:00     86\n",
       "11  Día 1  2020-06-08  21:00     73\n",
       "12  Día 1  2020-06-08  22:00    107\n",
       "13  Día 1  2020-06-08  23:00     91\n",
       "14  Día 2  2020-06-09  00:00     59\n",
       "15  Día 2  2020-06-09  01:00     65\n",
       "16  Día 2  2020-06-09  02:00     14\n",
       "17  Día 2  2020-06-09  03:00      2\n",
       "18  Día 2  2020-06-09  04:00      3\n",
       "19  Día 2  2020-06-09  05:00      2\n",
       "20  Día 2  2020-06-09  06:00      1\n",
       "21  Día 2  2020-06-09  07:00      1\n",
       "22  Día 2  2020-06-09  08:00      4\n",
       "23  Día 2  2020-06-09  09:00     17\n",
       "24  Día 2  2020-06-09  10:00     49\n",
       "25  Día 2  2020-06-09  11:00    150\n",
       "26  Día 2  2020-06-09  12:00    229\n",
       "27  Día 2  2020-06-09  13:00    326\n",
       "28  Día 2  2020-06-09  14:00    473\n",
       "29  Día 2  2020-06-09  15:00    421\n",
       "30  Día 2  2020-06-09  16:00    370\n",
       "31  Día 2  2020-06-09  17:00    642\n",
       "32  Día 2  2020-06-09  18:00    277"
      ],
      "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>Día</th>\n      <th>Fecha</th>\n      <th>Hora</th>\n      <th>Votos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>10:00</td>\n      <td>107</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>11:00</td>\n      <td>238</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>12:00</td>\n      <td>309</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>13:00</td>\n      <td>378</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>14:00</td>\n      <td>285</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>15:00</td>\n      <td>188</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>16:00</td>\n      <td>213</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>17:00</td>\n      <td>195</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>18:00</td>\n      <td>158</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>19:00</td>\n      <td>140</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>20:00</td>\n      <td>86</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>21:00</td>\n      <td>73</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>22:00</td>\n      <td>107</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Día 1</td>\n      <td>2020-06-08</td>\n      <td>23:00</td>\n      <td>91</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>00:00</td>\n      <td>59</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>01:00</td>\n      <td>65</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>02:00</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>03:00</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>04:00</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>05:00</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>06:00</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>07:00</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>08:00</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>09:00</td>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>10:00</td>\n      <td>49</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>11:00</td>\n      <td>150</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>12:00</td>\n      <td>229</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>13:00</td>\n      <td>326</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>14:00</td>\n      <td>473</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>15:00</td>\n      <td>421</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>16:00</td>\n      <td>370</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>17:00</td>\n      <td>642</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Día 2</td>\n      <td>2020-06-09</td>\n      <td>18:00</td>\n      <td>277</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "df_hour_2"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Día       Fecha   Hora  Votos\n",
       "0   Día 1  2020-08-13  10:00    117\n",
       "1   Día 1  2020-08-13  11:00    120\n",
       "2   Día 1  2020-08-13  12:00    184\n",
       "3   Día 1  2020-08-13  13:00    286\n",
       "4   Día 1  2020-08-13  14:00    225\n",
       "5   Día 1  2020-08-13  15:00    235\n",
       "6   Día 1  2020-08-13  16:00    224\n",
       "7   Día 1  2020-08-13  17:00    202\n",
       "8   Día 1  2020-08-13  18:00    157\n",
       "9   Día 1  2020-08-13  19:00    161\n",
       "10  Día 1  2020-08-13  20:00    171\n",
       "11  Día 1  2020-08-13  21:00    131\n",
       "12  Día 1  2020-08-13  22:00    116\n",
       "13  Día 1  2020-08-13  23:00     92\n",
       "14  Día 2  2020-08-14  00:00     64\n",
       "15  Día 2  2020-08-14  01:00     32\n",
       "16  Día 2  2020-08-14  02:00     18\n",
       "17  Día 2  2020-08-14  03:00      3\n",
       "18  Día 2  2020-08-14  04:00      1\n",
       "19  Día 2  2020-08-14  05:00      0\n",
       "20  Día 2  2020-08-14  06:00      0\n",
       "21  Día 2  2020-08-14  07:00      3\n",
       "22  Día 2  2020-08-14  08:00      2\n",
       "23  Día 2  2020-08-14  09:00     11\n",
       "24  Día 2  2020-08-14  10:00     16\n",
       "25  Día 2  2020-08-14  11:00     40\n",
       "26  Día 2  2020-08-14  12:00    102\n",
       "27  Día 2  2020-08-14  13:00    178\n",
       "28  Día 2  2020-08-14  14:00    258\n",
       "29  Día 2  2020-08-14  15:00    264\n",
       "30  Día 2  2020-08-14  16:00    276\n",
       "31  Día 2  2020-08-14  17:00    327"
      ],
      "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>Día</th>\n      <th>Fecha</th>\n      <th>Hora</th>\n      <th>Votos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>10:00</td>\n      <td>117</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>11:00</td>\n      <td>120</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>12:00</td>\n      <td>184</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>13:00</td>\n      <td>286</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>14:00</td>\n      <td>225</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>15:00</td>\n      <td>235</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>16:00</td>\n      <td>224</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>17:00</td>\n      <td>202</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>18:00</td>\n      <td>157</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>19:00</td>\n      <td>161</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>20:00</td>\n      <td>171</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>21:00</td>\n      <td>131</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>22:00</td>\n      <td>116</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Día 1</td>\n      <td>2020-08-13</td>\n      <td>23:00</td>\n      <td>92</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>00:00</td>\n      <td>64</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>01:00</td>\n      <td>32</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>02:00</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>03:00</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>04:00</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>05:00</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>06:00</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>07:00</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>08:00</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>09:00</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>10:00</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>11:00</td>\n      <td>40</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>12:00</td>\n      <td>102</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>13:00</td>\n      <td>178</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>14:00</td>\n      <td>258</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>15:00</td>\n      <td>264</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>16:00</td>\n      <td>276</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Día 2</td>\n      <td>2020-08-14</td>\n      <td>17:00</td>\n      <td>327</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 16
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "df_hour_3"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Día       Fecha   Hora  Votos\n",
       "0   Día 1  2021-07-19  09:00     21\n",
       "1   Día 1  2021-07-19  10:00     66\n",
       "2   Día 1  2021-07-19  11:00    100\n",
       "3   Día 1  2021-07-19  12:00    105\n",
       "4   Día 1  2021-07-19  13:00     97\n",
       "5   Día 1  2021-07-19  14:00    128\n",
       "6   Día 1  2021-07-19  15:00     73\n",
       "7   Día 1  2021-07-19  16:00     81\n",
       "8   Día 1  2021-07-19  17:00     61\n",
       "9   Día 1  2021-07-19  18:00     59\n",
       "10  Día 1  2021-07-19  19:00     48\n",
       "11  Día 1  2021-07-19  20:00    127\n",
       "12  Día 1  2021-07-19  21:00     62\n",
       "13  Día 1  2021-07-19  22:00     17\n",
       "14  Día 1  2021-07-19  23:00     41\n",
       "15  Día 2  2021-07-20  00:00     25\n",
       "16  Día 2  2021-07-20  01:00     11\n",
       "17  Día 2  2021-07-20  02:00      7\n",
       "18  Día 2  2021-07-20  03:00      2\n",
       "19  Día 2  2021-07-20  04:00      0\n",
       "20  Día 2  2021-07-20  05:00      0\n",
       "21  Día 2  2021-07-20  06:00      0\n",
       "22  Día 2  2021-07-20  07:00      0\n",
       "23  Día 2  2021-07-20  08:00      6\n",
       "24  Día 2  2021-07-20  09:00      9\n",
       "25  Día 2  2021-07-20  10:00     22\n",
       "26  Día 2  2021-07-20  11:00     44\n",
       "27  Día 2  2021-07-20  12:00    140\n",
       "28  Día 2  2021-07-20  13:00    132\n",
       "29  Día 2  2021-07-20  14:00     99\n",
       "30  Día 2  2021-07-20  15:00     73\n",
       "31  Día 2  2021-07-20  16:00     80\n",
       "32  Día 2  2021-07-20  17:00     78\n",
       "33  Día 2  2021-07-20  18:00     63\n",
       "34  Día 2  2021-07-20  19:00     68\n",
       "35  Día 2  2021-07-20  20:00    165"
      ],
      "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>Día</th>\n      <th>Fecha</th>\n      <th>Hora</th>\n      <th>Votos</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>09:00</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>10:00</td>\n      <td>66</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>11:00</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>12:00</td>\n      <td>105</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>13:00</td>\n      <td>97</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>14:00</td>\n      <td>128</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>15:00</td>\n      <td>73</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>16:00</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>17:00</td>\n      <td>61</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>18:00</td>\n      <td>59</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>19:00</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>20:00</td>\n      <td>127</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>21:00</td>\n      <td>62</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>22:00</td>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Día 1</td>\n      <td>2021-07-19</td>\n      <td>23:00</td>\n      <td>41</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>00:00</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>01:00</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>02:00</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>03:00</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>04:00</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>05:00</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>06:00</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>07:00</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>08:00</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>09:00</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>10:00</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>11:00</td>\n      <td>44</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>12:00</td>\n      <td>140</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>13:00</td>\n      <td>132</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>14:00</td>\n      <td>99</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>15:00</td>\n      <td>73</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>16:00</td>\n      <td>80</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>17:00</td>\n      <td>78</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>18:00</td>\n      <td>63</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>19:00</td>\n      <td>68</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>Día 2</td>\n      <td>2021-07-20</td>\n      <td>20:00</td>\n      <td>165</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "df_results = df_results.drop('Código Pregunta')\r\n",
    "df_results = df_results.drop('Pregunta')\r\n",
    "df_results\r\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                          Impulsar 2018 Conectemos 2020-1 Conectemos 2020-2  \\\n",
       "Alameda                           4e+02               NaN               NaN   \n",
       "Conservatorio                     4e+01             1e+01                 4   \n",
       "Oriente                           9e+01               NaN               NaN   \n",
       "El Claustro                       1e+02               NaN               NaN   \n",
       "Huechuraba                        2e+03               NaN               NaN   \n",
       "...                                 ...               ...               ...   \n",
       "Teatro                              NaN             6e+01                 8   \n",
       "Tecnologia Medica                   NaN             3e+02             2e+02   \n",
       "Terapia Ocupacional                 NaN             2e+02             2e+02   \n",
       "Ingenieria Comercial                NaN               NaN               NaN   \n",
       "Ingenieria en Computacion           NaN               NaN               NaN   \n",
       "\n",
       "                          Construyendo 2021  \n",
       "Alameda                                 NaN  \n",
       "Conservatorio                             0  \n",
       "Oriente                                 NaN  \n",
       "El Claustro                             NaN  \n",
       "Huechuraba                              NaN  \n",
       "...                                     ...  \n",
       "Teatro                                3e+01  \n",
       "Tecnologia Medica                     2e+02  \n",
       "Terapia Ocupacional                   7e+01  \n",
       "Ingenieria Comercial                  3e+01  \n",
       "Ingenieria en Computacion                 3  \n",
       "\n",
       "[61 rows x 4 columns]"
      ],
      "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>Impulsar 2018</th>\n      <th>Conectemos 2020-1</th>\n      <th>Conectemos 2020-2</th>\n      <th>Construyendo 2021</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Alameda</th>\n      <td>4e+02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Conservatorio</th>\n      <td>4e+01</td>\n      <td>1e+01</td>\n      <td>4</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>Oriente</th>\n      <td>9e+01</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>El Claustro</th>\n      <td>1e+02</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>Huechuraba</th>\n      <td>2e+03</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</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>Teatro</th>\n      <td>NaN</td>\n      <td>6e+01</td>\n      <td>8</td>\n      <td>3e+01</td>\n    </tr>\n    <tr>\n      <th>Tecnologia Medica</th>\n      <td>NaN</td>\n      <td>3e+02</td>\n      <td>2e+02</td>\n      <td>2e+02</td>\n    </tr>\n    <tr>\n      <th>Terapia Ocupacional</th>\n      <td>NaN</td>\n      <td>2e+02</td>\n      <td>2e+02</td>\n      <td>7e+01</td>\n    </tr>\n    <tr>\n      <th>Ingenieria Comercial</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3e+01</td>\n    </tr>\n    <tr>\n      <th>Ingenieria en Computacion</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n<p>61 rows × 4 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Visualización de los datos"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "import matplotlib.pyplot as plt\r\n",
    "import seaborn as sns\r\n",
    "sns.set(rc={'figure.figsize':(18,8)})\r\n",
    "sns.set_palette(\"tab10\")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Total de participación para cada elección"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "start, end = '0', '35'\r\n",
    "fig, ax = plt.subplots()\r\n",
    "ax.plot(df_hour_0['Votos'], label='Votación Impulsar 2018', marker='.')\r\n",
    "ax.plot(df_hour_1['Votos'], label='Votación Conectemos 2020-1', marker='.')\r\n",
    "ax.plot(df_hour_2['Votos'], label='Votación Conectemos 2020-2', marker='.')\r\n",
    "ax.plot(df_hour_3['Votos'], label='Votación Construyendo 2021', marker='.')\r\n",
    "ax.set_xlabel('Hora de la votación')\r\n",
    "ax.set_ylabel('Cantidad de votos')\r\n",
    "ax.legend()\r\n",
    "ax.set_title('Votos por hora')\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "source": [
    "sns.set(rc={'figure.figsize':(13,6)})\r\n",
    "sns.set_palette(\"tab10\")\r\n",
    "\r\n",
    "fig = plt.figure()\r\n",
    "ax = fig.add_axes([0,0,1,1])\r\n",
    "ax.bar('Impulsar 2018', df_hour_0['Votos'].sum())\r\n",
    "ax.bar('Conectemos 2020-1', df_hour_1['Votos'].sum())\r\n",
    "ax.bar('Conectemos 2020-2', df_hour_2['Votos'].sum())\r\n",
    "ax.bar('Construyendo 2021', df_hour_3['Votos'].sum())\r\n",
    "ax.set_xlabel('Lista de Mesa Directiva')\r\n",
    "ax.set_ylabel('Cantidad de votos')\r\n",
    "ax.set_title('Total de votos para cada votación')\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 936x432 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "fig = plt.figure()\r\n",
    "ax = fig.add_axes([0,0,1,1])\r\n",
    "ax.bar('Impulsar 2018', 100*df_program_0['Votó'].sum()/(df_program_0['Votó'].sum()+df_program_0['No votó'].sum()))\r\n",
    "ax.bar('Conectemos 2020-1', 100*df_program_1['Votó'].sum()/(df_program_1['Votó'].sum()+df_program_1['No votó'].sum()))\r\n",
    "ax.bar('Conectemos 2020-2', 100*df_program_2['Votó'].sum()/(df_program_2['Votó'].sum()+df_program_2['No votó'].sum()))\r\n",
    "ax.bar('Construyendo 2021', 100*df_program_3['Votó'].sum()/(df_program_3['Votó'].sum()+df_program_3['No votó'].sum()))\r\n",
    "ax.set_xlabel('Lista de Mesa Directiva')\r\n",
    "ax.set_ylabel('% Participación de votantes habilitados')\r\n",
    "ax.set_title('Total de participación')\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "ultimas_elecciones = df_results\n",
    "ultimas_elecciones = ultimas_elecciones.drop('Impulsar 2018', axis=1)\n",
    "ultimas_elecciones = ultimas_elecciones.drop('Alameda')\n",
    "ultimas_elecciones = ultimas_elecciones.drop('Oriente')\n",
    "ultimas_elecciones = ultimas_elecciones.drop('El Claustro')\n",
    "ultimas_elecciones = ultimas_elecciones.drop('Huechuraba')\n",
    "ultimas_elecciones = ultimas_elecciones.drop('Manuel Montt')\n",
    "ultimas_elecciones = ultimas_elecciones.drop('República')\n",
    "ultimas_elecciones = ultimas_elecciones.drop('Santo Domingo')\n",
    "ultimas_elecciones = ultimas_elecciones.drop('Estadio Mayor')\n",
    "ultimas_elecciones = ultimas_elecciones.drop('Alonso de Cordova')\n",
    "ultimas_elecciones.loc['Ingenieria Comercial', 'Conectemos 2020-2'] = ultimas_elecciones.loc['Ingenieria Comercial - Campus Manuel Montt','Conectemos 2020-2'] + ultimas_elecciones.loc['Ingenieria Comercial - Campus Oriente','Conectemos 2020-2'] \n",
    "ultimas_elecciones = ultimas_elecciones.drop('Ingenieria Comercial - Campus Manuel Montt')\n",
    "ultimas_elecciones = ultimas_elecciones.drop('Ingenieria Comercial - Campus Oriente')\n",
    "ultimas_elecciones.style.set_precision(0)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7faf32a7acd0>"
      ],
      "text/html": "<style  type=\"text/css\" >\n</style><table id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0a\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Conectemos 2020-1</th>        <th class=\"col_heading level0 col1\" >Conectemos 2020-2</th>        <th class=\"col_heading level0 col2\" >Construyendo 2021</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row0\" class=\"row_heading level0 row0\" >Conservatorio</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow0_col0\" class=\"data row0 col0\" >13</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow0_col1\" class=\"data row0 col1\" >4</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow0_col2\" class=\"data row0 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row1\" class=\"row_heading level0 row1\" >Administracion Pública</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow1_col0\" class=\"data row1 col0\" >0</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow1_col1\" class=\"data row1 col1\" >0</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow1_col2\" class=\"data row1 col2\" >nan</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row2\" class=\"row_heading level0 row2\" >Agronomia</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow2_col0\" class=\"data row2 col0\" >52</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow2_col1\" class=\"data row2 col1\" >24</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow2_col2\" class=\"data row2 col2\" >6</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row3\" class=\"row_heading level0 row3\" >Animacion Digital</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow3_col0\" class=\"data row3 col0\" >76</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow3_col1\" class=\"data row3 col1\" >112</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow3_col2\" class=\"data row3 col2\" >58</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row4\" class=\"row_heading level0 row4\" >Arquitectura</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow4_col0\" class=\"data row4 col0\" >125</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow4_col1\" class=\"data row4 col1\" >47</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow4_col2\" class=\"data row4 col2\" >4</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row5\" class=\"row_heading level0 row5\" >Bachillerato en Ciencias</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow5_col0\" class=\"data row5 col0\" >0</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow5_col1\" class=\"data row5 col1\" >0</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow5_col2\" class=\"data row5 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row6\" class=\"row_heading level0 row6\" >Bachillerato en Ciencias de la Salud</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow6_col0\" class=\"data row6 col0\" >7</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow6_col1\" class=\"data row6 col1\" >2</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow6_col2\" class=\"data row6 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row7\" class=\"row_heading level0 row7\" >Bachillerato en Humanidades</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow7_col0\" class=\"data row7 col0\" >1</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow7_col1\" class=\"data row7 col1\" >1</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow7_col2\" class=\"data row7 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row8\" class=\"row_heading level0 row8\" >Biotecnologia</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow8_col0\" class=\"data row8 col0\" >81</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow8_col1\" class=\"data row8 col1\" >32</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow8_col2\" class=\"data row8 col2\" >19</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row9\" class=\"row_heading level0 row9\" >Cine</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow9_col0\" class=\"data row9 col0\" >54</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow9_col1\" class=\"data row9 col1\" >62</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow9_col2\" class=\"data row9 col2\" >50</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row10\" class=\"row_heading level0 row10\" >Construccion Civil</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow10_col0\" class=\"data row10 col0\" >44</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow10_col1\" class=\"data row10 col1\" >32</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow10_col2\" class=\"data row10 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row11\" class=\"row_heading level0 row11\" >Contador Público Auditor</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow11_col0\" class=\"data row11 col0\" >1</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow11_col1\" class=\"data row11 col1\" >0</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow11_col2\" class=\"data row11 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row12\" class=\"row_heading level0 row12\" >Data Science</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow12_col0\" class=\"data row12 col0\" >0</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow12_col1\" class=\"data row12 col1\" >0</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow12_col2\" class=\"data row12 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row13\" class=\"row_heading level0 row13\" >Derecho - Campus Manuel Montt</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow13_col0\" class=\"data row13 col0\" >187</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow13_col1\" class=\"data row13 col1\" >157</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow13_col2\" class=\"data row13 col2\" >58</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row14\" class=\"row_heading level0 row14\" >Derecho - Campus Oriente</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow14_col0\" class=\"data row14 col0\" >30</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow14_col1\" class=\"data row14 col1\" >37</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow14_col2\" class=\"data row14 col2\" >8</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row15\" class=\"row_heading level0 row15\" >Diseño</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow15_col0\" class=\"data row15 col0\" >68</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow15_col1\" class=\"data row15 col1\" >37</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow15_col2\" class=\"data row15 col2\" >3</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row16\" class=\"row_heading level0 row16\" >Enfermeria</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow16_col0\" class=\"data row16 col0\" >265</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow16_col1\" class=\"data row16 col1\" >178</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow16_col2\" class=\"data row16 col2\" >69</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row17\" class=\"row_heading level0 row17\" >Fonoaudiologia</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow17_col0\" class=\"data row17 col0\" >143</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow17_col1\" class=\"data row17 col1\" >101</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow17_col2\" class=\"data row17 col2\" >53</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row18\" class=\"row_heading level0 row18\" >Geologia</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow18_col0\" class=\"data row18 col0\" >162</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow18_col1\" class=\"data row18 col1\" >158</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow18_col2\" class=\"data row18 col2\" >105</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row19\" class=\"row_heading level0 row19\" >Ingenieria Civil Electronica</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow19_col0\" class=\"data row19 col0\" >8</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow19_col1\" class=\"data row19 col1\" >4</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow19_col2\" class=\"data row19 col2\" >4</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row20\" class=\"row_heading level0 row20\" >Ingenieria Civil En Computacion e Informática</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow20_col0\" class=\"data row20 col0\" >21</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow20_col1\" class=\"data row20 col1\" >12</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow20_col2\" class=\"data row20 col2\" >nan</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row21\" class=\"row_heading level0 row21\" >Ingenieria Civil Industrial</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow21_col0\" class=\"data row21 col0\" >115</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow21_col1\" class=\"data row21 col1\" >37</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow21_col2\" class=\"data row21 col2\" >30</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row22\" class=\"row_heading level0 row22\" >Ingenieria Electronica</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow22_col0\" class=\"data row22 col0\" >0</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow22_col1\" class=\"data row22 col1\" >0</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow22_col2\" class=\"data row22 col2\" >nan</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row23\" class=\"row_heading level0 row23\" >Ingenieria en Administracion</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow23_col0\" class=\"data row23 col0\" >83</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow23_col1\" class=\"data row23 col1\" >53</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow23_col2\" class=\"data row23 col2\" >4</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row24\" class=\"row_heading level0 row24\" >Ingenieria en Construccion </th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow24_col0\" class=\"data row24 col0\" >15</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow24_col1\" class=\"data row24 col1\" >9</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow24_col2\" class=\"data row24 col2\" >nan</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row25\" class=\"row_heading level0 row25\" >Ingenieria en Informática</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow25_col0\" class=\"data row25 col0\" >2</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow25_col1\" class=\"data row25 col1\" >1</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow25_col2\" class=\"data row25 col2\" >nan</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row26\" class=\"row_heading level0 row26\" >Ingenieria en Logistica</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow26_col0\" class=\"data row26 col0\" >0</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow26_col1\" class=\"data row26 col1\" >0</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow26_col2\" class=\"data row26 col2\" >nan</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row27\" class=\"row_heading level0 row27\" >Ingenieria en Medio Ambiente y Sustentabilidad</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow27_col0\" class=\"data row27 col0\" >38</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow27_col1\" class=\"data row27 col1\" >8</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow27_col2\" class=\"data row27 col2\" >4</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row28\" class=\"row_heading level0 row28\" >Ingenieria Forestal</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow28_col0\" class=\"data row28 col0\" >26</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow28_col1\" class=\"data row28 col1\" >22</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow28_col2\" class=\"data row28 col2\" >18</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row29\" class=\"row_heading level0 row29\" >Ingenieria Industrial</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow29_col0\" class=\"data row29 col0\" >1</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow29_col1\" class=\"data row29 col1\" >1</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow29_col2\" class=\"data row29 col2\" >nan</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row30\" class=\"row_heading level0 row30\" >Kinesiologia</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow30_col0\" class=\"data row30 col0\" >138</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow30_col1\" class=\"data row30 col1\" >131</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow30_col2\" class=\"data row30 col2\" >45</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row31\" class=\"row_heading level0 row31\" >Medicina</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow31_col0\" class=\"data row31 col0\" >232</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow31_col1\" class=\"data row31 col1\" >127</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow31_col2\" class=\"data row31 col2\" >23</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row32\" class=\"row_heading level0 row32\" >Medicina Veterinaria</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow32_col0\" class=\"data row32 col0\" >566</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow32_col1\" class=\"data row32 col1\" >313</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow32_col2\" class=\"data row32 col2\" >176</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row33\" class=\"row_heading level0 row33\" >Nutricion y Dietetica</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow33_col0\" class=\"data row33 col0\" >87</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow33_col1\" class=\"data row33 col1\" >32</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow33_col2\" class=\"data row33 col2\" >56</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row34\" class=\"row_heading level0 row34\" >Obstetricia y Puericultura</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow34_col0\" class=\"data row34 col0\" >205</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow34_col1\" class=\"data row34 col1\" >170</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow34_col2\" class=\"data row34 col2\" >64</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row35\" class=\"row_heading level0 row35\" >Odontologia</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow35_col0\" class=\"data row35 col0\" >297</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow35_col1\" class=\"data row35 col1\" >256</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow35_col2\" class=\"data row35 col2\" >170</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row36\" class=\"row_heading level0 row36\" >Pedagogia en Educacion Parvularia</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow36_col0\" class=\"data row36 col0\" >59</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow36_col1\" class=\"data row36 col1\" >58</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow36_col2\" class=\"data row36 col2\" >31</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row37\" class=\"row_heading level0 row37\" >Pedagogia en Artes Musicales</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow37_col0\" class=\"data row37 col0\" >84</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow37_col1\" class=\"data row37 col1\" >43</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow37_col2\" class=\"data row37 col2\" >9</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row38\" class=\"row_heading level0 row38\" >Pedagogia en Educacion Diferencial</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow38_col0\" class=\"data row38 col0\" >90</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow38_col1\" class=\"data row38 col1\" >95</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow38_col2\" class=\"data row38 col2\" >17</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row39\" class=\"row_heading level0 row39\" >Pedagogia en Educacion Fisica Deportes y Recreacion</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow39_col0\" class=\"data row39 col0\" >104</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow39_col1\" class=\"data row39 col1\" >99</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow39_col2\" class=\"data row39 col2\" >67</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row40\" class=\"row_heading level0 row40\" >Pedagogia en Ingles</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow40_col0\" class=\"data row40 col0\" >111</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow40_col1\" class=\"data row40 col1\" >58</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow40_col2\" class=\"data row40 col2\" >14</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row41\" class=\"row_heading level0 row41\" >Periodismo</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow41_col0\" class=\"data row41 col0\" >15</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow41_col1\" class=\"data row41 col1\" >6</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow41_col2\" class=\"data row41 col2\" >2</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row42\" class=\"row_heading level0 row42\" >Psicologia</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow42_col0\" class=\"data row42 col0\" >141</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow42_col1\" class=\"data row42 col1\" >149</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow42_col2\" class=\"data row42 col2\" >98</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row43\" class=\"row_heading level0 row43\" >Psicopedagogia</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow43_col0\" class=\"data row43 col0\" >55</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow43_col1\" class=\"data row43 col1\" >45</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow43_col2\" class=\"data row43 col2\" >18</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row44\" class=\"row_heading level0 row44\" >Sociologia</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow44_col0\" class=\"data row44 col0\" >0</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow44_col1\" class=\"data row44 col1\" >0</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow44_col2\" class=\"data row44 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row45\" class=\"row_heading level0 row45\" >Teatro</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow45_col0\" class=\"data row45 col0\" >62</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow45_col1\" class=\"data row45 col1\" >8</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow45_col2\" class=\"data row45 col2\" >28</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row46\" class=\"row_heading level0 row46\" >Tecnologia Medica</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow46_col0\" class=\"data row46 col0\" >311</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow46_col1\" class=\"data row46 col1\" >200</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow46_col2\" class=\"data row46 col2\" >153</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row47\" class=\"row_heading level0 row47\" >Terapia Ocupacional</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow47_col0\" class=\"data row47 col0\" >248</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow47_col1\" class=\"data row47 col1\" >204</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow47_col2\" class=\"data row47 col2\" >70</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row48\" class=\"row_heading level0 row48\" >Ingenieria Comercial</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow48_col0\" class=\"data row48 col0\" >nan</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow48_col1\" class=\"data row48 col1\" >20</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow48_col2\" class=\"data row48 col2\" >27</td>\n            </tr>\n            <tr>\n                        <th id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0alevel0_row49\" class=\"row_heading level0 row49\" >Ingenieria en Computacion</th>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow49_col0\" class=\"data row49 col0\" >nan</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow49_col1\" class=\"data row49 col1\" >nan</td>\n                        <td id=\"T_553d60dc_ea7b_11eb_899b_3c15c2e5ab0arow49_col2\" class=\"data row49 col2\" >3</td>\n            </tr>\n    </tbody></table>"
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Aprobación lista Mesa Directiva (Dos últimas votaciones)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "source": [
    "ultimas_elecciones = ultimas_elecciones.dropna()\n",
    "ultimas_elecciones.style.set_precision(0)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7faf37d3ca90>"
      ],
      "text/html": "<style  type=\"text/css\" >\n</style><table id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0a\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Conectemos 2020-1</th>        <th class=\"col_heading level0 col1\" >Conectemos 2020-2</th>        <th class=\"col_heading level0 col2\" >Construyendo 2021</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row0\" class=\"row_heading level0 row0\" >Conservatorio</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow0_col0\" class=\"data row0 col0\" >13</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow0_col1\" class=\"data row0 col1\" >4</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow0_col2\" class=\"data row0 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row1\" class=\"row_heading level0 row1\" >Agronomia</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow1_col0\" class=\"data row1 col0\" >52</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow1_col1\" class=\"data row1 col1\" >24</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow1_col2\" class=\"data row1 col2\" >6</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row2\" class=\"row_heading level0 row2\" >Animacion Digital</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow2_col0\" class=\"data row2 col0\" >76</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow2_col1\" class=\"data row2 col1\" >112</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow2_col2\" class=\"data row2 col2\" >58</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row3\" class=\"row_heading level0 row3\" >Arquitectura</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow3_col0\" class=\"data row3 col0\" >125</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow3_col1\" class=\"data row3 col1\" >47</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow3_col2\" class=\"data row3 col2\" >4</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row4\" class=\"row_heading level0 row4\" >Bachillerato en Ciencias</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow4_col0\" class=\"data row4 col0\" >0</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow4_col1\" class=\"data row4 col1\" >0</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow4_col2\" class=\"data row4 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row5\" class=\"row_heading level0 row5\" >Bachillerato en Ciencias de la Salud</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow5_col0\" class=\"data row5 col0\" >7</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow5_col1\" class=\"data row5 col1\" >2</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow5_col2\" class=\"data row5 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row6\" class=\"row_heading level0 row6\" >Bachillerato en Humanidades</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow6_col0\" class=\"data row6 col0\" >1</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow6_col1\" class=\"data row6 col1\" >1</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow6_col2\" class=\"data row6 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row7\" class=\"row_heading level0 row7\" >Biotecnologia</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow7_col0\" class=\"data row7 col0\" >81</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow7_col1\" class=\"data row7 col1\" >32</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow7_col2\" class=\"data row7 col2\" >19</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row8\" class=\"row_heading level0 row8\" >Cine</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow8_col0\" class=\"data row8 col0\" >54</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow8_col1\" class=\"data row8 col1\" >62</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow8_col2\" class=\"data row8 col2\" >50</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row9\" class=\"row_heading level0 row9\" >Construccion Civil</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow9_col0\" class=\"data row9 col0\" >44</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow9_col1\" class=\"data row9 col1\" >32</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow9_col2\" class=\"data row9 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row10\" class=\"row_heading level0 row10\" >Contador Público Auditor</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow10_col0\" class=\"data row10 col0\" >1</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow10_col1\" class=\"data row10 col1\" >0</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow10_col2\" class=\"data row10 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row11\" class=\"row_heading level0 row11\" >Data Science</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow11_col0\" class=\"data row11 col0\" >0</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow11_col1\" class=\"data row11 col1\" >0</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow11_col2\" class=\"data row11 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row12\" class=\"row_heading level0 row12\" >Derecho - Campus Manuel Montt</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow12_col0\" class=\"data row12 col0\" >187</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow12_col1\" class=\"data row12 col1\" >157</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow12_col2\" class=\"data row12 col2\" >58</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row13\" class=\"row_heading level0 row13\" >Derecho - Campus Oriente</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow13_col0\" class=\"data row13 col0\" >30</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow13_col1\" class=\"data row13 col1\" >37</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow13_col2\" class=\"data row13 col2\" >8</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row14\" class=\"row_heading level0 row14\" >Diseño</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow14_col0\" class=\"data row14 col0\" >68</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow14_col1\" class=\"data row14 col1\" >37</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow14_col2\" class=\"data row14 col2\" >3</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row15\" class=\"row_heading level0 row15\" >Enfermeria</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow15_col0\" class=\"data row15 col0\" >265</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow15_col1\" class=\"data row15 col1\" >178</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow15_col2\" class=\"data row15 col2\" >69</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row16\" class=\"row_heading level0 row16\" >Fonoaudiologia</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow16_col0\" class=\"data row16 col0\" >143</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow16_col1\" class=\"data row16 col1\" >101</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow16_col2\" class=\"data row16 col2\" >53</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row17\" class=\"row_heading level0 row17\" >Geologia</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow17_col0\" class=\"data row17 col0\" >162</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow17_col1\" class=\"data row17 col1\" >158</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow17_col2\" class=\"data row17 col2\" >105</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row18\" class=\"row_heading level0 row18\" >Ingenieria Civil Electronica</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow18_col0\" class=\"data row18 col0\" >8</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow18_col1\" class=\"data row18 col1\" >4</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow18_col2\" class=\"data row18 col2\" >4</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row19\" class=\"row_heading level0 row19\" >Ingenieria Civil Industrial</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow19_col0\" class=\"data row19 col0\" >115</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow19_col1\" class=\"data row19 col1\" >37</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow19_col2\" class=\"data row19 col2\" >30</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row20\" class=\"row_heading level0 row20\" >Ingenieria en Administracion</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow20_col0\" class=\"data row20 col0\" >83</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow20_col1\" class=\"data row20 col1\" >53</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow20_col2\" class=\"data row20 col2\" >4</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row21\" class=\"row_heading level0 row21\" >Ingenieria en Medio Ambiente y Sustentabilidad</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow21_col0\" class=\"data row21 col0\" >38</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow21_col1\" class=\"data row21 col1\" >8</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow21_col2\" class=\"data row21 col2\" >4</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row22\" class=\"row_heading level0 row22\" >Ingenieria Forestal</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow22_col0\" class=\"data row22 col0\" >26</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow22_col1\" class=\"data row22 col1\" >22</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow22_col2\" class=\"data row22 col2\" >18</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row23\" class=\"row_heading level0 row23\" >Kinesiologia</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow23_col0\" class=\"data row23 col0\" >138</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow23_col1\" class=\"data row23 col1\" >131</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow23_col2\" class=\"data row23 col2\" >45</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row24\" class=\"row_heading level0 row24\" >Medicina</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow24_col0\" class=\"data row24 col0\" >232</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow24_col1\" class=\"data row24 col1\" >127</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow24_col2\" class=\"data row24 col2\" >23</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row25\" class=\"row_heading level0 row25\" >Medicina Veterinaria</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow25_col0\" class=\"data row25 col0\" >566</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow25_col1\" class=\"data row25 col1\" >313</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow25_col2\" class=\"data row25 col2\" >176</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row26\" class=\"row_heading level0 row26\" >Nutricion y Dietetica</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow26_col0\" class=\"data row26 col0\" >87</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow26_col1\" class=\"data row26 col1\" >32</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow26_col2\" class=\"data row26 col2\" >56</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row27\" class=\"row_heading level0 row27\" >Obstetricia y Puericultura</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow27_col0\" class=\"data row27 col0\" >205</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow27_col1\" class=\"data row27 col1\" >170</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow27_col2\" class=\"data row27 col2\" >64</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row28\" class=\"row_heading level0 row28\" >Odontologia</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow28_col0\" class=\"data row28 col0\" >297</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow28_col1\" class=\"data row28 col1\" >256</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow28_col2\" class=\"data row28 col2\" >170</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row29\" class=\"row_heading level0 row29\" >Pedagogia en Educacion Parvularia</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow29_col0\" class=\"data row29 col0\" >59</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow29_col1\" class=\"data row29 col1\" >58</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow29_col2\" class=\"data row29 col2\" >31</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row30\" class=\"row_heading level0 row30\" >Pedagogia en Artes Musicales</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow30_col0\" class=\"data row30 col0\" >84</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow30_col1\" class=\"data row30 col1\" >43</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow30_col2\" class=\"data row30 col2\" >9</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row31\" class=\"row_heading level0 row31\" >Pedagogia en Educacion Diferencial</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow31_col0\" class=\"data row31 col0\" >90</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow31_col1\" class=\"data row31 col1\" >95</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow31_col2\" class=\"data row31 col2\" >17</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row32\" class=\"row_heading level0 row32\" >Pedagogia en Educacion Fisica Deportes y Recreacion</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow32_col0\" class=\"data row32 col0\" >104</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow32_col1\" class=\"data row32 col1\" >99</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow32_col2\" class=\"data row32 col2\" >67</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row33\" class=\"row_heading level0 row33\" >Pedagogia en Ingles</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow33_col0\" class=\"data row33 col0\" >111</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow33_col1\" class=\"data row33 col1\" >58</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow33_col2\" class=\"data row33 col2\" >14</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row34\" class=\"row_heading level0 row34\" >Periodismo</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow34_col0\" class=\"data row34 col0\" >15</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow34_col1\" class=\"data row34 col1\" >6</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow34_col2\" class=\"data row34 col2\" >2</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row35\" class=\"row_heading level0 row35\" >Psicologia</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow35_col0\" class=\"data row35 col0\" >141</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow35_col1\" class=\"data row35 col1\" >149</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow35_col2\" class=\"data row35 col2\" >98</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row36\" class=\"row_heading level0 row36\" >Psicopedagogia</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow36_col0\" class=\"data row36 col0\" >55</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow36_col1\" class=\"data row36 col1\" >45</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow36_col2\" class=\"data row36 col2\" >18</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row37\" class=\"row_heading level0 row37\" >Sociologia</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow37_col0\" class=\"data row37 col0\" >0</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow37_col1\" class=\"data row37 col1\" >0</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow37_col2\" class=\"data row37 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row38\" class=\"row_heading level0 row38\" >Teatro</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow38_col0\" class=\"data row38 col0\" >62</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow38_col1\" class=\"data row38 col1\" >8</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow38_col2\" class=\"data row38 col2\" >28</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row39\" class=\"row_heading level0 row39\" >Tecnologia Medica</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow39_col0\" class=\"data row39 col0\" >311</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow39_col1\" class=\"data row39 col1\" >200</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow39_col2\" class=\"data row39 col2\" >153</td>\n            </tr>\n            <tr>\n                        <th id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0alevel0_row40\" class=\"row_heading level0 row40\" >Terapia Ocupacional</th>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow40_col0\" class=\"data row40 col0\" >248</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow40_col1\" class=\"data row40 col1\" >204</td>\n                        <td id=\"T_648b7498_ea7b_11eb_899b_3c15c2e5ab0arow40_col2\" class=\"data row40 col2\" >70</td>\n            </tr>\n    </tbody></table>"
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "source": [
    "#for i in if ultimas_elecciones['Conectemos 2020-2']\n",
    "for i in range(ultimas_elecciones.count()[0]):\n",
    "    if (ultimas_elecciones['Conectemos 2020-2'][i] < ultimas_elecciones['Construyendo 2021'][i]):\n",
    "        print(ultimas_elecciones.iloc[i])\n",
    "\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Conectemos 2020-2    32.0\n",
      "Construyendo 2021    56.0\n",
      "Name: Nutricion y Dietetica, dtype: object\n",
      "Conectemos 2020-2     8.0\n",
      "Construyendo 2021    28.0\n",
      "Name: Teatro, dtype: object\n",
      "Conectemos 2020-2    20.0\n",
      "Construyendo 2021    27.0\n",
      "Name: Ingenieria Comercial, dtype: object\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7faf36f3e6a0>"
      ],
      "text/html": "<style  type=\"text/css\" >\n</style><table id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0a\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Conectemos 2020-1</th>        <th class=\"col_heading level0 col1\" >Conectemos 2020-2</th>        <th class=\"col_heading level0 col2\" >Construyendo 2021</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row0\" class=\"row_heading level0 row0\" >Conservatorio</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow0_col0\" class=\"data row0 col0\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow0_col1\" class=\"data row0 col1\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow0_col2\" class=\"data row0 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row1\" class=\"row_heading level0 row1\" >Agronomia</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow1_col0\" class=\"data row1 col0\" >57</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow1_col1\" class=\"data row1 col1\" >5</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow1_col2\" class=\"data row1 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row2\" class=\"row_heading level0 row2\" >Animacion Digital</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow2_col0\" class=\"data row2 col0\" >3</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow2_col1\" class=\"data row2 col1\" >13</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow2_col2\" class=\"data row2 col2\" >8</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row3\" class=\"row_heading level0 row3\" >Arquitectura</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow3_col0\" class=\"data row3 col0\" >24</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow3_col1\" class=\"data row3 col1\" >5</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow3_col2\" class=\"data row3 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row4\" class=\"row_heading level0 row4\" >Bachillerato en Ciencias</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow4_col0\" class=\"data row4 col0\" >1</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow4_col1\" class=\"data row4 col1\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow4_col2\" class=\"data row4 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row5\" class=\"row_heading level0 row5\" >Bachillerato en Ciencias de la Salud</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow5_col0\" class=\"data row5 col0\" >1</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow5_col1\" class=\"data row5 col1\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow5_col2\" class=\"data row5 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row6\" class=\"row_heading level0 row6\" >Bachillerato en Humanidades</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow6_col0\" class=\"data row6 col0\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow6_col1\" class=\"data row6 col1\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow6_col2\" class=\"data row6 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row7\" class=\"row_heading level0 row7\" >Biotecnologia</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow7_col0\" class=\"data row7 col0\" >16</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow7_col1\" class=\"data row7 col1\" >9</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow7_col2\" class=\"data row7 col2\" >1</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row8\" class=\"row_heading level0 row8\" >Cine</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow8_col0\" class=\"data row8 col0\" >18</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow8_col1\" class=\"data row8 col1\" >10</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow8_col2\" class=\"data row8 col2\" >13</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row9\" class=\"row_heading level0 row9\" >Construccion Civil</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow9_col0\" class=\"data row9 col0\" >18</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow9_col1\" class=\"data row9 col1\" >8</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow9_col2\" class=\"data row9 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row10\" class=\"row_heading level0 row10\" >Contador Público Auditor</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow10_col0\" class=\"data row10 col0\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow10_col1\" class=\"data row10 col1\" >1</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow10_col2\" class=\"data row10 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row11\" class=\"row_heading level0 row11\" >Data Science</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow11_col0\" class=\"data row11 col0\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow11_col1\" class=\"data row11 col1\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow11_col2\" class=\"data row11 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row12\" class=\"row_heading level0 row12\" >Derecho - Campus Manuel Montt</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow12_col0\" class=\"data row12 col0\" >69</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow12_col1\" class=\"data row12 col1\" >39</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow12_col2\" class=\"data row12 col2\" >14</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row13\" class=\"row_heading level0 row13\" >Derecho - Campus Oriente</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow13_col0\" class=\"data row13 col0\" >23</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow13_col1\" class=\"data row13 col1\" >10</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow13_col2\" class=\"data row13 col2\" >9</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row14\" class=\"row_heading level0 row14\" >Diseño</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow14_col0\" class=\"data row14 col0\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow14_col1\" class=\"data row14 col1\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow14_col2\" class=\"data row14 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row15\" class=\"row_heading level0 row15\" >Enfermeria</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow15_col0\" class=\"data row15 col0\" >11</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow15_col1\" class=\"data row15 col1\" >15</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow15_col2\" class=\"data row15 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row16\" class=\"row_heading level0 row16\" >Fonoaudiologia</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow16_col0\" class=\"data row16 col0\" >30</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow16_col1\" class=\"data row16 col1\" >10</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow16_col2\" class=\"data row16 col2\" >13</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row17\" class=\"row_heading level0 row17\" >Geologia</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow17_col0\" class=\"data row17 col0\" >10</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow17_col1\" class=\"data row17 col1\" >25</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow17_col2\" class=\"data row17 col2\" >13</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row18\" class=\"row_heading level0 row18\" >Ingenieria Civil Electronica</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow18_col0\" class=\"data row18 col0\" >10</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow18_col1\" class=\"data row18 col1\" >5</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow18_col2\" class=\"data row18 col2\" >1</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row19\" class=\"row_heading level0 row19\" >Ingenieria Civil Industrial</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow19_col0\" class=\"data row19 col0\" >40</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow19_col1\" class=\"data row19 col1\" >10</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow19_col2\" class=\"data row19 col2\" >7</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row20\" class=\"row_heading level0 row20\" >Ingenieria en Administracion</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow20_col0\" class=\"data row20 col0\" >12</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow20_col1\" class=\"data row20 col1\" >24</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow20_col2\" class=\"data row20 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row21\" class=\"row_heading level0 row21\" >Ingenieria en Medio Ambiente y Sustentabilidad</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow21_col0\" class=\"data row21 col0\" >7</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow21_col1\" class=\"data row21 col1\" >3</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow21_col2\" class=\"data row21 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row22\" class=\"row_heading level0 row22\" >Ingenieria Forestal</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow22_col0\" class=\"data row22 col0\" >1</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow22_col1\" class=\"data row22 col1\" >2</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow22_col2\" class=\"data row22 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row23\" class=\"row_heading level0 row23\" >Kinesiologia</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow23_col0\" class=\"data row23 col0\" >28</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow23_col1\" class=\"data row23 col1\" >19</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow23_col2\" class=\"data row23 col2\" >1</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row24\" class=\"row_heading level0 row24\" >Medicina</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow24_col0\" class=\"data row24 col0\" >35</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow24_col1\" class=\"data row24 col1\" >17</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow24_col2\" class=\"data row24 col2\" >6</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row25\" class=\"row_heading level0 row25\" >Medicina Veterinaria</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow25_col0\" class=\"data row25 col0\" >115</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow25_col1\" class=\"data row25 col1\" >102</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow25_col2\" class=\"data row25 col2\" >23</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row26\" class=\"row_heading level0 row26\" >Nutricion y Dietetica</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow26_col0\" class=\"data row26 col0\" >4</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow26_col1\" class=\"data row26 col1\" >2</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow26_col2\" class=\"data row26 col2\" >10</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row27\" class=\"row_heading level0 row27\" >Obstetricia y Puericultura</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow27_col0\" class=\"data row27 col0\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow27_col1\" class=\"data row27 col1\" >3</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow27_col2\" class=\"data row27 col2\" >2</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row28\" class=\"row_heading level0 row28\" >Odontologia</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow28_col0\" class=\"data row28 col0\" >44</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow28_col1\" class=\"data row28 col1\" >19</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow28_col2\" class=\"data row28 col2\" >23</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row29\" class=\"row_heading level0 row29\" >Pedagogia en Educacion Parvularia</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow29_col0\" class=\"data row29 col0\" >7</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow29_col1\" class=\"data row29 col1\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow29_col2\" class=\"data row29 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row30\" class=\"row_heading level0 row30\" >Pedagogia en Artes Musicales</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow30_col0\" class=\"data row30 col0\" >21</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow30_col1\" class=\"data row30 col1\" >7</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow30_col2\" class=\"data row30 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row31\" class=\"row_heading level0 row31\" >Pedagogia en Educacion Diferencial</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow31_col0\" class=\"data row31 col0\" >16</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow31_col1\" class=\"data row31 col1\" >8</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow31_col2\" class=\"data row31 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row32\" class=\"row_heading level0 row32\" >Pedagogia en Educacion Fisica Deportes y Recreacion</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow32_col0\" class=\"data row32 col0\" >58</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow32_col1\" class=\"data row32 col1\" >62</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow32_col2\" class=\"data row32 col2\" >11</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row33\" class=\"row_heading level0 row33\" >Pedagogia en Ingles</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow33_col0\" class=\"data row33 col0\" >10</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow33_col1\" class=\"data row33 col1\" >2</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow33_col2\" class=\"data row33 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row34\" class=\"row_heading level0 row34\" >Periodismo</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow34_col0\" class=\"data row34 col0\" >4</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow34_col1\" class=\"data row34 col1\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow34_col2\" class=\"data row34 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row35\" class=\"row_heading level0 row35\" >Psicologia</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow35_col0\" class=\"data row35 col0\" >6</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow35_col1\" class=\"data row35 col1\" >38</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow35_col2\" class=\"data row35 col2\" >22</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row36\" class=\"row_heading level0 row36\" >Psicopedagogia</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow36_col0\" class=\"data row36 col0\" >4</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow36_col1\" class=\"data row36 col1\" >8</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow36_col2\" class=\"data row36 col2\" >3</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row37\" class=\"row_heading level0 row37\" >Sociologia</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow37_col0\" class=\"data row37 col0\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow37_col1\" class=\"data row37 col1\" >0</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow37_col2\" class=\"data row37 col2\" >0</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row38\" class=\"row_heading level0 row38\" >Teatro</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow38_col0\" class=\"data row38 col0\" >30</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow38_col1\" class=\"data row38 col1\" >1</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow38_col2\" class=\"data row38 col2\" >4</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row39\" class=\"row_heading level0 row39\" >Tecnologia Medica</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow39_col0\" class=\"data row39 col0\" >3</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow39_col1\" class=\"data row39 col1\" >17</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow39_col2\" class=\"data row39 col2\" >14</td>\n            </tr>\n            <tr>\n                        <th id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0alevel0_row40\" class=\"row_heading level0 row40\" >Terapia Ocupacional</th>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow40_col0\" class=\"data row40 col0\" >4</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow40_col1\" class=\"data row40 col1\" >19</td>\n                        <td id=\"T_fbeab57e_ea7b_11eb_899b_3c15c2e5ab0arow40_col2\" class=\"data row40 col2\" >11</td>\n            </tr>\n    </tbody></table>"
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "\n",
    "ultimas_elecciones_neg = df_negative\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('Impulsar 2018', axis=1)\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('Código Pregunta')\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('Pregunta')\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('Alameda')\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('Oriente')\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('El Claustro')\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('Huechuraba')\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('Manuel Montt')\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('República')\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('Santo Domingo')\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('Estadio Mayor')\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('Alonso de Cordova')\n",
    "ultimas_elecciones_neg.loc['Ingenieria Comercial', 'Conectemos 2020-2'] = ultimas_elecciones_neg.loc['Ingenieria Comercial - Campus Manuel Montt','Conectemos 2020-2'] + ultimas_elecciones_neg.loc['Ingenieria Comercial - Campus Oriente','Conectemos 2020-2'] \n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('Ingenieria Comercial - Campus Manuel Montt')\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.drop('Ingenieria Comercial - Campus Oriente')\n",
    "ultimas_elecciones_neg = ultimas_elecciones_neg.dropna()\n",
    "ultimas_elecciones_neg.style.set_precision(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "source": [
    "array1 = pd.DataFrame.to_numpy(ultimas_elecciones['Conectemos 2020-2'])\r\n",
    "array2 = pd.DataFrame.to_numpy(ultimas_elecciones['Construyendo 2021'])\r\n",
    "array1"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([4.0, 24.0, 112.0, 47.0, 0.0, 2.0, 1.0, 32.0, 62.0, 32.0, 0.0, 0.0,\n",
       "       157.0, 37.0, 37.0, 178.0, 101.0, 158.0, 4.0, 37.0, 53.0, 8.0, 22.0,\n",
       "       131.0, 127.0, 313.0, 32.0, 170.0, 256.0, 58.0, 43.0, 95.0, 99.0,\n",
       "       58.0, 6.0, 149.0, 45.0, 0.0, 8.0, 200.0, 204.0, 20.0], dtype=object)"
      ]
     },
     "metadata": {},
     "execution_count": 225
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "source": [
    "#ultimas_elecciones.index\r\n",
    "#ultimas_elecciones.plot(x=ultimas_elecciones.index, y=[])\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import numpy as np\r\n",
    "\r\n",
    "a = range(0,300)\r\n",
    "b = range(0,300)\r\n",
    "ind = np.arange(len(a))\r\n",
    "\r\n",
    "fig = plt.figure()\r\n",
    "ax = fig.add_subplot(111)\r\n",
    "ax.bar(ultimas_elecciones.index, array1+0.25, color='#deb0b0', align='center')#, b, 0.45, color='#deb0b0', align='center')\r\n",
    "\r\n",
    "ax2 = ax.twinx()\r\n",
    "ax2.bar(ultimas_elecciones.index, array2, color='#b0c4de', align='center') #a, 0.65, color='#b0c4de', align='center')\r\n",
    "\r\n",
    "#plt.xticks(array1, ultimas_elecciones.index, rotation=70)\r\n",
    "#plt.setp( ultimas_elecciones.index.get_majorticklabels(), rotation=70 )\r\n",
    "ax.yaxis.set_ticks_position(\"right\")\r\n",
    "ax2.yaxis.set_ticks_position(\"left\")\r\n",
    "ax.set_xticklabels(ultimas_elecciones.index, rotation=90)\r\n",
    "plt.ylim(0, 5)\r\n",
    "plt.tight_layout()\r\n",
    "plt.figure(figsize=(15, 9), dpi=80)\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-253-df42b17cc226>:21: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(ultimas_elecciones.index, rotation=90)\n",
      "<ipython-input-253-df42b17cc226>:23: UserWarning: Tight layout not applied. The bottom and top margins cannot be made large enough to accommodate all axes decorations. \n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ],
      "image/png": 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QBEHbhBAJgiAI2iaESBAEQdA2IUSCIAiCtgkhEgRBELRNCJEgCIKgbUKIBEEQBG0TQiQIgiBomxAiQRAEQduEEAmCIAjaJoRIEARB0DYhRIIgCIK2CSESBEEQtM2gKxsGQTsUVaWLinRBrzMzVluMkUgQBEHQNjES6SGmXXhhv3nLbL99F1oSBK0zM/bOZyZiJBIEQRC0TYxEgmCUET3/YDgJIRIEQR/dUJkWHXM4jht0hlBnBUEQBG0zU49EYtjfnLhGQdDbSHoImA68Bcwws7UlLQicDSwNPATsYmbPtbP/mVqIBEFGqFSCUc6mZva/3PQ3gavM7KeSvpmmv9HOjgclRIZawgVBUMyFN04rnL/9hjFGDFpie2CT9Ps04Fq6IUQSQybhgpmTUKEFQcvMImlCbvoEMzuhYR0DrpBkwB/S8kXN7PG0/Alg0bYb0O6GFXRMwgVBEASVzDCztZuss5GZPSZpEeBKSfflF5qZJQHTFoMVIm1LOEn7A/sDzDbbbINsRucZiTryyDcVBMFAMbPH0vdTki4A1gWelLSYmT0uaTHgqXb3P1gh0raESwLnBIC55567bSkYBEFQRKQJAklzA2PMbHr6/RHgCOAiYC/gp+m7uNfcAoMSIkMt4YIgCIJBsShwgSTw9/1ZZna5pNuBcyTtCzwM7NLuAdoWIsMh4YKg1xlNTgIjUcUbVGNmDwKrFcx/Bti8E8cYzEhkyCVcEARBMLJpW4gMh4QLgiAIRjaROysIgiBom0h7ErRNlY58JOrPR2KbgqDXiZFIEARB0DYhRIIgCIK2CXXWCGIw6pZQ1QRB0A1iJBIEQRC0TYxEgiDoI/KzBQMlhEgw0zCaoseDYKQQQiQIgiBHFPwaGKNeiITBOQiCkcBoFU5hWA+CIAjaZtSPRIJgqGm3bkXYaILRQIxEgiAIgraJkcgIInqmQRD0GiFEgmCQRGzFyCT+l+EhhEgQBIMmRtEzL2ETCYIgCNomRiJBEHSVGMX0NjESCYIgCNomhEgQBEHQNqHOCtpmZlFDROocZ2b5v4OBEUIkCIIRSwjwkc+QCRFJHwN+A4wFTjKznw7FcZrdZNF7mnmIF04Q9Geo38VDIkQkjQV+D2wBPArcLukiM7tnKI43FITwGRzduH6DOWbVtnEvBL3KcLyLh2oksi7wgJk9CCDpL8D2QM8IkcFQ1SOO3nIQtE4I8EEz5O/ioRIiiwOP5KYfBT6QX0HS/sD+adIkvdqB484CzGhz+VAs67X9xrmMzP3OLMccqv322rkMhDklTchNn2BmJ+Smm76LB42ZdfwD7Izr3rLpTwO/G4pjNRx3QrvLh2JZr+03zmVk7ndmOWacS+c/w/EuHqo4kceAJXLT707zgiAIguFjyN/FQyVEbgeWl7SMpNmATwIXDdGxgiAIgmKG/F08JDYRM5sh6QvAP3C3slPMbOpQHKuBEwaxfCiW9dp+41xG5n5nlmMO1X577Vw6xnC8i5X0ZEEQBEEwYCJ3VhAEQdA2IUSCIAiCtgkh0iUkjZE0bhiOsUGTdeaU9N6SZZ9oZV4QdILheCaCzjNqbCKS5gEws5earHcMUHrSZvbFJtsfBfwQeBW4HFgVOBRYpGo7M/ulpLOAA4G3cK+JccBvzOznVdtWtOViqs9lO0mTzGyNku23BX4BzGZmy0haHTjCzLZLy+8wszUbtrnDzNaUtAJwHLComa0saVVgOzP7YTvn0gkkrQysBMyRm31J1TZm9mzadiywKDVnk88Cz1ds98vBtHUwSFoe+AkN52pmy6blWwPvb1h2RAv7Ld1O0obAZDN7WdKngDWBl4HnyvaX7vmFgf2Apck58pjZPmm/HX0mBoukWYHPAR9Ks64DjjezN9PyxnvsRxRfA+HP5uFVxzOz8wff6u7S81l8Ja0CnA4s6JN6GtjLzO5OyxcAlqf2p78I/KvJPucA9qX/A7UP8BEz+7qkHYCHgB2B8UB2M7wXWIeaG922wG3p90pm9qKkPYDLgG8CE4GmD4ykuYCvAEua2X7pRXIjcHOTTa+StBNwvvXvMRyOp0W4Np3f5OQKuCWwFbC4pN/m1h9HLcr2ROBrwB/StlPSC+GHqb3zpf1/MK1/HS6gXsidU7+Xvpmd3sJLsvE/BdgU2CRtcymwJXAD8AP8YVbBtTFgWUkHA4cBTwJvp2UL40Ky8P+UNJ1iAS5vqvX1qMvOs2DbbP31gGOA9wGz4V41L+f2eWpq76/See9N0ipIOh6YK80/CQ82uy2374WBbzS2B38mSrdL12I1Savh9+FJ+P9/atl5JC4Ergf+iQuKRpo+E5IWxf8DgNvM7Kk0f8D3iZmNb9Le44BZgWPT9KfTvM9KOoz+99gduNAp4/CKZUbtvdG7DEfU5BBHZN4EbJqb3gS4Kf3+LHAX3lO4Bh89XN3CPs8FjgT+A+wFXIH3jgDuTt8nAR9Lv+/MbTsemDc3PS8wPv2eit+g5wIbF2y7Ht4bewl4A3/oXkzLzga+njv+XHjPsNm5TMdfjG/iAnR6bp+3pO9JufWnAKul8344fWefHYEF0nq3F2w7Off7PPwFvmz6HIYLsmz5Yek/eRJ/ET0B/DUtuwHYPLVlKfxBPKLqP03zxmTXEx9VXNniPfQA8I6SZaX/Z4v7rjrPwv8bmAC8B5iEC5C9gZ/k9jkxfd9VMG9Kw/c8wPW59a7AO0j3AhsDpwA/a2G7O9L394F98/OanH/lPUrzZ2KXdB+ehncWpwE7t3uftPCc3VnQxuyeavseG82frjdg0CfQ/E+fI7uRgRVJLzK8p/kLvEdxdfZJyyal7+yBmpXaC/enwH3pAZ817efW3LHvB2bPTc8O3J9+fxGPFr0U77Eu1fCglr48SGkSqH9pP587zymNnxau3cnA7mn95fHe7/G55V8v2OaQ9H0ZsBy1l8vOwGW59SYXbDs597v0gaT6JVn4n+I9VPBe7Lh0fe9rOP4C+MjrQ9knzb8GmKXkGpX+nw3rLQIsmX1aPM/C/zv3X0/J7Sf/v9+U9nk+8AVgh9w9ll2HW4B3pfY+UHAd8/u+nXQPV2x3HfAt4N/AO9Px70rL5gAOwnvvp2SftOyHwFYV92CzZ+JOYJHc9MK5azng+6SF5+wOYLnc/paldo+X3mNUCKbcvrbGO4Lfzz7NntFe+PS8Ogt4UNL3gDPS9KeAB9Pv18zsNUlImt3M7ssZkc/Ee/db4zrZvYCn07I30/fzSRXxBMnmYWbfTHaRF8zsLUkv41kxM07H1R0XpOmP470ozOy3QF499LCkTfMnY2YPSBprZm8Bp0qahD+8b0iak6RCkbQc3isD2KbqAknajpqO91ozy+wEBwPfAV4HzsIDkvI2jU8CRzXs7jN4bYKD8ICpFSU9ltqyR269VyVtZGY3pDZsiPcG+5ab2duSZiRj6lPU0jO8LmkM8O8UKPUY3jOG8v/0Oknz42q2ifjD3Kfqk/RZ4BA87cNk/KG/GdgMv1+ulfT3dC2APrtH6f+Zu7ZH4y/ep/CX4L24KrTZeZb939NTdPHkdK89Tr0TzCH4SPSL+Ih5U/z+Bbg4XYef4y9ES9ckI7u3H082kP/iquA/Fmx3Um67XfEOxz5m9oSkJampnM7AO1YfBY7A74N7c239tqQ3csc2S6q5Fp6JMZbUV4lncteinfska0DZc/ZV4BpJD1ITanunzSZU3GO/w5+Xc4G1gT2BFbLjNVMz9jTdlmKD/eC9y9/iN/4d+AsuU7lcAMyPD3PH4/rZS7PeRPqu65Gl78+m/X4If8E8BRyQW29lfJi9Z/ZpaNOa+MNzCLBGbv6ieO//sjS9Ekk1kKbH4zrw0/GX96HUel1b4L3Bp3EB+BCwSVr2ZeBdJdfnp8BVwD7pcyU51UjJNrsBF+OqgItyn2uAq9I6y6TvuUnqnmxe+r0a3ot8CFdHTAJWyy0/Nv03B+K920nAqWnZOvjL4N24Cuh8YL1m/2lu30sDqzbMq+qZHlb0yW27VtH/mZbdCbyD2uh1U+DkFs+z8P/GX1xz4r3dw4BfAu9p4VkYA2yQm54dmK9hnW2A+fB7+Br8Zbhdwzr9tkvzlwI+nH7Plfvfs3PvN3Jvoc3Nnomf452bz6TPZcBRg7lPKq772PR7dtxhZlVyo9Cqe4zmo8dKdWEvf7regGE7Udf/bod7IkFNPfUPfDSyBm4DGQPsUrGfwyjRcaflSxZ90rLLcOGTCYZZqB+KV7488JfV1ulFsFBDm6biBswv4B5TfTcv3pvLpsfmbuQrgflzyxZI12Mp3LZ0c7pu2WdNktqHAn04STA3zBsHjGvy39Q9kO38p7hKZ77csvmBj+emsw7C5OzFAExt2N88wDwlxypTV2Uvjzuz60yBirXoPNN1nqPs/64478L/Lf2eNMBruFn63rHok1tvP1xd8580vTy1DkWm5hmPC6eFgAdz226Hq45/AWzTcPzKZyLN2yldm18COwzmPmn2nGXnUrKf0nuMig5gWl6pLuzlT8+6+Er6tZl9qcTNdRYz20rSgkXbmtmzkrbBX7pL4LaAccDhZnaxpAlmtnbJce/Ce9mTzGy15DnyJzPbIrc8a8+ceP2c+83s/ZJuN7N18m63kiab2eotnvPi+AOQd5Ucn1u+Kq522Al41Mw+LGkKPmLJXFkXxFVaqxa5/1a5BKflK+KqmqNw75yMccDXzOz9ab3ZUzuWpt4L8Bxz1UKd63DiK2a2Rzv/KW7PWr3sXJI6am/gS7gK6zlg1rTPlXGVTLbv/+Gjy6kF6qolcT14dp7/xFVcP8Ffnk/hPeR9Ks4TM7ujcZ6kc8xsl4Z7KL/Nqo3n1Xiukn6BC/86bzxJXzezo9Tfxf0DwK24w0DBIftccSfj9qRbc9f0LjNbJakKz8N77qfiwvj7Zna8pJ+m63Fm2uduuOD9VtpHS89EUgVm99GPzOxz7T77RfNzx/kVPpI6G3dhzra7o6Rd2XVfCu9YzoYLkPmAY83sgbTe9/D3zOZ4pUHDU7R/r6o9vUAv20QyG8gvCpb9JH1PpL97p+HGsufM3U1fwFUQmd4e4J+Svkr/G+lZmuu4V8k3JL1EPp8mX5b0Dmp2jfWAF1p5eUj6GS4gplJzQzW8B5TxFD4yeoZa3MpPgEmSrknX4UO47hfgbUlLmtn/pfYslT++pB1xz51F0rbCRzJn472wbXPHng7sJ+lbZvYTXH3wAv4fvJ5b78t4MbKjG8+Tmj67nf+0KD4oL2x3SD8PT9diPjzOB9y282Uzuyad9ya43nsD3OawHvDP9LLYFLe7ZWyP23oOxW0B8+FeaT+tOE8DNkv32+HUOgZjky5+04Jt8lT9bwfg13iGpNeoxStk9qoJDfuamNZ5xczOqTjm62b2huSXXdIs2THNLLOdXIc/W3m2AlY3s7fTdqfhKr3sHix8JrKNJR2AX8/X8Pte+Ev+cwz8PlkYmKeJkF49TebjagzveBQFZ8+Stn04Tb+W2tu47yPTz/MkXQLMYTl3956m20OhwXzwF9qZbW5bpI7JvDCmFXweTMtKddwVx8q8WNbEYzteSN//wntvi6XlSxV90rI6L6GG/X8ej/WYir+UVmpYvhg+nN8OeGdu/seA/8MF8p9w28VHc8sfAN5Xcsz1S+ZfnL7vrrgeY4ANO3wvnIKrJZZLn18CfySp0vBRRr9PWlbl4VeprgJ+VrBtv3klbb4PjzVYBFdVvgN/0V3TZLvK/63N69eswNJRwLdTm7fAbQ4XpmVfLvqkZVOy65z7H/J2g8JnIrf83+RUt4M8x1aes2ULtlu26h5LyzbE1Yz/wu2o2acldWEvf3p5JIK5d9RSkmYzszeK1kkqnqWp9UpXwL1EFpb05dyq43ChhJmVlnA2s2xUcbyky/GX1JTc8fL7HIM/JP9N294haWM8gE24mivzWMFqvZkiHsR7YK8XLFsC+JKZTW5cIOkqM9ucXA2BbJ6ZXZ5GSuulRV8ys//lNn/SzO6lmAckfZv+6qosIO4mSauY2V2NG5qP5H6H26H6UdJTfAHvRV9ErqeaW/Yl/CV3dpp3Je5BdjZuQ8r3TPPfy1Lt4fe8PBvCeOBMSU+RG53iL9RvNLRny2xeGs018gJu6H/BzC4rOP+3Jc1nJT3Vqv9N0odKthmflhepgF5I5/lNXO3UOPoGDwLcN7X7ANwlN+uZF6nCMopGwt/M7b/ymcDtlK8U7bid+0TSLE2es7/iz2yec3HnioOB79H/HgN3DjgUv8/yQZVfxMMH8qP2DGMUBBv2rE0kQ9LpeGTvRdTf/L+UdAre08+rgBbFdcAHAsfndjUd70X/W9KeBYdazMx+1kzHLY9qzZiBeyfNoObeWLTt+Wnboijo7KGYE/dtv4qaIJnNzA4s0f/Onta7BjeSZ8P6ccDlZrZiOmapnUXSb/CYgL9R7/p6vqSbcJtS40MzJV3De1J7p6Vts0juTK9fqLtPy45K+zwrzfok7g30BP4Cmhvv4QpYBbgbVyN9zsyuaLwQch3MEpbUPwXLF8BVEBulWdfj9rHnJM2NqyhETV11Jm4M/jzeI30gt7t5gRvN7FNp338H1sf/B/D/YiJuK7svfc6nvnNwGC5gr6ReYB1rTewsSUhkzIHbMSaa2WapPb/BRzt/Tuvsigc4fgoXCk/ntjdL0d+DQdJi1EecP1EiXPMHzp6JNXA7y63U34NfHMR9shj965u/jI/u3okLg4w6W1/FOd5qZp2tXd4jjAYhcljRfDP7gaR7zGylku2WKuuRJONjxhy4MQwzWzb1qAoO5w9pyf5OTT8XwfXsV6fpTfHo+m3SekcCj+IPhfCHYjncdflwXM+e51AzW13SNPrrf+fFBeO7SCOhxIvAiWb2uzI7i9VyZ51Kf8zM9ikyMjac81JF87NrngTm3PhL4FVy6UJUnbPrBdyNdWqafzr+4lgA98+/ruF42bncZQ32qsEgT+uyAN7T/mZu0fRc7x1J/8CN9E+m6UVxD57d8JfW7Q27NnJxKA1saGb7D+QelLQE8Gsz2ylN325m6zSskxm3pza+LMvsB7mDrprsHIeY2fNpmwVwo/FOZQIP79VX7LbPoH8bHpl+F7V7FDM7bYD3yUq4nePreLDmYdQ/Zx/FVW0b4SrCjOm4t+OuJaM4zHPU/RTXZNR1CHKdyx/jrsn5a/QVM/tuxXXoCXpeiGSoIAGjpJOBo83snty8X1u5V1ffS6dh3/MDfzGzj1Ucv5VkiFfgeb0eT9sshutUP5qm7zSz1Rr2OzkJin7LWkHSwWZ2TMmy+3H9c5GKrNl+f4gLwEsr1tkIWN7MTpXnbJrHzKa1sO87gf3M7LY0vQ7+UlpN0qtmNmdu3bXwl+5BeBDXZ/P7MrPr0nqnAb8zs9tz21bdC1tRokZJ+x2X9rEc7gn3utwgvypweu5lUdeRSaOiqWa2kpp4wpUhaQ4ze63ZvMbjpel7cftJZpRfEncPfl9adhj1ObWua9xnHjN7uOg8JP3PzBZqp9PVsJ/SazSQ+yQtv9s8WWjRsuw5+7eZLd+wbC0zm5jUbkUnc12z8yy5Rv2EYC/S0zYRADW4Z0rqc8/Ee3w3S3qCmkplDlx/XuTZUcbLuPoBSQfhxvzn0/QCeK+ylf0tkQmQxJO4y2jGK5J2wfWy4FGtmefXCnJ33Tzz4KlJ/pqfKU+4uKx5JtTHilQHSV1QZWdB0rtxt8TMa+16vMf5KPWRyG+QG0mkbQ/DRwbvxdURs+I9vA3T8kw9tIyZHZl6zIulF8JngVNSx0D46GnfpFqaJOk44C+pTbsC9+D+989nQqOADwB7SHoY/z+b3QtHp5fDkXjE+BnUVFqL5dY7D1hb0ntwL68L8R7uVmn5tXJvnHPT9M7Af9O5zKN6G1rGxVQkFsR70o0vn5uANVXvwjsG9zbKuxN/BbhB0n/S+SwDfD51CmbB/+++BJZWkSgyxxhJC5jZcwBy9erjqc2VnmYl5/8CroKbDFwmaX/8muR7+M/Sxn0idz1/q+A5ywTwK+kZr0u+irtslwrUZueJe97NnnXY5NknZm+yTU/Q8yMRuW7+O1bvnvljM9tA0gO4p0jjULjKsNY4qhiDP8znmtk3itQ4rfYo5cbk5anXRz9gZgen5cviEffrp+U34/r3J3EhmXfPXCKt+zEzy+uxkbQQHseyhKpVUufhMS95OwuW0uFLuhJ/IeYNzntYiolpcq6Tcb3+HVbz/59iNZvIcfh/slnqBS8AXJFXtchVRlh95t85cVtEZr+4EfeYWxNXV7wbfxlmQm3ZtF2pei29dF61mhvqWNwT7pWS0WHfvJz65OtpH8eoPuZBuCdOvr3vMLM/qEQVixvrD8Oz9G5LLUvvscDiuDDenXo71/FmtqKkvXL7mQE8ZGY3NrR/djxiH9yQ/Zqaxz+VZhaW2xC/jQtK4S/lH5nZGWWdLjM7Nk2fhXc2MlvONrgdY+m0v6IMuX3/a9rHQO6T1/Dn+SfUP2eH4mlT/p7W3Z1aCpc+55gizFV68+H/WebYcB25rNWSvoH/l9nzuDdwkZk1phXqPWwEuIgN5kO1e+bNFdsVJS28ntqDu3H6bAi8u2E75abHkot8xoXEX/HecZ+rX275jukYv6KF6Fu8HsYqBfNXISVgLNmulQSMexV9cssnF2wzOX0LFyrfS9NLAOvm1suimDO36bmpd+3M5k8q+N/mw90nJ6TP0dRHCs+Wzn9lPGAQit1l35Hb5oyCczkjfd9CLlIdH+FlmaBvwl8kY/EX+R7ZsrT8Vnwkeje1VDB3Nxxn0XRPbUMumWDF/1KYWDD9P9fgevprcp8LSe6ipASZDfs7JPd7Vtxj6K/p84U0rzKBJc0zC78/7esL5FzMS+6h/H8+vuDaX4c7ktzT5DoN+D5p4dpPyj8/6drcgTufHJU+q6TPz4CfpvVKs1ana7kE7pqdRe4PyiV7JH263oBBn4D7q38P77ksDXwXuCAtOxbvSe9Gg292uhl+krshfoS/2L9BQToCku8/nsvnHNzYvnn6fXRuvdL01C2cy7vT+TyVPucVPYS59V+nIPtsuvGfpsR/n+TDn9adE3hvyf6vwgXF2PT5FLVUF8fhkbf3pukFSKlF0vRX8VojD+IpM24GDs4tvzXtMxMmC+ce4KoHchM8LuI6/AU0De/93Vp2ndJ2dzRMjyW9pIquMTVhuTT+kv5fuqZ/A5bOrbcSnrtttzS9DPCN3PKqVOan0T99ySlUZOlN6+3U6nmmeZNyv09Kx90sfU5N8yrjn2ieG2os7sTRmOqnWafrPnIveFzFk2XGnQR8glqOru+ma7JGu/dJxXP27rSsNIULBSllqN2/pfdQdh1aeQf04qfrDRj0CdQnYJwI/Dp7MNMD0vjJUlRXBRu+WrAs65mMwYfYWU/uAGBsbr2q9NSV6aJxl869cXXMLHjCuVcqzv25dE5z5+bNg/us30DNA+XfeC/taDwY6k9p3W3xIMZpaXp1fIid7Wsp3HX66fSw/Y3ay6F0JJGb3gIXur8AtmhYtkfa96O4AL8f+ERaNrngXCdn15Kc0MPjfibinms/x1UUa+Y+38J77jNwnfmLafoZaum/bwTWzO1zLSpGsQVtqxLEd1KeynxSwfqTqEgsmNZ5J/2TFv6BJkkzi/6jkv9tafonsBxPeXLQg3EhOxXvPN1F7Xlp1un6Hv7sHpY+E/A06XPjqtxsPxvhAbVbU8tDNeD7pOI5y9LzZ8lXN6aWfPXAbN/kgmRxT8vseDcDG+WWbZi/h3DBvc5A32+98Ol6AwZ9AunF02xewTp3Uq9+WQd/oWX2k7yaaxotRsZTXeuhmUpgcsH+nsM9UBrnfzY9kD9ND/DE9Hk6zcvUPOMpL5I1EVcJTMotL400bzh+6UgiTc9NEq64cX07GlQKuF7+oHSd3pebX/pAUqCmS//RNQWfq3PrlGYuTv/9f3B15g143EdWhOwYvJNS98lt20wQNyYTHEMtg8GdpIzTaXrBxvVL2luUtPA+miTNTOveQX29jG3xtO1rFn1y6y1FSbJIqot6VXa6ctf/kPRZu2HZpOz/A3ZvmDfg+6TiOes3r2Cdtahlpn4IFypr5v73bNnD9M9afR/eafwPDYK21z+jwbBe5Ste6l2U3AFPwXt8mWfHIXig0onU18bo8/1X/3xHjQbcdfCHcn4879J8uH/4LUqJHRsMzJOsZoS9Cu95Zob33UgPHT5ymZjmr03KXGseuJUFIoKr4vrqdqjBjTcZVaeY2Xsl3WJm6zW0YQqpXG4Z5oFee+COAWvivaydge+a2blpPxPx0rgL4C/mCcAbZtZ3XZORdQnqAx3vkNd6Py1dOwHPAp8xszvlAaRvU/Pl3wN/Ke1T1WYV1wj/jdXiVmbFhR24UPiYeTLOvUquwWm589wMT2qZXcO7zWzl9PvnuNtv3pliirmTRqFBGhfQX6N/EGjmLtp2Ik9Jm+P32IPpmOviL7jni0+z75hVzgfX4CPNxgC+llD/+vZYzQX5EtzgvQX+n72Kq5xWa/c+KXnO9jazzZNDwY/x0gpbyuNL1jezk3PtnS+1sc+Qn1s2Li17sWH+UkXnbk2cfHqBnhUiqtUB34VaGgLwntJKZrZuK95FJZ4d6+F62+lpehzeU75V0n0UpDcws2cK2rgAbvy2ND0e+DCug34Cd4P8jNU8fZbChd76uHfYTcAXzez/5In/Vk67nmpmV9MCkr6TrtEFadbH8Uy6P5bH0VyFB8vthBtcZ8XrjtyNj3T+C3VBjPkX6Iq4ikK4yqQvRUpOkB8MzGmeQbbvRZdcZz+D98ystuta/EDRA5mE4EHUR5cfi/eSD6PcO2YK7n20Kp5T6yQ85f/GafnKDKAOeq49hYI46ySk6Z3IdWTM7ILcspVwIQQ+crpHHv9wPP3vsYlpm2vx/+vKdI3XwwV1UVxLnet12n52cgLTWogTknQLXkvkpTQ9D+5Nt0G6j96LezbVFfVS8zroB1Orb/9Wrr1ZJ2su3CB9l3kmhMVwR5O+zAQDuU/M43mqnrPLcAHznSSoZsFHPquUCJhDzWw/FbsqY17YLGtTW3FTI51eFiKr4UPII3AdasZ0PIHdc0W9M0kPm9lSVX+6vMrZmrmX/xjcsLimStIbSPo+tTTns+MZYlfDdfG7m9k/0837FP6iPpSGdNFDhTxq+INpcryZTUrz58IrG34Ef3j/gY+e5sYNmrum9p+N10x5XtI4M3tRTVJtp2v4edxZYV/ztOp9UeNphLSK5XKelf0nuX3/smyZ3F35bmrR3p/G1Qk7puWZUPs+8JiZnZybdxiuClqJWozE2/TPeJtvSxYJXyiIzezAirZWXkNcOKxVsf2a+Etw5XTOC+Mq3DsrtukXK9TAPEUzM0Fa8ixNNg/QO6xk2x9IymxzvyLnrmxm30/7eAD4QGMnrMk1+hz1VTIbj1t6n5Qhz6k1o2qUVyJgHjazxauuQdrHYaS4KTNbQdK78LCBDYu26yV6NtgwPTB3SjrL6hO25XkmqS7yw9ast1aVNE6ZAEnHejvdMOClM39O/3xHu+IvYKiVKl0YN+idhqcSz4aur5JLF63+NR7qsBS30Q7yiOT/URuJoJRG3MxewYXIdxo2ew3vCR8vVwl+Eg/U+gZ+nvmEhn27pZbQEDyI71u4p9xUeQzMNbn178ZVfvnSp1X/yeclfaZi+duWUnskfiCPVcmYLulb+Gj0Q6ljMGtatjO1GIm9U4/zEorTuDdyMAUlhlWcBw3oC3KclfJreLqkz+P/WWOAHbgBe2NySQtJyRDT/11EUQLAvl1Tn6MrS/VzB25IB0/ZvqbV0nisRXqR516Uc6V7Ks+cZnaVJKX7/3C5CjDr+D1C/0SJ4NeyMXFmxnx4jFQpKk7XshA+4rmdWnBhxidx9/Cq1PQLmdk56T4iCZ2n89eggh1IcVNp/f9Kqrrfe4aeFSI5lpZUNlzeB++x/YrasPWjaXnVn/6gpC/ibqzgPeosq2s2CskXrTJc35/dtB/F06S8BdwraRX1jzbP08rLqhKVJ1L8O7WHKSuS9QLwDjVJ/ZJ6vLvh+ujLcO+WMyQJ2NhKEhqmfVxHLmWGmT2I99Izsuyud1P/ouyXdia15Y9lx0r8RdU13XfFA8j2tf41wotqxMxrFRHK6Rhjgb+bRys3CuKmLwgryRYtz4UG9UW/8gL6ZnM74NTcNnfgNoO/57aZg1xRtGbtaWjD/NSivcE7BedKytSb78SvKZLWx73F5gGWTFqCA8wzXlfVQYfy+vbbpO/Ca9QCl9E/OeNa+L2/Du7FmWf79P1l3KttOUk34h3BndOyUgGj4vxhR1vNVveGmZmkbNu52zyvEUfPqrMymg2XS7bZFPcIyqJ278XzKl2bli+Ce+Fsht8wV+Hptp/qv7e+fd6Ce0w9ifcM18r0nWnIvjm1tNF5G42Z2Tcb9tUvD1gVqiVSvIeaDt2KXshJMHzPzHZQSS6g1Nat8evyFzzrb53RVCUJDdVibjJJU3G31MZsAtepOt0KaaSQzwr7VHpxnY73UsG92vayXJr+MiQdixu4P4mnBXkJdxNfpqBH26izvwqPPSrqTWf7X42cOpEmnTcrqHqY9vNOmkSsF2yzJvB5M/tsmp6PCttRbrtZcU+99zbMy9tS3kzzb8VftBdZg3OB+juajAN+bma3pPUK1UDUItgLMXfAqHKcqXK4ySoy9j1nkh7FPc7AR3Wz49f3deAtczV3kRpxZzOboiZVQuVF7pbHO2Q/wTu4Z1lJXrteYjQIkYlmtpbq9e3ZvIXxQLelqT2478b/zCPwoaXwHtx3gS9YRULBtO9C7w1q+viF8aypR6b1twI+bWa7ldxofTe76vOACXfXzfKAVbVpQIkUcw9RoccN/hKdRk31l90kfS9QFSQ0TPtomqwurdcvm2xuH6UOEfKcRz/HPciEv5y/gbtrf1Xl3jF59dJsuDrpJfzl9m4zeySttzT+onvazB5X82zEF1KQtt1qqWMOwe/B89OiHfBr/Bg+Ulgbdw0VrlK7ndoIqZF5cWeEtam317wInGYpfXojDc9Goe0oXY/s+ozBR/bn4Mbzq1ViUzEvC3CrmX2g4aVZly6mRNVVimoJDRuv0aq4fXL9JvfJnZQnZ7wXV2fln7MlcQFR50SSO89MZTcLBbVP0vE2sfr8YdflO1qStiBnfzSzK1u9HiOZ0aDOqhouX4j3Tv5JrYf+A+DjVm+EnCxpAin5nKQVcFXWoqk3tSqwnZn9EPfsOZWa+uJfwNnpZunXE0xCKRNMkrShpVxGkjagvuRmVZnWKkoTKaqiSBY+wvowtdKycwJXkJJNNuEDwKckPUQtoWFfDz2NKBZOv58u2P56uRryIvqnzl7YzE7NrftHSV9Kv7+DB209lc5vYfz/zfTzdcIjt98+9VJSx22PB/CZpEvxrAWY2UMN2z2cRgDr4i/Z283sidwq51NdWGhf3HD8cjr2z3B11KaSzscdOO5Ky47F9fJF9gtLqpHTJO1kZucVHazJ/w0eI1JkOzokN28GbjB+VNIPaF5U6ZF0L1sarRyCjz5KVV24a3eVHXDTtH3jNVoZd7GH6vukKDnjZ1PHaQz9n7NLzOyIorZI2rFEiK4giSS8j8aTvTa6a+fP6co0apsl7XdBa1LzvRcYDULkELyexBfx4fKm1Azbc5lZXdU5ST+yAi+WNCRdNE2eiOuj/5BbdhbwQ4qNa2817q+EffEbO/Nrfw4f1mbMnd3Yad/XqjXd6Su4ICxKpJjXzc/AdebZC2gOy6nM0rB+LmvNd/2jZQskHY6rC8f4pGYAxzQ8pNmIbL3cPMNViEUOEZn3zhirVys+k44zSdJFeMxFfkTQ7wVvPvz+W1KlfBO4Q9I6jaOqdC6fxY3AV+P/2TGSjjCzU9K+TpM0G+5AAf0r84n6ol2ZGyu4p85duXZ9Xq7m+4FV1zu/Ue4VVhTLUPV/A7yqYtvRWnhn6LGGa3VY+t67oj0H4obuxfFO3BXUVLe/xu+Vi9J+7pRXXzyo/24KabxGd0t6X5osvU/Sf7mKil34Xyt4zsZWtOGveGDh5GwXuWWGp1o5Xe4wsGmav6PVl6A4gP614vN2rp5lNAiRt9KL8CXcHpLnEklbNaioXqacbNlcZnabd1j7yGwCVd4blZj7+a9WdGMnqsq0VpGluCg6Zp0DgaQ58F7luVR43LTAsnjSPfC4laxX92VcR72O1WxCywLHSTrUzH6VHtiLzOxXJfsucojI/tvL5YWe8sF7l+FqxGeoxVxArafc6OI6BleRZB46RWnis1HV1/BcTc+k/bwjteeUNL0Jrhp6KG23hKS9LFWHJFXlk5SP08kC16ZIOon6gLgpeOGkKiFyKgWjYeDkxv+7gANx76862xGuZrtC0rNpX+daKqSVzvOQdMzpeCdrTVwAX4UHbeaDc+sws0canqW3rInTQo6yawTV9wmStialdM+OnzoyRc/ZVRVt2BG3l62Kazf+bAVu+eZeiE+THHyUvCDT4q8CK1t9+enRgY2AsPnBfHC30XvxUcjKDcum41L/tfQ704tfVPC5GHgubXcZqaJgmt6ZWp6iNfFcSy+k73/RkGeooI2fSt9fLvrk1luAWh6wO/De3QItXofZcINfv4yleMT7VvhD8yQe8wHF6T7WanKcxfGI6utwQ+Qv0+/b0rJJ+GitcbvGtCi3DeI/3zF37B3SvA0L1svnOTo19zkRfwEvkpYtVfRJy27CyxDnr3M+i29pjqbcvDXxkfIXSckD0/w58HihC9Ln0DTvp/hLZwlcb78gsGBuu9vTd/56Tk7fV9I/qeM/ctNZpuFxwLj8vPR7VVwNcx/ulp7Nz1KsfDS19f3Uno8b8teo4dz/iqtj78BVrl/FPRdb/a8Lr1EL2x2PO1o8gjsS3IUL2eyaDPg5w+OndscFyQ24h2K2bDs8R93LuD3xbeoTTV6Od067/s7s9KfnRyLmuuV34lHZf5AbVs82sx9aTg+eoXKPJKgVJzoIt0+sKOkx/KbYIx3vjrSPfsa13DEao583xXtSlW6f5ka5AceElPWG0+/dcQFyGz5CWMaSgdPMbpdHnffzuKngd8BxZvbHhjbsiUeOz2oFvS0zezrpyzNulNdXOZvaCHBPc8+uwrgZq8XLTMQTV/5T0lxyf/tj6F+oqW+eVatjfmhmn244nwlJhfkAPpK4MLVpe2o9YdL53p9r478kzar6YLmH0ifb94Jm9qx5HY/jgUvz+5C0a/qZV/nkVR9Vo+GFLbmZpvY8J/c2zDgPtzHkbUd/xdVZ4O7NT+Cjuvx22VBiK7xy41TVhhcP4v/nRdSrEn9JtaqrKeka/R63exn1xuwqt9oNzB1AppgHPR6Ndw7bfs7wzugLuH1lKeqLVh2Jq2b/aWZryD1AP5Vb/i3gpmQT6Ve7p5fpeSECYG7o/K3co+PruA77hwCStqPmznitmV3S2i7tw5kRzsymS9odeLjAwJY3rmUui5tQH/18Q9pppapBbtD/KvXeZFjzUqJHAx/JXkRpP3fjHjzHAV9N5zDNPNfROsAjZvaEmb0pd13cKZ3f4fjIosjoKTxtww6NC8x1wt+hWl34Ru736uk7byfJopNLI8Ul7Qfsn9ZdDndF/jGussgblcfh1eRaCeSsi6FI6rYlcaH/n/TJuLBhFxMK1C0T6B8s17f7NL1sujd/jo9ulpHngjrCmsdGVMUyvJVXo8i9yyx1Ft4PzNdwD4/Dr93n8Y7Ywriqcz/L6fSBifLyzssA30qCO3PNzq7RGHIdpXQdK1VdzWiiLly1QGBmtrZMLfuKPDr8GZLzhAZQGjutvxmuzloXF2a/MbPGe/RNM3tG0hhJY8zsGkm/zi3/A25Xq3NpHw2MBhff9+F68Z3wG+Vs4Dzz2IGf4iqbM9Pqu+Hugd9qss8iH/P/mtm7VFEpMK13FyUV4lTsckxu2zupyJlU0d66XE1p3tP4C/1u/IV2IZ5/aFl5YNqHzexZuZHzL3jk9ep45bqvVBzu2qKXnNxD7l/4S6ZIkAhXQ8xasKxl5J5E6+LpwNdIo8Jz8Afz+Nyq03EVZebZtiEu2LM8a5/AXW1Xwr3SMvdT4cLuhGb3SWpPaY6mFrYtSt54F26j+TKedn9/ef6p9+Y7QCp3Nf0YPoq+jpoL9P54r/njuNolbz+bjv//2+BpeyaXtHUMfn88aJ7+5h3A4paLw1GBG688jmszy6W3aVi+AtXJJifiaYPyHaQ/m7vwl7rVJpvHMXjM0+9xoXGJeZ6rQm2EldhpJL2Njz5vSPuxhu2+KOmf+PX9KV4Q7SncLrhB2scka6H6aS8yGoTIzfhDcK6Z/bdh2RRgdauPg5jU+MLNrZ/11o6iPlp4HPA1ayHqV9Jt5skfM0+N6XjhphXlpXyvp7+QOC9tO9EqciZVHPMUCjKW4t5gm+DCcys8EG9fPNgw65X9Ho+JODxNT7ZcjqTUk10+qY7mxHvOs+PBl5nb6ty4cfO1VofnKo63OQuoioLfTg0xCellegewrVV4lcmDQTeyFDQpV61db5488SdlAiMJ/q/Tv+b2+Wb2u7TO+60klkfF2YN/bZ7sryyL8r34PbKnuYv5XLht5svWJGYj7WMhal5vt1hOvShpfTO7uaGNY3H9fT8X9dw6Hyqab2bjlXPjNbO6iHVJp+MdkyJVV9OOU0kHaUpSVe1JSVnehvVnxzswL6Tp0ozEJee+V9l1SW09Le3ztdSOPfBn7UyrOWT8GB9NXUxxKpuepafVWenPn2Zmv6lYbX48RTTUopnLeC/eI5ufer/46cBVkva1XEro1IZ98RQZv06zJshTRpyIPxgv4XUPoMDluIGLVZ0zqYzP4b3h7AWe9YaNVFsjvTQ/iguU9yklnMN7avvn9tV3TxSojt6N99zvwFVf2Ut7SVzl8O0m7czzR/p7GI3DVXM74mk1MqG4G+4QAHCdpG8Dc8qDtz6PP5gnSfqE1evH/2JmmSvyAmn/2bWcJ80D9+Kb24rTxJ+Jj162wfX7e+HBafvg9iFwh4VGe0zGcbhH3mr4CO+ktP7GwFS5mnRsGm18ERcWa5vZrpJ2A0gqSKVtymI2xgHny1WTUIsNWTKpt7Io+AfS9Vua+uf/ftV7EzWS71TNgY8Gs5HUryl244USVVeOGWZ2XMH8jDJ1YaZCnUDNI29H3I5ZKGRVUzuXxUcVxmNZylpdRbp3sniiZ3FnhnxSyd3Sd76zkrdz9S42Aqz7g/ngL8wyz5BP4gVi/oi/5KYBu6ZlK+Av+ivwB/NqUhEj3Oe+cV8TKajTjOuzC4vL0FAhDrfTbFVxLtMKPg+WrT+Ia3YY7ll2Ie5NlY1I3wPcmFtvcjq/Sbl5WUGlOamVFh6w1wnVHkYTCtbPyrOOwVWC56bPZxv3k9smv++9C+6FvdKyKdAXMT4JF8jXZf97tk6+7eQqYxYdO7cs82D6Pp63Kz9vLtwT6nb8xfgj/AV9U7q+2XrL0cSbDVe/QfPiXDfhtcF3wVXA2Wc8qbNEzmux4nhL4GpjqFUazF/vfhUUS/ZzON4RWIxiT7TZcdVeFtR5KMn7L79+7nMm3jn5O+6+fF76PIurs/rus4Z29Js3wPv5s/goOru/HgL26dQzO5I/PT0SSUyjwDME7x29jQ/rs/Qa37BatPG5+DD6RGrD6D3T9+5ZLzDHklbguWRmb8gp64miWizGIcC3Jb0OvEktHiFL1dFKpHh+v+eY2S4qzliKlajtzL1V/oE/uFdYegrwF/TBuVVfT+eXHW+W7Djmha/uon2qPIzmlrSsedJGJC0DLCrpIDP7PXBiGiUtDKwl6XngbRUYlHPnfKo8lXeWQDN/L8wwM5O0PZ7K5eQ0wgT/nwAel8cd/JeULkPSDumajWvs/VotyDHLHvxp4IPKZQ+2kizKcueGy3Ej8pm4PWfvtOy9+Ogwn/ftBHPbyRi8MNiNFde9cDQsaaDxC4/iaiqojli/Ek9T/3yabhwhZqqiwmST5ralzJ07a+slVDgtmNv9rsDrCj2etlkMf8HD4OKjymgWTzQrrjHoc/IB/lD0Tuk1RoMQKRwum2dl/bp55G9RIF6/YbTciwOKvYN2kLSo5QKw0jZZlHtVJl7DjYuVLr6SDsL1qM+n6QWA3czs2JJNDknf21Ttt7BBKQFew7x/NcwqUx11gioPo0PxzK4P4i+GpXC3yvz/OBvuljoPNbXYDZIaDcp5xuKqqFlwr7oVzL18qtLE/1AemPcV3FA7Ds9ouyNupAbvxedVTEYtFUqWPXgfq2UPfjN1egoxt/1MxDtAwt1Y/5dsD+fjhvMT0rI10rXa0bx65u+oZQMooigAF2uesTjv5ZYZ2TMVWZUbb6XLcVnHqVkHqWrbxBKZAEk8iatdoSIj8SB4Bh/JZUynlmUBXK05K+4GD96pOA4fwfQ0PW9Yz1CxZ0hWfzwfi4C5V9LhuAdFS/aHZMT7Iv4yyR6etXBD8++sBb1p2s8CeALIfNr68WnZZOtf+GeSNfHqSD31x83stTQ9J57366HcOgPKDJy2GYMb4vNFq06yQdw0yW5xbmrzI5TE28iNoVlv+z7gBsslbJT0OzP7QvqdGairDMpZpuOp1FwsLb2w34m/6G83s+vTi34Tc517X66z3L76zWtyznnnhLlwtcf/4VH3t6bzz/N9M9u8YR9X4V5jP7OUbTq3bGPgm+YOCr/AbXDnF/1P8kSUc6d9Zdfb8P/4GHx0MRsucF/ORsmqNy7PwNU125mX+f2EpbLIBcebiAeE5keIF1gt6ehcFHii4WrEygSYqnZa+B3+nOUzGzxgZgen4xZmJK5Cnu1hXxqcLMxL7p6Oq3Yb44ky77W9LJeQMu3vzsZ5PUmn9WPD/cEz6N4D/F+aXg03KkOFjaHJsmvI2Ulyny1x18lncOF0HbDlANr6WVwF9Fw6xqvU66vvIgn2NJ15zTTb7wT6R1VnNodVcD3/w/iLayINkf1tXvcN8Vxf4D34X5KivJtsd0f+u2SdPQs+T1as/x/8Rfwp/AUM3utcN7fO/bgHzkDPs187q9pesO5+uM3jP2l6edzu8DFcdz4Jt5VlL6YF8Yy1C1DT8y+NC9J/VRzn/vSdZWl4Ex+9TccDM1u5h96T2jMWV5/9pMk2d6XrXvVffizdd2fgxvGHgY/mlp+Ne7/dnabnomYbG4tXKS3bd6ktKy3fAfca/BUps0GaPyveIfxr+nyBAntnwfHOxYMK/4Or4a7AHTDA7YxVnzvw5JfZvpYdyH00kj+jQZ31a0o8Q6xiuFu1DA/4y5gDNzzOMLPLSFGvbXIIbp+5xTzSfkXczTXjcuBsSX9I0wekec2YxXJ++OZ2jNnS5B/on7H0BJpnBkZeHKnI1rIsxV5Hp+MeRFU8k/TVyxSpdMwDvvIp4rMqe69L2s/MTmxo4wF4NP6x+MtzMzyAcTpuUM329SANmY4l3WBmG6l/FULhKpvvAwurIIixyTnmOYgU15LO79+SFjGzy/E8YLPjnjvX4p2StYB3URvtgguD39E/N1yel9P+K1WmQGkArpk9IGmseTG1UyVNSnagwt0Ai+IdonkkvZjmWfZtZuPM7HK5vTAbIX7J6jMaLGfFnmiY2VuS3pY0nxXXa6myZYFfw+mWy2xgZtNpX7X0HjP7hKTtzd16z8Ide7DmgcQ34F6SeRVt1f/ZM4wGIYIVJHgDULGr3wvURgOFhi7rH9x3o6TbOtDU18zTOCBpdvN67O/NLf8GLjg+l6avxF/OzXha0nZmdhFAeqiyB7U0M7DcmF2qwqC+euMceIBeFlXe7AEuY2tc7XAGJXYkSyqHDLnL9PnA3nKX2Lw6cXY8yOsy84JDk9I+nssJUijOdHxHWrcsPc4m+DOSX/4iNdtNKxQ6JyThsTUuQJbGczmdYmaPSTrYCooVSfq+pN8WHEO4PYL0At4DT29zpKQlgMWsVlejMQD3kKQWeiVdr8mSjgIexwXp27hgOAu3h9UZoM3VShea2fZFJ5/a8zFgWTM7QtKSktbN2gO8kdSvmYPFctSXNHgJuEtuoG+s11Jqy1J/9/TFcUeazfEgwLwa6Wp5vEozMpXX8/LURk+QUsPIs2UUdbg2S99X5VR14CPHlur/jHRGgxAp9QzB9ZfrU6vtvQmuzlkGNwA+TEFvRB75mjEGf1nN10pjcg9x30MDvDM9NI+mF+LfgCslPZfaAPQ5A/wRV3Hd32/n5RwInJn0wOCeM5mnWVVm4N/hbtDn4gJjT2opzbF6P3eAX6tWH7vKGF1KGjHdImkDK64zUsTLeHT0e+UpKLKgz7+b2dUAkt6Uxw1lL6OFqU8vkbmtFpK2XZTaMzHNvCbKH61Janz1z5WGmZ2efl6n/s4J4HaLS/G073c37PIUSd+lv53ga5STOYPkR2RH4i/h31MbkW1FfQBuplLbBr/Xv4A7NiwB7JRGJyviwu4sXHV8Fu7VNyOda6EAKWhP0QjxMPp7on0mt33m2ltEVcnjwhFgWvaWpOXM7D/pGixLfbr+Mk6Q2zS/h99L81CrFV+ovZDbamRmZyShMSUd89OS3jKzs+h1uq1PG+wHWAjvVT2JG8r/BLwjLfsHbmDO1l00zVsQj1ht3FeWqXQa/qKdhmfmvAKPdm6lPcfhD+29aXoBkn2iYb2Nce+evC1jO1x3Py1Nr06Fr37BPufBo4bz8xaglrF0Iq7+WyAty2Iv8jEQk3K/18x91saFVXaN3okbRD+YppfEI6xbbesKuFqtKE7nYmov/UvSf/HTJvvbI63/KB5vcT/uWtpKWw7GR25T8VHqXdk1qWpnWn4Y3kl5EvcSe4KUJTktz8e1/DX9fptaVukXc59sutRO0MK5ZDan/P94Z+73FOrjMNbGR+d340boxZvsf9d0rb6GOzuQa3fddyvtSdPvwEdl21CcAXpO6jMlz4F7WP0OH7nPUrBNXewK3jnI/tPNcTvNtenzELBpq/fuAO7x23AhNk/BsrlpyPbcq5+eH4mY61fLErwtYfUuuU+lec9KsrLeiFXYS5IaYif65786Iv38gFWoVVJPZglqL5GVqalnDsN7T9embSfLvZgqkadUOMrqXYO/YmbfteqMpWUqjIy8uinzyNklte0Jcr775t43p9M6WZzOSfTvBf4i97uvyl7VzszszDRK2hxX73zczO5VhYto2m5VfPT6Xus/8mrWTnDV1mr4y2pvpVxpuf2/jcci5W05J1KBpAlWYidogWYjsp/gBbyuwa/TrriB/5d4J+YY3H05357F8RHrDrga+FDcw+rnqX1Vdphm7SFd978XbSxpW/x+yCep/DsuuK/HnV1Wor4yIxSPACdIeqfVVEsH4KrQK3BnhkIkfcrM/qR621i+/b+s0F68bAUekeYeZYPKIzdS6Hkhkl5+P8R1tZfj9RAONbM/4f7zl+AvAvCXf2YT+Bclhq4SW0rG99K2EykoR0vFQyPpSHyo/iA5N1NqaRveNLMXGt4XpS/AHFuaWV/KkSS4tgK+q+pgr0/TX4WxY24/m5YdUCU1y82sJbUfFekurPWCRTQ8vE9Rc+nMlrUSQ/MI5YXFmqXleNVcDTlDXobgKfw6Zm3YEI/KXgp/3voC4ir22cxOUMVvcbf1RST9CBdy380WmtmfJV1LTZ20lpkdkH7/XJ6csw953M28eJLLvanFPsymlNJe0irU3LHvsfo8YkXt+V6L5wJ+7Ro7Vu8ws0+l9p2M9/gb+Sauzr4LFxaX4rbGzAHlA2mdLPHoCZTbuuZO31XCMgt8FN7xmZaOf6JSSp38yvIsyLP120sP0vNCBE+B/nV59PBD+EtwPN4bPChNZxlWJ+DqrZeB1dOoosjQtS/uvXR1mt4Ujz59GljaquM2qh7iXXBvlMKMppTnUmrGWLmh/nXoixOZPS1byMqDvT5unnfsNbx0J/IKdr9Jv0tHXVZSs7yFtmaU5glTc4N/nvzDCzXB1srLOuNBvHPx94a2/LKqnelnVa408MSEh9KQYLAJh1NhJ0gv0aJRU9WIrDGjQjaymzuNeDIb3JzydOrZ9VwKv6YHUB+8qdSWh3FV5p1p3iqS/g/Y3sxeLGoPFUk2C6jsWJmXp+63URLsp+HqJEvn94Xc/7YrHul/HnCePDt0IWaWlcku9cAq014kIfdXSQdasq1JWhpXeZ9ctE2vMRqESHYOW+OZfPtuODOzNNJYD/csmkZK1VEw2niPagnaZqUgZUJSV7wpaRXL1X3OU/YQp8V348kdnyraFu8VfQd/WZ2F22+ObOEanIkniDw1Te+NqyigOh3IXiSBkeMzuXkX4j30slEX4NeZ+prlrbBX+i5Kd1Fp8G84dlN1XwtkwX+z0b93WNVOzCwzlB8v6XK8WuCU3LovmLuGN0WeUfksM7tCBRHruVVvSS+9U3GvNMvtY0ncE+3ihnmZanIO/JpmL/2lcHVdNgLJqynNzJauaO9v8Z79ZlYz1I/B06H/SO4Jthhui7gvdV6+hN9j72rY1yLUOyZkgqaoYzW73KWYdA5zKudibGbj5K7Jx1OLIVoGfxaaJh5tcr6NvIB3Tt9NcbaJX0h6CRivFPCLdzR+2mSE2zP0fMR6ulE/jquz1sVf0v/EPTp2oxax/lUzW0rSD8zsMFXUBZF0r5lleYGyB2Oqmb1P0j14UNY0/MWa+cZvUtXO1MNeG38x3019r3a7dJx+kb9F80quw5b4gwFwpZn9I80vqi9xFm6L2Yjk556YF3jbUrS0pLvNbOWS4+WF8Bj8xbSxma3frK0tnMsEM1tbuTTgKoncz/TV6XddJLmkL1hK195pJK2YXoyNPXwArJaX6af4SOp86v/zOxq3SaPAT+Iv3nPwuhmTCtYTnoV2H1wtdQ7eyfmXajYg4S/lZfBR9vvTtucDh2WdILln2eFmNhC35awd9+AJRmc0zJ8FH+mMxStDzo57af0Mt5sdleugbYcLuHfhnaulcKeUrL1z4R2rj6Td/wOvRPlak7bdB2xjqRZ6Ugnegqui/4ePntZMHc33AKeZ2YZN9nkCrrbLq8en4Y4B65vZwg3r192zSYWFeazKqKHnhQj06b5fMA9OmguX9Nfjrn/ZTfRgi6oNVJEyQSVpGHCdbfbwLokbIIULtf8zs2UkTcWD/+4iZ1zMbAAqLobVb95AUUM6EFzHuwxuZM2PHKbjvcas5sYJwDFFo64GIZwZ3U80s7JRVrbd183sqPS7TkBK+rGZfVvSePwleRLeM34c+IwVpIjIX5/Ga9Xs2kn6tXk53osp9vHfTuVpObZL09c0bueb9hVVqlxe0q6lcGHySdwz6c+4QGnMbYa8DOuf8P/0Tjz9yc255WsCnzezz6bpqdZQF6doXiuoIE1PbtlreK2YZ9NI6F94zfuJDevdidsEG8vKHoR7A76HWn30OmHVpG23W32aHOG2k4OpJR7N6uGsgHtQ9RPsDfu8JZ1DFoc2C/6e2Qh3Q58zGxXK7aJT2rmuvcZoESIbUK+3XxN3Qd0Q1y3/Bc/5tExum1KPpjS9A7VAxPFmdkFu243wXEinyg3n85jZtLTsRNxz5dI0vSWu0jqg8cbO7W9L3H9/F2qV98Cjo1cys3WbnH+RkftVM5unWU+5yX4LR11Wkh24FVp56aeX6JPpXA7FvVyOzToEDfvr6+0V9Pwm4fdE0U0uvFDR8qqodCfpbAoKRJnZ6mmEur4NII/WQJHbJ07Be/xj07x34C/aT+PX6WTcvXl1XKW7TMM+7rJaEbK/4J2sfH2OecysMWt1K227Dx/tNxolhLv/zplbtzBPVG7UeSeeBfft9Ps+PLgv88B6yMy+NIC2HYePas7B//9P4CrLfwKZ2npASLofT6XzQpqeD0/R/15JT+Kj/Xy2iUfM7CsDPU6v0fM2EUln4BGpk6kZLs3MPin3wtoe18Mukm6sC8zsCko8muR6/azK2wU0kJavjfdGT8Vf2H/CBRbAema2X26/l8k9yACul/QT/IHP2xj+i+tVt8NfWBnT8ZdoJVZs5D48zSqKCjdgMzU3YG9Zdky5S/Rv8BGO4cbkQy2lb69AJb/7ps2joGfDR3TnUZ0gz0p+Z9NNvbOs2husKi3H2yrJmqsW3ELLDph6uFviI5HN8VHu4blVbsaDRz9u9a7PEyRNzR1zDN6hylf83BsPjPsuPoK8nPqywmVtWg541Mxel7QJ7gX5NDk37wbGqt6GsFh+2moVMJ+X2wrG4wGzT+G9+pVygq/MA6uKOXABm3UQnsZHddtCXZblgXAU7g5/LX6vfgj4cXrPnI7bXwaabaLn6Xkhgr/QV8qGkXnScPUs4Kw00vgEnlrkCko8mpJKrKrK2w74SyNLmfHfTNeZ+K882jjf08se4uxlk/diylQbd8pz8YiaEbml7KIN55w3cle66dI8Yj3zJqkzeibOwj1MdkjTn8TVLh+gmmYvfdJL6jRcRSbcS2kvS9mOG1hRXlJWeFr5zKgtPGvAw03ag6RtcAeGRjfccTR3t71K0k70z5pb5RZaOPyXxzPsho9Kb8NH0Ptbg3soHtNSpkK4PXfMGXhMxXlJMP0YFyKPpOVL4qqiTD2zOP1rnWfX/Dxgbbn94ATctjfdzLYqOZe9GmY1phLK2B73DjyUWlnZI/CXcNaGQg+sKsxs7wFt0No+T5Z0KW57Bfi2pZLc8qwQ78HrsD9gBTYbVWc26Fl6Xp0l6Vzgi1ZfO6CV7b6B90ryHk0XmdlRSSe/Bv4gZw+wmdn2qtVQz1QvcwM3W80AvCAeNPgh/GUxHjjCWqilnNQqp5N7eeIppItenvntiozcO1BfirMOMztfTQzYqjB6qrj2ddPU1pLewq+p8J5hlr4/Uy/NKvdM2t1S6peks/6zFdSfV7mNKuNMK0+yaOaePA/gruB3Nb6cJX0EN+yuhHc+NgT2tlpCy+m4wHgLd+7IHC3eb2aPUICkbSwlPWyYfzUunM8zDxIdEPJAvPfgI+l7G5b9Chcuh1oy7KbOz9Gp3a/htr97qB/RZ04f2f3+NTwH3DGN6sNOkrtPoP5eyQv4qu3fjY+yMw3B9biXW2XQagvtyievvA5PyPpj3MnhYWrP7anAd7JOYOrUbYLfR5fiI80brA2HhpHGaBAi1+C64Nso8Hhqsm2ZR1NeR555NH0yvTy/ihvdt8AN0/vgbpn9EuYVHO/7RfMtRbsP5OXZsN8iI/fK+Iuh5JC2j5oYsFVi9DSzfeX1OZ7De8uGv4AWIOUuakVoVpxPkYDqN69TpHtoc0tuqgXL30HN3bauTknFPu8DPma5mi5p/t549cHlBt3w+v1+H7eTTMRHgz+xXMZjSf8GVigQkmNx+8PbuN2l0JVb0q14ypzvANua2TRVeO+10N6qDMpNhUQL+78SF8j5nHF7mNkWg9hnY/LK3fCR0xTqhfM4PMr+VTM7JM27i1pmg9WUMhsMpj0jhdEgREqNooPc7xp4crcsvuT8TFAktUNfoSYzu7J0R/X7zBvZ5sD19fea2T5p+YBfnukl8EUz+1XLJ1fbttKArRKjZ3oIplXs2qxFT7iSdp2Cv9TyKsGx2XVqc5+/xQXyzQXL1sHVWdfREGwo6SorKBBlNTdoUZA1F8/p9mtgazP7d1r3W/g9teVge8QF5zAVz077ShJ6l1u9d9K/zKww1kbSv3B9/iespGiZpJVwb6mbzaPelwF2MbOfdfI8OoWKC7yVepO1uM8p1CevHIuPjuYoE85mtnyazjQYE4FNcXvnvea2156m520i5h40i1JL43CbNXEzBVCxUflVvCedjy+RNdgVktBoSXA0bFdn5JZXoftHbtYESSdR//IsKtWb3+dbcqNvoRBJL5TDcDdEw3W2R5jZM+YG7IXTfoqiccuMnp0K8ivjc7iLZ2Z8vZ5atuV2mQh8T556/wI89Ut2bX+EeyzNQS3YcJakmlxIbk/LlPLjSGnXE4VZc81sHUmvA5dJ+jheq2Jd4EPtqKrypP9sP+o9Ehe1VNnTzJ6Re47luUfSntagg5dnmc08oSarPlV+nwHczO5JKuAl0/Q0PO4j20+hPUUV0fVpuwULZk+3wdcefyadW+amvxv15WrbZX4gG2XPB312yDrSc5mf3yyzQc8yGkYiu+Av/mupqZ6+ZmZ/bbLdBPoblU/BX5htxZe00fYF8Ay/70nTs+MvzyxNy/X4yKAyb1LSd89K/zLAd6RhfZYGBlwwbYILky/gNhTharBjrJZIErm957W0PDN6npleUmPxLAFLU//iKPU6Ggmkl9ZO+H+/pLmLbz+1jDzw70u4PegxakLkRTwe5ndpvcxW0GcfyNuGJH0QF1o34T33yiC5Fs/hJvzeyKdSOR2vmAi15yBvS/sc7pH0KjUj99q4rWEHXK3ZD0tln5VLhGge87Q63hnZTrXSw/3sKUmNNpmC6Pq034dwG0I+ruoJfIS8n/Wv7dMSaZR9DF4KwvDr/0UrdpZpdZ+74dH416S2fgjvbB5TIpx3KVKry9OeNGY26FlGgxC5E9giG32kXto/rbmBt8io/CBuWymNL2mhPaUJIVWfUXYssDD+IA4qqloVAW0lL8jH8aj5/a0W37Isnsb+8lZUY3IvldfoHzhZNKIZEOqftDDbdz9hrvIsvYUxLZLWxV942+PqhG3Tf/ZPc9fvxv0XFojKLb8Vz7N2exImC+MG+PdQCz6dHe/pv5VrV6nOv2SUnK93XqSqKVTrZlgtoHUzavVY7jGzTPAgd6su9AxMapjN8EqImbC82zx25n5K7ClJ3VcYXZ+Wn4inzs/skR/BhfypeOnZZt5+w4o8BVKf1oNaNoIy4TyvtZDZoKexEZCPfjAf3KMmPz2mcV7JduPxB/R03P/7UGq1MubGddcX4z374/BEj9m2s+GG65VpqM1MrT70DngQ2Hy5/S6V+yxOqoMAnJOdC26kq/u0cC7Lls3D/fg/ma7LGDyg8UmK6zYsjBcoyupCZJ+iOhFN2zWI//Q+3HtlETylxDtINWIK1l2q6pNb7yi8NszluCfe/Lll03FB+GrBeX4CfxGAx1acj6fLyLZtu45JxflX1jvHOylbdfiab4J7F12Xno1puOotW35L+p6Um5fV57iMgpoZBcfYFB/VPZ+Osz4Fz2puv5MHcT6nNfzHC+CVI9vZ15pVn7TOZng0/MG4k0a27Qnp+5qCz9XttGekfUbDSOTneG8/n6Jkipl9o8l2S+Fuq7NSERWtWnzJrma2uQpiGMi54eZ6ZyfhPazLJd1NzS2wiNnN7HGVuKta88p6RelS8q60c1MbLYzxXXr0c8G+7sbzHb0Tf2H+xQpUAEmFcZUV9N4Hi6RbrcM9UHkt9vOsBc+qhu2mmNmq8iwFP8RVp9/Pt09e+S9LuHmVNbjXttHWZq7XmVvxG9RKtpoNwqNJTTwD5QF/V+FpcnbC7VWzmtmBks7DPY/62VPUJLoeF+xX4aN+8Od3C7yk7u2N9/UAzqfvelXNa3FfRSP9DLOKFDZp+yHPbNBNelaIyIOeFjWzG+VxEpkd4Xlcb/+fITpus4etKCHkI3jAoXDPnew3lHgyyfNdPWMVf1B6eb0f72Xns8yOw+1ChXl7ioRO4zJ5Socd8VHMHLi95S+WXHflaWH+hAulN2lBTdMqGljSwkpXUTxNRZk6YUV81FOIuU1pkrl780/wXvNZ8nQqG5vZiyWGYWxwLs4t5w7rFGriGajiRIhHmkew71W0TzM7Te75dQZwqjV4pCVD/cnUHD8AbsTLEryA26z6pbpp8XzuBDax5MSQ/qfrLEXBDzftCrBeoJeFyCXAt6whOaC8QM6PzWzbJttXRSlXbdfUDVf9E0KOM68EWNZDWg832D2b2nQG7iI6Bs/ZdHlJW7bHBdZ21NcPn46/8G+SVDQCuoacAT6/S1LAX+4YY3BB8lv8uv4yzZ+G2xX6BegNliobTxv7OtHM9ivZ5+q40bcxPfqqeOng9dN99hjeO14T7xzchudF2iZdh8z20fdd1DEYQJsrXa/TOvmgt2stBS+qokxBk2NWulWrSYbpMnuKJLVyf8gDH81KXIzbOJ89gW/jox3hdX1+ZGZnVG5Yvc9ZcQeFvusO/MFa8CSTe2LeTP/MBj1PLwuRwmSGaVlfwrmK7UujlJts1zSGQRXpDUpUTxPwG34+PKXElmZ2Sxpp/LlZD0bS+lafuXVF/EE2eYbajDnw0dHEFobgG+BukR/EPbnONrPrc8vH4z29wgC94UYVcSAtbFuaHj11Aj6G3yf/TobVVYZCjTeA9hYFvU0ws29Juh435P8RH5G/0OI+Kz0DS+7bbNS6CRUq3ibHXQW3S2Yjuv+lbe9upd1N9r0StaqhV5vZPYPc30m4+vu0NOvTwFuWMiQ32bYws0EnRu7dppeFyL8tBfIULHvAkttsxfbXUBGlXLFd0cP2e0vVCtUkvUHJwzjZkreN+tcyaToMVn+PsLVxXfMW1hCTIA+G+7WZ7VSxv4dwteBf8OqOdSm4k5rnj3hhpsvoXw1wUCRV2mHUp5c4ouqFmFQqu+KJMS8gxYHIXS3V2AOVlL0AzlJFenR5GvN+WM5OpOqcUwNGTbzTVBz0Nimneloe94b6BD5qOtVaDIgtaEvTDNPNVLxN9n8Tnh7kmjS9CT7i3aDN9o4bQjVjv7Q+qgXfDqQa56iil4MNJ0jaz3KpHQAkfZbyZG95vg5cKq8hPZCX4IFpnb71lCspiw+bV8Mf6r3lgZDjVcusuoj6Z3bNC7JXG5a1IuWLSgTfgrsqN+ZoehS/0at4KB33o9Qi8/Pt2Qz33plGcTXAwXIK7oK8S5r+NO7yuWPZBubxDKepFgfysyQAnqWW2ibP+bgX0lnAFPUP8sx8+P9OQZEnkpusSmIkqI/RGCitlNSdn4agt4w0Yvou7uX1W2ANScITBtZlr5V0jpntonJX6U/TPMP0rJkAScf/V1L9tMLcmQBJ214rj09ql7PwTBBZ2eSMTN04mJivtyQtZ8neKneLz/6fymSm6fr3y2xgZgPNTjzi6GUh8iXgAkl7UO+fPRu1zLJVFEUpt8JeVJeUfdU8PcgMeQ6dp/ChepZZ9UT6Z3ZdTbXynlmpT6i9uJqRPbD5EsFvmNklko6h9jCNwe0Alb7pZrZJswNaB+JBKliuYaT0A1XUwG7gPbjBfCngXmDxIj27mb2ce9Htjeu6D0nT1+Fu3TSqRZWKPOVmfRzPqlsZEDpAmpXU/QkwKY2ms6C3b6b2rYqfz9Z4VoVt08jxXSSdfMO+snMuTJlvntXgbuCjSVAXUZppoWCU3Bc3ldZ9UJ4BN5/jqlk5gVLMbJv0PRQZFb4GXCOPJxN+j+2dO/YDksaaF606Ve6AkSVBLcxsQC3mpGfpWSFiZk8CG8iTAmbBdH83s6tb3MW7bADJ4+TRqrsDy0jKG7HnpdYjhOL0Bpc0eekO9oV8sTzh36vA5+QBb1lkdD5tygxczTBoV8N0jK/jPfK87WfAxu8CXpW0kZndkI61If1HaI3tOQrvPPwHV7scaWbPS7pX0tzWkE5dbsidLbX5NTxtzK/Ssg/iI82DGo+TXsh59+MHcSHeSSFyjdx1vdA7zTx31bXUXkDfsOS4gatUTsJHHa/mts1KFDSeT5b9egzweLoWyNPfL5rWeUvSEpJmy9S2DVSlqSkaJeczKOyD3//n57ZtO0daRrpnJqfOwqdwp4hf2yAi1s3sKtUqW4LbHbP/5xW5c8HkdC8+jl/TjA8k+9GktK/n0vo9T8/aRAaLKqKUS9ZfihZKyjZsszTDlN5ABR5heObZ9+BG4X9U7mDgx7uCVLseT8y3F/C0NYnPaXHfq+HG1kxN8xxubC29jiqJA5FnXd4cV0M+nOYtjfcCrzWzn6d5a+AG6l3IJdxsUD2OwV9G7zCzj6btSmMk2jv7cu80vMztkEQ/y507NsjZ9mYDbrTkvCLpdFwNehH1qXUq1b8qjptqWjJgsCS70Wr4yOePuGDdxcw2HuR+G6uoYmanq0ncmUoyGzSzd/YCM7MQybwlXqcDcQ6SVhyqB7zF4zfe3HviN/RN+Ev0YjM7soPHm2hma6k+lqDUY67FfdYVAkvqQMzsxYptml53SQfiaoV50uyXcJfqq3DBkU+4+VUz6wv6VCrulcjS7J+X67GXxkhUn+3AkXSCtVDXvc199zl35Oblc4AdVrDZJ5KAKLSnmAdpFsVNXWIpWFPF9e1fwEfQf7A2c42p5jn2feAx84JSpfFRLe6zrIpq0w5DUrvvindCTsNtp9+1BrfpXmSmFSLtohIvDNwbaEge8BbaVHRz7w4skhuZXG8teMs07LdIJfCbpCe/xczWk/QP3Hj7X7yn2XadDNXXXz/PKjzIcttUxYHUXfekwsJqdR/extUnLSXclGcveN4aHpqk+lnScsbldtAgSuoOFnmizmPM7KI0vT2esLDIKQFJcwCfNrMT1STTQsMoeW48jUwWN/UbPN1OPuPEi7hgGWdmn27zfK6jluLmQ/go4c5GG9cA93kvDVVU1cQxwerjxzqa2WDEYCMg98pwfoAV03dpHpwm21fmNWqy7ez4y/3bwPezTwfO6V5ShyA3746q6Rb3OwW/4VdL53sQHvULboidD7dHXYPbf7Yb5HlMKvo9hPfCx3E35kdwG9bmwLS07Pu5e2V23NX5Wfxl9OHcPrbFvbWy7VbHK2S2054D0vdhRZ/cegfRPy/U5wd5LZbDPfr+L12Pm4D3NKwzFnf3PQOPpP9rmr8MHqSarTcnsHT6PReecyzLIbU8sE1u3dsL2nJ7+p46iPN5J/Bl4INpekk8cHcw1+hc3KMqP2+x9L1UyWdcWr5g0Weo7/Hh+Mx0I5HBqgTUPK/RQXiQ1/NpegFgNzM7VtLl+FC9znXTGuqMtHFO/UoES3oFz4EFLgiWS9OF2W1L9ttxlUArx2v83WSbpnEgLexjbjz6fjfce+Z0PL5nGTMzSfvjwn9z3G3zNDNbN21bmt221fMeKCWqp0nm6VkWBr5B/2DXlkbC8voxWM6jTZ4heHdqtd83xBN8vpKWl9pTJJ2N3+97mqu+5gJuslxcFO759X9pekm80Nv71EaqkDRCOpBkCwROtgJ7ZTuoSRVVSe/EVXaGC8InJF1iQ5TZYKTQs95Z7WJm+6fvTdvcRTMvjP3M7Pe54z0naT/cW+XdZvaxNo9bxUJ40aH8zX0jXghpMEyXV+P7FPAheQqUJSQdYGZ/yK+YDNvLmNk3i3bUIlWuzmbF9qqDaR4HUom559ZZwFmqJdz8lNV6WB/FvdreAu6VlH9u3jR3qc7vclBR/JJOw+uBP5+mFwCOtlpWhLFSLZ2IPNgw8/Q5E7ftbE3O4aHF425N8rbLnc/++OjkONxeNF3StEyAJGaxnNeWmb2hmufRcma2q9y7EfPKi/mL9RXgBkn/wf/nZYDPJ8Hejl3pNNzGeT3eEViJmhvzYDm8bIE8Pu37+IhVwDGSjrChdTkeEcx0QiRPgTEaayguU8CncaHxBdwLYwk8uC2j6gG/SW3mNmrC4UUzrUn23xbYFe+B7pt6VUviRukTCtY9EVd/tS1ErCSzcBNmteZxIACoIh1Nbvo54ARJ+6T1nwQ2xb3QMubK/Z4qaXf8f18ed3O9qY3zyLNqJkCyNsm9xzIuB86WlAnyA9I8cM+xkyUdYl5D5DpJtzc7oKTj8fPaFPdk2hnvcf8VV/vtigfbXUh/3f/TkrazentK5iX3RrIZZc/DctT34i9N1y0rE3u/1Yzpv27W7gJWsmT3kGce7mQw3/9R4gaNx5CsYamKozx78U144CxpXkczG4wYuqlL6+YH1+vehI8Qjkmf3zbZZiyuqqpa5+d44Z3N0+ccvBcJHtX8Bq5Dn0KqH9Kh81kUt1NsgxvUh+q63V2xrG0d9iDacy8e9dw4f168xnU2fRhuu3kSj37v0+mX7PcDeIbfZ4Dv5eZvhY9Ksum58MDV23F72Y/I2QfaPKc7gQVy0wuSq7uBd2I+h7/g/4oLkbFpWVb34x/4aGQN4D8tHHNKw/c8uDMGeM96U7zz8Cju1r4LqYYIFfYUPOPBdfho6Ezcu23ThmOvnPa3Z/YZxLUbtC2wYt8T8MqO2fRs1Ow3NxUsuyk3/bN07pfidYoupk3b2Uj7dL0BXTvxAmN0i9vdkL9ZCpZXPeCFxrcOnMsueEGh03B9/jRg5w7sN1+c6jXcjjMDWL5g3eXxJIDD/T9+Fc/ftVRu3tJ4upKv5ebdlf6brEDYosCV3b4PS85pT1yAHYlHe9+He0K1sm1bDg/Aren7Frwk8OzAAwXrzZqOcSbwv4Zl81BQnAovKrZ12m6hhmUDEu4tnMdb1BdTm0FDobFB7Htywbzsfjoddz45PJ3THXh8ypfT5368blDX769Of2ZmddbduAfH481WbOBB4EZ51Hq/oCvzpHjHpU8d5q6xG+Ev4VOTEXSexvXa4DvAOtZQIljSBcDpZrZHOzs1s770LEmPvT2u3rpM0g+pTzfzLTwVzbBiZr+Q9BKen6wuDsTM8v9BUTqaJQZ7fA1BnIN58NoEahlodzSze1pxJ7WUEj61YSB2v0vkmRZ+jr8ADVdrNR7jTTwf2yVJnQMU21PM7AhJV5m7Cf89t242D4pzzWXR7APG2lOJtkqV2u4/6ZNxYfrOnqGhyGwwIpiZhUiRMRpLnhYVZDfLGPrnwMpiKw6nf52SZeUBW2vjaRNOxW+qP+HeLoNhTCZAEs+keW9JWkrl6Spaxry79bd0Dh/HdcAHp8V3AztZ5209rbbteOB4NcSBNFCUjuZmAHlRs5/h5XjFwAJPH6R/nMN03IvrRNyG1hKqz0D7BDmngDQvMxAX5bk6IK2Xz5XWhzUJiLNaIOp58hoqc1iTNPKW0qqU2FMmpjYvlBwDMmP6OLw0dMaQCPch4kDgTEm/x6/xo/ioEWuSS06e2WCypI5lNhgpzHQuvhnJbbEf5sbIVrafy+o9VLL591GQgdXMnpEnEVwD19Nm7qD9ilwNFBWXCL7LPGdRW+kq0n7zWXPH4AJwYzNbfzDt7TZqSEcjry2zrbUR/KWCKP1sngpSzDfZV6M7aN8iah2RsXi6nk0btt3WzC5WmxH0qnBNb6HdWQnh7Hse3K4zBleNPUZNiLwInGhmv0vbHovHTX0S99R6CVcb7d14nJGCit2gK3PJtfu/9AIz7UikUVgkNdNuuBGwFEnr46m65wGWlOd5OsDMssyuVRlY3zAzk5R5qgwm5XUfZvY11ZcIPsHMLki/K0dOTchXh8xSfmw/iKYOO6pIiyJpTfN0NE+WCRB5HMgpwFnWUJslMY9y6VqSB1umVhvQ6M9acAdNo8u3Jc2XHymY2cXpu92XUpVrejOyRI+vyLMFP4MLwffgiSBL0+3knpvj5XFUw5Jrrh2Squ3HePLWLeVFr9Y3s5OpuVZvQ4FrtXmp4I5kNhhpzLRCBCC5Te6OxwVMA85rYbNf43EDFwGY2Z2qL0FblYH1nOSWOX96QPfBVR7ttr+vzrx5nYjz0/yNlOoeZMPsot5TM0Zyb3AAfBmPdSgK6DTc7jBBHhT3N+r/s/PxUd3ewO3JTnEqnjgvGyl0LM6hSNDVNbaWf+0l4C55qpL86PKLad4nGkYUf7GUMLKCKtd05IWmvkZ/F9XNKLGnJIG3I+4g0PK55oT7SOOP+P//nTT9L1xwnEwT12pJ2wK/wK/pMpJWxwutNVOfj3hmOnVWehgqk+412f5WM/uA6qPU84nqrinYzHLD2i2oFXr6h7VZcS7tq2mdeXmswxnUlx/d08ymtrD/ZfE6KevhL4ab8VoQbdd7GEpUEgciD5Jc30pS4Es6tWC2WX3J4zF4L/M4XE15Kp5H7Fl5tcuiOIeBtj+7d0prvqf1ilQjls51spVEszc59s9xAZGPPXnEzL6Slt8JHE9/Ne3Ehv3MTs6eopLa4q2e60hA0ixmNiOnpsw/+5PNbHU1ySWnLmQ2GDZsBLiIDecHjya+jlxeIODBAWz/Vzyl8x24YfyreE+vG+fSL+9Qbtld6fsmcn75eOnem1rc/y24YXiW9PkUyRW0Yb2O+eIP4lpUuooyiFxc+IvtV7ib5m/xGJKvkFw+0/2wOx2Ic0j7Ox+v455Nr9xwLocUbHNI+p6Iq0yy+Uu18v9Q4Zqe7bdi29JcXriTwdt4FHk/V9tm5zoSPtn1A67F3ZWz6fVoMZcctfidSbl5HYkR6/an6w3owg3xcUqS7rW4/UK4/vNJ3JPkT/hQNlu+KD68vSxNr4RHfA/Fufy7YtkD6fvOgmX95pXso99NXrK/SSPgf62MA8FVCTtREBuEe1JdRQqkxIXGd9PviWnZ7jT4+acX4ICDVls4l35Bm/l5RUIh+w+Aj+FBf2eke/NhPDfVYK/v4XhFx8VoSCBIcfxES/dEs3MdCZ/ctV0TTyf0Qvr+V7pXxuIj9Kp9nJzuoSl4TNUxwPHdPrdOfGY6dVaGipPuXWAtFqmq2O9lJL2pma0mz7M0yQaRgrriWH8GrrbiOvNbmOcsugAfNeXLj65lZju0sP+f4QWh/oKrs3bFe5k/BzCzZ9N6PzSzflXzhhNJt5nZukltsCne473XzFZMy7P6MW/hhuA+N1552vCv4XEddaoGSctahfpOBenBO3Auf8ZtHflys/PgdrjdcQeK63ObzAu8bSn2QtJCeC8ZvAdcV6ir5JiNHmEAWEoQmJYXLLZl5XErq2bXINlTpljyTJO0HZ6OHVydk8WylJ6rme3WrM3DhaRH8UqX4B2V2fH753U8yecvs/uvYh9z4baUPlU2Xn2zLdXnSGKmFSJ5VEu6t6uV108o9L/PsOTvXaU3Tb9nw3u+4PrzNwfR7kWBC3AvoH515s3zXS2Alx/NPLeuBw63Yk+jxv0XvTgyzEZQBtLBuIo2+8+abNsvg/JgkWei/Ry1F+943BazKOXVNV83s6llBmtrYqiW53rKmAN/HhY0s++30N5Se4q8KNU6+OgdvNM2wcy+VXWuI+nlKulx/PqraLmZ/UDSr3D19tnUOzuMRAeBjhJCpEVKjJl9WHKtlNe+3glXpawpL2L1MzPbWNImuMfOQ/gNuQRe9nVQSdhUX2d+qrVeZ35UooKyxJKE93KXMbMjJS2B14K4LY0evwCcm/6znXEV5JYtHOsaKtKDD+IcBuQOqiGoeqhUvTL9ngv3dFsyHWd54L1mdklyOjiAWjblK6l5Z00BVjfP5JCNUiZZfbGmEe36qhbKErTgUDMkFRxHAiFEOkzqCR6Dv9TvxqOZdzazKUnVsnv2sCRPsT/bACsOttiOopu2j1ZecumB35r+mY6HrMLeQFGLZYklHYcbeDczr1WxAO6qu07yQjsBN5A/h7t772EtZEHWIINWS/a5Ha4ynM3M+rmDJtVc9t/OhveAX8ZLz5Z6oTU5Zv76ZYGln7Oa12FlXZCK/U4BNsmpPhfEVVpZLZ7Kcx0JtOLd1sI+fsMQVHAcCczUcSIDodWXsnlN743x1CaiXmU1a763ZWb/UkO68g7yi/S9I54jLNM574Y7BbTCxXjixbsoqJHRSdXcIGglDgTgA2mUMQn6gulmS78fBD6c7GRjLJc2Jb0sv4L3lPfL98DTtm0LiwoOw4sbXZuOMVlSXwCiFec0W888fcjv8KwIAyV//bLA0l1y80rrgjSxp/wEmJR66sLVVnlVXOW5jhAKVdx5VB2ICF60K5/Z4OKcGrWpu/1IJoRI6/yiaqGkzczsatWnCgFYQRLmgWsTJJ1EvRFxwhC0te/lJuloM1s7t+hiedBcK7zbSlKyFKnmJA1aNTdQkmplDO5NVdUDfzONrDLj78I0CEbz4lSNnIr3wLO4hceAc5NBuKhTMZC8W6Vttf6Frgo7MMmY/Td5TrNvAldJ2omGuIxmWPMibVV1QfL3V589Je33z0nFu07a9huW6qsnWj7XbpGNoprwR8oDEaGDmQ1GGiFEWqRZj1PSD/CqZtsWLDbcHfRzuE99lnTteuD3Bet3krmV8zBKvbxW061cJukjVuyxdjTwkUbVHNBx1VwzWuyB/xZ3QlhE0o/wJIGteJQV9sDzo4EhoLLQlYpzmmU69QPw0dkMSa/RRKhJ+nJVQ3Kqy8PwwldLSDoTTxr6mbTOMw2b/TqpbjOj/Pq4Y4fh75wLWj3XkY5SICKe4v4ceSVQzIMT38qt2ukKjiOGECItohbScKfvKm+gA9ND2WdTkHQIHhU+VBwKXCvpQfzmXYqU8bUFbgEuSD39N6l/IQ2naq4VKnvgZnZmerFtjp/Hx621hIuVlfmGiIPxHu3ruGD+B/WpQ0pzmrUh3LL134uPFi7KHaOvKqCZXSnpDtx1WHhw4/+g1J4yS1p2LJ5DK7MFHCDpw2Z2UMG5npXO9YcDPIduchseP/Ky3MMtu0/Www3nAFjnKziOGMKw3iKSFjOzxyUtVbLKTnjyxZMbttsXmNfMfl3k5dEJo10zVJ+W4z4za+klmHTd2+PR79aw7BRcHZRXzY21XKqQ4UQlcSC4Si5Lr96PnMG3LGXKR/CX3ErAFXgPfG8zK/LG6Tqqr9VROq9gu/HA1pk9SJ5W/+9m9qGq7dK6+WuRCbVfmNn98qzW78vun9QhmYqPGg/EBcxdwMmpR99TZM+vKhxqcuu2U457xBNCpEOkXu56jcblZLz9Nx6pWhkkNoRta1pbvGS78bhnTZFRfXZcNZePPzm2VQE1XKh/enXlv61W52UT/BpdCmwJ3GBmO6d9vINaD7yl4L0223pRk1WqbFlj8RHtNfi55Ot3XG4p6LLi2PfjAYOvp+nZ8YDB97bQ9Kr9XgIcZMnTLXXCfge8go9ur8ev90Nm9qXBHKsbqIVAxLTeGXgZ4cnUco+ZjYJ6IqHOGiAqKWCEp07p551kZm9IehW3ISxEvRfMdFy4DGV7C1+QeIR+Mx7EVWGXUR8D8cv0sqlTzXWT5ClUFAfSNL06FdX11LwyXydZH0/H82fgVvoHt63dbwsffe2Le+DtidfvyAe4vYi/tJtxOnCbPMMBeHqgZjVIWrGnzAvcKy/+Zrgn1gRgY9zV9w+STianOusxxuIG8sb/aq6G6bXpcGaDkUIIkYFzFAUFjCTdJWlRM3uyYf6iuAfKtdQ8fIaTwZQfnZY+s5FLCw6g/hUcgVqajC5wLCkOBLcfvIQ7LfS5VUpanP7tHU9xdb0l1Vplvk7yTmAL3A17d1xw/dlqGZf7nDuSuukQPE39X4CjzewpSQeb2TEDPbCZ/Sh1Fj6YZu1tZpMa15O0CLUR7btxvX+VPaUs4v1EUocqGaEH2uSRwuNmdkQL67VbjnvkYyMggVcvfYAbS+bvSa2HNW/6bALcjkelg6tEbsdfcG/gw9oXh7i9t6XvifgLULhdZLD7vQ8f1SyCZzZ9B7lElF34X7LMqpNy8+7M/f4Zrqu/FI9/uRi4KC07Fg/UOxBXPU7CRwLT8BHYg9QE6p3AF4bhfGbHvZ+ezh8Pd539YWrL4cACDdvNjXudnZCmlwe2qTjOZrnfyzQs2zH3e7t0bV5Ox36blCgRT1Uyb27deYHxuemlgA+n33Om5W/ho6Qss+8MCrL8jvQPrSeavAYPZP0HLmwvyu6/Xv/ESKRFcm6VZQWMTpf0NHAEblwz3ID4fatVOvwdntvpXHx4uye1YL2horS2eDNUXfKzqoJjN2gWB/JxPEiwn83GKqrrtduzb5dki9gaH40sTc01OctRtSMeXb+KFRcYOwX/nzdI04/h99slBeuCxz9lzh7n5X6DC6Pz0+8j8U7QP80NyZviyTzBc3rlYx3eSPOQF1/bHxd+y+Gjl+PNbGzZNegxWlVrHj6UjegmYVhvERUXLsowa8ErSdIEM1tbubrqw+GdlTv+0gyg/KikK0hFu6iV/ByDv9h2wfXBRRUchx1Je+CpJNbEdfk74wGI56bll+EV//J1sVtNmTIsXjWSTsc7IJfiNWrublj+Nn6tZ1DvZp7PSJzdY333lXJF0wqOmV+v7l5sWJbt905gDXP1353mmaq/g98PeXvKOWb2Y0mTcTvIrbl93WVDkNU66A4xEmkR60yp2FeSt9ZkSUfh+tExHdhvKXkjsJk91DivCUUlP1/E1RMZeWNvPs3IsGLN40Bewa/7VdSE3mb4S7s0ZUqZVw2tOSYMlE/h6qJDgC/m7ASZkGjlXhloXIuV/G6cfl5eYnk8cKakp1JbsWp7yuvmziWk9sxScJxRi+rznNUtYvCZDUYEIUQGiKTT8ECr59P0ArhRs5X4iE/jQuMLeBDgEnh8yVC0cw7cQ2QwhuHM2+xxSVvjJT+ftuYpMoYNSeOsFgfyFLWgNiQtaLWUFZkeOs9ENU+ZMmxeNS0KiWYcTv/I8qoO0LLJtVi531CLqs7YHo+KPxT3gpsP1/NnPGdmfUGzknY0T/VznaRvA3PKS0N/HrdHzRTY0GY2GBGEOmuAFKmfWlFJJX396Wa2x5A2sHa8Q4Av4S6fj1ETIi8CJ5pZU7dPSdvgfvxL4IFU44AfmNlFkn4MHNUgTL9iw1ycqpU4kNy6hSnHq/4/DUG9kKFmIHEtKslCnGEV6X6UC55VQyBtNp2E9L7UF2M6aTiEcjA8hBAZIEknvImlok6pB3xdpuNNhtGd6K9DP0LSDbg3zLAlXBsqw3CJMG1ad6FbSNoWNyL3Szku6Re4s0G/lCkaonohQ0WRqnIwcS2SbjCzjQrUMgLmygzkTewpCwOY2dPttCEY2YQ6a+AcDdyceqjCDbg/yi2/EPedn0h/XfSDwI1JZZCvfjZkAXtmdsxADcPJC+gBM/tDw/wDcDfQb+IJ82a3WoTznLhbateoiAMBV/M0phzPRilZ0sK35IGheX314cPR9sHSIfVlP8xso/TdTy0jz6XVt2rjppIOx1W3Y9L6bwHHWGtxFUGPEEJkgJi78mZ1vMF96e/JrfJuM/tYyeb/SZ8x1BLfDSltGoY3w117GzkRj7D/Jl7u9Kqc19redDEbqbwe/K7APdSfZyZEilKOvw3Vemszu06eqmN5M/unvL7ISHRPPYCa+nIi9erLViLWK1Fx7rEqe8qKwDPAOmY2Le1jWeA4SYea2a8G26ZgZBDqrDZRfeQuVqsTcALe27qrYtu5zOyVoW8lSLqXARqGJd1tZiuXLJtqZu9Pvz8GfDgtutLM/jHoBreJGnI/FSw/GbgKF4A74SnHZzWzA6XK0rl9cQ5mtpw8E+vx7aqHhpohVF8+hNvGnsOFxPzAs2n653j9jDwn4UWZ6uwxSbV1RTMbYtA7DKl76WhE0naS/o1H7V6HR0Hng+42wr1+7pc0RZ4OJQtcW1/SPXi0N5JWk6fKHkqydAsD4dX0sqwjzXs1N+tePLnfV4Hr5ak4usWDeJnYMg7Ggyaz9Oov4j138Ij19fFUI1BLmQKeZHLDtD5m9m88Sn+k8kT2P0j6rqTzVRIHk0fSCpJOlHSFpKuzT26VK4GtzGwhM3sHnq3gfDxg9rNmdl3+g7v29jPoJ7tIN0sGBB0m1FkDpypyF/zhKuPXwEdJrqZmdqekpqm2B8lCwD3yBHitGoa/jxek+iGuGgF3df0W6cWr/pHIiwPH03oEb6cpigPBUpbUNPL7DrXKc3lKS+fSe3EO3zOzcyVthI8Sfw4cB3ygyXbn4v/fidTUgXnWM7P9sgkzu0LSL8zsgORM0kiV80hPV/IL6gkhMnDeNLNnJI2RNMbMrpH062yhmT2cHuDlzezUNHyfJ7f8kQa9fNED20kOH+gGZnaZpI8DX8N78OAjmp1yarqDSJHIaZt/JxVftyiKA+lD0sX0f/m/gOc7m6HylCnXqbfiHLL7aWs8f9bfU2egGTPM7LiK5Y9L+gae7BHc/vRkum79SgUAq8kDUxsROTVw0PuEEBk4pZG7QJZ6fW08s+mp+ND9T7hK5JHkKWXyKoCH4CqhISMZhhells32NjN7qoXt7sbTnJQxonroZnaaSuJAEg/ihYKyYMRd8WR/K+A94wsoLp37TTzO4S7ceH0pru8fqTwm6Q94NuCfpVFCK2rriyV9Hr8O+ZFcFqy5O14i929p+sY0byye8qQOGz25sYImhGF9gMhrIr+KP5hZ5O6ZlupMy3MFrYFnlc385KeY2aqSFsILB30Y75FdgUe/N9ao7mR7d8FVGtemY34Q+JqZ/XWQ+z0KeB7XiR+M99DvMbMiddGQo4o4kLT8djNbp2Gb281sHUlTcWN7ljLlKmutdO6II3mPfQyvRvlvSYvhyRqvaLLdtILZZg2p/ZO9xaw+B9kK+Ki10b26KylwguElhMggSELhmbznk6TbzGxd1SJ25wZutpRwsQttvBPYIht9JFXNP60kId8A9ivgs4yQSOTkdr0ZXugoE959XmbJS+2jOS+6JXGPsvcmx4dNGnZ5ipl9XNJdFIywuvV/tkIypG+Et/tG60BSTEmr4G7hmavv//ASB3ene+x43H7Wp541s4n9dhSMOkKd1SKS1gN+irs1HgmcgRutx0ja08wuT6uek9QJ8yfj8z7Ac5J+W7ZvG9oSmWMa1FfPMEivvKQHn2pecvXEweyrg5TGgSS+Atwg6T/U8kI9mYT84rhtJJ8yJbtG2wx1wzuJpO8Dn6CWwv1USeeaWaVdJI1gvoyrA/dPnnjvNbMshfwfgC9bqi0vaRM8Jf0GNLenBKOYGIm0iKQJwLdx9dUJwJZmdoukFfHqc/mUD1tQ30N/V9W+zWzIgvTk0eerUm8LuMvMioIJq/bTmBvpQuDgrGffbVQRB5JbZ3Y8CA7gfjN7rcV9vxN3IjDgdjN7opNt7yTyeJnVsnNLdqLJ1qRWurxGzkRgTzNbOQmVm8xs9bS8Xzp51VLBH44nvyyzpwSjmBAiLSJpcu6ButfM3pdbNmkkB0/JC2ptlCavN7MLqtYv2cekBkE5Hrf93EZ9Cpeu5JRKL73vUC+8j8wLClWkf1FJyhRJn8Vdnq9O+90Yt7WcMsSn1BbyXF87WC0x5vx4TrBK+4Sa1CGR116/Ax+Bg7u1r2VmO1TZU9II/hjgfXiJ5bHAyzYKUqAHTqizWievGnm1YVmlJC5xL61tPAQvXknvARY1sxvNU3Kfn+ZvJGk5M/vPAHf594bp73WinZ2iSRxIZfoXVadM+RpehClznHgHcBNeQXDEIOkYvM0vAFMlXZmmt6BW77yKZnVI9gF+QE1Ndn2ah5nlU8Y30o1qnsEwEiORFpEnj3sZ743OiQe3kabnMLPSKFwNIt12u0i6BPiWNaRfSQbSH5vZth04Rr+cUmY2fbD7bbMtVXEgf8Drphemf1FFyhRJN+FZm99I07PhxvsNGtftJpKq3LGbqkyTCva7wEq41+CGwGfM7NoWjl1qT1GXq3kGQ0+MRFpkIH7v6UWT9bbuHwoh0QKLNgoQADO7S14md1Bo5EWsV8WBnEgt/UtRXZAsZUqfEJH05fTzAeDWZAMyvDhTS+WFh5NMSMiz+b4nzX6gVbuPmV0pz8qb1SE5xHJpS5oI6Y2A2ymu6z7s1TyD4SWESIdJXiun4Tm1hFeYm2Zmmw2zu+j8Fcvm7MD+R1rE+gYNcSAXN8SBPEV5+peilCnr4jnRsszLGRcO5Um0izzY88e4iulhavfeqcB3zOzNqu0BksquUW2ZUSWkNzOzT0jaLe3nFdXc5IatmmfQHUKIdJ6jgY9YippOgVjnpmXD6S46QdJ+ZlbngpsMxS377xeMqrKX0YiKWAfmkbRkQxxIlm7mDarTvxSWzh1Kr7kh4Od4eYFlMpWipHF4AOYv8OwIg6FKSO9dYU/5H/BGGhH9ILmHd7XuTNBZQoh0nlktl3bDzP6V9crM7OFhbMeXgAsk7UF9EsXZgB1a2UHJqGov80JP12lk5ZQqigP5fIoDOa1KpWgVKVMkrY0b6xs9t0ZasOE2wAp5m4957fnP4VmjBytEqoT0f+lf1/0zadlVeIaGLMJ9TtzmMqJsSkH7hGG9w0g6Bffk+lOatQducN4nudr+DE8lrvSxoXR3lGcZzmqDTDWzq6vWb9h2IrB7w6jqz2a2lkZg7eyiOBD1L+vatzrp2qu6dO79uIfWXeQ89Ia5Q9AUSf8ys0Kvp6plBeuW1cnZCrd51QlpPJ3Ofrjrb7+67nnX+Nwx+s0LepcQIh0mvcgOIheXAfw+qX4eALa1HsnLlPeoKZqnEVY7uyoOpMl2pSlTlGqMD1GTO4akv+HxIKc3zP8UsEszN3JJ2+Gq2Hfh9qOlgHstFSBL6ww4WFPSjXhQ6h1pei3gd2a2fqvnFoxsQoh0GEmHmNlviuZJutHMNuxW2wZK2agK+D9ytbPx2Iqu1s4uiwOxFlLKSLrFzNZTfaBdljRzc2A3XC2TN8ifX7K7riAPljwfj2HKqy/nxIMPH2uy/Z24IK2rk2Nm++bWWRl3Ac6PVCqFtKR18PTx/8VHKe8EdrXIqzVqCCHSYdSQHiTNexD4Kh7t/E48nfaIfSFllIyqZsFVWPtbQ+1svMphV2pnq40ywLltq0rn/gnvfU+lps4yM9unMy3vLJI2wys4gmdVvqrF7bJ4jjvx4Mq3VR+xfhiepHIlPB3+lsANZrZzC/ueFS+NAPXOGcEoIIRIh0jujbvjL9zrc4vmxR+gsvrjI/aFVIS8+t8WNsJqZ0s6F/iimRXFgTTbtjRliqT7rUneqdGApH8CHwd+gicWfQpYx1JQZXJPXw2YZJ4va1HgT2a2RW4feXvKBmb2l2QH7MdI7TgFAye8szrHTXgg1UK4bjljOjDFzGZ0pVWDQNKGuGts3jPp3Y0CBNwuknqc3aKdMsDZOlUpU26StJKZ3dOxlo5Mtgdew2M5sjo5efXkq2l0MiO5Dj+Fx3yU2VP+h6uxijIjGLX0KUGPE0KkQyRvnYeBUoOhpNPwSODn0/QCwNEjeCRyMv5SydeJqPLu6mbt7MPb3VDV0djr44GI03DhlHl1jTQX30FhZi/nJoviYybIkzmeiN8PLwE3p2VH4p5ZdfaUtN+9h6zRwYgg1FkdRhVZS1WQM6ho3khB0q1m9oGGeVkOsX6r0ySH2EhF0m/oH439Ii5YFsMFaR0jzcW3XTLvswJX6FL3c3nanHFmNiVNl9pTJB2Cl4mejgugNYFvWpNKi0HvECORzlOVtXSMpAXM7DkASQsyAv8DeWU8gGvk9UjOp6YiWsc6UCmvU7QSB9LCbipTpmQCIwUu7oB7a2092LaPBDL3ZTObt2h57l4oXJbuheclzYNnPT5T0lPUOhr7JM/EjwLvwNOgnIEHHAajgBH3AhsNmNkDksaa2Vt4ZblJwLdwvfHNyQgsYGfgR11sahlHN0yvnfttuCvoiKDs5TdAqqKx35S0A+408VHgPDzoblSROjSNTKd2L8yB3wd34vfuqtTUfVX2lCyH1lbA6WY2VaovPxn0NiFEOk9p1lIzOz0Ftm2a1t1xJBpszWzT5muNKopSppyYYk+Wxt1+T8dHYaNVx38Hbih/Dr8G8wNPAE/iEenfAda0lBk6xYwcDk3tKRMlXYFf029Jmpf62jxBjxM2kQ4jr7HxJG4PORTvlR1rZg/k1ilMLTHSkPRj4KgGR4CvmNl3u9qwIaAxGhvP7Hs9XlMji4d50MyW7VIThxRJJwJ/NbN/pOmP4MLzVOA3wDz56PW0zstmNneVPUWeHmd14EEze15e1GvxzJ4S9D4hRDqIPEPp6Wa2R8nypqklRhIljgD9gilHAwUpUzKV1ifwNOh/Ab5vZkt1pYFDjKS7zGyVhnlZ1P5k4F7czpHPXjCPme1Wsr8Vzey+MpvKSLKrBYMj1FkdxMzekrSUpNksVcJroNQVcoQyVtLslir+yTPdjro03k1SpnwzCZjdgFklXQZcYGYndKWxQ8fjkr6BC0twD7UnU8fobWBv4HPUsgGPx7MUlNlTvoKrwRrtazDC7GrB4IiRSIeRdDru3nsROVdYM/tls9QSI430UtkWV2mAv0guMrOjuteqztNqypSkmvkw8MkRHNvTFpIWAg6jluLmRrym+gt4ivwHVJ4u/yH621Oex+Om9gOmmdmzQ38WQTeIkUjnySrhjcFTnuSpcoUccZjZz5LA+3CadWSmMx9lVJXO7cPM3sZdU0ede2rKQnBwMnybmb2UW/xAUsX+HLf1LaNcunzgSvrbU87EU8Ufi5ceHnUq0MCJkcgQIWmulE4jP29uPMvqGGqukGealyUdkSRHgeXN7J8px9RYS5XzRguSrsGNvwNOmTJakLQK7oGWqab+B+xlZnen5UXp8u8ys1VK7CmvmtmcyZ5iIzWgNhg8MRLpMJLWx9OFzAMsKWk14AAz+3zOFfJtSX8HnmmmQukmkvYD9sdfLMsBi+MxEpt3s11DwOHdbsAI4A/Al83sGiCrankCtQqEb5rZCw0hHtm9W2RPeVteO2T2tL81qMWMhGF9FBEjkQ4j6VY8iPCiXI/tP8AjwLO4cf0MPGHgGGBPM7u8S82tJPUi1wVubex9drVhw0zqCHwwTV5vZnd2sz1DQZFtTvWp4KvS5RfZU1YHZuCu7K82HM7MLAzro4QYiQwBZvZIQ49tMdyzZT48geGWZnaLpBXxfE0jUogAr5tXZARA0iwUpxjpSVpJmZJyP+1HLevsnySdYGbHDFc7h4kHJX0P7+CAew0+mFt+MB5w+DpwFp4u/4fQ1J4SjHJCiHSeR5JLqMlTox+Cv4yvAJB0hJndApD86LvY1KZcJ+nbwJyStsANpRd3uU0do8WUKfsCH8hUkZJ+hmevHW1CZB/cGysTltcD+0iaAzgQeA9eZ359ayhr0GhPkVRnTwlGNyFEOs+BeITv4sBjuCdPPiK939B+mNrVDt8APou/PA7AK9qd1NUWDT+iFjtC+j2iJX87pKSg/UoJSzobeBMXKlvi7utfalitmT0lGMWETWQYUC19uvCa15nX1ohNn56CzKaa2YpNVx7FSPoysBdwQZr1ceCPZvbrbrVpKFB5TZXNgWXNqzzOAtzWmLGgmT0lGN3ESKRDSDqG8lHF71P0c8+Qou/vz2e3nRlJQaLXUjMa721mk7rYpKHiQfrXVJmOx3icCHzazGaUqF8r7SmSFqe+OiZmNr7TJxB0hxiJdAhJe1UtN7OianEjGknjgTXw+Il89P2oj5+QNM7MXixJ6cFoi8BWqp/SOI9akGDjSDrvfLAAbk/JBO31wOFm9lyyIe0K3EN9SplRfw/NLIQQCUqRtHHRfDO7brjbMtxIusTMtpGXxS3KUDuqsvmm1C8ftfqaKv8ws/cVJeIcwH7vB1bN8q8Fo49QZ3WIEp1yH73Y8zKz64oi1rvdruHAzLZJ38t0uy3DRFFNlc+nLAuVo+gKe8oE4CFcJRZCZJQSI5EOUdZrz+jF3ns+Yt3MlpO0PHC8mY22iPV+lKUwzxiNEddqqKliZq+1uF1VjfptcO+uq6hPKdNTNsKgnBAiQSkzc8R6yqcFJWVhzWz9brVtqJBXK1yJ+oJpp7ewXaE9xbxG/aN4kGIdvWgjDIoJdVaHkHSOme0i6S4K1FpmtmoXmjVYRnXEehWWSgRLOp+SsrCjCUmHAZvgQuRSPCbkBjyIsBlVNeqfxkcoK6Tp+83szQ42PegyIUQ6R1asZ5uutqKzjOqI9RZ5byZAAMzsbknv62aDhoidgdWASWa2t6RFqVUxbEaVPeVG4N+4bUTAEpL2Chff0UOos4JS5EWY9gU+gr8A/gGcNJIzD3caSX9mAGVhexVJt5nZuinl+6Z4jMi9rQabltlT0v52t1TIStIKwJ/NbK2On0TQFWIk0mEk7Qj8DFgEf/H2+dN3tWFtYF558W/A38zs6W63p0uUloUdZUyQND8eWDgReAnPEdYqywPvxe0pq0nK7CmzWq4Sopn9K+WUC0YJMRLpMJIeALY1s3u73ZZ2kRtBDgO+gKerBw8UO8bMjuhaw7qESsrCjlYkLQ2MM7MpLa5faE8xs50lnYLXaM+P5MbaKCsvPDMzpvkqwQB5spcFSOJQYENgHTNb0MwWBD4AbCjp0O42bXiRl4WdTErXL2l1SRd1tVEdRNKajR88G+8szdycc+yM59h6wsz2xm0r86Vln8Oj1b+YPvekecEoIUYiHSKpsQA2xut1/416v/jzCzYbkUiaBGyR6kTk5y8MXNFu9HIvooqysN1tWWfohCvzYO0pQW8TNpHOsW3u9yu4MTrDqNVp6AVmbRQgAGb29Eyoz64qC9vzdMiVuciekpXFHU0u70EBIUQ6RBrGjxbeaHPZaGSqpN2BsSli/4vATV1u01DQtiuzmX0+/Txe0uXAODw+BEaXy3tQQKizOoyk04BDzOz5NL0AcHQvGRJz9U/6LWKE1j8ZKlK+sO9Q7+Z8ZKspQXqFdlyZW0kNI2kZ4PGcy++cwKJm9lBHGh50nRAiHaYo4+lgsqAGwXAgL4P7OeBDadZ44LgqYdmKPUXSBGADM3sjbTMbcGNjmpSgdwl1VucZI2kB83KjpHoUcZ17jGYeWL2YlbkK88qFxwOXturK3KI9ZZZMgKRt3kiCJBglxMut8xwN3CzpXLxXtjPwo+42KWiD9YFH8LxPtzIK66rnSa7MPwdmA5aRtDpwRIvCssqe8rSk7czsonSc7YF+ThtB7xLqrCFA0vtxV0eAq83snm62Jxg48hrzWwC74eqZv+PpOqZ2tWFDxGBcmavsKZKWA84E3oUL4keAPc3sgSE4jaALhBAZIiQtQn1K7Zm2Tnmvk/JC7Yb31H9gZr/rcpM6jqRbzGy9vP1O0pRWXHFbsadImgfAzF7qfOuDbhLqrA6T1AJH4z2vp4ClgHuB93ezXcHAScJja1yALA38Frigm20aQtp2ZW5mT5G0NX7/z5HF28yM6XNGK5H2pPMcCawH/CuVVt0cuKW7TQoGiqTT8QSEa+Kjj3XM7Egze6zLTRsqDsZf9K8DZ+Hlbb/UyoZVqWGScNk17V/AJ/COVTBKCHVWh5E0wczWlnQnsEbKhHunma3W7bYFrSPpbWqxMvmHpGezMheRVFEHAu8B7gJONrMZA9xHqT0lU4nlvucBLjOzD3b4VIIuEeqszvN8elDGA2dKeoriwL1gBGNmM8so/TS8Bvr1ePbd99HiCCRHVWqYV9P3K5LeBTwDLNZ2a4MRRwiRzrM9/uAcinupzAeE/jcYqayUeWBJOhm4rY19VNlTLkl5tX4O3IELl5MG3epgxBDqrCFE0kLAMzNTJcCgt5B0h5mtWTbd4j7yqWHAU8P8sDHaPTkqzGFmLwyy2cEIIoRIh5C0HvBT4FncuH4GsBDuvLCnmV3exeYFQSENedIEzIlnoW5q+2nFniLpIODMhlxyu5nZsR0+laBLhBDpEClH0Ldx9dUJwJZmdoukFfEgtcidFYwqJJ1NvT3lITP7UsM6k81s9YZ5kUtuFBE2kc4xi5ldASDpCDO7BcDM7mswOAY9RCo29jNgEbx3Pqq8swZJK/aUsZKUqXRTJoDInTWKCCHSOd7O/X61YVkM93qXo4BtR0HJ46HgzeyHmc0o6SxdDpwt6Q9p+oA0LxglhDqrQ+R0y3m9MsyENThGE5JuNLMNu92OkUgr9hRJY3DBsXla70rgJDN7a7jbGwwNIUSCoAJJvwHeCfwNj+YGwMx6qdxxEAwZoc4KgmrG4b3rj+TmGRBCpAUkTaO4xvqyXWhOMASEEAmCCsxs7263ocdZO/d7Djx31oJdakswBIQ6KwgqkLQCcBxeF3xlSasC25nZD7vctJ5F0kQzW6vb7Qg6w8ySHygI2uVE4FskTyQzmwJ8sqst6iEkrZn7rC3pQEIDMqqIPzMIqpnLzG5rcF8dUJbbmZyjc79nAA8Bu3SnKcFQEEIkCKr5XyrxmgXL7Qw83t0m9Q5mtmnztYJeJmwiQVCBpGXxNDYbAM8B04A9zOzhrjZshCPpy1XLzeyXw9WWYGiJkUgQVGBmDwIfljQ3MMbMpne7TT3CvOn7vcA6wEVpelvaSzcfjFBiJBIEwZAhaTywdSZ8Jc0L/N3MPtTdlgWdIryzgiAYShYF3shNv5HmBaOEUGcFQTCUnA7cJumCNP1xvCRvMEoIdVYQNEHSysBKeMQ1AGZ2evda1FtIWhP4YJocb2aTutmeoLOEEAmCCiQdBmyCC5FL8eJLN5jZzt1s10hH0mZmdnX6vYyZTcst2zESWI4ewiYSBNXsjKcxfyLl0VoNr14ZVPOL3O/zGpZ9dzgbEgwtIUSCoJpXzextYIakccBTwBJdblMvoJLfRdNBDxOG9SCoZoKk+fEcWhOBl4Cbu9qi3sBKfhdNBz1M2ESCoEUkLQ2MS0kYgwokPQ+Mx0cdH0y/SdMbmdkCXWpa0GFCiARBAZJWNLP7kmdRP8zsjuFuUy8haeOq5WZ23XC1JRhaQogEQQGSTjCz/SVdU7DYzGyzYW9UEIxAQogEQQmSxgDrm9mN3W5LEIxUQogEQQWSJpnZGt1uRxCMVMLFNwiquUrSTmqoShUEgRMjkSCoQNJ0YG7gLeBV3LvIzGxcVxvWI6Qa9V8DliIXUhA2pdFDCJEgCIYMSXcCx+MxNm9l881sYtcaFXSUECJBUEFSY+0BLGNmR0paAljMzKKwUgtImmhma3W7HcHQEUIkCCqQdBzwNrCZmb1P0gLAFWa2Tpeb1hNIOhxPFXMB8Ho238ye7Vabgs4SQiQIKpB0h5mtmffSknSnma3W7bb1ApKmFcw2M1t22BsTDAmROysIqnlT0lhSvidJC+Mjk6AFzGyZbrchGFrCxTcIqvktropZRNKPgBuAH3e3Sb2DpLkkfVfSCWl6eUnbdLtdQecIdVYQNEHSinhNEQFXmdm9XW5SzyDpbNwza08zW1nSXMBNZrZ6d1sWdIoQIkFQgKRxZvaipAWLlodhuDUkTTCztcOmNHoJm0gQFHMWsA3eizZSkGHuOwzDrfGGpDmp2ZSWI+elFfQ+MRIJgmDIkLQFXg53JeAKYEPgM2Z2bTfbFXSOECJB0ARJi9M/bcf48i2CPJLeAayHj+JuMbP/dblJQQcJIRIEFUj6GbArcA+1tB1mZtt1r1VBMHIIIRIEFUi6H1jVzEKPHwQFRJxIEFTzIDBrtxsRBCOV8M4KgmpeASZLuor63E9f7F6Teg9JiwBzZNNm9n9dbE7QQUKIBEE1F6VP0AaStgOOBt6FJ2JcCrgXeH832xV0jrCJBEETUpzDkmZ2f7fb0mukeiKbAf80szUkbQp8ysz27XLTgg4RNpEgqEDStsBk4PI0vbqkGJm0zptm9gwwRtIYM7sGWLvbjQo6R6izgqCaw4F1gWsBzGyypIhWb53nJc0DjAfOlPQU8HKX2xR0kBiJBEE1b5rZCw3zIhV862yP16Y/FB/N/QfYtqstCjpKjESCoJqpknYHxkpaHvgicFOX29QzmFl+1HFa1xoSDBkxEgmCag7GPYleB/4MvAh8qZsN6gUk3ZC+p0t6MfeZLunFbrcv6BzhnRUEQRC0TaizgqACSReT0pjneAGYAPzBzF4b/lb1DiX1WKab2ZvD3phgSAh1VhBU8yDwEnBi+rwITAdWSNNBNXcATwP/Av6dfj8k6Q5Ja3W1ZUFHiJFIEFSzgZmtk5u+WNLtZraOpKlda1XvcCXwVzP7B4CkjwA7AacCxwIf6GLbgg4QI5EgqGYeSUtmE+n3PGnyje40qadYLxMgAGZ2BbC+md0CzN69ZgWdIkYiQVDNV4AbJP0HL6q0DPB5SXMTLqut8LikbwB/SdO7Ak9KGkvE24wKwjsrCJogaXZgxTR5fxjTW0fSQsBhwEZp1o3AD3DnhCXN7IFutS3oDCFEgqAJkjYAlqa+PO7pXWtQDyJpXrwi5EvdbkvQWUKdFQQVSDoDWA5PwthXHhcIIdICklbBr9WCafp/wF5mdndXGxZ0jBiJBEEFku4FVrJ4UNpC0k3Ad1L2XiRtAvzYzDboZruCzhHeWUFQzd3AO7vdiB5m7kyAAJjZtcDc3WtO0GlCnRUE1SwE3CPpNurL427XvSb1FA9K+h5wRpr+FB7AGYwSQp0VBBVI2rhovpldN9xt6UUkLYB7Y2XeWdcDh5vZc91rVdBJQogEQRAEbRPqrCAoQNJ0+ideBA84NDMbN8xN6kkigeXoJ0YiQRAMGZJ+AyyM12IBj1h/ERcs48zs091qW9AZQogEQTBkZMkqi+ZJmmpm7+9W24LOEC6+QRAMJZHAcpQTNpEgCIaSSGA5ygl1VhAEQ0oksBzdhBAJgmBIkbQysBIwRzYvEliOHkKIBEEwZEg6DNgEFyKXAlsCN5jZzt1sV9A5wrAeBMFQsjOwOfCEme0NrAbM190mBZ0khEgQBEPJq2b2NjBD0jjgKWCJLrcp6CDhnRUEwVAyQdL8wInAROAl4OautijoKGETCYJgWJC0NB6lPqXbbQk6RwiRIAg6jqQ1q5ab2R3D1ZZgaAkhEgRBx5GUFaKaA1gbuBMPNlwVmGBm63erbUFnCcN6EAQdx8w2NbNNgceBNc1sbTNbC1gDeKy7rQs6SQiRIAiGkvea2V3ZhJndDbyvi+0JOkx4ZwVBMJRMkXQS8Kc0vQcQhvVRRNhEgiAYMiTNAXwO+FCaNR44LvJnjR5CiARBMKRImhNY0szu73Zbgs4TNpEgCIYMSdsBk4HL0/Tqki7qaqOCjhJCJAiCoeQwYF3gef6/vTtGiSAIojD8F5qYeAqNxAN4FU9gouypBM8hGBgJi7lX2EBBRnkGO5sYGDTdDA7/F9VMVNmjapppIMkL+ztFtBKGiKSRpiS7X+/coa+Ip7MkjfRaVdfAUVWdA3fA08I9qSMnEUkj3QIXwCdwD+yAzZINqS8nEUndzUd7b4AzYAtcJflatiuN4BFfSd1V1QMwAY/sbzN8S7JZtCkNYYhI6q6qtkku5/oYeE7y55999T/5TUTSCNOhcI21bk4ikrqrqm/g/fAInAAfc50kp0v1pr4MEUlSM9dZkqRmhogkqZkhIklqZohIkpoZIpKkZoaIJKnZD4DpaoiAIV+jAAAAAElFTkSuQmCC"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1200x720 with 0 Axes>"
      ]
     },
     "metadata": {}
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   "source": [
    "## Créditos\r\n",
    "Melissa Silva S. - https://github.com/melipass"
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