MI_Strategy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MI Strategies To counter DC attacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "MI_Strategies=pd.read_csv(\"deliveries.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# what should MI do at the toss(bat/bowl)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_Toss=pd.read_csv(\"Matches IPL 2020.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1216492</td>\n",
       "      <td>2020</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>09/19/2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>CSK</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>AT Rayudu</td>\n",
       "      <td>Sheikh Zayed Stadium, Abu Dhabi</td>\n",
       "      <td>Chris Gaffaney</td>\n",
       "      <td>Virender Sharma</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1216493</td>\n",
       "      <td>2020</td>\n",
       "      <td>Dubai (DSC)</td>\n",
       "      <td>09/20/2020</td>\n",
       "      <td>DC</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>field</td>\n",
       "      <td>tie</td>\n",
       "      <td>0</td>\n",
       "      <td>DC</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>MP Stoinis</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>Anil Chaudhary</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>Paul Reiffel</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  season         city        date team1 team2 toss_winner  \\\n",
       "0  1216492    2020    Abu Dhabi  09/19/2020    MI   CSK         CSK   \n",
       "1  1216493    2020  Dubai (DSC)  09/20/2020    DC  KXIP        KXIP   \n",
       "\n",
       "  toss_decision  result  dl_applied winner  win_by_runs  win_by_wickets  \\\n",
       "0         field  normal           0    CSK            0               5   \n",
       "1         field     tie           0     DC            0               0   \n",
       "\n",
       "  player_of_match                                venue         umpire1  \\\n",
       "0       AT Rayudu      Sheikh Zayed Stadium, Abu Dhabi  Chris Gaffaney   \n",
       "1      MP Stoinis  Dubai International Cricket Stadium  Anil Chaudhary   \n",
       "\n",
       "           umpire2        umpire3  \n",
       "0  Virender Sharma  Sundaram Ravi  \n",
       "1      Nitin Menon   Paul Reiffel  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_Toss.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x280acb5c888>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='toss_decision',hue='winner',palette='Set2',data=MI_Toss)\n",
    "plt.legend(loc='right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count=[31,25]\n",
    "explode=[0,0]\n",
    "labels=['Batting Second','Batting First']\n",
    "plt.pie(count,labels=labels,explode=explode,autopct='%.2f')\n",
    "plt.title('Winning Percentage of batting First and Batting Second ')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "Dubai_Results=MI_Toss.loc[(MI_Toss['city']=='Dubai (DSC)')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1216535</td>\n",
       "      <td>2020</td>\n",
       "      <td>Dubai (DSC)</td>\n",
       "      <td>10-31-2020</td>\n",
       "      <td>DC</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Ishan Kishan</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>Yeshwant Barde</td>\n",
       "      <td>Paul Reiffel</td>\n",
       "      <td>Anil Chaudhary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>1216530</td>\n",
       "      <td>2020</td>\n",
       "      <td>Dubai (DSC)</td>\n",
       "      <td>11-01-20</td>\n",
       "      <td>KKR</td>\n",
       "      <td>RR</td>\n",
       "      <td>RR</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>KKR</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>PJ Cummins</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>Paul Reiffel</td>\n",
       "      <td>Anil Chaudhary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  season         city        date team1 team2 toss_winner  \\\n",
       "50  1216535    2020  Dubai (DSC)  10-31-2020    DC    MI          MI   \n",
       "53  1216530    2020  Dubai (DSC)    11-01-20   KKR    RR          RR   \n",
       "\n",
       "   toss_decision  result  dl_applied winner  win_by_runs  win_by_wickets  \\\n",
       "50         field  normal           0     MI            0               9   \n",
       "53         field  normal           0    KKR           60               0   \n",
       "\n",
       "   player_of_match                                venue         umpire1  \\\n",
       "50    Ishan Kishan  Dubai International Cricket Stadium  Yeshwant Barde   \n",
       "53      PJ Cummins  Dubai International Cricket Stadium     Nitin Menon   \n",
       "\n",
       "         umpire2         umpire3  \n",
       "50  Paul Reiffel  Anil Chaudhary  \n",
       "53  Paul Reiffel  Anil Chaudhary  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dubai_Results.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='toss_decision',hue='winner',palette='Set2',data=Dubai_Results)\n",
    "plt.legend(loc='right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Toss Decision In Dubai  No Of Times\n",
      "0      Batting First win           25\n",
      "1     Batting Second win           31\n"
     ]
    }
   ],
   "source": [
    "Data={'Toss Decision In Dubai':['Batting First win','Batting Second win'],\n",
    "     'No Of Times':[25,31]}\n",
    "df = pd.DataFrame(Data,columns = ['Toss Decision In Dubai','No Of Times'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=['Batting First','Batting Second']\n",
    "y=[25,31]\n",
    "plt.bar(x,y)\n",
    "plt.ylabel('Count')\n",
    "plt.title('Batting First or Batting Second which is more likely to win on Dubai Ground')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_DC1=MI_Toss.loc[(MI_Toss['team2']=='MI')&(MI_Toss['team1']=='DC')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_DC2=MI_Toss.loc[(MI_Toss['team1']=='MI')&(MI_Toss['team2']=='DC')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_DC=MI_DC2.append(MI_DC1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1216529</td>\n",
       "      <td>2020</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>10-11-20</td>\n",
       "      <td>MI</td>\n",
       "      <td>DC</td>\n",
       "      <td>DC</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>Sheikh Zayed Stadium, Abu Dhabi</td>\n",
       "      <td>Chris Gaffaney</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "      <td>Virender Sharma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1216535</td>\n",
       "      <td>2020</td>\n",
       "      <td>Dubai (DSC)</td>\n",
       "      <td>10-31-2020</td>\n",
       "      <td>DC</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Ishan Kishan</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>Yeshwant Barde</td>\n",
       "      <td>Paul Reiffel</td>\n",
       "      <td>Anil Chaudhary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  season         city        date team1 team2 toss_winner  \\\n",
       "26  1216529    2020    Abu Dhabi    10-11-20    MI    DC          DC   \n",
       "50  1216535    2020  Dubai (DSC)  10-31-2020    DC    MI          MI   \n",
       "\n",
       "   toss_decision  result  dl_applied winner  win_by_runs  win_by_wickets  \\\n",
       "26           bat  normal           0     MI            0               5   \n",
       "50         field  normal           0     MI            0               9   \n",
       "\n",
       "   player_of_match                                venue         umpire1  \\\n",
       "26       Q de Kock      Sheikh Zayed Stadium, Abu Dhabi  Chris Gaffaney   \n",
       "50    Ishan Kishan  Dubai International Cricket Stadium  Yeshwant Barde   \n",
       "\n",
       "          umpire2          umpire3  \n",
       "26  Sundaram Ravi  Virender Sharma  \n",
       "50   Paul Reiffel   Anil Chaudhary  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_DC.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1216529</th>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216535</th>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         season  dl_applied  win_by_runs  win_by_wickets\n",
       "id                                                      \n",
       "1216529    2020           0            0               5\n",
       "1216535    2020           0            0               9"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_DC.groupby('id').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x280ace9bc88>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "sns.set_style('whitegrid')\n",
    "sns.countplot(x='toss_decision',hue='winner',data=MI_DC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1216492</td>\n",
       "      <td>2020</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>09/19/2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>CSK</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>AT Rayudu</td>\n",
       "      <td>Sheikh Zayed Stadium, Abu Dhabi</td>\n",
       "      <td>Chris Gaffaney</td>\n",
       "      <td>Virender Sharma</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1216508</td>\n",
       "      <td>2020</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>09/23/2020</td>\n",
       "      <td>KKR</td>\n",
       "      <td>MI</td>\n",
       "      <td>KKR</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>Sheikh Zayed Stadium, Abu Dhabi</td>\n",
       "      <td>Chris Gaffaney</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "      <td>Virender Sharma</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  season       city        date team1 team2 toss_winner  \\\n",
       "0  1216492    2020  Abu Dhabi  09/19/2020    MI   CSK         CSK   \n",
       "4  1216508    2020  Abu Dhabi  09/23/2020   KKR    MI         KKR   \n",
       "\n",
       "  toss_decision  result  dl_applied winner  win_by_runs  win_by_wickets  \\\n",
       "0         field  normal           0    CSK            0               5   \n",
       "4         field  normal           0     MI           49               0   \n",
       "\n",
       "  player_of_match                            venue         umpire1  \\\n",
       "0       AT Rayudu  Sheikh Zayed Stadium, Abu Dhabi  Chris Gaffaney   \n",
       "4       RG Sharma  Sheikh Zayed Stadium, Abu Dhabi  Chris Gaffaney   \n",
       "\n",
       "           umpire2          umpire3  \n",
       "0  Virender Sharma    Sundaram Ravi  \n",
       "4    Sundaram Ravi  Virender Sharma  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_Winnig_Record=MI_Toss.loc[(MI_Toss['team1']=='MI')|(MI_Toss['team2']=='MI')]\n",
    "MI_Winnig_Record.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1216492</td>\n",
       "      <td>2020</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>09/19/2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>CSK</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>CSK</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>AT Rayudu</td>\n",
       "      <td>Sheikh Zayed Stadium, Abu Dhabi</td>\n",
       "      <td>Chris Gaffaney</td>\n",
       "      <td>Virender Sharma</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1216508</td>\n",
       "      <td>2020</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>09/23/2020</td>\n",
       "      <td>KKR</td>\n",
       "      <td>MI</td>\n",
       "      <td>KKR</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>Sheikh Zayed Stadium, Abu Dhabi</td>\n",
       "      <td>Chris Gaffaney</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "      <td>Virender Sharma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1216547</td>\n",
       "      <td>2020</td>\n",
       "      <td>Dubai (DSC)</td>\n",
       "      <td>09/28/2020</td>\n",
       "      <td>RCB</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>field</td>\n",
       "      <td>tie</td>\n",
       "      <td>0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>Paul Reiffel</td>\n",
       "      <td>Anil Chaudhary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1216503</td>\n",
       "      <td>2020</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>10-01-20</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>MI</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>KA Pollard</td>\n",
       "      <td>Sheikh Zayed Stadium, Abu Dhabi</td>\n",
       "      <td>Virender Sharma</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "      <td>Ulhas Gandhe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1216538</td>\n",
       "      <td>2020</td>\n",
       "      <td>Sharjah</td>\n",
       "      <td>10-04-20</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>MI</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>TA Boult</td>\n",
       "      <td>Sharjah Cricket Stadium</td>\n",
       "      <td>KN Ananthapadmanabhan</td>\n",
       "      <td>Richard Illingworth</td>\n",
       "      <td>Krishnamachari Srinivasan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1216511</td>\n",
       "      <td>2020</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>10-06-20</td>\n",
       "      <td>MI</td>\n",
       "      <td>RR</td>\n",
       "      <td>MI</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>AS Yadav</td>\n",
       "      <td>Sheikh Zayed Stadium, Abu Dhabi</td>\n",
       "      <td>Virender Sharma</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "      <td>Chris Gaffaney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1216529</td>\n",
       "      <td>2020</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>10-11-20</td>\n",
       "      <td>MI</td>\n",
       "      <td>DC</td>\n",
       "      <td>DC</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>Sheikh Zayed Stadium, Abu Dhabi</td>\n",
       "      <td>Chris Gaffaney</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "      <td>Virender Sharma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>1216526</td>\n",
       "      <td>2020</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>10-16-2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KKR</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>Sheikh Zayed Stadium, Abu Dhabi</td>\n",
       "      <td>Chris Gaffaney</td>\n",
       "      <td>Virender Sharma</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>1216517</td>\n",
       "      <td>2020</td>\n",
       "      <td>Dubai (DSC)</td>\n",
       "      <td>10-18-2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>MI</td>\n",
       "      <td>bat</td>\n",
       "      <td>tie</td>\n",
       "      <td>0</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>Paul Reiffel</td>\n",
       "      <td>Anil Chaudhary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>1216521</td>\n",
       "      <td>2020</td>\n",
       "      <td>Sharjah</td>\n",
       "      <td>10-23-2020</td>\n",
       "      <td>CSK</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>TA Boult</td>\n",
       "      <td>Sharjah Cricket Stadium</td>\n",
       "      <td>Chettithody Shamshuddin</td>\n",
       "      <td>Vineet Kulkarni</td>\n",
       "      <td>Richard Illingworth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>1216541</td>\n",
       "      <td>2020</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>10-25-2020</td>\n",
       "      <td>RR</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>RR</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>BA Stokes</td>\n",
       "      <td>Sheikh Zayed Stadium, Abu Dhabi</td>\n",
       "      <td>Ulhas Gandhe</td>\n",
       "      <td>Virender Sharma</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>1216499</td>\n",
       "      <td>2020</td>\n",
       "      <td>Abu Dhabi</td>\n",
       "      <td>10-28-2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>RCB</td>\n",
       "      <td>MI</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>AS Yadav</td>\n",
       "      <td>Sheikh Zayed Stadium, Abu Dhabi</td>\n",
       "      <td>Ulhas Gandhe</td>\n",
       "      <td>Chris Gaffaney</td>\n",
       "      <td>Sundaram Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>1216535</td>\n",
       "      <td>2020</td>\n",
       "      <td>Dubai (DSC)</td>\n",
       "      <td>10-31-2020</td>\n",
       "      <td>DC</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>MI</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Ishan Kishan</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>Yeshwant Barde</td>\n",
       "      <td>Paul Reiffel</td>\n",
       "      <td>Anil Chaudhary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>1216495</td>\n",
       "      <td>2020</td>\n",
       "      <td>Sharjah</td>\n",
       "      <td>11-03-20</td>\n",
       "      <td>SRH</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>SRH</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>S Nadeem</td>\n",
       "      <td>Sharjah Cricket Stadium</td>\n",
       "      <td>Chettithody Shamshuddin</td>\n",
       "      <td>Richard Illingworth</td>\n",
       "      <td>KN Ananthapadmanabhan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id  season         city        date team1 team2 toss_winner  \\\n",
       "0   1216492    2020    Abu Dhabi  09/19/2020    MI   CSK         CSK   \n",
       "4   1216508    2020    Abu Dhabi  09/23/2020   KKR    MI         KKR   \n",
       "9   1216547    2020  Dubai (DSC)  09/28/2020   RCB    MI          MI   \n",
       "12  1216503    2020    Abu Dhabi    10-01-20  KXIP    MI        KXIP   \n",
       "16  1216538    2020      Sharjah    10-04-20    MI   SRH          MI   \n",
       "19  1216511    2020    Abu Dhabi    10-06-20    MI    RR          MI   \n",
       "26  1216529    2020    Abu Dhabi    10-11-20    MI    DC          DC   \n",
       "31  1216526    2020    Abu Dhabi  10-16-2020    MI   KKR         KKR   \n",
       "35  1216517    2020  Dubai (DSC)  10-18-2020    MI  KXIP          MI   \n",
       "40  1216521    2020      Sharjah  10-23-2020   CSK    MI          MI   \n",
       "44  1216541    2020    Abu Dhabi  10-25-2020    RR    MI          MI   \n",
       "47  1216499    2020    Abu Dhabi  10-28-2020    MI   RCB          MI   \n",
       "50  1216535    2020  Dubai (DSC)  10-31-2020    DC    MI          MI   \n",
       "55  1216495    2020      Sharjah    11-03-20   SRH    MI         SRH   \n",
       "\n",
       "   toss_decision  result  dl_applied winner  win_by_runs  win_by_wickets  \\\n",
       "0          field  normal           0    CSK            0               5   \n",
       "4          field  normal           0     MI           49               0   \n",
       "9          field     tie           0    RCB            0               0   \n",
       "12         field  normal           0     MI           48               0   \n",
       "16           bat  normal           0     MI           34               0   \n",
       "19           bat  normal           0     MI           57               0   \n",
       "26           bat  normal           0     MI            0               5   \n",
       "31           bat  normal           0     MI            0               8   \n",
       "35           bat     tie           0   KXIP            0               0   \n",
       "40         field  normal           0     MI            0              10   \n",
       "44           bat  normal           0     RR            0               8   \n",
       "47         field  normal           0     MI            0               5   \n",
       "50         field  normal           0     MI            0               9   \n",
       "55         field  normal           0    SRH            0              10   \n",
       "\n",
       "   player_of_match                                venue  \\\n",
       "0        AT Rayudu      Sheikh Zayed Stadium, Abu Dhabi   \n",
       "4        RG Sharma      Sheikh Zayed Stadium, Abu Dhabi   \n",
       "9   AB de Villiers  Dubai International Cricket Stadium   \n",
       "12      KA Pollard      Sheikh Zayed Stadium, Abu Dhabi   \n",
       "16        TA Boult              Sharjah Cricket Stadium   \n",
       "19        AS Yadav      Sheikh Zayed Stadium, Abu Dhabi   \n",
       "26       Q de Kock      Sheikh Zayed Stadium, Abu Dhabi   \n",
       "31       Q de Kock      Sheikh Zayed Stadium, Abu Dhabi   \n",
       "35        KL Rahul  Dubai International Cricket Stadium   \n",
       "40        TA Boult              Sharjah Cricket Stadium   \n",
       "44       BA Stokes      Sheikh Zayed Stadium, Abu Dhabi   \n",
       "47        AS Yadav      Sheikh Zayed Stadium, Abu Dhabi   \n",
       "50    Ishan Kishan  Dubai International Cricket Stadium   \n",
       "55        S Nadeem              Sharjah Cricket Stadium   \n",
       "\n",
       "                    umpire1              umpire2                    umpire3  \n",
       "0            Chris Gaffaney      Virender Sharma              Sundaram Ravi  \n",
       "4            Chris Gaffaney        Sundaram Ravi            Virender Sharma  \n",
       "9               Nitin Menon         Paul Reiffel             Anil Chaudhary  \n",
       "12          Virender Sharma        Sundaram Ravi               Ulhas Gandhe  \n",
       "16    KN Ananthapadmanabhan  Richard Illingworth  Krishnamachari Srinivasan  \n",
       "19          Virender Sharma        Sundaram Ravi             Chris Gaffaney  \n",
       "26           Chris Gaffaney        Sundaram Ravi            Virender Sharma  \n",
       "31           Chris Gaffaney      Virender Sharma              Sundaram Ravi  \n",
       "35              Nitin Menon         Paul Reiffel             Anil Chaudhary  \n",
       "40  Chettithody Shamshuddin      Vineet Kulkarni        Richard Illingworth  \n",
       "44             Ulhas Gandhe      Virender Sharma              Sundaram Ravi  \n",
       "47             Ulhas Gandhe       Chris Gaffaney              Sundaram Ravi  \n",
       "50           Yeshwant Barde         Paul Reiffel             Anil Chaudhary  \n",
       "55  Chettithody Shamshuddin  Richard Illingworth      KN Ananthapadmanabhan  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_Winnig_Record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x280acf14788>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='toss_decision',hue='winner',palette='Set2',data=MI_Winnig_Record)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=['Batting First','Batting Second']\n",
    "y=[4,5]\n",
    "plt.bar(x,y)\n",
    "plt.ylabel('Count')\n",
    "plt.title('Mumbai Indians Winning Count On Batting first and Batting Second')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_Winnig_record=MI_Winnig_Record.loc[(MI_Winnig_Record['winner']=='MI')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1216499</th>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216503</th>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216508</th>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216511</th>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216521</th>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216526</th>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216529</th>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216535</th>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216538</th>\n",
       "      <td>2020</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         season  dl_applied  win_by_runs  win_by_wickets\n",
       "id                                                      \n",
       "1216499    2020           0            0               5\n",
       "1216503    2020           0           48               0\n",
       "1216508    2020           0           49               0\n",
       "1216511    2020           0           57               0\n",
       "1216521    2020           0            0              10\n",
       "1216526    2020           0            0               8\n",
       "1216529    2020           0            0               5\n",
       "1216535    2020           0            0               9\n",
       "1216538    2020           0           34               0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_Winnig_record.groupby('id').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_Runs=pd.read_csv('Deliveries IPL 2020.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1216492</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1216492</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1216492</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1216492</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1216492</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13823</th>\n",
       "      <td>1216495</td>\n",
       "      <td>2</td>\n",
       "      <td>SRH</td>\n",
       "      <td>MI</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>WP Saha</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NM Coulter-Nile</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13824</th>\n",
       "      <td>1216495</td>\n",
       "      <td>2</td>\n",
       "      <td>SRH</td>\n",
       "      <td>MI</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>WP Saha</td>\n",
       "      <td>NM Coulter-Nile</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13825</th>\n",
       "      <td>1216495</td>\n",
       "      <td>2</td>\n",
       "      <td>SRH</td>\n",
       "      <td>MI</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>WP Saha</td>\n",
       "      <td>NM Coulter-Nile</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13826</th>\n",
       "      <td>1216495</td>\n",
       "      <td>2</td>\n",
       "      <td>SRH</td>\n",
       "      <td>MI</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>WP Saha</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>NM Coulter-Nile</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13827</th>\n",
       "      <td>1216495</td>\n",
       "      <td>2</td>\n",
       "      <td>SRH</td>\n",
       "      <td>MI</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>WP Saha</td>\n",
       "      <td>HH Pandya</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13828 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       match_id  inning batting_team bowling_team  over  ball    batsman  \\\n",
       "0       1216492       1           MI          CSK     1     1  RG Sharma   \n",
       "1       1216492       1           MI          CSK     1     2  RG Sharma   \n",
       "2       1216492       1           MI          CSK     1     3  Q de Kock   \n",
       "3       1216492       1           MI          CSK     1     4  Q de Kock   \n",
       "4       1216492       1           MI          CSK     1     5  Q de Kock   \n",
       "...         ...     ...          ...          ...   ...   ...        ...   \n",
       "13823   1216495       2          SRH           MI    17     3    WP Saha   \n",
       "13824   1216495       2          SRH           MI    17     4  DA Warner   \n",
       "13825   1216495       2          SRH           MI    17     5  DA Warner   \n",
       "13826   1216495       2          SRH           MI    17     6    WP Saha   \n",
       "13827   1216495       2          SRH           MI    18     1  DA Warner   \n",
       "\n",
       "      non_striker           bowler  is_super_over  ...  bye_runs  legbye_runs  \\\n",
       "0       Q de Kock        DL Chahar              0  ...         0            0   \n",
       "1       Q de Kock        DL Chahar              0  ...         0            0   \n",
       "2       RG Sharma        DL Chahar              0  ...         0            0   \n",
       "3       RG Sharma        DL Chahar              0  ...         0            0   \n",
       "4       RG Sharma        DL Chahar              0  ...         0            0   \n",
       "...           ...              ...            ...  ...       ...          ...   \n",
       "13823   DA Warner  NM Coulter-Nile              0  ...         0            0   \n",
       "13824     WP Saha  NM Coulter-Nile              0  ...         0            0   \n",
       "13825     WP Saha  NM Coulter-Nile              0  ...         0            0   \n",
       "13826   DA Warner  NM Coulter-Nile              0  ...         0            0   \n",
       "13827     WP Saha        HH Pandya              0  ...         0            0   \n",
       "\n",
       "       noball_runs  penalty_runs  batsman_runs  extra_runs  total_runs  \\\n",
       "0                0             0             4           0           4   \n",
       "1                0             0             1           0           1   \n",
       "2                0             0             2           0           2   \n",
       "3                0             0             4           0           4   \n",
       "4                0             0             0           0           0   \n",
       "...            ...           ...           ...         ...         ...   \n",
       "13823            0             0             1           0           1   \n",
       "13824            0             0             0           0           0   \n",
       "13825            0             0             1           0           1   \n",
       "13826            0             0             0           0           0   \n",
       "13827            0             0             4           0           4   \n",
       "\n",
       "       player_dismissed dismissal_kind fielder  \n",
       "0                   NaN            NaN     NaN  \n",
       "1                   NaN            NaN     NaN  \n",
       "2                   NaN            NaN     NaN  \n",
       "3                   NaN            NaN     NaN  \n",
       "4                   NaN            NaN     NaN  \n",
       "...                 ...            ...     ...  \n",
       "13823               NaN            NaN     NaN  \n",
       "13824               NaN            NaN     NaN  \n",
       "13825               NaN            NaN     NaN  \n",
       "13826               NaN            NaN     NaN  \n",
       "13827               NaN            NaN     NaN  \n",
       "\n",
       "[13828 rows x 21 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_Runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_runs=MI_Runs.loc[(MI_Runs['batting_team']=='MI')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
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       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1216492</td>\n",
       "      <td>1</td>\n",
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       "      <td>CSK</td>\n",
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       "      <td>Q de Kock</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1216492</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1216492</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1216492</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1216492</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13716</th>\n",
       "      <td>1216495</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>KA Pollard</td>\n",
       "      <td>JL Pattinson</td>\n",
       "      <td>JO Holder</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13717</th>\n",
       "      <td>1216495</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>KA Pollard</td>\n",
       "      <td>JL Pattinson</td>\n",
       "      <td>JO Holder</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>KA Pollard</td>\n",
       "      <td>bowled</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13718</th>\n",
       "      <td>1216495</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>DS Kulkarni</td>\n",
       "      <td>JL Pattinson</td>\n",
       "      <td>JO Holder</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13719</th>\n",
       "      <td>1216495</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>JL Pattinson</td>\n",
       "      <td>DS Kulkarni</td>\n",
       "      <td>JO Holder</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13720</th>\n",
       "      <td>1216495</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>DS Kulkarni</td>\n",
       "      <td>JL Pattinson</td>\n",
       "      <td>JO Holder</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1646 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       match_id  inning batting_team bowling_team  over  ball       batsman  \\\n",
       "0       1216492       1           MI          CSK     1     1     RG Sharma   \n",
       "1       1216492       1           MI          CSK     1     2     RG Sharma   \n",
       "2       1216492       1           MI          CSK     1     3     Q de Kock   \n",
       "3       1216492       1           MI          CSK     1     4     Q de Kock   \n",
       "4       1216492       1           MI          CSK     1     5     Q de Kock   \n",
       "...         ...     ...          ...          ...   ...   ...           ...   \n",
       "13716   1216495       1           MI          SRH    20     2    KA Pollard   \n",
       "13717   1216495       1           MI          SRH    20     3    KA Pollard   \n",
       "13718   1216495       1           MI          SRH    20     4   DS Kulkarni   \n",
       "13719   1216495       1           MI          SRH    20     5  JL Pattinson   \n",
       "13720   1216495       1           MI          SRH    20     6   DS Kulkarni   \n",
       "\n",
       "        non_striker     bowler  is_super_over  ...  bye_runs  legbye_runs  \\\n",
       "0         Q de Kock  DL Chahar              0  ...         0            0   \n",
       "1         Q de Kock  DL Chahar              0  ...         0            0   \n",
       "2         RG Sharma  DL Chahar              0  ...         0            0   \n",
       "3         RG Sharma  DL Chahar              0  ...         0            0   \n",
       "4         RG Sharma  DL Chahar              0  ...         0            0   \n",
       "...             ...        ...            ...  ...       ...          ...   \n",
       "13716  JL Pattinson  JO Holder              0  ...         0            0   \n",
       "13717  JL Pattinson  JO Holder              0  ...         0            0   \n",
       "13718  JL Pattinson  JO Holder              0  ...         0            0   \n",
       "13719   DS Kulkarni  JO Holder              0  ...         0            0   \n",
       "13720  JL Pattinson  JO Holder              0  ...         0            0   \n",
       "\n",
       "       noball_runs  penalty_runs  batsman_runs  extra_runs  total_runs  \\\n",
       "0                0             0             4           0           4   \n",
       "1                0             0             1           0           1   \n",
       "2                0             0             2           0           2   \n",
       "3                0             0             4           0           4   \n",
       "4                0             0             0           0           0   \n",
       "...            ...           ...           ...         ...         ...   \n",
       "13716            0             0             6           0           6   \n",
       "13717            0             0             0           0           0   \n",
       "13718            0             0             1           0           1   \n",
       "13719            0             0             1           0           1   \n",
       "13720            0             0             2           0           2   \n",
       "\n",
       "       player_dismissed dismissal_kind fielder  \n",
       "0                   NaN            NaN     NaN  \n",
       "1                   NaN            NaN     NaN  \n",
       "2                   NaN            NaN     NaN  \n",
       "3                   NaN            NaN     NaN  \n",
       "4                   NaN            NaN     NaN  \n",
       "...                 ...            ...     ...  \n",
       "13716               NaN            NaN     NaN  \n",
       "13717        KA Pollard         bowled     NaN  \n",
       "13718               NaN            NaN     NaN  \n",
       "13719               NaN            NaN     NaN  \n",
       "13720               NaN            NaN     NaN  \n",
       "\n",
       "[1646 rows x 21 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(MI_runs['match_id'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2395"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_runs['total_runs'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average runs scored by MI in a single match in 2020IPL: 171\n"
     ]
    }
   ],
   "source": [
    "print(\"Average runs scored by MI in a single match in 2020IPL:\",2395//14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>inning</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>wide_runs</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>match_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1216492</th>\n",
       "      <td>124</td>\n",
       "      <td>1289</td>\n",
       "      <td>449</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>157</td>\n",
       "      <td>5</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216495</th>\n",
       "      <td>120</td>\n",
       "      <td>1260</td>\n",
       "      <td>420</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>1</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216499</th>\n",
       "      <td>242</td>\n",
       "      <td>1242</td>\n",
       "      <td>445</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "      <td>8</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216503</th>\n",
       "      <td>125</td>\n",
       "      <td>1317</td>\n",
       "      <td>455</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>185</td>\n",
       "      <td>6</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216508</th>\n",
       "      <td>131</td>\n",
       "      <td>1406</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>181</td>\n",
       "      <td>13</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216511</th>\n",
       "      <td>127</td>\n",
       "      <td>1335</td>\n",
       "      <td>470</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>12</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216517</th>\n",
       "      <td>141</td>\n",
       "      <td>1277</td>\n",
       "      <td>484</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>182</td>\n",
       "      <td>9</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216521</th>\n",
       "      <td>148</td>\n",
       "      <td>494</td>\n",
       "      <td>255</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>114</td>\n",
       "      <td>2</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216526</th>\n",
       "      <td>202</td>\n",
       "      <td>901</td>\n",
       "      <td>351</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>144</td>\n",
       "      <td>5</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216529</th>\n",
       "      <td>244</td>\n",
       "      <td>1260</td>\n",
       "      <td>438</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "      <td>4</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216535</th>\n",
       "      <td>174</td>\n",
       "      <td>669</td>\n",
       "      <td>304</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>110</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216538</th>\n",
       "      <td>123</td>\n",
       "      <td>1292</td>\n",
       "      <td>441</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>203</td>\n",
       "      <td>5</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216541</th>\n",
       "      <td>125</td>\n",
       "      <td>1289</td>\n",
       "      <td>456</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>186</td>\n",
       "      <td>9</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1216547</th>\n",
       "      <td>256</td>\n",
       "      <td>1323</td>\n",
       "      <td>477</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>6</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          inning  over  ball  is_super_over  wide_runs  bye_runs  legbye_runs  \\\n",
       "match_id                                                                        \n",
       "1216492      124  1289   449              0          4         0            1   \n",
       "1216495      120  1260   420              0          0         0            1   \n",
       "1216499      242  1242   445              0          6         0            2   \n",
       "1216503      125  1317   455              0          5         0            1   \n",
       "1216508      131  1406   500              0         10         0            2   \n",
       "1216511      127  1335   470              0          5         1            2   \n",
       "1216517      141  1277   484             14          3         4            2   \n",
       "1216521      148   494   255              0          0         0            2   \n",
       "1216526      202   901   351              0          0         0            5   \n",
       "1216529      244  1260   438              0          4         0            0   \n",
       "1216535      174   669   304              0          1         0            0   \n",
       "1216538      123  1292   441              0          2         0            1   \n",
       "1216541      125  1289   456              0          5         0            4   \n",
       "1216547      256  1323   477              6          5         1            0   \n",
       "\n",
       "          noball_runs  penalty_runs  batsman_runs  extra_runs  total_runs  \n",
       "match_id                                                                   \n",
       "1216492             0             0           157           5         162  \n",
       "1216495             0             0           148           1         149  \n",
       "1216499             0             0           158           8         166  \n",
       "1216503             0             0           185           6         191  \n",
       "1216508             1             0           181          13         194  \n",
       "1216511             4             0           177          12         189  \n",
       "1216517             0             0           182           9         191  \n",
       "1216521             0             0           114           2         116  \n",
       "1216526             0             0           144           5         149  \n",
       "1216529             0             0           162           4         166  \n",
       "1216535             0             0           110           1         111  \n",
       "1216538             2             0           203           5         208  \n",
       "1216541             0             0           186           9         195  \n",
       "1216547             0             0           202           6         208  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_runs.groupby('match_id').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>960</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>Z Khan</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>961</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Z Khan</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id  inning                 batting_team    bowling_team  over  \\\n",
       "960         5       1  Royal Challengers Bangalore  Delhi Capitals     1   \n",
       "961         5       1  Royal Challengers Bangalore  Delhi Capitals     1   \n",
       "\n",
       "     ball    batsman non_striker  bowler  is_super_over  ...  bye_runs  \\\n",
       "960     1   CH Gayle   SR Watson  Z Khan              0  ...         0   \n",
       "961     2  SR Watson    CH Gayle  Z Khan              0  ...         0   \n",
       "\n",
       "     legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "960            0            0             0             1           0   \n",
       "961            0            0             0             4           0   \n",
       "\n",
       "     total_runs  player_dismissed dismissal_kind fielder  \n",
       "960           1               NaN            NaN     NaN  \n",
       "961           4               NaN            NaN     NaN  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Deliveries=pd.read_csv(\"deliveries.csv\")\n",
    "Deliveries.replace('Delhi Daredevils','Delhi Capitals',inplace=True)\n",
    "Delhi_Bangalore1=Deliveries.loc[(Deliveries['batting_team' ] =='Royal Challengers Bangalore' ) & (Deliveries['bowling_team'] == 'Delhi Capitals')]\n",
    "Delhi_Bangalore2=Deliveries.loc[(Deliveries['bowling_team' ] =='Royal Challengers Bangalore') & (Deliveries['batting_team'] == 'Delhi Capitals')]\n",
    "Delhi_Bangalore=Delhi_Bangalore1.append(Delhi_Bangalore2)\n",
    "Delhi_Bangalore.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count=[31,26,23,25,22,18]\n",
    "explode=[0.2,0,0,0,0,0]\n",
    "labels=['Rohit_Sharma','Quinton de kock','surya Kumar yadav','Ishan Kishan','Krunal Pandya','Kieron pollard']\n",
    "plt.pie(count,labels=labels,explode=explode,autopct='%.2f')\n",
    "plt.title('Mumbai Indians Batsmans Average Runs against DC From IPL[2008-2019]Records')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MI Batsman Performance against DC in 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mum_Del1=MI_Runs.loc[(MI_Runs['batting_team']=='MI')&(MI_Runs['bowling_team']=='DC')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mum_Del2=MI_Runs.loc[(MI_Runs['batting_team']=='DC')&(MI_Runs['bowling_team']=='MI')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mum_Del=Mum_Del1.append(Mum_Del2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_Rohit=Mum_Del.loc[(Mum_Del['batsman']=='RG Sharma')]#played only one match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_Rohit['batsman_runs'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    3\n",
       "1    3\n",
       "5    2\n",
       "3    2\n",
       "7    1\n",
       "4    1\n",
       "2    1\n",
       "Name: ball, dtype: int64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_Rohit['ball'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_Sk=Mum_Del.loc[(Mum_Del['batsman']=='AS Yadav')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_Sk['batsman_runs'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    7\n",
       "2    7\n",
       "6    6\n",
       "1    6\n",
       "3    4\n",
       "5    3\n",
       "Name: ball, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_Sk['ball'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_qdk=Mum_Del.loc[(Mum_Del['batsman']=='Q de Kock')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_qdk['batsman_runs'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    12\n",
       "4    12\n",
       "2    12\n",
       "3    11\n",
       "1    10\n",
       "6     7\n",
       "Name: ball, dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_qdk['ball'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_Ik=Mum_Del.loc[(Mum_Del['batsman']=='Ishan Kishan')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_Ik['batsman_runs'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    12\n",
       "2    12\n",
       "6    10\n",
       "1    10\n",
       "5     9\n",
       "4     8\n",
       "7     2\n",
       "8     1\n",
       "Name: ball, dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_Ik['ball'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_hp=Mum_Del.loc[(Mum_Del['batsman']=='HH Pandya')]#played only one match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_hp['batsman_runs'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    1\n",
       "1    1\n",
       "Name: ball, dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_hp['ball'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_kp=Mum_Del.loc[(Mum_Del['batsman']=='KH Pandya')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_kp['batsman_runs'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    2\n",
       "4    2\n",
       "1    2\n",
       "2    1\n",
       "Name: ball, dtype: int64"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_kp['ball'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "MI_Kp=Mum_Del.loc[(Mum_Del['batsman']=='KA Pollard')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_Kp['batsman_runs'].sum()#played one innings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    4\n",
       "6    3\n",
       "5    2\n",
       "4    2\n",
       "1    2\n",
       "7    1\n",
       "2    1\n",
       "Name: ball, dtype: int64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MI_Kp['ball'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Player_Name  Batsman_Total_Runs  Balls_Faced  Strike_Rate  \\\n",
      "0       Rohit Sharma                   5           12         41.6   \n",
      "1    Quinton de kock                  79           64        123.4   \n",
      "2  Surya kumar Yadav                  53           33        160.6   \n",
      "3       Ishan Kishan                 100           64        156.6   \n",
      "4      krunal pandya                  12            7        171.4   \n",
      "5      hardik pandya                   0            2          0.0   \n",
      "6     kieron pollard                  11           15         73.3   \n",
      "\n",
      "   No_of_matches_played_against_DC_in_2020               Batsman_status  \n",
      "0                                        1                       played  \n",
      "1                                        2                       played  \n",
      "2                                        2                       played  \n",
      "3                                        2                       played  \n",
      "4                                        2   Didn't bat in second Match  \n",
      "5                                        1                       played  \n",
      "6                                        2  Didn't bat in  second Match  \n"
     ]
    }
   ],
   "source": [
    "Data={'Player_Name':['Rohit Sharma','Quinton de kock','Surya kumar Yadav','Ishan Kishan','krunal pandya','hardik pandya','kieron pollard'],\n",
    "     'Batsman_Total_Runs':[5,79,53,100,12,0,11],\n",
    "      'Balls_Faced':[12,64,33,64,7,2,15],\n",
    "      'Strike_Rate':[41.6,123.4,160.6,156.6,171.4,0,73.3],\n",
    "     'No_of_matches_played_against_DC_in_2020':[1,2,2,2,2,1,2],\n",
    "     'Batsman_status':['played','played','played','played',\"Didn't bat in second Match\",\"played\",\"Didn't bat in  second Match\"]}\n",
    "df1 = pd.DataFrame(Data,columns = ['Player_Name','Batsman_Total_Runs','Balls_Faced','Strike_Rate','No_of_matches_played_against_DC_in_2020','Batsman_status'])\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the dataframe \n",
    "df1.to_csv('file1.csv') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "File=pd.read_csv(\"file1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>Unnamed: 0</th>\n",
       "      <th>Player_Name</th>\n",
       "      <th>Batsman_Total_Runs</th>\n",
       "      <th>Balls_Faced</th>\n",
       "      <th>Strike_Rate</th>\n",
       "      <th>No_of_matches_played_against_DC_in_2020</th>\n",
       "      <th>Batsman_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Rohit Sharma</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>41.6</td>\n",
       "      <td>1</td>\n",
       "      <td>played</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Quinton de kock</td>\n",
       "      <td>79</td>\n",
       "      <td>64</td>\n",
       "      <td>123.4</td>\n",
       "      <td>2</td>\n",
       "      <td>played</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Surya kumar Yadav</td>\n",
       "      <td>53</td>\n",
       "      <td>33</td>\n",
       "      <td>160.6</td>\n",
       "      <td>2</td>\n",
       "      <td>played</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Ishan Kishan</td>\n",
       "      <td>100</td>\n",
       "      <td>64</td>\n",
       "      <td>156.6</td>\n",
       "      <td>2</td>\n",
       "      <td>played</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>krunal pandya</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>171.4</td>\n",
       "      <td>2</td>\n",
       "      <td>Didn't bat in second Match</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>hardik pandya</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>played</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>kieron pollard</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>73.3</td>\n",
       "      <td>2</td>\n",
       "      <td>Didn't bat in  second Match</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        Player_Name  Batsman_Total_Runs  Balls_Faced  \\\n",
       "0           0       Rohit Sharma                   5           12   \n",
       "1           1    Quinton de kock                  79           64   \n",
       "2           2  Surya kumar Yadav                  53           33   \n",
       "3           3       Ishan Kishan                 100           64   \n",
       "4           4      krunal pandya                  12            7   \n",
       "5           5      hardik pandya                   0            2   \n",
       "6           6     kieron pollard                  11           15   \n",
       "\n",
       "   Strike_Rate  No_of_matches_played_against_DC_in_2020  \\\n",
       "0         41.6                                        1   \n",
       "1        123.4                                        2   \n",
       "2        160.6                                        2   \n",
       "3        156.6                                        2   \n",
       "4        171.4                                        2   \n",
       "5          0.0                                        1   \n",
       "6         73.3                                        2   \n",
       "\n",
       "                Batsman_status  \n",
       "0                       played  \n",
       "1                       played  \n",
       "2                       played  \n",
       "3                       played  \n",
       "4   Didn't bat in second Match  \n",
       "5                       played  \n",
       "6  Didn't bat in  second Match  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "Dc_Bowlers_AN=MI_runs.loc[(MI_runs['bowler']=='Anrich Nortje')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dc_Bowlers_AN['batsman_runs'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    7\n",
       "1    7\n",
       "6    6\n",
       "5    6\n",
       "4    6\n",
       "3    6\n",
       "7    1\n",
       "Name: ball, dtype: int64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dc_Bowlers_AN['ball'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MI batsman VS DC Bowlers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x1=['Rabada','Ashwin','Axar Patel','Marcus Stoinis','Anrich Nortje']\n",
    "y1=[7,6,7,9,8.6]\n",
    "plt.bar(x1,y1)\n",
    "plt.ylabel('Count')\n",
    "plt.title('Economy of DC Bowlers against MI batsman')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MI Batsman vs DC bowlers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count=[31,26,23,25,22,18]\n",
    "explode=[0.2,0,0,0,0,0]\n",
    "labels=['Rohit_Sharma','Quinton de kock','surya Kumar yadav','Ishan Kishan','Krunal Pandya','Kieron pollard']\n",
    "plt.pie(count,labels=labels,explode=explode,autopct='%.2f')\n",
    "plt.title('Mumbai Indians Batsmans Average Runs against DC From IPL[2008-2019]Records')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Player_Name  Dismissal_Count_Of_Rabada  Stoinis  Anrich_nortje  \\\n",
      "0       Rohit Sharma                          1        0              0   \n",
      "1    Quinton de kock                          0        0              1   \n",
      "2  Surya kumar Yadav                          2        1              0   \n",
      "3       Ishan Kishan                          0        0              0   \n",
      "4      krunal pandya                          0        2              0   \n",
      "5      hardik pandya                          2        1              0   \n",
      "6     kieron pollard                          0        1              0   \n",
      "\n",
      "   Ashwin  Axar_patel  \n",
      "0       1           2  \n",
      "1       2           0  \n",
      "2       0           1  \n",
      "3       0           0  \n",
      "4       0           0  \n",
      "5       0           1  \n",
      "6       3           1  \n"
     ]
    }
   ],
   "source": [
    "Data1={'Player_Name':['Rohit Sharma','Quinton de kock','Surya kumar Yadav','Ishan Kishan','krunal pandya','hardik pandya','kieron pollard'],\n",
    "     'Dismissal_Count_Of_Rabada':[1,0,2,0,0,2,0],\n",
    "     'Stoinis':[0,0,1,0,2,1,1],\n",
    "     'Anrich_nortje':[0,1,0,0,0,0,0],\n",
    "     'Ashwin':[1,2,0,0,0,0,3],\n",
    "      'Axar_patel':[2,0,1,0,0,1,1],\n",
    "     }\n",
    "df2 = pd.DataFrame(Data1,columns = ['Player_Name','Dismissal_Count_Of_Rabada','Stoinis','Anrich_nortje','Ashwin','Axar_patel'])\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_csv('file2.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "file2=pd.read_csv(\"file2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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>Unnamed: 0</th>\n",
       "      <th>Player_Name</th>\n",
       "      <th>Dismissal_Count_Of_Rabada</th>\n",
       "      <th>Stoinis</th>\n",
       "      <th>Anrich_nortje</th>\n",
       "      <th>Ashwin</th>\n",
       "      <th>Axar_patel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Rohit Sharma</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Quinton de kock</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Surya kumar Yadav</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Ishan Kishan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>krunal pandya</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>hardik pandya</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>kieron pollard</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        Player_Name  Dismissal_Count_Of_Rabada  Stoinis  \\\n",
       "0           0       Rohit Sharma                          1        0   \n",
       "1           1    Quinton de kock                          0        0   \n",
       "2           2  Surya kumar Yadav                          2        1   \n",
       "3           3       Ishan Kishan                          0        0   \n",
       "4           4      krunal pandya                          0        2   \n",
       "5           5      hardik pandya                          2        1   \n",
       "6           6     kieron pollard                          0        1   \n",
       "\n",
       "   Anrich_nortje  Ashwin  Axar_patel  \n",
       "0              0       1           2  \n",
       "1              1       2           0  \n",
       "2              0       0           1  \n",
       "3              0       0           0  \n",
       "4              0       0           0  \n",
       "5              0       0           1  \n",
       "6              0       3           1  "
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count=[3,5,1,6,5]\n",
    "explode=[0,0,0,0.2,0]\n",
    "labels=['Rabada','Stoinis','Anrich Nortje','Ashwin','Axar Patel']\n",
    "plt.pie(count,labels=labels,explode=explode,autopct='%.2f')\n",
    "plt.title('MI Batsman Wickets Taken by DC Bowlers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DC batsman Runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count=[26,32,20,13,29,19,18]\n",
    "explode=[0,0.150,0,0,0,0,0]\n",
    "labels=['Dhawan','Rishab Pant','Shreyas Iyyer','prithvi Shaw','Rahane','Marcus Stoinis','Hetmyer']\n",
    "plt.pie(count,labels=labels,explode=explode,autopct='%.2f')\n",
    "plt.title('Delhi Capitals Batsmans Average Runs against MI From IPL[2008-2019]Records')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DC Batsman Performance against MI in 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mum_Del1=MI_Runs.loc[(MI_Runs['batting_team']=='MI')&(MI_Runs['bowling_team']=='DC')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mum_Del2=MI_Runs.loc[(MI_Runs['batting_team']=='DC')&(MI_Runs['bowling_team']=='MI')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "Mum_Del=Mum_Del1.append(Mum_Del2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Player_Name  Batsman_Total_Runs  Balls_Faced  Strike_Rate  \\\n",
      "0    Prithvi Shaw                  14           14        100.0   \n",
      "1          Dhawan                  69           54        127.7   \n",
      "2    Shreyas Iyer                  67           62        108.0   \n",
      "3    Rishabh Pant                  21           24         87.8   \n",
      "4  Marcus Stoinis                  15           10        150.0   \n",
      "5         Hetmyer                  11           13         84.6   \n",
      "6          Rahane                  15           15        100.0   \n",
      "\n",
      "   No_of_matches_played_against_MI_in_2020                Batsman_status  \n",
      "0                                        2                        played  \n",
      "1                                        2                        played  \n",
      "2                                        2                        played  \n",
      "3                                        1   Didn't play in second Match  \n",
      "4                                        2                        played  \n",
      "5                                        1  Didn't play in  second Match  \n",
      "6                                        1    Didn't play in First Match  \n"
     ]
    }
   ],
   "source": [
    "Data={'Player_Name':['Prithvi Shaw','Dhawan','Shreyas Iyer','Rishabh Pant','Marcus Stoinis','Hetmyer','Rahane'],\n",
    "     'Batsman_Total_Runs':[14,69,67,21,15,11,15],\n",
    "      'Balls_Faced':[14,54,62,24,10,13,15],\n",
    "      'Strike_Rate':[100,127.7,108.0,87.8,150,84.6,100.0],\n",
    "     'No_of_matches_played_against_MI_in_2020':[2,2,2,1,2,1,1],\n",
    "     'Batsman_status':['played','played','played',\"Didn't play in second Match\",\"played\",\"Didn't play in  second Match\",\"Didn't play in First Match\"]}\n",
    "df3 = pd.DataFrame(Data,columns = ['Player_Name','Batsman_Total_Runs','Balls_Faced','Strike_Rate','No_of_matches_played_against_MI_in_2020','Batsman_status'])\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3.to_csv('file3.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "file3=pd.read_csv(\"file3.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "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>Unnamed: 0</th>\n",
       "      <th>Player_Name</th>\n",
       "      <th>Batsman_Total_Runs</th>\n",
       "      <th>Balls_Faced</th>\n",
       "      <th>Strike_Rate</th>\n",
       "      <th>No_of_matches_played_against_MI_in_2020</th>\n",
       "      <th>Batsman_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Prithvi Shaw</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2</td>\n",
       "      <td>played</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Dhawan</td>\n",
       "      <td>69</td>\n",
       "      <td>54</td>\n",
       "      <td>127.7</td>\n",
       "      <td>2</td>\n",
       "      <td>played</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Shreyas Iyer</td>\n",
       "      <td>67</td>\n",
       "      <td>62</td>\n",
       "      <td>108.0</td>\n",
       "      <td>2</td>\n",
       "      <td>played</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Rishabh Pant</td>\n",
       "      <td>21</td>\n",
       "      <td>24</td>\n",
       "      <td>87.8</td>\n",
       "      <td>1</td>\n",
       "      <td>Didn't play in second Match</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Marcus Stoinis</td>\n",
       "      <td>15</td>\n",
       "      <td>10</td>\n",
       "      <td>150.0</td>\n",
       "      <td>2</td>\n",
       "      <td>played</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Hetmyer</td>\n",
       "      <td>11</td>\n",
       "      <td>13</td>\n",
       "      <td>84.6</td>\n",
       "      <td>1</td>\n",
       "      <td>Didn't play in  second Match</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Rahane</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Didn't play in First Match</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     Player_Name  Batsman_Total_Runs  Balls_Faced  Strike_Rate  \\\n",
       "0           0    Prithvi Shaw                  14           14        100.0   \n",
       "1           1          Dhawan                  69           54        127.7   \n",
       "2           2    Shreyas Iyer                  67           62        108.0   \n",
       "3           3    Rishabh Pant                  21           24         87.8   \n",
       "4           4  Marcus Stoinis                  15           10        150.0   \n",
       "5           5         Hetmyer                  11           13         84.6   \n",
       "6           6          Rahane                  15           15        100.0   \n",
       "\n",
       "   No_of_matches_played_against_MI_in_2020                Batsman_status  \n",
       "0                                        2                        played  \n",
       "1                                        2                        played  \n",
       "2                                        2                        played  \n",
       "3                                        1   Didn't play in second Match  \n",
       "4                                        2                        played  \n",
       "5                                        1  Didn't play in  second Match  \n",
       "6                                        1    Didn't play in First Match  "
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DC Batsman VS MI Bowlers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Player_Name  Dismissal_Count_Of_Bumrah  Trent Boult  Rahul Chahar  \\\n",
      "0    Prithvi Shaw                          0            2             1   \n",
      "1          Dhawan                          0            1             1   \n",
      "2    Shreyas Iyer                          2            0             2   \n",
      "3    Rishabh Pant                          5            0             0   \n",
      "4  Marcus Stoinis                          1            0             0   \n",
      "5          Rahane                          0            0             0   \n",
      "6   Harshal patel                          1            0             0   \n",
      "\n",
      "   Krunal Pandya  Hardik Pandya  \n",
      "0              0              0  \n",
      "1              0              1  \n",
      "2              1              1  \n",
      "3              2              0  \n",
      "4              0              0  \n",
      "5              1              0  \n",
      "6              0              0  \n"
     ]
    }
   ],
   "source": [
    "Data1={'Player_Name':['Prithvi Shaw','Dhawan','Shreyas Iyer','Rishabh Pant','Marcus Stoinis','Rahane','Harshal patel'],\n",
    "     'Dismissal_Count_Of_Bumrah':[0,0,2,5,1,0,1],\n",
    "     'Trent Boult':[2,1,0,0,0,0,0],\n",
    "     'Rahul Chahar':[1,1,2,0,0,0,0],\n",
    "     'Krunal Pandya':[0,0,1,2,0,1,0],\n",
    "      'Hardik Pandya':[0,1,1,0,0,0,0],\n",
    "     }\n",
    "df4 = pd.DataFrame(Data1,columns = ['Player_Name','Dismissal_Count_Of_Bumrah','Trent Boult','Rahul Chahar','Krunal Pandya','Hardik Pandya'])\n",
    "print(df4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4.to_csv('file4.csv') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "file4=pd.read_csv(\"file4.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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>Unnamed: 0</th>\n",
       "      <th>Player_Name</th>\n",
       "      <th>Dismissal_Count_Of_Bumrah</th>\n",
       "      <th>Trent Boult</th>\n",
       "      <th>Rahul Chahar</th>\n",
       "      <th>Krunal Pandya</th>\n",
       "      <th>Hardik Pandya</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Prithvi Shaw</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Dhawan</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Shreyas Iyer</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Rishabh Pant</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Marcus Stoinis</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Rahane</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Harshal patel</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0     Player_Name  Dismissal_Count_Of_Bumrah  Trent Boult  \\\n",
       "0           0    Prithvi Shaw                          0            2   \n",
       "1           1          Dhawan                          0            1   \n",
       "2           2    Shreyas Iyer                          2            0   \n",
       "3           3    Rishabh Pant                          5            0   \n",
       "4           4  Marcus Stoinis                          1            0   \n",
       "5           5          Rahane                          0            0   \n",
       "6           6   Harshal patel                          1            0   \n",
       "\n",
       "   Rahul Chahar  Krunal Pandya  Hardik Pandya  \n",
       "0             1              0              0  \n",
       "1             1              0              1  \n",
       "2             2              1              1  \n",
       "3             0              2              0  \n",
       "4             0              0              0  \n",
       "5             0              1              0  \n",
       "6             0              0              0  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "count=[9,3,4,4,2]\n",
    "explode=[0.2,0,0,0,0]\n",
    "labels=['Bumrah','Trent Boult','Rahul Chahar','Krunal Pandya','Hardik Pandya']\n",
    "plt.pie(count,labels=labels,explode=explode,autopct='%.2f')\n",
    "plt.title('DC Batsman Wickets Taken by MI Bowlers')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
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  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
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