Análisis ENMODO 2014.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#libraries\n",
    "import pandas as pd\n",
    "from pandas import DataFrame\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Levanto ENMODO 2014\n",
    "http://datar.info/dataset/enmodo-2014"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "hogares = pd.read_csv('hogares.csv', delimiter='|')\n",
    "personas = pd.read_csv('personas.csv', delimiter='|')\n",
    "viajes = pd.read_csv('viajes.csv', delimiter='|')\n",
    "etapas = pd.read_csv('etapas.csv', delimiter='|')\n",
    "\n",
    "hogares = hogares.drop_duplicates().reset_index(drop=True)\n",
    "personas = personas.drop_duplicates().reset_index(drop=True)\n",
    "viajes = viajes.drop_duplicates().reset_index(drop=True)\n",
    "etapas = etapas.drop_duplicates().reset_index(drop=True)\n",
    "\n",
    "hogares.f_expansion.sum().round(0)\n",
    "personas.f_expansion.sum().round(0)\n",
    "\n",
    "hogares.loc[hogares.cod_provincia==2, 'Zona'] = 'CABA'\n",
    "hogares.loc[hogares.cod_provincia==6, 'Zona'] = 'Conurbano'\n",
    "\n",
    "# Codifico motivo destino de viajes y creo variable motivos agregada\n",
    "viajes['Motivo'] = viajes.bivi06_02.replace([1,2,3,4,5,6,7,8,9,10,11,12,13], ['Hogar', 'Lugar de trabajo', 'Actividades relacionadas con el trabajo', 'Establecimiento educativo',\n",
    "                'Actividades relacionadas con el estudio', 'Acompañar a un miembro del hogar a un establecimiento educativo', \n",
    "                'Acompañar a un miembro del hogar a otro lugar', 'Acompañar a un miembro de otro hogar', 'Compras',\n",
    "                'Salud', 'Trámites', 'Ocio', 'Otros'])\n",
    "\n",
    "\n",
    "viajes['Motivo_agg'] = viajes.Motivo.replace(['Hogar', 'Lugar de trabajo', 'Actividades relacionadas con el trabajo', 'Establecimiento educativo', 'Actividades relacionadas con el estudio', \n",
    " 'Acompañar a un miembro del hogar a un establecimiento educativo', 'Acompañar a un miembro del hogar a otro lugar', 'Acompañar a un miembro de otro hogar', 'Compras',\n",
    "                'Salud', 'Trámites', 'Ocio', 'Otros'], ['Hogar', 'Trabajo', 'Trabajo', 'Estudio', 'Estudio', 'Cuidado', 'Cuidado', 'Cuidado', 'Compras', 'Salud', 'Personal', 'Personal', 'Otros'])\n",
    "\n",
    "# Creo variable de modos de viajes agregada\n",
    "viajes['Modo_tipo'] = viajes.tipo_viaje.replace([1,2,3,4], ['No Motorizado', 'Público', 'Privado', 'Mixto'])\n",
    "\n",
    "viajes['Zona_destino'] = 'Destino Conurbano'\n",
    "viajes.loc[viajes.bivi05_01=='CABA', 'Zona_destino'] = 'Destino CABA'\n",
    "\n",
    "viajes['Zona_origen'] = 'Origen Conurbano'\n",
    "viajes.loc[viajes.bivi04_01=='CABA', 'Zona_origen'] = 'Origen CABA'\n",
    "\n",
    "\n",
    "viajes = pd.concat([viajes, pd.get_dummies(viajes.Modo_tipo)], axis=1)\n",
    "\n",
    "# Incluyo factor de expansión y Zona \n",
    "personas = personas.set_index(['id_muestra', 'nro_vivienda', 'nro_hogar']).join(hogares[['id_muestra', 'nro_vivienda', 'nro_hogar', 'Zona']].set_index(['id_muestra', 'nro_vivienda', 'nro_hogar'])).reset_index()\n",
    "viajes = viajes.set_index(['id_muestra', 'nro_vivienda', 'nro_hogar']).join(hogares[['id_muestra', 'nro_vivienda', 'nro_hogar', 'Zona', 'f_expansion']].set_index(['id_muestra', 'nro_vivienda', 'nro_hogar'])).reset_index()\n",
    "etapas = etapas.set_index(['id_muestra', 'nro_vivienda', 'nro_hogar']).join(hogares[['id_muestra', 'nro_vivienda', 'nro_hogar', 'Zona', 'f_expansion']].set_index(['id_muestra', 'nro_vivienda', 'nro_hogar'])).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Motivo_agg\n",
       "Trabajo     0.43\n",
       "Cuidado     0.15\n",
       "Estudio     0.14\n",
       "Personal    0.13\n",
       "Compras     0.08\n",
       "Salud       0.06\n",
       "Otros       0.01\n",
       "Name: f_expansion, dtype: float64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(viajes[viajes.Motivo_agg!='Hogar'].groupby('Motivo_agg').f_expansion.sum().sort_values(ascending=False) / viajes[viajes.Motivo_agg!='Hogar'].f_expansion.sum()).round(2).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análisis de Etapas de viajes\n",
    "La tabla de Etapas de viajes incluye todos los modos de transporte utilizados para un viaje"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modos de transporte (Etapas)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Modo</th>\n",
       "      <th>Modo_agg</th>\n",
       "      <th>Etapas</th>\n",
       "      <th>Etapas_porc</th>\n",
       "      <th>CABA</th>\n",
       "      <th>CABA_porc</th>\n",
       "      <th>Conurbano</th>\n",
       "      <th>Conurbano_porc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colectivo</td>\n",
       "      <td>Público</td>\n",
       "      <td>9078326</td>\n",
       "      <td>37</td>\n",
       "      <td>1862695</td>\n",
       "      <td>34</td>\n",
       "      <td>7215631</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A pie</td>\n",
       "      <td>No Motorizado</td>\n",
       "      <td>5487974</td>\n",
       "      <td>22</td>\n",
       "      <td>1423676</td>\n",
       "      <td>26</td>\n",
       "      <td>4064298</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vehículo motorizado (auto, moto, etc.) como co...</td>\n",
       "      <td>Auto/Moto</td>\n",
       "      <td>4037207</td>\n",
       "      <td>16</td>\n",
       "      <td>815233</td>\n",
       "      <td>15</td>\n",
       "      <td>3221974</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vehículo motorizado (auto, moto, etc.) como ac...</td>\n",
       "      <td>Auto/Moto</td>\n",
       "      <td>2134948</td>\n",
       "      <td>8</td>\n",
       "      <td>396557</td>\n",
       "      <td>7</td>\n",
       "      <td>1738391</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ferrocarril</td>\n",
       "      <td>Público</td>\n",
       "      <td>1272688</td>\n",
       "      <td>5</td>\n",
       "      <td>99141</td>\n",
       "      <td>1</td>\n",
       "      <td>1173547</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Subte/Premetro</td>\n",
       "      <td>Público</td>\n",
       "      <td>703210</td>\n",
       "      <td>2</td>\n",
       "      <td>461547</td>\n",
       "      <td>8</td>\n",
       "      <td>241663</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Bicicleta</td>\n",
       "      <td>No Motorizado</td>\n",
       "      <td>640367</td>\n",
       "      <td>2</td>\n",
       "      <td>122655</td>\n",
       "      <td>2</td>\n",
       "      <td>517712</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Remis</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>306040</td>\n",
       "      <td>1</td>\n",
       "      <td>16032</td>\n",
       "      <td>0</td>\n",
       "      <td>290008</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Transporte escolar</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>183308</td>\n",
       "      <td>0</td>\n",
       "      <td>14836</td>\n",
       "      <td>0</td>\n",
       "      <td>168472</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Taxi</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>166341</td>\n",
       "      <td>0</td>\n",
       "      <td>125729</td>\n",
       "      <td>2</td>\n",
       "      <td>40612</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Charter/combi</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>112553</td>\n",
       "      <td>0</td>\n",
       "      <td>22339</td>\n",
       "      <td>0</td>\n",
       "      <td>90214</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bus de la empresa donde trabaja</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>54660</td>\n",
       "      <td>0</td>\n",
       "      <td>7585</td>\n",
       "      <td>0</td>\n",
       "      <td>47075</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Otro</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>15268</td>\n",
       "      <td>0</td>\n",
       "      <td>1286</td>\n",
       "      <td>0</td>\n",
       "      <td>13982</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Modo       Modo_agg   Etapas  \\\n",
       "1                                           Colectivo        Público  9078326   \n",
       "0                                               A pie  No Motorizado  5487974   \n",
       "2   Vehículo motorizado (auto, moto, etc.) como co...      Auto/Moto  4037207   \n",
       "3   Vehículo motorizado (auto, moto, etc.) como ac...      Auto/Moto  2134948   \n",
       "4                                         Ferrocarril        Público  1272688   \n",
       "5                                      Subte/Premetro        Público   703210   \n",
       "6                                           Bicicleta  No Motorizado   640367   \n",
       "8                                               Remis      A demanda   306040   \n",
       "9                                  Transporte escolar      A demanda   183308   \n",
       "7                                                Taxi      A demanda   166341   \n",
       "10                                      Charter/combi      A demanda   112553   \n",
       "11                    Bus de la empresa donde trabaja      A demanda    54660   \n",
       "12                                               Otro      A demanda    15268   \n",
       "\n",
       "    Etapas_porc     CABA  CABA_porc  Conurbano  Conurbano_porc  \n",
       "1            37  1862695         34    7215631              38  \n",
       "0            22  1423676         26    4064298              21  \n",
       "2            16   815233         15    3221974              17  \n",
       "3             8   396557          7    1738391               9  \n",
       "4             5    99141          1    1173547               6  \n",
       "5             2   461547          8     241663               1  \n",
       "6             2   122655          2     517712               2  \n",
       "8             1    16032          0     290008               1  \n",
       "9             0    14836          0     168472               0  \n",
       "7             0   125729          2      40612               0  \n",
       "10            0    22339          0      90214               0  \n",
       "11            0     7585          0      47075               0  \n",
       "12            0     1286          0      13982               0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Modo_agg</th>\n",
       "      <th>Etapas</th>\n",
       "      <th>Etapas_porc</th>\n",
       "      <th>CABA</th>\n",
       "      <th>CABA_porc</th>\n",
       "      <th>Conurbano</th>\n",
       "      <th>Conurbano_porc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Público</td>\n",
       "      <td>11054224</td>\n",
       "      <td>44</td>\n",
       "      <td>2423383</td>\n",
       "      <td>43</td>\n",
       "      <td>8630841</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Auto/Moto</td>\n",
       "      <td>6172155</td>\n",
       "      <td>24</td>\n",
       "      <td>1211790</td>\n",
       "      <td>22</td>\n",
       "      <td>4960365</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No Motorizado</td>\n",
       "      <td>6128341</td>\n",
       "      <td>24</td>\n",
       "      <td>1546331</td>\n",
       "      <td>28</td>\n",
       "      <td>4582010</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A demanda</td>\n",
       "      <td>838170</td>\n",
       "      <td>1</td>\n",
       "      <td>187807</td>\n",
       "      <td>2</td>\n",
       "      <td>650363</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        Modo_agg    Etapas  Etapas_porc     CABA  CABA_porc  Conurbano  \\\n",
       "3        Público  11054224           44  2423383         43    8630841   \n",
       "1      Auto/Moto   6172155           24  1211790         22    4960365   \n",
       "2  No Motorizado   6128341           24  1546331         28    4582010   \n",
       "0      A demanda    838170            1   187807          2     650363   \n",
       "\n",
       "   Conurbano_porc  \n",
       "3              45  \n",
       "1              26  \n",
       "2              23  \n",
       "0               1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# agrupo por biet03 (modo de transporte)\n",
    "print('Modos de transporte (Etapas)')\n",
    "etapas1 = (etapas.groupby('biet03').f_expansion.sum()).astype(int).astype(int).reset_index()\n",
    "etapas1['Porcentaje'] = (etapas1.f_expansion/etapas1.f_expansion.sum()*100).astype(int)\n",
    "etapas1 = pd.concat([etapas1, DataFrame(['A pie','Colectivo','Vehículo motorizado (auto, moto, etc.) como conductor',\n",
    "                                                       'Vehículo motorizado (auto, moto, etc.) como acompañante','Ferrocarril','Subte/Premetro','Bicicleta',\n",
    "                                                       'Taxi','Remis','Transporte escolar','Charter/combi','Bus de la empresa donde trabaja','Otro'], columns=['Modo'])], axis=1)\n",
    "\n",
    "etapas1 = pd.concat([etapas1, DataFrame(['No Motorizado','Público','Auto/Moto','Auto/Moto','Público','Público','No Motorizado','A demanda','A demanda',\n",
    "                                                       'A demanda','A demanda','A demanda','A demanda'], columns=['Modo_agg'])], axis=1)\n",
    "\n",
    "# Agrego descripción de modos a etapas\n",
    "etapas = etapas.set_index('biet03').join(etapas1[['biet03', 'Modo', 'Modo_agg']].set_index('biet03')).sort_values('id_muestra').reset_index()\n",
    "\n",
    "etapas1 = etapas1[['Modo', 'Modo_agg', 'f_expansion', 'Porcentaje']]\n",
    "etapas1.columns = ['Modo', 'Modo_agg', 'Etapas', 'Etapas_porc']\n",
    "\n",
    "#CABA\n",
    "etapas1_caba = (etapas[etapas.Zona=='CABA'].groupby('biet03').f_expansion.sum()).astype(int).astype(int).reset_index()\n",
    "etapas1_caba['CABA_porc'] = (etapas1_caba.f_expansion/etapas1_caba.f_expansion.sum()*100).astype(int)\n",
    "etapas1_caba = pd.concat([etapas1_caba, DataFrame(['A pie','Colectivo','Vehículo motorizado (auto, moto, etc.) como conductor',\n",
    "                                                       'Vehículo motorizado (auto, moto, etc.) como acompañante','Ferrocarril','Subte/Premetro','Bicicleta',\n",
    "                                                       'Taxi','Remis','Transporte escolar','Charter/combi','Bus de la empresa donde trabaja','Otro'], columns=['Modo'])], axis=1)\n",
    "etapas1_caba = etapas1_caba[['Modo', 'f_expansion', 'CABA_porc']]\n",
    "etapas1_caba.columns = ['Modo', 'CABA', 'CABA_porc']\n",
    "\n",
    "#AMBA\n",
    "etapas1_amba = (etapas[etapas.Zona=='Conurbano'].groupby('biet03').f_expansion.sum()).astype(int).astype(int).reset_index()\n",
    "etapas1_amba['Conurbano_porc'] = (etapas1_amba.f_expansion/etapas1_amba.f_expansion.sum()*100).astype(int)\n",
    "etapas1_amba = pd.concat([etapas1_amba, DataFrame(['A pie','Colectivo','Vehículo motorizado (auto, moto, etc.) como conductor',\n",
    "                                                       'Vehículo motorizado (auto, moto, etc.) como acompañante','Ferrocarril','Subte/Premetro','Bicicleta',\n",
    "                                                       'Taxi','Remis','Transporte escolar','Charter/combi','Bus de la empresa donde trabaja','Otro'], columns=['Modo'])], axis=1)\n",
    "etapas1_amba = etapas1_amba[['Modo', 'f_expansion', 'Conurbano_porc']]\n",
    "etapas1_amba.columns = ['Modo', 'Conurbano', 'Conurbano_porc']\n",
    "\n",
    "\n",
    "etapas1 = etapas1.set_index('Modo').join(etapas1_caba.set_index('Modo')).reset_index()\n",
    "etapas1 = etapas1.set_index('Modo').join(etapas1_amba.set_index('Modo')).reset_index()\n",
    "display(etapas1.sort_values('Etapas', ascending=False))\n",
    "\n",
    "display(etapas1.groupby('Modo_agg').sum().reset_index().sort_values('Etapas', ascending=False))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Etapas de viajes sin transporte no motorizado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sin transporte no motorizado (Etapas)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Modo</th>\n",
       "      <th>Modo_agg</th>\n",
       "      <th>Etapas</th>\n",
       "      <th>Etapas_porc</th>\n",
       "      <th>CABA</th>\n",
       "      <th>CABA_porc</th>\n",
       "      <th>Conurbano</th>\n",
       "      <th>Conurbano_porc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colectivo</td>\n",
       "      <td>Público</td>\n",
       "      <td>9078326</td>\n",
       "      <td>50</td>\n",
       "      <td>1862695</td>\n",
       "      <td>33</td>\n",
       "      <td>7215631</td>\n",
       "      <td>10021710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vehículo motorizado (auto, moto, etc.) como co...</td>\n",
       "      <td>Auto/Moto</td>\n",
       "      <td>4037207</td>\n",
       "      <td>22</td>\n",
       "      <td>815233</td>\n",
       "      <td>14</td>\n",
       "      <td>3221974</td>\n",
       "      <td>4474964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vehículo motorizado (auto, moto, etc.) como ac...</td>\n",
       "      <td>Auto/Moto</td>\n",
       "      <td>2134948</td>\n",
       "      <td>12</td>\n",
       "      <td>396557</td>\n",
       "      <td>7</td>\n",
       "      <td>1738391</td>\n",
       "      <td>2414432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ferrocarril</td>\n",
       "      <td>Público</td>\n",
       "      <td>1272688</td>\n",
       "      <td>7</td>\n",
       "      <td>99141</td>\n",
       "      <td>2</td>\n",
       "      <td>1173547</td>\n",
       "      <td>1629926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Subte/Premetro</td>\n",
       "      <td>Público</td>\n",
       "      <td>703210</td>\n",
       "      <td>4</td>\n",
       "      <td>461547</td>\n",
       "      <td>8</td>\n",
       "      <td>241663</td>\n",
       "      <td>335643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Remis</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>306040</td>\n",
       "      <td>2</td>\n",
       "      <td>16032</td>\n",
       "      <td>0</td>\n",
       "      <td>290008</td>\n",
       "      <td>402789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Transporte escolar</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>183308</td>\n",
       "      <td>1</td>\n",
       "      <td>14836</td>\n",
       "      <td>0</td>\n",
       "      <td>168472</td>\n",
       "      <td>233989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Taxi</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>166341</td>\n",
       "      <td>1</td>\n",
       "      <td>125729</td>\n",
       "      <td>2</td>\n",
       "      <td>40612</td>\n",
       "      <td>56406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Charter/combi</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>112553</td>\n",
       "      <td>1</td>\n",
       "      <td>22339</td>\n",
       "      <td>0</td>\n",
       "      <td>90214</td>\n",
       "      <td>125297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Bus de la empresa donde trabaja</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>54660</td>\n",
       "      <td>0</td>\n",
       "      <td>7585</td>\n",
       "      <td>0</td>\n",
       "      <td>47075</td>\n",
       "      <td>65382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Otro</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>15268</td>\n",
       "      <td>0</td>\n",
       "      <td>1286</td>\n",
       "      <td>0</td>\n",
       "      <td>13982</td>\n",
       "      <td>19419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Modo   Modo_agg   Etapas  \\\n",
       "1                                           Colectivo    Público  9078326   \n",
       "2   Vehículo motorizado (auto, moto, etc.) como co...  Auto/Moto  4037207   \n",
       "3   Vehículo motorizado (auto, moto, etc.) como ac...  Auto/Moto  2134948   \n",
       "4                                         Ferrocarril    Público  1272688   \n",
       "5                                      Subte/Premetro    Público   703210   \n",
       "8                                               Remis  A demanda   306040   \n",
       "9                                  Transporte escolar  A demanda   183308   \n",
       "7                                                Taxi  A demanda   166341   \n",
       "10                                      Charter/combi  A demanda   112553   \n",
       "11                    Bus de la empresa donde trabaja  A demanda    54660   \n",
       "12                                               Otro  A demanda    15268   \n",
       "\n",
       "    Etapas_porc     CABA  CABA_porc  Conurbano  Conurbano_porc  \n",
       "1            50  1862695         33    7215631        10021710  \n",
       "2            22   815233         14    3221974         4474964  \n",
       "3            12   396557          7    1738391         2414432  \n",
       "4             7    99141          2    1173547         1629926  \n",
       "5             4   461547          8     241663          335643  \n",
       "8             2    16032          0     290008          402789  \n",
       "9             1    14836          0     168472          233989  \n",
       "7             1   125729          2      40612           56406  \n",
       "10            1    22339          0      90214          125297  \n",
       "11            0     7585          0      47075           65382  \n",
       "12            0     1286          0      13982           19419  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Etapas</th>\n",
       "      <th>Etapas_porc</th>\n",
       "      <th>CABA</th>\n",
       "      <th>CABA_porc</th>\n",
       "      <th>Conurbano</th>\n",
       "      <th>Conurbano_porc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Modo_agg</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Público</th>\n",
       "      <td>11054224</td>\n",
       "      <td>61</td>\n",
       "      <td>2423383</td>\n",
       "      <td>43</td>\n",
       "      <td>8630841</td>\n",
       "      <td>11987279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auto/Moto</th>\n",
       "      <td>6172155</td>\n",
       "      <td>34</td>\n",
       "      <td>1211790</td>\n",
       "      <td>21</td>\n",
       "      <td>4960365</td>\n",
       "      <td>6889396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A demanda</th>\n",
       "      <td>838170</td>\n",
       "      <td>5</td>\n",
       "      <td>187807</td>\n",
       "      <td>2</td>\n",
       "      <td>650363</td>\n",
       "      <td>903282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Etapas  Etapas_porc     CABA  CABA_porc  Conurbano  \\\n",
       "Modo_agg                                                          \n",
       "Público    11054224           61  2423383         43    8630841   \n",
       "Auto/Moto   6172155           34  1211790         21    4960365   \n",
       "A demanda    838170            5   187807          2     650363   \n",
       "\n",
       "           Conurbano_porc  \n",
       "Modo_agg                   \n",
       "Público          11987279  \n",
       "Auto/Moto         6889396  \n",
       "A demanda          903282  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Sin transporte no motorizado (Etapas)')\n",
    "etapas2 = etapas1[etapas1.Modo_agg!='No Motorizado'].copy()\n",
    "etapas2['Etapas_porc'] = (etapas2['Etapas'] / etapas2['Etapas'].sum() * 100).round().astype(int)\n",
    "etapas2['CABA_porc'] = (etapas2['CABA'] / etapas2['CABA_porc'].sum() * 100).round().astype(int)\n",
    "etapas2['CABA_porc'] = (etapas2['CABA'] / etapas2['CABA_porc'].sum() * 100).round().astype(int)\n",
    "etapas2['Conurbano_porc'] = (etapas2['Conurbano'] / etapas2['Conurbano_porc'].sum() * 100).round().astype(int)\n",
    "display(etapas2.sort_values('Etapas', ascending=False))\n",
    "etapas2.groupby('Modo_agg').sum().sort_values('Etapas', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Etapas de viajes de solo los viajes en transporte público"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solo Transporte Público (Etapas)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Modo</th>\n",
       "      <th>Modo_agg</th>\n",
       "      <th>Etapas</th>\n",
       "      <th>Etapas_porc</th>\n",
       "      <th>CABA</th>\n",
       "      <th>CABA_porc</th>\n",
       "      <th>Conurbano</th>\n",
       "      <th>Conurbano_porc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Colectivo</td>\n",
       "      <td>Público</td>\n",
       "      <td>9078326</td>\n",
       "      <td>82</td>\n",
       "      <td>1862695</td>\n",
       "      <td>77</td>\n",
       "      <td>7215631</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ferrocarril</td>\n",
       "      <td>Público</td>\n",
       "      <td>1272688</td>\n",
       "      <td>12</td>\n",
       "      <td>99141</td>\n",
       "      <td>4</td>\n",
       "      <td>1173547</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Subte/Premetro</td>\n",
       "      <td>Público</td>\n",
       "      <td>703210</td>\n",
       "      <td>6</td>\n",
       "      <td>461547</td>\n",
       "      <td>19</td>\n",
       "      <td>241663</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Modo Modo_agg   Etapas  Etapas_porc     CABA  CABA_porc  \\\n",
       "1       Colectivo  Público  9078326           82  1862695         77   \n",
       "4     Ferrocarril  Público  1272688           12    99141          4   \n",
       "5  Subte/Premetro  Público   703210            6   461547         19   \n",
       "\n",
       "   Conurbano  Conurbano_porc  \n",
       "1    7215631              84  \n",
       "4    1173547              14  \n",
       "5     241663               3  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Solo Transporte Público (Etapas)')\n",
    "etapas3 = etapas1[etapas1.Modo_agg=='Público'].copy()\n",
    "etapas3['Etapas_porc'] = (etapas3['Etapas'] / etapas3['Etapas'].sum() * 100).round().astype(int)\n",
    "etapas3['CABA_porc'] = (etapas3['CABA'] / etapas3['CABA'].sum() * 100).round().astype(int)\n",
    "etapas3['Conurbano_porc'] = (etapas3['Conurbano'] / etapas3['Conurbano'].sum() * 100).round().astype(int)\n",
    "display(etapas3.sort_values('Etapas', ascending=False))\n",
    "etapas3.to_csv('Etapas_tpubl2014.csv', sep=';', encoding='latin1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análisis de viajes\n",
    "La tabla de viajes incluye todos los viajes realizados por cada una de las pesonas que viaja. \n",
    "Cuando el viaje incluye varios modos de transporte se categoriza como Público Multietapa (si el viaje incluye una etapa de transporte público) o Otros Multietapa cuando no incluye etapa de transporte público"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Zona</th>\n",
       "      <th>Modo</th>\n",
       "      <th>Modo_agg</th>\n",
       "      <th>Modo_agg2</th>\n",
       "      <th>Viajes</th>\n",
       "      <th>Viajes_porc</th>\n",
       "      <th>CABA</th>\n",
       "      <th>CABA_porc</th>\n",
       "      <th>Conurbano</th>\n",
       "      <th>Conurbano_porc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Colectivo</td>\n",
       "      <td>Público</td>\n",
       "      <td>Público</td>\n",
       "      <td>5921540.0</td>\n",
       "      <td>27.4</td>\n",
       "      <td>1507620.0</td>\n",
       "      <td>29.7</td>\n",
       "      <td>4413920.0</td>\n",
       "      <td>26.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A pie</td>\n",
       "      <td>No Motorizado</td>\n",
       "      <td>No Motorizado</td>\n",
       "      <td>5487974.0</td>\n",
       "      <td>25.4</td>\n",
       "      <td>1423676.0</td>\n",
       "      <td>28.1</td>\n",
       "      <td>4064298.0</td>\n",
       "      <td>24.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Vehículo motorizado (auto, moto, etc.) como co...</td>\n",
       "      <td>Auto/Moto</td>\n",
       "      <td>Auto/Moto</td>\n",
       "      <td>4020148.0</td>\n",
       "      <td>18.6</td>\n",
       "      <td>809415.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3210733.0</td>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Multietapa</td>\n",
       "      <td>Público Multietapa</td>\n",
       "      <td>Público</td>\n",
       "      <td>2104287.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>281576.0</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1822711.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Vehículo motorizado (auto, moto, etc.) como ac...</td>\n",
       "      <td>Auto/Moto</td>\n",
       "      <td>Auto/Moto</td>\n",
       "      <td>2068005.0</td>\n",
       "      <td>9.6</td>\n",
       "      <td>382748.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1685257.0</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bicicleta</td>\n",
       "      <td>No Motorizado</td>\n",
       "      <td>No Motorizado</td>\n",
       "      <td>615513.0</td>\n",
       "      <td>2.8</td>\n",
       "      <td>119473.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>496040.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Subte/Premetro</td>\n",
       "      <td>Público</td>\n",
       "      <td>Público</td>\n",
       "      <td>337558.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>329850.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>7708.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ferrocarril</td>\n",
       "      <td>Público</td>\n",
       "      <td>Público</td>\n",
       "      <td>323435.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>43515.0</td>\n",
       "      <td>0.9</td>\n",
       "      <td>279920.0</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Remis</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>273486.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>13295.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>260191.0</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Transporte escolar</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>177898.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>14143.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>163755.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Taxi</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>148572.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>120177.0</td>\n",
       "      <td>2.4</td>\n",
       "      <td>28395.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Charter/combi</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>79941.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>19681.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>60260.0</td>\n",
       "      <td>0.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bus de la empresa donde trabaja</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>30114.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>3562.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>26552.0</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Otros Multietapa</td>\n",
       "      <td>Otros Multietapa</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>14153.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>688.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13465.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Otro</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>A demanda</td>\n",
       "      <td>13261.0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>626.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12635.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Zona                                               Modo            Modo_agg  \\\n",
       "4                                             Colectivo             Público   \n",
       "0                                                 A pie       No Motorizado   \n",
       "14    Vehículo motorizado (auto, moto, etc.) como co...           Auto/Moto   \n",
       "8                                            Multietapa  Público Multietapa   \n",
       "13    Vehículo motorizado (auto, moto, etc.) como ac...           Auto/Moto   \n",
       "1                                             Bicicleta       No Motorizado   \n",
       "10                                       Subte/Premetro             Público   \n",
       "5                                           Ferrocarril             Público   \n",
       "9                                                 Remis           A demanda   \n",
       "12                                   Transporte escolar           A demanda   \n",
       "11                                                 Taxi           A demanda   \n",
       "3                                         Charter/combi           A demanda   \n",
       "2                       Bus de la empresa donde trabaja           A demanda   \n",
       "7                                      Otros Multietapa    Otros Multietapa   \n",
       "6                                                  Otro           A demanda   \n",
       "\n",
       "Zona      Modo_agg2     Viajes  Viajes_porc       CABA  CABA_porc  Conurbano  \\\n",
       "4           Público  5921540.0         27.4  1507620.0       29.7  4413920.0   \n",
       "0     No Motorizado  5487974.0         25.4  1423676.0       28.1  4064298.0   \n",
       "14        Auto/Moto  4020148.0         18.6   809415.0       16.0  3210733.0   \n",
       "8           Público  2104287.0          9.7   281576.0        5.6  1822711.0   \n",
       "13        Auto/Moto  2068005.0          9.6   382748.0        7.5  1685257.0   \n",
       "1     No Motorizado   615513.0          2.8   119473.0        2.4   496040.0   \n",
       "10          Público   337558.0          1.6   329850.0        6.5     7708.0   \n",
       "5           Público   323435.0          1.5    43515.0        0.9   279920.0   \n",
       "9         A demanda   273486.0          1.3    13295.0        0.3   260191.0   \n",
       "12        A demanda   177898.0          0.8    14143.0        0.3   163755.0   \n",
       "11        A demanda   148572.0          0.7   120177.0        2.4    28395.0   \n",
       "3         A demanda    79941.0          0.4    19681.0        0.4    60260.0   \n",
       "2         A demanda    30114.0          0.1     3562.0        0.1    26552.0   \n",
       "7         A demanda    14153.0          0.1      688.0        0.0    13465.0   \n",
       "6         A demanda    13261.0          0.1      626.0        0.0    12635.0   \n",
       "\n",
       "Zona  Conurbano_porc  \n",
       "4               26.7  \n",
       "0               24.6  \n",
       "14              19.4  \n",
       "8               11.0  \n",
       "13              10.2  \n",
       "1                3.0  \n",
       "10               0.0  \n",
       "5                1.7  \n",
       "9                1.6  \n",
       "12               1.0  \n",
       "11               0.2  \n",
       "3                0.4  \n",
       "2                0.2  \n",
       "7                0.1  \n",
       "6                0.1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viajes0 = pd.concat([etapas, pd.get_dummies(etapas.Modo), pd.get_dummies(etapas.Modo_agg)], axis=1)\n",
    "\n",
    "viajes0 = viajes0.groupby(['id_muestra', 'nro_vivienda', 'nro_hogar', 'Zona', 'biet01']).agg({'f_expansion':'max', 'Modo':'max', 'Modo_agg':'max', 'A pie':'sum', 'Bicicleta':'sum', 'Bus de la empresa donde trabaja':'sum', 'Charter/combi':'sum', \n",
    "                                                                                            'Colectivo':'sum', 'Ferrocarril':'sum', 'Otro':'sum', 'Remis':'sum', 'Subte/Premetro':'sum', 'Taxi':'sum',\n",
    "                                                                                            'Transporte escolar':'sum', 'Vehículo motorizado (auto, moto, etc.) como acompañante':'sum',\n",
    "                                                                                            'Vehículo motorizado (auto, moto, etc.) como conductor':'sum', 'A demanda':'max',\n",
    "                                                                                            'Auto/Moto':'max', 'No Motorizado':'max', 'Público':'max'}).reset_index()\n",
    "\n",
    "\n",
    "viajes0['cantidad_modos'] = viajes0[['A pie', 'Bicicleta',\n",
    "       'Bus de la empresa donde trabaja', 'Charter/combi', 'Colectivo',\n",
    "       'Ferrocarril', 'Otro', 'Remis', 'Subte/Premetro', 'Taxi',\n",
    "       'Transporte escolar',\n",
    "       'Vehículo motorizado (auto, moto, etc.) como acompañante',\n",
    "       'Vehículo motorizado (auto, moto, etc.) como conductor']].sum(axis=1)\n",
    "viajes0['cantidad_modosxtipo'] = viajes0[['A demanda','Auto/Moto', 'No Motorizado', 'Público']].sum(axis=1)\n",
    "\n",
    "\n",
    "viajes0.loc[(viajes0.cantidad_modos>1), 'Modo'] = ''\n",
    "viajes0.loc[(viajes0.cantidad_modos>1)&(viajes0.Público==1), 'Modo'] = 'Público Multietapa'\n",
    "viajes0.loc[(viajes0.cantidad_modos>1)&(viajes0.Público!=1), 'Modo'] = 'Otros Multietapa'\n",
    "\n",
    "viajes0.loc[(viajes0.cantidad_modos>1)&(viajes0.Público==1), 'Modo_agg'] = 'Público Multietapa'\n",
    "viajes0.loc[(viajes0.cantidad_modos>1)&(viajes0.Público!=1), 'Modo_agg'] = 'Otros Multietapa'\n",
    "\n",
    "\n",
    "viajes1 = viajes0.groupby(['Zona', 'Modo', 'Modo_agg']).f_expansion.sum().round(1).sort_values(ascending=False).reset_index()\n",
    "\n",
    "viajes1['Modo_agg2'] = viajes1.Modo_agg.replace(['Público', 'No Motorizado', 'Auto/Moto', 'Público Multietapa',\n",
    "                               'A demanda', 'Otros Multietapa'], ['Público', 'No Motorizado', 'Auto/Moto', 'Público', 'A demanda', 'A demanda'])\n",
    "\n",
    "\n",
    "viajes1 = pd.pivot_table(data=viajes1, index=['Modo', 'Modo_agg', 'Modo_agg2'], columns='Zona', values='f_expansion').reset_index()\n",
    "viajes1['Viajes'] = viajes1['CABA']+viajes1['Conurbano']\n",
    "\n",
    "viajes1['Viajes_porc'] = (viajes1['Viajes'] / viajes1['Viajes'].sum() * 100).round(1)\n",
    "viajes1['CABA_porc'] = (viajes1['CABA'] / viajes1['CABA'].sum() * 100).round(1)\n",
    "viajes1['Conurbano_porc'] = (viajes1['Conurbano'] / viajes1['Conurbano'].sum() * 100).round(1)\n",
    "\n",
    "viajes1 = viajes1[['Modo', 'Modo_agg', 'Modo_agg2', 'Viajes', 'Viajes_porc', 'CABA', 'CABA_porc', 'Conurbano',\n",
    "        'Conurbano_porc']].sort_values('Viajes', ascending=False)\n",
    "\n",
    "\n",
    "viajes1['Modo'] = viajes1.Modo.replace(['Colectivo', 'Público Multietapa', 'Subte/Premetro', 'Ferrocarril'],\n",
    "                                                 ['Colectivo', 'Multietapa', 'Subte/Premetro', 'Ferrocarril'])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "viajes1.to_csv('modos2014.csv', sep=';', encoding='latin1')\n",
    "\n",
    "viajes1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viajes motorizados por modo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Viajes por modo (solo motorizado)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Zona</th>\n",
       "      <th>Viajes</th>\n",
       "      <th>Viajes_porc</th>\n",
       "      <th>CABA</th>\n",
       "      <th>CABA_porc</th>\n",
       "      <th>Conurbano</th>\n",
       "      <th>Conurbano_porc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Modo_agg</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Público</th>\n",
       "      <td>6582533.0</td>\n",
       "      <td>42.44</td>\n",
       "      <td>1880985.0</td>\n",
       "      <td>53.33</td>\n",
       "      <td>4701548.0</td>\n",
       "      <td>39.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Auto/Moto</th>\n",
       "      <td>6088153.0</td>\n",
       "      <td>39.25</td>\n",
       "      <td>1192163.0</td>\n",
       "      <td>33.80</td>\n",
       "      <td>4895990.0</td>\n",
       "      <td>40.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Público Multietapa</th>\n",
       "      <td>2104287.0</td>\n",
       "      <td>13.57</td>\n",
       "      <td>281576.0</td>\n",
       "      <td>7.98</td>\n",
       "      <td>1822711.0</td>\n",
       "      <td>15.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A demanda</th>\n",
       "      <td>723272.0</td>\n",
       "      <td>4.67</td>\n",
       "      <td>171484.0</td>\n",
       "      <td>4.87</td>\n",
       "      <td>551788.0</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Otros Multietapa</th>\n",
       "      <td>14153.0</td>\n",
       "      <td>0.09</td>\n",
       "      <td>688.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>13465.0</td>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Zona                   Viajes  Viajes_porc       CABA  CABA_porc  Conurbano  \\\n",
       "Modo_agg                                                                      \n",
       "Público             6582533.0        42.44  1880985.0      53.33  4701548.0   \n",
       "Auto/Moto           6088153.0        39.25  1192163.0      33.80  4895990.0   \n",
       "Público Multietapa  2104287.0        13.57   281576.0       7.98  1822711.0   \n",
       "A demanda            723272.0         4.67   171484.0       4.87   551788.0   \n",
       "Otros Multietapa      14153.0         0.09      688.0       0.02    13465.0   \n",
       "\n",
       "Zona                Conurbano_porc  \n",
       "Modo_agg                            \n",
       "Público                      39.23  \n",
       "Auto/Moto                    40.85  \n",
       "Público Multietapa           15.21  \n",
       "A demanda                     4.61  \n",
       "Otros Multietapa              0.11  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Viajes por modo (solo motorizado)')\n",
    "viajes2 = viajes1[viajes1.Modo_agg2!='No Motorizado'].copy()\n",
    "viajes2['Viajes_porc'] = (viajes2['Viajes'] / viajes2['Viajes'].sum() * 100).round(2)\n",
    "viajes2['CABA_porc'] = (viajes2['CABA'] / viajes2['CABA'].sum() * 100).round(2)\n",
    "viajes2['Conurbano_porc'] = (viajes2['Conurbano'] / viajes2['Conurbano'].sum() * 100).round(2)\n",
    "viajes2.groupby('Modo_agg').sum().sort_values('Viajes', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viajes en transporte público por modo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Viajes por modo (transporte público)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Zona</th>\n",
       "      <th>Viajes</th>\n",
       "      <th>Viajes_porc</th>\n",
       "      <th>CABA</th>\n",
       "      <th>CABA_porc</th>\n",
       "      <th>Conurbano</th>\n",
       "      <th>Conurbano_porc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Modo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Colectivo</th>\n",
       "      <td>5921540.0</td>\n",
       "      <td>68.2</td>\n",
       "      <td>1507620.0</td>\n",
       "      <td>69.7</td>\n",
       "      <td>4413920.0</td>\n",
       "      <td>67.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Multietapa</th>\n",
       "      <td>2104287.0</td>\n",
       "      <td>24.2</td>\n",
       "      <td>281576.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1822711.0</td>\n",
       "      <td>27.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subte/Premetro</th>\n",
       "      <td>337558.0</td>\n",
       "      <td>3.9</td>\n",
       "      <td>329850.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>7708.0</td>\n",
       "      <td>0.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ferrocarril</th>\n",
       "      <td>323435.0</td>\n",
       "      <td>3.7</td>\n",
       "      <td>43515.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>279920.0</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Zona               Viajes  Viajes_porc       CABA  CABA_porc  Conurbano  \\\n",
       "Modo                                                                      \n",
       "Colectivo       5921540.0         68.2  1507620.0       69.7  4413920.0   \n",
       "Multietapa      2104287.0         24.2   281576.0       13.0  1822711.0   \n",
       "Subte/Premetro   337558.0          3.9   329850.0       15.3     7708.0   \n",
       "Ferrocarril      323435.0          3.7    43515.0        2.0   279920.0   \n",
       "\n",
       "Zona            Conurbano_porc  \n",
       "Modo                            \n",
       "Colectivo                 67.7  \n",
       "Multietapa                27.9  \n",
       "Subte/Premetro             0.1  \n",
       "Ferrocarril                4.3  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Viajes por modo (transporte público)')\n",
    "viajes3 = viajes1[viajes1.Modo_agg2=='Público'].copy()\n",
    "viajes3['Viajes_porc'] = (viajes3['Viajes'] / viajes3['Viajes'].sum() * 100).round(1)\n",
    "viajes3['CABA_porc'] = (viajes3['CABA'] / viajes3['CABA'].sum() * 100).round(1)\n",
    "viajes3['Conurbano_porc'] = (viajes3['Conurbano'] / viajes3['Conurbano'].sum() * 100).round(1)\n",
    "viajes3 = viajes3.groupby('Modo').sum().sort_values('Viajes', ascending=False)\n",
    "\n",
    "viajes3.to_csv('modos_publico2014.csv', sep=';', encoding='latin1')\n",
    "viajes3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Construcción de indicadores para el análisis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dato</th>\n",
       "      <th>Total</th>\n",
       "      <th>Conurbano</th>\n",
       "      <th>CABA</th>\n",
       "      <th>Conurbano_porc</th>\n",
       "      <th>CABA_porc</th>\n",
       "      <th>Total_porc_pobl</th>\n",
       "      <th>Conurbano_porc_pobl</th>\n",
       "      <th>CABA_porc_pobl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Población</td>\n",
       "      <td>14422940.00</td>\n",
       "      <td>11562732.00</td>\n",
       "      <td>2860208.00</td>\n",
       "      <td>80.17</td>\n",
       "      <td>19.83</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Viajes (partido hogar)</td>\n",
       "      <td>10009259.00</td>\n",
       "      <td>7596592.00</td>\n",
       "      <td>2412667.00</td>\n",
       "      <td>75.90</td>\n",
       "      <td>24.10</td>\n",
       "      <td>69.40</td>\n",
       "      <td>65.70</td>\n",
       "      <td>84.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Viajes (partido origen)</td>\n",
       "      <td>10009259.00</td>\n",
       "      <td>7184734.00</td>\n",
       "      <td>2824525.00</td>\n",
       "      <td>71.78</td>\n",
       "      <td>28.22</td>\n",
       "      <td>69.40</td>\n",
       "      <td>62.14</td>\n",
       "      <td>98.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Viajes (partido destino)</td>\n",
       "      <td>10009259.00</td>\n",
       "      <td>7003404.00</td>\n",
       "      <td>3005855.00</td>\n",
       "      <td>69.97</td>\n",
       "      <td>30.03</td>\n",
       "      <td>69.40</td>\n",
       "      <td>60.57</td>\n",
       "      <td>105.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Viajes en TPubl (partido hogar)</td>\n",
       "      <td>4103227.00</td>\n",
       "      <td>3077437.00</td>\n",
       "      <td>1025790.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>28.45</td>\n",
       "      <td>26.62</td>\n",
       "      <td>35.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Viajes en TPubl (partido origen)</td>\n",
       "      <td>4103227.00</td>\n",
       "      <td>2834290.00</td>\n",
       "      <td>1268937.00</td>\n",
       "      <td>69.07</td>\n",
       "      <td>30.93</td>\n",
       "      <td>28.45</td>\n",
       "      <td>24.51</td>\n",
       "      <td>44.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Viajes en TPubl (partido destino)</td>\n",
       "      <td>4103227.00</td>\n",
       "      <td>2721825.00</td>\n",
       "      <td>1381402.00</td>\n",
       "      <td>66.33</td>\n",
       "      <td>33.67</td>\n",
       "      <td>28.45</td>\n",
       "      <td>23.54</td>\n",
       "      <td>48.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Viajes (partido hogar) (sin vuelta Hogar)</td>\n",
       "      <td>6315913.00</td>\n",
       "      <td>4693682.00</td>\n",
       "      <td>1622231.00</td>\n",
       "      <td>74.32</td>\n",
       "      <td>25.68</td>\n",
       "      <td>43.79</td>\n",
       "      <td>40.59</td>\n",
       "      <td>56.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Viajes (partido origen) (sin vuelta Hogar)</td>\n",
       "      <td>6315913.00</td>\n",
       "      <td>4662579.00</td>\n",
       "      <td>1653334.00</td>\n",
       "      <td>73.82</td>\n",
       "      <td>26.18</td>\n",
       "      <td>43.79</td>\n",
       "      <td>40.32</td>\n",
       "      <td>57.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Viajes (partido destino) (sin vuelta Hogar)</td>\n",
       "      <td>6315913.00</td>\n",
       "      <td>4102677.00</td>\n",
       "      <td>2213236.00</td>\n",
       "      <td>64.96</td>\n",
       "      <td>35.04</td>\n",
       "      <td>43.79</td>\n",
       "      <td>35.48</td>\n",
       "      <td>77.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Viajes en TPubl (partido hogar) (sin vuelta Ho...</td>\n",
       "      <td>2532604.00</td>\n",
       "      <td>1847320.00</td>\n",
       "      <td>685284.00</td>\n",
       "      <td>72.94</td>\n",
       "      <td>27.06</td>\n",
       "      <td>17.56</td>\n",
       "      <td>15.98</td>\n",
       "      <td>23.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Viajes en TPubl (partido origen) (sin vuelta H...</td>\n",
       "      <td>2532604.00</td>\n",
       "      <td>1832695.00</td>\n",
       "      <td>699909.00</td>\n",
       "      <td>72.36</td>\n",
       "      <td>27.64</td>\n",
       "      <td>17.56</td>\n",
       "      <td>15.85</td>\n",
       "      <td>24.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Viajes en TPubl (partido destino) (sin vuelta ...</td>\n",
       "      <td>2532604.00</td>\n",
       "      <td>1492414.00</td>\n",
       "      <td>1040190.00</td>\n",
       "      <td>58.93</td>\n",
       "      <td>41.07</td>\n",
       "      <td>17.56</td>\n",
       "      <td>12.91</td>\n",
       "      <td>36.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Etapas</td>\n",
       "      <td>24192890.00</td>\n",
       "      <td>18823579.00</td>\n",
       "      <td>5369311.00</td>\n",
       "      <td>77.81</td>\n",
       "      <td>22.19</td>\n",
       "      <td>167.74</td>\n",
       "      <td>162.80</td>\n",
       "      <td>187.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Etapas TPubl</td>\n",
       "      <td>11054224.00</td>\n",
       "      <td>8630841.00</td>\n",
       "      <td>2423383.00</td>\n",
       "      <td>78.08</td>\n",
       "      <td>21.92</td>\n",
       "      <td>76.64</td>\n",
       "      <td>74.64</td>\n",
       "      <td>84.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Viajes por persona (promedio)</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.15</td>\n",
       "      <td>99.10</td>\n",
       "      <td>103.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Viajes por persona en TPubl (promedio)</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Personas que viajan (hogar)</td>\n",
       "      <td>9004648.00</td>\n",
       "      <td>6904494.00</td>\n",
       "      <td>2100154.00</td>\n",
       "      <td>76.68</td>\n",
       "      <td>23.32</td>\n",
       "      <td>62.43</td>\n",
       "      <td>59.71</td>\n",
       "      <td>73.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Personas que viajan (origen)</td>\n",
       "      <td>9004648.00</td>\n",
       "      <td>6532553.00</td>\n",
       "      <td>2472095.00</td>\n",
       "      <td>72.55</td>\n",
       "      <td>27.45</td>\n",
       "      <td>62.43</td>\n",
       "      <td>56.50</td>\n",
       "      <td>86.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Pers q viajan (hogar-dde viajes)</td>\n",
       "      <td>9017350.00</td>\n",
       "      <td>6905993.00</td>\n",
       "      <td>2097971.00</td>\n",
       "      <td>76.59</td>\n",
       "      <td>23.27</td>\n",
       "      <td>62.52</td>\n",
       "      <td>59.73</td>\n",
       "      <td>73.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Pers q viajan (origen-dde viajes)</td>\n",
       "      <td>9017350.00</td>\n",
       "      <td>6531576.00</td>\n",
       "      <td>2456109.00</td>\n",
       "      <td>72.43</td>\n",
       "      <td>27.24</td>\n",
       "      <td>62.52</td>\n",
       "      <td>56.49</td>\n",
       "      <td>85.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Personas que viajan en TPubl (hogar)</td>\n",
       "      <td>4103227.00</td>\n",
       "      <td>3077437.00</td>\n",
       "      <td>1025790.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>28.45</td>\n",
       "      <td>26.62</td>\n",
       "      <td>35.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Personas que viajan en TPubl (origen)</td>\n",
       "      <td>4103227.00</td>\n",
       "      <td>2834290.00</td>\n",
       "      <td>1268937.00</td>\n",
       "      <td>69.07</td>\n",
       "      <td>30.93</td>\n",
       "      <td>28.45</td>\n",
       "      <td>24.51</td>\n",
       "      <td>44.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Pers q viajan TP (hogar-dde viajes)</td>\n",
       "      <td>4103227.00</td>\n",
       "      <td>3077437.00</td>\n",
       "      <td>1025790.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>25.00</td>\n",
       "      <td>28.45</td>\n",
       "      <td>26.62</td>\n",
       "      <td>35.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Pers q viajan TP (origen-dde viajes)</td>\n",
       "      <td>4103227.00</td>\n",
       "      <td>2834290.00</td>\n",
       "      <td>1268937.00</td>\n",
       "      <td>69.07</td>\n",
       "      <td>30.93</td>\n",
       "      <td>28.45</td>\n",
       "      <td>24.51</td>\n",
       "      <td>44.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Trabajadores</td>\n",
       "      <td>6530099.00</td>\n",
       "      <td>5008045.00</td>\n",
       "      <td>1522054.00</td>\n",
       "      <td>76.69</td>\n",
       "      <td>23.31</td>\n",
       "      <td>45.28</td>\n",
       "      <td>43.31</td>\n",
       "      <td>53.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Rate Viajes/Persona (Trabajo)</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.90</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Rate Viajes/Persona (Otros)</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.89</td>\n",
       "      <td>100.00</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Viajes de CABA a</td>\n",
       "      <td>2412667.00</td>\n",
       "      <td>131434.00</td>\n",
       "      <td>2281233.00</td>\n",
       "      <td>5.45</td>\n",
       "      <td>94.55</td>\n",
       "      <td>16.73</td>\n",
       "      <td>1.14</td>\n",
       "      <td>79.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Viajes de Conurbano a</td>\n",
       "      <td>7596592.00</td>\n",
       "      <td>6871970.00</td>\n",
       "      <td>724622.00</td>\n",
       "      <td>90.46</td>\n",
       "      <td>9.54</td>\n",
       "      <td>52.67</td>\n",
       "      <td>59.43</td>\n",
       "      <td>25.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Viajes de CABA a (sin vuelta Hogar)</td>\n",
       "      <td>1622231.00</td>\n",
       "      <td>130948.00</td>\n",
       "      <td>1491283.00</td>\n",
       "      <td>8.07</td>\n",
       "      <td>91.93</td>\n",
       "      <td>11.25</td>\n",
       "      <td>1.13</td>\n",
       "      <td>52.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Viajes de Conurbano a (sin vuelta Hogar)</td>\n",
       "      <td>4693682.00</td>\n",
       "      <td>3971729.00</td>\n",
       "      <td>721953.00</td>\n",
       "      <td>84.62</td>\n",
       "      <td>15.38</td>\n",
       "      <td>32.54</td>\n",
       "      <td>34.35</td>\n",
       "      <td>25.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Viajes trabajo de CABA a</td>\n",
       "      <td>750878.00</td>\n",
       "      <td>94380.00</td>\n",
       "      <td>656498.00</td>\n",
       "      <td>12.57</td>\n",
       "      <td>87.43</td>\n",
       "      <td>5.21</td>\n",
       "      <td>0.82</td>\n",
       "      <td>22.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Viajes trabajo de Conurbano a</td>\n",
       "      <td>1975743.00</td>\n",
       "      <td>1480942.00</td>\n",
       "      <td>494801.00</td>\n",
       "      <td>74.96</td>\n",
       "      <td>25.04</td>\n",
       "      <td>13.70</td>\n",
       "      <td>12.81</td>\n",
       "      <td>17.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Viajes otros de CABA a</td>\n",
       "      <td>1661789.00</td>\n",
       "      <td>37054.00</td>\n",
       "      <td>1624735.00</td>\n",
       "      <td>2.23</td>\n",
       "      <td>97.77</td>\n",
       "      <td>11.52</td>\n",
       "      <td>0.32</td>\n",
       "      <td>56.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Viajes otros de Conurbano a</td>\n",
       "      <td>5620849.00</td>\n",
       "      <td>5391028.00</td>\n",
       "      <td>229821.00</td>\n",
       "      <td>95.91</td>\n",
       "      <td>4.09</td>\n",
       "      <td>38.97</td>\n",
       "      <td>46.62</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Viajes otros de CABA a (sin vuelta Hogar)</td>\n",
       "      <td>871353.00</td>\n",
       "      <td>36568.00</td>\n",
       "      <td>834785.00</td>\n",
       "      <td>4.20</td>\n",
       "      <td>95.80</td>\n",
       "      <td>6.04</td>\n",
       "      <td>0.32</td>\n",
       "      <td>29.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Viajes otros de Conurbano a (sin vuelta Hogar)</td>\n",
       "      <td>2717939.00</td>\n",
       "      <td>2490787.00</td>\n",
       "      <td>227152.00</td>\n",
       "      <td>91.64</td>\n",
       "      <td>8.36</td>\n",
       "      <td>18.84</td>\n",
       "      <td>21.54</td>\n",
       "      <td>7.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Cantidad de autos (totales en hogares)</td>\n",
       "      <td>2497812.00</td>\n",
       "      <td>1927515.00</td>\n",
       "      <td>570297.00</td>\n",
       "      <td>77.17</td>\n",
       "      <td>22.83</td>\n",
       "      <td>17.32</td>\n",
       "      <td>16.67</td>\n",
       "      <td>19.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 dato        Total  \\\n",
       "0                                           Población  14422940.00   \n",
       "1                              Viajes (partido hogar)  10009259.00   \n",
       "2                             Viajes (partido origen)  10009259.00   \n",
       "3                            Viajes (partido destino)  10009259.00   \n",
       "4                     Viajes en TPubl (partido hogar)   4103227.00   \n",
       "5                    Viajes en TPubl (partido origen)   4103227.00   \n",
       "6                   Viajes en TPubl (partido destino)   4103227.00   \n",
       "7           Viajes (partido hogar) (sin vuelta Hogar)   6315913.00   \n",
       "8          Viajes (partido origen) (sin vuelta Hogar)   6315913.00   \n",
       "9         Viajes (partido destino) (sin vuelta Hogar)   6315913.00   \n",
       "10  Viajes en TPubl (partido hogar) (sin vuelta Ho...   2532604.00   \n",
       "11  Viajes en TPubl (partido origen) (sin vuelta H...   2532604.00   \n",
       "12  Viajes en TPubl (partido destino) (sin vuelta ...   2532604.00   \n",
       "13                                             Etapas  24192890.00   \n",
       "14                                       Etapas TPubl  11054224.00   \n",
       "15                      Viajes por persona (promedio)         1.11   \n",
       "16             Viajes por persona en TPubl (promedio)         1.00   \n",
       "17                        Personas que viajan (hogar)   9004648.00   \n",
       "18                       Personas que viajan (origen)   9004648.00   \n",
       "19                   Pers q viajan (hogar-dde viajes)   9017350.00   \n",
       "20                  Pers q viajan (origen-dde viajes)   9017350.00   \n",
       "21               Personas que viajan en TPubl (hogar)   4103227.00   \n",
       "22              Personas que viajan en TPubl (origen)   4103227.00   \n",
       "23                Pers q viajan TP (hogar-dde viajes)   4103227.00   \n",
       "24               Pers q viajan TP (origen-dde viajes)   4103227.00   \n",
       "25                                       Trabajadores   6530099.00   \n",
       "26                      Rate Viajes/Persona (Trabajo)         0.90   \n",
       "27                        Rate Viajes/Persona (Otros)         0.89   \n",
       "28                                   Viajes de CABA a   2412667.00   \n",
       "29                              Viajes de Conurbano a   7596592.00   \n",
       "30                Viajes de CABA a (sin vuelta Hogar)   1622231.00   \n",
       "31           Viajes de Conurbano a (sin vuelta Hogar)   4693682.00   \n",
       "32                           Viajes trabajo de CABA a    750878.00   \n",
       "33                      Viajes trabajo de Conurbano a   1975743.00   \n",
       "34                             Viajes otros de CABA a   1661789.00   \n",
       "35                        Viajes otros de Conurbano a   5620849.00   \n",
       "36          Viajes otros de CABA a (sin vuelta Hogar)    871353.00   \n",
       "37     Viajes otros de Conurbano a (sin vuelta Hogar)   2717939.00   \n",
       "38             Cantidad de autos (totales en hogares)   2497812.00   \n",
       "\n",
       "      Conurbano        CABA  Conurbano_porc  CABA_porc  Total_porc_pobl  \\\n",
       "0   11562732.00  2860208.00           80.17      19.83           100.00   \n",
       "1    7596592.00  2412667.00           75.90      24.10            69.40   \n",
       "2    7184734.00  2824525.00           71.78      28.22            69.40   \n",
       "3    7003404.00  3005855.00           69.97      30.03            69.40   \n",
       "4    3077437.00  1025790.00           75.00      25.00            28.45   \n",
       "5    2834290.00  1268937.00           69.07      30.93            28.45   \n",
       "6    2721825.00  1381402.00           66.33      33.67            28.45   \n",
       "7    4693682.00  1622231.00           74.32      25.68            43.79   \n",
       "8    4662579.00  1653334.00           73.82      26.18            43.79   \n",
       "9    4102677.00  2213236.00           64.96      35.04            43.79   \n",
       "10   1847320.00   685284.00           72.94      27.06            17.56   \n",
       "11   1832695.00   699909.00           72.36      27.64            17.56   \n",
       "12   1492414.00  1040190.00           58.93      41.07            17.56   \n",
       "13  18823579.00  5369311.00           77.81      22.19           167.74   \n",
       "14   8630841.00  2423383.00           78.08      21.92            76.64   \n",
       "15         1.10        1.15           99.10     103.60             0.00   \n",
       "16         1.00        1.00          100.00     100.00             0.00   \n",
       "17   6904494.00  2100154.00           76.68      23.32            62.43   \n",
       "18   6532553.00  2472095.00           72.55      27.45            62.43   \n",
       "19   6905993.00  2097971.00           76.59      23.27            62.52   \n",
       "20   6531576.00  2456109.00           72.43      27.24            62.52   \n",
       "21   3077437.00  1025790.00           75.00      25.00            28.45   \n",
       "22   2834290.00  1268937.00           69.07      30.93            28.45   \n",
       "23   3077437.00  1025790.00           75.00      25.00            28.45   \n",
       "24   2834290.00  1268937.00           69.07      30.93            28.45   \n",
       "25   5008045.00  1522054.00           76.69      23.31            45.28   \n",
       "26         0.90        0.90          100.00     100.00             0.00   \n",
       "27         0.89        0.89          100.00     100.00             0.00   \n",
       "28    131434.00  2281233.00            5.45      94.55            16.73   \n",
       "29   6871970.00   724622.00           90.46       9.54            52.67   \n",
       "30    130948.00  1491283.00            8.07      91.93            11.25   \n",
       "31   3971729.00   721953.00           84.62      15.38            32.54   \n",
       "32     94380.00   656498.00           12.57      87.43             5.21   \n",
       "33   1480942.00   494801.00           74.96      25.04            13.70   \n",
       "34     37054.00  1624735.00            2.23      97.77            11.52   \n",
       "35   5391028.00   229821.00           95.91       4.09            38.97   \n",
       "36     36568.00   834785.00            4.20      95.80             6.04   \n",
       "37   2490787.00   227152.00           91.64       8.36            18.84   \n",
       "38   1927515.00   570297.00           77.17      22.83            17.32   \n",
       "\n",
       "    Conurbano_porc_pobl  CABA_porc_pobl  \n",
       "0                100.00          100.00  \n",
       "1                 65.70           84.35  \n",
       "2                 62.14           98.75  \n",
       "3                 60.57          105.09  \n",
       "4                 26.62           35.86  \n",
       "5                 24.51           44.37  \n",
       "6                 23.54           48.30  \n",
       "7                 40.59           56.72  \n",
       "8                 40.32           57.80  \n",
       "9                 35.48           77.38  \n",
       "10                15.98           23.96  \n",
       "11                15.85           24.47  \n",
       "12                12.91           36.37  \n",
       "13               162.80          187.72  \n",
       "14                74.64           84.73  \n",
       "15                 0.00            0.00  \n",
       "16                 0.00            0.00  \n",
       "17                59.71           73.43  \n",
       "18                56.50           86.43  \n",
       "19                59.73           73.35  \n",
       "20                56.49           85.87  \n",
       "21                26.62           35.86  \n",
       "22                24.51           44.37  \n",
       "23                26.62           35.86  \n",
       "24                24.51           44.37  \n",
       "25                43.31           53.21  \n",
       "26                 0.00            0.00  \n",
       "27                 0.00            0.00  \n",
       "28                 1.14           79.76  \n",
       "29                59.43           25.33  \n",
       "30                 1.13           52.14  \n",
       "31                34.35           25.24  \n",
       "32                 0.82           22.95  \n",
       "33                12.81           17.30  \n",
       "34                 0.32           56.80  \n",
       "35                46.62            8.04  \n",
       "36                 0.32           29.19  \n",
       "37                21.54            7.94  \n",
       "38                16.67           19.94  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Población\n",
    "datos = DataFrame([['Población', personas.f_expansion.sum().round().astype(int), personas[personas.Zona=='Conurbano'].f_expansion.sum().round().astype(int), \n",
    "                    personas[personas.Zona=='CABA'].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA'])\n",
    "\n",
    "\n",
    "# Viajes\n",
    "datos = datos.append(DataFrame([['Viajes (partido hogar)', viajes.f_expansion.sum().round().astype(int), \n",
    "                                 viajes[viajes.Zona=='Conurbano'].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[viajes.Zona=='CABA'].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "datos = datos.append(DataFrame([['Viajes (partido origen)', viajes.f_expansion.sum().round().astype(int), \n",
    "                                 viajes[viajes.Zona_origen=='Origen Conurbano'].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[viajes.Zona_origen=='Origen CABA'].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "datos = datos.append(DataFrame([['Viajes (partido destino)', viajes.f_expansion.sum().round().astype(int), \n",
    "                                 viajes[viajes.Zona_destino=='Destino Conurbano'].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[viajes.Zona_destino=='Destino CABA'].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "\n",
    "datos = datos.append(DataFrame([['Viajes en TPubl (partido hogar)', viajes[(viajes.Público==1)].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona=='Conurbano')&(viajes.Público==1)].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona=='CABA')&(viajes.Público==1)].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "datos = datos.append(DataFrame([['Viajes en TPubl (partido origen)', viajes[(viajes.Público==1)].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona_origen=='Origen Conurbano')&(viajes.Público==1)].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona_origen=='Origen CABA')&(viajes.Público==1)].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "datos = datos.append(DataFrame([['Viajes en TPubl (partido destino)', viajes[(viajes.Público==1)].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona_destino=='Destino Conurbano')&(viajes.Público==1)].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona_destino=='Destino CABA')&(viajes.Público==1)].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "\n",
    "# Viajes (sin vuelta a hogar)\n",
    "datos = datos.append(DataFrame([['Viajes (partido hogar) (sin vuelta Hogar)', viajes[(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona=='Conurbano')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona=='CABA')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "datos = datos.append(DataFrame([['Viajes (partido origen) (sin vuelta Hogar)', viajes[(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona_origen=='Origen Conurbano')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona_origen=='Origen CABA')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "datos = datos.append(DataFrame([['Viajes (partido destino) (sin vuelta Hogar)', viajes[(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona_destino=='Destino Conurbano')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona_destino=='Destino CABA')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "datos = datos.append(DataFrame([['Viajes en TPubl (partido hogar) (sin vuelta Hogar)', viajes[(viajes.Público==1)&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona=='Conurbano')&(viajes.Público==1)&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona=='CABA')&(viajes.Público==1)&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "datos = datos.append(DataFrame([['Viajes en TPubl (partido origen) (sin vuelta Hogar)', viajes[(viajes.Público==1)&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona_origen=='Origen Conurbano')&(viajes.Público==1)&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona_origen=='Origen CABA')&(viajes.Público==1)&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "datos = datos.append(DataFrame([['Viajes en TPubl (partido destino) (sin vuelta Hogar)', viajes[(viajes.Público==1)&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona_destino=='Destino Conurbano')&(viajes.Público==1)&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                 viajes[(viajes.Zona_destino=='Destino CABA')&(viajes.Público==1)&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "# Etapas\n",
    "datos = datos.append(DataFrame([['Etapas', etapas.f_expansion.sum().round().astype(int), etapas[etapas.Zona=='Conurbano'].f_expansion.sum().round().astype(int), \n",
    "                                 etapas[etapas.Zona=='CABA'].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "datos = datos.append(DataFrame([['Etapas TPubl', etapas[(etapas.Modo_agg =='Público')].f_expansion.sum().round().astype(int), \n",
    "                                 etapas[(etapas.Zona=='Conurbano')&(etapas.Modo_agg =='Público')].f_expansion.sum().round().astype(int), \n",
    "                                 etapas[(etapas.Zona=='CABA')&(etapas.Modo_agg =='Público')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "# Viajes promedio por persona (promedio ponderado por fex)\n",
    "pers_temp = viajes.groupby(['id_muestra', 'nro_vivienda', 'nro_hogar', 'bivi01', 'Zona', 'f_expansion']).size().reset_index()\n",
    "pers_temp['pond'] = pers_temp['f_expansion'] * pers_temp[0]\n",
    "datos = datos.append(DataFrame([['Viajes por persona (promedio)', (pers_temp.pond.sum() / pers_temp.f_expansion.sum()).round(2), \n",
    "            (pers_temp[pers_temp.Zona=='Conurbano'].pond.sum() / pers_temp[pers_temp.Zona=='Conurbano'].f_expansion.sum()).round(2), \n",
    "            (pers_temp[pers_temp.Zona=='CABA'].pond.sum() / pers_temp[pers_temp.Zona=='CABA'].f_expansion.sum()).round(2)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "# Viajes promedio por persona en TPubl (promedio ponderado por fex)\n",
    "pers_temp = viajes[viajes.Público==1].groupby(['id_muestra', 'nro_vivienda', 'nro_hogar', 'bivi01', 'Zona', 'f_expansion']).size().reset_index()\n",
    "pers_temp['pond'] = pers_temp['f_expansion'] * pers_temp[0]\n",
    "datos = datos.append(DataFrame([['Viajes por persona en TPubl (promedio)', (pers_temp.pond.sum() / pers_temp.f_expansion.sum()).round(2), \n",
    "            (pers_temp[pers_temp.Zona=='Conurbano'].pond.sum() / pers_temp[pers_temp.Zona=='Conurbano'].f_expansion.sum()).round(2), \n",
    "            (pers_temp[pers_temp.Zona=='CABA'].pond.sum() / pers_temp[pers_temp.Zona=='CABA'].f_expansion.sum()).round(2)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "\n",
    "# Personas que viajan (según la zona del hogar)\n",
    "pers_temp = viajes.groupby(['id_muestra', 'nro_vivienda', 'nro_hogar', 'bivi01', 'Zona', 'f_expansion']).agg({'Mixto':'max', 'No Motorizado':'max', 'Privado':'max', 'Público':'max'}).reset_index()\n",
    "datos = datos.append(DataFrame([['Personas que viajan (hogar)', pers_temp.f_expansion.sum().round().astype(int), \n",
    "            pers_temp[pers_temp.Zona=='Conurbano'].f_expansion.sum().round().astype(int), \n",
    "            pers_temp[pers_temp.Zona=='CABA'].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "pers_temp2 = viajes.groupby(['id_muestra', 'nro_vivienda', 'nro_hogar', 'bivi01', 'Zona_origen', 'f_expansion']).agg({'Mixto':'max', 'No Motorizado':'max', 'Privado':'max', 'Público':'max'}).reset_index()\n",
    "datos = datos.append(DataFrame([['Personas que viajan (origen)', pers_temp2.f_expansion.sum().round().astype(int), \n",
    "            pers_temp2[pers_temp2.Zona_origen=='Origen Conurbano'].f_expansion.sum().round().astype(int), \n",
    "            pers_temp2[pers_temp2.Zona_origen=='Origen CABA'].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "# Personas que viajan (construido desde viajes - viajes/promedio de viajes por persona)\n",
    "datos = datos.append(pd.DataFrame([['Pers q viajan (hogar-dde viajes)', (datos[datos.dato=='Viajes (partido hogar)'].Total.values[0] / datos[datos.dato=='Viajes por persona (promedio)'].Total.values[0]).round().astype(int), \\\n",
    "        (datos[datos.dato=='Viajes (partido hogar)'].Conurbano.values[0] / datos[datos.dato=='Viajes por persona (promedio)'].Conurbano.values[0]).round().astype(int), \\\n",
    "        (datos[datos.dato=='Viajes (partido hogar)'].CABA.values[0] / datos[datos.dato=='Viajes por persona (promedio)'].CABA.values[0]).round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "datos = datos.append(pd.DataFrame([['Pers q viajan (origen-dde viajes)', (datos[datos.dato=='Viajes (partido origen)'].Total.values[0] / datos[datos.dato=='Viajes por persona (promedio)'].Total.values[0]).round().astype(int), \\\n",
    "        (datos[datos.dato=='Viajes (partido origen)'].Conurbano.values[0] / datos[datos.dato=='Viajes por persona (promedio)'].Conurbano.values[0]).round().astype(int), \\\n",
    "        (datos[datos.dato=='Viajes (partido origen)'].CABA.values[0] / datos[datos.dato=='Viajes por persona (promedio)'].CABA.values[0]).round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "datos = datos.append(DataFrame([['Personas que viajan en TPubl (hogar)', pers_temp[(pers_temp['Público']==1)].f_expansion.sum().round().astype(int), \n",
    "            pers_temp[(pers_temp.Zona=='Conurbano')&(pers_temp['Público']==1)].f_expansion.sum().round().astype(int), \n",
    "            pers_temp[(pers_temp.Zona=='CABA')&(pers_temp['Público']==1)].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "datos = datos.append(DataFrame([['Personas que viajan en TPubl (origen)', pers_temp2[(pers_temp2['Público']==1)].f_expansion.sum().round().astype(int), \n",
    "            pers_temp2[(pers_temp2.Zona_origen=='Origen Conurbano')&(pers_temp2['Público']==1)].f_expansion.sum().round().astype(int), \n",
    "            pers_temp2[(pers_temp2.Zona_origen=='Origen CABA')&(pers_temp2['Público']==1)].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "datos = datos.append(pd.DataFrame([['Pers q viajan TP (hogar-dde viajes)', (datos[datos.dato=='Viajes en TPubl (partido hogar)'].Total.values[0] / datos[datos.dato=='Viajes por persona en TPubl (promedio)'].Total.values[0]).round().astype(int), \\\n",
    "        (datos[datos.dato=='Viajes en TPubl (partido hogar)'].Conurbano.values[0] / datos[datos.dato=='Viajes por persona en TPubl (promedio)'].Conurbano.values[0]).round().astype(int), \\\n",
    "        (datos[datos.dato=='Viajes en TPubl (partido hogar)'].CABA.values[0] / datos[datos.dato=='Viajes por persona en TPubl (promedio)'].CABA.values[0]).round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "datos = datos.append(pd.DataFrame([['Pers q viajan TP (origen-dde viajes)', (datos[datos.dato=='Viajes en TPubl (partido origen)'].Total.values[0] / datos[datos.dato=='Viajes por persona en TPubl (promedio)'].Total.values[0]).round().astype(int), \\\n",
    "        (datos[datos.dato=='Viajes en TPubl (partido origen)'].Conurbano.values[0] / datos[datos.dato=='Viajes por persona en TPubl (promedio)'].Conurbano.values[0]).round().astype(int), \\\n",
    "        (datos[datos.dato=='Viajes en TPubl (partido origen)'].CABA.values[0] / datos[datos.dato=='Viajes por persona en TPubl (promedio)'].CABA.values[0]).round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "datos = datos.append(DataFrame([['Trabajadores', personas[personas.bhmo03==1].f_expansion.sum().round().astype(int), \n",
    "                                 personas[(personas.Zona=='Conurbano')&(personas.bhmo03==1)].f_expansion.sum().round().astype(int), \n",
    "                                 personas[(personas.Zona=='CABA')&(personas.bhmo03==1)].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "\n",
    "# Rate Viajes/Personas\n",
    "v1 = viajes[viajes.Motivo_agg=='Trabajo'].groupby(['id_muestra', 'nro_vivienda', 'nro_hogar', 'bivi01', 'f_expansion']).size().reset_index()\n",
    "v1['tot_temp'] = v1['f_expansion'] * v1[0]\n",
    "v1 = (v1.f_expansion.sum() / v1.tot_temp.sum() ).round(2)\n",
    "datos = datos.append(pd.DataFrame([['Rate Viajes/Persona (Trabajo)', v1, v1, v1]], columns=['dato', 'Total', 'Conurbano', 'CABA'] ))\n",
    "\n",
    "v1 = viajes[(viajes.Motivo_agg!='Hogar')&(viajes.Motivo_agg!='Trabajo')].groupby(['id_muestra', 'nro_vivienda', 'nro_hogar', 'bivi01', 'f_expansion']).size().reset_index()\n",
    "v1['tot_temp'] = v1['f_expansion'] * v1[0]\n",
    "v1 = (v1.f_expansion.sum() / v1.tot_temp.sum()).round(2)\n",
    "datos = datos.append(pd.DataFrame([['Rate Viajes/Persona (Otros)', v1, v1, v1]], columns=['dato', 'Total', 'Conurbano', 'CABA'] ))\n",
    "\n",
    "\n",
    "# Construyo matriz de origen y destino de viajes total, motivo trabajo y motivo otros\n",
    "datos = datos.append(pd.DataFrame([['Viajes de CABA a', viajes[viajes.Zona=='CABA'].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='CABA')&(viajes.Zona_destino=='Destino Conurbano')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='CABA')&(viajes.Zona_destino=='Destino CABA')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "datos = datos.append(pd.DataFrame([['Viajes de Conurbano a', viajes[viajes.Zona=='Conurbano'].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='Conurbano')&(viajes.Zona_destino=='Destino Conurbano')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='Conurbano')&(viajes.Zona_destino=='Destino CABA')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "datos = datos.append(pd.DataFrame([['Viajes de CABA a (sin vuelta Hogar)', viajes[(viajes.Zona=='CABA')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='CABA')&(viajes.Zona_destino=='Destino Conurbano')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='CABA')&(viajes.Zona_destino=='Destino CABA')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "datos = datos.append(pd.DataFrame([['Viajes de Conurbano a (sin vuelta Hogar)', viajes[(viajes.Zona=='Conurbano')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='Conurbano')&(viajes.Zona_destino=='Destino Conurbano')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='Conurbano')&(viajes.Zona_destino=='Destino CABA')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "datos = datos.append(pd.DataFrame([['Viajes trabajo de CABA a', viajes[(viajes.Zona=='CABA')&(viajes['Motivo_agg']=='Trabajo')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='CABA')&(viajes.Zona_destino=='Destino Conurbano')&(viajes['Motivo_agg']=='Trabajo')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='CABA')&(viajes.Zona_destino=='Destino CABA')&(viajes['Motivo_agg']=='Trabajo')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "datos = datos.append(pd.DataFrame([['Viajes trabajo de Conurbano a', viajes[(viajes.Zona=='Conurbano')&(viajes['Motivo_agg']=='Trabajo')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='Conurbano')&(viajes.Zona_destino=='Destino Conurbano')&(viajes['Motivo_agg']=='Trabajo')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='Conurbano')&(viajes.Zona_destino=='Destino CABA')&(viajes['Motivo_agg']=='Trabajo')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "datos = datos.append(pd.DataFrame([['Viajes otros de CABA a', viajes[(viajes.Zona=='CABA')&(viajes['Motivo_agg']!='Trabajo')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='CABA')&(viajes.Zona_destino=='Destino Conurbano')&(viajes['Motivo_agg']!='Trabajo')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='CABA')&(viajes.Zona_destino=='Destino CABA')&(viajes['Motivo_agg']!='Trabajo')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "datos = datos.append(pd.DataFrame([['Viajes otros de Conurbano a', viajes[(viajes.Zona=='Conurbano')&(viajes['Motivo_agg']!='Trabajo')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='Conurbano')&(viajes.Zona_destino=='Destino Conurbano')&(viajes['Motivo_agg']!='Trabajo')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='Conurbano')&(viajes.Zona_destino=='Destino CABA')&(viajes['Motivo_agg']!='Trabajo')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "\n",
    "datos = datos.append(pd.DataFrame([['Viajes otros de CABA a (sin vuelta Hogar)', viajes[(viajes.Zona=='CABA')&(viajes['Motivo_agg']!='Trabajo')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='CABA')&(viajes.Zona_destino=='Destino Conurbano')&(viajes['Motivo_agg']!='Trabajo')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='CABA')&(viajes.Zona_destino=='Destino CABA')&(viajes['Motivo_agg']!='Trabajo')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "datos = datos.append(pd.DataFrame([['Viajes otros de Conurbano a (sin vuelta Hogar)', viajes[(viajes.Zona=='Conurbano')&(viajes['Motivo_agg']!='Trabajo')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='Conurbano')&(viajes.Zona_destino=='Destino Conurbano')&(viajes['Motivo_agg']!='Trabajo')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int), \n",
    "                                    viajes[(viajes.Zona=='Conurbano')&(viajes.Zona_destino=='Destino CABA')&(viajes['Motivo_agg']!='Trabajo')&(viajes['Motivo_agg']!='Hogar')].f_expansion.sum().round().astype(int)]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# BHVM02_01 - Cantidad de autos (totales en hogares)\n",
    "hogares['bhvm02_01'] = hogares['bhvm02_01'].fillna(0)\n",
    "hogares['Hog_Cant_autos'] = hogares['bhvm02_01'] * hogares['f_expansion']\n",
    "(hogares.Hog_Cant_autos.sum()).round(2), \\\n",
    "(hogares.Hog_Cant_autos.sum() / hogares.f_expansion.sum()).round(2)\n",
    "\n",
    "datos = datos.append(pd.DataFrame([['Cantidad de autos (totales en hogares)', (hogares.Hog_Cant_autos.sum()).round(2), \n",
    "                                    (hogares[hogares.Zona=='Conurbano'].Hog_Cant_autos.sum()).round(2), \n",
    "                                    (hogares[hogares.Zona=='CABA'].Hog_Cant_autos.sum()).round(2),]], columns=['dato', 'Total', 'Conurbano', 'CABA']))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# Construyo campos de porcentajes \n",
    "datos['Conurbano_porc'] = (datos['Conurbano'] / datos['Total'] * 100).round(2)\n",
    "datos['CABA_porc'] = (datos['CABA'] / datos['Total'] * 100).round(2)\n",
    "\n",
    "datos['Total_porc_pobl'] = (datos.Total / datos[datos.dato=='Población'].Total.values * 100).round(2)\n",
    "datos['Conurbano_porc_pobl'] = (datos.Conurbano / datos[datos.dato=='Población'].Conurbano.values * 100).round(2)\n",
    "datos['CABA_porc_pobl'] = (datos.CABA / datos[datos.dato=='Población'].CABA.values * 100).round(2)\n",
    "\n",
    "\n",
    "datos = datos.fillna(0)\n",
    "\n",
    "\n",
    "datos = datos.reset_index(drop=True)\n",
    "datos.to_csv('datos_2014.csv', sep=';', encoding='latin1')\n",
    "datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_muestra</th>\n",
       "      <th>nro_vivienda</th>\n",
       "      <th>nro_hogar</th>\n",
       "      <th>bivi01</th>\n",
       "      <th>bivi02</th>\n",
       "      <th>bivi03</th>\n",
       "      <th>bivi04_01</th>\n",
       "      <th>bivi04_02</th>\n",
       "      <th>bivi04_03</th>\n",
       "      <th>bivi04_04</th>\n",
       "      <th>bivi04_05</th>\n",
       "      <th>bivi05_01</th>\n",
       "      <th>bivi05_02</th>\n",
       "      <th>bivi05_03</th>\n",
       "      <th>bivi05_04</th>\n",
       "      <th>bivi05_05</th>\n",
       "      <th>bivi06_01</th>\n",
       "      <th>bivi06_02</th>\n",
       "      <th>bivi07</th>\n",
       "      <th>bivi08</th>\n",
       "      <th>bivi09</th>\n",
       "      <th>tipo_viaje</th>\n",
       "      <th>origen_latitud</th>\n",
       "      <th>origen_longitud</th>\n",
       "      <th>destino_latitud</th>\n",
       "      <th>destino_longitud</th>\n",
       "      <th>Motivo</th>\n",
       "      <th>Motivo_agg</th>\n",
       "      <th>Modo_tipo</th>\n",
       "      <th>Zona_destino</th>\n",
       "      <th>Zona_origen</th>\n",
       "      <th>Mixto</th>\n",
       "      <th>No Motorizado</th>\n",
       "      <th>Privado</th>\n",
       "      <th>Público</th>\n",
       "      <th>Zona</th>\n",
       "      <th>f_expansion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>146684</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>02/12</td>\n",
       "      <td>BERISSO</td>\n",
       "      <td>H</td>\n",
       "      <td>HOGAR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LA PLATA</td>\n",
       "      <td>C</td>\n",
       "      <td>37</td>\n",
       "      <td>E</td>\n",
       "      <td>AV. 7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>07:40</td>\n",
       "      <td>09:00</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>-34.89871</td>\n",
       "      <td>-57.91151</td>\n",
       "      <td>-34.90477</td>\n",
       "      <td>-57.96327</td>\n",
       "      <td>Lugar de trabajo</td>\n",
       "      <td>Trabajo</td>\n",
       "      <td>Público</td>\n",
       "      <td>Destino Conurbano</td>\n",
       "      <td>Origen Conurbano</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Conurbano</td>\n",
       "      <td>397.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>616429</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23/09</td>\n",
       "      <td>MORENO</td>\n",
       "      <td>H</td>\n",
       "      <td>HOGAR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VICENTE LOPEZ</td>\n",
       "      <td>H</td>\n",
       "      <td>HOSPITAL DR. B. HOUSSAY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>07:30</td>\n",
       "      <td>11:30</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-34.60522</td>\n",
       "      <td>-58.79516</td>\n",
       "      <td>-34.54195</td>\n",
       "      <td>-58.52828</td>\n",
       "      <td>Salud</td>\n",
       "      <td>Salud</td>\n",
       "      <td>Público</td>\n",
       "      <td>Destino Conurbano</td>\n",
       "      <td>Origen Conurbano</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Conurbano</td>\n",
       "      <td>323.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id_muestra  nro_vivienda  nro_hogar  bivi01  bivi02 bivi03 bivi04_01  \\\n",
       "0      146684             1          1       1       1  02/12   BERISSO   \n",
       "1      616429             1          1       1       1  23/09    MORENO   \n",
       "\n",
       "  bivi04_02 bivi04_03 bivi04_04 bivi04_05      bivi05_01 bivi05_02  \\\n",
       "0         H     HOGAR       NaN       NaN       LA PLATA         C   \n",
       "1         H     HOGAR       NaN       NaN  VICENTE LOPEZ         H   \n",
       "\n",
       "                 bivi05_03 bivi05_04 bivi05_05  bivi06_01  bivi06_02 bivi07  \\\n",
       "0                       37         E     AV. 7          1          2  07:40   \n",
       "1  HOSPITAL DR. B. HOUSSAY       NaN       NaN          1         10  07:30   \n",
       "\n",
       "  bivi08  bivi09  tipo_viaje origen_latitud origen_longitud destino_latitud  \\\n",
       "0  09:00       6           2      -34.89871       -57.91151       -34.90477   \n",
       "1  11:30       1           2      -34.60522       -58.79516       -34.54195   \n",
       "\n",
       "  destino_longitud            Motivo Motivo_agg Modo_tipo       Zona_destino  \\\n",
       "0        -57.96327  Lugar de trabajo    Trabajo   Público  Destino Conurbano   \n",
       "1        -58.52828             Salud      Salud   Público  Destino Conurbano   \n",
       "\n",
       "        Zona_origen  Mixto  No Motorizado  Privado  Público       Zona  \\\n",
       "0  Origen Conurbano      0              0        0        1  Conurbano   \n",
       "1  Origen Conurbano      0              0        0        1  Conurbano   \n",
       "\n",
       "   f_expansion  \n",
       "0        397.0  \n",
       "1        323.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viajes.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>f_expansion</th>\n",
       "      <th>tot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zona</th>\n",
       "      <th>Zona_destino</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">CABA</th>\n",
       "      <th>Destino CABA</th>\n",
       "      <td>988145.0</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destino Conurbano</th>\n",
       "      <td>63903.0</td>\n",
       "      <td>54.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Conurbano</th>\n",
       "      <th>Destino CABA</th>\n",
       "      <td>436262.0</td>\n",
       "      <td>64.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destino Conurbano</th>\n",
       "      <td>2738363.0</td>\n",
       "      <td>56.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             f_expansion   tot\n",
       "Zona      Zona_destino                        \n",
       "CABA      Destino CABA          988145.0  63.0\n",
       "          Destino Conurbano      63903.0  54.3\n",
       "Conurbano Destino CABA          436262.0  64.1\n",
       "          Destino Conurbano    2738363.0  56.4"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = viajes[viajes.Modo_tipo!='No Motorizado'].groupby(['Zona', 'Zona_destino', 'Modo_tipo']).f_expansion.sum().round().reset_index()\n",
    "v['tot'] = v.groupby(['Zona', 'Zona_destino']).f_expansion.transform('sum') \n",
    "v['tot'] = (v['f_expansion'] / v['tot'] * 100).round(1)\n",
    "v[(v.Modo_tipo=='Público')|(v.Modo_tipo=='Mixto')].groupby(['Zona', 'Zona_destino']).sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Mixto', 'Privado', 'Público'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v.Modo_tipo.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1808522.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # Origen y destino de los viajes por trabajo\n",
    "# (viajes[viajes['Motivo_agg']=='Trabajo'].groupby(['Zona', 'Zona_destino']).f_expansion.sum().round() / viajes[viajes['Motivo_agg']=='Trabajo'].groupby(['Zona', 'Zona_destino']).f_expansion.sum().round().sum() * 100 ).round().reset_index()\n",
    "\n",
    "viajes['Trabajo']=0\n",
    "viajes['Otros']=0\n",
    "viajes.loc[viajes.Motivo_agg=='Trabajo', 'Trabajo'] = 1\n",
    "viajes.loc[viajes.Motivo_agg!='Trabajo', 'Otros'] = 1\n",
    "\n",
    "pers_temp3 = viajes.groupby(['id_muestra', 'nro_vivienda', 'nro_hogar', 'bivi01', 'Zona', 'Zona_destino', 'f_expansion']).agg({'Trabajo':'max'}).reset_index()\n",
    "pers_temp3[(pers_temp3.Zona=='Conurbano')&(pers_temp3.Trabajo==1)].f_expansion.sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Motivos de los viajes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Motivo</th>\n",
       "      <th>Total</th>\n",
       "      <th>porc_tot</th>\n",
       "      <th>porc_tot_sinHogar</th>\n",
       "      <th>Total_porc_pobl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Trabajo</td>\n",
       "      <td>2726621</td>\n",
       "      <td>27.2</td>\n",
       "      <td>43.2</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cuidado</td>\n",
       "      <td>948187</td>\n",
       "      <td>9.5</td>\n",
       "      <td>15.0</td>\n",
       "      <td>6.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Estudio</td>\n",
       "      <td>910633</td>\n",
       "      <td>9.1</td>\n",
       "      <td>14.4</td>\n",
       "      <td>6.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Personal</td>\n",
       "      <td>833031</td>\n",
       "      <td>8.3</td>\n",
       "      <td>13.2</td>\n",
       "      <td>5.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Compras</td>\n",
       "      <td>489112</td>\n",
       "      <td>4.9</td>\n",
       "      <td>7.7</td>\n",
       "      <td>3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Salud</td>\n",
       "      <td>352673</td>\n",
       "      <td>3.5</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Otros</td>\n",
       "      <td>55656</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hogar</td>\n",
       "      <td>3693346</td>\n",
       "      <td>36.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Motivo    Total  porc_tot  porc_tot_sinHogar  Total_porc_pobl\n",
       "7   Trabajo  2726621      27.2               43.2            18.90\n",
       "1   Cuidado   948187       9.5               15.0             6.57\n",
       "2   Estudio   910633       9.1               14.4             6.31\n",
       "5  Personal   833031       8.3               13.2             5.78\n",
       "0   Compras   489112       4.9                7.7             3.39\n",
       "6     Salud   352673       3.5                5.6             2.45\n",
       "4     Otros    55656       0.6                0.9             0.39\n",
       "3     Hogar  3693346      36.9                0.0            25.61"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dest = viajes.groupby('Motivo_agg').f_expansion.sum().round().astype(int).reset_index()\n",
    "dest['porc_tot'] = (dest['f_expansion'] / dest['f_expansion'].sum() * 100).round(1)\n",
    "dest['porc_tot_sinHogar'] = 0 \n",
    "dest.loc[dest.Motivo_agg!='Hogar', 'porc_tot_sinHogar'] = (dest.loc[dest.Motivo_agg!='Hogar', 'f_expansion'] / dest.loc[dest.Motivo_agg!='Hogar', 'f_expansion'].sum() * 100).round(1)\n",
    "\n",
    "dest = dest.rename(columns={'f_expansion': 'Total', 'Motivo_agg': 'Motivo'})\n",
    "\n",
    "dest['Total_porc_pobl'] = (dest.Total / datos[datos.dato=='Población'].Total.values * 100).round(2)\n",
    "\n",
    "dest.to_csv('motivos2014.csv', sep=';')\n",
    "dest.sort_values('porc_tot_sinHogar', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Zona</th>\n",
       "      <th>AMBA</th>\n",
       "      <th>Conurbano</th>\n",
       "      <th>CABA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3693346.0</td>\n",
       "      <td>2902910.0</td>\n",
       "      <td>790436.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2726621.0</td>\n",
       "      <td>1975743.0</td>\n",
       "      <td>750878.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>948187.0</td>\n",
       "      <td>741285.0</td>\n",
       "      <td>206902.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>910633.0</td>\n",
       "      <td>748068.0</td>\n",
       "      <td>162565.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>833031.0</td>\n",
       "      <td>580948.0</td>\n",
       "      <td>252083.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>489112.0</td>\n",
       "      <td>348673.0</td>\n",
       "      <td>140439.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>352673.0</td>\n",
       "      <td>256196.0</td>\n",
       "      <td>96477.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55656.0</td>\n",
       "      <td>42769.0</td>\n",
       "      <td>12887.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Zona       AMBA  Conurbano      CABA\n",
       "3     3693346.0  2902910.0  790436.0\n",
       "7     2726621.0  1975743.0  750878.0\n",
       "1      948187.0   741285.0  206902.0\n",
       "2      910633.0   748068.0  162565.0\n",
       "5      833031.0   580948.0  252083.0\n",
       "0      489112.0   348673.0  140439.0\n",
       "6      352673.0   256196.0   96477.0\n",
       "4       55656.0    42769.0   12887.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viajes por motivo\n",
    "viajes_motivo = viajes.pivot_table('f_expansion', 'Motivo_agg', 'Zona', aggfunc='sum').reset_index()\n",
    "viajes_motivo['AMBA'] = viajes_motivo['CABA'] + viajes_motivo['Conurbano']\n",
    "viajes_motivo = viajes_motivo.sort_values('AMBA', ascending=False)\n",
    "viajes_motivo = viajes_motivo[['AMBA', 'Conurbano', 'CABA']]\n",
    "viajes_motivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.1, 1.12, 1.11, 1.11)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Promedio de viajes por persona (motivo trabajo, otros, total sin hogar, total)\n",
    "viajes[viajes.Motivo_agg=='Trabajo'].groupby(['id_muestra', 'nro_vivienda', 'nro_hogar', 'bivi01']).size().mean().round(2), \\\n",
    "viajes[(viajes.Motivo_agg!='Trabajo')&(viajes.Motivo_agg!='Hogar')].groupby(['id_muestra', 'nro_vivienda', 'nro_hogar', 'bivi01']).size().mean().round(2), \\\n",
    "viajes[(viajes.Motivo_agg!='Hogar')].groupby(['id_muestra', 'nro_vivienda', 'nro_hogar', 'bivi01']).size().mean().round(2), \\\n",
    "viajes.groupby(['id_muestra', 'nro_vivienda', 'nro_hogar', 'bivi01']).size().mean().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vehículos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2181599, 0.4611336800010653)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tiene Auto (si/no)\n",
    "hogares[hogares.bhvm02==1].f_expansion.sum().round().astype(int), \\\n",
    "hogares[hogares.bhvm02==1].f_expansion.sum() / hogares.f_expansion.sum().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(445970, 0.09)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tiene moto/ciclomotor (si/no)\n",
    "(hogares[hogares.bhmc01==1].f_expansion.sum()).round().astype(int), \\\n",
    "(hogares[hogares.bhmc01==1].f_expansion.sum() / hogares.f_expansion.sum()).round(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2408982, 0.51)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tiene auto o moto (si/no)\n",
    "hogares[(hogares.bhvm02==1)|(hogares.bhmc01==1)].f_expansion.sum().round().astype(int), \\\n",
    "(hogares[(hogares.bhvm02==1)|(hogares.bhmc01==1)].f_expansion.sum() / hogares.f_expansion.sum()).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2497812.0, 0.53)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BHVM02_01 - Cantidad de autos (totales en hogares)\n",
    "hogares['bhvm02_01'] = hogares['bhvm02_01'].fillna(0)\n",
    "hogares['Hog_Cant_autos'] = hogares['bhvm02_01'] * hogares['f_expansion']\n",
    "(hogares.Hog_Cant_autos.sum()).round(2), \\\n",
    "(hogares.Hog_Cant_autos.sum() / hogares.f_expansion.sum()).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2003538.03, 2722756.81)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estimado cantidad de autos de uso personal\n",
    "# (enmodmo 2009) %39 y (enmodo 2014) 53% de hogares con automovil - Hogares en AMBA: 5137277 (EPH 3T2019)\n",
    "5137277 * .39, 5137277 * .53"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Año</th>\n",
       "      <th>2009</th>\n",
       "      <th>2014</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>1621245</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>1692904</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011</td>\n",
       "      <td>1767730</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013</td>\n",
       "      <td>1845864</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>1927451</td>\n",
       "      <td>2408982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>2012644</td>\n",
       "      <td>2515459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>2101603</td>\n",
       "      <td>2626642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>2194494</td>\n",
       "      <td>2742740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018</td>\n",
       "      <td>2291491</td>\n",
       "      <td>2863969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019</td>\n",
       "      <td>2392775</td>\n",
       "      <td>2990556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Año     2009     2014\n",
       "0  2009  1621245        0\n",
       "0  2010  1692904        0\n",
       "0  2011  1767730        0\n",
       "0  2013  1845864        0\n",
       "0  2014  1927451  2408982\n",
       "0  2015  2012644  2515459\n",
       "0  2016  2101603  2626642\n",
       "0  2017  2194494  2742740\n",
       "0  2018  2291491  2863969\n",
       "0  2019  2392775  2990556"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tasa de crecimiento vehicular (CABA y AMBA)\n",
    "tasa_caba_amba = 0.0442\n",
    "autos_2009 = 1621245\n",
    "autos_2014 = 0\n",
    "\n",
    "autos = pd.DataFrame([[2009, autos_2009, 0]], columns=['Año', '2009', '2014'])\n",
    "for i in [2010, 2011, 2013, 2014, 2015, 2016, 2017, 2018, 2019]:\n",
    "           \n",
    "    autos_2009 = int(round(autos_2009+(autos_2009*tasa_caba_amba)))\n",
    "    \n",
    "    if i == 2014: autos_2014 = 2408982\n",
    "    if i > 2014: autos_2014 = int(round(autos_2014+(autos_2014*tasa_caba_amba)))\n",
    "        \n",
    "    autos = autos.append(pd.DataFrame([[i, autos_2009, autos_2014]], columns=['Año', '2009', '2014']))\n",
    "    \n",
    "    \n",
    "autos['Año'] = autos['Año'].astype(str)\n",
    "autos.to_csv('parque_vehicular.csv', sep=';')\n",
    "autos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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