Summer Olympics .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('summer.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "  </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",
       "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": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summer Olympics Data Analysis Assignment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. In how many cities Summer Olympics is held so far?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['City'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['City'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Which sport is having most number of Gold Medals so far? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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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>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": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports = df[ (df.Medal == 'Gold')]\n",
    "sports.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[]\n",
    "for sport in sports['Sport'].unique():\n",
    "    data.append([sport,len(sports[sports['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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
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      "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": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
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    {
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      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2650ff04d00>"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Sport',y='No. of gold medals',kind='bar',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Which sport is having most number of medals so far? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "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": 45,
   "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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2650ff5eca0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Sport',y='No. of medals',kind='bar',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Which player has won most number of medals? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[]\n",
    "\n",
    "for sport in df['Athlete'].unique():\n",
    "    data.append([sport,len(df[df['Athlete']==sport])])\n",
    "\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": 48,
   "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": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2650ffb6c10>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Athlete',y='No. of medals',kind='bar',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Which player has won most number Gold Medals of medals? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "player = df[ (df.Medal == 'Gold')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>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": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[]\n",
    "\n",
    "for sport in player['Athlete'].unique():\n",
    "    data.append([sport,len(player[player['Athlete']==sport])])\n",
    "\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": 53,
   "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": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2650ebdf3a0>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Athlete',y='No. of gold medals',kind='bar',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. In which year India won first Gold Medal in Summer Olympics?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>5512</th>\n",
       "      <td>1928</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Hockey</td>\n",
       "      <td>Hockey</td>\n",
       "      <td>ALLEN, Richard James</td>\n",
       "      <td>IND</td>\n",
       "      <td>Men</td>\n",
       "      <td>Hockey</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Year       City   Sport Discipline               Athlete Country Gender  \\\n",
       "5512  1928  Amsterdam  Hockey     Hockey  ALLEN, Richard James     IND    Men   \n",
       "\n",
       "       Event Medal  \n",
       "5512  Hockey  Gold  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "India_medals = df[(df.Country==\"IND\") & (df.Medal == 'Gold')]\n",
    "India_medals.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Which event is most popular in terms on number of players? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=[]\n",
    "\n",
    "for sport in df['Event'].unique():\n",
    "    data.append([sport,len(df[df['Event']==sport])])\n",
    "\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": 57,
   "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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2650ffafc70>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Event',y='No. of players',kind='bar',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 8. Which sport is having most female Gold Medalists? (Top 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "womens_gold = df[(df.Gender == 'Women') & (df.Medal == 'Gold')]\n",
    "data=[]\n",
    "\n",
    "for sport in womens_gold['Sport'].unique():\n",
    "    data.append([sport,len(womens_gold[womens_gold['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": 60,
   "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": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2651002edf0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(x='Sport',y='No. of female gold medalists',kind='bar',color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}