assignment 2 - olympics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "c4be90a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6e7d2e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"summer.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "325f419b",
   "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>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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31160</th>\n",
       "      <td>2012</td>\n",
       "      <td>London</td>\n",
       "      <td>Wrestling</td>\n",
       "      <td>Wrestling Freestyle</td>\n",
       "      <td>JANIKOWSKI, Damian</td>\n",
       "      <td>POL</td>\n",
       "      <td>Men</td>\n",
       "      <td>Wg 84 KG</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31161</th>\n",
       "      <td>2012</td>\n",
       "      <td>London</td>\n",
       "      <td>Wrestling</td>\n",
       "      <td>Wrestling Freestyle</td>\n",
       "      <td>REZAEI, Ghasem Gholamreza</td>\n",
       "      <td>IRI</td>\n",
       "      <td>Men</td>\n",
       "      <td>Wg 96 KG</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31162</th>\n",
       "      <td>2012</td>\n",
       "      <td>London</td>\n",
       "      <td>Wrestling</td>\n",
       "      <td>Wrestling Freestyle</td>\n",
       "      <td>TOTROV, Rustam</td>\n",
       "      <td>RUS</td>\n",
       "      <td>Men</td>\n",
       "      <td>Wg 96 KG</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31163</th>\n",
       "      <td>2012</td>\n",
       "      <td>London</td>\n",
       "      <td>Wrestling</td>\n",
       "      <td>Wrestling Freestyle</td>\n",
       "      <td>ALEKSANYAN, Artur</td>\n",
       "      <td>ARM</td>\n",
       "      <td>Men</td>\n",
       "      <td>Wg 96 KG</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31164</th>\n",
       "      <td>2012</td>\n",
       "      <td>London</td>\n",
       "      <td>Wrestling</td>\n",
       "      <td>Wrestling Freestyle</td>\n",
       "      <td>LIDBERG, Jimmy</td>\n",
       "      <td>SWE</td>\n",
       "      <td>Men</td>\n",
       "      <td>Wg 96 KG</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31165 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year    City      Sport           Discipline  \\\n",
       "0      1896  Athens   Aquatics             Swimming   \n",
       "1      1896  Athens   Aquatics             Swimming   \n",
       "2      1896  Athens   Aquatics             Swimming   \n",
       "3      1896  Athens   Aquatics             Swimming   \n",
       "4      1896  Athens   Aquatics             Swimming   \n",
       "...     ...     ...        ...                  ...   \n",
       "31160  2012  London  Wrestling  Wrestling Freestyle   \n",
       "31161  2012  London  Wrestling  Wrestling Freestyle   \n",
       "31162  2012  London  Wrestling  Wrestling Freestyle   \n",
       "31163  2012  London  Wrestling  Wrestling Freestyle   \n",
       "31164  2012  London  Wrestling  Wrestling Freestyle   \n",
       "\n",
       "                         Athlete Country Gender                       Event  \\\n",
       "0                  HAJOS, Alfred     HUN    Men              100M Freestyle   \n",
       "1               HERSCHMANN, Otto     AUT    Men              100M Freestyle   \n",
       "2              DRIVAS, Dimitrios     GRE    Men  100M Freestyle For Sailors   \n",
       "3             MALOKINIS, Ioannis     GRE    Men  100M Freestyle For Sailors   \n",
       "4             CHASAPIS, Spiridon     GRE    Men  100M Freestyle For Sailors   \n",
       "...                          ...     ...    ...                         ...   \n",
       "31160         JANIKOWSKI, Damian     POL    Men                    Wg 84 KG   \n",
       "31161  REZAEI, Ghasem Gholamreza     IRI    Men                    Wg 96 KG   \n",
       "31162             TOTROV, Rustam     RUS    Men                    Wg 96 KG   \n",
       "31163          ALEKSANYAN, Artur     ARM    Men                    Wg 96 KG   \n",
       "31164             LIDBERG, Jimmy     SWE    Men                    Wg 96 KG   \n",
       "\n",
       "        Medal  \n",
       "0        Gold  \n",
       "1      Silver  \n",
       "2      Bronze  \n",
       "3        Gold  \n",
       "4      Silver  \n",
       "...       ...  \n",
       "31160  Bronze  \n",
       "31161    Gold  \n",
       "31162  Silver  \n",
       "31163  Bronze  \n",
       "31164  Bronze  \n",
       "\n",
       "[31165 rows x 9 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c5c0ae",
   "metadata": {},
   "source": [
    "### 1. In how many cities Summer Olympics is held so far?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "68953573",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22\n",
      "['Athens' 'Paris' 'St Louis' 'London' 'Stockholm' 'Antwerp' 'Amsterdam'\n",
      " 'Los Angeles' 'Berlin' 'Helsinki' 'Melbourne / Stockholm' 'Rome' 'Tokyo'\n",
      " 'Mexico' 'Munich' 'Montreal' 'Moscow' 'Seoul' 'Barcelona' 'Atlanta'\n",
      " 'Sydney' 'Beijing']\n"
     ]
    }
   ],
   "source": [
    "#to display count\n",
    "print(df['City'].nunique())\n",
    "#to display names\n",
    "print(df['City'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d8a8fe7",
   "metadata": {},
   "source": [
    "### 2. Which sport is having most number of Gold Medals so far? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f2c9b542",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "\n",
    "for sport in df['Sport'].unique():\n",
    "    data.append([sport, len(df[(df['Sport'] == sport) & (df['Medal'] == 'Gold')])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ef2aa8a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Sport  No. of gold\n",
      "0     Aquatics         1421\n",
      "1    Athletics         1215\n",
      "17      Rowing          890\n",
      "4   Gymnastics          820\n",
      "3      Fencing          552\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sport'>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MwCVJktQyA5ckSVLLJhS4kjwhyReS3JDk+iTPS/LEJF9N8r3mccu+7Y9JsjzJjUleMfHyJUmSJr+J9nB9FPj3qnoGsBtwPfBu4MKqmgdc2CyTZCdgIbAzsB9wYpIZEzy+JEnSpDfuwJVkC+DFwKkAVfXrqvopcABwRrPZGcCBzfMDgLOr6v6quhlYDuw53uNLkiQNi4n0cD0VWAV8Osl3knwqyWOBravqDoDm8cnN9tsCt/Xtv7Jpe4QkS5IsS7Js1apVEyhRkiSpexMJXDOBPYCTqmp34Jc0w4frkBHaaqQNq+qUqlpQVQtmz549gRIlSZK6N5HAtRJYWVWXNctfoBfAfpxkG4Dm8c6+7bfr238OcPsEji9JkjQUxh24qupHwG1Jdmya9gG+CywFFjdti4ELmudLgYVJZiWZC8wDvjXe40uSJA2LmRPc/6+AM5M8GvgBcDi9EHduktcDK4CDAKrquiTn0gtlDwJHVNXqCR5fkiRp0ptQ4KqqK4EFI6zaZx3bHwccN5FjSpIkDRtnmpckSWqZgUuSJKllBi5JkqSWTfSkeUmSNMTyvpGmyZw66tgRp/wcOHu4JEmSWmbgkiRJapmBS5IkqWUGLkmSpJYZuCRJklpm4JIkSWqZgUuSJKllBi5JkqSWGbgkSZJaZuCSJElqmYFLkiSpZQYuSZKklhm4JEmSWmbgkiRJapmBS5IkqWUGLkmSpJYZuCRJklpm4JIkSWqZgUuSJKllBi5JkqSWGbgkSZJaZuCSJElqmYFLkiSpZRMOXElmJPlOkn9plp+Y5KtJvtc8btm37TFJlie5MckrJnpsSZKkYbAxeriOAq7vW343cGFVzQMubJZJshOwENgZ2A84McmMjXB8SZKkSW1CgSvJHOD3gU/1NR8AnNE8PwM4sK/97Kq6v6puBpYDe07k+JIkScNgoj1cHwGOBn7T17Z1Vd0B0Dw+uWnfFritb7uVTdsjJFmSZFmSZatWrZpgiZIkSd0ad+BKsj9wZ1VdPtZdRmirkTasqlOqakFVLZg9e/Z4S5QkSZoUZk5g3xcAr07yKmBTYIsk/wj8OMk2VXVHkm2AO5vtVwLb9e0/B7h9AseXJEkaCuPu4aqqY6pqTlXtQO9k+P+sqkOApcDiZrPFwAXN86XAwiSzkswF5gHfGnflkiRJQ2IiPVzr8n7g3CSvB1YABwFU1XVJzgW+CzwIHFFVq1s4viRJ0qSyUQJXVX0N+Frz/G5gn3Vsdxxw3MY4piRJ0rBwpnlJkqSWGbgkSZJaZuCSJElqmYFLkiSpZQYuSZKklhm4JEmSWmbgkiRJapmBS5IkqWUGLkmSpJYZuCRJklpm4JIkSWqZgUuSJKllBi5JkqSWGbgkSZJaZuCSJElqmYFLkiSpZQYuSZKklhm4JEmSWmbgkiRJapmBS5IkqWUGLkmSpJYZuCRJklpm4JIkSWqZgUuSJKllBi5JkqSWGbgkSZJaZuCSJElqmYFLkiSpZeMOXEm2S3JRkuuTXJfkqKb9iUm+muR7zeOWffsck2R5khuTvGJjfAOSJEmT3UR6uB4E3l5VzwT2Ao5IshPwbuDCqpoHXNgs06xbCOwM7AecmGTGRIqXJEkaBuMOXFV1R1Vd0Ty/B7ge2BY4ADij2ewM4MDm+QHA2VV1f1XdDCwH9hzv8SVJkobFRjmHK8kOwO7AZcDWVXUH9EIZ8ORms22B2/p2W9m0jfR6S5IsS7Js1apVG6NESZKkzkw4cCV5HHAe8Jaq+vn6Nh2hrUbasKpOqaoFVbVg9uzZEy1RkiSpUxMKXEk2oRe2zqyq85vmHyfZplm/DXBn074S2K5v9znA7RM5viRJ0jCYyFWKAU4Frq+q4/tWLQUWN88XAxf0tS9MMivJXGAe8K3xHl+SJGlYzJzAvi8ADgWuSXJl0/bXwPuBc5O8HlgBHARQVdclORf4Lr0rHI+oqtUTOL4kSdJQGHfgqqpLGPm8LIB91rHPccBx4z2mJEnSMHKmeUmSpJYZuCRJklpm4JIkSWqZgUuSJKllBi5JkqSWGbgkSZJaZuCSJElqmYFLkiSpZQYuSZKklhm4JEmSWmbgkiRJapmBS5IkqWUGLkmSpJYZuCRJklpm4JIkSWqZgUuSJKllBi5JkqSWGbgkSZJaZuCSJElqmYFLkiSpZQYuSZKklhm4JEmSWmbgkiRJapmBS5IkqWUGLkmSpJYZuCRJklpm4JIkSWrZwANXkv2S3JhkeZJ3D/r4kiRJgzbQwJVkBvAJ4JXATsCiJDsNsgZJkqRBG3QP157A8qr6QVX9GjgbOGDANUiSJA1UqmpwB0v+GNivqv68WT4UeG5VHbnWdkuAJc3ijsCNAyty8LYC7uq6CI2L791w8/0bXr53w22qv3/bV9XstRtnDriIjND2iMRXVacAp7RfTveSLKuqBV3XoQ3nezfcfP+Gl+/dcJuu79+ghxRXAtv1Lc8Bbh9wDZIkSQM16MD1bWBekrlJHg0sBJYOuAZJkqSBGuiQYlU9mORI4MvADOC0qrpukDVMQtNi6HSK8r0bbr5/w8v3brhNy/dvoCfNS5IkTUfONC9JktQyA5ckSVLLDFySJEktM3BJkiS1zMA1YElekOSxzfNDkhyfZPuu65KmgyR7jPD1tCSDngRaGyjJB5JskWSTJBcmuSvJIV3XJY2VgWvwTgJ+lWQ34GjgVuAz3ZaksfADf0o4EfgmvcvSPwlcSu+erjcl2bfLwjSqfavq58D+9CbRfjrwzm5L0lgluSfJz9f6ui3JF5M8tev6BsHANXgPVm8ujgOAj1bVR4HNO65JY+MH/vC7Bdi9qhZU1bOB3YFrgZcDH+iyMI1qk+bxVcBZVfWTLovRBjue3ufltvTuMvMOen/0nA2c1mFdA2PgGrx7khwDHAL8a5IZ/PaDRJObH/jD7xn9ky1X1XfpBbAfdFiTxuafk9wALAAuTDIbuK/jmjR2+1XV/6uqe6rq5809k19VVecAW3Zd3CAYuAbvYOB+4PVV9SN6af+D3ZakMfIDf/jdmOSkJC9pvk6kN5w4C3ig6+K0blX1buB5wIKqegD4Fb2RAg2H3yR5bZJHNV+v7Vs3LWZgd6b5AUsyF/hRVd3bLD8G2Lqqbum0MI1Jki2Bn1fV6ubih82b4Kwh0Py8vQl4IRDgEnrndd0HbFZVv+iwPK1HkiOAM6vqp83ylsCiqjqx08I0Js15Wh+lF5qL3rmUbwV+CDy7qi7psLyBMHANWJJlwPOr6tfN8qOB/66q53RbmUbjB77UnSRXVtWz1mr7TlXt3lFJ0gbxUujBm7kmbAFU1a+b0KXJ7w1V9Yk1C1X1P0neQK+HREMgyQuA9wLb0/f5V1XT4iqpIfeoJGkuOqI5/9XPziHRnILxBmAHHv6z92dd1TRoBq7BW5Xk1VW1FCDJAcBdHdeksfEDf/idSm8Y43Jgdce1aMN8GTg3ycn0hqT+Evj3bkvSBrgA+DrwH0zTnz2HFAcsydOAM4Gn0DuH5DbgsKpa3mlhGlWSD9L766z/A/+2qnp7l3Vp7JJcVlXP7boObbgkjwL+AtiH3mfnV4BPVdW0/OU9bEYaEp5uDFwdSfI4ev/+93Rdi8bGD/zhl+T9wAzgfHpXCwNQVVd0VpQ0DST5v8A3qurfuq6lKwauAUlySFX9Y5K3jbS+qo4fdE3SdJPkohGaq6peNvBiNCZJzq2q1ya5hhGmD6iq+R2UpQ2U5B7gsfT+0HmA3h+tVVVbdFrYAHkO1+A8tnkcaVZ5U+8k5gf+1FFVL+26Bm2wo5rH/TutQhNSVdP+jir2cA1YkhdU1X+P1qbJI8k2VXXHum4yXlW3DrombRh7mIdfkr+vqneN1qbJJckzquqGJHuMtH46DefbwzV4HwPW/o83Upsmiaq6o3n6ppE+8AE/8Ce/9fUwazj8Ho/8WXvlCG2aXN4GLAH+YYR1BUyb4Xx7uAYkyfOA5wNvAT7ct2oL4DVVtVsXdWnsklxRVXus1Xa1Q4rDI8mmVeXtmIZIkjfSuzvA04D+q7k3pzdp9CGdFCZtIHu4BufRwOPo/Zv3/5X9c+CPO6lIY9L3gf/UJFf3rdoc+EY3VWmcrk3yY3rzAV1M7xf2zzquSev3OeBLwN8B7+5rv8cbyA8P79RhD9fAJdnec36GS5LH07ubvR/4U0CS3wVeBLwAeBXw0+k+P9AwaOYwXFlV9yfZG5gPfGbNL3BNbt6aCR7VdQHT0K+SfDDJvyX5zzVfXReldauqn1XVLVW1CNgOeFkTmh/V3IxcQyLJHHpB60XA7sB1wDmdFqWxOg9YneR/0btjwFx6vV8aDo9KkjUL0/FOHQ4pDt6Z9D7g96c3U/liYFWnFWlMkhwLLAB2BD5N78PiH+n9AtdwWAF8G/jbqvrLrovRBvlNVT2Y5A+Bj1TVx5J8p+uiNGbT/tZMDikOWJLLq+rZ/SdbJ/mvqnpJ17Vp/ZJcSa9X5Io13eCeND9ckuwGvBB4MfC7wPeA/6qqUzstTKNKchnwEeA9wB9U1c1Jrq2qXbqtTGPhnTrs4erCA83jHUl+H7gdmNNhPRq7X1dVJVlz8+rHjraDJpequirJ94Hv0xtWPIRe+DJwTX6H0+sVOa4JW3Pp9TBrCFTVb4CTmq9pyR6uAUuyP70rpLajN//WFsD7qmppp4VpVEneAcyjNx/Q3wF/Bnyuqj7WaWEasyTLgFn0ri69BLjYi1ik9iV5AfBeYHt6nT1rbu3z1C7rGiQDl7QBkvwesC+9D4svV9VXOy5JGyDJ7KrynMkhlGQevT90dgI2XdM+nX5hD7MkNwBvBS4HHhpGrKq7OytqwBxSHLAkn2bk+/H9WQflaAM1AcuQNbx+neR4esOIAP8F/G/n4hoKnwaOpTdx9EvpDTFmvXtoMvlZVX2p6yK6ZA/XgCX5o77FTYHXALdX1Zs7KkmjaO5yP9IPyrS72/2wS3IecC1wRtN0KLBbVf1hd1VpLPouOLqmqnZt2r5eVS/qujaNLsn7gRnA+cD9a9q9l6JaU1Xn9S8nOQv4j47K0Rh4l/sp5WlV1f9Hz/uaq081+d3XXOn2vSRHAj8EntxxTRq75zaPC/raptW9FA1c3ZtH7/J0DYFmsr6t6fvZqaoV3VWkDXRvkhdW1SXw0Im893Zck8bmLcBmwJuB/0PvF/XiLgvS2FXVS7uuoWsOKQ5Y3/BUmscfAces3fOlySfJX9E7h+THwG+a5nIeruGR5Fn0hhMfT+9n8CfA4qq6en37SZqYJFsDfws8papemWQn4HnTaQ48A5c0RkmWA8+dTlfVTFVJ1px39yvg4Ko6s8t6NLokTwfeyW+nFQCgqqbNkNQwS/Ilehc+vKeqdksyE/jOmvPxpgOHFAcsyR7rWz+dTiAcQrcBXs02hJqAdQSwLXABvfMmjwDeAVxF75Zbmtw+D5wMfJK+aQU0NLaqqnOTHAPQ3KZpWr2PBq7BOxHYA7ia3pDGfOAyejPQT6sTCIdFkrc1T38AfC3Jv/Lwq2yO76QwbYjPAv8DXAq8ATia3r0wD6yqKzusS2P3YFVN21nKp4BfJnkSzRXfSfZimv0Ba+AavFuAN1TVNQBJdgHeUVV/2mVRWq81VymuaL4ezW/vcu+Y/HB4at9UAp8C7gJ+t6ru6bYsbYB/TvIm4Is8/A+en3RXkjbA24ClwNOS/DcwG/jjbksaLM/hGrAkV1bVs0Zr0+ST5KCq+vxobZp8klxRVXusa1mTX5KbR2ieVreGGUZJfnfNldzNeVs70hvdubGqHljvzlOMgWvAmnm3fknvpqtF7+a5j62q13VamEY10i9pf3EPh+ZckV+uWQQeQ++EeSevlVrU/xmZ5Ly15sGbVhxSHLzDgTfSm0smwBXADl0WpPVL8krgVcC2SU7oW7U5vXPvNMlV1Yyua9DENHPg/T69z8v+qxQ9h3Jy67/90rTujTRwDVhV3ZfkImAb4GBgS+AL3ValUdxO74arr24e19ieXi+JpPb9M3AfcA2/nQdPk1+t4/m045DigDRzyCwEFgF3A+fQO1l++04L05gl2QTYGXgd8FrgZuC8qvp4p4VJ00CSq51kePj0Def3D+XDNBzOt4drcG4Avg78QVUtB0jy1m5L0lisIyzHW1VIA/WlJPtW1Ve6LkRj53D+bxm4BueP6P3SvijJvwNn8/CxbU1ehmWpe98EvtjcwPoBpmEPiYbbo7ouYLqoqi9W1cHAM4CvAW8Ftk5yUpJ9Oy1Oo/kjeve8vCjJJ5Psg2FZGrR/AJ4HbFZVW1TV5oYtDRPP4epQkicCB9G7l5szzE9ySR4LHEhvaPFl9G6C/EWHOKT2Jfky8Mqq8oR5DSUDlzQOhmVpsJKcTm9agS/hrbU0hAxckqRJL8mxI7VX1fsGXYs0HgYuSdKkl2SXqrq26zqk8TJwSZImvSSX0Ltp/OnA56rqp50WJG0gr1KUJE16VfVCevee3Q5YluRzXuGtYWIPlyRpaDT3VDwQOAH4Ob0pWv66qs7vsi5pNAYuSdKkl2Q+cDi9G1h/FTi1qq5I8hTgUm+TpsnOwCVJmvSSXAx8EvhCVd271rpDq+qz3VQmjY2BS5I0aSU5AJhTVZ9oli8DZjer31VVn++sOGkDeNK8JGkyOxpY2rc8C3gOsDfwl10UJI2HN6+WJE1mj66q2/qWL6mqu4G7m9ttSUPBHi5J0mS2Zf9CVR3ZtzgbaUgYuCRJk9llSd6wdmOSvwC+1UE90rh40rwkadJK8mTgn+jdsPqKpvnZ9M7lOrCqftxRadIGMXBJkia9JC8Ddm4Wr6uq/+yyHmlDGbgkSZJa5jlckiRJLTNwSZIktczAJWlKSPKeJNcluTrJlUmeuxFec+8kz98Y9Uma3pz4VNLQS/I8YH9gj6q6P8lWwKMn+Joz6c1m/gvgGxMuUtK0ZuCSNBVsA9xVVfcDVNVdAEluAc4BXtps97qqWp5ke+A0ehNnrgIOr6oVSU4HfgLs3jy+AFid5BDgr6rq64P7liRNJQ4pSpoKvgJsl+SmJCcmeUnfup9X1Z7Ax4GPNG0fBz5TVfOBM4ET+rZ/OvDyqvoj4GTgw1X1LMOWpIkwcEkaelX1C3qTYS6h12N1TpI/bVaf1ff4vOb584DPNc8/C7yw7+U+X1WrWy1Y0rTjkKKkKaEJSV8DvpbkGmDxmlX9m61r977nv9z41Uma7uzhkjT0kuyYZF5f07OAW5vnB/c9Xto8/wawsHn+J8Al63jpe4DNN16lkqYre7gkTQWPAz6W5AnAg8ByesOL+wOzklxG7w/MRc32bwZOS/JOmpPm1/G6/wx8IckBeNK8pAnw1j6SpqzmKsUFa65alKSuOKQoSZLUMnu4JEmSWmYPlyRJUssMXJIkSS0zcEmSJLXMwCVJktQyA5ckSVLLDFySJEkt+/+MmonIkcMhgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data = pd.DataFrame(data,columns = [\"Sport\", \"No. of gold\"]).sort_values(by=\"No. of gold\", ascending= False).head()\n",
    "\n",
    "print(plot_data)\n",
    "\n",
    "plot_data.plot(x = 'Sport', y = 'No. of gold', kind = 'bar', figsize = (10,5), color = \"green\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f60c5193",
   "metadata": {},
   "source": [
    "### 3. Which sport is having most number of medals so far? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5eec22aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Sport  No. of medals\n",
      "0     Aquatics           4170\n",
      "1    Athletics           3638\n",
      "17      Rowing           2667\n",
      "4   Gymnastics           2307\n",
      "3      Fencing           1613\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sport'>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = []\n",
    "\n",
    "for sport in df['Sport'].unique():\n",
    "    data.append([sport, len(df[(df['Sport'] == sport)])])\n",
    "    \n",
    "plot_data = pd.DataFrame(data,columns = ['Sport', 'No. of medals']).sort_values(by='No. of medals' , ascending = False).head()\n",
    "\n",
    "print(plot_data)\n",
    "\n",
    "plot_data.plot(x = 'Sport', y = 'No. of medals', kind = 'bar', figsize = (10,5), color = 'green')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e230275a",
   "metadata": {},
   "source": [
    "### 4. Which player has won most number of medals? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8ee90c0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    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\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Athlete'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = []\n",
    "\n",
    "for athlete in df['Athlete'].unique():\n",
    "    data.append([athlete , len(df[(df['Athlete'] == athlete)])])\n",
    "\n",
    "plot_data = pd.DataFrame(data,columns = ['Athlete','No. of medals']).sort_values(by='No. of medals', ascending=False).head()\n",
    "print(plot_data)\n",
    "\n",
    "plot_data.plot(x = 'Athlete', y = 'No. of medals', kind = 'bar', figsize = (10,5), color = 'green')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a8c462c",
   "metadata": {},
   "source": [
    "### 5. . Which player has won most number Gold Medals of medals? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "30cb74b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Athlete  No. of gold medals\n",
      "18640   PHELPS, Michael                  18\n",
      "12468       LEWIS, Carl                   9\n",
      "8811        SPITZ, Mark                   9\n",
      "2262       NURMI, Paavo                   9\n",
      "7197   LATYNINA, Larisa                   9\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Athlete'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = []\n",
    "\n",
    "for athlete in df['Athlete'].unique():\n",
    "    data.append([athlete , len(df[(df['Athlete'] == athlete) & (df['Medal'] == \"Gold\")])])\n",
    "\n",
    "plot_data = pd.DataFrame(data,columns = ['Athlete','No. of gold medals']).sort_values(by='No. of gold medals', ascending=False).head()\n",
    "\n",
    "print(plot_data)\n",
    "\n",
    "plot_data.plot(x = 'Athlete', y = 'No. of gold medals', kind = 'bar', figsize = (10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c0fdf8b",
   "metadata": {},
   "source": [
    "### 6. In which year India won first Gold Medal in Summer Olympics?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b5c37ea7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "   Year  Gold\n",
      "0  1896     0\n",
      "1  1900     0\n",
      "2  1904     0\n",
      "3  1908     0\n",
      "4  1912     0\n",
      "5  1920     0\n",
      "6  1924     0\n",
      "7  1928    15\n",
      "8  1932    11\n",
      "9  1936    19\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Year'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = []\n",
    "for year in df['Year'].unique():\n",
    "    data.append([year , len(df[(df['Year'] == year) & (df['Medal'] == \"Gold\") & (df['Country'] == \"IND\")])])\n",
    "\n",
    "plot_data = pd.DataFrame(data,columns = ['Year','Gold']).sort_values(by='Year', ascending= True)\n",
    "\n",
    "print(\"\")\n",
    "print(plot_data.head(10))\n",
    "\n",
    "plot_data.plot(x = 'Year', y = 'Gold', kind = 'bar', figsize = (15,5), color = 'green')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35465a3d",
   "metadata": {},
   "source": [
    "### 7. Which event is most popular in terms on number of players? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "30eefd2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------top-5 most popular events in terms on number of players------------ \n",
      "\n",
      "                Event  Athletes\n",
      "80           Football      1497\n",
      "176            Hockey      1422\n",
      "138  Team Competition      1147\n",
      "327        Basketball      1012\n",
      "337          Handball       973\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Event'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = []\n",
    "for event in df['Event'].unique():\n",
    "    data.append([event , len(df[(df['Event'] == event)])])\n",
    "\n",
    "plot_data = pd.DataFrame(data,columns = ['Event','Athletes']).sort_values(by='Athletes', ascending=False).head()\n",
    "\n",
    "print(\"----------top-5 most popular events in terms on number of players------------ \\n\")\n",
    "print(plot_data)\n",
    "\n",
    "\n",
    "plot_data.plot(x = 'Event', y = 'Athletes', kind = 'bar', figsize = (10,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b8e1aa9",
   "metadata": {},
   "source": [
    "### 8. Which sport is having most female Gold Medalists? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6cf1055c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------top-5 Sport that having most female Gold Medalists------------ \n",
      "\n",
      "         Sport  No of female athlete\n",
      "0     Aquatics                   589\n",
      "1    Athletics                   389\n",
      "4   Gymnastics                   268\n",
      "17      Rowing                   217\n",
      "35  Volleyball                   166\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sport'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = []\n",
    "for sport in df['Sport'].unique():\n",
    "    data.append([sport , len(df[(df['Sport'] == sport) & (df['Medal'] == \"Gold\") & (df['Gender'] == \"Women\")])])\n",
    "\n",
    "plot_data = pd.DataFrame(data,columns = ['Sport','No of female athlete']).sort_values(by='No of female athlete', ascending=False).head()\n",
    "\n",
    "print(\"------------top-5 Sport that having most female Gold Medalists------------ \\n\")\n",
    "print(plot_data)\n",
    "\n",
    "plot_data.plot(x = 'Sport', y = 'No of female athlete', kind = 'bar', figsize = (10,8), color = \"green\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e3395f3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": 5
}