Data_Visualization_on_Olympic_Data.ipynb

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  "metadata": {
    "colab": {
      "name": "Data Visualization on Olympic Data.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nasiksami/Python-Projects/blob/main/Data_Visualization_on_Olympic_Data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZK77Chgo1OL"
      },
      "source": [
        "**Dataset Link: https://www.kaggle.com/heesoo37/120-years-of-olympic-history-athletes-and-results**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8elCb1gkQyec"
      },
      "source": [
        "#Importing All the Libraries And Dependencies\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import seaborn as sns\r\n",
        "from matplotlib import pyplot as plt\r\n",
        "import matplotlib\r\n",
        "%matplotlib inline\r\n",
        "import warnings\r\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wqiT8xKOoaVR",
        "outputId": "8c95b015-a10f-4b93-f85e-762fc9b11163"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xr8e7YdwQ-nA"
      },
      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/athlete_events.csv')"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "wsFFSnP2RN2m",
        "outputId": "88c136a1-1f5d-4bde-8671-298215cf162e"
      },
      "source": [
        "df.head(5)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>A Dijiang</td>\n",
              "      <td>M</td>\n",
              "      <td>24.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>China</td>\n",
              "      <td>CHN</td>\n",
              "      <td>1992 Summer</td>\n",
              "      <td>1992</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Men's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>A Lamusi</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>China</td>\n",
              "      <td>CHN</td>\n",
              "      <td>2012 Summer</td>\n",
              "      <td>2012</td>\n",
              "      <td>Summer</td>\n",
              "      <td>London</td>\n",
              "      <td>Judo</td>\n",
              "      <td>Judo Men's Extra-Lightweight</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Gunnar Nielsen Aaby</td>\n",
              "      <td>M</td>\n",
              "      <td>24.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>DEN</td>\n",
              "      <td>1920 Summer</td>\n",
              "      <td>1920</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Antwerpen</td>\n",
              "      <td>Football</td>\n",
              "      <td>Football Men's Football</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\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>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Christine Jacoba Aaftink</td>\n",
              "      <td>F</td>\n",
              "      <td>21.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>NED</td>\n",
              "      <td>1988 Winter</td>\n",
              "      <td>1988</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Calgary</td>\n",
              "      <td>Speed Skating</td>\n",
              "      <td>Speed Skating Women's 500 metres</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ID                      Name  ...                             Event  Medal\n",
              "0   1                 A Dijiang  ...       Basketball Men's Basketball    NaN\n",
              "1   2                  A Lamusi  ...      Judo Men's Extra-Lightweight    NaN\n",
              "2   3       Gunnar Nielsen Aaby  ...           Football Men's Football    NaN\n",
              "3   4      Edgar Lindenau Aabye  ...       Tug-Of-War Men's Tug-Of-War   Gold\n",
              "4   5  Christine Jacoba Aaftink  ...  Speed Skating Women's 500 metres    NaN\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "kMxyG6boRQyY",
        "outputId": "a47b2075-3200-428e-ea0c-a78d349ad2ca"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\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>ID</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>271116.000000</td>\n",
              "      <td>261642.000000</td>\n",
              "      <td>210945.000000</td>\n",
              "      <td>208241.000000</td>\n",
              "      <td>271116.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>68248.954396</td>\n",
              "      <td>25.556898</td>\n",
              "      <td>175.338970</td>\n",
              "      <td>70.702393</td>\n",
              "      <td>1978.378480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>39022.286345</td>\n",
              "      <td>6.393561</td>\n",
              "      <td>10.518462</td>\n",
              "      <td>14.348020</td>\n",
              "      <td>29.877632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>127.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>1896.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>34643.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>168.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>1960.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>68205.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>175.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>1988.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>102097.250000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>183.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>2002.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>135571.000000</td>\n",
              "      <td>97.000000</td>\n",
              "      <td>226.000000</td>\n",
              "      <td>214.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  ID            Age  ...         Weight           Year\n",
              "count  271116.000000  261642.000000  ...  208241.000000  271116.000000\n",
              "mean    68248.954396      25.556898  ...      70.702393    1978.378480\n",
              "std     39022.286345       6.393561  ...      14.348020      29.877632\n",
              "min         1.000000      10.000000  ...      25.000000    1896.000000\n",
              "25%     34643.000000      21.000000  ...      60.000000    1960.000000\n",
              "50%     68205.000000      24.000000  ...      70.000000    1988.000000\n",
              "75%    102097.250000      28.000000  ...      79.000000    2002.000000\n",
              "max    135571.000000      97.000000  ...     214.000000    2016.000000\n",
              "\n",
              "[8 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wShLnyP9RYv2",
        "outputId": "e9c84dbf-b71d-45ef-9edc-4bf6d80a99d7"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 271116 entries, 0 to 271115\n",
            "Data columns (total 15 columns):\n",
            " #   Column  Non-Null Count   Dtype  \n",
            "---  ------  --------------   -----  \n",
            " 0   ID      271116 non-null  int64  \n",
            " 1   Name    271116 non-null  object \n",
            " 2   Sex     271116 non-null  object \n",
            " 3   Age     261642 non-null  float64\n",
            " 4   Height  210945 non-null  float64\n",
            " 5   Weight  208241 non-null  float64\n",
            " 6   Team    271116 non-null  object \n",
            " 7   NOC     271116 non-null  object \n",
            " 8   Games   271116 non-null  object \n",
            " 9   Year    271116 non-null  int64  \n",
            " 10  Season  271116 non-null  object \n",
            " 11  City    271116 non-null  object \n",
            " 12  Sport   271116 non-null  object \n",
            " 13  Event   271116 non-null  object \n",
            " 14  Medal   39783 non-null   object \n",
            "dtypes: float64(3), int64(2), object(10)\n",
            "memory usage: 31.0+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "j4g6tHfDUyFj",
        "outputId": "59066359-5bbb-40cc-fbc6-2b3a564b28f4"
      },
      "source": [
        "corr = df.corr()\r\n",
        "plt.figure(figsize=(15,10))\r\n",
        "sns.heatmap(corr, annot = True, cmap=\"BuPu\")\r\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6865c6ce80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fLo19etzVurT",
        "outputId": "006a1ccc-9993-443b-bbd3-b4bd68d3080a"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID          int64\n",
              "Name       object\n",
              "Sex        object\n",
              "Age       float64\n",
              "Height    float64\n",
              "Weight    float64\n",
              "Team       object\n",
              "NOC        object\n",
              "Games      object\n",
              "Year        int64\n",
              "Season     object\n",
              "City       object\n",
              "Sport      object\n",
              "Event      object\n",
              "Medal      object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "aXyJLpxvWD_q",
        "outputId": "8a3bf9d6-0e28-4c71-c38f-1894143c6438"
      },
      "source": [
        "#showing the null values\r\n",
        "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')\r\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f68613e6d68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DCS5NuVyXG3K"
      },
      "source": [
        "Here we can see there are lots of missing values in the Height, Weight and Medal columns. We can use different methods to handle these missing values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hY93fGJvWzGe",
        "outputId": "641ee553-50da-4315-ecdb-bd38f2228501"
      },
      "source": [
        "# find the null values\r\n",
        "df.isnull().sum()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID             0\n",
              "Name           0\n",
              "Sex            0\n",
              "Age         9474\n",
              "Height     60171\n",
              "Weight     62875\n",
              "Team           0\n",
              "NOC            0\n",
              "Games          0\n",
              "Year           0\n",
              "Season         0\n",
              "City           0\n",
              "Sport          0\n",
              "Event          0\n",
              "Medal     231333\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "185Bl98QXhmF"
      },
      "source": [
        "df['Age'] = df['Age'].fillna(df['Age'].median())"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDfrIy6pX313",
        "outputId": "1cf8f88d-21ac-43f6-b0e9-a436eca0955d"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID             0\n",
              "Name           0\n",
              "Sex            0\n",
              "Age            0\n",
              "Height     60171\n",
              "Weight     62875\n",
              "Team           0\n",
              "NOC            0\n",
              "Games          0\n",
              "Year           0\n",
              "Season         0\n",
              "City           0\n",
              "Sport          0\n",
              "Event          0\n",
              "Medal     231333\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jmb-nr9mX9Xr"
      },
      "source": [
        "df['Height'] = df['Height'].fillna(df['Height'].median())\r\n",
        "df['Weight'] = df['Weight'].fillna(df['Weight'].median())"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O-7FgO3uYzjy"
      },
      "source": [
        "I've used Median method to eleminate the null values because it will sensibly handle the outliers from the dataset, if any outlier is available. Where mean method can lead to a biased output"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NIjkuNYtYj0L",
        "outputId": "70942168-03e3-4d6b-9468-f637371f43ad"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID             0\n",
              "Name           0\n",
              "Sex            0\n",
              "Age            0\n",
              "Height         0\n",
              "Weight         0\n",
              "Team           0\n",
              "NOC            0\n",
              "Games          0\n",
              "Year           0\n",
              "Season         0\n",
              "City           0\n",
              "Sport          0\n",
              "Event          0\n",
              "Medal     231333\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "my_RvtihZiBL",
        "outputId": "cf1dd1c9-67ea-44d1-c629-0cfd64d6ee69"
      },
      "source": [
        "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f68641a75f8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hPMb4oIYmVK"
      },
      "source": [
        "We are not filling in the missing values for Medal because it will affect the outpput of the visualization and give bias results."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "LFA8lVKmYk_T",
        "outputId": "ad929251-1d50-420c-b7fa-577472b7b910"
      },
      "source": [
        "#showing the countplot  to how many athletes were male and how many of them were female.\r\n",
        "sns.set_style('whitegrid')\r\n",
        "sns.countplot(x='Sex',data=df)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f68613c5518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "aIOXNqgKZRR1",
        "outputId": "bda30bf9-b18c-4c4f-9a7d-2109bc75d117"
      },
      "source": [
        "\r\n",
        "sns.set_style('whitegrid')\r\n",
        "sns.countplot(x='Sex',hue='Year',data=df,palette='RdBu_r')\r\n",
        "ax = sns.countplot(x=\"Year\", data=df)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "IbLLNVcua3Vb",
        "outputId": "0f64e4d5-377c-425a-d84e-f379656f10a4"
      },
      "source": [
        "ax = sns.countplot(x=\"Sex\", data=df, palette=\"Set3\")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JM5FWfhbdrHu",
        "outputId": "beec06b0-f863-4b5f-f9fa-f4245fd90032"
      },
      "source": [
        "df.City.unique()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Barcelona', 'London', 'Antwerpen', 'Paris', 'Calgary',\n",
              "       'Albertville', 'Lillehammer', 'Los Angeles', 'Salt Lake City',\n",
              "       'Helsinki', 'Lake Placid', 'Sydney', 'Atlanta', 'Stockholm',\n",
              "       'Sochi', 'Nagano', 'Torino', 'Beijing', 'Rio de Janeiro', 'Athina',\n",
              "       'Squaw Valley', 'Innsbruck', 'Sarajevo', 'Mexico City', 'Munich',\n",
              "       'Seoul', 'Berlin', 'Oslo', \"Cortina d'Ampezzo\", 'Melbourne',\n",
              "       'Roma', 'Amsterdam', 'Montreal', 'Moskva', 'Tokyo', 'Vancouver',\n",
              "       'Grenoble', 'Sapporo', 'Chamonix', 'St. Louis', 'Sankt Moritz',\n",
              "       'Garmisch-Partenkirchen'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hpYB11MCd2ed",
        "outputId": "acded269-5409-4b15-af62-05ddd438195d"
      },
      "source": [
        "df.Medal.unique()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([nan, 'Gold', 'Bronze', 'Silver'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rQIZzzm5dxYs",
        "outputId": "ee4f2bb0-8bfc-4426-ae19-1690fda9ec0a"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID          int64\n",
              "Name       object\n",
              "Sex        object\n",
              "Age       float64\n",
              "Height    float64\n",
              "Weight    float64\n",
              "Team       object\n",
              "NOC        object\n",
              "Games      object\n",
              "Year        int64\n",
              "Season     object\n",
              "City       object\n",
              "Sport      object\n",
              "Event      object\n",
              "Medal      object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "sXqzlwAedz9X",
        "outputId": "da26faf1-16a8-4c35-c7bd-b29e6268b274"
      },
      "source": [
        "\r\n",
        "def groupage(Age):\r\n",
        "    if Age < 18:\r\n",
        "        return \"Junior\"\r\n",
        "    elif Age >=18 and Age < 25:\r\n",
        "        return \"Matured\"\r\n",
        "    else:\r\n",
        "        return \"Senior\"\r\n",
        "      \r\n",
        "groupage = df['Age'].apply(groupage)\r\n",
        "df.insert(10, \"Groupage\", groupage, True)\r\n",
        "df.head(5)\r\n",
        "\r\n"
      ],
      "execution_count": 25,
      "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>ID</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>Groupage</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>0</th>\n",
              "      <td>1</td>\n",
              "      <td>A Dijiang</td>\n",
              "      <td>M</td>\n",
              "      <td>24.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>China</td>\n",
              "      <td>CHN</td>\n",
              "      <td>1992 Summer</td>\n",
              "      <td>1992</td>\n",
              "      <td>Matured</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Barcelona</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Basketball Men's Basketball</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>A Lamusi</td>\n",
              "      <td>M</td>\n",
              "      <td>23.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>China</td>\n",
              "      <td>CHN</td>\n",
              "      <td>2012 Summer</td>\n",
              "      <td>2012</td>\n",
              "      <td>Matured</td>\n",
              "      <td>Summer</td>\n",
              "      <td>London</td>\n",
              "      <td>Judo</td>\n",
              "      <td>Judo Men's Extra-Lightweight</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Gunnar Nielsen Aaby</td>\n",
              "      <td>M</td>\n",
              "      <td>24.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>Denmark</td>\n",
              "      <td>DEN</td>\n",
              "      <td>1920 Summer</td>\n",
              "      <td>1920</td>\n",
              "      <td>Matured</td>\n",
              "      <td>Summer</td>\n",
              "      <td>Antwerpen</td>\n",
              "      <td>Football</td>\n",
              "      <td>Football Men's Football</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Edgar Lindenau Aabye</td>\n",
              "      <td>M</td>\n",
              "      <td>34.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>Denmark/Sweden</td>\n",
              "      <td>DEN</td>\n",
              "      <td>1900 Summer</td>\n",
              "      <td>1900</td>\n",
              "      <td>Senior</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>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Christine Jacoba Aaftink</td>\n",
              "      <td>F</td>\n",
              "      <td>21.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>Netherlands</td>\n",
              "      <td>NED</td>\n",
              "      <td>1988 Winter</td>\n",
              "      <td>1988</td>\n",
              "      <td>Matured</td>\n",
              "      <td>Winter</td>\n",
              "      <td>Calgary</td>\n",
              "      <td>Speed Skating</td>\n",
              "      <td>Speed Skating Women's 500 metres</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ID                      Name  ...                             Event  Medal\n",
              "0   1                 A Dijiang  ...       Basketball Men's Basketball    NaN\n",
              "1   2                  A Lamusi  ...      Judo Men's Extra-Lightweight    NaN\n",
              "2   3       Gunnar Nielsen Aaby  ...           Football Men's Football    NaN\n",
              "3   4      Edgar Lindenau Aabye  ...       Tug-Of-War Men's Tug-Of-War   Gold\n",
              "4   5  Christine Jacoba Aaftink  ...  Speed Skating Women's 500 metres    NaN\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        },
        "id": "2N5_vuu3flEy",
        "outputId": "cfbfad4f-2922-4fcc-e994-a223b2b1a403"
      },
      "source": [
        "%matplotlib inline\r\n",
        "g = sns.catplot(x=\"Sex\", hue=\"Groupage\", col=\"Groupage\", data=df, kind=\"count\", height=5, aspect=1)\r\n",
        "plt.show()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "yaTHxDHNh7wd",
        "outputId": "8f5f006c-fff3-473c-fa65-1500714dba96"
      },
      "source": [
        "ax = sns.countplot(x=\"Medal\", hue=\"Groupage\",data=df, palette=\"Set3\")\r\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "OuWw7qOTiCou",
        "outputId": "27bff832-9af9-4a91-bd20-60bd4591b764"
      },
      "source": [
        "df['Age'].hist(color='green',bins=10,figsize=(8,4))"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f685f1bfc88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n9zYvfQ1lvmD",
        "outputId": "f69ecdcf-5bbd-49af-85c9-d1fe6e809592"
      },
      "source": [
        "{column: len(df[column].unique()) for column in df.select_dtypes('object').columns}"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'City': 42,\n",
              " 'Event': 765,\n",
              " 'Games': 51,\n",
              " 'Groupage': 3,\n",
              " 'Medal': 4,\n",
              " 'NOC': 230,\n",
              " 'Name': 134732,\n",
              " 'Season': 2,\n",
              " 'Sex': 2,\n",
              " 'Sport': 66,\n",
              " 'Team': 1184}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bvuNks-4p3kb"
      },
      "source": [
        "Denmark_Medals = (df[(df.NOC == 'DEN') & \r\n",
        "                    ((df.Medal == 'Gold') | \r\n",
        "                     (df.Medal == 'Silver')|\r\n",
        "                     (df.Medal == 'Bronze'))].loc[:,[\"Year\",\"Medal\"]])\r\n"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "bSdXCEXFqMSr",
        "outputId": "13a61d1c-1d8a-4e2a-9c5f-ea711271026a"
      },
      "source": [
        "sns.set(style=\"darkgrid\")\r\n",
        "plt.figure(figsize=(15, 8))\r\n",
        "sns.countplot(x='Year', data=Denmark_Medals)\r\n",
        "plt.title('Denmark Medals Over the Years')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Denmark Medals Over the Years')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "ftbXNcHjqWJO",
        "outputId": "d6e9892c-08ab-499f-f4c7-baf3b1b08432"
      },
      "source": [
        "ax = sns.countplot(y=\"Sex\", hue=\"Medal\", data=df)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_xO5wWNXuo5y"
      },
      "source": [
        "**Countries participation at Summer Olympic Games**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Ujd8oXSsXBI"
      },
      "source": [
        "countries_participated = df[df.Season==\"Summer\"].groupby([\"Year\"])\\\r\n",
        "[\"NOC\"].value_counts().reset_index(name='Count').sort_values([\"Year\"])\r\n"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 916
        },
        "id": "fjeLeJ3auxln",
        "outputId": "1850b374-54b0-4a81-a170-a8de3d6eedd8"
      },
      "source": [
        "sns.set(style=\"whitegrid\")\r\n",
        "plt.figure(figsize=(20, 15))\r\n",
        "sns.countplot('Year', data=countries_participated)\r\n",
        "plt.title(\"Countries participation at Summer Olympic Game\")"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Countries participation at Summer Olympic Game')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VSzUs18svQca"
      },
      "source": [
        ""
      ],
      "execution_count": 35,
      "outputs": []
    }
  ]
}