groupby_pandas.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "groupby_pandas",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vyk35S3KwnfN",
        "colab_type": "text"
      },
      "source": [
        "# GroupBy (pacote pandas) - Microdados do ENEM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pjZq99VCwuwF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib\n",
        "import zipfile"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pLPIJHniwx6Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "53df4cce-74f9-46da-c118-8af18222db3e"
      },
      "source": [
        "with zipfile.ZipFile('/content/drive/My Drive/microdados_enem2018.zip') as z:\n",
        "  print(*z.namelist(), sep='\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "LEIA-ME e DOCUMENTOS TÉCNICOS/\n",
            "LEIA-ME e DOCUMENTOS TÉCNICOS/edital_enem_2018.pdf\n",
            "LEIA-ME e DOCUMENTOS TÉCNICOS/matriz_referencia.pdf\n",
            "LEIA-ME e DOCUMENTOS TÉCNICOS/manual_de_redacao_do_enem_2018.pdf\n",
            "LEIA-ME e DOCUMENTOS TÉCNICOS/Leia_Me_Enem_2018.pdf\n",
            "PLANILHAS_TS_ITEM/\n",
            "PROVAS E GABARITOS/\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_02_DIA_1_AMARELO.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_03_DIA_1_BRANCO.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_09_DIA_1_LARANJA_LEDOR.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_10_DIA_1_VERDE_LIBRAS.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_01_DIA_1_AZUL.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_05_DIA_2_AMARELO.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_01_DIA_1_AZUL_AMPLIADA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_06_DIA_2_CINZA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_07_DIA_2_AZUL.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_08_DIA_2_ROSA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_11_DIA_2_LARANJA_LEDOR.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_12_DIA_2_VERDE_LIBRAS.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_05_DIA_2_SUPERAMPLIADA.pdf.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_01_DIA_1_AZUL.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_GAB_04_DIA_1_ROSA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_CAD_02_DIA_1_AMARELO.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_CAD_03_DIA_1_BRANCO.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_CAD_04_DIA_1_ROSA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_CAD_05_DIA_2_AMARELO.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_CAD_06_DIA_2_CINZA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_CAD_07_DIA_2_AZUL.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_CAD_08_DIA_2_ROSA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_GAB_01_DIA_1_AZUL.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_03_DIA_1_BRANCO.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_02_DIA_1_AMARELO.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_04_DIA_1_ROSA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_09_DIA_1_LARANJA_LEDOR.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_GAB_03_DIA_1_BRANCO.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_GAB_02_DIA_1_AMARELO.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_GAB_05_DIA_2_AMARELO.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_05_DIA_2_AMARELO.pdf.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_06_DIA_2_CINZA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_08_DIA_2_ROSA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_07_DIA_2_AZUL.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_12_DIA_2_VERDE_LIBRAS.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_GAB_06_DIA_2_CINZA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_GAB_07_DIA_2_AZUL.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_11_DIA_2_LARANJA_LEDOR.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_CAD_01_DIA_1_AZUL.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_04_DIA_1_ROSA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_10_DIA_1_VERDE_LIBRAS.pdf.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P2_GAB_08_DIA_2_ROSA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_01_DIA_1_AZUL_SUPERAMPLIADA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_CAD_05_DIA_2_AMPLIADA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_01_DIA_1_AZUL_AMPLIADA.pdf\n",
            "PROVAS E GABARITOS/ENEM_2018_P1_GAB_01_DIA_1_AZUL_SUPERAMPLIADA.pdf\n",
            "DADOS/\n",
            "DADOS/MICRODADOS_ENEM_2018.csv\n",
            "DADOS/ITENS_PROVA_2018.csv\n",
            "DICION╡RIO/\n",
            "DICION╡RIO/Dicionário_Microdados_Enem_2018.ods\n",
            "DICION╡RIO/Dicionário_Microdados_Enem_2018.xlsx\n",
            "INPUTS/\n",
            "INPUTS/INPUT_R_MICRODADOS_ENEM_2018.R\n",
            "INPUTS/INPUT_R_ITENS_PROVA_2018.R\n",
            "INPUTS/INPUT_SPSS_ITENS_PROVA_2018.sps\n",
            "INPUTS/INPUT_SAS_ITENS_PROVA_2018.sas\n",
            "INPUTS/INPUT_SPSS_MICRODADOS_ENEM_2018.sps\n",
            "INPUTS/INPUT_SAS_MICRODADOS_ENEM_2018.sas\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BivVBBQZxA49",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with zipfile.ZipFile('/content/drive/My Drive/microdados_enem2018.zip') as z:\n",
        "  with z.open('DADOS/MICRODADOS_ENEM_2018.csv') as f:\n",
        "    microdadosEnem = pd.read_csv(f, sep=';', encoding='ISO-8859-1', nrows=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TKbgJFkuxVJJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "outputId": "c96a4841-6473-4d51-b52d-4f071efe1643"
      },
      "source": [
        "microdadosEnem.columns.values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V3wpA5lexYr3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "colunasSelecionadas = ['TP_SEXO', 'NU_NOTA_REDACAO']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQI1VINMxj-_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with zipfile.ZipFile('/content/drive/My Drive/microdados_enem2018.zip') as z:\n",
        "  with z.open('DADOS/MICRODADOS_ENEM_2018.csv') as f:\n",
        "    microdadosEnemSexoRedacao = pd.read_csv(f, sep=';', encoding='ISO-8859-1', usecols=colunasSelecionadas)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OqA_5OYGxvCf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "318ea6b6-8a2f-4476-d0e8-c0c826928d63"
      },
      "source": [
        "microdadosEnemSexoRedacao.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "    }\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>TP_SEXO</th>\n",
              "      <th>NU_NOTA_REDACAO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>M</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F</td>\n",
              "      <td>280.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>F</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>360.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  TP_SEXO  NU_NOTA_REDACAO\n",
              "0       M              NaN\n",
              "1       F            280.0\n",
              "2       F              NaN\n",
              "3       F              NaN\n",
              "4       M            360.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w3Q9HrEfx54H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# para retirar o 'NOT A NUMBER' (NaN) utiliza-se método dropna do pacote pandas\n",
        "microdadosEnemSexoRedacao = microdadosEnemSexoRedacao.dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FvpKVipTyjQu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "1e6587f8-e66a-4286-8a7d-ccf873401967"
      },
      "source": [
        "microdadosEnemSexoRedacao.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>TP_SEXO</th>\n",
              "      <th>NU_NOTA_REDACAO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>F</td>\n",
              "      <td>280.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>M</td>\n",
              "      <td>360.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>F</td>\n",
              "      <td>580.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>M</td>\n",
              "      <td>680.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>M</td>\n",
              "      <td>340.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  TP_SEXO  NU_NOTA_REDACAO\n",
              "1       F            280.0\n",
              "4       M            360.0\n",
              "5       F            580.0\n",
              "7       M            680.0\n",
              "8       M            340.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7lKHPPJXym4_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "5501743f-806a-42e6-a097-5a66c5910c6d"
      },
      "source": [
        "# utilizando o Groupby do pandas para ver a distribuição entre masculino e feminino\n",
        "microdadosEnemSexoRedacao.groupby('TP_SEXO').count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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_NOTA_REDACAO</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TP_SEXO</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>2454691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>1693573</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         NU_NOTA_REDACAO\n",
              "TP_SEXO                 \n",
              "F                2454691\n",
              "M                1693573"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9u04sFoWy9_H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "bde0d2dc-5ad7-42de-a2a0-4d7879ed682e"
      },
      "source": [
        "# para verificar a nota máxima de cada sexo utilizando o método max do groupby\n",
        "microdadosEnemSexoRedacao.groupby('TP_SEXO').max()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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_NOTA_REDACAO</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TP_SEXO</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         NU_NOTA_REDACAO\n",
              "TP_SEXO                 \n",
              "F                 1000.0\n",
              "M                 1000.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gKObo51mztiv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "5887ec0f-beef-45b1-88b8-56adab2d34b0"
      },
      "source": [
        "# para verificar a nota mínima de cada sexo utilizando o método min do groupby\n",
        "microdadosEnemSexoRedacao.groupby('TP_SEXO').min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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_NOTA_REDACAO</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TP_SEXO</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         NU_NOTA_REDACAO\n",
              "TP_SEXO                 \n",
              "F                    0.0\n",
              "M                    0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6FNVfj7z8KG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "19a5ed89-9dad-4eb3-d2f3-4cdfb9740848"
      },
      "source": [
        "# observar a nota mínima acima de zero para os sexos\n",
        "microdadosEnemSexoRedacao[microdadosEnemSexoRedacao.NU_NOTA_REDACAO > 0].groupby('TP_SEXO').min()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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_NOTA_REDACAO</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TP_SEXO</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         NU_NOTA_REDACAO\n",
              "TP_SEXO                 \n",
              "F                   40.0\n",
              "M                   40.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqjcYdBn0jH-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "c5752ae6-4a9f-48cf-8e6d-ff620772136b"
      },
      "source": [
        "# para verificar a média da nota entre os sexos\n",
        "microdadosEnemSexoRedacao.groupby('TP_SEXO').mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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_NOTA_REDACAO</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TP_SEXO</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>512.529145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>503.102340</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         NU_NOTA_REDACAO\n",
              "TP_SEXO                 \n",
              "F             512.529145\n",
              "M             503.102340"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drRwJ7LA15cX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "c03f2769-7927-4fd1-ef55-39f501c697cf"
      },
      "source": [
        "# verificando a mediana da nota entre os sexos\n",
        "microdadosEnemSexoRedacao.groupby('TP_SEXO').median()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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_NOTA_REDACAO</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TP_SEXO</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>520.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>520.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         NU_NOTA_REDACAO\n",
              "TP_SEXO                 \n",
              "F                  520.0\n",
              "M                  520.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ktbWZwu-2L53",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "outputId": "024a85b0-595c-4a9d-cd04-9bf1a000a02e"
      },
      "source": [
        "# plotar um histograma do objeto groupby\n",
        "microdadosEnemSexoRedacao.groupby('TP_SEXO').hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TP_SEXO\n",
              "F    [[AxesSubplot(0.125,0.125;0.775x0.755)]]\n",
              "M    [[AxesSubplot(0.125,0.125;0.775x0.755)]]\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mLkyuzFa2ewW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "outputId": "94c3ba9a-18e0-47e0-a099-aa84f211f692"
      },
      "source": [
        "# utilizando o método describe para uma descrição estatística do groupby\n",
        "microdadosEnemSexoRedacao.groupby('TP_SEXO').describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"8\" halign=\"left\">NU_NOTA_REDACAO</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TP_SEXO</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>F</th>\n",
              "      <td>2454691.0</td>\n",
              "      <td>512.529145</td>\n",
              "      <td>187.440251</td>\n",
              "      <td>0.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>520.0</td>\n",
              "      <td>620.0</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>M</th>\n",
              "      <td>1693573.0</td>\n",
              "      <td>503.102340</td>\n",
              "      <td>187.437803</td>\n",
              "      <td>0.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>520.0</td>\n",
              "      <td>620.0</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        NU_NOTA_REDACAO                          ...                      \n",
              "                  count        mean         std  ...    50%    75%     max\n",
              "TP_SEXO                                          ...                      \n",
              "F             2454691.0  512.529145  187.440251  ...  520.0  620.0  1000.0\n",
              "M             1693573.0  503.102340  187.437803  ...  520.0  620.0  1000.0\n",
              "\n",
              "[2 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    }
  ]
}