Aula03 - Distribuições e correlações entre notas.ipynb

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    "colab": {
      "name": "Aula03.ipynb",
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      "name": "python3",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/JorgeCandido/ImersaoDados/blob/main/ImersaoDados_Aula03.ipynb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
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        "id": "C4RYey1KQ1go"
      },
      "source": [
        "##Aula 1"
      ]
    },
    {
      "cell_type": "code",
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        "id": "z9XYg7h2FcNq",
        "outputId": "4dda9269-5b45-447b-8aa6-0c0e3b272ff2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "import pandas as pd\n",
        "\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": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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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>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>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": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "roHaxkDKHfDt",
        "outputId": "2585965f-f73f-4852-8aae-300187b85368",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dados.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127380, 136)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nE1TlKlrHr1y",
        "outputId": "bea72a9e-ff2c-4c7f-8847-7a777403dd3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "dados[\"SG_UF_RESIDENCIA\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         PA\n",
              "1         PA\n",
              "2         PA\n",
              "3         PA\n",
              "4         PA\n",
              "          ..\n",
              "127375    MG\n",
              "127376    BA\n",
              "127377    BA\n",
              "127378    BA\n",
              "127379    MG\n",
              "Name: SG_UF_RESIDENCIA, Length: 127380, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKsMyeSjJvCa",
        "outputId": "22db9309-9618-434a-92e1-308e3d44645a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        }
      },
      "source": [
        "dados.columns.values"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['NU_INSCRICAO', 'NU_ANO', 'CO_MUNICIPIO_RESIDENCIA',\n",
              "       'NO_MUNICIPIO_RESIDENCIA', 'CO_UF_RESIDENCIA', 'SG_UF_RESIDENCIA',\n",
              "       'NU_IDADE', 'TP_SEXO', 'TP_ESTADO_CIVIL', 'TP_COR_RACA',\n",
              "       'TP_NACIONALIDADE', 'CO_MUNICIPIO_NASCIMENTO',\n",
              "       'NO_MUNICIPIO_NASCIMENTO', 'CO_UF_NASCIMENTO', 'SG_UF_NASCIMENTO',\n",
              "       'TP_ST_CONCLUSAO', 'TP_ANO_CONCLUIU', 'TP_ESCOLA', 'TP_ENSINO',\n",
              "       'IN_TREINEIRO', 'CO_ESCOLA', 'CO_MUNICIPIO_ESC',\n",
              "       'NO_MUNICIPIO_ESC', 'CO_UF_ESC', 'SG_UF_ESC',\n",
              "       'TP_DEPENDENCIA_ADM_ESC', 'TP_LOCALIZACAO_ESC', 'TP_SIT_FUNC_ESC',\n",
              "       'IN_BAIXA_VISAO', 'IN_CEGUEIRA', 'IN_SURDEZ',\n",
              "       'IN_DEFICIENCIA_AUDITIVA', 'IN_SURDO_CEGUEIRA',\n",
              "       'IN_DEFICIENCIA_FISICA', 'IN_DEFICIENCIA_MENTAL',\n",
              "       'IN_DEFICIT_ATENCAO', 'IN_DISLEXIA', 'IN_DISCALCULIA',\n",
              "       'IN_AUTISMO', 'IN_VISAO_MONOCULAR', 'IN_OUTRA_DEF', 'IN_GESTANTE',\n",
              "       'IN_LACTANTE', 'IN_IDOSO', 'IN_ESTUDA_CLASSE_HOSPITALAR',\n",
              "       'IN_SEM_RECURSO', 'IN_BRAILLE', 'IN_AMPLIADA_24', 'IN_AMPLIADA_18',\n",
              "       'IN_LEDOR', 'IN_ACESSO', 'IN_TRANSCRICAO', 'IN_LIBRAS',\n",
              "       'IN_TEMPO_ADICIONAL', 'IN_LEITURA_LABIAL', 'IN_MESA_CADEIRA_RODAS',\n",
              "       'IN_MESA_CADEIRA_SEPARADA', 'IN_APOIO_PERNA', 'IN_GUIA_INTERPRETE',\n",
              "       'IN_COMPUTADOR', 'IN_CADEIRA_ESPECIAL', 'IN_CADEIRA_CANHOTO',\n",
              "       'IN_CADEIRA_ACOLCHOADA', 'IN_PROVA_DEITADO', 'IN_MOBILIARIO_OBESO',\n",
              "       'IN_LAMINA_OVERLAY', 'IN_PROTETOR_AURICULAR', 'IN_MEDIDOR_GLICOSE',\n",
              "       'IN_MAQUINA_BRAILE', 'IN_SOROBAN', 'IN_MARCA_PASSO', 'IN_SONDA',\n",
              "       'IN_MEDICAMENTOS', 'IN_SALA_INDIVIDUAL', 'IN_SALA_ESPECIAL',\n",
              "       'IN_SALA_ACOMPANHANTE', 'IN_MOBILIARIO_ESPECIFICO',\n",
              "       'IN_MATERIAL_ESPECIFICO', 'IN_NOME_SOCIAL', 'CO_MUNICIPIO_PROVA',\n",
              "       'NO_MUNICIPIO_PROVA', 'CO_UF_PROVA', 'SG_UF_PROVA',\n",
              "       'TP_PRESENCA_CN', 'TP_PRESENCA_CH', 'TP_PRESENCA_LC',\n",
              "       'TP_PRESENCA_MT', 'CO_PROVA_CN', 'CO_PROVA_CH', 'CO_PROVA_LC',\n",
              "       'CO_PROVA_MT', 'NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC',\n",
              "       'NU_NOTA_MT', 'TX_RESPOSTAS_CN', 'TX_RESPOSTAS_CH',\n",
              "       'TX_RESPOSTAS_LC', 'TX_RESPOSTAS_MT', 'TP_LINGUA',\n",
              "       'TX_GABARITO_CN', 'TX_GABARITO_CH', 'TX_GABARITO_LC',\n",
              "       'TX_GABARITO_MT', 'TP_STATUS_REDACAO', 'NU_NOTA_COMP1',\n",
              "       'NU_NOTA_COMP2', 'NU_NOTA_COMP3', 'NU_NOTA_COMP4', 'NU_NOTA_COMP5',\n",
              "       'NU_NOTA_REDACAO', 'Q001', 'Q002', 'Q003', 'Q004', 'Q005', 'Q006',\n",
              "       'Q007', 'Q008', 'Q009', 'Q010', 'Q011', 'Q012', 'Q013', 'Q014',\n",
              "       'Q015', 'Q016', 'Q017', 'Q018', 'Q019', 'Q020', 'Q021', 'Q022',\n",
              "       'Q023', 'Q024', 'Q025'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4RinKz2RKws6",
        "outputId": "acb5c2cf-9f6d-4ff1-e139-7fc5cc59e62c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "dados[[\"SG_UF_RESIDENCIA\", \"Q025\"]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>SG_UF_RESIDENCIA</th>\n",
              "      <th>Q025</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>PA</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PA</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>PA</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>PA</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PA</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127375</th>\n",
              "      <td>MG</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127376</th>\n",
              "      <td>BA</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127377</th>\n",
              "      <td>BA</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127378</th>\n",
              "      <td>BA</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127379</th>\n",
              "      <td>MG</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>127380 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       SG_UF_RESIDENCIA Q025\n",
              "0                    PA    B\n",
              "1                    PA    B\n",
              "2                    PA    B\n",
              "3                    PA    B\n",
              "4                    PA    B\n",
              "...                 ...  ...\n",
              "127375               MG    B\n",
              "127376               BA    B\n",
              "127377               BA    B\n",
              "127378               BA    A\n",
              "127379               MG    B\n",
              "\n",
              "[127380 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9XyBWm9DL3hI",
        "outputId": "7581255d-0489-4c2a-ed04-e0455ea68d43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "dados[\"SG_UF_RESIDENCIA\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         PA\n",
              "1         PA\n",
              "2         PA\n",
              "3         PA\n",
              "4         PA\n",
              "          ..\n",
              "127375    MG\n",
              "127376    BA\n",
              "127377    BA\n",
              "127378    BA\n",
              "127379    MG\n",
              "Name: SG_UF_RESIDENCIA, Length: 127380, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WEH6_lABMT2T",
        "outputId": "a3a909d9-ab3b-4598-8203-8f94976bb3e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "dados[\"SG_UF_RESIDENCIA\"].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['PA', 'RO', 'MT', 'AC', 'AM', 'RR', 'MA', 'PR', 'BA', 'PI', 'CE',\n",
              "       'PE', 'AP', 'TO', 'SC', 'GO', 'MG', 'SP', 'SE', 'RJ', 'PB', 'AL',\n",
              "       'RN', 'ES', 'DF', 'RS', 'MS'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDXEe1rxMY0D",
        "outputId": "fbc772ad-b85e-4bdb-a5f7-9656f0807e5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(dados[\"SG_UF_RESIDENCIA\"].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yyQaMKoyMeaK",
        "outputId": "0e926736-aa2f-4819-8c19-17834b0d23fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "source": [
        "dados[\"SG_UF_RESIDENCIA\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SP    20264\n",
              "MG    13547\n",
              "BA    10040\n",
              "RJ     8467\n",
              "CE     7381\n",
              "PA     7170\n",
              "PE     6941\n",
              "MA     5543\n",
              "RS     5466\n",
              "PR     5259\n",
              "GO     4268\n",
              "PB     3690\n",
              "PI     3034\n",
              "RN     2959\n",
              "AM     2820\n",
              "SC     2673\n",
              "ES     2550\n",
              "DF     2408\n",
              "AL     2263\n",
              "MT     2230\n",
              "SE     1815\n",
              "MS     1655\n",
              "RO     1400\n",
              "TO     1178\n",
              "AP     1047\n",
              "AC      946\n",
              "RR      366\n",
              "Name: SG_UF_RESIDENCIA, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ML7wBPY1MrZL",
        "outputId": "f95db50d-555d-4647-d468-22dde61b915f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "dados[\"NU_IDADE\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18    21452\n",
              "17    21256\n",
              "19    14418\n",
              "20    10413\n",
              "16     7830\n",
              "      ...  \n",
              "75        2\n",
              "73        2\n",
              "82        1\n",
              "77        1\n",
              "76        1\n",
              "Name: NU_IDADE, Length: 65, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xj2Lmq2_NNPk",
        "outputId": "4c22e1a2-28ab-49f6-cb59-4cb37b996138",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "dados[\"NU_IDADE\"].value_counts().sort_index()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13        4\n",
              "14      141\n",
              "15     2009\n",
              "16     7830\n",
              "17    21256\n",
              "      ...  \n",
              "73        2\n",
              "75        2\n",
              "76        1\n",
              "77        1\n",
              "82        1\n",
              "Name: NU_IDADE, Length: 65, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ioQ7i7Q1Oln8",
        "outputId": "5691d71d-0d51-431c-ab10-db7e1c3ba329",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "dados[\"NU_IDADE\"].hist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ed599a518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kNYT+ju5hHb2pJyYmJuh2uxw6dIhutzur46w/9/AQuhvMxCmj+f6zfY2OmMvrO0r2O1z2O1yj7HegMEjyOnpB8NWq+kYrP59kaVXtb1M9B1p9H3Bm3+7LW20f0Dmq3m315dOM/y1VtRHYCDA5OVmdTodut0un05lu+DFdt+G+WY2fT+vPPcwXHh04g+fN7ms6c9pvLq/vKNnvcNnvcI2y30HuJgpwG/BkVf1l36YtwJE7gtYC9/TVr213Fa0CXmrTSduAS5IsaReOLwG2tW0vJ1nVvte1fceSJC2AQX5EfTfwp8CjSR5utf8E3ATcleR64FngyrZtK3A5MAW8AnwYoKoOJvkM8EAb9+mqOtiWPwrcDpwCfKs9JEkLZMYwqKr/DRzrvv+LpxlfwA3HONYmYNM09V3AOTP1IkkaDj+BLEkyDCRJhoEkCcNAkoRhIEnCMJAkYRhIkjAMJEkYBpIkDANJEoaBJAnDQJKEYSBJwjCQJGEYSJIwDCRJGAaSJAwDSRKGgSQJw0CShGEgScIwkCQBi2YakGQT8AHgQFWd02qnA18HVgC7gSur6oUkAW4GLgdeAa6rqu+3fdYC/6Ud9rNVtbnVLwBuB04BtgIfr6qap+enZsWG++a03/pzD3PdHPcF2H3T++e8r6SFM8iZwe3A6qNqG4AdVbUS2NHWAS4DVrbHOuBW+HV43AhcBFwI3JhkSdvnVuAjffsd/b0kSUM2YxhU1f8CDh5VXgNsbsubgSv66ndUz05gcZKlwKXA9qo6WFUvANuB1W3baVW1s50N3NF3LEnSApnrNYOJqtrflp8DJtryMmBP37i9rfZa9b3T1CVJC2jGawYzqapKsiBz/EnW0Zt+YmJigm63y6FDh+h2u7M6zvpzDw+hu8FMnDLa7z9bx9vvbP9sjtdc3g+jZL/DZb+Dm2sYPJ9kaVXtb1M9B1p9H3Bm37jlrbYP6BxV77b68mnGT6uqNgIbASYnJ6vT6dDtdul0OsfaZVrHc0H0eK0/9zBfePS4M3jBHG+/u6/pzF8zA5jL+2GU7He47Hdwc50m2gKsbctrgXv66temZxXwUptO2gZckmRJu3B8CbCtbXs5yap2J9K1fceSJC2QQW4t/Rq9n+rPSLKX3l1BNwF3JbkeeBa4sg3fSu+20il6t5Z+GKCqDib5DPBAG/fpqjpyUfqjvHpr6bfaQ5K0gGYMg6q6+hibLp5mbAE3HOM4m4BN09R3AefM1IckaXj8BLIkyTCQJBkGkiQMA0kShoEkCcNAkoRhIEnCMJAkMQ+/qE56LXP9T3Xmqv8/4/E/1pEG55mBJMkwkCQZBpIkDANJEoaBJAnDQJKEYSBJwjCQJOGHzvTP2EJ/4O0IP+ymE5FnBpIkw0CSZBhIkvCagTTvZnOtov8X6x0vr1XoeIzNmUGS1UmeSjKVZMOo+5Gk3yVjEQZJTgK+BFwGnA1cneTs0XYlSb87xmWa6EJgqqqeAUhyJ7AGeGKkXUknkIW4lfZY01pOUZ34xiUMlgF7+tb3AheNqBdJs+RnOk58qapR90CSDwGrq+rftfU/BS6qqo8dNW4dsK6tvg14CjgD+OkCtnu87He47He47He4ht3vv6qqt0y3YVzODPYBZ/atL2+131BVG4GN/bUku6pqcrjtzR/7HS77HS77Ha5R9jsWF5CBB4CVSc5KcjJwFbBlxD1J0u+MsTgzqKrDST4GbANOAjZV1eMjbkuSfmeMRRgAVNVWYOscdt0485CxYr/DZb/DZb/DNbJ+x+ICsiRptMblmoEkaYROmDBIsinJgSSP9dVOT7I9ydPt65JR9tgvyZlJvpvkiSSPJ/l4q49lz0nekOR7SX7Q+v1Uq5+V5P72a0K+3i7wj40kJyV5KMm9bX3c+92d5NEkDyfZ1Wpj+Z4ASLI4yd1JfpjkySTvGtd+k7ytva5HHi8n+cS49guQ5D+2v2+PJfla+3s4kvfwCRMGwO3A6qNqG4AdVbUS2NHWx8VhYH1VnQ2sAm5ov2JjXHv+BfDeqnoHcB6wOskq4PPAF6vqrcALwPUj7HE6Hwee7Fsf934B/riqzuu7hXBc3xMANwPfrqq3A++g91qPZb9V9VR7Xc8DLgBeAb7JmPabZBnwH4DJqjqH3s0zVzGq93BVnTAPYAXwWN/6U8DStrwUeGrUPb5G7/cA7zsRegZ+H/g+vU+B/xRY1OrvAraNur++PpfT+8v9XuBeIOPcb+tpN3DGUbWxfE8AbwJ+TLu2OO79HtXjJcD/Ged+efU3L5xO72aee4FLR/UePpHODKYzUVX72/JzwMQomzmWJCuAdwL3M8Y9tymXh4EDwHbgR8CLVXW4DdlL7w08Lv4K+HPg/7X1NzPe/QIU8PdJHmyfqIfxfU+cBfwE+Js2FfflJKcyvv32uwr4Wlsey36rah/w34B/APYDLwEPMqL38IkeBr9WvRgdu1ujkvwB8HfAJ6rq5f5t49ZzVf2qeqfYy+n98sC3j7ilY0ryAeBAVT046l5m6T1VdT6939B7Q5I/6t84Zu+JRcD5wK1V9U7g5xw1xTJm/QLQ5tg/CPz3o7eNU7/t2sUaeqH7L4FT+e2p8AVzoofB80mWArSvB0bcz29I8jp6QfDVqvpGK491zwBV9SLwXXqnqIuTHPk8yrS/JmRE3g18MMlu4E56U0U3M779Ar/+aZCqOkBvPvtCxvc9sRfYW1X3t/W76YXDuPZ7xGXA96vq+bY+rv3+W+DHVfWTqvon4Bv03tcjeQ+f6GGwBVjbltfSm5cfC0kC3AY8WVV/2bdpLHtO8pYki9vyKfSubzxJLxQ+1IaNTb9V9cmqWl5VK+hNCXynqq5hTPsFSHJqkjceWaY3r/0YY/qeqKrngD1J3tZKF9P7tfJj2W+fq3l1igjGt99/AFYl+f3278WR13c07+FRX0SZxcWWr9GbV/snej+xXE9vjngH8DTwP4HTR91nX7/voXc6+gjwcHtcPq49A/8GeKj1+xjwX1v9XwPfA6bonXa/ftS9TtN7B7h33Pttvf2gPR4H/nOrj+V7ovV2HrCrvS/+B7BkzPs9FfgZ8Ka+2jj3+yngh+3v3FeA14/qPewnkCVJJ/w0kSRpHhgGkiTDQJJkGEiSMAwkSRgGkiQMA0kShoEkCfj/d/6B1W1BlUIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYd8dPfNO1-n",
        "outputId": "661acc10-1315-4667-d153-984dd6d1cf7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        }
      },
      "source": [
        "dados[\"NU_IDADE\"].hist(bins = 20, figsize = (10,8))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ed58f6588>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Al4Yeg7wSFN6"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VrQVzO4vNyTP",
        "outputId": "357e96ef-848a-408d-c9a1-fbeea62339e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        }
      },
      "source": [
        "dados.query(\"IN_TREINEIRO == 1\")[\"NU_IDADE\"].value_counts().sort_index()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14      64\n",
              "15    1927\n",
              "16    7142\n",
              "17    4901\n",
              "18     858\n",
              "19     228\n",
              "20      83\n",
              "21      44\n",
              "22      30\n",
              "23      21\n",
              "24      13\n",
              "25       8\n",
              "26      13\n",
              "27       9\n",
              "28       6\n",
              "29      10\n",
              "30       4\n",
              "31       4\n",
              "32       2\n",
              "33       1\n",
              "34       3\n",
              "35       2\n",
              "36       2\n",
              "37       1\n",
              "38       4\n",
              "39       2\n",
              "40       4\n",
              "41       1\n",
              "42       4\n",
              "44       2\n",
              "45       1\n",
              "46       2\n",
              "47       1\n",
              "48       1\n",
              "51       1\n",
              "55       1\n",
              "62       1\n",
              "Name: NU_IDADE, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCPdzdkeNioz",
        "outputId": "50f05567-0ac6-4156-edb3-082be9162306",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "dados[\"NU_NOTA_REDACAO\"].hist(bins = 20, figsize=(8, 6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ed53f5a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2tJt5q4dSolR",
        "outputId": "eab4ae3c-7729-4c12-c0a1-eacc4f8ad7ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "dados[\"NU_NOTA_LC\"].hist(bins = 20, figsize=(8, 6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ed4087240>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BDLcSca0S8nd",
        "outputId": "45fedee3-80b0-456f-cf78-5903581bf59f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dados[\"NU_NOTA_REDACAO\"].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "571.5700253970197"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vz4VGeogTHRw",
        "outputId": "3e6a76e0-3175-46c4-dade-3fc2e76f6f45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dados[\"NU_NOTA_REDACAO\"].std()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "188.07645455749753"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YeKjZt4oTS5o",
        "outputId": "58d43219-5dda-4d1a-e136-e2a539917c96",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "provas = [\"NU_NOTA_CN\",\"NU_NOTA_CH\",\"NU_NOTA_MT\",\"NU_NOTA_LC\",\"NU_NOTA_REDACAO\"]\n",
        "\n",
        "dados[provas].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead 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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FMaMoSUyUD_8",
        "outputId": "8171820b-2be7-4cf5-ec5e-4e2f01a89420",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dados[\"NU_NOTA_LC\"].quantile(0.1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "434.5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "03RSXG1vVQOi",
        "outputId": "42648262-e348-4099-b5f9-2d5428b8ca40",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        }
      },
      "source": [
        "dados[\"NU_NOTA_LC\"].plot.box(grid = True, figsize=(8,6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ed4028e80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_62npBVWvW5",
        "outputId": "74daed93-93f5-4aba-c404-86ed4d7a16de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        }
      },
      "source": [
        "dados[provas].boxplot(grid=True, figsize= (10,8))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ed3fb4ac8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2iyYYnhjNzRr"
      },
      "source": [
        "Desafio01: Proporção dos inscritos por idade.\n",
        "\n",
        "Desafio02: Descobrir de quais estados são os inscritos com 13 anos.\n",
        "\n",
        "Desafio03: Adicionar título no gráfico\n",
        "\n",
        "Desafio04: Plotar os Histogramas das idades dos do treineiro e não treineiros.\n",
        "\n",
        "Desafio05: Comparar as distribuições das provas em inglês espanhol\n",
        "\n",
        "Desafio06: Explorar a documentações e visualizações com matplotlib ou pandas e gerar novas visualizações."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BZlUU335HJZS"
      },
      "source": [
        "## Aula 02"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nPG7t_YtHI5a",
        "outputId": "c6414a10-4c69-4002-ada7-743ff5453e24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        }
      },
      "source": [
        "dados.query(\"NU_IDADE == 13\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NU_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_CH</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>57359</th>\n",
              "      <td>190003297248</td>\n",
              "      <td>2019</td>\n",
              "      <td>3504107</td>\n",
              "      <td>Atibaia</td>\n",
              "      <td>35</td>\n",
              "      <td>SP</td>\n",
              "      <td>13</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3556701.0</td>\n",
              "      <td>Vinhedo</td>\n",
              "      <td>35.0</td>\n",
              "      <td>SP</td>\n",
              "      <td>4</td>\n",
              "      <td>0</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>CAEBCEBECAABCDBEEAEDDBCBACBCECAABDCACEABAEADC</td>\n",
              "      <td>BAAEC99999BCCDDEDACBAADECADBEADBADDBECADEBCBDA...</td>\n",
              "      <td>AABDCEACDDBEDABDDACBDECABACDDBAECDBECBBDEBCDA</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>200.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>920.0</td>\n",
              "      <td>E</td>\n",
              "      <td>G</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>3</td>\n",
              "      <td>F</td>\n",
              "      <td>A</td>\n",
              "      <td>C</td>\n",
              "      <td>C</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>D</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95630</th>\n",
              "      <td>190004825557</td>\n",
              "      <td>2019</td>\n",
              "      <td>5107602</td>\n",
              "      <td>Rondonópolis</td>\n",
              "      <td>51</td>\n",
              "      <td>MT</td>\n",
              "      <td>13</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5107602.0</td>\n",
              "      <td>Rondonópolis</td>\n",
              "      <td>51.0</td>\n",
              "      <td>MT</td>\n",
              "      <td>4</td>\n",
              "      <td>0</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>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>E</td>\n",
              "      <td>E</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>3</td>\n",
              "      <td>F</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>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98197</th>\n",
              "      <td>190004926952</td>\n",
              "      <td>2019</td>\n",
              "      <td>1600303</td>\n",
              "      <td>Macapá</td>\n",
              "      <td>16</td>\n",
              "      <td>AP</td>\n",
              "      <td>13</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1600303.0</td>\n",
              "      <td>Macapá</td>\n",
              "      <td>16.0</td>\n",
              "      <td>AP</td>\n",
              "      <td>4</td>\n",
              "      <td>0</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>NaN</td>\n",
              "      <td>NaN</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>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>G</td>\n",
              "      <td>E</td>\n",
              "      <td>D</td>\n",
              "      <td>B</td>\n",
              "      <td>4</td>\n",
              "      <td>K</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>D</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>C</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>E</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116501</th>\n",
              "      <td>190005659236</td>\n",
              "      <td>2019</td>\n",
              "      <td>2917508</td>\n",
              "      <td>Jacobina</td>\n",
              "      <td>29</td>\n",
              "      <td>BA</td>\n",
              "      <td>13</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2304400.0</td>\n",
              "      <td>Fortaleza</td>\n",
              "      <td>23.0</td>\n",
              "      <td>CE</td>\n",
              "      <td>4</td>\n",
              "      <td>0</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>ADCCCCDCEAAABCEEEDAD*CCCDEBADCDBBBCBEBDBDBECC</td>\n",
              "      <td>EBBAD99999BDCBCCBCEBBCADEDDEDECDECDECECACDCECC...</td>\n",
              "      <td>ECEADDBBABCCCDBDCAAECABDCBDBCECCECABCABBBCBAA</td>\n",
              "      <td>0</td>\n",
              "      <td>AAECACDEADCBCDDDBCBDADAEABCEBABEEBCBEECEBDADC</td>\n",
              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
              "      <td>EEEADBEBACABCDBABECECACDCBDCCEDCDABEDECDDDBAA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>D</td>\n",
              "      <td>F</td>\n",
              "      <td>D</td>\n",
              "      <td>B</td>\n",
              "      <td>4</td>\n",
              "      <td>D</td>\n",
              "      <td>A</td>\n",
              "      <td>E</td>\n",
              "      <td>E</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>B</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>D</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4 rows × 136 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        NU_INSCRICAO  NU_ANO  CO_MUNICIPIO_RESIDENCIA  ... Q023  Q024 Q025\n",
              "57359   190003297248    2019                  3504107  ...    B     B    B\n",
              "95630   190004825557    2019                  5107602  ...    A     A    B\n",
              "98197   190004926952    2019                  1600303  ...    A     B    B\n",
              "116501  190005659236    2019                  2917508  ...    B     C    B\n",
              "\n",
              "[4 rows x 136 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8Z64spVCxEd",
        "outputId": "0f7d1a1e-f53a-4bd3-a2c9-7c9bb79e81a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "dados.query(\"NU_IDADE <= 14\")[\"SG_UF_RESIDENCIA\"].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PE    18\n",
              "CE    15\n",
              "BA    14\n",
              "PA    12\n",
              "MG    11\n",
              "SP    11\n",
              "MA     9\n",
              "PI     7\n",
              "GO     7\n",
              "PB     7\n",
              "DF     4\n",
              "AM     4\n",
              "MT     4\n",
              "PR     3\n",
              "SE     3\n",
              "TO     3\n",
              "RO     2\n",
              "SC     2\n",
              "AL     2\n",
              "AP     1\n",
              "ES     1\n",
              "MS     1\n",
              "AC     1\n",
              "RN     1\n",
              "RJ     1\n",
              "RS     1\n",
              "Name: SG_UF_RESIDENCIA, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqS5PRDaIXVb",
        "outputId": "c37452f0-19d9-4671-9c66-76f5311d66e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "dados.query(\"NU_IDADE <= 14\")[\"SG_UF_RESIDENCIA\"].value_counts(normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PE    0.124138\n",
              "CE    0.103448\n",
              "BA    0.096552\n",
              "PA    0.082759\n",
              "MG    0.075862\n",
              "SP    0.075862\n",
              "MA    0.062069\n",
              "PI    0.048276\n",
              "GO    0.048276\n",
              "PB    0.048276\n",
              "DF    0.027586\n",
              "AM    0.027586\n",
              "MT    0.027586\n",
              "PR    0.020690\n",
              "SE    0.020690\n",
              "TO    0.020690\n",
              "RO    0.013793\n",
              "SC    0.013793\n",
              "AL    0.013793\n",
              "AP    0.006897\n",
              "ES    0.006897\n",
              "MS    0.006897\n",
              "AC    0.006897\n",
              "RN    0.006897\n",
              "RJ    0.006897\n",
              "RS    0.006897\n",
              "Name: SG_UF_RESIDENCIA, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TLl6Z1IeJJs8",
        "outputId": "4d15bb68-34a3-46f0-ecb3-b4cae94058b9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        }
      },
      "source": [
        "alunos_menor_quartoze = dados.query(\"NU_IDADE <= 14\")\n",
        "alunos_menor_quartoze[\"SG_UF_RESIDENCIA\"].value_counts().plot.pie(figsize=(10,8))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ed3e91b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OKGKHYz7KPcz",
        "outputId": "7777d569-c652-46ab-946f-1e6aa27da83c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        }
      },
      "source": [
        "alunos_menor_quartoze[\"SG_UF_RESIDENCIA\"].value_counts(normalize = True).plot.bar(figsize=(10,8))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ed3e2af98>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8pZMPrlWLqxk",
        "outputId": "6494308c-bc25-474f-9a40-a12702ce639e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(alunos_menor_quartoze)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "145"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBj4lqiOM-tL",
        "outputId": "7ed7fcaa-0e91-425c-fa55-4cb0d4110fa9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.boxplot(x=\"Q006\", y = \"NU_NOTA_MT\", data = dados)\n",
        "plt.title(\"Boxplot das notas de matemática pela renda\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Boxplot das notas de matemática pela renda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d7ec4hqHOMz0"
      },
      "source": [
        "renda_ordenada = dados[\"Q006\"].unique()\n",
        "renda_ordenada.sort()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gmKZikIfPPrD",
        "outputId": "fdf457d5-7b73-47b8-b2bf-831409c200a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "renda_ordenada"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M',\n",
              "       'N', 'O', 'P', 'Q'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sy5W5bGIPYNv",
        "outputId": "492d8272-390f-4c9c-cafd-c56be97aef0d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x=\"Q006\", y = \"NU_NOTA_MT\", data = dados, order = renda_ordenada)\n",
        "plt.title(\"Boxplot das notas de matemática pela renda\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Boxplot das notas de matemática pela renda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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//e5kDWDy5Mlce+21TY+jUqlwySWXsHDhwpY9FyuVCueccw47duxg/PjxfO1rX2vZY9EsV155JZ2dnc9Y3tXVBUB7e3vN7WbNmsU73/nOhsbWLP0dg7Vr1/bqN9nW1sbMmb+faKneYxARq1JKc2qts2VNGkAZ+kOoHKoTtVrlZvEuyOxz2ZMctLe3t+znsgzDlzz55JM8+eSThdVfBtOnT9/9PCI45JBDRryOcSO+R2mU6ejoYM2aNYX1hyiLVm/RmTx58jNa1pqt712QHR0dLfm7qFQqu1t0HnroITZu3NiSx6E6cX/Pe97T0Lr6axm64IILALj00ksbWn8ZDNQ6dvbZZ7Nx40bOOOOMhvwuTNakQfT0h2h1ixcv5o477mDx4sV84AMfaHr9RSeLCxcu5KKLLtpd/shHPtL0GGrdBdnoL+kyWrJkye4+Qq16HFotce/vEiSU41Ls9OnTefrppxv2T72XQSUNqlKpsGLFCgCWL19eyGWXoi//HX/88btb0yZPnswLXvCCpsfgXZAZj4PDl1Qrw6XY8ePHM2vWrIYlzLasSRrU4sWLe30xNLt1rSytCAsXLmThwoWFtKpBdhfkDTfcwM6dO1v6LkiPQ+2EdTS3Lg7UMtYKl2JN1lRqRV/6Uubmm2/uVV6xYkVTk7WyXP47/vjjueGGG5peb4+Ojg5uuukmoLjR2sugmcdhuJffGn3pzYS1tZisqdSa2YFW/esZ8LG/cqO1WitCf3ruTr7++usbfndyWZMUaO5xGEiRl95M3FuLyZpKqyyXvgQnnXQSy5Yt211+5Stf2dT6X/KSl/Sq/8QTT2xq/WVShruTi+4fBM07DmW9/FaWhFXNYbKmfhV9CbIsl74ECxYsYMWKFXR3dzNmzBgWLFhQdEgtq1l3J5c1SenhXdrlSNzVHCZr6lfRlyC99PV7q1ev5oILLuAf/uEfOProo5tef1tbG3PnzmXZsmXMmzev6cn77bff3qt82223FTJ8SLOUfZiCZinzpdgyMGFtHSZrqqkMlyDtQPt7ixYtYtu2bVx88cVcddVVhcTw+te/nttvv50//dM/bXrdc+fO5Xvf+x67du1i7NixDT8Xyjy1ThkuQZaBx0GtxGRNNZXhEqQdaDOrV69m3bp1QDYH3X333VdI69q1117Ltm3b+OY3v9n0Vq2ec6EnWSvqXGhWglD2S5DN4nGQMiZrJVV0f7EyXIK0A21m0aJFvcpFtK5VKpXdA48uW7aMBQsWNPX30exzwal1JJWJyVpJFd1frCyXIO1Ay+5WtR5r165tegyLFy/ePb1PSqmhg+L2dwly7dq1jB07dnf/vb5apZ+SWk+ZL8urOZxuqoT69hcrYmqfjo4OxozJTo8iL0H2dKBt1VY1KH6MM6g9KG6zPf300+yzzz6MHz++6XVLZVSGaZbUHLaslVAZ+ot5CbI8XvrSl3LrrbfuLr/sZS9regy7du0asDySvAQp9eZnQraslVBZJinu6Ojg2GOPLfQSZKVS4fzzzy+kdbEs/uqv/mrAsiRpdDNZ60eRScLcuXMZNy5r9Cyyv1gZLkFW991rVW1tbZxwwgkAvPjFLy7k91GGS7GS1KpM1vpRZJJQlv5iRStD372eOIpu3ZswYQIA++yzTyH199xc0F9ZktQ4Jms1FJ0ktLW18fKXvxyAV7ziFYW1bBWdpNTqu1eExYsXc8cdd7B48eJC6q9UKtx2220A3HrrrYX8Pnr+eeivLElqHG8wqKEMHfzLoOjhQ8ow1lulUtl95+Py5cubPr4YZMli9flYxLAZBxxwAJs2bdpdnjJlSq/hMxwiQJIax3+Payi6g3+lUuGHP/whAD/4wQ8KaUmpVCrceOONhV6CnDt37u6+URFRSN+9WolSs91yyy29yn2H0WiGGTNm9CofeuihTY9BklqVLWs1FD0gbBla9pYsWbI7Yd2xY0chMZx++un813/9F5D1kXr1q1/d1Pqh9vhizZ5qqZn9xQZqHXvTm97Epk2bOOWUU0b1JOqSVDa2rNVQdAf/olv2ILvkVz1i/bJly5oew3e/+91e5euvv77pMfQkzf2Vm6FvK1bfVq5mmTFjBvvttx8LFiwopH5JalUmazX0DAgbEYUMCDt37lzGjh0LwNixYwu5/HfIIYcMWG6Gvgni97///abHUIYhK/pegq5UKk2PAWD8+PHMmjXLAZIlqcm8DNqPIuek7Ojo2N2qlFIqJIaeOef6KzdDz1hz/ZWbYe7cub2SxkYmzv117p84cWKvKWUmTZpk535JaiG2rKmmnsuw/ZWbYevWrQOWm6FvcnbyySc3PYbp06fvfh4RhbRySpKKY8taP4octmLJkiWMGTOG7u5uxowZU0gMZRgEdebMmaxbt253+bDDDmt6DFdccUWv8uc+9zmuuuqqhtQ1UOvY2WefzcaNGznjjDNachgZSWpltqzVUPSguGW4waAMlyD7JmdFJGvVySLA2rVrmx4DZK1r++23X8vOZiFJrcxkrYaiR84vw9ygfVt53v3udzc9hpUrV/Yq/+xnP2t6DGVh535Jal0mazUU3bJV9NAhAPfff3+vcq2O741WhmEz2traepUPPvjgpscgSWptJms1FN2yVfTQIVCOYTPK0G+u7zAZGzZsaHoMkqTWZrJWQ0dHR69pjooavuPYY48trI9Szzhv/ZUlSVJzmKzV0NbWtnt4hOnTpxfSstXW1sZll11WWB+lJ554YsByM5QhYdxvv/0GLEuS1GgmazVUKpXdg8B2dXUVNpH6+eefX0jdZdE3Ue3bf6wZdu3aNWBZkqRGM1mrYfHixbv7R3V3d7N48eJCYrjjjjsKqRtg33337VWeOHFi02NYv359r/Kjjz7a9Bhe9KIX9SqfcMIJTY9BktTaTNZquPnmm3uVV6xY0dT6K5XK7jtQly1bVkjr2lNPPdWrXD3dUSu59957e5XvueeegiKRJLUqZzAooeqWvZQSixcv5gMf+EDBUY1u/c3LWat1r3peTnBuTklSY9myVsOhhx7aqzxjxoym1l90y54kSSoPW9ZqKHpsrTIMBttq+msZO/fcc3nooYd2l9vb27n00kubFZYkSbas1TJ9+vQBy43WM3tBf2U1z0c+8pFe5b/9278tKBJJUqsyC6ih712Hzb4L8bjjjutV/qM/+qOm1q/fmz17NuPHjweyVrWjjz664IgkSa3GZK2GE088sVf5pS99aVPr9w7EcjnyyCMZM2aMrWqSpELYZ62E+g6TsW3btoIiaZ7+7sbsq4g7MSdNmsSxxx5rq5okqRC2rNVw22239Sr/6Ec/KiiS1nbUUUf1Kj/rWc8qJhBJkgpky1oNhxxyCGvWrOlVVmP11zo2f/58APbZZx+++MUvNjMkSZJKobCWtYj464i4KyLujIh/i4h9I+JZEfHTiFgdEf8eERPy1+6Tl1fn649qZGxF32Cg3+tpXfv4xz9ebCCSJBWkkGQtImYC7wXmpJSOBcYCZwOfBv4xpTQbeAxYkG+yAHgsX/6P+esaZt68eb3KJ598ciOr0wAOOOAAjjvuOF7wghcUHYokSYUoss/aOGBiRIwDJgEPAXOB/8zXfxU4K39+Zl4mXz8vIqJRgRV9N6gkSVKPQpK1lNI64FLgAbIkbTOwCtiUUtqZv2wtMDN/PhN4MN92Z/76tr77jYjzImJlRKzsO6fjUHzhC1/oVf785z8/7H0Nh4PiSpKkHkVdBj2IrLXsWUA7sB9w2p7uN6X0pZTSnJTSnGnTpg17P9U3F9QqN5rTTUmSpB5FNdmcDNyfUlqfUtoBXAucCByYXxYFOAxYlz9fBxwOkK+fAvSewHME9Z24vdkTuUuSJPUoKll7AHhxREzK+57NA+4GbgbekL/mXOC6/PnSvEy+fkVKKTUquJ07d/Yq79q1q1FVSZIkDaioPms/JbtR4OfAHXkcXwIuBP4mIlaT9UlbnG+yGGjLl/8N8MFGxte3v5tDd0iSpKIUNihuSumjwEf7LL4P+OMar30KeGMz4gKICKob7hp446kkSdKAvM2whkMPPXTAsiRJUrOYrNXw2GOPDViWJElqFpO1GiZNmtSrvN9++xUUiSRJanUmazVs3LixV7lSadgoIZIkSQMyWZMkSSoxk7Uaxo4dO2BZkiSpWQobuqPMjjvuOH7xi1/sLj//+c8vMBpJktQsV155JZ2dnUPapuf1F1xwwZC2mzVrFu985zsHfZ3JWg133313r/Jdd91VUCSSJLWOMiRKnZ2drL771xwx5ZC69zVhVzYe6/Z19Y8e8cDm+gfcN1mroe/0Uk43JUlS42WJ0m84Yv+ZdW8zYWeWymx/8Im6t3lgy7oB1x8x5RA+/JK31r2/4bj49q/X/VqTtRr6zg3atyxJkhrjiP1n8qET3t3QOi756RUN3f9IM1mrodWmm6q32blvE3O919olSdLweTdoDdWJWq2yJElSs9iyVsO4ceN6XfocN250H6ZarWPz589/xrJLL720GeFIkqQqozsLGSb7rMHs2bNZvXr17vKzn/3sAqORJDVaGe7EVG0ma6rp85//fK/WtSuu2Ls6Y0qShqazs5Pf3r2aw6YcUfc243dNAODJddvr3mbt5geGHFurM1lTvyZMmMD27dttVZOkFnHYlCN4/4kLG1rH5bctauj+RyOTNfXrOc95DmBfNUmSimSyJklSwewvpoGYrEmSVLDOzk5+fc9qDpl6ZN3bRMr6iz32yI66t3l045ohx6bimaxJkgplq1LmkKlH0nHahxtax5IbLm7o/tUYJmuSpEJ1dnZyx72/ZWxb/fNB7krZ19fd67fVv01l4PkgpbIyWZMkFW5s20z2f+37G1rHlqWXN3T/UqOYrElSC/MS5PCOAYy+46DyMlmTpBbW2dnJr+79NWPapte9TXfKppW+c/2m+repPDLk2Jqls7OTe+5dzZS2+geDBdiVd/DvWl//gLCbKw4Iq6EzWZOkFjembToTX9vR0DqeXLqkofvfU1PajuCkMxvbuR/gluvs4F92XV1dPLF5Cxff/vWG1rNm86PsF0/W9doxDY1EkiRJe8SWNUmSpFx7ezvb02N8+CVvbWg9F9/+dSa0H1TXa03WJKkgdu6XVA+TtYLV+8e67x9m//BKe7+sc/+9RFtb3duklAC4Y/36+repVIYcm6TyMFmT1JLK0qoVbW1MeM1rh7S/odr+naUN3b80Urq6unhiy1Yu+ekVDa1nzZZ17Nc1uaF1jCSTtYLV+gM+f/78Zyy79NJLmxGO1DKyVq17iLYD694mpW4A7lj/UP3bVOof3kKSajFZK6F9992Xp556and54sSJBUYjjV7RdiDjXzu3oXXsWLqiofuXRpP29na273qCD53w7obWc8lPr2BC+34NrWMkOXRHCS1d2vuSxXXXXVdQJJIkqWi2rJWcrWoajZrZXwy8IUfS3s1kraSOO+44wL5qGp2y/mJ3w8FD6eC7A4BfbRjidD0btg7t9VKLykbuf4LLb1vU0HrWbl7DfrH3XIIsg5ZO1oby3331f/P+ly6NgIMnM+7M4xtezc7rVjW8DklqpJZO1iRJKoOuri62bN7GkhsaO3fooxvX8OSuSTXXtbe382TazvtPXNjQGC6/bRET2yc0tI7RpqWTtf5axxw6Q5IklUVLJ2v9ec973sPnPve53eX3v//9BUYjSRrt2tvbeWzsDjpO+3BD61lyw8UcNH18Q+vQyHPojhpe85rX9CqffvrpBUUiSZJanclaP2bMmAHYqiZJkorlZdB+TJs2jWnTptmqplFnOGOcQWPmxZQg61y/8/GtbFl6eUPr2VlZS9eOvWc+SKmHyZrUYrIxzu6Ag8cOcctdAPxqw931b7Jh1xDrkCT1ZbImtaKDxzL2dVMaXs2ub21ueB3a+7W3t7Np/Db2f21ju51sWXo57dNqD1shlZnJmtRkzZxqyUuQ0uC6urrY9PgT3HJdY8c4A9hUWQM7HL1fQ2OyJjVZZ2cnd937KyZPrX+bHSn7uebRX9W9zdaNQwxMLamrq4vux7fw5NIlDa2nu/IIXTu2NbQOabQyWZMKMHkqPP/V0dA6fnl9auj+pdGivb0dxm/npDMbO8YZwC3XXUz7NEfv19CYrElSC54mZdIAAB4wSURBVGtvb2fj+E1MfG1HQ+t5cukS2qcd2NA6pNHKZE2SJKnKA5sf5eLbv1736x954jEApu930JDqmD2zvtebrEmSJOVmzZo15G22d2adhCfUmXwBzJ55UN11maxJkiTlhnMHfc+d+pdeeulIhwMUmKxFxIHAPwPHAgl4B/Br4N+Bo4DfAW9KKT0WEQF8Bjgd2Aa8LaX08wLClqQR09XVRXr8cbZ/Z2lD60mVCl07djS0DkmNU2TL2meAG1JKb4iICcAk4EPA8pTSpyLig8AHgQuBVwHH5I8TgCvzn9KQOMaZJJXbA1vWcclPr6j79Y9s2wDA9EkHD6mO2Tx7yLEVpZBkLSKmAC8H3gaQUtoObI+IM4GT8pd9FbiFLFk7E7g6pZSAn0TEgRExI6X0UJND116us7OTe+75FQfW362A7u7s50MP1z/G2abHhhiYWlJ7ezuV8eOZ8JrXNrSe7d9ZSvu0aQ2tQxoJw+sv9jAAEw6vf7Dh2Tx7WHUVpaiWtWcB64F/iYjnA6uA9wHTqxKwh4Hp+fOZwINV26/Nl/VK1iLiPOA8gCOOOKJhwWvvduBBMO+Uxtax/PuN3b/2XHYJcjM7lq5oaD2psomuHY55J9WjjP3FymBMQfWOA14IXJlSegHwBNklz93yVrQh/YVLKX0ppTQnpTRnmv9FSpKkUWDQlrWIuCmlNH+E610LrE0p/TQv/ydZsvZIz+XNiJgBPJqvXwccXrX9Yfky7UXsL6YyyS5BBuNfO7eh9exYuoL2aTMaWoek0a2ey6Aj3kSVUno4Ih6MiP+VUvo1MA+4O3+cC3wq/3ldvslS4N0RcQ3ZjQWb7a+29+ns7OTee37FkAYxz/uLVR6qv7/Y+k1Di0vN19XVBY9vYed1qxpf2YYtdG3vanw9ktQg9SRrUyLi9f2tTCldO8y63wMsye8EvQ94O9ll2f+IiAXAGuBN+Wu/SzZsx2qyoTvePsw6VbBpB8Ib5za2q+Q3Vuxs6P4lSWqmupI14Ayg1qzTCRhWspZS+h9gTo1V82q8NgHvGk49ksqnvb2dDRN2Mu7M4xte187rVtF+cHvD65GkRqknWVuTUnpHwyORJEnSM9STrNVqUZMkSSPo0Y1rWHLDxXW//rEt2fhiB+1/6JDqOGj67CHHpmLVk6z974ZHIUlSCxvOAK0bt24H4KDp4+ve5qDpswesa+3mB7j8tkV172/9E48AMG2/6YO8sncdx8w0YRyKepK1n0RE9XhnQdZXLci6kx3QkMgkSWoRZRgMdjgJ447OLGGcOHNC3dscM3PghFHPVE+ythw4lOxGgmtSSg80NiRJktRsZUgYVdugMxiklM4CTiWbHurLEfGDiPiriJja8OgkSZJaXF0DXqWUNpPN4/lV4Gzgs8C+wD80MDZJUovYVVnHlqWX1//6zesBGDul/nHbd1XWwbRjhhybVLS6krWIeAnwFuBlwI+A16WUbm1kYBpZTvWkHtnsATvZ9a3Nja9sw05nD9CghtN/qfPxbPDrWdMm1b/RtGPsK6W9Uj1zg/4O2ARcA5wH7MyXvxAgpfTzBsanEdLZ2clv7vkV7VPqH4ll7K7svpKtXXfUvU3X5jT4iySpin2lpIHV07L2O7K7P08F5tN73LUENHYWZI2Y9inBX7y8/jt2huOLP9ze0P2PBl1dXWzdDL+8vrGJ7dYKdO18ZqtWNnvAJsa+bkpD6wfY9a3Nzh4gSXto0GQtpXRSPTuKiFNSSt/f44gkSZK020jOqP1pwGRNGkR7ezs7xm3g+a9u7OQgv7w+0X6IrVoaXHflEZ5cuqT+129+DIAxUw4aUh1MO3DIsUka2WTNaakkaS8zvM79lWzboSRf0w4sdef+zZUHuOW6+qd6Ati6ORu9f/KU+kfv31x5gPZpjt6voRnJZM2e5ZK0l7Fz//ASVoDOx7M+uu3T6u8L3D7N0fs1dCOZrEmStNcZ7lBDoy1pVXkNOoPBQCKievbY3+1ZKJIkSepryMlaZOZFxGJgbc/ylNLrRzQySZIk1X8ZNCJeDLwVOAuYCrwLGNrQ9i2qmbMHgDMISJI0mtQzg8ElwBuBB4B/A/4OWJlS+mqDYxs1Ojs7WX33XRwxpf5pUSbsyjqubl93/5DqemDztiG9vtV0dXWxeTMsb/AgM5seg9TtNEuSpD1XT8va/wF+A1wJfCel9HREeOfnEB0xZRIXvew5Da/nk7fe2/A6JI2cVKmw/TtL63/95mxO15hS/wwUqVKBafVPeC6pXOpJ1mYAp5BN5H55RNwMTIyIcSmlnQ2NboQ4ibl6tLe3E2M2MO+Uxtaz/Psw41AHpNXAhjfG2ePZtkNJvqZNc7gIaS9WT7I2M6V0A3BDROwDnAFMBNZFxPKU0lsbGuEI6OzsZPU993DklKl1bzMhn8R8R9cjdW+zZvPGIcfWSrq6unh8M3xjRWNz/PWb4OnkJUiVn2OcSapHPcnat4EXAqSUnga+CXwzIg4gu9lgr3DklKl8+GWnNrSOi2+9saH7lyRJraeeZK3mNFIppceBq0c2HI1m7e3t7BMbeOPcxo7F/I0VO2mb4SVISdLoUNdl0Ij4bH8rU0rvHcF4JKlpUmUTO5auqP/1m7cCEFMmD6kOps0YcmyS1KOeZO1JYFWjA5HUYjZsZed1Q/jT0jMszRCGwOmph4OfuXh4nfuzG49mDSX5mjbDzv2S9kg9yVrFMdUkjaRhJUqb80Tp4COGtuHBteuzc7+kvUU9ydr2hkchqaWYKElS/QZN1lJKL46ICUAH8Lx88V3A1/O7QyVJktQgg07kHhF/ANwNnEQ25dQD+fO7IuK5jQxOkiSp1dVzGfQK4J0ppV6zKUbEycA/Aa9sRGAaWV1dXWzdlPjiDxt7VbtrU2IyDkgrSdJIGbRljWwGg2dMe51SWgYcOvIhSZIkqUc9LWtjImKfvv3TImLfOrdXCbS3t7OVCn/x8gkNreeLP9zO5HYHpJUkaaTUk2xdTTa91LtSSmsAIuIo4LPA1xoXmjR6bd0Iv7w+1f36J7O5u5l4wNDq4JB+Vm7Yxa5vba5/ZwCbd2U/p4ytf5sNu2qOcSZJql89d4NeHBHvBm6NiElk009tBS5NKX2u0QFKo82wxhjbko0xduQhQ9j2kNp1DXeA1t+PczaE7fsZ40ySVL+6LmOmlK4AroiI/fPyloZGJY1iRY8xNpz6RzoGSVL9Bk3WIuKcGst2P08pOZn7ILq6unhi8zY+eeu9Da9rzeZt7BfejSlJ0mhRT8vai/pZ/lpgJlmfNkmSJDVAPX3W3tPzPLImtQ7gQuAnwKLGhTZ6tLe3sz09zUUve07D6/rkrfcywbsxJUkaNerqsxYR44C3AReQJWlvSCn9uoFxjaiuri62bdrMxbfe2NB61mzayCR2NbQO7blNj8HyZ4wc2L+teQ/NyfsPrY4ZjkIoSRoB9fRZexfwPmA5cFpK6XeNDkpqlGHdiflEdhfkjEPr33bGod4FKUkaGfW0rH0OeBR4KXBi1c0FAaSU0nENim3EtLe3s4OxfPhlpza0notvvZHx7dMbWsfebv0m+MaKnXW/ftPW7OeBk4dWR9uM2uuKvhNTkqShqidZe1bDo1BLGE5L06bOrFWrbUb927bNsFVLkjR61HODwZp6dhQRP04p/cmeh6TRylYtSZKGrp6J3Ou17wjuS5IkSYxsslb/RIeSJEmqS11Dd2h06Nqc+OIPt9f9+g1bs/z74MkxyCt71/Fsh3mTJGnEjGSyVv83uppuOB3uH8k7909ur3/bZ7fbuV+SpJE0ksnan43gvjTC7NwvSdLeqZ5BcbfQuz9aAjYANwMXppQqACmlOxsS4SjxwBAncn/kiacAmL7f0O7beGDzNmbPHNImkiSpxOoZuuMZk+xExEFk0099AXjjyIc18tZs3jik6aYezucYOnQIcwyt2byR2TUGxR3OZcHt+SXICTOHNszd7JlehpQkaaRdeeWVdObfzX399re/5emnn+Z973sf48eP77Vu1qxZw7q6VW1Yl0FTSo8B/xgRe3TpMyLGAiuBdSmlMyLiWcA1QBuwCvizlNL2iNgHuBo4HqgAbx7KtFfDS5ayofOHMiPB7PbpNevyEqQkSaNXd3c33d3dPPTQQxxxxBEjvv9h91mLiPF7sn3ufcA9wAF5+dPAP6aUromILwALgCvzn4+llGZHxNn5695cbyUmS5IkaU/0l0tUKhXe+ta3ArB582Y+9KEPMXXq1BGte9Bx1iLi9TUeC4Drgf8cbsURcRjwauCf83IAc6v2+VXgrPz5mXmZfP28qJqkVJIkqQiLFy8mpaxrf0qJxYsXj3gd9QyK+5o+jzOA5wCfSSl9fA/qvhz4v0B3Xm4DNqWUemb5Xgv0dJWfCTwIkK/fnL9ekiSpMCtWrOhVXr58+YjXUc8NBm8f6Uoj4gzg0ZTSqog4aQT3ex5wHtCQa8aSJEnVuru7ByyPhHqG7vjbAVanlNInhlHvicBrI+J0sjlFDwA+AxwYEePy1rPDgHX569cBhwNrI2IcMIXsRoO+wXwJ+BLAnDlznP5KkiTt9eq5DPpEjQdknf4vHE6lKaWLUkqHpZSOAs4GVqSUOsjGbntD/rJzgevy50vzMvn6FannArEkSdIoVs9l0Mt6nkfE/mR3cL6dbIiNy/rbbpguBK6JiIuBXwA9vfQWA1+LiNXARrIET5IkadSra+iNiJgK/A3QQXZX5gvzsdb2WErpFuCW/Pl9wB/XeM1T7CWD70qSpJE13AFpYWQGpS1aPUN3/D3wM2AL8IcppY+NVKImSZK0J3bt2kV3dzcPP/xw0aE0TD0ta+cDTwMfBhZWDW8WZDcYHNDfhpIkSXtqoAFpOzo6gGxA2iuvvHLEB6Qtg0Fb1lJKY1JKE1NK+6eUDqh67G+iJkmSirJ48eLdQ2V0d3c3ZEDaMtjT6aIkSaPQQH2Eepb3TMtXbTT0D9Le4+abb+5VXrFiBR/4wAcKiqZxTNYkqWSGmyhBc5KliRMnNnT/PUwYNZi+M08WMRPlmDFjeg2EO2ZMPaOiDY3JmiTtRZqVKJU92WmlhLG/GJqZuJchhlpOOukkli1btrv8yle+smF19Wfu3Lm9Ypg3b96I19HSyZofQmMYrP69IYZm/B7KEEMrnY9lT5SapezHoVkJY1nrL0MMCxYsYMWKFXR3dzNmzBgWLFhQSAzLly8npURENCSGlk7WBlL0CVh0/cZgDGWLoej6yxKDmqsMCaMx9K+trW13y9a8efMKuRO0ra2NefPmsWzZMk4++eSGxBCjddamOXPmpJUrVxYdhiRJaqBKpcIll1zCwoULCxu2YyRiiIhVKaU5NdeZrEmSJBVroGRt5G9ZkCRJ0ogxWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEiskWYuIwyPi5oi4OyLuioj35cunRsT3I+K3+c+D8uUREZ+NiNUR8auIeGERcUuSJDVbUS1rO4HzU0rPBV4MvCsingt8EFieUjoGWJ6XAV4FHJM/zgOubH7IkiRJzVdIspZSeiil9PP8+RbgHmAmcCbw1fxlXwXOyp+fCVydMj8BDoyIGU0OW5IkqekK77MWEUcBLwB+CkxPKT2Ur3oYmJ4/nwk8WLXZ2nyZJEnSqFZoshYRk4FvAu9PKT1evS6llIA0xP2dFxErI2Ll+vXrRzBSSZKkYhSWrEXEeLJEbUlK6dp88SM9lzfzn4/my9cBh1dtfli+rJeU0pdSSnNSSnOmTZvWuOAlSZKapKi7QQNYDNyTUvqHqlVLgXPz5+cC11UtPye/K/TFwOaqy6WSJEmj1riC6j0R+DPgjoj4n3zZh4BPAf8REQuANcCb8nXfBU4HVgPbgLc3N1xJkqRiFJKspZR+BEQ/q+fVeH0C3tXQoCRJkkqo8LtBJUmS1D+TNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIzWZMkSSoxk7V+rF69mrPOOov77ruvkPqvueYa5s+fzze+8Y1C6i9LDJVKhfPPP5+NGzcWFkMZrFq1itNOO41f/OIXhcVQ9GfCc0HqrejPZFmU4W9Do2PYq5K1iDgtIn4dEasj4oONrOvTn/4027Zt45Of/GQjq+nXVVddBcCXv/zlQuovSwxLlizhzjvvZMmSJYXFUAaLFi2iu7ubT3ziE4XFUPRnwnNB6q3oz2RZlOFvQ6Nj2GuStYgYC/wT8CrgucBbIuK5jahr9erVrFmzBoA1a9Y0/b+Wa665ple5iJatMsRQqVS46aabSClx4403tmyLyqpVq9i6dSsAW7duLaR1rejPhOeC1FvRn8myKMPfhmbEsNcka8AfA6tTSvellLYD1wBnNqKiT3/6073Kzf6vpadFq0cRLVtliGHJkiV0d3cD0N3d3bItKosWLepVLqJ1rejPhOeC1FvRn8myKMPfhmbEsDclazOBB6vKa/Nlu0XEeRGxMiJWrl+/ftgV9fy30l9ZzbFixQp27twJwM6dO1m+fHnBERWjp1Wtv3IzFP2Z8FyQeiv6M1kWZfjb0IwY9qZkbVAppS+llOaklOZMmzZt2Ps58sgjByyrOebOncu4ceMAGDduHPPmzSs4omJMnjx5wHIzFP2Z8FyQeiv6M1kWZfjb0IwY9qZkbR1weFX5sHzZiLvwwgt7lS+66KJGVNOvd7zjHb3Kf/7nf97U+ssSQ0dHB2PGZKfomDFj6OjoaHoMZbBw4cJe5Y985CNNj6Hoz4TngtRb0Z/JsijD34ZmxLA3JWs/A46JiGdFxATgbGBpIyqaPXv27v9SjjzySI4++uhGVNOvs88+u1f5jW98Y1PrL0sMbW1tzJ8/n4jg1FNPZerUqU2PoQyOP/743a1pkydP5gUveEHTYyj6M+G5IPVW9GeyLMrwt6EZMew1yVpKaSfwbuBG4B7gP1JKdzWqvgsvvJBJkyYV9t9KT8tWES1aZYqho6ODY489tuVbUhYuXMiYMWMKaVXrUfRnwnNB6q3oz2RZlOFvQ6NjiJRSQ3ZctDlz5qSVK1cWHYYkSdKgImJVSmlOrXV7TcuaJElSKzJZkyRJKjGTNUmSpBIzWZMkSSoxkzVJkqQSM1mTJEkqMZM1SZKkEjNZkyRJKjGTNUmSpBIbtTMYRMR6YM0e7uZgYMMIhLO31m8MxlC2GIqu3xiMoWwxFF2/MYxcDEemlKbVWjFqk7WREBEr+5v6oRXqNwZjKFsMRddvDMZQthiKrt8YmhODl0ElSZJKzGRNkiSpxEzWBvalFq8fjKGHMWSKjqHo+sEYehhDpugYiq4fjKFHw2Kwz5okSVKJ2bImSZJUYiZrNUTEWRGRIuI5BdW/KyL+JyJ+GRE/j4iXFBDDoRFxTUR0RsSqiPhuRDy7ifX3HIO78uNwfkQ0/XytiqPn8cESxHBUk+ufHhFfj4j78nPhxxHxuibHsLVP+W0RcUUzY+gvllaLobruiDg9In4TEUcWFUOz5d8N/1pVHhcR6yPivwqI47Kq8gUR8bEmx3BYRFwXEb/Nvys+ExETmhxDz9/HOyPiGxExqcn1Vx+D+yLiiojYZ6TrMVmr7S3Aj/KfRXgypfRHKaXnAxcBn2xm5RERwLeAW1JKs1JKx+dxTG9iGD3H4HnAKcCrgI82sf6+cfQ8PlWCGH7XrIrzc+HbwA9TSkfn58LZwGHNikHlFBHzgM8Cr0op7emYlnuTJ4BjI2JiXj4FWFdAHE8Dr4+Igwuou+dvw7XAt1NKxwDPBiYDi5ocSs/fx2OB7cBfNqviGsfgGGAi8P9Gui6TtT4iYjLwUmAB2ZdS0Q4AHmtyna8EdqSUvtCzIKX0y5TSrU2Oo6fuR4HzgHfnHw41z1xge59zYU1K6XMFxqSCRcTLgS8DZ6SUOouOpwDfBV6dP38L8G8FxLCTrEP7XxdQN2R/G55KKf0LQEppVx7LO5rdulXlVmB2E+vr7xick+cSI8Zk7ZnOBG5IKf0GqETE8QXEMDFv1r0X+GfgE02u/1hgVZPrHFBK6T5gLHBIk6vu+V30PN7c5Pr7xvCtJtf9PODnTa6zll6/B+DjRQfUwvYha209K6V0b9HBFOQa4OyI2Bc4DvhpQXH8E9AREVMKqPt59PmeSCk9DjxAcxMmILscTXYF5o4mVtvfMfgdI3wMxo3kzkaJtwCfyZ9fk5ebnbg8mVL6I4CI+BPg6og4NnnrbhF2/y5aPAYAIuKfyFqet6eUXtTEqnsdg4h4G1DoaOUtbAdwO9nVh/cVHEshUkq/yvuOvoWsla2oOB6PiKuB9wJPFhVHwSbm/8BB1rK2uMhgGsVkrUpETCVr1vzDiEhkLTkpIj5QVKKUUvpx3idhGvBok6q9C3hDk+qqS0QcDeyiecdAmbuAP+0ppJTelZ+PK4sLSQXrBt4ELI+ID6WULik6oIIsBS4FTgLaCozjcrLW739pcr130+d7IiIOAI4AVjcxjiL/me3vGBwK/HokK/IyaG9vAL6WUjoypXRUSulw4H7gZUUFlN+ROhaoNLHaFcA+EXFeVRzHRUQhxyEipgFfAK6wdbHpVgD7RsQ7q5YV1R9FJZFS2kbWZ6sjIhYUHU9BrgL+LqXUzMtuz5BS2gj8B1lLZzMtByZFxDkAETEWuAz4Sn5+tIL+jsEVKaURbek0WevtLWR3QVb7Js2/K3RiVd+cfwfOzTsuNkWeEL0OODm/HfsusjtSH25WDPz+GNwFLANuAv6uifX3jaPnUcTdoIXJz4WzgFdExP0R8d/AV4ELi42sdeV9c54uOo48STgN+HBEvLbJ1U+KiLVVj79pcv2klNamlD7b7Hr7cRnQ1LtCq74n3hgRvwV+AzwFfKiZcRSp6hi8IT8GFaA7pTTid8Q6g4Ek7UUi4vnAl1NKf1x0LJJ+L7IxUf8NeF1KaURvzDJZk6S9RET8JVln8venlG4qOh5JzWGyJkmSVGL2WZMkSSoxkzVJkqQSM1mTJEkqMZM1SS0tIg6LiOsi4rcRcV9EXBER++TrLoqI1RHx64g4tWqb0/JlqyPig1XLIyIWRcRvIuKeiHhvEe9J0uhisiapZUVEANcC304pHQMcA0wE/l9EPBc4m2z+v9OAz0fE2Hzgy38im4fwucBb8tcCvA04HHhOSukPyKask6Q94nRTklrZXOCplNK/AKSUdkXEXwNrgA3ANSmlp4H7I2I10DO22eqU0n0AEXENcCbZ1DPvBN6aUurO9+f0aJL2mC1rklrZ84BV1QtSSo8DvwNOAB6sWrUWmJk/ai0HmAW8OSJWRsT3IuKYBsUtqYWYrEnSyNmHrKVuDvBlsvkjJWmPmKxJamV3A8dXL4iIA4BDgdvI+p/1OAxYlz9qLYesle3a/Pm3gONGPmRJrcZkTVIrW042Kfg5APnNA5cBVwBLgbMjYp+IeBbZzQf/DfwMOCYinhURE8huQlia7+/bwCvz568gm9xakvaI001JamkRcTjZ3Z1/AEwD/j2l9Bf5uoXAO4CdZPNxfi9ffjpwOTAWuCqltChffiCwBDgC2Ar8ZUrpl819R5JGG5M1ScpFxEuAfwNel1L6edHxSBKYrEmSJJWafdYkSZJKzGRNkiSpxEzWJEmSSsxkTZIkqcRM1iRJkkrMZE2SJKnETNYkSZJK7P8Do5gFsu7Dk/sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K_Qk_WbaQuuB",
        "outputId": "451e9d7f-236b-4920-85cd-db91a54aefd4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "dados[provas].sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NU_NOTA_CN         44334986.1\n",
              "NU_NOTA_CH         49743676.1\n",
              "NU_NOTA_MT         48563933.8\n",
              "NU_NOTA_LC         51027844.9\n",
              "NU_NOTA_REDACAO    56038440.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqS_hIkvRA0O",
        "outputId": "b73d79f9-f4d8-4b0f-e387-746c52329c68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "\n",
        "dados[\"NU_NOTA_TOTAL\"] = dados[provas].sum(axis=1)\n",
        "dados.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>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": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbqC-d1mRcdQ",
        "outputId": "fbcddf0b-2771-4292-fff0-63ee924c9db6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x=\"Q006\", y = \"NU_NOTA_TOTAL\", data = dados, order = renda_ordenada)\n",
        "plt.title(\"Boxplot das notas de total pela renda\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Boxplot das notas de total pela renda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M1dZw-EUSf03",
        "outputId": "086e49f0-c543-43d9-99f4-98f3fa50ce00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "sns.displot(dados, x =\"NU_NOTA_TOTAL\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f9ec9804d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HX2i1uknS2uT",
        "outputId": "81e83c08-8029-4d07-b3fc-b75f71864863",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "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": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NU_NOTA_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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10</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>11</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>26</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>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",
              "      <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>...</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",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127365</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>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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127368</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>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": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKw2G33sUiHQ",
        "outputId": "d3753f35-c88d-457c-c927-85c96dce3ea6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "source": [
        "dados_sem_notas_zero = dados.query(\"NU_NOTA_TOTAL != 0\")\n",
        "dados_sem_notas_zero.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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",
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              "      <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",
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              "      <th>IN_BAIXA_VISAO</th>\n",
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              "      <th>IN_SURDEZ</th>\n",
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              "      <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",
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              "      <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",
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              "      <td>DEEDCAECDDEEECBCBECABEBAECBBCDAECAEBBBBBDCCDB</td>\n",
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              "      <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",
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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>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <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": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUEzdhqiXBpv",
        "outputId": "7eb1f1eb-08cb-4720-bb9e-6878174be4a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.boxplot(x=\"Q006\", y = \"NU_NOTA_TOTAL\", data = dados_sem_notas_zero, order = renda_ordenada)\n",
        "plt.title(\"Boxplot das notas de total pela renda\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Boxplot das notas de total pela renda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OeFkN4ttXiOH",
        "outputId": "d6c3ddd4-d9a2-4e37-aa09-23ec1c16a77b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        }
      },
      "source": [
        "plt.figure(figsize=(14, 8))\n",
        "sns.boxplot(x=\"Q006\", y = \"NU_NOTA_TOTAL\", data = dados_sem_notas_zero, \n",
        "            hue = \"IN_TREINEIRO\", order = renda_ordenada)\n",
        "plt.title(\"Boxplot das notas de total pela renda\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Boxplot das notas de total pela renda')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1008x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FH5KDYQDM_Wi"
      },
      "source": [
        "MEGA DESAFIO DA THAINÁ: Pegar a amostra completa dos alunos de 13 e 14 anos\n",
        "\n",
        "Desafio do Gui bonzinho: aumentar a amostra para alunos menor de idade e compara a proporção por estado.\n",
        "\n",
        "Desafio 3: Criar uma função para plotar o boxplot do seaborn\n",
        "\n",
        "Desafio 4: Verificar se quem zerou a prova foi eliminado ou não estava presente\n",
        "\n",
        "Desafio 5: Quem é eliminado tira zero ou será NaN (não teve registro de notas)\n",
        "\n",
        "DEsafio 6: Verificar a proporção dos participantes de rendas mais altas e mais baixas como treineiro e não treineiro.\n",
        "\n",
        "Desafio 7: Fazer o mesmo boxplot olhando para a questão 25 (tem internet ou não) e fazer uma reflexão sobre o assunto e o contexto de pandemia."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wDoPzeDiG1Mo"
      },
      "source": [
        "## Aula 03"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G7i10vedUd9m",
        "outputId": "c5d3721c-fa05-4de7-ef2d-fd3c168116a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        }
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.histplot(dados_sem_notas_zero, x = \"NU_NOTA_TOTAL\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-cfa3e52795b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdados_sem_notas_zero\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"NU_NOTA_TOTAL\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2wLEf24MnW0",
        "outputId": "c812287a-d1c2-46e6-8e56-779d6ed8fb98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        }
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.histplot(dados_sem_notas_zero, x = \"NU_NOTA_MT\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-4-d629f932a66c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdados_sem_notas_zero\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"NU_NOTA_MT\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Izw1KIclHYs0",
        "outputId": "10c92152-03b5-49e5-83df-c965a8e530f1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180
        }
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.histplot(dados_sem_notas_zero, x = \"NU_NOTA_LC\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-b5321bb7b380>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdados_sem_notas_zero\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"NU_NOTA_LC\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3lzMKIQtIkEf"
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.histplot(dados_sem_notas_zero, x = \"NU_NOTA_TOTAL\", hue=\"Q025\", kde=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D84AeV85Jb71"
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.histplot(dados_sem_notas_zero, x = \"NU_NOTA_TOTAL\", hue=\"Q025\", kde=True, stat=\"probability\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTCNlG_iKsO8"
      },
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "sns.histplot(dados_sem_notas_zero, x = \"NU_NOTA_TOTAL\", hue=\"Q025\", kde=True, stat=\"density\", cumulative=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qQo-NmTFLGgX"
      },
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "sns.scatterplot(data = dados_sem_notas_zero, x=\"NU_NOTA_MT\", y=\"NU_NOTA_LC\", hue=\"Q025\")\n",
        "plt.xlim((-50, 1050))\n",
        "plt.ylim((-50, 1050))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OLdCI88ROtYB"
      },
      "source": [
        "provas"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ksEK3KhxMTHo"
      },
      "source": [
        "sns.pairplot(dados_sem_notas_zero[provas])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "heCAW7TuO4Wk"
      },
      "source": [
        "correlacao = dados_sem_notas_zero[provas].corr()\n",
        "correlacao"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TUivMgz6SYXD"
      },
      "source": [
        "sns.heatmap(correlacao, cmap=\"Blues\", center=0, annot=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ILxPJbfYIuTE"
      },
      "source": [
        "Desafio: Plotar as médias, medianas e moda nas notas de LC e MT (matiplotlib linha vertical)\n",
        "\n",
        "Desafio2: Melhorar a visualização da matriz de correlação e analisar mais detalhadamente.\n",
        "\n",
        "Desafio3: Filtrar as notas por seu estado ou sua cidade e refazer as análises, verificando se são semelhantes ao geral.\n",
        "\n",
        "Desafio4: Pensar sobre a correlação entre matemática e Linguagens."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUdQdEvRU1bC"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}