summer.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Wlah_Hsx88os"
   },
   "source": [
    "# Summer Olympics Data Analysis Assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jai Krishna Pandey\n",
    "Batch2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Summer.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Discipline</th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Aquatics</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>HAJOS, Alfred</td>\n",
       "      <td>HUN</td>\n",
       "      <td>Men</td>\n",
       "      <td>100M Freestyle</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Aquatics</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>HERSCHMANN, Otto</td>\n",
       "      <td>AUT</td>\n",
       "      <td>Men</td>\n",
       "      <td>100M Freestyle</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Aquatics</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>DRIVAS, Dimitrios</td>\n",
       "      <td>GRE</td>\n",
       "      <td>Men</td>\n",
       "      <td>100M Freestyle For Sailors</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Aquatics</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>MALOKINIS, Ioannis</td>\n",
       "      <td>GRE</td>\n",
       "      <td>Men</td>\n",
       "      <td>100M Freestyle For Sailors</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Aquatics</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>CHASAPIS, Spiridon</td>\n",
       "      <td>GRE</td>\n",
       "      <td>Men</td>\n",
       "      <td>100M Freestyle For Sailors</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Year    City     Sport Discipline             Athlete Country Gender  \\\n",
       "0  1896  Athens  Aquatics   Swimming       HAJOS, Alfred     HUN    Men   \n",
       "1  1896  Athens  Aquatics   Swimming    HERSCHMANN, Otto     AUT    Men   \n",
       "2  1896  Athens  Aquatics   Swimming   DRIVAS, Dimitrios     GRE    Men   \n",
       "3  1896  Athens  Aquatics   Swimming  MALOKINIS, Ioannis     GRE    Men   \n",
       "4  1896  Athens  Aquatics   Swimming  CHASAPIS, Spiridon     GRE    Men   \n",
       "\n",
       "                        Event   Medal  \n",
       "0              100M Freestyle    Gold  \n",
       "1              100M Freestyle  Silver  \n",
       "2  100M Freestyle For Sailors  Bronze  \n",
       "3  100M Freestyle For Sailors    Gold  \n",
       "4  100M Freestyle For Sailors  Silver  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<p>31165 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Year   City  Sport  Discipline  Athlete  Country  Gender  Event  Medal\n",
       "0      False  False  False       False    False    False   False  False  False\n",
       "1      False  False  False       False    False    False   False  False  False\n",
       "2      False  False  False       False    False    False   False  False  False\n",
       "3      False  False  False       False    False    False   False  False  False\n",
       "4      False  False  False       False    False    False   False  False  False\n",
       "...      ...    ...    ...         ...      ...      ...     ...    ...    ...\n",
       "31160  False  False  False       False    False    False   False  False  False\n",
       "31161  False  False  False       False    False    False   False  False  False\n",
       "31162  False  False  False       False    False    False   False  False  False\n",
       "31163  False  False  False       False    False    False   False  False  False\n",
       "31164  False  False  False       False    False    False   False  False  False\n",
       "\n",
       "[31165 rows x 9 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year          0\n",
       "City          0\n",
       "Sport         0\n",
       "Discipline    0\n",
       "Athlete       0\n",
       "Country       4\n",
       "Gender        0\n",
       "Event         0\n",
       "Medal         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Aquatics', 'Athletics', 'Cycling', 'Fencing', 'Gymnastics',\n",
       "       'Shooting', 'Tennis', 'Weightlifting', 'Wrestling', 'Archery',\n",
       "       'Basque Pelota', 'Cricket', 'Croquet', 'Equestrian', 'Football',\n",
       "       'Golf', 'Polo', 'Rowing', 'Rugby', 'Sailing', 'Tug of War',\n",
       "       'Boxing', 'Lacrosse', 'Roque', 'Hockey', 'Jeu de paume', 'Rackets',\n",
       "       'Skating', 'Water Motorsports', 'Modern Pentathlon', 'Ice Hockey',\n",
       "       'Basketball', 'Canoe / Kayak', 'Handball', 'Judo', 'Volleyball',\n",
       "       'Table Tennis', 'Badminton', 'Baseball', 'Softball', 'Taekwondo',\n",
       "       'Triathlon', 'Canoe'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sport'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(31165, 9)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Year', 'City', 'Sport', 'Discipline', 'Athlete', 'Country', 'Gender',\n",
       "       'Event', 'Medal'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Year</th>\n",
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       "      <th>count</th>\n",
       "      <td>31165.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>1970.482785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>33.158416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1896.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1948.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1980.000000</td>\n",
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       "      <th>75%</th>\n",
       "      <td>2000.000000</td>\n",
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       "               Year\n",
       "count  31165.000000\n",
       "mean    1970.482785\n",
       "std       33.158416\n",
       "min     1896.000000\n",
       "25%     1948.000000\n",
       "50%     1980.000000\n",
       "75%     2000.000000\n",
       "max     2012.000000"
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     "metadata": {},
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   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 31165 entries, 0 to 31164\n",
      "Data columns (total 9 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Year        31165 non-null  int64 \n",
      " 1   City        31165 non-null  object\n",
      " 2   Sport       31165 non-null  object\n",
      " 3   Discipline  31165 non-null  object\n",
      " 4   Athlete     31165 non-null  object\n",
      " 5   Country     31161 non-null  object\n",
      " 6   Gender      31165 non-null  object\n",
      " 7   Event       31165 non-null  object\n",
      " 8   Medal       31165 non-null  object\n",
      "dtypes: int64(1), object(8)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s1_KPqsOadcL"
   },
   "source": [
    "### 1. In how many cities Summer Olympics is held so far?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Athens', 'Paris', 'St Louis', 'London', 'Stockholm', 'Antwerp',\n",
       "       'Amsterdam', 'Los Angeles', 'Berlin', 'Helsinki',\n",
       "       'Melbourne / Stockholm', 'Rome', 'Tokyo', 'Mexico', 'Munich',\n",
       "       'Montreal', 'Moscow', 'Seoul', 'Barcelona', 'Atlanta', 'Sydney',\n",
       "       'Beijing'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['City'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['City'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Fy5AvU-ubUtk"
   },
   "source": [
    "### 2. Which sport is having most number of Gold Medals so far? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Sport</th>\n",
       "      <th>Discipline</th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Aquatics</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>HAJOS, Alfred</td>\n",
       "      <td>HUN</td>\n",
       "      <td>Men</td>\n",
       "      <td>100M Freestyle</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Aquatics</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>MALOKINIS, Ioannis</td>\n",
       "      <td>GRE</td>\n",
       "      <td>Men</td>\n",
       "      <td>100M Freestyle For Sailors</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Aquatics</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>HAJOS, Alfred</td>\n",
       "      <td>HUN</td>\n",
       "      <td>Men</td>\n",
       "      <td>1200M Freestyle</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Aquatics</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>NEUMANN, Paul</td>\n",
       "      <td>AUT</td>\n",
       "      <td>Men</td>\n",
       "      <td>400M Freestyle</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>BURKE, Thomas</td>\n",
       "      <td>USA</td>\n",
       "      <td>Men</td>\n",
       "      <td>100M</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year    City      Sport Discipline             Athlete Country Gender  \\\n",
       "0   1896  Athens   Aquatics   Swimming       HAJOS, Alfred     HUN    Men   \n",
       "3   1896  Athens   Aquatics   Swimming  MALOKINIS, Ioannis     GRE    Men   \n",
       "6   1896  Athens   Aquatics   Swimming       HAJOS, Alfred     HUN    Men   \n",
       "9   1896  Athens   Aquatics   Swimming       NEUMANN, Paul     AUT    Men   \n",
       "13  1896  Athens  Athletics  Athletics       BURKE, Thomas     USA    Men   \n",
       "\n",
       "                         Event Medal  \n",
       "0               100M Freestyle  Gold  \n",
       "3   100M Freestyle For Sailors  Gold  \n",
       "6              1200M Freestyle  Gold  \n",
       "9               400M Freestyle  Gold  \n",
       "13                        100M  Gold  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medalOly = df[df.Medal == 'Gold']\n",
    "medalOly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[]\n",
    "for sport in medalOly['Sport'].unique():\n",
    "    data.append([sport,len(medalOly[medalOly['Sport']==sport])])\n",
    "data=pd.DataFrame(data, columns=['Sport','No. of gold medals']) \n",
    "data=data.sort_values(by='No. of gold medals', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "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>Sport</th>\n",
       "      <th>No. of gold medals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aquatics</td>\n",
       "      <td>1421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Athletics</td>\n",
       "      <td>1215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Rowing</td>\n",
       "      <td>890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fencing</td>\n",
       "      <td>552</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sport  No. of gold medals\n",
       "0     Aquatics                1421\n",
       "1    Athletics                1215\n",
       "17      Rowing                 890\n",
       "4   Gymnastics                 820\n",
       "3      Fencing                 552"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sport'>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6yCSfm9F+WFVtXVW7ZnRz39fHwzZWVtUB49kxXj3jHAAAmFoL1nRAVZ2e5BlJtq+q5UmOS/KeJGdV1VFJrk/y0iTp7quq6qwk30xyZ5Jjuvuu8Vu9IaMZNx6Y5AvjBwAATLU1BubuPnw1uw5ezfHHJzl+lvaLkuy5TtUBAMCEWekPAAAGCMwAADBAYAYAgAECMwAADBCYAQBggMAMAAADBGYAABggMAMAwACBGQAABgjMAAAwQGAGAIABAjMAAAwQmAEAYIDADAAAAwRmAAAYIDADAMAAgRkAAAYIzAAAMEBgBgCAAQIzAAAMEJgBAGCAwAwAAAMWTLoAgC1JvbsmXcLU6ON60iUAbBB6mAEAYIDADAAAAwRmAAAYIDADAMAAgRkAAAYIzAAAMEBgBgCAAQIzAAAMEJgBAGCAwAwAAAMEZgAAGCAwAwDAAIEZAAAGCMwAADBAYAYAgAECMwAADBCYAQBgwIJJFwAAW7p6d026hKnRx/WkS4B1pocZAAAGzCkwV9Vbq+qqqrqyqk6vqm2q6uFV9cWqunb8/LAZxx9bVcuq6pqqOmTu5QMAwMa13oG5qnZM8qYkS7p7zyRbJTksyTuSnNfduyU5b7ydqtpjvP8JSZ6T5MSq2mpu5QMAwMY11yEZC5I8sKoWJHlQkhuTHJrk1PH+U5O8cPz60CRndPft3X1dkmVJ9p/j9QEAYKNa78Dc3T9I8v4k1ye5KcnPuvvcJI/q7pvGx9yU5JHjU3ZMcsOMt1g+bruPqjq6qi6qqotWrFixviUCAMCczWVIxsMy6jXeNcmjkzy4qo4YOmWWtllvle3uk7p7SXcvWbhw4fqWCAAAczaXIRnPSnJdd6/o7juSnJ3kKUl+VFU7JMn4+ebx8cuT7DTj/EUZDeEAAICpNZfAfH2SA6rqQVVVSQ5OcnWSc5IcOT7myCSfG78+J8lhVbV1Ve2aZLckX5/D9QEAYKNb74VLuvtrVfXpJBcnuTPJJUlOSvKQJGdV1VEZheqXjo+/qqrOSvLN8fHHdPddc6wfAAA2qjmt9NfdxyU5bpXm2zPqbZ7t+OOTHD+XawIAwKZkpT8AABggMAMAwACBGQAABgjMAAAwQGAGAIABAjMAAAwQmAEAYIDADAAAAwRmAAAYIDADAMAAgRkAAAYIzAAAMEBgBgCAAQIzAAAMEJgBAGCAwAwAAAMEZgAAGCAwAwDAAIEZAAAGCMwAADBAYAYAgAECMwAADBCYAQBggMAMAAADBGYAABggMAMAwACBGQAABgjMAAAwQGAGAIABAjMAAAwQmAEAYIDADAAAAwRmAAAYIDADAMAAgRkAAAYIzAAAMEBgBgCAAQIzAAAMEJgBAGCAwAwAAAMEZgAAGCAwAwDAgDkF5qp6aFV9uqq+VVVXV9WBVfXwqvpiVV07fn7YjOOPraplVXVNVR0y9/IBAGDjmmsP818l+afuflySvZNcneQdSc7r7t2SnDfeTlXtkeSwJE9I8pwkJ1bVVnO8PgAAbFTrHZirarskT09ycpJ096+6+6dJDk1y6viwU5O8cPz60CRndPft3X1dkmVJ9l/f6wMAwKYwlx7m30qyIsnHq+qSqvrbqnpwkkd1901JMn5+5Pj4HZPcMOP85eO2+6iqo6vqoqq6aMWKFXMoEQAA5mYugXlBkv2SfKS7903yi4yHX6xGzdLWsx3Y3Sd195LuXrJw4cI5lAgAAHMzl8C8PMny7v7aePvTGQXoH1XVDkkyfr55xvE7zTh/UZIb53B9AADY6NY7MHf3D5PcUFW7j5sOTvLNJOckOXLcdmSSz41fn5PksKrauqp2TbJbkq+v7/UBAGBTWDDH8/9zktOq6gFJvpvktRmF8LOq6qgk1yd5aZJ091VVdVZGofrOJMd0911zvD4AAGxUcwrM3X1pkiWz7Dp4Nccfn+T4uVwTAAA2JSv9AQDAAIEZAAAGCMwAADBAYAYAgAECMwAADBCYAQBggMAMAAAD5rpwCQAA66HeXZMuYWr0cT3pEgbpYQYAgAECMwAADBCYAQBggMAMAAADBGYAABggMAMAwACBGQAABgjMAAAwQGAGAIABAjMAAAwQmAEAYIDADAAAAwRmAAAYIDADAMAAgRkAAAYIzAAAMEBgBgCAAQIzAAAMEJgBAGCAwAwAAAMEZgAAGCAwAwDAAIEZAAAGCMwAADBAYAYAgAECMwAADBCYAQBggMAMAAADBGYAABggMAMAwACBGQAABgjMAAAwQGAGAIABAjMAAAyYc2Cuqq2q6pKq+p/j7YdX1Rer6trx88NmHHtsVS2rqmuq6pC5XhsAADa2DdHD/OYkV8/YfkeS87p7tyTnjbdTVXskOSzJE5I8J8mJVbXVBrg+AABsNHMKzFW1KMnzkvztjOZDk5w6fn1qkhfOaD+ju2/v7uuSLEuy/1yuDwAAG9tce5g/lORPkvx6RtujuvumJBk/P3LcvmOSG2Yct3zcdh9VdXRVXVRVF61YsWKOJQIAwPpb78BcVc9PcnN3L13bU2Zp69kO7O6TuntJdy9ZuHDh+pYIAABztmAO5z41yQuq6rlJtkmyXVV9KsmPqmqH7r6pqnZIcvP4+OVJdppx/qIkN87h+gAAsNGtdw9zdx/b3Yu6e5eMbub71+4+Isk5SY4cH3Zkks+NX5+T5LCq2rqqdk2yW5Kvr3flAACwCcylh3l13pPkrKo6Ksn1SV6aJN19VVWdleSbSe5Mckx337URrg8AABvMBgnM3f1vSf5t/PqWJAev5rjjkxy/Ia4JAACbgpX+AABggMAMAAADBGYAABggMAMAwACBGQAABgjMAAAwQGAGAIABAjMAAAwQmAEAYIDADAAAAwRmAAAYIDADAMAAgRkAAAYIzAAAMEBgBgCAAQIzAAAMEJgBAGCAwAwAAAMEZgAAGCAwAwDAAIEZAAAGCMwAADBAYAYAgAECMwAADBCYAQBggMAMAAADBGYAABggMAMAwACBGQAABgjMAAAwQGAGAIABAjMAAAwQmAEAYIDADAAAAwRmAAAYIDADAMAAgRkAAAYIzAAAMEBgBgCAAQIzAAAMEJgBAGCAwAwAAAPWOzBX1U5V9aWqurqqrqqqN4/bH15VX6yqa8fPD5txzrFVtayqrqmqQzbEHwAAADamufQw35nkbd39+CQHJDmmqvZI8o4k53X3bknOG29nvO+wJE9I8pwkJ1bVVnMpHgAANrb1DszdfVN3Xzx+vTLJ1Ul2THJoklPHh52a5IXj14cmOaO7b+/u65IsS7L/+l4fAAA2hQ0yhrmqdkmyb5KvJXlUd9+UjEJ1kkeOD9sxyQ0zTls+bpvt/Y6uqouq6qIVK1ZsiBIBAGC9zDkwV9VDknwmyVu6++dDh87S1rMd2N0ndfeS7l6ycOHCuZYIAADrbU6Buarun1FYPq27zx43/6iqdhjv3yHJzeP25Ul2mnH6oiQ3zuX6AACwsc1lloxKcnKSq7v7AzN2nZPkyPHrI5N8bkb7YVW1dVXtmmS3JF9f3+sDAMCmsGAO5z41yauSXFFVl47b/izJe5KcVVVHJbk+yUuTpLuvqqqzknwzoxk2junuu+ZwfQAA2OjWOzB39wWZfVxykhy8mnOOT3L8+l4TAAA2NSv9AQDAAIEZAAAGCMwAADBAYAYAgAECMwAADBCYAQBggMAMAAADBGYAABggMAMAwACBGQAABgjMAAAwQGAGAIABAjMAAAwQmAEAYIDADAAAAwRmAAAYIDADAMAAgRkAAAYIzAAAMEBgBgCAAQIzAAAMEJgBAGCAwAwAAAMEZgAAGCAwAwDAAIEZAAAGCMwAADBAYAYAgAECMwAADBCYAQBggMAMAAADBGYAABggMAMAwACBGQAABgjMAAAwQGAGAIABAjMAAAwQmAEAYIDADAAAAwRmAAAYIDADAMAAgRkAAAZs8sBcVc+pqmuqallVvWNTXx8AANbFJg3MVbVVkr9J8vtJ9khyeFXtsSlrAACAdbGpe5j3T7Ksu7/b3b9KckaSQzdxDQAAsNaquzfdxar+IMlzuvv/HG+/KsmTu/uNqxx3dJKjx5u7J7lmkxU53bZP8uNJF8HU8blgVT4TzMbngtn4XPyHnbt74Ww7FmziQmqWtvsk9u4+KclJG7+czUtVXdTdSyZdB9PF54JV+UwwG58LZuNzsXY29ZCM5Ul2mrG9KMmNm7gGAABYa5s6MH8jyW5VtWtVPSDJYUnO2cQ1AADAWtukQzK6+86qemOSf06yVZJTuvuqTVnDZs4wFWbjc8GqfCaYjc8Fs/G5WAub9KY/AADY3FjpDwAABgjMAAAwQGAGAIABAjMAAAwQmKdYVT21qh48fn1EVX2gqnaedF1MVlW9t6q2q6r7V9V5VfXjqjpi0nUxWVW13yyPx1bVpl6giini+wI2DIF5un0kyS+rau8kf5Lk+0k+OdmSmALP7u6fJ3l+RosB/XaSt0+2JKbAiUm+mtEUUR9LcmGSM5J8u6qePcnCmCjfF9xHVa2sqp+v8rihqj5bVb816fqmkcA83e7s0bx/hyb5q+7+qyTbTrgmJu/+4+fnJjm9u38yyWKYGt9Lsm93L+nuJybZN8mVSZ6V5L2TLIyJ8n3BbD6Q0T+cdsxo1eU/zugf2mckOWWCdU0tgXm6rayqY5MckeQfq2qr/MeXH/PX56vqW0mWJDmvqhYmuW3CNTF5j5u5EFR3fzOjAP3dCdbE5Pm+YDbP6e7/3t0ru/vn3X1Skud295lJHjbp4qaRwDzdXp7k9iRHdfcPM/qX4PsmWxKT1t3vSHJgkiXdfUeSX2b0KwTz2zVV9ZGq+p3x48SMhmNsneSOSRfHZPi+YDV+XVUvq6r7jR8vm7HPinazEJin2wOTfKS7vzzeXpHk/AnWwxSoqmOSdHffNW56QJIXT7AkpsNrkixL8pYkb03y3XHbHUmeOamimCzfF6zGK5O8KsnNSX40fn1EVT0wyRsnWdi0sjT2FKuqi5I8pbt/Nd5+QJL/3d1PmmxlTFJVXdrd+6zSdkl37zuhkoAp5fsCNgzTDU23BXeH5STp7l+NQzPz2/2qqsY3hGY8tt3nYp6rqqcmeVeSnTPju7273fE+v/m+4D7GY9n/MMkuuff3xesmVdO0E5in24qqekF3n5MkVXVokh9PuCYm75+TnFVVH81orNnrk/zTZEtiCpyc0VCMpUnuWsOxzB++L5jN55J8Ocm/xPfFWjEkY4pV1WOTnJbk0UkqyQ1JXt3dyyZaGBNVVfdL8p+SHJzR5+LcJH87Y4wi81BVfa27nzzpOpguvi+YzWxDdRgmMG8GquohGf1drZx0LcB0qqr3JNkqydkZza6TJOnuiydWFDCVqur/TvKV7v5/J13L5kJgnkJVdUR3f6qq/sts+7v7A5u6Jiavqs7q7pdV1RWZZdqf7l48gbKYElX1pVmau7t/d5MXw8T5vmBIVa1M8uCM/nF9R0a/PnR3bzfRwqaYMczT6cHj59lW9fMvnPnrzePn50+0CqZSd5s6jpl8X7Ba3W3V4HWkh3mKVdVTu/t/r6mN+aWq/lt3/+ma2pgf/CLFEN8XzFRVj+vub1XVfrPtN4Rr9SxcMt3+ei3bmF9+b5a239/kVTAtZv4iNduD+c33BTPd/Q/rv5zl8f5JFbU50MM8harqwCRPyWjFrg/O2LVdkhd1996TqIvJqqo3JPmjJL+V5Dszdm2b0c0br5xIYUyFqtqmu2+bdB1MhxnfF4/NaAXIu22b0QJYR0ykMNhMGcM8nR6Q5CEZ/f3M7CH6eZI/mEhFTIO/T/KFJP9PknfMaF/Z3T+ZTElMkSur6kcZza16fkah6GcTronJ8X3Bao2XTD+tu3863n5YksO7+8SJFjbF9DBPsaraubu/P+k6mD5V9bQku3X3x6tq+yTbdvd1k66LyaqqxyQ5KMlTkzw3yU/NtTq/jefzX97dt1fVM5IsTvLJu4MS85Ml09edHubp9suqel+SJyTZ5u5G00TNb1V1XJIlSXZP8vGMfpH4VEYhiXmqqhZl9Bk4KMneSa5KcsFEi2IafCbJkqr6PzJaDfKcjHqfnzvRqpg0S6avI4F5up2W5MyMpgV6fZIjk6yYaEVMgxcl2TfJxUnS3TdWlZu7uD7JN5L81+5+/aSLYWr8urvvrKoXJ/lQd/91VV0y6aKYOEumryOzZEy3R3T3yUnu6O7/1d2vS3LApIti4n417hW4u2fgwWs4nvlh3ySfTPKKqrqwqj5ZVUdNuigm7o6qOjzJq5P8z3Hb/SdYD9PhT5P8a5I3JDkmyXlJ/mSiFU05PczT7Y7x801V9bwkNyZZNMF6mA5nVdV/T/LQqvrDJK9L8rEJ18SEdfdlVfWdjGZQOSjJEUmentHP8Mxfr82o9/D47r6uqnbNaAgX81h3/zrJR8YP1oKb/qZYVT0/ozved8po/uXtkry7u8+ZaGFMXFX9XpJnZ7Sc6T939xcnXBITVlUXJdk6yVcyGrt8vpuGgdlU1VOTvCvJzhl1nt69NPZvTbKuaSYwA2wBqmphd7vHgXupqt0ymlpuj9z75nHBaB6rqm8leWuSpUnuuru9u2+ZWFFTzpCMKVZVH894nOpM47HMzDNVtTKzfB7yHz0D223ikpguv6qqD2Q0DCNJ/leS/8tczPPex5Mcl9EiWM/MaIhGTbQipsHPuvsLky5ic6KHeYpV1UtmbG6T0ewIN3b3myZUEjClquozSa5Mcuq46VVJ9u7uF0+uKiatqpZ29xOr6oru3mvc9uXuPmjStTE5VfWeJFslOTvJ7Xe3d/fFEytqyulhnmLd/ZmZ21V1epJ/mVA5TJHxnJmPyoz/D3f39ZOriCnw2O6e+Y/sd1fVpZMqhqlxW1XdL8m1VfXGJD9I8sgJ18TkPXn8vGRGWyexzsNqCMybl92SPGbSRTBZVfWfM/qJ9UdJfj1u7oxW8GL+urWqntbdFyT33NRz64RrYvLekuRBSd6U5C8yCkRHTrIgJq+7nznpGjY3hmRMsRljVmv8/MMkx67a88z8UlXLkjzZzRnMVFX7ZDQc4zcy+s74SZIju/vySdYFTJ+qelSS/5rk0d39+1W1R5IDx2s/MAuBGTYzVfWlJL/X3XdOuhamT1XdffPnL5O8vLtPm2Q9TFZV/XaSt+c/pg9LknS3n97nsar6QkY3hL6zu/euqgVJLrl7nDv3ZUjGFKuq/Yb2G5w/v1TVfxm//G6Sf6uqf8y9b9b4wEQKY6LGAfmYJDsm+VxG9zkck+SPk1yWRGCe3/4hyUczWtzorjUcy/yxfXefVVXHJsl4+XSfjwEC83Q7Mcl+SS7P6CfWxUm+ltEKgAbnzz/bjp+vHz8eMH4ks083x/zwd0n+vyQXJvnDjJa3fUCSF3b3pROsi+lwZ3dbzY1V/aKqHpHxfzuq6oAkpqAcYEjGFKuqMzJazvSK8faeSf64u18z0cKYqKp6aXf/w5ramB9WmS5sqyQ/TvKY7l452cqYBlX1riQ3J/ls7v2L1E8mVROTN/4F+6+T7JnRdJQLk/yBex5WT2CeYlV1aXfvs6Y25pequri791tTG/PDqn/3PgvMVFXXzdJsCeR5qqoec/cUpONxy7tn9Av2Nd19x0SLm3KGZEy3q6vqb5N8KqOfTY5I8s3JlsSkVNXvJ3lukh2r6oQZu7bNaJgO89PeVfXz8etK8sDxthUgSXfvOukamCr/I6Ohnkly5ipztzNAYJ5ur03yhozmz6wkFyfZZZIFMVE3Jlma5AXj57vtnNGMCMxD3b3VpGtgeo2H6Twvo/92zJwlw03C89PMZdH9yrAOBOYp1t23jacQ2yHJy5M8LMmnJ1sVk9LdlyW5rKpOS/KEJK9I8rIk1yUxNzcwm88nuS3JFfmPhY6Yv3o1r1kDgXkKjefNPCzJ4UluSXJmknT3MyZYFhO2ms9FWbEJGLCou60Cyt32njFk64GrDOcyhGuAm/6mUFX9OsmXkxzV3cvGbd91k8b85nMBrKuq+m9JzuvucyddC2zO7jfpApjVSzJaBvtLVfWxqjo49x53xPzkcwGsq68m+WxV3VpVP6+qlTN6FYG1pId5ilXVg5O8MKOf4H83yalJPqunYH7zuQDWVlV9N6Pviyvaf/BhvQnMm4mqeniSlyZ5eXdb4Y8kPhfAsKr65yS/391u+IM5EJgBYAtVVZ/IaPqwL+TeK/2ZVg7WgVkyAGDLdd348YDxA1gPepgBYAtVVXt295WTrgM2dwIzAGyhquqCjHqWP5Hk77v7pxMtCDZTppUDgC1Udz8tyRFJdkpyUVX9fVU9e8JlwWZHDzMAbOGqaquMppc7IcndK739WXefPcm6YHMhMAPAFqqqFid5bZLnJflikpO7++KqenSSC7t754kWCJsJgRkAtlBVdX6SjyX5dHffusq+V3X3302mMti8CMwAsIWpqkOTLOruvxlvfy3JwvHuP+3uf5hYcbAZctMfAGx5/iTJOTO2t07ypCTPSPL6SRQEmzMLlwDAlucB3X3DjO0LuvuWJLdU1YMnVRRsrvQwA8CW52EzN7r7jTM2FwZYJwIzAGx5vlZVf7hqY1X9pyRfn0A9sFlz0x8AbGGq6pFJ/keS25NcPG5+YkZjmV/Y3T+aUGmwWRKYAWALVVW/m+QJ482ruvtfJ1kPbK4EZgAAGGAMMwAADBCYAQBggMAMsBmoqndW1VVVdXlVXVpVT94A7/mMqnrKhqgPYEtm4RKAKVdVByZ5fpL9uvv2qto+yQPm+J4LMlr17d+TfGXORQJswQRmgOm3Q5Ifd/ftSdLdP06SqvpekjOTPHN83Cu6e1lV7ZzklIwWqFiR5LXdfX1VfSLJT5LsO35+apK7quqIJP+5u7+86f5IAJsPQzIApt+5SXaqqm9X1YlV9Tsz9v28u/dP8uEkHxq3fTjJJ7t7cZLTkpww4/jfTvKs7n5Jko8m+WB37yMsA6yewAww5br73zNadOLojHqMz6yq14x3nz7j+cDx6wOT/P349d8ledqMt/uH7r5roxYMsIUxJANgMzAOuf+W5N+q6ookR969a+Zhqzt9xutfbPjqALZsepgBplxV7V5Vu81o2ifJ98evXz7j+cLx668kOWz8+pVJLljNW69Msu2GqxRgy6SHGWD6PSTJX1fVQ5PcmWRZRsMznp9k66r6WkYdIIePj39TklOq6u0Z3/S3mvf9fJJPV9WhcdMfwGpZGhtgMzWeJWPJ3bNmALBxGJIBAAAD9DADAMAAPcwAADBAYAYAgAECMwAADBCYAQBggMAMAAADBGYAABjw/wM/Kf7dTj61IQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Sport',y='No. of gold medals',kind='bar',figsize=(12,7),color='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "is3yTC6QbcDn"
   },
   "source": [
    "### 3. Which sport is having most number of medals so far? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "CYK4Es_lbScv"
   },
   "outputs": [],
   "source": [
    "data=[]\n",
    "for sport in df['Sport'].unique():\n",
    "    data.append([sport,len(df[df['Sport']==sport])])\n",
    "data=pd.DataFrame(data, columns=['Sport','No. of medals']) \n",
    "data=data.sort_values(by='No. of medals', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "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>Sport</th>\n",
       "      <th>No. of medals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Aquatics</td>\n",
       "      <td>4170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Athletics</td>\n",
       "      <td>3638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Rowing</td>\n",
       "      <td>2667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>2307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Fencing</td>\n",
       "      <td>1613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sport  No. of medals\n",
       "0     Aquatics           4170\n",
       "1    Athletics           3638\n",
       "17      Rowing           2667\n",
       "4   Gymnastics           2307\n",
       "3      Fencing           1613"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sport'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Sport',y='No. of medals',kind='bar',figsize=(12,7),color='r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iQ0CLLV4blW8"
   },
   "source": [
    "### 4. Which player has won most number of medals? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "C3aeMgfVbRNS"
   },
   "outputs": [],
   "source": [
    "data=[]\n",
    "for sport in df['Athlete'].unique():\n",
    "    data.append([sport,len(df[df['Athlete']==sport])])\n",
    "data=pd.DataFrame(data, columns=['Athlete','No. of medals']) \n",
    "data=data.sort_values(by='No. of medals', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "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>Athlete</th>\n",
       "      <th>No. of medals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18640</th>\n",
       "      <td>PHELPS, Michael</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7197</th>\n",
       "      <td>LATYNINA, Larisa</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9948</th>\n",
       "      <td>ANDRIANOV, Nikolay</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6591</th>\n",
       "      <td>ONO, Takashi</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5103</th>\n",
       "      <td>MANGIAROTTI, Edoardo</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Athlete  No. of medals\n",
       "18640       PHELPS, Michael             22\n",
       "7197       LATYNINA, Larisa             18\n",
       "9948     ANDRIANOV, Nikolay             15\n",
       "6591           ONO, Takashi             13\n",
       "5103   MANGIAROTTI, Edoardo             13"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Athlete'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Athlete',y='No. of medals',kind='bar',color='y',figsize=(12,7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5FSXQaTBbsMK"
   },
   "source": [
    "### 5. Which player has won most number Gold Medals of medals? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "cS6ZpExVbQ8l"
   },
   "outputs": [
    {
     "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>Year</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Discipline</th>\n",
       "      <th>Athlete</th>\n",
       "      <th>Country</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Event</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Aquatics</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>HAJOS, Alfred</td>\n",
       "      <td>HUN</td>\n",
       "      <td>Men</td>\n",
       "      <td>100M Freestyle</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Aquatics</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>MALOKINIS, Ioannis</td>\n",
       "      <td>GRE</td>\n",
       "      <td>Men</td>\n",
       "      <td>100M Freestyle For Sailors</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Aquatics</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>HAJOS, Alfred</td>\n",
       "      <td>HUN</td>\n",
       "      <td>Men</td>\n",
       "      <td>1200M Freestyle</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Aquatics</td>\n",
       "      <td>Swimming</td>\n",
       "      <td>NEUMANN, Paul</td>\n",
       "      <td>AUT</td>\n",
       "      <td>Men</td>\n",
       "      <td>400M Freestyle</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1896</td>\n",
       "      <td>Athens</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>Athletics</td>\n",
       "      <td>BURKE, Thomas</td>\n",
       "      <td>USA</td>\n",
       "      <td>Men</td>\n",
       "      <td>100M</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Year    City      Sport Discipline             Athlete Country Gender  \\\n",
       "0   1896  Athens   Aquatics   Swimming       HAJOS, Alfred     HUN    Men   \n",
       "3   1896  Athens   Aquatics   Swimming  MALOKINIS, Ioannis     GRE    Men   \n",
       "6   1896  Athens   Aquatics   Swimming       HAJOS, Alfred     HUN    Men   \n",
       "9   1896  Athens   Aquatics   Swimming       NEUMANN, Paul     AUT    Men   \n",
       "13  1896  Athens  Athletics  Athletics       BURKE, Thomas     USA    Men   \n",
       "\n",
       "                         Event Medal  \n",
       "0               100M Freestyle  Gold  \n",
       "3   100M Freestyle For Sailors  Gold  \n",
       "6              1200M Freestyle  Gold  \n",
       "9               400M Freestyle  Gold  \n",
       "13                        100M  Gold  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playerOly = df[df.Medal =='Gold']\n",
    "playerOly.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[]\n",
    "for sport in playerOly['Athlete'].unique():\n",
    "    data.append([sport,len(playerOly[playerOly['Athlete']==sport])])\n",
    "data=pd.DataFrame(data, columns=['Athlete','No. of gold medals']) \n",
    "data=data.sort_values(by='No. of gold medals', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "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>Athlete</th>\n",
       "      <th>No. of gold medals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6851</th>\n",
       "      <td>PHELPS, Michael</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3320</th>\n",
       "      <td>SPITZ, Mark</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2714</th>\n",
       "      <td>LATYNINA, Larisa</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4630</th>\n",
       "      <td>LEWIS, Carl</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>907</th>\n",
       "      <td>NURMI, Paavo</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Athlete  No. of gold medals\n",
       "6851   PHELPS, Michael                  18\n",
       "3320       SPITZ, Mark                   9\n",
       "2714  LATYNINA, Larisa                   9\n",
       "4630       LEWIS, Carl                   9\n",
       "907       NURMI, Paavo                   9"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Athlete'>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Athlete',y='No. of gold medals',kind='bar',figsize=(12,7),color='orange')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l5sYSF--b1I4"
   },
   "source": [
    "### 6. In which year India won first Gold Medal in Summer Olympics?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "IMJ41ZePbQsS"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1928    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IndiaOly = df[(df.Country==\"IND\") & (df.Medal == 'Gold')]\n",
    "IndiaOly.head(1).value_counts('Year')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Hxmh3IoHb8ag"
   },
   "source": [
    "### 7. Which event is most popular in terms on number of players? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "90okLR3pbQWK"
   },
   "outputs": [],
   "source": [
    "data=[]\n",
    "for sport in df['Event'].unique():\n",
    "    data.append([sport,len(df[df['Event']==sport])])\n",
    "data=pd.DataFrame(data, columns=['Event','No. of players']) \n",
    "data=data.sort_values(by='No. of players', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "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>Event</th>\n",
       "      <th>No. of players</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>Football</td>\n",
       "      <td>1497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>Hockey</td>\n",
       "      <td>1422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Team Competition</td>\n",
       "      <td>1147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>Basketball</td>\n",
       "      <td>1012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>Handball</td>\n",
       "      <td>973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Event  No. of players\n",
       "80           Football            1497\n",
       "176            Hockey            1422\n",
       "138  Team Competition            1147\n",
       "327        Basketball            1012\n",
       "337          Handball             973"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Event'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Event',y='No. of players',kind='bar',figsize=(12,7),color='black')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6MZcyOXecEtD"
   },
   "source": [
    "### 8. Which sport is having most female Gold Medalists? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "trTyNocQbP0A"
   },
   "outputs": [],
   "source": [
    "womenOly = df[(df.Gender == 'Women') & (df.Medal == 'Gold')]\n",
    "data=[]\n",
    "for sport in womenOly['Sport'].unique():\n",
    "    data.append([sport,len(womenOly[womenOly['Sport']==sport])])\n",
    "data=pd.DataFrame(data, columns=['Sport','No. of female gold medalists']) \n",
    "data=data.sort_values(by='No. of female gold medalists', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "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>Sport</th>\n",
       "      <th>No. of female gold medalists</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Aquatics</td>\n",
       "      <td>589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Athletics</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Gymnastics</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Rowing</td>\n",
       "      <td>217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Volleyball</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sport  No. of female gold medalists\n",
       "4     Aquatics                           589\n",
       "7    Athletics                           389\n",
       "8   Gymnastics                           268\n",
       "14      Rowing                           217\n",
       "11  Volleyball                           166"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sport'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Sport',y='No. of female gold medalists',kind = 'bar',figsize=(12,7),color='pink')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thank you"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyOz7DvpLZVCKTdOYesPogDO",
   "collapsed_sections": [],
   "name": "Summer.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}