DesafioImersaoDadosAula01_Correção-Alura.ipynb

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      "name": "Aula01 desafios.ipynb",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/JorgeCandido/ImersaoDados/blob/main/DesafioImersaoDadosAula01_Corre%C3%A7%C3%A3o-Alura.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "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>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": "a8e5c390-dcd2-49fa-a9f1-ee5be8f900d9",
        "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": "83bc4077-bc45-41b2-e4a8-05d4cde7ad81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "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": "f94b69f3-15bc-4663-9199-4df971b2b4b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        }
      },
      "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": "3d23a0ca-e566-49e2-8a91-23323bb11e86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "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": "6d3ad94f-cd7d-41d5-9bb2-cde7510ad3d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "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": "ee120e91-5ddf-45cd-a7ba-c8094049472f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "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": "f37d82ee-71db-4e04-c735-14bcebb3c8a5",
        "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": "107e5c40-d933-450a-c081-25e10ac706b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "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": "12445b64-bb1b-46ab-b93c-3708e6d6f9cd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "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": "26e83959-5e4b-4aef-9e3f-d41fc3116de1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "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": "c90c974e-d329-4f06-8db8-f4d06ce02074",
        "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 0x7fed5d931550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYd8dPfNO1-n",
        "outputId": "e970e76a-9d9c-4dd3-a2b6-42c08d3e86ca",
        "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 0x7fed5d912128>"
            ]
          },
          "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": "41045df0-e68d-459c-ba4c-4860653b2479",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        }
      },
      "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": "f9443fa0-da6c-4c2b-eae6-9ac36194cb16",
        "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 0x7fed5d36b0f0>"
            ]
          },
          "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": "b02c5b89-60a0-46d9-a219-2157a5b83213",
        "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 0x7fed5c05b358>"
            ]
          },
          "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": "ad045718-c4e7-4903-adac-9192c1552f61",
        "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": "3228510b-930c-48c0-9eec-5f743c2cf0a2",
        "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": "c850f9d0-d0e3-4a44-f816-7adb856355cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "provas = [\"NU_NOTA_CN\",\"NU_NOTA_CH\",\"NU_NOTA_MT\",\"NU_NOTA_LC\",\"NU_NOTA_REDACAO\"]\n",
        "\n",
        "dados[provas].describe()"
      ],
      "execution_count": 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": "74ee6901-d7cb-44de-a628-ac5ca6668939",
        "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": "01382dc6-1334-496d-947e-93c06d8a2ddd",
        "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 0x7fed5c064d68>"
            ]
          },
          "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": "81d5db4b-a4c8-47d0-8c92-3e6de09f9916",
        "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 0x7fed5bf45d30>"
            ]
          },
          "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": "M457i8dUnaDp"
      },
      "source": [
        "Desafio01: Proporção dos inscritos por idade."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X-vN4RHqZrak",
        "outputId": "f7c6f993-962d-4a0c-d0db-4915e00aa27e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "dados['NU_IDADE'].value_counts(normalize=True).sort_values(ascending=False).head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18    0.168409\n",
              "17    0.166871\n",
              "19    0.113189\n",
              "20    0.081748\n",
              "16    0.061470\n",
              "Name: NU_IDADE, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d46qR5E6nmfi"
      },
      "source": [
        "Desafio02: Descobrir de quais estados são os inscritos com 13 anos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fNru4GVUnbfY",
        "outputId": "04c9fe15-aef2-4664-ce75-f7bc3bf383e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dados.query(\"NU_IDADE == 13\")['SG_UF_RESIDENCIA'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['SP', 'MT', 'AP', 'BA'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G20SkCf0nr6Q"
      },
      "source": [
        "Desafio03: Adicionar título no gráfico"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uEKszQjfsg1w"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t0eIDN-xnrH4",
        "outputId": "e1d293eb-bdec-49e2-bcd6-0e2c121f3009",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "dados['NU_NOTA_REDACAO'].plot.hist(bins=20, figsize=(8, 6), title=\"Grafico de distribuição das notas de redação\", grid=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed5ba63860>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "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": "C5yU-ZSGoRFn",
        "outputId": "e8cd09ba-fde4-4e81-b804-6b7f5f108ba9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        }
      },
      "source": [
        "dados[provas].plot.box(grid=True, figsize= (10,8), title=\"Grafico de distribuição das notas de redação\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed5bdba9e8>"
            ]
          },
          "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": "markdown",
      "metadata": {
        "id": "dd8OMU1FoZih"
      },
      "source": [
        "Desafio04: Plotar os Histogramas das idades dos do treineiro e não treineiros."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bFrKsT4Yoc7l",
        "outputId": "69654d94-8e3a-425c-dad0-b50be3f9f41c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "dados_nao_treineiros = dados.query(\"IN_TREINEIRO == 0\")\n",
        "dados_nao_treineiros['NU_IDADE'].hist(bins=20, figsize=(8, 6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed5bdba2e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "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": "SsBld2zPovVc",
        "outputId": "ca8c6b7b-013b-4a0f-b65d-1c652760cd74",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "dados_sim_treineiros = dados.query(\"IN_TREINEIRO == 1\")\n",
        "dados_sim_treineiros['NU_IDADE'].hist(bins=10, figsize=(8, 6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed5b8eeba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "t9x7rMiqo3WF"
      },
      "source": [
        "Desafio05: Comparar as distribuições das provas em inglês espanhol"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCC_xKSioxDE",
        "outputId": "15cec607-ccc6-49e2-fd9a-d972724c502a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "dados_ingles = dados.query(\"TP_LINGUA == 0\")['NU_NOTA_LC']\n",
        "dados_ingles.plot.box(grid=True, figsize=(8, 6))\n",
        "_ = plt.title(\"Grafico de caixa das notas de linguagem e códigos para quem escolheu inglês\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "XRUvYkzKsXil",
        "outputId": "34365321-4ba9-47cb-9e71-f37812920b11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "dados_espanhol = dados.query(\"TP_LINGUA == 1\")['NU_NOTA_LC']\n",
        "dados_espanhol.plot.box(grid=True, figsize=(8, 6))\n",
        "_ = plt.title(\"Grafico de caixa das notas de linguagem e códigos para quem escolheu espanhol\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "markdown",
      "metadata": {
        "id": "7k9zk7z6s2gH"
      },
      "source": [
        "Desafio06: Explorar a documentações e visualizações com matplotlib ou pandas e gerar novas visualizações."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "udltp8G3s1AA",
        "outputId": "7aa72b09-383a-40c0-f360-12d95935f57a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "idiomas = ['Espanhol','Ingles']\n",
        "dados_por_idioma = pd.concat([dados_espanhol, dados_ingles], axis=1, names = idiomas)\n",
        "dados_por_idioma.columns = idiomas\n",
        "dados_por_idioma.plot.box(grid=True, figsize=(8, 6))\n",
        "_ = plt.title(\"Grafico de caixa das notas de linguagem e códigos separado entre inglês e espanhol\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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": "Jk3c5Gw-s52v",
        "outputId": "660b42f4-d28e-45e4-ec10-c478f9cab244",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "idiomas = ['Espanhol','Ingles']\n",
        "\n",
        "ing = dados.query(\"TP_LINGUA == 0\")['NU_NOTA_LC']\n",
        "esp = dados.query(\"TP_LINGUA == 1\")['NU_NOTA_LC']\n",
        "\n",
        "\n",
        "dados_por_idioma = pd.concat([esp, ing], axis=1, names = idiomas)\n",
        "dados_por_idioma.columns = idiomas\n",
        "dados_por_idioma.plot.box(grid=True, figsize=(8, 6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fed5b5a5898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "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"
          }
        }
      ]
    }
  ]
}