analise_pt2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "rqg1qPbO27zR"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VdhaVSwZ8IQK",
    "outputId": "c23335d2-0938-46a9-a6c5-05358bd7aa53"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    from google.colab import drive\n",
    "    my_path = 'drive/My Drive/techlabs/'\n",
    "    drive.mount('/content/drive')\n",
    "except:\n",
    "    my_path = 'dados/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 261
    },
    "id": "AuSOsTqjtnFn",
    "outputId": "7d05c86e-4085-4fc9-adf6-f28fc08e1741"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape: (3931570, 42)\n",
      "Memory: 383 MB\n"
     ]
    },
    {
     "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>NO_MUNICIPIO_RESIDENCIA</th>\n",
       "      <th>SG_UF_RESIDENCIA</th>\n",
       "      <th>NU_IDADE</th>\n",
       "      <th>TP_SEXO</th>\n",
       "      <th>TP_ESTADO_CIVIL</th>\n",
       "      <th>TP_COR_RACA</th>\n",
       "      <th>TP_NACIONALIDADE</th>\n",
       "      <th>NO_MUNICIPIO_NASCIMENTO</th>\n",
       "      <th>SG_UF_NASCIMENTO</th>\n",
       "      <th>TP_ST_CONCLUSAO</th>\n",
       "      <th>TP_ANO_CONCLUIU</th>\n",
       "      <th>TP_ESCOLA</th>\n",
       "      <th>TP_ENSINO</th>\n",
       "      <th>IN_TREINEIRO</th>\n",
       "      <th>NO_MUNICIPIO_PROVA</th>\n",
       "      <th>SG_UF_PROVA</th>\n",
       "      <th>NU_NOTA_CN</th>\n",
       "      <th>NU_NOTA_CH</th>\n",
       "      <th>NU_NOTA_LC</th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <th>TP_LINGUA</th>\n",
       "      <th>TP_STATUS_REDACAO</th>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "      <th>Q001</th>\n",
       "      <th>Q002</th>\n",
       "      <th>Q003</th>\n",
       "      <th>Q004</th>\n",
       "      <th>Q005</th>\n",
       "      <th>Q006</th>\n",
       "      <th>Q007</th>\n",
       "      <th>Q008</th>\n",
       "      <th>Q009</th>\n",
       "      <th>Q010</th>\n",
       "      <th>Q011</th>\n",
       "      <th>Q012</th>\n",
       "      <th>Q022</th>\n",
       "      <th>Q023</th>\n",
       "      <th>Q024</th>\n",
       "      <th>Q025</th>\n",
       "      <th>IN_ESPECIALIZADO</th>\n",
       "      <th>IN_ESPECIFICO</th>\n",
       "      <th>IN_RECURSO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Santarém</td>\n",
       "      <td>PA</td>\n",
       "      <td>21</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>Santarém</td>\n",
       "      <td>PA</td>\n",
       "      <td>472.899994</td>\n",
       "      <td>466.000000</td>\n",
       "      <td>497.299988</td>\n",
       "      <td>369.100006</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>780.0</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mãe do Rio</td>\n",
       "      <td>PA</td>\n",
       "      <td>16</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Paragominas</td>\n",
       "      <td>PA</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>Mãe do Rio</td>\n",
       "      <td>PA</td>\n",
       "      <td>358.899994</td>\n",
       "      <td>495.399994</td>\n",
       "      <td>482.299988</td>\n",
       "      <td>416.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>600.0</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>5</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Paragominas</td>\n",
       "      <td>PA</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Paragominas</td>\n",
       "      <td>PA</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Paragominas</td>\n",
       "      <td>PA</td>\n",
       "      <td>574.200012</td>\n",
       "      <td>538.700012</td>\n",
       "      <td>582.599976</td>\n",
       "      <td>571.500000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>640.0</td>\n",
       "      <td>H</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>3</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>E</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ananindeua</td>\n",
       "      <td>PA</td>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Belém</td>\n",
       "      <td>PA</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Belém</td>\n",
       "      <td>PA</td>\n",
       "      <td>522.000000</td>\n",
       "      <td>592.599976</td>\n",
       "      <td>582.000000</td>\n",
       "      <td>605.299988</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>580.0</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>C</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Marabá</td>\n",
       "      <td>PA</td>\n",
       "      <td>30</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Marabá</td>\n",
       "      <td>PA</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Marabá</td>\n",
       "      <td>PA</td>\n",
       "      <td>407.399994</td>\n",
       "      <td>480.100006</td>\n",
       "      <td>464.299988</td>\n",
       "      <td>581.500000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>360.0</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  NO_MUNICIPIO_RESIDENCIA SG_UF_RESIDENCIA  ...  IN_ESPECIFICO IN_RECURSO\n",
       "0                Santarém               PA  ...          False      False\n",
       "1              Mãe do Rio               PA  ...          False      False\n",
       "2             Paragominas               PA  ...          False      False\n",
       "3              Ananindeua               PA  ...          False      False\n",
       "4                  Marabá               PA  ...          False      False\n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_pickle(my_path + 'final-3.6.pkl.xz')\n",
    "df = df.reset_index(drop=True)\n",
    "print(f'Shape: {df.shape}')\n",
    "print(f'Memory: {df.memory_usage().sum() // (1024**2) + 1} MB')\n",
    "display(df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2hx7TVruNIbJ"
   },
   "source": [
    "***Colunas que eu fiquei para analisar***\n",
    "\n",
    "'NO_MUNICIPIO_RESIDENCIA', 'SG_UF_RESIDENCIA', 'SG_UF_NASCIMENTO', 'NO_MUNICIPIO_NASCIMENTO',  'NO_MUNICIPIO_PROVA', 'SG_UF_PROVA',\n",
    "'NU_IDADE', 'TP_SEXO','TP_ESTADO_CIVIL','TP_COR_RACA','TP_NACIONALIDADE','TP_LINGUA'\n",
    "\n",
    "|Coluna|Descrição|\n",
    "|---|---|\n",
    "|NO_MUNICIPIO_RESIDENCIA|Nome do município de residência|\n",
    "|SG_UF_RESIDENCIA|Sigla da Unidade da Federação de residência|\n",
    "|SG_UF_NASCIMENTO|Sigla da Unidade da Federação de nascimento|\n",
    "|NO_MUNICIPIO_NASCIMENTO|Nome do município de nascimento|\n",
    "|NO_MUNICIPIO_PROVA|Nome do município da aplicação da prova|\n",
    "|SG_UF_PROVA|Sigla da Unidade da Federação da aplicação da prova|\n",
    "|NU_IDADE|Idade2|\n",
    "|TP_SEXO|Sexo|\n",
    "|TP_ESTADO_CIVIL|Estado Civil|\n",
    "|TP_COR_RACA|Cor/raça|\n",
    "|TP_NACIONALIDADE|Nacionalidade|\n",
    "|TP_LINGUA|Língua Estrangeira|\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "J4_9ubjPD0f4"
   },
   "outputs": [],
   "source": [
    "provas = ['NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC', 'NU_NOTA_MT', 'NU_NOTA_REDACAO']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "UBO7AYazPMwZ"
   },
   "outputs": [],
   "source": [
    "colunas = [\n",
    "  'NO_MUNICIPIO_RESIDENCIA', 'SG_UF_RESIDENCIA', 'SG_UF_NASCIMENTO',\n",
    "  'NO_MUNICIPIO_NASCIMENTO',  'NO_MUNICIPIO_PROVA', 'SG_UF_PROVA', 'NU_IDADE',\n",
    "  'TP_SEXO','TP_ESTADO_CIVIL','TP_COR_RACA','TP_NACIONALIDADE','TP_LINGUA'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "e2nMkF0uPK7a"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "with open(my_path + 'categorias.json', 'r') as f:\n",
    "    c = json.load(f)\n",
    "    categorias = {\n",
    "        **c['participante'], **c['escola'], **c['especializado'], **c['especifico'],\n",
    "        **c['recurso'], **c['local_prova'], **c['prova'], **c['redacao'], **c['socioeconomico']\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 416
    },
    "id": "uDX1Sve5PbwI",
    "outputId": "92b8340b-2aa7-4c79-d850-36a2a48b866d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP_SEXO\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'F': 'Feminino ', 'M': 'Masculino'}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP_ESTADO_CIVIL\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'0': 'Não informado',\n",
       " '1': 'Solteiro(a)',\n",
       " '2': 'Casado(a)/Mora com companheiro(a)',\n",
       " '3': 'Divorciado(a)/Desquitado(a)/Separado(a)',\n",
       " '4': 'Viúvo(a)'}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP_COR_RACA\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'0': 'Não declarado',\n",
       " '1': 'Branca',\n",
       " '2': 'Preta',\n",
       " '3': 'Parda',\n",
       " '4': 'Amarela',\n",
       " '5': 'Indígena '}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP_NACIONALIDADE\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'0': 'Não informado',\n",
       " '1': 'Brasileiro(a)',\n",
       " '2': 'Brasileiro(a) Naturalizado(a)',\n",
       " '3': 'Estrangeiro(a)',\n",
       " '4': 'Brasileiro(a) Nato(a), nascido(a) no exterior'}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP_LINGUA\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'0': 'Inglês', '1': 'Espanhol'}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for c in colunas:\n",
    "    if c in categorias:\n",
    "        print(c)\n",
    "        display(categorias[c])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9f8CHC8Njz_a"
   },
   "source": [
    "## Análises"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "P-LJQmMPO8_b"
   },
   "outputs": [],
   "source": [
    "Norte = ['AM','RR','AP','PA','TO','RO','AC']\n",
    "Nordeste = ['MA','PI','CE','RN','PE','PB','SE','AL','BA']\n",
    "Centro_Oeste = ['MT','MS','GO']\n",
    "Sudeste = ['SP','RJ','ES','MG']\n",
    "Sul = ['PR','SC','RS']\n",
    "regioes = [Norte,Nordeste,Centro_Oeste,Sudeste,Sul]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "q98XIksMduBj"
   },
   "outputs": [],
   "source": [
    "dicionario_regioes ={'AM':'NORTE','RR':'NORTE','AP':'NORTE','PA':'NORTE','TO':'NORTE','RO':'NORTE','AC':'NORTE',\n",
    "       'MA':'NORDESTE','PI':'NORDESTE','CE':'NORDESTE','RN':'NORDESTE','PE':'NORDESTE','PB':'NORDESTE',\n",
    "       'SE':'NORDESTE','AL':'NORDESTE','BA':'NORDESTE','MT':'CENTRO-OESTE','MS':'CENTRO-OESTE',\n",
    "       'GO':'CENTRO-OESTE','DF':'CENTRO-OESTE','SP':'SUDESTE','RJ':'SUDESTE','ES':'SUDESTE','MG':'SUDESTE','PR':'SUL',\n",
    "       'SC':'SUL','RS':'SUL'}\n",
    "\n",
    "df['Regiao_moradia'] = df['SG_UF_RESIDENCIA'].apply(lambda x : dicionario_regioes[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u7G91x82k17t",
    "outputId": "0f214099-cff7-46d0-fdc7-7e153d4a6464"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "São Paulo         174342\n",
       "Rio de Janeiro    107276\n",
       "Fortaleza          73880\n",
       "Brasília           71267\n",
       "Salvador           65606\n",
       "                   ...  \n",
       "Sério                  6\n",
       "Guaporema              6\n",
       "Araguainha             4\n",
       "Uiramutã               4\n",
       "Forquetinha            4\n",
       "Name: NO_MUNICIPIO_RESIDENCIA, Length: 5299, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['NO_MUNICIPIO_RESIDENCIA'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "BQgCIKGIjEbO"
   },
   "outputs": [],
   "source": [
    "Capital = [\n",
    "  'Rio Branco', 'Maceió', 'Macapá', 'Manaus','Salavador',\n",
    "  'Fortaleza', 'Brasília',  'Vitória', 'Goiânia',\n",
    "  'São Luís', 'Cuiabá', 'Campo Grande', 'Belo Horizonte', 'Belém'\n",
    "  'João Pessoa', 'Curitiba', 'Recife', 'Teresina', 'Rio de Janeiro'\n",
    "  'Natal', 'Porto Alegre', 'Porto Velho', 'Boa Vista', 'Florianópolis',\n",
    "  'São Paulo', 'Aracaju', 'Palmas'\n",
    "]\n",
    "\n",
    "df['Tipo_cidade'] = np.where(df['NO_MUNICIPIO_RESIDENCIA'].isin(Capital), 'Capital', 'Interior')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 402
    },
    "id": "zeHoCZ5Rc6pV",
    "outputId": "defc99c8-d3e2-4890-c2f1-e9f0c543ea9d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15,5))\n",
    "variavel = 'Regiao_moradia'\n",
    "df[variavel].value_counts(normalize=True).plot.bar(ax=axs[0], grid=True)\n",
    "axs[0].set_xlabel(variavel)\n",
    "\n",
    "variavel = 'Tipo_cidade'\n",
    "df[variavel].value_counts(normalize=True).plot.bar(ax=axs[1], grid=True)\n",
    "axs[1].set_xlabel(variavel)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "O9nh2JiHzA1c"
   },
   "outputs": [],
   "source": [
    "df['mudou_endereco'] = np.where(df['NO_MUNICIPIO_NASCIMENTO']==df['NO_MUNICIPIO_RESIDENCIA'], False, True)\n",
    "df['moveu_prova'] = np.where(df['NO_MUNICIPIO_RESIDENCIA']==df['NO_MUNICIPIO_PROVA'], False, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "flYT7XHidnHH",
    "outputId": "c4cd8efd-656e-4292-c245-c756592b3c8c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, figsize=(15,5))\n",
    "variavel = 'mudou_endereco'\n",
    "df[variavel].value_counts(normalize=True).plot.bar(ax=axs[0], grid=True)\n",
    "axs[0].set_xlabel(variavel)\n",
    "\n",
    "variavel = 'moveu_prova'\n",
    "df[variavel].value_counts(normalize=True).plot.bar(ax=axs[1], grid=True)\n",
    "axs[1].set_xlabel(variavel)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4FJekPYcuhkr",
    "outputId": "fb99f607-11d6-4989-c9ef-f8fbbc34880c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17    779146\n",
       "18    743880\n",
       "19    454445\n",
       "20    304136\n",
       "16    297390\n",
       "       ...  \n",
       "84         1\n",
       "85         1\n",
       "86         1\n",
       "88         1\n",
       "1          1\n",
       "Name: NU_IDADE, Length: 89, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['NU_IDADE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "eZUHPSh9TnVs"
   },
   "outputs": [],
   "source": [
    "df['faixa_idades']=pd.cut(\n",
    "   df['NU_IDADE'],\n",
    "   bins=[0,16, 19, 22, 25, 29, 35, 40, 50, 60, 70, 80, 90],\n",
    "   labels=['0-15','16-18','19-21','22-24','25-28', '29-34', '35-39', '40-49', '50-59','60-69','70-79','80-89']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "Wsmd5c-Y2BMU",
    "outputId": "3efa8a24-bc10-4422-fed6-ade27bb4a9b6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa11342c828>"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['faixa_idades'].value_counts(normalize=True).sort_index().plot.bar(figsize=(10,6), grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 443
    },
    "id": "LF_DDIBAga6o",
    "outputId": "0a38c4f1-01e3-45cf-fc76-60604b5b50db"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "faixa_idades\n"
     ]
    },
    {
     "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>descricao</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>porcentagem</th>\n",
       "      <th>acumulado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0-15</td>\n",
       "      <td>374882</td>\n",
       "      <td>0.095352</td>\n",
       "      <td>0.095352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16-18</td>\n",
       "      <td>1977471</td>\n",
       "      <td>0.502973</td>\n",
       "      <td>0.598324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19-21</td>\n",
       "      <td>667491</td>\n",
       "      <td>0.169777</td>\n",
       "      <td>0.768102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>22-24</td>\n",
       "      <td>302316</td>\n",
       "      <td>0.076895</td>\n",
       "      <td>0.844996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25-28</td>\n",
       "      <td>205566</td>\n",
       "      <td>0.052286</td>\n",
       "      <td>0.897282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29-34</td>\n",
       "      <td>175903</td>\n",
       "      <td>0.044741</td>\n",
       "      <td>0.942023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35-39</td>\n",
       "      <td>96279</td>\n",
       "      <td>0.024489</td>\n",
       "      <td>0.966512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>40-49</td>\n",
       "      <td>95193</td>\n",
       "      <td>0.024212</td>\n",
       "      <td>0.990725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50-59</td>\n",
       "      <td>30824</td>\n",
       "      <td>0.007840</td>\n",
       "      <td>0.998565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60-69</td>\n",
       "      <td>5178</td>\n",
       "      <td>0.001317</td>\n",
       "      <td>0.999882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>70-79</td>\n",
       "      <td>445</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.999995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>80-89</td>\n",
       "      <td>20</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   descricao  frequencia  porcentagem  acumulado\n",
       "0       0-15      374882     0.095352   0.095352\n",
       "1      16-18     1977471     0.502973   0.598324\n",
       "2      19-21      667491     0.169777   0.768102\n",
       "3      22-24      302316     0.076895   0.844996\n",
       "4      25-28      205566     0.052286   0.897282\n",
       "5      29-34      175903     0.044741   0.942023\n",
       "6      35-39       96279     0.024489   0.966512\n",
       "7      40-49       95193     0.024212   0.990725\n",
       "8      50-59       30824     0.007840   0.998565\n",
       "9      60-69        5178     0.001317   0.999882\n",
       "10     70-79         445     0.000113   0.999995\n",
       "11     80-89          20     0.000005   1.000000"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "variavel = 'faixa_idades'\n",
    "tabela = pd.DataFrame({\n",
    "    'descricao': list(df[variavel].dtype.categories),\n",
    "    'frequencia': list(df[variavel].value_counts().sort_index()),\n",
    "    'porcentagem': list(df[variavel].value_counts(normalize=True).sort_index())\n",
    "})\n",
    "tabela['acumulado'] = tabela['porcentagem'].cumsum()\n",
    "print(variavel)\n",
    "display(tabela)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "ii55DbNpZDtg"
   },
   "outputs": [],
   "source": [
    "def cria_tabela(variavel):\n",
    "    tabela = pd.DataFrame({\n",
    "        'descricao': list(categorias[variavel].values()),\n",
    "        'frequencia': list(df[variavel].value_counts().sort_index()),\n",
    "        'porcentagem': list(df[variavel].value_counts(normalize=True).sort_index())\n",
    "    })\n",
    "    print(variavel)\n",
    "    display(tabela)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "DOGSj5oUxK9M",
    "outputId": "5c2baefa-76f4-4a5b-886d-619cd6f5d3dc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP_SEXO\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
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       "\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>descricao</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Masculino</td>\n",
       "      <td>1590845</td>\n",
       "      <td>0.404634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feminino</td>\n",
       "      <td>2340725</td>\n",
       "      <td>0.595366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   descricao  frequencia  porcentagem\n",
       "0  Masculino     1590845     0.404634\n",
       "1  Feminino      2340725     0.595366"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa111a4f470>"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4dakMvv19Ky+VSdIEGGNJkpr5oR+SJDUzxpIkNTPGkiQ1M8aSJDUzxpIkNfs/VrlLc/X6R3gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df['TP_SEXO'].value_counts(normalize=True).plot.bar(figsize=(10,6), grid=True)\n",
    "cria_tabela('TP_SEXO')\n",
    "df['TP_SEXO'].value_counts(normalize=True).sort_index().plot.bar(figsize=(8,4), grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "K9MXrOLua_Mx",
    "outputId": "8ccc7ac9-fbba-410b-b444-ede0264d728b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP_ESTADO_CIVIL\n"
     ]
    },
    {
     "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>descricao</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Não informado</td>\n",
       "      <td>140463</td>\n",
       "      <td>0.035727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Solteiro(a)</td>\n",
       "      <td>3488267</td>\n",
       "      <td>0.887245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Casado(a)/Mora com companheiro(a)</td>\n",
       "      <td>242327</td>\n",
       "      <td>0.061636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Divorciado(a)/Desquitado(a)/Separado(a)</td>\n",
       "      <td>56426</td>\n",
       "      <td>0.014352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Viúvo(a)</td>\n",
       "      <td>4087</td>\n",
       "      <td>0.001040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 descricao  frequencia  porcentagem\n",
       "0                            Não informado      140463     0.035727\n",
       "1                              Solteiro(a)     3488267     0.887245\n",
       "2        Casado(a)/Mora com companheiro(a)      242327     0.061636\n",
       "3  Divorciado(a)/Desquitado(a)/Separado(a)       56426     0.014352\n",
       "4                                 Viúvo(a)        4087     0.001040"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa111a3da90>"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E3OjH6aQ7gIX+A5m5ANwREZ+uGalzfj4zvwc/iMY5P8JrbxOsBvR+M+CvRMRNwIvV8zTJvzOWJKmYP2csSVIxYyxJUjFjLElSMWMsSVIxYyxJUrH/AztdxXHh2VHxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df['TP_ESTADO_CIVIL'].value_counts(normalize=True)\n",
    "cria_tabela('TP_ESTADO_CIVIL')\n",
    "df['TP_ESTADO_CIVIL'].value_counts(normalize=True).sort_index().plot.bar(figsize=(8,4), grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 517
    },
    "id": "u7D8tDU75Syl",
    "outputId": "9f614125-4440-4ccf-d4ae-86400adb2eef"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP_COR_RACA\n"
     ]
    },
    {
     "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>descricao</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Não declarado</td>\n",
       "      <td>78400</td>\n",
       "      <td>0.019941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Branca</td>\n",
       "      <td>1453618</td>\n",
       "      <td>0.369730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Preta</td>\n",
       "      <td>483856</td>\n",
       "      <td>0.123069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Parda</td>\n",
       "      <td>1802255</td>\n",
       "      <td>0.458406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amarela</td>\n",
       "      <td>90038</td>\n",
       "      <td>0.022901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Indígena</td>\n",
       "      <td>23403</td>\n",
       "      <td>0.005953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       descricao  frequencia  porcentagem\n",
       "0  Não declarado       78400     0.019941\n",
       "1         Branca     1453618     0.369730\n",
       "2          Preta      483856     0.123069\n",
       "3          Parda     1802255     0.458406\n",
       "4        Amarela       90038     0.022901\n",
       "5      Indígena        23403     0.005953"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa111b0f780>"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Rw/jXgc9ExJeBZ7tjbwfeAUxf9k9tTKPAe4EXlo0H8E9Xv5yB9VxE3JyZTwJ075B/FngIGPjvzK+i1yLiLZn5CnDLG4MRsRW/Ef62zHwd+P2I+HT3v8+xsb9mX8lW4Aydr0kZETdk5r9HxAjr5BvhDbtNDRAR19D5IX/vL3Cd6n7Xrq6I+ATwycz8fJ9zD2fm+wrKGjgRsYPOXd/X+5x7T2b+Y0FZAycirs3Mb/YZ3wbckJlnC8oaeBGxH3hPZv5WdS3rRUS8BRjNzH+rrmUlGzqMJUkaBH7OWJKkYoaxJEnFDGNJkooZxpIkFTOMJUkq9r/QMxwOdUt50QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df['TP_COR_RACA'].value_counts(normalize=True).plot.bar(figsize=(10,6), grid=True)\n",
    "#df['TP_COR_RACA'].value_counts(normalize=True)\n",
    "cria_tabela('TP_COR_RACA')\n",
    "df['TP_COR_RACA'].value_counts(normalize=True).sort_index().plot.bar(figsize=(8,4), grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "id": "YIUOvKwy5ZOM",
    "outputId": "6fdce6a4-5bb8-43fb-9b39-fb91a3c4b9d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP_LINGUA\n"
     ]
    },
    {
     "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>descricao</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inglês</td>\n",
       "      <td>1939397</td>\n",
       "      <td>0.493288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Espanhol</td>\n",
       "      <td>1992173</td>\n",
       "      <td>0.506712</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  descricao  frequencia  porcentagem\n",
       "0    Inglês     1939397     0.493288\n",
       "1  Espanhol     1992173     0.506712"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa111a2dd68>"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df['TP_LINGUA'].value_counts(normalize=True).plot.bar(figsize=(10,6), grid=True)\n",
    "cria_tabela('TP_LINGUA')\n",
    "df['TP_LINGUA'].value_counts(normalize=True).sort_index().plot.bar(figsize=(8,4), grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 486
    },
    "id": "hRY9zwMw5Hb_",
    "outputId": "afee8f43-b84e-491c-ef63-eee1c9960de6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TP_NACIONALIDADE\n"
     ]
    },
    {
     "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>descricao</th>\n",
       "      <th>frequencia</th>\n",
       "      <th>porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Não informado</td>\n",
       "      <td>5353</td>\n",
       "      <td>0.001362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brasileiro(a)</td>\n",
       "      <td>3818053</td>\n",
       "      <td>0.971127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brasileiro(a) Naturalizado(a)</td>\n",
       "      <td>96799</td>\n",
       "      <td>0.024621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Estrangeiro(a)</td>\n",
       "      <td>4516</td>\n",
       "      <td>0.001149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brasileiro(a) Nato(a), nascido(a) no exterior</td>\n",
       "      <td>6849</td>\n",
       "      <td>0.001742</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       descricao  frequencia  porcentagem\n",
       "0                                  Não informado        5353     0.001362\n",
       "1                                  Brasileiro(a)     3818053     0.971127\n",
       "2                  Brasileiro(a) Naturalizado(a)       96799     0.024621\n",
       "3                                 Estrangeiro(a)        4516     0.001149\n",
       "4  Brasileiro(a) Nato(a), nascido(a) no exterior        6849     0.001742"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa11198fe80>"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9aPyd/X1qQEQ8dqpdwMQoa+mwt5y8NJ2ZxyJiCrgnIs5noc86ffOZ+b/ACxHxr5n5HEBmvhgR/n/RjEngRuDTwKcy89GIeLELIXySYby03wC+HhH/wqt/JOM84F3AzrKqumcC+EXgmUXjAfzD6MvppCcj4qLMfBSgf4b8YeAOYFNtaZ3xUkT8SGa+AFxycjAizsVf3huRmS8Dn4+Ir/a/PknH8qtT30xTMvO+iHg3C38+cvANXPv7vwGrGX8NrDoZFIMiojf6cjrpemB+cCAz54HrI+KLNSV1zi9k5vfhldA46a28eptgNSAzjwO/HBFXAc9V19MkXzOWJKmYnzOWJKmYYSxJUjHDWJKkYoaxJEnFDGNJkor9Hx/MEGVMelLvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df['TP_NACIONALIDADE'].value_counts(normalize=True).plot.bar(figsize=(10,6), grid=True)\n",
    "cria_tabela('TP_NACIONALIDADE')\n",
    "df['TP_NACIONALIDADE'].value_counts(normalize=True).sort_index().plot.bar(figsize=(8,4), grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HPEvw9DNdloP"
   },
   "source": [
    "## Correlação\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "xUPVRS-4HX3b"
   },
   "outputs": [],
   "source": [
    "def plota_boxplot_provas(variavel, rotate=False):\n",
    "    fig, axs = plt.subplots(1, 5, figsize=(24,5))\n",
    "    for idx, p in enumerate(provas):\n",
    "        ax = sns.boxplot(x=variavel, y=p, data=df, ax=axs[idx])\n",
    "        ax.set_ylim(-50, 1050)\n",
    "        ax.set_ylabel('')\n",
    "        ax.set_xlabel(p)\n",
    "    if rotate:\n",
    "        plt.draw()\n",
    "        for ax in axs:\n",
    "            ax.set_xticklabels(ax.get_xticklabels(), rotation=30, ha='right')\n",
    "    fig.suptitle(variavel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 335
    },
    "id": "AyUEpnBO5mFx",
    "outputId": "ea90e8e7-7132-4525-c1ee-c67d4e038eae"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gTD3jGc9YtHzqqacOVAl9OXjwYC699NIcPLjs1KgAAAxIkxYAgCd54IEHFi3ff//9A1VCX/bu3ZubbropV1xxxdClAACwhCYtAABP8vjjjx91mdFy8ODBXHPNNWmt5c///M+NpgUA2GTWNCctACu3Z8+ezM7Obshz7dq1q9f9TU1NZefOnb3uE4D1t3fv3jz22GNJksceeyxXXHFFXvva1w5cFQAA84ykBQCAMXfttdc+cfG31lre//73D1wRAAALGUkLsMHWayTq9PT0k9bt3r17XZ4LgNFy+umn584771y0DADA5mEkLQAAjLl//Md/POoyAADD0qQFGBMzMzNHXQYAAAA2J9MdHAcX+QEAYJSdfvrpuffeexctA+PP37IAo8NIWoAxsmPHjuzYscMoWgAWWdigXW4ZAIBhGUl7HFzkBwAAgFHjb1mA0WEkLQAAAADAgDRpB+QiPwAAAIwaf8sC9E+TFgAAAABgQOakHdiOHTuSmL8HAACA0eFv2fGxZ8+ezM7ODl3Gis3XvGvXroErWZ2pqal1mzea0aRJCwAAADChZmdn8/FP3ZzDJ506dCkrcsKjLUlyw633DlzJym156L6hS2AT0qQFAAAAmGCHTzo1Dz/3pUOXMTG23XL10CWwCWnSAsAqbfSpYX2fyuUUKwAAgM3BhcMAAAAAAAZkJC0ArNJ6jkKdnp5+0joX5mA5Gzmi22huAABYH0bSAgAAAAAMyEhaANiEZmZmFo2mnZmZGawWNrf1GolqNDcAAGwcI2kBAAAAAAakSQsAm9SOHTuyY8cOo2gZxNL3nfchAACsH01aAAAAAIABadICALAso7kBAGBjaNICAAAAAAxIkxYAAAAAYEBbhy4AAACYs2fPnszOzm7Ic+3atavX/U1NTWXnzp297hMAYFIYSQsAAAAAMCAjaQEAYJNYr5Go09PTT1q3e/fudXkuAABWTpOWiTLKpxAmTiMEAAAAGEemOwAAgDE3MzNz1GUAAIZlJC0TxSmEAAAAwLjayDOI+zRf83qclbze+jrrWZMWAAAmwI4dO5L4Inkc+AN845l2DBgVs7Oz+cynP5ZnnXJ46FJW5MTH5k72f+T2fQNXsjJ3PLilt31p0kIPZmZmFo2mdQohAADrZXZ2Nh//1M05fNKpQ5eyIic82pIkN9x678CVrMyWh+4bugSAFXnWKYfz8+d9YegyJsJbbvya3valSQsAADBiDp90ah5+7kuHLmMibLvl6qFLAGACuHAY9GTHjh3ZsWOHUbQAAAAArIgmLQAAAADAgDRpAQAAAAAGtKYmbVU9var+qKpuqaqbq+rbq+rUqrq2qj7T/fuMbtuqqt+oqtmq+mRVndfPSwDGhUwB+iRTgD7JFKBvcgVYaK0jaXcnuaa19twkO5LcnOQNST7YWjs3yQe75SR5SZJzu59Lkvz2Gp8bGD8yBeiTTAH6JFOAvskV4AmrbtJW1dOSfE+SdyZJa+3R1tr9SS5IsrfbbG+Sl3W3L0hyRZvzkSRPr6qzVl05MFZkCtAnmQL0SaYAfZMrwFJb1/DY5yQ5kOS/VNWOJDck2ZXkzNba3d029yQ5s7u9PcmdCx5/V7fu7gDIFKBfMgXok0wB+rZpcmX//v3Z8tAD2XbL1WvdFcdpy0MHs3//oaHLYJNZy3QHW5Ocl+S3W2vfkuRL+cow/CRJa60laSvZaVVdUlX7qmrfgQMH1lAeMGJkCtAnmQL0aV0yJZErMMF8VgEWWctI2ruS3NVa+2i3/EeZC5R7q+qs1trd3dD7z3X370/yzAWPP7tbt0hr7bIklyXJ+eefv+IPOcDIkilAn2QK0Kd1yZRErsAE2zSfVbZv3557Htmah5/70tW9ElZs2y1XZ/v2M4+9IRNl1U3a1to9VXVnVX19a+3vk7wwyd91PxcneWv371XdQ96X5Ker6sok35bkgQVD+IEJt9kyZc+ePZmdne1rdxtmvuZdu3YNXMnKTU1NZefOnUOXwZjYbJkCjDaZAvRNrgBLrWUkbZLsTPKuqjoxya1JfixzUyi8t6peneT2JC/vtr06yUuTzCZ5qNsWYKFNkymzs7P5+KduzuGTTu1zt+vuhEfnviy/4dZ7B65kZbY8dN/QJTCeNk2mAGNBpgB9kyvAE9bUpG2tfTzJ+cvc9cJltm1JXrOW5wPG22bLlMMnneqUnw3iIgWsh82UKUbnbzyj8+nbZsoUYDzIFWChtY6kBQDgGIzO31hG5wMAMGo0aQEANoDR+RvH6HwAAEbNCUMXAAAAAAAwyTRpAQAAAAAGZLoDAAAAABgD+/fvz5e+uCVvufFrhi5lItz+xS05ef/+XvY1Nk1aV03eeK6aDAAAAABrNzZNWldN3liumgwAALB2BhxtPAOOGGfbt2/PI4fuzs+f94WhS5kIb7nxa/LU7dt72dfYNGkTV03eSK6aDAAAsHazs7P5zKc/lmedcnjoUlbkxMfmLnHzyO37Bq5kZe54cMvQJQAsa6yatAAAm9H+/fuz5aEHfMm5QbY8dDD79x9al32P6oi3xKg32Myedcpho942iHk6gc1KkxaAsaahMgwNFcbVqI54S4x6Gye++NlY6/nFDwDM06QFWIY/fjbWev7xo6Gy8TRUnmz79u2555GtpmXaINtuuTrbt5+5bvs34m1jGfUGAEwCTVoAxp6GysbSUAFYX7742Vjr/cUPACSatADL8sfPxvLHDwAAAJPshKELAAAAAACYZEbSAgAAAEywLQ/dN3LX4zjhy3PTmT3+VaM31daWh+5L4kxCFtOkBQAAAJhQU1NTQ5ewKrOzX0ySTH3dKDY7zxzZ3zvrR5MWAAAAYELt3Llz6BJWZdeuXUmS3bt3D1wJ9EOTFgAAjtP+/fvzpS9uyVtuHL1TK0fV7V/ckpP37x+6DACAdaVJy6azZ8+ezM7ODl3Gis3XPP9t3qiZmpoa2W9QAUaBud42jnneAAAYNZq0bDqzs7P5zKc/lmedcnjoUlbkxMdOSJI8cvu+gStZuTse3DJ0CQBjbVTnHBvdud7Wb5637du355FDd+fnz/vCuuyfJ3vLjV+Tp27fPnQZAADramyatPv378+Whx4YuREqo2rLQwezf/+hddv/s0457I+fDeSUTYD1NapnKpjrDQAANsYJQxcAAAAAADDJxmYk7fbt23PPI1vz8HNfOnQpE2HbLVdn+/ZRO/URmEQu8rPxXOQHAABgZYykBQAAAAAY0NiMpAWA5bjIz8ZzkR8AAICV0aQFAACAgZiaaWOZlgnYrDRpAY5gy0P3ZdstVw9dxoqc8OW50aKPf9Vofcjf8tB9ScxzDQAAwGTSpAVYxtTU1NAlrMrs7BeTJFNfN2oNzzNH9ncOTJ47HhzNEW/3PjR3OYozT3p84EpW5o4Ht+TcoYuAdWRqpo1lWiZgs9KkBVjGzp07hy5hVXbt2pUk2b1798CVAIynUf5C6dHZ2STJU589Wq/h3Iz27x0A4Hho0gIAwHEa1S/xEl/kAcCkGMWzfpzxo0kLAAAwcsydv3HMnQ+MklE9+8QZP5q0bEKubrrxXOEUAGB0jOof4ObOB1h/o3rWjzN+NGkBAABGij/AAWD8aNKy6bi66cZzhVMAAACA4ZwwdAEAAAAAAJNsrEbSmjx/45g8HwAAAAD6MTZN2lGdyN3k+QDr744HR/NihPc+NHfCy5knPT5wJStzx4Nbcu7QRQAAAIyQsWnSmjwfgOWM8hdKj87OJkme+uzReg3nZrR/7wAAABttbJq0ALCcUf0SL/FFHgBMilE868cZPwD90qQFAACAgYzq2SfO+AHolyYtAAAADGRUz/pxxg9Av05Y6w6qaktVfayq/qxbfk5VfbSqZqvqPVV1Yrf+qd3ybHf/OWt9bmD8yBSgTzIF6JNMAfokU4CF+hhJuyvJzUnmJ9D55SRva61dWVXvSPLqJL/d/ftPrbWpqrqw2+6He3h+xpA5mTbWJpuXSaYAfZIpQJ9kCtAnmQI8YU1N2qo6O8n3J3lzkp+pqkryvUl+pNtkb5JfzFyoXNDdTpI/SvKbVVWttbaWGhg/ozo/0KjOyZRsnnmZZArQJ5kC9EmmAH2SKcBSax1J+/Yk/3eSr+6WT0tyf2vtULd8V5Lt3e3tSe5Mktbaoap6oNv+82usgTFjTqaJJlOAPsmUNfrEJz6RJJmens7MzMywxcDwZArQJ5kCLLLqOWmr6l8m+Vxr7YYe60lVXVJV+6pq34EDB/rcNbCJyRSgTzIF6NN6ZUq3b7kCE0amAMtZy4XDvjPJv66q25Jcmblh+buTPL2q5kfonp1kf3d7f5JnJkl3/9OSHFy609baZa2181tr559xxhlrKA8YMTIF6JNMWaPp6emjLsOEWZdMSSYrV4AnyBTgSVY93UFr7eeS/FySVNV0kte11l5RVf81yQ9mLmguTnJV95D3dcsf7u6/zvwpwDyZAvRpkjJlz549me3mRV9v81P79GVqampkpzliskxSpgDrT6YAy1nLSNojeX3mJr2ezdwcKe/s1r8zyWnd+p9J8oZ1eG5g/MgUoE8yBeiTTAH6JFNggq31wmFJktbaTJKZ7vatSZ6/zDZfTvJDfTwfMN5kCtCncc+U9RqJutz0Bi6OCeOfKcDGkinAvPUYSQsAAAAAwHHSpAUAAAAAGJAmLQAATIBPfOIT+cQnPrHsVBYAAAxLkxYAAAAAYEC9XDgMAADYvJaOnp2ens7MzMwgtbC57dmzJ7Ozs+uy7/n97tq1a132PzU1tW4XUxxVjifA6NCkBQCATWI9GypL9d1Y0VDhWLZt2zZ0CfTI8QTolyYtAAAASaLRPmYcT4DRoUkLAACbxHo1VJa7WNju3bvX5bkAAFg5Fw4DAAAAABiQJi0AAAAAwIA0aQEAAAAABqRJCwAAAAAwIBcOOw579uzJ7Ozsuux7fr+7du1al/1PTU25oucCo3wsE8cTAAAAYBxp0g5s27ZtQ5dATxxLAAAAAFZDk/Y4GLk4PhxLNoNRHtFtNPdi63ksE8cTAABgUmjSAowRI7rHi+MJAAAwGTRpATaYkYvjw7EEAACgDycMXQAAAJvPzMzMUZcZLY4nAMDmpkkLAAAAADAg0x0AALAsoy3Hi+MJALB5GUkLAAAAADAgTVoAAAAAgAFp0gIAAAAADEiTFgAAAABgQJq0AAAAAAAD0qQFAAAAABiQJi0AAAAAwIA0aQEAAAAABqRJCwAAAAAwIE1aAAAAAIABadICAAAAAAxIkxYAAAAAYECatAAAAAAAA9KkBQAAAAAYkCYtAAAAAMCANGkBAAAAAAakSQsAAAAAMCBNWgAAAACAAW0dugAAAAAAxs+ePXsyOzu7Lvue3++uXbvWZf9TU1PZuXPnuuwblqNJCwAAAMBI2bZt29AlQK9W3aStqmcmuSLJmUlakstaa7ur6tQk70lyTpLbkry8tfZPVVVJdid5aZKHkryqtXbj2soHxoVMAfokU4A+yRSgb5OSK+s5EnV6ejpJ8vnPfz4zMzPr9jywUdYyJ+2hJP9Xa+15SV6Q5DVV9bwkb0jywdbauUk+2C0nyUuSnNv9XJLkt9fw3MD4kSlAn2QK0CeZAvRNrgCLrLpJ21q7e/5bm9baF5PcnGR7kguS7O0225vkZd3tC5Jc0eZ8JMnTq+qsVVcOjBWZAvRJpgB9kilA3+TK2syPoj3SMoyiXuakrapzknxLko8mObO1dnd31z2ZG7qfzIXNnQsedle37u4ALCBTgD7JFKBPMgXom1xhVLgQ3Ppay3QHSZKqOiXJHyf5P1trX1h4X2utZW5ulZXs75Kq2ldV+w4cOLDW8oARI1OAPskUoE99Z0q3T7kCE8xnFZizbdu2ib8Y3JpG0lbVUzIXJu9qrf1Jt/reqjqrtXZ3N/T+c936/UmeueDhZ3frFmmtXZbksiQ5//zzV/whBxhdMgXok0wB+rQemZLIFZhkPqswalwIbn2teiRtd2XBdya5ubX26wvuel+Si7vbFye5asH6V9acFyR5YMEQfmDCyRSgTzIF6JNMAfomV4Cl1jKS9juT/GiSm6rq4926n0/y1iTvrapXJ7k9ycu7+65O8tIks0keSvJja3huYPzIFKBPMgXok0wB+iZXoLPchTisjOcAAA0zSURBVOAmcTTtqpu0rbUPJakj3P3CZbZvSV6z2ucDxptMAfokU4A+yRSgb3IFWGrNFw4DAAAAAGD1NGkBAAAAAAakSQsAAAAAMCBNWgAAAACAAWnSAgAAAAAMSJMWAAAAAGBAmrQAAAAAAAPSpAUAAAAAGJAmLQAAAADAgDRpAQAAAAAGpEkLAAAAADAgTVoAAAAAgAFp0gIAAAAADEiTFgAAAABgQJq0AAAAAAAD0qQFAAAAABiQJi0AAAAAwIA0aQEAAAAABqRJCwAAAMDIeMpTnrJo+cQTTxyoEuiPJi0AAAAAI+Oxxx5btPzoo48OVAn0R5MWAAAAAGBAmrQAAAAAAAPSpAUAAABgZJiTlnGkSQsAAADAyDAnLeNIkxYAAAAAYECatAAAAAAAA9KkBQAAAAAYkCYtAAAAAMCANGkBAAAAGBnf9E3ftGj5vPPOG6gS6I8mLQAAAAAj45Of/OSi5RtvvHGgSqA/mrQAAAAAAAPSpAUAAAAAGJAmLQAAAAAjw5y0jCNNWgAAAABGxoEDBxYtf+5znxuoEuiPJi0AAAAAI+Puu+9etHzXXXcNVAn0R5MWAAAAAGBAmrQAAAAAAAPSpAUAAABgZJx11lmLls8+++yBKoH+aNICAAAAMDLe/e53L1r+gz/4g4EqoQ8zMzNHXZ4UmrQAAAAAjJT50bRG0TIutg5dAAAAAACsxNLRtIy2SR09u9CGN2mr6sVJdifZkuR3W2tv3egaNpPp6eknbntDjjbHchgyZTHvw/HieG48mbKY9+B4cTw3nkxZzHtwvDieG0+mLOY9OF4czw2e7qCqtiT5rSQvSfK8JBdV1fM2sgZgfMgUoE8yBeiTTAH6JFNg/G30nLTPTzLbWru1tfZokiuTXLDBNWwaC78lWG6Z0eFYDkamLOB9OF4cz0HIlAW8B8eL4zkImbKA9+B4cTwHIVMW8B4cL47nnI1u0m5PcueC5bu6dQCrIVOAPskUoE8yBeiTTIExt9FN2mOqqkuqal9V7Ttw4MDQ5QAjTqYAfZIpQN/kCtAnmQKja6ObtPuTPHPB8tnduie01i5rrZ3fWjv/jDPO2NDigJEjU4A+yRSgT8fMlESuAMdNpsCY2+gm7d8mObeqnlNVJya5MMn7NrgGYHzIFKBPMgXok0wB+iRTYMxtaJO2tXYoyU8n+YskNyd5b2vt0xtZw2YyMzNz1GVGh2M5DJmymPfheHE8N55MWcx7cLw4nhtPpizmPTheHM+NJ1MW8x4cL47nnK0b/YSttauTXL3RzwuMJ5kC9EmmAH2SKUCfZAqMtw1v0rLYpH47MI4cSzYD78Px4ngyNO/B8eJ4MjTvwfHieDI078Hx4nhu/Jy0AAAAAAAsoEkLAAAAADAgTVoAAAAAgAFp0gIAAAAADEiTFgAAAABgQJq0AAAAAAADqtba0DUcUVUdSHL70HVsgNOTfH7oIujFpBzLZ7fWzhi6iJWSKYyoSTieMmVzm4T34CSZhOM5kpmSTEyuTMJ7cJJMwvGUKZvbJLwHJ8mkHM9lc2VTN2knRVXta62dP3QdrJ1jyWbgfTheHE+G5j04XhxPhuY9OF4cT4bmPTheJv14mu4AAAAAAGBAmrQAAAAAAAPSpN0cLhu6AHrjWLIZeB+OF8eToXkPjhfHk6F5D44Xx5OheQ+Ol4k+nuakBQAAAAAYkJG0AAAAAAAD0qQdWFW9uKr+vqpmq+oNQ9fD6lTV71XV56rqU0PXwmSTKeNDrrAZyJTxIVPYDGTK+JApbAYyZXzIlDmatAOqqi1JfivJS5I8L8lFVfW8YatilS5P8uKhi2CyyZSxc3nkCgOSKWPn8sgUBiRTxs7lkSkMSKaMncsjUzRpB/b8JLOttVtba48muTLJBQPXxCq01v4qyX1D18HEkyljRK6wCciUMSJT2ARkyhiRKWwCMmWMyJQ5mrTD2p7kzgXLd3XrAFZDpgB9kilAn2QK0CeZwtjRpAUAAAAAGJAm7bD2J3nmguWzu3UAqyFTgD7JFKBPMgXok0xh7GjSDutvk5xbVc+pqhOTXJjkfQPXBIwumQL0SaYAfZIpQJ9kCmNHk3ZArbVDSX46yV8kuTnJe1trnx62Klajqt6d5MNJvr6q7qqqVw9dE5NHpowXucLQZMp4kSkMTaaMF5nC0GTKeJEpc6q1NnQNAAAAAAATy0haAAAAAIABadICAAAAAAxIkxYAAAAAYECatAAAAAAAA9KkBQAAAAAYkCYtAAAAAMCANGknRFW1qvq1Bcuvq6pf7G5fXlU/uGT7B4+yr3O6/e1csO43q+pV3e2qqn9fVZ+pqn+oquur6n/t7vtoVX28qu6oqgPd7Y93+9zarXvrcbyep1TVW7vnuLGqPlxVL+nuu62q/njBtj9YVZcf568KOE4TmCunL9h2uqr+7Hh/V8DxGcNcmamq85dZ//yq+quq+vuq+lhV/W5VnXTs3xBwvMY0T+6oqlqw7r9X1YNV9Y0L9ntfVX22u/2B4/+Nwfga0zz4+6r6RFX9bVV984L7bquqmxbs+zcWvM7Pdo/5h6q6oqrOXrLfl3Wv7blL1h/1c0uXRR9Z8pgj/h44Mk3ayfFIkh+oBU2GNfpckl1VdeIy970myXck2dFa+1+S/Mck76uqr2qtfVtr7ZuT/L9J3tNa++bu57Yk35fkH5L80MIPH0fwxiRnJfmG1tp5SV6W5KsX3P+tVfW8tbxA4JgmLVeA9TduufIkVXVmkv+a5PWtta9vrX1Lkmsib6Bv45gn9yf5ziSpqqdn7nNLWms3ze83yfuS/Gy3/KI1vWIYH+OYB69ore1I8p+T/MqS+/73Bfu+dMH6n+0e8/VJPpbkuiWv4aIkH+r+TXLszy1dFn1rkqdV1dcdz+/hOF7bxNKknRyHklyW5LU97e9Akg8muXiZ+16f5Kdbaw8lSWvt/Un+JskrjrHPi5LsTnJHkm8/0kbdNzY/kWRna+2R7jnuba29d8Fmv5bk/zm+lwKs0qTlCrD+xiZXjuI1Sfa21j48v6K19kettXtXsS/gyMYxT65McmF3+weS/MlxPAYYzzyY9+Ek21ewfdqctyW5J8n8mYOnJPmuJK/OV3ImOfbnlh9I8qdZnE/J6n8PE02TdrL8VpJXVNXTetrfLyd5XVVtmV9RVV+T5OTW2q1Ltt2X5IhD27tvU16Uuf+4350F39wsYyrJHa21Lxxlm/cmOa+qpo7+EoA1mqRcuX7+tKEkv3v0lwGswbjkypF8Q5IbVvE4YOXGLU8+mOR7uue/MMl7VlY+TLRxy4N5L07y35ese+Lvlqo6WmP6xiTzUxtckOSa1to/JDlYVd/arT/W55aLupqfqHu1vwc0aSdK13y4IsmlS+9abvPj2N+tST6a5EfWXl3+ZZLrW2sPJ/njJC9bGHarcDhzQ/5/rofagCOYsFx54rShJD/eQ33AMiYsV4B1NIZ5cjhzpyJfmGRbd4o0cBzGMA/eVVWfzdwZxL+15L6F0x287Sj7WDitwkWZGw2b7t9jNoq7qRDOTfKhrrn7WFV9w7Eex5Fp0k6et2du+PrJC9YdTPKM+YWqOjXJ549zf2/J3DD2Sp4Ivi8tmYskmZuj5NNH2c9FSV5UVbdl7lua05J87xG2nU3yrO7bmaP5/STfk+SZx9gOWJtJyhVgY4xDrhzJp7vnATbGuOXJlUl+I3NnDgIrM0558IokX5dkb5I9x1nvUt+S5ObuNX9vkt/tavjZJC/v5sY92ueWl2fud/fZ7nHnJLloDb+HiadJO2Faa/dl7n/or16weibJDy+YMPpVSa4/zv3dkuTvkvyrBat/JclvVNW2JKmqF2VubpM/XG4fXVPku5M8q7V2TmvtnMzNe7LsNzfdnCbvTLJ7vuaqOqOqfmjJdo8leVv6m3cGWMYk5QqwMcYhV47iN5NcXFXftmDfP9CNRgF6NoZ58teZuwDPu4+nXuArxi0PWmstyX9I8oKqeu6xtl/wnFVVl2bu4oPXJPnBJL/fWnt2V8Mzk3y2q+ton1suSvLiBXV/a74yL+2Kfg/M2Tp0AQzi15L89PxCa+3PuvlGbqiqw0n+R5KfWsH+3py5KwPO25O5b1Nu6vZ3T5ILuqH7y/k3Sa6bv1hP56ok/6mqnrpk/bx/n+RNSf6uqr6c5EuZu0LiUu/stgXW1yTlCrAxxiFXkuT/q6rHutsfbq39UFVdmORXq+prkzye5K8y90cSsD7GJU/mmzK/uoJagcXGJg+6+h+uql/L3OjX+ebz9d1zJ8knW2uv7G7/SlX9hyQnJflI5qZFeLSqLsrcHLsL/XHmRsX+uyN8brklybO7/czX8tmqeqBr6K7090CSmst4AAAAAACGYLoDAAAAAIABme6AI6qqb8zcxbcWeqS19m3Lbb9ONfy3JM9Zsvr1rbW/2KgagP7IFaBvcgXoizwB5skDhmC6AwAAAACAAZnuAAAAAABgQJq0AAAAAAAD0qQFAAAAABiQJi0AAAAAwIA0aQEAAAAABvT/A4o49vcHq35KAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1728x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.boxplot(x='TP_LINGUA',y='NU_NOTA_LC',data=df)\n",
    "plota_boxplot_provas('TP_LINGUA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 353
    },
    "id": "4Cki1Kn2jkz9",
    "outputId": "b301f58f-4d47-4180-9deb-2986ae31e439"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'0': 'Não declarado', '1': 'Branca', '2': 'Preta', '3': 'Parda', '4': 'Amarela', '5': 'Indígena '}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1728x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(categorias['TP_COR_RACA'])\n",
    "plota_boxplot_provas('TP_COR_RACA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 335
    },
    "id": "A30K9ImZs0yV",
    "outputId": "0079dd83-3794-44c5-9a94-a5ddf1afa625"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plota_boxplot_provas('TP_SEXO')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "CY-fuCSQtj4t",
    "outputId": "49cba091-73a9-450d-8805-3da8c3712bea"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': 'Não informado',\n",
       " '1': 'Solteiro(a)',\n",
       " '2': 'Casado(a)/Mora com companheiro(a)',\n",
       " '3': 'Divorciado(a)/Desquitado(a)/Separado(a)',\n",
       " '4': 'Viúvo(a)'}"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1728x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(categorias['TP_ESTADO_CIVIL'])\n",
    "plota_boxplot_provas('TP_ESTADO_CIVIL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MfdF7eCOI4cp",
    "outputId": "c5202633-89c9-4850-cb9a-784ca9b2dbc2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CategoricalDtype(categories=['0-15', '16-18', '19-21', '22-24', '25-28', '29-34', '35-39',\n",
       "                  '40-49', '50-59', '60-69', '70-79', '80-89'],\n",
       "                 ordered=True)"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['faixa_idades'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 347
    },
    "id": "yEEgl2XSIdvm",
    "outputId": "9497b684-ac9d-4b01-928d-40268e837450"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plota_boxplot_provas('faixa_idades', rotate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 368
    },
    "id": "aBTFrh-0vCq1",
    "outputId": "b0895f4d-bfc2-4668-d561-91017f102d15"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plota_boxplot_provas('Regiao_moradia', rotate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 335
    },
    "id": "zpE_Ogk2vnoM",
    "outputId": "13cf1282-0654-4ccb-f1fc-3048dc2af973"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x360 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plota_boxplot_provas('Tipo_cidade')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "zi4ZKgRVP0Hx",
    "outputId": "6ac53413-c022-42ed-e9d9-ed3b5085fb31"
   },
   "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>media</th>\n",
       "      <th>mediana</th>\n",
       "      <th>minimo</th>\n",
       "      <th>maximo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_CN</th>\n",
       "      <td>477.129486</td>\n",
       "      <td>470.299988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>860.900024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_CH</th>\n",
       "      <td>507.144409</td>\n",
       "      <td>510.600006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>835.099976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_LC</th>\n",
       "      <td>520.025940</td>\n",
       "      <td>526.200012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>801.700012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <td>522.217407</td>\n",
       "      <td>501.100006</td>\n",
       "      <td>0.0</td>\n",
       "      <td>985.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NU_NOTA_REDACAO</th>\n",
       "      <td>571.218384</td>\n",
       "      <td>580.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      media     mediana  minimo       maximo\n",
       "NU_NOTA_CN       477.129486  470.299988     0.0   860.900024\n",
       "NU_NOTA_CH       507.144409  510.600006     0.0   835.099976\n",
       "NU_NOTA_LC       520.025940  526.200012     0.0   801.700012\n",
       "NU_NOTA_MT       522.217407  501.100006     0.0   985.500000\n",
       "NU_NOTA_REDACAO  571.218384  580.000000     0.0  1000.000000"
      ]
     },
     "execution_count": 37,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\n",
    "    'media': df[provas].mean(),\n",
    "    'mediana': df[provas].median(),\n",
    "    'minimo': df[provas].min(),\n",
    "    'maximo': df[provas].max()\n",
    "})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qBGWsdxRwAux"
   },
   "source": [
    "## Conclusões\n",
    "\n",
    "**Distribuições e características**\n",
    "\n",
    "- As regiões com maior número de participantes são os estados de Sudeste e Nordeste.\n",
    "- Basicamente 80% das pessoas que realizam o ENEM moram no interior\n",
    "- Quase 70% dos participantes moram no mesmo local onde nasceram.\n",
    "- Menos de 20% das pessoas fizeram prova em um município diferente do que moram.\n",
    "- Mais de 50% de quem faz a prova tem entre 16 e 18 anos, mais de 80% tem menos de 28.\n",
    "- Aproximadamente 60% dos participantes é do sexo feminino.\n",
    "- Cerca de 90% dos participantes são solteiros.\n",
    "- Quase metade dos participantes se declara pardo, depois 37% se declara branco, e 12% preto. A porcentagem de quem não se declarou é baixa.\n",
    "- A quantidade que escolhe inglês e espanhol é quase igual.\n",
    "- O número de não brasileiros fazendo a prova é quase desprezível.\n",
    " \n",
    "**Correlação.**\n",
    "\n",
    "- Alunos que escolheram inglês tiveram uma nota levemente superior.\n",
    "- Brancos obtiveram notas mais altas, sobretudo em matemática. \n",
    "- Os homens tiram notas maiores em matemática enquanto as mulheres se sobressaem levemente na redação.\n",
    "- Aparentemente pessoas que tem/tiveram um companheiro tiveram um desempenho levemente pior.\n",
    "- Conforme a idade aumenta as notas são menores, sobretudo em matemática e redacao.\n",
    "- A regiao sudeste apresenta as melhores notas, mas essa diferença é mais significativa na nota de matemática.\n",
    "- Capital e interior não mostraram diferenças significativas de notas."
   ]
  }
 ],
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