Deliveries_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IPL Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPL is the short form of Indian Premier League which is a twenty- twenty cricket league played in India. It was founded in 2008 by BCCI. This league mainly concerns with finding the hidden player in country who are capable to play at international level and give a chance to play for Indian international team. It is very famous league around the world."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It has eight teams named as CSK Chennai super King and this team is owned by N. Srinivasan, Delhi Capitals owned by GM Rao, Kings XI Punjab owned by famous bollywood actress Priety Zinta, Kolkata Knight Riders owned by King of bollywood Shah Rukh Khan, Mumbai Indians owned by Mukesh Ambani and his wife Neeta Ambani, Rajasthan Royal owned by Manoj Badale and Royal Challengers Bangalore owned by Vijay Mallya. All the team participates in the league and only four team qualify for the semi final. IPL is a good platform for them who want their career in cricket and by performing well they may get chances for playing with Indian cricket team. \n",
    "\n",
    "In cricket and IPL, Data Science is used in a somewhat unique and interesting manner. In 2008, IPL came, which completely revolutionized the cricket world, because before IPL never had such an immense amount of money invested into cricket. Considering the auction 2020, totaled ~400 crore INR spent on the players. So much money is being spent in IPL. Data Collection and Data Analysis in IPL has breached the next level, because as IPL spending lot of money on players, it has become necessary for IPL teams to find out that, “Should they spend on a particular player or not?” or “How valuable is the player going to be for the team?”   How should they judge in detail, “Which player should they buy and which one they shouldn’t it?”, “How much money should be spent on which player?” or “What are the values of the different players?”. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset consists of two seperate CSV files named deliveries and matches. These files contain the information of ball by ball  of each ball bowled and match by match details of every match during 2008-2017 period including 10 seasons.\n",
    "\n",
    "Analysis is done in two seperate notebooks, this being the first One\n",
    "\n",
    "Our analysis focuses on extracting useful and exciting statistics from the data by using python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis covered in this notebook :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Indepth Analysis of Virat Kohli Performance\n",
    "2. Score Distribution For Teams by Innings\n",
    "3. Analysing How many times teams scores more than 200\n",
    "4. Batsman Comparison\n",
    "5. Top Individual Scores\n",
    "6. Highest Wicket Taker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reading csv data File and printing first 10 Row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <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",
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       "      <td>...</td>\n",
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       "      <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",
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       "      <td>1</td>\n",
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       "      <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",
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       "      <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",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <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",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</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>6</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>TS Mills</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>6</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>7</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>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>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>4</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 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",
       "5         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "6         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "7         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     2   \n",
       "8         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     2   \n",
       "9         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     2   \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",
       "5     6   S Dhawan   DA Warner     TS Mills              0  ...         0   \n",
       "6     7   S Dhawan   DA Warner     TS Mills              0  ...         0   \n",
       "7     1   S Dhawan   DA Warner  A Choudhary              0  ...         0   \n",
       "8     2  DA Warner    S Dhawan  A Choudhary              0  ...         0   \n",
       "9     3  DA Warner    S Dhawan  A Choudhary              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",
       "5            0            0             0             0           0   \n",
       "6            1            0             0             0           1   \n",
       "7            0            0             0             1           0   \n",
       "8            0            0             0             4           0   \n",
       "9            0            1             0             0           1   \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",
       "5           0               NaN            NaN     NaN  \n",
       "6           1               NaN            NaN     NaN  \n",
       "7           1               NaN            NaN     NaN  \n",
       "8           4               NaN            NaN     NaN  \n",
       "9           1               NaN            NaN     NaN  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df=pd.read_csv(r'C:\\Users\\Eren Yaeger\\Downloads\\IPL Analysis/deliveries.csv')\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting an idea of size of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150460, 21)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indepth Analysis of Virat Kohli Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filtering out data in which Virat Kohli is on_strike"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
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       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
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       "      <td>Mumbai Indians</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>TG Southee</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>2594</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>TG Southee</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>2597</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Harbhajan Singh</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2598</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Harbhajan Singh</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2603</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>TG Southee</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>2604</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>TG Southee</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>2605</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>TG Southee</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>2606</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>TG Southee</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>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id  inning                 batting_team    bowling_team  over  \\\n",
       "2590        12       1  Royal Challengers Bangalore  Mumbai Indians     1   \n",
       "2591        12       1  Royal Challengers Bangalore  Mumbai Indians     1   \n",
       "2593        12       1  Royal Challengers Bangalore  Mumbai Indians     1   \n",
       "2594        12       1  Royal Challengers Bangalore  Mumbai Indians     1   \n",
       "2597        12       1  Royal Challengers Bangalore  Mumbai Indians     2   \n",
       "2598        12       1  Royal Challengers Bangalore  Mumbai Indians     2   \n",
       "2603        12       1  Royal Challengers Bangalore  Mumbai Indians     3   \n",
       "2604        12       1  Royal Challengers Bangalore  Mumbai Indians     3   \n",
       "2605        12       1  Royal Challengers Bangalore  Mumbai Indians     3   \n",
       "2606        12       1  Royal Challengers Bangalore  Mumbai Indians     3   \n",
       "\n",
       "      ball  batsman non_striker           bowler  is_super_over  ...  \\\n",
       "2590     2  V Kohli    CH Gayle       TG Southee              0  ...   \n",
       "2591     3  V Kohli    CH Gayle       TG Southee              0  ...   \n",
       "2593     5  V Kohli    CH Gayle       TG Southee              0  ...   \n",
       "2594     6  V Kohli    CH Gayle       TG Southee              0  ...   \n",
       "2597     1  V Kohli    CH Gayle  Harbhajan Singh              0  ...   \n",
       "2598     2  V Kohli    CH Gayle  Harbhajan Singh              0  ...   \n",
       "2603     1  V Kohli    CH Gayle       TG Southee              0  ...   \n",
       "2604     2  V Kohli    CH Gayle       TG Southee              0  ...   \n",
       "2605     3  V Kohli    CH Gayle       TG Southee              0  ...   \n",
       "2606     4  V Kohli    CH Gayle       TG Southee              0  ...   \n",
       "\n",
       "      bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "2590         0            0            0             0             0   \n",
       "2591         0            0            0             0             1   \n",
       "2593         0            0            0             0             0   \n",
       "2594         0            0            0             0             1   \n",
       "2597         0            0            0             0             0   \n",
       "2598         0            0            0             0             1   \n",
       "2603         0            0            0             0             6   \n",
       "2604         0            0            0             0             2   \n",
       "2605         0            0            0             0             4   \n",
       "2606         0            0            0             0             4   \n",
       "\n",
       "      extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
       "2590           1           1               NaN            NaN     NaN  \n",
       "2591           0           1               NaN            NaN     NaN  \n",
       "2593           0           0               NaN            NaN     NaN  \n",
       "2594           0           1               NaN            NaN     NaN  \n",
       "2597           0           0               NaN            NaN     NaN  \n",
       "2598           0           1               NaN            NaN     NaN  \n",
       "2603           0           6               NaN            NaN     NaN  \n",
       "2604           0           2               NaN            NaN     NaN  \n",
       "2605           0           4               NaN            NaN     NaN  \n",
       "2606           0           4               NaN            NaN     NaN  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kohli=df[df['batsman']=='V Kohli']\n",
    "df_kohli.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Count of Various Dismissal Count of Virat Kohli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "caught               76\n",
       "bowled               24\n",
       "run out              14\n",
       "lbw                   7\n",
       "stumped               3\n",
       "caught and bowled     2\n",
       "Name: dismissal_kind, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kohli['dismissal_kind'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting the same on Pie Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='dismissal_kind'>"
      ]
     },
     "execution_count": 123,
     "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": [
    "df_kohli['dismissal_kind'].value_counts().plot.pie()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total fours by Virat Kohli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_kohli[df_kohli['batsman_runs']==4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Total 6s by Virat Kohli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_kohli[df_kohli['batsman_runs']==6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Total runs made by Virat Kohli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4588"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_kohli['total_runs'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of 1s,2s,3s,4s,6s scored by Virat Kohli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count(df,runs):\n",
    "    return len(df_kohli[df_kohli['batsman_runs']==runs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1410"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count(df_kohli,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count(df_kohli,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count(df_kohli,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "384"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count(df_kohli,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "160"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count(df_kohli,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pie Chart of Runs Distribution of Kohli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "slices=[1410*1,242*2,11*3,384*4,160*6]\n",
    "labels=[1,2,3,4,6]\n",
    "explode=[0,0,0,0.1,0.1]\n",
    "plt.pie(slices,labels=labels,autopct='%1.1f%%',explode=explode)\n",
    "plt.title(\"Virat Kohli total runs contribution\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### So Virat Kohli scored 23.9% of his runs through Sixes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Unique Team Names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Royal Challengers Bangalore', 'Sunrisers Hyderabad',\n",
       "       'Rising Pune Supergiant', 'Mumbai Indians',\n",
       "       'Kolkata Knight Riders', 'Gujarat Lions', 'Kings XI Punjab',\n",
       "       'Delhi Daredevils', 'Chennai Super Kings', 'Rajasthan Royals',\n",
       "       'Deccan Chargers', 'Kochi Tuskers Kerala', 'Pune Warriors',\n",
       "       'Rising Pune Supergiants'], dtype=object)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bowling_team'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Replacing the Team Names with their abbreviations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "Teams={\n",
    "    'Royal Challengers Bangalore':'RCB', \n",
    "    'Sunrisers Hyderabad':'SRH',\n",
    "       'Rising Pune Supergiant':'RPS',\n",
    "    'Mumbai Indians':'MI',\n",
    "       'Kolkata Knight Riders':'KKR', \n",
    "    'Gujarat Lions':'GL',\n",
    "    'Kings XI Punjab':'KXIP',\n",
    "       'Delhi Daredevils':'DD',\n",
    "    'Chennai Super Kings':'CSK',\n",
    "    'Rajasthan Royals':'RR',\n",
    "       'Deccan Chargers':'DC',\n",
    "    'Kochi Tuskers Kerala':'KTK',\n",
    "    'Pune Warriors':'PW',\n",
    "       'Rising Pune Supergiants':'RPS'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['batting_team']=df['batting_team'].map(Teams)\n",
    "df['bowling_team']=df['bowling_team'].map(Teams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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>SRH</td>\n",
       "      <td>RCB</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>SRH</td>\n",
       "      <td>RCB</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>SRH</td>\n",
       "      <td>RCB</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>SRH</td>\n",
       "      <td>RCB</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>SRH</td>\n",
       "      <td>RCB</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  ball    batsman  \\\n",
       "0         1       1          SRH          RCB     1     1  DA Warner   \n",
       "1         1       1          SRH          RCB     1     2  DA Warner   \n",
       "2         1       1          SRH          RCB     1     3  DA Warner   \n",
       "3         1       1          SRH          RCB     1     4  DA Warner   \n",
       "4         1       1          SRH          RCB     1     5  DA Warner   \n",
       "\n",
       "  non_striker    bowler  is_super_over  ...  bye_runs  legbye_runs  \\\n",
       "0    S Dhawan  TS Mills              0  ...         0            0   \n",
       "1    S Dhawan  TS Mills              0  ...         0            0   \n",
       "2    S Dhawan  TS Mills              0  ...         0            0   \n",
       "3    S Dhawan  TS Mills              0  ...         0            0   \n",
       "4    S Dhawan  TS Mills              0  ...         0            0   \n",
       "\n",
       "   noball_runs  penalty_runs  batsman_runs  extra_runs  total_runs  \\\n",
       "0            0             0             0           0           0   \n",
       "1            0             0             0           0           0   \n",
       "2            0             0             4           0           4   \n",
       "3            0             0             0           0           0   \n",
       "4            0             0             0           2           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",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Score Distribution For Teams by Innings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping Dataframe by Innings and Runs Scored by them in that Innings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>RPS</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>GL</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>634</td>\n",
       "      <td>2</td>\n",
       "      <td>KKR</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>635</td>\n",
       "      <td>1</td>\n",
       "      <td>GL</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>635</td>\n",
       "      <td>2</td>\n",
       "      <td>SRH</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>636</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>636</td>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1284 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id  inning batting_team  total_runs\n",
       "0            1       1          SRH         207\n",
       "1            1       2          RCB         172\n",
       "2            2       1           MI         184\n",
       "3            2       2          RPS         187\n",
       "4            3       1           GL         183\n",
       "...        ...     ...          ...         ...\n",
       "1279       634       2          KKR         140\n",
       "1280       635       1           GL         162\n",
       "1281       635       2          SRH         163\n",
       "1282       636       1          SRH         208\n",
       "1283       636       2          RCB         200\n",
       "\n",
       "[1284 rows x 4 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runs=df.groupby(['match_id','inning','batting_team'])[['total_runs']].sum().reset_index()\n",
    "runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Runs scored by CSK in various Innings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>61</td>\n",
       "      <td>1</td>\n",
       "      <td>CSK</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>67</td>\n",
       "      <td>1</td>\n",
       "      <td>CSK</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>71</td>\n",
       "      <td>2</td>\n",
       "      <td>CSK</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>74</td>\n",
       "      <td>1</td>\n",
       "      <td>CSK</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>79</td>\n",
       "      <td>1</td>\n",
       "      <td>CSK</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id  inning batting_team  total_runs\n",
       "122        61       1          CSK         240\n",
       "134        67       1          CSK         208\n",
       "143        71       2          CSK         152\n",
       "148        74       1          CSK         178\n",
       "158        79       1          CSK         169"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csk_innings=runs[runs['batting_team']=='CSK']\n",
    "csk_innings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Seperating Runs Dataframe by Innings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "inning1=runs[runs['inning']==1]\n",
    "inning2=runs[runs['inning']==2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\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>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>GL</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>RPS</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>RCB</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning batting_team  total_runs\n",
       "0         1       1          SRH         207\n",
       "2         2       1           MI         184\n",
       "4         3       1           GL         183\n",
       "6         4       1          RPS         163\n",
       "8         5       1          RCB         157"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inning1.head()"
   ]
  },
  {
   "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",
       "    }\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>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>RPS</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>KKR</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>DD</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning batting_team  total_runs\n",
       "1         1       2          RCB         172\n",
       "3         2       2          RPS         187\n",
       "5         3       2          KKR         184\n",
       "7         4       2         KXIP         164\n",
       "9         5       2           DD         142"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inning2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Boxplot of Runs scored by various teams in all Matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb13e0bcc88>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='batting_team',y='total_runs',data=inning1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### According to this plot, the batting by CSK & RCB seems best. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb13e284348>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='batting_team',y='total_runs',data=inning2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### this figure also conveys the same story.in this, we see a point near 0 for RCB which may seem to be outlier. But it is on account of the match was disrupted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysing How many times teams scores more than 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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>total_runs</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>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1279</th>\n",
       "      <td>634</td>\n",
       "      <td>2</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>635</td>\n",
       "      <td>1</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>635</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>636</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>636</td>\n",
       "      <td>2</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1284 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id  inning                 batting_team  \\\n",
       "0            1       1          Sunrisers Hyderabad   \n",
       "1            1       2  Royal Challengers Bangalore   \n",
       "2            2       1               Mumbai Indians   \n",
       "3            2       2       Rising Pune Supergiant   \n",
       "4            3       1                Gujarat Lions   \n",
       "...        ...     ...                          ...   \n",
       "1279       634       2        Kolkata Knight Riders   \n",
       "1280       635       1                Gujarat Lions   \n",
       "1281       635       2          Sunrisers Hyderabad   \n",
       "1282       636       1          Sunrisers Hyderabad   \n",
       "1283       636       2  Royal Challengers Bangalore   \n",
       "\n",
       "                     bowling_team  total_runs  \n",
       "0     Royal Challengers Bangalore         207  \n",
       "1             Sunrisers Hyderabad         172  \n",
       "2          Rising Pune Supergiant         184  \n",
       "3                  Mumbai Indians         187  \n",
       "4           Kolkata Knight Riders         183  \n",
       "...                           ...         ...  \n",
       "1279          Sunrisers Hyderabad         140  \n",
       "1280          Sunrisers Hyderabad         162  \n",
       "1281                Gujarat Lions         163  \n",
       "1282  Royal Challengers Bangalore         208  \n",
       "1283          Sunrisers Hyderabad         200  \n",
       "\n",
       "[1284 rows x 5 columns]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_scores=df.groupby(['match_id', 'inning','batting_team','bowling_team'])['total_runs'].sum().reset_index() \n",
    "high_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering out innings with >=200 Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>DD</td>\n",
       "      <td>RPS</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>RCB</td>\n",
       "      <td>GL</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>KKR</td>\n",
       "      <td>209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>620</td>\n",
       "      <td>1</td>\n",
       "      <td>RCB</td>\n",
       "      <td>GL</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1256</th>\n",
       "      <td>623</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>DD</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1262</th>\n",
       "      <td>626</td>\n",
       "      <td>1</td>\n",
       "      <td>RCB</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1282</th>\n",
       "      <td>636</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1283</th>\n",
       "      <td>636</td>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>67 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id  inning batting_team bowling_team  total_runs\n",
       "0            1       1          SRH          RCB         207\n",
       "16           9       1           DD          RPS         205\n",
       "38          20       1          RCB           GL         213\n",
       "62          32       1          SRH         KXIP         207\n",
       "72          36       1          SRH          KKR         209\n",
       "...        ...     ...          ...          ...         ...\n",
       "1250       620       1          RCB           GL         248\n",
       "1256       623       1           MI           DD         206\n",
       "1262       626       1          RCB         KXIP         211\n",
       "1282       636       1          SRH          RCB         208\n",
       "1283       636       2          RCB          SRH         200\n",
       "\n",
       "[67 rows x 5 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_200=high_scores[high_scores['total_runs']>=200]\n",
    "score_200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering out innings with <100 Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "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>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>total_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>KKR</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RPS</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>MI</td>\n",
       "      <td>GL</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>34</td>\n",
       "      <td>4</td>\n",
       "      <td>GL</td>\n",
       "      <td>MI</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>DD</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1167</th>\n",
       "      <td>578</td>\n",
       "      <td>2</td>\n",
       "      <td>KKR</td>\n",
       "      <td>DD</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1207</th>\n",
       "      <td>598</td>\n",
       "      <td>2</td>\n",
       "      <td>RPS</td>\n",
       "      <td>SRH</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1237</th>\n",
       "      <td>613</td>\n",
       "      <td>2</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1253</th>\n",
       "      <td>621</td>\n",
       "      <td>2</td>\n",
       "      <td>KKR</td>\n",
       "      <td>RPS</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1261</th>\n",
       "      <td>625</td>\n",
       "      <td>2</td>\n",
       "      <td>RPS</td>\n",
       "      <td>DD</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>82 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id  inning batting_team bowling_team  total_runs\n",
       "53          27       2          RCB          KKR          49\n",
       "65          33       2          RCB          RPS          96\n",
       "68          34       3           MI           GL          11\n",
       "69          34       4           GL           MI           6\n",
       "70          35       1           DD         KXIP          67\n",
       "...        ...     ...          ...          ...         ...\n",
       "1167       578       2          KKR           DD          99\n",
       "1207       598       2          RPS          SRH          94\n",
       "1237       613       2           MI          SRH          92\n",
       "1253       621       2          KKR          RPS          66\n",
       "1261       625       2          RPS           DD          76\n",
       "\n",
       "[82 rows x 5 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_100=high_scores[high_scores['total_runs']<100]\n",
    "score_100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting how many times Various teams scored >=200 runs in an innings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Eren Yaeger\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='batting_team', ylabel='count'>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(score_200['batting_team'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting how many times Various teams scored <100 runs in an innings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Eren Yaeger\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='batting_team', ylabel='count'>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(score_100['batting_team'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Counting the number of times a bowling team has conceeded above 200 runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Eren Yaeger\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='bowling_team', ylabel='count'>"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(score_200['bowling_team'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1st two graph shows the number of times a team has scored above 200 runs. 2nd graph shows the number of times a bowling team has conceeded above 200 runs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Counting how many times team scores >200 runs against various team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_200=df.groupby(['match_id', 'inning','batting_team','bowling_team'])['total_runs'].sum().reset_index()\n",
    "high_200.set_index(['match_id'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Maximum runs in an Innings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "263"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_200['total_runs'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>RPS</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>RPS</td>\n",
       "      <td>MI</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>GL</td>\n",
       "      <td>KKR</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          inning batting_team bowling_team  total_runs\n",
       "match_id                                              \n",
       "1              1          SRH          RCB         207\n",
       "1              2          RCB          SRH         172\n",
       "2              1           MI          RPS         184\n",
       "2              2          RPS           MI         187\n",
       "3              1           GL          KKR         183"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_200.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"38\" valign=\"top\">1</th>\n",
       "      <th rowspan=\"7\" valign=\"top\">CSK</th>\n",
       "      <th>DD</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KKR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KXIP</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCB</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRH</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <th>RR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">DD</th>\n",
       "      <th>KXIP</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPS</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GL</th>\n",
       "      <th>DD</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">KKR</th>\n",
       "      <th>DC</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KXIP</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCB</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">KXIP</th>\n",
       "      <th>CSK</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCB</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">MI</th>\n",
       "      <th>CSK</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DD</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCB</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"8\" valign=\"top\">RCB</th>\n",
       "      <th>CSK</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DD</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GL</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KXIP</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PW</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRH</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">RR</th>\n",
       "      <th>CSK</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DD</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KXIP</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">SRH</th>\n",
       "      <th>KKR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KXIP</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCB</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">2</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">CSK</th>\n",
       "      <th>KXIP</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RCB</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DD</th>\n",
       "      <th>GL</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KKR</th>\n",
       "      <th>KXIP</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">KXIP</th>\n",
       "      <th>CSK</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DD</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KKR</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRH</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MI</th>\n",
       "      <th>CSK</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KXIP</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RCB</th>\n",
       "      <th>KXIP</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SRH</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">RR</th>\n",
       "      <th>CSK</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DC</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MI</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  count\n",
       "inning batting_team bowling_team       \n",
       "1      CSK          DD                1\n",
       "                    KKR               1\n",
       "                    KXIP              2\n",
       "                    MI                1\n",
       "                    RCB               1\n",
       "                    RR                1\n",
       "                    SRH               2\n",
       "       DC           RR                1\n",
       "       DD           KXIP              1\n",
       "                    MI                1\n",
       "                    RPS               1\n",
       "       GL           DD                1\n",
       "       KKR          DC                1\n",
       "                    KXIP              1\n",
       "                    RCB               1\n",
       "       KXIP         CSK               2\n",
       "                    MI                1\n",
       "                    RCB               2\n",
       "                    RR                1\n",
       "       MI           CSK               1\n",
       "                    DD                4\n",
       "                    RCB               1\n",
       "                    RR                1\n",
       "       RCB          CSK               1\n",
       "                    DD                1\n",
       "                    GL                2\n",
       "                    KXIP              3\n",
       "                    MI                1\n",
       "                    PW                1\n",
       "                    RR                1\n",
       "                    SRH               1\n",
       "       RR           CSK               1\n",
       "                    DD                1\n",
       "                    KXIP              1\n",
       "       SRH          KKR               1\n",
       "                    KXIP              2\n",
       "                    RCB               2\n",
       "                    RR                1\n",
       "2      CSK          KXIP              1\n",
       "                    RCB               1\n",
       "                    RR                1\n",
       "       DD           GL                1\n",
       "       KKR          KXIP              1\n",
       "       KXIP         CSK               2\n",
       "                    DD                1\n",
       "                    KKR               1\n",
       "                    SRH               1\n",
       "       MI           CSK               1\n",
       "                    KXIP              1\n",
       "       RCB          KXIP              1\n",
       "                    SRH               1\n",
       "       RR           CSK               1\n",
       "                    DC                1\n",
       "                    MI                1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high=high_200.rename(columns={'total_runs':'count'})\n",
    "high=high[high['count']>=200].groupby(['inning','batting_team','bowling_team']).count()\n",
    "high"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batsman Comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Batsman and total balls played by them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batsman</th>\n",
       "      <th>ball</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Ashish Reddy</td>\n",
       "      <td>196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Chandila</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Chopra</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Flintoff</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>YV Takawale</td>\n",
       "      <td>183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>Yashpal Singh</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>Younis Khan</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>2050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>Z Khan</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>461 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            batsman  ball\n",
       "0    A Ashish Reddy   196\n",
       "1        A Chandila     7\n",
       "2          A Chopra    75\n",
       "3       A Choudhary    20\n",
       "4        A Flintoff    57\n",
       "..              ...   ...\n",
       "456     YV Takawale   183\n",
       "457   Yashpal Singh    67\n",
       "458     Younis Khan     7\n",
       "459    Yuvraj Singh  2050\n",
       "460          Z Khan   141\n",
       "\n",
       "[461 rows x 2 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balls=df.groupby(['batsman'])['ball'].count().reset_index()\n",
    "balls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Batsman and total runs scored by them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batsman</th>\n",
       "      <th>runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Ashish Reddy</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Chandila</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Chopra</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Flintoff</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>YV Takawale</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>Yashpal Singh</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>Younis Khan</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>2591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>Z Khan</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>461 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            batsman  runs\n",
       "0    A Ashish Reddy   280\n",
       "1        A Chandila     4\n",
       "2          A Chopra    53\n",
       "3       A Choudhary    25\n",
       "4        A Flintoff    62\n",
       "..              ...   ...\n",
       "456     YV Takawale   192\n",
       "457   Yashpal Singh    47\n",
       "458     Younis Khan     3\n",
       "459    Yuvraj Singh  2591\n",
       "460          Z Khan   117\n",
       "\n",
       "[461 rows x 2 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runs=df.groupby(['batsman'])['batsman_runs'].sum().reset_index()\n",
    "runs.columns=['batsman','runs']\n",
    "runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "batsman\n",
       "A Ashish Reddy     280\n",
       "A Chandila           4\n",
       "A Chopra            53\n",
       "A Choudhary         25\n",
       "A Flintoff          62\n",
       "                  ... \n",
       "YV Takawale        192\n",
       "Yashpal Singh       47\n",
       "Younis Khan          3\n",
       "Yuvraj Singh      2591\n",
       "Z Khan             117\n",
       "Name: batsman_runs, Length: 461, dtype: int64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['batsman'])['batsman_runs'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merging balls and runs table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batsman</th>\n",
       "      <th>ball</th>\n",
       "      <th>runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Ashish Reddy</td>\n",
       "      <td>196</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Chandila</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Chopra</td>\n",
       "      <td>75</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Flintoff</td>\n",
       "      <td>57</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>YV Takawale</td>\n",
       "      <td>183</td>\n",
       "      <td>192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>Yashpal Singh</td>\n",
       "      <td>67</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>Younis Khan</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>2050</td>\n",
       "      <td>2591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>Z Khan</td>\n",
       "      <td>141</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>461 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            batsman  ball  runs\n",
       "0    A Ashish Reddy   196   280\n",
       "1        A Chandila     7     4\n",
       "2          A Chopra    75    53\n",
       "3       A Choudhary    20    25\n",
       "4        A Flintoff    57    62\n",
       "..              ...   ...   ...\n",
       "456     YV Takawale   183   192\n",
       "457   Yashpal Singh    67    47\n",
       "458     Younis Khan     7     3\n",
       "459    Yuvraj Singh  2050  2591\n",
       "460          Z Khan   141   117\n",
       "\n",
       "[461 rows x 3 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balls=balls.merge(runs,left_on='batsman',right_on='batsman',how='outer')\n",
    "balls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "four=df[df['batsman_runs']==4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Counting no. of fours by various Batsman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batsman</th>\n",
       "      <th>4s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Ashish Reddy</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Chopra</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Flintoff</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Kumble</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>YV Takawale</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>Yashpal Singh</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>Z Khan</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>368 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            batsman   4s\n",
       "0    A Ashish Reddy   16\n",
       "1          A Chopra    7\n",
       "2       A Choudhary    1\n",
       "3        A Flintoff    5\n",
       "4          A Kumble    3\n",
       "..              ...  ...\n",
       "363       YK Pathan  240\n",
       "364     YV Takawale   26\n",
       "365   Yashpal Singh    5\n",
       "366    Yuvraj Singh  205\n",
       "367          Z Khan   11\n",
       "\n",
       "[368 rows x 2 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runs_4=four.groupby('batsman')['batsman_runs'].count().reset_index()\n",
    "runs_4.columns=['batsman','4s']\n",
    "runs_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batsman</th>\n",
       "      <th>4s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       batsman   4s\n",
       "112  G Gambhir  484"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_4= runs_4[runs_4['4s']==runs_4['4s'].max()]\n",
    "max_4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Gautam Gambhir hitted most 4s in IPL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Counting no. of Six by various Batsman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "six=df[df['batsman_runs']==6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batsman</th>\n",
       "      <th>6s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Ashish Reddy</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Chopra</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Flintoff</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Kumble</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>YV Takawale</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>Yashpal Singh</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>367</th>\n",
       "      <td>Z Khan</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>368 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            batsman   6s\n",
       "0    A Ashish Reddy   16\n",
       "1          A Chopra    7\n",
       "2       A Choudhary    1\n",
       "3        A Flintoff    5\n",
       "4          A Kumble    3\n",
       "..              ...  ...\n",
       "363       YK Pathan  240\n",
       "364     YV Takawale   26\n",
       "365   Yashpal Singh    5\n",
       "366    Yuvraj Singh  205\n",
       "367          Z Khan   11\n",
       "\n",
       "[368 rows x 2 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runs_6=four.groupby('batsman')['batsman_runs'].count().reset_index()\n",
    "runs_6.columns=['batsman','6s']\n",
    "runs_6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenating runs, balls, 4s, 6s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batsman</th>\n",
       "      <th>runs</th>\n",
       "      <th>ball</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Ashish Reddy</td>\n",
       "      <td>280</td>\n",
       "      <td>196</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Chandila</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Chopra</td>\n",
       "      <td>53</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Flintoff</td>\n",
       "      <td>62</td>\n",
       "      <td>57</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>YV Takawale</td>\n",
       "      <td>192</td>\n",
       "      <td>183</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>Yashpal Singh</td>\n",
       "      <td>47</td>\n",
       "      <td>67</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>Younis Khan</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>2591</td>\n",
       "      <td>2050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>Z Khan</td>\n",
       "      <td>117</td>\n",
       "      <td>141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>461 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            batsman  runs  ball    4s   6s\n",
       "0    A Ashish Reddy   280   196  16.0   15\n",
       "1        A Chandila     4     7   7.0    0\n",
       "2          A Chopra    53    75   1.0    0\n",
       "3       A Choudhary    25    20   5.0    1\n",
       "4        A Flintoff    62    57   3.0    2\n",
       "..              ...   ...   ...   ...  ...\n",
       "456     YV Takawale   192   183   NaN    3\n",
       "457   Yashpal Singh    47    67   NaN    0\n",
       "458     Younis Khan     3     7   NaN    0\n",
       "459    Yuvraj Singh  2591  2050   NaN  141\n",
       "460          Z Khan   117   141   NaN    2\n",
       "\n",
       "[461 rows x 5 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player=pd.concat([runs,balls.iloc[:,1],runs_4.iloc[:,1],six.iloc[:,1]],axis=1)\n",
    "player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batsman</th>\n",
       "      <th>runs</th>\n",
       "      <th>ball</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "      <th>strike_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Ashish Reddy</td>\n",
       "      <td>280</td>\n",
       "      <td>196</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Chandila</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.571429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Chopra</td>\n",
       "      <td>53</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.706667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Flintoff</td>\n",
       "      <td>62</td>\n",
       "      <td>57</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.087719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          batsman  runs  ball    4s  6s  strike_rate\n",
       "0  A Ashish Reddy   280   196  16.0  15     1.428571\n",
       "1      A Chandila     4     7   7.0   0     0.571429\n",
       "2        A Chopra    53    75   1.0   0     0.706667\n",
       "3     A Choudhary    25    20   5.0   1     1.250000\n",
       "4      A Flintoff    62    57   3.0   2     1.087719"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player['strike_rate']=player['runs']/player['ball']\n",
    "player.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No. of Missing Values in each Coulumn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "batsman         0\n",
       "runs            0\n",
       "ball            0\n",
       "4s             93\n",
       "6s              0\n",
       "strike_rate     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Filling Missing values with zero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "player['4s'].fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "batsman        0\n",
       "runs           0\n",
       "ball           0\n",
       "4s             0\n",
       "6s             0\n",
       "strike_rate    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Checking if any missing value left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player.isnull().values.any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Every Scores by Every Batsman in Every Match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "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>batsman</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>batsman_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>RCB</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>SRH</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>RCB</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>SRH</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>SRH</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9510</th>\n",
       "      <td>636</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>RCB</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9511</th>\n",
       "      <td>636</td>\n",
       "      <td>STR Binny</td>\n",
       "      <td>RCB</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9512</th>\n",
       "      <td>636</td>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>RCB</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9513</th>\n",
       "      <td>636</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>RCB</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9514</th>\n",
       "      <td>636</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>SRH</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9515 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id       batsman batting_team  batsman_runs\n",
       "0            1   A Choudhary          RCB             6\n",
       "1            1   BCJ Cutting          SRH            16\n",
       "2            1      CH Gayle          RCB            32\n",
       "3            1     DA Warner          SRH            14\n",
       "4            1      DJ Hooda          SRH            16\n",
       "...        ...           ...          ...           ...\n",
       "9510       636     SR Watson          RCB            11\n",
       "9511       636     STR Binny          RCB             9\n",
       "9512       636   Sachin Baby          RCB            18\n",
       "9513       636       V Kohli          RCB            54\n",
       "9514       636  Yuvraj Singh          SRH            38\n",
       "\n",
       "[9515 rows x 4 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grp=df.groupby(['match_id','batsman','batting_team'])['batsman_runs'].sum().reset_index()\n",
    "grp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Maximum runs Scored by Every Batsman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batsman</th>\n",
       "      <th>max_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Ashish Reddy</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Chandila</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Chopra</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Flintoff</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>YV Takawale</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>Yashpal Singh</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>Younis Khan</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>Z Khan</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>461 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            batsman  max_runs\n",
       "0    A Ashish Reddy        36\n",
       "1        A Chandila         4\n",
       "2          A Chopra        24\n",
       "3       A Choudhary        15\n",
       "4        A Flintoff        24\n",
       "..              ...       ...\n",
       "456     YV Takawale        45\n",
       "457   Yashpal Singh        20\n",
       "458     Younis Khan         3\n",
       "459    Yuvraj Singh        83\n",
       "460          Z Khan        23\n",
       "\n",
       "[461 rows x 2 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max=grp.groupby(['batsman'])['batsman_runs'].max().reset_index()\n",
    "max.columns=['batsman','max_runs']\n",
    "max"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Concatenating Max_runs with player Database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>batsman</th>\n",
       "      <th>runs</th>\n",
       "      <th>ball</th>\n",
       "      <th>4s</th>\n",
       "      <th>6s</th>\n",
       "      <th>strike_rate</th>\n",
       "      <th>max_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A Ashish Reddy</td>\n",
       "      <td>280</td>\n",
       "      <td>196</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.428571</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A Chandila</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A Chopra</td>\n",
       "      <td>53</td>\n",
       "      <td>75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.706667</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A Flintoff</td>\n",
       "      <td>62</td>\n",
       "      <td>57</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.087719</td>\n",
       "      <td>24</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>YV Takawale</td>\n",
       "      <td>192</td>\n",
       "      <td>183</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.049180</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>457</th>\n",
       "      <td>Yashpal Singh</td>\n",
       "      <td>47</td>\n",
       "      <td>67</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.701493</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>Younis Khan</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>2591</td>\n",
       "      <td>2050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>141</td>\n",
       "      <td>1.263902</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>Z Khan</td>\n",
       "      <td>117</td>\n",
       "      <td>141</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.829787</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>461 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            batsman  runs  ball    4s   6s  strike_rate  max_runs\n",
       "0    A Ashish Reddy   280   196  16.0   15     1.428571        36\n",
       "1        A Chandila     4     7   7.0    0     0.571429         4\n",
       "2          A Chopra    53    75   1.0    0     0.706667        24\n",
       "3       A Choudhary    25    20   5.0    1     1.250000        15\n",
       "4        A Flintoff    62    57   3.0    2     1.087719        24\n",
       "..              ...   ...   ...   ...  ...          ...       ...\n",
       "456     YV Takawale   192   183   0.0    3     1.049180        45\n",
       "457   Yashpal Singh    47    67   0.0    0     0.701493        20\n",
       "458     Younis Khan     3     7   0.0    0     0.428571         3\n",
       "459    Yuvraj Singh  2591  2050   0.0  141     1.263902        83\n",
       "460          Z Khan   117   141   0.0    2     0.829787        23\n",
       "\n",
       "[461 rows x 7 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player2=pd.concat([player,max.iloc[:,1:2]],axis=1)\n",
    "player2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(461, 7)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top 10 Batsman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb13e273188>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "max_runs=df.groupby(['batsman'])['batsman_runs'].sum()\n",
    "max_runs.sort_values(ascending=False)[:10].plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### SK Raina has highest total runs across all seasons Whereas Virat Kohliis just few runs behind with the second spot "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top Individual Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>batsman</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>batsman_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6200</th>\n",
       "      <td>411</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>RCB</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>60</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>KKR</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8426</th>\n",
       "      <td>562</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>RCB</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9257</th>\n",
       "      <td>620</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>RCB</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5585</th>\n",
       "      <td>372</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>RCB</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3135</th>\n",
       "      <td>206</td>\n",
       "      <td>M Vijay</td>\n",
       "      <td>CSK</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>36</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>SRH</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7752</th>\n",
       "      <td>516</td>\n",
       "      <td>V Sehwag</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3686</th>\n",
       "      <td>243</td>\n",
       "      <td>PC Valthaty</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4223</th>\n",
       "      <td>279</td>\n",
       "      <td>V Sehwag</td>\n",
       "      <td>DD</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id         batsman batting_team  batsman_runs\n",
       "6200       411        CH Gayle          RCB           175\n",
       "900         60     BB McCullum          KKR           158\n",
       "8426       562  AB de Villiers          RCB           133\n",
       "9257       620  AB de Villiers          RCB           129\n",
       "5585       372        CH Gayle          RCB           128\n",
       "3135       206         M Vijay          CSK           127\n",
       "529         36       DA Warner          SRH           126\n",
       "7752       516        V Sehwag         KXIP           122\n",
       "3686       243     PC Valthaty         KXIP           120\n",
       "4223       279        V Sehwag           DD           119"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby([\"match_id\",\"batsman\",'batting_team'])[\"batsman_runs\"].sum().reset_index().sort_values(by='batsman_runs', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Not only Gayle but there are many RCB players on the top scores list. Looks like RCB is a very formidable batting side."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Highest Wicket Taker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'caught', 'bowled', 'run out', 'lbw', 'caught and bowled',\n",
       "       'stumped', 'retired hurt', 'hit wicket', 'obstructing the field'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dismissal_kind'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "dismissal_kinds = ['caught', 'bowled', 'lbw', 'caught and bowled',\n",
    "       'stumped', 'hit wicket']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>A Choudhary</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>DA Warner</td>\n",
       "      <td>caught</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>STR Binny</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>S Dhawan</td>\n",
       "      <td>caught</td>\n",
       "      <td>Sachin Baby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>YS Chahal</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>MC Henriques</td>\n",
       "      <td>caught</td>\n",
       "      <td>Sachin Baby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>DJ Hooda</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>Yuvraj Singh</td>\n",
       "      <td>bowled</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>bowled</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  ball        batsman  \\\n",
       "11          1       1          SRH          RCB     2     5      DA Warner   \n",
       "64          1       1          SRH          RCB    11     3       S Dhawan   \n",
       "94          1       1          SRH          RCB    16     2   MC Henriques   \n",
       "116         1       1          SRH          RCB    19     4   Yuvraj Singh   \n",
       "160         1       2          RCB          SRH     6     4  Mandeep Singh   \n",
       "\n",
       "      non_striker       bowler  is_super_over  ...  bye_runs  legbye_runs  \\\n",
       "11       S Dhawan  A Choudhary              0  ...         0            0   \n",
       "64   MC Henriques    STR Binny              0  ...         0            0   \n",
       "94   Yuvraj Singh    YS Chahal              0  ...         0            0   \n",
       "116      DJ Hooda     TS Mills              0  ...         0            0   \n",
       "160      CH Gayle  Rashid Khan              0  ...         0            0   \n",
       "\n",
       "     noball_runs  penalty_runs  batsman_runs  extra_runs  total_runs  \\\n",
       "11             0             0             0           0           0   \n",
       "64             0             0             0           0           0   \n",
       "94             0             0             0           0           0   \n",
       "116            0             0             0           0           0   \n",
       "160            0             0             0           0           0   \n",
       "\n",
       "     player_dismissed dismissal_kind        fielder  \n",
       "11          DA Warner         caught  Mandeep Singh  \n",
       "64           S Dhawan         caught    Sachin Baby  \n",
       "94       MC Henriques         caught    Sachin Baby  \n",
       "116      Yuvraj Singh         bowled            NaN  \n",
       "160     Mandeep Singh         bowled            NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hwt=df[df[\"dismissal_kind\"].isin(dismissal_kinds)]\n",
    "hwt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb13e3bff48>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hwt['bowler'].value_counts()[:10].plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>4/5/2017</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>4/6/2017</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>4/7/2017</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>4/8/2017</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>4/8/2017</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>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>4/9/2017</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>4/9/2017</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",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>4/10/2017</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>AR Patel</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>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>4/11/2017</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>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2017</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>4/12/2017</td>\n",
       "      <td>Sunrisers Hyderabad</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>JJ Bumrah</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",
       "0   1    2017  Hyderabad   4/5/2017          Sunrisers Hyderabad   \n",
       "1   2    2017       Pune   4/6/2017               Mumbai Indians   \n",
       "2   3    2017     Rajkot   4/7/2017                Gujarat Lions   \n",
       "3   4    2017     Indore   4/8/2017       Rising Pune Supergiant   \n",
       "4   5    2017  Bangalore   4/8/2017  Royal Challengers Bangalore   \n",
       "5   6    2017  Hyderabad   4/9/2017                Gujarat Lions   \n",
       "6   7    2017     Mumbai   4/9/2017        Kolkata Knight Riders   \n",
       "7   8    2017     Indore  4/10/2017  Royal Challengers Bangalore   \n",
       "8   9    2017       Pune  4/11/2017             Delhi Daredevils   \n",
       "9  10    2017     Mumbai  4/12/2017          Sunrisers Hyderabad   \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",
       "5          Sunrisers Hyderabad          Sunrisers Hyderabad         field   \n",
       "6               Mumbai Indians               Mumbai Indians         field   \n",
       "7              Kings XI Punjab  Royal Challengers Bangalore           bat   \n",
       "8       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "9               Mumbai Indians               Mumbai Indians         field   \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",
       "5  normal           0          Sunrisers Hyderabad            0   \n",
       "6  normal           0               Mumbai Indians            0   \n",
       "7  normal           0              Kings XI Punjab            0   \n",
       "8  normal           0             Delhi Daredevils           97   \n",
       "9  normal           0               Mumbai Indians            0   \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",
       "5               9     Rashid Khan  Rajiv Gandhi International Stadium, Uppal   \n",
       "6               4          N Rana                           Wankhede Stadium   \n",
       "7               8        AR Patel                     Holkar Cricket Stadium   \n",
       "8               0       SV Samson    Maharashtra Cricket Association Stadium   \n",
       "9               4       JJ Bumrah                           Wankhede 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  \n",
       "5      A Deshmukh       NJ Llong      NaN  \n",
       "6     Nitin Menon      CK Nandan      NaN  \n",
       "7    AK Chaudhary  C Shamshuddin      NaN  \n",
       "8     AY Dandekar         S Ravi      NaN  \n",
       "9     Nitin Menon      CK Nandan      NaN  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(r'C:\\Users\\Eren Yaeger\\Downloads\\IPL Analysis/matches.csv')\n",
    "df.head(10)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Lasith Malinga leads the chart, "
   ]
  }
 ],
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