02b - Interseccion espacial de Radios Censales a Circuitos CNE.ipynb

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   "metadata": {},
   "source": [
    "## Radios en Circuitos\n",
    "\n",
    "**En esta notebook se usan los archivos:**\n",
    "\n",
    "Radios censales 2010 por IGN\n",
    "\n",
    "- `radios = './radios_IGN_2010_WGS84/'`\n",
    "\n",
    "Circuitos compilados por Tartagalensis en base a CNE\n",
    "\n",
    "- `circuitos_ARG = './mapaelectoral/circuitos-CNE-TTGL.geojson'`\n",
    "\n",
    "Geometrías de Dptos y Provincias de IGN\n",
    "\n",
    "- `dptos_url = 'https://dnsg.ign.gob.ar/apps/api/v1/capas-sig/Geodesia+y+demarcación/Límites/departamento/json'`\n",
    "\n",
    "- `provs_url = 'https://dnsg.ign.gob.ar/apps/api/v1/capas-sig/Geodesia+y+demarcación/Límites/provincia/json'`\n",
    "\n",
    "\n",
    "**Y se generan los archivos:**\n",
    "\n",
    "Secciones según circuitos de Tartagalensis - CNE\n",
    "\n",
    "```python\n",
    "prov_dpto_IN1[['codprov', 'coddepto', 'IN1']].to_csv('./info/secciones_departamentos-TTGL.csv', index=False)\n",
    "\n",
    "ref_dptos[['in1prov', 'namprov', 'in1', 'nam', 'IN1', 'geometry']].to_file('./mapaelectoral/ref-dptosIGN.geojson', driver='GeoJSON')\n",
    "\n",
    "ref_dptos[['in1prov', 'namprov', 'in1', 'nam', 'IN1']].to_csv('./info/ref-dptosIGN.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from shapely.validation import make_valid\n",
    "\n",
    "def fix_geom(in_feature):\n",
    "\n",
    "    # avoid changing original geodf\n",
    "    in_feature = in_feature.copy(deep=True)    \n",
    "        \n",
    "    # drop any missing geometries\n",
    "    in_feature = in_feature[~(in_feature.is_empty)]\n",
    "    \n",
    "    # Repair broken geometries\n",
    "    for index, row in in_feature.iterrows(): # Looping over all polygons\n",
    "        if row['geometry'].is_valid:\n",
    "            next\n",
    "        else:\n",
    "            fix = make_valid(row['geometry'])\n",
    "\n",
    "            try:\n",
    "                in_feature.loc[[index],'geometry'] =  fix # issue with Poly > Multipolygon\n",
    "            except ValueError:\n",
    "                in_feature.loc[[index],'geometry'] =  in_feature.loc[[index], 'geometry'].buffer(0)\n",
    "    return in_feature\n",
    "\n",
    "\n",
    "def curated_overlay(lower, higher, low_index, high_index):\n",
    "    # Perform overlay to intersect radios with higher-level polygons\n",
    "    overlay = gpd.overlay(lower, higher, how='intersection')\n",
    "    \n",
    "    if not overlay.empty:  # If there is an intersection\n",
    "        # Calculate the area of the intersected polygons\n",
    "        overlay['area'] = overlay.area\n",
    "\n",
    "        # Select the radio with the largest intersection area for each higher-level unit\n",
    "        lower_in_higher = overlay.groupby(low_index).apply(\n",
    "            lambda x: x.nlargest(1, 'area')\n",
    "        ).reset_index(drop=True)[low_index + high_index + ['geometry']]\n",
    "\n",
    "        return lower_in_higher\n",
    "    else:\n",
    "        print('No intersection with any higher-level unit.')\n",
    "        return gpd.GeoDataFrame()  # Return an empty GeoDataFrame if there is no intersection\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# if __name__ == \"__main__\":\n",
    "#     IGN, radios, censo10_IGN_ref, circuitos_ARG = load_data()\n",
    "\n",
    "#     radios_dpto_IGN = process_radios_dpto_IGN(IGN, radios, censo10_IGN_ref)\n",
    "\n",
    "#     circs_dpto_IGN = process_circs_dpto_IGN(IGN, circuitos_ARG)\n",
    "\n",
    "#     results = process_radio_circ_IGN(radios_dpto_IGN, circs_dpto_IGN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data():\n",
    "    IGN = gpd.read_file('./../geoespacial-censo-IGN/IGN_shp/ign_departamento')\n",
    "    radios = gpd.read_file('./radios_IGN_2010_WGS84/').drop('IN1', axis=1)\n",
    "    censo10_IGN_ref = gpd.read_file(repo + '/info/censo10_IGN_ref.geojson')\n",
    "    circuitos_ARG = gpd.read_file('./mapaelectoral/circuitos-CNE-TTGL.geojson')\n",
    "    circuitos_ARG = fix_geom(circuitos_ARG.dropna(subset='geometry'))\n",
    "    \n",
    "    return IGN, radios, censo10_IGN_ref, circuitos_ARG\n",
    "\n",
    "\n",
    "IGN, radios, censo10_IGN_ref, circuitos_ARG = load_data()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "radios_dpto_IGN = radios.merge(censo10_IGN_ref[['PROV_', 'DEPTO_', 'IN1', 'NAM']], how='left')\n",
    "# radios_dpto_IGN.to_file('./info/radio_dpto_IGN.geojson', driver='GeoJSON')\n",
    "radios_dpto_IGN.drop('geometry', axis = 1).to_csv('./info/radio_dpto_CNE_IGN.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def process_circs_dpto_IGN(IGN, circuitos_ARG):\n",
    "    lower = circuitos_ARG[['circuito', 'codprov', 'coddepto', 'geometry']]\n",
    "    higher = IGN[['IN1', 'NAM', 'geometry']]\n",
    "    circs_dpto_IGN = curated_overlay(lower, higher, low_index=['codprov', 'coddepto', 'circuito'], \n",
    "                                     high_index=['IN1', 'NAM'])\n",
    "    # circs_dpto_IGN.to_file('./info/circ_dpto_IGN.geojson', driver='GeoJSON')\n",
    "\n",
    "    ## Equivalencia DEPTOS CNE - DEPTOS IGN\n",
    "    counts = circs_dpto_IGN.groupby(['codprov', 'coddepto', 'IN1', 'NAM']).size().reset_index(name='counts')\n",
    "    idx_max_counts = counts.groupby(['codprov', 'coddepto'])['counts'].idxmax() # Mayor cantidad de circuitos en depto, para desambiguar.\n",
    "    ref = counts.loc[idx_max_counts] # Select the corresponding rows from the original DataFrame using the indices\n",
    "    print(ref.shape)\n",
    "\n",
    "    # Referencia de equivalencia entre deptos CNE y deptos IGN\n",
    "    ref.to_csv('./info/dpto_CNE_IGN_ref.csv', index=False)\n",
    "    \n",
    "    return circs_dpto_IGN\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(527, 5)\n"
     ]
    }
   ],
   "source": [
    "circs_dpto_IGN = process_circs_dpto_IGN(IGN, circuitos_ARG)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "metadata": {},
   "outputs": [],
   "source": [
    "circs_dpto_IGN.to_file('./info/circ_dpto_IGN.geojson', driver='GeoJSON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [],
   "source": [
    "circs_dpto_IGN.drop('geometry', axis = 1).to_csv('./info/circ_dpto_CNE_IGN.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def process_radio_circ_IGN(radios_dpto_IGN, circs_dpto_IGN):\n",
    "    circs_dpto_IGN = circs_dpto_IGN.to_crs(radios_dpto_IGN.crs)\n",
    "    \n",
    "    unique_in1_nam_censo = radios_dpto_IGN[['IN1', 'NAM']].drop_duplicates()\n",
    "    unique_in1_nam_circs = circs_dpto_IGN[['IN1', 'NAM']].drop_duplicates()\n",
    "\n",
    "    results = []\n",
    "\n",
    "    for _, row in unique_in1_nam_censo.iterrows():\n",
    "        in1_val = row['IN1']\n",
    "        nam_val = row['NAM']\n",
    "        print(in1_val, nam_val)\n",
    "\n",
    "        radios_selection = radios_dpto_IGN[\n",
    "            (radios_dpto_IGN['IN1'] == in1_val) & (radios_dpto_IGN['NAM'] == nam_val)\n",
    "        ]\n",
    "\n",
    "        circuits_selection = circs_dpto_IGN[\n",
    "            (circs_dpto_IGN['IN1'] == in1_val) & (circs_dpto_IGN['NAM'] == nam_val)\n",
    "        ]\n",
    "\n",
    "        result = curated_overlay(radios_selection, circuits_selection, low_index=['PROV_', 'DEPTO_', 'COD_2010'],\n",
    "                                 high_index=['codprov', 'coddepto', 'circuito'])\n",
    "\n",
    "        results += [result]\n",
    "\n",
    "    results = pd.concat(results)\n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
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      "02091 Comuna 13\n",
      "02084 Comuna 12\n",
      "02098 Comuna 14\n",
      "02105 Comuna 15\n",
      "02014 Comuna 2\n",
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      "02007 Comuna 1\n",
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      "54056 Guaraní\n",
      "54098 San Ignacio\n",
      "54014 Cainguás\n",
      "54091 Oberá\n",
      "54119 25de Mayo\n",
      "54021 Candelaria\n",
      "54028 Capital\n",
      "54070 Leandro N. Alem\n",
      "54105 San Javier\n",
      "54007 Apóstoles\n",
      "54035 Concepción\n",
      "58042 Chos Malal\n",
      "58077 Minas\n",
      "58091 Pehuenches\n",
      "58084 Ñorquín\n",
      "58063 Loncopué\n",
      "58014 Añelo\n",
      "58105 Picunches\n",
      "58035 Confluencia\n",
      "58112 Zapala\n",
      "58007 Aluminé\n",
      "58098 Picún Leufú\n",
      "58021 Catán Lil\n",
      "58049 Huiliches\n",
      "58028 Collón Curá\n",
      "58056 Lácar\n",
      "58070 Los Lagos\n",
      "62042 General Roca\n",
      "62014 Avellaneda\n",
      "62063 Pichi Mahuida\n",
      "62035 El Cuy\n",
      "62049 9 de julio\n",
      "62028 Conesa\n",
      "62084 Valcheta\n",
      "62077 San Antonio\n",
      "62070 Pilcaniyeu\n",
      "62091 25 de Mayo\n",
      "62007 Adolfo Alsina\n",
      "62021 Bariloche\n",
      "62056 Ñorquinco\n",
      "66056 General José de San Martín\n",
      "66133 Rivadavia\n",
      "66161 Santa Victoria\n",
      "66070 Iruya\n",
      "66126 Orán\n",
      "66091 La Poma\n",
      "66105 Los Andes\n",
      "66147 Rosario de Lerma\n",
      "66007 Anta\n",
      "66077 La Caldera\n",
      "66049 General Güemes\n",
      "66028 Capital\n",
      "66014 Cachi\n",
      "66035 Cerrillos\n",
      "66042 Chicoana\n",
      "66112 Metán\n",
      "66119 Molinos\n",
      "66098 La Viña\n",
      "66154 San Carlos\n",
      "66063 Guachipas\n",
      "66140 Rosario de la Frontera\n",
      "66084 La Candelaria\n",
      "66021 Cafayate\n",
      "70049 Iglesia\n",
      "70056 Jachal\n",
      "70119 Valle Fértil\n",
      "70021 Calingasta\n",
      "70112 Ullum\n",
      "70035 Caucete\n",
      "70014 Angaco\n",
      "70007 Albardón\n",
      "70133 Zonda\n",
      "70091 San Martín\n",
      "70042 Chimbas\n",
      "70084 Rivadavia\n",
      "70098 Santa Lucía\n",
      "70028 Capital\n",
      "70063 9 de Julio\n",
      "70077 Rawson\n",
      "70070 Pocito\n",
      "70126 25 de Mayo\n",
      "70105 Sarmiento\n",
      "74007 Ayacucho\n",
      "74049 Junín\n",
      "74063 Libertador General San Martín\n",
      "74028 Chacabuco\n",
      "74014 Belgrano\n",
      "74021 Coronel Pringles\n",
      "74035 General Pedernera\n",
      "74056 Juan Martín de Pueyrredón\n",
      "74042 Gobernador Dupuy\n",
      "78014 Deseado\n",
      "78035 Lago Buenos Aires\n",
      "78049 Río Chico\n",
      "78042 Magallanes\n",
      "78028 Lago Argentino\n",
      "78007 Corpen Aike\n",
      "78021 Güer Aike\n",
      "82077 9 de Julio\n",
      "82133 Vera\n",
      "82049 General Obligado\n",
      "82098 San Javier\n",
      "82091 San Cristóbal\n",
      "82112 San Justo\n",
      "82070 Las Colonias\n",
      "82035 Garay\n",
      "82021 Castellanos\n",
      "82063 La Capital\n",
      "82126 San Martín\n",
      "82105 San Jerónimo\n",
      "82007 Belgrano\n",
      "82056 Iriondo\n",
      "82119 San Lorenzo\n",
      "82084 Rosario\n",
      "82014 Caseros\n",
      "82028 Constitución\n",
      "82042 General López\n",
      "86056 Copo\n",
      "86133 Pellegrini\n",
      "86014 Alberdi\n",
      "86091 Jiménez\n",
      "86070 Figueroa\n",
      "86119 Moreno\n",
      "86035 Banda\n",
      "86147 Río Hondo\n",
      "86049 Capital\n",
      "86084 Guasayán\n",
      "86161 Robles\n",
      "86098 Juan F. Ibarra\n",
      "86182 Sarmiento\n",
      "86175 San Martín\n",
      "86189 Silipica\n",
      "86063 Choya\n",
      "86028 Avellaneda\n",
      "86105 Loreto\n",
      "86077 General Taboada\n",
      "86021 Atamisqui\n",
      "86168 Salavina\n",
      "86126 Ojo de Agua\n",
      "86007 Aguirre\n",
      "86042 Belgrano\n",
      "86140 Quebrachos\n",
      "86112 Mitre\n",
      "86154 Rivadavia\n",
      "90112 Trancas\n",
      "90098 Tafí del Valle\n",
      "90007 Burruyacú\n",
      "90105 Tafí Viejo\n",
      "90063 Lules\n",
      "90119 Yerba Buena\n",
      "90014 Cruz Alta\n",
      "90084 Capital\n",
      "90028 Famaillá\n",
      "90070 Monteros\n",
      "90056 Leales\n",
      "90021 Chicligasta\n",
      "90091 Simoca\n",
      "90077 Río Chico\n",
      "90042 Juan Bautista Alberdi\n",
      "90035 Graneros\n",
      "90049 La Cocha\n",
      "94007 Río Grande\n",
      "94014 Ushuaia\n"
     ]
    }
   ],
   "source": [
    "results = process_radio_circ_IGN(radios_dpto_IGN, circs_dpto_IGN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dpto_CNE_IGN_ref = pd.read_csv('./info/dpto_CNE_IGN_ref.csv', dtype={'codprov': str, 'coddepto': str})\n",
    "\n",
    "results = results.merge(dpto_CNE_IGN_ref[['codprov', 'coddepto', 'IN1', 'NAM']].drop_duplicates())\n",
    "\n",
    "results.drop('geometry', axis=1).to_csv('./info/radio_circ_IGN.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "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>codprov</th>\n",
       "      <th>coddepto</th>\n",
       "      <th>IN1</th>\n",
       "      <th>NAM</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>2007</td>\n",
       "      <td>Comuna 1</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01</td>\n",
       "      <td>002</td>\n",
       "      <td>2014</td>\n",
       "      <td>Comuna 2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01</td>\n",
       "      <td>003</td>\n",
       "      <td>2021</td>\n",
       "      <td>Comuna 3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01</td>\n",
       "      <td>004</td>\n",
       "      <td>2028</td>\n",
       "      <td>Comuna 4</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>005</td>\n",
       "      <td>2035</td>\n",
       "      <td>Comuna 5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  codprov coddepto   IN1       NAM  counts\n",
       "0      01      001  2007  Comuna 1      19\n",
       "1      01      002  2014  Comuna 2      10\n",
       "2      01      003  2021  Comuna 3      13\n",
       "3      01      004  2028  Comuna 4      15\n",
       "4      01      005  2035  Comuna 5      10"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpto_CNE_IGN_ref.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\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>PROV_</th>\n",
       "      <th>DEPTO_</th>\n",
       "      <th>COD_2010</th>\n",
       "      <th>codprov</th>\n",
       "      <th>coddepto</th>\n",
       "      <th>circuito</th>\n",
       "      <th>geometry</th>\n",
       "      <th>IN1</th>\n",
       "      <th>NAM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>020130101</td>\n",
       "      <td>01</td>\n",
       "      <td>013</td>\n",
       "      <td>00134</td>\n",
       "      <td>POLYGON Z ((-58.47254 -34.53737 0.00000, -58.4...</td>\n",
       "      <td>2091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>020130102</td>\n",
       "      <td>01</td>\n",
       "      <td>013</td>\n",
       "      <td>00134</td>\n",
       "      <td>POLYGON Z ((-58.47030 -34.53644 0.00000, -58.4...</td>\n",
       "      <td>2091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>020130103</td>\n",
       "      <td>01</td>\n",
       "      <td>013</td>\n",
       "      <td>00134</td>\n",
       "      <td>POLYGON Z ((-58.46836 -34.53548 0.00000, -58.4...</td>\n",
       "      <td>2091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>020130104</td>\n",
       "      <td>01</td>\n",
       "      <td>013</td>\n",
       "      <td>00134</td>\n",
       "      <td>POLYGON Z ((-58.46794 -34.53643 0.00000, -58.4...</td>\n",
       "      <td>2091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>020130105</td>\n",
       "      <td>01</td>\n",
       "      <td>013</td>\n",
       "      <td>00134</td>\n",
       "      <td>POLYGON Z ((-58.46631 -34.53668 0.00000, -58.4...</td>\n",
       "      <td>2091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PROV_ DEPTO_   COD_2010 codprov coddepto circuito  \\\n",
       "0    02    013  020130101      01      013    00134   \n",
       "1    02    013  020130102      01      013    00134   \n",
       "2    02    013  020130103      01      013    00134   \n",
       "3    02    013  020130104      01      013    00134   \n",
       "4    02    013  020130105      01      013    00134   \n",
       "\n",
       "                                            geometry   IN1        NAM  \n",
       "0  POLYGON Z ((-58.47254 -34.53737 0.00000, -58.4...  2091  Comuna 13  \n",
       "1  POLYGON Z ((-58.47030 -34.53644 0.00000, -58.4...  2091  Comuna 13  \n",
       "2  POLYGON Z ((-58.46836 -34.53548 0.00000, -58.4...  2091  Comuna 13  \n",
       "3  POLYGON Z ((-58.46794 -34.53643 0.00000, -58.4...  2091  Comuna 13  \n",
       "4  POLYGON Z ((-58.46631 -34.53668 0.00000, -58.4...  2091  Comuna 13  "
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51940, 9)"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cargar geometrias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Departamentos API Georef\n",
    "\n",
    "https://datos.gob.ar/ar/dataset/ign-unidades-territoriales/archivo/ign_01.03.02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Para bajar estos poligonos consultar el repo geoespacial-censo-IGN\n",
    "# './IGN_shp/ign_departamento'\n",
    "\n",
    "IGN = gpd.read_file('./../geoespacial-censo-IGN/IGN_shp/ign_departamento')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Radios Censales"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PROV_</th>\n",
       "      <th>DEPTO_</th>\n",
       "      <th>FRACC_</th>\n",
       "      <th>COD_2010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>03</td>\n",
       "      <td>020130302</td>\n",
       "      <td>POLYGON ((-58.46740 -34.53485, -58.46740 -34.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>11</td>\n",
       "      <td>020131101</td>\n",
       "      <td>POLYGON ((-58.45868 -34.53715, -58.45868 -34.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>01</td>\n",
       "      <td>020130104</td>\n",
       "      <td>POLYGON ((-58.46758 -34.53733, -58.46794 -34.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>01</td>\n",
       "      <td>020130103</td>\n",
       "      <td>POLYGON ((-58.46846 -34.53531, -58.46846 -34.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>01</td>\n",
       "      <td>020130102</td>\n",
       "      <td>POLYGON ((-58.47269 -34.53719, -58.47269 -34.5...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PROV_ DEPTO_ FRACC_   COD_2010  \\\n",
       "0    02    013     03  020130302   \n",
       "1    02    013     11  020131101   \n",
       "2    02    013     01  020130104   \n",
       "3    02    013     01  020130103   \n",
       "4    02    013     01  020130102   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-58.46740 -34.53485, -58.46740 -34.5...  \n",
       "1  POLYGON ((-58.45868 -34.53715, -58.45868 -34.5...  \n",
       "2  POLYGON ((-58.46758 -34.53733, -58.46794 -34.5...  \n",
       "3  POLYGON ((-58.46846 -34.53531, -58.46846 -34.5...  \n",
       "4  POLYGON ((-58.47269 -34.53719, -58.47269 -34.5...  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radios = gpd.read_file('./radios_IGN_2010_WGS84/').drop('IN1', axis = 1)\n",
    "radios.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# claves.\n",
    "\n",
    "repo = './../geoespacial-censo-IGN'\n",
    "\n",
    "## Para bajar estos poligonos consultar el repo geoespacial-censo-IGN\n",
    "censo10_IGN_ref = gpd.read_file(repo+'/info/censo10_IGN_ref.geojson') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>DEPTO_</th>\n",
       "      <th>IN1</th>\n",
       "      <th>NAM</th>\n",
       "      <th>area_km2</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "      <td>02007</td>\n",
       "      <td>Comuna 1</td>\n",
       "      <td>0.001742</td>\n",
       "      <td>POLYGON Z ((-58.39154 -34.58087 0.00000, -58.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>002</td>\n",
       "      <td>02014</td>\n",
       "      <td>Comuna 2</td>\n",
       "      <td>0.000615</td>\n",
       "      <td>POLYGON Z ((-58.40451 -34.59804 0.00000, -58.4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02</td>\n",
       "      <td>003</td>\n",
       "      <td>02021</td>\n",
       "      <td>Comuna 3</td>\n",
       "      <td>0.000627</td>\n",
       "      <td>POLYGON Z ((-58.39991 -34.62857 0.00000, -58.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02</td>\n",
       "      <td>004</td>\n",
       "      <td>02028</td>\n",
       "      <td>Comuna 4</td>\n",
       "      <td>0.002131</td>\n",
       "      <td>MULTIPOLYGON Z (((-58.35579 -34.61814 0.00000,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02</td>\n",
       "      <td>005</td>\n",
       "      <td>02035</td>\n",
       "      <td>Comuna 5</td>\n",
       "      <td>0.000654</td>\n",
       "      <td>POLYGON Z ((-58.42926 -34.61512 0.00000, -58.4...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PROV_ DEPTO_    IN1       NAM  area_km2  \\\n",
       "0    02    001  02007  Comuna 1  0.001742   \n",
       "1    02    002  02014  Comuna 2  0.000615   \n",
       "2    02    003  02021  Comuna 3  0.000627   \n",
       "3    02    004  02028  Comuna 4  0.002131   \n",
       "4    02    005  02035  Comuna 5  0.000654   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON Z ((-58.39154 -34.58087 0.00000, -58.3...  \n",
       "1  POLYGON Z ((-58.40451 -34.59804 0.00000, -58.4...  \n",
       "2  POLYGON Z ((-58.39991 -34.62857 0.00000, -58.3...  \n",
       "3  MULTIPOLYGON Z (((-58.35579 -34.61814 0.00000,...  \n",
       "4  POLYGON Z ((-58.42926 -34.61512 0.00000, -58.4...  "
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censo10_IGN_ref.head()\n",
    "# Clave departamentos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(525, 6)"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censo10_IGN_ref.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>PROV_</th>\n",
       "      <th>DEPTO_</th>\n",
       "      <th>FRACC_</th>\n",
       "      <th>COD_2010</th>\n",
       "      <th>geometry</th>\n",
       "      <th>IN1</th>\n",
       "      <th>NAM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>03</td>\n",
       "      <td>020130302</td>\n",
       "      <td>POLYGON ((-58.46740 -34.53485, -58.46740 -34.5...</td>\n",
       "      <td>02091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>11</td>\n",
       "      <td>020131101</td>\n",
       "      <td>POLYGON ((-58.45868 -34.53715, -58.45868 -34.5...</td>\n",
       "      <td>02091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>01</td>\n",
       "      <td>020130104</td>\n",
       "      <td>POLYGON ((-58.46758 -34.53733, -58.46794 -34.5...</td>\n",
       "      <td>02091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>01</td>\n",
       "      <td>020130103</td>\n",
       "      <td>POLYGON ((-58.46846 -34.53531, -58.46846 -34.5...</td>\n",
       "      <td>02091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>01</td>\n",
       "      <td>020130102</td>\n",
       "      <td>POLYGON ((-58.47269 -34.53719, -58.47269 -34.5...</td>\n",
       "      <td>02091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52396</th>\n",
       "      <td>94</td>\n",
       "      <td>014</td>\n",
       "      <td>07</td>\n",
       "      <td>940140705</td>\n",
       "      <td>POLYGON ((-68.36060 -54.82677, -68.35857 -54.8...</td>\n",
       "      <td>94014</td>\n",
       "      <td>Ushuaia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52397</th>\n",
       "      <td>94</td>\n",
       "      <td>014</td>\n",
       "      <td>07</td>\n",
       "      <td>940140710</td>\n",
       "      <td>POLYGON ((-68.33741 -54.82805, -68.33741 -54.8...</td>\n",
       "      <td>94014</td>\n",
       "      <td>Ushuaia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52398</th>\n",
       "      <td>94</td>\n",
       "      <td>014</td>\n",
       "      <td>08</td>\n",
       "      <td>940140813</td>\n",
       "      <td>POLYGON ((-68.33292 -54.82781, -68.33292 -54.8...</td>\n",
       "      <td>94014</td>\n",
       "      <td>Ushuaia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52399</th>\n",
       "      <td>94</td>\n",
       "      <td>014</td>\n",
       "      <td>07</td>\n",
       "      <td>940140709</td>\n",
       "      <td>POLYGON ((-68.33740 -54.82804, -68.33740 -54.8...</td>\n",
       "      <td>94014</td>\n",
       "      <td>Ushuaia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52400</th>\n",
       "      <td>94</td>\n",
       "      <td>014</td>\n",
       "      <td>07</td>\n",
       "      <td>940140707</td>\n",
       "      <td>POLYGON ((-68.35771 -54.83116, -68.35827 -54.8...</td>\n",
       "      <td>94014</td>\n",
       "      <td>Ushuaia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52401 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PROV_ DEPTO_ FRACC_   COD_2010  \\\n",
       "0        02    013     03  020130302   \n",
       "1        02    013     11  020131101   \n",
       "2        02    013     01  020130104   \n",
       "3        02    013     01  020130103   \n",
       "4        02    013     01  020130102   \n",
       "...     ...    ...    ...        ...   \n",
       "52396    94    014     07  940140705   \n",
       "52397    94    014     07  940140710   \n",
       "52398    94    014     08  940140813   \n",
       "52399    94    014     07  940140709   \n",
       "52400    94    014     07  940140707   \n",
       "\n",
       "                                                geometry    IN1        NAM  \n",
       "0      POLYGON ((-58.46740 -34.53485, -58.46740 -34.5...  02091  Comuna 13  \n",
       "1      POLYGON ((-58.45868 -34.53715, -58.45868 -34.5...  02091  Comuna 13  \n",
       "2      POLYGON ((-58.46758 -34.53733, -58.46794 -34.5...  02091  Comuna 13  \n",
       "3      POLYGON ((-58.46846 -34.53531, -58.46846 -34.5...  02091  Comuna 13  \n",
       "4      POLYGON ((-58.47269 -34.53719, -58.47269 -34.5...  02091  Comuna 13  \n",
       "...                                                  ...    ...        ...  \n",
       "52396  POLYGON ((-68.36060 -54.82677, -68.35857 -54.8...  94014    Ushuaia  \n",
       "52397  POLYGON ((-68.33741 -54.82805, -68.33741 -54.8...  94014    Ushuaia  \n",
       "52398  POLYGON ((-68.33292 -54.82781, -68.33292 -54.8...  94014    Ushuaia  \n",
       "52399  POLYGON ((-68.33740 -54.82804, -68.33740 -54.8...  94014    Ushuaia  \n",
       "52400  POLYGON ((-68.35771 -54.83116, -68.35827 -54.8...  94014    Ushuaia  \n",
       "\n",
       "[52401 rows x 7 columns]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radios_dpto_IGN = radios.merge(censo10_IGN_ref[['PROV_', 'DEPTO_', 'IN1', 'NAM']], how = 'left')\n",
    "radios_dpto_IGN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "radios_dpto_IGN.to_file('./info/radio_dpto_IGN.geojson', driver = 'GeoJSON')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Circuitos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>circuito</th>\n",
       "      <th>codprov</th>\n",
       "      <th>coddepto</th>\n",
       "      <th>seccion</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00001</td>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((-58.36620 -34.62040, -58.36610...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00002</td>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MULTIPOLYGON (((-58.37740 -34.61740, -58.37740...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00003</td>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GEOMETRYCOLLECTION (POLYGON ((-58.35720 -34.59...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00004</td>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((-58.36760 -34.60760, -58.36770 -34.6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00005</td>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GEOMETRYCOLLECTION (MULTIPOLYGON (((-58.36080 ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  circuito codprov coddepto  seccion  \\\n",
       "0    00001      01      001      NaN   \n",
       "1    00002      01      001      NaN   \n",
       "2    00003      01      001      NaN   \n",
       "3    00004      01      001      NaN   \n",
       "4    00005      01      001      NaN   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((-58.36620 -34.62040, -58.36610...  \n",
       "1  MULTIPOLYGON (((-58.37740 -34.61740, -58.37740...  \n",
       "2  GEOMETRYCOLLECTION (POLYGON ((-58.35720 -34.59...  \n",
       "3  POLYGON ((-58.36760 -34.60760, -58.36770 -34.6...  \n",
       "4  GEOMETRYCOLLECTION (MULTIPOLYGON (((-58.36080 ...  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# circuitos_ARG = gpd.read_file('./mapaelectoral/circuitos-CNE.geojson')\n",
    "circuitos_ARG = gpd.read_file('./mapaelectoral/circuitos-CNE-TTGL.geojson')\n",
    "circuitos_ARG = fix_geom(circuitos_ARG.dropna(subset = 'geometry'))\n",
    "circuitos_ARG.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     codprov coddepto circuito    IN1         NAM  \\\n",
      "0         01      001    00001  02007    Comuna 1   \n",
      "1         01      001    00002  02007    Comuna 1   \n",
      "2         01      001    00003  02007    Comuna 1   \n",
      "3         01      001    00004  02007    Comuna 1   \n",
      "4         01      001    00005  02007    Comuna 1   \n",
      "...      ...      ...      ...    ...         ...   \n",
      "5498      24       02     0014  94007  Río Grande   \n",
      "5499      24       02     0016  94007  Río Grande   \n",
      "5500      24       02     0021  94011     Tolhuin   \n",
      "5501      24       02     0210  94007  Río Grande   \n",
      "5502      24       02     0221  94007  Río Grande   \n",
      "\n",
      "                                               geometry  \n",
      "0     POLYGON Z ((-58.36610 -34.62150 0.00000, -58.3...  \n",
      "1     POLYGON Z ((-58.37740 -34.61630 0.00000, -58.3...  \n",
      "2     POLYGON Z ((-58.35720 -34.59510 0.00000, -58.3...  \n",
      "3     POLYGON Z ((-58.36770 -34.60740 0.00000, -58.3...  \n",
      "4     POLYGON Z ((-58.37984 -34.58016 0.00000, -58.3...  \n",
      "...                                                 ...  \n",
      "5498  MULTIPOLYGON Z (((-67.63620 -53.80993 0.00000,...  \n",
      "5499  POLYGON Z ((-68.30958 -53.32111 0.00000, -68.3...  \n",
      "5500  POLYGON Z ((-67.24502 -54.48237 0.00000, -67.1...  \n",
      "5501  POLYGON Z ((-67.73932 -53.77926 0.00000, -67.7...  \n",
      "5502  POLYGON Z ((-67.73517 -53.74868 0.00000, -67.7...  \n",
      "\n",
      "[5503 rows x 6 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "lower = circuitos_ARG[['circuito', 'codprov', 'coddepto', 'geometry']]\n",
    "higher = IGN[['IN1', 'NAM', 'geometry']]\n",
    "\n",
    "circs_dpto_IGN = curated_overlay(lower, higher, low_index = ['codprov', 'coddepto', 'circuito'], \n",
    "                         high_index = ['IN1', 'NAM'])\n",
    "print(circs_dpto_IGN)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "DriverError",
     "evalue": "path/to/censo10_IGN_ref.shp: No such file or directory",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mCPLE_OpenFailedError\u001b[0m                      Traceback (most recent call last)",
      "File \u001b[0;32mfiona/ogrext.pyx:136\u001b[0m, in \u001b[0;36mfiona.ogrext.gdal_open_vector\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mfiona/_err.pyx:291\u001b[0m, in \u001b[0;36mfiona._err.exc_wrap_pointer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mCPLE_OpenFailedError\u001b[0m: path/to/censo10_IGN_ref.shp: No such file or directory",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mDriverError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[265], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# Load GeoDataFrames\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m censo10_IGN_ref \u001b[39m=\u001b[39m gpd\u001b[39m.\u001b[39;49mread_file(\u001b[39m'\u001b[39;49m\u001b[39mpath/to/censo10_IGN_ref.shp\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m      3\u001b[0m circs_dpto_IGN \u001b[39m=\u001b[39m gpd\u001b[39m.\u001b[39mread_file(\u001b[39m'\u001b[39m\u001b[39mpath/to/circs_dpto_IGN.shp\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      5\u001b[0m \u001b[39m# Adjust CRS to match (if needed)\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/geopandas/io/file.py:281\u001b[0m, in \u001b[0;36m_read_file\u001b[0;34m(filename, bbox, mask, rows, engine, **kwargs)\u001b[0m\n\u001b[1;32m    278\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    279\u001b[0m         path_or_bytes \u001b[39m=\u001b[39m filename\n\u001b[0;32m--> 281\u001b[0m     \u001b[39mreturn\u001b[39;00m _read_file_fiona(\n\u001b[1;32m    282\u001b[0m         path_or_bytes, from_bytes, bbox\u001b[39m=\u001b[39;49mbbox, mask\u001b[39m=\u001b[39;49mmask, rows\u001b[39m=\u001b[39;49mrows, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\n\u001b[1;32m    283\u001b[0m     )\n\u001b[1;32m    285\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    286\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39munknown engine \u001b[39m\u001b[39m'\u001b[39m\u001b[39m{\u001b[39;00mengine\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/geopandas/io/file.py:322\u001b[0m, in \u001b[0;36m_read_file_fiona\u001b[0;34m(path_or_bytes, from_bytes, bbox, mask, rows, where, **kwargs)\u001b[0m\n\u001b[1;32m    319\u001b[0m     reader \u001b[39m=\u001b[39m fiona\u001b[39m.\u001b[39mopen\n\u001b[1;32m    321\u001b[0m \u001b[39mwith\u001b[39;00m fiona_env():\n\u001b[0;32m--> 322\u001b[0m     \u001b[39mwith\u001b[39;00m reader(path_or_bytes, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs) \u001b[39mas\u001b[39;00m features:\n\u001b[1;32m    323\u001b[0m         crs \u001b[39m=\u001b[39m features\u001b[39m.\u001b[39mcrs_wkt\n\u001b[1;32m    324\u001b[0m         \u001b[39m# attempt to get EPSG code\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/fiona/env.py:457\u001b[0m, in \u001b[0;36mensure_env_with_credentials.<locals>.wrapper\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m    454\u001b[0m     session \u001b[39m=\u001b[39m DummySession()\n\u001b[1;32m    456\u001b[0m \u001b[39mwith\u001b[39;00m env_ctor(session\u001b[39m=\u001b[39msession):\n\u001b[0;32m--> 457\u001b[0m     \u001b[39mreturn\u001b[39;00m f(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/fiona/__init__.py:292\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(fp, mode, driver, schema, crs, encoding, layer, vfs, enabled_drivers, crs_wkt, allow_unsupported_drivers, **kwargs)\u001b[0m\n\u001b[1;32m    289\u001b[0m     path \u001b[39m=\u001b[39m parse_path(fp)\n\u001b[1;32m    291\u001b[0m \u001b[39mif\u001b[39;00m mode \u001b[39min\u001b[39;00m (\u001b[39m\"\u001b[39m\u001b[39ma\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mr\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 292\u001b[0m     colxn \u001b[39m=\u001b[39m Collection(\n\u001b[1;32m    293\u001b[0m         path,\n\u001b[1;32m    294\u001b[0m         mode,\n\u001b[1;32m    295\u001b[0m         driver\u001b[39m=\u001b[39;49mdriver,\n\u001b[1;32m    296\u001b[0m         encoding\u001b[39m=\u001b[39;49mencoding,\n\u001b[1;32m    297\u001b[0m         layer\u001b[39m=\u001b[39;49mlayer,\n\u001b[1;32m    298\u001b[0m         enabled_drivers\u001b[39m=\u001b[39;49menabled_drivers,\n\u001b[1;32m    299\u001b[0m         allow_unsupported_drivers\u001b[39m=\u001b[39;49mallow_unsupported_drivers,\n\u001b[1;32m    300\u001b[0m         \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\n\u001b[1;32m    301\u001b[0m     )\n\u001b[1;32m    302\u001b[0m \u001b[39melif\u001b[39;00m mode \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mw\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m    303\u001b[0m     colxn \u001b[39m=\u001b[39m Collection(\n\u001b[1;32m    304\u001b[0m         path,\n\u001b[1;32m    305\u001b[0m         mode,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    314\u001b[0m         \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs\n\u001b[1;32m    315\u001b[0m     )\n",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/fiona/collection.py:243\u001b[0m, in \u001b[0;36mCollection.__init__\u001b[0;34m(self, path, mode, driver, schema, crs, encoding, layer, vsi, archive, enabled_drivers, crs_wkt, ignore_fields, ignore_geometry, include_fields, wkt_version, allow_unsupported_drivers, **kwargs)\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmode \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mr\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m    242\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msession \u001b[39m=\u001b[39m Session()\n\u001b[0;32m--> 243\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msession\u001b[39m.\u001b[39;49mstart(\u001b[39mself\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m    244\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmode \u001b[39min\u001b[39;00m (\u001b[39m\"\u001b[39m\u001b[39ma\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mw\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[1;32m    245\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msession \u001b[39m=\u001b[39m WritingSession()\n",
      "File \u001b[0;32mfiona/ogrext.pyx:588\u001b[0m, in \u001b[0;36mfiona.ogrext.Session.start\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mfiona/ogrext.pyx:143\u001b[0m, in \u001b[0;36mfiona.ogrext.gdal_open_vector\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mDriverError\u001b[0m: path/to/censo10_IGN_ref.shp: No such file or directory"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>codprov</th>\n",
       "      <th>coddepto</th>\n",
       "      <th>circuito</th>\n",
       "      <th>IN1</th>\n",
       "      <th>NAM</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>00001</td>\n",
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       "      <td>Comuna 1</td>\n",
       "      <td>POLYGON Z ((-58.36610 -34.62150 0.00000, -58.3...</td>\n",
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       "    <tr>\n",
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       "      <td>001</td>\n",
       "      <td>00002</td>\n",
       "      <td>02007</td>\n",
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       "      <td>POLYGON Z ((-58.37740 -34.61630 0.00000, -58.3...</td>\n",
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       "    <tr>\n",
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       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>00003</td>\n",
       "      <td>02007</td>\n",
       "      <td>Comuna 1</td>\n",
       "      <td>POLYGON Z ((-58.35720 -34.59510 0.00000, -58.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>00004</td>\n",
       "      <td>02007</td>\n",
       "      <td>Comuna 1</td>\n",
       "      <td>POLYGON Z ((-58.36770 -34.60740 0.00000, -58.3...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>00005</td>\n",
       "      <td>02007</td>\n",
       "      <td>Comuna 1</td>\n",
       "      <td>POLYGON Z ((-58.37984 -34.58016 0.00000, -58.3...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  codprov coddepto circuito    IN1       NAM  \\\n",
       "0      01      001    00001  02007  Comuna 1   \n",
       "1      01      001    00002  02007  Comuna 1   \n",
       "2      01      001    00003  02007  Comuna 1   \n",
       "3      01      001    00004  02007  Comuna 1   \n",
       "4      01      001    00005  02007  Comuna 1   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON Z ((-58.36610 -34.62150 0.00000, -58.3...  \n",
       "1  POLYGON Z ((-58.37740 -34.61630 0.00000, -58.3...  \n",
       "2  POLYGON Z ((-58.35720 -34.59510 0.00000, -58.3...  \n",
       "3  POLYGON Z ((-58.36770 -34.60740 0.00000, -58.3...  \n",
       "4  POLYGON Z ((-58.37984 -34.58016 0.00000, -58.3...  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circs_dpto_IGN.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "circs_dpto_IGN.to_file('./info/circ_dpto_IGN.geojson', driver = 'GeoJSON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = circs_dpto_IGN\n",
    "counts = df.groupby(['codprov', 'coddepto', 'IN1', 'NAM']).size().reset_index(name='counts')\n",
    "\n",
    "# Get the row indices with the highest counts within each group\n",
    "idx_max_counts = counts.groupby(['codprov', 'coddepto'])['counts'].idxmax()\n",
    "# Select the corresponding rows from the original DataFrame using the indices\n",
    "ref = counts.loc[idx_max_counts]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>codprov</th>\n",
       "      <th>coddepto</th>\n",
       "      <th>IN1</th>\n",
       "      <th>NAM</th>\n",
       "      <th>counts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>02007</td>\n",
       "      <td>Comuna 1</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01</td>\n",
       "      <td>002</td>\n",
       "      <td>02014</td>\n",
       "      <td>Comuna 2</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01</td>\n",
       "      <td>003</td>\n",
       "      <td>02021</td>\n",
       "      <td>Comuna 3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01</td>\n",
       "      <td>004</td>\n",
       "      <td>02028</td>\n",
       "      <td>Comuna 4</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>005</td>\n",
       "      <td>02035</td>\n",
       "      <td>Comuna 5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  codprov coddepto    IN1       NAM  counts\n",
       "0      01      001  02007  Comuna 1      19\n",
       "1      01      002  02014  Comuna 2      10\n",
       "2      01      003  02021  Comuna 3      13\n",
       "3      01      004  02028  Comuna 4      15\n",
       "4      01      005  02035  Comuna 5      10"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Claves 527 deptos, de CNE a IGN\n",
    "ref.head(5)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Radios en Circuitos\n",
    "#### Interseccion espacial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Load GeoDataFrames\n",
    "radios_dpto_IGN = gpd.read_file('./info/radio_dpto_IGN.geojson')\n",
    "circs_dpto_IGN = gpd.read_file('./info/circ_dpto_IGN.geojson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "      <th>IN1</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>020130103</td>\n",
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       "    <tr>\n",
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       "      <td>01</td>\n",
       "      <td>020130102</td>\n",
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       "      <td>07</td>\n",
       "      <td>940140705</td>\n",
       "      <td>POLYGON ((-68.36060 -54.82677, -68.35857 -54.8...</td>\n",
       "      <td>94014</td>\n",
       "      <td>Ushuaia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52397</th>\n",
       "      <td>94</td>\n",
       "      <td>014</td>\n",
       "      <td>07</td>\n",
       "      <td>940140710</td>\n",
       "      <td>POLYGON ((-68.33741 -54.82805, -68.33741 -54.8...</td>\n",
       "      <td>94014</td>\n",
       "      <td>Ushuaia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52398</th>\n",
       "      <td>94</td>\n",
       "      <td>014</td>\n",
       "      <td>08</td>\n",
       "      <td>940140813</td>\n",
       "      <td>POLYGON ((-68.33292 -54.82781, -68.33292 -54.8...</td>\n",
       "      <td>94014</td>\n",
       "      <td>Ushuaia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52399</th>\n",
       "      <td>94</td>\n",
       "      <td>014</td>\n",
       "      <td>07</td>\n",
       "      <td>940140709</td>\n",
       "      <td>POLYGON ((-68.33740 -54.82804, -68.33740 -54.8...</td>\n",
       "      <td>94014</td>\n",
       "      <td>Ushuaia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52400</th>\n",
       "      <td>94</td>\n",
       "      <td>014</td>\n",
       "      <td>07</td>\n",
       "      <td>940140707</td>\n",
       "      <td>POLYGON ((-68.35771 -54.83116, -68.35827 -54.8...</td>\n",
       "      <td>94014</td>\n",
       "      <td>Ushuaia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52401 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      PROV_ DEPTO_ FRACC_   COD_2010  \\\n",
       "0        02    013     03  020130302   \n",
       "1        02    013     11  020131101   \n",
       "2        02    013     01  020130104   \n",
       "3        02    013     01  020130103   \n",
       "4        02    013     01  020130102   \n",
       "...     ...    ...    ...        ...   \n",
       "52396    94    014     07  940140705   \n",
       "52397    94    014     07  940140710   \n",
       "52398    94    014     08  940140813   \n",
       "52399    94    014     07  940140709   \n",
       "52400    94    014     07  940140707   \n",
       "\n",
       "                                                geometry    IN1        NAM  \n",
       "0      POLYGON ((-58.46740 -34.53485, -58.46740 -34.5...  02091  Comuna 13  \n",
       "1      POLYGON ((-58.45868 -34.53715, -58.45868 -34.5...  02091  Comuna 13  \n",
       "2      POLYGON ((-58.46758 -34.53733, -58.46794 -34.5...  02091  Comuna 13  \n",
       "3      POLYGON ((-58.46846 -34.53531, -58.46846 -34.5...  02091  Comuna 13  \n",
       "4      POLYGON ((-58.47269 -34.53719, -58.47269 -34.5...  02091  Comuna 13  \n",
       "...                                                  ...    ...        ...  \n",
       "52396  POLYGON ((-68.36060 -54.82677, -68.35857 -54.8...  94014    Ushuaia  \n",
       "52397  POLYGON ((-68.33741 -54.82805, -68.33741 -54.8...  94014    Ushuaia  \n",
       "52398  POLYGON ((-68.33292 -54.82781, -68.33292 -54.8...  94014    Ushuaia  \n",
       "52399  POLYGON ((-68.33740 -54.82804, -68.33740 -54.8...  94014    Ushuaia  \n",
       "52400  POLYGON ((-68.35771 -54.83116, -68.35827 -54.8...  94014    Ushuaia  \n",
       "\n",
       "[52401 rows x 7 columns]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# radios_dpto_IGN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "02091 Comuna 13\n",
      "02084 Comuna 12\n",
      "02098 Comuna 14\n",
      "02105 Comuna 15\n",
      "02014 Comuna 2\n",
      "02077 Comuna 11\n",
      "02007 Comuna 1\n",
      "02035 Comuna 5\n",
      "02021 Comuna 3\n",
      "02042 Comuna 6\n",
      "02070 Comuna 10\n",
      "02049 Comuna 7\n",
      "02028 Comuna 4\n",
      "02063 Comuna 9\n",
      "02056 Comuna 8\n",
      "06763 San Nicolás\n",
      "06665 Ramallo\n",
      "06623 Pergamino\n",
      "06175 Colón\n",
      "06770 San Pedro\n",
      "06070 Baradero\n",
      "06077 Arrecifes\n",
      "06686 Rojas\n",
      "06882 Zárate\n",
      "06294 General Arenales\n",
      "06714 Salto\n",
      "06126 Campana\n",
      "06140 Capitán Sarmiento\n",
      "06735 San Antonio de Areco\n",
      "06749 San Fernando\n",
      "06266 Exaltación de la Cruz\n",
      "06161 Carmen de Areco\n",
      "06462 Leandro N. Alem\n",
      "06392 General Villegas\n",
      "06728 San Andrés de Giles\n",
      "60441 La Plata\n",
      "06413 Junín\n",
      "06252 Escobar\n",
      "06351 General Pinto\n",
      "06805 Tigre\n",
      "06210 Chacabuco\n",
      "06497 Luján\n",
      "06638 Pilar\n",
      "06277 Florentino Ameghino\n",
      "06784 Suipacha\n",
      "06469 Lincoln\n",
      "06532 Mercedes\n",
      "06112 Bragado\n",
      "06224 Chivilcoy\n",
      "06385 General Viamonte\n",
      "06364 General Rodríguez\n",
      "06525 Marcos Paz\n",
      "06021 Alberti\n",
      "06329 General Las Heras\n",
      "06574 Navarro\n",
      "06091 Berazategui\n",
      "06245 Ensenada\n",
      "06154 Carlos Tejedor\n",
      "06270 Ezeiza\n",
      "06098 Berisso\n",
      "06134 Cañuelas\n",
      "06483 Lobos\n",
      "06679 Rivadavia\n",
      "06778 San Vicente\n",
      "06505 Magdalena\n",
      "06119 Brandsen\n",
      "06588 9 de Julio\n",
      "06854 25 de Mayo\n",
      "06343 General Paz\n",
      "06547 Monte\n",
      "06147 Carlos Casares\n",
      "06693 Roque Pérez\n",
      "06655 Punta Indio\n",
      "06609 Pehuajó\n",
      "06218 Chascomús\n",
      "06826 Trenque Lauquen\n",
      "06707 Saladillo\n",
      "06301 General Belgrano\n",
      "06455 Las Flores\n",
      "06287 General Alvear\n",
      "06616 Pellegrini\n",
      "06105 Bolívar\n",
      "06168 Castelli\n",
      "06630 Pila\n",
      "06406 Hipólito Yrigoyen\n",
      "06798 Tapalqué\n",
      "06847 Tres Lomas\n",
      "06231 Daireaux\n",
      "06672 Rauch\n",
      "06238 Dolores\n",
      "06049 Azul\n",
      "06595 Olavarría\n",
      "06812 Tordillo\n",
      "06399 Guaminí\n",
      "06721 Salliqueló\n",
      "06308 General Guido\n",
      "06007 Adolfo Alsina\n",
      "06420 La Costa\n",
      "06336 General Lavalle\n",
      "06042 Ayacucho\n",
      "06511 Maipú\n",
      "06322 General La Madrid\n",
      "06203 Coronel Suárez\n",
      "06791 Tandil\n",
      "06315 General Juan Madariaga\n",
      "06448 Laprida\n",
      "06084 Benito Juárez\n",
      "06644 Pinamar\n",
      "06700 Saavedra\n",
      "06651 Puán\n",
      "06518 Mar Chiquita\n",
      "06868 Villa Gesell\n",
      "06063 Balcarce\n",
      "06014 Adolfo Gonzales Chaves\n",
      "06196 Coronel Pringles\n",
      "06476 Lobería\n",
      "06581 Necochea\n",
      "06357 General Pueyrredón\n",
      "06819 Tornquist\n",
      "06833 Tres Arroyos\n",
      "06742 San Cayetano\n",
      "06280 General Alvarado\n",
      "06189 Coronel Dorrego\n",
      "06056 Bahía Blanca\n",
      "06875 Villarino\n",
      "06182 Coronel de Marina Leonardo Rosales\n",
      "06553 Monte Hermoso\n",
      "06602 Patagones\n",
      "06515 Malvinas Argentinas\n",
      "06412 José C. Paz\n",
      "06756 San Isidro\n",
      "06371 General San Martín\n",
      "06560 Moreno\n",
      "06861 Vicente López\n",
      "06760 San Miguel\n",
      "06840 Tres de Febrero\n",
      "06408 Hurlingham\n",
      "06568 Morón\n",
      "06410 Ituzaingó\n",
      "06539 Merlo\n",
      "06427 La Matanza\n",
      "06035 Avellaneda\n",
      "06434 Lanús\n",
      "06490 Lomas de Zamora\n",
      "06658 Quilmes\n",
      "06260 Esteban Echeverría\n",
      "06028 Almirante Brown\n",
      "06274 Florencio Varela\n",
      "06648 Presidente Perón\n",
      "10028 Antofagasta de la Sierra\n",
      "10035 Belén\n",
      "10091 Santa María\n",
      "10105 Tinogasta\n",
      "10021 Andalgalá\n",
      "10007 Ambato\n",
      "10084 Pomán\n",
      "10077 Paclín\n",
      "10098 Santa Rosa\n",
      "10056 El Alto\n",
      "10063 Fray Mamerto Esquiú\n",
      "10049 Capital\n",
      "10042 Capayán\n",
      "10112 Valle viejo\n",
      "10014 Ancasti\n",
      "10070 La Paz\n",
      "14154 Sobremonte\n",
      "14175 Tulumba\n",
      "14112 Río Seco\n",
      "14028 Cruz del Eje\n",
      "14049 Ischilín\n",
      "14140 San Justo\n",
      "14168 Totoral\n",
      "14105 Río Primero\n",
      "14070 Minas\n",
      "14091 Punilla\n",
      "14021 Colón\n",
      "14077 Pocho\n",
      "14014 Capital\n",
      "14119 Río Segundo\n",
      "14147 Santa María\n",
      "14126 San Alberto\n",
      "14007 Calamuchita\n",
      "14133 San Javier\n",
      "14161 Tercero Arriba\n",
      "14182 Unión\n",
      "14042 General San Martín\n",
      "14063 Marcos Juárez\n",
      "14098 Río Cuarto\n",
      "14056 Juárez Celman\n",
      "14084 Presidente Roque Sáenz Peña\n",
      "14035 General Roca\n",
      "18077 Itatí\n",
      "18133 San Cosme\n",
      "18084 Ituzaingó\n",
      "18014 Berón de Astrada\n",
      "18021 Capital\n",
      "18140 San Luis del Palmar\n",
      "18063 General Paz\n",
      "18154 San Miguel\n",
      "18042 Empedrado\n",
      "18168 Santo Tomé\n",
      "18098 Mburucuyá\n",
      "18028 Concepción\n",
      "18126 Saladas\n",
      "18007 Bella Vista\n",
      "18147 San Martín\n",
      "18161 San Roque\n",
      "18056 General Alvear\n",
      "18035 Curuzú Cuatiá\n",
      "18091 Lavalle\n",
      "18070 Goya\n",
      "18119 Paso de los Libres\n",
      "18049 Esquina\n",
      "18175 Sauce\n",
      "18112 Monte Caseros\n",
      "22063 General Güemes\n",
      "22007 Almirante Brown\n",
      "22084 Libertador General San Martín\n",
      "22091 Maipú\n",
      "22133 Quitilipi\n",
      "22168 25 de Mayo\n",
      "22070 Independencia\n",
      "22021 Comandante Fernández\n",
      "22154 Sargento Cabral\n",
      "22105 9 de Julio\n",
      "22049 General Belgrano\n",
      "22014 Bermejo\n",
      "22028 Chacabuco\n",
      "22119 Presidencia de la Plaza\n",
      "22036 12 de Octubre\n",
      "22126 1ro. de Mayo\n",
      "22112 O'Higgins\n",
      "22056 General Donovan\n",
      "22147 San Lorenzo\n",
      "22077 Libertad\n",
      "22161 Tapenagá\n",
      "22098 Mayor Luis Jorge Fontana\n",
      "22140 San Fernando\n",
      "22039 2 de Abril\n",
      "22043 Fray Justo Santa María de Oro\n",
      "26105 Telsen\n",
      "26007 Biedma\n",
      "26049 Gastre\n",
      "26014 Cushamen\n",
      "26035 Futaleufú\n",
      "26056 Languiñeo\n",
      "26077 Rawson\n",
      "26042 Gaiman\n",
      "26063 Mártires\n",
      "26070 Paso de Indios\n",
      "26028 Florentino Ameghino\n",
      "26098 Tehuelches\n",
      "26021 Escalante\n",
      "26091 Sarmiento\n",
      "26084 Río Senguer\n",
      "30042 Feliciano\n",
      "30070 La Paz\n",
      "30028 Federación\n",
      "30035 Federal\n",
      "30015 Concordia\n",
      "30084 Paraná\n",
      "30113 Villaguay\n",
      "30088 San Salvador\n",
      "30008 Colón\n",
      "30021 Diamante\n",
      "30077 Nogoyá\n",
      "30091 Tala\n",
      "30098 Uruguay\n",
      "30105 Victoria\n",
      "30056 Gualeguaychú\n",
      "30049 Gualeguay\n",
      "30063 Islas del Ibicuy\n",
      "34063 Ramón Lista\n",
      "34007 Bermejo\n",
      "34028 Matacos\n",
      "34035 Patiño\n",
      "34042 Pilagás\n",
      "34049 Pilcomayo\n",
      "34056 Pirané\n",
      "34014 Formosa\n",
      "34021 Laishí\n",
      "38077 Santa Catalina\n",
      "38112 Yavi\n",
      "38049 Rinconada\n",
      "38007 Cochinoca\n",
      "38028 Humahuaca\n",
      "38084 Susques\n",
      "38105 Valle Grande\n",
      "38094 Tilcara\n",
      "38098 Tumbaya\n",
      "38035 Ledesma\n",
      "38070 Santa Bárbara\n",
      "38021 Dr. Manuel Belgrano\n",
      "38063 San Pedro\n",
      "38042 Palpalá\n",
      "38056 San Antonio\n",
      "38014 El Carmen\n",
      "42126 Rancul\n",
      "42133 Realicó\n",
      "42056 Chapaleufú\n",
      "42147 Trenel\n",
      "42105 Maracó\n",
      "42035 Conhelo\n",
      "42119 Quemú Quemú\n",
      "42098 Loventué\n",
      "42049 Chalileo\n",
      "42063 Chical Co\n",
      "42140 Toay\n",
      "42021 Capital\n",
      "42028 Catriló\n",
      "42091 Limay Mahuida\n",
      "42112 Puelén\n",
      "42007 Atreucó\n",
      "42154 Utracán\n",
      "42070 Guatraché\n",
      "42042 Curacó\n",
      "42084 Lihuel Calel\n",
      "42077 Hucal\n",
      "42014 Caleu Caleu\n",
      "46098 Vinchina\n",
      "46077 General Lamadrid\n",
      "46119 San Blas de Los Sauces\n",
      "46007 Arauco\n",
      "46049 Famatina\n",
      "46021 Castro Barros\n",
      "46014 Capital\n",
      "46126 Sanagasta\n",
      "46028 Coronel Felipe Varela\n",
      "46042 Chilecito\n",
      "46105 Independencia\n",
      "46056 General  Angel V. Peñaloza\n",
      "46035 Chamical\n",
      "46063 General Belgrano\n",
      "46070 General Juan F. Quiroga\n",
      "46084 General Ocampo\n",
      "46112 Rosario Vera Peñaloza\n",
      "46091 General San Martín\n",
      "50049 Las Heras\n",
      "50056 Lavalle\n",
      "50063 Cuyo\n",
      "50098 San Martín\n",
      "50028 Guaymallén\n",
      "50112 Santa Rosa\n",
      "50070 Maipú\n",
      "50007 Capital\n",
      "50021 Godoy Cruz\n",
      "50126 Tupungato\n",
      "50042 La Paz\n",
      "50035 Junín\n",
      "50084 Rivadavia\n",
      "50119 Tunuyán\n",
      "50091 San Carlos\n",
      "50105 San Rafael\n",
      "50014 General Alvear\n",
      "50077 Malargüe\n",
      "54049 General Manuel Belgrano\n",
      "54063 Iguazú\n",
      "54042 Eldorado\n",
      "54112 San Pedro\n",
      "54084 Montecarlo\n",
      "54077 Libertador General San Martín\n",
      "54056 Guaraní\n",
      "54098 San Ignacio\n",
      "54014 Cainguás\n",
      "54091 Oberá\n",
      "54119 25de Mayo\n",
      "54021 Candelaria\n",
      "54028 Capital\n",
      "54070 Leandro N. Alem\n",
      "54105 San Javier\n",
      "54007 Apóstoles\n",
      "54035 Concepción\n",
      "58042 Chos Malal\n",
      "58077 Minas\n",
      "58091 Pehuenches\n",
      "58084 Ñorquín\n",
      "58063 Loncopué\n",
      "58014 Añelo\n",
      "58105 Picunches\n",
      "58035 Confluencia\n",
      "58112 Zapala\n",
      "58007 Aluminé\n",
      "58098 Picún Leufú\n",
      "58021 Catán Lil\n",
      "58049 Huiliches\n",
      "58028 Collón Curá\n",
      "58056 Lácar\n",
      "58070 Los Lagos\n",
      "62042 General Roca\n",
      "62014 Avellaneda\n",
      "62063 Pichi Mahuida\n",
      "62035 El Cuy\n",
      "62049 9 de julio\n",
      "62028 Conesa\n",
      "62084 Valcheta\n",
      "62077 San Antonio\n",
      "62070 Pilcaniyeu\n",
      "62091 25 de Mayo\n",
      "62007 Adolfo Alsina\n",
      "62021 Bariloche\n",
      "62056 Ñorquinco\n",
      "66056 General José de San Martín\n",
      "66133 Rivadavia\n",
      "66161 Santa Victoria\n",
      "66070 Iruya\n",
      "66126 Orán\n",
      "66091 La Poma\n",
      "66105 Los Andes\n",
      "66147 Rosario de Lerma\n",
      "66007 Anta\n",
      "66077 La Caldera\n",
      "66049 General Güemes\n",
      "66028 Capital\n",
      "66014 Cachi\n",
      "66035 Cerrillos\n",
      "66042 Chicoana\n",
      "66112 Metán\n",
      "66119 Molinos\n",
      "66098 La Viña\n",
      "66154 San Carlos\n",
      "66063 Guachipas\n",
      "66140 Rosario de la Frontera\n",
      "66084 La Candelaria\n",
      "66021 Cafayate\n",
      "70049 Iglesia\n",
      "70056 Jachal\n",
      "70119 Valle Fértil\n",
      "70021 Calingasta\n",
      "70112 Ullum\n",
      "70035 Caucete\n",
      "70014 Angaco\n",
      "70007 Albardón\n",
      "70133 Zonda\n",
      "70091 San Martín\n",
      "70042 Chimbas\n",
      "70084 Rivadavia\n",
      "70098 Santa Lucía\n",
      "70028 Capital\n",
      "70063 9 de Julio\n",
      "70077 Rawson\n",
      "70070 Pocito\n",
      "70126 25 de Mayo\n",
      "70105 Sarmiento\n",
      "74007 Ayacucho\n",
      "74049 Junín\n",
      "74063 Libertador General San Martín\n",
      "74028 Chacabuco\n",
      "74014 Belgrano\n",
      "74021 Coronel Pringles\n",
      "74035 General Pedernera\n",
      "74056 Juan Martín de Pueyrredón\n",
      "74042 Gobernador Dupuy\n",
      "78014 Deseado\n",
      "78035 Lago Buenos Aires\n",
      "78049 Río Chico\n",
      "78042 Magallanes\n",
      "78028 Lago Argentino\n",
      "78007 Corpen Aike\n",
      "78021 Güer Aike\n",
      "82077 9 de Julio\n",
      "82133 Vera\n",
      "82049 General Obligado\n",
      "82098 San Javier\n",
      "82091 San Cristóbal\n",
      "82112 San Justo\n",
      "82070 Las Colonias\n",
      "82035 Garay\n",
      "82021 Castellanos\n",
      "82063 La Capital\n",
      "82126 San Martín\n",
      "82105 San Jerónimo\n",
      "82007 Belgrano\n",
      "82056 Iriondo\n",
      "82119 San Lorenzo\n",
      "82084 Rosario\n",
      "82014 Caseros\n",
      "82028 Constitución\n",
      "82042 General López\n",
      "86056 Copo\n",
      "86133 Pellegrini\n",
      "86014 Alberdi\n",
      "86091 Jiménez\n",
      "86070 Figueroa\n",
      "86119 Moreno\n",
      "86035 Banda\n",
      "86147 Río Hondo\n",
      "86049 Capital\n",
      "86084 Guasayán\n",
      "86161 Robles\n",
      "86098 Juan F. Ibarra\n",
      "86182 Sarmiento\n",
      "86175 San Martín\n",
      "86189 Silipica\n",
      "86063 Choya\n",
      "86028 Avellaneda\n",
      "86105 Loreto\n",
      "86077 General Taboada\n",
      "86021 Atamisqui\n",
      "86168 Salavina\n",
      "86126 Ojo de Agua\n",
      "86007 Aguirre\n",
      "86042 Belgrano\n",
      "86140 Quebrachos\n",
      "86112 Mitre\n",
      "86154 Rivadavia\n",
      "90112 Trancas\n",
      "90098 Tafí del Valle\n",
      "90007 Burruyacú\n",
      "90105 Tafí Viejo\n",
      "90063 Lules\n",
      "90119 Yerba Buena\n",
      "90014 Cruz Alta\n",
      "90084 Capital\n",
      "90028 Famaillá\n",
      "90070 Monteros\n",
      "90056 Leales\n",
      "90021 Chicligasta\n",
      "90091 Simoca\n",
      "90077 Río Chico\n",
      "90042 Juan Bautista Alberdi\n",
      "90035 Graneros\n",
      "90049 La Cocha\n",
      "94007 Río Grande\n",
      "94014 Ushuaia\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Adjust CRS to match (if needed)\n",
    "circs_dpto_IGN = circs_dpto_IGN.to_crs(radios_dpto_IGN.crs)\n",
    "\n",
    "# Get unique values of 'IN1' and 'NAM' in both GeoDataFrames\n",
    "unique_in1_nam_censo = radios_dpto_IGN[['IN1', 'NAM']].drop_duplicates()\n",
    "unique_in1_nam_circs = circs_dpto_IGN[['IN1', 'NAM']].drop_duplicates()\n",
    "\n",
    "# Iterate over unique values and perform the curated overlay for each combination\n",
    "results = []\n",
    "\n",
    "# for in1_prov in circs_dpto_IGN['IN1'].str[:2].unique()[:3]:\n",
    "    # print(in1_prov)\n",
    "for _, row in unique_in1_nam_censo.iterrows():\n",
    "    # in1_prov = row['IN1'].str[:2]\n",
    "    in1_val = row['IN1']\n",
    "    nam_val = row['NAM']\n",
    "    print(in1_val, nam_val)\n",
    "\n",
    "    # Select radios with the current IN1 and NAM values\n",
    "    # radios_selection = radios_dpto_IGN[radios_dpto_IGN['IN1'].str[:2]  == in1_prov]\n",
    "    radios_selection = radios_dpto_IGN[\n",
    "        (radios_dpto_IGN['IN1'] == in1_val) & (radios_dpto_IGN['NAM'] == nam_val)\n",
    "    ]\n",
    "    \n",
    "    # Select circuits with the current IN1 and NAM values\n",
    "    # circuits_selection = circs_dpto_IGN[circs_dpto_IGN['IN1'].str[:2] == in1_prov]\n",
    "    circuits_selection = circs_dpto_IGN[\n",
    "        (circs_dpto_IGN['IN1'] == in1_val) & (circs_dpto_IGN['NAM'] == nam_val)\n",
    "    ]\n",
    "    \n",
    "    # Perform curated overlay for the selected radios and circuits\n",
    "    result = curated_overlay(radios_selection, circuits_selection, low_index=['PROV_', 'DEPTO_', 'COD_2010'], high_index=['codprov', 'coddepto', 'circuito'])\n",
    "\n",
    "    # Do something with the 'result' GeoDataFrame (e.g., save it, process it, etc.)\n",
    "    results += [result]\n",
    "\n",
    "results = pd.concat(results)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 299,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['codprov', 'coddepto', 'circuito', 'IN1', 'NAM', 'geometry'], dtype='object')"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censo10_IGN_ref.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51940, 7)"
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51940, 9)"
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.merge(censo10_IGN_ref[['PROV_', 'DEPTO_', 'IN1', 'NAM']].drop_duplicates()).shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(525, 2)"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[['PROV_', 'DEPTO_']].drop_duplicates().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(526, 2)"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[['codprov', 'coddepto']].drop_duplicates().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51940, 9)"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.merge(ref[['codprov', 'coddepto', 'IN1', 'NAM']].drop_duplicates()).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = results.merge(ref[['codprov', 'coddepto', 'IN1', 'NAM']].drop_duplicates())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# results.to_file('./info/radio_circ_IGN.geojson', driver = 'GeoJSON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.drop('geometry', axis = 1).to_csv('./info/radio_circ_IGN.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>PROV_</th>\n",
       "      <th>DEPTO_</th>\n",
       "      <th>COD_2010</th>\n",
       "      <th>codprov</th>\n",
       "      <th>coddepto</th>\n",
       "      <th>circuito</th>\n",
       "      <th>geometry</th>\n",
       "      <th>IN1</th>\n",
       "      <th>NAM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>020130101</td>\n",
       "      <td>01</td>\n",
       "      <td>013</td>\n",
       "      <td>00134</td>\n",
       "      <td>POLYGON Z ((-58.47254 -34.53737 0.00000, -58.4...</td>\n",
       "      <td>02091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>020130102</td>\n",
       "      <td>01</td>\n",
       "      <td>013</td>\n",
       "      <td>00134</td>\n",
       "      <td>POLYGON Z ((-58.47030 -34.53644 0.00000, -58.4...</td>\n",
       "      <td>02091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>020130103</td>\n",
       "      <td>01</td>\n",
       "      <td>013</td>\n",
       "      <td>00134</td>\n",
       "      <td>POLYGON Z ((-58.46836 -34.53548 0.00000, -58.4...</td>\n",
       "      <td>02091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>020130104</td>\n",
       "      <td>01</td>\n",
       "      <td>013</td>\n",
       "      <td>00134</td>\n",
       "      <td>POLYGON Z ((-58.46794 -34.53643 0.00000, -58.4...</td>\n",
       "      <td>02091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02</td>\n",
       "      <td>013</td>\n",
       "      <td>020130105</td>\n",
       "      <td>01</td>\n",
       "      <td>013</td>\n",
       "      <td>00134</td>\n",
       "      <td>POLYGON Z ((-58.46631 -34.53668 0.00000, -58.4...</td>\n",
       "      <td>02091</td>\n",
       "      <td>Comuna 13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PROV_ DEPTO_   COD_2010 codprov coddepto circuito  \\\n",
       "0    02    013  020130101      01      013    00134   \n",
       "1    02    013  020130102      01      013    00134   \n",
       "2    02    013  020130103      01      013    00134   \n",
       "3    02    013  020130104      01      013    00134   \n",
       "4    02    013  020130105      01      013    00134   \n",
       "\n",
       "                                            geometry    IN1        NAM  \n",
       "0  POLYGON Z ((-58.47254 -34.53737 0.00000, -58.4...  02091  Comuna 13  \n",
       "1  POLYGON Z ((-58.47030 -34.53644 0.00000, -58.4...  02091  Comuna 13  \n",
       "2  POLYGON Z ((-58.46836 -34.53548 0.00000, -58.4...  02091  Comuna 13  \n",
       "3  POLYGON Z ((-58.46794 -34.53643 0.00000, -58.4...  02091  Comuna 13  \n",
       "4  POLYGON Z ((-58.46631 -34.53668 0.00000, -58.4...  02091  Comuna 13  "
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ref.loc[ref.IN1 == '82084']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# codprov\tcoddepto con mas de un depto IGN:\n",
    "\n",
    "# Lugares que bordean con otros deptos y pueden dar lugar a error. Circuitos con depto incorrecto.\n",
    "# duplicates = df.groupby(['codprov', 'coddepto']).agg({'IN1' : ['nunique', 'unique'], 'NAM': 'unique'}).sort_values(('IN1', 'nunique')).tail(20)\n",
    "# duplicates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Asignamos radios y circuitos espacialmente a los departamentos de IGN. Para evitar todo tipo de problemas con los codigos de deptos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "circuito    5589\n",
       "codprov     5563\n",
       "coddepto    5563\n",
       "seccion     1066\n",
       "geometry    5559\n",
       "dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuitos_ARG.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "circuito    2058\n",
       "codprov       24\n",
       "coddepto     138\n",
       "seccion        8\n",
       "geometry    5550\n",
       "dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circuitos_ARG.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Agregar codigos de provincia de INDEC\n",
    "codprovs = pd.read_csv('./codprovs.csv', dtype = 'str')\n",
    "codprovs['codprov'] = codprovs['codprov'].str.zfill(2)\n",
    "\n",
    "circuitos_ARG = circuitos_ARG.merge(codprovs, how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Ciudad Autónoma de Buenos Aires' nan]\n",
      "['Catamarca' nan]\n",
      "['Chaco' nan]\n",
      "['Chubut' nan]\n",
      "['Córdoba' nan]\n",
      "['Corrientes' nan]\n",
      "[nan 'Entre Ríos']\n",
      "[nan 'Formosa']\n",
      "[nan 'Jujuy']\n",
      "[nan 'La Pampa']\n",
      "[nan 'La Rioja']\n",
      "[nan 'Mendoza']\n",
      "[nan 'Misiones']\n",
      "[nan 'Neuquén']\n",
      "[nan 'Buenos Aires']\n",
      "[nan 'Río Negro']\n",
      "[nan 'Salta']\n",
      "[nan 'San Luis']\n",
      "[nan 'Santa Cruz']\n",
      "[nan 'Santa Fe']\n",
      "[nan 'Santiago del Estero']\n",
      "[nan 'San Juan']\n",
      "[nan 'Tierra del Fuego, Antártida e Islas del Atlántico Sur']\n",
      "[nan 'Tucumán']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "result_list = []\n",
    "\n",
    "for indec_p in circuitos_ARG.indec_p.dropna().unique():\n",
    "    cne_prov = circuitos_ARG.loc[circuitos_ARG.indec_p.fillna(indec_p) == indec_p]\n",
    "    cne_prov = fix_geom(cne_prov.dropna(subset = 'geometry'))\n",
    "    print(cne_prov.nomprov.unique())\n",
    "\n",
    "    radios_prov = radios.loc[radios.PROV_ == indec_p]\n",
    "\n",
    "#     ## Correccion de errores\n",
    "#     radios_prov.loc[radios_prov.IN1 == '26028', 'DEPTO_'] = '063' # Esto es para adaptarse a un erorr en CNE\n",
    "# #             (la CNE tiene unido camarones al dpto martires, por error)\n",
    "\n",
    "    result_prov = []\n",
    "\n",
    "#     for dpto in cne_prov.indec_d.unique():\n",
    "\n",
    "#         if (cne_prov.indec_p.unique()[0] == '94') | (cne_prov.indec_p.unique()[0] == '22'):\n",
    "# #                     print('chaco o tdf')\n",
    "#             radios_dpto = radios_prov # En chaco y tdf, usar toda la provincia porque los codigos estan cambiados...\n",
    "#         else:\n",
    "#             radios_dpto = radios_prov.loc[radios_prov.DEPTO_ == dpto]\n",
    "\n",
    "# #         circ_dpto = cne_prov.loc[cne_prov.indec_d == dpto]\n",
    "\n",
    "# #     Check...\n",
    "#     fig, axs = plt.subplots(1)\n",
    "#     radios_prov.plot(ax = axs, alpha = .4)\n",
    "#     cne_prov.plot(ax = axs, alpha = .4)\n",
    "#     plt.show()\n",
    "\n",
    "    ## Intersectar los radios en cuestion con los polis de IGS.\n",
    "    overlay = gpd.overlay(radios_prov, cne_prov[['circuito', 'codprov', 'coddepto', 'geometry']], how='intersection')\n",
    "    \n",
    "    \n",
    "    if len(overlay) != 0: # Si hay interseccion\n",
    "        overlay['area'] = overlay.area\n",
    "\n",
    "        year = '2010'\n",
    "        # Quedarse con el que mas interseca (el radio se asigna al depto donde este mayor parte de su area.)\n",
    "        radios_in_circu = overlay.groupby(['PROV_', 'DEPTO_', 'FRACC_', 'COD_'+year]\n",
    "                                        ).apply(lambda x: x.nlargest(1, 'area')).reset_index(drop = True\n",
    "                    )[['codprov', 'coddepto', 'PROV_', 'DEPTO_', 'FRACC_', 'COD_'+year, 'IN1', 'circuito']]\n",
    "        \n",
    "        # radios_in_circu['FRACC_2'] = radios_in_circu['COD_'+year].str[5:7]\n",
    "        result_prov += [radios_in_circu]\n",
    "    else:\n",
    "#             print('sin interseccion en '+str(circ_dpto.departamen.unique()[0]))\n",
    "        print('sin interseccion en '+str(cne_prov.nomprov.unique()[0]))\n",
    "\n",
    "    result_prov = pd.concat(result_prov)\n",
    "\n",
    "    ## Tomar radios que se asociaron a mas de 1 circuito.\n",
    "#     Esto pasa cuando se usa la provincia entera para intersecar (ie. chaco, tdf)\n",
    "    duplicados = result_prov[result_prov.duplicated('COD_2010', keep = False)].sort_values('COD_2010')\n",
    "\n",
    "    if len(duplicados) > 0:\n",
    "\n",
    "        ## Volcarlos en 1 solo circuito.\n",
    "        overlay = gpd.overlay(gpd.GeoDataFrame(duplicados.drop('circuito', axis = 1).merge(radios)), \n",
    "                             gpd.GeoDataFrame(duplicados.merge(cne_prov))[['circuito', 'geometry']], how='intersection')\n",
    "\n",
    "        if len(overlay) != 0: # Si hay interseccion\n",
    "            overlay['area'] = overlay.area\n",
    "\n",
    "            year = '2010'\n",
    "            # Quedarse con el que mas interseca (el radio se asigna al depto donde este mayor parte de su area.)\n",
    "            res_dup_prov = overlay.groupby(['PROV_', 'DEPTO_', 'FRACC_', 'COD_'+year]\n",
    "                                        ).apply(lambda x: x.nlargest(1, 'area')\n",
    "                                               ).reset_index(drop = True)[['PROV_', 'DEPTO_', 'FRACC_', 'COD_'+year, 'IN1', 'circuito']]\n",
    "        result_prov = pd.concat([result_prov[~result_prov.duplicated('COD_2010', keep = False)], res_dup_prov])\n",
    "\n",
    "    result_list += [result_prov]\n",
    "\n",
    "circs_dpto_IGN = pd.concat(result_list).drop_duplicates()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PROV_          24\n",
       "DEPTO_        154\n",
       "FRACC_         99\n",
       "COD_2010    52401\n",
       "IN1           525\n",
       "geometry    52401\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "radios.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "codprov        24\n",
       "coddepto      138\n",
       "PROV_          24\n",
       "DEPTO_        154\n",
       "FRACC_         99\n",
       "COD_2010    52159\n",
       "IN1           525\n",
       "circuito     1974\n",
       "dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circs_dpto_IGN.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Esto es lo extraido de la base\n",
    "# seccion_dpto = pd.read_csv('./../elecciones-ARG/datos/BD/seccion_table.csv', dtype = 'str')\n",
    "# seccion_dpto['distrito_id'] = seccion_dpto['distrito_id'].str.zfill(2)\n",
    "# seccion_dpto['seccion_id'] = seccion_dpto['seccion_id'].str.zfill(3)\n",
    "# seccion_dpto.nunique()\n",
    "\n",
    "## Este archivo esta basado en lo extraido de la base, pero trae los codigos usados en INDEC, etc (PROV_, DEPTO_)\n",
    "# result = pd.read_csv('./info/radios_circuitos-TTGL.csv', dtype= 'str')\n",
    "secciones_dptos_ref = pd.read_csv('./info/secciones_dptos_ref.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "secciones_dptos_ref = secciones_dptos_ref.dropna()\n",
    "secciones_dptos_ref['PROV_'] = secciones_dptos_ref.IDPROV.astype(int).astype(str).str.zfill(2)\n",
    "secciones_dptos_ref['DEPTO_'] = secciones_dptos_ref.IDDPTO.astype(int).astype(str).str.zfill(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>PROV_</th>\n",
       "      <th>DEPTO_</th>\n",
       "      <th>distrito_id</th>\n",
       "      <th>seccion_id</th>\n",
       "      <th>seccionprovincial_id</th>\n",
       "      <th>seccion_nombre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Comuna 01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02</td>\n",
       "      <td>002</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Comuna 02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02</td>\n",
       "      <td>003</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Comuna 03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02</td>\n",
       "      <td>004</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Comuna 04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02</td>\n",
       "      <td>005</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Comuna 05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PROV_ DEPTO_  distrito_id  seccion_id  seccionprovincial_id seccion_nombre\n",
       "0    02    001            1           1                     0      Comuna 01\n",
       "1    02    002            1           2                     0      Comuna 02\n",
       "2    02    003            1           3                     0      Comuna 03\n",
       "3    02    004            1           4                     0      Comuna 04\n",
       "4    02    005            1           5                     0      Comuna 05"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "claves = secciones_dptos_ref[['PROV_', 'DEPTO_', 'distrito_id', 'seccion_id', 'seccionprovincial_id', 'seccion_nombre']]\n",
    "claves.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>codprov</th>\n",
       "      <th>coddepto</th>\n",
       "      <th>PROV_</th>\n",
       "      <th>DEPTO_</th>\n",
       "      <th>FRACC_</th>\n",
       "      <th>COD_2010</th>\n",
       "      <th>IN1</th>\n",
       "      <th>circuito</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "      <td>01</td>\n",
       "      <td>020010101</td>\n",
       "      <td>0214007</td>\n",
       "      <td>00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>020010201</td>\n",
       "      <td>0214007</td>\n",
       "      <td>00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>020010202</td>\n",
       "      <td>0214007</td>\n",
       "      <td>00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>020010203</td>\n",
       "      <td>0214007</td>\n",
       "      <td>00005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>020010204</td>\n",
       "      <td>0214007</td>\n",
       "      <td>00005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  codprov coddepto PROV_ DEPTO_ FRACC_   COD_2010      IN1 circuito\n",
       "0      01      001    02    001     01  020010101  0214007    00005\n",
       "1      01      001    02    001     02  020010201  0214007    00005\n",
       "2      01      001    02    001     02  020010202  0214007    00005\n",
       "3      01      001    02    001     02  020010203  0214007    00005\n",
       "4      01      001    02    001     02  020010204  0214007    00005"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circs_dpto_IGN.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_c = circs_dpto_IGN.merge(claves)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# result_c.to_csv('./info/radios_circuitos_secciones_ref.csv', index = False)  #### Hay problema con IN1... a revisar. O bien en el codigo que sigue debajo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_c.drop('IN1', axis = 1).to_csv('./../elecciones-ARG/datos/censo/radios_circuitos_secciones_ref.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>codprov</th>\n",
       "      <th>coddepto</th>\n",
       "      <th>PROV_</th>\n",
       "      <th>DEPTO_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>01</td>\n",
       "      <td>002</td>\n",
       "      <td>02</td>\n",
       "      <td>002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>01</td>\n",
       "      <td>003</td>\n",
       "      <td>02</td>\n",
       "      <td>003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>01</td>\n",
       "      <td>004</td>\n",
       "      <td>02</td>\n",
       "      <td>004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>01</td>\n",
       "      <td>005</td>\n",
       "      <td>02</td>\n",
       "      <td>005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1474</th>\n",
       "      <td>23</td>\n",
       "      <td>016</td>\n",
       "      <td>90</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1597</th>\n",
       "      <td>23</td>\n",
       "      <td>014</td>\n",
       "      <td>90</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1641</th>\n",
       "      <td>23</td>\n",
       "      <td>015</td>\n",
       "      <td>90</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1647</th>\n",
       "      <td>23</td>\n",
       "      <td>016</td>\n",
       "      <td>90</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>23</td>\n",
       "      <td>002</td>\n",
       "      <td>90</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>629 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     codprov coddepto PROV_ DEPTO_\n",
       "0         01      001    02    001\n",
       "329       01      002    02    002\n",
       "527       01      003    02    003\n",
       "781       01      004    02    004\n",
       "1033      01      005    02    005\n",
       "...      ...      ...   ...    ...\n",
       "1474      23      016    90    105\n",
       "1597      23      014    90    112\n",
       "1641      23      015    90    119\n",
       "1647      23      016    90    119\n",
       "1701      23      002    90    119\n",
       "\n",
       "[629 rows x 4 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "circs_dpto_IGN[['codprov', 'coddepto', 'PROV_', 'DEPTO_']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>codprov</th>\n",
       "      <th>coddepto</th>\n",
       "      <th>PROV_</th>\n",
       "      <th>DEPTO_</th>\n",
       "      <th>FRACC_</th>\n",
       "      <th>COD_2010</th>\n",
       "      <th>circuito</th>\n",
       "      <th>distrito_id</th>\n",
       "      <th>seccion_id</th>\n",
       "      <th>seccionprovincial_id</th>\n",
       "      <th>seccion_nombre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "      <td>01</td>\n",
       "      <td>020010101</td>\n",
       "      <td>00005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Comuna 01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>020010201</td>\n",
       "      <td>00005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Comuna 01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>020010202</td>\n",
       "      <td>00005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Comuna 01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>020010203</td>\n",
       "      <td>00005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Comuna 01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>020010204</td>\n",
       "      <td>00005</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Comuna 01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  codprov coddepto PROV_ DEPTO_ FRACC_   COD_2010 circuito  distrito_id  \\\n",
       "0      01      001    02    001     01  020010101    00005            1   \n",
       "1      01      001    02    001     02  020010201    00005            1   \n",
       "2      01      001    02    001     02  020010202    00005            1   \n",
       "3      01      001    02    001     02  020010203    00005            1   \n",
       "4      01      001    02    001     02  020010204    00005            1   \n",
       "\n",
       "   seccion_id  seccionprovincial_id seccion_nombre  \n",
       "0           1                     0      Comuna 01  \n",
       "1           1                     0      Comuna 01  \n",
       "2           1                     0      Comuna 01  \n",
       "3           1                     0      Comuna 01  \n",
       "4           1                     0      Comuna 01  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_c.drop('IN1', axis = 1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>codprov</th>\n",
       "      <th>coddepto</th>\n",
       "      <th>PROV_</th>\n",
       "      <th>DEPTO_</th>\n",
       "      <th>distrito_id</th>\n",
       "      <th>seccion_id</th>\n",
       "      <th>seccion_nombre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>02</td>\n",
       "      <td>001</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Comuna 01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>01</td>\n",
       "      <td>002</td>\n",
       "      <td>02</td>\n",
       "      <td>002</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Comuna 02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>01</td>\n",
       "      <td>003</td>\n",
       "      <td>02</td>\n",
       "      <td>003</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Comuna 03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>01</td>\n",
       "      <td>004</td>\n",
       "      <td>02</td>\n",
       "      <td>004</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Comuna 04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>01</td>\n",
       "      <td>005</td>\n",
       "      <td>02</td>\n",
       "      <td>005</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Comuna 05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51916</th>\n",
       "      <td>23</td>\n",
       "      <td>016</td>\n",
       "      <td>90</td>\n",
       "      <td>105</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>Tafí Viejo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52039</th>\n",
       "      <td>23</td>\n",
       "      <td>014</td>\n",
       "      <td>90</td>\n",
       "      <td>112</td>\n",
       "      <td>23</td>\n",
       "      <td>14</td>\n",
       "      <td>Trancas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52083</th>\n",
       "      <td>23</td>\n",
       "      <td>015</td>\n",
       "      <td>90</td>\n",
       "      <td>119</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Yerba Buena</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52089</th>\n",
       "      <td>23</td>\n",
       "      <td>016</td>\n",
       "      <td>90</td>\n",
       "      <td>119</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Yerba Buena</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52143</th>\n",
       "      <td>23</td>\n",
       "      <td>002</td>\n",
       "      <td>90</td>\n",
       "      <td>119</td>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "      <td>Yerba Buena</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>629 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      codprov coddepto PROV_ DEPTO_  distrito_id  seccion_id seccion_nombre\n",
       "0          01      001    02    001            1           1      Comuna 01\n",
       "329        01      002    02    002            1           2      Comuna 02\n",
       "527        01      003    02    003            1           3      Comuna 03\n",
       "781        01      004    02    004            1           4      Comuna 04\n",
       "1033       01      005    02    005            1           5      Comuna 05\n",
       "...       ...      ...   ...    ...          ...         ...            ...\n",
       "51916      23      016    90    105           23          16     Tafí Viejo\n",
       "52039      23      014    90    112           23          14        Trancas\n",
       "52083      23      015    90    119           23          15    Yerba Buena\n",
       "52089      23      016    90    119           23          15    Yerba Buena\n",
       "52143      23      002    90    119           23          15    Yerba Buena\n",
       "\n",
       "[629 rows x 7 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_c[['codprov', 'coddepto', 'PROV_', 'DEPTO_', 'distrito_id', 'seccion_id', 'seccion_nombre']].drop_duplicates()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Codprov - Coddepto - IN1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "incompatible index of inserted column with frame index",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/pandas/core/frame.py:11610\u001b[0m, in \u001b[0;36m_reindex_for_setitem\u001b[0;34m(value, index)\u001b[0m\n\u001b[1;32m  11609\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m> 11610\u001b[0m     reindexed_value \u001b[39m=\u001b[39m value\u001b[39m.\u001b[39;49mreindex(index)\u001b[39m.\u001b[39m_values\n\u001b[1;32m  11611\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mValueError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m  11612\u001b[0m     \u001b[39m# raised in MultiIndex.from_tuples, see test_insert_error_msmgs\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/pandas/core/series.py:4918\u001b[0m, in \u001b[0;36mSeries.reindex\u001b[0;34m(self, index, axis, method, copy, level, fill_value, limit, tolerance)\u001b[0m\n\u001b[1;32m   4901\u001b[0m \u001b[39m@doc\u001b[39m(\n\u001b[1;32m   4902\u001b[0m     NDFrame\u001b[39m.\u001b[39mreindex,  \u001b[39m# type: ignore[has-type]\u001b[39;00m\n\u001b[1;32m   4903\u001b[0m     klass\u001b[39m=\u001b[39m_shared_doc_kwargs[\u001b[39m\"\u001b[39m\u001b[39mklass\u001b[39m\u001b[39m\"\u001b[39m],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4916\u001b[0m     tolerance\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m,\n\u001b[1;32m   4917\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Series:\n\u001b[0;32m-> 4918\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49mreindex(\n\u001b[1;32m   4919\u001b[0m         index\u001b[39m=\u001b[39;49mindex,\n\u001b[1;32m   4920\u001b[0m         method\u001b[39m=\u001b[39;49mmethod,\n\u001b[1;32m   4921\u001b[0m         copy\u001b[39m=\u001b[39;49mcopy,\n\u001b[1;32m   4922\u001b[0m         level\u001b[39m=\u001b[39;49mlevel,\n\u001b[1;32m   4923\u001b[0m         fill_value\u001b[39m=\u001b[39;49mfill_value,\n\u001b[1;32m   4924\u001b[0m         limit\u001b[39m=\u001b[39;49mlimit,\n\u001b[1;32m   4925\u001b[0m         tolerance\u001b[39m=\u001b[39;49mtolerance,\n\u001b[1;32m   4926\u001b[0m     )\n",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/pandas/core/generic.py:5360\u001b[0m, in \u001b[0;36mNDFrame.reindex\u001b[0;34m(self, labels, index, columns, axis, method, copy, level, fill_value, limit, tolerance)\u001b[0m\n\u001b[1;32m   5359\u001b[0m \u001b[39m# perform the reindex on the axes\u001b[39;00m\n\u001b[0;32m-> 5360\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_reindex_axes(\n\u001b[1;32m   5361\u001b[0m     axes, level, limit, tolerance, method, fill_value, copy\n\u001b[1;32m   5362\u001b[0m )\u001b[39m.\u001b[39m__finalize__(\u001b[39mself\u001b[39m, method\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mreindex\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/pandas/core/generic.py:5375\u001b[0m, in \u001b[0;36mNDFrame._reindex_axes\u001b[0;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[1;32m   5374\u001b[0m ax \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_axis(a)\n\u001b[0;32m-> 5375\u001b[0m new_index, indexer \u001b[39m=\u001b[39m ax\u001b[39m.\u001b[39;49mreindex(\n\u001b[1;32m   5376\u001b[0m     labels, level\u001b[39m=\u001b[39;49mlevel, limit\u001b[39m=\u001b[39;49mlimit, tolerance\u001b[39m=\u001b[39;49mtolerance, method\u001b[39m=\u001b[39;49mmethod\n\u001b[1;32m   5377\u001b[0m )\n\u001b[1;32m   5379\u001b[0m axis \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_axis_number(a)\n",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/pandas/core/indexes/base.py:4278\u001b[0m, in \u001b[0;36mIndex.reindex\u001b[0;34m(self, target, method, level, limit, tolerance)\u001b[0m\n\u001b[1;32m   4276\u001b[0m             indexer, _ \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_indexer_non_unique(target)\n\u001b[0;32m-> 4278\u001b[0m target \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_wrap_reindex_result(target, indexer, preserve_names)\n\u001b[1;32m   4279\u001b[0m \u001b[39mreturn\u001b[39;00m target, indexer\n",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/pandas/core/indexes/multi.py:2490\u001b[0m, in \u001b[0;36mMultiIndex._wrap_reindex_result\u001b[0;34m(self, target, indexer, preserve_names)\u001b[0m\n\u001b[1;32m   2489\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 2490\u001b[0m     target \u001b[39m=\u001b[39m MultiIndex\u001b[39m.\u001b[39;49mfrom_tuples(target)\n\u001b[1;32m   2491\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m   2492\u001b[0m     \u001b[39m# not all tuples, see test_constructor_dict_multiindex_reindex_flat\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/pandas/core/indexes/multi.py:211\u001b[0m, in \u001b[0;36mnames_compat.<locals>.new_meth\u001b[0;34m(self_or_cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m    209\u001b[0m     kwargs[\u001b[39m\"\u001b[39m\u001b[39mnames\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mpop(\u001b[39m\"\u001b[39m\u001b[39mname\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 211\u001b[0m \u001b[39mreturn\u001b[39;00m meth(self_or_cls, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/pandas/core/indexes/multi.py:590\u001b[0m, in \u001b[0;36mMultiIndex.from_tuples\u001b[0;34m(cls, tuples, sortorder, names)\u001b[0m\n\u001b[1;32m    588\u001b[0m         tuples \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray(tuples\u001b[39m.\u001b[39m_values)\n\u001b[0;32m--> 590\u001b[0m     arrays \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(lib\u001b[39m.\u001b[39mtuples_to_object_array(tuples)\u001b[39m.\u001b[39mT)\n\u001b[1;32m    591\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39misinstance\u001b[39m(tuples, \u001b[39mlist\u001b[39m):\n",
      "File \u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/pandas/_libs/lib.pyx:2894\u001b[0m, in \u001b[0;36mpandas._libs.lib.tuples_to_object_array\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Buffer dtype mismatch, expected 'Python object' but got 'long'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_915914/147721104.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m## Cuando codprov y coddpto tienen mas de un IN1, elegir el que mas radios tiene en la interseccion.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprov_dpto_nuniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'codprov'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coddepto'\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[0mnunique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\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----> 4\u001b[0;31m \u001b[0mprov_dpto_nuniques\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'pct'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprov_dpto_nuniques\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'codprov'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'coddepto'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'COD_2010'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\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      5\u001b[0m \u001b[0mprov_dpto_nuniques\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprov_dpto_nuniques\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'pct'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# test.sort_values('test').head(20).sort_values('IN1')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   3946\u001b[0m             \u001b[0;31m# Column to set is duplicated\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3947\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3948\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3949\u001b[0m             \u001b[0;31m# set column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3950\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_item\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   4139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4140\u001b[0m         \u001b[0mSeries\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mTimeSeries\u001b[0m \u001b[0mwill\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mconformed\u001b[0m \u001b[0mto\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mDataFrames\u001b[0m \u001b[0mindex\u001b[0m \u001b[0mto\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4141\u001b[0m         \u001b[0mensure\u001b[0m \u001b[0mhomogeneity\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4142\u001b[0m         \"\"\"\n\u001b[0;32m-> 4143\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\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   4144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4145\u001b[0m         if (\n\u001b[1;32m   4146\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m   4863\u001b[0m         \u001b[0;31m# or through loc single_block_path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4864\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\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   4865\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0m_reindex_for_setitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4866\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mis_dict_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\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-> 4867\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0m_reindex_for_setitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4869\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\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   4870\u001b[0m             \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrequire_length_match\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/base2/lib/python3.11/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(value, index)\u001b[0m\n\u001b[1;32m  11613\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11614\u001b[0m             \u001b[0;31m# duplicate axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11615\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11616\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m> 11617\u001b[0;31m         raise TypeError(\n\u001b[0m\u001b[1;32m  11618\u001b[0m             \u001b[0;34m\"incompatible index of inserted column with frame index\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  11619\u001b[0m         ) from err\n\u001b[1;32m  11620\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mreindexed_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: incompatible index of inserted column with frame index"
     ]
    }
   ],
   "source": [
    "## Cuando codprov y coddpto tienen mas de un IN1, elegir el que mas radios tiene en la interseccion.\n",
    "\n",
    "prov_dpto_nuniques = circs_dpto_IGN.groupby(['codprov', 'coddepto', 'IN1']).nunique().reset_index()\n",
    "prov_dpto_nuniques['pct'] = prov_dpto_nuniques.groupby(['codprov', 'coddepto'])['COD_2010'].apply(lambda x: x/x.sum()).round(2)\n",
    "prov_dpto_nuniques['test'] = abs(prov_dpto_nuniques['pct'] - .5)\n",
    "\n",
    "# test.sort_values('test').head(20).sort_values('IN1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'prompt_toolkit.formatted_text'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "## Lezama y Chascomus\n",
    "prov_dpto_nuniques.loc[prov_dpto_nuniques.IN1 == '06217']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'prompt_toolkit.formatted_text'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "## Tierra del Fuego\n",
    "prov_dpto_nuniques.loc[prov_dpto_nuniques.IN1.str[:2] == '94']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'prompt_toolkit.formatted_text'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "prov_dpto_IN1 = prov_dpto_nuniques.groupby(['codprov', 'coddepto']).apply(lambda x: x.nlargest(1, 'COD_2010')).reset_index(drop = True)\n",
    "\n",
    "prov_dpto_IN1[['codprov', 'coddepto', 'IN1']].to_csv('./info/secciones_departamentos-TTGL.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'prompt_toolkit.formatted_text'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "prov_dpto_IN1[['codprov', 'coddepto', 'IN1']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unir secciones a departamentos IGN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'prompt_toolkit.formatted_text'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "\n",
    "# Unir secciones a departamentos IGN\n",
    "radios_diss_IN1 = radios.copy()\n",
    "radios_diss_IN1['geometry'] = radios_diss_IN1.buffer(0.001)\n",
    "radios_diss_IN1 = radios_diss_IN1.dissolve('IN1').reset_index()[['IN1', 'geometry']]\n",
    "\n",
    "# Si bien en teoria este dissolve nos daria los departmantos, el archivo de radios IGN tiene varios faltantes y errores.\n",
    "# Por eso es mejor hacer spatial join de los radios disueltos, con la fuente de DPTOS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Radios de IGN en DPTOS y PROVS de IGN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'prompt_toolkit.formatted_text'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "## Si este paso falla, se pueden bajar manualmente los archivos y abrirlos localmente.\n",
    "\n",
    "try: \n",
    "    dptos_url = u'https://dnsg.ign.gob.ar/apps/api/v1/capas-sig/Geodesia+y+demarcación/Límites/departamento/json'\n",
    "    dptos_IGN = gpd.read_file(dptos_url)\n",
    "\n",
    "    # Agregar código y nombres de provincia\n",
    "    provs_url = 'https://dnsg.ign.gob.ar/apps/api/v1/capas-sig/Geodesia+y+demarcación/Límites/provincia/json'\n",
    "    provs_IGN = gpd.read_file(provs_url)\n",
    "\n",
    "except:\n",
    "    dptos_IGN = gpd.read_file('./datos/departamento.json')\n",
    "    provs_IGN = gpd.read_file('./datos/provincia.json')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'prompt_toolkit.formatted_text'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "\n",
    "dptos_IN1_from_radios = radios_diss_IN1\n",
    "\n",
    "overlay = gpd.overlay(dptos_IGN[['nam', 'in1', 'geometry']], dptos_IN1_from_radios, how='intersection')\n",
    "overlay['area'] = overlay.area\n",
    "\n",
    "overlay = overlay.groupby(['nam', 'in1']).apply(lambda x: x.nlargest(1, 'area')).reset_index(drop=True)\n",
    "\n",
    "provnames = provs_IGN.rename(columns={'in1': 'in1prov', 'nam': 'namprov'})[['in1prov', 'namprov']]\n",
    "overlay['in1prov'] = overlay['in1'].str[:2]\n",
    "overlay = overlay.merge(provnames, on='in1prov')\n",
    "\n",
    "overlay.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'prompt_toolkit.formatted_text'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "# Lugares con código diferente (in1, de radios IGN, vs. IN1 de departamentos IGN)\n",
    "# Son las comunas de CABA, además de Lezama-Chascomús y Tierra del Fuego\n",
    "overlay.loc[overlay.in1 != overlay.IN1].sort_values('in1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Guardar los resultados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'prompt_toolkit.formatted_text'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": [
    "# Geojson\n",
    "overlay[['in1prov', 'namprov', 'in1', 'nam', 'IN1', 'geometry']].to_file('./mapaelectoral/ref-dptosIGN.geojson', driver='GeoJSON')\n",
    "\n",
    "# CSV\n",
    "overlay[['in1prov', 'namprov', 'in1', 'nam', 'IN1']].to_csv('./info/ref-dptosIGN.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mThe kernel failed to start due to the missing module 'prompt_toolkit.formatted_text'. Consider installing this module.\n",
      "\u001b[1;31mClick <a href='https://aka.ms/kernelFailuresMissingModule'>here</a> for more info."
     ]
    }
   ],
   "source": []
  }
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