AnaliseOlimpiadas.ipynb

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  "metadata": {
    "colab": {
      "name": "Projeto_AnáliseELimpeza_TA.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d1FPM1UWXJxC"
      },
      "source": [
        "# Projetinho 1 TA - Análise e Limpeza de Dados\n",
        "**Escrito por:** Letícia Agudo Falconer\n",
        "\n",
        "Esse projeto tem como objetivo colocar em prática os conceitos vistos no Turing Academy até o momento, utilizando o dataset apresentado a seguir para encontrar possíveis padrões entre os ganhadores de medalhas das edições passadas das Olimpíadas.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ibc8Xe91bLP4"
      },
      "source": [
        "##Importação das Bibliotecas:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w513JOdMbTaR"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "akCGLmOfZGEe"
      },
      "source": [
        "##Sobre o Dataset:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mEFI_NWarnj"
      },
      "source": [
        "O dataset que será utilizado traz informações à respeito dos atletas que participaram dos jogos olimpicos passados\n",
        "\n",
        "**Colunas:**\n",
        "1. **Athlete_ID** - Um número de identificação único de cada atleta\n",
        "2. **Name** - Nome do atleta\n",
        "3. **Sex** - Gênero do atleta: M (masculino) ou F (feminino)\n",
        "4. **Age** - Idade\n",
        "5. **Height** - Altura em centímetros\n",
        "6. **Weight** - Peso em kg\n",
        "7. **Team** - Nome do time ao qual o atleta pertence\n",
        "8. **NOC** - Nome do comitê olímpico nacional ao qual o atleta pertence, sempre\n",
        "será um código de 3 letras (BRA para Brasil, USA para Estados Unidos, etc.)\n",
        "9. **Games** - Ano e época dos jogos\n",
        "10. **Year** - Ano da edição que o atleta participou\n",
        "11. **Season** - Estação na qual ocorreu os jogos Summer (verão) ou Winter (inverno) \n",
        "12. **City** - Cidade onde ocorreu a edição dos jogos\n",
        "13. **Sport** - Esporte do atleta\n",
        "14. **Event** - Especificação a respeito da categoria do esporte (Ex. Futebol\n",
        "masculino, vôlei feminino, corrida 500m, etc.)\n",
        "15. **Medal** - Medalha ganha pelo atleta: Gold (ouro), Silver (prata), Bronze, ou NA\n",
        "(nenhuma medalha)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ZKOfJKWa7V1"
      },
      "source": [
        "Em primeiro lugar, vamos importá-lo e definir a coluna \"ID\" como index do dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZG3ePpEXHF4"
      },
      "source": [
        "jogos = pd.read_csv(\"df_athlete_events.csv\") \n",
        "jogos.set_index(\"Athlete_ID\", inplace=True)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGfjbiQ9cLJj"
      },
      "source": [
        "Usando as funções .head() e .info(), vamos realizar algumas observações iniciais"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 376
        },
        "id": "cAssQrg3c6fG",
        "outputId": "5d96a892-fef9-4ec2-e325-2c20a3c4039c"
      },
      "source": [
        "jogos.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Team</th>\n",
              "      <th>NOC</th>\n",
              "      <th>Games</th>\n",
              "      <th>Year</th>\n",
              "      <th>Season</th>\n",
              "      <th>City</th>\n",
              "      <th>Sport</th>\n",
              "      <th>Event</th>\n",
              "      <th>Medal</th>\n",
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              "    <tr>\n",
              "      <th>Athlete_ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Edgar Lindenau Aabye</td>\n",
              "      <td>M</td>\n",
              "      <td>34.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Denmark/Sweden</td>\n",
              "      <td>DEN</td>\n",
              "      <td>1900 Summer</td>\n",
              "      <td>1900</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Paris</td>\n",
              "      <td>Tug-Of-War</td>\n",
              "      <td>Tug-Of-War Men's Tug-Of-War</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Per Knut Aaland</td>\n",
              "      <td>M</td>\n",
              "      <td>31.0</td>\n",
              "      <td>188.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA</td>\n",
              "      <td>1992 Winter</td>\n",
              "      <td>1992</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Albertville</td>\n",
              "      <td>Cross Country Skiing</td>\n",
              "      <td>Cross Country Skiing Men's 10/15 kilometres Pu...</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Arvo Ossian Aaltonen</td>\n",
              "      <td>M</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Finland</td>\n",
              "      <td>FIN</td>\n",
              "      <td>1920 Summer</td>\n",
              "      <td>1920</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Antwerpen</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>Swimming Men's 200 metres Breaststroke</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Arvo Ossian Aaltonen</td>\n",
              "      <td>M</td>\n",
              "      <td>30.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Finland</td>\n",
              "      <td>FIN</td>\n",
              "      <td>1920 Summer</td>\n",
              "      <td>1920</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Antwerpen</td>\n",
              "      <td>Swimming</td>\n",
              "      <td>Swimming Men's 400 metres Breaststroke</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Juhamatti Tapio Aaltonen</td>\n",
              "      <td>M</td>\n",
              "      <td>28.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>Finland</td>\n",
              "      <td>FIN</td>\n",
              "      <td>2014 Winter</td>\n",
              "      <td>2014</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Sochi</td>\n",
              "      <td>Ice Hockey</td>\n",
              "      <td>Ice Hockey Men's Ice Hockey</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                Name  ...   Medal\n",
              "Athlete_ID                            ...        \n",
              "4               Edgar Lindenau Aabye  ...    Gold\n",
              "6                    Per Knut Aaland  ...     NaN\n",
              "15              Arvo Ossian Aaltonen  ...  Bronze\n",
              "15              Arvo Ossian Aaltonen  ...  Bronze\n",
              "16          Juhamatti Tapio Aaltonen  ...  Bronze\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JFDFuc2DdLW5",
        "outputId": "49b2c740-005e-439b-eebe-1b2fdb5eb466"
      },
      "source": [
        "jogos.info()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 45567 entries, 4 to 135563\n",
            "Data columns (total 14 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   Name    45567 non-null  object \n",
            " 1   Sex     45567 non-null  object \n",
            " 2   Age     44594 non-null  float64\n",
            " 3   Height  35547 non-null  float64\n",
            " 4   Weight  34926 non-null  object \n",
            " 5   Team    45567 non-null  object \n",
            " 6   NOC     45567 non-null  object \n",
            " 7   Games   45567 non-null  object \n",
            " 8   Year    45567 non-null  int64  \n",
            " 9   Season  45567 non-null  object \n",
            " 10  City    45567 non-null  object \n",
            " 11  Sport   45567 non-null  object \n",
            " 12  Event   45567 non-null  object \n",
            " 13  Medal   39804 non-null  object \n",
            "dtypes: float64(2), int64(1), object(11)\n",
            "memory usage: 5.2+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oo1LJoiCd7Qj"
      },
      "source": [
        "- São 45567 entradas no total, havendo muitos dados faltantes em diversas colunas\n",
        "- A maioria de nossos dados são categóricos (Dtype = object). Assim, é necessário checar se todos estes estão no formato correto, mas vamos analizar isso na próxima etapa do projeto."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZa-QlNYfRGw"
      },
      "source": [
        "#Limpeza dos dados"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KH1ikJNQfYCB"
      },
      "source": [
        "##Dados Categóricos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HaGfCTSlfbuN"
      },
      "source": [
        "Primeiro vamos utilizar o método .describe()."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaUhVaClhFKv"
      },
      "source": [
        ""
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "q_vcbqamfUE4",
        "outputId": "a0d04467-4488-40dc-a9a0-b230487ae0d0"
      },
      "source": [
        "jogos.describe(include=[\"object\"])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Team</th>\n",
              "      <th>NOC</th>\n",
              "      <th>Games</th>\n",
              "      <th>Season</th>\n",
              "      <th>City</th>\n",
              "      <th>Sport</th>\n",
              "      <th>Event</th>\n",
              "      <th>Medal</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>45567</td>\n",
              "      <td>45567</td>\n",
              "      <td>34926</td>\n",
              "      <td>45567</td>\n",
              "      <td>45567</td>\n",
              "      <td>45567</td>\n",
              "      <td>45567</td>\n",
              "      <td>45567</td>\n",
              "      <td>45567</td>\n",
              "      <td>45567</td>\n",
              "      <td>39804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>32905</td>\n",
              "      <td>6</td>\n",
              "      <td>188</td>\n",
              "      <td>602</td>\n",
              "      <td>201</td>\n",
              "      <td>51</td>\n",
              "      <td>5</td>\n",
              "      <td>43</td>\n",
              "      <td>66</td>\n",
              "      <td>760</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>Michael Fred Phelps, II</td>\n",
              "      <td>M</td>\n",
              "      <td>70.0</td>\n",
              "      <td>United States</td>\n",
              "      <td>USA</td>\n",
              "      <td>2008 Summer</td>\n",
              "      <td>Summer</td>\n",
              "      <td>London</td>\n",
              "      <td>Athletics</td>\n",
              "      <td>Football Men's Football</td>\n",
              "      <td>Gold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>28</td>\n",
              "      <td>31664</td>\n",
              "      <td>1443</td>\n",
              "      <td>5515</td>\n",
              "      <td>5957</td>\n",
              "      <td>2321</td>\n",
              "      <td>38607</td>\n",
              "      <td>4091</td>\n",
              "      <td>4811</td>\n",
              "      <td>1379</td>\n",
              "      <td>13320</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           Name    Sex  ...                    Event  Medal\n",
              "count                     45567  45567  ...                    45567  39804\n",
              "unique                    32905      6  ...                      760      4\n",
              "top     Michael Fred Phelps, II      M  ...  Football Men's Football   Gold\n",
              "freq                         28  31664  ...                     1379  13320\n",
              "\n",
              "[4 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dgpDn17_gGk0"
      },
      "source": [
        "A princípio, podemos concluir que:\n",
        "- Temos um número maior de IDs do que de \"Name\", mas isso não será importante na análise desse dataset\n",
        "- Weight deveria ser do data type float;\n",
        "- As colunas Sex e Season deveriam possuir apenas 2 classificações diferentes.\n",
        "- Devemos checar as categorias dos outros dados\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xOE5-K6Ilf95"
      },
      "source": [
        "###Coluna \"Sex\":"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D1VavI-vnfZ8"
      },
      "source": [
        "Observando os diferentes dados de cada categoria:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXrDC3xVlwkv",
        "outputId": "c7ef3daf-a9a5-46e9-ffd1-b8c86d376404"
      },
      "source": [
        "print(jogos[\"Sex\"].unique())"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['M' 'MALE' 'FEMALE' 'F' 'Vic vai te dar um socao' 'P&D >> Comunidade']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JgQv7x0Qnk2r"
      },
      "source": [
        "Podemos perceber que \"MALE\" deveria ser tratado como \"M\", assim como \"FEMALE\" deveria ser apenas \"F\". Além disso, 'Vic vai te dar um socao' e 'P&D >> Comunidade' não deveriam estar nessa coluna. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YUYx1iBwoDHy"
      },
      "source": [
        "Para os dados \"M\" e \"F\":"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QCKijdUYoHd1",
        "outputId": "e8103e81-efac-4446-95e7-214d740e04cd"
      },
      "source": [
        "# Substituindo \"MALE\" por \"M\".\n",
        "jogos[\"Sex\"].replace(\"MALE\", \"M\", inplace=True)\n",
        "\n",
        "# Substituindo \"FEMALE\" por \"F\".\n",
        "jogos[\"Sex\"].replace(\"FEMALE\", \"F\", inplace=True)\n",
        "\n",
        "# Olhando novamente os dados.\n",
        "print(jogos[\"Sex\"].unique())"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['M' 'F' 'Vic vai te dar um socao' 'P&D >> Comunidade']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8LJOuH6omqs"
      },
      "source": [
        "Para decidir o que faremos com 'Vic vai te dar um socao' e 'P&D >> Comunidade', vamos ver quantos desses dados existem:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qsyf8KxVof6P",
        "outputId": "2fb80177-a106-4df7-a3ae-ea9407b7cd7b"
      },
      "source": [
        "print(\"Número de 'Vic vai te dar um socao': \", len(jogos[jogos['Sex'] == 'Vic vai te dar um socao']))\n",
        "print(\"Número de 'P&D >> Comunidade': \", len(jogos[jogos['Sex'] == 'P&D >> Comunidade']))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Número de 'Vic vai te dar um socao':  312\n",
            "Número de 'P&D >> Comunidade':  333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kotOhr_xpf3N"
      },
      "source": [
        "Temos duas opções: deletar estes dados, ou substituí-los pela moda. Como a quantidade de dados é pequena em comparação com a total, vamos neste caso optar pela exclusão."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQQ5HS3Bp4IH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9a110eb-b0dd-4ce2-b4fa-18d37ce70610"
      },
      "source": [
        "Vic = jogos[jogos[\"Sex\"] == 'Vic vai te dar um socao'].index\n",
        "jogos.drop(Vic, inplace=True)\n",
        "\n",
        "comunas = jogos[jogos[\"Sex\"] == 'P&D >> Comunidade'].index\n",
        "jogos.drop(comunas, inplace=True)\n",
        "\n",
        "# Olhando novamente os dados.\n",
        "print(jogos[\"Sex\"].unique())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['M' 'F']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Zxb4WemreWY"
      },
      "source": [
        "### Coluna \"Season\":"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QUAL-VWkmY-l",
        "outputId": "1422db92-6e62-4b12-84d4-436a2d0bbabf"
      },
      "source": [
        "print(jogos[\"Season\"].unique())"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Summer' 'Winter' '#ForaBolsonaro' 'Ele não' 'Ele nunca']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ynL-rxgwrn3n"
      },
      "source": [
        "Como essa categoria deveria se tratar apenas de \"Summer\" e \"Winter\", vamos ver a quantidade dos dados diferentes, para decidir o que faremos:\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HH_-rB2fr-PN",
        "outputId": "8f2b9d1f-d8d9-4337-a1f2-e8303d1af633"
      },
      "source": [
        "print(\"Número de '#ForaBolsonaro': \", len(jogos[jogos['Season'] == '#ForaBolsonaro']))\n",
        "print(\"Número de 'Ele não': \", len(jogos[jogos['Season'] == 'Ele não']))\n",
        "print(\"Número de 'Ele nunca': \", len(jogos[jogos['Season'] == 'Ele nunca']))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Número de '#ForaBolsonaro':  70\n",
            "Número de 'Ele não':  49\n",
            "Número de 'Ele nunca':  28\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C7rxq2AFu1kX"
      },
      "source": [
        "Como temos quantidades pequenas de dados, vamos simplesmente deletá-las."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XrW2K79Mu9sf",
        "outputId": "588289c5-bfa7-4183-fa65-874933ee69d0"
      },
      "source": [
        "fora = jogos[jogos[\"Season\"] == '#ForaBolsonaro'].index\n",
        "jogos.drop(fora, inplace=True)\n",
        "\n",
        "nao = jogos[jogos[\"Season\"] == 'Ele não'].index\n",
        "jogos.drop(nao, inplace=True)\n",
        "\n",
        "nunca = jogos[jogos[\"Season\"] == 'Ele nunca'].index\n",
        "jogos.drop(nunca, inplace=True)\n",
        "\n",
        "# Olhando novamente os dados.\n",
        "print(jogos[\"Season\"].unique())"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Summer' 'Winter']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UaLKESVLvbaa"
      },
      "source": [
        "###Coluna \"Weight\":"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZVkyvOjOhy_Z"
      },
      "source": [
        "Iremos transformar essa coluna em um float."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4faLRILXvjGF",
        "outputId": "189c32d2-d5bb-4e0b-b1ab-e4f70b349d93"
      },
      "source": [
        "jogos[\"Weight\"].unique()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, '75.0', '85.0', '64.0', '90.0', '87.0', '49.0', 'hsuahusahu',\n",
              "       '76.0', '83.0', '86.0', '82.0', '55.0', '67.0', '60.0', '79.0',\n",
              "       '96.0', '97.0', '70.0', '88.0', '56.0', '80.0', '59.0', '91.0',\n",
              "       '72.0', '62.0', '63.0', '130.0', '104.0', '73.0', '84.0', '125.0',\n",
              "       '50.0', '102.0', '48.0', '58.0', '78.0', '40.0', '68.0', '92.0',\n",
              "       '61.0', '95.0', '89.0', '65.0', '93.0', '77.0', '46.0', '81.0',\n",
              "       '52.0', '54.0', '57.0', '69.0', '99.0', '69.5', '51.0', '120.0',\n",
              "       '74.0', '110.0', '100.0', '66.0', '47.0', '116.0', '107.0', '53.0',\n",
              "       '105.0', '109.0', '156.0', '71.0', '98.0', '160.0', '106.0',\n",
              "       '146.0', '44.0', '138.0', '141.0', '94.0', '118.0', '101.0',\n",
              "       '39.0', '32.0', '112.0', '115.0', '74.5', '121.0', '135.0',\n",
              "       '111.0', '43.0', '36.0', '67.5', '45.0', '114.0', '132.0', '65.5',\n",
              "       '42.0', '59.5', '127.5', '108.0', '35.0', '117.0', '103.0', '58.5',\n",
              "       '214.0', '113.0', '146.5', '116.5', '140.0', '38.0', '101.5',\n",
              "       '154.0', '57.5', '136.0', '127.0', '102.5', '170.0', '126.0',\n",
              "       '77.3333333333333', '122.0', '55.5', '41.0', '123.0', '34.0',\n",
              "       '150.0', '109.5', '37.0', '70.5', '71.5', '30.0', '76.5', '64.5',\n",
              "       '33.0', '129.0', '137.0', '128.0', '73.5', '54.5', '56.5', '63.5',\n",
              "       '134.0', '147.0', '100.5', '78.5', '82.5', '61.5', '83.5', '77.5',\n",
              "       '53.5', '87.5', '124.0', '72.5', '88.5', '81.5', '89.5', '167.0',\n",
              "       '91.5', '79.5', '68.5', '66.5', '85.5', '108.5', '142.0', '86.5',\n",
              "       '106.5', '60.5', '135.5', '31.0', '148.0', '93.5', '97.5', '103.5',\n",
              "       '175.0', '90.5', '131.0', '145.0', '119.0', '155.0', '139.0',\n",
              "       '52.5', '104.5', '143.0', '105.5', '75.5', '84.5', '92.5', '182.0',\n",
              "       '62.5', '130.5', '28.0', '163.0'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2eJgnPDZ2moy"
      },
      "source": [
        "Podemos perceber que o único dado que não se trata de um float é o 'hsuahusahu', a seguir veremos quantos desse dado existem e lidaremos com o mesmo."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yhSwZUMHzsTm",
        "outputId": "7eedca3a-f505-49c1-a48c-de82e250e643"
      },
      "source": [
        "len(jogos[jogos[\"Weight\"] == 'hsuahusahu'])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "460"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQfQUtvI0UE9"
      },
      "source": [
        "# Substituindo 'hsuahusahu' pela moda.\n",
        "jogos[\"Weight\"].replace('hsuahusahu', jogos['Weight'].mode()[0], inplace=True)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DKsCwJ2c0c4p"
      },
      "source": [
        "# Transformando em um float.\n",
        "jogos[\"Weight\"] = jogos[\"Weight\"].astype(float)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghzzag4K3oz_"
      },
      "source": [
        "### Coluna \"Medal\":"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQ9NNKih3zY8",
        "outputId": "a8224534-dde6-4901-ae15-8c9e294bc46e"
      },
      "source": [
        "jogos[\"Medal\"].unique()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Gold', nan, 'Bronze', 'Silver', 'Nelson'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXNMinzniMek"
      },
      "source": [
        "Podemos perceber que \"Nelson\" não faz parte dessa categoria. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HkI4oBIrkMqL",
        "outputId": "1d5b181a-090a-4224-f59d-9378500577be"
      },
      "source": [
        "len(jogos[jogos[\"Medal\"] == \"Nelson\"])"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "161"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oV1CcStsiWgf"
      },
      "source": [
        "Assim como os outros dados que tratamos, vamos excluir o \"Nelson\", já que sua quantidade não é relevante"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTyyzti6keVi",
        "outputId": "dfa3a06b-c770-4618-f676-658bc8036c4b"
      },
      "source": [
        "fora = jogos[jogos[\"Medal\"] == 'Nelson'].index\n",
        "jogos.drop(fora, inplace=True)\n",
        "\n",
        "# Olhando novamente os dados.\n",
        "print(jogos[\"Medal\"].unique())"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Gold' nan 'Bronze' 'Silver']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aYw254CVjGE_"
      },
      "source": [
        "###Arrumando a coluna \"City\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3mM52voWk5wt",
        "outputId": "89fc8a2a-aeb0-45cf-a3e2-9fd1636494eb"
      },
      "source": [
        "print(jogos[\"City\"].unique())"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Paris' 'Albertville' 'Antwerpen' 'Sochi' 'London' 'Helsinki'\n",
            " 'Lillehammer' 'Salt Lake City' 'Torino' 'Beijing' 'Seoul' 'Atlanta'\n",
            " 'Oslo' 'Squaw Valley' 'Stockholm' 'Melbourne' 'Rio de Janeiro' 'Sydney'\n",
            " 'Athina' 'Moskva' 'Los Angeles' 'Barcelona' 'Berlin' 'Tokyo' 'Roma'\n",
            " 'Munich' 'Chamonix' 'St. Louis' 'Sankt Moritz' 'Calgary' 'Grenoble'\n",
            " 'Montreal' 'Nagano' 'Vancouver' 'Amsterdam' 'Mexico City' 'Sapporo'\n",
            " 'Lake Placid' 'Sarajevo' 'Garmisch-Partenkirchen' \"Cortina d'Ampezzo\"\n",
            " 'Innsbruck' '######']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vURAIp48imWu"
      },
      "source": [
        "Para facilitar a leitura, iremos transformar a cidade categorizada como \"######\" para um dado do tipo NaN."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UuoJyovCjLrx",
        "outputId": "cbe63831-cdcf-4e07-c817-14bc23bbb628"
      },
      "source": [
        "jogos[\"City\"].replace(\"######\", np.nan, inplace = True)\n",
        "\n",
        "# Olhando novamente os dados.\n",
        "print(jogos[\"City\"].unique())"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Paris' 'Albertville' 'Antwerpen' 'Sochi' 'London' 'Helsinki'\n",
            " 'Lillehammer' 'Salt Lake City' 'Torino' 'Beijing' 'Seoul' 'Atlanta'\n",
            " 'Oslo' 'Squaw Valley' 'Stockholm' 'Melbourne' 'Rio de Janeiro' 'Sydney'\n",
            " 'Athina' 'Moskva' 'Los Angeles' 'Barcelona' 'Berlin' 'Tokyo' 'Roma'\n",
            " 'Munich' 'Chamonix' 'St. Louis' 'Sankt Moritz' 'Calgary' 'Grenoble'\n",
            " 'Montreal' 'Nagano' 'Vancouver' 'Amsterdam' 'Mexico City' 'Sapporo'\n",
            " 'Lake Placid' 'Sarajevo' 'Garmisch-Partenkirchen' \"Cortina d'Ampezzo\"\n",
            " 'Innsbruck' nan]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KQwRrtDw887Q"
      },
      "source": [
        "##Dados Numéricos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x4-WYAasi-Y6"
      },
      "source": [
        "O método describe() nos dá uma visão geral, e olhando para os valores de mínimo e máximo de cada dado, já podemos tirar algumas conclusões."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "mFZg1aOJaYBV",
        "outputId": "7985df25-f178-41f3-d868-0cd23f385c13"
      },
      "source": [
        "jogos.describe()"
      ],
      "execution_count": 22,
      "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>Age</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>42715.000000</td>\n",
              "      <td>3.389400e+04</td>\n",
              "      <td>33299.000000</td>\n",
              "      <td>43681.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>25.872457</td>\n",
              "      <td>9.565149e+02</td>\n",
              "      <td>73.700256</td>\n",
              "      <td>1971.864243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.969756</td>\n",
              "      <td>3.379120e+04</td>\n",
              "      <td>14.905732</td>\n",
              "      <td>95.099006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.320000e+02</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>-2992.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>22.000000</td>\n",
              "      <td>1.700000e+02</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>1952.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>1.780000e+02</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>1984.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>1.850000e+02</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>2002.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>73.000000</td>\n",
              "      <td>1.976779e+06</td>\n",
              "      <td>214.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Age        Height        Weight          Year\n",
              "count  42715.000000  3.389400e+04  33299.000000  43681.000000\n",
              "mean      25.872457  9.565149e+02     73.700256   1971.864243\n",
              "std        5.969756  3.379120e+04     14.905732     95.099006\n",
              "min       10.000000  1.320000e+02     28.000000  -2992.000000\n",
              "25%       22.000000  1.700000e+02     63.000000   1952.000000\n",
              "50%       25.000000  1.780000e+02     72.000000   1984.000000\n",
              "75%       29.000000  1.850000e+02     82.000000   2002.000000\n",
              "max       73.000000  1.976779e+06    214.000000   2016.000000"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7GAzO8qhjVO1"
      },
      "source": [
        "###Coluna \"Year\":"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VdFHhACWjZTN"
      },
      "source": [
        "Plotando um gráfico com os anos que apareceram no dataset e suas respectivas quantidades:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ZFXBDPEybr2_",
        "outputId": "a83b1d4f-5c63-450c-a062-3b54c3e3a674"
      },
      "source": [
        "plt.figure(figsize=(30,10))\n",
        "sns.countplot(x = 'Year', data = jogos, palette = \"RdYlBu\") \n",
        "plt.xlabel(\"Anos\")\n",
        "plt.ylabel(\"Contagem\")\n",
        "plt.title(\"Anos das Olimpíadas\", fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tG6voZgSjoWr"
      },
      "source": [
        "É notável que os anos inferiores a 1896 estão incorretos, até por que este foi o ano em que surgiram os jogos olímpicos. Vamos retirá-los"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "ptduUHUVclyZ",
        "outputId": "f90cd620-0fe7-41f3-d468-4a9a6e0c4443"
      },
      "source": [
        "jogos = jogos[jogos['Year'] >= 1896]\n",
        "\n",
        "#vamos plotar novamente o gráfico e ver o que mudou\n",
        "plt.figure(figsize=(18,5))\n",
        "sns.countplot(x = 'Year', data = jogos, palette = \"RdYlBu\") \n",
        "plt.xlabel(\"Anos\")\n",
        "plt.ylabel(\"Contagem\")\n",
        "plt.title(\"Anos das Olimpíadas\", fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uakEdP4-drMf"
      },
      "source": [
        "Podemos perceber uma discrepância entre os anos inferiores a 1994 com seus superiores. Isso ocorre, pois a partir de 1994, os jogos de Verão e Inverno foram colocados em alternância aos anos pares, o que não acontecia antes.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BIG3kKt9klpf"
      },
      "source": [
        "###Coluna \"Height\":"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x89JVjcZm2Yd"
      },
      "source": [
        "Para encontrar os outliers dessa categoria, um gráfico comum seria o boxplot. Contudo, podemos ver abaixo o que ocorre nesse caso, já que os outliers possuem valores extremamente discrepantes, o gáfico fica ilegível"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "ehUL0PCClSj7",
        "outputId": "55a1c3f4-23f3-4390-8fea-0a28d8133ea3"
      },
      "source": [
        "plt.figure(figsize=(30,8))\n",
        "sns.boxplot( jogos[\"Height\"])\n",
        "plt.title(\"Testando o Boxplot\", fontsize=18)\n",
        "plt.xlabel(\"Altura\")\n",
        "plt.show()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D4WYrEDGks21"
      },
      "source": [
        "Assim, vamos visualizar esse dado divindindo-o em duas tabelas, de modo que poderemos ver o que ocorre com alturas muito grandes e muito pequenas."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nkJ99tA8lDJh"
      },
      "source": [
        "Analisando primeiro alturas acima de 210cm:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "UCYSMozZfHec",
        "outputId": "40cbe166-5cf6-4f5d-e23b-ad1f37ff4078"
      },
      "source": [
        "plt.figure(figsize=(30,8))\n",
        "sns.countplot(x = \"Height\", data = jogos[jogos[\"Height\"] > 210], palette = \"RdYlBu\")\n",
        "plt.xlabel(\"Altura\")\n",
        "plt.ylabel(\"Contagem\")\n",
        "plt.title(\"Alturas maiores que 2,10m\", fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JISiDLWlnYQk"
      },
      "source": [
        "Há um salto muito grande de 223cm (que é uma altura possível) para 233157, de modo que todos os valores acima disso devem ser retirados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YqyUnMWunr6U"
      },
      "source": [
        "Para alturas menores que 160cm:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "id": "J4HGhxMKh7pe",
        "outputId": "33a5f961-4d0e-4a25-8467-1076943f882f"
      },
      "source": [
        "plt.figure(figsize=(30,8))\n",
        "sns.countplot(x = \"Height\", data = jogos[jogos[\"Height\"] < 160], palette = \"RdYlBu\")\n",
        "plt.xlabel(\"Altura\")\n",
        "plt.ylabel(\"Contagem\")\n",
        "plt.title(\"Alturas menores que 1,60m\", fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XCUWQQcpnxlH"
      },
      "source": [
        "Apesar de haverem algumas alturas muito pequenas, não vamos retirá-las, já que seus valores são possíveis e existem poucos dados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qWEI7nGoFyC"
      },
      "source": [
        "Arrumando então os dados de altura:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "4YskDg96fZa3",
        "outputId": "45edd92f-cfba-47e5-bb60-765eac8f2936"
      },
      "source": [
        "jogos = jogos[jogos['Height'] <= 223]\n",
        "\n",
        "#vamos plotar novamente o gráfico e ver o que mudou\n",
        "plt.figure(figsize=(18,5))\n",
        "sns.distplot(x=jogos['Height'])\n",
        "plt.xlabel(\"Alturas\")\n",
        "plt.ylabel(\"Densidade\")\n",
        "plt.title('Alturas dos Atletas')\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6CdelU05Yh_F"
      },
      "source": [
        "#Análise de Dados\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hxov8RAilgNk"
      },
      "source": [
        "##Participação mulheres vs homens"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "id": "amtCzH4JloOe",
        "outputId": "014a2ed3-d6a8-430e-f74a-4a05817488a8"
      },
      "source": [
        "plt.figure(figsize=(10,5))\n",
        "sns.countplot(x = 'Sex', data = jogos, palette={\"M\": \"#518cc4\", \"F\": \"#f280b2\"}) \n",
        "plt.xlabel(\"Sexo\")\n",
        "plt.ylabel(\"Contagem\")\n",
        "plt.title(\"Sexos nas Olimpíadas\", fontsize=18)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Sexos nas Olimpíadas')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ris55IxWoRF0"
      },
      "source": [
        "É possível percebermos claramente a maior quantidade de homens nos jogos olímpicos. Para termos uma melhor visualização, vamos ver o que ocorreu ao longo dos anos:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "zi3YvCsbYo2c",
        "outputId": "93df0c03-9031-4891-e162-86392e1983f7"
      },
      "source": [
        "plt.figure(figsize=(18,5))\n",
        "sns.countplot(x = 'Year', hue=\"Sex\", data = jogos, palette={\"M\": \"#518cc4\", \"F\": \"#f280b2\"}) \n",
        "plt.xlabel(\"Idade\")\n",
        "plt.ylabel(\"Contagem\")\n",
        "plt.title(\"Participação de cada sexo ao longo dos anos\", fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0_wR9kL3ojKR"
      },
      "source": [
        "Apesar de o número de homens ser maior que o de mulheres, a diferença entre os dois sexos vem ficando cada vez menor. Contudo, não podemos tirar nenhuma conclusão, já que não sabemos o motivo dessa diferença."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aqDxTbbqpLVX"
      },
      "source": [
        "##Top 5 jogos femininos e masculinos:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "gFXeHnuSfMdP",
        "outputId": "34c60355-d529-4bfe-e20b-04f75c6896c9"
      },
      "source": [
        "mulheres = jogos[jogos[\"Sex\"] == \"F\"]\n",
        "homens = jogos[jogos[\"Sex\"] == \"M\"]\n",
        "\n",
        "fig, ax = plt.subplots(1, 2, figsize=(10,5))\n",
        "fig.suptitle('Top 5 Jogos')\n",
        "plt.subplots_adjust(wspace=1)\n",
        "\n",
        "ax[0].set_title('Femininos')\n",
        "sns.countplot(ax = ax[0], y = \"Sport\", data = mulheres, palette = \"PuRd\",\n",
        "              order=mulheres[\"Sport\"].value_counts().index[:5])\n",
        "\n",
        "ax[1].set_title('Masculinos')\n",
        "sns.countplot(ax = ax[1], y = \"Sport\", data = homens, palette = \"Blues\",\n",
        "              order=homens[\"Sport\"].value_counts().index[:5])\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQ2Ec9RlpoLV"
      },
      "source": [
        "\"Athletics\" é o jogo que mais aparece nas duas categorias. Além disso, há uma participação grande das mulheres em jogos como ginástica e volley, enquanto os homens possuem presença em hockey no gelo e luta."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cO8nOeI_qNec"
      },
      "source": [
        "Temos abaixo uma visão mais geral dos jogos:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "uO9sFpwyx1GK",
        "outputId": "506f697a-64d3-4f21-c60e-d9c2920f9ce0"
      },
      "source": [
        "plt.figure(figsize = (8,10))\n",
        "sns.countplot(y = \"Sport\", hue = \"Sex\", data = jogos, palette={\"M\": \"#518cc4\", \"F\": \"#f280b2\"},order=jogos[\"Sport\"].value_counts().index)\n",
        "plt.ylim(20.5)\n",
        "plt.xlabel(\"Contagem\")\n",
        "plt.ylabel(\"Esporte\")\n",
        "plt.title(\"Participação nos esportes\", fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8NmjO36dqW_Q"
      },
      "source": [
        "###Distribuição de idades nos top 5 jogos:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jTbXKxDf8voR"
      },
      "source": [
        "Separando apenas os dados referentes aos 5 top jogos de cada sexo, temos:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e4_JYg0zn7JA"
      },
      "source": [
        "top5_mulheres = mulheres[\"Sport\"].value_counts(sort=True).index[:5]\n",
        "top5_mulheres = mulheres[mulheres[\"Sport\"].isin(top5_mulheres)]\n",
        "\n",
        "top5_homens = homens[\"Sport\"].value_counts(sort=True).index[:5]\n",
        "top5_homens = homens[homens[\"Sport\"].isin(top5_homens)]"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "aPXSVBdxg1oG",
        "outputId": "6af38c2d-5b43-48ab-9ee8-bff3aa6a35fd"
      },
      "source": [
        "\n",
        "plt.figure(figsize=(18,5))\n",
        "sns.distplot(top5_homens[\"Age\"], color = \"#518cc4\") \n",
        "sns.distplot(top5_mulheres[\"Age\"], color = \"#f280b2\")\n",
        "plt.xlabel(\"Idade\")\n",
        "plt.ylabel(\"Densidade\")\n",
        "plt.title(\"Idades dos top 5 jogos\", fontsize=18) \n",
        "plt.show()"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G7y2_sMIqcuy"
      },
      "source": [
        "O gráfico acima nos mostra que existem números maiores de mulheres mais novas participando das Olimpíadas do que homens. Isso pode ser explicado já que esportes como a ginástica, que possuem grande participação feminina, requerem uma faixa etária de competidoras muito mais novas do que outros esportes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZGJ01bR2rLrg"
      },
      "source": [
        "##Medalhas por país"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "KHB8iRRiuJRm",
        "outputId": "e83485db-9f42-48d2-d8ad-bc9dcc83c72c"
      },
      "source": [
        "plt.figure(figsize = (8,10))\n",
        "sns.countplot(y = \"NOC\", hue = \"Medal\", data = jogos, hue_order = [\"Gold\", \"Silver\", \"Bronze\"], \n",
        "              palette = {\"Gold\": \"#ffcb78\", \"Silver\": \"#c4c4c4\", \"Bronze\": \"#de9273\"}, \n",
        "              order=jogos[\"NOC\"].value_counts().index)\n",
        "plt.ylim(15.5)\n",
        "plt.xlabel(\"Contagem\")\n",
        "plt.ylabel(\"País\")\n",
        "plt.title(\"Medalhas por País\", fontsize=18)\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzvMA1uX88Dv"
      },
      "source": [
        "Há um destaque muito grande para o número de medalhas acumuladas ao longo dos anos pelos Estados Unidos, principalmente as de ouro."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YSko1kiVsiIF"
      },
      "source": [
        "###Jogos de Verão vs Inverno"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x6o8hxCR9VbR"
      },
      "source": [
        "Por se tratarem de jogos completmante diferentes, é importante a análise dos jogos de Verão e Inverno separadamente"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "BilhtAyIrx7c",
        "outputId": "838ccf9c-7452-4190-f6c1-4bcc076bb887"
      },
      "source": [
        "fig, ax = plt.subplots(1, 2, figsize=(10, 5))\n",
        "fig.suptitle('Medalhas por País')\n",
        "plt.subplots_adjust(wspace=1)\n",
        "\n",
        "ax[0].set_title('Jogos de Inverno')\n",
        "sns.countplot(ax = ax[0], y = \"NOC\", data = jogos[jogos[\"Season\"] == \"Winter\"], palette=\"Blues\", \n",
        "              order=jogos[jogos[\"Season\"] == \"Winter\"][\"NOC\"].value_counts().index[:8])\n",
        "\n",
        "ax[1].set_title('Jogos de Verão')\n",
        "sns.countplot(ax = ax[1], y = \"NOC\", data = jogos[jogos[\"Season\"] == \"Summer\"], palette=\"YlOrRd\",\n",
        "              order=jogos[jogos[\"Season\"] == \"Summer\"][\"NOC\"].value_counts().index[:8])\n",
        "\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kzC9gFHv9hYY"
      },
      "source": [
        "O único país que permanece em destaque em ambas as Olimpíadas é os Estados Unidos. Já os outros são completamente diferentes, e podemos perceber que na de inverno, se destacam países com essa estação forte e culturas que valorizam os jogos nela praticados."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cLpL4DzMyWvk"
      },
      "source": [
        "##Competidores com mais medalhas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "UKP0J71JvdlI",
        "outputId": "1fc5026c-2347-468d-e9f5-f92588dc9819"
      },
      "source": [
        "fig, ax = plt.subplots(2, sharex=True, figsize=(5,10))\n",
        "fig.suptitle('Top Medalhistas')\n",
        "sns.set_style(\"whitegrid\")\n",
        "\n",
        "ax[0].set_title('Jogos de Inverno')\n",
        "sns.countplot(ax = ax[0], y = \"Name\", data = jogos[jogos[\"Season\"] == \"Winter\"], palette=\"Blues\", \n",
        "              order=jogos[jogos[\"Season\"] == \"Winter\"][\"Name\"].value_counts().index[:8])\n",
        "\n",
        "ax[1].set_title('Jogos de Verão')\n",
        "sns.countplot(ax = ax[1], y = \"Name\", data = jogos[jogos[\"Season\"] == \"Summer\"], palette=\"YlOrRd\", \n",
        "              order=jogos[jogos[\"Season\"] == \"Summer\"][\"Name\"].value_counts().index[:8])\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fHN_EHv65Lu7"
      },
      "source": [
        "#Conclusão"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z1-PijLM5R2T"
      },
      "source": [
        "Com esse projeto, foi possível colocar em prática os conceitos aprendidos nas aulas do Turing Academy de limpeza e análise de dados, de modo que se tornaram possíveis as diversas análises acerca dos jogos olímpicos. Ao comparar algumas métricas interessantes, sejam elas entre os sexos, seus respectivos jogos, ou até mesmo entre as modalidades de Inverno e Verão e os países com mais medalhas, o entendimento desse dataset se tornou eminente."
      ]
    }
  ]
}