Aula01.ipynb

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  "metadata": {
    "colab": {
      "name": "Aula01",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNbc6Uicojvk8NvA8kLU0uF",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/eduardonsm/ImersaoDados/blob/main/Aula01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zKb0HwCsLI_j",
        "outputId": "1c9c5945-5558-4c91-f05f-09c2fd568139",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "import pandas as pd\n",
        "fonte = \"https://raw.githubusercontent.com/alura-cursos/imersao-dados-2-2020/master/MICRODADOS_ENEM_2019_SAMPLE_43278.csv\"\n",
        "dados = pd.read_csv(fonte)\n",
        "dados.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>1</td>\n",
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              "      <td>Belém</td>\n",
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              "      <td>1</td>\n",
              "      <td>12</td>\n",
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              "      <td>120.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>560.0</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>B</td>\n",
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              "      <td>C</td>\n",
              "      <td>A</td>\n",
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              "      <th>3</th>\n",
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              "      <td>2019</td>\n",
              "      <td>1507300</td>\n",
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              "      <td>15</td>\n",
              "      <td>PA</td>\n",
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              "      <td>120.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>500.0</td>\n",
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              "      <td>A</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>190001004776</td>\n",
              "      <td>2019</td>\n",
              "      <td>1500800</td>\n",
              "      <td>Ananindeua</td>\n",
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              "      <td>16</td>\n",
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              "      <td>1</td>\n",
              "      <td>1500800.0</td>\n",
              "      <td>Ananindeua</td>\n",
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              "      <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": "1eXg17CIMNT9",
        "outputId": "83f30faf-5e73-4878-e737-0f5d1297d912",
        "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": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KP5UOD0IN0aZ",
        "outputId": "4bb266f8-c62a-4a8f-b9ab-65ade046b482",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "dados[['SG_UF_RESIDENCIA','NO_MUNICIPIO_RESIDENCIA']]"
      ],
      "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>NO_MUNICIPIO_RESIDENCIA</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>PA</td>\n",
              "      <td>Redenção</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>PA</td>\n",
              "      <td>Marabá</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>PA</td>\n",
              "      <td>Belém</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>PA</td>\n",
              "      <td>São Félix do Xingu</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>PA</td>\n",
              "      <td>Ananindeua</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>Montalvânia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127376</th>\n",
              "      <td>BA</td>\n",
              "      <td>Barra da Estiva</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127377</th>\n",
              "      <td>BA</td>\n",
              "      <td>Salvador</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127378</th>\n",
              "      <td>BA</td>\n",
              "      <td>Simões Filho</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127379</th>\n",
              "      <td>MG</td>\n",
              "      <td>Belo Horizonte</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>127380 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       SG_UF_RESIDENCIA NO_MUNICIPIO_RESIDENCIA\n",
              "0                    PA                Redenção\n",
              "1                    PA                  Marabá\n",
              "2                    PA                   Belém\n",
              "3                    PA      São Félix do Xingu\n",
              "4                    PA              Ananindeua\n",
              "...                 ...                     ...\n",
              "127375               MG             Montalvânia\n",
              "127376               BA         Barra da Estiva\n",
              "127377               BA                Salvador\n",
              "127378               BA            Simões Filho\n",
              "127379               MG          Belo Horizonte\n",
              "\n",
              "[127380 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BcCA9FDLPGUW",
        "outputId": "0f98eeb0-5eb0-43ec-952c-22b9b5f3216d",
        "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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MeulAeZ9Pd4l",
        "outputId": "d4ccecb9-5971-4c6a-cb06-92f51aefc360",
        "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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b11-wFOUPu4C",
        "outputId": "2b6b305f-26fb-4e63-bdee-a663b158b639",
        "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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DEMRtkiXQRxu",
        "outputId": "a491bbef-3f58-4be8-efb7-c7b0f160782e",
        "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": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8s_AoYHXEwLA"
      },
      "source": [
        "**Desafio 01 - Porcentagem de inscritos de acordo com a idade**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TR26utddAeU",
        "outputId": "512a311b-f560-48d4-f794-c458940c4c93",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "source": [
        "dados[\"NU_IDADE\"].value_counts().sort_index() * 100 / dados['NU_IDADE'].count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13     0.003140\n",
              "14     0.110692\n",
              "15     1.577171\n",
              "16     6.146962\n",
              "17    16.687078\n",
              "        ...    \n",
              "73     0.001570\n",
              "75     0.001570\n",
              "76     0.000785\n",
              "77     0.000785\n",
              "82     0.000785\n",
              "Name: NU_IDADE, Length: 65, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nWDxdQRvDtLn"
      },
      "source": [
        "**Desafio 02 - descobrir em quais estados estão as pessoas com 13 anos**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EY0K4AA3Qvei",
        "outputId": "da9effdc-8958-4ffd-e104-fffa03e23757",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        }
      },
      "source": [
        "dados.query(\"NU_IDADE == 13\")[\"SG_UF_RESIDENCIA\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "57359     SP\n",
              "95630     MT\n",
              "98197     AP\n",
              "116501    BA\n",
              "Name: SG_UF_RESIDENCIA, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VNU65EV3SmMC",
        "outputId": "ca44fe9c-0728-482c-c935-8268be49a2aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "dados[['SG_UF_RESIDENCIA',  \"NU_IDADE\"]].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SG_UF_RESIDENCIA  NU_IDADE\n",
              "SP                17          5594\n",
              "                  18          3592\n",
              "MG                18          2370\n",
              "                  17          2087\n",
              "SP                19          2072\n",
              "                              ... \n",
              "MS                60             1\n",
              "                  59             1\n",
              "                  14             1\n",
              "MA                66             1\n",
              "AC                14             1\n",
              "Length: 1333, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pk8jEeR_Z-3I",
        "outputId": "ccd060f9-22bd-4b57-d83e-1a0b5d264524",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "dados['NU_IDADE'].hist(bins = 20, figsize = (10,8)).set_title(\"Quantidade de inscritos por idade\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Quantidade de inscritos por idade')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "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": "GhLzKnZsFMlN",
        "outputId": "d79871ab-00bb-49a0-e3e2-ff20a412a491",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        }
      },
      "source": [
        "idade = len(dados[\"NU_IDADE\"].unique())\n",
        "dados.query(\"IN_TREINEIRO == 1\")[\"NU_IDADE\"].value_counts()"
      ],
      "execution_count": null,
      "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": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "85OErkMgKAnN"
      },
      "source": [
        "\n",
        "**Desafio 04 - Plotar os Histogramas das idades dos do treineiro e não treineiros**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zid3hYDCHmys",
        "outputId": "eb2c261e-3be1-4d34-fbe8-7cbdaaa4ca85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(8,6))\n",
        "dados.query(\"IN_TREINEIRO == 1\")[\"NU_IDADE\"].plot(kind ='hist', title = \"Treneiros\", bins=60)\n",
        "plt.ylabel(\"Quantidade\", size='x-large')\n",
        "plt.xlabel(\"Idade\", size='x-large')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Idade')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "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": "0I-BL3PsLHwE",
        "outputId": "736cc1c2-489f-4f1d-9dfd-a4073a2dc18e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        }
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "dados.query(\"IN_TREINEIRO != 1\")[\"NU_IDADE\"].plot(kind ='hist', title = \"Não treineiros\", bins=60, color = \"green\")\n",
        "plt.ylabel(\"Quantidade\", size='x-large')\n",
        "plt.xlabel(\"Idade\", size='x-large')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Idade')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "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": "duHCyfUGLynw",
        "outputId": "529ae5f3-bd29-4324-ec48-d11d57c9cf62",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "dados[\"NU_NOTA_REDACAO\"].mean() #média"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "571.5700253970197"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ath8T_GCMSW4",
        "outputId": "bfa1dfb7-b7de-43bd-d62c-5fd4e6e91e65",
        "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": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d33rKQqQMiL_",
        "outputId": "df206763-995e-4d25-e78a-968670496d24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 550
        }
      },
      "source": [
        "dados[[\"NU_NOTA_CH\",\"NU_NOTA_MT\"]].hist(bins = 20, figsize = (10,8)) #ciencias humanas e matematica"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f7d11ea87f0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f7d121ce8d0>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x9DisNOSM1Fe",
        "outputId": "2f0613df-94e3-4e95-e330-7f754bc560a9",
        "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": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XLADaVMbR1VE"
      },
      "source": [
        "**Desafio 05 - Comparar as notas de inglês e espanhol**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UlpOXoFlOL_x",
        "outputId": "25f81da7-9ca2-4101-8604-50804720e32e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "ingles = dados.query(\"TP_LINGUA == 0\")[\"NU_NOTA_LC\"]\n",
        "espanhol = dados.query(\"TP_LINGUA == 1\")[\"NU_NOTA_LC\"]\n",
        "\n",
        "ingles.hist(bins = 20, figsize = (10,6))\n",
        "espanhol.hist(bins = 20, figsize = (10,6))\n",
        "\n",
        "plt.legend([\"Inglês\" , \"Espanhol\"])\n",
        "plt.title(\"Notas de inglês e espanhol\")\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(12,8), sharey = True, constrained_layout = True)\n",
        "fig.suptitle(\"Distribuição das notas separadas por inglês e espanhol\")\n",
        "ingles.plot.box(ax = axes[0], ylabel = \"Notas\", label = \"Inglês\", grid = True, color = \"green\")\n",
        "espanhol.plot.box(ax = axes[1], label = \"Espanhol\", grid = True, color = \"blue\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d0efe85c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R-Dp-B8WVFpy"
      },
      "source": [
        "**Desafio 06 - Explorar a documentações e visualizações com matplotlib ou pandas e gerar novas visualizações**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XKnKUbvR9Wn",
        "outputId": "b3e92724-844b-436f-ee75-7e6a2c19284e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        }
      },
      "source": [
        "dados.query(\"SG_UF_RESIDENCIA == 'PB'\")[\"NO_MUNICIPIO_RESIDENCIA\"].value_counts().sort_values(ascending=False)[:10].plot(kind = 'bar', figsize=(10, 8))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f7d0f09d048>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        },
        {
          "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": "4hnxA3lyVP2I"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
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}