ExploracЗ╞o_Conceitos Iniciais.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: center\">\n",
    "<div style=\"width: 150px; display: inline-block;\">\n",
    "    <a href=\"https://opendataday.org\">\n",
    "        <img src=\"./imagens/logo_open_data.png\" style=\"width:110px;height:110px;\">\n",
    "    </a>                                           \n",
    "</div>\n",
    "\n",
    "<div style=\"width: 150px; display: inline-block;\">\n",
    "    <a href=\"https://www.linkedin.com/company/py013/\">\n",
    "        <img src=\"./imagens/logo_py013.png\" style=\"width:100px;height:100px;\"> \n",
    "    </a>\n",
    "    \n",
    "</div>\n",
    "\n",
    "<div style=\"width: 150px; display: inline-block;\">\n",
    "    <a href=\"https://www.observatoriodasvacinas.com.br/\">\n",
    "        <img src=\"./imagens/observatorio_vacinas.jpeg\" style=\"width:100px;height:100px;\">\n",
    "    </a>\n",
    "</div>\n",
    "\n",
    "<div style=\"width: 150px; display: inline-block;\">\n",
    "    <a href=\"https://www.unisantos.br/pesquisa/laboratorios/laboratorios-do-ipeci/laboratorio-de-inteligencia-artificial/\">\n",
    "        <img src=\"./imagens/unisantos-vertical.png\" style=\"width:100px;height:130px;\">\n",
    "        <figcaption>Laboratório de IA</figcaption>\n",
    "    </a>\n",
    "</div>\n",
    "</div>\n",
    "\n",
    "# OPEND DATA DAY 2021"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importação das bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## __DataFrame das Vacinas__\n",
    "\n",
    "### Vacina: Sarampo, rubéola e caxumba - Tríplice viral - __SCR__\n",
    "## Ano: 2016, 2017 e 2018\n",
    "### Região Norte\n",
    "\n",
    "### __Descrição das Colunas__\n",
    "\n",
    "| __Coluna__         | __Descrição__                                                                               |\n",
    "|-------------------:|:------------------------------------------------------------------------------------------------|\n",
    "|COD\t             |                                                                                                 |\n",
    "|CODMUN7\t         |                                                                                                 |\n",
    "|Estado\t             | Uf                                                                                              |\n",
    "|ano                 | Ano do programa de vacinação\t                                                                   |\n",
    "|Nome                | Nome do municipio                                                                               |\n",
    "| CV_SCR1 (12 meses) | Primeira dose da Sarampo                                                                        |\n",
    "| CV_SCR2 (15 meses) | Segunda dose da sarampo                                                                         |\n",
    "| Poprural           | População rural                                                                                 |\n",
    "| PopUrb             | População urbana                                                                                |\n",
    "| RDPC               | Renda Domiciliar per capita                                                                     |\n",
    "| PEA                | PEA - 10 anos ou mais                                                                           |\n",
    "| IDHM               | IDHM                                                                                            |\n",
    "| IDHM_E             | IDHM Educação                                                                                   |\n",
    "| IDHM_L             | IDHM Longevidade                                                                                |\n",
    "| IDHM_R             | IDHM Renda                                                                                      |\n",
    "| T_ANALF18M         | Taxa de analfabetismo - 18 anos ou mais                                                         |\n",
    "| T_MED18M           | % população com 18 anos ou mais com médio completo                                              |\n",
    "| T_SUPER25M         | % população com 25 anos ou mais com superior completo                                           |\n",
    "| T_AGUA             | % da população em domicílios com água encanada                                                  |\n",
    "| T_BANAGUA          | % da população em domicílios com banheiro e água encanada                                       |\n",
    "| T_DENS             | % da população em domicílios com com densidade > 2 pessoas/dormitório                           |\n",
    "| T_LIXO             | % da população em domicílios com coleta de lixo                                                 |\n",
    "| T_LUZ              | % da população em domicílios com energia elétrica                                               |\n",
    "| AGUA_ESGOTO        | % de pessoas em domicílios com abastecimento de água e esgotamento sanitário inadequados        |\n",
    "| POPDOMCHEFMULHERES | % de população feminina de 10 anos ou mais de idade, responsáveis pelos domicílios particulares |\n",
    "| DENSDEMOG2010      | Densidade demográfica (pessoas/área)                                                            |\n",
    "| PERC_NV_7CPN       | % Nascidos Vivos de mães que realizaram 7 ou mais consultas em PN no ano de 2016                |\n",
    "| CoberturaAB        | % Cobertura de Atenção Básica em Saúde                                                          |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vacina = pd.read_excel(\n",
    "    \"./base/bancoOPENCOMPLETO.xlsx\"\n",
    "    , sheet_name = 0\n",
    "    , na_values = ['', ' ']\n",
    "    , dtype  = {'ano': np.int32, 'Estado': 'category'\n",
    "        , 'CV_SCR1 (1 ano)': np.float32, 'CV_SCR2 (15 meses)': np.float32, 'Poprural': np.float32\n",
    "        , 'PopUrb': np.float32, 'RDPC': np.float32, 'PEA': np.float32, 'PEA_A': np.float32\n",
    "        , 'IDHM': np.float32, 'IDHM_E': np.float32, 'IDHM_L': np.float32, 'IDHM_R': np.float32\n",
    "        , 'T_ANALF18M': np.float32, 'T_MED18M': np.float32,'T_SUPER25M': np.float32, 'T_AGUA': np.float32\n",
    "        , 'T_BANAGUA': np.float32, 'T_DENS': np.float32, 'T_LIXO': np.float32\n",
    "        , 'T_LUZ': np.float32,'AGUA_ESGOTO': np.float32, 'POPDOMCHEFMULHERES': np.float32, 'DENSDEMOG2010': np.float32\n",
    "        , 'PERC_NV_7CPN': np.float32,'CoberturaAB': np.float32\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>COD</th>\n",
       "      <th>CODMUN7</th>\n",
       "      <th>Estado</th>\n",
       "      <th>ano</th>\n",
       "      <th>Nome</th>\n",
       "      <th>CV_SCR1 (1 ano)</th>\n",
       "      <th>CV_SCR2 (15 meses)</th>\n",
       "      <th>Poprural</th>\n",
       "      <th>PopUrb</th>\n",
       "      <th>RDPC</th>\n",
       "      <th>...</th>\n",
       "      <th>T_AGUA</th>\n",
       "      <th>T_BANAGUA</th>\n",
       "      <th>T_DENS</th>\n",
       "      <th>T_LIXO</th>\n",
       "      <th>T_LUZ</th>\n",
       "      <th>AGUA_ESGOTO</th>\n",
       "      <th>POPDOMCHEFMULHERES</th>\n",
       "      <th>DENSDEMOG2010</th>\n",
       "      <th>PERC_NV_7CPN</th>\n",
       "      <th>CoberturaAB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>110001</td>\n",
       "      <td>1100015.0</td>\n",
       "      <td>Rondonia</td>\n",
       "      <td>2016</td>\n",
       "      <td>Alta Floresta D'Oeste</td>\n",
       "      <td>96.449997</td>\n",
       "      <td>92.080002</td>\n",
       "      <td>42.730000</td>\n",
       "      <td>57.270000</td>\n",
       "      <td>476.989990</td>\n",
       "      <td>...</td>\n",
       "      <td>93.690002</td>\n",
       "      <td>80.199997</td>\n",
       "      <td>22.58</td>\n",
       "      <td>94.050003</td>\n",
       "      <td>93.980003</td>\n",
       "      <td>9.31</td>\n",
       "      <td>26.920000</td>\n",
       "      <td>3.450000</td>\n",
       "      <td>67.180000</td>\n",
       "      <td>80.930000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>110002</td>\n",
       "      <td>1100023.0</td>\n",
       "      <td>Rondonia</td>\n",
       "      <td>2016</td>\n",
       "      <td>Ariquemes</td>\n",
       "      <td>98.919998</td>\n",
       "      <td>89.519997</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>84.699997</td>\n",
       "      <td>689.950012</td>\n",
       "      <td>...</td>\n",
       "      <td>98.540001</td>\n",
       "      <td>85.349998</td>\n",
       "      <td>27.15</td>\n",
       "      <td>96.720001</td>\n",
       "      <td>98.580002</td>\n",
       "      <td>11.40</td>\n",
       "      <td>32.630001</td>\n",
       "      <td>20.410000</td>\n",
       "      <td>72.620003</td>\n",
       "      <td>61.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>110003</td>\n",
       "      <td>1100031.0</td>\n",
       "      <td>Rondonia</td>\n",
       "      <td>2016</td>\n",
       "      <td>Cabixi</td>\n",
       "      <td>109.639999</td>\n",
       "      <td>77.110001</td>\n",
       "      <td>57.340000</td>\n",
       "      <td>42.660000</td>\n",
       "      <td>457.170013</td>\n",
       "      <td>...</td>\n",
       "      <td>95.489998</td>\n",
       "      <td>91.629997</td>\n",
       "      <td>19.93</td>\n",
       "      <td>99.139999</td>\n",
       "      <td>96.360001</td>\n",
       "      <td>3.31</td>\n",
       "      <td>16.420000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>65.750000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110004</td>\n",
       "      <td>1100049.0</td>\n",
       "      <td>Rondonia</td>\n",
       "      <td>2016</td>\n",
       "      <td>Cacoal</td>\n",
       "      <td>99.180000</td>\n",
       "      <td>82.320000</td>\n",
       "      <td>21.190001</td>\n",
       "      <td>78.809998</td>\n",
       "      <td>738.059998</td>\n",
       "      <td>...</td>\n",
       "      <td>97.959999</td>\n",
       "      <td>93.440002</td>\n",
       "      <td>20.50</td>\n",
       "      <td>98.169998</td>\n",
       "      <td>98.889999</td>\n",
       "      <td>1.85</td>\n",
       "      <td>31.730000</td>\n",
       "      <td>20.719999</td>\n",
       "      <td>74.589996</td>\n",
       "      <td>71.190002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110005</td>\n",
       "      <td>1100056.0</td>\n",
       "      <td>Rondonia</td>\n",
       "      <td>2016</td>\n",
       "      <td>Cerejeiras</td>\n",
       "      <td>99.279999</td>\n",
       "      <td>85.510002</td>\n",
       "      <td>15.330000</td>\n",
       "      <td>84.669998</td>\n",
       "      <td>577.179993</td>\n",
       "      <td>...</td>\n",
       "      <td>97.529999</td>\n",
       "      <td>86.870003</td>\n",
       "      <td>16.66</td>\n",
       "      <td>91.889999</td>\n",
       "      <td>98.849998</td>\n",
       "      <td>5.34</td>\n",
       "      <td>39.970001</td>\n",
       "      <td>6.120000</td>\n",
       "      <td>76.809998</td>\n",
       "      <td>95.910004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      COD    CODMUN7    Estado   ano                   Nome  CV_SCR1 (1 ano)  \\\n",
       "0  110001  1100015.0  Rondonia  2016  Alta Floresta D'Oeste        96.449997   \n",
       "1  110002  1100023.0  Rondonia  2016              Ariquemes        98.919998   \n",
       "2  110003  1100031.0  Rondonia  2016                 Cabixi       109.639999   \n",
       "3  110004  1100049.0  Rondonia  2016                 Cacoal        99.180000   \n",
       "4  110005  1100056.0  Rondonia  2016             Cerejeiras        99.279999   \n",
       "\n",
       "   CV_SCR2 (15 meses)   Poprural     PopUrb        RDPC  ...     T_AGUA  \\\n",
       "0           92.080002  42.730000  57.270000  476.989990  ...  93.690002   \n",
       "1           89.519997  15.300000  84.699997  689.950012  ...  98.540001   \n",
       "2           77.110001  57.340000  42.660000  457.170013  ...  95.489998   \n",
       "3           82.320000  21.190001  78.809998  738.059998  ...  97.959999   \n",
       "4           85.510002  15.330000  84.669998  577.179993  ...  97.529999   \n",
       "\n",
       "   T_BANAGUA  T_DENS     T_LIXO      T_LUZ  AGUA_ESGOTO  POPDOMCHEFMULHERES  \\\n",
       "0  80.199997   22.58  94.050003  93.980003         9.31           26.920000   \n",
       "1  85.349998   27.15  96.720001  98.580002        11.40           32.630001   \n",
       "2  91.629997   19.93  99.139999  96.360001         3.31           16.420000   \n",
       "3  93.440002   20.50  98.169998  98.889999         1.85           31.730000   \n",
       "4  86.870003   16.66  91.889999  98.849998         5.34           39.970001   \n",
       "\n",
       "   DENSDEMOG2010  PERC_NV_7CPN  CoberturaAB  \n",
       "0       3.450000     67.180000    80.930000  \n",
       "1      20.410000     72.620003    61.919998  \n",
       "2       4.800000     65.750000   100.000000  \n",
       "3      20.719999     74.589996    71.190002  \n",
       "4       6.120000     76.809998    95.910004  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vacina.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1350 entries, 0 to 1349\n",
      "Data columns (total 29 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   COD                 1350 non-null   int64   \n",
      " 1   CODMUN7             1347 non-null   float64 \n",
      " 2   UF                  1350 non-null   category\n",
      " 3   ANO                 1350 non-null   int32   \n",
      " 4   MUNICIPIO           1350 non-null   object  \n",
      " 5   CV_SCR1             1350 non-null   float32 \n",
      " 6   CV_SCR2             1350 non-null   float32 \n",
      " 7   POPRURAL            1347 non-null   float32 \n",
      " 8   POPURB              1347 non-null   float32 \n",
      " 9   RDPC                1347 non-null   float32 \n",
      " 10  PEA                 1347 non-null   float32 \n",
      " 11  PEA_A               1347 non-null   float32 \n",
      " 12  IDHM                1347 non-null   float32 \n",
      " 13  IDHM_E              1347 non-null   float32 \n",
      " 14  IDHM_L              1347 non-null   float32 \n",
      " 15  IDHM_R              1347 non-null   float32 \n",
      " 16  T_ANALF18M          1347 non-null   float32 \n",
      " 17  T_MED18M            1347 non-null   float32 \n",
      " 18  T_SUPER25M          1347 non-null   float32 \n",
      " 19  T_AGUA              1347 non-null   float32 \n",
      " 20  T_BANAGUA           1347 non-null   float32 \n",
      " 21  T_DENS              1347 non-null   float32 \n",
      " 22  T_LIXO              1347 non-null   float32 \n",
      " 23  T_LUZ               1347 non-null   float32 \n",
      " 24  AGUA_ESGOTO         1347 non-null   float32 \n",
      " 25  POPDOMCHEFMULHERES  1347 non-null   float32 \n",
      " 26  DENSDEMOG2010       1347 non-null   float32 \n",
      " 27  PERC_NV_7CPN        1348 non-null   float32 \n",
      " 28  COBERTURA_AB        1350 non-null   float32 \n",
      "dtypes: category(1), float32(24), float64(1), int32(1), int64(1), object(1)\n",
      "memory usage: 165.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_vacina.rename(columns={'Estado': 'UF', 'ano':'ANO','Nome': 'MUNICIPIO', 'CV_SCR1 (1 ano)': 'CV_SCR1'\n",
    "                          , 'CV_SCR2 (15 meses)': 'CV_SCR2', 'Poprural': 'POPRURAL', 'PopUrb': 'POPURB'\n",
    "                          , 'CoberturaAB': 'COBERTURA_AB'}, inplace=True)\n",
    "\n",
    "df_vacina.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### __Dimensões do DataFrame__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1350, 29)"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vacina.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1350"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Linhas\n",
    "df_vacina.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Colunas\n",
    "df_vacina.shape[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### __Estatística Descritiva__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>COD</th>\n",
       "      <th>CODMUN7</th>\n",
       "      <th>ANO</th>\n",
       "      <th>CV_SCR1</th>\n",
       "      <th>CV_SCR2</th>\n",
       "      <th>POPRURAL</th>\n",
       "      <th>POPURB</th>\n",
       "      <th>RDPC</th>\n",
       "      <th>PEA</th>\n",
       "      <th>PEA_A</th>\n",
       "      <th>...</th>\n",
       "      <th>T_AGUA</th>\n",
       "      <th>T_BANAGUA</th>\n",
       "      <th>T_DENS</th>\n",
       "      <th>T_LIXO</th>\n",
       "      <th>T_LUZ</th>\n",
       "      <th>AGUA_ESGOTO</th>\n",
       "      <th>POPDOMCHEFMULHERES</th>\n",
       "      <th>DENSDEMOG2010</th>\n",
       "      <th>PERC_NV_7CPN</th>\n",
       "      <th>COBERTURA_AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1350.00</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>1350.00</td>\n",
       "      <td>1350.00</td>\n",
       "      <td>1350.00</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>1347.00</td>\n",
       "      <td>1348.00</td>\n",
       "      <td>1350.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>147896.68</td>\n",
       "      <td>1478913.89</td>\n",
       "      <td>2017.00</td>\n",
       "      <td>99.89</td>\n",
       "      <td>80.86</td>\n",
       "      <td>43.28</td>\n",
       "      <td>56.72</td>\n",
       "      <td>345.09</td>\n",
       "      <td>15342.52</td>\n",
       "      <td>40.30</td>\n",
       "      <td>...</td>\n",
       "      <td>81.82</td>\n",
       "      <td>56.05</td>\n",
       "      <td>45.14</td>\n",
       "      <td>87.48</td>\n",
       "      <td>88.61</td>\n",
       "      <td>25.89</td>\n",
       "      <td>32.27</td>\n",
       "      <td>22.24</td>\n",
       "      <td>53.51</td>\n",
       "      <td>87.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20575.04</td>\n",
       "      <td>205975.96</td>\n",
       "      <td>0.82</td>\n",
       "      <td>31.30</td>\n",
       "      <td>28.44</td>\n",
       "      <td>19.65</td>\n",
       "      <td>19.65</td>\n",
       "      <td>140.97</td>\n",
       "      <td>55295.09</td>\n",
       "      <td>6.37</td>\n",
       "      <td>...</td>\n",
       "      <td>16.74</td>\n",
       "      <td>22.55</td>\n",
       "      <td>17.48</td>\n",
       "      <td>13.46</td>\n",
       "      <td>10.84</td>\n",
       "      <td>18.21</td>\n",
       "      <td>8.28</td>\n",
       "      <td>141.97</td>\n",
       "      <td>16.74</td>\n",
       "      <td>19.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>110001.00</td>\n",
       "      <td>1100015.00</td>\n",
       "      <td>2016.00</td>\n",
       "      <td>5.13</td>\n",
       "      <td>4.94</td>\n",
       "      <td>0.25</td>\n",
       "      <td>4.18</td>\n",
       "      <td>121.32</td>\n",
       "      <td>307.00</td>\n",
       "      <td>20.41</td>\n",
       "      <td>...</td>\n",
       "      <td>14.95</td>\n",
       "      <td>6.68</td>\n",
       "      <td>13.26</td>\n",
       "      <td>0.06</td>\n",
       "      <td>27.41</td>\n",
       "      <td>1.04</td>\n",
       "      <td>15.25</td>\n",
       "      <td>0.13</td>\n",
       "      <td>3.45</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>130270.00</td>\n",
       "      <td>1302702.00</td>\n",
       "      <td>2016.00</td>\n",
       "      <td>80.10</td>\n",
       "      <td>61.41</td>\n",
       "      <td>29.81</td>\n",
       "      <td>43.14</td>\n",
       "      <td>239.06</td>\n",
       "      <td>2472.00</td>\n",
       "      <td>36.07</td>\n",
       "      <td>...</td>\n",
       "      <td>76.32</td>\n",
       "      <td>37.97</td>\n",
       "      <td>30.94</td>\n",
       "      <td>83.27</td>\n",
       "      <td>83.19</td>\n",
       "      <td>9.73</td>\n",
       "      <td>26.59</td>\n",
       "      <td>1.87</td>\n",
       "      <td>41.60</td>\n",
       "      <td>78.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>150477.50</td>\n",
       "      <td>1504802.00</td>\n",
       "      <td>2017.00</td>\n",
       "      <td>96.70</td>\n",
       "      <td>78.40</td>\n",
       "      <td>44.19</td>\n",
       "      <td>55.81</td>\n",
       "      <td>320.54</td>\n",
       "      <td>5695.00</td>\n",
       "      <td>40.29</td>\n",
       "      <td>...</td>\n",
       "      <td>88.19</td>\n",
       "      <td>57.02</td>\n",
       "      <td>43.69</td>\n",
       "      <td>91.77</td>\n",
       "      <td>92.20</td>\n",
       "      <td>22.20</td>\n",
       "      <td>32.02</td>\n",
       "      <td>4.27</td>\n",
       "      <td>53.94</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>170370.00</td>\n",
       "      <td>1703701.00</td>\n",
       "      <td>2018.00</td>\n",
       "      <td>114.49</td>\n",
       "      <td>96.90</td>\n",
       "      <td>56.86</td>\n",
       "      <td>70.19</td>\n",
       "      <td>414.87</td>\n",
       "      <td>11706.00</td>\n",
       "      <td>44.18</td>\n",
       "      <td>...</td>\n",
       "      <td>93.68</td>\n",
       "      <td>74.15</td>\n",
       "      <td>58.11</td>\n",
       "      <td>96.27</td>\n",
       "      <td>96.70</td>\n",
       "      <td>38.64</td>\n",
       "      <td>37.91</td>\n",
       "      <td>10.76</td>\n",
       "      <td>65.93</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>172210.00</td>\n",
       "      <td>1722107.00</td>\n",
       "      <td>2018.00</td>\n",
       "      <td>291.23</td>\n",
       "      <td>199.05</td>\n",
       "      <td>95.82</td>\n",
       "      <td>99.75</td>\n",
       "      <td>1087.35</td>\n",
       "      <td>849048.00</td>\n",
       "      <td>57.03</td>\n",
       "      <td>...</td>\n",
       "      <td>99.66</td>\n",
       "      <td>97.28</td>\n",
       "      <td>88.64</td>\n",
       "      <td>100.00</td>\n",
       "      <td>99.97</td>\n",
       "      <td>85.36</td>\n",
       "      <td>68.46</td>\n",
       "      <td>2477.55</td>\n",
       "      <td>90.48</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             COD     CODMUN7      ANO  CV_SCR1  CV_SCR2  POPRURAL   POPURB  \\\n",
       "count    1350.00     1347.00  1350.00  1350.00  1350.00   1347.00  1347.00   \n",
       "mean   147896.68  1478913.89  2017.00    99.89    80.86     43.28    56.72   \n",
       "std     20575.04   205975.96     0.82    31.30    28.44     19.65    19.65   \n",
       "min    110001.00  1100015.00  2016.00     5.13     4.94      0.25     4.18   \n",
       "25%    130270.00  1302702.00  2016.00    80.10    61.41     29.81    43.14   \n",
       "50%    150477.50  1504802.00  2017.00    96.70    78.40     44.19    55.81   \n",
       "75%    170370.00  1703701.00  2018.00   114.49    96.90     56.86    70.19   \n",
       "max    172210.00  1722107.00  2018.00   291.23   199.05     95.82    99.75   \n",
       "\n",
       "          RDPC        PEA    PEA_A  ...   T_AGUA  T_BANAGUA   T_DENS   T_LIXO  \\\n",
       "count  1347.00    1347.00  1347.00  ...  1347.00    1347.00  1347.00  1347.00   \n",
       "mean    345.09   15342.52    40.30  ...    81.82      56.05    45.14    87.48   \n",
       "std     140.97   55295.09     6.37  ...    16.74      22.55    17.48    13.46   \n",
       "min     121.32     307.00    20.41  ...    14.95       6.68    13.26     0.06   \n",
       "25%     239.06    2472.00    36.07  ...    76.32      37.97    30.94    83.27   \n",
       "50%     320.54    5695.00    40.29  ...    88.19      57.02    43.69    91.77   \n",
       "75%     414.87   11706.00    44.18  ...    93.68      74.15    58.11    96.27   \n",
       "max    1087.35  849048.00    57.03  ...    99.66      97.28    88.64   100.00   \n",
       "\n",
       "         T_LUZ  AGUA_ESGOTO  POPDOMCHEFMULHERES  DENSDEMOG2010  PERC_NV_7CPN  \\\n",
       "count  1347.00      1347.00             1347.00        1347.00       1348.00   \n",
       "mean     88.61        25.89               32.27          22.24         53.51   \n",
       "std      10.84        18.21                8.28         141.97         16.74   \n",
       "min      27.41         1.04               15.25           0.13          3.45   \n",
       "25%      83.19         9.73               26.59           1.87         41.60   \n",
       "50%      92.20        22.20               32.02           4.27         53.94   \n",
       "75%      96.70        38.64               37.91          10.76         65.93   \n",
       "max      99.97        85.36               68.46        2477.55         90.48   \n",
       "\n",
       "       COBERTURA_AB  \n",
       "count       1350.00  \n",
       "mean          87.53  \n",
       "std           19.64  \n",
       "min            0.00  \n",
       "25%           78.15  \n",
       "50%          100.00  \n",
       "75%          100.00  \n",
       "max          100.00  \n",
       "\n",
       "[8 rows x 27 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vacina.describe().round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1350.000000\n",
       "mean       80.857712\n",
       "std        28.443947\n",
       "min         4.935065\n",
       "25%        61.407501\n",
       "50%        78.403542\n",
       "75%        96.901253\n",
       "max       199.050003\n",
       "Name: CV_SCR2, dtype: float64"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vacina['CV_SCR2'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vacina['CV_SCR1'] = df_vacina['CV_SCR1'].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## __Adicionando e Removendo Colunas__\n",
    "\n",
    "### __Range de Cobertura Vacinal__\n",
    "\n",
    "<img src=\"./imagens/range_cobertura_vacinal.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Adicionando uma nova coluna__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_vacina['GP_SCR1'] = np.where(df_vacina['CV_SCR1'] <= 50, 'Muito Baixa'\n",
    "         , np.where(df_vacina['CV_SCR1'] <= 95, 'Baixa'\n",
    "                    , np.where(df_vacina['CV_SCR1'] <= 120, 'Adequada', 'Elevada'\n",
    "                              )\n",
    "                   )\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">CV_SCR1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>amin</th>\n",
       "      <th>amax</th>\n",
       "      <th>count_nonzero</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GP_SCR1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Adequada</th>\n",
       "      <td>95.040001</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>437.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Baixa</th>\n",
       "      <td>50.369999</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>585.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elevada</th>\n",
       "      <td>120.114944</td>\n",
       "      <td>291.230011</td>\n",
       "      <td>281.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Muito Baixa</th>\n",
       "      <td>5.130000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                CV_SCR1                          \n",
       "                   amin        amax count_nonzero\n",
       "GP_SCR1                                          \n",
       "Adequada      95.040001  120.000000         437.0\n",
       "Baixa         50.369999   95.000000         585.0\n",
       "Elevada      120.114944  291.230011         281.0\n",
       "Muito Baixa    5.130000   50.000000          47.0"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Forma de Verificar o Grupo Criado\n",
    "df_vacina.groupby(['GP_SCR1']).agg({'CV_SCR1': [np.min, np.max, np.count_nonzero]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1350 entries, 0 to 1349\n",
      "Data columns (total 30 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   COD                 1350 non-null   int64   \n",
      " 1   CODMUN7             1347 non-null   float64 \n",
      " 2   UF                  1350 non-null   category\n",
      " 3   ANO                 1350 non-null   int32   \n",
      " 4   MUNICIPIO           1350 non-null   object  \n",
      " 5   CV_SCR1             1350 non-null   float32 \n",
      " 6   CV_SCR2             1350 non-null   float32 \n",
      " 7   POPRURAL            1347 non-null   float32 \n",
      " 8   POPURB              1347 non-null   float32 \n",
      " 9   RDPC                1347 non-null   float32 \n",
      " 10  PEA                 1347 non-null   float32 \n",
      " 11  PEA_A               1347 non-null   float32 \n",
      " 12  IDHM                1347 non-null   float32 \n",
      " 13  IDHM_E              1347 non-null   float32 \n",
      " 14  IDHM_L              1347 non-null   float32 \n",
      " 15  IDHM_R              1347 non-null   float32 \n",
      " 16  T_ANALF18M          1347 non-null   float32 \n",
      " 17  T_MED18M            1347 non-null   float32 \n",
      " 18  T_SUPER25M          1347 non-null   float32 \n",
      " 19  T_AGUA              1347 non-null   float32 \n",
      " 20  T_BANAGUA           1347 non-null   float32 \n",
      " 21  T_DENS              1347 non-null   float32 \n",
      " 22  T_LIXO              1347 non-null   float32 \n",
      " 23  T_LUZ               1347 non-null   float32 \n",
      " 24  AGUA_ESGOTO         1347 non-null   float32 \n",
      " 25  POPDOMCHEFMULHERES  1347 non-null   float32 \n",
      " 26  DENSDEMOG2010       1347 non-null   float32 \n",
      " 27  PERC_NV_7CPN        1348 non-null   float32 \n",
      " 28  COBERTURA_AB        1350 non-null   float32 \n",
      " 29  GP_SCR1             1350 non-null   object  \n",
      "dtypes: category(1), float32(24), float64(1), int32(1), int64(1), object(2)\n",
      "memory usage: 175.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_vacina.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1350 entries, 0 to 1349\n",
      "Data columns (total 30 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   COD                 1350 non-null   int64   \n",
      " 1   CODMUN7             1347 non-null   float64 \n",
      " 2   UF                  1350 non-null   category\n",
      " 3   ANO                 1350 non-null   int32   \n",
      " 4   MUNICIPIO           1350 non-null   object  \n",
      " 5   CV_SCR1             1350 non-null   float32 \n",
      " 6   CV_SCR2             1350 non-null   float32 \n",
      " 7   POPRURAL            1347 non-null   float32 \n",
      " 8   POPURB              1347 non-null   float32 \n",
      " 9   RDPC                1347 non-null   float32 \n",
      " 10  PEA                 1347 non-null   float32 \n",
      " 11  PEA_A               1347 non-null   float32 \n",
      " 12  IDHM                1347 non-null   float32 \n",
      " 13  IDHM_E              1347 non-null   float32 \n",
      " 14  IDHM_L              1347 non-null   float32 \n",
      " 15  IDHM_R              1347 non-null   float32 \n",
      " 16  T_ANALF18M          1347 non-null   float32 \n",
      " 17  T_MED18M            1347 non-null   float32 \n",
      " 18  T_SUPER25M          1347 non-null   float32 \n",
      " 19  T_AGUA              1347 non-null   float32 \n",
      " 20  T_BANAGUA           1347 non-null   float32 \n",
      " 21  T_DENS              1347 non-null   float32 \n",
      " 22  T_LIXO              1347 non-null   float32 \n",
      " 23  T_LUZ               1347 non-null   float32 \n",
      " 24  AGUA_ESGOTO         1347 non-null   float32 \n",
      " 25  POPDOMCHEFMULHERES  1347 non-null   float32 \n",
      " 26  DENSDEMOG2010       1347 non-null   float32 \n",
      " 27  PERC_NV_7CPN        1348 non-null   float32 \n",
      " 28  COBERTURA_AB        1350 non-null   float32 \n",
      " 29  GP_SCR1             1350 non-null   category\n",
      "dtypes: category(2), float32(24), float64(1), int32(1), int64(1), object(1)\n",
      "memory usage: 166.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_vacina['GP_SCR1'] = df_vacina['GP_SCR1'].astype('category') \n",
    "df_vacina.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Removendo__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df_vacina['GP_SCR1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1350 entries, 0 to 1349\n",
      "Data columns (total 29 columns):\n",
      " #   Column              Non-Null Count  Dtype   \n",
      "---  ------              --------------  -----   \n",
      " 0   COD                 1350 non-null   int64   \n",
      " 1   CODMUN7             1347 non-null   float64 \n",
      " 2   UF                  1350 non-null   category\n",
      " 3   ANO                 1350 non-null   int32   \n",
      " 4   MUNICIPIO           1350 non-null   object  \n",
      " 5   CV_SCR1             1350 non-null   float32 \n",
      " 6   CV_SCR2             1350 non-null   float32 \n",
      " 7   POPRURAL            1347 non-null   float32 \n",
      " 8   POPURB              1347 non-null   float32 \n",
      " 9   RDPC                1347 non-null   float32 \n",
      " 10  PEA                 1347 non-null   float32 \n",
      " 11  PEA_A               1347 non-null   float32 \n",
      " 12  IDHM                1347 non-null   float32 \n",
      " 13  IDHM_E              1347 non-null   float32 \n",
      " 14  IDHM_L              1347 non-null   float32 \n",
      " 15  IDHM_R              1347 non-null   float32 \n",
      " 16  T_ANALF18M          1347 non-null   float32 \n",
      " 17  T_MED18M            1347 non-null   float32 \n",
      " 18  T_SUPER25M          1347 non-null   float32 \n",
      " 19  T_AGUA              1347 non-null   float32 \n",
      " 20  T_BANAGUA           1347 non-null   float32 \n",
      " 21  T_DENS              1347 non-null   float32 \n",
      " 22  T_LIXO              1347 non-null   float32 \n",
      " 23  T_LUZ               1347 non-null   float32 \n",
      " 24  AGUA_ESGOTO         1347 non-null   float32 \n",
      " 25  POPDOMCHEFMULHERES  1347 non-null   float32 \n",
      " 26  DENSDEMOG2010       1347 non-null   float32 \n",
      " 27  PERC_NV_7CPN        1348 non-null   float32 \n",
      " 28  COBERTURA_AB        1350 non-null   float32 \n",
      "dtypes: category(1), float32(24), float64(1), int32(1), int64(1), object(1)\n",
      "memory usage: 165.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df_vacina.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## __Agrupamento__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>CV_SCR1</th>\n",
       "      <th>CV_SCR2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UF</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amapa</th>\n",
       "      <td>87.787155</td>\n",
       "      <td>72.669998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Para</th>\n",
       "      <td>84.338669</td>\n",
       "      <td>66.787209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rondonia</th>\n",
       "      <td>114.199997</td>\n",
       "      <td>93.549149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Roraima</th>\n",
       "      <td>90.458015</td>\n",
       "      <td>85.559998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tocantins</th>\n",
       "      <td>104.629997</td>\n",
       "      <td>91.230003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acre</th>\n",
       "      <td>92.329224</td>\n",
       "      <td>67.697891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amazonas</th>\n",
       "      <td>96.013260</td>\n",
       "      <td>72.742767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              CV_SCR1    CV_SCR2\n",
       "UF                              \n",
       "Amapa       87.787155  72.669998\n",
       "Para        84.338669  66.787209\n",
       "Rondonia   114.199997  93.549149\n",
       "Roraima     90.458015  85.559998\n",
       "Tocantins  104.629997  91.230003\n",
       "acre        92.329224  67.697891\n",
       "amazonas    96.013260  72.742767"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vacina.groupby(['UF']).agg({'CV_SCR1': np.median, 'CV_SCR2': np.median})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "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>UF</th>\n",
       "      <th>ANO</th>\n",
       "      <th>CV_SCR1</th>\n",
       "      <th>CV_SCR2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Amapa</td>\n",
       "      <td>2016</td>\n",
       "      <td>75.880005</td>\n",
       "      <td>74.860001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Amapa</td>\n",
       "      <td>2017</td>\n",
       "      <td>86.684303</td>\n",
       "      <td>78.726250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amapa</td>\n",
       "      <td>2018</td>\n",
       "      <td>92.430000</td>\n",
       "      <td>66.574997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Para</td>\n",
       "      <td>2016</td>\n",
       "      <td>77.300003</td>\n",
       "      <td>68.114998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Para</td>\n",
       "      <td>2017</td>\n",
       "      <td>83.663177</td>\n",
       "      <td>66.958321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      UF   ANO    CV_SCR1    CV_SCR2\n",
       "0  Amapa  2016  75.880005  74.860001\n",
       "1  Amapa  2017  86.684303  78.726250\n",
       "2  Amapa  2018  92.430000  66.574997\n",
       "3   Para  2016  77.300003  68.114998\n",
       "4   Para  2017  83.663177  66.958321"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uf = df_vacina.groupby(['UF', 'ANO']).agg({'CV_SCR1': np.median, 'CV_SCR2': np.median}).reset_index()\n",
    "df_uf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pivot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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>UF</th>\n",
       "      <th>Amapa</th>\n",
       "      <th>Para</th>\n",
       "      <th>Rondonia</th>\n",
       "      <th>Roraima</th>\n",
       "      <th>Tocantins</th>\n",
       "      <th>acre</th>\n",
       "      <th>amazonas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>74.860001</td>\n",
       "      <td>68.114998</td>\n",
       "      <td>97.854996</td>\n",
       "      <td>84.580002</td>\n",
       "      <td>102.730003</td>\n",
       "      <td>61.849998</td>\n",
       "      <td>70.455002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>78.726250</td>\n",
       "      <td>66.958321</td>\n",
       "      <td>91.331543</td>\n",
       "      <td>91.714287</td>\n",
       "      <td>92.592590</td>\n",
       "      <td>68.113388</td>\n",
       "      <td>63.170052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>66.574997</td>\n",
       "      <td>65.354996</td>\n",
       "      <td>82.914993</td>\n",
       "      <td>85.430000</td>\n",
       "      <td>84.180000</td>\n",
       "      <td>69.095001</td>\n",
       "      <td>78.244995</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "UF        Amapa       Para   Rondonia    Roraima   Tocantins       acre  \\\n",
       "ANO                                                                       \n",
       "2016  74.860001  68.114998  97.854996  84.580002  102.730003  61.849998   \n",
       "2017  78.726250  66.958321  91.331543  91.714287   92.592590  68.113388   \n",
       "2018  66.574997  65.354996  82.914993  85.430000   84.180000  69.095001   \n",
       "\n",
       "UF     amazonas  \n",
       "ANO              \n",
       "2016  70.455002  \n",
       "2017  63.170052  \n",
       "2018  78.244995  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_uf.pivot_table(\n",
    "    values = 'CV_SCR2' #Valores da tabela\n",
    "    , index = 'ANO' #Linhas da tabela\n",
    "    , columns = 'UF' #Colunas da tabela\n",
    "    , aggfunc = np.mean\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## __Filtrando o DataFrame__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Filtro__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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>COD</th>\n",
       "      <th>CODMUN7</th>\n",
       "      <th>UF</th>\n",
       "      <th>ANO</th>\n",
       "      <th>MUNICIPIO</th>\n",
       "      <th>CV_SCR1</th>\n",
       "      <th>CV_SCR2</th>\n",
       "      <th>POPRURAL</th>\n",
       "      <th>POPURB</th>\n",
       "      <th>RDPC</th>\n",
       "      <th>...</th>\n",
       "      <th>T_AGUA</th>\n",
       "      <th>T_BANAGUA</th>\n",
       "      <th>T_DENS</th>\n",
       "      <th>T_LIXO</th>\n",
       "      <th>T_LUZ</th>\n",
       "      <th>AGUA_ESGOTO</th>\n",
       "      <th>POPDOMCHEFMULHERES</th>\n",
       "      <th>DENSDEMOG2010</th>\n",
       "      <th>PERC_NV_7CPN</th>\n",
       "      <th>COBERTURA_AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>150010</td>\n",
       "      <td>1500107.0</td>\n",
       "      <td>Para</td>\n",
       "      <td>2016</td>\n",
       "      <td>Abaetetuba</td>\n",
       "      <td>74.400002</td>\n",
       "      <td>74.360001</td>\n",
       "      <td>41.180000</td>\n",
       "      <td>58.820000</td>\n",
       "      <td>293.010010</td>\n",
       "      <td>...</td>\n",
       "      <td>68.860001</td>\n",
       "      <td>44.709999</td>\n",
       "      <td>62.090000</td>\n",
       "      <td>97.860001</td>\n",
       "      <td>91.800003</td>\n",
       "      <td>39.799999</td>\n",
       "      <td>29.950001</td>\n",
       "      <td>87.610001</td>\n",
       "      <td>53.400002</td>\n",
       "      <td>46.970001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>150013</td>\n",
       "      <td>1500131.0</td>\n",
       "      <td>Para</td>\n",
       "      <td>2016</td>\n",
       "      <td>Abel Figueiredo</td>\n",
       "      <td>78.570000</td>\n",
       "      <td>61.430000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>390.119995</td>\n",
       "      <td>...</td>\n",
       "      <td>91.760002</td>\n",
       "      <td>63.790001</td>\n",
       "      <td>38.230000</td>\n",
       "      <td>91.230003</td>\n",
       "      <td>95.970001</td>\n",
       "      <td>26.330000</td>\n",
       "      <td>33.740002</td>\n",
       "      <td>11.040000</td>\n",
       "      <td>39.320000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>150020</td>\n",
       "      <td>1500206.0</td>\n",
       "      <td>Para</td>\n",
       "      <td>2016</td>\n",
       "      <td>Acar</td>\n",
       "      <td>63.970001</td>\n",
       "      <td>55.020000</td>\n",
       "      <td>76.440002</td>\n",
       "      <td>23.559999</td>\n",
       "      <td>199.339996</td>\n",
       "      <td>...</td>\n",
       "      <td>64.010002</td>\n",
       "      <td>20.250000</td>\n",
       "      <td>63.180000</td>\n",
       "      <td>78.480003</td>\n",
       "      <td>73.660004</td>\n",
       "      <td>55.709999</td>\n",
       "      <td>29.360001</td>\n",
       "      <td>12.330000</td>\n",
       "      <td>31.330000</td>\n",
       "      <td>44.669998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>150030</td>\n",
       "      <td>1500305.0</td>\n",
       "      <td>Para</td>\n",
       "      <td>2016</td>\n",
       "      <td>Afu</td>\n",
       "      <td>36.020000</td>\n",
       "      <td>25.850000</td>\n",
       "      <td>72.949997</td>\n",
       "      <td>27.049999</td>\n",
       "      <td>163.979996</td>\n",
       "      <td>...</td>\n",
       "      <td>26.559999</td>\n",
       "      <td>14.790000</td>\n",
       "      <td>78.760002</td>\n",
       "      <td>96.900002</td>\n",
       "      <td>70.230003</td>\n",
       "      <td>76.980003</td>\n",
       "      <td>26.559999</td>\n",
       "      <td>4.190000</td>\n",
       "      <td>17.670000</td>\n",
       "      <td>18.450001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>150034</td>\n",
       "      <td>1500347.0</td>\n",
       "      <td>Para</td>\n",
       "      <td>2016</td>\n",
       "      <td>µgua Azul do Norte</td>\n",
       "      <td>65.150002</td>\n",
       "      <td>55.049999</td>\n",
       "      <td>80.540001</td>\n",
       "      <td>19.459999</td>\n",
       "      <td>266.019989</td>\n",
       "      <td>...</td>\n",
       "      <td>94.370003</td>\n",
       "      <td>72.930000</td>\n",
       "      <td>38.540001</td>\n",
       "      <td>93.720001</td>\n",
       "      <td>94.230003</td>\n",
       "      <td>19.469999</td>\n",
       "      <td>16.129999</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>54.599998</td>\n",
       "      <td>65.580002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        COD    CODMUN7    UF   ANO           MUNICIPIO    CV_SCR1    CV_SCR2  \\\n",
       "151  150010  1500107.0  Para  2016          Abaetetuba  74.400002  74.360001   \n",
       "152  150013  1500131.0  Para  2016     Abel Figueiredo  78.570000  61.430000   \n",
       "153  150020  1500206.0  Para  2016               Acar   63.970001  55.020000   \n",
       "154  150030  1500305.0  Para  2016                Afu   36.020000  25.850000   \n",
       "155  150034  1500347.0  Para  2016  µgua Azul do Norte  65.150002  55.049999   \n",
       "\n",
       "      POPRURAL     POPURB        RDPC  ...     T_AGUA  T_BANAGUA     T_DENS  \\\n",
       "151  41.180000  58.820000  293.010010  ...  68.860001  44.709999  62.090000   \n",
       "152  11.000000  89.000000  390.119995  ...  91.760002  63.790001  38.230000   \n",
       "153  76.440002  23.559999  199.339996  ...  64.010002  20.250000  63.180000   \n",
       "154  72.949997  27.049999  163.979996  ...  26.559999  14.790000  78.760002   \n",
       "155  80.540001  19.459999  266.019989  ...  94.370003  72.930000  38.540001   \n",
       "\n",
       "        T_LIXO      T_LUZ  AGUA_ESGOTO  POPDOMCHEFMULHERES  DENSDEMOG2010  \\\n",
       "151  97.860001  91.800003    39.799999           29.950001      87.610001   \n",
       "152  91.230003  95.970001    26.330000           33.740002      11.040000   \n",
       "153  78.480003  73.660004    55.709999           29.360001      12.330000   \n",
       "154  96.900002  70.230003    76.980003           26.559999       4.190000   \n",
       "155  93.720001  94.230003    19.469999           16.129999       3.520000   \n",
       "\n",
       "     PERC_NV_7CPN  COBERTURA_AB  \n",
       "151     53.400002     46.970001  \n",
       "152     39.320000    100.000000  \n",
       "153     31.330000     44.669998  \n",
       "154     17.670000     18.450001  \n",
       "155     54.599998     65.580002  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vacina[ (df_vacina['UF'] == 'Para') & (df_vacina['ANO'] == 2016) ].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Travando Colunas__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>ANO</th>\n",
       "      <th>UF</th>\n",
       "      <th>MUNICIPIO</th>\n",
       "      <th>CV_SCR1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>Rondonia</td>\n",
       "      <td>Alta Floresta D'Oeste</td>\n",
       "      <td>96.449997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>Rondonia</td>\n",
       "      <td>Ariquemes</td>\n",
       "      <td>98.919998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>Rondonia</td>\n",
       "      <td>Cabixi</td>\n",
       "      <td>109.639999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>Rondonia</td>\n",
       "      <td>Cacoal</td>\n",
       "      <td>99.180000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>Rondonia</td>\n",
       "      <td>Cerejeiras</td>\n",
       "      <td>99.279999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ANO        UF              MUNICIPIO     CV_SCR1\n",
       "0  2016  Rondonia  Alta Floresta D'Oeste   96.449997\n",
       "1  2016  Rondonia              Ariquemes   98.919998\n",
       "2  2016  Rondonia                 Cabixi  109.639999\n",
       "3  2016  Rondonia                 Cacoal   99.180000\n",
       "4  2016  Rondonia             Cerejeiras   99.279999"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vacina.loc[:,['ANO', 'UF', 'MUNICIPIO', 'CV_SCR1']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## __Régua de Correlação__\n",
    "\n",
    "|Range de Valores | Descrição|\n",
    "|---------------:|:--------:|\n",
    "|__-1.0 ~ -0.5__ |Correlação forte inversa. Quando um sobe o outro desce. |\n",
    "|__-0.5 ~ 0.0__ |Correlação fraca inversa. Quando um sobe o outro desce mas não tem uma alta interferencia.|\n",
    "|__0.0 ~ 0.5__ |Correlação fraca direta. Quando um sobe o outro sobre mas não com uma alta.|\n",
    "|__0.5 ~ 1.0__ |Correlação forte direta. Quando um sobre o outro sobre também!|\n",
    "\n",
    "__Logo .... quanto mais próximo do 1, temos uma correlação forte positiva, quanto mais próximo do -1, temos uma correlação forte negativa.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __HeatMap__ --> https://seaborn.pydata.org/examples/many_pairwise_correlations.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Gráfico de Correlação das Variavéis')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = df_vacina.corr().round(4)\n",
    "corr = corr[corr > 0]\n",
    "\n",
    "mask = np.triu(np.ones_like(corr, dtype=bool))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(11, 9))\n",
    "\n",
    "ax = sns.heatmap(corr, mask=mask, cmap='Blues',\n",
    "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
    "\n",
    "ax.set_title(\"Gráfico de Correlação das Variavéis\", fontdict={'fontsize': 16, 'color': 'Blue'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "\n",
    "## __Distribuição dos Valores__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __Boxplot__ -> https://seaborn.pydata.org/examples/grouped_boxplot.html\n",
    "* __Hist__ -> https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.hist.html\n",
    "* __KDE__ -> https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.plot.kde.html\n",
    "* __Subplot__ -> https://matplotlib.org/devdocs/gallery/subplots_axes_and_figures/subplots_demo.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Kernel Density Estimate')"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Cria uma figura com uma linha e três colunas do tamanho de 17,6\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(nrows = 1, ncols = 3, figsize=(17, 6))\n",
    "\n",
    "# Adiciona um titulo a figura\n",
    "fig.suptitle('Distribuição de Valores - PRIMEIRA DOSE DO SARAMPO', fontsize=16)\n",
    "\n",
    "#ax1 -> Boxplot\n",
    "ax = sns.boxplot(data = df_vacina, x = 'CV_SCR1', color = 'red', ax=ax1)\n",
    "ax.set_title(\"BoxPlot\")\n",
    "ax.set_xlabel(\"\")\n",
    "\n",
    "#ax2 -> Histogram\n",
    "ax2 = df_vacina['CV_SCR1'].hist(bins=6, alpha=0.4, ax=ax2)\n",
    "ax2.set_title(\"Histogram\")\n",
    "\n",
    "#ax3 -> KDE - Estimativa de Densidade do Kernel\n",
    "ax = df_vacina['CV_SCR1'].plot.kde(ax=ax3)\n",
    "ax.set_title(\"Kernel Density Estimate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Corrigindo Strings Similares__\n",
    "\n",
    "<br>__Em teoria da informação , linguística e ciência da computação , a distância de Levenshtein é uma métrica de string para medir a diferença entre duas sequências. Informalmente, a distância Levenshtein entre duas palavras é o número mínimo de edições de um único caractere (inserções, exclusões ou substituições) necessárias para transformar uma palavra na outra. É nomeado após o matemático soviético Vladimir Levenshtein , que considerou esta distância em 1965.__\n",
    "\n",
    "> O índice de similaridade vai de 0 a 100\n",
    "\n",
    "* [Função de verificar caracteres](https://stackoverflow.com/questions/60287306/how-to-check-if-string-contains-chars-which-are-not-in-a-list)\n",
    "* [Wikipedia Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: fuzzywuzzy in c:\\users\\lmbr4\\anaconda3\\lib\\site-packages (0.18.0)\n"
     ]
    }
   ],
   "source": [
    "# https://www.microsoft.com/pt-br/download/details.aspx?id=48145\n",
    "# !pip install fuzzywuzzy\n",
    "# !pip install python-Levenshtein"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fuzzywuzzy import fuzz\n",
    "from fuzzywuzzy import process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzz.ratio('Mazagão', 'MazagÆo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzz.ratio('Mazagão', '######MazagÆo########')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzz.partial_ratio('Mazagão', '######MazagÆo########')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuzz.partial_token_sort_ratio('Mazagão', '######MazagÆo########')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_cidades = list(df_vacina['MUNICIPIO'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Alta Floresta D'Oeste\",\n",
       " 'Ariquemes',\n",
       " 'Cabixi',\n",
       " 'Cacoal',\n",
       " 'Cerejeiras',\n",
       " 'Colorado do Oeste',\n",
       " 'Corumbiara',\n",
       " 'Costa Marques',\n",
       " \"EspigÆo D'Oeste\",\n",
       " 'Guajar\\xa00Mirim',\n",
       " 'Jaru',\n",
       " 'Ji0Paran\\xa0',\n",
       " \"Machadinho D'Oeste\",\n",
       " \"Nova Brasilƒndia D'Oeste\",\n",
       " 'Ouro Preto do Oeste',\n",
       " 'Pimenta Bueno',\n",
       " 'Porto Velho',\n",
       " 'Presidente M‚dici',\n",
       " 'Rio Crespo',\n",
       " 'Rolim de Moura',\n",
       " \"Santa Luzia D'Oeste\",\n",
       " 'Vilhena',\n",
       " 'SÆo Miguel do Guapor‚',\n",
       " 'Nova Mamor‚',\n",
       " \"Alvorada D'Oeste\",\n",
       " 'Alto Alegre dos Parecis',\n",
       " 'Alto Para¡so',\n",
       " 'Buritis',\n",
       " 'Novo Horizonte do Oeste',\n",
       " 'Cacaulƒndia',\n",
       " 'Campo Novo de Rond“nia',\n",
       " 'Candeias do Jamari',\n",
       " 'Castanheiras',\n",
       " 'Chupinguaia',\n",
       " 'Cujubim',\n",
       " 'Governador Jorge Teixeira',\n",
       " 'ItapuÆ do Oeste',\n",
       " 'Ministro Andreazza',\n",
       " 'Mirante da Serra',\n",
       " 'Monte Negro',\n",
       " 'Nova UniÆo',\n",
       " 'Parecis',\n",
       " 'Pimenteiras do Oeste',\n",
       " 'Primavera de Rond“nia',\n",
       " \"SÆo Felipe D'Oeste\",\n",
       " 'SÆo Francisco do Guapor‚',\n",
       " 'Seringueiras',\n",
       " 'Teixeir¢polis',\n",
       " 'Theobroma',\n",
       " 'Urup\\xa0',\n",
       " 'Vale do Anari',\n",
       " 'Vale do Para¡so',\n",
       " 'Acrelƒndia',\n",
       " 'Assis Brasil',\n",
       " 'Brasil‚ia',\n",
       " 'Bujari',\n",
       " 'Capixaba',\n",
       " 'Cruzeiro do Sul',\n",
       " 'Epitaciolƒndia',\n",
       " 'Feij¢',\n",
       " 'JordÆo',\n",
       " 'Mƒncio Lima',\n",
       " 'Manoel Urbano',\n",
       " 'Marechal Thaumaturgo',\n",
       " 'Pl\\xa0cido de Castro',\n",
       " 'Porto Walter',\n",
       " 'Rio Branco',\n",
       " 'Rodrigues Alves',\n",
       " 'Santa Rosa do Purus',\n",
       " 'Senador Guiomard',\n",
       " 'Sena Madureira',\n",
       " 'Tarauac\\xa0',\n",
       " 'Xapuri',\n",
       " 'Porto Acre',\n",
       " 'AlvarÆes',\n",
       " 'Amatur\\xa0',\n",
       " 'AnamÆ',\n",
       " 'Anori',\n",
       " 'Apu¡',\n",
       " 'Atalaia do Norte',\n",
       " 'Autazes',\n",
       " 'Barcelos',\n",
       " 'Barreirinha',\n",
       " 'Benjamin Constant',\n",
       " 'Beruri',\n",
       " 'Boa Vista do Ramos',\n",
       " 'Boca do Acre',\n",
       " 'Borba',\n",
       " 'Caapiranga',\n",
       " 'Canutama',\n",
       " 'Carauari',\n",
       " 'Careiro',\n",
       " 'Careiro da V\\xa0rzea',\n",
       " 'Coari',\n",
       " 'Codaj\\xa0s',\n",
       " 'Eirunep‚',\n",
       " 'Envira',\n",
       " 'Fonte Boa',\n",
       " 'Guajar\\xa0',\n",
       " 'Humait\\xa0',\n",
       " 'Ipixuna',\n",
       " 'Iranduba',\n",
       " 'Itacoatiara',\n",
       " 'Itamarati',\n",
       " 'Itapiranga',\n",
       " 'Japur\\xa0',\n",
       " 'Juru\\xa0',\n",
       " 'Juta¡',\n",
       " 'L\\xa0brea',\n",
       " 'Manacapuru',\n",
       " 'Manaquiri',\n",
       " 'Manaus',\n",
       " 'Manicor‚',\n",
       " 'MaraÆ',\n",
       " 'Mau‚s',\n",
       " 'Nhamund\\xa0',\n",
       " 'Nova Olinda do Norte',\n",
       " 'Novo AirÆo',\n",
       " 'Novo AripuanÆ',\n",
       " 'Parintins',\n",
       " 'Pauini',\n",
       " 'Presidente Figueiredo',\n",
       " 'Rio Preto da Eva',\n",
       " 'Santa Isabel do Rio Negro',\n",
       " 'Santo Ant“nio do I‡\\xa0',\n",
       " 'SÆo Gabriel da Cachoeira',\n",
       " 'SÆo Paulo de Oliven‡a',\n",
       " 'SÆo SebastiÆo do UatumÆ',\n",
       " 'Silves',\n",
       " 'Tabatinga',\n",
       " 'Tapau\\xa0',\n",
       " 'Tef‚',\n",
       " 'Tonantins',\n",
       " 'Uarini',\n",
       " 'Urucar\\xa0',\n",
       " 'Urucurituba',\n",
       " 'Amajari',\n",
       " 'Alto Alegre',\n",
       " 'Boa Vista',\n",
       " 'Bonfim',\n",
       " 'Cant\\xa0',\n",
       " 'Caracara¡',\n",
       " 'Caroebe',\n",
       " 'Iracema',\n",
       " 'Mucaja¡',\n",
       " 'Normandia',\n",
       " 'Pacaraima',\n",
       " 'Rorain¢polis',\n",
       " 'SÆo JoÆo da Baliza',\n",
       " 'SÆo Luiz',\n",
       " 'UiramutÆ',\n",
       " 'Abaetetuba',\n",
       " 'Abel Figueiredo',\n",
       " 'Acar\\xa0',\n",
       " 'Afu\\xa0',\n",
       " 'µgua Azul do Norte',\n",
       " 'Alenquer',\n",
       " 'Almeirim',\n",
       " 'Altamira',\n",
       " 'Anaj\\xa0s',\n",
       " 'Ananindeua',\n",
       " 'Anapu',\n",
       " 'Augusto Corrˆa',\n",
       " 'Aurora do Par\\xa0',\n",
       " 'Aveiro',\n",
       " 'Bagre',\n",
       " 'BaiÆo',\n",
       " 'Bannach',\n",
       " 'Barcarena',\n",
       " 'Bel‚m',\n",
       " 'Belterra',\n",
       " 'Benevides',\n",
       " 'Bom Jesus do Tocantins',\n",
       " 'Bonito',\n",
       " 'Bragan‡a',\n",
       " 'Brasil Novo',\n",
       " 'Brejo Grande do Araguaia',\n",
       " 'Breu Branco',\n",
       " 'Breves',\n",
       " 'Bujaru',\n",
       " 'Cachoeira do Piri\\xa0',\n",
       " 'Cachoeira do Arari',\n",
       " 'Camet\\xa0',\n",
       " 'CanaÆ dos Caraj\\xa0s',\n",
       " 'Capanema',\n",
       " 'CapitÆo Po‡o',\n",
       " 'Castanhal',\n",
       " 'Chaves',\n",
       " 'Colares',\n",
       " 'Concei‡Æo do Araguaia',\n",
       " 'Conc¢rdia do Par\\xa0',\n",
       " 'Cumaru do Norte',\n",
       " 'Curion¢polis',\n",
       " 'Curralinho',\n",
       " 'Curu\\xa0',\n",
       " 'Curu‡\\xa0',\n",
       " 'Dom Eliseu',\n",
       " 'Eldorado do Caraj\\xa0s',\n",
       " 'Faro',\n",
       " 'Floresta do Araguaia',\n",
       " 'GarrafÆo do Norte',\n",
       " 'Goian‚sia do Par\\xa0',\n",
       " 'Gurup\\xa0',\n",
       " 'Igarap‚0A‡u',\n",
       " 'Igarap‚0Miri',\n",
       " 'Inhangapi',\n",
       " 'Ipixuna do Par\\xa0',\n",
       " 'Irituia',\n",
       " 'Itaituba',\n",
       " 'Itupiranga',\n",
       " 'Jacareacanga',\n",
       " 'Jacund\\xa0',\n",
       " 'Juruti',\n",
       " 'Limoeiro do Ajuru',\n",
       " 'MÆe do Rio',\n",
       " 'MagalhÆes Barata',\n",
       " 'Marab\\xa0',\n",
       " 'MaracanÆ',\n",
       " 'Marapanim',\n",
       " 'Marituba',\n",
       " 'Medicilƒndia',\n",
       " 'Melga‡o',\n",
       " 'Mocajuba',\n",
       " 'Moju',\n",
       " 'Moju¡ dos Campos',\n",
       " 'Monte Alegre',\n",
       " 'Muan\\xa0',\n",
       " 'Nova Esperan‡a do Piri\\xa0',\n",
       " 'Nova Ipixuna',\n",
       " 'Nova Timboteua',\n",
       " 'Novo Progresso',\n",
       " 'Novo Repartimento',\n",
       " 'àbidos',\n",
       " 'Oeiras do Par\\xa0',\n",
       " 'Oriximin\\xa0',\n",
       " 'Our‚m',\n",
       " 'Ourilƒndia do Norte',\n",
       " 'Pacaj\\xa0',\n",
       " 'Palestina do Par\\xa0',\n",
       " 'Paragominas',\n",
       " 'Parauapebas',\n",
       " \"Pau D'Arco\",\n",
       " 'Peixe0Boi',\n",
       " 'Pi‡arra',\n",
       " 'Placas',\n",
       " 'Ponta de Pedras',\n",
       " 'Portel',\n",
       " 'Porto de Moz',\n",
       " 'Prainha',\n",
       " 'Primavera',\n",
       " 'Quatipuru',\n",
       " 'Reden‡Æo',\n",
       " 'Rio Maria',\n",
       " 'Rondon do Par\\xa0',\n",
       " 'Rur¢polis',\n",
       " 'Salin¢polis',\n",
       " 'Salvaterra',\n",
       " 'Santa B\\xa0rbara do Par\\xa0',\n",
       " 'Santa Cruz do Arari',\n",
       " 'Santa Izabel do Par\\xa0',\n",
       " 'Santa Luzia do Par\\xa0',\n",
       " 'Santa Maria das Barreiras',\n",
       " 'Santa Maria do Par\\xa0',\n",
       " 'Santana do Araguaia',\n",
       " 'Santar‚m',\n",
       " 'Santar‚m Novo',\n",
       " 'Santo Ant“nio do Tau\\xa0',\n",
       " 'SÆo Caetano de Odivelas',\n",
       " 'SÆo Domingos do Araguaia',\n",
       " 'SÆo Domingos do Capim',\n",
       " 'SÆo F‚lix do Xingu',\n",
       " 'SÆo Francisco do Par\\xa0',\n",
       " 'SÆo Geraldo do Araguaia',\n",
       " 'SÆo JoÆo da Ponta',\n",
       " 'SÆo JoÆo de Pirabas',\n",
       " 'SÆo JoÆo do Araguaia',\n",
       " 'SÆo Miguel do Guam\\xa0',\n",
       " 'SÆo SebastiÆo da Boa Vista',\n",
       " 'Sapucaia',\n",
       " 'Senador Jos‚ Porf¡rio',\n",
       " 'Soure',\n",
       " 'Tailƒndia',\n",
       " 'Terra Alta',\n",
       " 'Terra Santa',\n",
       " 'Tom‚0A‡u',\n",
       " 'Tracuateua',\n",
       " 'TrairÆo',\n",
       " 'TucumÆ',\n",
       " 'Tucuru¡',\n",
       " 'Ulian¢polis',\n",
       " 'Uruar\\xa0',\n",
       " 'Vigia',\n",
       " 'Viseu',\n",
       " 'Vit¢ria do Xingu',\n",
       " 'Xinguara',\n",
       " 'Serra do Navio',\n",
       " 'Amap\\xa0',\n",
       " 'Pedra Branca do Amapari',\n",
       " 'Cal‡oene',\n",
       " 'Cutias',\n",
       " 'Ferreira Gomes',\n",
       " 'Itaubal',\n",
       " 'Laranjal do Jari',\n",
       " 'Macap\\xa0',\n",
       " 'MazagÆo',\n",
       " 'Oiapoque',\n",
       " 'Porto Grande',\n",
       " 'Pracu£ba',\n",
       " 'Santana',\n",
       " 'Tartarugalzinho',\n",
       " 'Vit¢ria do Jari',\n",
       " 'Abreulƒndia',\n",
       " 'Aguiarn¢polis',\n",
       " 'Alian‡a do Tocantins',\n",
       " 'Almas',\n",
       " 'Alvorada',\n",
       " 'Anan\\xa0s',\n",
       " 'Angico',\n",
       " 'Aparecida do Rio Negro',\n",
       " 'Aragominas',\n",
       " 'Araguacema',\n",
       " 'Aragua‡u',\n",
       " 'Aragua¡na',\n",
       " 'AraguanÆ',\n",
       " 'Araguatins',\n",
       " 'Arapoema',\n",
       " 'Arraias',\n",
       " 'Augustin¢polis',\n",
       " 'Aurora do Tocantins',\n",
       " 'Axix\\xa0 do Tocantins',\n",
       " 'Baba‡ulƒndia',\n",
       " 'Bandeirantes do Tocantins',\n",
       " 'Barra do Ouro',\n",
       " 'Barrolƒndia',\n",
       " 'Bernardo SayÆo',\n",
       " 'Brasilƒndia do Tocantins',\n",
       " 'Brejinho de Nazar‚',\n",
       " 'Buriti do Tocantins',\n",
       " 'Cachoeirinha',\n",
       " 'Campos Lindos',\n",
       " 'Cariri do Tocantins',\n",
       " 'Carmolƒndia',\n",
       " 'Carrasco Bonito',\n",
       " 'Caseara',\n",
       " 'Centen\\xa0rio',\n",
       " 'Chapada de Areia',\n",
       " 'Chapada da Natividade',\n",
       " 'Colinas do Tocantins',\n",
       " 'Combinado',\n",
       " 'Concei‡Æo do Tocantins',\n",
       " 'Couto MagalhÆes',\n",
       " 'Cristalƒndia',\n",
       " 'Crix\\xa0s do Tocantins',\n",
       " 'Darcin¢polis',\n",
       " 'Dian¢polis',\n",
       " 'Divin¢polis do Tocantins',\n",
       " 'Dois IrmÆos do Tocantins',\n",
       " 'Duer‚',\n",
       " 'Esperantina',\n",
       " 'F\\xa0tima',\n",
       " 'Figueir¢polis',\n",
       " 'Filad‚lfia',\n",
       " 'Formoso do Araguaia',\n",
       " 'Fortaleza do TabocÆo',\n",
       " 'Goianorte',\n",
       " 'Goiatins',\n",
       " 'Guara¡',\n",
       " 'Gurupi',\n",
       " 'Ipueiras',\n",
       " 'Itacaj\\xa0',\n",
       " 'Itaguatins',\n",
       " 'Itapiratins',\n",
       " 'ItaporÆ do Tocantins',\n",
       " 'Ja£ do Tocantins',\n",
       " 'Juarina',\n",
       " 'Lagoa da ConfusÆo',\n",
       " 'Lagoa do Tocantins',\n",
       " 'Lajeado',\n",
       " 'Lavandeira',\n",
       " 'Lizarda',\n",
       " 'Luzin¢polis',\n",
       " 'Marian¢polis do Tocantins',\n",
       " 'Mateiros',\n",
       " 'Maurilƒndia do Tocantins',\n",
       " 'Miracema do Tocantins',\n",
       " 'Miranorte',\n",
       " 'Monte do Carmo',\n",
       " 'Monte Santo do Tocantins',\n",
       " 'Palmeiras do Tocantins',\n",
       " 'Muricilƒndia',\n",
       " 'Natividade',\n",
       " 'Nazar‚',\n",
       " 'Nova Olinda',\n",
       " 'Nova Rosalƒndia',\n",
       " 'Novo Acordo',\n",
       " 'Novo Alegre',\n",
       " 'Novo Jardim',\n",
       " 'Oliveira de F\\xa0tima',\n",
       " 'Palmeirante',\n",
       " 'Palmeir¢polis',\n",
       " 'Para¡so do Tocantins',\n",
       " 'ParanÆ',\n",
       " 'Pedro Afonso',\n",
       " 'Peixe',\n",
       " 'Pequizeiro',\n",
       " 'Colm‚ia',\n",
       " 'Pindorama do Tocantins',\n",
       " 'Piraquˆ',\n",
       " 'Pium',\n",
       " 'Ponte Alta do Bom Jesus',\n",
       " 'Ponte Alta do Tocantins',\n",
       " 'Porto Alegre do Tocantins',\n",
       " 'Porto Nacional',\n",
       " 'Praia Norte',\n",
       " 'Presidente Kennedy',\n",
       " 'Pugmil',\n",
       " 'Recursolƒndia',\n",
       " 'Riachinho',\n",
       " 'Rio da Concei‡Æo',\n",
       " 'Rio dos Bois',\n",
       " 'Rio Sono',\n",
       " 'Sampaio',\n",
       " 'Sandolƒndia',\n",
       " 'Santa F‚ do Araguaia',\n",
       " 'Santa Maria do Tocantins',\n",
       " 'Santa Rita do Tocantins',\n",
       " 'Santa Rosa do Tocantins',\n",
       " 'Santa Tereza do Tocantins',\n",
       " 'Santa Terezinha do Tocantins',\n",
       " 'SÆo Bento do Tocantins',\n",
       " 'SÆo F‚lix do Tocantins',\n",
       " 'SÆo Miguel do Tocantins',\n",
       " 'SÆo Salvador do Tocantins',\n",
       " 'SÆo SebastiÆo do Tocantins',\n",
       " 'SÆo Val‚rio',\n",
       " 'Silvan¢polis',\n",
       " 'S¡tio Novo do Tocantins',\n",
       " 'Sucupira',\n",
       " 'Taguatinga',\n",
       " 'Taipas do Tocantins',\n",
       " 'TalismÆ',\n",
       " 'Palmas',\n",
       " 'Tocant¡nia',\n",
       " 'Tocantin¢polis',\n",
       " 'Tupirama',\n",
       " 'Tupiratins',\n",
       " 'Wanderlƒndia',\n",
       " 'Xambio\\xa0',\n",
       " \"Espigão D'Oeste\",\n",
       " 'Guajará-Mirim',\n",
       " 'Ji-Paraná',\n",
       " \"Nova Brasilândia D'Oeste\",\n",
       " 'Presidente Médici',\n",
       " 'São Miguel do Guaporé',\n",
       " 'Nova Mamoré',\n",
       " 'Alto Paraíso',\n",
       " 'Cacaulândia',\n",
       " 'Campo Novo de Rondônia',\n",
       " 'Itapuã do Oeste',\n",
       " 'Nova União',\n",
       " 'Primavera de Rondônia',\n",
       " \"São Felipe D'Oeste\",\n",
       " 'São Francisco do Guaporé',\n",
       " 'Teixeirópolis',\n",
       " 'Urupá',\n",
       " 'Vale do Paraíso',\n",
       " 'Acrelândia',\n",
       " 'Brasiléia',\n",
       " 'Epitaciolândia',\n",
       " 'Feijó',\n",
       " 'Jordão',\n",
       " 'Mâncio Lima',\n",
       " 'Plácido de Castro',\n",
       " 'Tarauacá',\n",
       " 'Alvarães',\n",
       " 'Amaturá',\n",
       " 'Anamã',\n",
       " 'Apuí',\n",
       " 'Careiro da Várzea',\n",
       " 'Codajás',\n",
       " 'Eirunepé',\n",
       " 'Guajará',\n",
       " 'Humaitá',\n",
       " 'Japurá',\n",
       " 'Juruá',\n",
       " 'Jutaí',\n",
       " 'Lábrea',\n",
       " 'Manicoré',\n",
       " 'Maraã',\n",
       " 'Maués',\n",
       " 'Nhamundá',\n",
       " 'Novo Airão',\n",
       " 'Novo Aripuanã',\n",
       " 'Santo Antônio do Içá',\n",
       " 'São Gabriel da Cachoeira',\n",
       " 'São Paulo de Olivença',\n",
       " 'São Sebastião do Uatumã',\n",
       " 'Tapauá',\n",
       " 'Tefé',\n",
       " 'Urucará',\n",
       " 'Cantá',\n",
       " 'Caracaraí',\n",
       " 'Mucajaí',\n",
       " 'Rorainópolis',\n",
       " 'São João da Baliza',\n",
       " 'São Luiz',\n",
       " 'Uiramutã',\n",
       " 'Acará',\n",
       " 'Afuá',\n",
       " 'Água Azul do Norte',\n",
       " 'Anajás',\n",
       " 'Augusto Corrêa',\n",
       " 'Aurora do Pará',\n",
       " 'Baião',\n",
       " 'Belém',\n",
       " 'Bragança',\n",
       " 'Cachoeira do Piriá',\n",
       " 'Cametá',\n",
       " 'Canaã dos Carajás',\n",
       " 'Capitão Poço',\n",
       " 'Conceição do Araguaia',\n",
       " 'Concórdia do Pará',\n",
       " 'Curionópolis',\n",
       " 'Curuá',\n",
       " 'Curuçá',\n",
       " 'Eldorado do Carajás',\n",
       " 'Garrafão do Norte',\n",
       " 'Goianésia do Pará',\n",
       " 'Gurupá',\n",
       " 'Igarapé-Açu',\n",
       " 'Igarapé-Miri',\n",
       " 'Ipixuna do Pará',\n",
       " 'Jacundá',\n",
       " 'Mãe do Rio',\n",
       " 'Magalhães Barata',\n",
       " 'Marabá',\n",
       " 'Maracanã',\n",
       " 'Medicilândia',\n",
       " 'Melgaço',\n",
       " 'Mojuí dos Campos',\n",
       " 'Muaná',\n",
       " 'Nova Esperança do Piriá',\n",
       " 'Óbidos',\n",
       " 'Oeiras do Pará',\n",
       " 'Oriximiná',\n",
       " 'Ourém',\n",
       " 'Ourilândia do Norte',\n",
       " 'Pacajá',\n",
       " 'Palestina do Pará',\n",
       " 'Peixe-Boi',\n",
       " 'Piçarra',\n",
       " 'Redenção',\n",
       " 'Rondon do Pará',\n",
       " 'Rurópolis',\n",
       " 'Salinópolis',\n",
       " 'Santa Bárbara do Pará',\n",
       " 'Santa Izabel do Pará',\n",
       " 'Santa Luzia do Pará',\n",
       " 'Santa Maria do Pará',\n",
       " 'Santarém',\n",
       " 'Santarém Novo',\n",
       " 'Santo Antônio do Tauá',\n",
       " 'São Caetano de Odivelas',\n",
       " 'São Domingos do Araguaia',\n",
       " 'São Domingos do Capim',\n",
       " 'São Félix do Xingu',\n",
       " 'São Francisco do Pará',\n",
       " 'São Geraldo do Araguaia',\n",
       " 'São João da Ponta',\n",
       " 'São João de Pirabas',\n",
       " 'São João do Araguaia',\n",
       " 'São Miguel do Guamá',\n",
       " 'São Sebastião da Boa Vista',\n",
       " 'Senador José Porfírio',\n",
       " 'Tailândia',\n",
       " 'Tomé-Açu',\n",
       " 'Trairão',\n",
       " 'Tucumã',\n",
       " 'Tucuruí',\n",
       " 'Ulianópolis',\n",
       " 'Uruará',\n",
       " 'Vitória do Xingu',\n",
       " 'Amapá',\n",
       " 'Calçoene',\n",
       " 'Macapá',\n",
       " 'Mazagão',\n",
       " 'Pracuúba',\n",
       " 'Vitória do Jari',\n",
       " 'Abreulândia',\n",
       " 'Aguiarnópolis',\n",
       " 'Aliança do Tocantins',\n",
       " 'Ananás',\n",
       " 'Araguaçu',\n",
       " 'Araguaína',\n",
       " 'Araguanã',\n",
       " 'Augustinópolis',\n",
       " 'Axixá do Tocantins',\n",
       " 'Babaçulândia',\n",
       " 'Barrolândia',\n",
       " 'Bernardo Sayão',\n",
       " 'Brasilândia do Tocantins',\n",
       " 'Brejinho de Nazaré',\n",
       " 'Carmolândia',\n",
       " 'Centenário',\n",
       " 'Conceição do Tocantins',\n",
       " 'Couto Magalhães',\n",
       " 'Cristalândia',\n",
       " 'Crixás do Tocantins',\n",
       " 'Darcinópolis',\n",
       " 'Dianópolis',\n",
       " 'Divinópolis do Tocantins',\n",
       " 'Dois Irmãos do Tocantins',\n",
       " 'Dueré',\n",
       " 'Fátima',\n",
       " 'Figueirópolis',\n",
       " 'Filadélfia',\n",
       " 'Fortaleza do Tabocão',\n",
       " 'Guaraí',\n",
       " 'Itacajá',\n",
       " 'Itaporã do Tocantins',\n",
       " 'Jaú do Tocantins',\n",
       " 'Lagoa da Confusão',\n",
       " 'Luzinópolis',\n",
       " 'Marianópolis do Tocantins',\n",
       " 'Maurilândia do Tocantins',\n",
       " 'Muricilândia',\n",
       " 'Nazaré',\n",
       " 'Nova Rosalândia',\n",
       " 'Oliveira de Fátima',\n",
       " 'Palmeirópolis',\n",
       " 'Paraíso do Tocantins',\n",
       " 'Paranã',\n",
       " 'Colméia',\n",
       " 'Piraquê',\n",
       " 'Recursolândia',\n",
       " 'Rio da Conceição',\n",
       " 'Sandolândia',\n",
       " 'Santa Fé do Araguaia',\n",
       " 'São Bento do Tocantins',\n",
       " 'São Félix do Tocantins',\n",
       " 'São Miguel do Tocantins',\n",
       " 'São Salvador do Tocantins',\n",
       " 'São Sebastião do Tocantins',\n",
       " 'São Valério',\n",
       " 'Silvanópolis',\n",
       " 'Sítio Novo do Tocantins',\n",
       " 'Talismã',\n",
       " 'Tocantínia',\n",
       " 'Tocantinópolis',\n",
       " 'Wanderlândia',\n",
       " 'Xambioá']"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista_cidades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "\n",
    "filtro = frozenset(\n",
    "    string.ascii_letters \n",
    "    + string.digits\n",
    "    + '-._' + ' ' + \"'\"+'ç' +'á'+'à'+ 'ã'+ 'â'+'é'+ 'è'+ 'ê'+ 'í'+ 'ì'+ 'î'+'ó'+ 'ó'+ 'õ'+ 'ô'+'ú'+'ù'+ 'û'\n",
    ")\n",
    "\n",
    "def verifica_caracter_especial(string, filtro=filtro):\n",
    "    return not set(string) <= filtro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#TalismÆ\n",
    "verifica_caracter_especial(\"TalismÆ\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temos 201 municipios preenchidos de forma errada e 450 preenchidos corretamente.\n"
     ]
    }
   ],
   "source": [
    "municipio_errado = []\n",
    "municipio_correto = []\n",
    "\n",
    "for i in lista_cidades:\n",
    "    if verifica_caracter_especial(i.lower()):\n",
    "        municipio_errado.append(i)\n",
    "    else:\n",
    "        municipio_correto.append(i)\n",
    "\n",
    "print(\"Temos {0} municipios preenchidos de forma errada e {1} preenchidos corretamente.\".format(\n",
    "    len(municipio_errado),len(municipio_correto)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## __Validando os resultados encontrados__\n",
    "\n",
    "<img src=\"./imagens/exemplo_municio_tocantins.png\">\n",
    "\n",
    "__Exemplo:__ \n",
    "\n",
    "O municipio Palmeiras do Tocantins teve um match com  Para¡so do Tocantins de 86 pontos.\n",
    "<br> Mas os dois municipios existem, apenas as letras que tem uma correlação alta devido ao __\"do Tocantins\"__, por ambas começarem com __\"P\"__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Santana do Araguaia --> Santa F‚ do Araguaia = 87\n",
      "Colinas do Tocantins --> Alian‡a do Tocantins = 85\n",
      "Palmeiras do Tocantins --> Para¡so do Tocantins = 86\n",
      "Guajará-Mirim --> Guajar 0Mirim = 85\n",
      "São Miguel do Guaporé --> SÆo Miguel do Guapor‚ = 90\n",
      "Nova União --> Nova UniÆo = 90\n",
      "Acrelândia --> Acrelƒndia = 90\n",
      "Brasiléia --> Brasil‚ia = 89\n",
      "Tarauacá --> Tarauac  = 88\n",
      "Alvarães --> AlvarÆes = 88\n",
      "Amaturá --> Amatur  = 86\n",
      "Codajás --> Codaj s = 86\n",
      "Eirunepé --> Eirunep‚ = 88\n",
      "Guajará --> Guajar  = 86\n",
      "Humaitá --> Humait  = 86\n",
      "Manicoré --> Manicor‚ = 88\n",
      "Nhamundá --> Nhamund  = 88\n",
      "Novo Airão --> Novo AirÆo = 90\n",
      "Santo Antônio do Içá --> Santo Ant“nio do I‡  = 85\n",
      "São Paulo de Olivença --> SÆo Paulo de Oliven‡a = 90\n",
      "São Sebastião do Uatumã --> SÆo SebastiÆo do UatumÆ = 87\n",
      "Urucará --> Urucar  = 86\n",
      "Caracaraí --> Caracara¡ = 89\n",
      "Mucajaí --> Mucaja¡ = 86\n",
      "São João da Baliza --> SÆo JoÆo da Baliza = 89\n",
      "São Luiz --> SÆo Luiz = 88\n",
      "Uiramutã --> UiramutÆ = 88\n",
      "Bragança --> Bragan‡a = 88\n",
      "Canaã dos Carajás --> CanaÆ dos Caraj s = 88\n",
      "Conceição do Araguaia --> Concei‡Æo do Araguaia = 90\n",
      "Concórdia do Pará --> Conc¢rdia do Par  = 88\n",
      "Goianésia do Pará --> Goian‚sia do Par  = 88\n",
      "Jacundá --> Jacund  = 86\n",
      "Mãe do Rio --> MÆe do Rio = 90\n",
      "Maracanã --> MaracanÆ = 88\n",
      "Melgaço --> Melga‡o = 86\n",
      "Oriximiná --> Oriximin  = 89\n",
      "Piçarra --> Pi‡arra = 86\n",
      "Rurópolis --> Rur¢polis = 89\n",
      "Santa Bárbara do Pará --> Santa B rbara do Par  = 90\n",
      "Santarém --> Santar‚m = 88\n",
      "Santo Antônio do Tauá --> Santo Ant“nio do Tau  = 90\n",
      "São Félix do Xingu --> SÆo F‚lix do Xingu = 89\n",
      "São Francisco do Pará --> SÆo Francisco do Par  = 90\n",
      "São João da Ponta --> SÆo JoÆo da Ponta = 88\n",
      "São João de Pirabas --> SÆo JoÆo de Pirabas = 89\n",
      "São João do Araguaia --> SÆo JoÆo do Araguaia = 90\n",
      "São Miguel do Guamá --> SÆo Miguel do Guam  = 89\n",
      "Senador José Porfírio --> Senador Jos‚ Porf¡rio = 90\n",
      "Tailândia --> Tailƒndia = 89\n",
      "Trairão --> TrairÆo = 86\n",
      "Tucuruí --> Tucuru¡ = 86\n",
      "Calçoene --> Cal‡oene = 88\n",
      "Mazagão --> MazagÆo = 86\n",
      "Pracuúba --> Pracu£ba = 88\n",
      "Araguaçu --> Aragua‡u = 88\n",
      "Araguaína --> Aragua¡na = 89\n",
      "Araguanã --> AraguanÆ = 88\n",
      "Centenário --> Centen rio = 90\n",
      "Dianópolis --> Dian¢polis = 90\n",
      "Filadélfia --> Filad‚lfia = 90\n",
      "Itacajá --> Itacaj  = 86\n",
      "Colméia --> Colm‚ia = 86\n",
      "Piraquê --> Piraquˆ = 86\n",
      "Rio da Conceição --> Rio da Concei‡Æo = 88\n",
      "Talismã --> TalismÆ = 86\n",
      "Tocantínia --> Tocant¡nia = 90\n",
      "Xambioá --> Xambio  = 86\n"
     ]
    }
   ],
   "source": [
    "for correto in municipio_correto:\n",
    "    for errado in municipio_errado:\n",
    "        if 85 <= fuzz.ratio(correto, errado) <= 90 :\n",
    "            print(\"{0} --> {1} = {2}\".format(correto, errado, fuzz.ratio(correto, errado)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>COD</th>\n",
       "      <th>CODMUN7</th>\n",
       "      <th>UF</th>\n",
       "      <th>ANO</th>\n",
       "      <th>MUNICIPIO</th>\n",
       "      <th>CV_SCR1</th>\n",
       "      <th>CV_SCR2</th>\n",
       "      <th>POPRURAL</th>\n",
       "      <th>POPURB</th>\n",
       "      <th>RDPC</th>\n",
       "      <th>...</th>\n",
       "      <th>T_AGUA</th>\n",
       "      <th>T_BANAGUA</th>\n",
       "      <th>T_DENS</th>\n",
       "      <th>T_LIXO</th>\n",
       "      <th>T_LUZ</th>\n",
       "      <th>AGUA_ESGOTO</th>\n",
       "      <th>POPDOMCHEFMULHERES</th>\n",
       "      <th>DENSDEMOG2010</th>\n",
       "      <th>PERC_NV_7CPN</th>\n",
       "      <th>COBERTURA_AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>172120</td>\n",
       "      <td>1721208.0</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>2016</td>\n",
       "      <td>Tocantin¢polis</td>\n",
       "      <td>93.279999</td>\n",
       "      <td>102.730003</td>\n",
       "      <td>19.01</td>\n",
       "      <td>80.989998</td>\n",
       "      <td>414.079987</td>\n",
       "      <td>...</td>\n",
       "      <td>93.82</td>\n",
       "      <td>77.339996</td>\n",
       "      <td>34.639999</td>\n",
       "      <td>93.449997</td>\n",
       "      <td>98.389999</td>\n",
       "      <td>9.25</td>\n",
       "      <td>43.349998</td>\n",
       "      <td>21.0</td>\n",
       "      <td>61.990002</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>172120</td>\n",
       "      <td>1721208.0</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>2017</td>\n",
       "      <td>Tocantinópolis</td>\n",
       "      <td>101.305481</td>\n",
       "      <td>95.822456</td>\n",
       "      <td>19.01</td>\n",
       "      <td>80.989998</td>\n",
       "      <td>414.079987</td>\n",
       "      <td>...</td>\n",
       "      <td>93.82</td>\n",
       "      <td>77.339996</td>\n",
       "      <td>34.639999</td>\n",
       "      <td>93.449997</td>\n",
       "      <td>98.389999</td>\n",
       "      <td>9.25</td>\n",
       "      <td>43.349998</td>\n",
       "      <td>21.0</td>\n",
       "      <td>67.272728</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>172120</td>\n",
       "      <td>1721208.0</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>2018</td>\n",
       "      <td>Tocantinópolis</td>\n",
       "      <td>98.570000</td>\n",
       "      <td>87.699997</td>\n",
       "      <td>19.01</td>\n",
       "      <td>80.989998</td>\n",
       "      <td>414.079987</td>\n",
       "      <td>...</td>\n",
       "      <td>93.82</td>\n",
       "      <td>77.339996</td>\n",
       "      <td>34.639999</td>\n",
       "      <td>93.449997</td>\n",
       "      <td>98.389999</td>\n",
       "      <td>9.25</td>\n",
       "      <td>43.349998</td>\n",
       "      <td>21.0</td>\n",
       "      <td>67.848701</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         COD    CODMUN7         UF   ANO       MUNICIPIO     CV_SCR1  \\\n",
       "445   172120  1721208.0  Tocantins  2016  Tocantin¢polis   93.279999   \n",
       "895   172120  1721208.0  Tocantins  2017  Tocantinópolis  101.305481   \n",
       "1345  172120  1721208.0  Tocantins  2018  Tocantinópolis   98.570000   \n",
       "\n",
       "         CV_SCR2  POPRURAL     POPURB        RDPC  ...  T_AGUA  T_BANAGUA  \\\n",
       "445   102.730003     19.01  80.989998  414.079987  ...   93.82  77.339996   \n",
       "895    95.822456     19.01  80.989998  414.079987  ...   93.82  77.339996   \n",
       "1345   87.699997     19.01  80.989998  414.079987  ...   93.82  77.339996   \n",
       "\n",
       "         T_DENS     T_LIXO      T_LUZ  AGUA_ESGOTO  POPDOMCHEFMULHERES  \\\n",
       "445   34.639999  93.449997  98.389999         9.25           43.349998   \n",
       "895   34.639999  93.449997  98.389999         9.25           43.349998   \n",
       "1345  34.639999  93.449997  98.389999         9.25           43.349998   \n",
       "\n",
       "      DENSDEMOG2010  PERC_NV_7CPN  COBERTURA_AB  \n",
       "445            21.0     61.990002         100.0  \n",
       "895            21.0     67.272728         100.0  \n",
       "1345           21.0     67.848701         100.0  \n",
       "\n",
       "[3 rows x 29 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vacina[df_vacina['MUNICIPIO'].isin(['Tocantinópolis', 'Tocantin¢polis'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### __Atualizando o DataFrame__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "for correto in municipio_correto:\n",
    "    \n",
    "    for index, errado in df_vacina[df_vacina['MUNICIPIO'].isin(municipio_errado)].iterrows():\n",
    "        \n",
    "        if fuzz.ratio(correto, errado['MUNICIPIO']) >= 90:\n",
    "            \n",
    "            df_vacina.loc[df_vacina['MUNICIPIO'] == errado['MUNICIPIO'], ['MUNICIPIO']] = correto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>COD</th>\n",
       "      <th>CODMUN7</th>\n",
       "      <th>UF</th>\n",
       "      <th>ANO</th>\n",
       "      <th>MUNICIPIO</th>\n",
       "      <th>CV_SCR1</th>\n",
       "      <th>CV_SCR2</th>\n",
       "      <th>POPRURAL</th>\n",
       "      <th>POPURB</th>\n",
       "      <th>RDPC</th>\n",
       "      <th>...</th>\n",
       "      <th>T_AGUA</th>\n",
       "      <th>T_BANAGUA</th>\n",
       "      <th>T_DENS</th>\n",
       "      <th>T_LIXO</th>\n",
       "      <th>T_LUZ</th>\n",
       "      <th>AGUA_ESGOTO</th>\n",
       "      <th>POPDOMCHEFMULHERES</th>\n",
       "      <th>DENSDEMOG2010</th>\n",
       "      <th>PERC_NV_7CPN</th>\n",
       "      <th>COBERTURA_AB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>172120</td>\n",
       "      <td>1721208.0</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>2016</td>\n",
       "      <td>Tocantinópolis</td>\n",
       "      <td>93.279999</td>\n",
       "      <td>102.730003</td>\n",
       "      <td>19.01</td>\n",
       "      <td>80.989998</td>\n",
       "      <td>414.079987</td>\n",
       "      <td>...</td>\n",
       "      <td>93.82</td>\n",
       "      <td>77.339996</td>\n",
       "      <td>34.639999</td>\n",
       "      <td>93.449997</td>\n",
       "      <td>98.389999</td>\n",
       "      <td>9.25</td>\n",
       "      <td>43.349998</td>\n",
       "      <td>21.0</td>\n",
       "      <td>61.990002</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>172120</td>\n",
       "      <td>1721208.0</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>2017</td>\n",
       "      <td>Tocantinópolis</td>\n",
       "      <td>101.305481</td>\n",
       "      <td>95.822456</td>\n",
       "      <td>19.01</td>\n",
       "      <td>80.989998</td>\n",
       "      <td>414.079987</td>\n",
       "      <td>...</td>\n",
       "      <td>93.82</td>\n",
       "      <td>77.339996</td>\n",
       "      <td>34.639999</td>\n",
       "      <td>93.449997</td>\n",
       "      <td>98.389999</td>\n",
       "      <td>9.25</td>\n",
       "      <td>43.349998</td>\n",
       "      <td>21.0</td>\n",
       "      <td>67.272728</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1345</th>\n",
       "      <td>172120</td>\n",
       "      <td>1721208.0</td>\n",
       "      <td>Tocantins</td>\n",
       "      <td>2018</td>\n",
       "      <td>Tocantinópolis</td>\n",
       "      <td>98.570000</td>\n",
       "      <td>87.699997</td>\n",
       "      <td>19.01</td>\n",
       "      <td>80.989998</td>\n",
       "      <td>414.079987</td>\n",
       "      <td>...</td>\n",
       "      <td>93.82</td>\n",
       "      <td>77.339996</td>\n",
       "      <td>34.639999</td>\n",
       "      <td>93.449997</td>\n",
       "      <td>98.389999</td>\n",
       "      <td>9.25</td>\n",
       "      <td>43.349998</td>\n",
       "      <td>21.0</td>\n",
       "      <td>67.848701</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         COD    CODMUN7         UF   ANO       MUNICIPIO     CV_SCR1  \\\n",
       "445   172120  1721208.0  Tocantins  2016  Tocantinópolis   93.279999   \n",
       "895   172120  1721208.0  Tocantins  2017  Tocantinópolis  101.305481   \n",
       "1345  172120  1721208.0  Tocantins  2018  Tocantinópolis   98.570000   \n",
       "\n",
       "         CV_SCR2  POPRURAL     POPURB        RDPC  ...  T_AGUA  T_BANAGUA  \\\n",
       "445   102.730003     19.01  80.989998  414.079987  ...   93.82  77.339996   \n",
       "895    95.822456     19.01  80.989998  414.079987  ...   93.82  77.339996   \n",
       "1345   87.699997     19.01  80.989998  414.079987  ...   93.82  77.339996   \n",
       "\n",
       "         T_DENS     T_LIXO      T_LUZ  AGUA_ESGOTO  POPDOMCHEFMULHERES  \\\n",
       "445   34.639999  93.449997  98.389999         9.25           43.349998   \n",
       "895   34.639999  93.449997  98.389999         9.25           43.349998   \n",
       "1345  34.639999  93.449997  98.389999         9.25           43.349998   \n",
       "\n",
       "      DENSDEMOG2010  PERC_NV_7CPN  COBERTURA_AB  \n",
       "445            21.0     61.990002         100.0  \n",
       "895            21.0     67.272728         100.0  \n",
       "1345           21.0     67.848701         100.0  \n",
       "\n",
       "[3 rows x 29 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vacina[df_vacina['MUNICIPIO'].isin(['Tocantinópolis', 'Tocantin¢polis'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temos 94 municipios preenchidos de forma errada e 450 preenchidos corretamente.\n"
     ]
    }
   ],
   "source": [
    "municipio_errado = []\n",
    "municipio_correto = []\n",
    "\n",
    "for i in list(df_vacina['MUNICIPIO'].unique()):\n",
    "    if verifica_caracter_especial(i.lower()):\n",
    "        municipio_errado.append(i)\n",
    "    else:\n",
    "        municipio_correto.append(i)\n",
    "\n",
    "print(\"Temos {0} municipios preenchidos de forma errada e {1} preenchidos corretamente.\".format(\n",
    "    len(municipio_errado),len(municipio_correto)))\n",
    "\n",
    "# Antes tinhamos 201 errados "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## __Material de Apoio__\n",
    "\n",
    "* [Github - Introdução a biblioteca pandas](https://github.com/lucasmbribeiro/exploratory-analysis/tree/master/Introdu%C3%A7%C3%A3o%20a%20Biblioteca%20Pandas)\n",
    "* [Medium - Pandas](https://levelup.gitconnected.com/introducing-pandas-e0581a6683c9)\n",
    "* [Youtube - Por onde iniciar em Data Analytics?](https://www.youtube.com/watch?v=MjThnO_SOFc&t=10s)"
   ]
  },
  {
   "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
}