{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://raw.githubusercontent.com/rafneta/CienciaDatosPythonCIDE/master/imagenes/banner.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejemplos Pandas\n",
    "\n",
    "\n",
    "## Base de datos ESRU-EMOVI 2017\n",
    "\n",
    "\n",
    "\n",
    "Por primera vez y gracias al enfoque territorial de la ESRU – EMOVI 2017, es posible medir la movilidad social por regiones. La encuesta de 2017, al igual que las anteriores, tiene como finalidad principal medir la movilidad social intergeneracional. Cuenta con 17,665 entrevistas y es representativa de hombres y mujeres entre 25 y 64 años a nivel nacional, para la Ciudad de México y cinco regiones del país: norte, norte-occidente, centro, centro-norte y sur. Los objetivos de la ESRU-EMOVI 2017 son:\n",
    "\n",
    "- Contar con información actualizada en las distintas dimensiones de la movilidad social a nivel nacional.\n",
    "\n",
    "- Generar estimaciones de movilidad para cinco regiones del país y la Ciudad de México.\n",
    "\n",
    "- Analizar los patrones de movilidad social desde la perspectiva de la desigualdad de oportunidades.\n",
    "\n",
    "Encuesta financiada por la Fundación ESRU. [CEEY](https://ceey.org.mx/contenido/que-hacemos/emovi/) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "folio           object\n",
       "Estado         float64\n",
       "folio_ageb      object\n",
       "consecutivo     object\n",
       "Origen         float64\n",
       "                ...   \n",
       "cmo2_2          object\n",
       "cmo3_2          object\n",
       "cmo4_2          object\n",
       "cmo5_2          object\n",
       "tamhog         float64\n",
       "Length: 366, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_stata('ESRU-EMOVI-2017-Entrevistado.dta',\n",
    "                   convert_categoricals= False\n",
    "                  )\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>folio</th>\n",
       "      <th>Estado</th>\n",
       "      <th>folio_ageb</th>\n",
       "      <th>consecutivo</th>\n",
       "      <th>Origen</th>\n",
       "      <th>Latitud</th>\n",
       "      <th>Longitud</th>\n",
       "      <th>LatitudGP</th>\n",
       "      <th>LongitudGP</th>\n",
       "      <th>recontacto</th>\n",
       "      <th>...</th>\n",
       "      <th>region</th>\n",
       "      <th>cdmx</th>\n",
       "      <th>tot_int</th>\n",
       "      <th>rururb</th>\n",
       "      <th>cmo1_2</th>\n",
       "      <th>cmo2_2</th>\n",
       "      <th>cmo3_2</th>\n",
       "      <th>cmo4_2</th>\n",
       "      <th>cmo5_2</th>\n",
       "      <th>tamhog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0100100010286020830102</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0100100010286</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.901323</td>\n",
       "      <td>-102.310598</td>\n",
       "      <td>21.901477</td>\n",
       "      <td>-102.310429</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>13</td>\n",
       "      <td>.</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0100100010286020850201</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0100100010286</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.901323</td>\n",
       "      <td>-102.310598</td>\n",
       "      <td>21.900773</td>\n",
       "      <td>-102.311138</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>41</td>\n",
       "      <td>.</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0100100010286025830201</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0100100010286</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.900830</td>\n",
       "      <td>-102.311818</td>\n",
       "      <td>21.900549</td>\n",
       "      <td>-102.313361</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>81</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>11</td>\n",
       "      <td>.</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0100100010286025840101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0100100010286</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.901188</td>\n",
       "      <td>-102.310700</td>\n",
       "      <td>21.900765</td>\n",
       "      <td>-102.313144</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0100100010286025850101</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0100100010286</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>21.901188</td>\n",
       "      <td>-102.310700</td>\n",
       "      <td>21.900577</td>\n",
       "      <td>-102.312733</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>52</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>2.0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>17660</th>\n",
       "      <td>3205700010022019460402</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3205700010022</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.755409</td>\n",
       "      <td>-102.513985</td>\n",
       "      <td>22.755409</td>\n",
       "      <td>-102.513985</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>71</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17661</th>\n",
       "      <td>3205700010022025450501</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3205700010022</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.288405</td>\n",
       "      <td>-101.577532</td>\n",
       "      <td>22.288405</td>\n",
       "      <td>-101.577532</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>82</td>\n",
       "      <td>71</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17662</th>\n",
       "      <td>3205700010022025460301</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3205700010022</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.758625</td>\n",
       "      <td>-102.499375</td>\n",
       "      <td>22.758625</td>\n",
       "      <td>-102.499375</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>53</td>\n",
       "      <td>52</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17663</th>\n",
       "      <td>3205700010022025460302</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3205700010022</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.755420</td>\n",
       "      <td>-102.513997</td>\n",
       "      <td>22.755420</td>\n",
       "      <td>-102.513997</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>.</td>\n",
       "      <td>52</td>\n",
       "      <td>.</td>\n",
       "      <td>53</td>\n",
       "      <td>62</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17664</th>\n",
       "      <td>3205700010022025460501</td>\n",
       "      <td>32.0</td>\n",
       "      <td>3205700010022</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.758625</td>\n",
       "      <td>-102.499375</td>\n",
       "      <td>22.758625</td>\n",
       "      <td>-102.499375</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>.</td>\n",
       "      <td>41</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17665 rows × 366 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        folio  Estado     folio_ageb consecutivo  Origen  \\\n",
       "0      0100100010286020830102     1.0  0100100010286           1     1.0   \n",
       "1      0100100010286020850201     1.0  0100100010286           1     2.0   \n",
       "2      0100100010286025830201     1.0  0100100010286           1     1.0   \n",
       "3      0100100010286025840101     1.0  0100100010286           1     1.0   \n",
       "4      0100100010286025850101     1.0  0100100010286           1     2.0   \n",
       "...                       ...     ...            ...         ...     ...   \n",
       "17660  3205700010022019460402    32.0  3205700010022           2     1.0   \n",
       "17661  3205700010022025450501    32.0  3205700010022           2     1.0   \n",
       "17662  3205700010022025460301    32.0  3205700010022           2     1.0   \n",
       "17663  3205700010022025460302    32.0  3205700010022           2     1.0   \n",
       "17664  3205700010022025460501    32.0  3205700010022           7     1.0   \n",
       "\n",
       "         Latitud    Longitud  LatitudGP  LongitudGP recontacto  ...  region  \\\n",
       "0      21.901323 -102.310598  21.901477 -102.310429          2  ...     3.0   \n",
       "1      21.901323 -102.310598  21.900773 -102.311138          1  ...     3.0   \n",
       "2      21.900830 -102.311818  21.900549 -102.313361          1  ...     3.0   \n",
       "3      21.901188 -102.310700  21.900765 -102.313144          1  ...     3.0   \n",
       "4      21.901188 -102.310700  21.900577 -102.312733          1  ...     3.0   \n",
       "...          ...         ...        ...         ...        ...  ...     ...   \n",
       "17660  22.755409 -102.513985  22.755409 -102.513985          1  ...     2.0   \n",
       "17661  22.288405 -101.577532  22.288405 -101.577532          1  ...     2.0   \n",
       "17662  22.758625 -102.499375  22.758625 -102.499375          1  ...     2.0   \n",
       "17663  22.755420 -102.513997  22.755420 -102.513997          1  ...     2.0   \n",
       "17664  22.758625 -102.499375  22.758625 -102.499375          1  ...     2.0   \n",
       "\n",
       "       cdmx  tot_int  rururb  cmo1_2  cmo2_2  cmo3_2  cmo4_2  cmo5_2  tamhog  \n",
       "0       NaN      5.0     0.0      41       .       .      13       .     5.0  \n",
       "1       NaN      1.0     0.0      41       .       .      41       .     1.0  \n",
       "2       NaN      2.0     0.0      81       .       .      11       .     2.0  \n",
       "3       NaN      1.0     0.0      52       .       .       .       .     1.0  \n",
       "4       NaN      2.0     0.0      52       .       .       .       .     2.0  \n",
       "...     ...      ...     ...     ...     ...     ...     ...     ...     ...  \n",
       "17660   NaN      4.0     0.0      71       .       .       .      71     4.0  \n",
       "17661   NaN      4.0     0.0       .       .       .      82      71     4.0  \n",
       "17662   NaN      6.0     0.0       .       .       .      53      52     6.0  \n",
       "17663   NaN      5.0     0.0       .      52       .      53      62     5.0  \n",
       "17664   NaN     10.0     0.0       .       .       .       .      41    10.0  \n",
       "\n",
       "[17665 rows x 366 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De acuerdo a las preguntas, hacemos un filtro de acuerdo lo que necesitemos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valores cohort de ingreso [4 3 7 5 1 2 6]\n"
     ]
    }
   ],
   "source": [
    "# pregunta p02, comparten el mimso gasto para comer: 1 ) Si 2 ) No\n",
    "df2 = df[df.p02 == 1 ]\n",
    "\n",
    "# pregunta p08, es el jefe del hogar: 1 ) Si \n",
    "#df2 = df2[df2.p08 == 1 ]\n",
    "\n",
    "# pregunta p12. actualmente estudia, 1)Si 2)No\n",
    "df2 = df2[df2.p12 == 2 ]\n",
    "\n",
    "# aós alacnazado podria ser importante p14\n",
    "\n",
    "# p13, nivel escuela 1-12. 97 = no fue a la escuela\n",
    "#df2 = df2[df2.p13 = 97 ] # quitamos la no asistencia\n",
    "#print(df2.p13.unique())\n",
    "\n",
    "\n",
    "# p63, Escuela publica o privada, 8 = No aplica \n",
    "# estonces sino aplica con quedamos con el más alto \n",
    "# esta valirvale es mas complicada, por el momento no la utiizamos\n",
    "# (preguntas por cada caso)\n",
    "\n",
    "\n",
    "\n",
    "# p68, Personas que trabajan, 1. Si, 2. No\n",
    "# p69, negocio vacaciones, 1. Si, 2. No\n",
    "df2 = df2[(df2.p68 == 1) | (df2.p69 == 1)] # quitamos los no ocupados\n",
    "\n",
    "\n",
    "# p120, material de la casa opciones: 1,2,3\n",
    "\n",
    "# p121, numero de cuartos para dormir, libre\n",
    "\n",
    "# p122, numeor totales de cuartos, libre\n",
    "\n",
    "# p123, casa propia o  del conyugue, 1. Sí, 2. No \n",
    "\n",
    "# p125, servicios básicos de la vivienda, \n",
    "# 125a - 125e (preguntas por cada caso, 1. Si, 2. No)\n",
    "\n",
    "# p126, articulos propiedad del hogar \n",
    "# 126a - 126r (preguntas por cada caso, 1. Si , 2. No)\n",
    "\n",
    "# p127, prestamo variable 1-9, 8 es otro caso\n",
    "#df2 = df2[df2.p127 != 8 ] # quitamos la otra posibilidad\n",
    "\n",
    "# p128, ahorros, tarjeta de credito, cuent abancaria, \n",
    "# a-f (preguntas por cada caso) 1. Si 2. No\n",
    "\n",
    "# p129, pertenencias propias o de conyugue, \n",
    "# a-e (pregunta por cada caso) 1. Si, 2. No\n",
    "\n",
    "# p130, apoyo economico porgramas u otro medio, \n",
    "# a-f, (preguntas por cada caso)  \n",
    "\n",
    "# p131, numero de automoviles propios, libre\n",
    "\n",
    "# p132,  numero de miembros que aportan ingreso al hogar\n",
    "df2 = df2[df2.p132 == 1] # un solo sosten\n",
    "\n",
    "# Cohort  de ingreso todas las personas que aportan ingresos\n",
    "df2 = df2[(df2.p133 != 8) & ( df2.p133 != 9) ] #ingreso no reportado o no diponible\n",
    "\n",
    "print('valores cohort de ingreso',df2.p133.unique())\n",
    "\n",
    "\n",
    "# p134, condiciones del barrio\n",
    "# a-i, (preguntas por cada caso) 1. Si , 2. No, 8 son respuesta \n",
    "p = \"p134\"\n",
    "\n",
    "\n",
    "for _ in \"abcdefghi\":\n",
    "    df2 = df2[df2[p+_] != 8]\n",
    "\n",
    "\n",
    "# p147 percepción de 1 más pobre + 10 más rico\n",
    "    \n",
    "    \n",
    "#df2.p08.plot.hist()\n",
    "\n",
    "\n",
    "#df2.p133.plot.hist()\n",
    "\n",
    "Estado = [\"Estado\"]\n",
    "p5 = ['p05']\n",
    "p6 = ['p06']\n",
    "p13  = ['p13']\n",
    "SINCO = ['SINCO3']\n",
    "p63 = ['p63a','p63b','p63c','p63d'] # por el momento la  quitamos de la estimación\n",
    "p120  = ['p120']\n",
    "p121  = ['p121']\n",
    "p122  = ['p122']\n",
    "p123  = ['p123']\n",
    "p125 = ['p125a','p125b','p125c','p125d','p125e']\n",
    "import string\n",
    "p126 = ['p126' + i for i in string.ascii_lowercase[0:18] ]\n",
    "p127 = ['p127']\n",
    "p128 = ['p128a','p128b','p128c','p128d','p128e','p128f']\n",
    "p129 = ['p129a','p129b','p129c','p129d','p129e']\n",
    "p130 = ['p130a','p130b','p130c','p130d','p130e', 'p130f']\n",
    "p131 = ['p131']\n",
    "p132 = ['p132']\n",
    "p134 = ['p134a','p134b','p134c','p134d','p134e', 'p134f','p134g','p134h','p134i']\n",
    "p147 = ['p147']\n",
    "\n",
    "#indexX =  p13  +  p120  + p121 +  p122  + p123 + p125  + p126 + p127 + p128 + p129  + p130 + p131  \n",
    "\n",
    "#indexX =  p13   + p121 +  p122 + p127 + p131 \n",
    "\n",
    "\n",
    "#indexX =  p13  + p131 \n",
    "\n",
    "indexX = Estado + p5 + p6 + p13 + SINCO\n",
    "\n",
    "\n",
    "indexY = ['p133']\n",
    "\n",
    "M  = df2[indexX+indexY].dropna() \n",
    "\n",
    "M.loc[M.p133 == 2,'p133'] = 1\n",
    "M.loc[M.p133 != 1,'p133'] = M.p133 - 1\n",
    "\n",
    "\n",
    "\n",
    "X = M[indexX].to_numpy()\n",
    "Xc = M[indexX].astype('category')\n",
    "y = M[indexY].to_numpy()\n",
    "yc = M[indexY].astype('category')\n",
    "y = np.ravel(y)\n",
    "y = y-1;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3699 entries, 6 to 17653\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype   \n",
      "---  ------  --------------  -----   \n",
      " 0   Estado  3699 non-null   category\n",
      " 1   p05     3699 non-null   category\n",
      " 2   p06     3699 non-null   category\n",
      " 3   p13     3699 non-null   category\n",
      " 4   SINCO3  3699 non-null   category\n",
      "dtypes: category(5)\n",
      "memory usage: 226.8 KB\n"
     ]
    }
   ],
   "source": [
    "Xc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Estado</th>\n",
       "      <th>p05</th>\n",
       "      <th>p06</th>\n",
       "      <th>p13</th>\n",
       "      <th>SINCO3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3699.0</td>\n",
       "      <td>3699.0</td>\n",
       "      <td>3699</td>\n",
       "      <td>3699.0</td>\n",
       "      <td>3699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>32.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>13.0</td>\n",
       "      <td>305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>600.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>2254</td>\n",
       "      <td>943.0</td>\n",
       "      <td>489</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Estado     p05   p06     p13 SINCO3\n",
       "count   3699.0  3699.0  3699  3699.0   3699\n",
       "unique    32.0    40.0     2    13.0    305\n",
       "top        9.0    40.0     1     2.0   4111\n",
       "freq     600.0   146.0  2254   943.0    489"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xc.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Estado  p05   p06  p13   SINCO3\n",
       "7.0     25.0  2    3.0   9998      4\n",
       "        62.0  1    2.0   6111      4\n",
       "4.0     64.0  1    2.0   6111      3\n",
       "6.0     62.0  1    2.0   7121      3\n",
       "9.0     26.0  1    6.0   4211      3\n",
       "                                  ..\n",
       "19.0    27.0  2    11.0  1511      1\n",
       "                   4.0   4111      1\n",
       "                   2.0   7513      1\n",
       "              1    6.0   5114      1\n",
       "1.0     25.0  1    6.0   2815      1\n",
       "Length: 3606, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Estado    category\n",
       "p05       category\n",
       "p06       category\n",
       "p13       category\n",
       "SINCO3    category\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xc.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([1, 2], dtype='int64')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xc.p06.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2254\n",
       "2    1445\n",
       "Name: p06, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xc.p06.value_counts(dropna=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mc = M.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAHE9HDdUP6e5fqqrvS3Lm6nO6e12/D19Vp3T3A9PLFyW5c3p+U5Jfr6o3ZmVbmbOT3LKeuQEAAAAAYG5DoXpV/ZusbNtyW5JHp+FOcsRQvareneQ5WdmPfX+SX0jynKo6bzr381nZXibdfVdV3Zjk7iQHk1ze3Y+uMS0AAAAAAGyaoVA9yZ4k53T3mvucr6W7X7rG8NuPcvzVSa4enR8AAAAAABbtsT6o9JA7k3zznIUAAAAAAMCyG12pfnKSu6vqliSPHBrs7r8/S1UAAAAAALCERkP1185ZBAAAAAAAbAVDoXp3/87chQAAAAAAwLIbCtWr6stJDn1I6ROSPD7JX3T3N8xVGAAAAAAALJvRlepPXv26ql6Y5II5CgIAAAAAgGX1uI2c1N2/keS5x7cUAAAAAABYbqPbv/zwqpePS7InX90OBgAAAAAAdoShUD3JD616fjDJ55NcdNyrAQAAAACAJTa6p/qPz10IAAAAAAAsu6E91avqtKr6QFU9VFUPVtX7quq0uYsDAAAAAIBlMvpBpb+a5KYkT0tyapLfnMYAAAAAAGDHGA3Vd3f3r3b3wenxziS7Z6wLAAAAAACWzmio/nBV/VhVnTA9fizJn85ZGAAAAAAALJvRUP0nkvxIkv+W5IEkL07iw0sBAAAAANhRdg0e94tJLu3uLyZJVZ2U5PVZCdsBAAAAYGmdf8X1s8x76zWXzDIvsNxGV6o/81CgniTd/YUkz5qnJAAAAAAAWE6jofrjqurEQy+mleqjq9wBAAAAAGBbGA3G35Dk96rqvUk6K/urXz1bVQAAAAAAsISGQvXuvr6q9iZ5bpJK8sPdffeslQEAAAAAwJIZ3sJlCtEF6QAAAAAA7Fije6oDAAAAAMCOJ1QHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEG7NrsAAAAAALane686d7a5z7jyjtnmBjgaK9UBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYNCuzS4AAHaye686d5Z5z7jyjlnmBQAAgJ3OSnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBs4XqVfWOqnqoqu5cNXZSVX2kqj47fT1x1Xuvqap9VfWZqnr+XHUBAAAAAMBGzblS/Z1JLjxs7NVJbu7us5PcPL1OVZ2T5OIkz5jOeWtVnTBjbQAAAAAAsG6zherd/bEkXzhs+KIk103Pr0vywlXjN3T3I939uST7klwwV20AAAAAALARi95T/and/UCSTF+fMo2fmuS+Vcftn8YAAAAAAGBpLMsHldYaY73mgVWXVdXeqtp74MCBmcsCINF7ARZN3wVYLH0XgPVYdKj+YFWdkiTT14em8f1JTl913GlJ7l9rgu6+trv3dPee3bt3z1osACv0XoDF0ncBFkvfBWA9Fh2q35Tk0un5pUk+uGr84qp6YlWdleTsJLcsuDYAAAAAADiqXXNNXFXvTvKcJCdX1f4kv5DkdUlurKqXJ7k3yUuSpLvvqqobk9yd5GCSy7v70blqAwAAAACAjZgtVO/ulx7hrecd4firk1w9Vz0AAAAAAHCsZgvVAYCt4fwrrp9t7luvuWS2uQEAAGAzLHpPdQAAAAAA2LKE6gAAAAAAMEioDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDhOoAAAAAADBIqA4AAAAAAIOE6gAAAAAAMEioDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDhOoAAAAAADBIqA4AAAAAAIOE6gAAAAAAMEioDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDhOoAAAAAADBIqA4AAAAAAIOE6gAAAAAAMEioDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDhOoAAAAAADBIqA4AAAAAAIOE6gAAAAAAMEioDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDhOoAAAAAADBIqA4AAAAAAIOE6gAAAAAAMEioDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDhOoAAAAAADBIqA4AAAAAAIOE6gAAAAAAMEioDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDdm12AQAAAACwFd171bmzzX3GlXfMNjdwbKxUBwAAAACAQUJ1AAAAAAAYZPsXAADYgs6/4vpZ5r31mktmmRcAALYLK9UBAAAAAGCQUB0AAAAAAAbZ/gUAAPiKe686d5Z5z7jyjlnmBQCARduUUL2qPp/ky0keTXKwu/dU1UlJ3pPkzCSfT/Ij3f3FzagPAAAAAADWspnbv/zd7j6vu/dMr1+d5ObuPjvJzdNrAAAAAABYGsu0p/pFSa6bnl+X5IWbVwoAAAAAAHytzdpTvZP8+6rqJP9vd1+b5Knd/UCSdPcDVfWUtU6sqsuSXJYkZ5xxxqLqBdjR9F6AxdJ3ARZL32WrO/+K62eZ99ZrLpllXtjqNmul+rO7+7uS/GCSy6vq+0dP7O5ru3tPd+/ZvXv3fBUC8BV6L8Bi6bsAi6XvArAemxKqd/f909eHknwgyQVJHqyqU5Jk+vrQZtQGAAAAAABHsvDtX6rq65M8rru/PD3/e0muSnJTkkuTvG76+sFF1wYAACwHv8YOAMCy2ow91Z+a5ANVdej6v97dH66qTya5sapenuTeJC/ZhNoAAAAAAOCIFh6qd/cfJ/nONcb/NMnzFl0PAAAAAACM2qwPKgUAAAAAgC1HqA4AAAAAAIOE6gAAAAAAMEioDgAAAAAAgxb+QaUAwM5x71XnzjLvGVfeMcu8AAAA8FisVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQbs2uwAAAIBFufeqc2eZ94wr75hlXgAAlo+V6gAAAAAAMEioDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDhOoAAAAAADBo12YXAAAwp/OvuH62uW+95pLZ5ga2t7l6k74EADA/K9UBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGDQrs0uAAAAAABYPvdede5sc59x5R2zzQ1zE6oDAABsE3OFH4IPAICvEqoDAAAAAEvr/Cuun2XeW6+5ZJZ52f7sqQ4AAAAAAIOE6gAAAAAAMMj2LwAAABxXfk0fANjOrFQHAAAAAIBBVqoDAGzQvVedO8u8Z1x5xyzzAgAAcOysVAcAAAAAgEFWqgMAbGFz7Vuc2LsYAABgLVaqAwAAAADAICvV12GufVMTe6cCAAAAAGwFVqoDAAAAAMCgbblSfa69RT/w5FmmBQAAAABgi1i6UL2qLkzy5iQnJPmV7n7dJpcEAAAAXzHXQi4fEA2wWLZ6ZqOWavuXqjohyVuS/GCSc5K8tKrO2dyqAAAAAABgxbKtVL8gyb7u/uMkqaobklyU5O5NrQpgYlUSsJPMtXJnWVbtbPf7g+3I/7dbmxWhAGuTNWw91d2bXcNXVNWLk1zY3f9oev2yJN/d3a9YdcxlSS6bXn5rks8ssMSTkzy8wOstmvvb2tzf1rbo+3u4uy8cPVjvnZX729rc39a2tL1X352V+9va3N/Wpu+uzX/3rc39bW3u7/haV9awlS1bqP6SJM8/LFS/oLt/anMrW1FVe7t7z2bXMRf3t7W5v61tu9/fsdjufzbub2tzf1vbdr+/jdrufy7ub2tzf1vbdr+/jdrufy7ub2tzf1vbdr+/zbRUe6on2Z/k9FWvT0ty/ybVAgAAAAAAf8WyheqfTHJ2VZ1VVU9IcnGSmza5JgAAAAAASLJkH1Ta3Qer6hVJfjvJCUne0d13bXJZq1272QXMzP1tbe5va9vu93cstvufjfvb2tzf1rbd72+jtvufi/vb2tzf1rbd72+jtvufi/vb2tzf1rbd72/TLNWe6gAAAAAAsMyWbfsXAAAAAABYWkJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUhzVU1Suqal9VdVWdvGr8oqq6vapuq6q9VfW3j8O1nlNV/32a87aquvIIx51VVZ+oqs9W1Xuq6gnHem2AZbHIvjvN+5xpzruq6neOcIy+C2xbC/777hWr/q57Z1U9WlUnrXGcvgtsawvuvX+jqn6zqv5o+jvvjx/hOL0XNqC6e7NrgKVTVc9K8sUk/zHJnu5+eBp/UpK/6O6uqmcmubG7v+0Yr/WcJP+0u/+PxzjuxiTv7+4bqupfJ/mj7n7bsVwbYFksuO9+Y5LfS3Jhd99bVU/p7ofWOE7fBbatRfbdw677Q0n+7+5+7hrv6bvAtrbgv/P+fJK/0d0/V1W7k3wmyTd3918edpzeCxtgpTo7VlWdWVWfrqrrpp8Iv7eqvi5JuvsPu/vzh5/T3X/eX/1J1NcnWchPpaqqkjw3yXunoeuSvHAR1wY4Xpao7/7DrPzD4d7pGmsF6vousOUtUd9d7aVJ3r1GrfousC0sUe/tJE+e+uuTknwhycHDatV7YYOE6ux035rk2u5+ZpIvJfknj3VCVb2oqj6d5N8m+YkjHPOeVb/iuvpxyRGm/d7pV7L+XVU9Y433vynJn3X3oW+A+5Oc+li1AiyhZei735LkxKr6j1V16xGO0XeB7WIZ+u6hc74uyYVJ3rfG2/ousJ0sQ+/9V0m+Pcn9Se5I8sru/p+HHaP3wgbt2uwCYJPd192/Oz3/tSQ/neT1Rzuhuz+Q5ANV9f1JfjHJD6xxzI+uo4ZPJfmb3f3nVfWCJL+R5OzDjqm1SlnHNQCWxTL03V1Jzk/yvCR/PcnvV9UfdPd/XnWMvgtsF8vQdw/5oSS/291fWOM9fRfYTpah9z4/yW1ZWYn+9CQfqar/1N1fWnWM3gsbJFRnpzv8m8XwN4/u/lhVPb2qTj60D9ohVfWerPxk+nBv7O7rD5vnS6uef6iq3rrGnA8n+caq2jX9BPm0rPy0GWCr2fS+m5UVOA93918k+Yuq+liS70yyOlTXd4HtYhn67iEXZ42tXyb6LrCdLEPv/fEkr5u2ldlXVZ9L8m1Jbll1jN4LGyRUZ6c7o6q+t7t/Pyv7O378aAdX1d9K8l+mDw/5riRPSPKnhx+3np8eV9U3J3lwmvOCrGzL9FfmnN77aJIXJ7khyaVJPjh6DYAlsul9Nyv9819V1a5pvu9O8qbD5tN3ge1iGfpuqupvJPk7SX5srff1XWCbWYbee29WfjPzP1XVU7MSxv/xYfPpvbBB9lRnp7snyaVVdXuSk5K8LUmq6qeran9Wfkp7e1X9ynT8P0hyZ1XdluQtSX501YeJbNSLpzn/KMm/THLxoTmr6kNV9bTpuJ9L8rNVtS8r+569/RivC7AZNr3vdvc9ST6c5PasrNT5le6+c6pD3wW2m03vu5MXJfn3028JfYW+C2xTy9B7fzHJ91XVHUluTvJzh1a+671w7Or4/P0Itp6qOjPJb3X3d2x2LQA7gb4LsFj6LsDi6b2wM1ipDgAAAAAAg6xUBwAAAACAQVaqAwAAAADAIKE6AAAAAAAM2rXZBRyLCy+8sD/84Q9vdhkAW1Ft9ES9F2DDNtR79V2ADdN3ARZrw1nDVrOlV6o//PDDm10CwI6j9wIslr4LsFj6LgCPZUuH6gAAAAAAsEhCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGDRbqF5Vp1fVR6vqnqq6q6peOY2/tqr+pKpumx4vWHXOa6pqX1V9pqqeP1dtAAAAAACwEbtmnPtgkld196eq6slJbq2qj0zvvam7X7/64Ko6J8nFSZ6R5GlJ/kNVfUt3PzpjjQAAAAAAMGy2lerd/UB3f2p6/uUk9yQ59SinXJTkhu5+pLs/l2Rfkgvmqg8AAAAAANZrIXuqV9WZSZ6V5BPT0Cuq6vaqekdVnTiNnZrkvlWn7c8aIXxVXVZVe6tq74EDB+YsG4CJ3guwWPouwGLpuwCsx+yhelU9Kcn7kvxMd38pyduSPD3JeUkeSPKGQ4eucXp/zUD3td29p7v37N69e56iAfgr9F6AxdJ3ARZL3wVgPWYN1avq8VkJ1N/V3e9Pku5+sLsf7e7/meSX89UtXvYnOX3V6acluX/O+gAAAAAAYD1mC9WrqpK8Pck93f3GVeOnrDrsRUnunJ7flOTiqnpiVZ2V5Owkt8xVHwAAAAAArNeuGed+dpKXJbmjqm6bxn4+yUur6rysbO3y+SQ/mSTdfVdV3Zjk7iQHk1ze3Y/OWB8AAAAAAKzLbKF6d388a++T/qGjnHN1kqvnqgkAAAAAAI7F7B9UCgAAAAAA24VQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAbNFqpX1elV9dGquqeq7qqqV07jJ1XVR6rqs9PXE1ed85qq2ldVn6mq589VGwAAAAAAbMScK9UPJnlVd397ku9JcnlVnZPk1Ulu7u6zk9w8vc703sVJnpHkwiRvraoTZqwPAAAAAADWZbZQvbsf6O5PTc+/nOSeJKcmuSjJddNh1yV54fT8oiQ3dPcj3f25JPuSXDBXfQAAAAAAsF4L2VO9qs5M8qwkn0jy1O5+IFkJ3pM8ZTrs1CT3rTpt/zR2+FyXVdXeqtp74MCBWesGYIXeC7BY+i7AYum7AKzH7KF6VT0pyfuS/Ex3f+loh64x1l8z0H1td+/p7j27d+8+XmUCcBR6L8Bi6bsAi6XvArAes4bqVfX4rATq7+ru90/DD1bVKdP7pyR5aBrfn+T0VaefluT+OesDAAAAAID1mC1Ur6pK8vYk93T3G1e9dVOSS6fnlyb54Krxi6vqiVV1VpKzk9wyV30AAAAAALBeu2ac+9lJXpbkjqq6bRr7+SSvS3JjVb08yb1JXpIk3X1XVd2Y5O4kB5Nc3t2PzlgfAAAAAACsy2yhend/PGvvk54kzzvCOVcnuXqumgAAAAAA4FjM/kGlAAAAAACwXQjVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABs0WqlfVO6rqoaq6c9XYa6vqT6rqtunxglXvvaaq9lXVZ6rq+XPVBQAAAAAAGzXnSvV3JrlwjfE3dfd50+NDSVJV5yS5OMkzpnPeWlUnzFgbAAAAAACs21CoXlU3j4yt1t0fS/KFwTouSnJDdz/S3Z9Lsi/JBYPnAgAAAADAQhw1VK+qv1ZVJyU5uapOrKqTpseZSZ62wWu+oqpun7aHOXEaOzXJfauO2T+NrVXTZVW1t6r2HjhwYIMlALAeei/AYum7AIul7wKwHo+1Uv0nk9ya5Numr4ceH0zylg1c721Jnp7kvCQPJHnDNF5rHNtrTdDd13b3nu7es3v37g2UAMB66b0Ai6XvAiyWvgvAeuw62pvd/eYkb66qn+ruXzrWi3X3g4eeV9UvJ/mt6eX+JKevOvS0JPcf6/UAAAAAAOB4Omqofkh3/1JVfV+SM1ef093Xr+diVXVKdz8wvXxRkjun5zcl+fWqemNWtpU5O8kt65kbAAAAAADmNhSqV9W/ycq2LbcleXQa7iRHDNWr6t1JnpOV/dj3J/mFJM+pqvOmcz+fle1l0t13VdWNSe5OcjDJ5d396BrTAgAAAADAphkK1ZPsSXJOd6+5z/lauvulawy//SjHX53k6tH5AQAAAABg0R7rg0oPuTPJN89ZCAAAAAAALLvRleonJ7m7qm5J8sihwe7++7NUBQAAAAAAS2g0VH/tnEUAAAAAAMBWMBSqd/fvzF0IAAAAAAAsu6FQvaq+nOTQh5Q+Icnjk/xFd3/DXIUBAAAAAMCyGV2p/uTVr6vqhUkumKMgAAAAAABYVo/byEnd/RtJnnt8SwEAAAAAgOU2uv3LD696+bgke/LV7WAAAAAAAGBHGArVk/zQqucHk3w+yUXHvRoAAAAAAFhio3uq//jchQAAAAAAwLIb3f7ltCS/lOTZWdn25eNJXtnd+2esDQBYgPOvuH62uW+95pLZ5gYAAIDNMPpBpb+a5KYkT0tyapLfnMYAAAAAAGDHGA3Vd3f3r3b3wenxziS7Z6wLAAAAAACWzmio/nBV/VhVnTA9fizJn85ZGAAAAAAALJvRUP0nkvxIkv+W5IEkL07iw0sBAAAAANhRhj6oNMkvJrm0u7+YJFV1UpLXZyVsBwAAAACAHWF0pfozDwXqSdLdX0jyrHlKAgAAAACA5TQaqj+uqk489GJaqT66yh0AAAAAALaF0WD8DUl+r6rem6Szsr/61bNVBQAAAAAAS2goVO/u66tqb5LnJqkkP9zdd89aGQAAAAAALJnhLVymEF2QDgAAAADAjjW6pzoAAAAAAOx4QnUAAAAAABgkVAcAAAAAgEHDe6oDwE51/hXXzzb3rddcMtvcAAAAwPFnpToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMmi1Ur6p3VNVDVXXnqrGTquojVfXZ6euJq957TVXtq6rPVNXz56oLAAAAAAA2as6V6u9McuFhY69OcnN3n53k5ul1quqcJBcnecZ0zlur6oQZawMAAAAAgHWbLVTv7o8l+cJhwxcluW56fl2SF64av6G7H+nuzyXZl+SCuWoDAAAAAICNWPSe6k/t7geSZPr6lGn81CT3rTpu/zT2NarqsqraW1V7Dxw4MGuxAKzQewEWS98FWCx9F4D1WJYPKq01xnqtA7v72u7e0917du/ePXNZACR6L8Ci6bsAi6XvArAeiw7VH6yqU5Jk+vrQNL4/yemrjjstyf0Lrg0AAAAAAI5q0aH6TUkunZ5fmuSDq8YvrqonVtVZSc5OcsuCawMAAAAAgKPaNdfEVfXuJM9JcnJV7U/yC0lel+TGqnp5knuTvCRJuvuuqroxyd1JDia5vLsfnas2AAAAAADYiNlC9e5+6RHeet4Rjr86ydVz1QMAAAAAAMdqtlAdgJ3j/Cuun23uW6+5ZLa5AQAAANZr0XuqAwAAAADAliVUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABi0a7MLAICjufeqc2eZ94wr75hlXgAAAGB7s1IdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAbt2uwCAGAnu/eqc2eZ94wr75hlXgAAANjprFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEH2VAcAtrXzr7h+trlvveaS2eYGAABgOVmpDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDhOoAAAAAADBIqA4AAAAAAIOE6gAAAAAAMEioDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDdm3GRavq80m+nOTRJAe7e09VnZTkPUnOTPL5JD/S3V/cjPoAAAAAAGAtm7lS/e9293ndvWd6/eokN3f32Ulunl4DAAAAAMDSWKbtXy5Kct30/LokL9y8UgAAAAAA4GttVqjeSf59Vd1aVZdNY0/t7geSZPr6lLVOrKrLqmpvVe09cODAgsoF2Nn0XoDF0ncBFkvfBWA9NitUf3Z3f1eSH0xyeVV9/+iJ3X1td+/p7j27d++er0IAvkLvBVgsfRdgsfRdANZjU0L17r5/+vpQkg8kuSDJg1V1SpJMXx/ajNoAAAAAAOBIFh6qV9XXV9WTDz1P8veS3JnkpiSXToddmuSDi64NAAAAAACOZtcmXPOpST5QVYeu/+vd/eGq+mSSG6vq5UnuTfKSTagNAAAAAACOaOGhenf/cZLvXGP8T5M8b9H1AAAAAADAqM36oFIAAAAAANhyhOoAAAAAADBoM/ZU5xidf8X1s8x76zWXzDIvADvXvVedO8u8Z1x5xyzzAgAAwGOxUh0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABg0K7NLmArufeqc2eb+4wr75htbgAAAAAAjg8r1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYNCuzS4ADnf+FdfPMu+t11wyy7wAAAAAwM4hVOcr7r3q3NnmPuPKO2abGwAAAABgUWz/AgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDhOoAAAAAADBIqA4AAAAAAIN2bXYBAABb1b1XnTvLvGdceccs8wIAAHDsrFQHAAAAAIBBQnUAAAAAABhk+xcAgC3s/Cuun23uW6+5ZLa5AQAAtqptGarP9Y/LDzx5lmkBAAAAANgibP8CAAAAAACDhOoAAAAAADBIqA4AAAAAAIOE6gAAAAAAMGhbflApAAAAwFZw71Xnzjb3GVfeMdvcADuZUB0AAADgMZx/xfWzzPuBJ88yLQAzEqqzY/jpPwAAAOxMc/1Q5NZrLpllXmC5CdVhwXwjBwAAYNn4typrsUAR1iZUh23CNzoAgMc219+Z/H0JYG3+rQpsR4/b7AIAAAAAAGCrsFIdAAD4Ciu5AWDr8UG6sFhCdQAAgAWbL/y4ZpZ5/VAEYG22t4GdyfYvAAAAAAAwyEp14LjyifEAwPHg19gBAFhWQnUAAACOKwstAIDtbOlC9aq6MMmbk5yQ5Fe6+3WbXBIAAAAAsEn8sJZls1ShelWdkOQtSf63JPuTfLKqburuuze3MgCAnWeuD95az4duzfUPqGTr/yPK9ijsRMvQlwAAlu2DSi9Isq+7/7i7/zLJDUku2uSaAAAAAAAgyZKtVE9yapL7Vr3en+S7N6kWYInMtSopWY4Vk1t9tSTAVmTFK7BsluXvvAAs1ny/gXjNLPMmvq9Ud292DV9RVS9J8vzu/kfT65cluaC7f2rVMZcluWx6+a1JPrPAEk9O8vACr7do7m9rc39b26Lv7+HuvnD0YL13Vu5va3N/W9vS9l59d1bub2tzf1ubvrs2/923Nve3tbm/42tdWcNWtmyh+vcmeW13P396/Zok6e7/Z1MLm1TV3u7es9l1zMX9bW3ub2vb7vd3LLb7n43729rc39a23e9vo7b7n4v729rc39a23e9vo7b7n4v729rc39a23e9vMy3bnuqfTHJ2VZ1VVU9IcnGSmza5JgAAAAAASLJke6p398GqekWS305yQpJ3dPddm1wWAAAAAAAkWbJQPUm6+0NJPrTZdRzBtZtdwMzc39bm/ra27X5/x2K7/9m4v63N/W1t2/3+Nmq7/7m4v63N/W1t2/3+Nmq7/7m4v63N/W1t2/3+vqKqzkpyQ5KTknwqycu6+y+n956T5F8keXxW9n3/O8d8vWXaUx0AAAAAANajqm5M8v7uvqGq/nWSP+rut1XVNyb5vSQXdve9VfWU7n7oWK+3bHuqAwAAAADA16iqM6vq01V1XVXdXlXvraqvT/LcJO+dDrsuyQun5/8wK2H7vUlyPAL1RKgOAAAAAMDW8a1Jru3uZyb5UpL/K8mfdffB6f39SU6dnn9LkhOr6j9W1a1VdcnxKGDp9lQHAAAAAIAjuK+7f3d6/mtJXrXGMYf2PN+V5Pwkz0vy15P8flX9QXf/52MpQKgOAAAAAMBWcfiHhP5lkm+sql3TavXTktw/vbc/Kx9O+hdJ/qKqPpbkO5McU6hu+xcAAAAAALaKM6rqe6fnL03y8SQfTfLiaezSJB+cnn8wyf9aVbuq6uuSfHeSe461AKE6AAAAAABbxT1JLq2q25OclORtSX4uyc9W1b4k35Tk7UnS3fck+XCS25PckuRXuvvOYy2gug9fLQ8AAAAAAMulqs5M8lvd/R2bWYeV6gAAAAAAMMhKdQAAAAAAGGSlOgAAAAAADBKqAwAAAADAIKE6rKGqXlFV+6qqq+rkVeMXVdXtVXVbVe2tqr99HK51YlV9YJr3lqpa84MWquqsqvpEVX22qt5TVU841msDbKYF99pvq6rfr6pHquqfHvbehVX1mamWVx/h/Kqqfzkdc3tVfdex1gSwaEvUd99RVQ9V1Z1HOV/fBba8Zei7VXV6VX20qu6pqruq6pVHOF/fhXUQqsPafjfJDyT5r4eN35zkO7v7vCQ/keRXjsO1fj7Jbd39zCSXJHnzEY7750ne1N1nJ/likpcfh2sDbKZF9tovJPnpJK9fPVhVJyR5S5IfTHJOkpdW1TlrnP+DSc6eHpcledtxqAlg0Ta9707emeTCxzhf3wW2g2XouweTvKq7vz3J9yS53N934dgJ1dmxqurMqvp0VV03/RT2vVX1dUnS3X/Y3Z8//Jzu/vP+6qf7fn2S4/FJv+dk5RtquvvTSc6sqqceVmsleW6S905D1yV54XG4NsCslqXXdvdD3f3JJP/jsLcuSLKvu/+4u/8yyQ1JLlpjiouSXN8r/iDJN1bVKcdaF8DxtgX6brr7Y1kJf45G3wW2hGXvu939QHd/anr+5ST3JDl1jSn0XXaskd+iO9yuOQuCLeBbk7y8u3+3qt6R5J9k7dU0X1FVL0ry/yR5SpL//QjHvGea+3Bv7O7rDxv7oyQ/nOTjVXVBkr+Z5LQkD6465puS/Fl3H5xe78/a3wQBltEy9NojOTXJfate70/y3YPHnZrkgcHrACzSMvfdUfousJVsib5bVWcmeVaST6zxtr7LUjj/iuuPxwLWr7j1mktq4LB3JvlXSYb/vxKqs9Pd192/Oz3/tRz5V1S/ors/kOQDVfX9SX4xK7/KdfgxP7qOGl6X5M1VdVuSO5L8YVZ+PWu1tRrAcW0yADNahl57JKP9VR8GtpJl7ruj9F1gK1n6vltVT0ryviQ/091fWuuQtco8XteHZdbdH5t+6DRMqM5Od/g3iOFvGNP/cE+vqpO7++HV763np8nTN7Mfn86rJJ+bHqs9nJVfvdo1rVY/Lcn9o7UCbLJN77VHsT/J6ateH6m/jh4HsAyWue+O0neBrWSp+25VPT4rgfq7uvv9RzhM34V1EKqz051RVd/b3b+f5KVJPn60g6vqbyX5L93d0ydhPyHJnx5+3Hp+mlxV35jk/5v28v1HST52+E+Np+t9NMmLs7Lf76VJPjh6DYBNtum99ig+meTsqjoryZ8kuTjJP1zjuJuSvKKqbsjK9jD/vbv9KiywrJa5747Sd4GtZGn77rR47+1J7unuNx7lUH0X1sEHlbLT3ZPk0qq6PclJmT7duqp+uqr2Z+Uns7dX1aFP4v4HSe6ctmp5S5IfXfXhIhv17UnuqqpPZ+XTtl956I2q+lBVPW16+XNJfraq9mVlj/W3H+N1ARZl03ttVX3zdK2fTfLPqmp/VX3D9Ns/r0jy21OdN3b3XdM5/7iq/vE0xYeS/HGSfUl+OSv7ZAIsq6Xtu9N7707y+0m+dRp/+TSu7wJb1TL33WcneVmS51bVbdPjBdM5+i5sUB17Hghb07RX0m9193dsdi0A25VeC7BY+i7AYum7cHxt0geVrvv/ZSvVAQAAAADYkY70W3RHPcdKdQAAAAAAGGOlOgAAAAAADBKqAwAAAADAIKE6AAAAAAAM2rXZBRyLCy+8sD/84Q9vdhkAW9HQp1+vRe8F2LAN9V59F2DD9F2Axdpw1rDVbOmV6g8//PBmlwCw4+i9AIul7wIslr4LwGPZ0qE6AAAAAAAsklAdAAAAAIAdp6pOr6qPVtU9VXVXVb1y5Lwtvac6AAAAAADbw71XndvHc74zrrzjsfZ5P5jkVd39qap6cpJbq+oj3X330U6yUh0AAAAAgB2nux/o7k9Nz7+c5J4kpz7WeUJ1AAAAAAB2tKo6M8mzknzisY4VqgMAAAAAsGNV1ZOSvC/Jz3T3lx7reKE6AAAAAAA7UlU9PiuB+ru6+/0j5wjVAQAAAADYcaqqkrw9yT3d/cbR84TqAAAAAADsRM9O8rIkz62q26bHCx7rpF3z1wUAAAAAAEd3xpV31CKv190fT7Lua1qpDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDhOoAAAAAADBIqA4AAAAAAIOE6gAAAAAAMEioDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDhOoAAAAAADBIqA4AAAAAAIOE6gAAAAAAMEioDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDhOoAAAAAADBIqA4AAAAAAIOE6gAAAAAAMEioDgAAAAAAg4TqAAAAAAAwSKgOAAAAAACDZgvVq+r0qvpoVd1TVXdV1Sun8ddW1Z9U1W3T4wWrznlNVe2rqs9U1fPnqg0AAAAAADZi14xzH0zyqu7+VFU9OcmtVfWR6b03dffrVx9cVeckuTjJM5I8Lcl/qKpv6e5HZ6wRAAAAAACGzbZSvbsf6O5PTc+/nOSeJKce5ZSLktzQ3Y909+eS7EtywVz1AQAAAADAei1kT/WqOjPJs5J8Yhp6RVXdXlXvqKoTp7FTk9y36rT9OXoIDwAAAAAACzV7qF5VT0ryviQ/091fSvK2JE9Pcl6SB5K84dCha5zea8x3WVXtraq9Bw4cmKdoAP4KvRdgsfRdgMXSdwFYj1lD9ap6fFYC9Xd19/uTpLsf7O5Hu/t/JvnlfHWLl/1JTl91+mlJ7j98zu6+trv3dPee3bt3z1k+ABO9F2Cx9F2AxdJ3AViP2UL1qqokb09yT3e/cdX4KasOe1GSO6fnNyW5uKqeWFVnJTk7yS1z1QcAAAAAAOu1a8a5n53kZUnuqKrbprGfT/LSqjovK1u7fD7JTyZJd99VVTcmuTvJwSSXd/ejM9YHAAAAAADrMluo3t0fz9r7pH/oKOdcneTquWoCAAAAAIBjMfsHlQIAAAAAwHYhVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABg0W6heVadX1Uer6p6ququqXjmNn1RVH6mqz05fT1x1zmuqal9Vfaaqnj9XbQAAAAAAsBFzrlQ/mORV3f3tSb4nyeVVdU6SVye5ubvPTnLz9DrTexcneUaSC5O8tapOmLE+AAAAAABYl9lC9e5+oLs/NT3/cpJ7kpya5KIk102HXZfkhdPzi5Lc0N2PdPfnkuxLcsFc9QEAAAAAwHotZE/1qjozybOSfCLJU7v7gWQleE/ylOmwU5Pct+q0/dPY4XNdVlV7q2rvgQMHZq0bgBV6L8Bi6bsAi6XvArAes4fqVfWkJO9L8jPd/aWjHbrGWH/NQPe13b2nu/fs3r37eJUJwFHovQCLpe8CLJa+C8B6zBqqV9XjsxKov6u73z8NP1hVp0zvn5LkoWl8f5LTV51+WpL756wPAAAAAADWY7ZQvaoqyduT3NPdb1z11k1JLp2eX5rkg6vGL66qJ1bVWUnOTnLLXPUBAAAAAMB67Zpx7mcneVmSO6rqtmns55O8LsmNVfXyJPcmeUmSdPddVXVjkruTHExyeXc/OmN9AAAAAACwLrOF6t398ay9T3qSPO8I51yd5Oq5agIAAAAAgGMx+weVAgAAAADAdiFUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEGzhepV9Y6qeqiq7lw19tqq+pOqum16vGDVe6+pqn1V9Zmqev5cdQEAAAAAwEbNuVL9nUkuXGP8Td193vT4UJJU1TlJLk7yjOmct1bVCTPWBgAAAAAA6zYUqlfVzSNjq3X3x5J8YbCOi5Lc0N2PdPfnkuxLcsHguQAAAAAAsBBHDdWr6q9V1UlJTq6qE6vqpOlxZpKnbfCar6iq26ftYU6cxk5Nct+qY/ZPY2vVdFlV7a2qvQcOHNhgCQCsh94LsFj6LsBi6bsArMdjrVT/ySS3Jvm26euhxweTvGUD13tbkqcnOS/JA0neMI3XGsf2WhN097Xdvae79+zevXsDJQCwXnovwGLpuwCLpe8CsB67jvZmd785yZur6qe6+5eO9WLd/eCh51X1y0l+a3q5P8npqw49Lcn9x3o9AAAAAAA4no4aqh/S3b9UVd+X5MzV53T39eu5WFWd0t0PTC9flOTO6flNSX69qt6YlW1lzk5yy3rmBgAAAACAuQ2F6lX1b7KybcttSR6dhjvJEUP1qnp3kudkZT/2/Ul+Iclzquq86dzPZ2V7mXT3XVV1Y5K7kxxMcnl3P7rGtAAAAAAAsGmGQvUke5Kc091r7nO+lu5+6RrDbz/K8VcnuXp0fgAAAAAAWLTH+qDSQ+5M8s1zFgIAAAAAAMtudKX6yUnurqpbkjxyaLC7//4sVQEAAAAAwBIaDdVfO2cRAAAAAACwFQyF6t39O3MXAgAAAAAAy24oVK+qLyc59CGlT0jy+CR/0d3fMFdhAAAAAACwbEZXqj959euqemGSC+YoCAAAAAAAltXjNnJSd/9Gkuce31IAAAAAAGC5jW7/8sOrXj4uyZ58dTsYAAAAAADYEYZC9SQ/tOr5wSSfT3LRca8GAAAAAACW2Oie6j8+dyEAAAAAALDshvZUr6rTquoDVfVQVT1YVe+rqtPmLg4AAAAAAJbJ6AeV/mqSm5I8LcmpSX5zGgMAAAAAgB1jNFTf3d2/2t0Hp8c7k+yesS4AAAAAAFg6o6H6w1X1Y1V1wvT4sSR/OmdhAAAAAACwbEZD9Z9I8iNJ/luSB5K8OIkPLwUAAAAAYEfZNXjcLya5tLu/mCRVdVKS12clbAcAAAAAgB1hdKX6Mw8F6knS3V9I8qx5SgIAAAAAgOU0Gqo/rqpOPPRiWqk+usodAAAAAAC2hdFg/A1Jfq+q3puks7K/+tWzVQUAAAAAAEtoKFTv7uuram+S5yapJD/c3XfPWhkAAAAAACyZ4S1cphBdkA4AAAAAwI41uqc6AAAAAADseEJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBs4XqVfWOqnqoqu5cNXZSVX2kqj47fT1x1Xuvqap9VfWZqnr+XHUBAAAAAMBGzblS/Z1JLjxs7NVJbu7us5PcPL1OVZ2T5OIkz5jOeWtVnTBjbQAAAAAAsG6zherd/bEkXzhs+KIk103Pr0vywlXjN3T3I939uST7klwwV20AAAAAALARi95T/and/UCSTF+fMo2fmuS+Vcftn8YAAAAAAGBpLMsHldYaY73mgVWXVdXeqtp74MCBmcsCINF7ARZN3wVYLH0XgPVYdKj+YFWdkiTT14em8f1JTl913GlJ7l9rgu6+trv3dPee3bt3z1osACv0XoDF0ncBFkvfBWA9Fh2q35Tk0un5pUk+uGr84qp6YlWdleTsJLcsuDYAAAAAADiqXXNNXFXvTvKcJCdX1f4kv5DkdUlurKqXJ7k3yUuSpLvvqqobk9yd5GCSy7v70blqA+D/b+/+Yy29yvvQf5947JAYEFgeXMc/akpcKAnUhiNzqSNEgQST5sZOGhIjBVv5IbcqJqBWVJA/SCCKFImGJq0IkmNI7AZwfAEXXzfXDdcl1yVtMGMw/k3rgmtP7DBjIAVzJSI7T/847zjHw7G9Zubs8+595vORtvZ+137fdz/rWLPk893rrAUAAADA4VhYqN7db3iCt179BOf/epJfX1Q9AAAAAABwpJZlo1IAAAAAAFh6QnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBu+YuAAAAgJ3lpW+7ciH3vfk9Fy3kvgAAh8JMdQAAAAAAGCRUBwAAAACAQZZ/AQAA2GaWRwEAWF1mqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADNo1x4dW1b1Jvpnk0SSPdPdaVZ2Q5A+TnJHk3iQ/3d1fn6M+AABgXi9925ULue/N77loIfcFAODoMedM9X/Y3Wd199p0/PYkN3T3mUlumI4BAAAAAGBpLNPyL+cnuWJ6fUWSC+YrBQAAAAAAvtNcoXon+eOqurmqLpnaTuruB5Nken7OZhdW1SVVtaeq9uzfv3+bygU4uhl7AbaXcRdgexl3ATgUc4Xq53b3S5K8LsmbquoVoxd292Xdvdbda7t3715chQA8xtgLsL2MuwDby7gLwKGYJVTv7gem531JrklyTpKvVNXJSTI975ujNgAAAAAAeCLbHqpX1fFV9YwDr5P8SJLbk1yb5OLptIuTfGK7awMAAAAAgCeza4bPPCnJNVV14PM/3N3XV9Vnk1xdVb+Q5L4kr5+hNgAAAAAAeELbHqp395eS/P1N2r+a5NXbXQ8AAAAAAIyaa6NSAAAAAABYOUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABi0a+4CAAAAYJW89G1XLuS+N7/nooXcFwDYWmaqAwAAAADAIDPVAQAAWAn3vftFC7nv6e+8bSH3BQB2JjPVAQAAAABgkJnqAACwgqzpDAAA8zBTHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGWVMdAAA4atz37hct5L6nv/O2hdwXAIDlI1QHAADYIXxpAACweJZ/AQAAAACAQUJ1AAAAAAAYZPkXADjKvfRtVy7s3je/56KF3RsAAADmIFQHAAAeY01uAAB4cpZ/AQAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABhko1IAYEd76duuXNi9b37PRQu7NwBHn0VtFJzYLBgAtpKZ6gAAAAAAMEioDgAAAAAAgyz/AsARs7wGAAAAcLQwUx0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABllTHQAAAHjMovbLsVcOADuFUB0A4DDd9+4XLeS+p7/ztoXcFwAAgCMnVAdgqQktYT7+/QEAPLlF/f9S4v+ZYJkJ1QFgRkJLAAAAWC02KgUAAAAAgEFmqgPAU1jUZl1Jcs0zFnZrAAAAVoANolePUJ2jxk5f52yn9w9YTZa34Ugt8kstv2QAwNFjUf9PsSyTZISyh0eWwuFaulC9qs5L8ttJjklyeXf/xswlPWZZ/qEZKAGAA/wlBQCrYll+pwaAI7VUoXpVHZPkfUl+OMneJJ+tqmu7+855Kzs6LMv/4Pj2+PAsS/8AAACAnUFWBJtbqlA9yTlJ7unuLyVJVV2V5PwkhxSq+4cG8/GXFAAAwE7kd53DsyyhLMBWqu6eu4bHVNVPJTmvu39xOn5jkpd196UbzrkkySXT4fOTfHEbSzwxyUPb+HnbTf9Wm/6ttu3u30Pdfd7oycbehdK/1aZ/q21px17j7kLp32rTv9Vm3N2c/+6rTf9Wm/5trUPKGlbZsoXqr0/y2oNC9XO6+83zVrauqvZ099rcdSyK/q02/VttO71/R2Kn/2z0b7Xp32rb6f07XDv956J/q03/VttO79/h2uk/F/1bbfq32nZ6/+b0XXMXcJC9SU7bcHxqkgdmqgUAAAAAAB5n2UL1zyY5s6qeW1XHJbkwybUz1wQAAAAAAEmWbKPS7n6kqi5N8h+THJPkg919x8xlbXTZ3AUsmP6tNv1bbTu9f0dip/9s9G+16d9q2+n9O1w7/eeif6tN/1bbTu/f4drpPxf9W236t9p2ev9ms1RrqgMAAAAAwDJbtuVfAAAAAABgaQnVAQAAAABgkFAdgKVUVR+sqn1VdfvctWy1qjqtqj5VVXdV1R1V9Za5a9pKVfW0qrqpqr4w9e9dc9e0CFV1TFV9vqqum7uWrVZV91bVbVV1S1XtmbuerVZVz6qqj1bV3dO/w5fPXdMy2MnjbmLs3Ql28ribGHuPRsbd1XY0jLvJzh57jbscCWuqA7CUquoVSR5OcmV3/+Dc9Wylqjo5ycnd/bmqekaSm5Nc0N13zlzalqiqSnJ8dz9cVccm+XSSt3T3n81c2paqqn+eZC3JM7v7x+auZytV1b1J1rr7oblrWYSquiLJf+7uy6vquCTf291/OXNZs9vJ425i7N0JdvK4mxh7j0bG3dV2NIy7yc4ee427HAkz1QFYSt19Y5KvzV3HInT3g939uen1N5PcleSUeavaOr3u4enw2Omxo77Fr6pTk/yjJJfPXQuHpqqemeQVST6QJN39V365WLeTx93E2LvqjLurzdi7OePuatvp425i7F1lxt3FE6oDwIyq6owkZyf5zMylbKnpz0RvSbIvySe7e0f1L8lvJfmXSf565joWpZP8cVXdXFWXzF3MFvs7SfYn+b3pT5kvr6rj5y6K7WXsXUm/lZ097ibGXnYw4+7K+q3s7LHXuMthE6oDwEyq6ulJPpbkrd39jbnr2Urd/Wh3n5Xk1CTnVNWO+ZPmqvqxJPu6++a5a1mgc7v7JUlel+RN05+n7xS7krwkyfu7++wk30ry9nlLYjsZe1fPUTLuJsZedijj7mo6SsZe4y6HTagOADOY1l38WJIPdffH565nUaY/MfyTJOfNW8mWOjfJj09rMF6V5FVV9QfzlrS1uvuB6XlfkmuSnDNvRVtqb5K9G2aSfTTrv3BwFDD2rqwdP+4mxl52JuPuStvxY69xlyMhVAeAbTZtavSBJHd193vnrmerVdXuqnrW9Pp7krwmyd2zFrWFuvsd3X1qd5+R5MIk/6m7f3bmsrZMVR0/bSaW6U9EfyTJ7fNWtXW6+y+S3F9Vz5+aXp1kR2yYxpMz9q6unT7uJsZedibj7mrb6WOvcZcjtWvuAgBgM1X1kSSvTHJiVe1N8ivd/YF5q9oy5yZ5Y5LbpjUYk+SXu/uP5itpS52c5IqqOibrX+Bf3d3XzVwT405Kcs3678HZleTD3X39vCVtuTcn+VBVHZfkS0l+buZ6lsIOH3cTYy/Lzdh7FDLurjzj7moz7nJEqntHbUwMAAAAAAALY/kXAAAAAAAYJFSHJFV1aVXdU1VdVSduaD+/qm6tqluqak9V/dAWfNazq+qa6b43HdgdvKqeP33Ogcc3quqtm1xfVfVvpnpvrSobTQAAAADANrH8CySpqrOTfD3ru3WvdfdDU/vTk3yru7uqXpz1NdJecISf9Z4kD3f3u6rqBUne192vPuicY5L8eZKXdff/POi9H836ulg/muRlSX67u192JDUBAAAAAGPMVOeoUVVnVNXdVXXFNMP7o1X1vUnS3Z/v7nsPvqa7H+6/+ebp+CRb8S3UC5PcMN3/7iRnVNVJB53z6iT/4+BAfXJ+kit73Z8leVZVnbwFdQEAAAAAT0GoztHm+Uku6+4XJ/lGkn/2VBdU1U9U1d1J/kOSn3+Cc/7woKVbDjwu2uT0LyT5yem6c5L87SSnHnTOhUk+8gQlnZLk/g3He6c2AAAAAGDBhOocbe7v7j+dXv9BkqdcI727r5mWfLkgya89wTk/091nbfK4cpPTfyPJs6vqlqwv4/L5JI8ceLOqjkvy40n+rycoqTYr4an6AbCTHOpeGFX1tGkfiy9U1R1V9a75qgdYPcZdgO1n7IXltWvuAmCbHRw+D4fR3X1jVT2vqk48sOb6AVX1h1mfBX+w9x4crHf3N5L83HRdJfny9DjgdUk+191feYJS9iY5bcPxqUkeGO0HwA7xp0muy/peGBvdkOTajXthJHlBkm8neVV3P1xVxyb5dFX9P9MyWgA8NeMuwPYz9sKSEqpztDm9ql7e3f81yRuSfPrJTq6q78/62uZdVS9JclySrx58Xnf/zGgBVfWsJP9/d/9Vkl9McuMUtB/whjzx0i9Jcm2SS6vqqqxvVPq/uvvB0c8HWCVVdUaS65N8JsnZSf5bkou6+/PT+487v7sf3nD42F4Y0/4YB947dnr4Kx+Agxh3AbafsRdWj+VfONrcleTiqro1yQlJ3p8kVfVLVbU367O+b62qy6fz/3GS26elWt6X5Gc2bFx6uP5ekjumddpfl+QtB96YNk794SQf33hBVf3Tqvqn0+EfJflSknuS/G4G1oUHWHGHtB/GE+2FUVXHTOP5viSf7O7PLK5kgJVm3AXYfsZeWCF15PkgrIbpm9/ruvsH564FgDHT2H1jd58+Hb8qyS919wXT8b1J1g5elmt67xVJ3tndrzmo/VlJrkny5u6+fZH1A6wa4y7A9jP2wuoxUx0AWHaHtR9Gd9+Y5HkbN3Wa2v8y6+tSnrcVxQHsQMZdgO1n7IUVIlTnqNHd95qlDrCSTq+ql0+vn3Q/jKr6/mkT6GzcC6Oqdk+zdVJV35PkNUnuXmjVAKvLuAuw/Yy9sEKE6gDAsvuO/TAOYy+Mk5N8arrHZ7O+vuR1290RgBVh3AXYfsZeWCHWVAcAlpb9MAC2l3EXYPsZe2H17Jq7gCNx3nnn9fXXXz93GQCrqOYuAAAAAGAVrXSo/tBD37HpMQCwg3T3vUnM2AHYJsZdgO1n7IXVY011AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBCwvVq+q0qvpUVd1VVXdU1Vum9l+tqj+vqlumx49uuOYdVXVPVX2xql67qNoAAAAAAOBw7FrgvR9J8i+6+3NV9YwkN1fVJ6f3/nV3/6uNJ1fVC5NcmOQHknxfkv+3qv5udz+6wBoBAAAAAGDYwmaqd/eD3f256fU3k9yV5JQnueT8JFd197e7+8tJ7klyzqLqAwAAAACAQ7Uta6pX1RlJzk7ymanp0qq6tao+WFXPntpOSXL/hsv2ZpMQvqouqao9VbVn//79iywbAAAAAAAeZ+GhelU9PcnHkry1u7+R5P1JnpfkrCQPJvnNA6ducnl/R0P3Zd291t1ru3fvXkzRAAAAAACwiYWG6lV1bNYD9Q9198eTpLu/0t2PdvdfJ/nd/M0SL3uTnLbh8lOTPLDI+gAAAAAA4FAsLFSvqkrygSR3dfd7N7SfvOG0n0hy+/T62iQXVtV3V9Vzk5yZ5KZF1QcAAAAAAIdq1wLvfW6SNya5rapumdp+OckbquqsrC/tcm+Sf5Ik3X1HVV2d5M4kjyR5U3c/usD6AAAAAADgkFT3dyxbvjLW1tZ6z549c5cBsIo228cCAAAAgKew8I1KAQAAAABgpxCqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwKCFhepVdVpVfaqq7qqqO6rqLVP7CVX1yar679Pzszdc846quqeqvlhVr11UbQAAAAAAcDgWOVP9kST/orv/XpL/I8mbquqFSd6e5IbuPjPJDdNxpvcuTPIDSc5L8jtVdcwC6wMAAAAAgEOysFC9ux/s7s9Nr7+Z5K4kpyQ5P8kV02lXJLlgen1+kqu6+9vd/eUk9yQ5Z1H1AQAAAADAodqWNdWr6owkZyf5TJKTuvvBZD14T/Kc6bRTkty/4bK9UxsAAAAAACyFhYfqVfX0JB9L8tbu/saTnbpJW29yv0uqak9V7dm/f/9WlQkAAAAAAE9poaF6VR2b9UD9Q9398an5K1V18vT+yUn2Te17k5y24fJTkzxw8D27+7LuXuvutd27dy+ueAAAAAAAOMjCQvWqqiQfSHJXd793w1vXJrl4en1xkk9saL+wqr67qp6b5MwkNy2qPgAAAAAAOFS7Fnjvc5O8McltVXXL1PbLSX4jydVV9QtJ7kvy+iTp7juq6uokdyZ5JMmbuvvRBdYHAAAAAACHpLq/Y9nylbG2ttZ79uyZuwyAVbTZPhYAAAAAPIWFb1QKAAAAAAA7hVAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABg0MJC9ar6YFXtq6rbN7T9alX9eVXdMj1+dMN776iqe6rqi1X12kXVBQAAAAAAh2uRM9V/P8l5m7T/6+4+a3r8UZJU1QuTXJjkB6ZrfqeqjllgbQAAAAAAcMiGQvWqumGkbaPuvjHJ1wbrOD/JVd397e7+cpJ7kpwzeC0AAAAAAGyLJw3Vq+ppVXVCkhOr6tlVdcL0OCPJ9x3mZ15aVbdOy8M8e2o7Jcn9G87ZO7VtVtMlVbWnqvbs37//MEsAAAAAAIBD91Qz1f9JkpuTvGB6PvD4RJL3HcbnvT/J85KcleTBJL85tdcm5/ZmN+juy7p7rbvXdu/efRglAAAAAADA4dn1ZG92928n+e2qenN3/9sj/bDu/sqB11X1u0mumw73Jjltw6mnJnngSD8PAAAAAAC20pOG6gd097+tqn+Q5IyN13T3lYfyYVV1cnc/OB3+RJLbp9fXJvlwVb0368vKnJnkpkO5NwAAAAAALNpQqF5V/y7ry7bckuTRqbmTPGGoXlUfSfLKrK/HvjfJryR5ZVWdNV17b9aXl0l331FVVye5M8kjSd7U3Y9uclsAAAAAAJhNdW+6dPnjT6q6K8kLe+TkbbS2ttZ79uyZuwyAVbTZXhYAAAAAPIWn2qj0gNuT/K1FFgIAAAAAAMtuaPmXJCcmubOqbkry7QON3f3jC6kKAAAAAACW0Gio/quLLAIAAAAAAFbBUKje3f/fogsBAAAAAIBlNxSqV9U3kxzYpPS4JMcm+VZ3P3NRhQEAAAAAwLIZnan+jI3HVXVBknMWURAAAAAAACyr7zqci7r73yd51daWAgAAAAAAy210+Zef3HD4XUnW8jfLwQAAAAAAwFFhKFRP8n9ueP1IknuTnL/l1QAAAAAAwBIbXVP95xZdCAAAAAAALLuhNdWr6tSquqaq9lXVV6rqY1V16qKLAwAAAACAZTK6UenvJbk2yfclOSXJ/z21AQAAAADAUWM0VN/d3b/X3Y9Mj99PsnuBdQEAAAAAwNIZDdUfqqqfrapjpsfPJvnqIgsDAAAAAIBlMxqq/3ySn07yF0keTPJTSWxeCgAAAADAUWXX4Hm/luTi7v56klTVCUn+VdbDdgAAAAAAOCqMzlR/8YFAPUm6+2tJzl5MSQAAAAAAsJxGQ/XvqqpnHziYZqqPznIHAAAAAIAdYTQY/80k/6WqPpqks76++q8vrCoAAAAAAFhCQ6F6d19ZVXuSvCpJJfnJ7r5zoZUBAAAAAMCSGV7CZQrRBekAAAAAABy1RtdUBwAAAACAo55QHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYJBQHQAAAAAABgnVAQAAAABg0MJC9ar6YFXtq6rbN7SdUFWfrKr/Pj0/e8N776iqe6rqi1X12kXVBQAAAAAAh2uRM9V/P8l5B7W9PckN3X1mkhum41TVC5NcmOQHpmt+p6qOWWBtAAAAAABwyBYWqnf3jUm+dlDz+UmumF5fkeSCDe1Xdfe3u/vLSe5Jcs6iagMAAAAAgMOx3Wuqn9TdDybJ9Pycqf2UJPdvOG/v1PYdquqSqtpTVXv279+/0GIBAAAAAGCjZdmotDZp681O7O7Lunutu9d279694LIAAAAAAOBvbHeo/pWqOjlJpud9U/veJKdtOO/UJA9sc20AAAAAAPCktjtUvzbJxdPri5N8YkP7hVX13VX13CRnJrlpm2sDAAAAAIAntWtRN66qjyR5ZZITq2pvkl9J8htJrq6qX0hyX5LXJ0l331FVVye5M8kjSd7U3Y8uqjYAAAAAADgc1b3p0uUrYW1trffs2TN3GQCraLO9LAAAAAB4CsuyUSkAAAAAACw9oToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqgMAAAAAwCChOgAAAAAADBKqAwAAAADAoF1zF7BK7nv3ixZ279PfedvC7g0AAAAAwNaYJVSvqnuTfDPJo0ke6e61qjohyR8mOSPJvUl+uru/Pkd9AAAAAACwmTmXf/mH3X1Wd69Nx29PckN3n5nkhukYAAAAAACWxjKtqX5+kium11ckuWC+UgAAAAAA4DvNFap3kj+uqpur6pKp7aTufjBJpufnbHZhVV1SVXuqas/+/fu3qVwAAAAAAJhvo9Jzu/uBqnpOkk9W1d2jF3b3ZUkuS5K1tbVeVIEAAAAAAHCwWWaqd/cD0/O+JNckOSfJV6rq5CSZnvfNURsAAAAAADyRbQ/Vq+r4qnrGgddJfiTJ7UmuTXLxdNrFST6x3bUBAAAAAMCTmWP5l5OSXFNVBz7/w919fVV9NsnVVfULSe5L8voZagMAAAAAgCe07aF6d38pyd/fpP2rSV693fUAAAAAAMCoWdZUBwAAAACAVSRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABi0a+4CFuGlb7tyIfe95hkLuS0AAAAAACvCTHUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYJFQHAAAAAIBBQnUAAAAAABgkVAcAAAAAgEFCdQAAAAAAGCRUBwAAAACAQUJ1AAAAAAAYtGvuAjh0L33blQu5783vuWgh9wUAAAAA2CnMVAcAAAAAgEFmqrN0zMQHAAAAAJaVmeoAAAAAADDITHUec9+7X7Swe5/+ztsWdm8AAAAAgO0iVOeo4UsDAAAAAOBIWf4FAAAAAAAGCdUBAAAAAGDQ0oXqVXVeVX2xqu6pqrfPXQ8AAAAAABywVKF6VR2T5H1JXpfkhUneUFUvnLcqAAAAAABYt2wblZ6T5J7u/lKSVNVVSc5PcuesVcEKsBErAAAAACxedffcNTymqn4qyXnd/YvT8RuTvKy7L91wziVJLpkOn5/ki9tY4olJHtrGz9tu+rfa9G+1bXf/Huru87bx8wAAAAB2hGWbqV6btD0u9e/uy5Jctj3lPF5V7enutTk+ezvo32rTv9W20/sHAAAAsFMs1ZrqSfYmOW3D8alJHpipFgAAAAAAeJxlC9U/m+TMqnpuVR2X5MIk185cEwAAAAAAJFmy5V+6+5GqujTJf0xyTJIPdvcdM5e10SzLzmwj/Vtt+rfadnr/AAAAAHaEpdqoFAAAAAAAltmyLf8CAAAAAABLS6gOAAAAAACDhOoDquqDVbWvqm6fu5ZFqKrTqupTVXVXVd1RVW+Zu6atVFVPq6qbquoLU//eNXdNi1BVx1TV56vqurlr2WpVdW9V3VZVt1TVnrnr2WpV9ayq+mhV3T39O3z53DUBAAAAsDlrqg+oqlckeTjJld39g3PXs9Wq6uQkJ3f356rqGUluTnJBd985c2lboqoqyfHd/XBVHZvk00ne0t1/NnNpW6qq/nmStSTP7O4fm7uerVRV9yZZ6+6H5q5lEarqiiT/ubsvr6rjknxvd//lzGUBAAAAsAkz1Qd0941JvjZ3HYvS3Q929+em199McleSU+atauv0uoenw2Onx476NqmqTk3yj5JcPnctHJqqemaSVyT5QJJ0918J1AEAAACWl1Cdx6mqM5KcneQzM5eypaalUW5Jsi/JJ7t7R/UvyW8l+ZdJ/nrmOhalk/xxVd1cVZfMXcwW+ztJ9if5vWn5nsur6vi5iwIAAABgc0J1HlNVT0/ysSRv7e5vzF3PVuruR7v7rCSnJjmnqnbMMj5V9WNJ9nX3zXPXskDndvdLkrwuyZumJZl2il1JXpLk/d19dpJvJXn7vCUBAAAA8ESE6iRJprXGP5bkQ9398bnrWZRpWY0/SXLevJVsqXOT/Pi07vhVSV5VVX8wb0lbq7sfmJ73JbkmyTnzVrSl9ibZu+GvJz6a9ZAdAAAAgCUkVOfARp4fSHJXd7937nq2WlXtrqpnTa+/J8lrktw9a1FbqLvf0d2ndvcZSS5M8p+6+2dnLmvLVNXx0wa6mZZF+ZEkt89b1dbp7r9Icn9VPX9qenWSHbFJMAAAAMBOtGvuAlZBVX0kySuTnFhVe5P8Snd/YN6qttS5Sd6Y5LZp3fEk+eXu/qP5StpSJye5oqqOyfoXSVd393Uz18S4k5Jcs/7dT3Yl+XB3Xz9vSVvuzUk+VFXHJflSkp+buR4AAAAAnkB199w1AAAAAADASrD8CwAAAAAADBKqAwAAAADAIKE6AAAAAAAMEqoDAAAAAMAgoToAAAAAAAwSqsMWqKpLq+qequqqOnFD+/lVdWtV3VJVe6rqh6b2p1XVTVX1haq6o6reNV/1AAAAAMCo6u65a4CVV1VnJ/l6kj9JstbdD03tT0/yre7uqnpxkqu7+wVVVUmO7+6Hq+rYJJ9O8pbu/rOZugAAAAAADNg1dwGwSqrqjCTXJ/lMkrOT/LckF3X356f3H3d+dz+84fD4JD21d5ID7x07PXzDBQAAAABLzvIvcOien+Sy7n5xkm8k+WdPdnJV/URV3Z3kPyT5+Q3tx1TVLUn2Jflkd39mcSUDAAAAAFtBqA6H7v7u/tPp9R8k+aEnO7m7r+nuFyS5IMmvbWh/tLvPSnJqknOq6gcXUy4AAAAAsFWE6nDoDl6mZWjZlu6+McnzNm5kOrX/ZdbXYj9vK4oDAAAAABZHqA6H7vSqevn0+g1Z32R0U1X1/dOmpKmqlyQ5LslXq2p3VT1rav+eJK9JcvdCqwYAAAAAjphQHQ7dXUkurqpbk5yQ5P1V9UtVtTfrS7ncWlWXT+f+4yS3T2unvy/Jz0yblJ6c5FPTPT6b9TXVr9vujgAAAAAAh6bW8z1gRFWdkeS67rb+OQAAAAAchcxUBwAAAACAQWaqAwAAAADAIDPVAQAAAABgkFAdAAAAAAAGCdUBAAAAAGCQUB0AAAAAAAYJ1QEAAAAAYND/Bg6v9uh6uPmAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1482.38x1440 with 13 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x = \"p133\",\n",
    "            data = Mc,\n",
    "            col=\"p13\", \n",
    "            col_wrap=4,\n",
    "           kind = 'count',\n",
    "           hue = 'p06')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encuesta Nacional de Ingresos y Gastos de los Hogares (ENIGH) 2016\n",
    "\n",
    "La Encuesta Nacional de Ingresos y Gastos de los Hogares 2016 se llevó a cabo del 21 de agosto al 28 de noviembre de 2016. Su objetivo es proporcionar un panorama estadístico del comportamiento de los ingresos y gastos de los hogares en cuanto a su monto, procedencia y distribución; adicionalmente, ofrece información sobre las características ocupacionales y sociodemográficas de los integrantes del hogar, así como las características de la infraestructura de la vivienda y el equipamiento del hogar. [ENIGH-2016](https://www.inegi.org.mx/programas/enigh/nc/2016/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "folioviv     object\n",
       "foliohog     object\n",
       "numren       object\n",
       "clave        object\n",
       "mes_1        object\n",
       "mes_2        object\n",
       "mes_3        object\n",
       "mes_4        object\n",
       "mes_5        object\n",
       "mes_6        object\n",
       "ing_1       float64\n",
       "ing_2       float64\n",
       "ing_3       float64\n",
       "ing_4       float64\n",
       "ing_5       float64\n",
       "ing_6       float64\n",
       "ing_tri     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = \"ingresos.dta\"\n",
    "df = pd.read_stata(url,\n",
    "                   convert_categoricals= False\n",
    "                  )\n",
    "df.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>folioviv</th>\n",
       "      <th>foliohog</th>\n",
       "      <th>numren</th>\n",
       "      <th>clave</th>\n",
       "      <th>mes_1</th>\n",
       "      <th>mes_2</th>\n",
       "      <th>mes_3</th>\n",
       "      <th>mes_4</th>\n",
       "      <th>mes_5</th>\n",
       "      <th>mes_6</th>\n",
       "      <th>ing_1</th>\n",
       "      <th>ing_2</th>\n",
       "      <th>ing_3</th>\n",
       "      <th>ing_4</th>\n",
       "      <th>ing_5</th>\n",
       "      <th>ing_6</th>\n",
       "      <th>ing_tri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0100003801</td>\n",
       "      <td>1</td>\n",
       "      <td>02</td>\n",
       "      <td>P009</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>7500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1844.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0100003801</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "      <td>P001</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>04</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>53114.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0100003801</td>\n",
       "      <td>1</td>\n",
       "      <td>02</td>\n",
       "      <td>P001</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>04</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>15000.0</td>\n",
       "      <td>44262.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0100003801</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "      <td>P009</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1475.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0100003802</td>\n",
       "      <td>1</td>\n",
       "      <td>02</td>\n",
       "      <td>P040</td>\n",
       "      <td>10</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2459.01</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>334332</th>\n",
       "      <td>3260801906</td>\n",
       "      <td>1</td>\n",
       "      <td>02</td>\n",
       "      <td>P022</td>\n",
       "      <td>10</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>4010.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334333</th>\n",
       "      <td>3260801906</td>\n",
       "      <td>1</td>\n",
       "      <td>02</td>\n",
       "      <td>P053</td>\n",
       "      <td>10</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>635.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334334</th>\n",
       "      <td>3260801906</td>\n",
       "      <td>1</td>\n",
       "      <td>04</td>\n",
       "      <td>P014</td>\n",
       "      <td>10</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>528.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334335</th>\n",
       "      <td>3260801906</td>\n",
       "      <td>1</td>\n",
       "      <td>04</td>\n",
       "      <td>P001</td>\n",
       "      <td>10</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>831.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334336</th>\n",
       "      <td>3260801906</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "      <td>P053</td>\n",
       "      <td>10</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>700.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1369.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>334337 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          folioviv foliohog numren clave mes_1 mes_2 mes_3 mes_4 mes_5 mes_6  \\\n",
       "0       0100003801        1     02  P009                                       \n",
       "1       0100003801        1     01  P001    09    08    07    06    05    04   \n",
       "2       0100003801        1     02  P001    09    08    07    06    05    04   \n",
       "3       0100003801        1     01  P009                                       \n",
       "4       0100003802        1     02  P040    10    09    08    07    06    05   \n",
       "...            ...      ...    ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "334332  3260801906        1     02  P022    10    09    08    07    06    05   \n",
       "334333  3260801906        1     02  P053    10    09    08    07    06    05   \n",
       "334334  3260801906        1     04  P014    10    09    08    07    06    05   \n",
       "334335  3260801906        1     04  P001    10    09    08    07    06    05   \n",
       "334336  3260801906        1     01  P053    10    09    08    07    06    05   \n",
       "\n",
       "          ing_1    ing_2    ing_3    ing_4    ing_5    ing_6   ing_tri  \n",
       "0        7500.0      NaN      NaN      NaN      NaN      NaN   1844.26  \n",
       "1       18000.0  18000.0  18000.0  18000.0  18000.0  18000.0  53114.75  \n",
       "2       15000.0  15000.0  15000.0  15000.0  15000.0  15000.0  44262.29  \n",
       "3        6000.0      NaN      NaN      NaN      NaN      NaN   1475.40  \n",
       "4           0.0      0.0      0.0      0.0   5000.0      0.0   2459.01  \n",
       "...         ...      ...      ...      ...      ...      ...       ...  \n",
       "334332      0.0      0.0   2200.0   2000.0   2000.0   2000.0   4010.86  \n",
       "334333    500.0      0.0    300.0    200.0      0.0    300.0    635.86  \n",
       "334334   1080.0      0.0      0.0      0.0      0.0      0.0    528.26  \n",
       "334335   1200.0    500.0      0.0      0.0      0.0      0.0    831.52  \n",
       "334336    700.0    300.0    200.0   1200.0    200.0    200.0   1369.56  \n",
       "\n",
       "[334337 rows x 17 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creamos una función para filtar los folios de hogares y el tipo de persona deseada. ¿Qué es cada tipo de dato creado dentro de la función?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def familiasintegrantes(df,foliohog,numren):\n",
    "    \n",
    "    confolio = [ df.foliohog == _ for _ in foliohog]\n",
    "    confnum = [ df.numren == _ for _ in numren]\n",
    "    \n",
    "    aconfolio = 0\n",
    "    for c in confolio:\n",
    "        aconfolio = aconfolio | c \n",
    "    \n",
    "    aconfnum = 0\n",
    "    for c in confnum:\n",
    "        aconfnum = aconfnum | c \n",
    "    \n",
    "    con = aconfolio & aconfnum\n",
    "    \n",
    "    return df[con]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>folioviv</th>\n",
       "      <th>foliohog</th>\n",
       "      <th>numren</th>\n",
       "      <th>clave</th>\n",
       "      <th>mes_1</th>\n",
       "      <th>mes_2</th>\n",
       "      <th>mes_3</th>\n",
       "      <th>mes_4</th>\n",
       "      <th>mes_5</th>\n",
       "      <th>mes_6</th>\n",
       "      <th>ing_1</th>\n",
       "      <th>ing_2</th>\n",
       "      <th>ing_3</th>\n",
       "      <th>ing_4</th>\n",
       "      <th>ing_5</th>\n",
       "      <th>ing_6</th>\n",
       "      <th>ing_tri</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0100003801</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "      <td>P001</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>04</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>18000.0</td>\n",
       "      <td>53114.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0100003801</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "      <td>P009</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1475.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0100003802</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "      <td>P001</td>\n",
       "      <td>10</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>47213.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0100003803</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "      <td>P001</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>04</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>82622.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0100003804</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "      <td>P001</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>04</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>29508.19</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\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>334328</th>\n",
       "      <td>3260801904</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "      <td>P072</td>\n",
       "      <td>10</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>5135.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334329</th>\n",
       "      <td>3260801905</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "      <td>P032</td>\n",
       "      <td>10</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>17608.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334330</th>\n",
       "      <td>3260801905</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "      <td>P044</td>\n",
       "      <td>10</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1614.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334331</th>\n",
       "      <td>3260801906</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "      <td>P001</td>\n",
       "      <td>10</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>2200.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>6163.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334336</th>\n",
       "      <td>3260801906</td>\n",
       "      <td>1</td>\n",
       "      <td>01</td>\n",
       "      <td>P053</td>\n",
       "      <td>10</td>\n",
       "      <td>09</td>\n",
       "      <td>08</td>\n",
       "      <td>07</td>\n",
       "      <td>06</td>\n",
       "      <td>05</td>\n",
       "      <td>700.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1369.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151177 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          folioviv foliohog numren clave mes_1 mes_2 mes_3 mes_4 mes_5 mes_6  \\\n",
       "1       0100003801        1     01  P001    09    08    07    06    05    04   \n",
       "3       0100003801        1     01  P009                                       \n",
       "8       0100003802        1     01  P001    10    09    08    07    06    05   \n",
       "10      0100003803        1     01  P001    09    08    07    06    05    04   \n",
       "13      0100003804        1     01  P001    09    08    07    06    05    04   \n",
       "...            ...      ...    ...   ...   ...   ...   ...   ...   ...   ...   \n",
       "334328  3260801904        1     01  P072    10    09    08    07    06    05   \n",
       "334329  3260801905        1     01  P032    10    09    08    07    06    05   \n",
       "334330  3260801905        1     01  P044    10    09    08    07    06    05   \n",
       "334331  3260801906        1     01  P001    10    09    08    07    06    05   \n",
       "334336  3260801906        1     01  P053    10    09    08    07    06    05   \n",
       "\n",
       "          ing_1    ing_2    ing_3    ing_4    ing_5    ing_6   ing_tri  \n",
       "1       18000.0  18000.0  18000.0  18000.0  18000.0  18000.0  53114.75  \n",
       "3        6000.0      NaN      NaN      NaN      NaN      NaN   1475.40  \n",
       "8       16000.0  16000.0  16000.0  16000.0  16000.0  16000.0  47213.11  \n",
       "10      28000.0  28000.0  28000.0  28000.0  28000.0  28000.0  82622.95  \n",
       "13      10000.0  10000.0  10000.0  10000.0  10000.0  10000.0  29508.19  \n",
       "...         ...      ...      ...      ...      ...      ...       ...  \n",
       "334328   1750.0   1750.0   1750.0   1750.0   1750.0   1750.0   5135.86  \n",
       "334329   6000.0   6000.0   6000.0   6000.0   6000.0   6000.0  17608.69  \n",
       "334330   1100.0      0.0   1100.0      0.0   1100.0      0.0   1614.13  \n",
       "334331   2200.0   2200.0   2200.0   2000.0   2000.0   2000.0   6163.04  \n",
       "334336    700.0    300.0    200.0   1200.0    200.0    200.0   1369.56  \n",
       "\n",
       "[151177 rows x 17 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = familiasintegrantes(df,['1','2'],['01'])\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '2'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.foliohog.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como cada persona está repetida de acuerdo al tipo de ingresos que tiene, creamos una función para sumar los ingresos de cada persona, y una nueva variable (`ing_men`) para tener el ingreso mensual acumulado\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "         folioviv foliohog numren    ing_1    ing_2    ing_3    ing_4  \\\n",
       "0      0100003801        1     01  24000.0  18000.0  18000.0  18000.0   \n",
       "1      0100003802        1     01  16000.0  16000.0  16000.0  16000.0   \n",
       "2      0100003803        1     01  28000.0  28000.0  28000.0  28000.0   \n",
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       "...           ...      ...    ...      ...      ...      ...      ...   \n",
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       "67818  3260610709        2     01   3600.0   3600.0   3600.0   3600.0   \n",
       "\n",
       "         ing_5    ing_6   ing_tri    claves       ing_men  \n",
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       "1      16000.0  16000.0  47213.11      P001  15737.703333  \n",
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       "...        ...      ...       ...       ...           ...  \n",
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       "67818   3600.0   3600.0  10622.95      P001   3540.983333  \n",
       "\n",
       "[67819 rows x 12 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def ingresosacu(df):\n",
    "    df2 = df.groupby(by = ['numren','foliohog','folioviv']).sum()\n",
    "    df2['claves'] = df.groupby(['numren','foliohog','folioviv'])['clave'].sum()\n",
    "    df2.reset_index(level=0, inplace=True)\n",
    "    df2.reset_index(level=0, inplace=True)\n",
    "    df2.reset_index(level=0, inplace=True)\n",
    "\n",
    "    df2['ing_men'] = df2.ing_tri/3\n",
    "    return df2\n",
    "\n",
    "df2 = ingresosacu(df1)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cremos una variable para indicar en que rango de ingreso cae cada perosona. Los rangos están predefinidos "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2</td>\n",
       "      <td>01</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>10622.95</td>\n",
       "      <td>P001</td>\n",
       "      <td>3540.983333</td>\n",
       "      <td>C2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67819 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         folioviv foliohog numren    ing_1    ing_2    ing_3    ing_4  \\\n",
       "0      0100003801        1     01  24000.0  18000.0  18000.0  18000.0   \n",
       "1      0100003802        1     01  16000.0  16000.0  16000.0  16000.0   \n",
       "2      0100003803        1     01  28000.0  28000.0  28000.0  28000.0   \n",
       "3      0100003804        1     01  15000.0  10000.0  10000.0  10000.0   \n",
       "4      0100003805        1     01  40000.0  12000.0  12000.0  12000.0   \n",
       "...           ...      ...    ...      ...      ...      ...      ...   \n",
       "67814  3260438625        2     01   4000.0   4000.0   4000.0   4000.0   \n",
       "67815  3260547718        2     01   6000.0   6000.0   6000.0   6000.0   \n",
       "67816  3260547909        2     01   1700.0    750.0   1700.0   3200.0   \n",
       "67817  3260601317        2     01    950.0      0.0    950.0      0.0   \n",
       "67818  3260610709        2     01   3600.0   3600.0   3600.0   3600.0   \n",
       "\n",
       "         ing_5    ing_6   ing_tri    claves       ing_men cohort  \n",
       "0      18000.0  18000.0  54590.15  P001P009  18196.716667     C5  \n",
       "1      16000.0  16000.0  47213.11      P001  15737.703333     C5  \n",
       "2      28000.0  28000.0  82622.95      P001  27540.983333     C6  \n",
       "3      10000.0  10000.0  30737.69  P001P009  10245.896667     C4  \n",
       "4      12000.0  12000.0  42295.07  P001P009  14098.356667     C5  \n",
       "...        ...      ...       ...       ...           ...    ...  \n",
       "67814   4000.0   4000.0  11803.27      P001   3934.423333     C2  \n",
       "67815   6000.0   6000.0  17704.91      P001   5901.636667     C3  \n",
       "67816   4150.0   3200.0   7190.21  P042P022   2396.736667     C1  \n",
       "67817    950.0      0.0   1394.02      P042    464.673333     C1  \n",
       "67818   3600.0   3600.0  10622.95      P001   3540.983333     C2  \n",
       "\n",
       "[67819 rows x 13 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cohorts(df):\n",
    "    \n",
    "    cohorts = ['C'+ c for c in '123456']\n",
    "    I = [(0,2400),(2400, 4800),(4800,7200),(7200,12000),(12000,24000),(24000,np.inf)]\n",
    "    #L = [(c,i) for c,i in zip(cohorts,I)]\n",
    "\n",
    "    df3 = df.copy()\n",
    "    \n",
    "    def coho(ing_men,cohorts,I):\n",
    "        for a in range(len(I)):\n",
    "            if I[a][0] < ing_men <= I[a][1]:\n",
    "                return cohorts[a]\n",
    "        return 0\n",
    "    \n",
    "    df3['cohort']=df3['ing_men'].apply(lambda x: coho(x,cohorts,I))\n",
    "    \n",
    "    return df3\n",
    "\n",
    "df3 = cohorts(df2)\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizamos cuantas personas caen en cada cohort de ingreso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def histogramacohort(df):\n",
    "    \n",
    "    # hacemos a la variables cohort categorica ordenada\n",
    "    \n",
    "    df.cohort= pd.Categorical(df.cohort, \n",
    "                      categories=['C'+ c for c in '123456'],\n",
    "                      ordered=True)\n",
    "    \n",
    "    \n",
    "    sns.catplot(x = \"cohort\",\n",
    "            data = df,\n",
    "            kind = 'count')\n",
    "    plt.show()\n",
    "\n",
    "histogramacohort(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}