03b - Interseccion espacial de Secciones CNE a Departamentos IGN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generar geografico de secciones de TTGL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# secciones_ARG = gpd.read_file('./mapaelectoral/geojson_CNE/secciones.geojson')\n",
    "# secciones_ARG['gid'] = secciones_ARG['gid'].astype(str).str[2:]\n",
    "\n",
    "# ## Corregir nombres de provincia para igualar IGN\n",
    "# secciones_ARG['provincia'] = secciones_ARG['provincia'].str.replace('Tierra del Fuego, Antártida e Islas del Atlántico', 'Tierra del Fuego, Antártida e Islas del Atlántico Sur')\n",
    "# secciones_ARG['provincia'] = secciones_ARG['provincia'].str.replace('Neuquen', 'Neuquén')\n",
    "\n",
    "# provs_CNE_ARG = secciones_ARG.dissolve('provincia')[['geometry']].reset_index()\n",
    "\n",
    "# # secciones_ARG.loc[secciones_ARG.departamen == 'Vicente Lopez']\n",
    "# secciones_ARG[['gid', 'provincia', 'departamen', 'geometry']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "secciones_ARG = pd.read_csv('./info/secciones_departamentos-TTGL.csv', dtype = 'str')\n",
    "dptos_IN1_from_radios = gpd.read_file('./info/dptos_fromIGNradios.geojson')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 504x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig, ax = plt.subplots(1, figsize = (7, 10))\n",
    "dptos_IN1_from_radios.plot(ax = ax)\n",
    "\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Archivos geojson de IGN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "provs_url = 'https://dnsg.ign.gob.ar/apps/api/v1/capas-sig/Geodesia+y+demarcaci%C3%B3n/L%C3%ADmites/provincia/json'\n",
    "dptos_url = 'https://dnsg.ign.gob.ar/apps/api/v1/capas-sig/Geodesia+y+demarcaci%C3%B3n/L%C3%ADmites/departamento/json'\n",
    "\n",
    "provs_IGN = gpd.read_file(provs_url)\n",
    "dptos_IGN = gpd.read_file(dptos_url)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>gid</th>\n",
       "      <th>objeto</th>\n",
       "      <th>fna</th>\n",
       "      <th>gna</th>\n",
       "      <th>nam</th>\n",
       "      <th>in1</th>\n",
       "      <th>fdc</th>\n",
       "      <th>sag</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Departamento</td>\n",
       "      <td>Departamento Federal</td>\n",
       "      <td>Departamento</td>\n",
       "      <td>Federal</td>\n",
       "      <td>30035</td>\n",
       "      <td>ATER - Direc. de Catastro</td>\n",
       "      <td>IGN</td>\n",
       "      <td>MULTIPOLYGON (((-58.57731 -30.69016, -58.57512...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Departamento</td>\n",
       "      <td>Departamento Gualeguaychú</td>\n",
       "      <td>Departamento</td>\n",
       "      <td>Gualeguaychú</td>\n",
       "      <td>30056</td>\n",
       "      <td>ATER - Direc. de Catastro</td>\n",
       "      <td>IGN</td>\n",
       "      <td>MULTIPOLYGON (((-58.13011 -33.02912, -58.13087...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Departamento</td>\n",
       "      <td>Departamento Islas del Ibicuy</td>\n",
       "      <td>Departamento</td>\n",
       "      <td>Islas del Ibicuy</td>\n",
       "      <td>30063</td>\n",
       "      <td>ATER - Direc. de Catastro</td>\n",
       "      <td>IGN</td>\n",
       "      <td>MULTIPOLYGON (((-58.57589 -34.02675, -58.57593...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Departamento</td>\n",
       "      <td>Departamento Victoria</td>\n",
       "      <td>Departamento</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>30105</td>\n",
       "      <td>ATER - Direc. de Catastro</td>\n",
       "      <td>IGN</td>\n",
       "      <td>MULTIPOLYGON (((-59.77158 -32.58791, -59.77198...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Departamento</td>\n",
       "      <td>Departamento Diamante</td>\n",
       "      <td>Departamento</td>\n",
       "      <td>Diamante</td>\n",
       "      <td>30021</td>\n",
       "      <td>ATER - Direc. de Catastro</td>\n",
       "      <td>IGN</td>\n",
       "      <td>MULTIPOLYGON (((-60.25784 -32.05170, -60.25723...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gid        objeto                            fna           gna  \\\n",
       "0    2  Departamento           Departamento Federal  Departamento   \n",
       "1    3  Departamento      Departamento Gualeguaychú  Departamento   \n",
       "2    4  Departamento  Departamento Islas del Ibicuy  Departamento   \n",
       "3    5  Departamento          Departamento Victoria  Departamento   \n",
       "4    6  Departamento          Departamento Diamante  Departamento   \n",
       "\n",
       "                nam    in1                        fdc  sag  \\\n",
       "0           Federal  30035  ATER - Direc. de Catastro  IGN   \n",
       "1      Gualeguaychú  30056  ATER - Direc. de Catastro  IGN   \n",
       "2  Islas del Ibicuy  30063  ATER - Direc. de Catastro  IGN   \n",
       "3          Victoria  30105  ATER - Direc. de Catastro  IGN   \n",
       "4          Diamante  30021  ATER - Direc. de Catastro  IGN   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((-58.57731 -30.69016, -58.57512...  \n",
       "1  MULTIPOLYGON (((-58.13011 -33.02912, -58.13087...  \n",
       "2  MULTIPOLYGON (((-58.57589 -34.02675, -58.57593...  \n",
       "3  MULTIPOLYGON (((-59.77158 -32.58791, -59.77198...  \n",
       "4  MULTIPOLYGON (((-60.25784 -32.05170, -60.25723...  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dptos_IGN.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## Agregar codigo IGN a info de CNE\n",
    "# provs_CNE_ARG = provs_CNE_ARG.merge(provs_IGN[['nam', 'in1']], left_on = 'provincia', right_on = 'nam').drop('nam', axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dptos_IGN.loc[dptos_IGN.gna == 'Comuna'].sort_values('in1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# secciones_ARG.dtypes\n",
    "\n",
    "# secciones_ARG\n",
    "\n",
    "# dptos_IGN.merge(secciones_ARG, left_on = 'in1', right_on = 'IN1', how = 'outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "ref_dptos = gpd.overlay(dptos_IGN[['nam', 'in1', 'geometry']], dptos_IN1_from_radios, how='intersection') # Demora un poco\n",
    "ref_dptos['area'] = ref_dptos.area\n",
    "\n",
    "inter_a = ref_dptos.groupby(['nam', 'in1']).apply(lambda x: x.nlargest(1, 'area')).reset_index(drop = True)\n",
    "# inter_b = inters.groupby(['IN1']).apply(lambda x: x.nlargest(1, 'area')).reset_index(drop = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nam         445\n",
       "in1         527\n",
       "IN1         525\n",
       "geometry    527\n",
       "area        527\n",
       "dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter_a.nunique()\n",
    "# inter_b.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IN1\n",
       "06217    2\n",
       "94007    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Lezama - Chascomus y Rio-Grande - Tolhuin\n",
    "# inter_a.groupby('IN1').size().loc[inter_a.groupby('IN1').size() != 1]\n",
    "\n",
    "# IN1\n",
    "# 06217\n",
    "# 94007"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>nam</th>\n",
       "      <th>in1</th>\n",
       "      <th>IN1</th>\n",
       "      <th>geometry</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Chascomús</td>\n",
       "      <td>06218</td>\n",
       "      <td>06217</td>\n",
       "      <td>POLYGON ((-57.79617 -35.44492, -57.79633 -35.4...</td>\n",
       "      <td>0.314180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Lezama</td>\n",
       "      <td>06466</td>\n",
       "      <td>06217</td>\n",
       "      <td>POLYGON ((-57.48964 -35.81152, -57.49059 -35.8...</td>\n",
       "      <td>0.104489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Río Grande</td>\n",
       "      <td>94008</td>\n",
       "      <td>94007</td>\n",
       "      <td>POLYGON ((-68.56935 -52.70694, -68.56996 -52.7...</td>\n",
       "      <td>0.893861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>Tolhuin</td>\n",
       "      <td>94011</td>\n",
       "      <td>94007</td>\n",
       "      <td>POLYGON ((-68.36912 -54.55150, -68.36831 -54.4...</td>\n",
       "      <td>0.697862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            nam    in1    IN1  \\\n",
       "101   Chascomús  06218  06217   \n",
       "287      Lezama  06466  06217   \n",
       "405  Río Grande  94008  94007   \n",
       "489     Tolhuin  94011  94007   \n",
       "\n",
       "                                              geometry      area  \n",
       "101  POLYGON ((-57.79617 -35.44492, -57.79633 -35.4...  0.314180  \n",
       "287  POLYGON ((-57.48964 -35.81152, -57.49059 -35.8...  0.104489  \n",
       "405  POLYGON ((-68.56935 -52.70694, -68.56996 -52.7...  0.893861  \n",
       "489  POLYGON ((-68.36912 -54.55150, -68.36831 -54.4...  0.697862  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter_a.loc[inter_a.IN1.isin(['06217', '94007'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Lugares con codigo diferente (in1, de radios IGN, vs. IN1 de dptos IGN)\n",
    "## Son las comunas de CABA, ademas de Lezama-Chasco y Tierra del Fuego"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>nam</th>\n",
       "      <th>in1</th>\n",
       "      <th>IN1</th>\n",
       "      <th>geometry</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Comuna 1</td>\n",
       "      <td>02007</td>\n",
       "      <td>0214007</td>\n",
       "      <td>POLYGON ((-58.38609 -34.57823, -58.38592 -34.5...</td>\n",
       "      <td>0.001757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>Comuna 2</td>\n",
       "      <td>02014</td>\n",
       "      <td>0214014</td>\n",
       "      <td>POLYGON ((-58.37387 -34.57871, -58.37389 -34.5...</td>\n",
       "      <td>0.000621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Comuna 3</td>\n",
       "      <td>02021</td>\n",
       "      <td>0214021</td>\n",
       "      <td>POLYGON ((-58.39290 -34.59966, -58.39273 -34.6...</td>\n",
       "      <td>0.000628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>Comuna 4</td>\n",
       "      <td>02028</td>\n",
       "      <td>0214028</td>\n",
       "      <td>POLYGON ((-58.35517 -34.61945, -58.35511 -34.6...</td>\n",
       "      <td>0.002220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>Comuna 5</td>\n",
       "      <td>02035</td>\n",
       "      <td>0214035</td>\n",
       "      <td>POLYGON ((-58.41597 -34.59787, -58.41566 -34.5...</td>\n",
       "      <td>0.000655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>Comuna 6</td>\n",
       "      <td>02042</td>\n",
       "      <td>0214042</td>\n",
       "      <td>POLYGON ((-58.42672 -34.62702, -58.42705 -34.6...</td>\n",
       "      <td>0.000673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>Comuna 7</td>\n",
       "      <td>02049</td>\n",
       "      <td>0214049</td>\n",
       "      <td>POLYGON ((-58.42672 -34.62702, -58.42647 -34.6...</td>\n",
       "      <td>0.001221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>Comuna 8</td>\n",
       "      <td>02056</td>\n",
       "      <td>0214056</td>\n",
       "      <td>POLYGON ((-58.44910 -34.65118, -58.44879 -34.6...</td>\n",
       "      <td>0.002211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>Comuna 9</td>\n",
       "      <td>02063</td>\n",
       "      <td>0214063</td>\n",
       "      <td>POLYGON ((-58.51922 -34.63303, -58.51904 -34.6...</td>\n",
       "      <td>0.001632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Comuna 10</td>\n",
       "      <td>02070</td>\n",
       "      <td>0214070</td>\n",
       "      <td>POLYGON ((-58.47777 -34.62240, -58.47738 -34.6...</td>\n",
       "      <td>0.001243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Comuna 11</td>\n",
       "      <td>02077</td>\n",
       "      <td>0214077</td>\n",
       "      <td>POLYGON ((-58.50343 -34.59397, -58.50305 -34.5...</td>\n",
       "      <td>0.001383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>Comuna 12</td>\n",
       "      <td>02084</td>\n",
       "      <td>0214084</td>\n",
       "      <td>POLYGON ((-58.46805 -34.57293, -58.46825 -34.5...</td>\n",
       "      <td>0.001541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>Comuna 13</td>\n",
       "      <td>02091</td>\n",
       "      <td>0214091</td>\n",
       "      <td>POLYGON ((-58.42566 -34.55225, -58.42672 -34.5...</td>\n",
       "      <td>0.001473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>Comuna 14</td>\n",
       "      <td>02098</td>\n",
       "      <td>0214098</td>\n",
       "      <td>POLYGON ((-58.42560 -34.55227, -58.42312 -34.5...</td>\n",
       "      <td>0.001557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>Comuna 15</td>\n",
       "      <td>02105</td>\n",
       "      <td>0214105</td>\n",
       "      <td>POLYGON ((-58.44441 -34.58319, -58.44437 -34.5...</td>\n",
       "      <td>0.001407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Chascomús</td>\n",
       "      <td>06218</td>\n",
       "      <td>06217</td>\n",
       "      <td>POLYGON ((-57.79617 -35.44492, -57.79633 -35.4...</td>\n",
       "      <td>0.314180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>Lezama</td>\n",
       "      <td>06466</td>\n",
       "      <td>06217</td>\n",
       "      <td>POLYGON ((-57.48964 -35.81152, -57.49059 -35.8...</td>\n",
       "      <td>0.104489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>Río Grande</td>\n",
       "      <td>94008</td>\n",
       "      <td>94007</td>\n",
       "      <td>POLYGON ((-68.56935 -52.70694, -68.56996 -52.7...</td>\n",
       "      <td>0.893861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>Tolhuin</td>\n",
       "      <td>94011</td>\n",
       "      <td>94007</td>\n",
       "      <td>POLYGON ((-68.36912 -54.55150, -68.36831 -54.4...</td>\n",
       "      <td>0.697862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>Ushuaia</td>\n",
       "      <td>94015</td>\n",
       "      <td>94014</td>\n",
       "      <td>MULTIPOLYGON (((-64.32925 -54.73391, -64.32744...</td>\n",
       "      <td>1.032857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            nam    in1      IN1  \\\n",
       "116    Comuna 1  02007  0214007   \n",
       "123    Comuna 2  02014  0214014   \n",
       "124    Comuna 3  02021  0214021   \n",
       "125    Comuna 4  02028  0214028   \n",
       "126    Comuna 5  02035  0214035   \n",
       "127    Comuna 6  02042  0214042   \n",
       "128    Comuna 7  02049  0214049   \n",
       "129    Comuna 8  02056  0214056   \n",
       "130    Comuna 9  02063  0214063   \n",
       "117   Comuna 10  02070  0214070   \n",
       "118   Comuna 11  02077  0214077   \n",
       "119   Comuna 12  02084  0214084   \n",
       "120   Comuna 13  02091  0214091   \n",
       "121   Comuna 14  02098  0214098   \n",
       "122   Comuna 15  02105  0214105   \n",
       "101   Chascomús  06218    06217   \n",
       "287      Lezama  06466    06217   \n",
       "405  Río Grande  94008    94007   \n",
       "489     Tolhuin  94011    94007   \n",
       "506     Ushuaia  94015    94014   \n",
       "\n",
       "                                              geometry      area  \n",
       "116  POLYGON ((-58.38609 -34.57823, -58.38592 -34.5...  0.001757  \n",
       "123  POLYGON ((-58.37387 -34.57871, -58.37389 -34.5...  0.000621  \n",
       "124  POLYGON ((-58.39290 -34.59966, -58.39273 -34.6...  0.000628  \n",
       "125  POLYGON ((-58.35517 -34.61945, -58.35511 -34.6...  0.002220  \n",
       "126  POLYGON ((-58.41597 -34.59787, -58.41566 -34.5...  0.000655  \n",
       "127  POLYGON ((-58.42672 -34.62702, -58.42705 -34.6...  0.000673  \n",
       "128  POLYGON ((-58.42672 -34.62702, -58.42647 -34.6...  0.001221  \n",
       "129  POLYGON ((-58.44910 -34.65118, -58.44879 -34.6...  0.002211  \n",
       "130  POLYGON ((-58.51922 -34.63303, -58.51904 -34.6...  0.001632  \n",
       "117  POLYGON ((-58.47777 -34.62240, -58.47738 -34.6...  0.001243  \n",
       "118  POLYGON ((-58.50343 -34.59397, -58.50305 -34.5...  0.001383  \n",
       "119  POLYGON ((-58.46805 -34.57293, -58.46825 -34.5...  0.001541  \n",
       "120  POLYGON ((-58.42566 -34.55225, -58.42672 -34.5...  0.001473  \n",
       "121  POLYGON ((-58.42560 -34.55227, -58.42312 -34.5...  0.001557  \n",
       "122  POLYGON ((-58.44441 -34.58319, -58.44437 -34.5...  0.001407  \n",
       "101  POLYGON ((-57.79617 -35.44492, -57.79633 -35.4...  0.314180  \n",
       "287  POLYGON ((-57.48964 -35.81152, -57.49059 -35.8...  0.104489  \n",
       "405  POLYGON ((-68.56935 -52.70694, -68.56996 -52.7...  0.893861  \n",
       "489  POLYGON ((-68.36912 -54.55150, -68.36831 -54.4...  0.697862  \n",
       "506  MULTIPOLYGON (((-64.32925 -54.73391, -64.32744...  1.032857  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inter_a.loc[inter_a.in1 != inter_a.IN1].sort_values('in1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Catamarca\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'in1'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3628\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3629\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3630\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'in1'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-31-fea0d9f99482>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprovs_CNE_ARG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'provincia'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mdptos_IGN_prov\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdptos_IGN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mdptos_IGN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Antártida'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdptos_IGN\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0min1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'in1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0msecciones_ARG_prov\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msecciones_ARG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0msecciones_ARG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdepartamen\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Antartida'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msecciones_ARG\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprovincia\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'provincia'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    956\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    957\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 958\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    959\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    960\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_get_value\u001b[0;34m(self, label, takeable)\u001b[0m\n\u001b[1;32m   1067\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1068\u001b[0m         \u001b[0;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1069\u001b[0;31m         \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1070\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1071\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3629\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3630\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3631\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3632\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3633\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'in1'"
     ]
    }
   ],
   "source": [
    "# import warnings\n",
    "# warnings.filterwarnings('ignore')\n",
    "\n",
    "# inters_result = []\n",
    "\n",
    "# for i, row in provs_CNE_ARG.iloc[1:3].iterrows():\n",
    "#     print(row['provincia'])\n",
    "#     dptos_IGN_prov = dptos_IGN.loc[~dptos_IGN.nam.str.contains('Antártida')].loc[dptos_IGN.in1.str[:2] == row['in1']]\n",
    "#     secciones_ARG_prov = secciones_ARG.loc[~secciones_ARG.departamen.str.contains('Antartida')].loc[secciones_ARG.provincia == row['provincia']]\n",
    "\n",
    "#     inters = gpd.overlay(dptos_IGN_prov[['nam', 'in1', 'geometry']], secciones_ARG_prov[['gid', 'provincia', 'departamen', 'geometry']], how='intersection') # Demora un poco\n",
    "#     inters['area'] = inters.area\n",
    "\n",
    "#     inter_prov = inters.groupby(['provincia', 'gid']).apply(lambda x: x.nlargest(1, 'area')).reset_index(drop = True)\n",
    "\n",
    "#     inters_result += [inter_prov]\n",
    "        \n",
    "# secciones_CNE_IGN = pd.concat(inters_result)\n",
    "# secciones_CNE_IGN = gpd.GeoDataFrame(secciones_CNE_IGN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Guardar resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "secciones_CNE_IGN[['in1', 'gid', 'provincia', 'departamen', 'nam', 'geometry']].to_file('./mapaelectoral/secciones_IGN.geojson', driver = 'GeoJSON')\n",
    "secciones_CNE_IGN[['in1', 'gid', 'provincia', 'departamen', 'nam']].to_csv('./info/CNE_IGN_spatialjoin.csv', index = False)\n"
   ]
  }
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