Fiddling With IPL Matches data-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'plotly'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-f064e3d452ac>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mseaborn\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mmlt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstyle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0muse\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'fivethirtyeight'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moffline\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      7\u001b[0m \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minit_notebook_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconnected\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph_objs\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'plotly'"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as mlt\n",
    "import seaborn as sns\n",
    "mlt.style.use('fivethirtyeight')\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  season       city        date                        team1  \\\n",
       "0   1    2017  Hyderabad  2017-04-05          Sunrisers Hyderabad   \n",
       "1   2    2017       Pune  2017-04-06               Mumbai Indians   \n",
       "2   3    2017     Rajkot  2017-04-07                Gujarat Lions   \n",
       "3   4    2017     Indore  2017-04-08       Rising Pune Supergiant   \n",
       "4   5    2017  Bangalore  2017-04-08  Royal Challengers Bangalore   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3              Kings XI Punjab              Kings XI Punjab         field   \n",
       "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "1  normal           0       Rising Pune Supergiant            0   \n",
       "2  normal           0        Kolkata Knight Riders            0   \n",
       "3  normal           0              Kings XI Punjab            0   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
       "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
       "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "\n",
       "          umpire1        umpire2  umpire3  \n",
       "0     AY Dandekar       NJ Llong      NaN  \n",
       "1  A Nand Kishore         S Ravi      NaN  \n",
       "2     Nitin Menon      CK Nandan      NaN  \n",
       "3    AK Chaudhary  C Shamshuddin      NaN  \n",
       "4             NaN            NaN      NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = pd.read_csv(\"matches.csv\")\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  season       city        date                        team1  \\\n",
       "0   1    2017  Hyderabad  2017-04-05          Sunrisers Hyderabad   \n",
       "1   2    2017       Pune  2017-04-06               Mumbai Indians   \n",
       "2   3    2017     Rajkot  2017-04-07                Gujarat Lions   \n",
       "3   4    2017     Indore  2017-04-08       Rising Pune Supergiant   \n",
       "4   5    2017  Bangalore  2017-04-08  Royal Challengers Bangalore   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3              Kings XI Punjab              Kings XI Punjab         field   \n",
       "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "1  normal           0       Rising Pune Supergiant            0   \n",
       "2  normal           0        Kolkata Knight Riders            0   \n",
       "3  normal           0              Kings XI Punjab            0   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
       "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
       "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "\n",
       "          umpire1        umpire2  \n",
       "0     AY Dandekar       NJ Llong  \n",
       "1  A Nand Kishore         S Ravi  \n",
       "2     Nitin Menon      CK Nandan  \n",
       "3    AK Chaudhary  C Shamshuddin  \n",
       "4             NaN            NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data = raw_data.drop([\"umpire3\"], axis = 1)\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 636 entries, 0 to 635\n",
      "Data columns (total 17 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   id               636 non-null    int64 \n",
      " 1   season           636 non-null    int64 \n",
      " 2   city             629 non-null    object\n",
      " 3   date             636 non-null    object\n",
      " 4   team1            636 non-null    object\n",
      " 5   team2            636 non-null    object\n",
      " 6   toss_winner      636 non-null    object\n",
      " 7   toss_decision    636 non-null    object\n",
      " 8   result           636 non-null    object\n",
      " 9   dl_applied       636 non-null    int64 \n",
      " 10  winner           633 non-null    object\n",
      " 11  win_by_runs      636 non-null    int64 \n",
      " 12  win_by_wickets   636 non-null    int64 \n",
      " 13  player_of_match  633 non-null    object\n",
      " 14  venue            636 non-null    object\n",
      " 15  umpire1          635 non-null    object\n",
      " 16  umpire2          635 non-null    object\n",
      "dtypes: int64(5), object(12)\n",
      "memory usage: 84.6+ KB\n"
     ]
    }
   ],
   "source": [
    "raw_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Team_Names = {'Sunrisers Hyderabad':\"SRH\", 'Rising Pune Supergiant':\"RPS\",\n",
    "       'Kolkata Knight Riders':\"KKR\", 'Kings XI Punjab':\"KXIP\",\n",
    "       'Royal Challengers Bangalore':\"RCB\", 'Mumbai Indians':\"MI\",\n",
    "       'Delhi Daredevils':\"DD\", 'Gujarat Lions':\"GL\", 'Chennai Super Kings':\"CSK\",\n",
    "       'Rajasthan Royals':\"RR\", 'Deccan Chargers':\"DC\", 'Pune Warriors':\"PWI\",\n",
    "       'Kochi Tuskers Kerala':\"KTK\", 'Rising Pune Supergiants':\"RPS\"}\n",
    "#Team_Names.keys()\n",
    "raw_data = raw_data.replace(Team_Names.keys() , Team_Names.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>MI</td>\n",
       "      <td>RPS</td>\n",
       "      <td>RPS</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>RPS</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>GL</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KKR</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>KKR</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>RPS</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>RCB</td>\n",
       "      <td>DD</td>\n",
       "      <td>RCB</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>RCB</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  season       city        date team1 team2 toss_winner toss_decision  \\\n",
       "0   1    2017  Hyderabad  2017-04-05   SRH   RCB         RCB         field   \n",
       "1   2    2017       Pune  2017-04-06    MI   RPS         RPS         field   \n",
       "2   3    2017     Rajkot  2017-04-07    GL   KKR         KKR         field   \n",
       "3   4    2017     Indore  2017-04-08   RPS  KXIP        KXIP         field   \n",
       "4   5    2017  Bangalore  2017-04-08   RCB    DD         RCB           bat   \n",
       "\n",
       "   result winner  win_by_runs  win_by_wickets player_of_match  \\\n",
       "0  normal    SRH           35               0    Yuvraj Singh   \n",
       "1  normal    RPS            0               7       SPD Smith   \n",
       "2  normal    KKR            0              10         CA Lynn   \n",
       "3  normal   KXIP            0               6      GJ Maxwell   \n",
       "4  normal    RCB           15               0       KM Jadhav   \n",
       "\n",
       "                                       venue         umpire1        umpire2  \n",
       "0  Rajiv Gandhi International Stadium, Uppal     AY Dandekar       NJ Llong  \n",
       "1    Maharashtra Cricket Association Stadium  A Nand Kishore         S Ravi  \n",
       "2     Saurashtra Cricket Association Stadium     Nitin Menon      CK Nandan  \n",
       "3                     Holkar Cricket Stadium    AK Chaudhary  C Shamshuddin  \n",
       "4                      M Chinnaswamy Stadium             NaN            NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#toss_stats = raw_data['toss_winner']\n",
    "#toss_stats.head()\n",
    "#toss_stats[['toss_winner']['winner']] = raw_data[['toss_winner']['winner']]\n",
    "#toss_stats.head()\n",
    "#type(toss_stats)\n",
    "\n",
    "only_full_matches_data = raw_data[ raw_data[\"dl_applied\"] == 0 ]\n",
    "\n",
    "only_full_matches_data[\"dl_applied\"].nunique()\n",
    "only_full_matches_data = only_full_matches_data.drop(['dl_applied'], axis = 1)\n",
    "only_full_matches_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 620 entries, 0 to 635\n",
      "Data columns (total 16 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   id               620 non-null    int64 \n",
      " 1   season           620 non-null    int64 \n",
      " 2   city             613 non-null    object\n",
      " 3   date             620 non-null    object\n",
      " 4   team1            620 non-null    object\n",
      " 5   team2            620 non-null    object\n",
      " 6   toss_winner      620 non-null    object\n",
      " 7   toss_decision    620 non-null    object\n",
      " 8   result           620 non-null    object\n",
      " 9   winner           617 non-null    object\n",
      " 10  win_by_runs      620 non-null    int64 \n",
      " 11  win_by_wickets   620 non-null    int64 \n",
      " 12  player_of_match  617 non-null    object\n",
      " 13  venue            620 non-null    object\n",
      " 14  umpire1          619 non-null    object\n",
      " 15  umpire2          619 non-null    object\n",
      "dtypes: int64(4), object(12)\n",
      "memory usage: 82.3+ KB\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 609 entries, 1 to 636\n",
      "Data columns (total 15 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   season           609 non-null    int64 \n",
      " 1   city             609 non-null    object\n",
      " 2   date             609 non-null    object\n",
      " 3   team1            609 non-null    object\n",
      " 4   team2            609 non-null    object\n",
      " 5   toss_winner      609 non-null    object\n",
      " 6   toss_decision    609 non-null    object\n",
      " 7   result           609 non-null    object\n",
      " 8   winner           609 non-null    object\n",
      " 9   win_by_runs      609 non-null    int64 \n",
      " 10  win_by_wickets   609 non-null    int64 \n",
      " 11  player_of_match  609 non-null    object\n",
      " 12  venue            609 non-null    object\n",
      " 13  umpire1          609 non-null    object\n",
      " 14  umpire2          609 non-null    object\n",
      "dtypes: int64(3), object(12)\n",
      "memory usage: 76.1+ KB\n"
     ]
    },
    {
     "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>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>MI</td>\n",
       "      <td>RPS</td>\n",
       "      <td>RPS</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>RPS</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>GL</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KKR</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>KKR</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>RPS</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>GL</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>A Deshmukh</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    season       city        date team1 team2 toss_winner toss_decision  \\\n",
       "id                                                                        \n",
       "1     2017  Hyderabad  2017-04-05   SRH   RCB         RCB         field   \n",
       "2     2017       Pune  2017-04-06    MI   RPS         RPS         field   \n",
       "3     2017     Rajkot  2017-04-07    GL   KKR         KKR         field   \n",
       "4     2017     Indore  2017-04-08   RPS  KXIP        KXIP         field   \n",
       "6     2017  Hyderabad  2017-04-09    GL   SRH         SRH         field   \n",
       "\n",
       "    result winner  win_by_runs  win_by_wickets player_of_match  \\\n",
       "id                                                               \n",
       "1   normal    SRH           35               0    Yuvraj Singh   \n",
       "2   normal    RPS            0               7       SPD Smith   \n",
       "3   normal    KKR            0              10         CA Lynn   \n",
       "4   normal   KXIP            0               6      GJ Maxwell   \n",
       "6   normal    SRH            0               9     Rashid Khan   \n",
       "\n",
       "                                        venue         umpire1        umpire2  \n",
       "id                                                                            \n",
       "1   Rajiv Gandhi International Stadium, Uppal     AY Dandekar       NJ Llong  \n",
       "2     Maharashtra Cricket Association Stadium  A Nand Kishore         S Ravi  \n",
       "3      Saurashtra Cricket Association Stadium     Nitin Menon      CK Nandan  \n",
       "4                      Holkar Cricket Stadium    AK Chaudhary  C Shamshuddin  \n",
       "6   Rajiv Gandhi International Stadium, Uppal      A Deshmukh       NJ Llong  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_full_matches_data.info()\n",
    "#test = only_full_matches_data.dropna([\"city\"], axis=1)\n",
    "only_full_matches_data = only_full_matches_data.dropna(axis=0)\n",
    "only_full_matches_data.set_index(\"id\",inplace=True)\n",
    "only_full_matches_data.info()\n",
    "only_full_matches_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CSK     5\n",
       "RCB     4\n",
       "MI      3\n",
       "RR      2\n",
       "KKR     2\n",
       "DC      1\n",
       "DD      1\n",
       "SRH     1\n",
       "KXIP    1\n",
       "Name: winner, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = only_full_matches_data.sort_values(by=\"win_by_runs\", ascending = False)\n",
    "test_2 = test[test[\"win_by_runs\"]>80]\n",
    "test_2\n",
    "test_2[\"winner\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "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>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>GL</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>A Deshmukh</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>KKR</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>KKR</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-05-06</td>\n",
       "      <td>RPS</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>RPS</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>JD Unadkat</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>KN Ananthapadmanabhan</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-05-08</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>MI</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>KN Ananthapadmanabhan</td>\n",
       "      <td>M Erasmus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2017</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>2017-05-13</td>\n",
       "      <td>GL</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Mohammed Siraj</td>\n",
       "      <td>Green Park</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>Nitin Menon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>2013</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2013-04-07</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>bat</td>\n",
       "      <td>tie</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>GH Vihari</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>2013</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2013-04-12</td>\n",
       "      <td>DD</td>\n",
       "      <td>SRH</td>\n",
       "      <td>DD</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>Aleem Dar</td>\n",
       "      <td>Subroto Das</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>2013</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2013-04-14</td>\n",
       "      <td>KKR</td>\n",
       "      <td>SRH</td>\n",
       "      <td>KKR</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>KKR</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>M Erasmus</td>\n",
       "      <td>VA Kulkarni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>2013</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2013-04-19</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>SRH</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>GH Vihari</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>CK Nandan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>2013</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2013-05-01</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>MI</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>I Sharma</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>Asad Rauf</td>\n",
       "      <td>S Asnani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>2013</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2013-05-04</td>\n",
       "      <td>DD</td>\n",
       "      <td>SRH</td>\n",
       "      <td>DD</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>DJG Sammy</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>Asad Rauf</td>\n",
       "      <td>S Asnani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>2013</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2013-05-08</td>\n",
       "      <td>CSK</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>CSK</td>\n",
       "      <td>77</td>\n",
       "      <td>0</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>S Das</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>2013</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2013-05-19</td>\n",
       "      <td>KKR</td>\n",
       "      <td>SRH</td>\n",
       "      <td>KKR</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>PA Patel</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>Asad Rauf</td>\n",
       "      <td>S Asnani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>2014</td>\n",
       "      <td>Sharjah</td>\n",
       "      <td>2014-04-22</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Sharjah Cricket Stadium</td>\n",
       "      <td>M Erasmus</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>2014</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2014-05-20</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>2014</td>\n",
       "      <td>Ranchi</td>\n",
       "      <td>2014-05-22</td>\n",
       "      <td>CSK</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>JSCA International Stadium Complex</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>2015</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2015-04-11</td>\n",
       "      <td>CSK</td>\n",
       "      <td>SRH</td>\n",
       "      <td>CSK</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>CSK</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>RK Illingworth</td>\n",
       "      <td>VA Kulkarni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>525</th>\n",
       "      <td>2015</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2015-04-13</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>RM Deshpande</td>\n",
       "      <td>RK Illingworth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>2015</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2015-04-18</td>\n",
       "      <td>DD</td>\n",
       "      <td>SRH</td>\n",
       "      <td>DD</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>DD</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>JP Duminy</td>\n",
       "      <td>Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...</td>\n",
       "      <td>PG Pathak</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>2015</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2015-04-25</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>MI</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>MI</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>CB Gaffaney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>2015</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2015-05-04</td>\n",
       "      <td>KKR</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>KKR</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>UT Yadav</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>M Erasmus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>2016</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2016-04-12</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>RCB</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>VK Sharma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>2016</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2016-04-18</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>VK Sharma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>591</th>\n",
       "      <td>2016</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2016-04-21</td>\n",
       "      <td>GL</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>K Bharatan</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>2016</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2016-04-23</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Mustafizur Rahman</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>CK Nandan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>610</th>\n",
       "      <td>2016</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2016-05-06</td>\n",
       "      <td>GL</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>M Erasmus</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>2016</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2016-05-15</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>SRH</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>HM Amla</td>\n",
       "      <td>Punjab Cricket Association IS Bindra Stadium, ...</td>\n",
       "      <td>KN Ananthapadmanabhan</td>\n",
       "      <td>M Erasmus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>2016</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>2016-05-22</td>\n",
       "      <td>KKR</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>KKR</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>KN Ananthapadmanabhan</td>\n",
       "      <td>M Erasmus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>2016</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2016-05-27</td>\n",
       "      <td>GL</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>M Erasmus</td>\n",
       "      <td>CK Nandan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     season           city        date team1 team2 toss_winner toss_decision  \\\n",
       "id                                                                             \n",
       "6      2017      Hyderabad  2017-04-09    GL   SRH         SRH         field   \n",
       "14     2017        Kolkata  2017-04-15   KKR   SRH         SRH         field   \n",
       "43     2017      Hyderabad  2017-05-06   RPS   SRH         SRH         field   \n",
       "47     2017      Hyderabad  2017-05-08    MI   SRH          MI           bat   \n",
       "52     2017         Kanpur  2017-05-13    GL   SRH         SRH         field   \n",
       "388    2013      Hyderabad  2013-04-07   RCB   SRH         RCB           bat   \n",
       "394    2013          Delhi  2013-04-12    DD   SRH          DD           bat   \n",
       "397    2013        Kolkata  2013-04-14   KKR   SRH         KKR           bat   \n",
       "405    2013      Hyderabad  2013-04-19  KXIP   SRH        KXIP           bat   \n",
       "423    2013      Hyderabad  2013-05-01    MI   SRH          MI           bat   \n",
       "428    2013      Hyderabad  2013-05-04    DD   SRH          DD           bat   \n",
       "435    2013      Hyderabad  2013-05-08   CSK   SRH         SRH         field   \n",
       "453    2013      Hyderabad  2013-05-19   KKR   SRH         KKR           bat   \n",
       "466    2014        Sharjah  2014-04-22  KXIP   SRH         SRH         field   \n",
       "503    2014      Hyderabad  2014-05-20   RCB   SRH         RCB           bat   \n",
       "507    2014         Ranchi  2014-05-22   CSK   SRH         SRH         field   \n",
       "521    2015        Chennai  2015-04-11   CSK   SRH         CSK           bat   \n",
       "525    2015      Bangalore  2015-04-13   RCB   SRH         SRH         field   \n",
       "531    2015  Visakhapatnam  2015-04-18    DD   SRH          DD           bat   \n",
       "541    2015         Mumbai  2015-04-25    MI   SRH          MI           bat   \n",
       "555    2015        Kolkata  2015-05-04   KKR   SRH         SRH         field   \n",
       "580    2016      Bangalore  2016-04-12   RCB   SRH         SRH         field   \n",
       "588    2016      Hyderabad  2016-04-18    MI   SRH         SRH         field   \n",
       "591    2016         Rajkot  2016-04-21    GL   SRH         SRH         field   \n",
       "594    2016      Hyderabad  2016-04-23  KXIP   SRH         SRH         field   \n",
       "610    2016      Hyderabad  2016-05-06    GL   SRH         SRH         field   \n",
       "622    2016     Chandigarh  2016-05-15  KXIP   SRH        KXIP           bat   \n",
       "631    2016        Kolkata  2016-05-22   KKR   SRH         SRH         field   \n",
       "635    2016          Delhi  2016-05-27    GL   SRH         SRH         field   \n",
       "\n",
       "     result winner  win_by_runs  win_by_wickets    player_of_match  \\\n",
       "id                                                                   \n",
       "6    normal    SRH            0               9        Rashid Khan   \n",
       "14   normal    KKR           17               0         RV Uthappa   \n",
       "43   normal    RPS           12               0         JD Unadkat   \n",
       "47   normal    SRH            0               7           S Dhawan   \n",
       "52   normal    SRH            0               8     Mohammed Siraj   \n",
       "388     tie    SRH            0               0          GH Vihari   \n",
       "394  normal    SRH            0               3           A Mishra   \n",
       "397  normal    KKR           48               0          G Gambhir   \n",
       "405  normal    SRH            0               5          GH Vihari   \n",
       "423  normal    SRH            0               7           I Sharma   \n",
       "428  normal    SRH            0               6          DJG Sammy   \n",
       "435  normal    CSK           77               0           SK Raina   \n",
       "453  normal    SRH            0               5           PA Patel   \n",
       "466  normal   KXIP           72               0         GJ Maxwell   \n",
       "503  normal    SRH            0               7          DA Warner   \n",
       "507  normal    SRH            0               6          DA Warner   \n",
       "521  normal    CSK           45               0        BB McCullum   \n",
       "525  normal    SRH            0               8          DA Warner   \n",
       "531  normal     DD            4               0          JP Duminy   \n",
       "541  normal     MI           20               0         SL Malinga   \n",
       "555  normal    KKR           35               0           UT Yadav   \n",
       "580  normal    RCB           45               0     AB de Villiers   \n",
       "588  normal    SRH            0               7          DA Warner   \n",
       "591  normal    SRH            0              10            B Kumar   \n",
       "594  normal    SRH            0               5  Mustafizur Rahman   \n",
       "610  normal    SRH            0               5            B Kumar   \n",
       "622  normal    SRH            0               7            HM Amla   \n",
       "631  normal    KKR           22               0          YK Pathan   \n",
       "635  normal    SRH            0               4          DA Warner   \n",
       "\n",
       "                                                 venue                umpire1  \\\n",
       "id                                                                              \n",
       "6            Rajiv Gandhi International Stadium, Uppal             A Deshmukh   \n",
       "14                                        Eden Gardens            AY Dandekar   \n",
       "43           Rajiv Gandhi International Stadium, Uppal  KN Ananthapadmanabhan   \n",
       "47           Rajiv Gandhi International Stadium, Uppal  KN Ananthapadmanabhan   \n",
       "52                                          Green Park           AK Chaudhary   \n",
       "388          Rajiv Gandhi International Stadium, Uppal           AK Chaudhary   \n",
       "394                                   Feroz Shah Kotla              Aleem Dar   \n",
       "397                                       Eden Gardens              M Erasmus   \n",
       "405          Rajiv Gandhi International Stadium, Uppal        HDPK Dharmasena   \n",
       "423          Rajiv Gandhi International Stadium, Uppal              Asad Rauf   \n",
       "428          Rajiv Gandhi International Stadium, Uppal              Asad Rauf   \n",
       "435          Rajiv Gandhi International Stadium, Uppal                  S Das   \n",
       "453          Rajiv Gandhi International Stadium, Uppal              Asad Rauf   \n",
       "466                            Sharjah Cricket Stadium              M Erasmus   \n",
       "503          Rajiv Gandhi International Stadium, Uppal           AK Chaudhary   \n",
       "507                 JSCA International Stadium Complex           BNJ Oxenford   \n",
       "521                    MA Chidambaram Stadium, Chepauk         RK Illingworth   \n",
       "525                              M Chinnaswamy Stadium           RM Deshpande   \n",
       "531  Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...              PG Pathak   \n",
       "541                                   Wankhede Stadium        HDPK Dharmasena   \n",
       "555                                       Eden Gardens           AK Chaudhary   \n",
       "580                              M Chinnaswamy Stadium        HDPK Dharmasena   \n",
       "588          Rajiv Gandhi International Stadium, Uppal        HDPK Dharmasena   \n",
       "591             Saurashtra Cricket Association Stadium             K Bharatan   \n",
       "594          Rajiv Gandhi International Stadium, Uppal           AK Chaudhary   \n",
       "610          Rajiv Gandhi International Stadium, Uppal              M Erasmus   \n",
       "622  Punjab Cricket Association IS Bindra Stadium, ...  KN Ananthapadmanabhan   \n",
       "631                                       Eden Gardens  KN Ananthapadmanabhan   \n",
       "635                                   Feroz Shah Kotla              M Erasmus   \n",
       "\n",
       "             umpire2  \n",
       "id                    \n",
       "6           NJ Llong  \n",
       "14          NJ Llong  \n",
       "43      AK Chaudhary  \n",
       "47         M Erasmus  \n",
       "52       Nitin Menon  \n",
       "388           S Ravi  \n",
       "394      Subroto Das  \n",
       "397      VA Kulkarni  \n",
       "405        CK Nandan  \n",
       "423         S Asnani  \n",
       "428         S Asnani  \n",
       "435         NJ Llong  \n",
       "453         S Asnani  \n",
       "466           S Ravi  \n",
       "503         NJ Llong  \n",
       "507    C Shamshuddin  \n",
       "521      VA Kulkarni  \n",
       "525   RK Illingworth  \n",
       "531           S Ravi  \n",
       "541      CB Gaffaney  \n",
       "555        M Erasmus  \n",
       "580        VK Sharma  \n",
       "588        VK Sharma  \n",
       "591  HDPK Dharmasena  \n",
       "594        CK Nandan  \n",
       "610           S Ravi  \n",
       "622        M Erasmus  \n",
       "631        M Erasmus  \n",
       "635        CK Nandan  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SRH_GL = only_full_matches_data[only_full_matches_data[\"team2\"]==\"SRH\"]\n",
    "SRH_GL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Biggest wins in terms of runs has been registered by Mumbai Indians - 146 runs.\n",
    "Chennai Super Kings have 5 out of the top 20 biggest wins by runs, most for any team.\n",
    "Royal Challengers Bangalore have 4.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "gb = only_full_matches_data.groupby(['team1','team2'])\n",
    "a = gb.get_group((\"MI\",\"CSK\"))\n",
    "b = gb.get_group((\"CSK\",\"MI\"))\n",
    "CSK_MI = pd.concat([a,b],axis = 0)\n",
    "\n",
    "CSK_MI = CSK_MI.drop([\"team1\",\"team2\",\"umpire1\",\"umpire2\",\"result\"], axis = 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys([('CSK', 'DC'), ('CSK', 'DD'), ('CSK', 'KKR'), ('CSK', 'KTK'), ('CSK', 'KXIP'), ('CSK', 'MI'), ('CSK', 'PWI'), ('CSK', 'RCB'), ('CSK', 'RR'), ('CSK', 'SRH'), ('DC', 'CSK'), ('DC', 'DD'), ('DC', 'KKR'), ('DC', 'KTK'), ('DC', 'KXIP'), ('DC', 'MI'), ('DC', 'PWI'), ('DC', 'RCB'), ('DC', 'RR'), ('DD', 'CSK'), ('DD', 'DC'), ('DD', 'KKR'), ('DD', 'KTK'), ('DD', 'KXIP'), ('DD', 'MI'), ('DD', 'PWI'), ('DD', 'RCB'), ('DD', 'RPS'), ('DD', 'RR'), ('DD', 'SRH'), ('GL', 'DD'), ('GL', 'KKR'), ('GL', 'MI'), ('GL', 'RCB'), ('GL', 'RPS'), ('GL', 'SRH'), ('KKR', 'CSK'), ('KKR', 'DC'), ('KKR', 'DD'), ('KKR', 'GL'), ('KKR', 'KXIP'), ('KKR', 'MI'), ('KKR', 'PWI'), ('KKR', 'RCB'), ('KKR', 'RPS'), ('KKR', 'RR'), ('KKR', 'SRH'), ('KTK', 'KKR'), ('KTK', 'KXIP'), ('KTK', 'PWI'), ('KTK', 'RCB'), ('KTK', 'RR'), ('KXIP', 'CSK'), ('KXIP', 'DC'), ('KXIP', 'DD'), ('KXIP', 'GL'), ('KXIP', 'KKR'), ('KXIP', 'MI'), ('KXIP', 'PWI'), ('KXIP', 'RCB'), ('KXIP', 'RPS'), ('KXIP', 'RR'), ('KXIP', 'SRH'), ('MI', 'CSK'), ('MI', 'DC'), ('MI', 'DD'), ('MI', 'GL'), ('MI', 'KKR'), ('MI', 'KTK'), ('MI', 'KXIP'), ('MI', 'PWI'), ('MI', 'RCB'), ('MI', 'RPS'), ('MI', 'RR'), ('MI', 'SRH'), ('PWI', 'CSK'), ('PWI', 'DC'), ('PWI', 'DD'), ('PWI', 'KKR'), ('PWI', 'KXIP'), ('PWI', 'MI'), ('PWI', 'RCB'), ('PWI', 'RR'), ('RCB', 'CSK'), ('RCB', 'DC'), ('RCB', 'DD'), ('RCB', 'GL'), ('RCB', 'KKR'), ('RCB', 'KXIP'), ('RCB', 'MI'), ('RCB', 'PWI'), ('RCB', 'RPS'), ('RCB', 'RR'), ('RCB', 'SRH'), ('RPS', 'GL'), ('RPS', 'KKR'), ('RPS', 'KXIP'), ('RPS', 'MI'), ('RPS', 'RCB'), ('RPS', 'SRH'), ('RR', 'CSK'), ('RR', 'DC'), ('RR', 'DD'), ('RR', 'KKR'), ('RR', 'KTK'), ('RR', 'KXIP'), ('RR', 'MI'), ('RR', 'PWI'), ('RR', 'RCB'), ('SRH', 'CSK'), ('SRH', 'DD'), ('SRH', 'KKR'), ('SRH', 'KXIP'), ('SRH', 'MI'), ('SRH', 'PWI'), ('SRH', 'RCB'), ('SRH', 'RPS'), ('SRH', 'RR')])\n"
     ]
    }
   ],
   "source": [
    "s = gb.groups.keys()\n",
    "print(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "head_to_head = [[]]\n",
    "for i in range(len(head_to_head)):\n",
    "    head_to_head.pop()\n",
    "    \n",
    "head_to_head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "'''for i in range(len(teams)):\n",
    "    #team_1 = teams[i]\n",
    "    for j in range(i+1,len(teams)):\n",
    "        #team_2 = teams[j]\n",
    "        head_to_head = \"_\".join([teams[i],teams[j]])\n",
    "        \n",
    "        a = gb.get_group((teams[i],teams[j]))\n",
    "        b = gb.get_group((teams[j], teams[i]))\n",
    "        head_to_head = pd.concat([a,b], axis = 0)\n",
    "        head_to_head = head_to_head.drop([\"team1\",\"team2\",\"umpire1\",\"umpire2\",\"result\"], axis = 1)'''\n",
    "# Creates a list of all teams which have played head to head in IPL\n",
    "i = 0 \n",
    "for key in gb.groups.keys():\n",
    "    key = list(key)\n",
    "    head_to_head.append(key)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import copy\n",
    "head_to_heads = copy.deepcopy(head_to_head)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removes duplicates from the list\n",
    "#For example, ['CSK',\"MI\"] and [\"MI\",\"CSK\"] are two different values in the list\n",
    "\n",
    "\n",
    "for i in range(len(head_to_heads)):\n",
    "    team1 = head_to_heads[i][0]\n",
    "    team2 = head_to_heads[i][1]\n",
    "    for j in range(i):\n",
    "        if head_to_heads[j][0] == team2 and head_to_heads[j][1] == team1:\n",
    "            head_to_heads[i][0] = 'x'\n",
    "            break\n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['CSK', 'DC'],\n",
       " ['CSK', 'DD'],\n",
       " ['CSK', 'KKR'],\n",
       " ['CSK', 'KTK'],\n",
       " ['CSK', 'KXIP'],\n",
       " ['CSK', 'MI'],\n",
       " ['CSK', 'PWI'],\n",
       " ['CSK', 'RCB'],\n",
       " ['CSK', 'RR'],\n",
       " ['CSK', 'SRH'],\n",
       " ['x', 'CSK'],\n",
       " ['DC', 'DD'],\n",
       " ['DC', 'KKR'],\n",
       " ['DC', 'KTK'],\n",
       " ['DC', 'KXIP'],\n",
       " ['DC', 'MI'],\n",
       " ['DC', 'PWI'],\n",
       " ['DC', 'RCB'],\n",
       " ['DC', 'RR'],\n",
       " ['x', 'CSK'],\n",
       " ['x', 'DC'],\n",
       " ['DD', 'KKR'],\n",
       " ['DD', 'KTK'],\n",
       " ['DD', 'KXIP'],\n",
       " ['DD', 'MI'],\n",
       " ['DD', 'PWI'],\n",
       " ['DD', 'RCB'],\n",
       " ['DD', 'RPS'],\n",
       " ['DD', 'RR'],\n",
       " ['DD', 'SRH'],\n",
       " ['GL', 'DD'],\n",
       " ['GL', 'KKR'],\n",
       " ['GL', 'MI'],\n",
       " ['GL', 'RCB'],\n",
       " ['GL', 'RPS'],\n",
       " ['GL', 'SRH'],\n",
       " ['x', 'CSK'],\n",
       " ['x', 'DC'],\n",
       " ['x', 'DD'],\n",
       " ['x', 'GL'],\n",
       " ['KKR', 'KXIP'],\n",
       " ['KKR', 'MI'],\n",
       " ['KKR', 'PWI'],\n",
       " ['KKR', 'RCB'],\n",
       " ['KKR', 'RPS'],\n",
       " ['KKR', 'RR'],\n",
       " ['KKR', 'SRH'],\n",
       " ['KTK', 'KKR'],\n",
       " ['KTK', 'KXIP'],\n",
       " ['KTK', 'PWI'],\n",
       " ['KTK', 'RCB'],\n",
       " ['KTK', 'RR'],\n",
       " ['x', 'CSK'],\n",
       " ['x', 'DC'],\n",
       " ['x', 'DD'],\n",
       " ['KXIP', 'GL'],\n",
       " ['x', 'KKR'],\n",
       " ['KXIP', 'MI'],\n",
       " ['KXIP', 'PWI'],\n",
       " ['KXIP', 'RCB'],\n",
       " ['KXIP', 'RPS'],\n",
       " ['KXIP', 'RR'],\n",
       " ['KXIP', 'SRH'],\n",
       " ['x', 'CSK'],\n",
       " ['x', 'DC'],\n",
       " ['x', 'DD'],\n",
       " ['x', 'GL'],\n",
       " ['x', 'KKR'],\n",
       " ['MI', 'KTK'],\n",
       " ['x', 'KXIP'],\n",
       " ['MI', 'PWI'],\n",
       " ['MI', 'RCB'],\n",
       " ['MI', 'RPS'],\n",
       " ['MI', 'RR'],\n",
       " ['MI', 'SRH'],\n",
       " ['x', 'CSK'],\n",
       " ['x', 'DC'],\n",
       " ['x', 'DD'],\n",
       " ['x', 'KKR'],\n",
       " ['x', 'KXIP'],\n",
       " ['x', 'MI'],\n",
       " ['PWI', 'RCB'],\n",
       " ['PWI', 'RR'],\n",
       " ['x', 'CSK'],\n",
       " ['x', 'DC'],\n",
       " ['x', 'DD'],\n",
       " ['x', 'GL'],\n",
       " ['x', 'KKR'],\n",
       " ['x', 'KXIP'],\n",
       " ['x', 'MI'],\n",
       " ['x', 'PWI'],\n",
       " ['RCB', 'RPS'],\n",
       " ['RCB', 'RR'],\n",
       " ['RCB', 'SRH'],\n",
       " ['x', 'GL'],\n",
       " ['x', 'KKR'],\n",
       " ['x', 'KXIP'],\n",
       " ['x', 'MI'],\n",
       " ['x', 'RCB'],\n",
       " ['RPS', 'SRH'],\n",
       " ['x', 'CSK'],\n",
       " ['x', 'DC'],\n",
       " ['x', 'DD'],\n",
       " ['x', 'KKR'],\n",
       " ['x', 'KTK'],\n",
       " ['x', 'KXIP'],\n",
       " ['x', 'MI'],\n",
       " ['x', 'PWI'],\n",
       " ['x', 'RCB'],\n",
       " ['x', 'CSK'],\n",
       " ['x', 'DD'],\n",
       " ['x', 'KKR'],\n",
       " ['x', 'KXIP'],\n",
       " ['x', 'MI'],\n",
       " ['SRH', 'PWI'],\n",
       " ['x', 'RCB'],\n",
       " ['x', 'RPS'],\n",
       " ['SRH', 'RR']]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "head_to_heads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "i = 0\n",
    "while i < len(head_to_heads):\n",
    "    if head_to_heads[i][0] == 'x':\n",
    "        head_to_heads.pop(i)\n",
    "    i+=1\n",
    "    \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['CSK', 'DC'],\n",
       " ['CSK', 'DD'],\n",
       " ['CSK', 'KKR'],\n",
       " ['CSK', 'KTK'],\n",
       " ['CSK', 'KXIP'],\n",
       " ['CSK', 'MI'],\n",
       " ['CSK', 'PWI'],\n",
       " ['CSK', 'RCB'],\n",
       " ['CSK', 'RR'],\n",
       " ['CSK', 'SRH'],\n",
       " ['DC', 'DD'],\n",
       " ['DC', 'KKR'],\n",
       " ['DC', 'KTK'],\n",
       " ['DC', 'KXIP'],\n",
       " ['DC', 'MI'],\n",
       " ['DC', 'PWI'],\n",
       " ['DC', 'RCB'],\n",
       " ['DC', 'RR'],\n",
       " ['DD', 'KKR'],\n",
       " ['DD', 'KTK'],\n",
       " ['DD', 'KXIP'],\n",
       " ['DD', 'MI'],\n",
       " ['DD', 'PWI'],\n",
       " ['DD', 'RCB'],\n",
       " ['DD', 'RPS'],\n",
       " ['DD', 'RR'],\n",
       " ['DD', 'SRH'],\n",
       " ['GL', 'DD'],\n",
       " ['GL', 'KKR'],\n",
       " ['GL', 'MI'],\n",
       " ['GL', 'RCB'],\n",
       " ['GL', 'RPS'],\n",
       " ['GL', 'SRH'],\n",
       " ['KKR', 'KXIP'],\n",
       " ['KKR', 'MI'],\n",
       " ['KKR', 'PWI'],\n",
       " ['KKR', 'RCB'],\n",
       " ['KKR', 'RPS'],\n",
       " ['KKR', 'RR'],\n",
       " ['KKR', 'SRH'],\n",
       " ['KTK', 'KKR'],\n",
       " ['KTK', 'KXIP'],\n",
       " ['KTK', 'PWI'],\n",
       " ['KTK', 'RCB'],\n",
       " ['KTK', 'RR'],\n",
       " ['KXIP', 'GL'],\n",
       " ['KXIP', 'MI'],\n",
       " ['KXIP', 'PWI'],\n",
       " ['KXIP', 'RCB'],\n",
       " ['KXIP', 'RPS'],\n",
       " ['KXIP', 'RR'],\n",
       " ['KXIP', 'SRH'],\n",
       " ['MI', 'KTK'],\n",
       " ['MI', 'PWI'],\n",
       " ['MI', 'RCB'],\n",
       " ['MI', 'RPS'],\n",
       " ['MI', 'RR'],\n",
       " ['MI', 'SRH'],\n",
       " ['PWI', 'RCB'],\n",
       " ['PWI', 'RR'],\n",
       " ['RCB', 'RPS'],\n",
       " ['RCB', 'RR'],\n",
       " ['RCB', 'SRH'],\n",
       " ['RPS', 'SRH'],\n",
       " ['SRH', 'PWI'],\n",
       " ['SRH', 'RR']]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "head_to_heads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creates a dictionary of dataframes where team names are the keys and the corresponding head to head matches data are the values \n",
    "\n",
    "frames = {}\n",
    "\n",
    "for i in range(len(head_to_heads)):\n",
    "    a = gb.get_group((head_to_heads[i][0],head_to_heads[i][1]))\n",
    "    if [head_to_heads[i][1],head_to_heads[i][0]] in head_to_head:\n",
    "        b = gb.get_group((head_to_heads[i][1],head_to_heads[i][0]))\n",
    "        frames[head_to_heads[i][0] + \"v\" + head_to_heads[i][1]] = pd.concat([a,b],axis = 0)\n",
    "        frames[head_to_heads[i][0] + \"v\" + head_to_heads[i][1]] = frames[head_to_heads[i][0] + \"v\" + head_to_heads[i][1]].drop([\"team1\",\"team2\",\"umpire1\",\"umpire2\",\"result\"], axis = 1)\n",
    "    else:\n",
    "        frames[head_to_heads[i][0] + \"v\" + head_to_heads[i][1]] = a\n",
    "        #frames[head_to_heads[i][0] + \"v\" + head_to_heads[i][1]] = frames[head_to_heads[i][0] + \"v\" + head_to_heads[i][1]].drop([\"team1\",\"team2\",\"umpire1\",\"umpire2\",\"result\"], axis = 1)\n",
    "\n"
   ]
  },
  {
   "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>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>2008</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2008-04-28</td>\n",
       "      <td>CSK</td>\n",
       "      <td>bat</td>\n",
       "      <td>CSK</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2009</td>\n",
       "      <td>Port Elizabeth</td>\n",
       "      <td>2009-04-20</td>\n",
       "      <td>CSK</td>\n",
       "      <td>bat</td>\n",
       "      <td>CSK</td>\n",
       "      <td>92</td>\n",
       "      <td>0</td>\n",
       "      <td>M Muralitharan</td>\n",
       "      <td>St George's Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>2009</td>\n",
       "      <td>Durban</td>\n",
       "      <td>2009-05-14</td>\n",
       "      <td>CSK</td>\n",
       "      <td>bat</td>\n",
       "      <td>RCB</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>LRPL Taylor</td>\n",
       "      <td>Kingsmead</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>2009</td>\n",
       "      <td>Johannesburg</td>\n",
       "      <td>2009-05-23</td>\n",
       "      <td>RCB</td>\n",
       "      <td>field</td>\n",
       "      <td>RCB</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>New Wanderers Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2011</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011-04-16</td>\n",
       "      <td>CSK</td>\n",
       "      <td>bat</td>\n",
       "      <td>CSK</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>2011</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2011-05-22</td>\n",
       "      <td>RCB</td>\n",
       "      <td>field</td>\n",
       "      <td>RCB</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>2011</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2011-05-28</td>\n",
       "      <td>CSK</td>\n",
       "      <td>bat</td>\n",
       "      <td>CSK</td>\n",
       "      <td>58</td>\n",
       "      <td>0</td>\n",
       "      <td>M Vijay</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>2014</td>\n",
       "      <td>Ranchi</td>\n",
       "      <td>2014-05-18</td>\n",
       "      <td>CSK</td>\n",
       "      <td>bat</td>\n",
       "      <td>RCB</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>JSCA International Stadium Complex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>2015</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2015-04-22</td>\n",
       "      <td>RCB</td>\n",
       "      <td>field</td>\n",
       "      <td>CSK</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>2015</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2015-05-04</td>\n",
       "      <td>CSK</td>\n",
       "      <td>bat</td>\n",
       "      <td>CSK</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>2008</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2008-05-21</td>\n",
       "      <td>RCB</td>\n",
       "      <td>bat</td>\n",
       "      <td>RCB</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>A Kumble</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>2010</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2010-03-23</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>RCB</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>2010</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2010-03-31</td>\n",
       "      <td>RCB</td>\n",
       "      <td>bat</td>\n",
       "      <td>CSK</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>M Vijay</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>2011</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>2011-05-24</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>CSK</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>2012</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2012-04-12</td>\n",
       "      <td>RCB</td>\n",
       "      <td>bat</td>\n",
       "      <td>CSK</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>2013</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>2013-04-13</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>CSK</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>2013</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2013-05-18</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>RCB</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>2014</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2014-05-24</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>CSK</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>2015</td>\n",
       "      <td>Ranchi</td>\n",
       "      <td>2015-05-22</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>CSK</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>JSCA International Stadium Complex</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     season            city        date toss_winner toss_decision winner  \\\n",
       "id                                                                         \n",
       "74     2008       Bangalore  2008-04-28         CSK           bat    CSK   \n",
       "122    2009  Port Elizabeth  2009-04-20         CSK           bat    CSK   \n",
       "159    2009          Durban  2009-05-14         CSK           bat    RCB   \n",
       "173    2009    Johannesburg  2009-05-23         RCB         field    RCB   \n",
       "248    2011         Chennai  2011-04-16         CSK           bat    CSK   \n",
       "302    2011       Bangalore  2011-05-22         RCB         field    RCB   \n",
       "307    2011         Chennai  2011-05-28         CSK           bat    CSK   \n",
       "499    2014          Ranchi  2014-05-18         CSK           bat    RCB   \n",
       "538    2015       Bangalore  2015-04-22         RCB         field    CSK   \n",
       "554    2015         Chennai  2015-05-04         CSK           bat    CSK   \n",
       "107    2008         Chennai  2008-05-21         RCB           bat    RCB   \n",
       "192    2010       Bangalore  2010-03-23         CSK         field    RCB   \n",
       "202    2010         Chennai  2010-03-31         RCB           bat    CSK   \n",
       "304    2011          Mumbai  2011-05-24         CSK         field    CSK   \n",
       "320    2012         Chennai  2012-04-12         RCB           bat    CSK   \n",
       "396    2013         Chennai  2013-04-13         CSK         field    CSK   \n",
       "452    2013       Bangalore  2013-05-18         CSK         field    RCB   \n",
       "510    2014       Bangalore  2014-05-24         CSK         field    CSK   \n",
       "575    2015          Ranchi  2015-05-22         CSK         field    CSK   \n",
       "\n",
       "     win_by_runs  win_by_wickets player_of_match  \\\n",
       "id                                                 \n",
       "74            13               0        MS Dhoni   \n",
       "122           92               0  M Muralitharan   \n",
       "159            0               2     LRPL Taylor   \n",
       "173            0               6       MK Pandey   \n",
       "248           21               0      MEK Hussey   \n",
       "302            0               8        CH Gayle   \n",
       "307           58               0         M Vijay   \n",
       "499            0               5  AB de Villiers   \n",
       "538           27               0        SK Raina   \n",
       "554           24               0        SK Raina   \n",
       "107           14               0        A Kumble   \n",
       "192           36               0      RV Uthappa   \n",
       "202            0               5         M Vijay   \n",
       "304            0               6        SK Raina   \n",
       "320            0               5    F du Plessis   \n",
       "396            0               4       RA Jadeja   \n",
       "452           24               0         V Kohli   \n",
       "510            0               8        MS Dhoni   \n",
       "575            0               3         A Nehra   \n",
       "\n",
       "                                  venue  \n",
       "id                                       \n",
       "74                M Chinnaswamy Stadium  \n",
       "122                    St George's Park  \n",
       "159                           Kingsmead  \n",
       "173               New Wanderers Stadium  \n",
       "248     MA Chidambaram Stadium, Chepauk  \n",
       "302               M Chinnaswamy Stadium  \n",
       "307     MA Chidambaram Stadium, Chepauk  \n",
       "499  JSCA International Stadium Complex  \n",
       "538               M Chinnaswamy Stadium  \n",
       "554     MA Chidambaram Stadium, Chepauk  \n",
       "107     MA Chidambaram Stadium, Chepauk  \n",
       "192               M Chinnaswamy Stadium  \n",
       "202     MA Chidambaram Stadium, Chepauk  \n",
       "304                    Wankhede Stadium  \n",
       "320     MA Chidambaram Stadium, Chepauk  \n",
       "396     MA Chidambaram Stadium, Chepauk  \n",
       "452               M Chinnaswamy Stadium  \n",
       "510               M Chinnaswamy Stadium  \n",
       "575  JSCA International Stadium Complex  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames[\"CSKvRCB\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbwAAAE0CAYAAABXU4ZqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nO3deVgVdf//8ecBBATFAwaYArkhLqgoKuBSbmluudutd2q5o2WlltaNWrlVmmul3maaZpb+UMlMxX3HLU1bbiW9XTJLU0AFUZDz+8Mv5+7E4kE4oJ3X47q8Lpn5zMx7sGtefT4znxlDYmKiCRERkb85h6IuQEREpDAo8ERExC4o8ERExC4o8ERExC4o8ERExC4o8ERExC4o8ETEKteuXWPMmDHUrl2bRx55BKPRyK5du4q6LBGrORV1ASIFwWg0ApCYmFjElRSes2fPUrt2bRo1asS6detsfrzx48ezaNEiGjduTPfu3XF0dCQgICDbtu3atWPPnj1W77tnz57MnTu3oEoVyZYCT0SssnHjRkqUKMHq1aspVqxYrm179epF48aNLZbt3r2bPXv20KhRoyzratasWeD1ivyVAk9ErHLx4kX8/PzuGXYA//znP7MsmzJlCnv27KFx48a8/vrrtihRJFe6hyd/W2fPnsVoNNKuXTsuXbrEsGHDCAwMpGzZsrRq1co85Hbjxg3eeOMNgoOD8fHxISwsjDVr1mTZ37JlyzAajUyZMoUDBw7QsWNH/P398ff3p1u3bhw9ejTLNhcvXuSdd96hVatWVKlSBW9vb6pWrUr//v356aefcqz96NGjDBgwgBo1auDj40NgYCBt2rRh4cKF5lpq164NwJ49ezAajeY/U6ZMser3c/r0aYYOHUr16tXx9vYmMDCQ5557juPHj1u0a9euHUajEZPJxPnz583HadeunVXHyauEhATGjRtHaGgovr6+PPbYY3Tq1InY2Nhs269atYq2bdtSpUoVfHx8qFq1Km3atOGDDz6waHfhwgVee+01QkNDKVu2LAEBAdSrV49BgwZx8uRJm5yLPFjUw5O/vaSkJFq3bo2npyfdu3fn119/JSYmhq5duxIbG8vLL79MSkoKbdu25fr160RHR/P8889Trlw56tevn2V/hw8fZsaMGTRr1oyBAwdy6tQp1q5dy549e1izZg1hYWHmtnv37mXWrFk0adKEp59+Gjc3N06dOkVMTAzr169nw4YN1KpVy2L/S5cu5ZVXXgGgVatWBAUFkZCQwPfff8+sWbPo378/NWvWZMiQIcybNw9/f3969epl3v6vw4XZOXLkCB07duTatWu0bt2aGjVq8N///pe1a9eyfv16PvvsM5588kngf8OT7777Lh4eHkRGRgLkeP8uPy5fvkzr1q05ffo0oaGhdOjQgStXrhATE0OPHj14++23GT58uLn97NmzGTduHI8++iht2rShdOnSXL58mZ9++omlS5fywgsvAHf/G2jZsiW//fYbzZs3p23btmRkZPDLL7+wefNmWrduTZUqVQr8fOTBYtDLo+XvILuHVjIf6gAYPHgw77zzDgaDAYDp06fz9ttv4+HhQbNmzfj444/NQ3UrV65k4MCBtGvXjmXLlpn3t2zZMoYNGwbA1KlTGThwoHldTEwMffv2JTAwkAMHDpiPc/nyZVxdXSlZsqRFvUePHqVt27ZEREQQHR1tXv6f//yHxo0b4+rqytdff01ISIjFdr/88gt+fn4W55fXh1ZMJhPh4eGcOHGCjz76yCIst2/fTufOnfHy8uL48eO4ublZ/I79/f2z9ACtNWXKFN59911Gjx6d45DmwIEDzb//qVOnmpefOXOGZs2akZSUxO7du6levToAoaGhXLp0iWPHjuHp6WmxrytXrlC6dGngf/+mI0eOZOzYsRbt0tLSSElJoVSpUvd1XvLw0JCm/O25u7szbtw4cwgB9OjRA7j7qP3EiRMt7kt16dKFYsWK5Xhhr1ixIv3797dY1rFjR8LCwoiPj2f//v3m5d7e3lnCDiAkJIQmTZqwe/du0tLSzMsXLlxIeno6I0eOzBJ2gDns8mP//v2cOHGCunXrWoQdQNOmTWnfvj1XrlwplCc//+zGjRusXr2aUqVKZQml8uXL8+KLL5KRkcFnn31mXu7g4ICTkxNOTlkHqzLDLrMdQPHixbO0K1asmMLOTijw5G+vUqVKuLu7WywrU6YM8L9ey585Ojri7e3Nr7/+mu3+IiIizBfQP2vYsCEAx44ds1i+ceNGnnnmGYKCgszz14xGIxs3buTWrVtcuXLF3PbQoUPA3aFMW/nuu+8AePzxx7Nd37RpU4t2heXHH38kPT2dkJAQPDw8rKqrR48eJCQkEBYWxhtvvMHXX3/N5cuXs2zbrFkzvL29mTx5Mt27d2f+/PkcOXKEO3fu2Ox85MGje3jyt5ddDyuzR5DdOrgbeunp6dmu8/HxyXa5t7c3cLfXmGnevHmMGTMGo9FIs2bN8Pf3x9XVFYPBwLp16/j++++5deuWuX1SUhIA5cqVs+LM7k9mfTmdh6+vr0W7wpJ5vMzj/1V2dY0aNYpHH32UxYsXM2/ePD766CMMBgPh4eG8+eab5vupXl5ebN26lXfeeYcNGzawadMm8/K+ffsyZswYXFxcbHl68gBQ4Ink0aVLl7JdntmzyOydpKenM2XKFHx9fdmxY4e5V5np4MGDWfaRObT266+/mu9LFrTM+nI6j99//92iXWHJPF7m8f8qu7oMBgPPPvsszz77LImJiRw4cIBvvvmGpUuX0rVrV/bt22fuwfv7+/Phhx9iMpn4z3/+w65du/jkk0+YMWMGKSkpvPvuuzY+QylqGtIUyaO4uDgyMjKyLN+7dy+A+anLK1eukJSURIMGDbKE3Y0bN7IdMsx8KjSnR/D/zNHRESDbWnKT+SBPTq8F27FjB0C29xBtqXr16hQrVoyjR49m27u8V11Go5FWrVoxc+ZM+vfvz40bN9i+fXuWdgaDgWrVqjFo0CDWrVuHg4NDod+vlKKhwBPJo1OnTpnnw2WKiYlh//79BAYGmofRvL29cXNz48iRI9y4ccPcNi0tjTFjxljcu8vUv39/ihUrxvvvv5/tQzMXLlww/93T0xODwcD58+fzVH9YWBhBQUEcPnyYL7/80mLdjh07WLt2LaVLl6Zt27Z52m9+lShRgs6dO5OUlMTkyZMt1p09e5Y5c+bg4OBgMak9NjY22/twmb1tV1dX4O591V9++SXbdhkZGRrOtBMa0hTJo5YtWxIVFcXmzZupUaOGeR5e8eLFmTNnjvlpUAcHBwYPHsyMGTNo2LAhbdu2JS0tjV27dpGQkECTJk2y9LKCgoKYPn06L7/8Ms2aNaN169YEBQWRlJTEDz/8wK+//mp+KMbd3Z3w8HD27dvHM888Q0hICE5OTjRs2JBGjRrlWL/BYGDu3Ll06tSJIUOGsHr1avM8vK+++gpnZ2fmzZtnMSWhsEyaNImDBw8yb948Dh8+TOPGjbl69SqrV6/m2rVrvPXWW+YpCXD3HZyPPPII4eHh+Pv7YzKZOHDgAAcPHiQoKMgc2hs3bmTKlCmEhYURGBjII488wsWLF809u5dffrnQz1UKnwJPJI9CQ0N57bXXmDhxIv/+97+Bu08Bjh07Nstw27/+9S9Kly7N0qVLWbx4MR4eHjRt2pSoqKgc34jSu3dvqlevzpw5c9i7dy+xsbF4enoSGBjIiBEjLNrOmzePf/3rX+zdu5dNmzaRkZHB6NGjcw08gLp167J9+3amTp3K9u3b2bJlC6VKlaJdu3aMHDkyy2T4wuLt7c3WrVuZPn06X3/9NR9++CGurq7UqVOHoUOH0rp1a4v2kyZNYseOHRw5coTY2FicnZ3x9/cnKiqKAQMGmJ/ObdOmDX/88QdxcXGsW7eO69ev4+PjQ5MmTYiMjLRqsr48/DTxXMRKmRPPc5s4LSIPLt3DExERu6DAExERu6DAExERu6B7eCIiYhfUwxMREbugwBMREbugwBMREbugwCsg8fHxRV2CiMhDqbCunwo8ERGxCwo8ERGxCwo8ERGxCwo8ERGxCwo8ERGxCwo8ERGxC0UaeJGRkRiNRoxGI6VLlyY4OJgRI0aQmJho0e7YsWM8//zzVKlSBV9fX+rUqUNkZCQ//PADcPdryJn7MRqN+Pj4EBoaypw5c4ritERE5AFU5D28pk2bcuLECY4dO8bs2bPZsGEDI0eONK/fsGEDLVu2JDk5mfnz57N//34++eQTypQpw1tvvWWxr+joaE6cOMGhQ4d45ZVXeOutt1i1alVhn5KIiDyAivyL5y4uLvj6+gJQrlw5OnfuzOeffw5ASkoKw4YNo3nz5nzxxRfmbcqXL0+dOnWy9AS9vLzM+3r22Wf5+OOP+e677+jSpYvN6neaN4/i//oXoXfuYHJ05OakSaQPGWKz44mI/F04Rkfj9uKL1E1NBVdXUubM4U7XrjY7XpH38P7szJkzbNmyhWLFigGwZcsWrly5wiuvvJJte6PRmO1yk8lEXFwcJ0+eJDQ01Gb1Os2bh9uYMTjcuYMBcLhzB7cxY3CaN89mxxQR+TtwjI7GvX9/HFJScMjIwCElBff+/XGMjrbZMYu8h7d582bKlSvHnTt3SE1NBWDSpEkAnD59GoCgoCCr9tW2bVscHBy4ffs2aWlpREZG8vTTT9umcKD4v/6F4S/LDP+3/Lp6eSIiOXJ78cVsr59uL77IdRv18oo88Bo2bMisWbO4efMmn376KWfOnGHI/4WFyZS3T/UtWLCA6tWrk5aWxo8//sjo0aNxd3cnKioqx23y8w630Dt3sl1uuHNH79YUEclF3f/r4GSRmpqv62dgYGCO64o88Nzc3KhYsSIA7733Hu3bt+e9997j9ddfp1KlSgCcOHGCsLCwe+6rXLly5n0FBQVx5swZJk2axKhRo3B1dc12m9x+OfdicnTEkE3omRwd87VfEZG/PVdXSEnJdrmtrp8P1D08gNGjRzNr1iwuXrxI8+bNKV26NDNmzMi27V8fWvkrR0dH0tPTuX37ti1K5eakSfy1D2r6v+UiIpKzlDlzsr1+pthwOtkDF3hNmjShatWqTJs2DXd3d2bPns2WLVvo0aMH27Zt4+zZsxw9epSJEycyaNAgi22vXr3K77//zoULF9i0aRPz5s2jSZMmeHh42KTW9CFDSHnnHTIcHTEBGY6OpLzzjp7SFBG5hztdu5K8cCEZbm5kODiQ4eZG8sKFNn1K05CYmJi3G2UFKDIykqtXr/Lll19aLF+5ciXDhg3j0KFDBAQEcPToUWbMmMHevXtJSkqibNmyREREMHz4cKpVq8bZs2epXbu2eXtHR0fKlClDy5YtGTt2LI888ojNzyU+Pl7DmCIi96Gwrp9FGnh/Jwo8EZH7U1jXzwduSFNERMQWFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXFHgiImIXrAq8W7dusXz5cg4fPmzrekRERGzCqsBzcXHhpZde4vjx47auR0RExCasHtIMDAzk999/t2UtIiIiNmN14L322mssWLCAH374wZb1iIiI2ISTtQ137tyJt7c3jz/+OA0aNKBChQoUL17coo3BYGDatGkFXqSIiEh+GRITE03WNPT09Lz3zgwGrl69mu+iHkbx8fEEBgYWdRkiIg+dwrp+Wt3DS0hIsGUdIiIiNqV5eCIiYhes7uFliouLY+fOnVy+fJnBgwdTuXJlkpOT+c9//kNgYCAeHh62qFNERCRfrA6827dv069fP7755htMJhMGg4H27dtTuXJlHB0d6datG8OGDWPUqFG2rFdEROS+WD2kOWXKFDZu3MjUqVM5ePAgJtP/nnVxdXWlU6dOrF+/3iZFioiI5JfVgbdy5Uqee+45+vfvj5eXV5b1gYGBnDlzpiBrExERKTBWB97ly5epWbNmjutdXFxITk4ukKJEREQKmtWB5+vrm2sP7vDhwzz22GMFUZOIiEiBszrwnn76aRYtWsTPP/9sXmYwGABYv349K1eupEuXLgVfoYiISAGw+k0r169fp127dpw6dYqwsDC2b9/O448/zo0bN/j2228JDQ1l7dq1uLq62rrmB5LetCIicn8K6/ppdQ+vZMmSxMbGMmLECC5fvoyrqytxcXEkJyfz+uuv23XYiYjIg8/qHp7kTj08EZH788D18ERERB5meXq1WHx8PJ999hlnzpwhISHBYvI53H2I5auvvirQAkVERAqC1YEXHR3N4MGDcXR0JDAwEKPRmKXNXwNQRETkQWF14E2ePJnq1asTHR2Nt7e3LWsSEREpcFbfw7tw4QJ9+vRR2ImIyEPJ6sCrUqUKV65csWUtIiIiNmN14I0bNy7Lm1ZEREQeFlbfw1u/fj3e3t40bNiQxx9/HD8/PxwdHS3aGAwGpk2bVuBFioiI5JfVE889PT3vvTODgatXr+a7qIeRJp6LiNyfwrp+Wt3DS0hIsGUdIiIiNqU3rYiIiF3I05tWMiUnJ2f7phUAf3//fBclIiJS0KwOvNu3b/Pee+/x6aef5jo9wV7v4YmIyIPN6sB77bXXWLJkCW3atKFRo0bZvlpMRETkQWV14K1Zs4ZevXrxwQcf2LIeERERm7D6oZWMjAzq1atny1pERERsxurAa9GiBXFxcbasRURExGasDrz33nuP77//nsmTJ/P777/bsiYREZECZ/WbVsqUKYPJZCItLQ2AYsWK4eBgmZcGg4Fff/214Kt8COhNKyIi9+eBe9NK586dMRgMtqxFRETEZqwOvLlz59qyDhEREZvSq8VERMQu5NjD27NnDwCNGjWy+PleMtuLiIg8SHIMvPbt22MwGPjtt99wdnY2/5wTk8lk158HEhGRB1uOgbd27VoAnJ2dLX4WERF5GOUYeI0bN871ZxERkYdJrg+tzJ07l6NHj5KRkVFY9YiIiNhErhPPPT09MRgMuLu7U79+fSIiIggPD6d+/fq4uroWZp0PPE08Fyk46enpJCcnF3UZUkiuXbuGh4eH1e3d3d1xcsr751xz3WLdunXExcURFxfHgQMH2LZtGwaDAScnJ2rXrk1ERIQ5BD09PfN8cBGRv0pPT+f69esYjUa97MJOuLi4WN2JMplMJCYmUrJkyTyHntWvFgP48ccfiYuLY9++fezfv5/z58/f3YnBQFBQEBEREUyfPj1PBfxdqIcnUjCSkpLw8PBQ2NmR1NTUPI0amkwmrl27RqlSpfJ0nDwF3l9dvHiR3bt3s3DhQvbv32/X0xIUeCIFIykpKc8XMnm45TXw4P7+O8nzIGhqaioHDx40D3UePHjQPPzQoEGDvO5ORESkUNwz8K5cucK+ffvMAXfs2DHS0tKoUKECYWFhTJgwgbCwMKpWrVoY9YqIiNyXXAOvQYMG/Pzzzzg7O1OrVi0iIiJ46aWXCA8Px9vbu7BqFBERybdc5+HFx8fj6OhI27ZtefbZZ+nduzcdOnRQ2ImI/InRaMz1T2RkZJHW17JlS6Kiooq0BmukpqZiNBrZsGGDTfZv9bSEcePGce3aNfO9uvDwcMLCwggNDTW/fkxExB6dOHHC/PeNGzcyfPhwi2Wat/xgyLWH17BhQ0aMGMGKFSs4c+YMu3btIioqCg8PDz755BPatm2Lv78/rVu3Zty4caxbt66w6hYReWD4+vqa/2Q+OZjdsu+++4527dpRpkwZKlSowIsvvsj169fN+8lc7+fnh5+fH02aNGHfvn0A3Lp1i5EjRxIUFISPjw/BwcFMmjTpvupt2bIlY8aM4bXXXiMgIIDKlSuzcOFCbt68yUsvvURAQADBwcGsWrXKvM3JkycxGo2sWrWKJ598El9fX8LDw9m5c6e5za1btxg6dCg1a9akTJkyhIaG8uGHH2IyWU4GWLJkCeHh4fj4+FClShVGjRoFQK1atQD4xz/+gdFopH79+vd1fjnJ0/fwatSoQb9+/fj3v//NsWPH+PHHH/nggw8wGAx88MEH9OnTp0CLExG5X4azZyk+cCDu7dtTfOBADGfPFmk9165do2vXrnh7e7N161YWL17Mzp07GTFihLnN888/z2OPPca2bdvYuXMno0aNwsXFBYA5c+awadMmFi9ezKFDh/j444+pWLHifdezbNkyfH192bZtG0OHDmXUqFH06dOH4OBgtm3bRteuXRk2bBh//PGHxXbjxo3jxRdfZOfOnYSHh9OzZ08uX74M3H1pQEBAAEuWLCEuLo7XX3+dKVOmsHLlSvP2c+fOZfTo0Tz33HPs3buXL774gkqVKgGwdetWAObPn8+JEycKfGizwKYlAOZ/GBGRomQ4exb3Tp1w/O9/zcscDx0iec0aTI89ViQ1LV++nIyMDObOnUvx4sUBeP/99+nevTvjx4+nXLlyXLhwgZYtW5rn9P450M6fP29+wQdAQEAA4eHh911P7dq1GTlyJAAvv/wyM2fOxN3dnYEDBwIwZswYZs+ezaFDh3jqqafM2w0ePJinn37aXP+WLVtYvHgxr776Ku7u7owZM8bctnz58hw6dIjo6Gh69OhBRkYG06ZNY/jw4QwZMsTcrnr16gA88sgjAJQqVQpfX9/7Prec3Ne0hPT0dEwmE0ajkYYNG5pfL1a3bt0CL1BEJK9cJ060CDsAx//+F9eJE7m5YEGR1HTy5Elq1aplDjuA8PBwTCYTJ0+exM/Pj6FDhzJo0CCWLFnC448/TseOHc29n969e9O1a1fq1atH8+bNadWqFS1atLjvN9LUqFHD/HcHBwe8vLzMwQNQvHhxSpYsae69ZfrzfGtHR0fq1q1rcb9y/vz5fP755/zyyy+kpqaSlpZG5cqVAbhw4QJXrlzhiSeeuK+a88uqaQlw91Uufn5+dOrUyRxw1apVK5QiRUTywuHixeyX//ZbIVfyP5kfyc5O5vLx48fTq1cvYmNj2bZtG1OmTOHDDz+kR48e1KtXj2PHjrFlyxZ27NjBgAEDqF+/PitWrLiv0CtWrFiWGv66LLNua33++ee8+eabTJo0idDQUEqWLMlHH33Ejh078rwvW8g18JycnOjXrx/h4eFERERQrly5wqpLROS+ZTz6aPbLy5Qp5Er+JygoiDVr1nDz5k1zLy8uLg6DwWDxWsLAwEACAwMZNmwYQ4cOZenSpfTo0QO4O9TXpUsXunTpQvfu3Wnfvj2//PIL/v7+hXYeBw8eJCwsDIA7d+5w5MgRnn32WfP5RERE0K9fP3P7U6dOmf/u5+dH6dKl2bFjBw0bNsyybycnJwwGA3fu3LFJ7bkG3t69e21yUBERW0qNisLx0CGLYc07FSqQWoRz0Xr27MnUqVMZOnQor776Kn/88QejRo2iW7du+Pn5kZSUxKRJk3j66acJCAjg4sWLHDx4kKZNmwIwa9Ys/P39qVmzJg4ODqxatQqj0WiTe125mT9/PuXLl6dKlSrMmzePP/74g+eeew6AypUrs2bNGrZt24a/vz9ffPEFhw8fNtfo4ODAiBEjmDhxIl5eXrRs2ZIbN26wY8cOhg8fjpOTE+XKlWP79u3Uq1cPFxcXjEZjgdWe9w8KiYg84EyPPUbymjW4TpyIw2+/kVGmDKlRUUX2wAqAh4cH0dHRvPHGGzRv3pzixYvTvn17Jk+eDNwdYvzjjz8YMmQIly5donTp0rRp04Y333wTADc3N6ZPn85///tfHB0dqV27NtHR0YU+D/rNN99k+vTpfP/991SoUIHly5fj4+MDwKBBg/jxxx/p27cvBoOBzp07M2jQIGJiYszbDxs2jOLFi/PRRx8RFRWFl5cXrVu3Nq+fNGkS48ePZ9GiRVSoUIGDBw8WWO35+lqC/I++liBSMPS1hAfTyZMnadCgAXv37rV4uKUgFNbXEvI0D09ERORhpSFNEZGH2Pbt2/nnP/+Z7ToXFxdOnz5dyBU9uBR4IiIPsQYNGrBr165s1zk4FNwgXpUqVUhMTCyw/RUFBZ6IyEPMzc0tX68Ysyd5Crz4+Hg+++wzzpw5Q0JCQpZJhAaDga+++qpACxQRESkIVgdedHQ0gwcPxtHRkcDAwGznRhT1LHoREZGcWB14kydPpnr16kRHR+sDsCJiM05OTiQnJ+Pm5nbf74mUvy+TyURKSgpOTnm/I2f1FhcuXGDixIkKOxGxKXd3d27dusW1a9eKuhQpJNeuXcPDw8Pq9q6urvf1dR6rA69KlSpcuXIlzwcQEckrFxcXfW7Mjly6dKlQ3gdq9TOr48aNY9GiReavJ4iIiDxMcuzhZX4Y8M+8vb1p2LAhjz/+OH5+fjg6OlqsNxgMTJs2reCrFBERyacc36Xp6emZ950ZDFy9ejXfRT2M9C5NEZH7U1jXzxx7eAkJCTY/uIiISGHRy6NFRMQuWB14cXFxzJgxI8f1M2bM4MCBAwVSlIiISEGzelrCu+++m+uXZ7///nt2795NdHR0gRQmIiJSkKzu4R07dowGDRrkuL5+/fp89913BVKUiIhIQbM68FJSUu75mp8bN27kuyARERFbsDrwKleuzKZNm3JcHxsbq09UiIjIA8vqwOvTpw9bt25lxIgRFq8Yu3LlCiNHjmT79u307t3bJkWKiIjkl9UPrQwcOJDjx4+zaNEiFi9ejLe3NwaDgUuXLmEymejVqxeRkZG2rFVEROS+5en7CrNnz6Z79+589dVXnDlzBpPJRIUKFejYsSONGze2VY0iIiL5lucPCjVp0oQmTZrYohYRERGbsfoenpeXFytXrsxx/apVq/Dy8iqQokRERAqa1YFnMmX7jmmzjIwMfZ1YREQeWHl6l2ZugXbo0KFc38QiIiJSlHK9hzd37lzmzZtn/vn1119nwoQJWVqQeS4AABVFSURBVNolJSVx7do1nnnmmYKvUEREpADkGnilS5c2f6Po3Llz+Pr6UqZMGYs2BoMBNzc36tSpw6BBg2xXqYiISD7k+AHYv2rfvj2vvvoqTzzxhK1reijpA7AiIvenyD8A+1dff/21LesQERGxqTzPw0tLSyM+Pp6kpCQyMjKyrG/UqFGBFCYiIlKQrA48k8nEpEmTmD9/PsnJyTm2u3r1aoEUJiIiUpCsnpYwe/Zs3n//fTp37szcuXMxmUy8+eabzJgxg2rVqlGzZk1Wr15ty1pFRETum9WBt3TpUtq3b8/s2bN58sknAahduzZ9+/Zl69at3Llzh927d9usUBERkfywOvDOnz9Ps2bN7m7kcHez27dvA+Di4sIzzzzD8uXLbVCiiIhI/lkdeEajkZSUFAA8PDxwdnbmwoUL5vUuLi66fyciIg8sqwOvWrVqHDt27O5GDg7UrVuXjz/+mAsXLnD+/HkWL16seWgiIvLAsjrwunfvTnx8PKmpqQCMGzeOU6dOUbNmTWrXrs2pU6cYN26czQoVERHJD6vftJKdM2fOsH79ehwdHWnRogWVKlUqyNoeKnrTiojI/Xng3rSSnfLlyxMZGVlQtYiIiNhMnj4PJCIi8rDKtYcXFhaWp50ZDAbi4uLyVZCIiIgt5Bp4J0+epHjx4oSEhJjn3omIiDyMcg28OnXqcOTIEU6fPk2XLl3o0aMHISEhhVWbiIhIgcm127Z161YOHz5M37592bhxI82bN6d+/fpMnTqVM2fOFFKJIiIi+ZenaQmHDh3iyy+/JCYmhj/++IPQ0FB69OhB165d8fLysmWdDzxNSxARuT+Fdf3M0425evXqMXXqVH766SdWrFiBi4sLo0ePZsGCBbaqT0REpEDkeR5eUlISMTExrFy5kr179+Lh4aGejYiIPPCsCry0tDQ2bNjAypUriY2NBaBVq1Z8+umntG7dGmdnZ5sWKSIikl+5Bt7u3btZuXIlMTExXL9+nUaNGjF16lQ6duyIh4dHYdUoIiKSb7kGXocOHShevDitWrWia9eulC1bFrh7gzEnoaGhBVuhiIhIAbjnkObNmzeJiYnhq6++yrWdyWTCYDDom3giIvJAyjXwPvzww8KqQ0RExKZyDbxevXoVVh0iIiI2pRdkioiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXVDgiYiIXXggAu/SpUuMHj2akJAQfHx8qFatGt26dSM2NhaA48eP07NnT6pUqYKvry/BwcH07t2bc+fOmfdhNBqJiYkx/3znzh2GDh1KhQoVOHToUKGfk4iIPFicirqAs2fP8tRTT1GiRAnGjx9PcHAwGRkZ7NixgxEjRrB9+3Y6duxIixYtWLFiBZ6enpw/f57Y2FiuX7+e7T5TU1Pp168fR48eZf369VStWrWQz0pERB40hsTERFNRFtC9e3eOHz/OoUOHKFGihMW6xMREdu/eTZ8+ffjtt99wdnbOcT9Go5FPP/2UZs2a0bNnTy5dusSqVavw9/e3af0unTvjvG0bBsAE3G7WjFurV9v0mCIifweO0dG4vfgipKaCqyspc+Zwp2tXmx2vSIc0ExIS2Lx5MwMHDswSdnA3xHx9fcnIyCAmJgaTKfdsvnz5Mu3btyc5OZn169cXSti5bNuGA2Dg7i/TZds2XDp3tulxRUQedo7R0bj3749DSgoOGRk4pKTg3r8/jtHRNjtmkfbwDh8+TIsWLVi6dCkdOnTIsd2ECROYNWsW7u7u1K1bl8aNG9O9e3cCAgLMbYxGI87OzpQtW5Zdu3ZRsmRJm9df0mjM9v8YMoDriYk2P76IyMOqZNmyOKSkZFme4ebG9V9/tckxi/Qe3r16bJnGjh3LsGHD2LlzJwcPHmTp0qW8//77LF++nCeeeMLcrnXr1nzzzTfMnz+fUaNGWbXv+Pj4+6odIDSH5YZ87ldE5O+ubmpq9itSU/N1/QwMDMxxXZH28BISEqhYsSJRUVGMHDnS6u1u377N448/TunSpVm3bh3wv3t4aWlpDB48mNdee43Ro0fbqnRAPTwRkftVFD28Ir2H5+npSYsWLViwYAE3btzIsj4xh9BwdnamfPnyJCcnZ1nXrVs3Fi5cyNSpU5k0aVKB1/xnt5s146//t5D54IqIiOQsZc6cbK+fKXPm2OyYRT4Pb9q0aZhMJpo1a8aaNWuIj4/n5MmTLFy4kMaNG7NhwwYGDRrEhg0b+Pnnn4mPj2fOnDls2rSJ9u3bZ7vPTp06sWjRImbOnMmECRNsVvut1au51awZGdz9h8oAbukpTRGRe7rTtSvJCxeS4eZGhoMDGW5uJC9caNOnNIt8WgLAb7/9xvvvv09sbCwXL17Ey8uL4OBgBg8eTGBgILNmzWL37t1cuHABJycnAgIC6NWrF5GRkRgMBuB/Q5odO3Y073f9+vX07duXyMhI3nrrLZueQ3x8fK5jxyIikr3Cun4+EIH3d6DAExG5P4V1/SzyIU0REZHCoMATERG7oMATERG7oMATERG7oMATERG7oMATERG7oGkJIiJiF9TDExERu6DAExERu6DAExERu6DAExERu6DAExERu6DAy8WlS5cYPXo0ISEh+Pj4UK1aNbp160ZsbCwAx48fp2fPnlSpUgVfX1+Cg4Pp3bs3586dM+/DaDQSExNj/vnOnTsMHTqUChUqcOjQoUI/JxERW4mMjMRoNGI0GildujTBwcGMGDEiy7dNjx07xvPPP2++dtapU4fIyEh++OEHAM6ePWvej9FoxMfHh9DQUObk81t5Tvna+m/s7NmzPPXUU5QoUYLx48cTHBxMRkYGO3bsYMSIEWzfvp2OHTvSokULVqxYgaenJ+fPnyc2Npbr169nu8/U1FT69evH0aNHWb9+PVWrVi3ksxIRsa2mTZsyf/580tPTOXHiBC+88AJJSUksXLgQgA0bNtCnTx9zuwoVKpCQkMBXX33FW2+9xYoVK8z7io6OJjg4mFu3brFz505efvllypUrR5cuXe6rNgVeDkaNGoXJZGLbtm2UKFHCvDwoKIgePXqwe/duEhMT+fDDD3F2dgbgscceo3Hjxtnu79q1a/Ts2ZNLly6xceNG/P39C+U8REQKk4uLC76+vgCUK1eOzp078/nnnwOQkpLCsGHDaN68OV988YV5m/Lly1OnTp0sPUEvLy/zvp599lk+/vhjvvvuu/sOPA1pZiMhIYHNmzczcOBAi7DLZDQa8fX1JSMjg5iYGEym3OfuX758mfbt25OcnMz69esVdiJiF86cOcOWLVsoVqwYAFu2bOHKlSu88sor2bY3Go3ZLjeZTMTFxXHy5ElCQ0Pvux718LJx+vRpTCYTVapUybFN/fr1GTlyJJGRkYwaNYq6devSuHFjunfvTkBAgEXbN954g7Jly7Jr1y5Klixp6/JFRIrM5s2bKVeuHHfu3CE1NRWASZMmAXevrXB3pMwabdu2xcHBgdu3b5OWlkZkZCRPP/30fdemHl427tVjyzR27FhOnjzJrFmzqF69OkuXLiU8PJwdO3ZYtGvdujXnz59n/vz5tihXROSB0bBhQ3bt2sWWLVsYNGgQrVq1YsiQIYD119ZMCxYsYNeuXezevZtFixYRHR3NxIkT77s2BV42KlWqhMFg4OTJk/ds6+XlRadOnZg0aRIHDhwgICCA9957z6JNt27dmD9/PlOmTOHdd9+1VdkiIkXOzc2NihUrUqNGDd577z1SUlLM18RKlSoBcOLECav2Va5cOSpWrEhQUBCdO3cmMjKSmTNnmnuOeaXAy4anpyctWrRgwYIF3LhxI8v6v95YzeTs7Ez58uVJTk7Osq5bt24sXLiQqVOnmrv3IiJ/d6NHj2bWrFlcvHiR5s2bU7p0aWbMmJFt25yurZkcHR1JT0/n9u3b91WLAi8H06ZNw2Qy0axZM9asWUN8fDwnT55k4cKFNG7cmA0bNjBo0CA2bNjAzz//THx8PHPmzGHTpk20b98+23126tSJRYsWMXPmTCZMmFDIZyQiUviaNGlC1apVmTZtGu7u7syePZstW7bQo0cPtm3bxtmzZzl69CgTJ05k0KBBFttevXqV33//nQsXLrBp0ybmzZtHkyZN8PDwuK9a9NBKDsqXL8+OHTt4//33GT9+PBcvXsTLy4vg4GBmzJhBYGAg7u7ujB07lgsXLuDk5ERAQAATJkwgMjIyx/126NCBJUuW0LdvX9LT03nrrbcK8axERArfsGHDGDZsGC+99BLt2rVj06ZNzJgxg0GDBpGUlETZsmWJiIjIcj3s2rUrcLdnV6ZMGZ588knGjh1733Xoe3giImIXNKQpIiJ2QYEnIiJ2QYEnIiJ2QYEnIiJ2QYEnIiJ2QYEnIiJ2QYEnIsDdl/4ajUYOHjxY1KWI2IQCTyQbf/7acm5/li1bVtSlAhAaGkqjRo2yLN+6dStGozHXdZkf5hT5u9ObVkSy8dcvWyxevJhDhw7xwQcfWCwPCwsrzLJyFB4ezvLly0lMTLT4pti+fftwcnLip59+ynYdQEREBADNmzfnt99+w8XFpXCLFykkCjyRbDzzzDMWP2/fvp1vv/02y/IHRXh4OMuWLePAgQO0atXKvDwuLo5OnTqxevXqbNcZjUaqVasGgIODA66uroVee15kZGRw+/btB75OeTBpSFOkACQnJxMVFUVwcDA+Pj6EhITwzjvvkJaWZtHu8OHDdOnShYoVK/Loo48SEhJCZGQkt27dMrdZsWIFTzzxBH5+fgQEBNCwYUOmT5+e6/EbNmwI3A2xTOnp6Xz77be0aNGC6tWrZ7suLCwMg8EAZH8Pr1+/fgQEBHDx4kV69+6Nn58f5cuXZ+TIkRZvrE9NTcVoNDJmzBi++eYbGjVqhK+vL/Xq1SMmJiZLvYmJiYwZM4bg4GC8vb2pWbMmEydOtPh9/XmfK1euJCIiAl9fX9atW5fr70IkJ+rhieRTRkYG//jHP9i1axf//Oc/qVOnDrt37+add94hPj7efI/s4sWLdO7cmUcffZRXXnmFUqVKce7cOb755htu3ryJi4sLGzduZNCgQbRo0YI+ffoAcPLkSfPwY04qVaqEj4+PRbtjx46RnJxMREQE3377bbbrMoMyN+np6XTq1InQ0FDefvtt4uLiWLhwIT4+PowePdqi7Z49e1i9ejX9+vXDaDSyePFi+vXrx+HDhylfvjwAN27coG3btly8eJHnn3+egIAAjh49yvTp0zl9+jSffPKJxT537NjB//t//48BAwbg7e1NxYoV71mzSHYUeCL5tHbtWnbt2kVUVBSjRo0CYMCAAYwYMYJPPvmEAQMGEBERwb59+7h27RobNmygevXq5u2joqLMf9+4cSOPPPIIK1euxMEhbwMwYWFhbNq0idu3b+Ps7My+ffvw9fWlfPnyNGjQgKVLl1qsg7tDofeSkpJCt27dePXVV4G7vb4rV66wZMmSLIF34sQJ9u/fT4UKFQBo164dISEhfPbZZ+bznDVrFufOnWPnzp0W4VW5cmWioqJ44YUXqFu3rnn5yZMn2bt3L0FBQXn6fYj8lYY0RfJpw4YNFCtWjCFDhlgsHz58OACxsbEAlCxZ0tw+PT09232VLFmSa9eusXPnzjzXERERQWpqKkeOHAHuDm9mPlQTFhaWZZ2rqyt16tSxat8DBgyw+LlRo0b8+uuvFkOxcPfBl8ywA8xDoGfPnjUvW7NmDY0aNaJUqVJcuXLF/KdZs2YAWc69cePGCjspEAo8kXw6d+4cZcuWpUSJEhbLy5cvT/HixTl37hxwNwxat27N22+/TcWKFenZsydLliwhJSXFvM2gQYPw8/OjU6dO1KhRg6FDh7Jhwwar6sh82jLzXt2BAwfMgRcQEEDZsmUt1tWtWxdnZ+d77rdEiRJ4enpaLDMajZhMJpKSkiyW+/v7Z9neaDSSkJAAgMlk4vTp02zcuJFKlSpZ/MmcOnH58mWL7f8coCL5oSFNkULi6OjIl19+yYEDB4iNjWXr1q0MHz6cGTNmsGXLFry8vChXrhz79u1j69atbNmyhc2bN/P555/Trl27e875q1WrFiVKlGDfvn106NCB33//3WLIMiwszGJd7969rao7t6FVk8nyc5qOjo65tjOZTGRkZNCyZUteeOGFbNv6+flZ/KwnMqWgKPBE8ikgIIADBw5w48YNi17e2bNnuXnzJgEBARbtGzRoQIMGDYiKimLt2rX07t2bZcuW8eKLLwLg4uJCmzZtaNOmDSaTiTfeeIO5c+dy9OhRQkJCcqzD0dGRevXqsX//fvbu3Yubmxu1atUyrw8LC+Pdd99l7969wP96hIXJwcGBxx57jOTkZJo2bVroxxf7piFNkXx66qmnSEtL49///rfF8jlz5gCY575lDuv9We3atQHMQ4NXr161WG8wGKhZsyZw91H+ewkPDychIYGlS5dSt25dihUrlu06BwcH6tevb+0pFqiuXbuae7F/lZKSYjHEK1KQ1MMTyacOHTrQpEkTJkyYwJkzZ6hVq5b58fwuXbqYe1KLFy9m2bJltGvXjgoVKpCcnMxnn31GsWLF6NChA3D3Ht7Nmzdp0qQJZcuW5cKFCyxYsAA/Pz8aNGhwz1oyj7V//37zE6OZgoODcXd3Z//+/dSqVQsPD48C/k1YZ8SIEWzevJkePXrQs2dPQkJCuHnzJvHx8axZs4a1a9da9ExFCooCTySfHBwc+OKLL5g8eTJr1qxh+fLllC1bltGjR1uEzhNPPMH3339PdHQ0ly9fxsPDg5CQEGbOnGnu6fXs2ZNly5axcOFCkpKS8PHxoW3btowePRo3N7d71lKvXj2cnJxIT0/P8tozJycnQkND2blzp1XTEWzF3d2ddevWMXPmTFavXs2XX35JiRIlqFChAi+88IIeUhGbMSQmJpru3UxEROThpnt4IiJiFxR4IiJiFxR4IiJiFxR4IiJiFxR4IiJiFxR4IiJiFxR4IiJiFxR4IiJiFxR4IiJiFxR4IiJiF/4/RvKqjbvwWWMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlt.scatter(frames[\"CSKvRCB\"].toss_winner, frames[\"CSKvRCB\"].winner, label = 'Toss_Impact', color = 'r')\n",
    "\n",
    "mlt.xlabel('Toss Winner')\n",
    "mlt.ylabel('Match Winner')\n",
    "mlt.title(\"Impact of Toss\")\n",
    "mlt.legend()\n",
    "mlt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MI     12\n",
       "CSK     9\n",
       "Name: winner, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSK_MI.sort_index(inplace = True)\n",
    "CSK_MI\n",
    "CSK_MI[\"winner\"].value_counts()\n",
    "#MI_Wins = CSK_MI.groupby(\"winner\").get_group(\"Mumbai Indians\")\n",
    "#MI_Wins\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mumbai Indians vs Chennai Super Kings\n",
    "Matches - 21\n",
    "MI - 12\n",
    "CSK - 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal    602\n",
       "tie         7\n",
       "Name: result, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data[\"result\"].value_counts()\n",
    "#raw_data = raw_data.drop([raw_data[\"result\"]==\"no result\"], axis = 1)\n",
    "only_full_matches_data[\"result\"].value_counts()\n",
    "#only_full_matches_data.set_index(\"id\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CH Gayle          16\n",
       "AB de Villiers    15\n",
       "YK Pathan         15\n",
       "SK Raina          14\n",
       "RG Sharma         14\n",
       "DA Warner         14\n",
       "G Gambhir         13\n",
       "MS Dhoni          13\n",
       "AM Rahane         12\n",
       "MEK Hussey        12\n",
       "DR Smith          11\n",
       "V Sehwag          11\n",
       "SR Watson         10\n",
       "JH Kallis         10\n",
       "KA Pollard         9\n",
       "SE Marsh           9\n",
       "V Kohli            9\n",
       "A Mishra           9\n",
       "SR Tendulkar       8\n",
       "RV Uthappa         7\n",
       "Name: player_of_match, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_MoMs = only_full_matches_data[\"player_of_match\"].value_counts()\n",
    "most_MoMs = most_MoMs[:20]\n",
    "most_MoMs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x265557f4648>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "most_MoMs.plot.bar(title = \"Most Man Of The Match Awards\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2655590eb08>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "venues = raw_data[\"venue\"].value_counts()\n",
    "venues = venues[:20]\n",
    "venues.plot.barh(figsize=(6,8), fontsize=10, title = \"Venues Which Have Hosted Most IPL Matches\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 88 entries, HDPK Dharmasena to SD Fry\n",
      "Data columns (total 1 columns):\n",
      " #   Column         Non-Null Count  Dtype\n",
      "---  ------         --------------  -----\n",
      " 0   no_of_matches  88 non-null     int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 1.4+ KB\n"
     ]
    },
    {
     "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>no_of_matches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HDPK Dharmasena</th>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asad Rauf</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AK Chaudhary</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aleem Dar</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Ravi</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BF Bowden</th>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BR Doctrove</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M Erasmus</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Asnani</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RE Koertzen</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JD Cloete</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CB Gaffaney</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BG Jerling</th>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NJ Llong</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AY Dandekar</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VA Kulkarni</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Das</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RK Illingworth</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A Nand Kishore</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SS Hazare</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 no_of_matches\n",
       "HDPK Dharmasena             70\n",
       "Asad Rauf                   49\n",
       "AK Chaudhary                41\n",
       "Aleem Dar                   36\n",
       "S Ravi                      36\n",
       "BF Bowden                   35\n",
       "BR Doctrove                 34\n",
       "M Erasmus                   31\n",
       "S Asnani                    19\n",
       "RE Koertzen                 19\n",
       "JD Cloete                   15\n",
       "CB Gaffaney                 14\n",
       "BG Jerling                  13\n",
       "NJ Llong                    12\n",
       "AY Dandekar                 12\n",
       "VA Kulkarni                 11\n",
       "S Das                       10\n",
       "RK Illingworth              10\n",
       "A Nand Kishore              10\n",
       "SS Hazare                   10"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "umpires = pd.DataFrame()\n",
    "umpires2 = pd.DataFrame()\n",
    "umpires[\"no_of_matches\"] = only_full_matches_data[\"umpire1\"].value_counts()\n",
    "umpires2[\"no_of_matches\"] = only_full_matches_data[\"umpire2\"].value_counts()\n",
    "umpires2\n",
    "umpires = pd.concat([umpires,umpires2], axis = 0)\n",
    "umpires.sort_values(by=\"no_of_matches\", ascending = False)\n",
    "umpires.info()\n",
    "top_umpires = umpires[:20]\n",
    "top_umpires\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x265559bf6c8>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_umpires.plot.barh(figsize = (10,7), title = \"Top 20 Umpires of IPL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>MI</td>\n",
       "      <td>RPS</td>\n",
       "      <td>RPS</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>RPS</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>GL</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KKR</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>KKR</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>RPS</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>GL</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>A Deshmukh</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    season       city        date team1 team2 toss_winner toss_decision  \\\n",
       "id                                                                        \n",
       "1     2017  Hyderabad  2017-04-05   SRH   RCB         RCB         field   \n",
       "2     2017       Pune  2017-04-06    MI   RPS         RPS         field   \n",
       "3     2017     Rajkot  2017-04-07    GL   KKR         KKR         field   \n",
       "4     2017     Indore  2017-04-08   RPS  KXIP        KXIP         field   \n",
       "6     2017  Hyderabad  2017-04-09    GL   SRH         SRH         field   \n",
       "\n",
       "    result winner  win_by_runs  win_by_wickets player_of_match  \\\n",
       "id                                                               \n",
       "1   normal    SRH           35               0    Yuvraj Singh   \n",
       "2   normal    RPS            0               7       SPD Smith   \n",
       "3   normal    KKR            0              10         CA Lynn   \n",
       "4   normal   KXIP            0               6      GJ Maxwell   \n",
       "6   normal    SRH            0               9     Rashid Khan   \n",
       "\n",
       "                                        venue         umpire1        umpire2  \n",
       "id                                                                            \n",
       "1   Rajiv Gandhi International Stadium, Uppal     AY Dandekar       NJ Llong  \n",
       "2     Maharashtra Cricket Association Stadium  A Nand Kishore         S Ravi  \n",
       "3      Saurashtra Cricket Association Stadium     Nitin Menon      CK Nandan  \n",
       "4                      Holkar Cricket Stadium    AK Chaudhary  C Shamshuddin  \n",
       "6   Rajiv Gandhi International Stadium, Uppal      A Deshmukh       NJ Llong  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_full_matches_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "ball_by_ball_data = pd.read_csv(\"deliveries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "1            0            0             0             0           0   \n",
       "2            0            0             0             4           0   \n",
       "3            0            0             0             0           0   \n",
       "4            0            0             0             0           2   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "2           4               NaN            NaN     NaN  \n",
       "3           0               NaN            NaN     NaN  \n",
       "4           2               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ball_by_ball_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "ball_by_ball_data_refined = ball_by_ball_data.drop(['is_super_over', 'wide_runs',\n",
    "       'bye_runs', 'legbye_runs', 'noball_runs', 'penalty_runs',\n",
    "       'batsman_runs', 'extra_runs','player_dismissed',\n",
    "       'dismissal_kind', 'fielder'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['match_id', 'inning', 'batting_team', 'bowling_team', 'over', 'ball',\n",
       "       'batsman', 'non_striker', 'bowler', 'is_super_over', 'wide_runs',\n",
       "       'bye_runs', 'legbye_runs', 'noball_runs', 'penalty_runs',\n",
       "       'batsman_runs', 'extra_runs', 'total_runs', 'player_dismissed',\n",
       "       'dismissal_kind', 'fielder'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ball_by_ball_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150455</th>\n",
       "      <td>636</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>CJ Jordan</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150456</th>\n",
       "      <td>636</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>CJ Jordan</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150457</th>\n",
       "      <td>636</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>Iqbal Abdulla</td>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150458</th>\n",
       "      <td>636</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>Iqbal Abdulla</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150459</th>\n",
       "      <td>636</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>Iqbal Abdulla</td>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning                 batting_team         bowling_team  \\\n",
       "150455       636       2  Royal Challengers Bangalore  Sunrisers Hyderabad   \n",
       "150456       636       2  Royal Challengers Bangalore  Sunrisers Hyderabad   \n",
       "150457       636       2  Royal Challengers Bangalore  Sunrisers Hyderabad   \n",
       "150458       636       2  Royal Challengers Bangalore  Sunrisers Hyderabad   \n",
       "150459       636       2  Royal Challengers Bangalore  Sunrisers Hyderabad   \n",
       "\n",
       "        over  ball        batsman    non_striker   bowler  total_runs  \n",
       "150455    20     2    Sachin Baby      CJ Jordan  B Kumar           2  \n",
       "150456    20     3    Sachin Baby      CJ Jordan  B Kumar           0  \n",
       "150457    20     4  Iqbal Abdulla    Sachin Baby  B Kumar           1  \n",
       "150458    20     5    Sachin Baby  Iqbal Abdulla  B Kumar           1  \n",
       "150459    20     6  Iqbal Abdulla    Sachin Baby  B Kumar           4  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ball_by_ball_data_refined.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150331</th>\n",
       "      <td>636</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150332</th>\n",
       "      <td>636</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150333</th>\n",
       "      <td>636</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150334</th>\n",
       "      <td>636</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150335</th>\n",
       "      <td>636</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team                 bowling_team  \\\n",
       "150331       636       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "150332       636       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "150333       636       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "150334       636       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "150335       636       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "\n",
       "        over  ball      batsman  non_striker     bowler  total_runs  \n",
       "150331    20     2  BCJ Cutting      B Kumar  SR Watson           6  \n",
       "150332    20     3  BCJ Cutting      B Kumar  SR Watson           6  \n",
       "150333    20     4  BCJ Cutting      B Kumar  SR Watson           1  \n",
       "150334    20     5      B Kumar  BCJ Cutting  SR Watson           1  \n",
       "150335    20     6  BCJ Cutting      B Kumar  SR Watson           6  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_innings_ball_by_ball_data_refined = ball_by_ball_data_refined[ball_by_ball_data_refined['inning'] == 1]\n",
    "first_innings_ball_by_ball_data_refined.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     4011\n",
       "2     4009\n",
       "3     3965\n",
       "6     3959\n",
       "4     3956\n",
       "5     3952\n",
       "7     3941\n",
       "9     3918\n",
       "8     3914\n",
       "10    3908\n",
       "13    3874\n",
       "11    3873\n",
       "15    3873\n",
       "17    3872\n",
       "12    3866\n",
       "16    3863\n",
       "14    3858\n",
       "18    3858\n",
       "19    3830\n",
       "20    3729\n",
       "Name: over, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_innings_ball_by_ball_data_refined['over'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "sep_matches = first_innings_ball_by_ball_data_refined.groupby([\"match_id\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Toss Decisions in %\n",
      " field    57.075472\n",
      "bat      42.924528\n",
      "Name: toss_decision, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('Toss Decisions in %\\n',((raw_data['toss_decision']).value_counts())/636*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlt.subplots(figsize=(12,6))\n",
    "sns.countplot(x='season',hue='toss_decision',data=raw_data, palette='Set1_r')\n",
    "mlt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlt.subplots(figsize=(13,6))\n",
    "ax=raw_data['toss_winner'].value_counts().plot.bar(width=0.8,color=sns.color_palette('RdYlGn',5))\n",
    "for p in ax.patches:\n",
    "    ax.annotate(format(p.get_height()), (p.get_x()+0.25, p.get_height()+2))\n",
    "mlt.show()"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}