Filtrando e Exportando para CSV os dados do ENEM por Estado Brasileiro.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": "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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"550\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/L50fiScthSc\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x10ab3b1d0>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo \n",
    "YouTubeVideo('L50fiScthSc', width=550, height=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtrando e Exportando para CSV os dados do ENEM por Estado Brasileiro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importando o pacote <strong>Pandas</strong> único que será utilizado nessa aula:\n",
    "\n",
    "<em>Documentação do Pandas</em>: https://pandas.pydata.org/pandas-docs/stable/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Link para os microdados do enem 2019: http://inep.gov.br/microdados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import zipfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LEIA-ME E DOCUMENTOS TÉCNICOS/\n",
      "LEIA-ME E DOCUMENTOS TÉCNICOS/matriz_referencia_enem.pdf\n",
      "LEIA-ME E DOCUMENTOS TÉCNICOS/Leia_Me_Enem_2019.pdf\n",
      "LEIA-ME E DOCUMENTOS TÉCNICOS/manual_de_redacao_do_enem_2019.pdf\n",
      "LEIA-ME E DOCUMENTOS TÉCNICOS/Edital_enem_2019.pdf\n",
      "PROVAS E GABARITOS/\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_02_DIA_1_AMARELO.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_02_DIA_1_AMARELO_AMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_02_DIA_1_AMARELO_SUPERAMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_03_DIA_1_BRANCO.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_04_DIA_1_ROSA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_09_DIA_1_LARANJA_LEDOR.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_10_DIA_1_VERDE_LIBRAS.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_05_DIA_2_AMARELO.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_02_DIA_1_AMARELO.pdf.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_02_DIA_1_AMARELO_AMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_02_DIA_1_AMARELO_SUPERAMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_03_DIA_1_BRANCO.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_04_DIA_1_ROSA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_09_DIA_1_LARANJA_LEDOR.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_10_DIA_1_VERDE_LIBRAS.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_05_DIA_2_AMARELO.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_05_DIA_2_AMARELO_SUPERAMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_06_DIA_2_CINZA_AMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_06_DIA_2_CINZA_SUPERAMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_07_DIA_2_AZUL.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_08_DIA_2_ROSA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_06_DIA_2_CINZA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_11_DIA_2_LARANJA_LEDOR.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_12_DIA_2_VERDE_LIBRAS.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_01_DIA_1_AZUL.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_06_DIA_2_CINZA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_06_DIA_2_CINZA_AMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_06_DIA_2_CINZA_SUPERAMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_07_DIA_2_AZUL.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_08_DIA_2_ROSA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_11_DIA_2_LARANJA_LEDOR.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_GAB_12_DIA_2_VERDE_LIBRAS.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_01_DIA_1_AZUL.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_01_DIA_1_AZUL_AMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_01_DIA_1_AZUL_AMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_01_DIA_1_AZUL_SUPERAMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_02_DIA_1_AMARELO.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_03_DIA_1_BRANCO.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_04_DIA_1_ROSA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_09_DIA_1_LARANJA_LEDOR.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_05_DIA_2_AMARELO.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_06_DIA_2_CINZA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_02_DIA_1_AMARELO.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_03_DIA_1_BRANCO.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_04_DIA_1_ROSA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_09_DIA_1_LARANJA_LEDOR.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_05_DIA_2_AMARELO.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_05_DIA_2_AMARELO_AMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_05_DIA_2_AMARELO_SUPERAMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_06_DIA_2_CINZA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_07_DIA_2_AZUL.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_08_DIA_2_ROSA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_CAD_11_DIA_2_LARANJA_LEDOR.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_01_DIA_1_AZUL.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_05_DIA_2_AMARELO_AMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P1_CAD_01_DIA_1_AZUL.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_01_DIA_1_AZUL_SUPERAMPLIADA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_07_DIA_2_AZUL.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_08_DIA_2_ROSA.pdf\n",
      "PROVAS E GABARITOS/ENEM_2019_P2_GAB_11_DIA_2_LARANJA_LEDOR.pdf\n",
      "DADOS/\n",
      "DADOS/MICRODADOS_ENEM_2019.csv\n",
      "DADOS/ITENS_PROVA_2019.csv\n",
      "DICION╡RIO/\n",
      "DICION╡RIO/Dicionário_Microdados_Enem_2019.ods\n",
      "DICION╡RIO/Dicionário_Microdados_Enem_2019.xlsx\n",
      "INPUTS/\n",
      "INPUTS/INPUT_R_MICRODADOS_ENEM_2019.R\n",
      "INPUTS/INPUT_SAS_ITENS_PROVA_2019.sas\n",
      "INPUTS/INPUT_SAS_MICRODADOS_ENEM_2019.sas\n",
      "INPUTS/INPUT_SPSS_ITENS_PROVA_2019.sps\n",
      "INPUTS/INPUT_SPSS_MICRODADOS_ENEM_2019.sps\n",
      "INPUTS/INPUT_R_ITENS_PROVA_2019.R\n"
     ]
    }
   ],
   "source": [
    "with zipfile.ZipFile('../dados/microdados_enem_2019.zip') as z:\n",
    "    print(*z.namelist(),sep=\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 16s, sys: 35.3 s, total: 1min 51s\n",
      "Wall time: 2min 15s\n"
     ]
    }
   ],
   "source": [
    "with zipfile.ZipFile('../dados/microdados_enem_2019.zip') as z:\n",
    "    with z.open('DADOS/MICRODADOS_ENEM_2019.csv') as f:\n",
    "        %time microdados_enem = pd.read_csv(f,sep=';',encoding='ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "171 µs ± 47.4 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit\n",
    "microdados_enem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>NU_INSCRICAO</th>\n",
       "      <th>NU_ANO</th>\n",
       "      <th>CO_MUNICIPIO_RESIDENCIA</th>\n",
       "      <th>NO_MUNICIPIO_RESIDENCIA</th>\n",
       "      <th>CO_UF_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>...</th>\n",
       "      <th>Q016</th>\n",
       "      <th>Q017</th>\n",
       "      <th>Q018</th>\n",
       "      <th>Q019</th>\n",
       "      <th>Q020</th>\n",
       "      <th>Q021</th>\n",
       "      <th>Q022</th>\n",
       "      <th>Q023</th>\n",
       "      <th>Q024</th>\n",
       "      <th>Q025</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>190001004627</td>\n",
       "      <td>2019</td>\n",
       "      <td>1506807</td>\n",
       "      <td>Santarém</td>\n",
       "      <td>15</td>\n",
       "      <td>PA</td>\n",
       "      <td>21</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>190001004628</td>\n",
       "      <td>2019</td>\n",
       "      <td>1504059</td>\n",
       "      <td>Mãe do Rio</td>\n",
       "      <td>15</td>\n",
       "      <td>PA</td>\n",
       "      <td>16</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>190001004629</td>\n",
       "      <td>2019</td>\n",
       "      <td>1505502</td>\n",
       "      <td>Paragominas</td>\n",
       "      <td>15</td>\n",
       "      <td>PA</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190001004630</td>\n",
       "      <td>2019</td>\n",
       "      <td>1507706</td>\n",
       "      <td>São Sebastião da Boa Vista</td>\n",
       "      <td>15</td>\n",
       "      <td>PA</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>190001004631</td>\n",
       "      <td>2019</td>\n",
       "      <td>1503903</td>\n",
       "      <td>Juruti</td>\n",
       "      <td>15</td>\n",
       "      <td>PA</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 136 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   NU_INSCRICAO  NU_ANO  CO_MUNICIPIO_RESIDENCIA     NO_MUNICIPIO_RESIDENCIA  \\\n",
       "0  190001004627    2019                  1506807                    Santarém   \n",
       "1  190001004628    2019                  1504059                  Mãe do Rio   \n",
       "2  190001004629    2019                  1505502                 Paragominas   \n",
       "3  190001004630    2019                  1507706  São Sebastião da Boa Vista   \n",
       "4  190001004631    2019                  1503903                      Juruti   \n",
       "\n",
       "   CO_UF_RESIDENCIA SG_UF_RESIDENCIA  NU_IDADE TP_SEXO  TP_ESTADO_CIVIL  \\\n",
       "0                15               PA        21       M                1   \n",
       "1                15               PA        16       F                1   \n",
       "2                15               PA        18       F                1   \n",
       "3                15               PA        23       M                0   \n",
       "4                15               PA        23       M                1   \n",
       "\n",
       "   TP_COR_RACA  ...  Q016  Q017 Q018  Q019 Q020  Q021  Q022  Q023  Q024  Q025  \n",
       "0            3  ...     A     A    A     C    B     A     D     A     B     A  \n",
       "1            3  ...     A     A    A     B    B     A     B     A     A     A  \n",
       "2            1  ...     B     A    A     D    B     B     D     A     C     B  \n",
       "3            3  ...     A     A    A     C    A     A     D     A     A     A  \n",
       "4            3  ...     A     A    A     B    A     A     D     A     A     A  \n",
       "\n",
       "[5 rows x 136 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microdados_enem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['NU_INSCRICAO', 'NU_ANO', 'CO_MUNICIPIO_RESIDENCIA',\n",
       "       'NO_MUNICIPIO_RESIDENCIA', 'CO_UF_RESIDENCIA', 'SG_UF_RESIDENCIA',\n",
       "       'NU_IDADE', 'TP_SEXO', 'TP_ESTADO_CIVIL', 'TP_COR_RACA',\n",
       "       'TP_NACIONALIDADE', 'CO_MUNICIPIO_NASCIMENTO',\n",
       "       'NO_MUNICIPIO_NASCIMENTO', 'CO_UF_NASCIMENTO', 'SG_UF_NASCIMENTO',\n",
       "       'TP_ST_CONCLUSAO', 'TP_ANO_CONCLUIU', 'TP_ESCOLA', 'TP_ENSINO',\n",
       "       'IN_TREINEIRO', 'CO_ESCOLA', 'CO_MUNICIPIO_ESC',\n",
       "       'NO_MUNICIPIO_ESC', 'CO_UF_ESC', 'SG_UF_ESC',\n",
       "       'TP_DEPENDENCIA_ADM_ESC', 'TP_LOCALIZACAO_ESC', 'TP_SIT_FUNC_ESC',\n",
       "       'IN_BAIXA_VISAO', 'IN_CEGUEIRA', 'IN_SURDEZ',\n",
       "       'IN_DEFICIENCIA_AUDITIVA', 'IN_SURDO_CEGUEIRA',\n",
       "       'IN_DEFICIENCIA_FISICA', 'IN_DEFICIENCIA_MENTAL',\n",
       "       'IN_DEFICIT_ATENCAO', 'IN_DISLEXIA', 'IN_DISCALCULIA',\n",
       "       'IN_AUTISMO', 'IN_VISAO_MONOCULAR', 'IN_OUTRA_DEF', 'IN_GESTANTE',\n",
       "       'IN_LACTANTE', 'IN_IDOSO', 'IN_ESTUDA_CLASSE_HOSPITALAR',\n",
       "       'IN_SEM_RECURSO', 'IN_BRAILLE', 'IN_AMPLIADA_24', 'IN_AMPLIADA_18',\n",
       "       'IN_LEDOR', 'IN_ACESSO', 'IN_TRANSCRICAO', 'IN_LIBRAS',\n",
       "       'IN_TEMPO_ADICIONAL', 'IN_LEITURA_LABIAL', 'IN_MESA_CADEIRA_RODAS',\n",
       "       'IN_MESA_CADEIRA_SEPARADA', 'IN_APOIO_PERNA', 'IN_GUIA_INTERPRETE',\n",
       "       'IN_COMPUTADOR', 'IN_CADEIRA_ESPECIAL', 'IN_CADEIRA_CANHOTO',\n",
       "       'IN_CADEIRA_ACOLCHOADA', 'IN_PROVA_DEITADO', 'IN_MOBILIARIO_OBESO',\n",
       "       'IN_LAMINA_OVERLAY', 'IN_PROTETOR_AURICULAR', 'IN_MEDIDOR_GLICOSE',\n",
       "       'IN_MAQUINA_BRAILE', 'IN_SOROBAN', 'IN_MARCA_PASSO', 'IN_SONDA',\n",
       "       'IN_MEDICAMENTOS', 'IN_SALA_INDIVIDUAL', 'IN_SALA_ESPECIAL',\n",
       "       'IN_SALA_ACOMPANHANTE', 'IN_MOBILIARIO_ESPECIFICO',\n",
       "       'IN_MATERIAL_ESPECIFICO', 'IN_NOME_SOCIAL', 'CO_MUNICIPIO_PROVA',\n",
       "       'NO_MUNICIPIO_PROVA', 'CO_UF_PROVA', 'SG_UF_PROVA',\n",
       "       'TP_PRESENCA_CN', 'TP_PRESENCA_CH', 'TP_PRESENCA_LC',\n",
       "       'TP_PRESENCA_MT', 'CO_PROVA_CN', 'CO_PROVA_CH', 'CO_PROVA_LC',\n",
       "       'CO_PROVA_MT', 'NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n",
       "       'NU_NOTA_MT', 'TX_RESPOSTAS_CN', 'TX_RESPOSTAS_CH',\n",
       "       'TX_RESPOSTAS_LC', 'TX_RESPOSTAS_MT', 'TP_LINGUA',\n",
       "       'TX_GABARITO_CN', 'TX_GABARITO_CH', 'TX_GABARITO_LC',\n",
       "       'TX_GABARITO_MT', 'TP_STATUS_REDACAO', 'NU_NOTA_COMP1',\n",
       "       'NU_NOTA_COMP2', 'NU_NOTA_COMP3', 'NU_NOTA_COMP4', 'NU_NOTA_COMP5',\n",
       "       'NU_NOTA_REDACAO', 'Q001', 'Q002', 'Q003', 'Q004', 'Q005', 'Q006',\n",
       "       'Q007', 'Q008', 'Q009', 'Q010', 'Q011', 'Q012', 'Q013', 'Q014',\n",
       "       'Q015', 'Q016', 'Q017', 'Q018', 'Q019', 'Q020', 'Q021', 'Q022',\n",
       "       'Q023', 'Q024', 'Q025'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microdados_enem.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>NU_INSCRICAO</th>\n",
       "      <th>NU_ANO</th>\n",
       "      <th>CO_MUNICIPIO_RESIDENCIA</th>\n",
       "      <th>NO_MUNICIPIO_RESIDENCIA</th>\n",
       "      <th>CO_UF_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>...</th>\n",
       "      <th>Q016</th>\n",
       "      <th>Q017</th>\n",
       "      <th>Q018</th>\n",
       "      <th>Q019</th>\n",
       "      <th>Q020</th>\n",
       "      <th>Q021</th>\n",
       "      <th>Q022</th>\n",
       "      <th>Q023</th>\n",
       "      <th>Q024</th>\n",
       "      <th>Q025</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2142</th>\n",
       "      <td>190001006769</td>\n",
       "      <td>2019</td>\n",
       "      <td>1302603</td>\n",
       "      <td>Manaus</td>\n",
       "      <td>13</td>\n",
       "      <td>AM</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3189</th>\n",
       "      <td>190001007816</td>\n",
       "      <td>2019</td>\n",
       "      <td>1302603</td>\n",
       "      <td>Manaus</td>\n",
       "      <td>13</td>\n",
       "      <td>AM</td>\n",
       "      <td>41</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8425</th>\n",
       "      <td>190001013052</td>\n",
       "      <td>2019</td>\n",
       "      <td>1302603</td>\n",
       "      <td>Manaus</td>\n",
       "      <td>13</td>\n",
       "      <td>AM</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>D</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10902</th>\n",
       "      <td>190001015529</td>\n",
       "      <td>2019</td>\n",
       "      <td>1302603</td>\n",
       "      <td>Manaus</td>\n",
       "      <td>13</td>\n",
       "      <td>AM</td>\n",
       "      <td>18</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20496</th>\n",
       "      <td>190001025124</td>\n",
       "      <td>2019</td>\n",
       "      <td>1302405</td>\n",
       "      <td>Lábrea</td>\n",
       "      <td>13</td>\n",
       "      <td>AM</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094636</th>\n",
       "      <td>190006119562</td>\n",
       "      <td>2019</td>\n",
       "      <td>1301209</td>\n",
       "      <td>Coari</td>\n",
       "      <td>13</td>\n",
       "      <td>AM</td>\n",
       "      <td>21</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094637</th>\n",
       "      <td>190006119563</td>\n",
       "      <td>2019</td>\n",
       "      <td>1302603</td>\n",
       "      <td>Manaus</td>\n",
       "      <td>13</td>\n",
       "      <td>AM</td>\n",
       "      <td>17</td>\n",
       "      <td>M</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094638</th>\n",
       "      <td>190006119564</td>\n",
       "      <td>2019</td>\n",
       "      <td>1302603</td>\n",
       "      <td>Manaus</td>\n",
       "      <td>13</td>\n",
       "      <td>AM</td>\n",
       "      <td>18</td>\n",
       "      <td>F</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>C</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094639</th>\n",
       "      <td>190006119565</td>\n",
       "      <td>2019</td>\n",
       "      <td>1302603</td>\n",
       "      <td>Manaus</td>\n",
       "      <td>13</td>\n",
       "      <td>AM</td>\n",
       "      <td>34</td>\n",
       "      <td>F</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5094640</th>\n",
       "      <td>190006119566</td>\n",
       "      <td>2019</td>\n",
       "      <td>1302603</td>\n",
       "      <td>Manaus</td>\n",
       "      <td>13</td>\n",
       "      <td>AM</td>\n",
       "      <td>17</td>\n",
       "      <td>M</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>A</td>\n",
       "      <td>B</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>118087 rows × 136 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         NU_INSCRICAO  NU_ANO  CO_MUNICIPIO_RESIDENCIA  \\\n",
       "2142     190001006769    2019                  1302603   \n",
       "3189     190001007816    2019                  1302603   \n",
       "8425     190001013052    2019                  1302603   \n",
       "10902    190001015529    2019                  1302603   \n",
       "20496    190001025124    2019                  1302405   \n",
       "...               ...     ...                      ...   \n",
       "5094636  190006119562    2019                  1301209   \n",
       "5094637  190006119563    2019                  1302603   \n",
       "5094638  190006119564    2019                  1302603   \n",
       "5094639  190006119565    2019                  1302603   \n",
       "5094640  190006119566    2019                  1302603   \n",
       "\n",
       "        NO_MUNICIPIO_RESIDENCIA  CO_UF_RESIDENCIA SG_UF_RESIDENCIA  NU_IDADE  \\\n",
       "2142                     Manaus                13               AM        18   \n",
       "3189                     Manaus                13               AM        41   \n",
       "8425                     Manaus                13               AM        18   \n",
       "10902                    Manaus                13               AM        18   \n",
       "20496                    Lábrea                13               AM        18   \n",
       "...                         ...               ...              ...       ...   \n",
       "5094636                   Coari                13               AM        21   \n",
       "5094637                  Manaus                13               AM        17   \n",
       "5094638                  Manaus                13               AM        18   \n",
       "5094639                  Manaus                13               AM        34   \n",
       "5094640                  Manaus                13               AM        17   \n",
       "\n",
       "        TP_SEXO  TP_ESTADO_CIVIL  TP_COR_RACA  ...  Q016  Q017 Q018  Q019  \\\n",
       "2142          F                1            3  ...     A     A    A     B   \n",
       "3189          F                0            3  ...     A     A    A     B   \n",
       "8425          F                1            3  ...     A     A    A     B   \n",
       "10902         M                1            3  ...     A     A    A     B   \n",
       "20496         F                1            3  ...     A     A    A     B   \n",
       "...         ...              ...          ...  ...   ...   ...  ...   ...   \n",
       "5094636       M                1            3  ...     A     A    A     A   \n",
       "5094637       M                3            3  ...     B     A    A     C   \n",
       "5094638       F                1            3  ...     B     A    A     B   \n",
       "5094639       F                2            3  ...     A     A    A     B   \n",
       "5094640       M                1            3  ...     B     A    A     B   \n",
       "\n",
       "        Q020  Q021  Q022  Q023  Q024  Q025  \n",
       "2142       A     A     B     A     A     A  \n",
       "3189       A     A     B     A     A     A  \n",
       "8425       A     B     D     A     A     B  \n",
       "10902      A     A     C     A     A     A  \n",
       "20496      A     A     C     A     A     A  \n",
       "...      ...   ...   ...   ...   ...   ...  \n",
       "5094636    A     A     B     A     A     A  \n",
       "5094637    A     B     C     A     B     B  \n",
       "5094638    A     B     C     A     B     B  \n",
       "5094639    A     B     B     A     A     A  \n",
       "5094640    A     B     B     A     A     A  \n",
       "\n",
       "[118087 rows x 136 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microdados_enem[microdados_enem['SG_UF_RESIDENCIA']=='AM']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista_ufs = sorted(microdados_enem['SG_UF_RESIDENCIA'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lista_ufs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_csv.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for uf in lista_ufs:\n",
    "    caminho_do_arquivo = \"../dados/microdados_enem_{}_2019.csv\".format(uf)\n",
    "    microdados_enem[microdados_enem['SG_UF_RESIDENCIA']== uf].to_csv(caminho_do_arquivo, index=False)"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}