Aula_04_Imersao_Dados.ipynb

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    "colab": {
      "name": "Aula_04_Imersao_Dados.ipynb",
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      "name": "python3",
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        "outputId": "a8bb4bb5-9747-4053-ba63-383a6c632dfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
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      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "fonte = \"https://github.com/alura-cursos/imersao-dados-2-2020/blob/master/MICRODADOS_ENEM_2019_SAMPLE_43278.csv?raw=true\"\n",
        "\n",
        "dados = pd.read_csv(fonte)\n",
        "dados.head()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>CCBCBEACABACABBBBACCBCEBDCBABCADADCBCADBAADEA</td>\n",
              "      <td>EBCAB99999BBCECBCACBEACBDAABDBCBBDACEBBACDCAEC...</td>\n",
              "      <td>AECCBBCBBCBDEDECACBAABEDABBEDDADCEADDBEBDBBEB</td>\n",
              "      <td>0</td>\n",
              "      <td>DEADBAAAEBEECEBCBCBCBDADAEABCEDDDDADCBEECACBC</td>\n",
              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
              "      <td>AADDDBEEEBEDDBEBACABCDBABECECACAECDCBDCCEDCDA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>780.0</td>\n",
              "      <td>E</td>\n",
              "      <td>E</td>\n",
              "      <td>B</td>\n",
              "      <td>D</td>\n",
              "      <td>3</td>\n",
              "      <td>E</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>D</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 136 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   NU_INSCRICAO  NU_ANO  CO_MUNICIPIO_RESIDENCIA  ... Q023  Q024 Q025\n",
              "0  190001004661    2019                  1506138  ...    A     B    B\n",
              "1  190001004674    2019                  1504208  ...    A     A    B\n",
              "2  190001004722    2019                  1501402  ...    A     A    B\n",
              "3  190001004735    2019                  1507300  ...    A     A    B\n",
              "4  190001004776    2019                  1500800  ...    A     A    B\n",
              "\n",
              "[5 rows x 136 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YeKjZt4oTS5o",
        "outputId": "5b75df0f-b4c8-4018-c916-52572786527b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "provas = [\"NU_NOTA_CN\",\"NU_NOTA_CH\",\"NU_NOTA_MT\",\"NU_NOTA_LC\",\"NU_NOTA_REDACAO\"]\n",
        "\n",
        "dados[provas].describe()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NU_NOTA_CN</th>\n",
              "      <th>NU_NOTA_CH</th>\n",
              "      <th>NU_NOTA_MT</th>\n",
              "      <th>NU_NOTA_LC</th>\n",
              "      <th>NU_NOTA_REDACAO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>92758.000000</td>\n",
              "      <td>98043.000000</td>\n",
              "      <td>92758.000000</td>\n",
              "      <td>98043.000000</td>\n",
              "      <td>98043.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>477.964015</td>\n",
              "      <td>507.365912</td>\n",
              "      <td>523.555206</td>\n",
              "      <td>520.463928</td>\n",
              "      <td>571.570025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>76.296099</td>\n",
              "      <td>82.972839</td>\n",
              "      <td>109.416939</td>\n",
              "      <td>64.556578</td>\n",
              "      <td>188.076455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>418.000000</td>\n",
              "      <td>447.900000</td>\n",
              "      <td>435.300000</td>\n",
              "      <td>483.700000</td>\n",
              "      <td>480.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>470.200000</td>\n",
              "      <td>510.900000</td>\n",
              "      <td>501.600000</td>\n",
              "      <td>526.100000</td>\n",
              "      <td>580.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>533.300000</td>\n",
              "      <td>567.100000</td>\n",
              "      <td>598.200000</td>\n",
              "      <td>565.100000</td>\n",
              "      <td>680.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>797.300000</td>\n",
              "      <td>809.400000</td>\n",
              "      <td>985.000000</td>\n",
              "      <td>801.700000</td>\n",
              "      <td>1000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         NU_NOTA_CN    NU_NOTA_CH    NU_NOTA_MT    NU_NOTA_LC  NU_NOTA_REDACAO\n",
              "count  92758.000000  98043.000000  92758.000000  98043.000000     98043.000000\n",
              "mean     477.964015    507.365912    523.555206    520.463928       571.570025\n",
              "std       76.296099     82.972839    109.416939     64.556578       188.076455\n",
              "min        0.000000      0.000000      0.000000      0.000000         0.000000\n",
              "25%      418.000000    447.900000    435.300000    483.700000       480.000000\n",
              "50%      470.200000    510.900000    501.600000    526.100000       580.000000\n",
              "75%      533.300000    567.100000    598.200000    565.100000       680.000000\n",
              "max      797.300000    809.400000    985.000000    801.700000      1000.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqS_hIkvRA0O",
        "outputId": "67319664-7b87-4c13-918c-354d0543ae99",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "dados[\"NU_NOTA_TOTAL\"] = dados[provas].sum(axis=1)\n",
        "dados.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NU_INSCRICAO</th>\n",
              "      <th>NU_ANO</th>\n",
              "      <th>CO_MUNICIPIO_RESIDENCIA</th>\n",
              "      <th>NO_MUNICIPIO_RESIDENCIA</th>\n",
              "      <th>CO_UF_RESIDENCIA</th>\n",
              "      <th>SG_UF_RESIDENCIA</th>\n",
              "      <th>NU_IDADE</th>\n",
              "      <th>TP_SEXO</th>\n",
              "      <th>TP_ESTADO_CIVIL</th>\n",
              "      <th>TP_COR_RACA</th>\n",
              "      <th>TP_NACIONALIDADE</th>\n",
              "      <th>CO_MUNICIPIO_NASCIMENTO</th>\n",
              "      <th>NO_MUNICIPIO_NASCIMENTO</th>\n",
              "      <th>CO_UF_NASCIMENTO</th>\n",
              "      <th>SG_UF_NASCIMENTO</th>\n",
              "      <th>TP_ST_CONCLUSAO</th>\n",
              "      <th>TP_ANO_CONCLUIU</th>\n",
              "      <th>TP_ESCOLA</th>\n",
              "      <th>TP_ENSINO</th>\n",
              "      <th>IN_TREINEIRO</th>\n",
              "      <th>CO_ESCOLA</th>\n",
              "      <th>CO_MUNICIPIO_ESC</th>\n",
              "      <th>NO_MUNICIPIO_ESC</th>\n",
              "      <th>CO_UF_ESC</th>\n",
              "      <th>SG_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>IN_BAIXA_VISAO</th>\n",
              "      <th>IN_CEGUEIRA</th>\n",
              "      <th>IN_SURDEZ</th>\n",
              "      <th>IN_DEFICIENCIA_AUDITIVA</th>\n",
              "      <th>IN_SURDO_CEGUEIRA</th>\n",
              "      <th>IN_DEFICIENCIA_FISICA</th>\n",
              "      <th>IN_DEFICIENCIA_MENTAL</th>\n",
              "      <th>IN_DEFICIT_ATENCAO</th>\n",
              "      <th>IN_DISLEXIA</th>\n",
              "      <th>IN_DISCALCULIA</th>\n",
              "      <th>IN_AUTISMO</th>\n",
              "      <th>IN_VISAO_MONOCULAR</th>\n",
              "      <th>...</th>\n",
              "      <th>TX_RESPOSTAS_LC</th>\n",
              "      <th>TX_RESPOSTAS_MT</th>\n",
              "      <th>TP_LINGUA</th>\n",
              "      <th>TX_GABARITO_CN</th>\n",
              "      <th>TX_GABARITO_CH</th>\n",
              "      <th>TX_GABARITO_LC</th>\n",
              "      <th>TX_GABARITO_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>Q001</th>\n",
              "      <th>Q002</th>\n",
              "      <th>Q003</th>\n",
              "      <th>Q004</th>\n",
              "      <th>Q005</th>\n",
              "      <th>Q006</th>\n",
              "      <th>Q007</th>\n",
              "      <th>Q008</th>\n",
              "      <th>Q009</th>\n",
              "      <th>Q010</th>\n",
              "      <th>Q011</th>\n",
              "      <th>Q012</th>\n",
              "      <th>Q013</th>\n",
              "      <th>Q014</th>\n",
              "      <th>Q015</th>\n",
              "      <th>Q016</th>\n",
              "      <th>Q017</th>\n",
              "      <th>Q018</th>\n",
              "      <th>Q019</th>\n",
              "      <th>Q020</th>\n",
              "      <th>Q021</th>\n",
              "      <th>Q022</th>\n",
              "      <th>Q023</th>\n",
              "      <th>Q024</th>\n",
              "      <th>Q025</th>\n",
              "      <th>NU_NOTA_TOTAL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>190001004661</td>\n",
              "      <td>2019</td>\n",
              "      <td>1506138</td>\n",
              "      <td>Redenção</td>\n",
              "      <td>15</td>\n",
              "      <td>PA</td>\n",
              "      <td>17</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1506138.0</td>\n",
              "      <td>Redenção</td>\n",
              "      <td>15.0</td>\n",
              "      <td>PA</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>99999CCCABBCAADDBCEBCCADBEEBDECBAABDEACACAEABB...</td>\n",
              "      <td>DEEDCAECDDEEECBCBECABEBAECBBCDAECAEBBBBBDCCDB</td>\n",
              "      <td>1</td>\n",
              "      <td>DEADBAAAEBEECEBCBCBCBDADAEABCEDDDDADCBEECACBC</td>\n",
              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
              "      <td>AADDDBEEEBEDDBEBACABCDBABECECACAECDCBDCCEDCDA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>4</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>C</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>C</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>2288.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>190001004674</td>\n",
              "      <td>2019</td>\n",
              "      <td>1504208</td>\n",
              "      <td>Marabá</td>\n",
              "      <td>15</td>\n",
              "      <td>PA</td>\n",
              "      <td>23</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1504208.0</td>\n",
              "      <td>Marabá</td>\n",
              "      <td>15.0</td>\n",
              "      <td>PA</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>99999ABCEBCEDDECEEEBBCCEECCABDABEEBCDEAEBBEAEA...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CBABADBBCEEEBCBADCBEEDBBEADBBACDBBACCCCADACAC</td>\n",
              "      <td>BBEDABDACACBABAECBBCCADCEBDBBCDDEEAAADDBECDECA...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>400.0</td>\n",
              "      <td>C</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>4</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>1184.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>190001004722</td>\n",
              "      <td>2019</td>\n",
              "      <td>1501402</td>\n",
              "      <td>Belém</td>\n",
              "      <td>15</td>\n",
              "      <td>PA</td>\n",
              "      <td>35</td>\n",
              "      <td>F</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1501402.0</td>\n",
              "      <td>Belém</td>\n",
              "      <td>15.0</td>\n",
              "      <td>PA</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>99999ECBCECEBBABECCCECECCEECCDECEEEDADDDDECEDD...</td>\n",
              "      <td>DDBEADEBBCADCEEACABECBCEBADECADCEDAACBDABABCE</td>\n",
              "      <td>1</td>\n",
              "      <td>AAECACDEADCBCDDDBCBDADAEABCEBABEEBCBEECEBDADC</td>\n",
              "      <td>CBABADBBCEEEBCBADCBEEDBBEADBBACDBBACCCCADACAC</td>\n",
              "      <td>BBEDABDACACBABAECBBCCADCEBDBBCDDEEAAADDBECDECA...</td>\n",
              "      <td>EEEADBEBACABCDBABECECACDCBDCCEDCDABEDECDDDBAA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>560.0</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>5</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>2350.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>190001004735</td>\n",
              "      <td>2019</td>\n",
              "      <td>1507300</td>\n",
              "      <td>São Félix do Xingu</td>\n",
              "      <td>15</td>\n",
              "      <td>PA</td>\n",
              "      <td>23</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1505437.0</td>\n",
              "      <td>Ourilândia do Norte</td>\n",
              "      <td>15.0</td>\n",
              "      <td>PA</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>99999BABCDABBCBDDAAECADBDDADDCBCEEDCAEBCEAEABD...</td>\n",
              "      <td>CBDEBBCBDEBCABCDBDEDADCCBEDBCAECEBEBDEBAEECBC</td>\n",
              "      <td>1</td>\n",
              "      <td>BEEAAEBEEBADEADDADAEABCEDDDBCBCBCCACBCDADCCEB</td>\n",
              "      <td>EEBCEEDBADBBCBABCCADCEBACDBBACCACACBEADBBADCB</td>\n",
              "      <td>ADBBEDCABAABBCBCDAAECDDDBAAAECADECDCEBDEEAECBD...</td>\n",
              "      <td>BEDEEEAADBEBACABCDBABECECACADCBDCCEDCDABECDDD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>E</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>1</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>2555.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>190001004776</td>\n",
              "      <td>2019</td>\n",
              "      <td>1500800</td>\n",
              "      <td>Ananindeua</td>\n",
              "      <td>15</td>\n",
              "      <td>PA</td>\n",
              "      <td>16</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1500800.0</td>\n",
              "      <td>Ananindeua</td>\n",
              "      <td>15.0</td>\n",
              "      <td>PA</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>EBCAB99999BBCECBCACBEACBDAABDBCBBDACEBBACDCAEC...</td>\n",
              "      <td>AECCBBCBBCBDEDECACBAABEDABBEDDADCEADDBEBDBBEB</td>\n",
              "      <td>0</td>\n",
              "      <td>DEADBAAAEBEECEBCBCBCBDADAEABCEDDDDADCBEECACBC</td>\n",
              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
              "      <td>AADDDBEEEBEDDBEBACABCDBABECECACAECDCBDCCEDCDA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>780.0</td>\n",
              "      <td>E</td>\n",
              "      <td>E</td>\n",
              "      <td>B</td>\n",
              "      <td>D</td>\n",
              "      <td>3</td>\n",
              "      <td>E</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>D</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>2803.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 137 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   NU_INSCRICAO  NU_ANO  CO_MUNICIPIO_RESIDENCIA  ... Q024  Q025 NU_NOTA_TOTAL\n",
              "0  190001004661    2019                  1506138  ...    B     B        2288.9\n",
              "1  190001004674    2019                  1504208  ...    A     B        1184.6\n",
              "2  190001004722    2019                  1501402  ...    A     B        2350.5\n",
              "3  190001004735    2019                  1507300  ...    A     B        2555.7\n",
              "4  190001004776    2019                  1500800  ...    A     B        2803.5\n",
              "\n",
              "[5 rows x 137 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HX2i1uknS2uT",
        "outputId": "197d39c1-69e0-45a6-cb37-14a42da0f49f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "provas = [\"NU_NOTA_CN\",\"NU_NOTA_CH\",\"NU_NOTA_MT\",\"NU_NOTA_LC\",\"NU_NOTA_REDACAO\"]\n",
        "provas.append(\"NU_NOTA_TOTAL\")\n",
        "dados[provas].query(\"NU_NOTA_TOTAL == 0\")"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NU_NOTA_CN</th>\n",
              "      <th>NU_NOTA_CH</th>\n",
              "      <th>NU_NOTA_MT</th>\n",
              "      <th>NU_NOTA_LC</th>\n",
              "      <th>NU_NOTA_REDACAO</th>\n",
              "      <th>NU_NOTA_TOTAL</th>\n",
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              "      <th>10</th>\n",
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              "      <th>11</th>\n",
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              "      <th>26</th>\n",
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              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127351</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>127366</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127374</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29162 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        NU_NOTA_CN  NU_NOTA_CH  ...  NU_NOTA_REDACAO  NU_NOTA_TOTAL\n",
              "10             NaN         NaN  ...              NaN            0.0\n",
              "11             NaN         NaN  ...              NaN            0.0\n",
              "26             NaN         NaN  ...              NaN            0.0\n",
              "38             NaN         NaN  ...              NaN            0.0\n",
              "40             NaN         NaN  ...              NaN            0.0\n",
              "...            ...         ...  ...              ...            ...\n",
              "127351         NaN         NaN  ...              NaN            0.0\n",
              "127365         NaN         NaN  ...              NaN            0.0\n",
              "127366         NaN         NaN  ...              NaN            0.0\n",
              "127368         NaN         NaN  ...              NaN            0.0\n",
              "127374         NaN         NaN  ...              NaN            0.0\n",
              "\n",
              "[29162 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKw2G33sUiHQ",
        "outputId": "ee3cb2f3-ae2a-4d5c-f97d-2b18e9005beb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "dados_sem_notas_zero = dados.query(\"NU_NOTA_TOTAL != 0\")\n",
        "dados_sem_notas_zero.head()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NU_INSCRICAO</th>\n",
              "      <th>NU_ANO</th>\n",
              "      <th>CO_MUNICIPIO_RESIDENCIA</th>\n",
              "      <th>NO_MUNICIPIO_RESIDENCIA</th>\n",
              "      <th>CO_UF_RESIDENCIA</th>\n",
              "      <th>SG_UF_RESIDENCIA</th>\n",
              "      <th>NU_IDADE</th>\n",
              "      <th>TP_SEXO</th>\n",
              "      <th>TP_ESTADO_CIVIL</th>\n",
              "      <th>TP_COR_RACA</th>\n",
              "      <th>TP_NACIONALIDADE</th>\n",
              "      <th>CO_MUNICIPIO_NASCIMENTO</th>\n",
              "      <th>NO_MUNICIPIO_NASCIMENTO</th>\n",
              "      <th>CO_UF_NASCIMENTO</th>\n",
              "      <th>SG_UF_NASCIMENTO</th>\n",
              "      <th>TP_ST_CONCLUSAO</th>\n",
              "      <th>TP_ANO_CONCLUIU</th>\n",
              "      <th>TP_ESCOLA</th>\n",
              "      <th>TP_ENSINO</th>\n",
              "      <th>IN_TREINEIRO</th>\n",
              "      <th>CO_ESCOLA</th>\n",
              "      <th>CO_MUNICIPIO_ESC</th>\n",
              "      <th>NO_MUNICIPIO_ESC</th>\n",
              "      <th>CO_UF_ESC</th>\n",
              "      <th>SG_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>IN_BAIXA_VISAO</th>\n",
              "      <th>IN_CEGUEIRA</th>\n",
              "      <th>IN_SURDEZ</th>\n",
              "      <th>IN_DEFICIENCIA_AUDITIVA</th>\n",
              "      <th>IN_SURDO_CEGUEIRA</th>\n",
              "      <th>IN_DEFICIENCIA_FISICA</th>\n",
              "      <th>IN_DEFICIENCIA_MENTAL</th>\n",
              "      <th>IN_DEFICIT_ATENCAO</th>\n",
              "      <th>IN_DISLEXIA</th>\n",
              "      <th>IN_DISCALCULIA</th>\n",
              "      <th>IN_AUTISMO</th>\n",
              "      <th>IN_VISAO_MONOCULAR</th>\n",
              "      <th>...</th>\n",
              "      <th>TX_RESPOSTAS_LC</th>\n",
              "      <th>TX_RESPOSTAS_MT</th>\n",
              "      <th>TP_LINGUA</th>\n",
              "      <th>TX_GABARITO_CN</th>\n",
              "      <th>TX_GABARITO_CH</th>\n",
              "      <th>TX_GABARITO_LC</th>\n",
              "      <th>TX_GABARITO_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>Q001</th>\n",
              "      <th>Q002</th>\n",
              "      <th>Q003</th>\n",
              "      <th>Q004</th>\n",
              "      <th>Q005</th>\n",
              "      <th>Q006</th>\n",
              "      <th>Q007</th>\n",
              "      <th>Q008</th>\n",
              "      <th>Q009</th>\n",
              "      <th>Q010</th>\n",
              "      <th>Q011</th>\n",
              "      <th>Q012</th>\n",
              "      <th>Q013</th>\n",
              "      <th>Q014</th>\n",
              "      <th>Q015</th>\n",
              "      <th>Q016</th>\n",
              "      <th>Q017</th>\n",
              "      <th>Q018</th>\n",
              "      <th>Q019</th>\n",
              "      <th>Q020</th>\n",
              "      <th>Q021</th>\n",
              "      <th>Q022</th>\n",
              "      <th>Q023</th>\n",
              "      <th>Q024</th>\n",
              "      <th>Q025</th>\n",
              "      <th>NU_NOTA_TOTAL</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>190001004661</td>\n",
              "      <td>2019</td>\n",
              "      <td>1506138</td>\n",
              "      <td>Redenção</td>\n",
              "      <td>15</td>\n",
              "      <td>PA</td>\n",
              "      <td>17</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1506138.0</td>\n",
              "      <td>Redenção</td>\n",
              "      <td>15.0</td>\n",
              "      <td>PA</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>99999CCCABBCAADDBCEBCCADBEEBDECBAABDEACACAEABB...</td>\n",
              "      <td>DEEDCAECDDEEECBCBECABEBAECBBCDAECAEBBBBBDCCDB</td>\n",
              "      <td>1</td>\n",
              "      <td>DEADBAAAEBEECEBCBCBCBDADAEABCEDDDDADCBEECACBC</td>\n",
              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
              "      <td>AADDDBEEEBEDDBEBACABCDBABECECACAECDCBDCCEDCDA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>420.0</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>4</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>C</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>C</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>2288.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>190001004674</td>\n",
              "      <td>2019</td>\n",
              "      <td>1504208</td>\n",
              "      <td>Marabá</td>\n",
              "      <td>15</td>\n",
              "      <td>PA</td>\n",
              "      <td>23</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1504208.0</td>\n",
              "      <td>Marabá</td>\n",
              "      <td>15.0</td>\n",
              "      <td>PA</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>99999ABCEBCEDDECEEEBBCCEECCABDABEEBCDEAEBBEAEA...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>CBABADBBCEEEBCBADCBEEDBBEADBBACDBBACCCCADACAC</td>\n",
              "      <td>BBEDABDACACBABAECBBCCADCEBDBBCDDEEAAADDBECDECA...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>400.0</td>\n",
              "      <td>C</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>4</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>1184.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>190001004722</td>\n",
              "      <td>2019</td>\n",
              "      <td>1501402</td>\n",
              "      <td>Belém</td>\n",
              "      <td>15</td>\n",
              "      <td>PA</td>\n",
              "      <td>35</td>\n",
              "      <td>F</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1501402.0</td>\n",
              "      <td>Belém</td>\n",
              "      <td>15.0</td>\n",
              "      <td>PA</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>99999ECBCECEBBABECCCECECCEECCDECEEEDADDDDECEDD...</td>\n",
              "      <td>DDBEADEBBCADCEEACABECBCEBADECADCEDAACBDABABCE</td>\n",
              "      <td>1</td>\n",
              "      <td>AAECACDEADCBCDDDBCBDADAEABCEBABEEBCBEECEBDADC</td>\n",
              "      <td>CBABADBBCEEEBCBADCBEEDBBEADBBACDBBACCCCADACAC</td>\n",
              "      <td>BBEDABDACACBABAECBBCCADCEBDBBCDDEEAAADDBECDECA...</td>\n",
              "      <td>EEEADBEBACABCDBABECECACDCBDCCEDCDABEDECDDDBAA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>560.0</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>5</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>2350.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>190001004735</td>\n",
              "      <td>2019</td>\n",
              "      <td>1507300</td>\n",
              "      <td>São Félix do Xingu</td>\n",
              "      <td>15</td>\n",
              "      <td>PA</td>\n",
              "      <td>23</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1505437.0</td>\n",
              "      <td>Ourilândia do Norte</td>\n",
              "      <td>15.0</td>\n",
              "      <td>PA</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>99999BABCDABBCBDDAAECADBDDADDCBCEEDCAEBCEAEABD...</td>\n",
              "      <td>CBDEBBCBDEBCABCDBDEDADCCBEDBCAECEBEBDEBAEECBC</td>\n",
              "      <td>1</td>\n",
              "      <td>BEEAAEBEEBADEADDADAEABCEDDDBCBCBCCACBCDADCCEB</td>\n",
              "      <td>EEBCEEDBADBBCBABCCADCEBACDBBACCACACBEADBBADCB</td>\n",
              "      <td>ADBBEDCABAABBCBCDAAECDDDBAAAECADECDCEBDEEAECBD...</td>\n",
              "      <td>BEDEEEAADBEBACABCDBABECECACADCBDCCEDCDABECDDD</td>\n",
              "      <td>1.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>E</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>1</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>2555.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>190001004776</td>\n",
              "      <td>2019</td>\n",
              "      <td>1500800</td>\n",
              "      <td>Ananindeua</td>\n",
              "      <td>15</td>\n",
              "      <td>PA</td>\n",
              "      <td>16</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1500800.0</td>\n",
              "      <td>Ananindeua</td>\n",
              "      <td>15.0</td>\n",
              "      <td>PA</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>EBCAB99999BBCECBCACBEACBDAABDBCBBDACEBBACDCAEC...</td>\n",
              "      <td>AECCBBCBBCBDEDECACBAABEDABBEDDADCEADDBEBDBBEB</td>\n",
              "      <td>0</td>\n",
              "      <td>DEADBAAAEBEECEBCBCBCBDADAEABCEDDDDADCBEECACBC</td>\n",
              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
              "      <td>AADDDBEEEBEDDBEBACABCDBABECECACAECDCBDCCEDCDA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>780.0</td>\n",
              "      <td>E</td>\n",
              "      <td>E</td>\n",
              "      <td>B</td>\n",
              "      <td>D</td>\n",
              "      <td>3</td>\n",
              "      <td>E</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>D</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>2803.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 137 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   NU_INSCRICAO  NU_ANO  CO_MUNICIPIO_RESIDENCIA  ... Q024  Q025 NU_NOTA_TOTAL\n",
              "0  190001004661    2019                  1506138  ...    B     B        2288.9\n",
              "1  190001004674    2019                  1504208  ...    A     B        1184.6\n",
              "2  190001004722    2019                  1501402  ...    A     B        2350.5\n",
              "3  190001004735    2019                  1507300  ...    A     B        2555.7\n",
              "4  190001004776    2019                  1500800  ...    A     B        2803.5\n",
              "\n",
              "[5 rows x 137 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aqtcVuDbmaIf",
        "outputId": "11bb7c45-b60f-44c2-b314-c4627b12e416",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "provas"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['NU_NOTA_CN',\n",
              " 'NU_NOTA_CH',\n",
              " 'NU_NOTA_MT',\n",
              " 'NU_NOTA_LC',\n",
              " 'NU_NOTA_REDACAO',\n",
              " 'NU_NOTA_TOTAL']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b9MD2C52eTyL"
      },
      "source": [
        "provas_entrada = [\"NU_NOTA_CH\",\"NU_NOTA_LC\", \"NU_NOTA_CN\",\"NU_NOTA_REDACAO\"]\n",
        "prova_saida = \"NU_NOTA_MT\"\n",
        "dados_sem_notas_zero = dados_sem_notas_zero[provas].dropna()\n",
        "notas_entrada = dados_sem_notas_zero[provas_entrada]\n",
        "notas_saida = dados_sem_notas_zero[prova_saida]\n",
        "\n"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KX6Lj8_Lj1nP",
        "outputId": "69e3327a-946f-407f-e908-5997f1887858",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "notas_entrada"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NU_NOTA_CH</th>\n",
              "      <th>NU_NOTA_LC</th>\n",
              "      <th>NU_NOTA_CN</th>\n",
              "      <th>NU_NOTA_REDACAO</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>512.3</td>\n",
              "      <td>488.6</td>\n",
              "      <td>435.6</td>\n",
              "      <td>420.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>499.1</td>\n",
              "      <td>441.0</td>\n",
              "      <td>423.2</td>\n",
              "      <td>560.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>578.1</td>\n",
              "      <td>551.5</td>\n",
              "      <td>426.2</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>571.3</td>\n",
              "      <td>511.2</td>\n",
              "      <td>516.5</td>\n",
              "      <td>780.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>618.7</td>\n",
              "      <td>607.5</td>\n",
              "      <td>559.6</td>\n",
              "      <td>900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127373</th>\n",
              "      <td>501.5</td>\n",
              "      <td>485.3</td>\n",
              "      <td>480.4</td>\n",
              "      <td>520.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127375</th>\n",
              "      <td>559.6</td>\n",
              "      <td>525.7</td>\n",
              "      <td>502.4</td>\n",
              "      <td>740.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127376</th>\n",
              "      <td>380.9</td>\n",
              "      <td>450.3</td>\n",
              "      <td>449.1</td>\n",
              "      <td>520.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127378</th>\n",
              "      <td>416.2</td>\n",
              "      <td>517.3</td>\n",
              "      <td>564.7</td>\n",
              "      <td>620.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127379</th>\n",
              "      <td>425.5</td>\n",
              "      <td>477.1</td>\n",
              "      <td>466.7</td>\n",
              "      <td>480.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>92537 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        NU_NOTA_CH  NU_NOTA_LC  NU_NOTA_CN  NU_NOTA_REDACAO\n",
              "0            512.3       488.6       435.6            420.0\n",
              "2            499.1       441.0       423.2            560.0\n",
              "3            578.1       551.5       426.2            500.0\n",
              "4            571.3       511.2       516.5            780.0\n",
              "5            618.7       607.5       559.6            900.0\n",
              "...            ...         ...         ...              ...\n",
              "127373       501.5       485.3       480.4            520.0\n",
              "127375       559.6       525.7       502.4            740.0\n",
              "127376       380.9       450.3       449.1            520.0\n",
              "127378       416.2       517.3       564.7            620.0\n",
              "127379       425.5       477.1       466.7            480.0\n",
              "\n",
              "[92537 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dsxn6pYOj580"
      },
      "source": [
        "x = notas_entrada\n",
        "y = notas_saida "
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZQpYtCQkGeM"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "SEED = 4321\n",
        "\n",
        "x_treino, x_teste, y_treino, y_teste = train_test_split(x, y, test_size = 0.25,\n",
        "                                                        random_state=SEED)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQTUUyAalgvI",
        "outputId": "d2642d63-ec09-4fb5-8503-d90c5050daec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "from sklearn.svm import LinearSVR\n",
        "\n",
        "modelo = LinearSVR(random_state = SEED)\n",
        "modelo.fit(x_treino, y_treino)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/svm/_base.py:947: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  \"the number of iterations.\", ConvergenceWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,\n",
              "          intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
              "          random_state=4321, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCMgH0TSl_za"
      },
      "source": [
        "predicoes_matematica = modelo.predict(x_teste)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vxxvvZegm_4E",
        "outputId": "f40052a7-01be-4882-a963-283e95304113",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "y_teste[:5]"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "114991    459.7\n",
              "104685    617.2\n",
              "91028     520.0\n",
              "115802    703.8\n",
              "93303     627.1\n",
              "Name: NU_NOTA_MT, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YFvp8RjWnEqH",
        "outputId": "f9948327-cc01-4db1-c4ea-ff3a0369d03c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        }
      },
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "sns.scatterplot(x=predicoes_matematica, y=y_teste)\n",
        "plt.xlim((-50, 1050))\n",
        "plt.ylim((-50, 1050))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-50.0, 1050.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JiQtFgn3np6s",
        "outputId": "3ce30890-405b-4599-85e7-94ab44393032",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        }
      },
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "sns.scatterplot(x=y_teste, y=y_teste - predicoes_matematica)\n"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7aceb690b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9RNd_gLKoUKk",
        "outputId": "c895d692-9b6e-4cf3-8ab3-de49a62511f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        }
      },
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "sns.scatterplot(x=y_teste, y=x_teste.mean(axis=1))\n",
        "plt.xlim((-50, 1050))\n",
        "plt.ylim((-50, 1050))"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-50.0, 1050.0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okNf_N2hq6SJ"
      },
      "source": [
        "resultados = pd.DataFrame()\n",
        "resultados[\"Real\"] = y_teste\n",
        "resultados[\"Previsao\"] = predicoes_matematica\n",
        "resultados[\"diferenca\"] = resultados[\"Real\"] - resultados[\"Previsao\"]\n",
        "resultados[\"quadrado_diferenca\"] = (resultados[\"Real\"] - resultados[\"Previsao\"])**2"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yOh_dQIcrkDX",
        "outputId": "1597910c-64d2-40cc-bed3-7993f3fc82fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "resultados"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Real</th>\n",
              "      <th>Previsao</th>\n",
              "      <th>diferenca</th>\n",
              "      <th>quadrado_diferenca</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>114991</th>\n",
              "      <td>459.7</td>\n",
              "      <td>564.486105</td>\n",
              "      <td>-104.786105</td>\n",
              "      <td>10980.127878</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104685</th>\n",
              "      <td>617.2</td>\n",
              "      <td>628.305027</td>\n",
              "      <td>-11.105027</td>\n",
              "      <td>123.321625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91028</th>\n",
              "      <td>520.0</td>\n",
              "      <td>509.980986</td>\n",
              "      <td>10.019014</td>\n",
              "      <td>100.380650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115802</th>\n",
              "      <td>703.8</td>\n",
              "      <td>593.681144</td>\n",
              "      <td>110.118856</td>\n",
              "      <td>12126.162420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>93303</th>\n",
              "      <td>627.1</td>\n",
              "      <td>579.784588</td>\n",
              "      <td>47.315412</td>\n",
              "      <td>2238.748252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81393</th>\n",
              "      <td>688.6</td>\n",
              "      <td>626.291851</td>\n",
              "      <td>62.308149</td>\n",
              "      <td>3882.305380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40159</th>\n",
              "      <td>479.1</td>\n",
              "      <td>652.313922</td>\n",
              "      <td>-173.213922</td>\n",
              "      <td>30003.062777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64083</th>\n",
              "      <td>501.4</td>\n",
              "      <td>607.787172</td>\n",
              "      <td>-106.387172</td>\n",
              "      <td>11318.230438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>84661</th>\n",
              "      <td>772.5</td>\n",
              "      <td>734.227663</td>\n",
              "      <td>38.272337</td>\n",
              "      <td>1464.771772</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79106</th>\n",
              "      <td>369.0</td>\n",
              "      <td>466.919690</td>\n",
              "      <td>-97.919690</td>\n",
              "      <td>9588.265665</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23135 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Real    Previsao   diferenca  quadrado_diferenca\n",
              "114991  459.7  564.486105 -104.786105        10980.127878\n",
              "104685  617.2  628.305027  -11.105027          123.321625\n",
              "91028   520.0  509.980986   10.019014          100.380650\n",
              "115802  703.8  593.681144  110.118856        12126.162420\n",
              "93303   627.1  579.784588   47.315412         2238.748252\n",
              "...       ...         ...         ...                 ...\n",
              "81393   688.6  626.291851   62.308149         3882.305380\n",
              "40159   479.1  652.313922 -173.213922        30003.062777\n",
              "64083   501.4  607.787172 -106.387172        11318.230438\n",
              "84661   772.5  734.227663   38.272337         1464.771772\n",
              "79106   369.0  466.919690  -97.919690         9588.265665\n",
              "\n",
              "[23135 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g13t7Af7r_X7",
        "outputId": "bfa3094f-9fd6-473a-a14b-ee5fb23e6ca6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "resultados[\"quadrado_diferenca\"].mean()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6604.415306219529"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1eB2rkIuHYB",
        "outputId": "c53dde02-a177-4876-af50-d117057b1012",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "resultados[\"quadrado_diferenca\"].mean()**(1/2)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "81.26755383435341"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RffZg4L-uO-v"
      },
      "source": [
        "from sklearn.dummy import DummyRegressor\n",
        "\n",
        "modelo_dummy = DummyRegressor()\n",
        "modelo_dummy.fit(x_treino, y_treino)\n",
        "dummy_predicoes = modelo_dummy.predict(x_teste)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGAw4on7uz68",
        "outputId": "03e5bcd8-d010-480f-d37b-a07d7b3241a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "mean_squared_error(y_teste, dummy_predicoes)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12063.645588509502"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hX1q2akDvGik",
        "outputId": "926e84b9-962d-491c-d79b-0c1ed40de863",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "mean_squared_error(y_teste, predicoes_matematica)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6604.41530621957"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMphahWFcYvs"
      },
      "source": [
        "**Desafio 01:** Procurar outro modelo de ML para treinar e comparar com os modelos criados em aula\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a58-m8xSdgdG",
        "outputId": "f6632d9e-b5ba-4b52-a7b2-1f9d8b9797c2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "est = GradientBoostingRegressor(n_estimators=100, learning_rate=0.1,\n",
        "     max_depth=1, random_state=SEED, loss='ls').fit(x_treino, y_treino)\n",
        "gbr_predicoes = est.predict(x_teste)\n",
        "mean_squared_error(y_teste, gbr_predicoes)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5382.369553389232"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sC-Nn3yrfNRZ"
      },
      "source": [
        "**Desafio 02:** Ler a documentação do Dummy e alterar o método de regressão"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w1hWLqsTfLVu",
        "outputId": "4c025d75-3899-4f15-8b95-691c2cc88443",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.dummy import DummyRegressor\n",
        "\n",
        "new_modelo_dummy = DummyRegressor(strategy=\"median\")\n",
        "new_modelo_dummy.fit(x_treino, y_treino)\n",
        "new_dummy_predicoes = new_modelo_dummy.predict(x_teste)\n",
        "mean_squared_error(y_teste, new_dummy_predicoes)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12599.969971795981"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "arh9zRaTviIT"
      },
      "source": [
        "**Desafio 03:** Buscar outra métrica para avaliar modelos de regressão\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HGHrE2-daGAc"
      },
      "source": [
        "Raiz do erro médio quadrático e logarítmico (RMSLE)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NfCl8_ZcZ4dz",
        "outputId": "13031ce1-839e-48fb-8696-c9c0e787a36e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from sklearn.metrics import mean_squared_log_error\n",
        "\n",
        "RMSLE = mean_squared_log_error(y_teste,predicoes_matematica)\n",
        "print(RMSLE)\n"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.031331617158242835\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RljCjj1XcAI1",
        "outputId": "7aab4a9d-a6a5-4ea7-8326-5ae6d6504e1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "RMSLE = mean_squared_log_error(y_teste,dummy_predicoes)\n",
        "print(RMSLE)\n"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.047637595382193004\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Uhai2xEpeAn_",
        "outputId": "3a635d56-4f91-43cd-c346-caed39ad35dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "RMSLE = mean_squared_log_error(y_teste,gbr_predicoes)\n",
        "print(RMSLE)\n"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.025844828688516196\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}