Olympic_EDA.ipynb

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  "metadata": {
    "colab": {
      "name": "Olympic EDA.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyM+qpyzIJbKTIofvLUZZDRa",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DataMinati/Super-Notebooks-1K/blob/main/Olympic_EDA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_2LVxej3Uv-z"
      },
      "source": [
        "## AIM OF ANALYSIS:\n",
        "\n",
        "### Analysis of Athletes informations in Tokyo 2021\n",
        "\n",
        "1.Which country competes in olympics the most athletes?\n",
        "\n",
        "2.Which country competes in olympics the least athletes?\n",
        "\n",
        "3.Which Olympic game where the most athletes compete?\n",
        "\n",
        "4.Which Olympic game where the least athletes compete?\n",
        "\n",
        "----------------------------------------------------\n",
        "\n",
        "### Analysis of Teams informations in Tokyo 2021\n",
        "\n",
        "1.How many teams participate in each Olympic game?\n",
        "\n",
        "2.What are the Men teams participate in Football game?\n",
        "\n",
        "3.What are the Women teams participate in Football game?\n",
        "\n",
        "4.What are the Men teams participate in Baskitball game?\n",
        "\n",
        "5.What are the Women teams participate in Baskitball game?\n",
        "\n",
        "6.What are the Men teams participate in Volleyball game?\n",
        "\n",
        "7.What are the Women teams participate in Volleyball game?\n",
        "\n",
        "8.What are the Men teams participate in Handball game?\n",
        "\n",
        "9.What are the Women teams participate in Handball game?\n",
        "\n",
        "10.What are the Men teams participate in Hockey game?\n",
        "\n",
        "11.What are the Women teams participate in Hockey game?\n",
        "\n",
        "------------------------------------------------------\n",
        "\n",
        "### Analysis of Coaches informations in Tokyo 2021\n",
        "\n",
        "1.What are the most participating countries by number of Coaches?\n",
        "\n",
        "2.What are the least participating countries by number of Coaches?\n",
        "\n",
        "3.How many of Coaches participate in each olympic game?\n",
        "\n",
        "4.How many of Coaches by each Country participate in Football game?\n",
        "\n",
        "5.How many of Coaches by each Country participate in Baskitball game?\n",
        "\n",
        "6.How many of Coaches by each Country participate in Volleyball game?\n",
        "\n",
        "7.How many of Coaches by each Country participate in Handball game?\n",
        "\n",
        "8.How many of Coaches by each Country participate in Swimming game?\n",
        "\n",
        "-----------------------------------------------------\n",
        "\n",
        "### Analysis of Athletes by Gender informations in Tokyo 2021\n",
        "\n",
        "1.Which Olympic Games the most participation by females?\n",
        "\n",
        "2.Which Olympic Games the least participation by females?\n",
        "\n",
        "3.Which Olympic Games the most participation by males?\n",
        "\n",
        "4.Which Olympic Games the least participation by males?\n",
        "\n",
        "5.Which Olympic Games the most percentage participation by male?\n",
        "\n",
        "6.Which Olympic Games the most percentage participation by female?\n",
        "\n",
        "7.Which Olympic Games the least percentage participation by female?\n",
        "\n",
        "8.Which Olympic Games the most percentage participation by male?\n",
        "\n",
        "9.Which Olympic Games the least percentage participation by male?\n",
        "\n",
        "10.Which Olympic Games the most participation by both genders?\n",
        "\n",
        "11.Which Olympic Games the least participation by both genders?\n",
        "\n",
        "-----------------------------------------------------\n",
        "\n",
        "### Analysis of Medals by Countriy informations in Tokyo 2021\n",
        "\n",
        "1.Which countries acheved the most gold medals?\n",
        "\n",
        "2.Which countries acheved the least gold medals?\n",
        "\n",
        "3.Which countries acheved the most bronze medals?\n",
        "\n",
        "4.Which countries acheved the least bronze medals?\n",
        "\n",
        "5.Which countries acheved the most total medals?\n",
        "\n",
        "6.Which countries acheved the least total medals?\n",
        "\n",
        "-----------------------------------------------------\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H18nuejQWMqb"
      },
      "source": [
        "### Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r9Gf-Pn0UsxX"
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "plt.rcParams[\"figure.figsize\"] = (10,5)\n",
        "%matplotlib inline"
      ],
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xm-vSN_pWO7V"
      },
      "source": [
        "Athletes = pd.read_csv('https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Tokyo-Olympics/Athletes.csv',error_bad_lines=False)\n",
        "Teams = pd.read_csv('https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Tokyo-Olympics/Teams.csv',encoding = \"ISO-8859-1\")\n",
        "Coaches = pd.read_csv('https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Tokyo-Olympics/Coaches.csv',error_bad_lines=False)\n",
        "Medals = pd.read_csv('https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Tokyo-Olympics/Medals.csv',error_bad_lines=False)\n",
        "Gender = pd.read_csv('https://raw.githubusercontent.com/MainakRepositor/Datasets-/master/Tokyo-Olympics/Gender.csv',error_bad_lines=False)"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfYAMUkMXDXg",
        "outputId": "a5d94771-7932-40e8-d557-3b5098cd64ca"
      },
      "source": [
        "Athletes.shape"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11085, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "i7nISzWdakFD",
        "outputId": "6eb79d48-3334-4955-cb5e-0ddf9b459ea9"
      },
      "source": [
        "Athletes.head()"
      ],
      "execution_count": 80,
      "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>Name</th>\n",
              "      <th>Country</th>\n",
              "      <th>Play</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AALERUD Katrine</td>\n",
              "      <td>Norway</td>\n",
              "      <td>Cycling Road</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ABAD Nestor</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Artistic Gymnastics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ABAGNALE Giovanni</td>\n",
              "      <td>Italy</td>\n",
              "      <td>Rowing</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ABALDE Alberto</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Basketball</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ABALDE Tamara</td>\n",
              "      <td>Spain</td>\n",
              "      <td>Basketball</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Name Country                 Play\n",
              "0    AALERUD Katrine  Norway         Cycling Road\n",
              "1        ABAD Nestor   Spain  Artistic Gymnastics\n",
              "2  ABAGNALE Giovanni   Italy               Rowing\n",
              "3     ABALDE Alberto   Spain           Basketball\n",
              "4      ABALDE Tamara   Spain           Basketball"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sx9mF-pIanK_",
        "outputId": "616e3ebf-a27e-45dc-ee13-7ed86e25bebb"
      },
      "source": [
        "# Country with most number of competitors\n",
        "Athletes.groupby('Country')['Name'].count().sort_values(ascending=False).head(15)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "United States of America      615\n",
              "Japan                         586\n",
              "Australia                     470\n",
              "People's Republic of China    401\n",
              "Germany                       400\n",
              "France                        377\n",
              "Canada                        368\n",
              "Great Britain                 366\n",
              "Italy                         356\n",
              "Spain                         324\n",
              "ROC                           318\n",
              "Brazil                        291\n",
              "Netherlands                   274\n",
              "Republic of Korea             223\n",
              "New Zealand                   202\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "rd4zq_OTavcz",
        "outputId": "7b26e8ea-c0e0-4b43-d407-497d28ae42dd"
      },
      "source": [
        "Athletes.groupby('Country')['Name'].count().sort_values(ascending=False).head(15).plot(legend=False,kind='barh',\n",
        "                                                                        colormap='BuPu_r',\n",
        "                                                                        figsize=(8,6),title='')\n",
        "plt.show()"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h4YGPh0Wa2-1",
        "outputId": "5aa70cd6-9afc-4284-cd27-2ca27984f50a"
      },
      "source": [
        "# Country with least number of competitors\n",
        "Athletes.groupby('Country')['Name'].count().sort_values(ascending=False).tail(15)"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "Marshall Islands                 2\n",
              "Mauritania                       2\n",
              "Bermuda                          2\n",
              "Andorra                          2\n",
              "Vanuatu                          2\n",
              "United Republic of Tanzania      2\n",
              "Tuvalu                           2\n",
              "South Sudan                      2\n",
              "Myanmar                          2\n",
              "Nauru                            2\n",
              "Saint Kitts and Nevis            2\n",
              "Brunei Darussalam                2\n",
              "Lesotho                          2\n",
              "St Vincent and the Grenadines    2\n",
              "Dominica                         2\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nxm84_z7bD6e",
        "outputId": "f2c2b6a1-b5f2-40dd-f5cc-4fab8cad4a1d"
      },
      "source": [
        "# Most participated sport\n",
        "Athletes.groupby('Play')['Name'].count().sort_values(ascending=False).head(15)"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Play\n",
              "Athletics       2068\n",
              "Swimming         743\n",
              "Football         567\n",
              "Rowing           496\n",
              "Hockey           406\n",
              "Judo             373\n",
              "Handball         343\n",
              "Shooting         342\n",
              "Sailing          336\n",
              "Rugby Sevens     283\n",
              "Basketball       280\n",
              "Wrestling        279\n",
              "Volleyball       274\n",
              "Boxing           270\n",
              "Water Polo       269\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "HeH1X2jtbKD5",
        "outputId": "97a1314f-025b-4de1-dbc4-1ff349a46ff5"
      },
      "source": [
        "Athletes.groupby('Play')['Name'].count().sort_values(ascending=False).head(15).plot(legend=False,kind='barh',\n",
        "                                                                        colormap='ocean',\n",
        "                                                                        figsize=(8,6),title='')\n",
        "plt.show()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BNjvovWCbOW0",
        "outputId": "df108cd1-b053-4e95-e3b4-39ccf9371912"
      },
      "source": [
        "# Least athletes competed\n",
        "Athletes.groupby('Play')['Name'].count().sort_values(ascending=False).tail(15)"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Play\n",
              "Artistic Swimming        98\n",
              "Rhythmic Gymnastics      95\n",
              "Beach Volleyball         90\n",
              "Canoe Slalom             78\n",
              "Skateboarding            77\n",
              "Karate                   77\n",
              "Cycling Mountain Bike    74\n",
              "Modern Pentathlon        69\n",
              "3x3 Basketball           62\n",
              "Marathon Swimming        49\n",
              "Cycling BMX Racing       43\n",
              "Surfing                  38\n",
              "Sport Climbing           37\n",
              "Trampoline Gymnastics    31\n",
              "Cycling BMX Freestyle    19\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oCUINTxObVyx",
        "outputId": "0e4886eb-5ad3-4398-eee0-5cfa95e193a4"
      },
      "source": [
        "Teams.shape"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(743, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "6VkoZIHAbZ9m",
        "outputId": "4462fe91-4b33-491f-fcbe-5448f7ae780d"
      },
      "source": [
        "Teams.head()"
      ],
      "execution_count": 88,
      "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>Name</th>\n",
              "      <th>Play</th>\n",
              "      <th>Event</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>3x3 Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>China</td>\n",
              "      <td>3x3 Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>China</td>\n",
              "      <td>3x3 Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>France</td>\n",
              "      <td>3x3 Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Italy</td>\n",
              "      <td>3x3 Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Name            Play  Event\n",
              "0  Belgium  3x3 Basketball    Men\n",
              "1    China  3x3 Basketball    Men\n",
              "2    China  3x3 Basketball  Women\n",
              "3   France  3x3 Basketball  Women\n",
              "4    Italy  3x3 Basketball  Women"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3H0i69FNbbyB",
        "outputId": "53b2c5a4-ada9-4adf-d2f9-892ff1b8e7a3"
      },
      "source": [
        "Teams['Play'].unique()"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['3x3 Basketball', 'Archery', 'Artistic Gymnastics',\n",
              "       'Artistic Swimming', 'Athletics', 'Baseball/Softball',\n",
              "       'Basketball', 'Beach Volleyball', 'Cycling Track', 'Fencing',\n",
              "       'Football', 'Handball', 'Hockey', 'Rhythmic Gymnastics',\n",
              "       'Rugby Sevens', 'Swimming', 'Table Tennis', 'Triathlon',\n",
              "       'Volleyball', 'Water Polo'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ffqcwkHwbdjB",
        "outputId": "2efc64e4-33b6-495c-dd03-df7254b9b230"
      },
      "source": [
        "# Teams in each olympic games\n",
        "Teams.groupby('Play')['Name'].count().sort_values(ascending=False)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Play\n",
              "Swimming               113\n",
              "Athletics               79\n",
              "Cycling Track           64\n",
              "Archery                 53\n",
              "Fencing                 52\n",
              "Table Tennis            48\n",
              "Beach Volleyball        48\n",
              "Artistic Swimming       32\n",
              "Football                28\n",
              "Rugby Sevens            24\n",
              "Artistic Gymnastics     24\n",
              "Hockey                  24\n",
              "Handball                24\n",
              "Volleyball              24\n",
              "Basketball              24\n",
              "Water Polo              22\n",
              "Triathlon               18\n",
              "3x3 Basketball          16\n",
              "Rhythmic Gymnastics     14\n",
              "Baseball/Softball       12\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "m1l-QPKdbhbY",
        "outputId": "cf9ad2d0-c6d7-4610-af99-1e7e6026096c"
      },
      "source": [
        "Teams.groupby('Play')['Name'].count().sort_values(ascending=False).plot(legend=False,kind='barh',\n",
        "                                                                        colormap='Accent_r',\n",
        "                                                                        figsize=(8,6),title='number of teams per play')\n",
        "plt.show()"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "id": "v78Zw1MtbljA",
        "outputId": "e84fcb9b-1f80-4867-945d-363a87abf336"
      },
      "source": [
        "# Men teams in Football\n",
        "Teams[(Teams['Play']=='Football') & (Teams['Event']=='Men')]"
      ],
      "execution_count": 92,
      "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>Name</th>\n",
              "      <th>Play</th>\n",
              "      <th>Event</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>404</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>405</th>\n",
              "      <td>Australia</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>407</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>412</th>\n",
              "      <td>Cï¿½ï¿½te dï¿</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>Egypt</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>414</th>\n",
              "      <td>France</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>415</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>Honduras</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>418</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>420</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>422</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>424</th>\n",
              "      <td>Republic of Korea</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>425</th>\n",
              "      <td>Romania</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>426</th>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>427</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>428</th>\n",
              "      <td>Spain</td>\n",
              "      <td>Football</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Name      Play Event\n",
              "404          Argentina  Football   Men\n",
              "405          Australia  Football   Men\n",
              "407             Brazil  Football   Men\n",
              "412      Cï¿½ï¿½te dï¿  Football   Men\n",
              "413              Egypt  Football   Men\n",
              "414             France  Football   Men\n",
              "415            Germany  Football   Men\n",
              "417           Honduras  Football   Men\n",
              "418              Japan  Football   Men\n",
              "420             Mexico  Football   Men\n",
              "422        New Zealand  Football   Men\n",
              "424  Republic of Korea  Football   Men\n",
              "425            Romania  Football   Men\n",
              "426       Saudi Arabia  Football   Men\n",
              "427       South Africa  Football   Men\n",
              "428              Spain  Football   Men"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "djP2zMbKbq7y",
        "outputId": "8bf4e657-bc2e-4767-d754-9251a6fd6667"
      },
      "source": [
        "# Women teams in Football\n",
        "Teams[(Teams['Play']=='Football') & (Teams['Event']=='Women')]"
      ],
      "execution_count": 93,
      "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>Name</th>\n",
              "      <th>Play</th>\n",
              "      <th>Event</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>406</th>\n",
              "      <td>Australia</td>\n",
              "      <td>Football</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>408</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Football</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>409</th>\n",
              "      <td>Canada</td>\n",
              "      <td>Football</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>410</th>\n",
              "      <td>Chile</td>\n",
              "      <td>Football</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>411</th>\n",
              "      <td>China</td>\n",
              "      <td>Football</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>416</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>Football</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>419</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Football</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>421</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>Football</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>423</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>Football</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>429</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>Football</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>430</th>\n",
              "      <td>United States</td>\n",
              "      <td>Football</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>431</th>\n",
              "      <td>Zambia</td>\n",
              "      <td>Football</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Name      Play  Event\n",
              "406      Australia  Football  Women\n",
              "408         Brazil  Football  Women\n",
              "409         Canada  Football  Women\n",
              "410          Chile  Football  Women\n",
              "411          China  Football  Women\n",
              "416  Great Britain  Football  Women\n",
              "419          Japan  Football  Women\n",
              "421    Netherlands  Football  Women\n",
              "423    New Zealand  Football  Women\n",
              "429         Sweden  Football  Women\n",
              "430  United States  Football  Women\n",
              "431         Zambia  Football  Women"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "uY488pd2bynq",
        "outputId": "48f77f92-9738-47f3-f6bd-b46a9a5ff253"
      },
      "source": [
        "# Men teams in Basketball\n",
        "Teams[(Teams['Play']=='Basketball') & (Teams['Event']=='Men')]"
      ],
      "execution_count": 94,
      "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>Name</th>\n",
              "      <th>Play</th>\n",
              "      <th>Event</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>216</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>217</th>\n",
              "      <td>Australia</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222</th>\n",
              "      <td>Czech Republic</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>223</th>\n",
              "      <td>France</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>225</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>226</th>\n",
              "      <td>Islamic Rep. of Iran</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>227</th>\n",
              "      <td>Italy</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>228</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>230</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>235</th>\n",
              "      <td>Slovenia</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>236</th>\n",
              "      <td>Spain</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>238</th>\n",
              "      <td>United States</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Name        Play Event\n",
              "216             Argentina  Basketball   Men\n",
              "217             Australia  Basketball   Men\n",
              "222        Czech Republic  Basketball   Men\n",
              "223                France  Basketball   Men\n",
              "225               Germany  Basketball   Men\n",
              "226  Islamic Rep. of Iran  Basketball   Men\n",
              "227                 Italy  Basketball   Men\n",
              "228                 Japan  Basketball   Men\n",
              "230               Nigeria  Basketball   Men\n",
              "235              Slovenia  Basketball   Men\n",
              "236                 Spain  Basketball   Men\n",
              "238         United States  Basketball   Men"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "SdNBnPmUb3s2",
        "outputId": "8caa08c4-52d1-4d4f-eed5-7b688f7f61d7"
      },
      "source": [
        "# Women teams in Basketball\n",
        "Teams[(Teams['Play']=='Basketball') & (Teams['Event']=='Women')]"
      ],
      "execution_count": 95,
      "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>Name</th>\n",
              "      <th>Play</th>\n",
              "      <th>Event</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>218</th>\n",
              "      <td>Australia</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>220</th>\n",
              "      <td>Canada</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>221</th>\n",
              "      <td>China</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>224</th>\n",
              "      <td>France</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>229</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>232</th>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>233</th>\n",
              "      <td>Republic of Korea</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>Serbia</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>237</th>\n",
              "      <td>Spain</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>239</th>\n",
              "      <td>United States</td>\n",
              "      <td>Basketball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Name        Play  Event\n",
              "218          Australia  Basketball  Women\n",
              "219            Belgium  Basketball  Women\n",
              "220             Canada  Basketball  Women\n",
              "221              China  Basketball  Women\n",
              "224             France  Basketball  Women\n",
              "229              Japan  Basketball  Women\n",
              "231            Nigeria  Basketball  Women\n",
              "232        Puerto Rico  Basketball  Women\n",
              "233  Republic of Korea  Basketball  Women\n",
              "234             Serbia  Basketball  Women\n",
              "237              Spain  Basketball  Women\n",
              "239      United States  Basketball  Women"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "RtuY5d1cb8T7",
        "outputId": "945f21e3-01d3-4b3f-f9da-3290d278729f"
      },
      "source": [
        "# Men teams in Volleyball\n",
        "Teams[(Teams['Play']=='Volleyball') & (Teams['Event']=='Men')]"
      ],
      "execution_count": 96,
      "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>Name</th>\n",
              "      <th>Play</th>\n",
              "      <th>Event</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>697</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>699</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>701</th>\n",
              "      <td>Canada</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>704</th>\n",
              "      <td>France</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>705</th>\n",
              "      <td>Islamic Rep. of Iran</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>706</th>\n",
              "      <td>Italy</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>708</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>711</th>\n",
              "      <td>Poland</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>713</th>\n",
              "      <td>ROC</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>716</th>\n",
              "      <td>Tunisia</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>718</th>\n",
              "      <td>United States</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>720</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Name        Play Event\n",
              "697             Argentina  Volleyball   Men\n",
              "699                Brazil  Volleyball   Men\n",
              "701                Canada  Volleyball   Men\n",
              "704                France  Volleyball   Men\n",
              "705  Islamic Rep. of Iran  Volleyball   Men\n",
              "706                 Italy  Volleyball   Men\n",
              "708                 Japan  Volleyball   Men\n",
              "711                Poland  Volleyball   Men\n",
              "713                   ROC  Volleyball   Men\n",
              "716               Tunisia  Volleyball   Men\n",
              "718         United States  Volleyball   Men\n",
              "720             Venezuela  Volleyball   Men"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "bIS7aEtScAvJ",
        "outputId": "428a72b3-a95d-4a0f-c28a-dbc115718f9a"
      },
      "source": [
        "# Women teams in Volleyball\n",
        "Teams[(Teams['Play']=='Volleyball') & (Teams['Event']=='Women')]"
      ],
      "execution_count": 97,
      "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>Name</th>\n",
              "      <th>Play</th>\n",
              "      <th>Event</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>698</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>700</th>\n",
              "      <td>Brazil</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>702</th>\n",
              "      <td>China</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>703</th>\n",
              "      <td>Dominican Rep.</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>707</th>\n",
              "      <td>Italy</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>709</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>710</th>\n",
              "      <td>Kenya</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>712</th>\n",
              "      <td>Republic of Korea</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>714</th>\n",
              "      <td>ROC</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>715</th>\n",
              "      <td>Serbia</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>717</th>\n",
              "      <td>Turkey</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>719</th>\n",
              "      <td>United States</td>\n",
              "      <td>Volleyball</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Name        Play  Event\n",
              "698          Argentina  Volleyball  Women\n",
              "700             Brazil  Volleyball  Women\n",
              "702              China  Volleyball  Women\n",
              "703     Dominican Rep.  Volleyball  Women\n",
              "707              Italy  Volleyball  Women\n",
              "709              Japan  Volleyball  Women\n",
              "710              Kenya  Volleyball  Women\n",
              "712  Republic of Korea  Volleyball  Women\n",
              "714                ROC  Volleyball  Women\n",
              "715             Serbia  Volleyball  Women\n",
              "717             Turkey  Volleyball  Women\n",
              "719      United States  Volleyball  Women"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "q1MXJ4rscE1f",
        "outputId": "fc53d9e1-30a1-492e-abe4-a414bb042406"
      },
      "source": [
        "# Men teams in Hockey\n",
        "Teams[(Teams['Play']=='Hockey') & (Teams['Event']=='Men')]"
      ],
      "execution_count": 98,
      "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>Name</th>\n",
              "      <th>Play</th>\n",
              "      <th>Event</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>456</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>458</th>\n",
              "      <td>Australia</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>461</th>\n",
              "      <td>Canada</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>463</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>465</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>467</th>\n",
              "      <td>India</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>470</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>472</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>474</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>476</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>478</th>\n",
              "      <td>Spain</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Men</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Name    Play Event\n",
              "456      Argentina  Hockey   Men\n",
              "458      Australia  Hockey   Men\n",
              "460        Belgium  Hockey   Men\n",
              "461         Canada  Hockey   Men\n",
              "463        Germany  Hockey   Men\n",
              "465  Great Britain  Hockey   Men\n",
              "467          India  Hockey   Men\n",
              "470          Japan  Hockey   Men\n",
              "472    Netherlands  Hockey   Men\n",
              "474    New Zealand  Hockey   Men\n",
              "476   South Africa  Hockey   Men\n",
              "478          Spain  Hockey   Men"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "vgWKNztVcME5",
        "outputId": "f0d53a68-d90e-472b-b3d1-feedec03af07"
      },
      "source": [
        "# Women teams in Hockey\n",
        "Teams[(Teams['Play']=='Hockey') & (Teams['Event']=='Women')]"
      ],
      "execution_count": 99,
      "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>Name</th>\n",
              "      <th>Play</th>\n",
              "      <th>Event</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>457</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>459</th>\n",
              "      <td>Australia</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>462</th>\n",
              "      <td>China</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>464</th>\n",
              "      <td>Germany</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>466</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>468</th>\n",
              "      <td>India</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>469</th>\n",
              "      <td>Ireland</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>471</th>\n",
              "      <td>Japan</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>473</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>475</th>\n",
              "      <td>New Zealand</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>477</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>479</th>\n",
              "      <td>Spain</td>\n",
              "      <td>Hockey</td>\n",
              "      <td>Women</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Name    Play  Event\n",
              "457      Argentina  Hockey  Women\n",
              "459      Australia  Hockey  Women\n",
              "462          China  Hockey  Women\n",
              "464        Germany  Hockey  Women\n",
              "466  Great Britain  Hockey  Women\n",
              "468          India  Hockey  Women\n",
              "469        Ireland  Hockey  Women\n",
              "471          Japan  Hockey  Women\n",
              "473    Netherlands  Hockey  Women\n",
              "475    New Zealand  Hockey  Women\n",
              "477   South Africa  Hockey  Women\n",
              "479          Spain  Hockey  Women"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QjS08y5KcU33",
        "outputId": "508c9ad3-7afc-4d97-b407-7db9bf37c9a7"
      },
      "source": [
        "Coaches.shape"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(394, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "DIY0ggbzcQ50",
        "outputId": "d9d39100-a619-4134-97e5-be16858b67fb"
      },
      "source": [
        "Coaches.head()"
      ],
      "execution_count": 101,
      "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>Name</th>\n",
              "      <th>Country</th>\n",
              "      <th>Play</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ABDELMAGID Wael</td>\n",
              "      <td>Egypt</td>\n",
              "      <td>Football</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ABE Junya</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Volleyball</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ABE Katsuhiko</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Basketball</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ADAMA Cherif</td>\n",
              "      <td>Cote d'Ivoir</td>\n",
              "      <td>Football</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AGEBA Yuya</td>\n",
              "      <td>Japan</td>\n",
              "      <td>Volleyball</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Name       Country        Play\n",
              "0  ABDELMAGID Wael         Egypt    Football\n",
              "1        ABE Junya         Japan  Volleyball\n",
              "2    ABE Katsuhiko         Japan  Basketball\n",
              "3     ADAMA Cherif  Cote d'Ivoir    Football\n",
              "4       AGEBA Yuya         Japan  Volleyball"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_53_7fG_cUAK",
        "outputId": "07e3f14c-87b1-467e-94d4-c8649b800e11"
      },
      "source": [
        "# Coaches of the most participating countries\n",
        "Coaches.groupby('Country')['Name'].count().sort_values(ascending=False).head(15)"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "Japan                         35\n",
              "United States of America      28\n",
              "Spain                         28\n",
              "Australia                     22\n",
              "Canada                        16\n",
              "Italy                         14\n",
              "Egypt                         12\n",
              "South Africa                  12\n",
              "People's Republic of China    12\n",
              "ROC                           12\n",
              "Argentina                     11\n",
              "Venezuela                     10\n",
              "Netherlands                   10\n",
              "France                        10\n",
              "Germany                        9\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "wkNCkAsqcdNd",
        "outputId": "5e947390-ce6e-407f-dfb1-f078819361d6"
      },
      "source": [
        "Coaches.groupby('Country')['Name'].count().sort_values(ascending=False).head(15).plot(legend=False,kind='barh',\n",
        "                                                                        colormap='CMRmap',\n",
        "                                                                        figsize=(8,6),title='number of Coachs per Nation')\n",
        "plt.show()"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MjGABL20/l7f3a9FJxn4B3FxVPx6mr3ck2bttb9La3An8ETi9lc8H/rJtv4BO0gjwdeDTbftS4Ogkq7b4hkzKqmoendFCr76UJKkxKZs4XwAuA77SVbYS8PyqeqC7YleS1m1xd5Wu109V1ZcGtZ8J3DdUJ0l2pZMM7lhVf0hyLjCj7f5TPXI/gyU8+vflMclUVZ3XRuZeARyT5HNV9bWhjitJkh7N6csJUlV3Ad8E3txVfCbw9oE3SWa1zXuBtUfR7RnAmwZG15JsnOTPltJmHeB3LSHbAnj+KI7zI2Dftr1/V7ybAr+pqi8DRwHbjaIvSZKESdlEOwTovgrzHcDstkj/GjoL/AFOA/YetND/MarqTOB/gIuSLAS+xdKTue8BqyT5CZ21acNNcXZ7J/DWdoyNu8p3Ba5IcjnwWjprzyRJ0ih4R39NqNmzZ1d/v+v9JUnTg3f0lyRJ6nEmZZIkST3ApEySJKkHmJRJkiT1AJMySZKkHmBSJkmS1ANMyiRJknqASZkkSVIPMCmTJEnqASZlkiRJPcDHLGlCJfEXcIrzb4wkPcLHLEmSJPU4k7IelOScJH81qOxdSY6YqJiGkuSYJPtMdBySJE0FJmW96Thg30Fl+7ZySZI0BZmU9aZvAa9I8gSAJDOBjYDVk1yU5LIkJyZZq+2/KcnHWvnCJFu08jWTHJ3kkiSXJ9mzlR+VZEH7+W2SjybZNcnpAwEkOSzJnLbdl+SHSeYnOSPJhoMDTvKRJJcmuSrJvCQZ349IkqSpxaSsB1XVXcAlwMtb0b7AmcAHgZdU1XZAP/CermZ3tPIjgPe2sg8CP6iqHYDdgM8mWbOq/qGqZgF7AncAxwwXS5JVgUOBfaqqDzga+MQQVQ+rqu2ramtgdWCPZT9zSZKmL5Oy3tU9hbkvcAuwJfCjJAuANwKbdtU/ub3OB2a27ZcCH2j1zwVmAE8DSDIDOBF4e1XdPEIczwa2Bs5q/XwIeOoQ9XZLcnGShcDuwFbDdZhkbpL+JP0jHFeSpGlllYkOQMP6X+DzSbYD1gAuA86qqv2Gqb+4vS7hke81wKur6roh6h8JnFxVZ7f3D/LoJH1GVx9XV9WOwwXaErzDgdlVdUuSg7raP0ZVzQPmtbbeL0GSJBwp61lVtQg4h8504XHAj4EXJHkmPLxebPOldHMG8PaB9V1Jntde3wqsXVUHd9W9GdgyyWpJ1gVe3MqvA9ZPsmNru2qSwaNgAwnYHW2dm1dkSpK0jEzKettxwLbAcVX1W2AOcFySK4GLgC2W0v7jwKrAlUmubu+hs+Zsm67F/gdU1S3AN4Gr2uvlAFX1RzpJ1qeTXAEsAHbqPkhV3Q18ubU9A7h0uc5akqRpyDv6a0I5fTn1+TdGkh4x0h39XVOmCdXX10d/v+v9JUly+lKSJKkHmJRJkiT1AJMySZKkHmBSJkmS1ANMyiRJknqASZkkSVIPMCmTJEnqASZlkiRJPcCkTJIkqQeYlEmSJPUAn32pCeWzLzWYf5MkTWUjPfvSkTJJkqQeYFI2SSTZK0kl2WIFHGtWkr/uev83ST4w3seVJGk6MymbPPYDLmivj5JklTE+1izg4aSsqk6tqoPH+BiSJKmLSdkkkGQtYGfgzcC+rWzXJOcnORW4JslKSQ5Pcm2Ss5J8N8k+rW5fkh8mmZ/kjCQbtvJzk3w6ySVJrk+yS5InAP8GvDbJgiSvTTInyWGtzTFJvpjkwiQ3dB1jrSTfT3JZkoVJ9lzxn5QkSZOXSdnksCfwvaq6HrgzSV8r3w54Z1VtDrwKmAlsCbwe2BEgyarAocA+VdUHHA18oqvvVapqB+BdwEer6o/AR4ATqmpWVZ0wRDwb0kkS9wAGRtAeAPauqu2A3YBDkmSok0kyN0l/kv7H8VlIkjQljfW0l8bHfsB/tu3j2/vTgUuq6sZWvjNwYlU9BPw6yTmt/NnA1sBZLUdaGbitq++T2+t8OkndaHy7HeeaJBu0sgCfTPJC4CFgY2AD4NeDG1fVPGAeePWlJEkDTMp6XJL1gN2BbVoCszJQwHeA+0bTBXB1Ve04zP7F7XUJo/99WNy1PTAatj+wPtBXVX9KchMwY5T9SZI07Tl92fv2Ab5eVZtW1cyq2gS4EdhlUL0fAa9ua8s2AHZt5dcB6yd5eDozyVZLOea9wNrLGOc6wO0tIdsN2HQZ20uSNK2ZlPW+/YBTBpWdxGOvwjwJ+CVwDfAN4DLgnrZGbB/g00muABYAOy3lmOcAWw4s9B9lnMcCs5MsBN4AXDvKdpIkCe/oP6UkWauqFiV5MnAJ8IKqesyarl7imjIN5t8kSVPZSHf0d03Z1HJ6knWBJwAf7/WEDKCvr4/+fi/ClCTJpGwKqapdJzoGSZL0+LimTJIkqQeYlEmSJPUAkzJJkqQeYFImSZLUA0zKJEmSeoBJmSRJUg8wKZMkSeoBJmWSJEk9wKRMkiSpB/jsS00on32p5eXfMEmTyUjPvnSkTJIkqQeYlOlRkixJsiDJVUlOaw84H9i3VZIfJLkuyU+TfDhJuva/PEl/kmuSXJ7kkIk5C0mSJh+TMg12f1XNqqqtgbuAtwIkWR04FTi4qp4NbAvsBPxz2781cBjwd1W1JTAb+NkExC9J0qRkUqaRXARs3LZfB/yoqs4EqKo/AG8DPtD2Hwh8oqqubfuXVNURKzheSZImLZMyDSnJysCL6YyOAWwFzO+uU1U/B9ZK8kRg68H7R+h7bpvm7B/DkCVJmtRMyjTY6kkWAL8GNgDOGusDVNW8qpo93NUnkiRNRyZlGuz+qpoFbAqEtqYMuAbo666Y5OnAoqr6PXD14P2SJGn0TMo0pLZm7B3AvyRZBTgW2DnJS+Dhhf9fBD7TmnwW+H9JNm/7V0pywIqPXJKkycmkTMOqqsuBK4H9qup+YE/gQ0muAxYCl9K54pKquhJ4F3Bckp8AVwFPn5DAJUmahLyjvyaUd/TX8vJvmKTJZKQ7+q+yooORuvX19dHf70WYkiQ5fSlJktQDTMokSZJ6gEmZJElSDzApkyRJ6gEmZZIkST3ApEySJKkHmJRJkiT1AJMySZKkHmBSJkmS1ANMyiRJknqAz77UhPLZl1pe/g2TNJmM9OxLR8okSZJ6wLglZUmWJFmQ5KokJyZZY4z7XzSKOjctQ3/nJrkuyRVJLk0ya7kCHP4Yj8mOk8xJcljbPiDJG8bgWO9I8pMkxw6xb4ck57XzvTzJUUnWSHJQkvcO09+FyxuTJEka3niOlN1fVbOqamvgj8AB43issbJ/VW0LHA58diICqKojq+prY9DVPwN/WVX7dxcm2QA4EXh/VT27qp4HfA9Yeylx7TQGMUmSpGGsqOnL84FnJlkzydFJLmkjNHsCJJmR5CtJFrby3Vr5nCT/20aYfprko0N1nuR9bXTryiQf69r127Z/wzYyNDByt8tS4r0I2Li1HS7mIWNLMjPJVV2xvTfJQV19v74rjh2GOJeHR6uSPDPJ2W307rIkzxii/ntaX1cleVcrOxJ4OvB/Sd49qMlbga9W1UUDBVX1rar6TXu7ZTunG5K8o+s4i9rrrm3/t5Jcm+TYJGn7PtK+h6uSzBsolyRJS7fKeB8gySrAy+mMxnwQ+EFVvSnJusAlSc6mM4pWVbVNki2AM5Ns3rrYAdga+ANwaZLvVFV/V/8vBZ7V6gU4NckLq+q8qtq+VXsdcEZVfSLJysDSplJfBny7bQ8X85CxAXcspe81qmpWkhcCR7f2wzkWOLiqTkkyg0FJdJI+4O+Bv2jnfnGSH1bVAUleBuxWVYPj2Rr46gjH3ALYjc7I2XVJjqiqPw2q8zxgK+BXwI+AFwAXAIdV1b+12L4O7AGcNvgASeYCc0eIQZKkaWc8k7LVkyxo2+cD/w1cCPxN17qlGcDTgJ2BQwGq6tokNwMDSdlZVXUnQJKTW92HkzLgpe3n8vZ+LTpJ2nlddS4Fjk6yKvDtqlrA0I5N8oTWx8CaspcOE/NwsX2bkR3XzvO8JE9sid5jJFkb2LiqTmn1Hxii2s7AKVV1X1cMu/DIZ/F4fKeqFgOLk9wObAD8clCdS6rql+2YC4CZdJKy3ZIcSCfpXQ+4miGSsqqaB8xr7b10TpIkxjcpu7+qHrVYvk1nvbqqrhtUPlI/g//RHvw+wKeq6kvDdtBJgF4IvAI4Jsnnhlm3tT8wn856skOBV7X+h4r5L4aJ7UEePaI1YxnPZ7xdDfQB/zvM/sVd20sY+nfkMXXaSN7hwOyquqVN2Q4+d0mSNIwVfUuMM4C3d61Bel4rP59OQkSbtnwaMJAE/WWS9ZKsDuxFZ7pscJ9vSrJWa79xkj/rrpBkU+A3VfVl4Chgu+ECrM5Njz4MPL9NpQ4X83Cx/Qb4syRPTrIanSm8bq9t/ewM3FNV9wwTx73AL5Ps1eqvlsdewXo+sFc6V06uCezdykZyGPDGllTS+n5VOhcALI+BBOyO9l3ss5z9SZI0rYxqpCzJkwem6ZbTx4EvAFcmWQm4kU7ScjhwRJKFdEaa5lTV4pYHXQKcBDwV+Eb3ejKAqjozyXOAi1r9RcDfAbd3VdsVeF+SP7X9I95yoqruT3II8D7gbcPEPGxsSf6t7bsVuHZQ9w8kuRxYFXjTiJ8WvB74UuvvT8DfAjd0xXlZkmPasQCOqqoRpy6r6jdJ9gX+oyWvD9GZ6v3eUmIZUVXdneTLwFXAr+lMGUuSpFEa1R39k/wUWAB8Bfi/WkG30E4yh8502NtWxPGWRS/HNpnMnj27+vv7l15RkqQpIGNwR//N6SzMfj3w0ySf7Lo6UpIkSctpmZ99mc49xL4BrAlcAXyg+55X0rJwpEySNJ2MNFI26jVldNZpvZ7OQva3A6fSuW3EicBmYxOqJEnS9DTaW2JcBHwd2Gvg/lRNf7t7vCRJkpbDUpOydgf806rq40Ptr6pPj3lUkiRJ08xSF/pX1RLAh1FLkiSNo9FOXy5Iciqd9WP3DRRW1cnjEpUkSdI0M9qkbAZwJ7B7V1kBJmWSJEljYLRJ2VFV9ajHGyV5wTjEI0mSNC2N9uaxh46yTJIkSY/DiCNlSXaks8h//STv6dr1RGDl8QxM08P8+fNpzyyVHpcV9NQ3SRp3S5u+fAKwVqu3dlf574F9xisoSZKk6Wa0DyTftKpuXgHxTAtJPgi8DlgCPAT8Y1Vd/Dj62RX4Y1Vd2N4fA5xeVd8aRdu9gFOA51TVta1sfeB0Osn4O6rq/EFtjgI+V1XXLGusI8ThMIeWiyNlkiaT5X7MErBaknnAzO42VbX7sC00pDYlvAewXVUtTvIUOknQ47ErsAi48HG03Q+4oL1+tJW9GFhYVf8wuHKSlYcqlyRJY2O0C/1PBC4HPgS8r+tHy25D4I6qWgxQVXdU1a8Akrw4yeVJFiY5OslqrfymlryRZHaSc5PMBA4A3p1kQZJdWv8vTHJhkhuSDDnFnGQtYGfgzcC+rWwW8Blgz9bf6kkWJTkkyRXAju24s1v9lyW5LMkVSb7fynZIclE7hwuTPHvsPz5Jkqam0SZlD1bVEVV1SVXNH/gZ18imrjOBTZJcn+TwJC8CSDIDOAZ4bVVtQ2dE8p+G66SqbgKOBD5fVbO6pho3pJNw7QEcPEzzPYHvVdX1wJ1J+qpqAfAR4ITW3/3AmsDFVbVtVV0w0LhNc34ZeHVVbQv8bdt1LbBLVT2v9fXJZfpkJEmaxkablJ2W5J+TbJhkvYGfcY1siqqqRUAfMBf4LXBCkjnAs4EbW6IE8FXghY/jEN+uqofauq8NhqmzH3B82z6+vR/KEuCkIcqfD5xXVTcCVNVdrXwd4MQkVwGfB7YaqtMkc5P0J+lf6tlIkjRNjHZN2Rvba/eUZQFPH9twpof2PNFzgXOTLKTz+V4+QpMHeSSBnrGU7hd3bT/mXhMtmd4d2KYtsl8ZqCRDTUc/0GIdrY8D51TV3m169dyhKlXVPGBei8dV2pIkMcqRsqrabIgfE7LHIcmzkzyrq2gWcDNwHTAzyTNb+euBH7btm+iMrgG8uqvtvTz6ViWjsQ/w9aratKpmVtUmwI3ALktp1+3HdNaubQYPJ3rQGSm7tW3PWca4JEma1kY1UpbkDUOVV9XXxjacaWEt4NAk69IZAfsZMEOh2W4AABf+SURBVLeqHkjy93Sm/1YBLqWzZgzgY8B/J/k4jx59Og34VpI9gbeP8vj7AZ8eVHZSKx/VbTmq6rdJ5gInJ1kJuB34SzoXCnw1yYeA74wyHkmSxOjvU9b9SKUZdG6dcFlVeQNZLRenL7W8vE+ZpMlkue9TVlWPGoVpozzHD1NdGrW+vj76+13vL0nSaK++HOw+YLOxDESSJGk6G+2astPoXG0Jnav1ngN8c7yCkiRJmm5Ge0uM/+jafhC4uap+OQ7xSJIkTUujvSXGD+ncrX1t4EnAH8czKEmSpOlmVElZktcAl9B5nM5rgIuHe66iJEmSlt1opy8/CGxfVbfDw88+PBv41ngFJkmSNJ2M9urLlQYSsubOZWgrSZKkpRjtSNn3kpwBHNfevxb47viEJEmSNP2MmJS15zBuUFXvS/IqYOe26yLg2PEOTpIkabpY2kjZF4B/Baiqk4GTAZJs0/a9clyjkyRJmiaWlpRtUFULBxdW1cIkM8clIk0r8+fPJ8lEh6FJzGdfSpoqlrZYf90R9q0+loFIkiRNZ0tLyvqTvGVwYZJ/AOaPT0gab0mWJFnQ9fOBMe5/VpK/Hss+JUma6pY2ffku4JQk+/NIEjYbeAKw93gGpnF1f1XNGsf+Z9H5PfEKXUmSRimjWY+RZDdg6/b26qr6wbhGpXGVZFFVrTVE+V8DnwPuA34EPB34G+A6YKeq+m2SlYDrgR2BzwIP0EnAngi8BzgT+Bmd6e1bgU9V1QkjxOKCIC0X15RJmkySzK+q2UPtG9V9yqrqHOCcMY1KE2n1JAu63n8K+F/gS8ALq+rGJMcBVNVDSb4B7E/nituXAFe0BA1gJrAD8Aw6vyPPBD4CzK6qtw118CRzgbnjcWKSJE1W3pV/erq/qmZ1/ZwAbAHcUFU3tjrHddU/GnhD234T8JWufd+sqoeq6qfADa2fEVXVvKqaPdz/FCRJmo5MyrRUVXUL8Jsku9MZFfu/7t2Dq6+wwCRJmkJMyjTgOuDpXfefe+2g/UcB3wBOrKolXeV/m2SlJM+gswbtOuBeYO3xDVeSpKnFpGx6Wn3QLTEOrqr7gX+m85zT+XQSq3u62pwKrMWjpy4BfgFcQmf07ICqeoDO2rItW9+DkztJkjSE0T6QXFNIVa08zK5zqmqLdFbw/xfQ37VvWzoL/K8d1ObsqjpgUP93AduPWcCSJE0DJmXq9pYkb6RzH7rL6VyNSbu57D/RuQJzTPX19dHf37/0ipIkTXGjuk+ZNF5mz55dJmWSpOlipPuUuaZMkiSpB5iUSZIk9QCTMkmSpB5gUiZJktQDTMokSZJ6gEmZJElSDzApkyRJ6gEmZZIkST3ApEySJKkHeEd/Tagk/gKq5/l3UtJY8Y7+kiRJPc6kTI+SZFF7nZnkdaOoPzPJVeMfmSRJU5tJmYYzE1hqUiZJksaGSZmGczCwS5IFSd7dRsTOT3JZ+9lpcIMk5yWZ1fX+giTbrtCoJUmapEzKNJwPAOdX1ayq+jxwO/CXVbUd8Frgi0O0+W9gDkCSzYEZVXXF4EpJ5ibpT9I/btFLkjTJmJRptFYFvpxkIXAisOUQdU4E9kiyKvAm4JihOqqqeVU1e7irTyRJmo5WmegANGm8G/gNsC2dZP6BwRWq6g9JzgL2BF4D9K3QCCVJmsRMyjSce4G1u96vA/yyqh5K8kZg5WHaHQWcRmfq83fjHKMkSVOG05cazpXAkiRXJHk3cDjwxiRXAFsA9w3VqKrmA78HvrLCIpUkaQrwjv4aU0k2As4Ftqiqh0ZR319A9Tz/TkoaKyPd0d/pS42ZJG8APgG8ZzQJGUBfXx/9/V6EKUmSSZnGTFV9DfjaRMchSdJk5JoySZKkHmBSJkmS1ANMyiRJknqASZkkSVIPMCmTJEnqASZlkiRJPcCkTJIkqQeYlEmSJPUAkzJJkqQe4LMvNaF89qXkszWl6WSkZ186UiZJktQDTMqmmCR/nuT4JD9PMj/Jd5NsPo7HWzRefUuSNJ34QPIpJEmAU4CvVtW+rWxbYAPg+omMTZIkjcyRsqllN+BPVXXkQEFVXQFcnuT7SS5LsjDJngBJZib5SZIvJ7k6yZlJVm/73pLk0iRXJDkpyRqtfLMkF7V+/n3gOEnWGuoYkiRpdEzKppatgflDlD8A7F1V29FJ3A5po2oAzwL+q6q2Au4GXt3KT66q7atqW+AnwJtb+X8CR1TVNsBtozzGoySZm6Q/Sf/jPlNJkqYYk7LpIcAnk1wJnA1sTGdKE+DGqlrQtucDM9v21knOT7IQ2B/YqpW/ADiubX99lMd4lKqaV1Wzh7v6RJKk6cg1ZVPL1cA+Q5TvD6wP9FXVn5LcBMxo+xZ31VsCrN62jwH2qqorkswBdu2qN9T1+yMdQ5IkLYUjZVPLD4DVkswdKEjyXGBT4PaWLO3W3i/N2sBtSValk3AN+BGwb9vuLl/ncRxDkiQ1JmVTSHXuQLk38JJ2S4yrgU8B3wVmt6nINwDXjqK7DwMX00nCuuu/E3hr62vjrvJjH8cxJElS4x39NaFmz55d/f2u95ckTQ/e0V+SJKnHmZRJkiT1AJMySZKkHmBSJkmS1ANMyiRJknqASZkkSVIPMCmTJEnqASZlkiRJPcCkTJIkqQeYlEmSJPUAH7OkCZXEX0BJS+W/VZoqfMySJElSjzMpm6SS7JWkkmyxHO23fBzt5iQ5rG0fkOQNj+f4kiTp0UzKJq/9gAva6+OxFzBkUpZkldF0UFVHVtXXHufxJUlSF5OySSjJWsDOwJuBfVvZrklO76pzWJI5bfvgJNckuTLJfyTZCfgb4LNJFiR5RpJzk3whST/wziSvTHJxksuTnJ1kgyHiOCjJe9v2W5JcmuSKJCclWWPcPwhJkqaQUY2IqOfsCXyvqq5PcmeSvuEqJnkysDewRVVVknWr6u4kpwKnV9W3Wj2AJwwsPkzyJOD5rc0/AAcC/zJCTCdX1Zdb23+nkzAeuvynKknS9OBI2eS0H3B82z6ekacw7wEeAP47yauAP4xQ94Su7acCZyRZCLwP2GopMW2d5PxWf/+R6ieZm6S/jcpJkiRMyiadJOsBuwNHJbmJTsL0GmAJj/4+ZwBU1YPADsC3gD2A743Q/X1d24cCh1XVNsA/DvQ3gmOAt7X6HxupflXNq6rZw10SLEnSdGRSNvnsA3y9qjatqplVtQlwI53vcsskqyVZF3gxPLz+bJ2q+i7wbmDb1s+9wNojHGcd4Na2/cZRxLU2cFuSVemMlEmSpGVgUjb57AecMqjsJDoL/r8JXNVeL2/71gZOT3Ilnas139PKjwfe1xbyP2OI4xwEnJhkPnDHKOL6MHAx8CPg2lGfjSRJAryjvyaYd/SXNBr+W6WpYqQ7+nv1pSZUX18f/f2u95ckyelLSZKkHmBSJkmS1ANMyiRJknqASZkkSVIPMCmTJEnqASZlkiRJPcCkTJIkqQeYlEmSJPUAkzJJkqQeYFImSZLUA3z2pSaUz76UpLHhv+eTw0jPvnSkTJIkqQeYlE1TST6Y5OokVyZZkOQvHkcff5PkA+MRnyRJ080qEx2AVrwkOwJ7ANtV1eIkTwGesKz9VNWpwKljHZ8kSdORI2XT04bAHVW1GKCq7qiqXyW5KclnkixMckmSZwIkeWWSi5NcnuTsJBu08jlJDmvbxyT5YpILk9yQZJ8JOztJkiYhk7Lp6UxgkyTXJzk8yYu69t1TVdsAhwFfaGUXAM+vqucBxwMHDtPvhsDOdEbhDh7u4EnmJulP0r+8JyJJ0lTh9OU0VFWLkvQBuwC7ASd0rQ07ruv18237qa3OhnSmOW8cputvV9VDwDUDo2nDHH8eMA+8+lKSpAGOlE1TVbWkqs6tqo8CbwNePbCru1p7PRQ4rI2g/SMwY5huF3dtZyzjlSRpqjMpm4aSPDvJs7qKZgE3t+3Xdr1e1LbXAW5t228c/wglSZp+nL6cntYCDk2yLvAg8DNgLp21YE9KciWdUa/9Wv2DgBOT/A74AbDZCo9YkqQpzjv662FJbgJmV9UdK/CY/gJK0hjw3/PJYaQ7+jtSpgnV19dHf78XYUqSZFKmh1XVzImOQZKk6cqF/pIkST3ApEySJKkHmJRJkiT1AJMySZKkHmBSJkmS1ANMyiRJknqASZkkSVIPMCmTJEnqASZlkiRJPcBnX2pC+exLSRob/ns+OYz07EtHyiRJknrACk/KksxMctWgsoOSvHcp7WYn+WLb3jXJTo/j2DclecoQ5W9KsjDJlUmuSrJnK5+TZKNR9DuqessjyXEtvncPs39BkuPHOYajkmw5nseQJGm6mjQPJK+qfqC/vd0VWARcuLz9Jnkq8EFgu6q6J8lawPpt9xzgKuBXS+lmtPUeb4x/DmxfVc8cZv9zgJWBXZKsWVX3jUMMK1fVP4x1v5IkqaPnpi+TnJvk00kuSXJ9kl1a+a5JTk8yEzgAeHcbHdolyfpJTkpyaft5QWvz5CRnJrk6yVFAhjjknwH30knyqKpFVXVjkn2A2cCx7TirJ/lI6/+qJPPSMVS9viQ/TDI/yRlJNmzxvCPJNW3E6zGjWklmJPlKG7W7PMlubdeZwMYD5zvEOewHfL3V23PQZ/n5JP1JfpJk+yQnJ/lpkn/vqvd37fNekORLSVZu5YuSHJLkCmDH1t/stu9lSS5LckWS77eyHZJc1GK/MMmzR/GVS5Ik6CwMXJE/wEzgqkFlBwHvbdvnAoe07b8Gzm7buwKnD67f3v8PsHPbfhrwk7b9ReAjbfsVQAFPGXTslYEzgF8AXwFe2bXvXGB21/v1ura/PlC3ux6wKp0RvPXb+9cCR7ftXwGrte11h/hs/qWr7hYtphlDfWaD2l3XzvulwGmD4v90235nO/6GwGrAL4EnA88BTgNWbfUOB97Qtgt4zeDPg85I4i3AZt2fC/BEYJW2/RLgpGHinUtn1LO/HcMff/zxx5/l/NHkAPTXMP+eT8T0ZY2i/OT2Op9OQrI0LwG2TB4eCHtim4Z8IfAqgKr6TpLfPeagVUuSvAzYHngx8PkkfVV10BDH2S3JgcAawHrA1XQSmm7PBrYGzmrxrAzc1vZdSWdE7dvAt4fof2fg0BbXtUluBjYHfj/cibeRqzuq6hdJbgWOTrJeVd3VqpzaXhcCV1fVba3dDcAm7Zh9wKUt3tWB21ubJcBJQxz2+cB5VXVji3XgWOsAX03yLDrf56pDxVxV84B5LY7hfh8kSZpWJiIpuxN40qCy9YAbu94vbq9LGF2MKwHPr6oHugu7krQRtcz1EuCSJGfRGTE7aFBfM+iMIs2uqluSHERnFGuw0El+dhxi3yvoJIqvBD6YZJuqenBUQQ5vP2CLJDe1908EXg18ub0f+Cwf6toeeL9Ki/erVfWvQ/T9QFUtWYZYPg6cU1V7t2nmc5ehrSRJ09oKX1NWVYuA25LsDpBkPeBlwAXL0M29wNpd788E3j7wJsmstnke8LpW9nIemwySZKMk23UVzQJuHuI4AwnYHW0Ubp9h4rkOWD/Jjq3/VZNslWQlYJOqOgd4P51RpbUGhXM+sH9rtzmdKcnrhvkMaH2+BtimqmZW1Uw6a8r2G67NEL4P7JPkz1qf6yXZdCltfgy8MMlmA21a+TrArW17zjLEIEnStDdRC/3fAHw4yQLgB8DHqurny9D+NGDvroXv7wBmtwX019C5EADgY3SSh6vpTGP+Yoi+VgX+I8m1LZ7X0ll/BXAMcGQrX0xn9OkqOmvQLu3qo7veynQStk+3BfILgJ1a+TeSLAQuB75YVXcPiuVwYKVW5wRgTlUtZni7ALdWVfdVn+fRmcrdcIR2D6uqa4APAWcmuRI4i866s5Ha/JbOurCT2zme0HZ9BvhUksuZRFf2SpLUC7yjvyaUa8okaWz47/nkkBHu6O9ohiZUX18f/f39S68oSdIU13P3KZMkSZqOTMokSZJ6gEmZJElSDzApkyRJ6gEmZZIkST3ApEySJKkHmJRJkiT1AJMySZKkHmBSJkmS1AN8zJImlI9ZkiT1qvHIkUZ6zJIjZZIkST3ApGyaSrJoomOQJEmPMCmTJEnqASZl01iStZJ8P8llSRYm2bOVz0xybZJjk/wkybeSrNH2fSTJpUmuSjIvSVr5uUk+neSSJNcn2WUiz02SpMnGpGx6ewDYu6q2A3YDDhlIsoBnA4dX1XOA3wP/3MoPq6rtq2prYHVgj67+VqmqHYB3AR9dIWcgSdIUYVI2vQX4ZJIrgbOBjYEN2r5bqupHbfsbwM5te7ckFydZCOwObNXV38ntdT4wc9iDJnOT9CfpH5vTkCRp8ltlogPQhNofWB/oq6o/JbkJmNH2Db4OuJLMAA4HZlfVLUkO6qoPsLi9LmGE362qmgfMA2+JIUnSAEfKprd1gNtbQrYbsGnXvqcl2bFtvw64gEcSsDuSrAXss+JClSRpajMpm4aSrEJnVOtYYHabinwDcG1XteuAtyb5CfAk4Iiquhv4MnAVcAZw6QoNXJKkKcw7+k9DSbYFvtwW5Q+1fyZwelvMP96x+AsoSepJK/qO/q4pm2aSHAC8g84VkhOur6+P/n7X+0uSZFI2zVTVkcCRS6lzEzDuo2SSJOkRrimTJEnqASZlkiRJPcCkTJIkqQeYlEmSJPUAb4mhCZXkXjr3RJsungLcMdFBrGCe8/Qw3c55up0veM5jZdOqWn+oHV59qYl23XD3a5mKkvRPp/MFz3m6mG7nPN3OFzznFcHpS0mSpB5gUiZJktQDTMo00eZNdAAr2HQ7X/Ccp4vpds7T7XzBcx53LvSXJEnqAY6USZIk9QCTMk2IJC9Lcl2SnyX5wETHsyIkuSnJwiQLkkzJp7AnOTrJ7Umu6ipbL8lZSX7aXp80kTGOtWHO+aAkt7bvekGSv57IGMdSkk2SnJPkmiRXJ3lnK5+y3/MI5zwlv+ckM5JckuSKdr4fa+WbJbm4/d0+IckTJjrWsTLCOR+T5Mau73jWuMbh9KVWtCQrA9cDfwn8ErgU2K+qrpnQwMZZkpuA2VU1Ze/zk+SFwCLga1W1dSv7DHBXVR3cEvAnVdX7JzLOsTTMOR8ELKqq/5jI2MZDkg2BDavqsiRrA/OBvYA5TNHveYRzfg1T8HtOEmDNqlqUZFXgAuCdwHuAk6vq+CRHAldU1RETGetYGeGcDwBOr6pvrYg4HCnTRNgB+FlV3VBVfwSOB/ac4Jg0BqrqPOCuQcV7Al9t21+l84/ZlDHMOU9ZVXVbVV3Wtu8FfgJszBT+nkc45ympOha1t6u2nwJ2BwaSk6n2HQ93ziuUSZkmwsbALV3vf8kU/gPXpYAzk8xPMneig1mBNqiq29r2r4ENJjKYFehtSa5s05tTZiqvW5KZwPOAi5km3/Ogc4Yp+j0nWTnJAuB24Czg58DdVfVgqzLl/m4PPueqGviOP9G+488nWW08YzApk1acnatqO+DlwFvbtNe0Up31EtNhzcQRwDOAWcBtwCETG87YS7IWcBLwrqr6ffe+qfo9D3HOU/Z7rqolVTULeCqd2Y0tJjikcTf4nJNsDfwrnXPfHlgPGNcpeZMyTYRbgU263j+1lU1pVXVre70dOIXOH7rp4DdtTc7A2pzbJziecVdVv2l/4B8C/n979+8SRxQEcPw7KEKwuSK2FoG09oEUViGtIKIQsIx/QxohkDakD4mVPxCUxD/AFCktDETQMpZ2adM4Fu8JW+hVOXd9fD/VsncHbxhY5t6bYT/TWK5rz80BsJ2Zh/V203m+K+bW8wyQmX+BH8ALYBQRt69nbPa53Yn5dT26zsz8B2wx4RxblKkPJ8DzOskzA6wCRz2vaaIiYrY2CBMRs8Ar4Gz8r5pxBKzX63Xge49reRC3xUm1REO5rg3RX4DzzPzY+ajZPN8Xc6t5joi5iBjV6yeUoaxzSqGyXL/WWo7vivmi80cjKD10E82x05fqRR0d/wRMAV8z80PPS5qoiHhG2R0DmAZ2Wow5InaBReApcAVsAt+AfWAeuARWMrOZxvh7Yl6kHGkl8Ad42+m3etQi4iXwE/gNXNfb7yg9Vk3meUzMazSY54hYoDTyT1E2b/Yz8319ju1RjvFOgTd1B+nRGxPzMTAHBPAL2OgMBPz/dViUSZIk9c/jS0mSpAGwKJMkSRoAizJJkqQBsCiTJEkaAIsySZKkAbAokyRJGgCLMkmSpAGwKJMkSRqAG0fZoZ2C3hYoAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j0JagCwTcg3H",
        "outputId": "f9269730-c82f-43fe-f6fa-b2087f15ce5d"
      },
      "source": [
        "# Coaches of least participating countries\n",
        "Coaches.groupby('Country')['Name'].count().sort_values(ascending=False).tail(15)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "Montenegro                  2\n",
              "Islamic Republic of Iran    2\n",
              "San Marino                  2\n",
              "Turkey                      1\n",
              "Tunisia                     1\n",
              "Slovakia                    1\n",
              "Bahrain                     1\n",
              "Belarus                     1\n",
              "Honduras                    1\n",
              "Cambodia                    1\n",
              "Portugal                    1\n",
              "Cote d'Ivoir                1\n",
              "Croatia                     1\n",
              "Liechtenstein               1\n",
              "Angola                      1\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MBQU8L0Hcm9d",
        "outputId": "4979c294-1c9e-42fc-a695-363fa144cabb"
      },
      "source": [
        "# Coaches participating in Olympics\n",
        "Coaches.groupby('Play')['Name'].count().sort_values(ascending=False)"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Play\n",
              "Basketball           74\n",
              "Artistic Swimming    69\n",
              "Football             59\n",
              "Volleyball           45\n",
              "Hockey               45\n",
              "Handball             29\n",
              "Baseball/Softball    26\n",
              "Rugby Sevens         25\n",
              "Water Polo           22\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "TrmqJK3hcsNv",
        "outputId": "dbbabf14-e8be-491a-f94a-59cdc0feb754"
      },
      "source": [
        "Coaches.groupby('Play')['Name'].count().sort_values(ascending=False).plot(legend=False,kind='barh',\n",
        "                                                                        colormap='Greens_r',\n",
        "                                                                        figsize=(8,6),title='number of Coachs per Nation')\n",
        "plt.show()"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qrNSLhsycv_9",
        "outputId": "3a1ce55f-08bc-42bc-e4bd-9e2aade48962"
      },
      "source": [
        "# Coaches in Football\n",
        "Football_coatches=Coaches[Coaches['Play']=='Football'].drop(['Play'],axis=1)\n",
        "Football_coatches.groupby('Country')['Name'].count().sort_values(ascending=False)"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "Japan                         6\n",
              "Chile                         6\n",
              "Egypt                         5\n",
              "Spain                         4\n",
              "South Africa                  4\n",
              "C����                         4\n",
              "United States of America      3\n",
              "Mexico                        2\n",
              "Australia                     2\n",
              "Brazil                        2\n",
              "Germany                       2\n",
              "Zambia                        2\n",
              "New Zealand                   2\n",
              "Republic of Korea             2\n",
              "Romania                       2\n",
              "Saudi Arabia                  2\n",
              "Netherlands                   1\n",
              "Honduras                      1\n",
              "Great Britain                 1\n",
              "France                        1\n",
              "People's Republic of China    1\n",
              "Cote d'Ivoir                  1\n",
              "Canada                        1\n",
              "Sweden                        1\n",
              "Argentina                     1\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "a0PFe75Vc1zn",
        "outputId": "fa247be0-2848-4243-9091-4f98858da91c"
      },
      "source": [
        "Football_coatches.groupby('Country')['Name'].count().sort_values(ascending=False).plot(legend=False,kind='barh',\n",
        "                                                                        colormap='PRGn',\n",
        "                                                                        figsize=(8,6),title='')\n",
        "plt.show()"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fci46IS6c43T",
        "outputId": "4afc78b0-d6a2-44e6-fb9b-e026424a79a3"
      },
      "source": [
        "# Basketball coaches in Olympics\n",
        "Basketball_coatches=Coaches[Coaches['Play']=='Basketball'].drop(['Play'],axis=1)\n",
        "Basketball_coatches.groupby('Country')['Name'].count().sort_values(ascending=False)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "Spain                         10\n",
              "Nigeria                        9\n",
              "United States of America       9\n",
              "Venezuela                      6\n",
              "Australia                      5\n",
              "Japan                          5\n",
              "France                         4\n",
              "Dominican Republic             4\n",
              "Slovenia                       3\n",
              "Puerto Rico                    3\n",
              "Belgium                        3\n",
              "Serbia                         2\n",
              "People's Republic of China     2\n",
              "Germany                        2\n",
              "Czech Republic                 2\n",
              "Canada                         1\n",
              "Italy                          1\n",
              "Islamic Republic of Iran       1\n",
              "Republic of Korea              1\n",
              "Argentina                      1\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "hKGwDzR3c9-s",
        "outputId": "2441fec8-0a6e-4e55-ce61-5905d334cead"
      },
      "source": [
        "Basketball_coatches.groupby('Country')['Name'].count().sort_values(ascending=False).plot(legend=False,kind='barh',\n",
        "                                                                        colormap='Paired_r',\n",
        "                                                                        figsize=(8,6),title='')\n",
        "plt.show()"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D38aLfpldAWj",
        "outputId": "2f53fd0b-0f69-4caa-f363-4ddb9a0c1ae7"
      },
      "source": [
        "# Volleyball coaches in Olympics\n",
        "Volleyball_coatches=Coaches[Coaches['Play']=='Volleyball'].drop(['Play'],axis=1)\n",
        "Volleyball_coatches.groupby('Country')['Name'].count().sort_values(ascending=False)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "ROC                           5\n",
              "Japan                         5\n",
              "United States of America      5\n",
              "Venezuela                     4\n",
              "Argentina                     4\n",
              "Poland                        3\n",
              "People's Republic of China    3\n",
              "Italy                         3\n",
              "Kenya                         3\n",
              "Brazil                        2\n",
              "Republic of Korea             1\n",
              "Serbia                        1\n",
              "Tunisia                       1\n",
              "Turkey                        1\n",
              "Islamic Republic of Iran      1\n",
              "France                        1\n",
              "Dominican Republic            1\n",
              "Canada                        1\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "HlSSIA1xdFvI",
        "outputId": "8004ce7d-b1e3-41f0-eb0a-d32e5e1450f8"
      },
      "source": [
        "Volleyball_coatches.groupby('Country')['Name'].count().sort_values(ascending=False).plot(legend=False,kind='barh',\n",
        "                                                                        colormap='PiYG',\n",
        "                                                                        figsize=(8,6),title='')\n",
        "plt.show()"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ePpLGN2ydIvL",
        "outputId": "d0cfb55c-8b45-4305-ae01-b573f7710f04"
      },
      "source": [
        "# Swimming coaches in Olympics\n",
        "Swimming_coatches=Coaches[Coaches['Play']=='Artistic Swimming'].drop(['Play'],axis=1)\n",
        "Swimming_coatches.groupby('Country')['Name'].count().sort_values(ascending=False)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Country\n",
              "Spain                         8\n",
              "Italy                         7\n",
              "Egypt                         6\n",
              "Greece                        5\n",
              "Canada                        4\n",
              "ROC                           4\n",
              "Ukraine                       4\n",
              "Japan                         4\n",
              "Austria                       3\n",
              "People's Republic of China    3\n",
              "Colombia                      2\n",
              "Great Britain                 2\n",
              "Mexico                        2\n",
              "San Marino                    2\n",
              "Australia                     2\n",
              "Cambodia                      1\n",
              "Belarus                       1\n",
              "United States of America      1\n",
              "France                        1\n",
              "Israel                        1\n",
              "Kazakhstan                    1\n",
              "Liechtenstein                 1\n",
              "Netherlands                   1\n",
              "Serbia                        1\n",
              "Slovakia                      1\n",
              "Argentina                     1\n",
              "Name: Name, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "71iUFi4fdadc",
        "outputId": "2b876230-146c-4060-822c-96f6071761b3"
      },
      "source": [
        "Swimming_coatches.groupby('Country')['Name'].count().sort_values(ascending=False).plot(legend=False,kind='barh',\n",
        "                                                                        colormap='vlag_r',\n",
        "                                                                        figsize=(8,6),title='')\n",
        "plt.show()"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zeslY_DfdhKo",
        "outputId": "70c38e88-c4a6-4c58-d3b6-d71d42c193c3"
      },
      "source": [
        "Gender.shape"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(46, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "4GWpZFj9dc1i",
        "outputId": "6a0bc55c-7ed2-4225-af86-b3ae0a635425"
      },
      "source": [
        "Gender.head()"
      ],
      "execution_count": 116,
      "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>Play</th>\n",
              "      <th>Female</th>\n",
              "      <th>Male</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3x3 Basketball</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Archery</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Artistic Gymnastics</td>\n",
              "      <td>98</td>\n",
              "      <td>98</td>\n",
              "      <td>196</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Artistic Swimming</td>\n",
              "      <td>105</td>\n",
              "      <td>0</td>\n",
              "      <td>105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Athletics</td>\n",
              "      <td>969</td>\n",
              "      <td>1072</td>\n",
              "      <td>2041</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Play  Female  Male  Total\n",
              "0       3x3 Basketball      32    32     64\n",
              "1              Archery      64    64    128\n",
              "2  Artistic Gymnastics      98    98    196\n",
              "3    Artistic Swimming     105     0    105\n",
              "4            Athletics     969  1072   2041"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O3DUlkspdjQE"
      },
      "source": [
        "Gender['Female_%']=(Gender['Female'] / Gender['Total']) * 100\n",
        "Gender['Male_%']=(Gender['Male'] / Gender['Total']) * 100"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "j-7RLgagdqIM",
        "outputId": "4831d73b-8045-4b5b-fed7-9f2a12462b09"
      },
      "source": [
        "Gender.head()"
      ],
      "execution_count": 118,
      "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>Play</th>\n",
              "      <th>Female</th>\n",
              "      <th>Male</th>\n",
              "      <th>Total</th>\n",
              "      <th>Female_%</th>\n",
              "      <th>Male_%</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3x3 Basketball</td>\n",
              "      <td>32</td>\n",
              "      <td>32</td>\n",
              "      <td>64</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Archery</td>\n",
              "      <td>64</td>\n",
              "      <td>64</td>\n",
              "      <td>128</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Artistic Gymnastics</td>\n",
              "      <td>98</td>\n",
              "      <td>98</td>\n",
              "      <td>196</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Artistic Swimming</td>\n",
              "      <td>105</td>\n",
              "      <td>0</td>\n",
              "      <td>105</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Athletics</td>\n",
              "      <td>969</td>\n",
              "      <td>1072</td>\n",
              "      <td>2041</td>\n",
              "      <td>47.476727</td>\n",
              "      <td>52.523273</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Play  Female  Male  Total    Female_%     Male_%\n",
              "0       3x3 Basketball      32    32     64   50.000000  50.000000\n",
              "1              Archery      64    64    128   50.000000  50.000000\n",
              "2  Artistic Gymnastics      98    98    196   50.000000  50.000000\n",
              "3    Artistic Swimming     105     0    105  100.000000   0.000000\n",
              "4            Athletics     969  1072   2041   47.476727  52.523273"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "YdD32772dshH",
        "outputId": "90117478-5371-48ac-a9a8-043bb128d32f"
      },
      "source": [
        "# Games participated most by the female candidates\n",
        "female=Gender.sort_values(['Female'], ascending=False).drop(['Male','Total','Female_%','Male_%'],axis=1).head(10)\n",
        "female"
      ],
      "execution_count": 119,
      "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>Play</th>\n",
              "      <th>Female</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Athletics</td>\n",
              "      <td>969</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Swimming</td>\n",
              "      <td>361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Football</td>\n",
              "      <td>264</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Rowing</td>\n",
              "      <td>257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Judo</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Hockey</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Shooting</td>\n",
              "      <td>178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Sailing</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Handball</td>\n",
              "      <td>168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Rugby Sevens</td>\n",
              "      <td>146</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Play  Female\n",
              "4      Athletics     969\n",
              "36      Swimming     361\n",
              "20      Football     264\n",
              "29        Rowing     257\n",
              "24          Judo     192\n",
              "23        Hockey     192\n",
              "32      Shooting     178\n",
              "31       Sailing     175\n",
              "22      Handball     168\n",
              "30  Rugby Sevens     146"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "2CjAwEMXd2Dc",
        "outputId": "1496fa07-20be-4fc7-a032-1af965d6f3aa"
      },
      "source": [
        "sns.barplot(y='Play', x='Female' , data=female.sort_values(['Female']),palette=\"gnuplot2_r\")\n",
        "plt.show()"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "fhC246QZd6td",
        "outputId": "5b1266c1-d9c3-4a21-99e8-e441ea4e7fd2"
      },
      "source": [
        "# Games least paritcipated by the females\n",
        "female2=Gender.sort_values(['Female'], ascending=False).drop(['Male','Total','Female_%','Male_%'],axis=1).tail(10)\n",
        "female2"
      ],
      "execution_count": 121,
      "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>Play</th>\n",
              "      <th>Female</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Karate</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Cycling Mountain Bike</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Modern Pentathlon</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3x3 Basketball</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Marathon Swimming</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Cycling BMX Racing</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Surfing</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Sport Climbing</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Trampoline Gymnastics</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Cycling BMX Freestyle</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Play  Female\n",
              "25                 Karate      40\n",
              "14  Cycling Mountain Bike      38\n",
              "27      Modern Pentathlon      36\n",
              "0          3x3 Basketball      32\n",
              "26      Marathon Swimming      25\n",
              "13     Cycling BMX Racing      24\n",
              "35                Surfing      20\n",
              "34         Sport Climbing      20\n",
              "40  Trampoline Gymnastics      16\n",
              "12  Cycling BMX Freestyle      10"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "iGTNLQ9EeWqt",
        "outputId": "b4fa6ceb-8585-4c49-d38e-43938b4f42c9"
      },
      "source": [
        "sns.barplot(y='Play', x='Female' , data=female2.sort_values(['Female']),palette=\"inferno_r\")\n",
        "plt.show()"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "-AwB4TDnechy",
        "outputId": "f77506b4-60aa-4f41-95ff-b6c7c0e3139c"
      },
      "source": [
        "# Games most participated by Males\n",
        "male=Gender.sort_values(['Male'], ascending=False).drop(['Female','Total','Female_%','Male_%'],axis=1).head(10)\n",
        "male"
      ],
      "execution_count": 123,
      "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>Play</th>\n",
              "      <th>Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Athletics</td>\n",
              "      <td>1072</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Swimming</td>\n",
              "      <td>418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Football</td>\n",
              "      <td>344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Rowing</td>\n",
              "      <td>265</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Judo</td>\n",
              "      <td>201</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Wrestling</td>\n",
              "      <td>193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Hockey</td>\n",
              "      <td>192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Boxing</td>\n",
              "      <td>187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Shooting</td>\n",
              "      <td>178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Sailing</td>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Play  Male\n",
              "4   Athletics  1072\n",
              "36   Swimming   418\n",
              "20   Football   344\n",
              "29     Rowing   265\n",
              "24       Judo   201\n",
              "45  Wrestling   193\n",
              "23     Hockey   192\n",
              "9      Boxing   187\n",
              "32   Shooting   178\n",
              "31    Sailing   175"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "N0JoSWEieipv",
        "outputId": "04639d87-f9f8-4db1-ac72-028dc2383176"
      },
      "source": [
        "sns.barplot(y='Play', x='Male' , data=male.sort_values(['Male']),palette=\"terrain_r\")\n",
        "plt.show()"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "shLYZV-LemFA",
        "outputId": "d65ce0a4-5bbf-455f-dadc-50e7796df8a9"
      },
      "source": [
        "# Games participated least by Males\n",
        "male2=Gender.sort_values(['Male'], ascending=False).drop(['Female','Total','Female_%','Male_%'],axis=1).tail(10)\n",
        "male2"
      ],
      "execution_count": 125,
      "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>Play</th>\n",
              "      <th>Male</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Modern Pentathlon</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3x3 Basketball</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Marathon Swimming</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Cycling BMX Racing</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Surfing</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Sport Climbing</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Trampoline Gymnastics</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Cycling BMX Freestyle</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Artistic Swimming</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Rhythmic Gymnastics</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Play  Male\n",
              "27      Modern Pentathlon    36\n",
              "0          3x3 Basketball    32\n",
              "26      Marathon Swimming    25\n",
              "13     Cycling BMX Racing    24\n",
              "35                Surfing    20\n",
              "34         Sport Climbing    20\n",
              "40  Trampoline Gymnastics    16\n",
              "12  Cycling BMX Freestyle     9\n",
              "3       Artistic Swimming     0\n",
              "28    Rhythmic Gymnastics     0"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "U9IR0SRketo2",
        "outputId": "15f0807a-bd64-4bc7-b578-962faca0a5b9"
      },
      "source": [
        "sns.barplot(y='Play', x='Male' , data=male2.sort_values(['Male']),palette=\"tab20c_r\")"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f04582e3190>"
            ]
          },
          "metadata": {},
          "execution_count": 126
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "fKbDQM0fewfp",
        "outputId": "e69e3ffe-e46f-476e-af40-e64d41c08c48"
      },
      "source": [
        "# Most participated games\n",
        "both_genders=Gender.sort_values(['Total'], ascending=False).drop(['Female','Male_%','Male','Female_%'],axis=1).head(10)\n",
        "both_genders"
      ],
      "execution_count": 127,
      "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>Play</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Athletics</td>\n",
              "      <td>2041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Swimming</td>\n",
              "      <td>779</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Football</td>\n",
              "      <td>608</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Rowing</td>\n",
              "      <td>522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Judo</td>\n",
              "      <td>393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Hockey</td>\n",
              "      <td>384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Shooting</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Sailing</td>\n",
              "      <td>350</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Handball</td>\n",
              "      <td>336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Rugby Sevens</td>\n",
              "      <td>297</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Play  Total\n",
              "4      Athletics   2041\n",
              "36      Swimming    779\n",
              "20      Football    608\n",
              "29        Rowing    522\n",
              "24          Judo    393\n",
              "23        Hockey    384\n",
              "32      Shooting    356\n",
              "31       Sailing    350\n",
              "22      Handball    336\n",
              "30  Rugby Sevens    297"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "iguW5_G7e8UJ",
        "outputId": "c0b5be5f-7b12-4322-d4f8-ab17bfb38eec"
      },
      "source": [
        "# Most participated games\n",
        "sns.barplot(y='Play', x='Total' , data=both_genders.sort_values(['Total']),palette=\"Greys\")\n",
        "plt.show()"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "WiQzGYmtfAeY",
        "outputId": "d6794190-3da3-4662-f86a-7e9da239be03"
      },
      "source": [
        "# Least participated games\n",
        "both_genders2=Gender.sort_values(['Total'], ascending=False).drop(['Female','Male_%','Male','Female_%'],axis=1).tail(10)\n",
        "both_genders2"
      ],
      "execution_count": 129,
      "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>Play</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Skateboarding</td>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Cycling Mountain Bike</td>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Modern Pentathlon</td>\n",
              "      <td>72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3x3 Basketball</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Marathon Swimming</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Cycling BMX Racing</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Sport Climbing</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Surfing</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Trampoline Gymnastics</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Cycling BMX Freestyle</td>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Play  Total\n",
              "33          Skateboarding     80\n",
              "14  Cycling Mountain Bike     76\n",
              "27      Modern Pentathlon     72\n",
              "0          3x3 Basketball     64\n",
              "26      Marathon Swimming     50\n",
              "13     Cycling BMX Racing     48\n",
              "34         Sport Climbing     40\n",
              "35                Surfing     40\n",
              "40  Trampoline Gymnastics     32\n",
              "12  Cycling BMX Freestyle     19"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "f_34uNMCgjxX",
        "outputId": "34ef1660-2949-40a6-a421-e3169f85bd03"
      },
      "source": [
        "sns.barplot(y='Play', x='Total' , data=both_genders2.sort_values(['Total']),palette=\"Greens\")\n",
        "plt.show()"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jclf72HXguYW",
        "outputId": "fb07e0aa-5aa3-4bd6-cf1b-32020b31f34d"
      },
      "source": [
        "Medals.shape"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(93, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "id": "uw4TLLpkgott",
        "outputId": "27a8fc68-50f8-470c-fc51-a1f46f937ae3"
      },
      "source": [
        "Medals.head()"
      ],
      "execution_count": 132,
      "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>Team</th>\n",
              "      <th>Gold</th>\n",
              "      <th>Silver</th>\n",
              "      <th>Bronze</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Australia</td>\n",
              "      <td>17</td>\n",
              "      <td>7</td>\n",
              "      <td>22</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Austria</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Azerbaijan</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Team  Gold  Silver  Bronze  Total\n",
              "0   Argentina     0       1       2      3\n",
              "1     Armenia     0       2       2      4\n",
              "2   Australia    17       7      22     46\n",
              "3     Austria     1       1       5      7\n",
              "4  Azerbaijan     0       3       4      7"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "6bjwVvCcgwcp",
        "outputId": "d5395d76-3736-46ed-850d-f83320d4564e"
      },
      "source": [
        "# Countries with most gold medals\n",
        "Med_Go=Medals.sort_values(['Gold'], ascending=False).drop(['Silver','Bronze','Total'],axis=1).head(10)\n",
        "Med_Go"
      ],
      "execution_count": 133,
      "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>Team</th>\n",
              "      <th>Gold</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>United States of America</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>China</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Japan</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>ROC</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Australia</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Italy</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Germany</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>France</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Team  Gold\n",
              "90  United States of America    39\n",
              "15                     China    38\n",
              "46                     Japan    27\n",
              "34             Great Britain    22\n",
              "72                       ROC    20\n",
              "2                  Australia    17\n",
              "44                     Italy    10\n",
              "32                   Germany    10\n",
              "30                    France    10\n",
              "60               Netherlands    10"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "S_sZnU1Og0ws",
        "outputId": "b1079c8c-16bd-42e8-8bcf-9a12216b9f62"
      },
      "source": [
        "sns.barplot(y='Team', x='Gold' , data=Med_Go.sort_values(['Gold']),palette=\"RdGy\")\n",
        "plt.show()"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "5UByLxdgg4Qm",
        "outputId": "749a8ef7-b468-447a-9c2d-25c10690c77f"
      },
      "source": [
        "# Least Gold medals\n",
        "Medals.sort_values(['Gold'], ascending=False).drop(['Silver','Bronze','Total'],axis=1).tail(20)"
      ],
      "execution_count": 135,
      "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>Team</th>\n",
              "      <th>Gold</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Azerbaijan</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Finland</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>North Macedonia</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>Mongolia</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Namibia</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Kyrgyzstan</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Kuwait</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Kazakhstan</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>San Marino</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Jordan</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Armenia</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Grenada</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>Republic of Moldova</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Ghana</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>55</th>\n",
              "      <td>Malaysia</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Argentina</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Team  Gold\n",
              "4            Azerbaijan     0\n",
              "13         Burkina Faso     0\n",
              "29              Finland     0\n",
              "75         Saudi Arabia     0\n",
              "63      North Macedonia     0\n",
              "56               Mexico     0\n",
              "57             Mongolia     0\n",
              "59              Namibia     0\n",
              "52           Kyrgyzstan     0\n",
              "51               Kuwait     0\n",
              "62              Nigeria     0\n",
              "48           Kazakhstan     0\n",
              "74           San Marino     0\n",
              "47               Jordan     0\n",
              "1               Armenia     0\n",
              "36              Grenada     0\n",
              "71  Republic of Moldova     0\n",
              "33                Ghana     0\n",
              "55             Malaysia     0\n",
              "0             Argentina     0"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "q5mgTrnRg9zV",
        "outputId": "e67d8fde-88f9-4101-ec9a-47b1009ae1ca"
      },
      "source": [
        "# Countries with most silvers\n",
        "Med_Sil=Medals.sort_values(['Silver'], ascending=False).drop(['Gold','Bronze','Total'],axis=1).head(10)\n",
        "Med_Sil"
      ],
      "execution_count": 136,
      "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>Team</th>\n",
              "      <th>Silver</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>United States of America</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>China</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>ROC</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Japan</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>France</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Germany</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Italy</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>Spain</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Team  Silver\n",
              "90  United States of America      41\n",
              "15                     China      32\n",
              "72                       ROC      28\n",
              "34             Great Britain      21\n",
              "46                     Japan      14\n",
              "30                    France      12\n",
              "60               Netherlands      12\n",
              "32                   Germany      11\n",
              "44                     Italy      10\n",
              "80                     Spain       8"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "cAYUERL2hC3H",
        "outputId": "6529d065-ed2e-4a6d-eabd-98418e9dbc95"
      },
      "source": [
        "sns.barplot(y='Team', x='Silver' , data=Med_Sil.sort_values(['Silver']),palette=\"RdPu\")\n",
        "plt.show()"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "1vZeFRzxhIrN",
        "outputId": "31df817e-a679-410a-c169-d19813681673"
      },
      "source": [
        "# Least silver medals\n",
        "Medals.sort_values(['Silver'], ascending=False).drop(['Gold','Bronze','Total'],axis=1).tail(20)"
      ],
      "execution_count": 138,
      "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>Team</th>\n",
              "      <th>Silver</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Fiji</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Bermuda</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Kuwait</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Estonia</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Ghana</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Grenada</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Ireland</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Israel</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Kazakhstan</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Kosovo</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>Latvia</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>Republic of Moldova</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Mexico</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Morocco</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Finland</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>Qatar</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Botswana</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Cote d'Ivoir</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Team  Silver\n",
              "28                 Fiji       0\n",
              "9               Bermuda       0\n",
              "51               Kuwait       0\n",
              "26              Estonia       0\n",
              "33                Ghana       0\n",
              "36              Grenada       0\n",
              "41              Ireland       0\n",
              "43               Israel       0\n",
              "48           Kazakhstan       0\n",
              "50               Kosovo       0\n",
              "53               Latvia       0\n",
              "71  Republic of Moldova       0\n",
              "56               Mexico       0\n",
              "58              Morocco       0\n",
              "29              Finland       0\n",
              "13         Burkina Faso       0\n",
              "68          Puerto Rico       0\n",
              "69                Qatar       0\n",
              "10             Botswana       0\n",
              "18         Cote d'Ivoir       0"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "GgMCUqzlhsFl",
        "outputId": "24b0ab72-59d3-4c9e-f698-aac466e9bc9d"
      },
      "source": [
        "# Bronze medals won\n",
        "Med_Bro=Medals.sort_values(['Bronze'], ascending=False).drop(['Gold','Silver','Total'],axis=1).head(10)\n",
        "Med_Bro"
      ],
      "execution_count": 139,
      "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>Team</th>\n",
              "      <th>Bronze</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>United States of America</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>ROC</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Australia</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Italy</td>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>China</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Japan</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Germany</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>89</th>\n",
              "      <td>Ukraine</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Team  Bronze\n",
              "90  United States of America      33\n",
              "72                       ROC      23\n",
              "2                  Australia      22\n",
              "34             Great Britain      22\n",
              "44                     Italy      20\n",
              "15                     China      18\n",
              "46                     Japan      17\n",
              "32                   Germany      16\n",
              "60               Netherlands      14\n",
              "89                   Ukraine      12"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "v4E0gMJmhwO7",
        "outputId": "a98844c9-d4ac-40dc-83db-e8154854442c"
      },
      "source": [
        "sns.barplot(y='Team', x='Bronze' , data=Med_Bro.sort_values(['Bronze']),palette=\"RdBu_r\")\n",
        "plt.show()"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MERER0c966nHfVV5fkLQOta85RdKOwLPNDiyaR9LC8tom6aAG6rdJmtv8yCIiojc93Zym8voZaneTbyLpV8BoYP9mBxb9og04CPhxi+OIiIgG9dTjHi3pM8BE4Ergm8D/AmcDezY/tOgHpwC7Spop6ZjSs75V0j3lZ+fOO0i6RdKEuvXbJI3v16gjIoawnnrcw6h9ZludyldtXjjRz44HjrW9N4CkVYF3214kaVPgYqDzQ9x/CEwCjpa0GTDC9qzODUuaDEwG2HCDfPlERERf6SlxP2L7y/0WSQwEw4HTS496CbBZF3V+Cpwg6bPAodQ+dbAU21OBqQDbTRjvpkQbETEENTLHHUPHMcCjwHhq0yiLOlew/RdJ1wP7Ah8GtuvXCCMihrieEve7+i2KaJXngfoHZY8C/mj7VUmHUJsu6co5wM+AW20/3eQYIyKiTrc3p9l+qj8DiZaYDSyRNEvSMcAZwCGSZgFbAC90tZPt6cBzwHn9FmlERACNPWQkBhnbI8vry8AenTZvXbf8uVJvATCuo1DSW6i96buuqYFGRMRSGvnK04jXSPoYcCfwBduvtjqeiIihJj3uWCa2LwQubHUcERFDVXrcERERFZIedzSdVh7OiHXzJSwREX0hPe6IiIgKSeKOiIiokCTuiIiICskcdzTdkr88x7PTrm11GBGDxqj297Y6hGih9LgjIiIqJIk7IiKiQpK4IyIiKiSJOyIiokKSuCMiIiokiXuAk/R+SZa0xQrsP3Y59psk6fSyfHh5uEhERLRYEvfAdyBwW3ldHu8Hukzckhr6OKDts8rDRSIiosWSuAcwSSOBXYCPAx8pZRMlXVNX53RJk8ryKZLulTRb0r9L2hnYB/iWpJmSNpF0k6TvSpoGHCXpfZLulDRD0i8kjekijimSji3Ln5R0t6RZki6XtGrTfxEREfGafAHLwLYv8HPbv5H0pKTtuqsoaR1gP2AL25a0pu1nJF0NXGP7slIP4A2228v6WsCOZZ9PAMcB/9pDTFfYPrvs+1VqbypO6yKeycBkgA3f/KZlPvGIiOhaetwD24HAJWX5EnoeLn8WWAT8UNIHgL/0UPcndcsbANdKmgN8Ftiyl5jGSbq11D+4u/q2p9put92+zlqjemkyIiIalcQ9QElaG9gDOEfSAmpJ9cPAEv76uo0AsP0KsANwGbA38PMemn+hbvk04HTbWwGHdbTXg/OBT5X6JzVQPyIi+lAS98C1P/Aj2xvZbrO9IfAgtWs2VtIbJa0JvAtemw8fZft/gGOA8aWd54HVezjOKODhsnxIA3GtDjwiaTi1HndERPSjJO6B60Dgyk5ll1O7Se1SYG55nVG2rQ5cI2k2tbvQP1PKLwE+W24+26SL40wBfippOvBEA3GdANwJ/Aq4v+GziYiIPiHbrY4hBrltxm7mmy5c6v61iFhOeTrY0CBpeseNxPXS446IiKiQJO6IiIgKyee4o+mGrbpGhvYiIvpIetwREREVksQdERFRIUncERERFZLEHRERUSG5OS2abvHjj/DAD77S6jAiBo1NDjuh1SFEC6XHHRERUSFJ3BERERWSxB0REVEhSdwREREVksTdhySNkfRjSb+XNF3S7ZL266O22yQd1MO2FyXNlDRL0q8lbd5N3bdIuqwsT5D09w0cu13S91bsDCIioi8kcfcRSQKuAm6xvbHt7ag9gnODLuouz938bUCXibt4wPYE2+OBC4DPd3Vc23+yvX8pmgD0mrhtT7N95HLEHBERfSyJu+/sAbxk+6yOAtsP2T4NQNIkSVdLugH4paTVJJ0r6a7yrOx9S702SbdKuqf87FyaOwXYtfSqj+klljWAp7s5bpukuZLeAHwZOKC0eYCkHcoowYz6XrukiZKuKctTStw3lZGFJPSIiH6Uz3H3nS2Be3qpsy2wte2nJH0NuMH2oZLWBO6S9AvgMeDdthdJ2hS4GGgHjgeOtb13N21vImkmsDqwKvA33Ry3DcD2S5JOBNptfwpA0hrArrZfkbQn8DXgg10cawtg93Ks+ZLOtP1yfQVJk4HJAG9Ze1Qvv5aIiGhUEneTSPo+sAu1Xvj2pfh620+V5fcA+0g6tqyPAN4K/Ak4XdIEYAmwWYOHfMD2hHLsA4CpwF5dHLcno4ALyhsGA8O7qfffthcDiyU9BowB/lhfwfbUEgNbbbS+GzyHiIjoRRJ335lHXe/U9hGS1gWm1dV5oW5ZwAdtz69vRNIU4FFgPLWpjEXLEcvVwHndHLcnXwFutL1f6Znf1E29xXXLS8i/o4iIfpM57r5zAzBC0j/Xla3aQ/1rgU+Xm9qQtE0pHwU8YvtV4B+BYaX8eWpD043YBXiggXqd2xwFPFyWJzV4rIiI6EdJ3H3EtoH3A38r6UFJd1G7u/tz3ezyFWpD0bMlzSvrAGcAh0iaRW0uuaO3PBtYUj7u1dXNaZt0fByM2tz0JxoI+0ZgbMfNacA3ga9LmkF60RERA5Jq+SaiebbaaH1f9fnDWx1GxKCRh4wMDZKm227vXJ4ed0RERIUkcUdERFRIEndERESF5AakaLo3jl4vc3IREX0kPe6IiIgKSeKOiIiokCTuiIiICskcdzTd8wt+z43/9JFWhxExpOx+3iWtDiGaJD3uiIiICknijoiIqJAk7oiIiApJ4o6IiKiQJO6IiIgKSeIegiQtKY/ynCvpZ5LWrNu2paQbJM2X9FtJJ3Q8M7xs/ztJ0yTdK2mGpFNbcxYREUNTEvfQ9KLtCbbHAU8BRwBIWgW4GjjF9ubAeGBn4F/K9nHA6cBHbY8F2oHftSD+iIghK4k7bgfWL8sHAb+yfR2A7b8AnwKOL9uPA062fX/ZvsT2mf0cb0TEkJbEPYRJGga8i1ovG2BLYHp9HdsPACMlrQGM67y9h7YnlyH1ac8uWtyHUUdEDG1J3EPTKpJmAn8GxgDX9/UBbE+13W67fdSIN/Z18xERQ1YS99D0ou0JwEaAKHPcwL3AdvUVJW0MLLT9HDCv8/aIiOhfSdxDWJnDPhL4V0krAxcBu0jaE167We17wDfLLt8CPi9ps7J9JUmH93/kERFDVxL3EGd7BjAbOND2i8C+wBclzQfmAHdTu5Mc27OBo4GLJd0HzAU2bkngERFDVJ4ONgTZHtlp/X11y3OAiT3sew1wTdOCi4iIHqXHHRERUSFJ3BERERWSxB0REVEhmeOOplu9bWN2P++SVocRETEopMcdERFRIUncERERFZLEHRERUSGZ446me3ze/fzgHTu2OoyIiH512H13NKXd9LgjIiIqJIk7IiKiQpK4IyIiKiSJOyIiokKSuCMiIiqkKYlbUpukuZ3Kpkg6tpf92iV9ryxPlLTzchx7gaR1uyg/VNIcSbMlzZW0bymfJOktDbTbUL0VIeniEt8x3WyfKampX0Em6RxJY5t5jIiIWH4D6uNgtqcB08rqRGAh8OsVbVfSBsAXgG1tPytpJDC6bJ5E7bnSf+qlmUbrLW+Mbwa2t/32bra/AxgG7CppNdsvNCGGYbY/0dftRkRE32nJULmkmyR9Q9Jdkn4jaddSPlHSNZLagMOBY0ovc1dJoyVdLunu8vPOss86kq6TNE/SOYC6OOSbgOepvRHA9kLbD0raH2gHLirHWUXSiaX9uZKmqqarettJulnSdEnXSlqvxHOkpHtLz3mp3rGkEZLOK73/GZJ2L5uuA9bvON8uzuFA4Eel3r6dfpffkTRN0n2Stpd0haTfSvpqXb2Plt/3TEk/kDSslC+UdKqkWcBOpb32sm0vSfdImiXpl6VsB0m3l9h/LWnzBi55RET0kVbOca9sewfgaOBL9RtsLwDOAr5je4LtW4H/KOvbAx8EzinVvwTcZntL4ErgrV0caxbwKPBgSZrvK8e5jFoP/+BynBeB021vb3scsAqwd+d6wCvAacD+trcDzgVOLsc6HtjG9tbU3nx0dkTt0N6KWjK+QNIIYB/ggbrz7ewA4BLg4rJfvZdst5ff2X+VY4wDJpU3Nu8o+7+zxL8EOLjsuxpwp+3xtm/raFDSaOBs4IO2xwMfKpvuB3a1vQ1wIvC1LmJF0uTyZmLawlde7qpKREQsh2YNlbuB8ivK63SgrYE29wTGSq91qNcoQ967AR8AsP3fkp5e6qD2Ekl7AdsD7wK+I2k721O6OM7uko4DVgXWBuYBP+tUZ3NqifH6Es8w4JGybTa1nvlVwFVdtL8LtaSP7fslPQRsBjzX3YmXHvATtv9P0sPAuZLWtv1UqXJ1eZ0DzLP9SNnv98CG5ZjbAXeXeFcBHiv7LAEu7+KwOwK32H6wxNpxrFHU3mxsSu16Du8qZttTgakAG60ysrt/DxERsYyalbifBNbqVLY28GDd+uLyuqTBOFYCdrS9qL6wLpH3yLaBu4C7JF0PnAdM6dTWCOAMoN32HyRNAUZ00ZyoJcidutj2D9TeTLwP+IKkrWy/0lCQ3TsQ2ELSgrK+BrVRh7PLesfv8tW65Y71lUu8F9j+ty7aXmR7yTLE8hXgRtv7lSmNm5Zh34iIWEFNGSq3vRB4RNIeAJLWBvYCbutxx7/2PLB63fp1wKc7ViRNKIu3AAeVsr9j6TcMSHqLpG3riiYAD3VxnI4k/UTpze/fTTzzgdGSdirtD5e0paSVgA1t3wh8jlrvdGSncG6lDFNL2oza0P78bn4HlDY/DGxlu812G7U57s7D5T35JbC/pDeVNteWtFEv+9wB7CbpbR37lPJRwMNledIyxBAREX2gmXPcHwNOkDQTuAE4yfYDy7D/z4D96m7WOhJoLzd93cvr88cnUUsw86gNmf9fF20NB/5d0v0lngOAo8q284GzSvliar3YucC1wN11bdTXG0YtqX+j3NQ1E9i5lP+npDnADOB7tp/pFMsZwEqlzk+ASbYX071dgYdt19/Nfgu1aYP1etjvNbbvBb4IXCdpNnA90OO+th8HJgNXlHP8Sdn0TeDrkmYwwD6VEBExFKg2ghzRPButMtKfbxvX6jAiIvrVij4dTNL0cuPxX8k3p0VERFRIEndERESFJHFHRERUSG4uiqYbveUWHDZtxeZ6IiKiJj3uiIiICsld5dF0kp6nh8+qV8C6wBOtDmIFVf0cEn/rVf0cqhj/RrZHdy7MUHn0h/ldfaShKiRNq3L8UP1zSPytV/VzqHr89TJUHhERUSFJ3BERERWSxB39YWqrA1hBVY8fqn8Oib/1qn4OVY//Nbk5LSIiokLS446IiKiQJO6IiIgKSeKOppG0l6T5kn4n6fhWx7M8JC2QNKc8XnZaq+PpjaRzJT0maW5d2dqSrpf02/K61DPrB5JuzmGKpIfLdZgp6e9bGWNPJG0o6UZJ90qaJ+moUl6J69BD/JW4BpJGSLpL0qwS/0ml/G2S7ix/j34i6Q2tjnV5ZY47mkLSMOA3wLuBP1J7tvmB5dnglSFpAdBuuxJf3CBpN2AhcKHtcaXsm8BTtk8pb6DWsv25VsbZk27OYQqw0Pa/tzK2RkhaD1jP9j2SVgemA+8HJlGB69BD/B+mAtdAkoDVbC+UNBy4DTgK+Axwhe1LJJ0FzLJ9ZitjXV7pcUez7AD8zvbvbb8EXALs2+KYBj3btwBPdSreF7igLF9A7Y/wgNXNOVSG7Uds31OWnwfuA9anItehh/grwTULy+rw8mNgD+CyUj5gf/+NSOKOZlkf+EPd+h+p0H/+OgaukzRd0uRWB7Ocxth+pCz/GRjTymBWwKckzS5D6QNymLkzSW3ANsCdVPA6dIofKnINJA2TNBN4DLgeeAB4xvYrpUpV/x4BSdwRvdnF9rbA3wFHlGHcynJtbqyK82NnApsAE4BHgFNbG07vJI0ELgeOtv1c/bYqXIcu4q/MNbC9xPYEYANqo39btDikPpXEHc3yMLBh3foGpaxSbD9cXh8DrqT2R6BqHi3zlh3zl4+1OJ5lZvvR8sf4VeBsBvh1KHOrlwMX2b6iFFfmOnQVf9WuAYDtZ4AbgZ2ANSV1PJ+jkn+POiRxR7PcDWxa7uR8A/AR4OoWx7RMJK1Wbs5B0mrAe4C5Pe81IF0NHFKWDwH+q4WxLJeOhFfsxwC+DuXmqB8C99n+dt2mSlyH7uKvyjWQNFrSmmV5FWo3yN5HLYHvX7T/h+YAAAKVSURBVKoN2N9/I3JXeTRN+bjId4FhwLm2T25xSMtE0sbUetlQe5Lejwf6OUi6GJhI7RGGjwJfAq4CLgXeCjwEfNj2gL35q5tzmEhtiNbAAuCwuvniAUXSLsCtwBzg1VL8eWrzxAP+OvQQ/4FU4BpI2prazWfDqHVOL7X95fL/+RJgbWAG8FHbi1sX6fJL4o6IiKiQDJVHRERUSBJ3REREhSRxR0REVEgSd0RERIUkcUdERFRIEndEVJakJeVJVbMk3SNp51bHFNFs+ThYRFSWpIW2R5bl9wKft/23neqsXPcd1RGVlx53RAwWawBPA0iaKOlWSVcD95ZnNJ9Xnq0+Q9Lupd4kSVdI+nl5TvY3S/k+dc+dni/pwVK+naSby0Nnru30bWIR/WLl3qtERAxYq5SnQI0A1qP26MYO2wLjbD8o6V+pPdtjK0lbUHvi22al3gRqT8BaDMyXdJrtqylf0SvpUuDm8v3dpwH72n5c0gHAycCh/XCeEa9J4o6IKnuxPAUKSTsBF0oaV7bdZfvBsrwLtaSL7fslPQR0JO5f2n62tHEvsBHlkbSSjivH+H5pdxxwfe3rvBlG7SlZEf0qiTsiBgXbt0taFxhdil5ocNf676teQvm7KGlP4ENAx6NcBcyzvVMfhBux3DLHHRGDQhkCHwY82cXmW4GDS73NqD3oY34PbW0EfB/4kO0XS/F8YHTp2SNpuKQt++4MIhqTHndEVFnHHDfUesSH2F5ShrLrnQGcKWkO8AowyfbiLup1mASsA1xV6vzJ9t9L2h/4nqRR1P5+fheY15cnFNGbfBwsIiKiQjJUHhERUSFJ3BERERWSxB0REVEhSdwREREVksQdERFRIUncERERFZLEHRERUSH/D0UhJQmhxcCAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "q15l15nqh1Yj",
        "outputId": "3ce9f1fa-cdbf-42d5-d566-3a4d78dd2a3f"
      },
      "source": [
        "# Least bronze\n",
        "Medals.sort_values(['Bronze'], ascending=False).drop(['Gold','Silver','Total'],axis=1).tail(20)"
      ],
      "execution_count": 141,
      "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>Team</th>\n",
              "      <th>Bronze</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Ghana</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Georgia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>Uganda</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Bahrain</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Tunisia</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Bahamas</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Bermuda</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Turkmenistan</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>South Africa</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>73</th>\n",
              "      <td>Romania</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Ecuador</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>North Macedonia</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Namibia</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Morocco</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Lithuania</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Kosovo</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>92</th>\n",
              "      <td>Venezuela</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Team  Bronze\n",
              "33            Ghana       1\n",
              "31          Georgia       1\n",
              "88           Uganda       1\n",
              "6           Bahrain       0\n",
              "85          Tunisia       0\n",
              "5           Bahamas       0\n",
              "9           Bermuda       0\n",
              "87     Turkmenistan       0\n",
              "68      Puerto Rico       0\n",
              "81           Sweden       0\n",
              "79     South Africa       0\n",
              "75     Saudi Arabia       0\n",
              "73          Romania       0\n",
              "24          Ecuador       0\n",
              "63  North Macedonia       0\n",
              "59          Namibia       0\n",
              "58          Morocco       0\n",
              "54        Lithuania       0\n",
              "50           Kosovo       0\n",
              "92        Venezuela       0"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "gQkzk7xAh4U1",
        "outputId": "02d6a41e-e4cc-4896-91fa-4e3b4b63882f"
      },
      "source": [
        "# Most medals won\n",
        "Med_Total=Medals.sort_values(['Total'], ascending=False).drop(['Gold','Silver','Bronze'],axis=1).head(10)\n",
        "Med_Total"
      ],
      "execution_count": 142,
      "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>Team</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>90</th>\n",
              "      <td>United States of America</td>\n",
              "      <td>113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>China</td>\n",
              "      <td>88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>ROC</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Great Britain</td>\n",
              "      <td>65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Japan</td>\n",
              "      <td>58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Australia</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Italy</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Germany</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>France</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        Team  Total\n",
              "90  United States of America    113\n",
              "15                     China     88\n",
              "72                       ROC     71\n",
              "34             Great Britain     65\n",
              "46                     Japan     58\n",
              "2                  Australia     46\n",
              "44                     Italy     40\n",
              "32                   Germany     37\n",
              "60               Netherlands     36\n",
              "30                    France     33"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "Apr8e0o2h7WC",
        "outputId": "decac319-8aaa-4349-a0d6-423b5b502c01"
      },
      "source": [
        "sns.barplot(y='Team', x='Total' , data=Med_Total.sort_values(['Total']),palette=\"OrRd\")\n",
        "plt.show()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S/n+ZnyAp318ZERHRBgO5Oe1Mqncuv7LM/xo4qlUBRURERN/6TNySerrR17P9Q8oLRmw/BywZhtgiIiKil/5a3DeVz6ckrUv1NadI2hl4rNWBxdCQ9GS7Y4iIiKHT381pKp8fobqbfHNJ1wHr0/y9zhEREdFi/SXu9SV9pEz/CPhfqmS+GNgDmN/i2GKISBoLXASsDYwGPmn7IkldwE+B2cAOwCLgnbb/IunTwN5UL5n5JfB+25Z0FXAjMB1YC3iP7WuG94giIkau/rrKR1E9s70GsDpVkh8FrFbKoj6eBvazvQNVwv2qpJ4ela2Ak2y/Bngc+FApP9H2jrYnUiXvvRq2t7LtnahuUvxMsx1KmiGpW1L3w0893YJDiogYmfprcd9r+7PDFkm0koDPS3od1U2GGwLjy7I/2L6uTH8XOAL4CjBd0rFUF2rrULXGf1Lq9bzSdTbQ1WyHtmcBswC222h9D+XBRESMZAMZ4476O4Tq3oQptp+VdBcvvOmtd1J1eSPcScBU23+QNLOhPlTDJVA9XTCQb9+LiIgh0l9X+RuGLYpotXHA/SVpTwc2aVj2KkmvLdNvB67lhST9YBkfz82IEREriD4Tt+2HhzOQGHrlWfzFwPeAqZIWAO8EbmuodjtwuKRfUd28drLtR4FTgYVUX75z87AGHhERfUo3Z2fbBrjD9oPAa3svLHeVP2f7Hb2X2f4k8Mkm5dMaph+kjzHuiIhojYF85WnUkKQPAOfQJPlGRER9pcXdoWyfAvT74mHbdwEThyWgiIgYEmlxR0RE1Eha3NFyq7xiEzb6WL+N/4iIGKC0uCMiImokiTsiIqJGkrgjIiJqJGPc0XJP/+FObj/qkHaHETHktvra99odQoxAaXFHRETUSBJ3REREjSRxR0RE1EgSd0RERI0kcUdERNRIEvcQkjRe0vcl/U7SbEnXS9pviLbdJent/Sz7q6S5kuZJ+qWkrfqo+0pJ55fpyZL+cQD7nirpG4M7goiIGApJ3ENEkoAfA1fb3sz2FOAgYKMmdZfnMbwuoGniLu6wPdn2JOAs4N+b7df2n2wfUIomA0tN3La7bR+xHDFHRMQQS+IeOq8Hnilv5QLA9t22vwkg6TBJF0u6Avi5pNUlnS7pJklzJO1T6nVJukbSLeVnl7K5LwK7l1b10UuJZU3gkT722yVpoaRVgM8CB5ZtHihpp9JLMKex1S5pmqRLyvTMEvdVpWchCT0iYhjlC1iGzjbALUupswOwne2HJX0euML2uyWtBdwk6WfA/cAbbT8taQuqd2pPBT4OHGN7rz62vbmkucAawGrA3/Wx3y4A289I+jQw1fa/AkhaE9jd9nOS9gA+D+zfZF9bA9PLvm6XdLLtZxsrSJoBzAB45RqrLeXXEhERA5XE3SKSvgXsRtUK37EUX2774TL9JuCtko4p82OAVwF/Ak6UNBlYAmw5wF3eYXty2feBwCxgzyb77c844KxywWBgdB/1/sf2YmCxpPuB8cAfGyvYnlViYOL4dT3AY4iIiKVI4h46i2hondo+XNJ6QHdDnacapgXsb/v2xo1Imgn8GZhENZTx9HLEcjFwRh/77c9/AFfa3q+0zK/qo97ihukl5O8oImLYZIx76FwBjJH0wYay/vqILwU+XG5qQ9L2pXwccK/t54F/AUaV8ieouqYHYjfgjgHU673NccA9ZfqwAe4rIiKGURL3ELFtYF/g7yXdKekmqru7P9bHKv9B1RU9X9KiMg9wEnCopHlUY8k9reX5wJLyuFezm9M273kcjGps+r0DCPtKYELPzWnAl4EvSJpDWtERESskVfkmonUmjl/XFxy859IrRtRM3g4WrSRptu2pvcvT4o6IiKiRJO6IiIgayThmtNyYjTdNl2JExBBJizsiIqJGkrgjIiJqJIk7IiKiRpK4IyIiaiQ3p0XLPfnb27lur93bHUbECmnXS65pdwhRM2lxR0RE1EgSd0RERI0kcUdERNRIEndERESNJHGPQJKWlDeCLZT0E0lrNSzbRtIVkm6X9BtJn+p59WhZ/mZJ3ZJulTRH0lfbcxQRESNTEvfI9Ffbk21PBB4GDgeQtCpwMfBF21sBk4BdgA+V5ROBE4F32J4ATAV+24b4IyJGrCTuuB7YsEy/HbjO9mUAtv8C/Cvw8bL8WOB427eV5UtsnzzM8UZEjGhJ3COYpFHAG6ha2QDbALMb69i+AxgraU1gYu/l/Wx7RulS7370mWeHMOqIiJEtiXtkWlXSXOA+YDxw+VDvwPYs21NtT11rldFDvfmIiBEriXtk+qvtycAmgChj3MCtwJTGipI2A560/TiwqPfyiIgYXkncI1gZwz4C+KiklYHvAbtJ2gP+drPaN4Avl1X+E/h3SVuW5StJ+sDwRx4RMXIlcY9wtucA84GDbf8V2Af4pKTbgQXAzVR3kmN7PnAUcI6kXwELgc3aEnhExAiVl4yMQLbH9prfu2F6ATCtn3UvAS5pWXAREdGvtLgjIiJqJIk7IiKiRpK4IyIiaiRj3NFyY1+9Fbteck27w4iI6AhpcUdERNRIEndERESNJHFHRETUSMa4o+UeWbSQC7bZot1hREQL7L/oN+0OYcRJizsiIqJGkrgjIiJqJIk7IiKiRpK4IyIiaiSJOyIiokaSuDuYpFdIOlfSHZJmS/pfSTMkNX27l6TTJE0Y7jgjImLg8jhYh5Ik4EfAWbYPKmWTgLf2tY7t9w5TeBERsZzS4u5c04FnbZ/SU2B7HnANMFbS+ZJuk/S9kuSRdJWkqWX6SUnHS5on6QZJ40v53pJulDRH0s96yiMiYngkcXeuicDsPpZtDxwFTAA2A3ZtUmd14Abbk4CrgfeV8muBnW1vD5wLHNtsB6VLvltS9+NLliz/UURExIukq3xkusn2HwEkzQW6qBJyo2eAnrHw2cAby/RGwA8kbQCsAtzZbAe2ZwGzADZfdYyHMviIiJEsLe7OtQiY0seyxQ3TS2h+AfesbTep803gRNvbAu8HxgxBrBERMUBJ3J3rCuBlkmb0FEjaDth9kNsdB9xTpg8d5LYiImIZJXF3qNJa3g/YozwOtgj4AnDfIDc9EzhP0mzgwUFuKyIilpFe6A2NaI3NVx3jL2+2cbvDiIgWyNvBWkfSbNtTe5enxR0REVEjSdwRERE1ksQdERFRI3mOO1pu7W0msn93d7vDiIjoCGlxR0RE1EgSd0RERI0kcUdERNRIxrij5e6fN4+vv/zl7Q4jImJYHXn//S3ZblrcERERNZLEHRERUSNJ3BERETWSxB0REVEjSdwRERE10pLELalL0sJeZTMlHbOU9aZK+kaZniZpl+XY912S1mtS/m5JCyTNl7RQ0j6l/DBJrxzAdgdUbzAknVPiO7qP5XMlndviGE6TNKGV+4iIiOW3Qj0OZrsb6PluzGnAk8AvB7tdSRsBnwB2sP2YpLHA+mXxYcBC4E9L2cxA6y1vjK8AdrT96j6WvwYYBewuaXXbT7UghlG23zvU242IiKHTlq5ySVdJ+pKkmyT9WtLupXyapEskdQEfAI4urczdJa0v6QJJN5efXcs660q6TNIiSacBarLLlwNPUF0IYPtJ23dKOgCYCnyv7GdVSZ8u218oaZYqzepNkfQLSbMlXSppgxLPEZJuLS3nl7SOJY2RdEZp/c+RNL0sugzYsOd4mxzDwcB3Sr19ev0uT5DULelXknaUdKGk30j6XEO9d5Tf91xJ/y1pVCl/UtJXJc0DXlu2N7Us21PSLZLmSfp5KdtJ0vUl9l9K2moApzwiIoZIO8e4V7a9E3AU8JnGBbbvAk4BTrA92fY1wNfL/I7A/sBppfpngGttbwP8CHhVk33NA/4M3FmS5t5lP+dTtfAPKfv5K3Ci7R1tTwRWBfbqXQ94DvgmcIDtKcDpwPFlXx8Htre9HdXFR2+HV7v2tlTJ+CxJY4C3Anc0HG9vBwLnAueU9Ro9U162fgpwUdnHROCwcmHzmrL+riX+JcAhZd3VgRttT7J9bc8GJa0PnArsb3sS8M9l0W3A7ra3Bz4NfL5JrEiaUS4mup98/vlmVSIiYjm0qqvcAyi/sHzOBroGsM09gAnS3xrUa5Yu79cB/wRg+38kPfKSndpLJO0J7Ai8AThB0hTbM5vsZ7qkY4HVgHWARcBPetXZiioxXl7iGQXcW5bNp2qZ/xj4cZPt70aV9LF9m6S7gS2Bx/s68NICftD27yXdA5wuaR3bD5cqF5fPBcAi2/eW9X4HbFz2OQW4ucS7KtDzlT5LgAua7HZn4Grbd5ZYe/Y1jupiYwuq8zm6Wcy2ZwGzAF41enRffw8REbGMWpW4HwLW7lW2DnBnw/zi8rlkgHGsBOxs++nGwoZE3i/bBm4CbpJ0OXAGMLPXtsYAJwFTbf9B0kxgTJPNiSpBvrbJsrdQXUzsDXxC0ra2nxtQkH07GNha0l1lfk2qXodTy3zP7/L5hume+ZVLvGfZ/rcm237a9pJliOU/gCtt71eGNK5ahnUjImKQWtJVbvtJ4F5JrweQtA6wJ3Btvyu+2BPAGg3zlwEf7pmRNLlMXg28vZS9mZdeMCDplZJ2aCiaDNzdZD89SfrB0po/oI94bgfWl/Tasv3RkraRtBKwse0rgY9RtU7H9grnGko3taQtqbr2b+/jd0DZ5tuAbW132e6iGuPu3V3en58DB0h6ednmOpI2Wco6NwCvk7RpzzqlfBxwT5k+bBliiIiIIdDKMe53Ap+SNBe4AjjO9h3LsP5PgP0abtY6Aphabvq6lRfGj4+jSjCLqLrMf99kW6OBr0i6rcRzIHBkWXYmcEopX0zVil0IXArc3LCNxnqjqJL6l8pNXXOBXUr5dyUtAOYA37D9aK9YTgJWKnV+ABxmezF92x24x3bj3exXUw0bbNDPen9j+1bgk8BlkuYDlwP9rmv7AWAGcGE5xh+URV8GviBpDivYUwkRESOBqh7kiNZ51ejR/ujaL+kIiYjoaIN9O5ik2eXG4xfJN6dFRETUSBJ3REREjSRxR0RE1EhuLoqWe/mkSRzZ3b30ihERsVRpcUdERNRI7iqPlpP0BP08q94B1gMebHcQLdbpx9jpxwedf4ydeHyb2F6/d2G6ymM43N7skYZOIam7k48POv8YO/34oPOPsdOPr1G6yiMiImokiTsiIqJGkrhjOMxqdwAt1unHB51/jJ1+fND5x9jpx/c3uTktIiKiRtLijoiIqJEk7oiIiBpJ4o6WkbSnpNsl/VbSx9sdz1CQtLGkKyXdKmmRpCNL+TqSLpf0m/JZ69ehSRolaY6kS8r8ppJuLOfyB5JWaXeMgyFpLUnnl1f9/krSazvpHEo6uvx9LpR0jqQxdT+Hkk6XdL+khQ1lTc+ZKt8oxzpf0g7ti3zoJXFHS0gaBXwLeDMwAThY0oT2RjUkngM+ansCsDNweDmujwM/t70F8PMyX2dHAr9qmP8ScILtVwOPAO9pS1RD5+vAT21vDUyiOtaOOIeSNgSOAKbangiMAg6i/ufwTGDPXmV9nbM3A1uUnxnAycMU47BI4o5W2Qn4re3f2X4GOBfYp80xDZrte23fUqafoPqHvyHVsZ1Vqp0F7NueCAdP0kbAW4DTyryA1wPnlyp1P75xwOuAbwPYfsb2o3TQOaT6cq1VJa0MrAbcS83Poe2rgYd7Ffd1zvYBznblBmAtSRsMT6Stl8QdrbIh8IeG+T+Wso4hqQvYHrgRGG/73rLoPmB8m8IaCl8DjgWeL/PrAo/afq7M1/1cbgo8AJxRhgNOk7Q6HXIObd8DfAX4PVXCfgyYTWedwx59nbOO/v+TxB2xHCSNBS4AjrL9eOMyV89Y1vI5S0l7Affbnt3uWFpoZWAH4GTb2wNP0atbvObncG2qFuemwCuB1XlpF3PHqfM5W1ZJ3NEq9wAbN8xvVMpqT9JoqqT9PdsXluI/93TFlc/72xXfIO0KvFXSXVTDG6+nGg9eq3S7Qv3P5R+BP9q+scyfT5XIO+Uc7gHcafsB288CF1Kd1046hz36Omcd+/8HkrijdW4Gtih3sq5CdXPMxW2OadDKeO+3gV/Z/q+GRRcDh5bpQ4GLhju2oWD732xvZLuL6pxdYfsQ4ErggFKttscHYPs+4A+StmOHqaMAAAIzSURBVCpFbwBupUPOIVUX+c6SVit/rz3H1zHnsEFf5+xi4J3l7vKdgccautRrL9+cFi0j6R+pxktHAafbPr7NIQ2apN2Aa4AFvDAG/O9U49w/BF4F3A28zXbvG2lqRdI04Bjbe0najKoFvg4wB3iH7cXtjG8wJE2muvluFeB3wLuoGjIdcQ4lHQccSPUUxBzgvVRjvLU9h5LOAaZRvb7zz8BngB/T5JyVC5YTqYYI/gK8y3Z3O+JuhSTuiIiIGklXeURERI0kcUdERNRIEndERESNJHFHRETUSBJ3REREjSRxR0RHkbSupLnl5z5J9zTMr9Kr7lGSVhvANq+SNLV1UUcM3MpLrxIRUR+2HwImA0iaCTxp+yt9VD8K+C7Vs74RtZAWd0R0PElvKC8UWVDe6/wySUdQfZf3lZKuLPVOltRd3mV9XHujjmguiTsiOt0Yqnc5H2h7W6qexg/a/gbwJ2C67eml7idsTwW2A/5e0nbtCDiiP0ncEdHpRlG9dOPXZf4sqvdxN/M2SbdQfSXoNsCEYYgvYplkjDsiApC0KXAMsKPtRySdSdVaj1ihpMUdEZ1uCdAl6dVl/l+AX5TpJ4A1yvSaVO/mfkzSeODNwxplxAClxR0Rne5pqrd/nVfeR30zcEpZNgv4qaQ/2Z4uaQ5wG/AH4Lq2RBuxFHk7WERERI2kqzwiIqJGkrgjIiJqJIk7IiKiRpK4IyIiaiSJOyIiokaSuCMiImokiTsiIqJG/g8rzs0DW8ChkAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "u9-IktLCh903",
        "outputId": "637b0fcd-36b0-4c29-88a1-a8f4473f61a6"
      },
      "source": [
        "# Least medals won\n",
        "Medals.sort_values(['Total'], ascending=False).drop(['Gold','Silver','Bronze'],axis=1).tail(20)"
      ],
      "execution_count": 144,
      "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>Team</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>Tunisia</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50</th>\n",
              "      <td>Kosovo</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Nigeria</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Botswana</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Bahrain</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Burkina Faso</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Bermuda</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>Syrian Arab Republic</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>Turkmenistan</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>Lithuania</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Cote d'Ivoir</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>Saudi Arabia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>Republic of Moldova</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Grenada</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Puerto Rico</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>North Macedonia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>51</th>\n",
              "      <td>Kuwait</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Namibia</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>Morocco</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Ghana</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    Team  Total\n",
              "85               Tunisia      2\n",
              "50                Kosovo      2\n",
              "62               Nigeria      2\n",
              "10              Botswana      1\n",
              "6                Bahrain      1\n",
              "13          Burkina Faso      1\n",
              "9                Bermuda      1\n",
              "83  Syrian Arab Republic      1\n",
              "87          Turkmenistan      1\n",
              "54             Lithuania      1\n",
              "18          Cote d'Ivoir      1\n",
              "75          Saudi Arabia      1\n",
              "71   Republic of Moldova      1\n",
              "36               Grenada      1\n",
              "68           Puerto Rico      1\n",
              "63       North Macedonia      1\n",
              "51                Kuwait      1\n",
              "59               Namibia      1\n",
              "58               Morocco      1\n",
              "33                 Ghana      1"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LgxuPNHiCRX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}