imersao_de_dados_aula_01.ipynb

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    "colab": {
      "name": "imersao-de-dados-aula-01.ipynb",
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      "authorship_tag": "ABX9TyMgZRkKad5H3onq5m3kQk5D",
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      "name": "python3",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/RianBrug/imers-o-dados-alura/blob/main/imersao_de_dados_aula_01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
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        "id": "5GgQ4mynvtoU",
        "outputId": "3cc9b5bc-ceb6-47a8-b803-f17fb086a074",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "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": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>NaN</td>\n",
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              "      <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": "qzcxgx7ayTbI",
        "outputId": "875eafa7-06aa-4895-ef42-d25f28e8bffa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "dados.shape"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(127380, 136)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BlYosLCwyrsp",
        "outputId": "d47f3c6c-5dcf-4fa0-eb6c-d75cf62941dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        }
      },
      "source": [
        "dados[\"NU_IDADE\"]"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         17\n",
              "1         23\n",
              "2         35\n",
              "3         23\n",
              "4         16\n",
              "          ..\n",
              "127375    17\n",
              "127376    18\n",
              "127377    22\n",
              "127378    26\n",
              "127379    19\n",
              "Name: NU_IDADE, Length: 127380, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gax5LNmuy6Pq",
        "outputId": "4a92ce59-296d-4d99-97ea-5e9c26feca3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 742
        }
      },
      "source": [
        "dados.columns.values"
      ],
      "execution_count": 5,
      "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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMNOJ6zF0Jye",
        "outputId": "36af2122-c571-4bd0-9990-1f146667de8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "source": [
        "dados[[\"SG_UF_RESIDENCIA\", \"Q025\"]]"
      ],
      "execution_count": 7,
      "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": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHs9f6lh0O9r",
        "outputId": "237926fe-f50d-4163-cd15-208562f4262c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        }
      },
      "source": [
        "dados[\"SG_UF_RESIDENCIA\"].unique()"
      ],
      "execution_count": 11,
      "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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nVMY7uzF0U7G",
        "outputId": "0aadd8a1-59e3-4e57-fd9b-89bd9fa7beb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "len(dados[\"SG_UF_RESIDENCIA\"].unique())"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UaJF2ZMh0fWq",
        "outputId": "4a72885f-a214-4cb2-aeb0-502196387fc9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        }
      },
      "source": [
        "dados[\"SG_UF_RESIDENCIA\"].value_counts()"
      ],
      "execution_count": 15,
      "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": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-_y7jvJm0qT3",
        "outputId": "9f853e98-c60b-44f2-d768-390aacf33971",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        }
      },
      "source": [
        "dados[\"NU_IDADE\"].value_counts()"
      ],
      "execution_count": 16,
      "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": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yAdmCUMM02U6",
        "outputId": "b8d7408a-a557-4640-ee07-b1e55ed109b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        }
      },
      "source": [
        "dados[\"NU_IDADE\"].value_counts().sort_index()"
      ],
      "execution_count": 19,
      "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": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HVDOAoxE3L_A",
        "outputId": "25ea496d-838c-40f2-e186-1857dbbd0480",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "dados[\"NU_IDADE\"].hist()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1fb1cbb438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "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": "FiQQTFf83jhx",
        "outputId": "ebf6af66-4b4f-4ada-c4a3-eaaa6c9c70b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "dados[\"NU_IDADE\"].hist(bins=20)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1fa9f87ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        },
        {
          "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": "lKm_N8sR3mul",
        "outputId": "4e12aab7-34fe-4eff-df4f-a7860ddcd726",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        }
      },
      "source": [
        "dados[\"NU_IDADE\"].hist(bins=20, figsize = (10,8))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1fa9d94048>"
            ]
          },
          "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": "ZJ94kvTj5GBa",
        "outputId": "69e4270f-a24d-4317-9007-ba3351f0a5b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        }
      },
      "source": [
        "dados.query(\"IN_TREINEIRO == 1\")[\"NU_IDADE\"].value_counts()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "16    7142\n",
              "17    4901\n",
              "15    1927\n",
              "18     858\n",
              "19     228\n",
              "20      83\n",
              "14      64\n",
              "21      44\n",
              "22      30\n",
              "23      21\n",
              "24      13\n",
              "26      13\n",
              "29      10\n",
              "27       9\n",
              "25       8\n",
              "28       6\n",
              "38       4\n",
              "40       4\n",
              "31       4\n",
              "30       4\n",
              "42       4\n",
              "34       3\n",
              "36       2\n",
              "39       2\n",
              "32       2\n",
              "46       2\n",
              "44       2\n",
              "35       2\n",
              "45       1\n",
              "41       1\n",
              "33       1\n",
              "62       1\n",
              "48       1\n",
              "51       1\n",
              "37       1\n",
              "47       1\n",
              "55       1\n",
              "Name: NU_IDADE, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "utBehT445QZD",
        "outputId": "133774fa-b342-4945-e1ce-1b598a1092c0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 724
        }
      },
      "source": [
        "dados.query(\"IN_TREINEIRO == 1\")[\"NU_IDADE\"].value_counts().sort_index()"
      ],
      "execution_count": 31,
      "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": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3wUqmj4n52QB",
        "outputId": "e39cdd27-1fc7-4452-b75d-9b893d4df30e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        }
      },
      "source": [
        "dados[\"NU_NOTA_REDACAO\"].hist(bins = 20, figsize = (10,8))"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1fa89922e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "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": "uynWmysB55wX",
        "outputId": "c77b66f7-291f-4663-affb-c996d4d92091",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        }
      },
      "source": [
        "dados[\"NU_NOTA_LC\"].hist(bins = 20, figsize = (10,8))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1fa8992b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        },
        {
          "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": "3YmJgv5L6H-T",
        "outputId": "15acfc27-acbb-4ad5-ad7a-f68a6f7fac1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "dados[\"NU_NOTA_REDACAO\"].mean()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "571.5700253970197"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VHtlnl196c0o"
      },
      "source": [
        "média aferida pelo Pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6oiP61W6fGZ",
        "outputId": "4f40b745-ec21-4739-ec45-bd29cba983ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "dados[\"NU_NOTA_REDACAO\"].std()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "188.07645455749753"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tkhyh1846iH0"
      },
      "source": [
        "Desvio padrão calculado pelo Pandas\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Af-355a7OZ8",
        "outputId": "5a87a08a-330e-4124-919c-0c37ef9d618d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "source": [
        "provas = [\"NU_NOTA_CN\",\"NU_NOTA_CH\",\"NU_NOTA_MT\",\"NU_NOTA_LC\",\"NU_NOTA_REDACAO\"]\n",
        "dados[provas].describe()"
      ],
      "execution_count": 42,
      "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": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fE-qMrNk8JIF",
        "outputId": "6fa6c76b-6ebb-486c-cfb8-bb3a755c84e3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "source": [
        "dados[\"NU_NOTA_LC\"].quantile(0.9)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "596.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0pq0qKeH8OCd"
      },
      "source": [
        "analise dos 10% mais altos, \"10% tirou acima disso\")"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iqbSTo3L8hxc",
        "outputId": "d39c715b-190b-4cff-b2ef-c68870fda67b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "dados[\"NU_NOTA_LC\"].plot.box(grid = True, figsize=(8,6))"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1fa87f5908>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeYAAAFmCAYAAAC4IzkXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAYSUlEQVR4nO3df4xd5X3n8fe3njgmLMX8SK/AdmqqeFNTIwgZAdl0V2MmqYB2a7YCCskWF03lXYnQtN0K6Hq12UjrXbJLl0IasbUyuzVRGUJpEd6AIMjMVbRqoLULdQKTlglr8DjmZ8B0+BlPvvvHPIaLM3juzNzhPpl5v6TRfc73PPfc70VcfXyeOXNuZCaSJKkOP9XtBiRJ0tsMZkmSKmIwS5JUEYNZkqSKGMySJFXEYJYkqSI93W4A4MQTT8zVq1d3uw1p0XnllVc4+uiju92GtOjs2rXr+cz84FT7qgjm1atXs3Pnzm63IS06zWaTvr6+brchLToR8eS77XMpW5KkihjMkiRVxGCWJKkiBrMkSRUxmCVJqojBLElSRQxmSZIqYjBLklQRg1mSpIoYzJIkVaStYI6I342IRyPiOxExFBHLIuKUiHgoIkYj4msRsbTMfX/ZHi37V8/nG5A0c0NDQ6xbt47+/n7WrVvH0NBQt1uSVEx7r+yIWAH8NnBqZr4WEbcDlwIXADdk5m0R8T+BAeDm8vhiZn44Ii4Fvgj8+ry9A0kzMjQ0xObNmxkcHGRiYoIlS5YwMDAAwGWXXdbl7iS1u5TdAxwVET3AB4D9wLnAHWX/NuDCMt5Qtin7+yMiOtOupLnasmULg4ODrF+/np6eHtavX8/g4CBbtmzpdmuSaOOMOTP3RcT1wFPAa8A3gF3AS5l5sEwbA1aU8Qpgb3nuwYg4AJwAPN963IjYBGwCaDQaNJvNOb8ZSdMbGRlhYmKCZrPJ+Pg4zWaTiYkJRkZG/BxKFWhnKfs4Js+CTwFeAv4cOG+uL5yZW4GtAL29velXz0nvjbVr17JkyRL6+vre+trH4eFh1q5d61dAShVoZyn7k8D/y8znMvOHwF8CnwCWl6VtgJXAvjLeB6wCKPuPBV7oaNeSZm3z5s0MDAwwPDzMwYMHGR4eZmBggM2bN3e7NUm0ccbM5BL2ORHxASaXsvuBncAwcBFwG7ARuKvM3162v1X2P5CZ2eG+Jc3SoQu8rrrqKkZGRli7di1btmzxwi+pEtFOZkbEF5i8svog8DDwW0z+Lvk24PhS+9eZ+UZELAO+CnwU+AFwaWY+caTj9/b25s6dO+fyPiTNwqGlbEnvrYjYlZm9U+1r54yZzPw88PnDyk8AZ00x93Xg4pk2KUmSvPOXJElVMZglSaqIwSxJUkUMZkmSKmIwS5JUEYNZkqSKGMySJFXEYJYkqSIGs7QIDQ0NsW7dOvr7+1m3bh1DQ0PdbklS0dadvyQtHENDQ2zevJnBwUEmJiZYsmQJAwMDAN4vW6qAZ8zSIrNlyxYGBwdZv349PT09rF+/nsHBQbZs2dLt1iRhMEuLzsjICGNjY+9Yyh4bG2NkZKTbrUnCpWxp0Tn55JO5+uqrufXWW99ayv70pz/NySef3O3WJOEZs7QoRcQRtyV1j2fM0iLz/e9/n3PPPZf+/n4yk4igv7+fBx54oNutScJglhad5cuXMzw8zPXXX8+pp57KY489xtVXX83y5cu73ZokDGZp0Xn55ZdZtmwZX/rSl3jqqaf40Ic+xLJly3j55Ze73Zok/B2ztOgcPHiQiYkJ9uzZw49+9CP27NnDxMQEBw8e7HZrkjCYpUXp9ddfp9FoEBE0Gg1ef/31brckqXApW1qknnnmmXc8SqqDZ8ySJFXEYJYkqSIGsyRJFTGYJUmqiMEsSVJFDGZJkipiMEuSVBGDWZKkikwbzBHxkYh4pOXn5Yj4nYg4PiLuj4jHy+NxZX5ExE0RMRoRuyPizPl/G5IkLQzTBnNm/n1mnpGZZwAfA14F7gSuBXZk5hpgR9kGOB9YU342ATfPR+OSJC1EM13K7ge+l5lPAhuAbaW+DbiwjDcAt+SkB4HlEXFSR7qVJGmBm+m9si8Fhsq4kZn7y/hpoFHGK4C9Lc8ZK7X9LTUiYhOTZ9Q0Gg2azeYMW5HUaX4Ope5rO5gjYinwq8AfHL4vMzMiciYvnJlbga0Avb292dfXN5OnS5oHfg6l7pvJUvb5wN9m5qGvonnm0BJ1eXy21PcBq1qet7LUJEnSNGYSzJfx9jI2wHZgYxlvBO5qqV9ers4+BzjQsuQtSZKOoK2l7Ig4GvgU8G9aytcBt0fEAPAkcEmp3wNcAIwyeQX3FR3rVpKkBa6tYM7MV4ATDqu9wORV2ofPTeDKjnQn6YgioorjTX7sJXXCTK/KllSR2QTikcLXgJW6z1tySovMaaedNqO6pPeWwSwtMrt37/6xED7ttNPYvXt3lzqS1Mpglhah3bt3k5n87DVfJzMNZakiBrMkSRUxmCVJqojBLElSRQxmSZIqYjBLklQRg1mSpIoYzJIkVcRgliSpIgazJEkVMZglSaqIwSxJUkUMZkmSKmIwS5JUEYNZkqSKGMySJFXEYJYkqSIGsyRJFTGYJUmqiMEsSVJFDGZJkipiMEuSVBGDWZKkihjMkiRVxGCWJKkibQVzRCyPiDsi4rsRMRIRH4+I4yPi/oh4vDweV+ZGRNwUEaMRsTsizpzftyBJ0sLR7hnzjcC9mfnzwOnACHAtsCMz1wA7yjbA+cCa8rMJuLmjHUuStIBNG8wRcSzwL4BBgMx8MzNfAjYA28q0bcCFZbwBuCUnPQgsj4iTOt65JEkLUE8bc04BngP+d0ScDuwCPgc0MnN/mfM00CjjFcDeluePldr+lhoRsYnJM2oajQbNZnOWb0HSXPjZk+rSTjD3AGcCV2XmQxFxI28vWwOQmRkROZMXzsytwFaA3t7e7Ovrm8nTJXXCvXfjZ0+qSzu/Yx4DxjLzobJ9B5NB/cyhJery+GzZvw9Y1fL8laUmSZKmMW0wZ+bTwN6I+Egp9QOPAduBjaW2EbirjLcDl5ers88BDrQseUuSpCNoZykb4CrgzyJiKfAEcAWToX57RAwATwKXlLn3ABcAo8CrZa4kSWpDW8GcmY8AvVPs6p9ibgJXzrEvSZIWJe/8JUlSRQxmSZIqYjBLklQRg1mSpIoYzJIkVcRgliSpIgazJEkVafcGI5I67PQvfIMDr/2w222w+tq7u90Cxx71Pv7u87/U7TakKhjMUpcceO2H7Lnul7vaQ7PZrOJLLGr4x4FUC5eyJUmqiMEsSVJFDGZJkipiMEuSVBGDWZKkihjMkiRVxGCWJKkiBrMkSRUxmCVJqojBLElSRQxmSZIqYjBLklQRv8RC6pJj1l7Laduu7XYbsK3bDcAxawG6+4UeUi0MZqlL/nHkOr9dqvDbpaS3uZQtSVJFDGZJkipiMEuSVBGDWZKkihjMkiRVpK1gjog9EfHtiHgkInaW2vERcX9EPF4ejyv1iIibImI0InZHxJnz+QYkSVpIZnLGvD4zz8jM3rJ9LbAjM9cAO8o2wPnAmvKzCbi5U81KkrTQzWUpewNv35pgG3BhS/2WnPQgsDwiTprD60iStGi0e4ORBL4REQn8SWZuBRqZub/sfxpolPEKYG/Lc8dKbX9LjYjYxOQZNY1Gg2azOas3IP0k6/b/9+Pj413v4ZBa+pC6rd1g/sXM3BcRPwPcHxHfbd2ZmVlCu20l3LcC9Pb2Zg13H5LeU/fe3fW7btVy568a/ltItWhrKTsz95XHZ4E7gbOAZw4tUZfHZ8v0fcCqlqevLDVJkjSNaYM5Io6OiGMOjYFfAr4DbAc2lmkbgbvKeDtwebk6+xzgQMuStyRJOoJ2lrIbwJ0RcWj+rZl5b0T8DXB7RAwATwKXlPn3ABcAo8CrwBUd71qSpAVq2mDOzCeA06eovwD0T1FP4MqOdCdJ0iLj1z5KXVTF1x3e2/0ejj3qfd1uQaqGwSx1Sbe/ixkm/2FQQx+S3ua9siVJqojBLElSRQxmSZIqYjBLklQRg1mSpIoYzJIkVcRgliSpIgazJEkVMZglSaqIwSxJUkUMZkmSKmIwS5JUEYNZkqSKGMySJFXEYJYkqSIGsyRJFTGYJUmqiMEsSVJFDGZJkipiMEuSVBGDWZKkihjMkiRVxGCWJKkiBrMkSRUxmCVJqojBLElSRdoO5ohYEhEPR8TXy/YpEfFQRIxGxNciYmmpv79sj5b9q+endUmSFp6ZnDF/Dhhp2f4icENmfhh4ERgo9QHgxVK/ocyTJEltaCuYI2Il8MvAV8p2AOcCd5Qp24ALy3hD2abs7y/zJUnSNHranPdHwNXAMWX7BOClzDxYtseAFWW8AtgLkJkHI+JAmf986wEjYhOwCaDRaNBsNmf5FiTNhZ89qS7TBnNE/ArwbGbuioi+Tr1wZm4FtgL09vZmX1/HDi2pXffejZ89qS7tnDF/AvjViLgAWAb8NHAjsDwiespZ80pgX5m/D1gFjEVED3As8ELHO5ckaQGa9nfMmfkHmbkyM1cDlwIPZOZngGHgojJtI3BXGW8v25T9D2RmdrRrSZIWqLn8HfM1wO9FxCiTv0MeLPVB4IRS/z3g2rm1KEnS4tHuxV8AZGYTaJbxE8BZU8x5Hbi4A71JkrToeOcvSZIqYjBLklQRg1mSpIoYzJIkVcRgliSpIjO6KlvSwtB6+/ooXzPj7QakOnjGLC0y7/adMn7XjFQHg1mSpIq4lC39BOv0We5sj+cyuNQ5BrP0E2w2gXik8DVgpe5zKVuSpIoYzJIkVcRgliSpIgazJEkVMZglSaqIwSxJUkUMZkmSKmIwS5JUEYNZkqSKGMySJFXEYJYkqSIGsyRJFTGYJUmqiMEsSVJFDGZJkipiMEuSVBGDWVpkMnNGdUnvLYNZWoQyk8xkeHj4rbGkOkwbzBGxLCL+OiL+LiIejYgvlPopEfFQRIxGxNciYmmpv79sj5b9q+f3LUiStHC0c8b8BnBuZp4OnAGcFxHnAF8EbsjMDwMvAgNl/gDwYqnfUOZJkqQ2TBvMOWm8bL6v/CRwLnBHqW8DLizjDWWbsr8/IqJjHUuStID1tDMpIpYAu4APA18Gvge8lJkHy5QxYEUZrwD2AmTmwYg4AJwAPH/YMTcBmwAajQbNZnNOb0TSzI2Pj/vZkyrTVjBn5gRwRkQsB+4Efn6uL5yZW4GtAL29vdnX1zfXQ0qaoWaziZ89qS4zuio7M18ChoGPA8sj4lCwrwT2lfE+YBVA2X8s8EJHupUkaYFr56rsD5YzZSLiKOBTwAiTAX1RmbYRuKuMt5dtyv4H0r/FkCSpLe0sZZ8EbCu/Z/4p4PbM/HpEPAbcFhH/GXgYGCzzB4GvRsQo8APg0nnoW5KkBWnaYM7M3cBHp6g/AZw1Rf114OKOdCdJ0iLjnb8kSaqIwSxJUkUMZkmSKmIwS5JUEYNZkqSKGMySJFXEYJYkqSIGsyRJFTGYJUmqiMEsSVJFDGZJkipiMEuSVBGDWZKkihjMkiRVxGCWJKkiBrMkSRUxmCVJqojBLElSRQxmSZIqYjBLklQRg1mSpIoYzJIkVcRgliSpIgazJEkVMZglSaqIwSxJUkUMZkmSKmIwS5JUkWmDOSJWRcRwRDwWEY9GxOdK/fiIuD8iHi+Px5V6RMRNETEaEbsj4sz5fhOSJC0U7ZwxHwT+XWaeCpwDXBkRpwLXAjsycw2wo2wDnA+sKT+bgJs73rUkSQvUtMGcmfsz82/L+B+BEWAFsAHYVqZtAy4s4w3ALTnpQWB5RJzU8c4lSVqAemYyOSJWAx8FHgIambm/7HoaaJTxCmBvy9PGSm1/S42I2MTkGTWNRoNmszmzziXN2fj4uJ89qTJtB3NE/BPgL4DfycyXI+KtfZmZEZEzeeHM3ApsBejt7c2+vr6ZPF1SBzSbTfzsSXVp66rsiHgfk6H8Z5n5l6X8zKEl6vL4bKnvA1a1PH1lqUmSpGm0c1V2AIPASGb+j5Zd24GNZbwRuKulfnm5Ovsc4EDLkrckSTqCdpayPwH8BvDtiHik1P49cB1we0QMAE8Cl5R99wAXAKPAq8AVHe1YkqQFbNpgzsz/C8S77O6fYn4CV86xL0mSFiXv/CVJUkUMZkmSKmIwS5JUEYNZkqSKGMySJFXEYJYkqSIGsyRJFTGYJUmqiMEsSVJFDGZJkipiMEuSVBGDWZKkihjMkiRVxGCWJKkiBrMkSRUxmCVJqojBLElSRQxmSZIqYjBLklQRg1mSpIoYzJIkVcRgliSpIgazJEkVMZglSaqIwSxJUkUMZkmSKmIwS5JUEYNZkqSKTBvMEfG/IuLZiPhOS+34iLg/Ih4vj8eVekTETRExGhG7I+LM+WxekqSFpp0z5j8Fzjusdi2wIzPXADvKNsD5wJryswm4uTNtSpK0OEwbzJn5TeAHh5U3ANvKeBtwYUv9lpz0ILA8Ik7qVLOSJC10PbN8XiMz95fx00CjjFcAe1vmjZXafg4TEZuYPKum0WjQbDZn2Yqk2RofH/ezJ1VmtsH8lszMiMhZPG8rsBWgt7c3+/r65tqKpBlqNpv42ZPqMtursp85tERdHp8t9X3AqpZ5K0tNkiS1YbbBvB3YWMYbgbta6peXq7PPAQ60LHlLkqRpTLuUHRFDQB9wYkSMAZ8HrgNuj4gB4EngkjL9HuACYBR4FbhiHnqWJGnBmjaYM/Oyd9nVP8XcBK6ca1OSJC1W3vlLkqSKGMySJFXEYJYkqSIGsyRJFTGYJUmqiMEsSVJFDGZJkipiMEuSVBGDWZKkihjMkiRVxGCWJKkiBrMkSRUxmCVJqojBLElSRQxmSZIqYjBLklQRg1mSpIoYzJIkVcRgliSpIgazJEkVMZglSaqIwSxJUkUMZkmSKmIwS5JUEYNZkqSKGMySJFXEYJYkqSIGsyRJFemZj4NGxHnAjcAS4CuZed18vI6k2YmIH6tlZhc6kXS4jp8xR8QS4MvA+cCpwGURcWqnX0fS7LSG8tKlS6esS+qe+VjKPgsYzcwnMvNN4DZgwzy8jqQ5yEzuu+8+z5SlyszHUvYKYG/L9hhw9uGTImITsAmg0WjQbDbnoRVJU1m6dCnNZpPx8XGazSZLly7lzTff9HMoVSA6/a/liLgIOC8zf6ts/wZwdmZ+9t2e09vbmzt37uxoH5KmdmjJOjNpNpv09fW9oyZp/kXErszsnWrffJwx7wNWtWyvLDVJFYmIt86UJdVjPn7H/DfAmog4JSKWApcC2+fhdSTNQutZcWsoe7Ys1aHjwZyZB4HPAvcBI8Dtmflop19H0uxlJpnJ8PDwW2NJdZiXv2POzHuAe+bj2JIkLWTe+UuSpIoYzJIkVcRgliSpIgazJEkVMZglSaqIwSxJUkUMZkmSKmIwS5JUEYNZkqSKdPzbpWbVRMRzwJPd7kNahE4Enu92E9Ii9LOZ+cGpdlQRzJK6IyJ2vttXz0nqDpeyJUmqiMEsSVJFDGZpcdva7QYkvZO/Y5YkqSKeMUuSVBGDWZKkihjMUpdFREbEH7Zs/35E/Kcy/tOIuOiw+eNHONbqcryrWmp/HBG/WcYREf8hIh6PiH+IiOGI+IWy76GIeCQinoqI58r4kXLMnlK7ro3304yIH/sTrIg4KyK+GRF/HxEPR8RXIuID0/8XkhYXg1nqvjeAX4uIEzt0vGeBz0XE0in2XQn8M+D0zPynwH8FtkfEssw8OzPPAP4j8LXMPKP87AE+BfwDcHFExEwbiogG8OfANZn5kcz8KHAvcMxs3qC0kBnMUvcdZPLq6N/t0PGeA3YAG6fYdw3w2cx8FSAzvwH8FfCZaY55GXAj8BTw8Vn0dCWwLTO/daiQmXdk5jOzOJa0oBnMUh2+DHwmIo7t0PG+CPx+RCw5VIiInwaOzswnDpu7E/iFdztQRCwDPgn8H2CIyZCeqXXArlk8T1p0DGapApn5MnAL8NuH75pqehvHewJ4CPj03LvjV4DhzHwN+AvgwtbAl9RZBrNUjz8CBoCjW2ovAMcd2oiI42n/Syf+C5NL1wFvhf8rEfFzh837GPDoEY5zGfDJiNjD5FnvCcC5bfZwyKPldSRNw2CWKpGZPwBuZzKcD2kCv95yIddvAsNtHu+7wGPAv2wp/3fgpog4CiAiPgn8InDrVMcoy9//HPhQZq7OzNVM/r54psvZfwxsjIizW479a+WiMEkterrdgKR3+EPgs4c2MvPrEfExYFdETADfA/7tDI63BXi4ZftLTJ6Bf7sc72lgQ1mmnsq/Ah7IzDdaancB/y0i3n9YvdXdEfHDMv5WZl4cEZcC10fEzwA/Ar7J5JXZklp4S05JkiriUrYkSRVxKVv6CRQRpwFfPaz8RmaePdX8eerhTuCUw8rXZOZ971UP0kLkUrYkSRVxKVuSpIoYzJIkVcRgliSpIgazJEkVMZglSarI/wdklxiEm+Wf3gAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hy3XF8Zm89zJ"
      },
      "source": [
        "usado para verificar a media dos alunos, a função plotbox\n",
        "\n",
        "\"pegado um ramo de provas na mão e dado uma conferida, distribuição das notas\"\n",
        "a maioria das amostras tiraram entre 450~570\n",
        "linha verde representa a mediana (onde se divide pela metade)\n",
        "bolinhas são pontos dispersos, mas não são maioria "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R3bZo5wu9qdy"
      },
      "source": [
        "Quem tem a maior media? Respondido por questão de ordenação, no caso, comparar entre alguns vários.\n",
        "\n",
        "visualização entre tabelas e numeros diferencia do uso de plot gox, dependendo de qual pergunta deve ser respondida."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W11hRad799Du",
        "outputId": "0c6b7a77-e6df-4720-f79c-0a50f25709fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        }
      },
      "source": [
        "dados[provas].boxplot(grid=True, figsize=(10,8))"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1fa86a7f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "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": "asBhbAPe-c3X"
      },
      "source": [
        "auxilia a medir a dispersão entre os dados. \n",
        "\n",
        "ver sobre MODA, tendencia, mediana, média, etc, mathematics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OKphfwMF-kjE"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9JDJlqYS3Cie"
      },
      "source": [
        "Desafio01: Proporção dos inscritos por idade\n",
        "\n",
        "Desafio02: Descobrir de quais estados são os inscritos com 13 anos\n",
        "\n",
        "Desafio03: Adicionar títulos ao gráfico\n",
        "imersao-de-dados-aula-01.ipynb_\n",
        "\n",
        "Desafio04: Plotar os Histogramas das idades dos treineiro e não treineiros\n",
        "\n",
        "Desafio05: Descobrir similaridade entre bloxpot de prova EN e ES\n",
        "Comparar as distribuições das provas em ingles e espanhol\n",
        "//fazer uma query separando as pessoas de cada linguaguem, e dps verificar bloxpot (verificar se há mesmo shape)\n",
        "\n",
        "Desafio06: Explorar as documentações e visualizações do Matplotlib e Panda\n"
      ]
    }
  ]
}