Variáveis categóricas - Dica Pandas #6.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variáveis categóricas - Dica Pandas #6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.cut.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import YouTubeVideo, Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"550\"\n",
       "            height=\"310\"\n",
       "            src=\"https://www.youtube.com/embed/Sp9oqlVBgJc\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x104b39890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(YouTubeVideo('Sp9oqlVBgJc', width=550, height=310))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "microdados_enem = pd.read_csv('../dados/microdados-enem-2018/DADOS/MICRODADOS_ENEM_2018.csv',nrows=0,sep=';',encoding='ISO-8859-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>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",
       "      <th>Q026</th>\n",
       "      <th>Q027</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 137 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [NU_INSCRICAO, NU_ANO, CO_MUNICIPIO_RESIDENCIA, NO_MUNICIPIO_RESIDENCIA, CO_UF_RESIDENCIA, SG_UF_RESIDENCIA, NU_IDADE, TP_SEXO, TP_ESTADO_CIVIL, TP_COR_RACA, TP_NACIONALIDADE, CO_MUNICIPIO_NASCIMENTO, NO_MUNICIPIO_NASCIMENTO, CO_UF_NASCIMENTO, SG_UF_NASCIMENTO, TP_ST_CONCLUSAO, TP_ANO_CONCLUIU, TP_ESCOLA, TP_ENSINO, IN_TREINEIRO, CO_ESCOLA, CO_MUNICIPIO_ESC, NO_MUNICIPIO_ESC, CO_UF_ESC, SG_UF_ESC, TP_DEPENDENCIA_ADM_ESC, TP_LOCALIZACAO_ESC, TP_SIT_FUNC_ESC, IN_BAIXA_VISAO, IN_CEGUEIRA, IN_SURDEZ, IN_DEFICIENCIA_AUDITIVA, IN_SURDO_CEGUEIRA, IN_DEFICIENCIA_FISICA, IN_DEFICIENCIA_MENTAL, IN_DEFICIT_ATENCAO, IN_DISLEXIA, IN_DISCALCULIA, IN_AUTISMO, IN_VISAO_MONOCULAR, IN_OUTRA_DEF, IN_GESTANTE, IN_LACTANTE, IN_IDOSO, IN_ESTUDA_CLASSE_HOSPITALAR, IN_SEM_RECURSO, IN_BRAILLE, IN_AMPLIADA_24, IN_AMPLIADA_18, IN_LEDOR, IN_ACESSO, IN_TRANSCRICAO, IN_LIBRAS, IN_LEITURA_LABIAL, IN_MESA_CADEIRA_RODAS, IN_MESA_CADEIRA_SEPARADA, IN_APOIO_PERNA, IN_GUIA_INTERPRETE, IN_COMPUTADOR, IN_CADEIRA_ESPECIAL, IN_CADEIRA_CANHOTO, IN_CADEIRA_ACOLCHOADA, IN_PROVA_DEITADO, IN_MOBILIARIO_OBESO, IN_LAMINA_OVERLAY, IN_PROTETOR_AURICULAR, IN_MEDIDOR_GLICOSE, IN_MAQUINA_BRAILE, IN_SOROBAN, IN_MARCA_PASSO, IN_SONDA, IN_MEDICAMENTOS, IN_SALA_INDIVIDUAL, IN_SALA_ESPECIAL, IN_SALA_ACOMPANHANTE, IN_MOBILIARIO_ESPECIFICO, IN_MATERIAL_ESPECIFICO, IN_NOME_SOCIAL, CO_MUNICIPIO_PROVA, NO_MUNICIPIO_PROVA, CO_UF_PROVA, SG_UF_PROVA, TP_PRESENCA_CN, TP_PRESENCA_CH, TP_PRESENCA_LC, TP_PRESENCA_MT, CO_PROVA_CN, CO_PROVA_CH, CO_PROVA_LC, CO_PROVA_MT, NU_NOTA_CN, NU_NOTA_CH, NU_NOTA_LC, NU_NOTA_MT, TX_RESPOSTAS_CN, TX_RESPOSTAS_CH, TX_RESPOSTAS_LC, TX_RESPOSTAS_MT, TP_LINGUA, TX_GABARITO_CN, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 137 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microdados_enem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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_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', 'Q026', 'Q027'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microdados_enem.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['NU_NOTA_MT', 'Q027']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "microdados_enem = pd.read_csv('../dados/microdados-enem-2018/DADOS/MICRODADOS_ENEM_2018.csv',usecols=cols,sep=';',encoding='ISO-8859-1',\n",
    "                             dtype={'Q027':'category'})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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_NOTA_MT</th>\n",
       "      <th>Q027</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>480.3</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NU_NOTA_MT Q027\n",
       "0         NaN    A\n",
       "1       480.3    A\n",
       "2         NaN    A\n",
       "3         NaN    A\n",
       "4         NaN    A"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microdados_enem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5513747 entries, 0 to 5513746\n",
      "Data columns (total 2 columns):\n",
      "NU_NOTA_MT    float64\n",
      "Q027          category\n",
      "dtypes: category(1), float64(1)\n",
      "memory usage: 47.3 MB\n"
     ]
    }
   ],
   "source": [
    "microdados_enem.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NU_NOTA_MT    1608648\n",
       "Q027                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microdados_enem.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "microdados_enem.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NU_NOTA_MT    0\n",
       "Q027          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microdados_enem.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3905099 entries, 1 to 5513746\n",
      "Data columns (total 2 columns):\n",
      "NU_NOTA_MT    float64\n",
      "Q027          category\n",
      "dtypes: category(1), float64(1)\n",
      "memory usage: 63.3 MB\n"
     ]
    }
   ],
   "source": [
    "microdados_enem.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nota: Adicionei o comando abaixo, após um comentário no vídeo sobre o uso de memória"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "microdados_enem = microdados_enem.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3905099 entries, 0 to 3905098\n",
      "Data columns (total 2 columns):\n",
      "NU_NOTA_MT    float64\n",
      "Q027          category\n",
      "dtypes: category(1), float64(1)\n",
      "memory usage: 33.5 MB\n"
     ]
    }
   ],
   "source": [
    "microdados_enem.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "microdados_enem['Q027_GRUPOS'] = microdados_enem['Q027'].cat.codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    2850097\n",
       "D     526557\n",
       "B     327804\n",
       "C     104161\n",
       "E      94813\n",
       "F       1667\n",
       "Name: Q027, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microdados_enem['Q027'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2850097\n",
       "3     526557\n",
       "1     327804\n",
       "2     104161\n",
       "4      94813\n",
       "5       1667\n",
       "Name: Q027_GRUPOS, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microdados_enem['Q027_GRUPOS'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <th>Q027</th>\n",
       "      <th>Q027_GRUPOS</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>480.3</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538.8</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733.8</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>459.0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>525.4</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NU_NOTA_MT Q027  Q027_GRUPOS\n",
       "0       480.3    A            0\n",
       "1       538.8    A            0\n",
       "2       733.8    A            0\n",
       "3       459.0    A            0\n",
       "4       525.4    A            0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microdados_enem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <th>Q027_GRUPOS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.905099e+06</td>\n",
       "      <td>3.905099e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.354056e+02</td>\n",
       "      <td>6.410552e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.031512e+02</td>\n",
       "      <td>1.179987e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.553000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.166000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.007000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.961000e+02</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         NU_NOTA_MT   Q027_GRUPOS\n",
       "count  3.905099e+06  3.905099e+06\n",
       "mean   5.354056e+02  6.410552e-01\n",
       "std    1.031512e+02  1.179987e+00\n",
       "min    0.000000e+00  0.000000e+00\n",
       "25%    4.553000e+02  0.000000e+00\n",
       "50%    5.166000e+02  0.000000e+00\n",
       "75%    6.007000e+02  1.000000e+00\n",
       "max    9.961000e+02  5.000000e+00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microdados_enem.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "média baixa    1465276\n",
       "média alta     1224868\n",
       "alta            942984\n",
       "baixa           232752\n",
       "Name: NU_NOTA_MT, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.cut(microdados_enem.NU_NOTA_MT, bins=[0,400,500,600,800],labels=['baixa','média baixa','média alta','alta']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "microdados_enem['NU_NOTA_MT_GRUPOS']=pd.qcut(microdados_enem.NU_NOTA_MT, q=4,labels=['baixa','média baixa','média alta','alta'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>NU_NOTA_MT</th>\n",
       "      <th>Q027</th>\n",
       "      <th>Q027_GRUPOS</th>\n",
       "      <th>NU_NOTA_MT_GRUPOS</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>480.3</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>média baixa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>538.8</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>média alta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>733.8</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>alta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>459.0</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>média baixa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>525.4</td>\n",
       "      <td>A</td>\n",
       "      <td>0</td>\n",
       "      <td>média alta</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NU_NOTA_MT Q027  Q027_GRUPOS NU_NOTA_MT_GRUPOS\n",
       "0       480.3    A            0       média baixa\n",
       "1       538.8    A            0        média alta\n",
       "2       733.8    A            0              alta\n",
       "3       459.0    A            0       média baixa\n",
       "4       525.4    A            0        média alta"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "microdados_enem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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