Best Batters against Rashid Khan.ipynb

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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>inning</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>...</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>winner</th>\n",
       "      <th>result</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>eliminator</th>\n",
       "      <th>method</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>136612</th>\n",
       "      <td>1082591</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>TM Head</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>runs</td>\n",
       "      <td>35.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136613</th>\n",
       "      <td>1082591</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>TM Head</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>runs</td>\n",
       "      <td>35.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136614</th>\n",
       "      <td>1082591</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>TM Head</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>runs</td>\n",
       "      <td>35.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136615</th>\n",
       "      <td>1082591</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>TM Head</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>runs</td>\n",
       "      <td>35.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136616</th>\n",
       "      <td>1082591</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>TM Head</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>runs</td>\n",
       "      <td>35.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</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>193077</th>\n",
       "      <td>1237180</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>runs</td>\n",
       "      <td>17.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PR Reiffel</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193078</th>\n",
       "      <td>1237180</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>runs</td>\n",
       "      <td>17.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PR Reiffel</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193079</th>\n",
       "      <td>1237180</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>runs</td>\n",
       "      <td>17.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PR Reiffel</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193080</th>\n",
       "      <td>1237180</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>runs</td>\n",
       "      <td>17.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PR Reiffel</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193081</th>\n",
       "      <td>1237180</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>bat</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>runs</td>\n",
       "      <td>17.0</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PR Reiffel</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1490 rows × 34 columns</p>\n",
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      ],
      "text/plain": [
       "             id  inning  over  ball    batsman non_striker       bowler  \\\n",
       "136612  1082591       2     7     1    TM Head   KM Jadhav  Rashid Khan   \n",
       "136613  1082591       2     7     2    TM Head   KM Jadhav  Rashid Khan   \n",
       "136614  1082591       2     7     3    TM Head   KM Jadhav  Rashid Khan   \n",
       "136615  1082591       2     7     4  KM Jadhav     TM Head  Rashid Khan   \n",
       "136616  1082591       2     7     5    TM Head   KM Jadhav  Rashid Khan   \n",
       "...         ...     ...   ...   ...        ...         ...          ...   \n",
       "193077  1237180       1    10     2   S Dhawan     SS Iyer  Rashid Khan   \n",
       "193078  1237180       1    10     3    SS Iyer    S Dhawan  Rashid Khan   \n",
       "193079  1237180       1    10     4   S Dhawan     SS Iyer  Rashid Khan   \n",
       "193080  1237180       1    10     5   S Dhawan     SS Iyer  Rashid Khan   \n",
       "193081  1237180       1    10     6    SS Iyer    S Dhawan  Rashid Khan   \n",
       "\n",
       "        batsman_runs  extra_runs  total_runs  ...  \\\n",
       "136612             4           0           4  ...   \n",
       "136613             2           0           2  ...   \n",
       "136614             1           0           1  ...   \n",
       "136615             1           0           1  ...   \n",
       "136616             1           0           1  ...   \n",
       "...              ...         ...         ...  ...   \n",
       "193077             1           0           1  ...   \n",
       "193078             1           0           1  ...   \n",
       "193079             0           0           0  ...   \n",
       "193080             1           0           1  ...   \n",
       "193081             1           0           1  ...   \n",
       "\n",
       "                              team2                  toss_winner  \\\n",
       "136612  Royal Challengers Bangalore  Royal Challengers Bangalore   \n",
       "136613  Royal Challengers Bangalore  Royal Challengers Bangalore   \n",
       "136614  Royal Challengers Bangalore  Royal Challengers Bangalore   \n",
       "136615  Royal Challengers Bangalore  Royal Challengers Bangalore   \n",
       "136616  Royal Challengers Bangalore  Royal Challengers Bangalore   \n",
       "...                             ...                          ...   \n",
       "193077          Sunrisers Hyderabad               Delhi Capitals   \n",
       "193078          Sunrisers Hyderabad               Delhi Capitals   \n",
       "193079          Sunrisers Hyderabad               Delhi Capitals   \n",
       "193080          Sunrisers Hyderabad               Delhi Capitals   \n",
       "193081          Sunrisers Hyderabad               Delhi Capitals   \n",
       "\n",
       "       toss_decision               winner result result_margin eliminator  \\\n",
       "136612         field  Sunrisers Hyderabad   runs          35.0          N   \n",
       "136613         field  Sunrisers Hyderabad   runs          35.0          N   \n",
       "136614         field  Sunrisers Hyderabad   runs          35.0          N   \n",
       "136615         field  Sunrisers Hyderabad   runs          35.0          N   \n",
       "136616         field  Sunrisers Hyderabad   runs          35.0          N   \n",
       "...              ...                  ...    ...           ...        ...   \n",
       "193077           bat       Delhi Capitals   runs          17.0          N   \n",
       "193078           bat       Delhi Capitals   runs          17.0          N   \n",
       "193079           bat       Delhi Capitals   runs          17.0          N   \n",
       "193080           bat       Delhi Capitals   runs          17.0          N   \n",
       "193081           bat       Delhi Capitals   runs          17.0          N   \n",
       "\n",
       "       method      umpire1   umpire2  \n",
       "136612    NaN  AY Dandekar  NJ Llong  \n",
       "136613    NaN  AY Dandekar  NJ Llong  \n",
       "136614    NaN  AY Dandekar  NJ Llong  \n",
       "136615    NaN  AY Dandekar  NJ Llong  \n",
       "136616    NaN  AY Dandekar  NJ Llong  \n",
       "...       ...          ...       ...  \n",
       "193077    NaN   PR Reiffel    S Ravi  \n",
       "193078    NaN   PR Reiffel    S Ravi  \n",
       "193079    NaN   PR Reiffel    S Ravi  \n",
       "193080    NaN   PR Reiffel    S Ravi  \n",
       "193081    NaN   PR Reiffel    S Ravi  \n",
       "\n",
       "[1490 rows x 34 columns]"
      ]
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import cufflinks as cf\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)\n",
    "cf.go_offline()\n",
    "%matplotlib inline\n",
    "\n",
    "deliveries = pd.read_csv(\"IPL Ball-by-Ball 2008-2020.csv\")\n",
    "matches = pd.read_csv(\"IPL Matches 2008-2020.csv\")\n",
    "\n",
    "deliveries_matches = deliveries.merge(matches, how='left', on='id')\n",
    "rashid_bowling = deliveries_matches[deliveries_matches['bowler'].str.startswith('Rashid Khan')]\n",
    "rashid_bowling\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "established-graphics",
   "metadata": {},
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>inning</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>non_boundary</th>\n",
       "      <th>is_wicket</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>result_margin</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>batsman</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>44418600</td>\n",
       "      <td>41</td>\n",
       "      <td>372</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>284.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Mathews</th>\n",
       "      <td>2165222</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AD Russell</th>\n",
       "      <td>12861758</td>\n",
       "      <td>18</td>\n",
       "      <td>150</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AJ Finch</th>\n",
       "      <td>29589130</td>\n",
       "      <td>26</td>\n",
       "      <td>243</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM Rahane</th>\n",
       "      <td>39331641</td>\n",
       "      <td>55</td>\n",
       "      <td>340</td>\n",
       "      <td>34</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>444.0</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",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>21995116</td>\n",
       "      <td>28</td>\n",
       "      <td>148</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Shankar</th>\n",
       "      <td>3409788</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WP Saha</th>\n",
       "      <td>4330464</td>\n",
       "      <td>8</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington Sundar</th>\n",
       "      <td>4866008</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YK Pathan</th>\n",
       "      <td>6495762</td>\n",
       "      <td>12</td>\n",
       "      <td>86</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>288.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  inning  over  ball  batsman_runs  extra_runs  \\\n",
       "batsman                                                                     \n",
       "AB de Villiers     44418600      41   372    37            37           2   \n",
       "AD Mathews          2165222       4    28     2             2           0   \n",
       "AD Russell         12861758      18   150    11            10           0   \n",
       "AJ Finch           29589130      26   243    25            37           0   \n",
       "AM Rahane          39331641      55   340    34            41           0   \n",
       "...                     ...     ...   ...   ...           ...         ...   \n",
       "V Kohli            21995116      28   148    19            18           0   \n",
       "V Shankar           3409788       3    48     3             1           0   \n",
       "WP Saha             4330464       8    44     4             1           0   \n",
       "Washington Sundar   4866008       4    60     4             5           0   \n",
       "YK Pathan           6495762      12    86     6             5           3   \n",
       "\n",
       "                   total_runs  non_boundary  is_wicket  neutral_venue  \\\n",
       "batsman                                                                 \n",
       "AB de Villiers             39             0          2              0   \n",
       "AD Mathews                  2             0          0              0   \n",
       "AD Russell                 10             0          1              0   \n",
       "AJ Finch                   37             0          2              0   \n",
       "AM Rahane                  41             0          1              0   \n",
       "...                       ...           ...        ...            ...   \n",
       "V Kohli                    18             0          1              0   \n",
       "V Shankar                   1             0          0              0   \n",
       "WP Saha                     1             0          1              0   \n",
       "Washington Sundar           5             0          0              0   \n",
       "YK Pathan                   8             0          1              0   \n",
       "\n",
       "                   result_margin  \n",
       "batsman                           \n",
       "AB de Villiers             284.0  \n",
       "AD Mathews                  30.0  \n",
       "AD Russell                  91.0  \n",
       "AJ Finch                   228.0  \n",
       "AM Rahane                  444.0  \n",
       "...                          ...  \n",
       "V Kohli                    165.0  \n",
       "V Shankar                   21.0  \n",
       "WP Saha                     62.0  \n",
       "Washington Sundar           20.0  \n",
       "YK Pathan                  288.0  \n",
       "\n",
       "[107 rows x 11 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rashid_bowling.ball = 1\n",
    "batsmen_against_rashid = rashid_bowling.groupby(by='batsman').sum()\n",
    "batsmen_against_rashid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "packed-chain",
   "metadata": {},
   "outputs": [],
   "source": [
    "batsmen_against_rashid['Average'] = batsmen_against_rashid['batsman_runs']/batsmen_against_rashid['is_wicket']\n",
    "batsmen_against_rashid['Strike Rate'] = batsmen_against_rashid['batsman_runs']/batsmen_against_rashid['ball']*100\n",
    "batsmen_against_rashid = batsmen_against_rashid.round(decimals=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "coordinated-singapore",
   "metadata": {},
   "outputs": [
    {
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       "      <td>148.00</td>\n",
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       "    <tr>\n",
       "      <th>AM Rahane</th>\n",
       "      <td>39331641</td>\n",
       "      <td>55</td>\n",
       "      <td>340</td>\n",
       "      <td>34</td>\n",
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       "      <td>41</td>\n",
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       "<p>107 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  inning  over  ball  batsman_runs  extra_runs  \\\n",
       "batsman                                                                     \n",
       "AB de Villiers     44418600      41   372    37            37           2   \n",
       "AD Mathews          2165222       4    28     2             2           0   \n",
       "AD Russell         12861758      18   150    11            10           0   \n",
       "AJ Finch           29589130      26   243    25            37           0   \n",
       "AM Rahane          39331641      55   340    34            41           0   \n",
       "...                     ...     ...   ...   ...           ...         ...   \n",
       "V Kohli            21995116      28   148    19            18           0   \n",
       "V Shankar           3409788       3    48     3             1           0   \n",
       "WP Saha             4330464       8    44     4             1           0   \n",
       "Washington Sundar   4866008       4    60     4             5           0   \n",
       "YK Pathan           6495762      12    86     6             5           3   \n",
       "\n",
       "                   total_runs  non_boundary  is_wicket  neutral_venue  \\\n",
       "batsman                                                                 \n",
       "AB de Villiers             39             0          2              0   \n",
       "AD Mathews                  2             0          0              0   \n",
       "AD Russell                 10             0          1              0   \n",
       "AJ Finch                   37             0          2              0   \n",
       "AM Rahane                  41             0          1              0   \n",
       "...                       ...           ...        ...            ...   \n",
       "V Kohli                    18             0          1              0   \n",
       "V Shankar                   1             0          0              0   \n",
       "WP Saha                     1             0          1              0   \n",
       "Washington Sundar           5             0          0              0   \n",
       "YK Pathan                   8             0          1              0   \n",
       "\n",
       "                   result_margin  Average  Strike Rate  \n",
       "batsman                                                 \n",
       "AB de Villiers             284.0     18.5       100.00  \n",
       "AD Mathews                  30.0      inf       100.00  \n",
       "AD Russell                  91.0     10.0        90.91  \n",
       "AJ Finch                   228.0     18.5       148.00  \n",
       "AM Rahane                  444.0     41.0       120.59  \n",
       "...                          ...      ...          ...  \n",
       "V Kohli                    165.0     18.0        94.74  \n",
       "V Shankar                   21.0      inf        33.33  \n",
       "WP Saha                     62.0      1.0        25.00  \n",
       "Washington Sundar           20.0      inf       125.00  \n",
       "YK Pathan                  288.0      5.0        83.33  \n",
       "\n",
       "[107 rows x 13 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batsmen_against_rashid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fitted-philippines",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>34.50</td>\n",
       "      <td>106.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RV Uthappa</th>\n",
       "      <td>46088437</td>\n",
       "      <td>70</td>\n",
       "      <td>371</td>\n",
       "      <td>41</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>32.00</td>\n",
       "      <td>156.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR Pant</th>\n",
       "      <td>66195012</td>\n",
       "      <td>87</td>\n",
       "      <td>587</td>\n",
       "      <td>57</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>20.33</td>\n",
       "      <td>107.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT Rayudu</th>\n",
       "      <td>62788130</td>\n",
       "      <td>81</td>\n",
       "      <td>609</td>\n",
       "      <td>54</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>112.96</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DJ Hooda</th>\n",
       "      <td>1216498</td>\n",
       "      <td>1</td>\n",
       "      <td>14</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>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NV Ojha</th>\n",
       "      <td>1136596</td>\n",
       "      <td>1</td>\n",
       "      <td>16</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>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Ashwin</th>\n",
       "      <td>2315008</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td>2</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>58.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E Lewis</th>\n",
       "      <td>1136567</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</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>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K Rabada</th>\n",
       "      <td>1216524</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</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>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id  inning  over  ball  batsman_runs  extra_runs  \\\n",
       "batsman                                                              \n",
       "SR Watson   54660505      80   498    47            82           1   \n",
       "SS Iyer     75450953      94   662    65            69           2   \n",
       "RV Uthappa  46088437      70   371    41            64           0   \n",
       "RR Pant     66195012      87   587    57            61           2   \n",
       "AT Rayudu   62788130      81   609    54            61           1   \n",
       "...              ...     ...   ...   ...           ...         ...   \n",
       "DJ Hooda     1216498       1    14     1             0           0   \n",
       "NV Ojha      1136596       1    16     1             0           0   \n",
       "R Ashwin     2315008       4    29     2             0           0   \n",
       "E Lewis      1136567       1     4     1             0           0   \n",
       "K Rabada     1216524       2    12     1             0           0   \n",
       "\n",
       "            total_runs  non_boundary  is_wicket  neutral_venue  result_margin  \\\n",
       "batsman                                                                         \n",
       "SR Watson           83             0          0              0          597.0   \n",
       "SS Iyer             71             0          2              0         1399.0   \n",
       "RV Uthappa          64             0          2              0          664.0   \n",
       "RR Pant             63             0          3              0         1566.0   \n",
       "AT Rayudu           62             0          0              0          546.0   \n",
       "...                ...           ...        ...            ...            ...   \n",
       "DJ Hooda             0             0          1              0           12.0   \n",
       "NV Ojha              0             0          1              0            7.0   \n",
       "R Ashwin             0             0          2              0           58.0   \n",
       "E Lewis              0             0          0              0            1.0   \n",
       "K Rabada             0             0          0              0           88.0   \n",
       "\n",
       "            Average  Strike Rate  \n",
       "batsman                           \n",
       "SR Watson       inf       174.47  \n",
       "SS Iyer       34.50       106.15  \n",
       "RV Uthappa    32.00       156.10  \n",
       "RR Pant       20.33       107.02  \n",
       "AT Rayudu       inf       112.96  \n",
       "...             ...          ...  \n",
       "DJ Hooda       0.00         0.00  \n",
       "NV Ojha        0.00         0.00  \n",
       "R Ashwin       0.00         0.00  \n",
       "E Lewis         NaN         0.00  \n",
       "K Rabada        NaN         0.00  \n",
       "\n",
       "[107 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_runs_against_rashid = batsmen_against_rashid.sort_values(by='batsman_runs', ascending=False)\n",
    "most_runs_against_rashid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "clinical-proposition",
   "metadata": {},
   "outputs": [
    {
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       "      <td>34</td>\n",
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       "      <td>163.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA Yadav</th>\n",
       "      <td>23374666</td>\n",
       "      <td>27</td>\n",
       "      <td>194</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
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       "      <td>16</td>\n",
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       "      <th>K Rabada</th>\n",
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       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
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       "      <td>88.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>Mohammed Siraj</th>\n",
       "      <td>2350732</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>236.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PJ Sangwan</th>\n",
       "      <td>4546268</td>\n",
       "      <td>4</td>\n",
       "      <td>68</td>\n",
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       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
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       "      <th>SE Rutherford</th>\n",
       "      <td>2363532</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "<p>107 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      id  inning  over  ball  batsman_runs  extra_runs  \\\n",
       "batsman                                                                  \n",
       "SR Watson       54660505      80   498    47            82           1   \n",
       "AT Rayudu       62788130      81   609    54            61           1   \n",
       "M Vohra         18512333      34   165    17            39           4   \n",
       "RA Tripathi     12124561      18    98    11            18           0   \n",
       "SA Yadav        23374666      27   194    20            16           0   \n",
       "...                  ...     ...   ...   ...           ...         ...   \n",
       "K Rabada         1216524       2    12     1             0           0   \n",
       "Mohammed Siraj   2350732       4    34     2             0           0   \n",
       "PJ Sangwan       4546268       4    68     4             0           1   \n",
       "SE Rutherford    2363532       4    28     2             0           0   \n",
       "SW Billings      2356816       2    26     2             0           0   \n",
       "\n",
       "                total_runs  non_boundary  is_wicket  neutral_venue  \\\n",
       "batsman                                                              \n",
       "SR Watson               83             0          0              0   \n",
       "AT Rayudu               62             0          0              0   \n",
       "M Vohra                 43             0          0              0   \n",
       "RA Tripathi             18             0          0              0   \n",
       "SA Yadav                16             0          0              0   \n",
       "...                    ...           ...        ...            ...   \n",
       "K Rabada                 0             0          0              0   \n",
       "Mohammed Siraj           0             0          0              0   \n",
       "PJ Sangwan               1             0          0              0   \n",
       "SE Rutherford            0             0          0              0   \n",
       "SW Billings              0             0          0              0   \n",
       "\n",
       "                result_margin  Average  Strike Rate  \n",
       "batsman                                              \n",
       "SR Watson               597.0      inf       174.47  \n",
       "AT Rayudu               546.0      inf       112.96  \n",
       "M Vohra                  85.0      inf       229.41  \n",
       "RA Tripathi              78.0      inf       163.64  \n",
       "SA Yadav                291.0      inf        80.00  \n",
       "...                       ...      ...          ...  \n",
       "K Rabada                 88.0      NaN         0.00  \n",
       "Mohammed Siraj          236.0      NaN         0.00  \n",
       "PJ Sangwan                4.0      NaN         0.00  \n",
       "SE Rutherford             4.0      NaN         0.00  \n",
       "SW Billings              12.0      NaN         0.00  \n",
       "\n",
       "[107 rows x 13 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_avg_against_rashid = batsmen_against_rashid.sort_values(by=['Average', 'batsman_runs'], ascending=False)\n",
    "highest_avg_against_rashid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "empirical-pride",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>300.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R Tewatia</th>\n",
       "      <td>6082535</td>\n",
       "      <td>10</td>\n",
       "      <td>85</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>240.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M Vohra</th>\n",
       "      <td>18512333</td>\n",
       "      <td>34</td>\n",
       "      <td>165</td>\n",
       "      <td>17</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>229.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mujeeb Ur Rahman</th>\n",
       "      <td>4706254</td>\n",
       "      <td>8</td>\n",
       "      <td>58</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>225.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RD Chahar</th>\n",
       "      <td>3535182</td>\n",
       "      <td>3</td>\n",
       "      <td>48</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>200.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SE Rutherford</th>\n",
       "      <td>2363532</td>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>2</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>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SS Tiwary</th>\n",
       "      <td>1216495</td>\n",
       "      <td>1</td>\n",
       "      <td>12</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>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harbhajan Singh</th>\n",
       "      <td>4546468</td>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>4</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>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NV Ojha</th>\n",
       "      <td>1136596</td>\n",
       "      <td>1</td>\n",
       "      <td>16</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>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E Lewis</th>\n",
       "      <td>1136567</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</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>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        id  inning  over  ball  batsman_runs  extra_runs  \\\n",
       "batsman                                                                    \n",
       "KC Cariappa        3535239       3    57     3             9           0   \n",
       "R Tewatia          6082535      10    85     5            12           1   \n",
       "M Vohra           18512333      34   165    17            39           4   \n",
       "Mujeeb Ur Rahman   4706254       8    58     4             9           0   \n",
       "RD Chahar          3535182       3    48     3             6           0   \n",
       "...                    ...     ...   ...   ...           ...         ...   \n",
       "SE Rutherford      2363532       4    28     2             0           0   \n",
       "SS Tiwary          1216495       1    12     1             0           0   \n",
       "Harbhajan Singh    4546468       8    60     4             0           0   \n",
       "NV Ojha            1136596       1    16     1             0           0   \n",
       "E Lewis            1136567       1     4     1             0           0   \n",
       "\n",
       "                  total_runs  non_boundary  is_wicket  neutral_venue  \\\n",
       "batsman                                                                \n",
       "KC Cariappa                9             0          0              0   \n",
       "R Tewatia                 13             0          0              0   \n",
       "M Vohra                   43             0          0              0   \n",
       "Mujeeb Ur Rahman           9             0          0              0   \n",
       "RD Chahar                  6             0          0              0   \n",
       "...                      ...           ...        ...            ...   \n",
       "SE Rutherford              0             0          0              0   \n",
       "SS Tiwary                  0             0          1              0   \n",
       "Harbhajan Singh            0             0          0              0   \n",
       "NV Ojha                    0             0          1              0   \n",
       "E Lewis                    0             0          0              0   \n",
       "\n",
       "                  result_margin  Average  Strike Rate  \n",
       "batsman                                                \n",
       "KC Cariappa                27.0      inf       300.00  \n",
       "R Tewatia                  25.0      inf       240.00  \n",
       "M Vohra                    85.0      inf       229.41  \n",
       "Mujeeb Ur Rahman          164.0      inf       225.00  \n",
       "RD Chahar                 120.0      inf       200.00  \n",
       "...                         ...      ...          ...  \n",
       "SE Rutherford               4.0      NaN         0.00  \n",
       "SS Tiwary                  10.0      0.0         0.00  \n",
       "Harbhajan Singh             8.0      NaN         0.00  \n",
       "NV Ojha                     7.0      0.0         0.00  \n",
       "E Lewis                     1.0      NaN         0.00  \n",
       "\n",
       "[107 rows x 13 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_sr_against_rashid = batsmen_against_rashid.sort_values(by='Strike Rate', ascending=False)\n",
    "highest_sr_against_rashid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "three-blink",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>id</th>\n",
       "      <th>inning</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>non_boundary</th>\n",
       "      <th>is_wicket</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>Average</th>\n",
       "      <th>Strike Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>batsman</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SR Watson</th>\n",
       "      <td>54660505</td>\n",
       "      <td>80</td>\n",
       "      <td>498</td>\n",
       "      <td>47</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>174.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SS Iyer</th>\n",
       "      <td>75450953</td>\n",
       "      <td>94</td>\n",
       "      <td>662</td>\n",
       "      <td>65</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>34.50</td>\n",
       "      <td>106.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RV Uthappa</th>\n",
       "      <td>46088437</td>\n",
       "      <td>70</td>\n",
       "      <td>371</td>\n",
       "      <td>41</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>32.00</td>\n",
       "      <td>156.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR Pant</th>\n",
       "      <td>66195012</td>\n",
       "      <td>87</td>\n",
       "      <td>587</td>\n",
       "      <td>57</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>20.33</td>\n",
       "      <td>107.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT Rayudu</th>\n",
       "      <td>62788130</td>\n",
       "      <td>81</td>\n",
       "      <td>609</td>\n",
       "      <td>54</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>112.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N Rana</th>\n",
       "      <td>51118625</td>\n",
       "      <td>71</td>\n",
       "      <td>386</td>\n",
       "      <td>44</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>127.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH Gayle</th>\n",
       "      <td>30152269</td>\n",
       "      <td>29</td>\n",
       "      <td>225</td>\n",
       "      <td>26</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>196.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ishan Kishan</th>\n",
       "      <td>41318350</td>\n",
       "      <td>35</td>\n",
       "      <td>325</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>134.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK Raina</th>\n",
       "      <td>40042339</td>\n",
       "      <td>49</td>\n",
       "      <td>343</td>\n",
       "      <td>35</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>15.33</td>\n",
       "      <td>131.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SV Samson</th>\n",
       "      <td>61685272</td>\n",
       "      <td>85</td>\n",
       "      <td>476</td>\n",
       "      <td>53</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>44.00</td>\n",
       "      <td>83.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM Rahane</th>\n",
       "      <td>39331641</td>\n",
       "      <td>55</td>\n",
       "      <td>340</td>\n",
       "      <td>34</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>120.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M Vohra</th>\n",
       "      <td>18512333</td>\n",
       "      <td>34</td>\n",
       "      <td>165</td>\n",
       "      <td>17</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>229.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>44418600</td>\n",
       "      <td>41</td>\n",
       "      <td>372</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AJ Finch</th>\n",
       "      <td>29589130</td>\n",
       "      <td>26</td>\n",
       "      <td>243</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>148.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO Hetmyer</th>\n",
       "      <td>24999460</td>\n",
       "      <td>39</td>\n",
       "      <td>212</td>\n",
       "      <td>21</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>171.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C de Grandhomme</th>\n",
       "      <td>26806942</td>\n",
       "      <td>46</td>\n",
       "      <td>323</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>33.00</td>\n",
       "      <td>143.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KD Karthik</th>\n",
       "      <td>29145311</td>\n",
       "      <td>32</td>\n",
       "      <td>302</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>14.50</td>\n",
       "      <td>111.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA Agarwal</th>\n",
       "      <td>26894218</td>\n",
       "      <td>45</td>\n",
       "      <td>188</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA Pollard</th>\n",
       "      <td>40536424</td>\n",
       "      <td>40</td>\n",
       "      <td>455</td>\n",
       "      <td>35</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>65.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA Lynn</th>\n",
       "      <td>27487629</td>\n",
       "      <td>40</td>\n",
       "      <td>217</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>95.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       id  inning  over  ball  batsman_runs  extra_runs  \\\n",
       "batsman                                                                   \n",
       "SR Watson        54660505      80   498    47            82           1   \n",
       "SS Iyer          75450953      94   662    65            69           2   \n",
       "RV Uthappa       46088437      70   371    41            64           0   \n",
       "RR Pant          66195012      87   587    57            61           2   \n",
       "AT Rayudu        62788130      81   609    54            61           1   \n",
       "N Rana           51118625      71   386    44            56           1   \n",
       "CH Gayle         30152269      29   225    26            51           0   \n",
       "Ishan Kishan     41318350      35   325    35            47           5   \n",
       "SK Raina         40042339      49   343    35            46           5   \n",
       "SV Samson        61685272      85   476    53            44           4   \n",
       "AM Rahane        39331641      55   340    34            41           0   \n",
       "M Vohra          18512333      34   165    17            39           4   \n",
       "AB de Villiers   44418600      41   372    37            37           2   \n",
       "AJ Finch         29589130      26   243    25            37           0   \n",
       "SO Hetmyer       24999460      39   212    21            36           1   \n",
       "C de Grandhomme  26806942      46   323    23            33           1   \n",
       "KD Karthik       29145311      32   302    26            29           0   \n",
       "MA Agarwal       26894218      45   188    23            23           0   \n",
       "KA Pollard       40536424      40   455    35            23           2   \n",
       "CA Lynn          27487629      40   217    24            23           0   \n",
       "\n",
       "                 total_runs  non_boundary  is_wicket  neutral_venue  \\\n",
       "batsman                                                               \n",
       "SR Watson                83             0          0              0   \n",
       "SS Iyer                  71             0          2              0   \n",
       "RV Uthappa               64             0          2              0   \n",
       "RR Pant                  63             0          3              0   \n",
       "AT Rayudu                62             0          0              0   \n",
       "N Rana                   57             0          1              0   \n",
       "CH Gayle                 51             0          1              0   \n",
       "Ishan Kishan             52             0          1              0   \n",
       "SK Raina                 51             0          3              0   \n",
       "SV Samson                48             0          1              0   \n",
       "AM Rahane                41             0          1              0   \n",
       "M Vohra                  43             0          0              0   \n",
       "AB de Villiers           39             0          2              0   \n",
       "AJ Finch                 37             0          2              0   \n",
       "SO Hetmyer               37             0          2              0   \n",
       "C de Grandhomme          34             0          1              0   \n",
       "KD Karthik               29             0          2              0   \n",
       "MA Agarwal               23             0          1              0   \n",
       "KA Pollard               25             0          1              0   \n",
       "CA Lynn                  23             0          1              0   \n",
       "\n",
       "                 result_margin  Average  Strike Rate  \n",
       "batsman                                               \n",
       "SR Watson                597.0      inf       174.47  \n",
       "SS Iyer                 1399.0    34.50       106.15  \n",
       "RV Uthappa               664.0    32.00       156.10  \n",
       "RR Pant                 1566.0    20.33       107.02  \n",
       "AT Rayudu                546.0      inf       112.96  \n",
       "N Rana                   203.0    56.00       127.27  \n",
       "CH Gayle                 356.0    51.00       196.15  \n",
       "Ishan Kishan             685.0    47.00       134.29  \n",
       "SK Raina                 221.0    15.33       131.43  \n",
       "SV Samson                443.0    44.00        83.02  \n",
       "AM Rahane                444.0    41.00       120.59  \n",
       "M Vohra                   85.0      inf       229.41  \n",
       "AB de Villiers           284.0    18.50       100.00  \n",
       "AJ Finch                 228.0    18.50       148.00  \n",
       "SO Hetmyer               218.0    18.00       171.43  \n",
       "C de Grandhomme         1808.0    33.00       143.48  \n",
       "KD Karthik               194.0    14.50       111.54  \n",
       "MA Agarwal               409.0    23.00       100.00  \n",
       "KA Pollard               506.0    23.00        65.71  \n",
       "CA Lynn                  194.0    23.00        95.83  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_runs_against_rashid.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "external-pledge",
   "metadata": {},
   "outputs": [],
   "source": [
    "most_runs_against_rashid['Average'][0] = 82\n",
    "most_runs_against_rashid['Average'][4] = 61"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "rocky-vacation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a45b7b7520>"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,11))\n",
    "sns.set(font_scale=1.4)\n",
    "# sns.set_style('darkgrid')\n",
    "sns.scatterplot(x='batsman_runs', y='Strike Rate', data=most_runs_against_rashid.head(10), size='Average', sizes=(40, 600), legend='full')\n",
    "plt.title(\"Most runs against Rashid Khan in IPL\")\n",
    "plt.xlabel(\"Runs\")\n",
    "plt.ylabel(\"Strike Rate\")\n",
    "# sns.despine(top=True, left=True, right=True, bottom=True)\n",
    "\n",
    "for line in range(0,most_runs_against_rashid.head(10).shape[0]):\n",
    "     plt.text(most_runs_against_rashid['batsman_runs'][line]-1.5, most_runs_against_rashid['Strike Rate'][line]+2.5, most_runs_against_rashid.index[line], horizontalalignment='left', size='small', color='black', )\n",
    "\n",
    "# plt.text(MS_RP_bowler_type['Average'][6]-6.5, MS_RP_bowler_type['Strike Rate'][6]+5.5, MS_RP_bowler_type['Bowler Category'][6], horizontalalignment='left', size='small', color='black', )\n",
    "plt.legend(bbox_to_anchor=(0.99, 0.7), loc=1, borderaxespad=0., title='Average', shadow=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "expensive-speed",
   "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>batsman</th>\n",
       "      <th>id</th>\n",
       "      <th>inning</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>non_boundary</th>\n",
       "      <th>is_wicket</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>Average</th>\n",
       "      <th>Strike Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SR Watson</td>\n",
       "      <td>54660505</td>\n",
       "      <td>80</td>\n",
       "      <td>498</td>\n",
       "      <td>47</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>174.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AT Rayudu</td>\n",
       "      <td>62788130</td>\n",
       "      <td>81</td>\n",
       "      <td>609</td>\n",
       "      <td>54</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>112.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M Vohra</td>\n",
       "      <td>18512333</td>\n",
       "      <td>34</td>\n",
       "      <td>165</td>\n",
       "      <td>17</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>229.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RA Tripathi</td>\n",
       "      <td>12124561</td>\n",
       "      <td>18</td>\n",
       "      <td>98</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>163.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SA Yadav</td>\n",
       "      <td>23374666</td>\n",
       "      <td>27</td>\n",
       "      <td>194</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>80.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>N Rana</td>\n",
       "      <td>51118625</td>\n",
       "      <td>71</td>\n",
       "      <td>386</td>\n",
       "      <td>44</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>127.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>30152269</td>\n",
       "      <td>29</td>\n",
       "      <td>225</td>\n",
       "      <td>26</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>196.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Ishan Kishan</td>\n",
       "      <td>41318350</td>\n",
       "      <td>35</td>\n",
       "      <td>325</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>134.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>SV Samson</td>\n",
       "      <td>61685272</td>\n",
       "      <td>85</td>\n",
       "      <td>476</td>\n",
       "      <td>53</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>44.00</td>\n",
       "      <td>83.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>AM Rahane</td>\n",
       "      <td>39331641</td>\n",
       "      <td>55</td>\n",
       "      <td>340</td>\n",
       "      <td>34</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>120.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>75450953</td>\n",
       "      <td>94</td>\n",
       "      <td>662</td>\n",
       "      <td>65</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>34.50</td>\n",
       "      <td>106.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>C de Grandhomme</td>\n",
       "      <td>26806942</td>\n",
       "      <td>46</td>\n",
       "      <td>323</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>33.00</td>\n",
       "      <td>143.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>46088437</td>\n",
       "      <td>70</td>\n",
       "      <td>371</td>\n",
       "      <td>41</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>32.00</td>\n",
       "      <td>156.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>27487629</td>\n",
       "      <td>40</td>\n",
       "      <td>217</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>95.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>KA Pollard</td>\n",
       "      <td>40536424</td>\n",
       "      <td>40</td>\n",
       "      <td>455</td>\n",
       "      <td>35</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>65.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>MA Agarwal</td>\n",
       "      <td>26894218</td>\n",
       "      <td>45</td>\n",
       "      <td>188</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>MM Ali</td>\n",
       "      <td>14775919</td>\n",
       "      <td>15</td>\n",
       "      <td>120</td>\n",
       "      <td>13</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>169.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>28068300</td>\n",
       "      <td>35</td>\n",
       "      <td>203</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>95.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Shubman Gill</td>\n",
       "      <td>31067893</td>\n",
       "      <td>43</td>\n",
       "      <td>291</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>84.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>RR Pant</td>\n",
       "      <td>66195012</td>\n",
       "      <td>87</td>\n",
       "      <td>587</td>\n",
       "      <td>57</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>20.33</td>\n",
       "      <td>107.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>30187706</td>\n",
       "      <td>40</td>\n",
       "      <td>261</td>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>74.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>EJG Morgan</td>\n",
       "      <td>30022979</td>\n",
       "      <td>49</td>\n",
       "      <td>245</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>73.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>44418600</td>\n",
       "      <td>41</td>\n",
       "      <td>372</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>AJ Finch</td>\n",
       "      <td>29589130</td>\n",
       "      <td>26</td>\n",
       "      <td>243</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>148.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>SO Hetmyer</td>\n",
       "      <td>24999460</td>\n",
       "      <td>39</td>\n",
       "      <td>212</td>\n",
       "      <td>21</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>171.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>DR Smith</td>\n",
       "      <td>10826242</td>\n",
       "      <td>10</td>\n",
       "      <td>98</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>180.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>15252644</td>\n",
       "      <td>16</td>\n",
       "      <td>152</td>\n",
       "      <td>14</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>128.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>21995116</td>\n",
       "      <td>28</td>\n",
       "      <td>148</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>94.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>18070485</td>\n",
       "      <td>27</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>40042339</td>\n",
       "      <td>49</td>\n",
       "      <td>343</td>\n",
       "      <td>35</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>15.33</td>\n",
       "      <td>131.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>LS Livingstone</td>\n",
       "      <td>9427360</td>\n",
       "      <td>16</td>\n",
       "      <td>39</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>187.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>TM Head</td>\n",
       "      <td>9743319</td>\n",
       "      <td>18</td>\n",
       "      <td>74</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>166.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>29145311</td>\n",
       "      <td>32</td>\n",
       "      <td>302</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>14.50</td>\n",
       "      <td>111.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>26154044</td>\n",
       "      <td>22</td>\n",
       "      <td>221</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>63.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>PP Shaw</td>\n",
       "      <td>10410044</td>\n",
       "      <td>13</td>\n",
       "      <td>64</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.00</td>\n",
       "      <td>144.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>KK Nair</td>\n",
       "      <td>15534420</td>\n",
       "      <td>27</td>\n",
       "      <td>129</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>85.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>36576653</td>\n",
       "      <td>53</td>\n",
       "      <td>430</td>\n",
       "      <td>32</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>71.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>AD Russell</td>\n",
       "      <td>12861758</td>\n",
       "      <td>18</td>\n",
       "      <td>150</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>10.00</td>\n",
       "      <td>90.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>HH Pandya</td>\n",
       "      <td>37797872</td>\n",
       "      <td>43</td>\n",
       "      <td>435</td>\n",
       "      <td>33</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>823.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>54.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>13722949</td>\n",
       "      <td>21</td>\n",
       "      <td>116</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>58.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>12991732</td>\n",
       "      <td>23</td>\n",
       "      <td>35</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>58.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>N Pooran</td>\n",
       "      <td>18171804</td>\n",
       "      <td>28</td>\n",
       "      <td>166</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>873.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>46.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>35329799</td>\n",
       "      <td>51</td>\n",
       "      <td>264</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>9388062</td>\n",
       "      <td>14</td>\n",
       "      <td>68</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>BA Stokes</td>\n",
       "      <td>20640263</td>\n",
       "      <td>21</td>\n",
       "      <td>177</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>5.50</td>\n",
       "      <td>61.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>MP Stoinis</td>\n",
       "      <td>8598316</td>\n",
       "      <td>10</td>\n",
       "      <td>74</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>71.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>6495762</td>\n",
       "      <td>12</td>\n",
       "      <td>86</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>83.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>3409701</td>\n",
       "      <td>3</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>133.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>PP Chawla</td>\n",
       "      <td>11700542</td>\n",
       "      <td>12</td>\n",
       "      <td>170</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>40.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>AR Patel</td>\n",
       "      <td>16459543</td>\n",
       "      <td>23</td>\n",
       "      <td>198</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>50.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            batsman        id  inning  over  ball  batsman_runs  extra_runs  \\\n",
       "0         SR Watson  54660505      80   498    47            82           1   \n",
       "1         AT Rayudu  62788130      81   609    54            61           1   \n",
       "2           M Vohra  18512333      34   165    17            39           4   \n",
       "3       RA Tripathi  12124561      18    98    11            18           0   \n",
       "4          SA Yadav  23374666      27   194    20            16           0   \n",
       "40           N Rana  51118625      71   386    44            56           1   \n",
       "41         CH Gayle  30152269      29   225    26            51           0   \n",
       "42     Ishan Kishan  41318350      35   325    35            47           5   \n",
       "43        SV Samson  61685272      85   476    53            44           4   \n",
       "44        AM Rahane  39331641      55   340    34            41           0   \n",
       "45          SS Iyer  75450953      94   662    65            69           2   \n",
       "46  C de Grandhomme  26806942      46   323    23            33           1   \n",
       "47       RV Uthappa  46088437      70   371    41            64           0   \n",
       "48          CA Lynn  27487629      40   217    24            23           0   \n",
       "49       KA Pollard  40536424      40   455    35            23           2   \n",
       "50       MA Agarwal  26894218      45   188    23            23           0   \n",
       "51           MM Ali  14775919      15   120    13            22           0   \n",
       "52         S Dhawan  28068300      35   203    23            22           0   \n",
       "53     Shubman Gill  31067893      43   291    26            22           0   \n",
       "54          RR Pant  66195012      87   587    57            61           2   \n",
       "55        SPD Smith  30187706      40   261    27            20           0   \n",
       "56       EJG Morgan  30022979      49   245    26            19           1   \n",
       "57   AB de Villiers  44418600      41   372    37            37           2   \n",
       "58         AJ Finch  29589130      26   243    25            37           0   \n",
       "59       SO Hetmyer  24999460      39   212    21            36           1   \n",
       "60         DR Smith  10826242      10    98    10            18           0   \n",
       "61        RG Sharma  15252644      16   152    14            18           0   \n",
       "62          V Kohli  21995116      28   148    19            18           0   \n",
       "63        KH Pandya  18070485      27   187    16            16           3   \n",
       "64         SK Raina  40042339      49   343    35            46           5   \n",
       "65   LS Livingstone   9427360      16    39     8            15           0   \n",
       "66          TM Head   9743319      18    74     9            15           0   \n",
       "67       KD Karthik  29145311      32   302    26            29           0   \n",
       "68        Q de Kock  26154044      22   221    22            14           2   \n",
       "69          PP Shaw  10410044      13    64     9            13           1   \n",
       "70          KK Nair  15534420      27   129    14            12           0   \n",
       "71         MS Dhoni  36576653      53   430    32            23           1   \n",
       "72       AD Russell  12861758      18   150    11            10           0   \n",
       "73        HH Pandya  37797872      43   435    33            18           0   \n",
       "74     F du Plessis  13722949      21   116    12             7           0   \n",
       "75        G Gambhir  12991732      23    35    12             7           2   \n",
       "76         N Pooran  18171804      28   166    15             7           0   \n",
       "77         KL Rahul  35329799      51   264    30            18           0   \n",
       "78        KM Jadhav   9388062      14    68     8             6           0   \n",
       "79        BA Stokes  20640263      21   177    18            11           0   \n",
       "80       MP Stoinis   8598316      10    74     7             5           1   \n",
       "81        YK Pathan   6495762      12    86     6             5           3   \n",
       "82      BCJ Cutting   3409701       3    51     3             4           0   \n",
       "83        PP Chawla  11700542      12   170    10             4           0   \n",
       "84         AR Patel  16459543      23   198    14             7           0   \n",
       "\n",
       "    total_runs  non_boundary  is_wicket  neutral_venue  result_margin  \\\n",
       "0           83             0          0              0          597.0   \n",
       "1           62             0          0              0          546.0   \n",
       "2           43             0          0              0           85.0   \n",
       "3           18             0          0              0           78.0   \n",
       "4           16             0          0              0          291.0   \n",
       "40          57             0          1              0          203.0   \n",
       "41          51             0          1              0          356.0   \n",
       "42          52             0          1              0          685.0   \n",
       "43          48             0          1              0          443.0   \n",
       "44          41             0          1              0          444.0   \n",
       "45          71             0          2              0         1399.0   \n",
       "46          34             0          1              0         1808.0   \n",
       "47          64             0          2              0          664.0   \n",
       "48          23             0          1              0          194.0   \n",
       "49          25             0          1              0          506.0   \n",
       "50          23             0          1              0          409.0   \n",
       "51          22             0          1              0          164.0   \n",
       "52          22             1          1              0          315.0   \n",
       "53          22             0          1              0          153.0   \n",
       "54          63             0          3              0         1566.0   \n",
       "55          20             0          1              0          229.0   \n",
       "56          20             0          1              0          347.0   \n",
       "57          39             0          2              0          284.0   \n",
       "58          37             0          2              0          228.0   \n",
       "59          37             0          2              0          218.0   \n",
       "60          18             0          1              0           84.0   \n",
       "61          18             0          1              0           85.0   \n",
       "62          18             0          1              0          165.0   \n",
       "63          19             0          1              0          379.0   \n",
       "64          51             0          3              0          221.0   \n",
       "65          15             0          1              0           56.0   \n",
       "66          15             0          1              0          315.0   \n",
       "67          29             0          2              0          194.0   \n",
       "68          16             0          1              0          204.0   \n",
       "69          14             0          1              0           43.0   \n",
       "70          12             0          1              0          171.0   \n",
       "71          24             0          2              0          232.0   \n",
       "72          10             0          1              0           91.0   \n",
       "73          18             0          2              0          823.0   \n",
       "74           7             0          1              0           34.0   \n",
       "75           9             0          1              0           94.0   \n",
       "76           7             0          1              0          873.0   \n",
       "77          18             0          3              0          537.0   \n",
       "78           6             0          1              0          110.0   \n",
       "79          11             0          2              0          158.0   \n",
       "80           6             0          1              0          113.0   \n",
       "81           8             0          1              0          288.0   \n",
       "82           4             0          1              0            3.0   \n",
       "83           4             0          1              0          100.0   \n",
       "84           7             0          2              0          388.0   \n",
       "\n",
       "    Average  Strike Rate  \n",
       "0       inf       174.47  \n",
       "1       inf       112.96  \n",
       "2       inf       229.41  \n",
       "3       inf       163.64  \n",
       "4       inf        80.00  \n",
       "40    56.00       127.27  \n",
       "41    51.00       196.15  \n",
       "42    47.00       134.29  \n",
       "43    44.00        83.02  \n",
       "44    41.00       120.59  \n",
       "45    34.50       106.15  \n",
       "46    33.00       143.48  \n",
       "47    32.00       156.10  \n",
       "48    23.00        95.83  \n",
       "49    23.00        65.71  \n",
       "50    23.00       100.00  \n",
       "51    22.00       169.23  \n",
       "52    22.00        95.65  \n",
       "53    22.00        84.62  \n",
       "54    20.33       107.02  \n",
       "55    20.00        74.07  \n",
       "56    19.00        73.08  \n",
       "57    18.50       100.00  \n",
       "58    18.50       148.00  \n",
       "59    18.00       171.43  \n",
       "60    18.00       180.00  \n",
       "61    18.00       128.57  \n",
       "62    18.00        94.74  \n",
       "63    16.00       100.00  \n",
       "64    15.33       131.43  \n",
       "65    15.00       187.50  \n",
       "66    15.00       166.67  \n",
       "67    14.50       111.54  \n",
       "68    14.00        63.64  \n",
       "69    13.00       144.44  \n",
       "70    12.00        85.71  \n",
       "71    11.50        71.88  \n",
       "72    10.00        90.91  \n",
       "73     9.00        54.55  \n",
       "74     7.00        58.33  \n",
       "75     7.00        58.33  \n",
       "76     7.00        46.67  \n",
       "77     6.00        60.00  \n",
       "78     6.00        75.00  \n",
       "79     5.50        61.11  \n",
       "80     5.00        71.43  \n",
       "81     5.00        83.33  \n",
       "82     4.00       133.33  \n",
       "83     4.00        40.00  \n",
       "84     3.50        50.00  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_avg_against_rashid.head(60)\n",
    "highest_avg_against_rashid.drop([39], inplace=True)\n",
    "highest_avg_against_rashid.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "bizarre-steps",
   "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>batsman</th>\n",
       "      <th>id</th>\n",
       "      <th>inning</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>non_boundary</th>\n",
       "      <th>is_wicket</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>Average</th>\n",
       "      <th>Strike Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SR Watson</td>\n",
       "      <td>54660505</td>\n",
       "      <td>80</td>\n",
       "      <td>498</td>\n",
       "      <td>47</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>174.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AT Rayudu</td>\n",
       "      <td>62788130</td>\n",
       "      <td>81</td>\n",
       "      <td>609</td>\n",
       "      <td>54</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>112.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M Vohra</td>\n",
       "      <td>18512333</td>\n",
       "      <td>34</td>\n",
       "      <td>165</td>\n",
       "      <td>17</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>229.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RA Tripathi</td>\n",
       "      <td>12124561</td>\n",
       "      <td>18</td>\n",
       "      <td>98</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>163.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SA Yadav</td>\n",
       "      <td>23374666</td>\n",
       "      <td>27</td>\n",
       "      <td>194</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>80.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>N Rana</td>\n",
       "      <td>51118625</td>\n",
       "      <td>71</td>\n",
       "      <td>386</td>\n",
       "      <td>44</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>127.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>30152269</td>\n",
       "      <td>29</td>\n",
       "      <td>225</td>\n",
       "      <td>26</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>196.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Ishan Kishan</td>\n",
       "      <td>41318350</td>\n",
       "      <td>35</td>\n",
       "      <td>325</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>134.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SV Samson</td>\n",
       "      <td>61685272</td>\n",
       "      <td>85</td>\n",
       "      <td>476</td>\n",
       "      <td>53</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>83.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>AM Rahane</td>\n",
       "      <td>39331641</td>\n",
       "      <td>55</td>\n",
       "      <td>340</td>\n",
       "      <td>34</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>120.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>75450953</td>\n",
       "      <td>94</td>\n",
       "      <td>662</td>\n",
       "      <td>65</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>106.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>C de Grandhomme</td>\n",
       "      <td>26806942</td>\n",
       "      <td>46</td>\n",
       "      <td>323</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>143.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>46088437</td>\n",
       "      <td>70</td>\n",
       "      <td>371</td>\n",
       "      <td>41</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>156.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            batsman        id  inning  over  ball  batsman_runs  extra_runs  \\\n",
       "0         SR Watson  54660505      80   498    47            82           1   \n",
       "1         AT Rayudu  62788130      81   609    54            61           1   \n",
       "2           M Vohra  18512333      34   165    17            39           4   \n",
       "3       RA Tripathi  12124561      18    98    11            18           0   \n",
       "4          SA Yadav  23374666      27   194    20            16           0   \n",
       "5            N Rana  51118625      71   386    44            56           1   \n",
       "6          CH Gayle  30152269      29   225    26            51           0   \n",
       "7      Ishan Kishan  41318350      35   325    35            47           5   \n",
       "8         SV Samson  61685272      85   476    53            44           4   \n",
       "9         AM Rahane  39331641      55   340    34            41           0   \n",
       "10          SS Iyer  75450953      94   662    65            69           2   \n",
       "11  C de Grandhomme  26806942      46   323    23            33           1   \n",
       "12       RV Uthappa  46088437      70   371    41            64           0   \n",
       "\n",
       "    total_runs  non_boundary  is_wicket  neutral_venue  result_margin  \\\n",
       "0           83             0          0              0          597.0   \n",
       "1           62             0          0              0          546.0   \n",
       "2           43             0          0              0           85.0   \n",
       "3           18             0          0              0           78.0   \n",
       "4           16             0          0              0          291.0   \n",
       "5           57             0          1              0          203.0   \n",
       "6           51             0          1              0          356.0   \n",
       "7           52             0          1              0          685.0   \n",
       "8           48             0          1              0          443.0   \n",
       "9           41             0          1              0          444.0   \n",
       "10          71             0          2              0         1399.0   \n",
       "11          34             0          1              0         1808.0   \n",
       "12          64             0          2              0          664.0   \n",
       "\n",
       "    Average  Strike Rate  \n",
       "0       inf       174.47  \n",
       "1       inf       112.96  \n",
       "2       inf       229.41  \n",
       "3       inf       163.64  \n",
       "4       inf        80.00  \n",
       "5      56.0       127.27  \n",
       "6      51.0       196.15  \n",
       "7      47.0       134.29  \n",
       "8      44.0        83.02  \n",
       "9      41.0       120.59  \n",
       "10     34.5       106.15  \n",
       "11     33.0       143.48  \n",
       "12     32.0       156.10  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_avg_against_rashid.head(13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "earned-radical",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a45aaf1e80>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,11))\n",
    "sns.set(font_scale=1.3)\n",
    "# sns.set_style('darkgrid')\n",
    "sns.scatterplot(x='Average', y='Strike Rate', data=highest_avg_against_rashid.head(13), size='batsman_runs', sizes=(20, 700), legend='full')\n",
    "plt.title(\"Highest Average against Rashid Khan in IPL\")\n",
    "plt.xlabel(\"Average\")\n",
    "plt.ylabel(\"Strike Rate\")\n",
    "# sns.despine(top=True, left=True, right=True, bottom=True)\n",
    "\n",
    "for line in range(0,highest_avg_against_rashid.head(13).shape[0]):\n",
    "     if(line < 5):\n",
    "        plt.text(highest_avg_against_rashid['Average'][line]-2.5 , highest_avg_against_rashid['Strike Rate'][line]+3.5, highest_avg_against_rashid['batsman'][line]+\"+\", horizontalalignment='left', size='small', color='black', )\n",
    "     plt.text(highest_avg_against_rashid['Average'][line]-2.5, highest_avg_against_rashid['Strike Rate'][line]+3.5, highest_avg_against_rashid['batsman'][line], horizontalalignment='left', size='small', color='black', )\n",
    "\n",
    "# plt.text(MS_RP_bowler_type['Average'][6]-6.5, MS_RP_bowler_type['Strike Rate'][6]+5.5, MS_RP_bowler_type['Bowler Category'][6], horizontalalignment='left', size='small', color='black', )\n",
    "plt.legend(bbox_to_anchor=(0.99, 0.5), loc=1, borderaxespad=0., title='Runs', shadow=True, fontsize=13)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "honey-syndicate",
   "metadata": {},
   "outputs": [],
   "source": [
    "highest_avg_against_rashid['Average'][0] = 82\n",
    "highest_avg_against_rashid['Average'][1] = 61\n",
    "highest_avg_against_rashid['Average'][2] = 39\n",
    "highest_avg_against_rashid['Average'][3] = 18\n",
    "highest_avg_against_rashid['Average'][4] = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "finnish-christopher",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>inning</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>non_boundary</th>\n",
       "      <th>is_wicket</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>Average</th>\n",
       "      <th>Strike Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>batsman</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M Vohra</th>\n",
       "      <td>18512333</td>\n",
       "      <td>34</td>\n",
       "      <td>165</td>\n",
       "      <td>17</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>229.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH Gayle</th>\n",
       "      <td>30152269</td>\n",
       "      <td>29</td>\n",
       "      <td>225</td>\n",
       "      <td>26</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>196.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR Watson</th>\n",
       "      <td>54660505</td>\n",
       "      <td>80</td>\n",
       "      <td>498</td>\n",
       "      <td>47</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>174.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO Hetmyer</th>\n",
       "      <td>24999460</td>\n",
       "      <td>39</td>\n",
       "      <td>212</td>\n",
       "      <td>21</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>171.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RV Uthappa</th>\n",
       "      <td>46088437</td>\n",
       "      <td>70</td>\n",
       "      <td>371</td>\n",
       "      <td>41</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>32.00</td>\n",
       "      <td>156.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AJ Finch</th>\n",
       "      <td>29589130</td>\n",
       "      <td>26</td>\n",
       "      <td>243</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>148.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C de Grandhomme</th>\n",
       "      <td>26806942</td>\n",
       "      <td>46</td>\n",
       "      <td>323</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>33.00</td>\n",
       "      <td>143.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ishan Kishan</th>\n",
       "      <td>41318350</td>\n",
       "      <td>35</td>\n",
       "      <td>325</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>134.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK Raina</th>\n",
       "      <td>40042339</td>\n",
       "      <td>49</td>\n",
       "      <td>343</td>\n",
       "      <td>35</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>15.33</td>\n",
       "      <td>131.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N Rana</th>\n",
       "      <td>51118625</td>\n",
       "      <td>71</td>\n",
       "      <td>386</td>\n",
       "      <td>44</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>127.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM Rahane</th>\n",
       "      <td>39331641</td>\n",
       "      <td>55</td>\n",
       "      <td>340</td>\n",
       "      <td>34</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>120.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AT Rayudu</th>\n",
       "      <td>62788130</td>\n",
       "      <td>81</td>\n",
       "      <td>609</td>\n",
       "      <td>54</td>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>62</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>546.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>112.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KD Karthik</th>\n",
       "      <td>29145311</td>\n",
       "      <td>32</td>\n",
       "      <td>302</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>14.50</td>\n",
       "      <td>111.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR Pant</th>\n",
       "      <td>66195012</td>\n",
       "      <td>87</td>\n",
       "      <td>587</td>\n",
       "      <td>57</td>\n",
       "      <td>61</td>\n",
       "      <td>2</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1566.0</td>\n",
       "      <td>20.33</td>\n",
       "      <td>107.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SS Iyer</th>\n",
       "      <td>75450953</td>\n",
       "      <td>94</td>\n",
       "      <td>662</td>\n",
       "      <td>65</td>\n",
       "      <td>69</td>\n",
       "      <td>2</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1399.0</td>\n",
       "      <td>34.50</td>\n",
       "      <td>106.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MA Agarwal</th>\n",
       "      <td>26894218</td>\n",
       "      <td>45</td>\n",
       "      <td>188</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>409.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KH Pandya</th>\n",
       "      <td>18070485</td>\n",
       "      <td>27</td>\n",
       "      <td>187</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>379.0</td>\n",
       "      <td>16.00</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AB de Villiers</th>\n",
       "      <td>44418600</td>\n",
       "      <td>41</td>\n",
       "      <td>372</td>\n",
       "      <td>37</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA Lynn</th>\n",
       "      <td>27487629</td>\n",
       "      <td>40</td>\n",
       "      <td>217</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>95.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S Dhawan</th>\n",
       "      <td>28068300</td>\n",
       "      <td>35</td>\n",
       "      <td>203</td>\n",
       "      <td>23</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>315.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>95.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V Kohli</th>\n",
       "      <td>21995116</td>\n",
       "      <td>28</td>\n",
       "      <td>148</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>94.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shubman Gill</th>\n",
       "      <td>31067893</td>\n",
       "      <td>43</td>\n",
       "      <td>291</td>\n",
       "      <td>26</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>22.00</td>\n",
       "      <td>84.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SV Samson</th>\n",
       "      <td>61685272</td>\n",
       "      <td>85</td>\n",
       "      <td>476</td>\n",
       "      <td>53</td>\n",
       "      <td>44</td>\n",
       "      <td>4</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>443.0</td>\n",
       "      <td>44.00</td>\n",
       "      <td>83.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SA Yadav</th>\n",
       "      <td>23374666</td>\n",
       "      <td>27</td>\n",
       "      <td>194</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>80.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MK Pandey</th>\n",
       "      <td>17321779</td>\n",
       "      <td>21</td>\n",
       "      <td>133</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>427.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>75.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPD Smith</th>\n",
       "      <td>30187706</td>\n",
       "      <td>40</td>\n",
       "      <td>261</td>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>74.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EJG Morgan</th>\n",
       "      <td>30022979</td>\n",
       "      <td>49</td>\n",
       "      <td>245</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>19.00</td>\n",
       "      <td>73.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS Dhoni</th>\n",
       "      <td>36576653</td>\n",
       "      <td>53</td>\n",
       "      <td>430</td>\n",
       "      <td>32</td>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>71.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KA Pollard</th>\n",
       "      <td>40536424</td>\n",
       "      <td>40</td>\n",
       "      <td>455</td>\n",
       "      <td>35</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>506.0</td>\n",
       "      <td>23.00</td>\n",
       "      <td>65.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RA Jadeja</th>\n",
       "      <td>25797623</td>\n",
       "      <td>35</td>\n",
       "      <td>277</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>138.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>63.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q de Kock</th>\n",
       "      <td>26154044</td>\n",
       "      <td>22</td>\n",
       "      <td>221</td>\n",
       "      <td>22</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>63.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BA Stokes</th>\n",
       "      <td>20640263</td>\n",
       "      <td>21</td>\n",
       "      <td>177</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>5.50</td>\n",
       "      <td>61.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KL Rahul</th>\n",
       "      <td>35329799</td>\n",
       "      <td>51</td>\n",
       "      <td>264</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>537.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HH Pandya</th>\n",
       "      <td>37797872</td>\n",
       "      <td>43</td>\n",
       "      <td>435</td>\n",
       "      <td>33</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>823.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>54.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GJ Maxwell</th>\n",
       "      <td>18008134</td>\n",
       "      <td>23</td>\n",
       "      <td>152</td>\n",
       "      <td>15</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>627.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>53.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N Pooran</th>\n",
       "      <td>18171804</td>\n",
       "      <td>28</td>\n",
       "      <td>166</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>873.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>46.67</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       id  inning  over  ball  batsman_runs  extra_runs  \\\n",
       "batsman                                                                   \n",
       "M Vohra          18512333      34   165    17            39           4   \n",
       "CH Gayle         30152269      29   225    26            51           0   \n",
       "SR Watson        54660505      80   498    47            82           1   \n",
       "SO Hetmyer       24999460      39   212    21            36           1   \n",
       "RV Uthappa       46088437      70   371    41            64           0   \n",
       "AJ Finch         29589130      26   243    25            37           0   \n",
       "C de Grandhomme  26806942      46   323    23            33           1   \n",
       "Ishan Kishan     41318350      35   325    35            47           5   \n",
       "SK Raina         40042339      49   343    35            46           5   \n",
       "N Rana           51118625      71   386    44            56           1   \n",
       "AM Rahane        39331641      55   340    34            41           0   \n",
       "AT Rayudu        62788130      81   609    54            61           1   \n",
       "KD Karthik       29145311      32   302    26            29           0   \n",
       "RR Pant          66195012      87   587    57            61           2   \n",
       "SS Iyer          75450953      94   662    65            69           2   \n",
       "MA Agarwal       26894218      45   188    23            23           0   \n",
       "KH Pandya        18070485      27   187    16            16           3   \n",
       "AB de Villiers   44418600      41   372    37            37           2   \n",
       "CA Lynn          27487629      40   217    24            23           0   \n",
       "S Dhawan         28068300      35   203    23            22           0   \n",
       "V Kohli          21995116      28   148    19            18           0   \n",
       "Shubman Gill     31067893      43   291    26            22           0   \n",
       "SV Samson        61685272      85   476    53            44           4   \n",
       "SA Yadav         23374666      27   194    20            16           0   \n",
       "MK Pandey        17321779      21   133    16            12           0   \n",
       "SPD Smith        30187706      40   261    27            20           0   \n",
       "EJG Morgan       30022979      49   245    26            19           1   \n",
       "MS Dhoni         36576653      53   430    32            23           1   \n",
       "KA Pollard       40536424      40   455    35            23           2   \n",
       "RA Jadeja        25797623      35   277    22            14           1   \n",
       "Q de Kock        26154044      22   221    22            14           2   \n",
       "BA Stokes        20640263      21   177    18            11           0   \n",
       "KL Rahul         35329799      51   264    30            18           0   \n",
       "HH Pandya        37797872      43   435    33            18           0   \n",
       "GJ Maxwell       18008134      23   152    15             8           0   \n",
       "N Pooran         18171804      28   166    15             7           0   \n",
       "\n",
       "                 total_runs  non_boundary  is_wicket  neutral_venue  \\\n",
       "batsman                                                               \n",
       "M Vohra                  43             0          0              0   \n",
       "CH Gayle                 51             0          1              0   \n",
       "SR Watson                83             0          0              0   \n",
       "SO Hetmyer               37             0          2              0   \n",
       "RV Uthappa               64             0          2              0   \n",
       "AJ Finch                 37             0          2              0   \n",
       "C de Grandhomme          34             0          1              0   \n",
       "Ishan Kishan             52             0          1              0   \n",
       "SK Raina                 51             0          3              0   \n",
       "N Rana                   57             0          1              0   \n",
       "AM Rahane                41             0          1              0   \n",
       "AT Rayudu                62             0          0              0   \n",
       "KD Karthik               29             0          2              0   \n",
       "RR Pant                  63             0          3              0   \n",
       "SS Iyer                  71             0          2              0   \n",
       "MA Agarwal               23             0          1              0   \n",
       "KH Pandya                19             0          1              0   \n",
       "AB de Villiers           39             0          2              0   \n",
       "CA Lynn                  23             0          1              0   \n",
       "S Dhawan                 22             1          1              0   \n",
       "V Kohli                  18             0          1              0   \n",
       "Shubman Gill             22             0          1              0   \n",
       "SV Samson                48             0          1              0   \n",
       "SA Yadav                 16             0          0              0   \n",
       "MK Pandey                12             0          0              0   \n",
       "SPD Smith                20             0          1              0   \n",
       "EJG Morgan               20             0          1              0   \n",
       "MS Dhoni                 24             0          2              0   \n",
       "KA Pollard               25             0          1              0   \n",
       "RA Jadeja                15             0          0              0   \n",
       "Q de Kock                16             0          1              0   \n",
       "BA Stokes                11             0          2              0   \n",
       "KL Rahul                 18             0          3              0   \n",
       "HH Pandya                18             0          2              0   \n",
       "GJ Maxwell                8             0          0              0   \n",
       "N Pooran                  7             0          1              0   \n",
       "\n",
       "                 result_margin  Average  Strike Rate  \n",
       "batsman                                               \n",
       "M Vohra                   85.0      inf       229.41  \n",
       "CH Gayle                 356.0    51.00       196.15  \n",
       "SR Watson                597.0      inf       174.47  \n",
       "SO Hetmyer               218.0    18.00       171.43  \n",
       "RV Uthappa               664.0    32.00       156.10  \n",
       "AJ Finch                 228.0    18.50       148.00  \n",
       "C de Grandhomme         1808.0    33.00       143.48  \n",
       "Ishan Kishan             685.0    47.00       134.29  \n",
       "SK Raina                 221.0    15.33       131.43  \n",
       "N Rana                   203.0    56.00       127.27  \n",
       "AM Rahane                444.0    41.00       120.59  \n",
       "AT Rayudu                546.0      inf       112.96  \n",
       "KD Karthik               194.0    14.50       111.54  \n",
       "RR Pant                 1566.0    20.33       107.02  \n",
       "SS Iyer                 1399.0    34.50       106.15  \n",
       "MA Agarwal               409.0    23.00       100.00  \n",
       "KH Pandya                379.0    16.00       100.00  \n",
       "AB de Villiers           284.0    18.50       100.00  \n",
       "CA Lynn                  194.0    23.00        95.83  \n",
       "S Dhawan                 315.0    22.00        95.65  \n",
       "V Kohli                  165.0    18.00        94.74  \n",
       "Shubman Gill             153.0    22.00        84.62  \n",
       "SV Samson                443.0    44.00        83.02  \n",
       "SA Yadav                 291.0      inf        80.00  \n",
       "MK Pandey                427.0      inf        75.00  \n",
       "SPD Smith                229.0    20.00        74.07  \n",
       "EJG Morgan               347.0    19.00        73.08  \n",
       "MS Dhoni                 232.0    11.50        71.88  \n",
       "KA Pollard               506.0    23.00        65.71  \n",
       "RA Jadeja                138.0      inf        63.64  \n",
       "Q de Kock                204.0    14.00        63.64  \n",
       "BA Stokes                158.0     5.50        61.11  \n",
       "KL Rahul                 537.0     6.00        60.00  \n",
       "HH Pandya                823.0     9.00        54.55  \n",
       "GJ Maxwell               627.0      inf        53.33  \n",
       "N Pooran                 873.0     7.00        46.67  "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_sr_against_rashid_min_fifteen_balls = highest_sr_against_rashid[highest_sr_against_rashid['ball'] >= 15]\n",
    "highest_sr_against_rashid_min_fifteen_balls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "willing-decision",
   "metadata": {},
   "outputs": [],
   "source": [
    "highest_sr_against_rashid_min_fifteen_balls['Average'][0] = 39\n",
    "highest_sr_against_rashid_min_fifteen_balls['Average'][2] = 82"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "successful-turtle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>inning</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>non_boundary</th>\n",
       "      <th>is_wicket</th>\n",
       "      <th>neutral_venue</th>\n",
       "      <th>result_margin</th>\n",
       "      <th>Average</th>\n",
       "      <th>Strike Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>batsman</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>M Vohra</th>\n",
       "      <td>18512333</td>\n",
       "      <td>34</td>\n",
       "      <td>165</td>\n",
       "      <td>17</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>39.00</td>\n",
       "      <td>229.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CH Gayle</th>\n",
       "      <td>30152269</td>\n",
       "      <td>29</td>\n",
       "      <td>225</td>\n",
       "      <td>26</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>51.00</td>\n",
       "      <td>196.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SR Watson</th>\n",
       "      <td>54660505</td>\n",
       "      <td>80</td>\n",
       "      <td>498</td>\n",
       "      <td>47</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>597.0</td>\n",
       "      <td>82.00</td>\n",
       "      <td>174.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SO Hetmyer</th>\n",
       "      <td>24999460</td>\n",
       "      <td>39</td>\n",
       "      <td>212</td>\n",
       "      <td>21</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>171.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RV Uthappa</th>\n",
       "      <td>46088437</td>\n",
       "      <td>70</td>\n",
       "      <td>371</td>\n",
       "      <td>41</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>664.0</td>\n",
       "      <td>32.00</td>\n",
       "      <td>156.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AJ Finch</th>\n",
       "      <td>29589130</td>\n",
       "      <td>26</td>\n",
       "      <td>243</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>228.0</td>\n",
       "      <td>18.50</td>\n",
       "      <td>148.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C de Grandhomme</th>\n",
       "      <td>26806942</td>\n",
       "      <td>46</td>\n",
       "      <td>323</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1808.0</td>\n",
       "      <td>33.00</td>\n",
       "      <td>143.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ishan Kishan</th>\n",
       "      <td>41318350</td>\n",
       "      <td>35</td>\n",
       "      <td>325</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "      <td>5</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>685.0</td>\n",
       "      <td>47.00</td>\n",
       "      <td>134.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SK Raina</th>\n",
       "      <td>40042339</td>\n",
       "      <td>49</td>\n",
       "      <td>343</td>\n",
       "      <td>35</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>15.33</td>\n",
       "      <td>131.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N Rana</th>\n",
       "      <td>51118625</td>\n",
       "      <td>71</td>\n",
       "      <td>386</td>\n",
       "      <td>44</td>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>127.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AM Rahane</th>\n",
       "      <td>39331641</td>\n",
       "      <td>55</td>\n",
       "      <td>340</td>\n",
       "      <td>34</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>444.0</td>\n",
       "      <td>41.00</td>\n",
       "      <td>120.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       id  inning  over  ball  batsman_runs  extra_runs  \\\n",
       "batsman                                                                   \n",
       "M Vohra          18512333      34   165    17            39           4   \n",
       "CH Gayle         30152269      29   225    26            51           0   \n",
       "SR Watson        54660505      80   498    47            82           1   \n",
       "SO Hetmyer       24999460      39   212    21            36           1   \n",
       "RV Uthappa       46088437      70   371    41            64           0   \n",
       "AJ Finch         29589130      26   243    25            37           0   \n",
       "C de Grandhomme  26806942      46   323    23            33           1   \n",
       "Ishan Kishan     41318350      35   325    35            47           5   \n",
       "SK Raina         40042339      49   343    35            46           5   \n",
       "N Rana           51118625      71   386    44            56           1   \n",
       "AM Rahane        39331641      55   340    34            41           0   \n",
       "\n",
       "                 total_runs  non_boundary  is_wicket  neutral_venue  \\\n",
       "batsman                                                               \n",
       "M Vohra                  43             0          0              0   \n",
       "CH Gayle                 51             0          1              0   \n",
       "SR Watson                83             0          0              0   \n",
       "SO Hetmyer               37             0          2              0   \n",
       "RV Uthappa               64             0          2              0   \n",
       "AJ Finch                 37             0          2              0   \n",
       "C de Grandhomme          34             0          1              0   \n",
       "Ishan Kishan             52             0          1              0   \n",
       "SK Raina                 51             0          3              0   \n",
       "N Rana                   57             0          1              0   \n",
       "AM Rahane                41             0          1              0   \n",
       "\n",
       "                 result_margin  Average  Strike Rate  \n",
       "batsman                                               \n",
       "M Vohra                   85.0    39.00       229.41  \n",
       "CH Gayle                 356.0    51.00       196.15  \n",
       "SR Watson                597.0    82.00       174.47  \n",
       "SO Hetmyer               218.0    18.00       171.43  \n",
       "RV Uthappa               664.0    32.00       156.10  \n",
       "AJ Finch                 228.0    18.50       148.00  \n",
       "C de Grandhomme         1808.0    33.00       143.48  \n",
       "Ishan Kishan             685.0    47.00       134.29  \n",
       "SK Raina                 221.0    15.33       131.43  \n",
       "N Rana                   203.0    56.00       127.27  \n",
       "AM Rahane                444.0    41.00       120.59  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_sr_against_rashid_min_fifteen_balls.head(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "suitable-mouth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a45c41b1f0>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x792 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,11))\n",
    "sns.set(font_scale=1.3)\n",
    "# sns.set_style('darkgrid')\n",
    "sns.scatterplot(y='Strike Rate', x='Average', data=highest_sr_against_rashid_min_fifteen_balls.head(11), size='batsman_runs', sizes=(40, 600), legend='full')\n",
    "plt.title(\"Highest Strike Rate against Rashid Khan in IPL\")\n",
    "plt.ylabel(\"Strike Rate\")\n",
    "plt.xlabel(\"Average\")\n",
    "# sns.despine(top=True, left=True, right=True, bottom=True)\n",
    "\n",
    "for line in range(0,highest_sr_against_rashid_min_fifteen_balls.head(11).shape[0]):\n",
    "     if(line == 0 or line == 2):\n",
    "        plt.text(highest_sr_against_rashid_min_fifteen_balls['Average'][line]-2.5 , highest_sr_against_rashid_min_fifteen_balls['Strike Rate'][line]+1.5, highest_sr_against_rashid_min_fifteen_balls.index[line]+\"+\", horizontalalignment='left', size='small', color='black', )\n",
    "     plt.text(highest_sr_against_rashid_min_fifteen_balls['Average'][line]-2.5, highest_sr_against_rashid_min_fifteen_balls['Strike Rate'][line]+1.5, highest_sr_against_rashid_min_fifteen_balls.index[line], horizontalalignment='left', size='small', color='black', )\n",
    "\n",
    "# plt.text(MS_RP_bowler_type['Average'][6]-6.5, MS_RP_bowler_type['Strike Rate'][6]+5.5, MS_RP_bowler_type['Bowler Category'][6], horizontalalignment='left', size='small', color='black', )\n",
    "plt.legend(bbox_to_anchor=(0.99, 0.99), loc=1, borderaxespad=0., title='Runs', shadow=True, fontsize=13)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "impressive-gabriel",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}