Análise_Geral_Microdados_Enem.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/Gus-1003/ENEM_2021-Data_analysis/blob/main/An%C3%A1lise_Geral_Microdados_Enem.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Contexto da Pesquisa:\n",
        "\n",
        "## Objetivo:\n",
        "\n",
        "\n",
        "> Analisar o padrão de resposta dos candidatos do ENEM 2021 e divulgar, de acordo com os fundamentos da Ciências dos Dados, métricas utilizadas pela Teoria de Resposta Item para cada questão do exame relacionando com dados populacionais.\n",
        "\n",
        "\n",
        "## Metodologia:\n",
        "\n",
        "> Processamento do banco de dados que contêm as respostas dos alunos para cada questão com ferramentas de Extração-Transformação-Carga (ETL, em inglês). Utilização de estatística descritiva para descrever as características de cada questão e da população em estudo. Utilização de estatística inferencial por métodos não-supervisionados (análise de cluster e PCA) para indicar casos de correspondência e correlação entre índice de acerto das questões e as variáveis populacionais."
      ],
      "metadata": {
        "id": "tyWoIQMozvTs"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# Ciclo de Vida dos Dados:\n",
        "\n",
        "## Coleta de dados:\n",
        "\n",
        "> Essa fase envolve a obtenção de dados brutos de várias fontes, como arquivos, bancos de dados, dispositivos IoT ou sensores.\n",
        "\n",
        "## Pré-processamento:\n",
        "> Nesta fase, os dados brutos são limpos, organizados e preparados para análise. Isso pode incluir a remoção de valores ausentes ou inconsistentes, a normalização de dados e a seleção de características relevantes.\n",
        "\n",
        "## Exploração de dados:\n",
        "> Nesta fase, os dados são analisados para entender padrões, tendências e correlações. Isso envolve a visualização de dados e a execução de análises estatísticas.\n",
        "\n",
        "## Modelagem:\n",
        "> Nesta fase, são criados modelos matemáticos ou estatísticos para prever ou classificar os dados. Isso pode incluir técnicas como regressão, árvores de decisão ou redes neurais.\n",
        "\n",
        "## Avaliação:\n",
        "> Nesta fase, os modelos criados são avaliados quanto à precisão e eficácia. Isso envolve o teste do modelo em dados separados para medir a precisão e ajustar o modelo conforme necessário."
      ],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# Análise Geral_Microdados_Enem"
      ],
      "metadata": {
        "id": "_-pmJ8bbjgR-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import de libs e ferramentas:"
      ],
      "metadata": {
        "id": "CGc_1Nrpji5O"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Antes de começar a análise exploratória, é necessário importar as bibliotecas necessárias, como pandas, numpy e matplotlib, por exemplo:"
      ],
      "metadata": {
        "id": "UCpSomrL1pvK"
      }
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    {
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      "execution_count": 1,
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No meu caso eu faço uso da nuvem do google drive para armazenar as informções do banco \"Microdados - Enem\". Porem quem tiver interesse pode encontrolo no Link:\n",
        "* https://www.gov.br/inep/pt-br/acesso-a-informacao/dados-abertos/microdados/enem"
      ],
      "metadata": {
        "id": "R8xKCHJ91tYJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')\n",
        "%cd /content/drive/MyDrive/"
      ],
      "metadata": {
        "id": "sFjeuSKMjc_W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79af0336-86bb-4daf-e161-387eadbe9f52"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n",
            "/content/drive/MyDrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Pré - Processamento:"
      ],
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exploração de dados, também conhecida como análise exploratória de dados, é um processo utilizado na área de ciência de dados para descobrir padrões, tendências e informações valiosas em um conjunto de dados. A exploração de dados envolve a aplicação de técnicas estatísticas e de visualização de dados para obter insights que possam ser utilizados para tomar decisões de negócios ou resolver problemas em diversas áreas, desde finanças e marketing até saúde e ciência.\n",
        "\n",
        "O processo de exploração de dados envolve diversas etapas, como a limpeza e pré-processamento dos dados, a identificação de padrões e tendências, a visualização dos dados em gráficos e tabelas, e a realização de testes estatísticos para validar hipóteses e encontrar relações entre variáveis.\n",
        "\n",
        "A exploração de dados é uma das principais etapas do processo de análise de dados, sendo fundamental para a descoberta de informações valiosas e insights que podem ser utilizados para melhorar processos e tomar decisões mais informadas."
      ],
      "metadata": {
        "id": "7lKt-eGu2IFB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"Para carregar os dados em Python, você pode usar a função read_csv() do pandas para carregar dados de um arquivo CSV, \n",
        "ou outras funções semelhantes para carregar dados de outros tipos de arquivos:\"\"\"\n",
        "\n",
        "dados = pd.read_csv('/content/drive/MyDrive/Pesquisa_Enem2021/Base de Dados/MICRODADOS_ENEM_2021.csv', encoding='latin-1', delimiter=\";\", quotechar='\"')"
      ],
      "metadata": {
        "id": "q4XPL4rajqS2"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Visualização dos dados:"
      ],
      "metadata": {
        "id": "qpZaVUlxu37d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''O comando \"display(df)\" é utilizado na biblioteca pandas do Python para exibir um DataFrame na saída. \n",
        "Ele é útil para visualizar os dados de maneira mais organizada e legível.\n",
        "o comando exibe as primeiras e últimas linhas do DataFrame, além de algumas informações básicas, \n",
        "como o número de linhas e colunas, nomes de colunas e tipo de dados de cada coluna.'''\n",
        "\n",
        "display(dados)"
      ],
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              "\n",
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    {
      "cell_type": "markdown",
      "source": [
        "Visualizando: Assim que compilamos o comando \"display()\", conseguimos extrair algumas informações sobre a composição do dataset \"ENEM 2021\":\n",
        "\n",
        "* 3.389.832 linhas = Quantidade de participantes da prova;\n",
        "* 76 atributos / características = dados referentes a esses alunos e ao exame;\n",
        "* De primeira, já podemos observar a presença da coluna \"NU_INSCRICAO\", que normalmente não possui utilidade para análise e, portanto, será apagada posteriormente.\n",
        "* Antes mesmo de uma busca aprofundada, já conseguimos identificar a presença de valores NaN no dataset - presentes na última amostra. Isso já gera uma hipótese de haver mais dados comprometidos, fazendo-se necessário um processo de limpeza desses valores."
      ],
      "metadata": {
        "id": "GbjwyQkBefg1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''O comando \"df.head()\" é utilizado na biblioteca pandas do Python para exibir as primeiras linhas de um DataFrame. Por padrão, \n",
        "o método \"head()\" exibe as 5 primeiras linhas do DataFrame, mas é possível passar um argumento inteiro para o método para especificar \n",
        "o número de linhas que deseja exibir.'''\n",
        "\n",
        "dados.head()"
      ],
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          "height": 300
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        "outputId": "a79bef25-299e-4f45-be23-3196038750bb"
      },
      "execution_count": 5,
      "outputs": [
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''O comando \"df.info()\" é utilizado na biblioteca pandas do Python para exibir informações sobre um DataFrame, como o número de linhas e colunas, \n",
        "o nome e tipo de cada coluna, a quantidade de valores não nulos e o tipo de objeto do DataFrame.'''\n",
        "\n",
        "dados.info()"
      ],
      "metadata": {
        "id": "Rwh0KcSbgpzU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2503cba6-b3b2-4b63-c066-d01a579f310d"
      },
      "execution_count": 6,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3389832 entries, 0 to 3389831\n",
            "Data columns (total 76 columns):\n",
            " #   Column                  Dtype  \n",
            "---  ------                  -----  \n",
            " 0   NU_INSCRICAO            int64  \n",
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            " 3   TP_SEXO                 object \n",
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            " 5   TP_COR_RACA             int64  \n",
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            " 8   TP_ANO_CONCLUIU         int64  \n",
            " 9   TP_ESCOLA               int64  \n",
            " 10  TP_ENSINO               float64\n",
            " 11  IN_TREINEIRO            int64  \n",
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            " 15  SG_UF_ESC               object \n",
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            " 17  TP_LOCALIZACAO_ESC      float64\n",
            " 18  TP_SIT_FUNC_ESC         float64\n",
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            " 21  CO_UF_PROVA             int64  \n",
            " 22  SG_UF_PROVA             object \n",
            " 23  TP_PRESENCA_CN          int64  \n",
            " 24  TP_PRESENCA_CH          int64  \n",
            " 25  TP_PRESENCA_LC          int64  \n",
            " 26  TP_PRESENCA_MT          int64  \n",
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            " 37  TX_RESPOSTAS_LC         object \n",
            " 38  TX_RESPOSTAS_MT         object \n",
            " 39  TP_LINGUA               int64  \n",
            " 40  TX_GABARITO_CN          object \n",
            " 41  TX_GABARITO_CH          object \n",
            " 42  TX_GABARITO_LC          object \n",
            " 43  TX_GABARITO_MT          object \n",
            " 44  TP_STATUS_REDACAO       float64\n",
            " 45  NU_NOTA_COMP1           float64\n",
            " 46  NU_NOTA_COMP2           float64\n",
            " 47  NU_NOTA_COMP3           float64\n",
            " 48  NU_NOTA_COMP4           float64\n",
            " 49  NU_NOTA_COMP5           float64\n",
            " 50  NU_NOTA_REDACAO         float64\n",
            " 51  Q001                    object \n",
            " 52  Q002                    object \n",
            " 53  Q003                    object \n",
            " 54  Q004                    object \n",
            " 55  Q005                    float64\n",
            " 56  Q006                    object \n",
            " 57  Q007                    object \n",
            " 58  Q008                    object \n",
            " 59  Q009                    object \n",
            " 60  Q010                    object \n",
            " 61  Q011                    object \n",
            " 62  Q012                    object \n",
            " 63  Q013                    object \n",
            " 64  Q014                    object \n",
            " 65  Q015                    object \n",
            " 66  Q016                    object \n",
            " 67  Q017                    object \n",
            " 68  Q018                    object \n",
            " 69  Q019                    object \n",
            " 70  Q020                    object \n",
            " 71  Q021                    object \n",
            " 72  Q022                    object \n",
            " 73  Q023                    object \n",
            " 74  Q024                    object \n",
            " 75  Q025                    object \n",
            "dtypes: float64(22), int64(17), object(37)\n",
            "memory usage: 1.9+ GB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizando: Assim que compilamos o comando \"dados.info()\", conseguimos extrair algumas informações sobre a composição do dataset \"ENEM 2021\":\n",
        "\n",
        "* Podemos identificar que dos 76 atributos originais, 39 são numéricos e 37 são nominais (textuais);\n",
        "* Entre os numéricos: 17 são inteiros (variáveis discretas) e 22 são flutuantes / decimais (variáveis contínuas).\n",
        "> **Lembrando que nem sempre os valores numéricos representam o real valor de uma coluna (como, por exemplo, 1 para estado civil). Provavelmente várias dessas colunas atualmente numéricas estão usando esses valores como códigos para uma opção de resposta.**"
      ],
      "metadata": {
        "id": "a3KAbWTt4c-v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''Ao executar \"df.describe()\", serão exibidas as estatísticas descritivas das colunas numéricas do DataFrame \"dados\", \n",
        "incluindo o número de valores não nulos, a média, o desvio padrão, o valor mínimo, o quartil 25%, o quartil 50% (mediana), \n",
        "o quartil 75% e o valor máximo. Esse comando é útil para entender a distribuição dos dados e detectar valores extremos \n",
        "ou anomalias nos dados.'''\n",
        "\n",
        "#dados.describe()"
      ],
      "metadata": {
        "id": "MAD9W9oigqH2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "80d1c5d4-cc3d-41d7-94f4-1bad182f60a5"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nAo executar \"df.describe()\", serão exibidas as estatísticas descritivas das colunas numéricas do DataFrame \"dados\", \\nincluindo o número de valores não nulos, a média, o desvio padrão, o valor mínimo, o quartil 25%, o quartil 50% (mediana), \\no quartil 75% e o valor máximo. Esse comando é útil para entender a distribuição dos dados e detectar valores extremos \\nou anomalias nos dados.\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Informação sobre o comando \"describe()\":**\n",
        "\n",
        "> É importante lembrar que o parâmetro \"include\" presente no método \"describe()\" pode ser configurado para gerar estatísticas descritivas apenas para um tipo específico de colunas do dataframe. Como \"include='all'\" para gerar estatísticas descritivas para todas as colunas, independentemente do tipo de dados, ou \"include=['int', 'float', 'object']\" para tipos específicos de valores.\n",
        "\n"
      ],
      "metadata": {
        "id": "2Bg53u14le18"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Informação sobre \"quartis\" presente nos resultados do comando \"describe()\":**\n",
        "\n",
        "> O comando \"describe()\" é usado para gerar uma breve estatística descritiva do conjunto de dados. Os quartis são uma parte dessa estatística descritiva e representam a divisão dos dados em quartos iguais.\n",
        "\n",
        "> Existem três quartis que dividem os dados em quatro partes iguais, com 25% dos dados em cada parte:\n",
        "\n",
        "* O primeiro quartil (Q1) é o valor abaixo do qual está 25% dos dados.\n",
        "* O segundo quartil (Q2) é o valor abaixo do qual está 50% dos dados. Este valor é equivalente à mediana.\n",
        "* O terceiro quartil (Q3) é o valor abaixo do qual está 75% dos dados.\n",
        "\n",
        "> A partir desses valores, pode-se calcular o intervalo interquartil (IQR) como a diferença entre o terceiro e o primeiro quartil, que representa a variação central dos dados."
      ],
      "metadata": {
        "id": "fL1h8TNGpVNA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados.describe(include='int')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "twG8QWKalMcQ",
        "outputId": "e7ff77b2-6d5f-4078-8785-071688cca7d6"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       NU_INSCRICAO     NU_ANO  TP_FAIXA_ETARIA  TP_ESTADO_CIVIL  \\\n",
              "count  3.389832e+06  3389832.0     3.389832e+06     3.389832e+06   \n",
              "mean   2.100527e+11     2021.0     5.306013e+00     1.052073e+00   \n",
              "std    9.962403e+05        0.0     3.902871e+00     4.129414e-01   \n",
              "min    2.100510e+11     2021.0     1.000000e+00     0.000000e+00   \n",
              "25%    2.100519e+11     2021.0     2.000000e+00     1.000000e+00   \n",
              "50%    2.100527e+11     2021.0     4.000000e+00     1.000000e+00   \n",
              "75%    2.100536e+11     2021.0     7.000000e+00     1.000000e+00   \n",
              "max    2.100545e+11     2021.0     2.000000e+01     4.000000e+00   \n",
              "\n",
              "        TP_COR_RACA  TP_NACIONALIDADE  TP_ST_CONCLUSAO  TP_ANO_CONCLUIU  \\\n",
              "count  3.389832e+06      3.389832e+06     3.389832e+06     3.389832e+06   \n",
              "mean   2.043491e+00      1.027106e+00     1.608430e+00     2.581980e+00   \n",
              "std    1.022561e+00      2.049293e-01     7.198306e-01     4.278731e+00   \n",
              "min    0.000000e+00      0.000000e+00     1.000000e+00     0.000000e+00   \n",
              "25%    1.000000e+00      1.000000e+00     1.000000e+00     0.000000e+00   \n",
              "50%    2.000000e+00      1.000000e+00     1.000000e+00     0.000000e+00   \n",
              "75%    3.000000e+00      1.000000e+00     2.000000e+00     3.000000e+00   \n",
              "max    6.000000e+00      4.000000e+00     4.000000e+00     1.500000e+01   \n",
              "\n",
              "          TP_ESCOLA  IN_TREINEIRO  CO_MUNICIPIO_PROVA   CO_UF_PROVA  \\\n",
              "count  3.389832e+06  3.389832e+06        3.389832e+06  3.389832e+06   \n",
              "mean   1.396214e+00  1.289710e-01        3.104218e+06  3.089625e+01   \n",
              "std    5.938455e-01  3.351679e-01        1.011035e+06  1.008222e+01   \n",
              "min    1.000000e+00  0.000000e+00        1.100015e+06  1.100000e+01   \n",
              "25%    1.000000e+00  0.000000e+00        2.408003e+06  2.400000e+01   \n",
              "50%    1.000000e+00  0.000000e+00        3.122306e+06  3.100000e+01   \n",
              "75%    2.000000e+00  0.000000e+00        3.550308e+06  3.500000e+01   \n",
              "max    3.000000e+00  1.000000e+00        5.300108e+06  5.300000e+01   \n",
              "\n",
              "       TP_PRESENCA_CN  TP_PRESENCA_CH  TP_PRESENCA_LC  TP_PRESENCA_MT  \\\n",
              "count    3.389832e+06    3.389832e+06    3.389832e+06    3.389832e+06   \n",
              "mean     6.633786e-01    7.040145e-01    7.040145e-01    6.633786e-01   \n",
              "std      4.734579e-01    4.590982e-01    4.590982e-01    4.734579e-01   \n",
              "min      0.000000e+00    0.000000e+00    0.000000e+00    0.000000e+00   \n",
              "25%      0.000000e+00    0.000000e+00    0.000000e+00    0.000000e+00   \n",
              "50%      1.000000e+00    1.000000e+00    1.000000e+00    1.000000e+00   \n",
              "75%      1.000000e+00    1.000000e+00    1.000000e+00    1.000000e+00   \n",
              "max      2.000000e+00    2.000000e+00    2.000000e+00    2.000000e+00   \n",
              "\n",
              "          TP_LINGUA  \n",
              "count  3.389832e+06  \n",
              "mean   4.704640e-01  \n",
              "std    4.991269e-01  \n",
              "min    0.000000e+00  \n",
              "25%    0.000000e+00  \n",
              "50%    0.000000e+00  \n",
              "75%    1.000000e+00  \n",
              "max    1.000000e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f132f6b7-5c0c-4aae-a1f4-bf47525034da\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>TP_FAIXA_ETARIA</th>\n",
              "      <th>TP_ESTADO_CIVIL</th>\n",
              "      <th>TP_COR_RACA</th>\n",
              "      <th>TP_NACIONALIDADE</th>\n",
              "      <th>TP_ST_CONCLUSAO</th>\n",
              "      <th>TP_ANO_CONCLUIU</th>\n",
              "      <th>TP_ESCOLA</th>\n",
              "      <th>IN_TREINEIRO</th>\n",
              "      <th>CO_MUNICIPIO_PROVA</th>\n",
              "      <th>CO_UF_PROVA</th>\n",
              "      <th>TP_PRESENCA_CN</th>\n",
              "      <th>TP_PRESENCA_CH</th>\n",
              "      <th>TP_PRESENCA_LC</th>\n",
              "      <th>TP_PRESENCA_MT</th>\n",
              "      <th>TP_LINGUA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3389832.0</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2.100527e+11</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>5.306013e+00</td>\n",
              "      <td>1.052073e+00</td>\n",
              "      <td>2.043491e+00</td>\n",
              "      <td>1.027106e+00</td>\n",
              "      <td>1.608430e+00</td>\n",
              "      <td>2.581980e+00</td>\n",
              "      <td>1.396214e+00</td>\n",
              "      <td>1.289710e-01</td>\n",
              "      <td>3.104218e+06</td>\n",
              "      <td>3.089625e+01</td>\n",
              "      <td>6.633786e-01</td>\n",
              "      <td>7.040145e-01</td>\n",
              "      <td>7.040145e-01</td>\n",
              "      <td>6.633786e-01</td>\n",
              "      <td>4.704640e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.962403e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.902871e+00</td>\n",
              "      <td>4.129414e-01</td>\n",
              "      <td>1.022561e+00</td>\n",
              "      <td>2.049293e-01</td>\n",
              "      <td>7.198306e-01</td>\n",
              "      <td>4.278731e+00</td>\n",
              "      <td>5.938455e-01</td>\n",
              "      <td>3.351679e-01</td>\n",
              "      <td>1.011035e+06</td>\n",
              "      <td>1.008222e+01</td>\n",
              "      <td>4.734579e-01</td>\n",
              "      <td>4.590982e-01</td>\n",
              "      <td>4.590982e-01</td>\n",
              "      <td>4.734579e-01</td>\n",
              "      <td>4.991269e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.100510e+11</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.100015e+06</td>\n",
              "      <td>1.100000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.100519e+11</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>2.408003e+06</td>\n",
              "      <td>2.400000e+01</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.100527e+11</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.122306e+06</td>\n",
              "      <td>3.100000e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.100536e+11</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>3.550308e+06</td>\n",
              "      <td>3.500000e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.100545e+11</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>2.000000e+01</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>5.300108e+06</td>\n",
              "      <td>5.300000e+01</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f132f6b7-5c0c-4aae-a1f4-bf47525034da button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f132f6b7-5c0c-4aae-a1f4-bf47525034da');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.describe(include='float')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "0Bkm9ggPlMA5",
        "outputId": "8fded0e6-ba24-477e-8860-ab886a1acde1"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          TP_ENSINO  CO_MUNICIPIO_ESC      CO_UF_ESC  TP_DEPENDENCIA_ADM_ESC  \\\n",
              "count  1.096828e+06      8.138060e+05  813806.000000           813806.000000   \n",
              "mean   1.006295e+00      3.182762e+06      31.681298                2.469393   \n",
              "std    7.909361e-02      1.041348e+06      10.387891                0.939162   \n",
              "min    1.000000e+00      1.100015e+06      11.000000                1.000000   \n",
              "25%    1.000000e+00      2.312908e+06      23.000000                2.000000   \n",
              "50%    1.000000e+00      3.170107e+06      31.000000                2.000000   \n",
              "75%    1.000000e+00      3.550308e+06      35.000000                4.000000   \n",
              "max    2.000000e+00      5.300108e+06      53.000000                4.000000   \n",
              "\n",
              "       TP_LOCALIZACAO_ESC  TP_SIT_FUNC_ESC   CO_PROVA_CN   CO_PROVA_CH  \\\n",
              "count       813806.000000    813806.000000  2.245844e+06  2.378379e+06   \n",
              "mean             1.034867         1.015533  9.160518e+02  8.867477e+02   \n",
              "std              0.183443         0.197323  2.586309e+01  2.788652e+01   \n",
              "min              1.000000         1.000000  9.090000e+02  8.790000e+02   \n",
              "25%              1.000000         1.000000  9.100000e+02  8.800000e+02   \n",
              "50%              1.000000         1.000000  9.110000e+02  8.810000e+02   \n",
              "75%              1.000000         1.000000  9.120000e+02  8.820000e+02   \n",
              "max              2.000000         4.000000  1.048000e+03  1.018000e+03   \n",
              "\n",
              "        CO_PROVA_LC   CO_PROVA_MT  ...    NU_NOTA_LC    NU_NOTA_MT  \\\n",
              "count  2.378379e+06  2.245844e+06  ...  2.378379e+06  2.245844e+06   \n",
              "mean   8.966545e+02  9.061433e+02  ...  5.025556e+02  5.350802e+02   \n",
              "std    2.751352e+01  2.621196e+01  ...  7.916405e+01  1.106267e+02   \n",
              "min    8.890000e+02  8.990000e+02  ...  0.000000e+00  0.000000e+00   \n",
              "25%    8.900000e+02  9.000000e+02  ...  4.476000e+02  4.447000e+02   \n",
              "50%    8.910000e+02  9.010000e+02  ...  5.059000e+02  5.154000e+02   \n",
              "75%    8.920000e+02  9.020000e+02  ...  5.588000e+02  6.139000e+02   \n",
              "max    1.028000e+03  1.038000e+03  ...  8.261000e+02  9.531000e+02   \n",
              "\n",
              "       TP_STATUS_REDACAO  NU_NOTA_COMP1  NU_NOTA_COMP2  NU_NOTA_COMP3  \\\n",
              "count       2.378379e+06   2.378379e+06   2.378379e+06   2.378379e+06   \n",
              "mean        1.140684e+00   1.192657e+02   1.380269e+02   1.153117e+02   \n",
              "std         7.910804e-01   3.391962e+01   4.563224e+01   3.634138e+01   \n",
              "min         1.000000e+00   0.000000e+00   0.000000e+00   0.000000e+00   \n",
              "25%         1.000000e+00   1.000000e+02   1.200000e+02   1.000000e+02   \n",
              "50%         1.000000e+00   1.200000e+02   1.200000e+02   1.200000e+02   \n",
              "75%         1.000000e+00   1.400000e+02   1.800000e+02   1.400000e+02   \n",
              "max         9.000000e+00   2.000000e+02   2.000000e+02   2.000000e+02   \n",
              "\n",
              "       NU_NOTA_COMP4  NU_NOTA_COMP5  NU_NOTA_REDACAO          Q005  \n",
              "count   2.378379e+06   2.378379e+06     2.378379e+06  3.389830e+06  \n",
              "mean    1.281853e+02   1.127109e+02     6.135005e+02  3.739557e+00  \n",
              "std     3.883252e+01   6.005667e+01     1.907142e+02  1.378942e+00  \n",
              "min     0.000000e+00   0.000000e+00     0.000000e+00  1.000000e+00  \n",
              "25%     1.200000e+02   8.000000e+01     5.200000e+02  3.000000e+00  \n",
              "50%     1.200000e+02   1.200000e+02     6.000000e+02  4.000000e+00  \n",
              "75%     1.600000e+02   1.600000e+02     7.400000e+02  4.000000e+00  \n",
              "max     2.000000e+02   2.000000e+02     1.000000e+03  2.000000e+01  \n",
              "\n",
              "[8 rows x 22 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-064953c0-16b2-467f-aef2-cb2f3dd2592a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>TP_ENSINO</th>\n",
              "      <th>CO_MUNICIPIO_ESC</th>\n",
              "      <th>CO_UF_ESC</th>\n",
              "      <th>TP_DEPENDENCIA_ADM_ESC</th>\n",
              "      <th>TP_LOCALIZACAO_ESC</th>\n",
              "      <th>TP_SIT_FUNC_ESC</th>\n",
              "      <th>CO_PROVA_CN</th>\n",
              "      <th>CO_PROVA_CH</th>\n",
              "      <th>CO_PROVA_LC</th>\n",
              "      <th>CO_PROVA_MT</th>\n",
              "      <th>...</th>\n",
              "      <th>NU_NOTA_LC</th>\n",
              "      <th>NU_NOTA_MT</th>\n",
              "      <th>TP_STATUS_REDACAO</th>\n",
              "      <th>NU_NOTA_COMP1</th>\n",
              "      <th>NU_NOTA_COMP2</th>\n",
              "      <th>NU_NOTA_COMP3</th>\n",
              "      <th>NU_NOTA_COMP4</th>\n",
              "      <th>NU_NOTA_COMP5</th>\n",
              "      <th>NU_NOTA_REDACAO</th>\n",
              "      <th>Q005</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.096828e+06</td>\n",
              "      <td>8.138060e+05</td>\n",
              "      <td>813806.000000</td>\n",
              "      <td>813806.000000</td>\n",
              "      <td>813806.000000</td>\n",
              "      <td>813806.000000</td>\n",
              "      <td>2.245844e+06</td>\n",
              "      <td>2.378379e+06</td>\n",
              "      <td>2.378379e+06</td>\n",
              "      <td>2.245844e+06</td>\n",
              "      <td>...</td>\n",
              "      <td>2.378379e+06</td>\n",
              "      <td>2.245844e+06</td>\n",
              "      <td>2.378379e+06</td>\n",
              "      <td>2.378379e+06</td>\n",
              "      <td>2.378379e+06</td>\n",
              "      <td>2.378379e+06</td>\n",
              "      <td>2.378379e+06</td>\n",
              "      <td>2.378379e+06</td>\n",
              "      <td>2.378379e+06</td>\n",
              "      <td>3.389830e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.006295e+00</td>\n",
              "      <td>3.182762e+06</td>\n",
              "      <td>31.681298</td>\n",
              "      <td>2.469393</td>\n",
              "      <td>1.034867</td>\n",
              "      <td>1.015533</td>\n",
              "      <td>9.160518e+02</td>\n",
              "      <td>8.867477e+02</td>\n",
              "      <td>8.966545e+02</td>\n",
              "      <td>9.061433e+02</td>\n",
              "      <td>...</td>\n",
              "      <td>5.025556e+02</td>\n",
              "      <td>5.350802e+02</td>\n",
              "      <td>1.140684e+00</td>\n",
              "      <td>1.192657e+02</td>\n",
              "      <td>1.380269e+02</td>\n",
              "      <td>1.153117e+02</td>\n",
              "      <td>1.281853e+02</td>\n",
              "      <td>1.127109e+02</td>\n",
              "      <td>6.135005e+02</td>\n",
              "      <td>3.739557e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.909361e-02</td>\n",
              "      <td>1.041348e+06</td>\n",
              "      <td>10.387891</td>\n",
              "      <td>0.939162</td>\n",
              "      <td>0.183443</td>\n",
              "      <td>0.197323</td>\n",
              "      <td>2.586309e+01</td>\n",
              "      <td>2.788652e+01</td>\n",
              "      <td>2.751352e+01</td>\n",
              "      <td>2.621196e+01</td>\n",
              "      <td>...</td>\n",
              "      <td>7.916405e+01</td>\n",
              "      <td>1.106267e+02</td>\n",
              "      <td>7.910804e-01</td>\n",
              "      <td>3.391962e+01</td>\n",
              "      <td>4.563224e+01</td>\n",
              "      <td>3.634138e+01</td>\n",
              "      <td>3.883252e+01</td>\n",
              "      <td>6.005667e+01</td>\n",
              "      <td>1.907142e+02</td>\n",
              "      <td>1.378942e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.100015e+06</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.090000e+02</td>\n",
              "      <td>8.790000e+02</td>\n",
              "      <td>8.890000e+02</td>\n",
              "      <td>8.990000e+02</td>\n",
              "      <td>...</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.312908e+06</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.100000e+02</td>\n",
              "      <td>8.800000e+02</td>\n",
              "      <td>8.900000e+02</td>\n",
              "      <td>9.000000e+02</td>\n",
              "      <td>...</td>\n",
              "      <td>4.476000e+02</td>\n",
              "      <td>4.447000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+02</td>\n",
              "      <td>1.200000e+02</td>\n",
              "      <td>1.000000e+02</td>\n",
              "      <td>1.200000e+02</td>\n",
              "      <td>8.000000e+01</td>\n",
              "      <td>5.200000e+02</td>\n",
              "      <td>3.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.170107e+06</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.110000e+02</td>\n",
              "      <td>8.810000e+02</td>\n",
              "      <td>8.910000e+02</td>\n",
              "      <td>9.010000e+02</td>\n",
              "      <td>...</td>\n",
              "      <td>5.059000e+02</td>\n",
              "      <td>5.154000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.200000e+02</td>\n",
              "      <td>1.200000e+02</td>\n",
              "      <td>1.200000e+02</td>\n",
              "      <td>1.200000e+02</td>\n",
              "      <td>1.200000e+02</td>\n",
              "      <td>6.000000e+02</td>\n",
              "      <td>4.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.550308e+06</td>\n",
              "      <td>35.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9.120000e+02</td>\n",
              "      <td>8.820000e+02</td>\n",
              "      <td>8.920000e+02</td>\n",
              "      <td>9.020000e+02</td>\n",
              "      <td>...</td>\n",
              "      <td>5.588000e+02</td>\n",
              "      <td>6.139000e+02</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.400000e+02</td>\n",
              "      <td>1.800000e+02</td>\n",
              "      <td>1.400000e+02</td>\n",
              "      <td>1.600000e+02</td>\n",
              "      <td>1.600000e+02</td>\n",
              "      <td>7.400000e+02</td>\n",
              "      <td>4.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>5.300108e+06</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.048000e+03</td>\n",
              "      <td>1.018000e+03</td>\n",
              "      <td>1.028000e+03</td>\n",
              "      <td>1.038000e+03</td>\n",
              "      <td>...</td>\n",
              "      <td>8.261000e+02</td>\n",
              "      <td>9.531000e+02</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>2.000000e+02</td>\n",
              "      <td>2.000000e+02</td>\n",
              "      <td>2.000000e+02</td>\n",
              "      <td>2.000000e+02</td>\n",
              "      <td>2.000000e+02</td>\n",
              "      <td>1.000000e+03</td>\n",
              "      <td>2.000000e+01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 22 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-064953c0-16b2-467f-aef2-cb2f3dd2592a')\"\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-064953c0-16b2-467f-aef2-cb2f3dd2592a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-064953c0-16b2-467f-aef2-cb2f3dd2592a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados.describe(include='object')"
      ],
      "metadata": {
        "id": "R8AckxEUgtHO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "outputId": "a4fa7057-a3a2-4da7-b3ba-cd4f72f18a51"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        TP_SEXO NO_MUNICIPIO_ESC SG_UF_ESC NO_MUNICIPIO_PROVA SG_UF_PROVA  \\\n",
              "count   3389832           813806    813806            3389832     3389832   \n",
              "unique        2             5271        27               1712          27   \n",
              "top           F        São Paulo        SP          São Paulo          SP   \n",
              "freq    2090526            37692    139473             141840      509954   \n",
              "\n",
              "                                      TX_RESPOSTAS_CN  \\\n",
              "count                                         2245844   \n",
              "unique                                        2244621   \n",
              "top     .............................................   \n",
              "freq                                              393   \n",
              "\n",
              "                                      TX_RESPOSTAS_CH  \\\n",
              "count                                         2378379   \n",
              "unique                                        2371495   \n",
              "top     .............................................   \n",
              "freq                                             4632   \n",
              "\n",
              "                                          TX_RESPOSTAS_LC  \\\n",
              "count                                             2378379   \n",
              "unique                                            2375813   \n",
              "top     .....99999.......................................   \n",
              "freq                                                 1297   \n",
              "\n",
              "                                      TX_RESPOSTAS_MT  \\\n",
              "count                                         2245844   \n",
              "unique                                        2244141   \n",
              "top     .............................................   \n",
              "freq                                              453   \n",
              "\n",
              "                                       TX_GABARITO_CN  ...     Q016     Q017  \\\n",
              "count                                         2245844  ...  3389830  3389830   \n",
              "unique                                              8  ...        5        5   \n",
              "top     ADCDCDBBBECDCCDCBCBDABCAADCBEBABEEBBBCABEDEDE  ...        B        A   \n",
              "freq                                           550219  ...  1698583  3282971   \n",
              "\n",
              "           Q018     Q019     Q020     Q021     Q022     Q023     Q024     Q025  \n",
              "count   3389830  3389830  3389830  3389830  3389830  3389830  3389830  3389830  \n",
              "unique        2        5        2        2        5        2        5        2  \n",
              "top           A        B        A        A        D        A        A        B  \n",
              "freq    2579763  2217847  2733449  2642067  1002077  2865104  1436524  3037632  \n",
              "\n",
              "[4 rows x 37 columns]"
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              "      <td>São Paulo</td>\n",
              "      <td>SP</td>\n",
              "      <td>.............................................</td>\n",
              "      <td>.............................................</td>\n",
              "      <td>.....99999.......................................</td>\n",
              "      <td>.............................................</td>\n",
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              "      <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",
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              "      <td>2090526</td>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e1a9bb59-6a7f-4a19-84f8-766b0da5098e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e1a9bb59-6a7f-4a19-84f8-766b0da5098e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualizando: focando no comando \"dados.describe(include='object')\", conseguimos extrair algumas informações sobre a composição do dataset \"ENEM 2021\":**\n",
        "\n",
        "* Podemos identificar que dos 3.389.832 cadidatos do exame, 2.090.526 são mulheres. Demonstrando um desejo do publico feminino pelo ensino superior no Brasil;\n",
        "* A cidade de São Paulo, como polo economico do Brasileiro, possui a maior quantidade de pontos de realização do exame e a maior quantidade de participante.\n",
        "\n",
        "> ***Hipoteses:*** O numero massivo de participantes e pontos de exame em um mesmo municipio pode tornar o nivel de educação exigido para entrar no ensino superior altissimo. O que pode acabar excluindo desse processo os alunos que não possuem condições sociais-financeiras que não puderam pagar uma educação de nivel mais elevado. No entanto, é importante lembrar que a exclusão desses alunos não é necessariamente uma regra e que existem políticas públicas e programas sociais que visam tornar o acesso à educação mais equitativo e inclusivo em todo o país."
      ],
      "metadata": {
        "id": "B9JyMJ3osdbP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Informações sobre técnicas de nomeclatura de váriaveis:**\n",
        "\n",
        "* Em programação, é uma boa prática nomear as variáveis com letras minúsculas.\n",
        "\n",
        "Porém podemos utilizar algumas tecnicas no caso de variaveis que são compostas por mais de um nome como: \n",
        "\n",
        "> A técnica ***camelCase*** é um padrão de nomenclatura de variáveis que consiste em escrever as palavras compostas ou frases juntas, eliminando espaços ou pontuações e capitalizando a primeira letra de cada palavra, exceto a primeira palavra. Por exemplo, se quisermos criar uma variável para armazenar o nome completo de uma pessoa, podemos usar a convenção camelCase e escrever \"nomeCompleto\" ou \"nomeCompletoPessoa\".\n",
        "\n",
        "> A técnica ***snake_case***, na qual as palavras são separadas por um sublinhado \"_\". Por exemplo, uma variável pode ser nomeada como \"numero_de_alunos\". É uma convenção de nomenclatura amplamente usada em Python e em outras linguagens de programação."
      ],
      "metadata": {
        "id": "guAkTGzOq9Mm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Variância dos atributos Numéricos:"
      ],
      "metadata": {
        "id": "qfZBCmi0u-ju"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# calcule a variância de cada coluna numérica do DataFrame\n",
        "variancia_colunas = dados.var(numeric_only=True)\n",
        "\n",
        "print(variancia_colunas)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lCTmU59moAvo",
        "outputId": "df13482c-46a1-42ae-a211-d7e1f56aa9a6"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "NU_INSCRICAO              9.924946e+11\n",
            "NU_ANO                    0.000000e+00\n",
            "TP_FAIXA_ETARIA           1.523240e+01\n",
            "TP_ESTADO_CIVIL           1.705206e-01\n",
            "TP_COR_RACA               1.045631e+00\n",
            "TP_NACIONALIDADE          4.199602e-02\n",
            "TP_ST_CONCLUSAO           5.181560e-01\n",
            "TP_ANO_CONCLUIU           1.830754e+01\n",
            "TP_ESCOLA                 3.526525e-01\n",
            "TP_ENSINO                 6.255800e-03\n",
            "IN_TREINEIRO              1.123375e-01\n",
            "CO_MUNICIPIO_ESC          1.084405e+12\n",
            "CO_UF_ESC                 1.079083e+02\n",
            "TP_DEPENDENCIA_ADM_ESC    8.820260e-01\n",
            "TP_LOCALIZACAO_ESC        3.365136e-02\n",
            "TP_SIT_FUNC_ESC           3.893641e-02\n",
            "CO_MUNICIPIO_PROVA        1.022192e+12\n",
            "CO_UF_PROVA               1.016512e+02\n",
            "TP_PRESENCA_CN            2.241624e-01\n",
            "TP_PRESENCA_CH            2.107712e-01\n",
            "TP_PRESENCA_LC            2.107712e-01\n",
            "TP_PRESENCA_MT            2.241624e-01\n",
            "CO_PROVA_CN               6.688995e+02\n",
            "CO_PROVA_CH               7.776581e+02\n",
            "CO_PROVA_LC               7.569940e+02\n",
            "CO_PROVA_MT               6.870668e+02\n",
            "NU_NOTA_CN                6.491002e+03\n",
            "NU_NOTA_CH                9.175821e+03\n",
            "NU_NOTA_LC                6.266946e+03\n",
            "NU_NOTA_MT                1.223826e+04\n",
            "TP_LINGUA                 2.491277e-01\n",
            "TP_STATUS_REDACAO         6.258082e-01\n",
            "NU_NOTA_COMP1             1.150540e+03\n",
            "NU_NOTA_COMP2             2.082301e+03\n",
            "NU_NOTA_COMP3             1.320696e+03\n",
            "NU_NOTA_COMP4             1.507964e+03\n",
            "NU_NOTA_COMP5             3.606803e+03\n",
            "NU_NOTA_REDACAO           3.637189e+04\n",
            "Q005                      1.901481e+00\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Selecione apenas as colunas numéricas do DataFrame\n",
        "colunas_numericas = dados.select_dtypes(include=['float64', 'int64'])\n",
        "\n",
        "# Calcule a variância de cada coluna numérica e encontre o índice (ou nome) da coluna com a maior variância\n",
        "coluna_maior_variancia = colunas_numericas.var().idxmax()\n",
        "\n",
        "print(\"A coluna numérica com a maior variância é:\", coluna_maior_variancia)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nKcxqs3pnddL",
        "outputId": "a705ebed-98d4-452a-90de-70935d3b9a66"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A coluna numérica com a maior variância é: CO_MUNICIPIO_ESC\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Limpeza dos valores corrompidos (NaN):"
      ],
      "metadata": {
        "id": "BJpGOY4wvTxm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Esse código retorna a contagem de valores nulos em cada coluna do DataFrame dados, ordenando do menor para o maior número de valores nulos.\n",
        "dados.isna().sum().sort_values()"
      ],
      "metadata": {
        "id": "Wz-KwDEOguvu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72513baf-9740-4681-b032-91a03284143d"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NU_INSCRICAO              0\n",
              "TP_LINGUA                 0\n",
              "TP_PRESENCA_MT            0\n",
              "TP_PRESENCA_LC            0\n",
              "TP_PRESENCA_CH            0\n",
              "                     ...   \n",
              "SG_UF_ESC           2576026\n",
              "CO_UF_ESC           2576026\n",
              "NO_MUNICIPIO_ESC    2576026\n",
              "CO_MUNICIPIO_ESC    2576026\n",
              "TP_SIT_FUNC_ESC     2576026\n",
              "Length: 76, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''O comando dados.dropna() é utilizado para remover as linhas com valores faltantes (NaN) do DataFrame dados. \n",
        "O resultado da operação é armazenado na variável dados_sem_nulos.'''\n",
        "\n",
        "# dados_sem_nulos = dados.drop(dados.isna().sum().idxmax(), axis=1) #Vai exigir que seja rodado varias vezes\n",
        "\n",
        "# contagem dos valores NaN por coluna\n",
        "nulos = dados.isnull().sum()\n",
        "\n",
        "# seleciona as colunas com mais de 1.500.000 valores NaN\n",
        "colunas_remover = nulos[nulos > 1500000].index\n",
        "\n",
        "# remove as colunas selecionadas\n",
        "dados_sem_nulos = dados.drop(columns=colunas_remover)"
      ],
      "metadata": {
        "id": "cFcuLUmfwv60"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Informação:**\n",
        "\n",
        "> Ao remover linhas com NaN o dataset dados se reduziu a 592.189 amostras, quase 6x menor que o numero original de 3.389.832;\n",
        "\n",
        "* Graças a isso eu optei por usar um comando que revome as \"colunas com maior quantidade de NaN\";"
      ],
      "metadata": {
        "id": "ohdK6dkb1rw0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados_sem_nulos.isna().sum().sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PHZmM65P0TTS",
        "outputId": "d3931593-0061-4991-85fc-19dea0de45f7"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NU_INSCRICAO             0\n",
              "TP_PRESENCA_MT           0\n",
              "TP_PRESENCA_LC           0\n",
              "TP_PRESENCA_CH           0\n",
              "TP_PRESENCA_CN           0\n",
              "                    ...   \n",
              "TX_RESPOSTAS_CN    1143988\n",
              "NU_NOTA_MT         1143988\n",
              "NU_NOTA_CN         1143988\n",
              "CO_PROVA_MT        1143988\n",
              "CO_PROVA_CN        1143988\n",
              "Length: 68, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "O comando shape() é utilizado para verificar a dimensão (número de linhas e colunas) de um DataFrame ou de um array no Python. \n",
        "A sintaxe para utilizar esse comando é bem simples.\n",
        "\n",
        "Esse comando irá retornar uma tupla com o número de linhas e colunas do DataFrame. Por exemplo, se o comando retornar (1000, 5), \n",
        "isso significa que o DataFrame possui 1000 linhas e 5 colunas.\n",
        "'''\n",
        "\n",
        "dados_sem_nulos.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sVIrZBte005S",
        "outputId": "0bd6a86c-c832-45f6-dbc0-0a040dd4965b"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3389832, 68)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(dados_sem_nulos)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "PIu-1YAy32UR",
        "outputId": "95c62bcc-8050-4a89-8ca7-b7861fb45825"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "         NU_INSCRICAO  NU_ANO  TP_FAIXA_ETARIA TP_SEXO  TP_ESTADO_CIVIL  \\\n",
              "0        210053865474    2021                5       F                1   \n",
              "1        210052384164    2021               12       M                1   \n",
              "2        210052589243    2021               13       F                3   \n",
              "3        210052128335    2021                3       M                1   \n",
              "4        210051353021    2021                2       F                1   \n",
              "...               ...     ...              ...     ...              ...   \n",
              "3389827  210053249138    2021                7       F                1   \n",
              "3389828  210053776013    2021                7       F                1   \n",
              "3389829  210052441508    2021               13       F                1   \n",
              "3389830  210051139675    2021                7       F                1   \n",
              "3389831  210052410399    2021               12       F                2   \n",
              "\n",
              "         TP_COR_RACA  TP_NACIONALIDADE  TP_ST_CONCLUSAO  TP_ANO_CONCLUIU  \\\n",
              "0                  1                 1                1                3   \n",
              "1                  1                 1                1               11   \n",
              "2                  1                 1                1               15   \n",
              "3                  3                 1                2                0   \n",
              "4                  3                 1                2                0   \n",
              "...              ...               ...              ...              ...   \n",
              "3389827            3                 1                1                0   \n",
              "3389828            1                 1                1                5   \n",
              "3389829            3                 1                1               15   \n",
              "3389830            1                 1                1                4   \n",
              "3389831            3                 1                1               15   \n",
              "\n",
              "         TP_ESCOLA  ...  Q016  Q017 Q018  Q019 Q020  Q021  Q022  Q023  Q024  \\\n",
              "0                1  ...     A     A    A     B    A     A     B     A     B   \n",
              "1                1  ...     A     A    A     B    A     A     C     A     A   \n",
              "2                1  ...     B     A    A     B    A     A     C     B     B   \n",
              "3                2  ...     A     A    A     B    A     A     B     A     B   \n",
              "4                2  ...     B     A    A     B    A     B     E     A     B   \n",
              "...            ...  ...   ...   ...  ...   ...  ...   ...   ...   ...   ...   \n",
              "3389827          1  ...     A     A    A     B    A     A     B     A     A   \n",
              "3389828          1  ...     B     A    B     C    A     A     E     B     B   \n",
              "3389829          1  ...     B     A    A     A    A     A     B     A     B   \n",
              "3389830          1  ...     B     A    B     B    A     B     E     A     C   \n",
              "3389831          1  ...   NaN   NaN  NaN   NaN  NaN   NaN   NaN   NaN   NaN   \n",
              "\n",
              "         Q025  \n",
              "0           B  \n",
              "1           A  \n",
              "2           B  \n",
              "3           B  \n",
              "4           B  \n",
              "...       ...  \n",
              "3389827     B  \n",
              "3389828     B  \n",
              "3389829     B  \n",
              "3389830     B  \n",
              "3389831   NaN  \n",
              "\n",
              "[3389832 rows x 68 columns]"
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NU_INSCRICAO</th>\n",
              "      <th>NU_ANO</th>\n",
              "      <th>TP_FAIXA_ETARIA</th>\n",
              "      <th>TP_SEXO</th>\n",
              "      <th>TP_ESTADO_CIVIL</th>\n",
              "      <th>TP_COR_RACA</th>\n",
              "      <th>TP_NACIONALIDADE</th>\n",
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              "      <th>TP_ANO_CONCLUIU</th>\n",
              "      <th>TP_ESCOLA</th>\n",
              "      <th>...</th>\n",
              "      <th>Q016</th>\n",
              "      <th>Q017</th>\n",
              "      <th>Q018</th>\n",
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              "      <th>Q020</th>\n",
              "      <th>Q021</th>\n",
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              "      <th>2</th>\n",
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              "      <td>2021</td>\n",
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              "      <td>F</td>\n",
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              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
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              "      <td>B</td>\n",
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              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>210052128335</td>\n",
              "      <td>2021</td>\n",
              "      <td>3</td>\n",
              "      <td>M</td>\n",
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              "      <td>3</td>\n",
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              "      <td>210052441508</td>\n",
              "      <td>2021</td>\n",
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              "      <td>1</td>\n",
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              "      <td>...</td>\n",
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              "      <td>B</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-484557f0-1877-42c0-99a5-4636356a2c08')\"\n",
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              "  <style>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-484557f0-1877-42c0-99a5-4636356a2c08 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-484557f0-1877-42c0-99a5-4636356a2c08');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados_sem_nulos.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4n5F2T3h4EdZ",
        "outputId": "70852756-e447-4376-dfdf-83c83f83f92c"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3389832 entries, 0 to 3389831\n",
            "Data columns (total 68 columns):\n",
            " #   Column              Dtype  \n",
            "---  ------              -----  \n",
            " 0   NU_INSCRICAO        int64  \n",
            " 1   NU_ANO              int64  \n",
            " 2   TP_FAIXA_ETARIA     int64  \n",
            " 3   TP_SEXO             object \n",
            " 4   TP_ESTADO_CIVIL     int64  \n",
            " 5   TP_COR_RACA         int64  \n",
            " 6   TP_NACIONALIDADE    int64  \n",
            " 7   TP_ST_CONCLUSAO     int64  \n",
            " 8   TP_ANO_CONCLUIU     int64  \n",
            " 9   TP_ESCOLA           int64  \n",
            " 10  IN_TREINEIRO        int64  \n",
            " 11  CO_MUNICIPIO_PROVA  int64  \n",
            " 12  NO_MUNICIPIO_PROVA  object \n",
            " 13  CO_UF_PROVA         int64  \n",
            " 14  SG_UF_PROVA         object \n",
            " 15  TP_PRESENCA_CN      int64  \n",
            " 16  TP_PRESENCA_CH      int64  \n",
            " 17  TP_PRESENCA_LC      int64  \n",
            " 18  TP_PRESENCA_MT      int64  \n",
            " 19  CO_PROVA_CN         float64\n",
            " 20  CO_PROVA_CH         float64\n",
            " 21  CO_PROVA_LC         float64\n",
            " 22  CO_PROVA_MT         float64\n",
            " 23  NU_NOTA_CN          float64\n",
            " 24  NU_NOTA_CH          float64\n",
            " 25  NU_NOTA_LC          float64\n",
            " 26  NU_NOTA_MT          float64\n",
            " 27  TX_RESPOSTAS_CN     object \n",
            " 28  TX_RESPOSTAS_CH     object \n",
            " 29  TX_RESPOSTAS_LC     object \n",
            " 30  TX_RESPOSTAS_MT     object \n",
            " 31  TP_LINGUA           int64  \n",
            " 32  TX_GABARITO_CN      object \n",
            " 33  TX_GABARITO_CH      object \n",
            " 34  TX_GABARITO_LC      object \n",
            " 35  TX_GABARITO_MT      object \n",
            " 36  TP_STATUS_REDACAO   float64\n",
            " 37  NU_NOTA_COMP1       float64\n",
            " 38  NU_NOTA_COMP2       float64\n",
            " 39  NU_NOTA_COMP3       float64\n",
            " 40  NU_NOTA_COMP4       float64\n",
            " 41  NU_NOTA_COMP5       float64\n",
            " 42  NU_NOTA_REDACAO     float64\n",
            " 43  Q001                object \n",
            " 44  Q002                object \n",
            " 45  Q003                object \n",
            " 46  Q004                object \n",
            " 47  Q005                float64\n",
            " 48  Q006                object \n",
            " 49  Q007                object \n",
            " 50  Q008                object \n",
            " 51  Q009                object \n",
            " 52  Q010                object \n",
            " 53  Q011                object \n",
            " 54  Q012                object \n",
            " 55  Q013                object \n",
            " 56  Q014                object \n",
            " 57  Q015                object \n",
            " 58  Q016                object \n",
            " 59  Q017                object \n",
            " 60  Q018                object \n",
            " 61  Q019                object \n",
            " 62  Q020                object \n",
            " 63  Q021                object \n",
            " 64  Q022                object \n",
            " 65  Q023                object \n",
            " 66  Q024                object \n",
            " 67  Q025                object \n",
            "dtypes: float64(16), int64(17), object(35)\n",
            "memory usage: 1.7+ GB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Separação dos grupos de atributos:"
      ],
      "metadata": {
        "id": "homihZPF4qVP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "A notação iloc permite fazer indexação baseada em posição (índices inteiros). Portanto, [:, 2:11] \n",
        "significa \"selecione todas as linhas (:) e as colunas de 2 a 10 (coluna 0, coluna 1, ..., coluna 10)\".\n",
        "\n",
        "Em outras palavras, esse comando retorna um novo DataFrame com as mesmas linhas do dados, mas apenas as 11 primeiras colunas (0 a 10).\n",
        "'''\n",
        "info_candidato = dados_sem_nulos.iloc[:,2:11]\n",
        "# info_exame = dados_sem_nulos.iloc[:,11:36]\n",
        "# info_redação = dados_sem_nulos.iloc[:,36:43]\n",
        "# info_quest_social = dados_sem_nulos.iloc[:,43:67]"
      ],
      "metadata": {
        "id": "hC47zZ2B3Qyh"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "Este comando seleciona as colunas 'TP_SEXO', 'TP_COR_RACA', 'TP_ESCOLA', 'NO_MUNICIPIO_PROVA', 'SG_UF_PROVA', 'TP_PRESENCA_CN', \n",
        "'CO_PROVA_CN', 'NU_NOTA_CN' e 'TX_RESPOSTAS_CN' do DataFrame 'dados' e as atribui a uma nova variável chamada 'nature_quest'.\n",
        "'''\n",
        "#nature_quest = dados[['TP_SEXO','TP_COR_RACA','TP_ESCOLA','NO_MUNICIPIO_PROVA','SG_UF_PROVA','TP_PRESENCA_CN', 'CO_PROVA_CN','NU_NOTA_CN','TX_RESPOSTAS_CN']]"
      ],
      "metadata": {
        "id": "L1t8ztUY09sy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "1a9677f3-425d-4af6-ea5c-0d28021026ff"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"\\nEste comando seleciona as colunas 'TP_SEXO', 'TP_COR_RACA', 'TP_ESCOLA', 'NO_MUNICIPIO_PROVA', 'SG_UF_PROVA', 'TP_PRESENCA_CN', \\n'CO_PROVA_CN', 'NU_NOTA_CN' e 'TX_RESPOSTAS_CN' do DataFrame 'dados' e as atribui a uma nova variável chamada 'nature_quest'.\\n\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Trabalhando com os dados dos candidatos:"
      ],
      "metadata": {
        "id": "ABpp4eRD4oeN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato.isna().sum() # Esse comando retorna a quantidade de valores NaN (Not a Number) em cada coluna do DataFrame infoCandidato..isna().sum() # Esse comando retorna a quantidade de valores NaN (Not a Number) em cada coluna do DataFrame infoCandidato."
      ],
      "metadata": {
        "id": "ta2mXf7OKjhl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10fec2db-84a8-4278-9dfc-55e0698a1c6a"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TP_FAIXA_ETARIA     0\n",
              "TP_SEXO             0\n",
              "TP_ESTADO_CIVIL     0\n",
              "TP_COR_RACA         0\n",
              "TP_NACIONALIDADE    0\n",
              "TP_ST_CONCLUSAO     0\n",
              "TP_ANO_CONCLUIU     0\n",
              "TP_ESCOLA           0\n",
              "IN_TREINEIRO        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display(info_candidato)"
      ],
      "metadata": {
        "id": "H5QSKNrS31Ei",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "f5f87d21-23ba-4032-9c44-c4adc18ef6ed"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "         NU_INSCRICAO  NU_ANO  TP_FAIXA_ETARIA TP_SEXO  TP_ESTADO_CIVIL  \\\n",
              "3        210052128335    2021                3       M                1   \n",
              "4        210051353021    2021                2       F                1   \n",
              "33       210053085526    2021                3       M                1   \n",
              "45       210052916275    2021                4       F                1   \n",
              "51       210051417174    2021                3       M                1   \n",
              "...               ...     ...              ...     ...              ...   \n",
              "3387074  210053404116    2021                3       F                1   \n",
              "3387928  210053232577    2021                3       F                1   \n",
              "3389073  210051724712    2021                3       M                1   \n",
              "3389288  210052134860    2021                3       F                0   \n",
              "3389480  210052612273    2021                3       F                1   \n",
              "\n",
              "         TP_COR_RACA  TP_NACIONALIDADE  TP_ST_CONCLUSAO  TP_ANO_CONCLUIU  \\\n",
              "3                  3                 1                2                0   \n",
              "4                  3                 1                2                0   \n",
              "33                 1                 1                2                0   \n",
              "45                 3                 1                2                0   \n",
              "51                 3                 1                2                0   \n",
              "...              ...               ...              ...              ...   \n",
              "3387074            3                 1                2                0   \n",
              "3387928            3                 1                2                0   \n",
              "3389073            3                 1                2                0   \n",
              "3389288            2                 1                2                0   \n",
              "3389480            3                 1                2                0   \n",
              "\n",
              "         TP_ESCOLA  TP_ENSINO  IN_TREINEIRO  \n",
              "3                2        1.0             0  \n",
              "4                2        1.0             0  \n",
              "33               2        1.0             0  \n",
              "45               2        1.0             0  \n",
              "51               2        1.0             0  \n",
              "...            ...        ...           ...  \n",
              "3387074          2        1.0             0  \n",
              "3387928          2        1.0             0  \n",
              "3389073          2        1.0             0  \n",
              "3389288          2        1.0             0  \n",
              "3389480          2        1.0             0  \n",
              "\n",
              "[592189 rows x 12 columns]"
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              "    <tr>\n",
              "      <th>45</th>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c787956f-a52d-4e34-ae2f-17dc878eecc4')\"\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c787956f-a52d-4e34-ae2f-17dc878eecc4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c787956f-a52d-4e34-ae2f-17dc878eecc4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KQrwgEkn42mE",
        "outputId": "362b6738-6f9a-428a-a662-b42fbc42b1ea"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3389832 entries, 0 to 3389831\n",
            "Data columns (total 9 columns):\n",
            " #   Column            Dtype \n",
            "---  ------            ----- \n",
            " 0   TP_FAIXA_ETARIA   int64 \n",
            " 1   TP_SEXO           object\n",
            " 2   TP_ESTADO_CIVIL   int64 \n",
            " 3   TP_COR_RACA       int64 \n",
            " 4   TP_NACIONALIDADE  int64 \n",
            " 5   TP_ST_CONCLUSAO   int64 \n",
            " 6   TP_ANO_CONCLUIU   int64 \n",
            " 7   TP_ESCOLA         int64 \n",
            " 8   IN_TREINEIRO      int64 \n",
            "dtypes: int64(8), object(1)\n",
            "memory usage: 232.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato.describe()"
      ],
      "metadata": {
        "id": "9wdYWkTDKfWt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "9a13f98b-c43e-4c63-af9e-c2c2850a32c0"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       TP_FAIXA_ETARIA  TP_ESTADO_CIVIL   TP_COR_RACA  TP_NACIONALIDADE  \\\n",
              "count     3.389832e+06     3.389832e+06  3.389832e+06      3.389832e+06   \n",
              "mean      5.306013e+00     1.052073e+00  2.043491e+00      1.027106e+00   \n",
              "std       3.902871e+00     4.129414e-01  1.022561e+00      2.049293e-01   \n",
              "min       1.000000e+00     0.000000e+00  0.000000e+00      0.000000e+00   \n",
              "25%       2.000000e+00     1.000000e+00  1.000000e+00      1.000000e+00   \n",
              "50%       4.000000e+00     1.000000e+00  2.000000e+00      1.000000e+00   \n",
              "75%       7.000000e+00     1.000000e+00  3.000000e+00      1.000000e+00   \n",
              "max       2.000000e+01     4.000000e+00  6.000000e+00      4.000000e+00   \n",
              "\n",
              "       TP_ST_CONCLUSAO  TP_ANO_CONCLUIU     TP_ESCOLA  IN_TREINEIRO  \n",
              "count     3.389832e+06     3.389832e+06  3.389832e+06  3.389832e+06  \n",
              "mean      1.608430e+00     2.581980e+00  1.396214e+00  1.289710e-01  \n",
              "std       7.198306e-01     4.278731e+00  5.938455e-01  3.351679e-01  \n",
              "min       1.000000e+00     0.000000e+00  1.000000e+00  0.000000e+00  \n",
              "25%       1.000000e+00     0.000000e+00  1.000000e+00  0.000000e+00  \n",
              "50%       1.000000e+00     0.000000e+00  1.000000e+00  0.000000e+00  \n",
              "75%       2.000000e+00     3.000000e+00  2.000000e+00  0.000000e+00  \n",
              "max       4.000000e+00     1.500000e+01  3.000000e+00  1.000000e+00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-78661481-95f8-4d7a-8d7d-bc84ce1fac1f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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_FAIXA_ETARIA</th>\n",
              "      <th>TP_ESTADO_CIVIL</th>\n",
              "      <th>TP_COR_RACA</th>\n",
              "      <th>TP_NACIONALIDADE</th>\n",
              "      <th>TP_ST_CONCLUSAO</th>\n",
              "      <th>TP_ANO_CONCLUIU</th>\n",
              "      <th>TP_ESCOLA</th>\n",
              "      <th>IN_TREINEIRO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "      <td>3.389832e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.306013e+00</td>\n",
              "      <td>1.052073e+00</td>\n",
              "      <td>2.043491e+00</td>\n",
              "      <td>1.027106e+00</td>\n",
              "      <td>1.608430e+00</td>\n",
              "      <td>2.581980e+00</td>\n",
              "      <td>1.396214e+00</td>\n",
              "      <td>1.289710e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.902871e+00</td>\n",
              "      <td>4.129414e-01</td>\n",
              "      <td>1.022561e+00</td>\n",
              "      <td>2.049293e-01</td>\n",
              "      <td>7.198306e-01</td>\n",
              "      <td>4.278731e+00</td>\n",
              "      <td>5.938455e-01</td>\n",
              "      <td>3.351679e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000e+01</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>4.000000e+00</td>\n",
              "      <td>1.500000e+01</td>\n",
              "      <td>3.000000e+00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-78661481-95f8-4d7a-8d7d-bc84ce1fac1f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-78661481-95f8-4d7a-8d7d-bc84ce1fac1f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-78661481-95f8-4d7a-8d7d-bc84ce1fac1f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploração dos Dados:"
      ],
      "metadata": {
        "id": "Ff7NDn3n3zfX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Analisando as Notas Grais:"
      ],
      "metadata": {
        "id": "HOwSpeBXw1sO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dados['NU_NOTA_CN'] = dados['NU_NOTA_CN'].astype(float)"
      ],
      "metadata": {
        "id": "ucsjWsYN_7OS"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dados['NU_NOTA_CN'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkyO_gIYA5Ag",
        "outputId": "59411de7-0593-4b6b-a6fe-f1f7b9feef68"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "491.7856752294459"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dados['NU_NOTA_CN'].std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q1np5N8cAJE5",
        "outputId": "a7e34f91-0ca3-47dc-e7a3-7f988b1db9d7"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "80.56675679752138"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "variancia = np.var(dados['TP_COR_RACA'])\n",
        "variancia"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d9XE0k-0A4XZ",
        "outputId": "458e4f46-a19d-42e5-8e85-204b1194d4af"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0456309517179945"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Candidatos:"
      ],
      "metadata": {
        "id": "_2aLyIPnxJDE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['TP_FAIXA_ETARIA'].value_counts().plot.pie(autopct='%.0f%%')"
      ],
      "metadata": {
        "id": "5WnctqUx5yRj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "468ceec0-46f1-4380-d489-2db3d6aa3def"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='TP_FAIXA_ETARIA'>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Significado das porcentagens:***\n",
        "\n",
        "* 1\t- Menor de 17 anos\n",
        "* 2\t- 17 anos\n",
        "* 3\t- 18 anos\n",
        "* 4\t- 19 anos\n",
        "* 5\t- 20 anos\n",
        "* 6 - 21 anos\n",
        "* 7\t- 22 anos\n",
        "* 8\t- 23 anos\n",
        "* 9\t- 24 anos\n",
        "* 10 - 25 anos\n",
        "* 11 - Entre 26 e 30 anos\n",
        "* 12 - Entre 31 e 35 anos\n",
        "* 13 - Entre 36 e 40 anos\n",
        "* 14 - Entre 41 e 45 anos\n",
        "* 15 - Entre 46 e 50 anos\n",
        "* 16 - Entre 51 e 55 anos\n",
        "* 17 - Entre 56 e 60 anos\n",
        "* 18 - Entre 61 e 65 anos\n",
        "* 19 - Entre 66 e 70 anos\n",
        "* 20 - Maior de 70 anos"
      ],
      "metadata": {
        "id": "gNFmnlPnTOHM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['TP_FAIXA_ETARIA'].value_counts()"
      ],
      "metadata": {
        "id": "lc1goYqaPqcz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b9139ad-e195-44a6-ec18-9a1597e49730"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3     699365\n",
              "2     620784\n",
              "4     394689\n",
              "1     274318\n",
              "5     250690\n",
              "11    227574\n",
              "6     177086\n",
              "7     132052\n",
              "12    130746\n",
              "8     100580\n",
              "13     93073\n",
              "9      85742\n",
              "10     71199\n",
              "14     58754\n",
              "15     35808\n",
              "16     20564\n",
              "17     10804\n",
              "18      4102\n",
              "19      1342\n",
              "20       560\n",
              "Name: TP_FAIXA_ETARIA, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['TP_SEXO'].value_counts().head().plot.pie(autopct='%.0f%%')"
      ],
      "metadata": {
        "id": "mRcojGux5P1i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "e8d66c62-a42d-4f9f-e16f-e3fd4792d996"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='TP_SEXO'>"
            ]
          },
          "metadata": {},
          "execution_count": 92
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Significado das porcentagens:***\n",
        "\n",
        "* M - Masculino\n",
        "* F - Feminino "
      ],
      "metadata": {
        "id": "BTefI5np673t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['TP_SEXO'].value_counts()"
      ],
      "metadata": {
        "id": "7kF-kR88PrZD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80faef40-64c0-4f31-d25c-2968b4f0c4c3"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F    2090526\n",
              "M    1299306\n",
              "Name: TP_SEXO, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['TP_ESTADO_CIVIL'].value_counts().head().plot.pie(autopct='%.0f%%')"
      ],
      "metadata": {
        "id": "iifXtNN2SpjY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "3c3b3655-6995-496b-f884-6629384e23f7"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='TP_ESTADO_CIVIL'>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Significado das porcentagens:***\n",
        "\n",
        "* 0\t- Não informado\n",
        "* 1\t- Solteiro(a)\n",
        "* 2\t- Casado(a)/Mora com companheiro(a)\n",
        "* 3\t- Divorciado(a)/Desquitado(a)/Separado(a)\n",
        "* 4\t- Viúvo(a)"
      ],
      "metadata": {
        "id": "wwqbzhN06U_7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['TP_ESTADO_CIVIL'].value_counts()"
      ],
      "metadata": {
        "id": "SwUn6mJKSrD4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5328b378-fa63-4491-81ae-36abc1651a21"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    2995915\n",
              "2     196285\n",
              "0     139630\n",
              "3      54141\n",
              "4       3861\n",
              "Name: TP_ESTADO_CIVIL, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['TP_COR_RACA'].value_counts().head().plot.pie(autopct='%.0f%%')"
      ],
      "metadata": {
        "id": "cNGt7aHm5muZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "7cb959fc-00fc-42f8-958f-4729a731179b"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='TP_COR_RACA'>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Significado das porcentagens:***\n",
        "\n",
        "* 0 - Não declarado\n",
        "* 1 - Branca\n",
        "* 2 - Preta\n",
        "* 3 - Parda\n",
        "* 4 - Amarela\n",
        "* 5 - Indígena \n",
        "* 6 - Não dispõe da informação"
      ],
      "metadata": {
        "id": "xqmZCR1M7W7a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['TP_COR_RACA'].value_counts()"
      ],
      "metadata": {
        "id": "Ykp408bBPsAa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab292811-8bd6-46d9-88a2-8596a72db188"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    1457454\n",
              "1    1362256\n",
              "2     411302\n",
              "0      71149\n",
              "4      68491\n",
              "5      19175\n",
              "6          5\n",
              "Name: TP_COR_RACA, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['TP_NACIONALIDADE'].value_counts().head().plot.pie(autopct='%.0f%%')"
      ],
      "metadata": {
        "id": "OWBHkLvX5rup",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "6ba2ec26-093a-415c-c39c-261b4c77098b"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='TP_NACIONALIDADE'>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Significado das porcentagens:***\n",
        "\n",
        "* 0 - Não informado\n",
        "* 1 - Brasileiro(a)\n",
        "* 2 - Brasileiro(a) Naturalizado(a)\n",
        "* 3 - Estrangeiro(a)\n",
        "* 4 - Brasileiro(a) Nato(a), nascido(a) no exterior"
      ],
      "metadata": {
        "id": "uR3Ndg0O7yaY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['TP_NACIONALIDADE'].value_counts()"
      ],
      "metadata": {
        "id": "k2gqJoB5PshD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce69fdc4-0855-4a5c-a68b-937889544644"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    3310876\n",
              "2      65525\n",
              "4       6526\n",
              "3       4562\n",
              "0       2343\n",
              "Name: TP_NACIONALIDADE, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['TP_ESCOLA'].value_counts().head().plot.pie(autopct='%.0f%%')"
      ],
      "metadata": {
        "id": "qz46hinF5_vF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "d0d6a4ac-133e-4829-8a17-3a0963f6b914"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='TP_ESCOLA'>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Significado das porcentagens:***\n",
        "\n",
        "* 1 - Não Respondeu\n",
        "* 2 - Pública\n",
        "* 3 - Privada"
      ],
      "metadata": {
        "id": "60atjIHS8Gbm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['TP_ESCOLA'].value_counts()"
      ],
      "metadata": {
        "id": "5Ps1yRa1PtCz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "01866de5-2335-4265-fdba-de8b7558b263"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    2238977\n",
              "2     958611\n",
              "3     192244\n",
              "Name: TP_ESCOLA, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['IN_TREINEIRO'].value_counts().head().plot.pie(autopct='%.0f%%')"
      ],
      "metadata": {
        "id": "X4aKwj1EIcOG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "0b7918a2-a6e0-4d6c-e2ba-0a9a5e761f2e"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='IN_TREINEIRO'>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Significado das porcentagens:***\n",
        "\n",
        "* 0 - Não é treineiro\n",
        "* 1 - É treineiro"
      ],
      "metadata": {
        "id": "-sjnr_08Iqx1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "info_candidato['IN_TREINEIRO'].value_counts()"
      ],
      "metadata": {
        "id": "dHylvsOlPts1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35d09173-0a8e-4ba6-88f6-a311144aca6c"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    2952642\n",
              "1     437190\n",
              "Name: IN_TREINEIRO, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    }
  ]
}