02-eda_mapas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Matplotlib is building the font cache; this may take a moment.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.font_manager as font_manager\n",
    "\n",
    "# funciones desarrolladas\n",
    "from functions.cargar_data import *\n",
    "from functions.guardar_data import *\n",
    "from functions.graficas import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "variables = ['DPTO', 'LOC','PERMI07', 'PERMI07_1', 'PERMI07_2', 'PERMI07_3', 'PERMI07_4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "censo = cargar_censo_vars(variables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DPTO</th>\n",
       "      <th>LOC</th>\n",
       "      <th>PERMI07</th>\n",
       "      <th>PERMI07_1</th>\n",
       "      <th>PERMI07_2</th>\n",
       "      <th>PERMI07_3</th>\n",
       "      <th>PERMI07_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DPTO  LOC  PERMI07  PERMI07_1  PERMI07_2  PERMI07_3  PERMI07_4\n",
       "0     1   20        0        NaN        NaN        NaN          0\n",
       "1     1   20        0        NaN        NaN        NaN          0\n",
       "2     1   20        0        NaN        NaN        NaN          0\n",
       "3     1   20        0        NaN        NaN        NaN          0\n",
       "4     1   20        0        NaN        NaN        NaN          0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conteo_migrantes(df):\n",
    "    \"Conteo de migrantes interno por departamento\"\n",
    "    lista_vars = ['total', 'cat_a', 'cat_b', 'cat_c']\n",
    "    df[lista_vars[0]] = 1\n",
    "    [df.insert(len(df.columns), lista_vars[i], 0) for i in range(1,4)]\n",
    "    # destino Mvdeo.\n",
    "    df.loc[(df.PERMI07 == 3) & (df.DPTO==1), lista_vars[1]] = 1\n",
    "    # destino interior\n",
    "    df.loc[(df.PERMI07 == 3) & (df.PERMI07_2== 1), lista_vars[2]] = 1\n",
    "    # entre interior\n",
    "    df.loc[(df.PERMI07 == 3) & (df.PERMI07_2!=1) & (df.DPTO!=1) & (df.PERMI07_2.isnull()==False), lista_vars[3]] = 1\n",
    "\n",
    "    # agrupa\n",
    "    df_group = df.groupby('DPTO').agg(\n",
    "                {   'total': sum,\n",
    "                     lista_vars[1]: sum,\n",
    "                     lista_vars[2]: sum,\n",
    "                     lista_vars[3]: sum\n",
    "                })\n",
    "\n",
    "    df_group['total_mig'] = df_group[lista_vars[1]] + df_group[lista_vars[2]] + df_group[lista_vars[3]]\n",
    "    \n",
    "    return df_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>cat_a</th>\n",
       "      <th>cat_b</th>\n",
       "      <th>cat_c</th>\n",
       "      <th>total_mig</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPTO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1318755</td>\n",
       "      <td>42444</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>73377</td>\n",
       "      <td>0</td>\n",
       "      <td>914</td>\n",
       "      <td>896</td>\n",
       "      <td>1810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>520173</td>\n",
       "      <td>0</td>\n",
       "      <td>33127</td>\n",
       "      <td>8286</td>\n",
       "      <td>41413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84698</td>\n",
       "      <td>0</td>\n",
       "      <td>1387</td>\n",
       "      <td>1614</td>\n",
       "      <td>3001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>123203</td>\n",
       "      <td>0</td>\n",
       "      <td>2100</td>\n",
       "      <td>3362</td>\n",
       "      <td>5462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>57084</td>\n",
       "      <td>0</td>\n",
       "      <td>982</td>\n",
       "      <td>1589</td>\n",
       "      <td>2571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>25050</td>\n",
       "      <td>0</td>\n",
       "      <td>378</td>\n",
       "      <td>933</td>\n",
       "      <td>1311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>67047</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>2257</td>\n",
       "      <td>3283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>58815</td>\n",
       "      <td>0</td>\n",
       "      <td>825</td>\n",
       "      <td>1760</td>\n",
       "      <td>2585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>164298</td>\n",
       "      <td>0</td>\n",
       "      <td>3914</td>\n",
       "      <td>8469</td>\n",
       "      <td>12383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>113107</td>\n",
       "      <td>0</td>\n",
       "      <td>1075</td>\n",
       "      <td>2511</td>\n",
       "      <td>3586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>54765</td>\n",
       "      <td>0</td>\n",
       "      <td>886</td>\n",
       "      <td>2018</td>\n",
       "      <td>2904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>103473</td>\n",
       "      <td>0</td>\n",
       "      <td>1665</td>\n",
       "      <td>1613</td>\n",
       "      <td>3278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>68088</td>\n",
       "      <td>0</td>\n",
       "      <td>1266</td>\n",
       "      <td>1842</td>\n",
       "      <td>3108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>124861</td>\n",
       "      <td>0</td>\n",
       "      <td>1547</td>\n",
       "      <td>2294</td>\n",
       "      <td>3841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>108304</td>\n",
       "      <td>0</td>\n",
       "      <td>4209</td>\n",
       "      <td>2784</td>\n",
       "      <td>6993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>82594</td>\n",
       "      <td>0</td>\n",
       "      <td>1173</td>\n",
       "      <td>1709</td>\n",
       "      <td>2882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>90051</td>\n",
       "      <td>0</td>\n",
       "      <td>1421</td>\n",
       "      <td>2128</td>\n",
       "      <td>3549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>48134</td>\n",
       "      <td>0</td>\n",
       "      <td>760</td>\n",
       "      <td>1595</td>\n",
       "      <td>2355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total  cat_a  cat_b  cat_c  total_mig\n",
       "DPTO                                         \n",
       "1     1318755  42444      0      0      42444\n",
       "2       73377      0    914    896       1810\n",
       "3      520173      0  33127   8286      41413\n",
       "4       84698      0   1387   1614       3001\n",
       "5      123203      0   2100   3362       5462\n",
       "6       57084      0    982   1589       2571\n",
       "7       25050      0    378    933       1311\n",
       "8       67047      0   1026   2257       3283\n",
       "9       58815      0    825   1760       2585\n",
       "10     164298      0   3914   8469      12383\n",
       "11     113107      0   1075   2511       3586\n",
       "12      54765      0    886   2018       2904\n",
       "13     103473      0   1665   1613       3278\n",
       "14      68088      0   1266   1842       3108\n",
       "15     124861      0   1547   2294       3841\n",
       "16     108304      0   4209   2784       6993\n",
       "17      82594      0   1173   1709       2882\n",
       "18      90051      0   1421   2128       3549\n",
       "19      48134      0    760   1595       2355"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "migrantes = conteo_migrantes(censo)\n",
    "migrantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Exporta CSV\n",
    "# migrantes.to_csv('tablas/migrantes_por_depto.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PERMI07_2</th>\n",
       "      <th>PERMI07_3</th>\n",
       "      <th>DPTO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>57982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>621.0</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>622.0</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>721.0</td>\n",
       "      <td>173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>19.0</td>\n",
       "      <td>949.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>891</th>\n",
       "      <td>19.0</td>\n",
       "      <td>951.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>19.0</td>\n",
       "      <td>953.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>19.0</td>\n",
       "      <td>960.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>19.0</td>\n",
       "      <td>999.0</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>895 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     PERMI07_2  PERMI07_3   DPTO\n",
       "0          1.0       20.0  57982\n",
       "1          1.0      621.0    125\n",
       "2          1.0      622.0    133\n",
       "3          1.0      721.0    173\n",
       "4          1.0      900.0    237\n",
       "..         ...        ...    ...\n",
       "890       19.0      949.0      2\n",
       "891       19.0      951.0      5\n",
       "892       19.0      953.0      1\n",
       "893       19.0      960.0      4\n",
       "894       19.0      999.0    108\n",
       "\n",
       "[895 rows x 3 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censo.groupby(['PERMI07_2', 'PERMI07_3']).DPTO.count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def conteo_migrantes_por_loc(df):\n",
    "    \"Conteo por localidad de migrantes internos con origen en Mvdeo.\"\n",
    "    df_filtered = df.loc[(df.PERMI07 == 3) & (df.PERMI07_2== 1)].copy()\n",
    "    df_filtered['CODLOC'] = df_filtered.DPTO.astype(str) + pad(df_filtered, 'LOC', 3)\n",
    "    df_grouped = df_filtered.groupby(['CODLOC']).DPTO.count().reset_index()\n",
    "    df_grouped.rename({'CODLOC':'loc_destino', 'DPTO':'personas'}, inplace=True, axis=1)\n",
    "    \n",
    "    return df_grouped.sort_values('personas', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "mig_loc = conteo_migrantes_por_loc(censo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "# exporta\n",
    "# mig_loc.to_csv('tablas/migrantes_no_mvo_por_loc.csv', index=False)\n",
    "# campos = 'Integer','Integer'\n",
    "# export_csvt(campos, 'tablas/migrantes_no_mvo_por_loc.csvt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loc_destino</th>\n",
       "      <th>personas</th>\n",
       "      <th>departamento</th>\n",
       "      <th>localidad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3828</td>\n",
       "      <td>2699</td>\n",
       "      <td>CANELONES</td>\n",
       "      <td>EL PINAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3953</td>\n",
       "      <td>2660</td>\n",
       "      <td>CANELONES</td>\n",
       "      <td>LOMAS DE SOLYMAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3732</td>\n",
       "      <td>2600</td>\n",
       "      <td>CANELONES</td>\n",
       "      <td>SOLYMAR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3221</td>\n",
       "      <td>2110</td>\n",
       "      <td>CANELONES</td>\n",
       "      <td>LAS PIEDRAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16521</td>\n",
       "      <td>2014</td>\n",
       "      <td>SAN JOSE</td>\n",
       "      <td>DELTA DEL TIGRE Y VILLAS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  loc_destino  personas departamento                 localidad\n",
       "0        3828      2699    CANELONES                  EL PINAR\n",
       "1        3953      2660    CANELONES          LOMAS DE SOLYMAR\n",
       "2        3732      2600    CANELONES                   SOLYMAR\n",
       "3        3221      2110    CANELONES               LAS PIEDRAS\n",
       "4       16521      2014     SAN JOSE  DELTA DEL TIGRE Y VILLAS"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_loc = loc_decode(mig_loc)\n",
    "merge_loc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27.15\n"
     ]
    }
   ],
   "source": [
    "# porcentaje de personas migrantes internas corresppondientes a las 7\n",
    "# localidades que más reciben migrantes\n",
    "print(round(merge_loc.head(7).personas.sum() / merge_loc.personas.sum() * 100, 2 ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "# identifica casos de AM\n",
    "am = pd.read_csv('tablas/localidades_am_mvo.csv', usecols=['codloc'], dtype= {'codloc': str,})\n",
    "migrantes_mvo = merge_loc.merge(am, how='left', left_on='loc_destino', right_on='codloc')\n",
    "migrantes_mvo.loc[~migrantes_mvo.codloc.isnull(), 'categoria'] = 'AREA METRO'\n",
    "migrantes_mvo.loc[migrantes_mvo.codloc.isnull(), 'categoria'] = migrantes_mvo.departamento\n",
    "\n",
    "# calcula n y porcentajes\n",
    "n_mig = migrantes_mvo.groupby(['categoria']).personas.sum()\n",
    "percent_mig = (migrantes_mvo.groupby(['categoria']).personas.sum()/migrantes_mvo.personas.sum()*100).round(2).rename('porcentaje', axis=1)\n",
    "migrantes_mvo_am= pd.concat([n_mig, percent_mig], axis=1).reset_index()\n",
    "\n",
    "# agrega total\n",
    "total = pd.DataFrame([['Total', sum(migrantes_mvo_am.personas), sum(migrantes_mvo_am.porcentaje)]], columns=['categoria', 'personas', 'porcentaje'])\n",
    "migrantes_mvo_am = migrantes_mvo_am.append(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "migrantes_mvo_am.loc[:, 'categoria'] = format_depto(migrantes_mvo_am, 'categoria')\n",
    "\n",
    "migrantes_mvo_am.rename({'categoria':'Entidad geográfica'}, inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Entidad geográfica      personas    porcentaje\n",
      "--------------------  ----------  ------------\n",
      "Área metropolitana         32750         58.45\n",
      "Artigas                      894          1.6\n",
      "Canelones                   1989          3.55\n",
      "Cerro Largo                 1355          2.42\n",
      "Colonia                     2015          3.6\n",
      "Durazno                      930          1.66\n",
      "Flores                       339          0.61\n",
      "Florida                      886          1.58\n",
      "Lavalleja                    746          1.33\n",
      "Maldonado                   3830          6.84\n",
      "Paysandú                    1059          1.89\n",
      "Río Negro                    821          1.47\n",
      "Rivera                      1626          2.9\n",
      "Rocha                       1211          2.16\n",
      "Salto                       1480          2.64\n",
      "San José                     921          1.64\n",
      "Soriano                     1117          1.99\n",
      "Tacuarembó                  1329          2.37\n",
      "Treinta y Tres               730          1.3\n",
      "Total                      56028        100\n"
     ]
    }
   ],
   "source": [
    "print(migrantes_mvo_am.to_markdown(index=False, tablefmt='pretty'))"
   ]
  }
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