6_EDA of IPL data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "18f0cefa",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis(EDA) of IPL Data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15018813",
   "metadata": {},
   "source": [
    "## The Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "920c6658",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the required libraries\n",
    "\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5d827f2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the ipl macthes dataset\n",
    "ipl=pd.read_csv('Documents\\matches.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49390501",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05-04-2017</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>06-04-2017</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>07-04-2017</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  season       city        date                        team1  \\\n",
       "0   1    2017  Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
       "1   2    2017       Pune  06-04-2017               Mumbai Indians   \n",
       "2   3    2017     Rajkot  07-04-2017                Gujarat Lions   \n",
       "3   4    2017     Indore  08-04-2017       Rising Pune Supergiant   \n",
       "4   5    2017  Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
       "\n",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3              Kings XI Punjab              Kings XI Punjab         field   \n",
       "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "1  normal           0       Rising Pune Supergiant            0   \n",
       "2  normal           0        Kolkata Knight Riders            0   \n",
       "3  normal           0              Kings XI Punjab            0   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
       "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
       "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "\n",
       "          umpire1        umpire2  umpire3  \n",
       "0     AY Dandekar       NJ Llong      NaN  \n",
       "1  A Nand Kishore         S Ravi      NaN  \n",
       "2     Nitin Menon      CK Nandan      NaN  \n",
       "3    AK Chaudhary  C Shamshuddin      NaN  \n",
       "4             NaN            NaN      NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Having a glance of first 5 records of the dataset\n",
    "ipl.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "47f52771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(636, 18)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lookin at the number of rows and column in the dataset\n",
    "ipl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "88b7f6d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CH Gayle          18\n",
       "YK Pathan         16\n",
       "DA Warner         15\n",
       "AB de Villiers    15\n",
       "RG Sharma         14\n",
       "                  ..\n",
       "AD Mathews         1\n",
       "LR Shukla          1\n",
       "R Bhatia           1\n",
       "A Singh            1\n",
       "BCJ Cutting        1\n",
       "Name: player_of_match, Length: 201, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the frequency of most man of the match awards\n",
    "ipl['player_of_match'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3f6f0faf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CH Gayle          18\n",
       "YK Pathan         16\n",
       "DA Warner         15\n",
       "AB de Villiers    15\n",
       "RG Sharma         14\n",
       "SK Raina          14\n",
       "MS Dhoni          13\n",
       "G Gambhir         13\n",
       "AM Rahane         12\n",
       "MEK Hussey        12\n",
       "Name: player_of_match, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting top 10 players with most man of the match awards\n",
    "ipl['player_of_match'].value_counts()[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "898d397d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CH Gayle          18\n",
       "YK Pathan         16\n",
       "DA Warner         15\n",
       "AB de Villiers    15\n",
       "RG Sharma         14\n",
       "Name: player_of_match, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting top 10 players with most man of the match awards\n",
    "ipl['player_of_match'].value_counts()[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab1b1096",
   "metadata": {},
   "source": [
    "## Bar Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f9ec1d5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.bar(list(ipl['player_of_match'].value_counts()[0:5].keys()),list(ipl['player_of_match'].value_counts()[0:5]),color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "23259105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a bar plot for the top 5 players with most man of the match awards\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.bar(list(ipl['player_of_match'].value_counts()[0:5].keys()),list(ipl['player_of_match'].value_counts()[0:5]),color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d9f9e282",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normal       626\n",
       "tie            7\n",
       "no result      3\n",
       "Name: result, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the frequency of result column\n",
    "ipl['result'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47785cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mumbai Indians                 85\n",
       "Kolkata Knight Riders          78\n",
       "Delhi Daredevils               72\n",
       "Royal Challengers Bangalore    70\n",
       "Kings XI Punjab                68\n",
       "Chennai Super Kings            66\n",
       "Rajasthan Royals               63\n",
       "Deccan Chargers                43\n",
       "Sunrisers Hyderabad            35\n",
       "Pune Warriors                  20\n",
       "Gujarat Lions                  15\n",
       "Kochi Tuskers Kerala            8\n",
       "Rising Pune Supergiants         7\n",
       "Rising Pune Supergiant          6\n",
       "Name: toss_winner, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the number of toss wins w.r.t. each team\n",
    "ipl['toss_winner'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1c09087b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting the records where a team won batting first\n",
    "batting_first=ipl[ipl['win_by_runs']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bcc43ec7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05-04-2017</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>11-04-2017</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>SV Samson</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>2017</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>15-04-2017</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>2017</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>15-04-2017</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>CJ Anderson</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>YC Barde</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id  season       city        date                        team1  \\\n",
       "0    1    2017  Hyderabad  05-04-2017          Sunrisers Hyderabad   \n",
       "4    5    2017  Bangalore  08-04-2017  Royal Challengers Bangalore   \n",
       "8    9    2017       Pune  11-04-2017             Delhi Daredevils   \n",
       "13  14    2017    Kolkata  15-04-2017        Kolkata Knight Riders   \n",
       "14  15    2017      Delhi  15-04-2017             Delhi Daredevils   \n",
       "\n",
       "                          team2                  toss_winner toss_decision  \\\n",
       "0   Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "4              Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "8        Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "13          Sunrisers Hyderabad          Sunrisers Hyderabad         field   \n",
       "14              Kings XI Punjab             Delhi Daredevils           bat   \n",
       "\n",
       "    result  dl_applied                       winner  win_by_runs  \\\n",
       "0   normal           0          Sunrisers Hyderabad           35   \n",
       "4   normal           0  Royal Challengers Bangalore           15   \n",
       "8   normal           0             Delhi Daredevils           97   \n",
       "13  normal           0        Kolkata Knight Riders           17   \n",
       "14  normal           0             Delhi Daredevils           51   \n",
       "\n",
       "    win_by_wickets player_of_match                                      venue  \\\n",
       "0                0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "4                0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "8                0       SV Samson    Maharashtra Cricket Association Stadium   \n",
       "13               0      RV Uthappa                               Eden Gardens   \n",
       "14               0     CJ Anderson                           Feroz Shah Kotla   \n",
       "\n",
       "        umpire1      umpire2  umpire3  \n",
       "0   AY Dandekar     NJ Llong      NaN  \n",
       "4           NaN          NaN      NaN  \n",
       "8   AY Dandekar       S Ravi      NaN  \n",
       "13  AY Dandekar     NJ Llong      NaN  \n",
       "14     YC Barde  Nitin Menon      NaN  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the head\n",
    "batting_first.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468365e0",
   "metadata": {},
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9e3b3a0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a histogram \n",
    "plt.figure(figsize=(5,5))\n",
    "plt.hist(batting_first[\"win_by_runs\"])\n",
    "plt.title(\"Distribution of runs\")\n",
    "plt.xlabel(\"Runs\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "130691c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mumbai Indians                 47\n",
       "Chennai Super Kings            46\n",
       "Kings XI Punjab                32\n",
       "Kolkata Knight Riders          31\n",
       "Royal Challengers Bangalore    30\n",
       "Sunrisers Hyderabad            23\n",
       "Rajasthan Royals               23\n",
       "Delhi Daredevils               21\n",
       "Deccan Chargers                18\n",
       "Pune Warriors                   6\n",
       "Rising Pune Supergiant          5\n",
       "Kochi Tuskers Kerala            2\n",
       "Rising Pune Supergiants         2\n",
       "Gujarat Lions                   1\n",
       "Name: winner, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding out the number of wins w.r.t. each team after batting first\n",
    "batting_first['winner'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "79f1a698",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a bar plot for top 3 teams with most wins after batting first\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.bar(list(batting_first['winner'].value_counts()[0:3].keys()),list(batting_first['winner'].value_counts()[0:3]),color=[\"blue\",\"yellow\",\"orange\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35c6cd3d",
   "metadata": {},
   "source": [
    "## Pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0b0171f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a Pie Chart for top 3 teams with most wins after batting first\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.pie(list(batting_first['winner'].value_counts()),labels=list(batting_first['winner'].value_counts().keys()),autopct='%0.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ba50252d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting those records where a team has won after batting second\n",
    "batting_second=ipl[ipl['win_by_wickets']!=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "de17a963",
   "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>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>06-04-2017</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>07-04-2017</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>CA Lynn</td>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>08-04-2017</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>09-04-2017</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>A Deshmukh</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2017</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>09-04-2017</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>N Rana</td>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>CK Nandan</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  season       city        date                   team1  \\\n",
       "1   2    2017       Pune  06-04-2017          Mumbai Indians   \n",
       "2   3    2017     Rajkot  07-04-2017           Gujarat Lions   \n",
       "3   4    2017     Indore  08-04-2017  Rising Pune Supergiant   \n",
       "5   6    2017  Hyderabad  09-04-2017           Gujarat Lions   \n",
       "6   7    2017     Mumbai  09-04-2017   Kolkata Knight Riders   \n",
       "\n",
       "                    team2             toss_winner toss_decision  result  \\\n",
       "1  Rising Pune Supergiant  Rising Pune Supergiant         field  normal   \n",
       "2   Kolkata Knight Riders   Kolkata Knight Riders         field  normal   \n",
       "3         Kings XI Punjab         Kings XI Punjab         field  normal   \n",
       "5     Sunrisers Hyderabad     Sunrisers Hyderabad         field  normal   \n",
       "6          Mumbai Indians          Mumbai Indians         field  normal   \n",
       "\n",
       "   dl_applied                  winner  win_by_runs  win_by_wickets  \\\n",
       "1           0  Rising Pune Supergiant            0               7   \n",
       "2           0   Kolkata Knight Riders            0              10   \n",
       "3           0         Kings XI Punjab            0               6   \n",
       "5           0     Sunrisers Hyderabad            0               9   \n",
       "6           0          Mumbai Indians            0               4   \n",
       "\n",
       "  player_of_match                                      venue         umpire1  \\\n",
       "1       SPD Smith    Maharashtra Cricket Association Stadium  A Nand Kishore   \n",
       "2         CA Lynn     Saurashtra Cricket Association Stadium     Nitin Menon   \n",
       "3      GJ Maxwell                     Holkar Cricket Stadium    AK Chaudhary   \n",
       "5     Rashid Khan  Rajiv Gandhi International Stadium, Uppal      A Deshmukh   \n",
       "6          N Rana                           Wankhede Stadium     Nitin Menon   \n",
       "\n",
       "         umpire2  umpire3  \n",
       "1         S Ravi      NaN  \n",
       "2      CK Nandan      NaN  \n",
       "3  C Shamshuddin      NaN  \n",
       "5       NJ Llong      NaN  \n",
       "6      CK Nandan      NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the head\n",
    "batting_second.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f0622c32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1.,  0.,  0.,  4.,  0.,  0., 15.,  0.,  0.,  0., 34.,  0.,  0.,\n",
       "        57.,  0.,  0., 68.,  0.,  0.,  0., 70.,  0.,  0., 48.,  0.,  0.,\n",
       "        32.,  0.,  0., 10.]),\n",
       " array([ 1. ,  1.3,  1.6,  1.9,  2.2,  2.5,  2.8,  3.1,  3.4,  3.7,  4. ,\n",
       "         4.3,  4.6,  4.9,  5.2,  5.5,  5.8,  6.1,  6.4,  6.7,  7. ,  7.3,\n",
       "         7.6,  7.9,  8.2,  8.5,  8.8,  9.1,  9.4,  9.7, 10. ]),\n",
       " <BarContainer object of 30 artists>)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a histogram for frequency of wins w.r.t. number of wickets\n",
    "\n",
    "plt.figure(figsize=(5,5))\n",
    "plt.hist(batting_second['win_by_wickets'],bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1518b415",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kolkata Knight Riders          46\n",
       "Mumbai Indians                 44\n",
       "Royal Challengers Bangalore    42\n",
       "Delhi Daredevils               41\n",
       "Rajasthan Royals               38\n",
       "Kings XI Punjab                36\n",
       "Chennai Super Kings            33\n",
       "Sunrisers Hyderabad            18\n",
       "Gujarat Lions                  12\n",
       "Deccan Chargers                11\n",
       "Pune Warriors                   6\n",
       "Rising Pune Supergiant          5\n",
       "Kochi Tuskers Kerala            4\n",
       "Rising Pune Supergiants         3\n",
       "Name: winner, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fnding out the frequency of number of wins w.r.t. each time after batting second\n",
    "batting_second['winner'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b6df0a8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a bar plot for top 3 teams with most wins after batting second\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.bar(list(batting_second['winner'].value_counts()[0:3].keys()),list(batting_second['winner'].value_counts()[0:3]),color=[\"purple\",\"blue\",\"red\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8a9e441f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a pie chart for distribution of most wins after batting second\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.pie(list(batting_second['winner'].value_counts()),labels=list(batting_second['winner'].value_counts().keys()),autopct='%0.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "67097397",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013    76\n",
       "2012    74\n",
       "2011    73\n",
       "2010    60\n",
       "2014    60\n",
       "2016    60\n",
       "2017    59\n",
       "2015    59\n",
       "2008    58\n",
       "2009    57\n",
       "Name: season, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the number of matches played each season\n",
    "ipl['season'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bcc207ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mumbai            85\n",
       "Bangalore         66\n",
       "Kolkata           61\n",
       "Delhi             60\n",
       "Hyderabad         49\n",
       "Chennai           48\n",
       "Chandigarh        46\n",
       "Jaipur            33\n",
       "Pune              32\n",
       "Durban            15\n",
       "Ahmedabad         12\n",
       "Centurion         12\n",
       "Visakhapatnam     11\n",
       "Rajkot            10\n",
       "Dharamsala         9\n",
       "Johannesburg       8\n",
       "Cape Town          7\n",
       "Abu Dhabi          7\n",
       "Ranchi             7\n",
       "Port Elizabeth     7\n",
       "Cuttack            7\n",
       "Raipur             6\n",
       "Sharjah            6\n",
       "Kochi              5\n",
       "Indore             5\n",
       "Kanpur             4\n",
       "Nagpur             3\n",
       "Kimberley          3\n",
       "East London        3\n",
       "Bloemfontein       2\n",
       "Name: city, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at the number of matches played in each city\n",
    "ipl['city'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "29bf3a1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "325"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding out how many times a team has won the match after winning the loss\n",
    "import numpy as np\n",
    "np.sum(ipl['toss_winner']==ipl['winner'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a83745e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5110062893081762"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "325/636"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "951360c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "deliveries=pd.read_csv('Documents\\deliveries.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c3de60e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "1            0            0             0             0           0   \n",
       "2            0            0             0             4           0   \n",
       "3            0            0             0             0           0   \n",
       "4            0            0             0             0           2   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "2           4               NaN            NaN     NaN  \n",
       "3           0               NaN            NaN     NaN  \n",
       "4           2               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deliveries.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "90217e9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    1,     2,     3,     4,     5,     6,     7,     8,     9,\n",
       "          10,    11,    12,    13,    14,    15,    16,    17,    18,\n",
       "          19,    20,    21,    22,    23,    24,    25,    26,    27,\n",
       "          28,    29,    30,    31,    32,    33,    34,    35,    36,\n",
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       "         505,   506,   507,   508,   509,   510,   511,   512,   513,\n",
       "         514,   515,   516,   517,   518,   519,   520,   521,   522,\n",
       "         523,   524,   525,   526,   527,   528,   529,   530,   531,\n",
       "         532,   533,   534,   535,   536,   537,   538,   539,   540,\n",
       "         541,   542,   543,   544,   545,   546,   547,   548,   549,\n",
       "         550,   551,   552,   553,   554,   555,   556,   557,   558,\n",
       "         559,   560,   561,   562,   563,   564,   565,   566,   567,\n",
       "         568,   569,   570,   571,   572,   573,   574,   575,   576,\n",
       "         577,   578,   579,   580,   581,   582,   583,   584,   585,\n",
       "         586,   587,   588,   589,   590,   591,   592,   593,   594,\n",
       "         595,   596,   597,   598,   599,   600,   601,   602,   603,\n",
       "         604,   605,   606,   607,   608,   609,   610,   611,   612,\n",
       "         613,   614,   615,   616,   617,   618,   619,   620,   621,\n",
       "         622,   623,   624,   625,   626,   627,   628,   629,   630,\n",
       "         631,   632,   633,   634,   635,   636,  7894,  7895,  7896,\n",
       "        7897,  7898,  7899,  7900,  7901,  7902,  7903,  7904,  7905,\n",
       "        7906,  7907,  7908,  7909,  7910,  7911,  7912,  7913,  7914,\n",
       "        7915,  7916,  7917,  7918,  7919,  7920,  7921,  7922,  7923,\n",
       "        7924,  7925,  7926,  7927,  7928,  7929,  7930,  7931,  7932,\n",
       "        7933,  7934,  7935,  7936,  7937,  7938,  7939,  7940,  7941,\n",
       "        7942,  7943,  7944,  7945,  7946,  7947,  7948,  7949,  7950,\n",
       "        7951,  7952,  7953, 11137, 11138, 11139, 11140, 11141, 11142,\n",
       "       11143, 11144, 11145, 11146, 11147, 11148, 11149, 11150, 11151,\n",
       "       11152, 11153, 11309, 11310, 11311, 11312, 11313, 11314, 11315,\n",
       "       11316, 11317, 11318, 11319, 11320, 11321, 11322, 11323, 11324,\n",
       "       11325, 11326, 11327, 11328, 11329, 11330, 11331, 11332, 11333,\n",
       "       11334, 11335, 11336, 11337, 11338, 11339, 11340, 11341, 11342,\n",
       "       11343, 11344, 11345, 11346, 11347, 11412, 11413, 11414, 11415],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deliveries['match_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9cee297d",
   "metadata": {},
   "outputs": [],
   "source": [
    "match_1=deliveries[deliveries['match_id']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8f6641b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "1            0            0             0             0           0   \n",
       "2            0            0             0             4           0   \n",
       "3            0            0             0             0           0   \n",
       "4            0            0             0             0           2   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "2           4               NaN            NaN     NaN  \n",
       "3           0               NaN            NaN     NaN  \n",
       "4           2               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "be5653e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(248, 21)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "match_1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ff96723d",
   "metadata": {},
   "outputs": [],
   "source": [
    "srh = match_1[match_1['inning']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "124c045d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    57\n",
       "0    32\n",
       "4    17\n",
       "6     9\n",
       "2     9\n",
       "3     1\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srh['batsman_runs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "495feb96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "caught    3\n",
       "bowled    1\n",
       "Name: dismissal_kind, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srh['dismissal_kind'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "1e7257be",
   "metadata": {},
   "outputs": [],
   "source": [
    "rcb=match_1[match_1['inning']==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "14d5445f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    49\n",
       "1    44\n",
       "4    15\n",
       "6     8\n",
       "2     7\n",
       "Name: batsman_runs, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcb['batsman_runs'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7e1bd75d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "caught     6\n",
       "bowled     2\n",
       "run out    2\n",
       "Name: dismissal_kind, dtype: int64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rcb['dismissal_kind'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3df83e73",
   "metadata": {},
   "source": [
    "# Analyzed by\n",
    "\n",
    "\n",
    "𝐌𝐝 𝐑𝐚𝐢𝐞𝐬𝐡,\n",
    "Enrollment number : 𝟏𝟗𝐔𝐌𝐄𝟏𝟏𝟔,\n",
    "Registration number : 𝟏𝟗𝟏𝟏𝟑𝟒𝟓,\n",
    "B Tech,7𝐭𝐡 semester,Section : 𝐀,\n",
    "Mechanical Engineering Department,\n",
    "National Institute of Technology Agartala, Tripura 799046,\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb329ecb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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