ipl-kxip-srh.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "%matplotlib inline\n",
    "import missingno as msg\n",
    "import math\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('deliveries.csv')\n",
    "df1 = pd.read_csv('matches.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "    }\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "    }\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'season', 'city', 'date', 'team1', 'team2', 'toss_winner',\n",
       "       'toss_decision', 'result', 'dl_applied', 'winner', 'win_by_runs',\n",
       "       'win_by_wickets', 'player_of_match', 'venue', 'umpire1', 'umpire2',\n",
       "       'umpire3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 1 - win ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "    }\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>18</th>\n",
       "      <td>19</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-17</td>\n",
       "      <td>Sunrisers Hyderabad</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>Sunrisers Hyderabad</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>A Deshmukh</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>2017</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>Sunrisers Hyderabad</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>Sunrisers Hyderabad</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>Punjab Cricket Association IS Bindra Stadium, ...</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>405</td>\n",
       "      <td>2013</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2013-04-19</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>440</td>\n",
       "      <td>2013</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2013-05-11</td>\n",
       "      <td>Sunrisers Hyderabad</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>Sunrisers Hyderabad</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>PA Patel</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>S Das</td>\n",
       "      <td>RJ Tucker</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>466</td>\n",
       "      <td>2014</td>\n",
       "      <td>Sharjah</td>\n",
       "      <td>2014-04-22</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>496</td>\n",
       "      <td>2014</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2014-05-14</td>\n",
       "      <td>Sunrisers Hyderabad</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>WP Saha</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>VA Kulkarni</td>\n",
       "      <td>PG Pathak</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>544</td>\n",
       "      <td>2015</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2015-04-27</td>\n",
       "      <td>Sunrisers Hyderabad</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>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>TA Boult</td>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>CB Gaffaney</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>564</td>\n",
       "      <td>2015</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2015-05-11</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>HDPK Dharmasena</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>594</td>\n",
       "      <td>2016</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2016-04-23</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>622</td>\n",
       "      <td>2016</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>2016-05-15</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>651</th>\n",
       "      <td>7909</td>\n",
       "      <td>2018</td>\n",
       "      <td>Mohali</td>\n",
       "      <td>19/04/18</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Punjab Cricket Association IS Bindra Stadium, ...</td>\n",
       "      <td>Nigel Llong</td>\n",
       "      <td>Anil Chaudhary</td>\n",
       "      <td>Vineet Kulkarni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>7918</td>\n",
       "      <td>2018</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>26/04/18</td>\n",
       "      <td>Sunrisers Hyderabad</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>Sunrisers Hyderabad</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>AS Rajpoot</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>O Nandan</td>\n",
       "      <td>Yeshwant Barde</td>\n",
       "      <td>Rod Tucker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>717</th>\n",
       "      <td>11313</td>\n",
       "      <td>2019</td>\n",
       "      <td>Mohali</td>\n",
       "      <td>08/04/19</td>\n",
       "      <td>Sunrisers Hyderabad</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>KL Rahul</td>\n",
       "      <td>IS Bindra Stadium</td>\n",
       "      <td>Marais Erasmus</td>\n",
       "      <td>Anil Dandekar</td>\n",
       "      <td>Nitin Menon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>743</th>\n",
       "      <td>11339</td>\n",
       "      <td>2019</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>29/04/19</td>\n",
       "      <td>Sunrisers Hyderabad</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>Sunrisers Hyderabad</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>O Nandan</td>\n",
       "      <td>Nanda Kishore</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  season        city        date                team1  \\\n",
       "18      19    2017   Hyderabad  2017-04-17  Sunrisers Hyderabad   \n",
       "31      32    2017  Chandigarh  2017-04-28  Sunrisers Hyderabad   \n",
       "404    405    2013   Hyderabad  2013-04-19      Kings XI Punjab   \n",
       "439    440    2013  Chandigarh  2013-05-11  Sunrisers Hyderabad   \n",
       "465    466    2014     Sharjah  2014-04-22      Kings XI Punjab   \n",
       "495    496    2014   Hyderabad  2014-05-14  Sunrisers Hyderabad   \n",
       "543    544    2015  Chandigarh  2015-04-27  Sunrisers Hyderabad   \n",
       "563    564    2015   Hyderabad  2015-05-11  Sunrisers Hyderabad   \n",
       "593    594    2016   Hyderabad  2016-04-23      Kings XI Punjab   \n",
       "621    622    2016  Chandigarh  2016-05-15      Kings XI Punjab   \n",
       "651   7909    2018      Mohali    19/04/18      Kings XI Punjab   \n",
       "660   7918    2018   Hyderabad    26/04/18  Sunrisers Hyderabad   \n",
       "717  11313    2019      Mohali    08/04/19  Sunrisers Hyderabad   \n",
       "743  11339    2019   Hyderabad    29/04/19  Sunrisers Hyderabad   \n",
       "\n",
       "                   team2          toss_winner toss_decision  result  \\\n",
       "18       Kings XI Punjab      Kings XI Punjab         field  normal   \n",
       "31       Kings XI Punjab      Kings XI Punjab         field  normal   \n",
       "404  Sunrisers Hyderabad      Kings XI Punjab           bat  normal   \n",
       "439      Kings XI Punjab      Kings XI Punjab         field  normal   \n",
       "465  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "495      Kings XI Punjab      Kings XI Punjab         field  normal   \n",
       "543      Kings XI Punjab      Kings XI Punjab         field  normal   \n",
       "563      Kings XI Punjab  Sunrisers Hyderabad           bat  normal   \n",
       "593  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "621  Sunrisers Hyderabad      Kings XI Punjab           bat  normal   \n",
       "651  Sunrisers Hyderabad      Kings XI Punjab           bat  normal   \n",
       "660      Kings XI Punjab      Kings XI Punjab         field  normal   \n",
       "717      Kings XI Punjab      Kings XI Punjab         field  normal   \n",
       "743      Kings XI Punjab      Kings XI Punjab         field  normal   \n",
       "\n",
       "     dl_applied               winner  win_by_runs  win_by_wickets  \\\n",
       "18            0  Sunrisers Hyderabad            5               0   \n",
       "31            0  Sunrisers Hyderabad           26               0   \n",
       "404           0  Sunrisers Hyderabad            0               5   \n",
       "439           0  Sunrisers Hyderabad           30               0   \n",
       "465           0      Kings XI Punjab           72               0   \n",
       "495           0      Kings XI Punjab            0               6   \n",
       "543           0  Sunrisers Hyderabad           20               0   \n",
       "563           0  Sunrisers Hyderabad            5               0   \n",
       "593           0  Sunrisers Hyderabad            0               5   \n",
       "621           0  Sunrisers Hyderabad            0               7   \n",
       "651           0      Kings XI Punjab           15               0   \n",
       "660           0  Sunrisers Hyderabad           13               0   \n",
       "717           0      Kings XI Punjab            0               6   \n",
       "743           0  Sunrisers Hyderabad           45               0   \n",
       "\n",
       "       player_of_match                                              venue  \\\n",
       "18             B Kumar          Rajiv Gandhi International Stadium, Uppal   \n",
       "31         Rashid Khan  Punjab Cricket Association IS Bindra Stadium, ...   \n",
       "404          GH Vihari          Rajiv Gandhi International Stadium, Uppal   \n",
       "439           PA Patel         Punjab Cricket Association Stadium, Mohali   \n",
       "465         GJ Maxwell                            Sharjah Cricket Stadium   \n",
       "495            WP Saha          Rajiv Gandhi International Stadium, Uppal   \n",
       "543           TA Boult         Punjab Cricket Association Stadium, Mohali   \n",
       "563          DA Warner          Rajiv Gandhi International Stadium, Uppal   \n",
       "593  Mustafizur Rahman          Rajiv Gandhi International Stadium, Uppal   \n",
       "621            HM Amla  Punjab Cricket Association IS Bindra Stadium, ...   \n",
       "651           CH Gayle  Punjab Cricket Association IS Bindra Stadium, ...   \n",
       "660         AS Rajpoot          Rajiv Gandhi International Stadium, Uppal   \n",
       "717           KL Rahul                                  IS Bindra Stadium   \n",
       "743          DA Warner                 Rajiv Gandhi Intl. Cricket Stadium   \n",
       "\n",
       "                   umpire1          umpire2          umpire3  \n",
       "18             AY Dandekar       A Deshmukh              NaN  \n",
       "31             Nitin Menon        CK Nandan              NaN  \n",
       "404        HDPK Dharmasena        CK Nandan              NaN  \n",
       "439                  S Das        RJ Tucker              NaN  \n",
       "465              M Erasmus           S Ravi              NaN  \n",
       "495            VA Kulkarni        PG Pathak              NaN  \n",
       "543        HDPK Dharmasena      CB Gaffaney              NaN  \n",
       "563           AK Chaudhary  HDPK Dharmasena              NaN  \n",
       "593           AK Chaudhary        CK Nandan              NaN  \n",
       "621  KN Ananthapadmanabhan        M Erasmus              NaN  \n",
       "651            Nigel Llong   Anil Chaudhary  Vineet Kulkarni  \n",
       "660               O Nandan   Yeshwant Barde       Rod Tucker  \n",
       "717         Marais Erasmus    Anil Dandekar      Nitin Menon  \n",
       "743                 S Ravi         O Nandan    Nanda Kishore  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df = df1[((df1['team1'] == 'Sunrisers Hyderabad') & (df1['team2'] == 'Kings XI Punjab')) | ((df1['team1'] == 'Kings XI Punjab') & (df1['team2'] == 'Sunrisers Hyderabad'))]\n",
    "main_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sunrisers Hyderabad    10\n",
      "Kings XI Punjab         4\n",
      "Name: winner, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize  =(7,7))\n",
    "sb.countplot(main_df['winner'])\n",
    "plt.grid()\n",
    "plt.show()\n",
    "print(main_df['winner'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# recent 3 seasons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "recent_match = main_df[main_df['season'] >= 2017]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sunrisers Hyderabad    10\n",
      "Kings XI Punjab         4\n",
      "Name: winner, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize  =(7,7))\n",
    "sb.countplot(main_df['winner'])\n",
    "plt.grid()\n",
    "plt.show()\n",
    "print(main_df['winner'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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></th>\n",
       "      <th>team2</th>\n",
       "      <th>team1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bat</th>\n",
       "      <th>Kings XI Punjab</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunrisers Hyderabad</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">field</th>\n",
       "      <th>Kings XI Punjab</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunrisers Hyderabad</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   team2  team1\n",
       "toss_decision winner                           \n",
       "bat           Kings XI Punjab          1      1\n",
       "              Sunrisers Hyderabad      3      3\n",
       "field         Kings XI Punjab          3      3\n",
       "              Sunrisers Hyderabad      7      7"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df.groupby(['toss_decision' , 'winner'])[['team2','team1']].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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></th>\n",
       "      <th>team2</th>\n",
       "      <th>team1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bat</th>\n",
       "      <th>Kings XI Punjab</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">field</th>\n",
       "      <th>Kings XI Punjab</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunrisers Hyderabad</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   team2  team1\n",
       "toss_decision winner                           \n",
       "bat           Kings XI Punjab          1      1\n",
       "field         Kings XI Punjab          1      1\n",
       "              Sunrisers Hyderabad      4      4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent_match.groupby(['toss_decision' , 'winner'])[['team2','team1']].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Take Aways : \n",
    "### 1 - Acc to this dataset , which was from INDIAN stadium ,SRH have won most of the match againt KXIP\n",
    "### 2 - BUt ,in this year IPL , Chances of KXIP winning is much over SRH ,as the openers are playing beautifully specially Rahul and Mayank\n",
    "### 3 - After the arrival of Gayle , the team looks strong now \n",
    "### 4 - The last match of both team went into a win\n",
    "### 5 - Since ,the match is in Dubai International Std , bowlers will play a vital role here !"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 1 ans - SRH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#########################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 2 - David Warner Status ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "        match_id  inning         batting_team         bowling_team  over  \\\n",
       "4312          19       1  Sunrisers Hyderabad      Kings XI Punjab     1   \n",
       "4313          19       1  Sunrisers Hyderabad      Kings XI Punjab     1   \n",
       "4314          19       1  Sunrisers Hyderabad      Kings XI Punjab     1   \n",
       "4315          19       1  Sunrisers Hyderabad      Kings XI Punjab     1   \n",
       "4316          19       1  Sunrisers Hyderabad      Kings XI Punjab     1   \n",
       "...          ...     ...                  ...                  ...   ...   \n",
       "176407     11339       2      Kings XI Punjab  Sunrisers Hyderabad    20   \n",
       "176408     11339       2      Kings XI Punjab  Sunrisers Hyderabad    20   \n",
       "176409     11339       2      Kings XI Punjab  Sunrisers Hyderabad    20   \n",
       "176410     11339       2      Kings XI Punjab  Sunrisers Hyderabad    20   \n",
       "176411     11339       2      Kings XI Punjab  Sunrisers Hyderabad    20   \n",
       "\n",
       "        ball         batsman     non_striker          bowler  is_super_over  \\\n",
       "4312       1       DA Warner        S Dhawan  Sandeep Sharma              0   \n",
       "4313       2       DA Warner        S Dhawan  Sandeep Sharma              0   \n",
       "4314       3       DA Warner        S Dhawan  Sandeep Sharma              0   \n",
       "4315       4       DA Warner        S Dhawan  Sandeep Sharma              0   \n",
       "4316       5        S Dhawan       DA Warner  Sandeep Sharma              0   \n",
       "...      ...             ...             ...             ...            ...   \n",
       "176407     2     M Ur Rahman        M Ashwin        S Sharma              0   \n",
       "176408     3     M Ur Rahman        M Ashwin        S Sharma              0   \n",
       "176409     4     M Ur Rahman        M Ashwin        S Sharma              0   \n",
       "176410     5  Mohammed Shami        M Ashwin        S Sharma              0   \n",
       "176411     6        M Ashwin  Mohammed Shami        S Sharma              0   \n",
       "\n",
       "        ...  bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "4312    ...         0            0            0             0             0   \n",
       "4313    ...         0            0            0             0             0   \n",
       "4314    ...         0            0            0             0             0   \n",
       "4315    ...         0            0            0             0             1   \n",
       "4316    ...         0            0            0             0             1   \n",
       "...     ...       ...          ...          ...           ...           ...   \n",
       "176407  ...         0            0            0             0             0   \n",
       "176408  ...         0            0            0             0             0   \n",
       "176409  ...         0            0            0             0             0   \n",
       "176410  ...         0            0            0             0             1   \n",
       "176411  ...         0            0            0             0             1   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
       "4312             0           0               NaN            NaN     NaN  \n",
       "4313             0           0               NaN            NaN     NaN  \n",
       "4314             0           0               NaN            NaN     NaN  \n",
       "4315             0           1               NaN            NaN     NaN  \n",
       "4316             0           1               NaN            NaN     NaN  \n",
       "...            ...         ...               ...            ...     ...  \n",
       "176407           0           0               NaN            NaN     NaN  \n",
       "176408           0           0               NaN            NaN     NaN  \n",
       "176409           0           0       M Ur Rahman         bowled     NaN  \n",
       "176410           0           1               NaN            NaN     NaN  \n",
       "176411           0           1               NaN            NaN     NaN  \n",
       "\n",
       "[3420 rows x 21 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
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    }
   ],
   "source": [
    "main_df_team = df[(((df['batting_team'] == 'Kings XI Punjab')  & (df['bowling_team'] == 'Sunrisers Hyderabad')) | ((df['batting_team'] == 'Sunrisers Hyderabad')  & (df['bowling_team'] == 'Kings XI Punjab')))]\n",
    "main_df_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first count the strike rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "single = main_df_team[main_df_team['match_id'] == 19]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "      <td>S Dhawan</td>\n",
       "      <td>Sandeep Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4316</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>Sandeep Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4556</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>Sandeep Sharma</td>\n",
       "      <td>I Sharma</td>\n",
       "      <td>S Kaul</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4557</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>Sandeep Sharma</td>\n",
       "      <td>I Sharma</td>\n",
       "      <td>S Kaul</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4558</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>I Sharma</td>\n",
       "      <td>Sandeep Sharma</td>\n",
       "      <td>S Kaul</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4559</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>I Sharma</td>\n",
       "      <td>Sandeep Sharma</td>\n",
       "      <td>S Kaul</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>4560</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>I Sharma</td>\n",
       "      <td>Sandeep Sharma</td>\n",
       "      <td>S Kaul</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>I Sharma</td>\n",
       "      <td>bowled</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>249 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id  inning         batting_team         bowling_team  over  ball  \\\n",
       "4312        19       1  Sunrisers Hyderabad      Kings XI Punjab     1     1   \n",
       "4313        19       1  Sunrisers Hyderabad      Kings XI Punjab     1     2   \n",
       "4314        19       1  Sunrisers Hyderabad      Kings XI Punjab     1     3   \n",
       "4315        19       1  Sunrisers Hyderabad      Kings XI Punjab     1     4   \n",
       "4316        19       1  Sunrisers Hyderabad      Kings XI Punjab     1     5   \n",
       "...        ...     ...                  ...                  ...   ...   ...   \n",
       "4556        19       2      Kings XI Punjab  Sunrisers Hyderabad    20     2   \n",
       "4557        19       2      Kings XI Punjab  Sunrisers Hyderabad    20     3   \n",
       "4558        19       2      Kings XI Punjab  Sunrisers Hyderabad    20     4   \n",
       "4559        19       2      Kings XI Punjab  Sunrisers Hyderabad    20     5   \n",
       "4560        19       2      Kings XI Punjab  Sunrisers Hyderabad    20     6   \n",
       "\n",
       "             batsman     non_striker          bowler  is_super_over  ...  \\\n",
       "4312       DA Warner        S Dhawan  Sandeep Sharma              0  ...   \n",
       "4313       DA Warner        S Dhawan  Sandeep Sharma              0  ...   \n",
       "4314       DA Warner        S Dhawan  Sandeep Sharma              0  ...   \n",
       "4315       DA Warner        S Dhawan  Sandeep Sharma              0  ...   \n",
       "4316        S Dhawan       DA Warner  Sandeep Sharma              0  ...   \n",
       "...              ...             ...             ...            ...  ...   \n",
       "4556  Sandeep Sharma        I Sharma          S Kaul              0  ...   \n",
       "4557  Sandeep Sharma        I Sharma          S Kaul              0  ...   \n",
       "4558        I Sharma  Sandeep Sharma          S Kaul              0  ...   \n",
       "4559        I Sharma  Sandeep Sharma          S Kaul              0  ...   \n",
       "4560        I Sharma  Sandeep Sharma          S Kaul              0  ...   \n",
       "\n",
       "      bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "4312         0            0            0             0             0   \n",
       "4313         0            0            0             0             0   \n",
       "4314         0            0            0             0             0   \n",
       "4315         0            0            0             0             1   \n",
       "4316         0            0            0             0             1   \n",
       "...        ...          ...          ...           ...           ...   \n",
       "4556         0            0            0             0             0   \n",
       "4557         0            0            0             0             1   \n",
       "4558         0            0            0             0             0   \n",
       "4559         0            0            0             0             0   \n",
       "4560         0            0            0             0             0   \n",
       "\n",
       "      extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
       "4312           0           0               NaN            NaN     NaN  \n",
       "4313           0           0               NaN            NaN     NaN  \n",
       "4314           0           0               NaN            NaN     NaN  \n",
       "4315           0           1               NaN            NaN     NaN  \n",
       "4316           0           1               NaN            NaN     NaN  \n",
       "...          ...         ...               ...            ...     ...  \n",
       "4556           1           1               NaN            NaN     NaN  \n",
       "4557           0           1               NaN            NaN     NaN  \n",
       "4558           1           1               NaN            NaN     NaN  \n",
       "4559           0           0               NaN            NaN     NaN  \n",
       "4560           0           0          I Sharma         bowled     NaN  \n",
       "\n",
       "[249 rows x 21 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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",
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       "<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>4312</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Sandeep Sharma</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <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>4313</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Sandeep Sharma</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>4314</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Sandeep Sharma</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>4315</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Sandeep Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4317</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Sandeep Sharma</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4318</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>I Sharma</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",
       "    <tr>\n",
       "      <th>4320</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>I Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4323</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>I Sharma</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>4324</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>I Sharma</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4325</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>I Sharma</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>4330</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Sandeep Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4333</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>I Sharma</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>4334</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>I Sharma</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>4335</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>I Sharma</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>4336</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>I Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4339</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>MM Sharma</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>4340</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>MM Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4344</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>I Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4351</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>KC Cariappa</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4353</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>KC Cariappa</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>4354</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>KC Cariappa</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4361</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>MM Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4362</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>KC Cariappa</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>4363</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>KC Cariappa</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4366</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>KC Cariappa</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>4367</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>KC Cariappa</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>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4372</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>AR Patel</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4377</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>KC Cariappa</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>4378</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>KC Cariappa</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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4379</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>KC Cariappa</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4381</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>AR Patel</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>4382</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>AR Patel</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4387</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>Sandeep Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4390</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>Sandeep Sharma</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>4391</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>13</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>Sandeep Sharma</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>4392</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>Sandeep Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4393</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>AR Patel</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>4394</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>AR Patel</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4396</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>AR Patel</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4400</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>I Sharma</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>4401</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>I Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4404</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>I Sharma</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4407</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>KC Cariappa</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4410</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>KC Cariappa</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4411</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>KC Cariappa</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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4412</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>KC Cariappa</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4413</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>MM Sharma</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>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4414</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>MM Sharma</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>4415</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>MM Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4417</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>MM Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4419</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>AR Patel</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4426</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>MM Sharma</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>4427</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>MM Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4429</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>19</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>MM Sharma</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>4430</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>MM Sharma</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>4434</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>Sandeep Sharma</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>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4435</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>Sandeep Sharma</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>57 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id  inning         batting_team     bowling_team  over  ball  \\\n",
       "4312        19       1  Sunrisers Hyderabad  Kings XI Punjab     1     1   \n",
       "4313        19       1  Sunrisers Hyderabad  Kings XI Punjab     1     2   \n",
       "4314        19       1  Sunrisers Hyderabad  Kings XI Punjab     1     3   \n",
       "4315        19       1  Sunrisers Hyderabad  Kings XI Punjab     1     4   \n",
       "4317        19       1  Sunrisers Hyderabad  Kings XI Punjab     1     6   \n",
       "4318        19       1  Sunrisers Hyderabad  Kings XI Punjab     2     1   \n",
       "4320        19       1  Sunrisers Hyderabad  Kings XI Punjab     2     3   \n",
       "4323        19       1  Sunrisers Hyderabad  Kings XI Punjab     2     6   \n",
       "4324        19       1  Sunrisers Hyderabad  Kings XI Punjab     2     7   \n",
       "4325        19       1  Sunrisers Hyderabad  Kings XI Punjab     2     8   \n",
       "4330        19       1  Sunrisers Hyderabad  Kings XI Punjab     3     5   \n",
       "4333        19       1  Sunrisers Hyderabad  Kings XI Punjab     4     2   \n",
       "4334        19       1  Sunrisers Hyderabad  Kings XI Punjab     4     3   \n",
       "4335        19       1  Sunrisers Hyderabad  Kings XI Punjab     4     4   \n",
       "4336        19       1  Sunrisers Hyderabad  Kings XI Punjab     4     5   \n",
       "4339        19       1  Sunrisers Hyderabad  Kings XI Punjab     5     2   \n",
       "4340        19       1  Sunrisers Hyderabad  Kings XI Punjab     5     3   \n",
       "4344        19       1  Sunrisers Hyderabad  Kings XI Punjab     6     1   \n",
       "4351        19       1  Sunrisers Hyderabad  Kings XI Punjab     7     2   \n",
       "4353        19       1  Sunrisers Hyderabad  Kings XI Punjab     7     4   \n",
       "4354        19       1  Sunrisers Hyderabad  Kings XI Punjab     7     5   \n",
       "4361        19       1  Sunrisers Hyderabad  Kings XI Punjab     8     6   \n",
       "4362        19       1  Sunrisers Hyderabad  Kings XI Punjab     9     1   \n",
       "4363        19       1  Sunrisers Hyderabad  Kings XI Punjab     9     2   \n",
       "4366        19       1  Sunrisers Hyderabad  Kings XI Punjab     9     5   \n",
       "4367        19       1  Sunrisers Hyderabad  Kings XI Punjab     9     6   \n",
       "4372        19       1  Sunrisers Hyderabad  Kings XI Punjab    10     5   \n",
       "4377        19       1  Sunrisers Hyderabad  Kings XI Punjab    11     3   \n",
       "4378        19       1  Sunrisers Hyderabad  Kings XI Punjab    11     4   \n",
       "4379        19       1  Sunrisers Hyderabad  Kings XI Punjab    11     5   \n",
       "4381        19       1  Sunrisers Hyderabad  Kings XI Punjab    12     1   \n",
       "4382        19       1  Sunrisers Hyderabad  Kings XI Punjab    12     2   \n",
       "4387        19       1  Sunrisers Hyderabad  Kings XI Punjab    13     1   \n",
       "4390        19       1  Sunrisers Hyderabad  Kings XI Punjab    13     4   \n",
       "4391        19       1  Sunrisers Hyderabad  Kings XI Punjab    13     5   \n",
       "4392        19       1  Sunrisers Hyderabad  Kings XI Punjab    13     6   \n",
       "4393        19       1  Sunrisers Hyderabad  Kings XI Punjab    14     1   \n",
       "4394        19       1  Sunrisers Hyderabad  Kings XI Punjab    14     2   \n",
       "4396        19       1  Sunrisers Hyderabad  Kings XI Punjab    14     4   \n",
       "4400        19       1  Sunrisers Hyderabad  Kings XI Punjab    15     2   \n",
       "4401        19       1  Sunrisers Hyderabad  Kings XI Punjab    15     3   \n",
       "4404        19       1  Sunrisers Hyderabad  Kings XI Punjab    15     6   \n",
       "4407        19       1  Sunrisers Hyderabad  Kings XI Punjab    16     2   \n",
       "4410        19       1  Sunrisers Hyderabad  Kings XI Punjab    16     5   \n",
       "4411        19       1  Sunrisers Hyderabad  Kings XI Punjab    16     6   \n",
       "4412        19       1  Sunrisers Hyderabad  Kings XI Punjab    16     7   \n",
       "4413        19       1  Sunrisers Hyderabad  Kings XI Punjab    17     1   \n",
       "4414        19       1  Sunrisers Hyderabad  Kings XI Punjab    17     2   \n",
       "4415        19       1  Sunrisers Hyderabad  Kings XI Punjab    17     3   \n",
       "4417        19       1  Sunrisers Hyderabad  Kings XI Punjab    17     5   \n",
       "4419        19       1  Sunrisers Hyderabad  Kings XI Punjab    18     1   \n",
       "4426        19       1  Sunrisers Hyderabad  Kings XI Punjab    19     2   \n",
       "4427        19       1  Sunrisers Hyderabad  Kings XI Punjab    19     3   \n",
       "4429        19       1  Sunrisers Hyderabad  Kings XI Punjab    19     5   \n",
       "4430        19       1  Sunrisers Hyderabad  Kings XI Punjab    19     6   \n",
       "4434        19       1  Sunrisers Hyderabad  Kings XI Punjab    20     4   \n",
       "4435        19       1  Sunrisers Hyderabad  Kings XI Punjab    20     5   \n",
       "\n",
       "        batsman    non_striker          bowler  is_super_over  ...  bye_runs  \\\n",
       "4312  DA Warner       S Dhawan  Sandeep Sharma              0  ...         0   \n",
       "4313  DA Warner       S Dhawan  Sandeep Sharma              0  ...         0   \n",
       "4314  DA Warner       S Dhawan  Sandeep Sharma              0  ...         0   \n",
       "4315  DA Warner       S Dhawan  Sandeep Sharma              0  ...         0   \n",
       "4317  DA Warner       S Dhawan  Sandeep Sharma              0  ...         0   \n",
       "4318  DA Warner       S Dhawan        I Sharma              0  ...         0   \n",
       "4320  DA Warner       S Dhawan        I Sharma              0  ...         0   \n",
       "4323  DA Warner       S Dhawan        I Sharma              0  ...         0   \n",
       "4324  DA Warner       S Dhawan        I Sharma              0  ...         0   \n",
       "4325  DA Warner       S Dhawan        I Sharma              0  ...         0   \n",
       "4330  DA Warner       S Dhawan  Sandeep Sharma              0  ...         0   \n",
       "4333  DA Warner       S Dhawan        I Sharma              0  ...         0   \n",
       "4334  DA Warner       S Dhawan        I Sharma              0  ...         0   \n",
       "4335  DA Warner       S Dhawan        I Sharma              0  ...         0   \n",
       "4336  DA Warner       S Dhawan        I Sharma              0  ...         0   \n",
       "4339  DA Warner       S Dhawan       MM Sharma              0  ...         0   \n",
       "4340  DA Warner       S Dhawan       MM Sharma              0  ...         0   \n",
       "4344  DA Warner   MC Henriques        I Sharma              0  ...         0   \n",
       "4351  DA Warner   MC Henriques     KC Cariappa              0  ...         0   \n",
       "4353  DA Warner   MC Henriques     KC Cariappa              0  ...         0   \n",
       "4354  DA Warner   MC Henriques     KC Cariappa              0  ...         0   \n",
       "4361  DA Warner   MC Henriques       MM Sharma              0  ...         0   \n",
       "4362  DA Warner   MC Henriques     KC Cariappa              0  ...         0   \n",
       "4363  DA Warner   MC Henriques     KC Cariappa              0  ...         0   \n",
       "4366  DA Warner   MC Henriques     KC Cariappa              0  ...         0   \n",
       "4367  DA Warner   MC Henriques     KC Cariappa              0  ...         0   \n",
       "4372  DA Warner        NV Ojha        AR Patel              0  ...         0   \n",
       "4377  DA Warner        NV Ojha     KC Cariappa              0  ...         0   \n",
       "4378  DA Warner        NV Ojha     KC Cariappa              0  ...         0   \n",
       "4379  DA Warner        NV Ojha     KC Cariappa              0  ...         0   \n",
       "4381  DA Warner        NV Ojha        AR Patel              0  ...         0   \n",
       "4382  DA Warner        NV Ojha        AR Patel              0  ...         0   \n",
       "4387  DA Warner        NV Ojha  Sandeep Sharma              0  ...         0   \n",
       "4390  DA Warner        NV Ojha  Sandeep Sharma              0  ...         0   \n",
       "4391  DA Warner        NV Ojha  Sandeep Sharma              0  ...         0   \n",
       "4392  DA Warner        NV Ojha  Sandeep Sharma              0  ...         0   \n",
       "4393  DA Warner        NV Ojha        AR Patel              0  ...         0   \n",
       "4394  DA Warner        NV Ojha        AR Patel              0  ...         0   \n",
       "4396  DA Warner        NV Ojha        AR Patel              0  ...         0   \n",
       "4400  DA Warner        NV Ojha        I Sharma              0  ...         0   \n",
       "4401  DA Warner        NV Ojha        I Sharma              0  ...         0   \n",
       "4404  DA Warner        NV Ojha        I Sharma              0  ...         1   \n",
       "4407  DA Warner        NV Ojha     KC Cariappa              0  ...         0   \n",
       "4410  DA Warner       DJ Hooda     KC Cariappa              0  ...         0   \n",
       "4411  DA Warner       DJ Hooda     KC Cariappa              0  ...         0   \n",
       "4412  DA Warner       DJ Hooda     KC Cariappa              0  ...         0   \n",
       "4413  DA Warner       DJ Hooda       MM Sharma              0  ...         0   \n",
       "4414  DA Warner       DJ Hooda       MM Sharma              0  ...         0   \n",
       "4415  DA Warner       DJ Hooda       MM Sharma              0  ...         0   \n",
       "4417  DA Warner       DJ Hooda       MM Sharma              0  ...         0   \n",
       "4419  DA Warner       DJ Hooda        AR Patel              0  ...         0   \n",
       "4426  DA Warner       DJ Hooda       MM Sharma              0  ...         0   \n",
       "4427  DA Warner       DJ Hooda       MM Sharma              0  ...         0   \n",
       "4429  DA Warner  Mohammad Nabi       MM Sharma              0  ...         0   \n",
       "4430  DA Warner  Mohammad Nabi       MM Sharma              0  ...         0   \n",
       "4434  DA Warner    Rashid Khan  Sandeep Sharma              0  ...         0   \n",
       "4435  DA Warner    Rashid Khan  Sandeep Sharma              0  ...         0   \n",
       "\n",
       "      legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "4312            0            0             0             0           0   \n",
       "4313            0            0             0             0           0   \n",
       "4314            0            0             0             0           0   \n",
       "4315            0            0             0             1           0   \n",
       "4317            1            0             0             0           1   \n",
       "4318            0            0             0             0           2   \n",
       "4320            0            0             0             1           0   \n",
       "4323            0            0             0             0           0   \n",
       "4324            0            0             0             0           1   \n",
       "4325            0            0             0             0           0   \n",
       "4330            0            0             0             1           0   \n",
       "4333            0            0             0             0           0   \n",
       "4334            0            0             0             0           0   \n",
       "4335            0            0             0             0           0   \n",
       "4336            0            0             0             1           0   \n",
       "4339            0            0             0             0           0   \n",
       "4340            0            0             0             1           0   \n",
       "4344            0            0             0             1           0   \n",
       "4351            0            0             0             1           0   \n",
       "4353            0            0             0             4           0   \n",
       "4354            0            0             0             1           0   \n",
       "4361            0            0             0             1           0   \n",
       "4362            0            0             0             0           0   \n",
       "4363            0            0             0             1           0   \n",
       "4366            0            0             0             0           0   \n",
       "4367            0            0             0             6           0   \n",
       "4372            0            0             0             1           0   \n",
       "4377            0            0             0             4           0   \n",
       "4378            0            0             0             2           0   \n",
       "4379            0            0             0             1           0   \n",
       "4381            0            0             0             0           0   \n",
       "4382            0            0             0             1           0   \n",
       "4387            0            0             0             1           0   \n",
       "4390            0            0             0             0           0   \n",
       "4391            0            0             0             4           0   \n",
       "4392            0            0             0             1           0   \n",
       "4393            0            0             0             0           0   \n",
       "4394            0            0             0             1           0   \n",
       "4396            0            0             0             1           0   \n",
       "4400            0            0             0             4           0   \n",
       "4401            0            0             0             1           0   \n",
       "4404            0            0             0             0           1   \n",
       "4407            0            0             0             1           0   \n",
       "4410            0            0             0             0           1   \n",
       "4411            0            0             0             2           0   \n",
       "4412            0            0             0             1           0   \n",
       "4413            0            0             0             2           0   \n",
       "4414            0            0             0             4           0   \n",
       "4415            0            0             0             1           0   \n",
       "4417            0            0             0             1           0   \n",
       "4419            0            0             0             1           0   \n",
       "4426            0            0             0             4           0   \n",
       "4427            0            0             0             1           0   \n",
       "4429            0            0             0             4           0   \n",
       "4430            0            0             0             0           0   \n",
       "4434            0            0             0             6           0   \n",
       "4435            1            0             0             0           1   \n",
       "\n",
       "      total_runs  player_dismissed dismissal_kind fielder  \n",
       "4312           0               NaN            NaN     NaN  \n",
       "4313           0               NaN            NaN     NaN  \n",
       "4314           0               NaN            NaN     NaN  \n",
       "4315           1               NaN            NaN     NaN  \n",
       "4317           1               NaN            NaN     NaN  \n",
       "4318           2               NaN            NaN     NaN  \n",
       "4320           1               NaN            NaN     NaN  \n",
       "4323           0               NaN            NaN     NaN  \n",
       "4324           1               NaN            NaN     NaN  \n",
       "4325           0               NaN            NaN     NaN  \n",
       "4330           1               NaN            NaN     NaN  \n",
       "4333           0               NaN            NaN     NaN  \n",
       "4334           0               NaN            NaN     NaN  \n",
       "4335           0               NaN            NaN     NaN  \n",
       "4336           1               NaN            NaN     NaN  \n",
       "4339           0               NaN            NaN     NaN  \n",
       "4340           1               NaN            NaN     NaN  \n",
       "4344           1               NaN            NaN     NaN  \n",
       "4351           1               NaN            NaN     NaN  \n",
       "4353           4               NaN            NaN     NaN  \n",
       "4354           1               NaN            NaN     NaN  \n",
       "4361           1               NaN            NaN     NaN  \n",
       "4362           0               NaN            NaN     NaN  \n",
       "4363           1               NaN            NaN     NaN  \n",
       "4366           0               NaN            NaN     NaN  \n",
       "4367           6               NaN            NaN     NaN  \n",
       "4372           1               NaN            NaN     NaN  \n",
       "4377           4               NaN            NaN     NaN  \n",
       "4378           2               NaN            NaN     NaN  \n",
       "4379           1               NaN            NaN     NaN  \n",
       "4381           0               NaN            NaN     NaN  \n",
       "4382           1               NaN            NaN     NaN  \n",
       "4387           1               NaN            NaN     NaN  \n",
       "4390           0               NaN            NaN     NaN  \n",
       "4391           4               NaN            NaN     NaN  \n",
       "4392           1               NaN            NaN     NaN  \n",
       "4393           0               NaN            NaN     NaN  \n",
       "4394           1               NaN            NaN     NaN  \n",
       "4396           1               NaN            NaN     NaN  \n",
       "4400           4               NaN            NaN     NaN  \n",
       "4401           1               NaN            NaN     NaN  \n",
       "4404           1               NaN            NaN     NaN  \n",
       "4407           1               NaN            NaN     NaN  \n",
       "4410           1               NaN            NaN     NaN  \n",
       "4411           2               NaN            NaN     NaN  \n",
       "4412           1               NaN            NaN     NaN  \n",
       "4413           2               NaN            NaN     NaN  \n",
       "4414           4               NaN            NaN     NaN  \n",
       "4415           1               NaN            NaN     NaN  \n",
       "4417           1               NaN            NaN     NaN  \n",
       "4419           1               NaN            NaN     NaN  \n",
       "4426           4               NaN            NaN     NaN  \n",
       "4427           1               NaN            NaN     NaN  \n",
       "4429           4               NaN            NaN     NaN  \n",
       "4430           0               NaN            NaN     NaN  \n",
       "4434           6               NaN            NaN     NaN  \n",
       "4435           1               NaN            NaN     NaN  \n",
       "\n",
       "[57 rows x 21 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "david = single[single['batsman'] == 'DA Warner']\n",
    "david"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">sum</th>\n",
       "      <th colspan=\"2\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman_runs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>208</td>\n",
       "      <td>70</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sum              count             \n",
       "         ball batsman_runs  ball batsman_runs\n",
       "match_id                                     \n",
       "19        208           70    57           57"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "david.pivot_table(index = ['match_id'] , aggfunc=['sum' , 'count'], values = ['batsman_runs','ball'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "david= main_df_team[(main_df_team['batsman'] == 'DA Warner') & (main_df_team['bowling_team'] == 'Kings XI Punjab')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "david = david.pivot_table(index = ['match_id'] , aggfunc=['sum' , 'count'], values = ['batsman_runs','ball'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">sum</th>\n",
       "      <th colspan=\"2\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman_runs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>208</td>\n",
       "      <td>70</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>95</td>\n",
       "      <td>51</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>51</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>84</td>\n",
       "      <td>44</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>135</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>205</td>\n",
       "      <td>81</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>124</td>\n",
       "      <td>59</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>143</td>\n",
       "      <td>52</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11313</th>\n",
       "      <td>232</td>\n",
       "      <td>74</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11339</th>\n",
       "      <td>221</td>\n",
       "      <td>91</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sum              count             \n",
       "         ball batsman_runs  ball batsman_runs\n",
       "match_id                                     \n",
       "19        208           70    57           57\n",
       "32         95           51    27           27\n",
       "466        51            8    12           12\n",
       "496        84           44    24           24\n",
       "544       135           58    41           41\n",
       "564       205           81    54           54\n",
       "594       124           59    32           32\n",
       "622       143           52    42           42\n",
       "11313     232           74    63           63\n",
       "11339     221           91    61           61"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "david"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "david = pd.DataFrame(david.to_records())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>('sum', 'ball')</th>\n",
       "      <th>('sum', 'batsman_runs')</th>\n",
       "      <th>('count', 'ball')</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>208</td>\n",
       "      <td>70</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>95</td>\n",
       "      <td>51</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>466</td>\n",
       "      <td>51</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>84</td>\n",
       "      <td>44</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>544</td>\n",
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       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
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       "      <th>5</th>\n",
       "      <td>564</td>\n",
       "      <td>205</td>\n",
       "      <td>81</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>594</td>\n",
       "      <td>124</td>\n",
       "      <td>59</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>622</td>\n",
       "      <td>143</td>\n",
       "      <td>52</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11313</td>\n",
       "      <td>232</td>\n",
       "      <td>74</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11339</td>\n",
       "      <td>221</td>\n",
       "      <td>91</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  ('sum', 'ball')  ('sum', 'batsman_runs')  ('count', 'ball')  \\\n",
       "0        19              208                       70                 57   \n",
       "1        32               95                       51                 27   \n",
       "2       466               51                        8                 12   \n",
       "3       496               84                       44                 24   \n",
       "4       544              135                       58                 41   \n",
       "5       564              205                       81                 54   \n",
       "6       594              124                       59                 32   \n",
       "7       622              143                       52                 42   \n",
       "8     11313              232                       74                 63   \n",
       "9     11339              221                       91                 61   \n",
       "\n",
       "   ('count', 'batsman_runs')  \n",
       "0                         57  \n",
       "1                         27  \n",
       "2                         12  \n",
       "3                         24  \n",
       "4                         41  \n",
       "5                         54  \n",
       "6                         32  \n",
       "7                         42  \n",
       "8                         63  \n",
       "9                         61  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "david"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "david = david.rename(columns={\"('sum', 'batsman_runs')\": \"runs\" , \"('count', 'ball')\":\"balls\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>('sum', 'ball')</th>\n",
       "      <th>runs</th>\n",
       "      <th>balls</th>\n",
       "      <th>('count', 'batsman_runs')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>208</td>\n",
       "      <td>70</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
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       "      <th>1</th>\n",
       "      <td>32</td>\n",
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       "      <td>51</td>\n",
       "      <td>27</td>\n",
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       "      <td>12</td>\n",
       "      <td>12</td>\n",
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       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>84</td>\n",
       "      <td>44</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>544</td>\n",
       "      <td>135</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
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       "      <td>32</td>\n",
       "      <td>32</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>622</td>\n",
       "      <td>143</td>\n",
       "      <td>52</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11313</td>\n",
       "      <td>232</td>\n",
       "      <td>74</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11339</td>\n",
       "      <td>221</td>\n",
       "      <td>91</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  ('sum', 'ball')  runs  balls  ('count', 'batsman_runs')\n",
       "0        19              208    70     57                         57\n",
       "1        32               95    51     27                         27\n",
       "2       466               51     8     12                         12\n",
       "3       496               84    44     24                         24\n",
       "4       544              135    58     41                         41\n",
       "5       564              205    81     54                         54\n",
       "6       594              124    59     32                         32\n",
       "7       622              143    52     42                         42\n",
       "8     11313              232    74     63                         63\n",
       "9     11339              221    91     61                         61"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "david "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "david['strike_rate'] = (david['runs'] / david['balls'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>('sum', 'ball')</th>\n",
       "      <th>runs</th>\n",
       "      <th>balls</th>\n",
       "      <th>('count', 'batsman_runs')</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>122.807018</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>95</td>\n",
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       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>188.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>466</td>\n",
       "      <td>51</td>\n",
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       "      <td>12</td>\n",
       "      <td>66.666667</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>496</td>\n",
       "      <td>84</td>\n",
       "      <td>44</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>183.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>544</td>\n",
       "      <td>135</td>\n",
       "      <td>58</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>141.463415</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>564</td>\n",
       "      <td>205</td>\n",
       "      <td>81</td>\n",
       "      <td>54</td>\n",
       "      <td>54</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>594</td>\n",
       "      <td>124</td>\n",
       "      <td>59</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>184.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>622</td>\n",
       "      <td>143</td>\n",
       "      <td>52</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>123.809524</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11313</td>\n",
       "      <td>232</td>\n",
       "      <td>74</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>117.460317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11339</td>\n",
       "      <td>221</td>\n",
       "      <td>91</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>149.180328</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  ('sum', 'ball')  runs  balls  ('count', 'batsman_runs')  \\\n",
       "0        19              208    70     57                         57   \n",
       "1        32               95    51     27                         27   \n",
       "2       466               51     8     12                         12   \n",
       "3       496               84    44     24                         24   \n",
       "4       544              135    58     41                         41   \n",
       "5       564              205    81     54                         54   \n",
       "6       594              124    59     32                         32   \n",
       "7       622              143    52     42                         42   \n",
       "8     11313              232    74     63                         63   \n",
       "9     11339              221    91     61                         61   \n",
       "\n",
       "   strike_rate  \n",
       "0   122.807018  \n",
       "1   188.888889  \n",
       "2    66.666667  \n",
       "3   183.333333  \n",
       "4   141.463415  \n",
       "5   150.000000  \n",
       "6   184.375000  \n",
       "7   123.809524  \n",
       "8   117.460317  \n",
       "9   149.180328  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "david"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,7))\n",
    "sb.kdeplot(david['strike_rate'])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "Avg_sr = david['strike_rate'].sum() / len(david)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142.79844902055885"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Avg_sr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets check wrt to all team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178462</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>178463</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>Mohammad Nabi</td>\n",
       "      <td>caught</td>\n",
       "      <td>AR Patel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178464</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>K Paul</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178465</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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>Rashid Khan</td>\n",
       "      <td>caught</td>\n",
       "      <td>RR Pant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178466</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>Basil Thampi</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12908 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team                 bowling_team  \\\n",
       "0              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "1              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "2              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "3              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "4              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "...          ...     ...                  ...                          ...   \n",
       "178462     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178463     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178464     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178465     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178466     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "\n",
       "        over  ball        batsman  non_striker    bowler  is_super_over  ...  \\\n",
       "0          1     1      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "1          1     2      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "2          1     3      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "3          1     4      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "4          1     5      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "...      ...   ...            ...          ...       ...            ...  ...   \n",
       "178462    20     5  Mohammad Nabi     DJ Hooda    K Paul              0  ...   \n",
       "178463    20     6  Mohammad Nabi     DJ Hooda    K Paul              0  ...   \n",
       "178464    20     7       DJ Hooda  Rashid Khan    K Paul              0  ...   \n",
       "178465    20     8    Rashid Khan      B Kumar    K Paul              0  ...   \n",
       "178466    20     9   Basil Thampi      B Kumar    K Paul              0  ...   \n",
       "\n",
       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "0              0            0            0             0             0   \n",
       "1              0            0            0             0             0   \n",
       "2              0            0            0             0             4   \n",
       "3              0            0            0             0             0   \n",
       "4              0            0            0             0             0   \n",
       "...          ...          ...          ...           ...           ...   \n",
       "178462         0            0            0             0             0   \n",
       "178463         0            0            0             0             0   \n",
       "178464         0            0            0             0             1   \n",
       "178465         0            0            0             0             0   \n",
       "178466         0            0            0             0             1   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind   fielder  \n",
       "0                0           0               NaN            NaN       NaN  \n",
       "1                0           0               NaN            NaN       NaN  \n",
       "2                0           4               NaN            NaN       NaN  \n",
       "3                0           0               NaN            NaN       NaN  \n",
       "4                2           2               NaN            NaN       NaN  \n",
       "...            ...         ...               ...            ...       ...  \n",
       "178462           0           0               NaN            NaN       NaN  \n",
       "178463           0           0     Mohammad Nabi         caught  AR Patel  \n",
       "178464           0           1               NaN            NaN       NaN  \n",
       "178465           0           0       Rashid Khan         caught   RR Pant  \n",
       "178466           0           1               NaN            NaN       NaN  \n",
       "\n",
       "[12908 rows x 21 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df_team_only_srh = df[ ((df['batting_team'] == 'Sunrisers Hyderabad'))]\n",
    "main_df_team_only_srh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">sum</th>\n",
       "      <th colspan=\"2\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman_runs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match_id</th>\n",
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       "      <th>1</th>\n",
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       "      <td>9</td>\n",
       "      <td>9</td>\n",
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       "      <td>34</td>\n",
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       "      <td>31</td>\n",
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       "    <tr>\n",
       "      <th>11324</th>\n",
       "      <td>96</td>\n",
       "      <td>51</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
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       "    <tr>\n",
       "      <th>11329</th>\n",
       "      <td>140</td>\n",
       "      <td>70</td>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>11332</th>\n",
       "      <td>158</td>\n",
       "      <td>59</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11336</th>\n",
       "      <td>108</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11339</th>\n",
       "      <td>221</td>\n",
       "      <td>91</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          sum              count             \n",
       "         ball batsman_runs  ball batsman_runs\n",
       "match_id                                     \n",
       "1          29           14     9            9\n",
       "6         162           76    47           47\n",
       "10        116           49    34           34\n",
       "14        112           26    31           31\n",
       "19        208           70    57           57\n",
       "...       ...          ...   ...          ...\n",
       "11324      96           51    26           26\n",
       "11329     140           70    39           39\n",
       "11332     158           59    47           47\n",
       "11336     108           40    33           33\n",
       "11339     221           91    61           61\n",
       "\n",
       "[71 rows x 4 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "david= main_df_team_only_srh[(main_df_team_only_srh['batsman'] == 'DA Warner')]\n",
    "david = david.pivot_table(index = ['match_id'] , aggfunc=['sum' , 'count'], values = ['batsman_runs','ball'])\n",
    "david"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "david = pd.DataFrame(david.to_records())\n",
    "david = david.rename(columns={\"('sum', 'batsman_runs')\": \"runs\" , \"('count', 'ball')\":\"balls\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>70</th>\n",
       "      <td>11339</td>\n",
       "      <td>221</td>\n",
       "      <td>91</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  ('sum', 'ball')  runs  balls  ('count', 'batsman_runs')\n",
       "0          1               29    14      9                          9\n",
       "1          6              162    76     47                         47\n",
       "2         10              116    49     34                         34\n",
       "3         14              112    26     31                         31\n",
       "4         19              208    70     57                         57\n",
       "..       ...              ...   ...    ...                        ...\n",
       "66     11324               96    51     26                         26\n",
       "67     11329              140    70     39                         39\n",
       "68     11332              158    59     47                         47\n",
       "69     11336              108    40     33                         33\n",
       "70     11339              221    91     61                         61\n",
       "\n",
       "[71 rows x 5 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "david"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "david['strike_rate'] = (david['runs'] / david['balls'])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,7))\n",
    "sb.distplot(david['strike_rate'] , color = \"green\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,7))\n",
    "sb.kdeplot(david['strike_rate'] , color = \"r\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "134.59947213423246"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Avg_srall = david['strike_rate'].sum() / len(david)\n",
    "Avg_srall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Hence sr > 125"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check for out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "david = david.pivot_table(index = ['match_id'] , aggfunc=[ 'count'], values = ['ball','player_dismissed'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ball</th>\n",
       "      <th>player_dismissed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>622</th>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11313</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11339</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count                 \n",
       "          ball player_dismissed\n",
       "match_id                       \n",
       "19          57                0\n",
       "32          27                1\n",
       "466         12                1\n",
       "496         24                1\n",
       "544         41                1\n",
       "564         54                1\n",
       "594         32                1\n",
       "622         42                2\n",
       "11313       63                1\n",
       "11339       61                1"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "david"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# majority of the time , david has got out !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets check wrt aall team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178462</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>178463</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>Mohammad Nabi</td>\n",
       "      <td>caught</td>\n",
       "      <td>AR Patel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178464</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>K Paul</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178465</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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>Rashid Khan</td>\n",
       "      <td>caught</td>\n",
       "      <td>RR Pant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178466</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>Basil Thampi</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12908 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team                 bowling_team  \\\n",
       "0              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "1              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "2              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "3              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "4              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "...          ...     ...                  ...                          ...   \n",
       "178462     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178463     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178464     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178465     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178466     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "\n",
       "        over  ball        batsman  non_striker    bowler  is_super_over  ...  \\\n",
       "0          1     1      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "1          1     2      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "2          1     3      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "3          1     4      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "4          1     5      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "...      ...   ...            ...          ...       ...            ...  ...   \n",
       "178462    20     5  Mohammad Nabi     DJ Hooda    K Paul              0  ...   \n",
       "178463    20     6  Mohammad Nabi     DJ Hooda    K Paul              0  ...   \n",
       "178464    20     7       DJ Hooda  Rashid Khan    K Paul              0  ...   \n",
       "178465    20     8    Rashid Khan      B Kumar    K Paul              0  ...   \n",
       "178466    20     9   Basil Thampi      B Kumar    K Paul              0  ...   \n",
       "\n",
       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "0              0            0            0             0             0   \n",
       "1              0            0            0             0             0   \n",
       "2              0            0            0             0             4   \n",
       "3              0            0            0             0             0   \n",
       "4              0            0            0             0             0   \n",
       "...          ...          ...          ...           ...           ...   \n",
       "178462         0            0            0             0             0   \n",
       "178463         0            0            0             0             0   \n",
       "178464         0            0            0             0             1   \n",
       "178465         0            0            0             0             0   \n",
       "178466         0            0            0             0             1   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind   fielder  \n",
       "0                0           0               NaN            NaN       NaN  \n",
       "1                0           0               NaN            NaN       NaN  \n",
       "2                0           4               NaN            NaN       NaN  \n",
       "3                0           0               NaN            NaN       NaN  \n",
       "4                2           2               NaN            NaN       NaN  \n",
       "...            ...         ...               ...            ...       ...  \n",
       "178462           0           0               NaN            NaN       NaN  \n",
       "178463           0           0     Mohammad Nabi         caught  AR Patel  \n",
       "178464           0           1               NaN            NaN       NaN  \n",
       "178465           0           0       Rashid Khan         caught   RR Pant  \n",
       "178466           0           1               NaN            NaN       NaN  \n",
       "\n",
       "[12908 rows x 21 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df_team_only_srh = df[ ((df['batting_team'] == 'Sunrisers Hyderabad'))]\n",
    "main_df_team_only_srh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11324</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11329</th>\n",
       "      <td>39</td>\n",
       "      <td>39</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11332</th>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11336</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11339</th>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         count                              \n",
       "          ball batsman_runs player_dismissed\n",
       "match_id                                    \n",
       "1            9            9                1\n",
       "6           47           47                0\n",
       "10          34           34                1\n",
       "14          31           31                1\n",
       "19          57           57                0\n",
       "...        ...          ...              ...\n",
       "11324       26           26                1\n",
       "11329       39           39                1\n",
       "11332       47           47                1\n",
       "11336       33           33                1\n",
       "11339       61           61                1\n",
       "\n",
       "[71 rows x 3 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "david= main_df_team_only_srh[(main_df_team_only_srh['batsman'] == 'DA Warner')]\n",
    "david = david.pivot_table(index = ['match_id'] , aggfunc=['count'], values = ['batsman_runs','ball' , 'player_dismissed'])\n",
    "david"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>match_id</th>\n",
       "      <th>('count', 'ball')</th>\n",
       "      <th>('count', 'batsman_runs')</th>\n",
       "      <th>('count', 'player_dismissed')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  match_id  ('count', 'ball')  ('count', 'batsman_runs')  \\\n",
       "0      0         1                  9                          9   \n",
       "1      1         6                 47                         47   \n",
       "2      2        10                 34                         34   \n",
       "3      3        14                 31                         31   \n",
       "4      4        19                 57                         57   \n",
       "\n",
       "   ('count', 'player_dismissed')  \n",
       "0                              1  \n",
       "1                              0  \n",
       "2                              1  \n",
       "3                              1  \n",
       "4                              0  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "david = pd.DataFrame(david.to_records())\n",
    "david.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([1], dtype=int64), count=array([57]))"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mode(david[\"('count', 'player_dismissed')\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ans is 1 - out  - 57 times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Take Aways :\n",
    "### 1 - Warner is a opening batsman for SRH\n",
    "### 2 - His SR againt KXIP is > 140\n",
    "### 3 - HIs SR againt all the other team is >133\n",
    "### 4 - He gets out most of the time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 2 ans - Out ,Strike rate > 125"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ques 3 - KL Rahul contribution for entire team total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>4437</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>HM Amla</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>B Kumar</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>HM Amla</td>\n",
       "      <td>lbw</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4438</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>B Kumar</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>4439</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>B Kumar</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>4440</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>B Kumar</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>4441</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>B Kumar</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176407</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>M Ur Rahman</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>S Sharma</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>176408</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>M Ur Rahman</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>S Sharma</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>176409</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>M Ur Rahman</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>S Sharma</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>M Ur Rahman</td>\n",
       "      <td>bowled</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176410</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>S Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176411</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>S Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1703 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning     batting_team         bowling_team  over  ball  \\\n",
       "4437          19       2  Kings XI Punjab  Sunrisers Hyderabad     1     1   \n",
       "4438          19       2  Kings XI Punjab  Sunrisers Hyderabad     1     2   \n",
       "4439          19       2  Kings XI Punjab  Sunrisers Hyderabad     1     3   \n",
       "4440          19       2  Kings XI Punjab  Sunrisers Hyderabad     1     4   \n",
       "4441          19       2  Kings XI Punjab  Sunrisers Hyderabad     1     5   \n",
       "...          ...     ...              ...                  ...   ...   ...   \n",
       "176407     11339       2  Kings XI Punjab  Sunrisers Hyderabad    20     2   \n",
       "176408     11339       2  Kings XI Punjab  Sunrisers Hyderabad    20     3   \n",
       "176409     11339       2  Kings XI Punjab  Sunrisers Hyderabad    20     4   \n",
       "176410     11339       2  Kings XI Punjab  Sunrisers Hyderabad    20     5   \n",
       "176411     11339       2  Kings XI Punjab  Sunrisers Hyderabad    20     6   \n",
       "\n",
       "               batsman     non_striker    bowler  is_super_over  ...  \\\n",
       "4437           HM Amla         M Vohra   B Kumar              0  ...   \n",
       "4438        GJ Maxwell         M Vohra   B Kumar              0  ...   \n",
       "4439        GJ Maxwell         M Vohra   B Kumar              0  ...   \n",
       "4440        GJ Maxwell         M Vohra   B Kumar              0  ...   \n",
       "4441        GJ Maxwell         M Vohra   B Kumar              0  ...   \n",
       "...                ...             ...       ...            ...  ...   \n",
       "176407     M Ur Rahman        M Ashwin  S Sharma              0  ...   \n",
       "176408     M Ur Rahman        M Ashwin  S Sharma              0  ...   \n",
       "176409     M Ur Rahman        M Ashwin  S Sharma              0  ...   \n",
       "176410  Mohammed Shami        M Ashwin  S Sharma              0  ...   \n",
       "176411        M Ashwin  Mohammed Shami  S Sharma              0  ...   \n",
       "\n",
       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "4437           0            0            0             0             0   \n",
       "4438           0            0            0             0             4   \n",
       "4439           0            0            0             0             0   \n",
       "4440           0            0            0             0             0   \n",
       "4441           0            0            0             0             0   \n",
       "...          ...          ...          ...           ...           ...   \n",
       "176407         0            0            0             0             0   \n",
       "176408         0            0            0             0             0   \n",
       "176409         0            0            0             0             0   \n",
       "176410         0            0            0             0             1   \n",
       "176411         0            0            0             0             1   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
       "4437             0           0           HM Amla            lbw     NaN  \n",
       "4438             0           4               NaN            NaN     NaN  \n",
       "4439             0           0               NaN            NaN     NaN  \n",
       "4440             0           0               NaN            NaN     NaN  \n",
       "4441             0           0               NaN            NaN     NaN  \n",
       "...            ...         ...               ...            ...     ...  \n",
       "176407           0           0               NaN            NaN     NaN  \n",
       "176408           0           0               NaN            NaN     NaN  \n",
       "176409           0           0       M Ur Rahman         bowled     NaN  \n",
       "176410           0           1               NaN            NaN     NaN  \n",
       "176411           0           1               NaN            NaN     NaN  \n",
       "\n",
       "[1703 rows x 21 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kxp_bat_srh_ball = df[(((df['batting_team'] == 'Kings XI Punjab')  & (df['bowling_team'] == 'Sunrisers Hyderabad')))]\n",
    "kxp_bat_srh_ball"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>total_runs</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>154</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>181</td>\n",
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       "      <th>405</th>\n",
       "      <td>123</td>\n",
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       "      <th>440</th>\n",
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       "      <th>544</th>\n",
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       "      <th>564</th>\n",
       "      <td>180</td>\n",
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       "      <th>594</th>\n",
       "      <td>143</td>\n",
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       "      <th>622</th>\n",
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       "      <th>7909</th>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7918</th>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11313</th>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11339</th>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sum\n",
       "         total_runs\n",
       "match_id           \n",
       "19              154\n",
       "32              181\n",
       "405             123\n",
       "440             120\n",
       "466             193\n",
       "496             211\n",
       "544             130\n",
       "564             180\n",
       "594             143\n",
       "622             179\n",
       "7909            201\n",
       "7918            121\n",
       "11313           152\n",
       "11339           174"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = kxp_bat_srh_ball.pivot_table(index = ['match_id'], aggfunc=['sum'] , values = ['total_runs'])\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7909</th>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7918</th>\n",
       "      <td>121</td>\n",
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       "      <th>11313</th>\n",
       "      <td>152</td>\n",
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       "    <tr>\n",
       "      <th>11339</th>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sum\n",
       "         total_runs\n",
       "match_id           \n",
       "7909            201\n",
       "7918            121\n",
       "11313           152\n",
       "11339           174"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_total = total.tail(4)\n",
    "main_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "rahul = kxp_bat_srh_ball[kxp_bat_srh_ball['batsman'] == 'KL Rahul']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7909,  7918, 11313, 11339], dtype=int64)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rahul['match_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "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>('sum', 'batsman_runs')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7909</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7918</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11313</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11339</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  ('sum', 'batsman_runs')\n",
       "0      7909                       18\n",
       "1      7918                       32\n",
       "2     11313                       71\n",
       "3     11339                       84"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rahul_score = rahul.pivot_table(index = ['match_id'], aggfunc=['sum'] , values = ['batsman_runs'])\n",
    "\n",
    "rahul_score = pd.DataFrame(rahul_score.to_records())\n",
    "rahul_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "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>('sum', 'total_runs')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7909</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7918</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11313</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11339</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  ('sum', 'total_runs')\n",
       "0      7909                    201\n",
       "1      7918                    121\n",
       "2     11313                    152\n",
       "3     11339                    174"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_total = pd.DataFrame(main_total.to_records())\n",
    "main_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "rahul_score = rahul_score.rename(columns = {\"('sum','batsman_runs')\" : \"runs\"})\n",
    "main_total = main_total.rename(columns = {\"('sum','total_runs')\" : \"total_runs\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_rahul = pd.merge(rahul_score , right = main_total,on = 'match_id' , how = 'inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>('sum', 'batsman_runs')</th>\n",
       "      <th>('sum', 'total_runs')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7909</td>\n",
       "      <td>18</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7918</td>\n",
       "      <td>32</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11313</td>\n",
       "      <td>71</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11339</td>\n",
       "      <td>84</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  ('sum', 'batsman_runs')  ('sum', 'total_runs')\n",
       "0      7909                       18                    201\n",
       "1      7918                       32                    121\n",
       "2     11313                       71                    152\n",
       "3     11339                       84                    174"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_rahul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_rahul['contrib'] = (main_rahul[\"('sum\\', \\'batsman_runs\\')\"] / main_rahul[\"('sum\\', \\'total_runs\\')\"])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>('sum', 'batsman_runs')</th>\n",
       "      <th>('sum', 'total_runs')</th>\n",
       "      <th>contrib</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7909</td>\n",
       "      <td>18</td>\n",
       "      <td>201</td>\n",
       "      <td>8.955224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7918</td>\n",
       "      <td>32</td>\n",
       "      <td>121</td>\n",
       "      <td>26.446281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11313</td>\n",
       "      <td>71</td>\n",
       "      <td>152</td>\n",
       "      <td>46.710526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11339</td>\n",
       "      <td>84</td>\n",
       "      <td>174</td>\n",
       "      <td>48.275862</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  ('sum', 'batsman_runs')  ('sum', 'total_runs')    contrib\n",
       "0      7909                       18                    201   8.955224\n",
       "1      7918                       32                    121  26.446281\n",
       "2     11313                       71                    152  46.710526\n",
       "3     11339                       84                    174  48.275862"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_rahul"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07a96a4c0>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sb.distplot(main_rahul[\"('sum\\', \\'total_runs\\')\"])\n",
    "sb.distplot(main_rahul[\"('sum\\', \\'batsman_runs\\')\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07abc7460>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sb.kdeplot(data = main_rahul ,  data2 = main_rahul[\"('sum\\', \\'total_runs\\')\"] , hue = main_rahul[\"('sum\\', \\'batsman_runs\\')\"] , multiple = 'stack')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07ac76b80>"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.kdeplot(main_rahul['contrib'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "# avg contribution of around 35-40 +"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets check wrt all team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "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>838</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>HM Amla</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>AB Dinda</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>839</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>HM Amla</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>AB Dinda</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>840</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>HM Amla</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>AB Dinda</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>841</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>HM Amla</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>AB Dinda</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>HM Amla</td>\n",
       "      <td>AB Dinda</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177869</th>\n",
       "      <td>11346</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>S Curran</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>Imran Tahir</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>177870</th>\n",
       "      <td>11346</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>S Curran</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>Imran Tahir</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177871</th>\n",
       "      <td>11346</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>S Curran</td>\n",
       "      <td>Imran Tahir</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177872</th>\n",
       "      <td>11346</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>S Curran</td>\n",
       "      <td>RA Jadeja</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177873</th>\n",
       "      <td>11346</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>S Curran</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>Imran Tahir</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>20931 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning     batting_team            bowling_team  over  ball  \\\n",
       "838            4       2  Kings XI Punjab  Rising Pune Supergiant     1     1   \n",
       "839            4       2  Kings XI Punjab  Rising Pune Supergiant     1     2   \n",
       "840            4       2  Kings XI Punjab  Rising Pune Supergiant     1     3   \n",
       "841            4       2  Kings XI Punjab  Rising Pune Supergiant     1     4   \n",
       "842            4       2  Kings XI Punjab  Rising Pune Supergiant     1     5   \n",
       "...          ...     ...              ...                     ...   ...   ...   \n",
       "177869     11346       2  Kings XI Punjab     Chennai Super Kings    18     3   \n",
       "177870     11346       2  Kings XI Punjab     Chennai Super Kings    18     4   \n",
       "177871     11346       2  Kings XI Punjab     Chennai Super Kings    18     5   \n",
       "177872     11346       2  Kings XI Punjab     Chennai Super Kings    17     6   \n",
       "177873     11346       2  Kings XI Punjab     Chennai Super Kings    18     6   \n",
       "\n",
       "              batsman    non_striker       bowler  is_super_over  ...  \\\n",
       "838           HM Amla        M Vohra     AB Dinda              0  ...   \n",
       "839           HM Amla        M Vohra     AB Dinda              0  ...   \n",
       "840           HM Amla        M Vohra     AB Dinda              0  ...   \n",
       "841           HM Amla        M Vohra     AB Dinda              0  ...   \n",
       "842           M Vohra        HM Amla     AB Dinda              0  ...   \n",
       "...               ...            ...          ...            ...  ...   \n",
       "177869       S Curran  Mandeep Singh  Imran Tahir              0  ...   \n",
       "177870       S Curran  Mandeep Singh  Imran Tahir              0  ...   \n",
       "177871  Mandeep Singh       S Curran  Imran Tahir              0  ...   \n",
       "177872  Mandeep Singh       S Curran    RA Jadeja              0  ...   \n",
       "177873       S Curran  Mandeep Singh  Imran Tahir              0  ...   \n",
       "\n",
       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "838            0            0            0             0             0   \n",
       "839            0            0            0             0             0   \n",
       "840            0            0            0             0             4   \n",
       "841            0            0            0             0             1   \n",
       "842            0            0            0             0             1   \n",
       "...          ...          ...          ...           ...           ...   \n",
       "177869         0            0            0             0             0   \n",
       "177870         0            0            0             0             1   \n",
       "177871         0            0            0             0             1   \n",
       "177872         0            0            0             0             1   \n",
       "177873         0            0            0             0             4   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
       "838              0           0               NaN            NaN     NaN  \n",
       "839              0           0               NaN            NaN     NaN  \n",
       "840              0           4               NaN            NaN     NaN  \n",
       "841              0           1               NaN            NaN     NaN  \n",
       "842              0           1               NaN            NaN     NaN  \n",
       "...            ...         ...               ...            ...     ...  \n",
       "177869           0           0               NaN            NaN     NaN  \n",
       "177870           0           1               NaN            NaN     NaN  \n",
       "177871           0           1               NaN            NaN     NaN  \n",
       "177872           0           1               NaN            NaN     NaN  \n",
       "177873           0           4               NaN            NaN     NaN  \n",
       "\n",
       "[20931 rows x 21 columns]"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kxp_bat_all_ball = df[(((df['batting_team'] == 'Kings XI Punjab') ))]\n",
    "kxp_bat_all_ball"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>sum</th>\n",
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       "      <th>11339</th>\n",
       "      <td>174</td>\n",
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       "      <th>11346</th>\n",
       "      <td>188</td>\n",
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       "</table>\n",
       "<p>176 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                sum\n",
       "         total_runs\n",
       "match_id           \n",
       "4               164\n",
       "8               150\n",
       "11              170\n",
       "15              137\n",
       "19              154\n",
       "...             ...\n",
       "11328           168\n",
       "11333           190\n",
       "11339           174\n",
       "11343           186\n",
       "11346           188\n",
       "\n",
       "[176 rows x 1 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = kxp_bat_all_ball.pivot_table(index = ['match_id'], aggfunc=['sum'] , values = ['total_runs'])\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
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       "      <td>188</td>\n",
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       "<p>176 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id  ('sum', 'total_runs')\n",
       "0           4                    164\n",
       "1           8                    150\n",
       "2          11                    170\n",
       "3          15                    137\n",
       "4          19                    154\n",
       "..        ...                    ...\n",
       "171     11328                    168\n",
       "172     11333                    190\n",
       "173     11339                    174\n",
       "174     11343                    186\n",
       "175     11346                    188\n",
       "\n",
       "[176 rows x 2 columns]"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = pd.DataFrame(total.to_records())\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "array([    4,     8,    11,    15,    19,    22,    26,    32,    35,\n",
       "          42,    46,    48,    50,    54,    61,    65,    69,    73,\n",
       "          77,    81,    84,    91,    94,    97,   100,   106,   108,\n",
       "         112,   116,   120,   123,   127,   130,   135,   139,   142,\n",
       "         145,   149,   151,   157,   161,   164,   169,   177,   181,\n",
       "         186,   190,   193,   196,   201,   205,   208,   212,   215,\n",
       "         218,   225,   228,   239,   243,   249,   256,   259,   270,\n",
       "         273,   280,   284,   287,   290,   293,   296,   300,   311,\n",
       "         315,   321,   325,   330,   333,   336,   340,   343,   350,\n",
       "         354,   359,   366,   369,   371,   374,   387,   391,   398,\n",
       "         400,   405,   409,   412,   415,   421,   425,   429,   436,\n",
       "         440,   443,   448,   450,   460,   464,   466,   472,   475,\n",
       "         479,   486,   488,   491,   496,   502,   505,   509,   512,\n",
       "         514,   516,   517,   520,   524,   528,   532,   536,   542,\n",
       "         544,   548,   552,   557,   560,   564,   566,   569,   579,\n",
       "         583,   586,   589,   594,   597,   604,   608,   612,   615,\n",
       "         619,   622,   626,   629,  7895,  7901,  7905,  7909,  7911,\n",
       "        7915,  7918,  7927,  7931,  7933,  7937,  7941,  7943,  7949,\n",
       "       11140, 11142, 11145, 11149, 11309, 11313, 11315, 11319, 11323,\n",
       "       11328, 11333, 11339, 11343, 11346], dtype=int64)"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total['match_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>7911</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>7915</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>7918</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>7927</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>7931</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>7933</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>7937</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>7941</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>7943</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>7949</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>11140</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>11142</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>11145</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>11149</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>11309</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>11313</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>11315</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>11319</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>11323</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>11328</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>11333</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>11339</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>11343</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>11346</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id  ('sum', 'total_runs')\n",
       "148      7895                    168\n",
       "149      7901                    161\n",
       "150      7905                    198\n",
       "151      7909                    201\n",
       "152      7911                    128\n",
       "153      7915                    157\n",
       "154      7918                    121\n",
       "155      7927                    184\n",
       "156      7931                    158\n",
       "157      7933                    149\n",
       "158      7937                    221\n",
       "159      7941                     93\n",
       "160      7943                    192\n",
       "161      7949                    164\n",
       "162     11140                    200\n",
       "163     11142                    196\n",
       "164     11145                    185\n",
       "165     11149                    175\n",
       "166     11309                    142\n",
       "167     11313                    152\n",
       "168     11315                    204\n",
       "169     11319                    180\n",
       "170     11323                    190\n",
       "171     11328                    168\n",
       "172     11333                    190\n",
       "173     11339                    174\n",
       "174     11343                    186\n",
       "175     11346                    188"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_total = total.tail(28)\n",
    "main_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "rahul = kxp_bat_all_ball[kxp_bat_all_ball['batsman'] == 'KL Rahul']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7895,  7901,  7905,  7909,  7911,  7915,  7918,  7927,  7931,\n",
       "        7933,  7937,  7941,  7943,  7949, 11140, 11142, 11145, 11149,\n",
       "       11309, 11313, 11315, 11319, 11323, 11328, 11333, 11339, 11343,\n",
       "       11346], dtype=int64)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rahul['match_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>('sum', 'batsman_runs')</th>\n",
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       "  </thead>\n",
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       "      <td>7933</td>\n",
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       "      <td>7941</td>\n",
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       "      <td>99</td>\n",
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       "      <th>13</th>\n",
       "      <td>7949</td>\n",
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       "      <th>19</th>\n",
       "      <td>11313</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11315</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11319</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11323</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
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       "      <th>23</th>\n",
       "      <td>11328</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11333</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11339</td>\n",
       "      <td>84</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>11343</td>\n",
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       "      <th>27</th>\n",
       "      <td>11346</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  ('sum', 'batsman_runs')\n",
       "0       7895                       51\n",
       "1       7901                       48\n",
       "2       7905                       37\n",
       "3       7909                       18\n",
       "4       7911                       61\n",
       "5       7915                       23\n",
       "6       7918                       32\n",
       "7       7927                       24\n",
       "8       7931                       86\n",
       "9       7933                      101\n",
       "10      7937                       68\n",
       "11      7941                       22\n",
       "12      7943                       99\n",
       "13      7949                        8\n",
       "14     11140                        4\n",
       "15     11142                        1\n",
       "16     11145                       72\n",
       "17     11149                       15\n",
       "18     11309                       55\n",
       "19     11313                       71\n",
       "20     11315                      104\n",
       "21     11319                       18\n",
       "22     11323                       53\n",
       "23     11328                       12\n",
       "24     11333                       44\n",
       "25     11339                       84\n",
       "26     11343                        2\n",
       "27     11346                       75"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rahul_total = rahul.pivot_table(index = ['match_id'], aggfunc=['sum'] , values = ['batsman_runs'])\n",
    "rahul_total = pd.DataFrame(rahul_total.to_records())\n",
    "rahul_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>match_id</th>\n",
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       "      <th>6</th>\n",
       "      <td>7918</td>\n",
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       "      <td>7941</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7943</td>\n",
       "      <td>99</td>\n",
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       "      <th>13</th>\n",
       "      <td>7949</td>\n",
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       "      <td>164</td>\n",
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       "      <th>15</th>\n",
       "      <td>11142</td>\n",
       "      <td>1</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11145</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11149</td>\n",
       "      <td>15</td>\n",
       "      <td>175</td>\n",
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       "      <th>18</th>\n",
       "      <td>11309</td>\n",
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       "      <th>19</th>\n",
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       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11315</td>\n",
       "      <td>104</td>\n",
       "      <td>204</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11319</td>\n",
       "      <td>18</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11323</td>\n",
       "      <td>53</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11328</td>\n",
       "      <td>12</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11333</td>\n",
       "      <td>44</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11339</td>\n",
       "      <td>84</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>11343</td>\n",
       "      <td>2</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>11346</td>\n",
       "      <td>75</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  ('sum', 'batsman_runs')  ('sum', 'total_runs')\n",
       "0       7895                       51                    168\n",
       "1       7901                       48                    161\n",
       "2       7905                       37                    198\n",
       "3       7909                       18                    201\n",
       "4       7911                       61                    128\n",
       "5       7915                       23                    157\n",
       "6       7918                       32                    121\n",
       "7       7927                       24                    184\n",
       "8       7931                       86                    158\n",
       "9       7933                      101                    149\n",
       "10      7937                       68                    221\n",
       "11      7941                       22                     93\n",
       "12      7943                       99                    192\n",
       "13      7949                        8                    164\n",
       "14     11140                        4                    200\n",
       "15     11142                        1                    196\n",
       "16     11145                       72                    185\n",
       "17     11149                       15                    175\n",
       "18     11309                       55                    142\n",
       "19     11313                       71                    152\n",
       "20     11315                      104                    204\n",
       "21     11319                       18                    180\n",
       "22     11323                       53                    190\n",
       "23     11328                       12                    168\n",
       "24     11333                       44                    190\n",
       "25     11339                       84                    174\n",
       "26     11343                        2                    186\n",
       "27     11346                       75                    188"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_rahul_all = pd.merge(rahul_total , right = main_total,on = 'match_id' , how = 'inner')\n",
    "main_rahul_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_rahul_all['contrib'] = (main_rahul_all[\"('sum\\', \\'batsman_runs\\')\"] / main_rahul_all[\"('sum\\', \\'total_runs\\')\"])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "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>('sum', 'batsman_runs')</th>\n",
       "      <th>('sum', 'total_runs')</th>\n",
       "      <th>contrib</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7895</td>\n",
       "      <td>51</td>\n",
       "      <td>168</td>\n",
       "      <td>30.357143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7901</td>\n",
       "      <td>48</td>\n",
       "      <td>161</td>\n",
       "      <td>29.813665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7905</td>\n",
       "      <td>37</td>\n",
       "      <td>198</td>\n",
       "      <td>18.686869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7909</td>\n",
       "      <td>18</td>\n",
       "      <td>201</td>\n",
       "      <td>8.955224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7911</td>\n",
       "      <td>61</td>\n",
       "      <td>128</td>\n",
       "      <td>47.656250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7915</td>\n",
       "      <td>23</td>\n",
       "      <td>157</td>\n",
       "      <td>14.649682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7918</td>\n",
       "      <td>32</td>\n",
       "      <td>121</td>\n",
       "      <td>26.446281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7927</td>\n",
       "      <td>24</td>\n",
       "      <td>184</td>\n",
       "      <td>13.043478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7931</td>\n",
       "      <td>86</td>\n",
       "      <td>158</td>\n",
       "      <td>54.430380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7933</td>\n",
       "      <td>101</td>\n",
       "      <td>149</td>\n",
       "      <td>67.785235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7937</td>\n",
       "      <td>68</td>\n",
       "      <td>221</td>\n",
       "      <td>30.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7941</td>\n",
       "      <td>22</td>\n",
       "      <td>93</td>\n",
       "      <td>23.655914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7943</td>\n",
       "      <td>99</td>\n",
       "      <td>192</td>\n",
       "      <td>51.562500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>7949</td>\n",
       "      <td>8</td>\n",
       "      <td>164</td>\n",
       "      <td>4.878049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11140</td>\n",
       "      <td>4</td>\n",
       "      <td>200</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11142</td>\n",
       "      <td>1</td>\n",
       "      <td>196</td>\n",
       "      <td>0.510204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11145</td>\n",
       "      <td>72</td>\n",
       "      <td>185</td>\n",
       "      <td>38.918919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11149</td>\n",
       "      <td>15</td>\n",
       "      <td>175</td>\n",
       "      <td>8.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11309</td>\n",
       "      <td>55</td>\n",
       "      <td>142</td>\n",
       "      <td>38.732394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11313</td>\n",
       "      <td>71</td>\n",
       "      <td>152</td>\n",
       "      <td>46.710526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11315</td>\n",
       "      <td>104</td>\n",
       "      <td>204</td>\n",
       "      <td>50.980392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>11319</td>\n",
       "      <td>18</td>\n",
       "      <td>180</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11323</td>\n",
       "      <td>53</td>\n",
       "      <td>190</td>\n",
       "      <td>27.894737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11328</td>\n",
       "      <td>12</td>\n",
       "      <td>168</td>\n",
       "      <td>7.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11333</td>\n",
       "      <td>44</td>\n",
       "      <td>190</td>\n",
       "      <td>23.157895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>11339</td>\n",
       "      <td>84</td>\n",
       "      <td>174</td>\n",
       "      <td>48.275862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>11343</td>\n",
       "      <td>2</td>\n",
       "      <td>186</td>\n",
       "      <td>1.075269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>11346</td>\n",
       "      <td>75</td>\n",
       "      <td>188</td>\n",
       "      <td>39.893617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  ('sum', 'batsman_runs')  ('sum', 'total_runs')    contrib\n",
       "0       7895                       51                    168  30.357143\n",
       "1       7901                       48                    161  29.813665\n",
       "2       7905                       37                    198  18.686869\n",
       "3       7909                       18                    201   8.955224\n",
       "4       7911                       61                    128  47.656250\n",
       "5       7915                       23                    157  14.649682\n",
       "6       7918                       32                    121  26.446281\n",
       "7       7927                       24                    184  13.043478\n",
       "8       7931                       86                    158  54.430380\n",
       "9       7933                      101                    149  67.785235\n",
       "10      7937                       68                    221  30.769231\n",
       "11      7941                       22                     93  23.655914\n",
       "12      7943                       99                    192  51.562500\n",
       "13      7949                        8                    164   4.878049\n",
       "14     11140                        4                    200   2.000000\n",
       "15     11142                        1                    196   0.510204\n",
       "16     11145                       72                    185  38.918919\n",
       "17     11149                       15                    175   8.571429\n",
       "18     11309                       55                    142  38.732394\n",
       "19     11313                       71                    152  46.710526\n",
       "20     11315                      104                    204  50.980392\n",
       "21     11319                       18                    180  10.000000\n",
       "22     11323                       53                    190  27.894737\n",
       "23     11328                       12                    168   7.142857\n",
       "24     11333                       44                    190  23.157895\n",
       "25     11339                       84                    174  48.275862\n",
       "26     11343                        2                    186   1.075269\n",
       "27     11346                       75                    188  39.893617"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_rahul_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1e07ab62b50>]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(main_rahul_all['contrib'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07a98a3d0>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.kdeplot(main_rahul_all['contrib'] , color= \"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07a9b12b0>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sb.kdeplot(data = main_rahul_all ,  data2 = main_rahul_all[\"('sum\\', \\'total_runs\\')\"] , hue = main_rahul_all[\"('sum\\', \\'batsman_runs\\')\"] , multiple = 'stack')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Avg contribution is around = 30-50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Take Aways :\n",
    "### 1 - Rahul is a class player ,and always scores the most runs for his team\n",
    "### 2 - He is the opener  ,so he should play a bit safe for starting 10 balls\n",
    "### 3 - Acc to stats , he scored around (30-50) % contribution for his team"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 3 ans - More than 23 %"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "############################################################################################################################3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 4- Balls taken by SRH to reach 50 ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "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>4312</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Sandeep Sharma</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>4313</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Sandeep Sharma</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>4314</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Sandeep Sharma</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>4315</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Sandeep Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4316</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>Sandeep Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176407</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>M Ur Rahman</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>S Sharma</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>176408</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>M Ur Rahman</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>S Sharma</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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>176409</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>M Ur Rahman</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>S Sharma</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>M Ur Rahman</td>\n",
       "      <td>bowled</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176410</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>S Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176411</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>S Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3420 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team         bowling_team  over  \\\n",
       "4312          19       1  Sunrisers Hyderabad      Kings XI Punjab     1   \n",
       "4313          19       1  Sunrisers Hyderabad      Kings XI Punjab     1   \n",
       "4314          19       1  Sunrisers Hyderabad      Kings XI Punjab     1   \n",
       "4315          19       1  Sunrisers Hyderabad      Kings XI Punjab     1   \n",
       "4316          19       1  Sunrisers Hyderabad      Kings XI Punjab     1   \n",
       "...          ...     ...                  ...                  ...   ...   \n",
       "176407     11339       2      Kings XI Punjab  Sunrisers Hyderabad    20   \n",
       "176408     11339       2      Kings XI Punjab  Sunrisers Hyderabad    20   \n",
       "176409     11339       2      Kings XI Punjab  Sunrisers Hyderabad    20   \n",
       "176410     11339       2      Kings XI Punjab  Sunrisers Hyderabad    20   \n",
       "176411     11339       2      Kings XI Punjab  Sunrisers Hyderabad    20   \n",
       "\n",
       "        ball         batsman     non_striker          bowler  is_super_over  \\\n",
       "4312       1       DA Warner        S Dhawan  Sandeep Sharma              0   \n",
       "4313       2       DA Warner        S Dhawan  Sandeep Sharma              0   \n",
       "4314       3       DA Warner        S Dhawan  Sandeep Sharma              0   \n",
       "4315       4       DA Warner        S Dhawan  Sandeep Sharma              0   \n",
       "4316       5        S Dhawan       DA Warner  Sandeep Sharma              0   \n",
       "...      ...             ...             ...             ...            ...   \n",
       "176407     2     M Ur Rahman        M Ashwin        S Sharma              0   \n",
       "176408     3     M Ur Rahman        M Ashwin        S Sharma              0   \n",
       "176409     4     M Ur Rahman        M Ashwin        S Sharma              0   \n",
       "176410     5  Mohammed Shami        M Ashwin        S Sharma              0   \n",
       "176411     6        M Ashwin  Mohammed Shami        S Sharma              0   \n",
       "\n",
       "        ...  bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "4312    ...         0            0            0             0             0   \n",
       "4313    ...         0            0            0             0             0   \n",
       "4314    ...         0            0            0             0             0   \n",
       "4315    ...         0            0            0             0             1   \n",
       "4316    ...         0            0            0             0             1   \n",
       "...     ...       ...          ...          ...           ...           ...   \n",
       "176407  ...         0            0            0             0             0   \n",
       "176408  ...         0            0            0             0             0   \n",
       "176409  ...         0            0            0             0             0   \n",
       "176410  ...         0            0            0             0             1   \n",
       "176411  ...         0            0            0             0             1   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
       "4312             0           0               NaN            NaN     NaN  \n",
       "4313             0           0               NaN            NaN     NaN  \n",
       "4314             0           0               NaN            NaN     NaN  \n",
       "4315             0           1               NaN            NaN     NaN  \n",
       "4316             0           1               NaN            NaN     NaN  \n",
       "...            ...         ...               ...            ...     ...  \n",
       "176407           0           0               NaN            NaN     NaN  \n",
       "176408           0           0               NaN            NaN     NaN  \n",
       "176409           0           0       M Ur Rahman         bowled     NaN  \n",
       "176410           0           1               NaN            NaN     NaN  \n",
       "176411           0           1               NaN            NaN     NaN  \n",
       "\n",
       "[3420 rows x 21 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>match_id</th>\n",
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       "      <td>0</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>1717 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team     bowling_team  over  ball  \\\n",
       "4312          19       1  Sunrisers Hyderabad  Kings XI Punjab     1     1   \n",
       "4313          19       1  Sunrisers Hyderabad  Kings XI Punjab     1     2   \n",
       "4314          19       1  Sunrisers Hyderabad  Kings XI Punjab     1     3   \n",
       "4315          19       1  Sunrisers Hyderabad  Kings XI Punjab     1     4   \n",
       "4316          19       1  Sunrisers Hyderabad  Kings XI Punjab     1     5   \n",
       "...          ...     ...                  ...              ...   ...   ...   \n",
       "176280     11339       1  Sunrisers Hyderabad  Kings XI Punjab    20     3   \n",
       "176281     11339       1  Sunrisers Hyderabad  Kings XI Punjab    20     4   \n",
       "176282     11339       1  Sunrisers Hyderabad  Kings XI Punjab    20     5   \n",
       "176283     11339       1  Sunrisers Hyderabad  Kings XI Punjab    20     6   \n",
       "176284     11339       1  Sunrisers Hyderabad  Kings XI Punjab    20     7   \n",
       "\n",
       "             batsman   non_striker          bowler  is_super_over  ...  \\\n",
       "4312       DA Warner      S Dhawan  Sandeep Sharma              0  ...   \n",
       "4313       DA Warner      S Dhawan  Sandeep Sharma              0  ...   \n",
       "4314       DA Warner      S Dhawan  Sandeep Sharma              0  ...   \n",
       "4315       DA Warner      S Dhawan  Sandeep Sharma              0  ...   \n",
       "4316        S Dhawan     DA Warner  Sandeep Sharma              0  ...   \n",
       "...              ...           ...             ...            ...  ...   \n",
       "176280     V Shankar  Ankit Sharma         A Singh              0  ...   \n",
       "176281  Ankit Sharma     V Shankar         A Singh              0  ...   \n",
       "176282  Ankit Sharma     V Shankar         A Singh              0  ...   \n",
       "176283  Ankit Sharma     V Shankar         A Singh              0  ...   \n",
       "176284     V Shankar  Ankit Sharma         A Singh              0  ...   \n",
       "\n",
       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "4312           0            0            0             0             0   \n",
       "4313           0            0            0             0             0   \n",
       "4314           0            0            0             0             0   \n",
       "4315           0            0            0             0             1   \n",
       "4316           0            0            0             0             1   \n",
       "...          ...          ...          ...           ...           ...   \n",
       "176280         0            0            0             0             1   \n",
       "176281         0            0            0             0             1   \n",
       "176282         0            0            0             0             4   \n",
       "176283         0            0            0             0             1   \n",
       "176284         0            0            0             0             2   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
       "4312             0           0               NaN            NaN     NaN  \n",
       "4313             0           0               NaN            NaN     NaN  \n",
       "4314             0           0               NaN            NaN     NaN  \n",
       "4315             0           1               NaN            NaN     NaN  \n",
       "4316             0           1               NaN            NaN     NaN  \n",
       "...            ...         ...               ...            ...     ...  \n",
       "176280           0           1               NaN            NaN     NaN  \n",
       "176281           1           2               NaN            NaN     NaN  \n",
       "176282           0           4               NaN            NaN     NaN  \n",
       "176283           0           1               NaN            NaN     NaN  \n",
       "176284           0           2               NaN            NaN     NaN  \n",
       "\n",
       "[1717 rows x 21 columns]"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df_team_bat_srh = main_df_team[ ((main_df_team['batting_team'] == 'Sunrisers Hyderabad'))]\n",
    "main_df_team_bat_srh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   19,    32,   405,   440,   466,   496,   544,   564,   594,\n",
       "         622,  7909,  7918, 11313, 11339], dtype=int64)"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches = main_df_team_bat_srh['match_id'].unique()\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "balls = []\n",
    "mat = []\n",
    "t =[]\n",
    "for i in matches:\n",
    "    c = 0\n",
    "    runs =0\n",
    "    m = main_df_team_bat_srh[main_df_team_bat_srh['match_id'] == i]\n",
    "    m = m.reset_index(drop = True)\n",
    "    for j in range(len(m)):\n",
    "        runs = runs + m.loc[j , 'total_runs']\n",
    "        \n",
    "        if runs <= 50:\n",
    "            c = c+1\n",
    "    balls.append(c)\n",
    "    mat.append(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[59, 30, 54, 46, 48, 36, 31, 26, 26, 35, 41, 46, 56, 21]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[19, 32, 405, 440, 466, 496, 544, 564, 594, 622, 7909, 7918, 11313, 11339]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mode and avg balls required to reach 50 runs against KXP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39.642857142857146, 38.5)"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(balls).mean() , np.median(np.array(balls))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "#avg is 40 balls and mode is 39"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07a79d0d0>"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.kdeplot(balls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07acd9fa0>"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q0YZdhIVZuriIv7l+XhqDDsMbx/QKYX+jBUCNmcNpOFTZxvTUaCJDdZE5f5MUHcrKqYnsP9uqVwj7GS0AasxKG8/R2TfIIv3077eumTGF0GAbbxTrUYA/0QKgxuxAZRthwTZmpkZbHUVNkPAQO1cVJHOyvpOypi6r46hxogVAjUnfoIPimnbmZcTpxG9+buXUJGLCgthWXKcXh/kJ/YtVY3KspoMBh9GTvwEgJMjGtTNTqGjp5kSdXhzmD7QAqDE5WNlGfEQwOYkRVkdRk2BJTjyJkSFsK67T2UL9gBYAdck6egcoaTjHwqw4bDr2PyDYbcLq2Sk0dPZxpLrd6jhqjLQAqEt2uLINAyzKirc6ippEczNimRIdyjsnGvQowMdpAVCX7FBVOxlx4SRFh1odRU0imwjXzpxCQ2cfR/UowKdpAVCXpOlcH9VtPSzI1DV/A9HcjFiSo0N5W48CfJoWAHVJDlW1IcC8TB39E4hsIlw7w3UUUFzTYXUcdYm0AKhRM8ZwqLKd3KRIYsODrY6jLDIvM5bkqFDePlGvRwE+SguAGrXa9l6azvUxX7t/AppNhGtmJlPf0ceJWr0uwBdpAVCjdqiqDZvAvHQtAIFuXkYc8RHBvHeqQa8O9kFaANSoOI3hcFU7BVOiidCZPwOe3SZcUZBMZWsP5c3dVsdRo6QFQI1KRXM37T0DLMjST//KZUlOPJEhdt471WB1FDVKWgDUqByqaiPYLsxKi7E6ivISwXYbl01L4lT9OV0vwMdoAVAeczgNR2s6mJEaQ2iQ3eo4yousyEskJMjGe6carY6iRkELgPJYWVMXXX2DzM/Q7h/1ceEhdpbnJnCkqp2Wrn6r4ygPaQFQHjtS3UZIkI0ZuvCLGsFl05IQgZ1nmqyOojykBUB5xOE0HK3uYFZqNMG68IsaQWx4MPMz49h7tpXeAYfVcZQHPPpLFpE1InJSREpE5IERnhcR+YX7+cMisnjIc4+LSIOIHB22z4MiUi0iB923dWN/OWqilDSco2fAwXyd+kFdwKqpSfQPOtlb3mJ1FOWBixYAEbEDDwFrgdnA7SIye1iztUCB+7YReHjIc78B1pzn2//cGLPQfds6yuxqEh2pdq37WzAlyuooyotlxIeTlxTJh2eacTj1wjBv58kRwDKgxBhTaozpB54H1g9rsx54yrjsAuJEJA3AGPM+oB8HfFjfoINjtR3MTovRdX/VRV0+LYn2ngGKa3SqaG/nyV9zBlA55HGVe9to24zkfneX0eMioquKeKn3TzXRO+BkXoZ2/6iLm5EaTWJkCB+UNOn0EF7OkwIw0lp/w/9VPWkz3MPAVGAhUAv8dMQfLrJRRIpEpKixUccYW+H1wzWEB9uZpt0/ygM2EVZNS6KqtYeKFp0ewpt5UgCqgKwhjzOBmkto8zHGmHpjjMMY4wQexdXVNFK7R4wxhcaYwuTkZA/iqvHUO+Dgz8fqmZMeg92m6/4qzyzOjics2MaHZ5qtjqIuwJMCsBcoEJE8EQkBbgM2D2uzGbjLPRpoBdBujKm90Df96ByB283A0fO1VdZ592QjXf0O5unUz2oUQoJsFOYkUFzTTnvPgNVx1HlctAAYYwaB+4FtwHHgRWNMsYhsEpFN7mZbgVKgBNen+Xs/2l9EngN2AjNEpEpEvux+6icickREDgPXAN8crxelxs+WI7UkRIaQn6TdP2p0VuQnYgzsKdOjAG/l0Xy+7iGaW4dt+9WQ+wa47zz73n6e7Rs8j6ms0NPv4K3j9Xx6UYZ2/6hRS4gMYUZqNHvKWrhmxhSr46gR6Jg+dV7vnmygu9/BDfPSLt5YqRGsnJpIV7+Dw9U6JNQbaQFQ5/X6kVqSokJYlpdgdRTlo6YlR5EcHcrOM806JNQLaQFQI+ruH+Tt4w2smZuqF3+pSyYirMxPpLqth/0VbVbHUcPoX7Ya0dsnGugZcHD9vHSroygftyg7jtAgG7/dWW51FDWMFgA1oi2Ha0mODtXuHzVmoUF2FmfHs/VIHU3n+qyOo4bQAqD+xrm+Qd4+0cDauak6+keNi+V5CfQ7nLywt/LijdWk0QKg/sZbx+vpG3Ryw3zt/lHjY0pMGJdNTeTZ3RU6S6gX0QKg/sbrh2tJiQmlMEfn51PjZ8OKHKrbenj7RIPVUZSbFgD1MZ29A7x3spF189KwafePGkerZ6eQEhPKUzvLrY6i3LQAqI9581g9/Q7t/lHjL8hu445lOWw/3URZU5fVcRRaANQwWw7Xkh4bxqIsnftfjb/bl2URZBOe2XXW6igKLQBqiPbuAd4/3cj187X7R02MKTFhfGpOKr/bV6ULx3sBLQDqL944VseAw3C9dv+oCfSF5dm09wyw5fAFZ4xXk0ALgPqL1w/XkhkfzgKd+19NoJVTE8lPjuTp3doNZDUtAAqAlq5+dpQ0cf38NES0+0dNHBHhC8tzOFDRpgvHW0wLgALgT0frGHQablqg3T9q4n12cQahQTae2V1hdZSApgVAAfDaoRrykyOZnRZjdRQVAOIiQrhxQTqvHKims1eXjLSKFgBFQ0cvu8qauXF+unb/qElz54ocuvsdvHKg2uooAUsLgGLLkVqMgRsX6MpfavIsyIxlTnoMz+yu0MViLKIFQPHaoRpmpcUwbUq01VFUAPnoZPCJuk72V7RaHScgaQEIcJUt3eyvaNNP/8oS6xemExUaxDO79GSwFbQABLgtR1wX49yoF38pC0SGBnHzogxeP1JLa1e/1XECjhaAAPfaoRoWZsWRlRBhdRQVoO5Ynk3/oJOX9ldZHSXgaAEIYCUNnRTXdOjYf2WpWWkxLMmJ55ndFTh1sZhJpQUggL1yoAabwA3a/68sdueKbMqauthZ2mx1lICiBSBAGWN49VA1q6YlMSU6zOo4KsCtnZtGXEQwT+s00ZNKC0CA2l/RRmVLD+sXZlgdRSnCgu3csiSTN47VU9/Ra3WcgKEFIEC9erCa0CAbn5qTYnUUpQC4Y3kODqfh+T2VVkcJGB4VABFZIyInRaRERB4Y4XkRkV+4nz8sIouHPPe4iDSIyNFh+ySIyJsictr9VVcgnyQDDievH67lE7NSiA4LtjqOUgDkJUVyRUESz+2pYNDhtDpOQLhoARARO/AQsBaYDdwuIrOHNVsLFLhvG4GHhzz3G2DNCN/6AeAtY0wB8Jb7sZoEH5Q00dLVz/qFOvpHeZc7V+RQ19HLWycarI4SEDw5AlgGlBhjSo0x/cDzwPphbdYDTxmXXUCciKQBGGPeB1pG+L7rgSfd958EPn0pL0CN3qsHqokJC+KqGclWR1HqY66bOYW02DA9GTxJPCkAGcDQTrkq97bRthkuxRhTC+D+OmWkRiKyUUSKRKSosbHRg7jqQrr6BnnjWD3Xz08jNMhudRylPibIbuO2pdlsP91EeVOX1XH8nicFYKT5gYdfreFJm0tijHnEGFNojClMTtZPrGP1p6N1dPc7uHlRptVRlBrRbcuysNuEZ/fo/EATzZMCUAVkDXmcCdRcQpvh6j/qJnJ/1U6/SfDS/iqyEyJYmqvn3JV3SokJ41NzUnixqJLeAYfVcfyaJwVgL1AgInkiEgLcBmwe1mYzcJd7NNAKoP2j7p0L2Azc7b5/N/DqKHKrS1Dd1sPO0mY+szhDF35RXm3DilzaugfYfOhinyPVWFy0ABhjBoH7gW3AceBFY0yxiGwSkU3uZluBUqAEeBS496P9ReQ5YCcwQ0SqROTL7qd+DKwWkdPAavdjNYFeOVCNMfAZ7f5RXm5FfgLTU6L47c6zuljMBArypJExZiuuN/mh23415L4B7jvPvrefZ3szcJ3HSdWYGGN4aV8Vy3ITyE7UmT+VdxMRNqzI4Z9eLeZgZRuLsrXLciLolcAB4mBlG6VNXXx2iU79oHzDzYsziQoN4rc7dUjoRNECECBe2l9FWLCNdfN05k/lG6JCg/jM4gxeP1xL87k+q+P4JS0AAaB3wMFrh2r51JxUnfpB+ZQNK3Lodzh5fq/ODzQRtAAEgDeO1dPeM8DnlujJX+VbClKiuWxqIs/sOqvzA00ALQAB4IW9FWTGh7NqapLVUZQatS9elktNey9vHKu3Oorf0QLg5yqau9lR0sznC7Ow2XTsv/I9181KISshnCd2lFkdxe9oAfBzLxZVYhP4XKF2/yjfZLcJd6/MZW95K0er262O41e0APixQYeT3+2r5KrpyaTFhlsdR6lLdkthFhEhdp7YUW51FL+iBcCPvX+6kfqOPj6/NNvqKEqNSWx4MJ9dnMlrh2po0iGh40YLgB97fk8lSVEhXDdrxJm2lfIpX1yVS7/DybO7dZbQ8aIFwE81uFdV+uziTILt+s+sfN/U5Ciump7MUzvP0jeos4SOB31n8FPP7qnA4TTcvky7f5T/+MoVeTSd6+PVgzpL6HjQAuCHBtyHyVdNTyY3KdLqOEqNm8unJTEzNZpfby/VWULHgRYAP/RGcT0NnX3ctTLH6ihKjSsRYeOV+ZyqP8d7p3SJ2LHSAuCHntpZTmZ8OFfP0JO/yv/cMD+dlJhQHt1eanUUn6cFwM+crOtkd1kLd67Iwa5X/io/FBJk40ur8thR0kxxjV4YNhZaAPzM07vOEhJk49bCrIs3VspH3b4sm8gQO4++r0cBY6EFwI909g7w8v4qbpyfTkJkiNVxlJowseHB3LYsm9cO11LZ0m11HJ+lBcCPvLC3kq5+B3dfpid/lf+754p8bAL//f4Zq6P4LC0AfmLA4eSJHeUsz0tgfmac1XGUmnCpsWF8bkkmLxZV0dDRa3Ucn6QFwE9sPVJLdVsP91yRb3UUpSbNpqumMuhw8usPdKroS6EFwA8YY3h0eyn5yZFcO1OHfqrAkZMYyY0L0nl611lau/qtjuNztAD4gZ2lzRyt7nD1ierQTxVg7r16Gt39Dn7zYbnVUXyOFgA/8OvtZSRFhXDzogyroyg16WakRvPJ2Sk8saOM9p4Bq+P4FC0APu5UfSdvn2hgw4pcwoLtVsdRyhJfu66Ajt5BHtNzAaOiBcDH/fLtEiJC7GzQeX9UAJubEcvauak8/kGZngsYBS0APqyk4RyvHa7hrpW5euGXCnjfXD2drv5B/luvDvaYFgAf9su3TxMWZOeeK/KsjqKU5aanRHPTgnSe/LCcxk5dNtITHhUAEVkjIidFpEREHhjheRGRX7ifPywiiy+2r4g8KCLVInLQfVs3Pi8pMJQ2nmPzoRo2rMwhMSrU6jhKeYWvX1dAv8PJw+/q1cGeuGgBEBE78BCwFpgN3C4is4c1WwsUuG8bgYc93PfnxpiF7tvWsb6YQPLLd0oICbLphV9KDZGfHMVnFmXw9O6zVLXqHEEX48kRwDKgxBhTaozpB54H1g9rsx54yrjsAuJEJM3DfdUolTd18erBGr6wPIfkaP30r9RQ31w9HQH+ddtJq6N4PU8KQAZQOeRxlXubJ20utu/97i6jx0UkfqQfLiIbRaRIRIoaG3UFIIB/feMkIXYbf3+VfvpXarj0uHC+ckUerx6s4WBlm9VxvJonBWCkS0uHL8Z5vjYX2vdhYCqwEKgFfjrSDzfGPGKMKTTGFCYnJ3sQ178dqGhly+Fa7rkynynRYVbHUcorffXqaSRFhfDPW47p2sEX4EkBqAKGri6SCdR42Oa8+xpj6o0xDmOME3gUV3eRugBjDD/64wmSokLYeKV++lfqfKJCg/jW6hnsLW9lW3Gd1XG8licFYC9QICJ5IhIC3AZsHtZmM3CXezTQCqDdGFN7oX3d5wg+cjNwdIyvxe+9dbyBPWUtfP0T04kKDbI6jlJe7dbCTKanRPGjP56gb9BhdRyvdNECYIwZBO4HtgHHgReNMcUisklENrmbbQVKgRJcn+bvvdC+7n1+IiJHROQwcA3wzfF7Wf5n0OHkR388Tn5yJLct1eUelbqYILuN714/m7PN3Tzynl4cNhKPPka6h2huHbbtV0PuG+A+T/d1b98wqqQB7rk9FZxp7OK/Nywh2K7X7ynliSunJ3P9vDR++U4JNy1MJycx0upIXkXfSXxAQ2cvP9l2klXTEvnk7BSr4yjlU/7phtkE221879ViPSE8jBYAH/AvW47TN+Dkh+vnIqLz/Ss1GqmxYbESRlEAAAweSURBVHxr9XTeO9XI1iN6QngoLQBebkdJE68crGHT1VPJT46yOo5SPumulTnMSY/hB68X09GrawZ8RAuAF+sbdPBPrxwlJzGCe6+eanUcpXxWkN3Gv9w8j8bOPn7w2jGr43gNLQBe7KG3Syht6uIH6+fqYi9KjdGCrDjuvXoav99XpdcGuGkB8FL7K1p56N0zfGZxBldN1yuglRoPX7uugNlpMfzjy0doOqdTRmsB8EJdfYN864WDpMaE8eBNc6yOo5TfCAmy8fPPL6Szd5D//YcjAT8qSAuAF/rnrcc529LNz25dQExYsNVxlPIrM1Kj+fYnp7OtuJ5ndldYHcdSWgC8zJ+P1fPs7go2XpnP8vxEq+Mo5Ze+ckU+V05P5gevHeNQAM8YqgXAi5Q1dfHNFw8yJz2Gb62ebnUcpfyW3Sb8x+cXkhwdyr3P7KclQBeS1wLgJc71DbLxqSKCbMKv7lxCaJCO+lFqIsVHhvDwnYtp7Ozj688fwOEMvPMBWgC8gNNp+PaLBylt6uKhOxaTlRBhdSSlAsL8zDi+v34O20838X8DcO0AnVPYC/z7W6fZVlzPd6+fxWXTkqyOo1RAuW1pFqfrz/H4jjLSY8O5J4DW2tACYLEnPyznF2+d5pYlmXz58jyr4ygVcESE714/i/rOXv5563GmxISyfuHwVW/9kxYAC71yoJr/s7mY1bNT+NFn5ulEb0pZxGYTfnrLApo6+/gfvztEXERIQFyAqecALPLmsXq+/btDrMxP5D9vX0SQzvGvlKXCgu08clchBVOiuefJIv58rN7qSBNO33Us8IcDVXz16X3MTY/h0bsLdZ4fpbxEbHgwz92zgllp0Wx6eh9bj9RaHWlCaQGYZI99UMY3XzjE0twEnv7Kcl3bVykvExsRzNNfWc7CrDjuf3Y/z+/x36uFtQBMkkGHk3/Zepwfvn6MtXNTeeJLS4nWaR6U8krRYcE8+XfLWDUtiQdePsKDm4sZdDitjjXutABMgsbOPjY8todH3i9lw4ocfnnHYu32UcrLRYYG8cQXl/J3q/L4zYflfPGJvbR1+9cVw1oAJtieshau/8V29le08q+fm88PPz0Xu01H+yjlC4LsNr5342x+8tn57C5rZs2/b+eD001Wxxo3WgAmyLm+QR7cXMznH9lJRIidP9y7ilsKs6yOpZS6BLcuzeKlr15GZKidOx/bzYObi+npd1gda8z0DOQ4M8bw1vEGvvfqUWo7erlrRQ7/c81MPdmrlI+bnxnHlq9dwY//eILffFjOm8fq+cd1s1g3L9Vnr+HRd6VxtLe8hZ/86QR7y1spmBLF7zddxpKceKtjKaXGSViwnQdvmsOn5qTy/deKue/Z/SzLTeA762ayKNv3/ta1AIyR02nYXtLEYx+U8f6pRqZEh/LDT8/l84VZhARpD5tS/mjl1ES2fO0KXthbyU/fOMnN//Uhl01NZNNVU7miIMlnjgi0AFyixs4+Xj9cw293nqW0qYukqFAeWDuTu1fmEh6iI3yU8nd2m3DH8mxuWpjOc7sr+PUHpdz1+B5mpERzS2Em6xdmkBwdanXMCxJfmv60sLDQFBUVWfbza9p6ePdkI1uO1LDzTDNOAwuz4vjiZbmsm5fm15/4nw3wpfPU2N2xPNvqCBOqb9DBqwdqeHZPBQcr2wiyCZcXJPGJWSlcN2sKabHhlmUTkX3GmMLh2/UI4DyMMVS0dHOwso39Z1vZXtJEaWMXALmJEdx3zTRumJ/OjNRoi5MqpbxBaJCdW5dmcevSLE7Xd/L7fVVsPVrLuycb+e4rMDM1muV5CRTmJrAkJ5602DDLu4o8OgIQkTXAfwB24NfGmB8Pe17cz68DuoEvGmP2X2hfEUkAXgBygXLgVmNM64VyTMQRQHf/INWtPVS19nC2uYvTDec43XCOU/WdtHUPABAWbGN5XiJXFCRxeUESM1KiLf+Hm2x6BKDGyt+PAEZijKGk4Rx/Pt7AjpIm9le00u0ePhofEczM1BhmpkWTlxRJTmIkOQkRpMaGjfuFopd8BCAiduAhYDVQBewVkc3GmGNDmq0FCty35cDDwPKL7PsA8JYx5sci8oD78T+M5UWez9sn6tlb3kpb9wDtPf20dPXT0NlHY2cfnb2DH2sbExZEQUo0a+akMj8zjgVZscxIidbZOpVSoyYiFKREU5ASzVevnsqAw8mxmg4OVrZxvLaD47UdPLengt6Bj08zER8RTEpMGIlRIcRHhJAQGcKdK3KYnjK+PQ6edAEtA0qMMaXuF/Q8sB4YWgDWA08Z1+HELhGJE5E0XJ/uz7fveuBq9/5PAu8yQQXg/VNNPL3rLHERIcRFBLsrbzRXFiSTHB1KZnw4mfERZCWEkxwVGnCf7pVSkyPYbmNBVhwLsuL+ss0YQ0NnH2ebuznb3EV9Ry91Hb3UtffR2t1PcU0HLV39rJmbakkByAAqhzyuwvUp/2JtMi6yb4oxphbAGFMrIlNG+uEishHY6H54TkROepB5uCTAG6/f9tZc4L3ZNNfoeUW2L/ztJq/INQKvzLXqQeDSs+WMtNGTAjDSx+HhJw7O18aTfS/IGPMI8Mho9hlORIpG6v+ymrfmAu/NprlGz1uzaa7RG+9snnRsVwFDJ7HJBGo8bHOhfevd3US4vzZ4HlsppdRYeVIA9gIFIpInIiHAbcDmYW02A3eJywqg3d29c6F9NwN3u+/fDbw6xteilFJqFC7aBWSMGRSR+4FtuIZyPm6MKRaRTe7nfwVsxTUEtATXMNAvXWhf97f+MfCiiHwZqABuGddX9nFj6kKaQN6aC7w3m+YaPW/NprlGb1yz+dSVwEoppcaPDm5XSqkApQVAKaUClN8XABFZIyInRaTEfcWxVTkeF5EGETk6ZFuCiLwpIqfdXyd9QnERyRKRd0TkuIgUi8jXvSGbiISJyB4ROeTO9X1vyDUkn11EDojI616Wq1xEjojIQREp8pZs7otDfy8iJ9z/11Z6Sa4Z7t/VR7cOEfmGl2T7pvv//lERec79NzGuufy6AAyZimItMBu4XURmWxTnN8CaYds+mg6jAHjL/XiyDQLfNsbMAlYA97l/R1Zn6wOuNcYsABYCa9wjzKzO9ZGvA8eHPPaWXADXGGMWDhkv7g3Z/gP4kzFmJrAA1+/O8lzGmJPu39VCYAmuQSx/sDqbiGQAXwMKjTFzcQ2iuW3ccxlj/PYGrAS2DXn8HeA7FubJBY4OeXwSSHPfTwNOesHv7FVcczd5TTYgAtiP6ypyy3Phup7lLeBa4HVv+rfENbFi0rBtlmYDYoAy3INOvCXXCDk/Cezwhmz8dRaFBFyjNV935xvXXH59BMD5p6jwFh+bDgMYcTqMySIiucAiYDdekM3dzXIQ10WCbxpjvCIX8O/A/wKGzuDlDbnAdaX9GyKyzz2NijdkywcagSfc3Wa/FpFIL8g13G3Ac+77lmYzxlQD/4ZriHwtrmur3hjvXP5eAMY8FUWgEJEo4CXgG8aYDqvzABhjHMZ1aJ4JLBORuVZnEpEbgAZjzD6rs5zHKmPMYlzdnveJyJVWB8L1CXYx8LAxZhHQhbVdZH/DfaHqTcDvrM4C4O7bXw/kAelApIjcOd4/x98LgCfTWFjJK6bDEJFgXG/+zxhjXvambADGmDZcs8Wu8YJcq4CbRKQceB64VkSe9oJcABhjatxfG3D1ZS/zgmxVQJX7CA7g97gKgtW5hloL7DfG1LsfW53tE0CZMabRGDMAvAxcNt65/L0AeDKNhZUsnw5DRAR4DDhujPmZt2QTkWQRiXPfD8f1B3HC6lzGmO8YYzKNMbm4/j+9bYy50+pcACISKSLRH93H1Wd81Opsxpg6oFJEZrg3XYdrSnjLf2dD3M5fu3/A+mwVwAoRiXD/jV6H68T5+Oay8qTLJJ1MWQecAs4A/9vCHM/h6ssbwPWJ6MtAIq6TiafdXxMsyHU5rm6xw8BB922d1dmA+cABd66jwPfc2y3/nQ3JeDV/PQlseS5cfe2H3Lfij/6/e0m2hUCR+9/zFSDeG3K5s0UAzUDskG2WZwO+j+tDz1Hgt0DoeOfSqSCUUipA+XsXkFJKqfPQAqCUUgFKC4BSSgUoLQBKKRWgtAAopVSA0gKglFIBSguAUkoFqP8Px52IVgKrJnQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(balls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets check wrt all team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178462</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>178463</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>Mohammad Nabi</td>\n",
       "      <td>caught</td>\n",
       "      <td>AR Patel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178464</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>K Paul</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178465</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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>Rashid Khan</td>\n",
       "      <td>caught</td>\n",
       "      <td>RR Pant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178466</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>Basil Thampi</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12908 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team                 bowling_team  \\\n",
       "0              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "1              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "2              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "3              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "4              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "...          ...     ...                  ...                          ...   \n",
       "178462     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178463     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178464     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178465     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178466     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "\n",
       "        over  ball        batsman  non_striker    bowler  is_super_over  ...  \\\n",
       "0          1     1      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "1          1     2      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "2          1     3      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "3          1     4      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "4          1     5      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "...      ...   ...            ...          ...       ...            ...  ...   \n",
       "178462    20     5  Mohammad Nabi     DJ Hooda    K Paul              0  ...   \n",
       "178463    20     6  Mohammad Nabi     DJ Hooda    K Paul              0  ...   \n",
       "178464    20     7       DJ Hooda  Rashid Khan    K Paul              0  ...   \n",
       "178465    20     8    Rashid Khan      B Kumar    K Paul              0  ...   \n",
       "178466    20     9   Basil Thampi      B Kumar    K Paul              0  ...   \n",
       "\n",
       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "0              0            0            0             0             0   \n",
       "1              0            0            0             0             0   \n",
       "2              0            0            0             0             4   \n",
       "3              0            0            0             0             0   \n",
       "4              0            0            0             0             0   \n",
       "...          ...          ...          ...           ...           ...   \n",
       "178462         0            0            0             0             0   \n",
       "178463         0            0            0             0             0   \n",
       "178464         0            0            0             0             1   \n",
       "178465         0            0            0             0             0   \n",
       "178466         0            0            0             0             1   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind   fielder  \n",
       "0                0           0               NaN            NaN       NaN  \n",
       "1                0           0               NaN            NaN       NaN  \n",
       "2                0           4               NaN            NaN       NaN  \n",
       "3                0           0               NaN            NaN       NaN  \n",
       "4                2           2               NaN            NaN       NaN  \n",
       "...            ...         ...               ...            ...       ...  \n",
       "178462           0           0               NaN            NaN       NaN  \n",
       "178463           0           0     Mohammad Nabi         caught  AR Patel  \n",
       "178464           0           1               NaN            NaN       NaN  \n",
       "178465           0           0       Rashid Khan         caught   RR Pant  \n",
       "178466           0           1               NaN            NaN       NaN  \n",
       "\n",
       "[12908 rows x 21 columns]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srh_bat_allball = df[( ((df['batting_team'] == 'Sunrisers Hyderabad')))]\n",
    "srh_bat_allball"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    1,     6,    10,    14,    19,    21,    25,    32,    36,\n",
       "          39,    43,    47,    52,    57,   384,   388,   394,   397,\n",
       "         402,   405,   414,   416,   423,   428,   432,   435,   440,\n",
       "         444,   449,   453,   455,   461,   466,   469,   474,   477,\n",
       "         481,   487,   489,   493,   496,   500,   503,   507,   511,\n",
       "         521,   525,   529,   531,   537,   541,   544,   551,   555,\n",
       "         558,   561,   564,   568,   572,   580,   584,   588,   591,\n",
       "         594,   598,   603,   610,   613,   616,   618,   622,   628,\n",
       "         631,   634,   635,   636,  7897,  7900,  7903,  7909,  7913,\n",
       "        7916,  7918,  7921,  7929,  7932,  7935,  7939,  7944,  7947,\n",
       "        7950,  7952,  7953, 11138, 11144, 11147, 11152, 11310, 11313,\n",
       "       11321, 11324, 11329, 11332, 11336, 11339, 11342, 11345, 11413],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches = srh_bat_allball['match_id'].unique()\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "balls = []\n",
    "mat = []\n",
    "t =[]\n",
    "for i in matches:\n",
    "    c = 0\n",
    "    runs =0\n",
    "    m = srh_bat_allball[srh_bat_allball['match_id'] == i]\n",
    "    m = m.reset_index(drop = True)\n",
    "    for j in range(len(m)):\n",
    "        runs = runs + m.loc[j , 'total_runs']\n",
    "        \n",
    "        if runs <= 50:\n",
    "            c = c+1\n",
    "    balls.append(c)\n",
    "    mat.append(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[35,\n",
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       " 32,\n",
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       " 29,\n",
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       " 27,\n",
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       " 34,\n",
       " 21,\n",
       " 28,\n",
       " 29,\n",
       " 34]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 11310,\n",
       " 11313,\n",
       " 11321,\n",
       " 11324,\n",
       " 11329,\n",
       " 11332,\n",
       " 11336,\n",
       " 11339,\n",
       " 11342,\n",
       " 11345,\n",
       " 11413]"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(41.074074074074076, 41.0)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# wrt all team\n",
    "np.array(balls).mean() , np.median(np.array(balls))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07c3717c0>"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ikuxRCcRGhbsdJaBER3i4LjORtw7b1c0m8HRbFFS1FXgIeAPvQPGLqnpQRNaIyBqn2UYgH8jD+6n/m87+hcA9wOIupp7+SET2i8g+4EbgO332rsxVq2tsYV+xjSf01k1TUyitbeRASV33jY3xIz59BHSmi27stO/JDs8VeLCL4z6i6/EGVPWeHiU1A2rHySraFbs+oZcWTx5BmMCbh84wPX2I23GM8Zld0Wy6tPlEJVHhYcwcneB2lIA0LDaSORnD2GTjCibAWFEwXdqaX8ms0UOJjvC4HSVgLctK5siZc5yqrHc7ijE+s6JgPqWusYVDpXXMG2ezjq7GTVkpAGw6dMblJMb4zoqC+ZSdBdWowtyxVhSuxujhg5icMti6kExAsaJgPmXbySoiPMLMUXbrzat1U1YyuQVVVF1odjuKMT6xomA+ZfvJSq5JTyAm0sYTrtayrBTaFVsLyQQMKwrmExqa29hfUmtXMfeRaWnxpA6J5o2DVhRMYLCiYD5hd1E1LW3KPBtP6BMiwmenpvDB8QrON7W6HceYbllRMJ+w/WQVIjA7w8YT+srK6ak0t7bzzhG7Tafxf1YUzCdsP1lFVmo88dF2/4S+MnvMUJIGR7FxX6nbUYzplhUFc0lzazu7TlXbVNQ+5gkTVkxL4d2j5VywLiTj56womEv2l9TS2NJu4wn9YOX0VJpa23n3qHUhGf9mRcFcsv2k96YwOTbzqM/NyRhGYlwUr+23q5uNf7OiYC7ZfrKS8UmxJMZFuR0l6HjChOXTknnnSDkNzXabTuO/rCgYANrbldxCG0/oTyunpdLQ0sZ71oVk/JgVBQPAsfJznGtsJWeMFYX+MnfsMIbHRvKn/TYLyfgvKwoGgB0F1QB2JXM/CveEsXxaCm8ftllIxn9ZUTAA7CyoImlwFKOGxbgdJaityk6joaWNN23lVOOnrCgYwHumMCdjKCJd3j3V9JGcMUNJS4jhlT0lbkcxpktWFAyltQ2U1DQw28YT+l1YmHBL9kg+PH6Ws+eb3I5jzKdYUTDkXhpPsPWOBsKt2Wm0tSsb9p52O4oxn+JTURCR5SJyVETyROSRLl4XEXnMeX2fiMxy9o8SkXdF5LCIHBSRhzscM0xE3hSR485X+43kkp2F1QyK9JCVGu92lJAwKWUwU1LjeWWPFQXjf7otCiLiAR4HVgBZwN0iktWp2Qog03msBp5w9rcC31XVKcB84MEOxz4CvK2qmcDbzrZxwY6CKrJHJRDusRPHgXJr9kj2FNVQcPaC21GM+QRffgvMBfJUNV9Vm4EXgFWd2qwCnlOvrUCCiKSqaqmq7gJQ1XPAYSCtwzHPOs+fBW69yvdieuF8UyuHS+tsaYsBdkv2SESwAWfjd3wpCmlAUYftYv78i93nNiKSAcwEtjm7klW1FMD5OsLX0Kbv7D5VTbvaeMJASx0Sw7yxw3h1z2lU1e04xlziS1Hoao5i53/FV2wjInHAS8C3VbXO93ggIqtFJFdEcisqKnpyqPHBjoJqwgRmjraiMNBun5XOybMXyC2sdjuKMZf4UhSKgVEdttOBziNkl20jIhF4C8KvVfXlDm3KRCTVaZMKdLkgjKo+pao5qpqTlJTkQ1zTEzsLq5iSGk9cVLjbUULO565JJS4qnBe2F3Xf2JgB4ktR2AFkishYEYkE7gLWd2qzHrjXmYU0H6hV1VLxXgn1NHBYVX/SxTH3Oc/vA17t9bswvdLa1s7uUzXkjLGzBDcMigzn8zNGsnF/KXWNLW7HMQbwoSioaivwEPAG3oHiF1X1oIisEZE1TrONQD6QB6wFvunsXwjcAywWkT3OY6Xz2qPAMhE5Dixzts0AOlx6jvrmNmbbILNr7pwzioaWNv5o1ywYP+FTn4GqbsT7i7/jvic7PFfgwS6O+4iuxxtQ1UpgSU/Cmr6VW+jcVMfOFFwzI30Ik5IH8+KOIv5i3hi34xhjVzSHsp2F1YwcEs3IBFsEzy0iwp1zRrG3uJbDpT2ag2FMv7CiEMJ2FlYzy84SXHfbzDQiPWH8docNOBv3WVEIUSU1DZTWNlrXkR8YGhvJTVOTeWVPCY0tdqtO4y4rCiEqt8AZT7BBZr/w5XmjqalvsQFn4zorCiHq4iJ4k1MGux3FAAvGDSdzRBzPbSm0K5yNq6wohKidhdXMHG2L4PkLEeHeBWPYX1LLnqIat+OYEGa/EULQxUXw7KY6/uW2WenERYXz3JZCt6OYEGZFIQTtOVVDu8JsG2T2K3FR4dw+K40/7Su1u7IZ11hRCEG5hVWIwMzRCW5HMZ3csyCD5rZ2m55qXGNFIQTtLKxmUvJg4qMj3I5iOpkwIo6FE4bzq62FtLa1ux3HhCArCiGmrV29i+DZ/RP81n0LMiitbeT1g2fcjmJCkBWFEHPkTB3nm1rJsUFmv7VkSjIZwwex9sOTNj3VDDgrCiFmp3NDFztT8F+eMOGBz4xlb1HNpb8vYwaKFYUQs6OgmpT4aNJsETy/dvvsdIbERLD2w3y3o5gQY0UhxOwsqCInYyje+x8ZfzUoMpyvzB/NpkNlFFZecDuOCSFWFEJISU0Dp20RvIBx74IMwsOEdR+ddDuKCSFWFEKILYIXWJLjo7llRhov5hZTU9/sdhwTIqwohJDcgmpibRG8gPK1RWNpaGnjv23pCzNArCiEkFznpjq2CF7gmJwSz+LJI3hmcwENzXavBdP/7LdDiKhrbOHImTpb7ygArbl+PFUXmnkx15a+MP3PikKI2H2qBlXsorUANCdjKLPHDOWpD/JpsaUvTD+zohAicguq8IQJ2bYIXsAREb5x/XhKahr4075St+OYIOdTURCR5SJyVETyROSRLl4XEXnMeX2fiMzq8No6ESkXkQOdjvm+iJSIyB7nsfLq3465nNyCaqakDiYuKtztKKYXFk8ewcTkOJ5474QtfWH6VbdFQUQ8wOPACiALuFtEsjo1WwFkOo/VwBMdXvslsPwy3/6nqprtPDb2MLvxUUtbO3uKaqzrKICFhQlfXzSeo2XneOdIudtxTBDz5UxhLpCnqvmq2gy8AKzq1GYV8Jx6bQUSRCQVQFU/AKr6MrTpmQMltTS0tDF3rBWFQHZL9kjSh8bwn+/m2dmC6Te+FIU0oOO0h2JnX0/bdOUhp7tpnYjYtJh+sv2ktybPsYvWAlqEJ4yvXz+e3adq2JJf6XYcE6R8KQpdLZLT+WOKL206ewIYD2QDpcCPu/zhIqtFJFdEcisqKrrLarqwo6CKcYmxJA2OcjuKuUpfmp1O0uAoHn83z+0oJkj5UhSKgVEdttOB071o8wmqWqaqbaraDqzF203VVbunVDVHVXOSkpJ8iGs6am9XdhRU21lCkIiO8PC168bycV4lu0/Zstqm7/lSFHYAmSIyVkQigbuA9Z3arAfudWYhzQdqVfWKc+cujjk4bgMOXK6t6b1j5eeobWix8YQg8hfzxpAwKILH3z3hdhQThLotCqraCjwEvAEcBl5U1YMiskZE1jjNNgL5QB7eT/3fvHi8iDwPbAEmiUixiDzgvPQjEdkvIvuAG4Hv9NWbMn92cTzBikLwiI0K5/5rx/LW4TKOnKlzO44JMj5NWnemi27stO/JDs8VePAyx959mf33+B7T9Nb2k1WkDokmfajdVCeYfPXaDNZ+mM/j757g/9090+04JojYFc1BTFXZfrKKuWOH2U11gsyQQRHcs2AMG/ad5kTFebfjmCBiRSGInaqqp/xckw0yB6kHPjOWqPAwfm5jC6YPWVEIYtuc8YR5Np4QlBLjoviLeWN4ZU8JRVX1bscxQcKKQhDbcbKKoYMimDAizu0opp+sXjQOjwg/f8/OFkzfsKIQxLYXVDEnw8YTgllyfDR3zEnn9zuLKK1tcDuOCQJWFIJUWV0jhZX1NhU1BKy5fjyq8F/v57sdxQQBKwpBassJ79o488cNdzmJ6W/pQwfxhVlpPL/9FOXnGt2OYwKcFYUgteVEJUNiIshKjXc7ihkA37xhAi1t7az9wM4WzNWxohCkNuefZd7YYYSF2XhCKMhIjGVVdhq/2nqKyvNNbscxAcyKQhAqrq6nqKqBBeOt6yiUPHjjBBpb2/jFRyfdjmICmBWFIHRxPOHa8YkuJzEDacKIOD43PZXnNhdQU9/sdhwToKwoBKEt+ZUMj41kYrJdnxBq/sfiTC40t7HOzhZML1lRCDKqypYTlcwfN9yuTwhBk1IGs3xqCs98XEBtfYvbcUwAsqIQZAor6ymtbWS+jSeErG8tyeRcUyvrPrazBdNzVhSCzMV7915rRSFkZY2M56asZNZ9fJK6RjtbMD1jRSHIbDlRyYjBUYxLjHU7inHRt5Zkcq6xlV9+XOB2FBNgrCgEEVVl84lKFoy38YRQNy1tCEunjODpj05yzs4WTA9YUQgieeXnOXu+iQW2tIUBHl4ykdqGFp7dXOB2FBNArCgEkQ+OnwXgM5l2fYKB6enes4W1H9rYgvGdFYUg8sGxCsYlxZI+dJDbUYyf+PZS79nCMx8VuB3FBAgrCkGisaWNbScrWZSZ5HYU40empQ3hpqxkfvFRvl23YHxiRSFI5BZU09jSzqKJ1nVkPunbSydyrrGVpz+yFVRN93wqCiKyXESOikieiDzSxesiIo85r+8TkVkdXlsnIuUicqDTMcNE5E0ROe58HXr1byd0fXC8ggiP2P0TzKdkjYxn5fQU1n1sayKZ7nVbFETEAzwOrACygLtFJKtTsxVApvNYDTzR4bVfAsu7+NaPAG+raibwtrNteumDYxXkjBnGoMhwt6MYP/TwkolcaG7lKbvfgumGL2cKc4E8Vc1X1WbgBWBVpzargOfUayuQICKpAKr6AVDVxfddBTzrPH8WuLU3b8BAeV0jR86cY9FEG08wXZuUMpjPXzOSZz4usLuzmSvypSikAUUdtoudfT1t01myqpYCOF9HdNVIRFaLSK6I5FZUVPgQN/RcnIpq4wnmSr6zbCLNbe08/k6e21GMH/OlKHR1aaz2ok2vqOpTqpqjqjlJSfZJuCsfHq8gMS6SKSl2601zeWMTY7kjZxS/2X6Koqp6t+MYP+VLUSgGRnXYTgdO96JNZ2UXu5icr+U+ZDGdtLcrHx4/y3WZSXbrTdOth5dkIiL89K1jbkcxfsqXorADyBSRsSISCdwFrO/UZj1wrzMLaT5Qe7Fr6ArWA/c5z+8DXu1BbuM4eLqOqgvNXGdXMRsfpAyJ5r4FY/jD7hKOlZ1zO47xQ90WBVVtBR4C3gAOAy+q6kERWSMia5xmG4F8IA9YC3zz4vEi8jywBZgkIsUi8oDz0qPAMhE5Dixztk0PvXW4DBG43gaZjY++ccMEYiPD+bc3jrodxfghn+YvqupGvL/4O+57ssNzBR68zLF3X2Z/JbDE56SmS28dLmP26KEMj4tyO4oJEMNiI1lz/Tj+fdMxtuVXMs+ubTEd2BXNAaykpoGDp+tYlpXsdhQTYB74zDhS4qP5l42HaW/vkzkhJkhYUQhgbx8uA2CpFQXTQzGRHv7ms5PYW1zLhv3dDf+ZUGJFIYC9eaiMcYmxjE+KczuKCUC3zUwjKzWeH752hMaWNrfjGD9hRSFAnWtsYWt+pXUdmV7zhAl//7kplNQ02I14zCVWFALU+8cqaGlT6zoyV2XhhEQWTx7B/3snz5a/MIAVhYD11qEyhg6KYNZoW1zWXJ1/uDmL5tZ2Hn3tiNtRjB+wohCAWtraeedIOYsnJ+Oxq5jNVRqbGMtfXTeWl3eVkFvQ1dqVJpRYUQhAOwqqqGtstfEE02ceWjyB1CHRfO/Vg7TZFNWQZkUhAG3YV0pMhMdWRTV9ZlBkOP/nc1kcKq3jN9sK3Y5jXGRFIcC0tLXz2v5SlmUl2w11TJ9aOT2FhROG86M3jlJWZ4POocqKQoD5KO8s1fUtfH7GSLejmCAjIvzzrdNpbm3nH145gHf1GhNqrCgEmD/uPU18dLh1HZl+MTYxlr9eNpFNh8p47cAZt+MYF1hRCCCNLW1sOljG8mkpRIV73I5jgtQDnxnL9LQhfO/VA9TUN7sdxwwwKwoB5L2j5ZxvarWuI9Ovwj1h/PD2a6ipb+EfNxxyO44ZYFYUAsj6vadJjItkgS11bPpZ1sh4vnYm06QAAA/QSURBVHHDeF7eVcJGWzAvpFhRCBDnm1p5+3A5K6enEu6xvzbT/761JJMZ6UN45KV9nK5pcDuOGSD22yVAbDp4hqbWdus6MgMmwhPGz+6aSWu78p3f7rGL2kKEFYUA8cL2IjKGD2K2rXVkBlBGYiw/uGUq205W8eT7J9yOYwaAFYUAkFd+ju0FVdw1dzRhttaRGWBfnJ3Ozdek8pM3j7HlRKXbcUw/s6IQAJ7fXkSER/ji7HS3o5gQJCL86xemkzF8EA/+ZhfF1fVuRzL9yIqCn2tsaeOlXcXcNDWFxLgot+OYEDU4OoKn7s2hpbWdr//3Thqa7U5twcqnoiAiy0XkqIjkicgjXbwuIvKY8/o+EZnV3bEi8n0RKRGRPc5jZd+8peDy+oEz1NS38OW5o92OYkLc+KQ4fnZ3NodK63jk5X22DEaQ6rYoiIgHeBxYAWQBd4tIVqdmK4BM57EaeMLHY3+qqtnOY+PVvplg9JvtpxgzfJBdm2D8wuLJyXx32URe3XOaf3vjqNtxTD/w5UxhLpCnqvmq2gy8AKzq1GYV8Jx6bQUSRCTVx2PNZeSVn2f7ySrummMDzMZ/PHjjBO6eO5qfv3eCpz866XYc08d8KQppQFGH7WJnny9tujv2Iae7aZ2I2FzLTp7bUmADzMbveFdTncbyqSn804ZDvLK7xO1Ipg/5UhS6+ojauTPxcm2udOwTwHggGygFftzlDxdZLSK5IpJbUVHhQ9zgcPZ8E7/dUcRtM9NIGmwDzMa/eMKE/7grmwXjhvM3v9vLH/eedjuS6SO+FIViYFSH7XSg87+Ay7W57LGqWqaqbaraDqzF29X0Kar6lKrmqGpOUlKSD3GDw7ObC2hua2f1ovFuRzGmS9ERHtbel8OsMUP51gu7+e2OU25HMn3Al6KwA8gUkbEiEgncBazv1GY9cK8zC2k+UKuqpVc61hlzuOg24MBVvpegcb6plWc3F3BTVjITRsS5HceYy4qLCufZ++dyXWYS/+ul/TbGEAS6vZ+jqraKyEPAG4AHWKeqB0VkjfP6k8BGYCWQB9QD91/pWOdb/0hEsvF2JxUAX+/LNxbInt92irrGVtZcb2cJxv/FRHpYe+9sHn5+D/+04RDF1fX8/coptnBjgJJAmmuck5Ojubm5bsfoV02tbSz60buMS4zj+dXz3Y5jjM9a29r5l41HWPfxSa4dP5zHvzyLobGRbscygIjsVNUcX9paKfczr+wuoayuiW/cYGcJJrCEe8L43uez+LcvXkNuQTW3PP4Re4pq3I5lesiKgh9paG7jp28eZ8aoBK7LtHswm8D0pZxR/Pbr82lrU25/YjM/2XSU5tZ2t2MZH1lR8CNrP8znTF0j/+dzUxCxi9VM4Jo5eiivf2cRt2an8dg7edz284/ZX1zrdizjAysKfqK8rpEn3z/BimkpzMkY5nYcY65afHQEP75jBv91z2zK6pq45fGP+Nvf7aX8XKPb0cwVdDv7yAyMH286RktbO4+smOx2FGP61GenpnDt+OH85zt5rPv4JBv3l/LAZ8Zy/8KxNhDth+xMwQ8cLq3jxZ1F3LsggzHDY92OY0yfGxwdwd+tnMKm71zPoolJPPZOHgt/+A7/uvEwZXV25uBPbEqqy9ralTv/awvHy8/z/t/eQMIg++Rkgt/RM+f4+Xt5/HHvaUSEz05N5ivzx7Bg3HAbT+sHPZmSat1HLnv6o3xyC6v56Z0zrCCYkDEpZTA/u2sm3102iV9vK+S3uUVs3H+GMcMHcWt2Gl+YlWZnzS6xMwUXHSs7x82PfcSNk5N48iuz7ROSCVmNLW1s3F/KS7uK2XyiElXIHpXAzdeksmJ6KmkJMW5HDGg9OVOwouCSlrZ2vvDzzZTUNLDpO4vsVpvGOEprG3hl92k27DvNwdN1gLdALJ+WwoppKXYG0QtWFALATzYd5bF38njyK7NYPi21+wOMCUEFZy/wp/2lvH7gDPtLvNc5TEmNZ4VTIDKTB7ucMDBYUfBzf9pXyoO/2cXts9L58R0z3I5jTEAoqqrnjYNneP3AGXILqwGYMCKOz01P5fMzUpkwwgrE5VhR8GP7imu447+2kJUaz2++Np/oCI/bkYwJOGV1jbxx8Ax/2lfK9oIqVGFyymBunZnGLTNGMtLGID7BioKfOlPbyC3/+RERnjBeeXCh3VHNmD5QVtfIxv2lrN97mt2nahCBeWOH8cXZo1g5PYVBkTbJ0oqCHyo/18g9v9hOcXU9L33zWianxLsdyZigU3D2Aq/uOc3Lu4sprKwnNtLD565J5c45o5k1OiFkZ/hZUfAzJTUNfOUX2zhT28gv7sth4QRbAdWY/qSq5BZW8/vcYjbsO82F5jYyR8Rx55xR3D4rPeSW17Ci4EdOnr3AX6zdyrmmVn55/xxmj7HF7owZSBeaWtmw7zTPby9iT1ENkeFhrJyWwpfnjWFOxtCQOHuwouAn3jpUxt/8fi8eEZ57YC5TRw5xO5IxIe1waR3Pbz/FH3aVcK6plfFJsdw9dzRfmJXOsCA+e7Ci4LLm1nYefc17W8KpI+N5/MuzyEi0C26M8Rf1za1s2FfKC9tPsetUDZGeMG6amswdOaNYOCERT1hwnT1YUXDRtvxKfvDHQxwqreOr12bwdysnExVu006N8VdHztTxwvYiXtlTQk19C2kJMazKHsltM9OC5uI4KwouKKy8wL9uPMLrB8+QOiSa798ylc9OTXE7ljHGR02tbbx1qJwXc4v4KO8sbe1KVmo8N89IZcW0VMYG8Nm+FYUBoqpsya/kuc2FbDp0hugID9+4fjx/dd04YiLt7MCYQFVxrokN+07z6p7T7CmqAbwXxy2dksz1k5KYOSqBcE/g3I6mz4uCiCwHfgZ4gF+o6qOdXhfn9ZVAPfBVVd11pWNFZBjwWyADKADuUNXqK+Xwh6LQ3q7sK6nlrUNlvHaglBMVFxg6KII754zmLxdmMCI+2tV8xpi+VVLTwBsHzvD6wTPsLKymrV0ZHB3OvLHDmJMxjDljhzFt5BAiw/23SPRpURARD3AMWAYUAzuAu1X1UIc2K4H/gbcozAN+pqrzrnSsiPwIqFLVR0XkEWCoqv6vK2UZ6KKgqlRdaOZ4+Xn2FtWw+1QNuYXVnD3fhCdMmJMxlNtnpfP5GSNtuQpjQkBtQwub887y/rEKtp2s4uTZCwBEeISJyYOZOjKeSSnxjEuKZVxiLGkJMX5xRtHXN9mZC+Spar7zzV8AVgGHOrRZBTyn3gqzVUQSRCQV71nA5Y5dBdzgHP8s8B5wxaLQW02tbTS2tNPS5n00tbTT0NJGfXMb9c2t1DW0UtvQQnV9MxXnmiira+R0bSMFZy9Q29By6fuMGT6IhROGc/3EJG6cNCLkLoAxJtQNiYlgxXTvPR7Au1JBbkE1+4prOXi6lrcOl/NibvGl9p4wIXlwFKkJMaTERzMsNpJhsZEMHRTB4OgIBkeHExcVTkykh5hID9HhHiLCw4j0eB8ejxAeJnjCvF8H4poKX4pCGlDUYbsY79lAd23Sujk2WVVLAVS1VERG9CB3j/zjHw/x622nfGo7ODqc5PhokuOjuPmaVMYlxTEuKZZr0oYw3O55YIzpYMTgaFZOT2WlUyQu9i7kn71AfsV5iqoaOF3bQGlNI4fP1FF1oZma+pZuvmvXnrl/DjdO6rdfk5f4UhS6Kk2d+5wu18aXY6/8w0VWA6udzfMicrQnx/sgETjbcceBPv4BV+FT2fyIZesdy9Y7IZ9t8Q97ddjFbGN8PcCXolAMjOqwnQ6c9rFN5BWOLRORVOcsIRUo7+qHq+pTwFM+5OwVEcn1ta9toFm23rFsvWPZeifYsvkyArIDyBSRsSISCdwFrO/UZj1wr3jNB2qdrqErHbseuM95fh/wak+CG2OM6XvdnimoaquIPAS8gXda6TpVPSgia5zXnwQ24p15lId3Sur9VzrW+daPAi+KyAPAKeBLffrOjDHG9JhPd59Q1Y14f/F33Pdkh+cKPOjrsc7+SmBJT8L2k37rmuoDlq13LFvvWLbeCapsAXVFszHGmP7l/lUVxhhj/EZIFQURWSci5SJyoMO+YSLypogcd74OdSHXKBF5V0QOi8hBEXnYj7JFi8h2EdnrZPuBv2TrkNEjIrtFZIMfZisQkf0iskdEcv0pn3OR6e9F5Ijzb2+BP2QTkUnOn9fFR52IfNsfsjn5vuP8XzggIs87/0f8JdvDTq6DIvJtZ1+PsoVUUQB+CSzvtO8R4G1VzQTedrYHWivwXVWdAswHHhSRLD/J1gQsVtUZQDaw3Jlh5g/ZLnoYONxh25+yAdyoqtkdpgb6S76fAa+r6mRgBt4/Q9ezqepR588rG5iNd/LKH/whm4ikAd8CclR1Gt4JNHf5SbZpwNfwrkIxA7hZRDJ7nE1VQ+qBd+mNAx22jwKpzvNU4KgfZHwV73pRfpUNGATswntVul9kw3vty9vAYmCDv/2d4l3sMbHTPtfzAfHASZxxRX/K1inPTcDH/pKNP6/SMAzvRJ0NTkZ/yPYlvIuOXtz+B+B/9jRbqJ0pdOUTy20A/X8d+RWISAYwE9iGn2Rzumf24L3A8E1V9ZtswH/g/Yff3mGfv2QD7xX8m0Rkp3N1PvhHvnFABfCM0/X2CxGJ9ZNsHd0FPO88dz2bqpYA/453Gn0p3muyNvlDNryLMSwSkeEiMgjvZQKjeprNioIfEZE44CXg26pa53aei1S1Tb2n8unAXOc01XUicjNQrqo73c5yBQtVdRawAm+34CK3AznCgVnAE6o6E7iA+91sn+Bc8HoL8Du3s1zk9MevAsYCI4FYEfmKu6m8VPUw8EPgTeB1YC/erukesaLgLLcBIFdYbqO/iUgE3oLwa1V92Z+yXaSqNXhXs12Of2RbCNwiIgXAC8BiEfmVn2QDQFVPO1/L8faLz/WTfMVAsXPWB/B7vEXCH7JdtALYpaplzrY/ZFsKnFTVClVtAV4GrvWTbKjq06o6S1UXAVXA8Z5ms6LgB8ttiIgATwOHVfUnfpYtSUQSnOcxeP9THPGHbKr6d6qarqoZeLsZ3lHVr/hDNgARiRWRwRef4+17PuAP+VT1DFAkIpOcXUvwLmnverYO7ubPXUfgH9lOAfNFZJDz/3YJ3gF6f8iGOKtNi8ho4At4//x6lm2gB0PcfDh/QKVAC95PSg8Aw/EOVB53vg5zIddn8PY97wP2OI+VfpLtGmC3k+0A8D1nv+vZOuW8gT8PNPtFNrz99nudx0Hg7/0sXzaQ6/zdvgIM9aNsg4BKYEiHff6S7Qd4PxgdAP4biPKjbB/iLe57gSW9+XOzK5qNMcZcYt1HxhhjLrGiYIwx5hIrCsYYYy6xomCMMeYSKwrGGGMusaJgjDHmEisKxhhjLrGiYIwx5pL/D3NE/54kXz7CAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.kdeplot(balls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07be61fa0>"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(balls)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "# avg balls = 40 , mode  = 35 wrt all team"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Take Aways:\n",
    "### 1 - SRH has a good opening batting\n",
    "### 2 - Avg against KXP is 40 balls , and avg againt all team is 41\n",
    "### 3 - Median against kxp is 39 , and against all is 41"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 4 ans - (37-42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "#############################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 5 - Runs scored by Gayle in first 10 balls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "gayle = main_df_team[main_df_team['batting_team'] == 'Kings XI Punjab']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "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>4437</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>HM Amla</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>B Kumar</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>HM Amla</td>\n",
       "      <td>lbw</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4438</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>B Kumar</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>4439</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>B Kumar</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>4440</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4441</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>M Vohra</td>\n",
       "      <td>B Kumar</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176407</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>M Ur Rahman</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>S Sharma</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>176408</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>M Ur Rahman</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>S Sharma</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>176409</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>M Ur Rahman</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>S Sharma</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>M Ur Rahman</td>\n",
       "      <td>bowled</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176410</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>S Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176411</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>S Sharma</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1703 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning     batting_team         bowling_team  over  ball  \\\n",
       "4437          19       2  Kings XI Punjab  Sunrisers Hyderabad     1     1   \n",
       "4438          19       2  Kings XI Punjab  Sunrisers Hyderabad     1     2   \n",
       "4439          19       2  Kings XI Punjab  Sunrisers Hyderabad     1     3   \n",
       "4440          19       2  Kings XI Punjab  Sunrisers Hyderabad     1     4   \n",
       "4441          19       2  Kings XI Punjab  Sunrisers Hyderabad     1     5   \n",
       "...          ...     ...              ...                  ...   ...   ...   \n",
       "176407     11339       2  Kings XI Punjab  Sunrisers Hyderabad    20     2   \n",
       "176408     11339       2  Kings XI Punjab  Sunrisers Hyderabad    20     3   \n",
       "176409     11339       2  Kings XI Punjab  Sunrisers Hyderabad    20     4   \n",
       "176410     11339       2  Kings XI Punjab  Sunrisers Hyderabad    20     5   \n",
       "176411     11339       2  Kings XI Punjab  Sunrisers Hyderabad    20     6   \n",
       "\n",
       "               batsman     non_striker    bowler  is_super_over  ...  \\\n",
       "4437           HM Amla         M Vohra   B Kumar              0  ...   \n",
       "4438        GJ Maxwell         M Vohra   B Kumar              0  ...   \n",
       "4439        GJ Maxwell         M Vohra   B Kumar              0  ...   \n",
       "4440        GJ Maxwell         M Vohra   B Kumar              0  ...   \n",
       "4441        GJ Maxwell         M Vohra   B Kumar              0  ...   \n",
       "...                ...             ...       ...            ...  ...   \n",
       "176407     M Ur Rahman        M Ashwin  S Sharma              0  ...   \n",
       "176408     M Ur Rahman        M Ashwin  S Sharma              0  ...   \n",
       "176409     M Ur Rahman        M Ashwin  S Sharma              0  ...   \n",
       "176410  Mohammed Shami        M Ashwin  S Sharma              0  ...   \n",
       "176411        M Ashwin  Mohammed Shami  S Sharma              0  ...   \n",
       "\n",
       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "4437           0            0            0             0             0   \n",
       "4438           0            0            0             0             4   \n",
       "4439           0            0            0             0             0   \n",
       "4440           0            0            0             0             0   \n",
       "4441           0            0            0             0             0   \n",
       "...          ...          ...          ...           ...           ...   \n",
       "176407         0            0            0             0             0   \n",
       "176408         0            0            0             0             0   \n",
       "176409         0            0            0             0             0   \n",
       "176410         0            0            0             0             1   \n",
       "176411         0            0            0             0             1   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
       "4437             0           0           HM Amla            lbw     NaN  \n",
       "4438             0           4               NaN            NaN     NaN  \n",
       "4439             0           0               NaN            NaN     NaN  \n",
       "4440             0           0               NaN            NaN     NaN  \n",
       "4441             0           0               NaN            NaN     NaN  \n",
       "...            ...         ...               ...            ...     ...  \n",
       "176407           0           0               NaN            NaN     NaN  \n",
       "176408           0           0               NaN            NaN     NaN  \n",
       "176409           0           0       M Ur Rahman         bowled     NaN  \n",
       "176410           0           1               NaN            NaN     NaN  \n",
       "176411           0           1               NaN            NaN     NaN  \n",
       "\n",
       "[1703 rows x 21 columns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gayle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['HM Amla', 'GJ Maxwell', 'M Vohra', 'EJG Morgan', 'DA Miller',\n",
       "       'WP Saha', 'AR Patel', 'MM Sharma', 'KC Cariappa', 'I Sharma',\n",
       "       'Sandeep Sharma', 'MJ Guptill', 'SE Marsh', 'Anureet Singh',\n",
       "       'Mandeep Singh', 'AC Gilchrist', 'PC Valthaty', 'DJ Hussey',\n",
       "       'PP Chawla', 'Gurkeerat Singh', 'Azhar Mahmood', 'P Kumar',\n",
       "       'AD Mascarenhas', 'LA Pomersbach', 'R Sathish', 'Harmeet Singh',\n",
       "       'P Awana', 'CA Pujara', 'V Sehwag', 'GJ Bailey', 'MG Johnson',\n",
       "       'M Vijay', 'R Dhawan', 'NS Naik', 'KL Rahul', 'CH Gayle',\n",
       "       'MA Agarwal', 'KK Nair', 'AJ Finch', 'MK Tiwary', 'R Ashwin',\n",
       "       'AJ Tye', 'BB Sran', 'AS Rajpoot', 'M Ur Rahman', 'S Curran',\n",
       "       'N Pooran', 'S Singh', 'Mohammed Shami', 'M Ashwin'], dtype=object)"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gayle['batsman'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<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>153984</th>\n",
       "      <td>7909</td>\n",
       "      <td>1</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>6</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <th>153985</th>\n",
       "      <td>7909</td>\n",
       "      <td>1</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>CH Gayle</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>CJ Jordan</td>\n",
       "      <td>0</td>\n",
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       "      <th>153986</th>\n",
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       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>2</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>KL Rahul</td>\n",
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       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "      <td>4</td>\n",
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       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153987</th>\n",
       "      <td>7909</td>\n",
       "      <td>1</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>CJ Jordan</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>7909</td>\n",
       "      <td>1</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>CH Gayle</td>\n",
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       "    <tr>\n",
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       "      <td>11313</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
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       "      <td>6</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>KL Rahul</td>\n",
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       "    <tr>\n",
       "      <th>169995</th>\n",
       "      <td>11313</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>Rashid Khan</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>CH Gayle</td>\n",
       "      <td>caught</td>\n",
       "      <td>DJ Hooda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176291</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>K Ahmed</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>176292</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>K Ahmed</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>176299</th>\n",
       "      <td>11339</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>K Ahmed</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>CH Gayle</td>\n",
       "      <td>caught</td>\n",
       "      <td>MK Pandey</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning     batting_team         bowling_team  over  ball  \\\n",
       "153984      7909       1  Kings XI Punjab  Sunrisers Hyderabad     1     6   \n",
       "153985      7909       1  Kings XI Punjab  Sunrisers Hyderabad     2     1   \n",
       "153986      7909       1  Kings XI Punjab  Sunrisers Hyderabad     2     2   \n",
       "153987      7909       1  Kings XI Punjab  Sunrisers Hyderabad     2     3   \n",
       "153988      7909       1  Kings XI Punjab  Sunrisers Hyderabad     2     4   \n",
       "...          ...     ...              ...                  ...   ...   ...   \n",
       "169994     11313       2  Kings XI Punjab  Sunrisers Hyderabad     3     6   \n",
       "169995     11313       2  Kings XI Punjab  Sunrisers Hyderabad     4     1   \n",
       "176291     11339       2  Kings XI Punjab  Sunrisers Hyderabad     1     7   \n",
       "176292     11339       2  Kings XI Punjab  Sunrisers Hyderabad     1     8   \n",
       "176299     11339       2  Kings XI Punjab  Sunrisers Hyderabad     3     1   \n",
       "\n",
       "         batsman non_striker       bowler  is_super_over  ...  bye_runs  \\\n",
       "153984  CH Gayle    KL Rahul      B Kumar              0  ...         0   \n",
       "153985  CH Gayle    KL Rahul    CJ Jordan              0  ...         0   \n",
       "153986  CH Gayle    KL Rahul    CJ Jordan              0  ...         0   \n",
       "153987  CH Gayle    KL Rahul    CJ Jordan              0  ...         0   \n",
       "153988  CH Gayle    KL Rahul    CJ Jordan              0  ...         0   \n",
       "...          ...         ...          ...            ...  ...       ...   \n",
       "169994  CH Gayle    KL Rahul      B Kumar              0  ...         0   \n",
       "169995  CH Gayle    KL Rahul  Rashid Khan              0  ...         0   \n",
       "176291  CH Gayle    KL Rahul      K Ahmed              0  ...         0   \n",
       "176292  CH Gayle    KL Rahul      K Ahmed              0  ...         0   \n",
       "176299  CH Gayle    KL Rahul      K Ahmed              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "153984            0            0             0             1           0   \n",
       "153985            0            0             0             0           0   \n",
       "153986            4            0             0             4           4   \n",
       "153987            0            0             0             0           0   \n",
       "153988            0            0             0             0           0   \n",
       "...             ...          ...           ...           ...         ...   \n",
       "169994            0            0             0             1           0   \n",
       "169995            0            0             0             0           0   \n",
       "176291            0            0             0             4           0   \n",
       "176292            0            0             0             0           0   \n",
       "176299            0            0             0             0           0   \n",
       "\n",
       "        total_runs  player_dismissed dismissal_kind    fielder  \n",
       "153984           1               NaN            NaN        NaN  \n",
       "153985           0               NaN            NaN        NaN  \n",
       "153986           8               NaN            NaN        NaN  \n",
       "153987           0               NaN            NaN        NaN  \n",
       "153988           0               NaN            NaN        NaN  \n",
       "...            ...               ...            ...        ...  \n",
       "169994           1               NaN            NaN        NaN  \n",
       "169995           0          CH Gayle         caught   DJ Hooda  \n",
       "176291           4               NaN            NaN        NaN  \n",
       "176292           0               NaN            NaN        NaN  \n",
       "176299           0          CH Gayle         caught  MK Pandey  \n",
       "\n",
       "[104 rows x 21 columns]"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gayle = gayle[gayle['batsman'] =='CH Gayle' ]\n",
    "gayle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches = gayle['match_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "runs, ma = [],[]\n",
    "for i in matches:\n",
    "    r = 0\n",
    "    m = gayle[gayle['match_id'] == i]\n",
    "    m = m.reset_index(drop = True)\n",
    "    if len(m) > 10:\n",
    "        m = m.head(10)\n",
    "    for j in range(len(m)):\n",
    "        r = r + m.loc[j , 'batsman_runs']\n",
    "    runs.append(r)\n",
    "    ma.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[17, 10, 15, 4]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[7909, 7918, 11313, 11339]"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07c0467c0>"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets check wrt all team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "gayle_all = df[df['batsman'] == 'CH Gayle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "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>125</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>A Nehra</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>B Kumar</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>132</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>B Kumar</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>133</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>B Kumar</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>A Nehra</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>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177820</th>\n",
       "      <td>11346</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>DJ Bravo</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177821</th>\n",
       "      <td>11346</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>DJ Bravo</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>177822</th>\n",
       "      <td>11346</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>DJ Bravo</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177825</th>\n",
       "      <td>11346</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>Harbhajan Singh</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>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177827</th>\n",
       "      <td>11346</td>\n",
       "      <td>2</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>N Pooran</td>\n",
       "      <td>Harbhajan Singh</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>CH Gayle</td>\n",
       "      <td>caught</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3131 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning                 batting_team         bowling_team  \\\n",
       "125            1       2  Royal Challengers Bangalore  Sunrisers Hyderabad   \n",
       "131            1       2  Royal Challengers Bangalore  Sunrisers Hyderabad   \n",
       "132            1       2  Royal Challengers Bangalore  Sunrisers Hyderabad   \n",
       "133            1       2  Royal Challengers Bangalore  Sunrisers Hyderabad   \n",
       "137            1       2  Royal Challengers Bangalore  Sunrisers Hyderabad   \n",
       "...          ...     ...                          ...                  ...   \n",
       "177820     11346       2              Kings XI Punjab  Chennai Super Kings   \n",
       "177821     11346       2              Kings XI Punjab  Chennai Super Kings   \n",
       "177822     11346       2              Kings XI Punjab  Chennai Super Kings   \n",
       "177825     11346       2              Kings XI Punjab  Chennai Super Kings   \n",
       "177827     11346       2              Kings XI Punjab  Chennai Super Kings   \n",
       "\n",
       "        over  ball   batsman    non_striker           bowler  is_super_over  \\\n",
       "125        1     1  CH Gayle  Mandeep Singh          A Nehra              0   \n",
       "131        2     1  CH Gayle  Mandeep Singh          B Kumar              0   \n",
       "132        2     2  CH Gayle  Mandeep Singh          B Kumar              0   \n",
       "133        2     3  CH Gayle  Mandeep Singh          B Kumar              0   \n",
       "137        3     1  CH Gayle  Mandeep Singh          A Nehra              0   \n",
       "...      ...   ...       ...            ...              ...            ...   \n",
       "177820    10     5  CH Gayle       KL Rahul         DJ Bravo              0   \n",
       "177821    10     6  CH Gayle       KL Rahul         DJ Bravo              0   \n",
       "177822    10     7  CH Gayle       KL Rahul         DJ Bravo              0   \n",
       "177825    11     2  CH Gayle       KL Rahul  Harbhajan Singh              0   \n",
       "177827    11     4  CH Gayle       N Pooran  Harbhajan Singh              0   \n",
       "\n",
       "        ...  bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "125     ...         0            0            0             0             1   \n",
       "131     ...         0            0            0             0             0   \n",
       "132     ...         0            0            0             0             0   \n",
       "133     ...         0            0            0             0             1   \n",
       "137     ...         0            0            0             0             0   \n",
       "...     ...       ...          ...          ...           ...           ...   \n",
       "177820  ...         0            0            0             0             1   \n",
       "177821  ...         0            0            0             0             0   \n",
       "177822  ...         0            0            0             0             1   \n",
       "177825  ...         0            0            0             0             1   \n",
       "177827  ...         0            0            0             0             0   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
       "125              0           1               NaN            NaN     NaN  \n",
       "131              0           0               NaN            NaN     NaN  \n",
       "132              0           0               NaN            NaN     NaN  \n",
       "133              0           1               NaN            NaN     NaN  \n",
       "137              0           0               NaN            NaN     NaN  \n",
       "...            ...         ...               ...            ...     ...  \n",
       "177820           1           2               NaN            NaN     NaN  \n",
       "177821           0           0               NaN            NaN     NaN  \n",
       "177822           0           1               NaN            NaN     NaN  \n",
       "177825           0           1               NaN            NaN     NaN  \n",
       "177827           0           0          CH Gayle         caught     NaN  \n",
       "\n",
       "[3131 rows x 21 columns]"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gayle_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches = gayle_all['match_id'].unique()\n",
    "runs, ma = [],[]\n",
    "for i in matches:\n",
    "    r = 0\n",
    "    m = gayle_all[gayle_all['match_id'] == i]\n",
    "    m = m.reset_index(drop = True)\n",
    "    if len(m) > 10:\n",
    "        m = m.head(10)\n",
    "    for j in range(len(m)):\n",
    "        r = r + m.loc[j , 'batsman_runs']\n",
    "    runs.append(r)\n",
    "    ma.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 4,\n",
       " 13,\n",
       " 6]"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "ma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07c118430>"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.kdeplot(runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07c3ad0a0>"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07bf48400>"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2sWjRIgJ5HWTqQUSPMhHKakUoqxW+gBfLoa18sWUz9957L61alTBhwhWMHDkSm03uPYgTk/CrIFwuF9dedx0H6ry4uo2TG28itnQYS80u7Pu+wFR/kPz8AiZOvJIxY8bIPLH4zvFWQST090xaax577DEOHDhIffuzpHxF7CkTwfwO1He7gIbOIzngNzN//nx+esUE3njjDdkGLX5QQhfwm2++yYoVK/CVlBPOKDI6jkhljZs+6ruOoaHL+dQEbfz+97/nyokTWbZsGcFg0OiEIg4lbAF/9dVX/O53vyeU1Qp/y15GxxHiO6GsYuq7nE9D55Hs98Bjjz3GpElX8/777xMOh42OJ+JIQs4BHzp0iOtvuJHDDX7cXceirWlGRxLi2LTGUrsbR+WnqIbDdOjYkeuvu45BgwahlDI6nYiRpJkD9vv93DtnDrVHjlDf8RwpXxHflCKY2xZ39wvxtD+LHXsPMHv2bG6aMoV169bJ4fEpLqEKWGvNvHnz2PzllzS0O5Nwer7RkYQ4McpEsKATrh4X4207mK927OH222/nllumy+HxKSyh1gE/9dRTvP322/ha9SaY197oOEI0nclEoKgrgYJOWA9s4fMtnzNjxgxO69WLq372M/r37y9TEykkYeaAX3zxRZ566in8hV3xtR0E8odUJINwEOuBr3Hs+wJ89XTqVMbPfjaRM888E7PZbHQ60UyONwecEAW8dOlS5s2bRyCvA94OZ0v5iuQTDmE9tB3Hvo3gqaOoRUsuu/QSRo8ejdPpNDqdOEUJW8B//vOfeeKJJwhmt46c8yD77UUy02Esh3dj378Jk2sfjrQ0Ro0cybhx4+TgnwSWcAWsteapp57ipZdeIpDbDm+Hs8CUUFPWQpwSU/1BbPs2YTu8Cx0O0a17d8aMHs2wYcPIyMgwOp5ogoQqYL/fz7x583j77bcb53wHynPdROoKerEe3Ibj4Bbw1GK1Whk6dCjDhw+nf//+cuZEAkiYAq6srOR/7ruPbVu34mvVG3+rcpnzFQJAa0z1B7Ee2ob98E50wIsjLY2hQ4YwdGjkQaMyXxyfEqKAP/roIx6eOxevP0RDu6FytKQQxxMOY3ZVRk5iO7IbHfBiNpvp3bs3AwYMoF+/frRv316WtMWJuH4m3IEDB1iwYAEffvghYWcBDd1/grbL87eEOC6TiVB2KaHsUnw6jNm9H0vtbtZ9uZ1169YBkJOTS9++fejVqxe9evWibdu2cmh8nDH0Ctjn8/HXv/6VZ559Fp8/gK/4dPwtTwOTrH8U4mQpnxtLXSXmur3Y6vejffUApKWn07VLF7p27Urnzp3p0KEDJSUl8uTnGIirK+CGhgaWLl3Ky6+8Qu3hwwSzS/GWDUQ7soyII0RS0fYMAoWdCRR2xqs1yufC7N6H332AdVv2sH7DZ6Ajp7JZrFZKS0pp06Y1JSUltGzZksLCQgoLC8nLyyMrKwur1XrKmYLBIIFAgEAgQCgUIhwOEwqFADCZTCilsFqt2Gw2bDZbylypx6yAtdZs3ryZd955h3ff/T/q692RR7t0GUAos1hutAkRDUqhHVkEHVkEC8rwAYSDmDxHMHlqMHsOs632CLv2b4RVqyEc+q9PYbc7SHc6cTjsOBwObDYbZpMJ1Vic4VCIUDhMMBDEH/Dj90eK1u/3E/D7CQT8TT6G02azk+50kp6eTk5ONrk5OeTk5JCXl0dBQQH5+fkUFhZSVFRETk5Ows51x6SA9+7dy10zZ7K3ogJlsuDPaY2/zTA5RF0II5gshJ35hJ35/Mcx8TqMCnhQ/npM/gZU0IMK+vAHfbhCfpQ3BA1BVNgHaPh2+lKpxgsoE9pkBpUGpgy0wwzp5sbXzGAyo5W58deb/n3RpXXj5wujwiEIh/CHArhDflSDn92uGiy7qlBBL9rv+ff7NrJabRQWFdKquJiWLVt+90+LFi1o0aIF+fn5cbutOyYFvHXrVvZWVOAr7Ye/sGvKPzrIvvtjTA010XuDkB8V9KMtNjDH31iH0/PwtRlodAzxfcqEtjnRNidxe2y8DqMCXlSgAZPfjfLX4/fV09DgpmLzN5g3boqU9FFMZjP5+QUUt2xBUVERBQUF311F5+XlkZeXR05ODk6nM+ZTHzGdAw7mtI6L8o16Af4Ic8MhVCgQtc/vcDgYO24sy5Ytw2vg13k8uuGQoeOfbFLqf2jKhLalo23phJ0Fx/41oQAmfz3K78bkc6P8bip99VTv3Idl667ITcljTLWYTCacGZlkZmaSlZlJVlYmTqeTtLQ0nE4nF154IaWlpc365fxoASulbgRuBGjTpk2zvrmIjrFjxzJ16lS01rz22mtGxxEitsxWwmk5kJbDf9csoDUq6IsUtOcw5oYaTJ5aTN4juOoi/1Qe47fZbDZuuOGGZo36owWstX4KeAoiy9Ca9d0NYvTVQtpXf8fiqo7a51+2bBlaa958882ovcepCKXn4+k62ugYIpnpcGQu2+dunNP+9sd6zMEGTP4GdMB73N+elp5ORkYmTmc66enpZDidDB48uNljxnQKwuSpJezISfkVD+H0PKL5jFx3yM+f/7YcbUmHzJwovtPJCafnGR1BJLrvphmOKlefC5PfjSXQgPa5/+tmXVZ2NkWFRRQVtfmPOeDc3Fxyc3PJzs4mKysLp9MZs5t2MSngkpISbHY7bP8AHFn48joSKOqass9zM/oKXIjj0jpSboH6yM2uoA8V8kHQjwoHIRxE6RBogHDkx6NWNejG1Q6oxhURJkvjaya0skSOk1UmQP3nhZiOrIIgHEKFg6hwIHIzORSIZAh4UEEvlpAX5W9AB/3/EVspRV5+Aa3aRVZAFBcXf7cK4tu1zfF4aFFMCrisrIy/vPEGK1euZPk777B+/Xoc1Z/jKyjD3/I0tF2O1hMipkIBTJ5azJ7DmLxHUN4jWPxuTD4X+gduEFusVqxWa+PmiciKgXA43LixIkgwEGjWB40qpUhLd0bWAOcWkpeXS2FhIfn5+RQUFNCiRWRlQ2FhYULu6DNkK3JFRQUvvvgiy5cvJxTW+Iu64SvpA+ZT33EjhPiecBhTw0HM9Qcwuw9i9RwEz5Hv/rXFaqW4uJjWpaUUFxf/1064zMxMMjIysNvtJ/SteTAYjGzCCATw+Xz4/f7vPj7RnXDp6enfrUBIhl1xcXka2v79+1m0aFHkZpHdiaf0DIK57VJ+jliIUxIOR8q2rhKLuxpL/QF0KHLXITcvj+7dun13FkT79u0pLi6O240KySIuC/hbmzZtYt6837J9+zYCue3xth8SlxsIhIhXKtCApbYCc+1ubO5qdNCPUooOHTrSu/fp9OrVix49elBQcJy1syKq4rqAAUKhEC+//DLPPPMMYXsm9R2GEU7Pj8p7CZEMlM+NpWYnttpvMLn3A5BfUMjgQQPp27cvffr0IStLDriKB3F1GtqxmM1mJk6cSM+ePbnv/gdg8zI87YYSzO9odDQh4oYKeLHU7MBWs+O70u1UVsZZZ45l8ODBdOzYMWEPpklFcVPA3zr99NN57tlnuO+++9m4cQXegJdAyx5GxxLCODqM+cherAe+xlpXAeEw7dt34LwrL2LYsGG0atXK6ITiJMVdAQPk5ubym9/8ml/96ld89NFHqKAHf0lfuTknUooKeLAe2IL90BbwusjKzmHUpZcyYsQIOnXqZHQ80QzisoAB7HY7999/P7/97W9ZtmwZKhTE12aAlLBIeqaGw1j3bcJesx0dDtG7vJyLLryQIUOGNMvh6CJ+xG0BQ2Re+PbbbyctLY1XX30VbbHhL+ljdCwhosLsqsZWtRHLkQqsNhujxozm0ksvpW1beThtsorrAobITpgpU6bgdrt566230GYbgZY9jY4lRPPQGnNdJfaqzzC7qsnMyuKya69l3Lhx5OTE3zkeonnFfQFDpITvuOMO6uvrWblyJdpiJ1hQZnQsIU5eY/E6qtZjcu0nP7+AK6dNY/To0aSlpeYZKakoIQoYItMR9957L3fNnMmGz/5BvSNbHmkkEpLZVY1j76eYXNXk5xcwacYMzj//fGw22XyUauJmI8aJqqur44Ybb2R/TR2ubhegbU5D8whxokz1B3HsXYf5yF5ycvO4etJVjBkzRoo3BRxvI0bCnXKRlZXFI3PnYjOFSd/2HoSjebKuEKfO5DmMY9t7OL9cSna4jl/84he8/NKLjB8/Xso3xSVcAQO0b9+eOffeG7mi+OafRscR4piUtw7HjpU4N/2FTO9+rrnmGl55+WWuuOIKHA6H0fFEHEiYOeDvGzp0KJMmTeKFF14gmNVKtiyLuKF8bmxVn2E7uBWrxcz4yy7jyiuvlFUN4r8kbAEDTJo0ibXr1rH563/ichaiHXLwiDCO8tZhq9qIrWYbZpOJcRddyMSJE+UEMnFcCV3AFouF/5kzh2uvu47QzhXUdxkdeRyKEDFkajiErfoLrDU7sJgtjB03jgkTJtCiRQujo4k4l9AFDNCyZUtm3nUX9913H7a9n+Jv3d/oSCIVNB6QY9+3KbKRwu7ggksu4YorrpArXnHCEr6AAc4++2zGjBnDm3//O8HctrI+WESN8tdjPbgV+8Et4HOTm5fPZTfeyAUXXEBmZqbR8USCSYoCBpgyZQprPvkEdq3C1X0cmJLmSxNGC3qx1uzCWrMTs6sKgD59+zLuggvkgBxxSpKmpZxOJ7NmzuSOO+7Atne9TEWIk6c1Jm8t5toKrEf2YHbvA60pKS3l3IuvZuTIkXIGr2gWSVPAAP369WPs2LEse/NNmYoQTaJ8bsyuKix1VdjcVWhfPQDtO3RgyIUTOfvss+nUqZM8bUI0q6QqYICbbrqJf368Br1rFe7uF8qqCHFMKuDBXFeJua4KW301eOoAyMjMpN+g/vTt25cBAwZQVCT/ExfRk3QF7HQ6ufOO25k1axa26i/wtzrd6EgiHmiNqaEGS+032I7sQdUfAiDd6aRP33J69+7N6aefTseOHTGZEnKDqEhASVfAAAMHDuSss87mo1WrCeS1lw0aKUz5XJHH+hzeAV4XSil6nnYaA84YT79+/SgrK8Nslu+ShDGSsoABpk2byif/+oTg7n/SUDZCHmWUSrTGXLcX274vsRypQClFv/79+cmwYQwePFi2BIu4kbQFXFhYyA3XX88f/vAHLDU7CeZ3MDqSiDatMbuqcFR+ism1n9y8fMZdfTWjR4+WXWkiLiVtAQNcdNFFvPX222z75hNcOaVglqP/kpXy1pG2+5+Yj+yVQ85Fwkjquw1ms5nbb7sN/A3YKzcYHUdEQziMrWojmV/+Bae/hptvvpkXX1zMhRdeKOUr4l5SXwEDdOvWjVGjRvH28ncIFHQmnCbzf8lC+dyk7/gAk/sAQ4YO5ZZbbpFlYyKhJH0BA9x4442sWLGS0J41ckMuSZhr9+DctRKHxczM++9n2LBhRkcSosmSegriW3l5eVx77WTMR/Ziqd1tdBxxKrTGVrGO9K3v0r5NKU8/vVDKVySslChggPHjx9O6TVvSKj6R58glqnAIx86V2Ks+Y9SoUTz5xBOUlJQYnUqIk5YyBWyxWLh1+i3gdWHbt8noOKKpQn7St76L9dB2rr32WmbOnIndbjc6lRCnJGUKGKBv374MHjwYR9VGVKDB6DjiRAV9ZHz9NlZ3NTNnzmTSpElyKI5ICilVwBA5N9hEGFvFp0ZHESci6CNjy9tYfLU89NBDnH/++UYnEqLZpFwBl5aWcskll2A7uAVTwyGj44gfEvSSseVtrL46Hn7oIQYNGmR0IiGaVcoVMMBVV11FRmYmjt1rQGuj44hjCfrI2LI8Ur4PP8SAAQOMTiREs0vJAs7MzOT6667D7KqWZWnxKBTEue3/sHhreeihX3HGGWcYnUiIqEjJAgYYO3Yspa1bk7Z3LYTDRscR3wqHSdv+Pib3fubMmSNXviKppWwBWywWptx0E3iOYD3wldFxBIDWOHauxHKkgjtuv102WIikl7IFDDBo0CB6l5eTVrUBgj6j46Q8+55/Ya3ZwQ033MDYsWONjiNE1KV0ASuluHnKFHTQh73qM6PjpDRr9SZs+75g/PjxXHnllUbHESImUrqAAcrKyhg1ciS2/ZtRPpfRcVKSpWYnjuquXZQAAAtUSURBVD1rGDp0KFOnTpVNFiJlpHwBA1x33XVYLWbsFWuNjpJyzK5q0neupHuPHsyZM0eezyZSihQwkccXTbjiCqw1OzG59xsdJ2Uo7xGc29+nuLglcx9+WM52EClHCrjRFVdcQXZODmkV/5LNGTGgAh4ytr5LRpqd3/z612RnZxsdSYiYkwJulJ6eznXXXovJtQ9L7TdGx0luoSDp297DGvby6CNz5UhJkbKkgI8yevRoWrduQ9redbI5I1p0mLSdKzDVH+B/5syhe/fuRicSwjBSwEexWCxMmSKbM6LJvucTLIe/YdrUqZx55plGxxHCUFLA3zNw4MCjNmf4jY6TVKzVX2Db9yWXXXYZl1xyidFxhDCcFPD3HL05wyabM5pNZK3vJ5x51lncdNNNRscRIi5IAR9DWVkZI847D8f+L2VzRjMw11WRtnMl3bv34N577sFkkj92QoAU8HFdf/31WMwm7BXrjI6S0EwNNTi3v0/rkhIeeWSurPUV4ihSwMdRVFTET3/6U6w1OzC5DxgdJyEpnxvntnfJycrgscd+Q1ZWltGRhIgrUsA/YMKECWRl50QeZS+bM5okstHiHdLMmv/vsd/QokULoyMJEXekgH+A0+nkhuuvi2zOOCybM07Yt4+QD9bz6COP0KFDB6MTCRGXpIB/xOjRo2nXrn3jkzNCRseJf+HILjeLp4YHH3yQXr16GZ1IiLglBfwjzGYzU6feDN46rPu/NDpOfAuHSdv+Aea6KmbNmiVPMRbiR0gBn4B+/fpxxoABpFVtRAU8RseJTzqMY+cKLLV7uPXWWxkxYoTRiYSIe1LAJ+jmKVNQ4QC2veuNjhJ/tMa+azXWmp38/Oc/56KLLjI6kRAJQQr4BLVt25bx48djO/g1poYao+PED62x7/4Y28GtTJo0iQkTJhidSIiEIQXcBNdccw0ZGRk49qyRZWkQKd89n2Dbv5nLL7+cyZMnG51IiIQiBdwEmZmZ3HD99ZjrqmRZmtbYKtZh27eJ8ePHc9NNN8mz3IRoIingJhozZkzjsrR/QThodBzD2CrXY6/eyAUXXMAtt9wi5SvESZACbiKLxcL06beA14Wt6nOj4xjCtnc99soNjBo1ihkzZkj5CnGSpIBPQnl5OWeffTaO6s9T7rQ0W+UG7JXrGTVqFHfddZecbCbEKZC/PSfp5ptvxma14Ni9xugoMWOr3IB976eMGDGCO++8U8pXiFMkf4NOUlFREZMnX4Oldjfm2t1Gx4k6W+Vn2Pd+ynnnncfMmTMxm81GRxIi4UkBn4JLL72U1m3akL5nTVLfkIuU7zrOO+88Zs2aJeUrRDORAj4FFouF22bMiNyQq0zOxxfZqiLle+6550r5CtHMpIBPUXl5OSNGjMBe/XnS7ZCzVX2GvSJSvnfffbeUrxDNTAq4Gdx8881kZmaS9s1q0GGj4zQLW9VG7BXrGD58uJSvEFEiBdwMsrOzuXX6LZjcB7Du22x0nFMWKd+1DB8+nNmzZ0v5ChElUsDN5JxzzokcWVn5aUKvDf62fM855xy58hUiyqSAm4lSittvuw2b1UzartUJeVjP0eU7e/ZsLBaL0ZGESGpSwM2oRYsWTJs6FXNdJdZ9ifX0DClfIWJPCriZjRkzhkGDBpG2dy0mz2Gj45wQW+Vn2CvW8pOfSPkKEUtSwM1MKcWdd95JZkYG6TtXxv2DPCPbiyOrHe65R8pXiFiSAo6CvLw8Zs68C1V/CPvedUbHOTatI6eaNW4vlitfIWJPCjhKhgwZwrhx47BVf4GlZpfRcf6T1tgq1n53qpnscBPCGFLAUTR16lS6dO1K+q6PMHlqjY4ToTX23WuwV3/OuHHjuOuuu6R8hTCIFHAU2Ww2fvngg2Q403Fufx9CAWMD6TD2Xaux7f+Syy+/nBkzZsiRkkIYSP72RVlRUREPPnA/ynuEtB0rjNuqHA6Stu19bAe3cPXVV8sz3ISIA1LAMVBeXs60adOw1O7Gvusfsd+kEfTh3LIcy5E9TJ8+ncmTJ0v5ChEH5LZ3jFx88cXU1tbywgsvoC12/K37x+R9lbcO57b/wxJwM+e++xg2bFhM3lcI8eOkgGNo8uTJ1NbWsnTpUrDY8BefHtX3Mx/Zi3Pnh6Tbbfzq4d9QXl4e1fcTQjSNFHAMKaWYPn06breb999/HxXw4Gt9BqhmngnSGuu+L3BUrKVt23Y8/PBDtGrVqnnfQwhxyqSAY8xsNnPPPfeQm5vLa6+9hslfj6f92WBunv8UyucmbdcqzHWVnHnWWdw9axbp6enN8rmFEM1LCtgAZrOZadOmUVxczOMLFpDx9Zs0tBtKOD3/5D+p1lgObSN9zydYzXDzjBmMGzdObrYJEcekgA106aWX0qpVKx559NeoL5fia9ETf0k5mJrwn0VrzHWVOCo/xeQ+QPcePZk9+25KSkqiF1wI0SyUbsKSqH79+um1a9dGMU5qqqur48knn+Stt94Cewa+/E4ECjqj7RnH/01BH5ba3dgObsXsqqagsJBrJ09m5MiRsrNNiDijlFqnte73X69LAceP9evX86fFi1m3bh1oTSizBSFHDtqeSdiahingQQUaMHtqMbuqQYfJLyhk4pUTGDt2LDabzegvQQhxDMcrYJmCiCPl5eWUl5dTXV3NW2+9xZo1a6jYW4n7wNff/Rq7w0GLFi0YPPpyzjrrLLp27SrbiYVIUHIFnADcbje1tbXk5eXJigYhEpBcASewjIwMMjJ+YD5YCJGQ5HtXIYQwiBSwEEIYRApYCCEMIgUshBAGkQIWQgiDSAELIYRBpICFEMIgTdqIoZQ6AHwTvTgxVQAcNDpEHJBx+DcZiwgZh4jmHIe2WuvC77/YpAJOJkqptcfamZJqZBz+TcYiQsYhIhbjIFMQQghhEClgIYQwSCoX8FNGB4gTMg7/JmMRIeMQEfVxSNk5YCGEMFoqXwELIYShpICFEMIgKVnASqlRSqmvlVLblFKzjM4TK0qpZ5VS+5VSXxz1Wp5S6l2l1NbGH3ONzBgLSqnWSqkPlFKblVKblFLTG19PqbFQSjmUUp8opT5rHIcHGl9PqXH4llLKrJRar5Ra1vhx1Mch5QpYKWUGFgDnA92BCUqp7samipnngVHfe20W8J7Wugx4r/HjZBcEbtdadwMGAjc3/hlItbHwAedorU8HegOjlFIDSb1x+NZ0YPNRH0d9HFKugIEzgG1a6x1aaz/wMnChwZliQmu9Eqj53ssXAosaf74IuCimoQygta7SWn/a+HMXkb90JaTYWOgId+OH1sZ/NCk2DgBKqVJgDPD0US9HfRxSsYBLgD1HfVzR+FqqaqG1roJIMQFFBueJKaVUO6AcWEMKjkXjt90bgP3Au1rrlBwH4HfAXUD4qNeiPg6pWMDqGK/JWrwUpJTKAF4DbtVa1xmdxwha65DWujdQCpyhlOppdKZYU0qNBfZrrdfF+r1TsYArgNZHfVwKVBqUJR7sU0oVAzT+uN/gPDGhlLISKd/FWuvXG19OybEA0FrXAh8SuUeQauMwBBinlNpFZEryHKXUn4jBOKRiAf8LKFNKtVdK2YArgKUGZzLSUuDqxp9fDfzVwCwxoZRSwDPAZq31vKP+VUqNhVKqUCmV0/jzNOBc4CtSbBy01ndrrUu11u2I9MH7WuufEYNxSMmdcEqp0UTmfMzAs1rrhwyOFBNKqZeAYUSO2dsH3Af8Bfgz0AbYDVymtf7+jbqkopQaCnwEfM6/5/xmE5kHTpmxUEr1InJzyUzkYuzPWusHlVL5pNA4HE0pNQy4Q2s9NhbjkJIFLIQQ8SAVpyCEECIuSAELIYRBpICFEMIgUsBCCGEQKWAhhDCIFLAQQhhEClgIIQzy/wDYY5y0khauWwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.violinplot(runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e00090be50>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets take last 5 seasons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [],
   "source": [
    "gayle_all = gayle_all.tail(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches = gayle_all['match_id'].unique()\n",
    "runs, ma = [],[]\n",
    "for i in matches:\n",
    "    r = 0\n",
    "    m = gayle_all[gayle_all['match_id'] == i]\n",
    "    m = m.reset_index(drop = True)\n",
    "    if len(m) > 10:\n",
    "        m = m.head(10)\n",
    "    for j in range(len(m)):\n",
    "        r = r + m.loc[j , 'batsman_runs']\n",
    "    runs.append(r)\n",
    "    ma.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1,\n",
       " 9,\n",
       " 10,\n",
       " 21,\n",
       " 21,\n",
       " 8,\n",
       " 14,\n",
       " 35,\n",
       " 1,\n",
       " 7,\n",
       " 10,\n",
       " 1,\n",
       " 0,\n",
       " 7,\n",
       " 5,\n",
       " 2,\n",
       " 9,\n",
       " 6,\n",
       " 1,\n",
       " 9,\n",
       " 11,\n",
       " 10,\n",
       " 17,\n",
       " 13,\n",
       " 10,\n",
       " 6,\n",
       " 8,\n",
       " 2,\n",
       " 14,\n",
       " 6,\n",
       " 18,\n",
       " 0,\n",
       " 9,\n",
       " 11,\n",
       " 18,\n",
       " 6,\n",
       " 15,\n",
       " 15,\n",
       " 6,\n",
       " 13,\n",
       " 24,\n",
       " 24,\n",
       " 4,\n",
       " 13,\n",
       " 6]"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[540,\n",
       " 543,\n",
       " 546,\n",
       " 550,\n",
       " 557,\n",
       " 562,\n",
       " 566,\n",
       " 568,\n",
       " 571,\n",
       " 574,\n",
       " 575,\n",
       " 580,\n",
       " 587,\n",
       " 606,\n",
       " 617,\n",
       " 620,\n",
       " 624,\n",
       " 626,\n",
       " 632,\n",
       " 633,\n",
       " 636,\n",
       " 7905,\n",
       " 7909,\n",
       " 7911,\n",
       " 7918,\n",
       " 7927,\n",
       " 7931,\n",
       " 7933,\n",
       " 7937,\n",
       " 7941,\n",
       " 7943,\n",
       " 7949,\n",
       " 11140,\n",
       " 11142,\n",
       " 11145,\n",
       " 11309,\n",
       " 11313,\n",
       " 11315,\n",
       " 11319,\n",
       " 11323,\n",
       " 11328,\n",
       " 11333,\n",
       " 11339,\n",
       " 11343,\n",
       " 11346]"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e0725be220>"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.kdeplot(runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07c0cff10>"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.distplot(runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e07acd6070>"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.violinplot(runs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e079e3f250>"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.boxplot(runs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Take Aways:\n",
    "### 1 - Gayle scores around (6-12) runs in the start 10 balls\n",
    "### 2 -Median is around 8 for all  stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 5 ans - Less than 10 runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}