diff --git a/SRH_RCB - Copy.ipynb b/SRH_RCB - Copy.ipynb
new file mode 100644
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--- /dev/null
+++ b/SRH_RCB - Copy.ipynb	
@@ -0,0 +1,6909 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analysis Of RCB vs SRH"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.setrecursionlimit(1000)\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Matches=pd.read_csv(\"matches.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>season</th>\n",
+       "      <th>city</th>\n",
+       "      <th>date</th>\n",
+       "      <th>team1</th>\n",
+       "      <th>team2</th>\n",
+       "      <th>toss_winner</th>\n",
+       "      <th>toss_decision</th>\n",
+       "      <th>result</th>\n",
+       "      <th>dl_applied</th>\n",
+       "      <th>winner</th>\n",
+       "      <th>win_by_runs</th>\n",
+       "      <th>win_by_wickets</th>\n",
+       "      <th>player_of_match</th>\n",
+       "      <th>venue</th>\n",
+       "      <th>umpire1</th>\n",
+       "      <th>umpire2</th>\n",
+       "      <th>umpire3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Hyderabad</td>\n",
+       "      <td>2017-04-05</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yuvraj Singh</td>\n",
+       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
+       "      <td>AY Dandekar</td>\n",
+       "      <td>NJ Llong</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Pune</td>\n",
+       "      <td>2017-04-06</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>Maharashtra Cricket Association Stadium</td>\n",
+       "      <td>A Nand Kishore</td>\n",
+       "      <td>S Ravi</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Rajkot</td>\n",
+       "      <td>2017-04-07</td>\n",
+       "      <td>Gujarat Lions</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>CA Lynn</td>\n",
+       "      <td>Saurashtra Cricket Association Stadium</td>\n",
+       "      <td>Nitin Menon</td>\n",
+       "      <td>CK Nandan</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Indore</td>\n",
+       "      <td>2017-04-08</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>GJ Maxwell</td>\n",
+       "      <td>Holkar Cricket Stadium</td>\n",
+       "      <td>AK Chaudhary</td>\n",
+       "      <td>C Shamshuddin</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Bangalore</td>\n",
+       "      <td>2017-04-08</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>KM Jadhav</td>\n",
+       "      <td>M Chinnaswamy Stadium</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>751</th>\n",
+       "      <td>11347</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Mumbai</td>\n",
+       "      <td>05/05/19</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>9</td>\n",
+       "      <td>HH Pandya</td>\n",
+       "      <td>Wankhede Stadium</td>\n",
+       "      <td>Nanda Kishore</td>\n",
+       "      <td>O Nandan</td>\n",
+       "      <td>S Ravi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>752</th>\n",
+       "      <td>11412</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Chennai</td>\n",
+       "      <td>07/05/19</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS Yadav</td>\n",
+       "      <td>M. A. Chidambaram Stadium</td>\n",
+       "      <td>Nigel Llong</td>\n",
+       "      <td>Nitin Menon</td>\n",
+       "      <td>Ian Gould</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>753</th>\n",
+       "      <td>11413</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Visakhapatnam</td>\n",
+       "      <td>08/05/19</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>RR Pant</td>\n",
+       "      <td>ACA-VDCA Stadium</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>754</th>\n",
+       "      <td>11414</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Visakhapatnam</td>\n",
+       "      <td>10/05/19</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>F du Plessis</td>\n",
+       "      <td>ACA-VDCA Stadium</td>\n",
+       "      <td>Sundaram Ravi</td>\n",
+       "      <td>Bruce Oxenford</td>\n",
+       "      <td>Chettithody Shamshuddin</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>755</th>\n",
+       "      <td>11415</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>Hyderabad</td>\n",
+       "      <td>12/05/19</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>JJ Bumrah</td>\n",
+       "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
+       "      <td>Nitin Menon</td>\n",
+       "      <td>Ian Gould</td>\n",
+       "      <td>Nigel Llong</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>756 rows × 18 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id  season           city        date                        team1  \\\n",
+       "0        1    2017      Hyderabad  2017-04-05          Sunrisers Hyderabad   \n",
+       "1        2    2017           Pune  2017-04-06               Mumbai Indians   \n",
+       "2        3    2017         Rajkot  2017-04-07                Gujarat Lions   \n",
+       "3        4    2017         Indore  2017-04-08       Rising Pune Supergiant   \n",
+       "4        5    2017      Bangalore  2017-04-08  Royal Challengers Bangalore   \n",
+       "..     ...     ...            ...         ...                          ...   \n",
+       "751  11347    2019         Mumbai    05/05/19        Kolkata Knight Riders   \n",
+       "752  11412    2019        Chennai    07/05/19          Chennai Super Kings   \n",
+       "753  11413    2019  Visakhapatnam    08/05/19          Sunrisers Hyderabad   \n",
+       "754  11414    2019  Visakhapatnam    10/05/19               Delhi Capitals   \n",
+       "755  11415    2019      Hyderabad    12/05/19               Mumbai Indians   \n",
+       "\n",
+       "                           team2                  toss_winner toss_decision  \\\n",
+       "0    Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "1         Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
+       "2          Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
+       "3                Kings XI Punjab              Kings XI Punjab         field   \n",
+       "4               Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
+       "..                           ...                          ...           ...   \n",
+       "751               Mumbai Indians               Mumbai Indians         field   \n",
+       "752               Mumbai Indians          Chennai Super Kings           bat   \n",
+       "753               Delhi Capitals               Delhi Capitals         field   \n",
+       "754          Chennai Super Kings          Chennai Super Kings         field   \n",
+       "755          Chennai Super Kings               Mumbai Indians           bat   \n",
+       "\n",
+       "     result  dl_applied                       winner  win_by_runs  \\\n",
+       "0    normal           0          Sunrisers Hyderabad           35   \n",
+       "1    normal           0       Rising Pune Supergiant            0   \n",
+       "2    normal           0        Kolkata Knight Riders            0   \n",
+       "3    normal           0              Kings XI Punjab            0   \n",
+       "4    normal           0  Royal Challengers Bangalore           15   \n",
+       "..      ...         ...                          ...          ...   \n",
+       "751  normal           0               Mumbai Indians            0   \n",
+       "752  normal           0               Mumbai Indians            0   \n",
+       "753  normal           0               Delhi Capitals            0   \n",
+       "754  normal           0          Chennai Super Kings            0   \n",
+       "755  normal           0               Mumbai Indians            1   \n",
+       "\n",
+       "     win_by_wickets player_of_match  \\\n",
+       "0                 0    Yuvraj Singh   \n",
+       "1                 7       SPD Smith   \n",
+       "2                10         CA Lynn   \n",
+       "3                 6      GJ Maxwell   \n",
+       "4                 0       KM Jadhav   \n",
+       "..              ...             ...   \n",
+       "751               9       HH Pandya   \n",
+       "752               6        AS Yadav   \n",
+       "753               2         RR Pant   \n",
+       "754               6    F du Plessis   \n",
+       "755               0       JJ Bumrah   \n",
+       "\n",
+       "                                         venue         umpire1  \\\n",
+       "0    Rajiv Gandhi International Stadium, Uppal     AY Dandekar   \n",
+       "1      Maharashtra Cricket Association Stadium  A Nand Kishore   \n",
+       "2       Saurashtra Cricket Association Stadium     Nitin Menon   \n",
+       "3                       Holkar Cricket Stadium    AK Chaudhary   \n",
+       "4                        M Chinnaswamy Stadium             NaN   \n",
+       "..                                         ...             ...   \n",
+       "751                           Wankhede Stadium   Nanda Kishore   \n",
+       "752                  M. A. Chidambaram Stadium     Nigel Llong   \n",
+       "753                           ACA-VDCA Stadium             NaN   \n",
+       "754                           ACA-VDCA Stadium   Sundaram Ravi   \n",
+       "755         Rajiv Gandhi Intl. Cricket Stadium     Nitin Menon   \n",
+       "\n",
+       "            umpire2                  umpire3  \n",
+       "0          NJ Llong                      NaN  \n",
+       "1            S Ravi                      NaN  \n",
+       "2         CK Nandan                      NaN  \n",
+       "3     C Shamshuddin                      NaN  \n",
+       "4               NaN                      NaN  \n",
+       "..              ...                      ...  \n",
+       "751        O Nandan                   S Ravi  \n",
+       "752     Nitin Menon                Ian Gould  \n",
+       "753             NaN                      NaN  \n",
+       "754  Bruce Oxenford  Chettithody Shamshuddin  \n",
+       "755       Ian Gould              Nigel Llong  \n",
+       "\n",
+       "[756 rows x 18 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(756, 18)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matches.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How many rows and columns are there?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 756 entries, 0 to 755\n",
+      "Data columns (total 18 columns):\n",
+      " #   Column           Non-Null Count  Dtype \n",
+      "---  ------           --------------  ----- \n",
+      " 0   id               756 non-null    int64 \n",
+      " 1   season           756 non-null    int64 \n",
+      " 2   city             749 non-null    object\n",
+      " 3   date             756 non-null    object\n",
+      " 4   team1            756 non-null    object\n",
+      " 5   team2            756 non-null    object\n",
+      " 6   toss_winner      756 non-null    object\n",
+      " 7   toss_decision    756 non-null    object\n",
+      " 8   result           756 non-null    object\n",
+      " 9   dl_applied       756 non-null    int64 \n",
+      " 10  winner           752 non-null    object\n",
+      " 11  win_by_runs      756 non-null    int64 \n",
+      " 12  win_by_wickets   756 non-null    int64 \n",
+      " 13  player_of_match  752 non-null    object\n",
+      " 14  venue            756 non-null    object\n",
+      " 15  umpire1          754 non-null    object\n",
+      " 16  umpire2          754 non-null    object\n",
+      " 17  umpire3          119 non-null    object\n",
+      "dtypes: int64(5), object(13)\n",
+      "memory usage: 106.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "Matches.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['id', 'season', 'city', 'date', 'team1', 'team2', 'toss_winner',\n",
+       "       'toss_decision', 'result', 'dl_applied', 'winner', 'win_by_runs',\n",
+       "       'win_by_wickets', 'player_of_match', 'venue', 'umpire1', 'umpire2',\n",
+       "       'umpire3'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matches.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No of Rows are: 756\n",
+      "No of columns are: 18\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"No of Rows are:\",len(Matches))\n",
+    "print(\"No of columns are:\",len(Matches.columns))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.describe of         id  season           city        date                        team1  \\\n",
+       "0        1    2017      Hyderabad  2017-04-05          Sunrisers Hyderabad   \n",
+       "1        2    2017           Pune  2017-04-06               Mumbai Indians   \n",
+       "2        3    2017         Rajkot  2017-04-07                Gujarat Lions   \n",
+       "3        4    2017         Indore  2017-04-08       Rising Pune Supergiant   \n",
+       "4        5    2017      Bangalore  2017-04-08  Royal Challengers Bangalore   \n",
+       "..     ...     ...            ...         ...                          ...   \n",
+       "751  11347    2019         Mumbai    05/05/19        Kolkata Knight Riders   \n",
+       "752  11412    2019        Chennai    07/05/19          Chennai Super Kings   \n",
+       "753  11413    2019  Visakhapatnam    08/05/19          Sunrisers Hyderabad   \n",
+       "754  11414    2019  Visakhapatnam    10/05/19               Delhi Capitals   \n",
+       "755  11415    2019      Hyderabad    12/05/19               Mumbai Indians   \n",
+       "\n",
+       "                           team2                  toss_winner toss_decision  \\\n",
+       "0    Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "1         Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
+       "2          Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
+       "3                Kings XI Punjab              Kings XI Punjab         field   \n",
+       "4               Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
+       "..                           ...                          ...           ...   \n",
+       "751               Mumbai Indians               Mumbai Indians         field   \n",
+       "752               Mumbai Indians          Chennai Super Kings           bat   \n",
+       "753               Delhi Capitals               Delhi Capitals         field   \n",
+       "754          Chennai Super Kings          Chennai Super Kings         field   \n",
+       "755          Chennai Super Kings               Mumbai Indians           bat   \n",
+       "\n",
+       "     result  dl_applied                       winner  win_by_runs  \\\n",
+       "0    normal           0          Sunrisers Hyderabad           35   \n",
+       "1    normal           0       Rising Pune Supergiant            0   \n",
+       "2    normal           0        Kolkata Knight Riders            0   \n",
+       "3    normal           0              Kings XI Punjab            0   \n",
+       "4    normal           0  Royal Challengers Bangalore           15   \n",
+       "..      ...         ...                          ...          ...   \n",
+       "751  normal           0               Mumbai Indians            0   \n",
+       "752  normal           0               Mumbai Indians            0   \n",
+       "753  normal           0               Delhi Capitals            0   \n",
+       "754  normal           0          Chennai Super Kings            0   \n",
+       "755  normal           0               Mumbai Indians            1   \n",
+       "\n",
+       "     win_by_wickets player_of_match  \\\n",
+       "0                 0    Yuvraj Singh   \n",
+       "1                 7       SPD Smith   \n",
+       "2                10         CA Lynn   \n",
+       "3                 6      GJ Maxwell   \n",
+       "4                 0       KM Jadhav   \n",
+       "..              ...             ...   \n",
+       "751               9       HH Pandya   \n",
+       "752               6        AS Yadav   \n",
+       "753               2         RR Pant   \n",
+       "754               6    F du Plessis   \n",
+       "755               0       JJ Bumrah   \n",
+       "\n",
+       "                                         venue         umpire1  \\\n",
+       "0    Rajiv Gandhi International Stadium, Uppal     AY Dandekar   \n",
+       "1      Maharashtra Cricket Association Stadium  A Nand Kishore   \n",
+       "2       Saurashtra Cricket Association Stadium     Nitin Menon   \n",
+       "3                       Holkar Cricket Stadium    AK Chaudhary   \n",
+       "4                        M Chinnaswamy Stadium             NaN   \n",
+       "..                                         ...             ...   \n",
+       "751                           Wankhede Stadium   Nanda Kishore   \n",
+       "752                  M. A. Chidambaram Stadium     Nigel Llong   \n",
+       "753                           ACA-VDCA Stadium             NaN   \n",
+       "754                           ACA-VDCA Stadium   Sundaram Ravi   \n",
+       "755         Rajiv Gandhi Intl. Cricket Stadium     Nitin Menon   \n",
+       "\n",
+       "            umpire2                  umpire3  \n",
+       "0          NJ Llong                      NaN  \n",
+       "1            S Ravi                      NaN  \n",
+       "2         CK Nandan                      NaN  \n",
+       "3     C Shamshuddin                      NaN  \n",
+       "4               NaN                      NaN  \n",
+       "..              ...                      ...  \n",
+       "751        O Nandan                   S Ravi  \n",
+       "752     Nitin Menon                Ian Gould  \n",
+       "753             NaN                      NaN  \n",
+       "754  Bruce Oxenford  Chettithody Shamshuddin  \n",
+       "755       Ian Gould              Nigel Llong  \n",
+       "\n",
+       "[756 rows x 18 columns]>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matches.describe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 756 rows are there that means we have details 756 matches that takes place between 2008-2019 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No of matches that takes place between 2008-2019 were: 756\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"No of matches that takes place between 2008-2019 were:\",len(Matches))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# It looks like between 2011-2013.Total no matches exceeds 70.That because in these periods(2011-2013)there were 10 teams(kochi tuskers kerala and pune warriors india were the new additions "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d92105c288>"
+      ]
+     },
+     "execution_count": 10,
+     "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": [
+    "year_wise_analysing=Matches.groupby('season')\n",
+    "year_wise_analysing.count()\n",
+    "year_wise_analysing['id'].count().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "season\n",
+       "2008    58\n",
+       "2009    57\n",
+       "2010    60\n",
+       "2011    73\n",
+       "2012    74\n",
+       "2013    76\n",
+       "2014    60\n",
+       "2015    59\n",
+       "2016    60\n",
+       "2017    59\n",
+       "2018    60\n",
+       "2019    60\n",
+       "Name: id, dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "year_wise_analysing['id'].count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "id                   0\n",
+       "season               0\n",
+       "city                 7\n",
+       "date                 0\n",
+       "team1                0\n",
+       "team2                0\n",
+       "toss_winner          0\n",
+       "toss_decision        0\n",
+       "result               0\n",
+       "dl_applied           0\n",
+       "winner               4\n",
+       "win_by_runs          0\n",
+       "win_by_wickets       0\n",
+       "player_of_match      4\n",
+       "venue                0\n",
+       "umpire1              2\n",
+       "umpire2              2\n",
+       "umpire3            637\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matches.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# from the below plot we can find huge missing values are there in umpire 3 columns .we can remove the column it will not create a impact on predicting the matches."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d921175408>"
+      ]
+     },
+     "execution_count": 13,
+     "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.heatmap(Matches.isnull(),yticklabels=False,cbar=False,cmap='PiYG')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>season</th>\n",
+       "      <th>city</th>\n",
+       "      <th>date</th>\n",
+       "      <th>team1</th>\n",
+       "      <th>team2</th>\n",
+       "      <th>toss_winner</th>\n",
+       "      <th>toss_decision</th>\n",
+       "      <th>result</th>\n",
+       "      <th>dl_applied</th>\n",
+       "      <th>winner</th>\n",
+       "      <th>win_by_runs</th>\n",
+       "      <th>win_by_wickets</th>\n",
+       "      <th>player_of_match</th>\n",
+       "      <th>venue</th>\n",
+       "      <th>umpire1</th>\n",
+       "      <th>umpire2</th>\n",
+       "      <th>umpire3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Hyderabad</td>\n",
+       "      <td>2017-04-05</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yuvraj Singh</td>\n",
+       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
+       "      <td>AY Dandekar</td>\n",
+       "      <td>NJ Llong</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Pune</td>\n",
+       "      <td>2017-04-06</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>Maharashtra Cricket Association Stadium</td>\n",
+       "      <td>A Nand Kishore</td>\n",
+       "      <td>S Ravi</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  season       city        date                team1  \\\n",
+       "0   1    2017  Hyderabad  2017-04-05  Sunrisers Hyderabad   \n",
+       "1   2    2017       Pune  2017-04-06       Mumbai Indians   \n",
+       "\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",
+       "\n",
+       "   result  dl_applied                  winner  win_by_runs  win_by_wickets  \\\n",
+       "0  normal           0     Sunrisers Hyderabad           35               0   \n",
+       "1  normal           0  Rising Pune Supergiant            0               7   \n",
+       "\n",
+       "  player_of_match                                      venue         umpire1  \\\n",
+       "0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal     AY Dandekar   \n",
+       "1       SPD Smith    Maharashtra Cricket Association Stadium  A Nand Kishore   \n",
+       "\n",
+       "    umpire2 umpire3  \n",
+       "0  NJ Llong     NaN  \n",
+       "1    S Ravi     NaN  "
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matches.head(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#  Removed the columns containing more missing values and have less impact on the predicting matches\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Matches=Matches.drop(['umpire3','city'],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "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>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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>2017-04-05</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yuvraj Singh</td>\n",
+       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
+       "      <td>AY Dandekar</td>\n",
+       "      <td>NJ Llong</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>2017-04-06</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>Maharashtra Cricket Association Stadium</td>\n",
+       "      <td>A Nand Kishore</td>\n",
+       "      <td>S Ravi</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   id  season        date                team1                        team2  \\\n",
+       "0   1    2017  2017-04-05  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
+       "1   2    2017  2017-04-06       Mumbai Indians       Rising Pune Supergiant   \n",
+       "\n",
+       "                   toss_winner toss_decision  result  dl_applied  \\\n",
+       "0  Royal Challengers Bangalore         field  normal           0   \n",
+       "1       Rising Pune Supergiant         field  normal           0   \n",
+       "\n",
+       "                   winner  win_by_runs  win_by_wickets player_of_match  \\\n",
+       "0     Sunrisers Hyderabad           35               0    Yuvraj Singh   \n",
+       "1  Rising Pune Supergiant            0               7       SPD Smith   \n",
+       "\n",
+       "                                       venue         umpire1   umpire2  \n",
+       "0  Rajiv Gandhi International Stadium, Uppal     AY Dandekar  NJ Llong  \n",
+       "1    Maharashtra Cricket Association Stadium  A Nand Kishore    S Ravi  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matches.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d91f64b308>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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aGPOVpKRwF2mI6izSmHkj0cHy56yLmew2Fx707CdpjauPSP0xvtIwXv/wR+933rhXSK6G2Aj4J9LQYlA4Ed9FTBjB6oWeXJ61taSisajerG1EXC1plzxccZakoetK+2JeIWm3qf/ljMe8AdhFLazwi4iDJX02Il473v0tnJGPtGhx+fNA3F6tcpDe4NuR3tiPKIh5qaTH97321wd+UFL3PEFFRNFy2LZFxMWS9hj1mCN1phsR55OWhK4PXAX8MSKWShr3jT5NvU+lmyMtBf0dqSayxNkR8TRJ3y2M03XMNlf43Tv/3fbkKRHxfuCdpLHh7wALgVdLGrqgPyI+JOnVEfENxp8db1q90Oby59UGS87yZffhhWFbm/SLsf4YD441l8HPp+Fy+hy39fpk4LsRsRg4rcW5kdZjjtSZbt8n84uBh0g6tneGWhDzxcCpwCNJ5R+bAsdIOrkg5kpSEmprdU5XMU8lJbA2V/i1LsaavhxA6kHwGuC8JvXEEbGrpOUR8U/j3S9pacNjfC9pBVrx8udpPFdpo6O1Fh5IajQWHRELgUeRhub6J7lWkn5Hf2kYt4v65N576B7gTtp9X7YXc8SS7jWkF8qngbdIurxp0p3gMrg3la3ZfjkMna3wa+2stC/mdZIeEREfA06V9J0oXMTRthjrXtZPJcNUOW7/63Qe8BjgvpKeXhBzV0nLB257hqTGJW4xTuvREtFBp791xUgNL5A+Tc8CLswJd3tSe7UmepfBC4DHkmYgIU3alMzeAxARWwIPY82KiKK4bccsSa6TeJqkN+Sz0ptI9ZXn0XBtf/aNPGZ6B3BEpN6tTZfrru7nMHgXDUuxYKx7WQf6h2vuAb5JujIr8bFIi4p6HdYOAl5NWV3xthHxHmAn1nx9Np1IbK0+OXIv65igxWOTq5EuYq6OPUpnul2IiO8Ci5WbI0fEZsBXVLAKKA9ZHEUqxbmKVAd7ceHkXBcxu2g43slZaf7AuVXS3yP1ydhMuQnQkHE66efQxRhkRKwHvFfS65vGmCDu9sBXSZU6i4D/APZXwVLtXEN7LHAC6cTlUFL+OLZhvB8CDyU1oCqqT46Ijyo1dGrtaqSLmKtjj1LSjdSk5EW0WFObz6AW9mbw85tnhaQdC2JeQzp7viSPRe4IvF3Sc0csZusz7nls81mks9LHkVb8nSnp8ZM+cPKYXXRDIyIemI9RwOVNknhfrNbHIHPcc1TYNGeCuA8nLdH9FfAsSUULWCL3K4m+fhMRcYGkvaZ67ATxxv1wbPqhuC4ZteGFLmpqTwEui4ivkd58B5DGjEvcKenOiCAiNsyXIQtGMGbrM+6S3hip/rd3Vnob5TW1S0jJ7An5+5tI9Z+Nk26+cngraVeTAE6MiOMkfbJhyNZ6JAy4KlKfhK+wZnP0obe8Gmdo5T6kyb9LIy2KKWmVeWeeoPtJRLwC+DVp55hGJN0YEYtIq+aW5CGloj61+aTtCNLZvUg7XZykhp3lOos5Yme6rdfU5riPYc32k0Vd9HMCP5Q0TvYk0oqvDVS2HUoXMVubcY+IJ0k6NybYH69JkuiL3UU3tBuAJ/TO6iMt5bxIUqMPsoj4KHBiG2OQA3GXjHOzmlzddTW0kmM/lnQCtAVpkcnmwPuVdjhpEu9Y0iq0BZIeHhEPIg377VlwjF8mVVX05hcOAraU1HiT0y5ijtqZbhc1tb0k01ppj6QD8pdvy2M+m5Nm8kcqJi02HCetyjmXNVeP9cdsnHTpphvaTazZa2Al6VK7qdZ6JPRT3vpqIhHxJknvmWasG/NjdgeuG5jH2InUl6HpcV6ev/wr6eSg1AHAo8nvS0m/ycdZYsHAB/V5EbFiwn89QzFHLel+NE+oHEOqNtiUsgYYnRnn0ujBpEmBkYnZ5ox7b8JkqiQxrIgIUl+Atruh/Zp0WX06KZn/K2mY6bXQaAVd6z0Spuk5wLSSbp+PkErPem4b57ahRMRupGXVgzt1N/3QuVuSIu8HlydPS10ZEbv3zr4j4vHA90ct5kgNL6wrOro0ai1mdLhkNyLeTbqsvDl/vyXwn5L+qyDmclJ9dq8b2iUq7IaWf54TkjStPc8iYr6kWyMtSx8vTuOdlaf5/EM3H4+82GTgttJFRjeQdtW+hrE+HiXVIK8jlUc+lfShchjweUmNt8KJtL/gAqC3Y/M2pCGRVTSvjGg95kid6UYHXdo70sWlUZsxO1uyC+wr6c29byT9JSL2AxonXdI219tL+mbx0WW9pBqpMY96l9oNfJ7Ul3k5Yz0SVj8NZTsrT0eTs6KfRcSrSGe3kCaCflZ4HH+UdMbU/2zaVpEmpW4l7RTzVknfK4zZxURn6zFHKunSTef3LnRxadRaTI0tcX5fySzrBNbL1RW9EryNgQ0LY+4DHB4RN5IuhYvHS/Pl8BLyB09E3AIcpoGVWlORtH/+8kLSopoLJP2o6XE10KQh8MtIm13+Fylpn0Nq9Vji2EgbfQ4uKW86lr8ZqTz0/4AvUt5kvVcRsSXwENYcAmk8n9NFzFFLul10fu/ClyPiZGCLSL1LD6O8z2oXMa+NtHvpBaSE8X2V72X2WeCcPOsu0nGWluB1MV76SeAISRfA6vHyJUDTRL6ENJl2YqTFB1eSEnDRZo8RcZ8phiiGbp0o6Q+knbnbdCiwI6mBUn+b0EZJN1+JvD0idiG1H10aETdJekrTA4yId5DmAn7K2BVC04njzmKOWtLtovN7F+5PWvFzK2m8561A4xdLVzElPTQitiGVy+0PfDgibh4c7xsy5vsj4up8bAG8Q9JZhcfZRUH8yl7Czc9xYaTmJY3kcrmlpAUs+5DOJncGSnfYvTTSdkdLgG9rYJJF0runGygi3pB/Pycyfoe1kkZHC1W4CecE/kCqUvozBXW/2YGk7aTuLj6qDmOOWtLtovN7F54q6Whg9RhURHwQOHqUYkbE1qRKgL1IjWmuI10ml7qetB3O2RGxSURsVjBm2pXL8pXDF0gJ6LnA+blme+jLw4g4hzRWfjHpyuGx+Yyy1MNJH2CHkc6ivwR8StKPG8TqLSS6ooXjGnRJROwk6YdtBIuIl5N+J72TjZe0EPtaUh1xG7+XzmKOVPVCpE70Z5HGTxaT6kyPKRk/aVN+oRxBmjz5ad9dm5Eu3Q8ehZh9sVcBlwPvVtozrlj0bQUjaYdIS3ZPUgdLWUvE+Gvme6QhF9xExAmkDVLvIpUMLSP1xmhtf7iI2Ic0fHNvYAXwRjXY3y8itpdUOnE2GPN60tY/rdQpR1q480WlnUjaOsbdgNNJibJ/3LlpD+VuYo5Y0u2tRFtEqmL4IPBmFazrb1OkfcG2JJW49G9dsrJp6VAXMftiLySNQz6RVOryE2BpSTVIvhR+HHCpxlaPrV6PP9tFxKak8c3XAQ+UVDSJmIfTDiZtZf970qTxGaQetl+RtF2DmMtINd6XMzb5V7SSLqbolRARW6phb922RMR1wMmsXdbWqIdyZzFHLOn2lgG/h7QV+eeb1CnamJwkFpGGGA4mnZ1sWxCv9a1guhIR/8LazZMadQWL1G9gL9LZ7o2MJbNzC4/xx6T+IEsk3TRw39GS3tcw7r1I4897k3ai2FRS6Rbnkz3fjG/dE2mXmXGb149SzFEb0/11Hod7CvC+SB3B5s3wMa2zIuIKUjnXRaSx3Ce2MGm1NFraCqZLEXESsAlp0uvjpLmBywpCbgwcDyyX1HibmnEsyKWC8wfHxgsSbu9Ddi9yFzjSOHSXWtvrvsDyfMJ2Bu3t7tF6zFE7092EVIx8jdIOplsBj1S7+4bNGRFxf0l/nOT+QzRko/MYZysY4OODs+4zrW+oqvf3pqR9rp4208fWb6CeOICbaVBPPBDz76TJtPcA32p5Nn+i5xyFM93eOH7vtdgbdy4pGWs95kid6Uq6nb66P0m/BX47c0e0bpss4WZHMWSNraRVpPrh0hrirvUmuG6PtKT6z8DQ46MVtF1PDHBfUtXKE4FX5QnViyUdU3qwI+78cW4rPRloPeZIJV2rbtqXhDHxNjhAUeOTrpwZEVsAHyAtrRaj+UHRaj1xjnFzRPyMVAW0NalP8QZNYkXEdpKm03RpFIYX/tr39Uak2vTSftytxxyp4QWra5hLwr7Z6yPz36fkv58P3N50gqqGPDewUf9qvIh4qsrX+pccU+/n/gLS2HN/PfFfJL1losdOI/ZPgRtI47gXkipNGg0xxNiOEZPucDGNlXXV5d/7GSrY5LOLmE66c1iTypCI+L4GOp+Nd9uom+kxyLbriAdiz8vDQBPdP+0evRFxJWnbnxeT9kcbPNCR3VU7Us+EyyQ9bJRienhhbmvSF/TeEbFI0oUAEfEExrqarUtm9HJY0+x13GSyc7KEmw3To/ffSXvirU83XetaMzAEth5ptVvRFVgnMX2mO3tFxFGkSZmVpLKpR5NWOTWuBomIXUmTP5uTXoy97l0jsWpwumb6THe6ujjOhlc4+6rl/fbaNrCA4x7g96XlfV3E9Jnu7HaYpP+OiKeTPqEPJSXhxkk3lzItjNSnNjTQtazJmZlNqosz8iZnWhdFxPGkigiApcBxg7//mdRCDXqVmF54MLv13rD7kVY8raClN7GkWyd4wx3VRvwKfjHTBzBNXVyKNnkNfJJ0xXRg/nMr6QPchuQz3dlteUR8l1Sf+qZIO1FMNd5XakbHSmOC3Yp7lJtuS5r0342QoX+e06gkGLpHL6m94eK+79+e+3DYkJx0Z7cXkRqn/EzS7ZH2+Wp1Y8lxzPQkwTMYWD008HXJrsUzoclkZ2s9evvcMTCBuidjC1BsCE66s9sewFWSbouIg0m7wZY23Z7KTBfJX9v3df+eZjP9YTCuXPe5GNiWNbeDOS7//YoGYdvs0dvzMuAzuSsewF+AQwrizVke053dPkJaBrsQeAOpO9ZnOn7O0i2vS22a/+wKvBzYCngQKWnsNIPHNZHTSdvD30PaH673pzEl35N0EKm+9hBSU/elEbFHw5grJC0kLU/eRdKjJa3e1ywinICnySVjs1iv3Cgi3gr8WtInSkuQuihD60Iey17c69qVx7O/IqmLHWMbi4hrJe3ccszWe/RO4znXiRK8UeAz3dltZaRNPl8AfDMi1qPhGvw+h0m6ldRlrFeG9t7CmF3YBuhf+no36RJ+1FwUEW03gL8YmA88S9K/SDpN0j2SrgBOavm5emZ6WGmd4THd2e25wPNIifJ3kTap/EBhzLXK0CJiFN9wp5Auqb9GGs89gPJdi7uwCHhhRLSyDU7Weo/eafAl8zR5eGGWi4gHkHYQgLRmvGiDvUhbrz+YVIa2kLQ08nxJuxYdaAdyU5m98rfLJF05k8cznphiG5yGMVvv0TuN5/QOL9PkpDuLRcSBpDPb80lvvr2A10v6akHMeYyVod2cy9C27p9UsalFxHxJt+af31pKOnZFxNXAkQM9ej/c9Ow5/86fLenLk/yb/21YaTHnOOnOYhGxgrS1+x/y9/cHzs6z0E1j7sk4ZWhdLJeczSLiTEn752GF/tI2SMML2xfEbr0TXEQsk/TEqf+lTcVJdxaLgV168xnLChXs3JvPonqlQ6eQZsb/TS1v3jdXRMQpjG1y+aPCWF326D2GtBjiS/SVtI1aD911gZPuLBYR7yclyC/km54LXC3p6IKYrZehzWUR8STGdmveHriSlICHXsTScY/e8XaPKDojn6ucdGexiHgfcCnpTR2kM6rdC5PuUuA7pNVOewF/JA03tF32NGfkUr7HknYufhlwh6QdO3w+d4KbQU66s9h4Z6CRd8ctiPlAUhna5ZIuyGVoe0vqeqXbrBQR55CawF9M3l6ntMJkGs859JVJpJ26XwtsI+mlEfEwUmnamZ0c5CzmxRGzUES8PHe8XxARV/f9+TlQVGUg6XfA54DNI2J/4E4n3CJXkxZu7EwaJ985Ijbu+Dmb1FUvIR3nE/L3NwHvbO2I5hCf6c5CuSnJlqQtWd7Yd9fK0omPLsrQDCJiU9LqvtcBD5S0YYfP1eRM9wpJu/XX40ZErx+DDcEr0mah3Fz8FuCgDsK/BXjsYBka4KTbQES8gvTBtSupIdEnScMMnT5tg8fcnc/ABRARO5BW0NmQnHRtWPMGxhz/jIepSmwMHPsMGPQAAAJ0SURBVA8sV+HeW0No0gnubaQJ1IdExOeAPYEXtnhMc4aHF2woXZShWbum6tFbEPe+wO6kM+VLJP2pJN5c5TNdG5aAkxkrQ/so6Y1oo+N00vDScloaAoiIr5KGPr6tqbd4t0n4TNeG0kUZmrWrox69TyFN9O1O2mPtU6Ur6OYqj8XZtHRZhmata71Hr6SzJT2f1GvjF8D3IuKiiDg0Ikp7NM8pPtO1aemyDM3aFRE/BB4KtNmjd3BHit+Q6rUXAY+UtHdJ7LnESddslumoR+9pwI6kJkefkvTbvvuukLRb09hzjZOu2SzRcY/eJ0k6t/nRWY+Trtks0WWP3hx/Z9KOyhv1BfUS8CE56ZrNMm326O2LeSywNynpfgvYl9Sc59ltxJ9LXL1gNvssAbYCToyIn0bEVyPiqMKYzwaeDPxO0qGkBTKd9YeYzbw4wmyWkXRu7nvc36N3Z2Doxuh97pC0KiLuiYj5wB9ITddtSE66ZrPMOD16VzcoKnBFRGwBfIy00u2vwGWFMeckj+mazTIRcQKpa9ldpOY2y4CLJd3RUvxtgfneAboZJ12zWaqNHr19m12OS9IPGh7enOWkazbLjNOjt1fJMHSd7cBml/3JorfKrfFml3OVx3TNZp/WevRK2gcgNzA/grTsV6Sx4o8UHuec5DNdM5tSRHwZuJXUbwHSriRbSDpw5o5q3eSka2ZTGm8/NO+R1owXR5jZdFwZEaub1UfE42m27c+c5zNdM5tSRFwPLAB+mW/aBrgeWEULbSPnEiddM5vSRO0ie0raRs41TrpmZhV5TNfMrCInXTOzipx0zcwqctI1M6vISdfMrKL/D36SFm7mqenoAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(Matches.isnull(),yticklabels=False,cbar=False,cmap='PiYG')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Today's match is between MI and SRH.so we only want information between the matches that take place between them"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "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>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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>2017-04-05</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yuvraj Singh</td>\n",
+       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
+       "      <td>AY Dandekar</td>\n",
+       "      <td>NJ Llong</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>2017-04-06</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>SPD Smith</td>\n",
+       "      <td>Maharashtra Cricket Association Stadium</td>\n",
+       "      <td>A Nand Kishore</td>\n",
+       "      <td>S Ravi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>2017-04-07</td>\n",
+       "      <td>Gujarat Lions</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kolkata Knight Riders</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>CA Lynn</td>\n",
+       "      <td>Saurashtra Cricket Association Stadium</td>\n",
+       "      <td>Nitin Menon</td>\n",
+       "      <td>CK Nandan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>2017-04-08</td>\n",
+       "      <td>Rising Pune Supergiant</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kings XI Punjab</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>GJ Maxwell</td>\n",
+       "      <td>Holkar Cricket Stadium</td>\n",
+       "      <td>AK Chaudhary</td>\n",
+       "      <td>C Shamshuddin</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>2017-04-08</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Delhi Daredevils</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>KM Jadhav</td>\n",
+       "      <td>M Chinnaswamy Stadium</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>751</th>\n",
+       "      <td>11347</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>05/05/19</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>9</td>\n",
+       "      <td>HH Pandya</td>\n",
+       "      <td>Wankhede Stadium</td>\n",
+       "      <td>Nanda Kishore</td>\n",
+       "      <td>O Nandan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>752</th>\n",
+       "      <td>11412</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>07/05/19</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>AS Yadav</td>\n",
+       "      <td>M. A. Chidambaram Stadium</td>\n",
+       "      <td>Nigel Llong</td>\n",
+       "      <td>Nitin Menon</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>753</th>\n",
+       "      <td>11413</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>08/05/19</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>RR Pant</td>\n",
+       "      <td>ACA-VDCA Stadium</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>754</th>\n",
+       "      <td>11414</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>10/05/19</td>\n",
+       "      <td>Delhi Capitals</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>F du Plessis</td>\n",
+       "      <td>ACA-VDCA Stadium</td>\n",
+       "      <td>Sundaram Ravi</td>\n",
+       "      <td>Bruce Oxenford</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>755</th>\n",
+       "      <td>11415</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>12/05/19</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>JJ Bumrah</td>\n",
+       "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
+       "      <td>Nitin Menon</td>\n",
+       "      <td>Ian Gould</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>756 rows × 16 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id  season        date                        team1  \\\n",
+       "0        1    2017  2017-04-05          Sunrisers Hyderabad   \n",
+       "1        2    2017  2017-04-06               Mumbai Indians   \n",
+       "2        3    2017  2017-04-07                Gujarat Lions   \n",
+       "3        4    2017  2017-04-08       Rising Pune Supergiant   \n",
+       "4        5    2017  2017-04-08  Royal Challengers Bangalore   \n",
+       "..     ...     ...         ...                          ...   \n",
+       "751  11347    2019    05/05/19        Kolkata Knight Riders   \n",
+       "752  11412    2019    07/05/19          Chennai Super Kings   \n",
+       "753  11413    2019    08/05/19          Sunrisers Hyderabad   \n",
+       "754  11414    2019    10/05/19               Delhi Capitals   \n",
+       "755  11415    2019    12/05/19               Mumbai Indians   \n",
+       "\n",
+       "                           team2                  toss_winner toss_decision  \\\n",
+       "0    Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "1         Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
+       "2          Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
+       "3                Kings XI Punjab              Kings XI Punjab         field   \n",
+       "4               Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
+       "..                           ...                          ...           ...   \n",
+       "751               Mumbai Indians               Mumbai Indians         field   \n",
+       "752               Mumbai Indians          Chennai Super Kings           bat   \n",
+       "753               Delhi Capitals               Delhi Capitals         field   \n",
+       "754          Chennai Super Kings          Chennai Super Kings         field   \n",
+       "755          Chennai Super Kings               Mumbai Indians           bat   \n",
+       "\n",
+       "     result  dl_applied                       winner  win_by_runs  \\\n",
+       "0    normal           0          Sunrisers Hyderabad           35   \n",
+       "1    normal           0       Rising Pune Supergiant            0   \n",
+       "2    normal           0        Kolkata Knight Riders            0   \n",
+       "3    normal           0              Kings XI Punjab            0   \n",
+       "4    normal           0  Royal Challengers Bangalore           15   \n",
+       "..      ...         ...                          ...          ...   \n",
+       "751  normal           0               Mumbai Indians            0   \n",
+       "752  normal           0               Mumbai Indians            0   \n",
+       "753  normal           0               Delhi Capitals            0   \n",
+       "754  normal           0          Chennai Super Kings            0   \n",
+       "755  normal           0               Mumbai Indians            1   \n",
+       "\n",
+       "     win_by_wickets player_of_match  \\\n",
+       "0                 0    Yuvraj Singh   \n",
+       "1                 7       SPD Smith   \n",
+       "2                10         CA Lynn   \n",
+       "3                 6      GJ Maxwell   \n",
+       "4                 0       KM Jadhav   \n",
+       "..              ...             ...   \n",
+       "751               9       HH Pandya   \n",
+       "752               6        AS Yadav   \n",
+       "753               2         RR Pant   \n",
+       "754               6    F du Plessis   \n",
+       "755               0       JJ Bumrah   \n",
+       "\n",
+       "                                         venue         umpire1         umpire2  \n",
+       "0    Rajiv Gandhi International Stadium, Uppal     AY Dandekar        NJ Llong  \n",
+       "1      Maharashtra Cricket Association Stadium  A Nand Kishore          S Ravi  \n",
+       "2       Saurashtra Cricket Association Stadium     Nitin Menon       CK Nandan  \n",
+       "3                       Holkar Cricket Stadium    AK Chaudhary   C Shamshuddin  \n",
+       "4                        M Chinnaswamy Stadium             NaN             NaN  \n",
+       "..                                         ...             ...             ...  \n",
+       "751                           Wankhede Stadium   Nanda Kishore        O Nandan  \n",
+       "752                  M. A. Chidambaram Stadium     Nigel Llong     Nitin Menon  \n",
+       "753                           ACA-VDCA Stadium             NaN             NaN  \n",
+       "754                           ACA-VDCA Stadium   Sundaram Ravi  Bruce Oxenford  \n",
+       "755         Rajiv Gandhi Intl. Cricket Stadium     Nitin Menon       Ian Gould  \n",
+       "\n",
+       "[756 rows x 16 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matches"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RCB_SRH1=Matches.loc[(Matches['team1']=='Royal Challengers Bangalore')&(Matches['team2'] =='Sunrisers Hyderabad')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RCB_SRH2=Matches.loc[(Matches['team2']=='Royal Challengers Bangalore')&(Matches['team1'] =='Sunrisers Hyderabad')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RCB_SRH=RCB_SRH1.append(RCB_SRH2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#   Complete details of matches that takes place between RCB and SRH"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        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>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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>387</th>\n",
+       "      <td>388</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>2013-04-07</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>tie</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>GH Vihari</td>\n",
+       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
+       "      <td>AK Chaudhary</td>\n",
+       "      <td>S Ravi</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>502</th>\n",
+       "      <td>503</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>2014-05-20</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>DA Warner</td>\n",
+       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
+       "      <td>AK Chaudhary</td>\n",
+       "      <td>NJ Llong</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>524</th>\n",
+       "      <td>525</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>2015-04-13</td>\n",
+       "      <td>Royal Challengers Bangalore</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>8</td>\n",
+       "      <td>DA Warner</td>\n",
+       "      <td>M Chinnaswamy Stadium</td>\n",
+       "      <td>RM Deshpande</td>\n",
+       "      <td>RK Illingworth</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>579</th>\n",
+       "      <td>580</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2016-04-12</td>\n",
+       "      <td>Royal Challengers Bangalore</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>Royal Challengers Bangalore</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AB de Villiers</td>\n",
+       "      <td>M Chinnaswamy Stadium</td>\n",
+       "      <td>HDPK Dharmasena</td>\n",
+       "      <td>VK Sharma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>686</th>\n",
+       "      <td>7944</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>17/05/18</td>\n",
+       "      <td>Royal Challengers Bangalore</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>Royal Challengers Bangalore</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "      <td>AB de Villiers</td>\n",
+       "      <td>M Chinnaswamy Stadium</td>\n",
+       "      <td>S Ravi</td>\n",
+       "      <td>Anil Dandekar</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>2017-04-05</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>field</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Yuvraj Singh</td>\n",
+       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
+       "      <td>AY Dandekar</td>\n",
+       "      <td>NJ Llong</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>431</th>\n",
+       "      <td>432</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>2013-04-09</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>V Kohli</td>\n",
+       "      <td>M Chinnaswamy Stadium</td>\n",
+       "      <td>S Ravi</td>\n",
+       "      <td>SJA Taufel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>480</th>\n",
+       "      <td>481</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>2014-05-04</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>Royal Challengers Bangalore</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>AB de Villiers</td>\n",
+       "      <td>M Chinnaswamy Stadium</td>\n",
+       "      <td>HDPK Dharmasena</td>\n",
+       "      <td>VA Kulkarni</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>567</th>\n",
+       "      <td>568</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>2015-05-15</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>V Kohli</td>\n",
+       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
+       "      <td>AK Chaudhary</td>\n",
+       "      <td>HDPK Dharmasena</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>602</th>\n",
+       "      <td>603</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2016-04-30</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>15</td>\n",
+       "      <td>0</td>\n",
+       "      <td>DA Warner</td>\n",
+       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
+       "      <td>AK Chaudhary</td>\n",
+       "      <td>HDPK Dharmasena</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>635</th>\n",
+       "      <td>636</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>2016-05-29</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>bat</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>BCJ Cutting</td>\n",
+       "      <td>M Chinnaswamy Stadium</td>\n",
+       "      <td>HDPK Dharmasena</td>\n",
+       "      <td>BNJ Oxenford</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>674</th>\n",
+       "      <td>7932</td>\n",
+       "      <td>2018</td>\n",
+       "      <td>07/05/18</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>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>KS Williamson</td>\n",
+       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
+       "      <td>Bruce Oxenford</td>\n",
+       "      <td>Virender Kumar Sharma</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>706</th>\n",
+       "      <td>11147</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>31/03/19</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>118</td>\n",
+       "      <td>0</td>\n",
+       "      <td>J Bairstow</td>\n",
+       "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
+       "      <td>S Ravi</td>\n",
+       "      <td>KN Anantapadmanabhan</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>749</th>\n",
+       "      <td>11345</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>04/05/19</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>Royal Challengers Bangalore</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>S Hetmyer</td>\n",
+       "      <td>M. Chinnaswamy Stadium</td>\n",
+       "      <td>Nigel Llong</td>\n",
+       "      <td>Anil Chaudhary</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id  season        date                        team1  \\\n",
+       "387    388    2013  2013-04-07  Royal Challengers Bangalore   \n",
+       "502    503    2014  2014-05-20  Royal Challengers Bangalore   \n",
+       "524    525    2015  2015-04-13  Royal Challengers Bangalore   \n",
+       "579    580    2016  2016-04-12  Royal Challengers Bangalore   \n",
+       "686   7944    2018    17/05/18  Royal Challengers Bangalore   \n",
+       "0        1    2017  2017-04-05          Sunrisers Hyderabad   \n",
+       "431    432    2013  2013-04-09          Sunrisers Hyderabad   \n",
+       "480    481    2014  2014-05-04          Sunrisers Hyderabad   \n",
+       "567    568    2015  2015-05-15          Sunrisers Hyderabad   \n",
+       "602    603    2016  2016-04-30          Sunrisers Hyderabad   \n",
+       "635    636    2016  2016-05-29          Sunrisers Hyderabad   \n",
+       "674   7932    2018    07/05/18          Sunrisers Hyderabad   \n",
+       "706  11147    2019    31/03/19          Sunrisers Hyderabad   \n",
+       "749  11345    2019    04/05/19          Sunrisers Hyderabad   \n",
+       "\n",
+       "                           team2                  toss_winner toss_decision  \\\n",
+       "387          Sunrisers Hyderabad  Royal Challengers Bangalore           bat   \n",
+       "502          Sunrisers Hyderabad  Royal Challengers Bangalore           bat   \n",
+       "524          Sunrisers Hyderabad          Sunrisers Hyderabad         field   \n",
+       "579          Sunrisers Hyderabad          Sunrisers Hyderabad         field   \n",
+       "686          Sunrisers Hyderabad          Sunrisers Hyderabad         field   \n",
+       "0    Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "431  Royal Challengers Bangalore          Sunrisers Hyderabad           bat   \n",
+       "480  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "567  Royal Challengers Bangalore          Sunrisers Hyderabad           bat   \n",
+       "602  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "635  Royal Challengers Bangalore          Sunrisers Hyderabad           bat   \n",
+       "674  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "706  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "749  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "\n",
+       "     result  dl_applied                       winner  win_by_runs  \\\n",
+       "387     tie           0          Sunrisers Hyderabad            0   \n",
+       "502  normal           0          Sunrisers Hyderabad            0   \n",
+       "524  normal           0          Sunrisers Hyderabad            0   \n",
+       "579  normal           0  Royal Challengers Bangalore           45   \n",
+       "686  normal           0  Royal Challengers Bangalore           14   \n",
+       "0    normal           0          Sunrisers Hyderabad           35   \n",
+       "431  normal           0  Royal Challengers Bangalore            0   \n",
+       "480  normal           0  Royal Challengers Bangalore            0   \n",
+       "567  normal           1  Royal Challengers Bangalore            0   \n",
+       "602  normal           0          Sunrisers Hyderabad           15   \n",
+       "635  normal           0          Sunrisers Hyderabad            8   \n",
+       "674  normal           0          Sunrisers Hyderabad            5   \n",
+       "706  normal           0          Sunrisers Hyderabad          118   \n",
+       "749  normal           0  Royal Challengers Bangalore            0   \n",
+       "\n",
+       "     win_by_wickets player_of_match  \\\n",
+       "387               0       GH Vihari   \n",
+       "502               7       DA Warner   \n",
+       "524               8       DA Warner   \n",
+       "579               0  AB de Villiers   \n",
+       "686               0  AB de Villiers   \n",
+       "0                 0    Yuvraj Singh   \n",
+       "431               7         V Kohli   \n",
+       "480               4  AB de Villiers   \n",
+       "567               6         V Kohli   \n",
+       "602               0       DA Warner   \n",
+       "635               0     BCJ Cutting   \n",
+       "674               0   KS Williamson   \n",
+       "706               0      J Bairstow   \n",
+       "749               4       S Hetmyer   \n",
+       "\n",
+       "                                         venue          umpire1  \\\n",
+       "387  Rajiv Gandhi International Stadium, Uppal     AK Chaudhary   \n",
+       "502  Rajiv Gandhi International Stadium, Uppal     AK Chaudhary   \n",
+       "524                      M Chinnaswamy Stadium     RM Deshpande   \n",
+       "579                      M Chinnaswamy Stadium  HDPK Dharmasena   \n",
+       "686                      M Chinnaswamy Stadium           S Ravi   \n",
+       "0    Rajiv Gandhi International Stadium, Uppal      AY Dandekar   \n",
+       "431                      M Chinnaswamy Stadium           S Ravi   \n",
+       "480                      M Chinnaswamy Stadium  HDPK Dharmasena   \n",
+       "567  Rajiv Gandhi International Stadium, Uppal     AK Chaudhary   \n",
+       "602  Rajiv Gandhi International Stadium, Uppal     AK Chaudhary   \n",
+       "635                      M Chinnaswamy Stadium  HDPK Dharmasena   \n",
+       "674  Rajiv Gandhi International Stadium, Uppal   Bruce Oxenford   \n",
+       "706         Rajiv Gandhi Intl. Cricket Stadium           S Ravi   \n",
+       "749                     M. Chinnaswamy Stadium      Nigel Llong   \n",
+       "\n",
+       "                   umpire2  \n",
+       "387                 S Ravi  \n",
+       "502               NJ Llong  \n",
+       "524         RK Illingworth  \n",
+       "579              VK Sharma  \n",
+       "686          Anil Dandekar  \n",
+       "0                 NJ Llong  \n",
+       "431             SJA Taufel  \n",
+       "480            VA Kulkarni  \n",
+       "567        HDPK Dharmasena  \n",
+       "602        HDPK Dharmasena  \n",
+       "635           BNJ Oxenford  \n",
+       "674  Virender Kumar Sharma  \n",
+       "706   KN Anantapadmanabhan  \n",
+       "749         Anil Chaudhary  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RCB_SRH"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# It looks like RCB and SRH played 14 matches against each other"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "won_by_runs_wickest=RCB_SRH.groupby('id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>season</th>\n",
+       "      <th>dl_applied</th>\n",
+       "      <th>win_by_runs</th>\n",
+       "      <th>win_by_wickets</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2017</td>\n",
+       "      <td>0</td>\n",
+       "      <td>35</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>0</td>\n",
+       "      <td>45</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>603</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7932</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7944</th>\n",
+       "      <td>2018</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11147</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>118</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11345</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       season  dl_applied  win_by_runs  win_by_wickets\n",
+       "id                                                    \n",
+       "1        2017           0           35               0\n",
+       "388      2013           0            0               0\n",
+       "432      2013           0            0               7\n",
+       "481      2014           0            0               4\n",
+       "503      2014           0            0               7\n",
+       "525      2015           0            0               8\n",
+       "568      2015           1            0               6\n",
+       "580      2016           0           45               0\n",
+       "603      2016           0           15               0\n",
+       "636      2016           0            8               0\n",
+       "7932     2018           0            5               0\n",
+       "7944     2018           0           14               0\n",
+       "11147    2019           0          118               0\n",
+       "11345    2019           0            0               4"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "won_by_runs_wickest.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7932     1\n",
+       "603      1\n",
+       "636      1\n",
+       "568      1\n",
+       "503      1\n",
+       "525      1\n",
+       "11147    1\n",
+       "11345    1\n",
+       "432      1\n",
+       "580      1\n",
+       "481      1\n",
+       "7944     1\n",
+       "388      1\n",
+       "1        1\n",
+       "Name: id, dtype: int64"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RCB_SRH['id'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 14 entries, 387 to 749\n",
+      "Data columns (total 16 columns):\n",
+      " #   Column           Non-Null Count  Dtype \n",
+      "---  ------           --------------  ----- \n",
+      " 0   id               14 non-null     int64 \n",
+      " 1   season           14 non-null     int64 \n",
+      " 2   date             14 non-null     object\n",
+      " 3   team1            14 non-null     object\n",
+      " 4   team2            14 non-null     object\n",
+      " 5   toss_winner      14 non-null     object\n",
+      " 6   toss_decision    14 non-null     object\n",
+      " 7   result           14 non-null     object\n",
+      " 8   dl_applied       14 non-null     int64 \n",
+      " 9   winner           14 non-null     object\n",
+      " 10  win_by_runs      14 non-null     int64 \n",
+      " 11  win_by_wickets   14 non-null     int64 \n",
+      " 12  player_of_match  14 non-null     object\n",
+      " 13  venue            14 non-null     object\n",
+      " 14  umpire1          14 non-null     object\n",
+      " 15  umpire2          14 non-null     object\n",
+      "dtypes: int64(5), object(11)\n",
+      "memory usage: 1.9+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "RCB_SRH.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.describe of         id  season        date                        team1  \\\n",
+       "387    388    2013  2013-04-07  Royal Challengers Bangalore   \n",
+       "502    503    2014  2014-05-20  Royal Challengers Bangalore   \n",
+       "524    525    2015  2015-04-13  Royal Challengers Bangalore   \n",
+       "579    580    2016  2016-04-12  Royal Challengers Bangalore   \n",
+       "686   7944    2018    17/05/18  Royal Challengers Bangalore   \n",
+       "0        1    2017  2017-04-05          Sunrisers Hyderabad   \n",
+       "431    432    2013  2013-04-09          Sunrisers Hyderabad   \n",
+       "480    481    2014  2014-05-04          Sunrisers Hyderabad   \n",
+       "567    568    2015  2015-05-15          Sunrisers Hyderabad   \n",
+       "602    603    2016  2016-04-30          Sunrisers Hyderabad   \n",
+       "635    636    2016  2016-05-29          Sunrisers Hyderabad   \n",
+       "674   7932    2018    07/05/18          Sunrisers Hyderabad   \n",
+       "706  11147    2019    31/03/19          Sunrisers Hyderabad   \n",
+       "749  11345    2019    04/05/19          Sunrisers Hyderabad   \n",
+       "\n",
+       "                           team2                  toss_winner toss_decision  \\\n",
+       "387          Sunrisers Hyderabad  Royal Challengers Bangalore           bat   \n",
+       "502          Sunrisers Hyderabad  Royal Challengers Bangalore           bat   \n",
+       "524          Sunrisers Hyderabad          Sunrisers Hyderabad         field   \n",
+       "579          Sunrisers Hyderabad          Sunrisers Hyderabad         field   \n",
+       "686          Sunrisers Hyderabad          Sunrisers Hyderabad         field   \n",
+       "0    Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "431  Royal Challengers Bangalore          Sunrisers Hyderabad           bat   \n",
+       "480  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "567  Royal Challengers Bangalore          Sunrisers Hyderabad           bat   \n",
+       "602  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "635  Royal Challengers Bangalore          Sunrisers Hyderabad           bat   \n",
+       "674  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "706  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "749  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
+       "\n",
+       "     result  dl_applied                       winner  win_by_runs  \\\n",
+       "387     tie           0          Sunrisers Hyderabad            0   \n",
+       "502  normal           0          Sunrisers Hyderabad            0   \n",
+       "524  normal           0          Sunrisers Hyderabad            0   \n",
+       "579  normal           0  Royal Challengers Bangalore           45   \n",
+       "686  normal           0  Royal Challengers Bangalore           14   \n",
+       "0    normal           0          Sunrisers Hyderabad           35   \n",
+       "431  normal           0  Royal Challengers Bangalore            0   \n",
+       "480  normal           0  Royal Challengers Bangalore            0   \n",
+       "567  normal           1  Royal Challengers Bangalore            0   \n",
+       "602  normal           0          Sunrisers Hyderabad           15   \n",
+       "635  normal           0          Sunrisers Hyderabad            8   \n",
+       "674  normal           0          Sunrisers Hyderabad            5   \n",
+       "706  normal           0          Sunrisers Hyderabad          118   \n",
+       "749  normal           0  Royal Challengers Bangalore            0   \n",
+       "\n",
+       "     win_by_wickets player_of_match  \\\n",
+       "387               0       GH Vihari   \n",
+       "502               7       DA Warner   \n",
+       "524               8       DA Warner   \n",
+       "579               0  AB de Villiers   \n",
+       "686               0  AB de Villiers   \n",
+       "0                 0    Yuvraj Singh   \n",
+       "431               7         V Kohli   \n",
+       "480               4  AB de Villiers   \n",
+       "567               6         V Kohli   \n",
+       "602               0       DA Warner   \n",
+       "635               0     BCJ Cutting   \n",
+       "674               0   KS Williamson   \n",
+       "706               0      J Bairstow   \n",
+       "749               4       S Hetmyer   \n",
+       "\n",
+       "                                         venue          umpire1  \\\n",
+       "387  Rajiv Gandhi International Stadium, Uppal     AK Chaudhary   \n",
+       "502  Rajiv Gandhi International Stadium, Uppal     AK Chaudhary   \n",
+       "524                      M Chinnaswamy Stadium     RM Deshpande   \n",
+       "579                      M Chinnaswamy Stadium  HDPK Dharmasena   \n",
+       "686                      M Chinnaswamy Stadium           S Ravi   \n",
+       "0    Rajiv Gandhi International Stadium, Uppal      AY Dandekar   \n",
+       "431                      M Chinnaswamy Stadium           S Ravi   \n",
+       "480                      M Chinnaswamy Stadium  HDPK Dharmasena   \n",
+       "567  Rajiv Gandhi International Stadium, Uppal     AK Chaudhary   \n",
+       "602  Rajiv Gandhi International Stadium, Uppal     AK Chaudhary   \n",
+       "635                      M Chinnaswamy Stadium  HDPK Dharmasena   \n",
+       "674  Rajiv Gandhi International Stadium, Uppal   Bruce Oxenford   \n",
+       "706         Rajiv Gandhi Intl. Cricket Stadium           S Ravi   \n",
+       "749                     M. Chinnaswamy Stadium      Nigel Llong   \n",
+       "\n",
+       "                   umpire2  \n",
+       "387                 S Ravi  \n",
+       "502               NJ Llong  \n",
+       "524         RK Illingworth  \n",
+       "579              VK Sharma  \n",
+       "686          Anil Dandekar  \n",
+       "0                 NJ Llong  \n",
+       "431             SJA Taufel  \n",
+       "480            VA Kulkarni  \n",
+       "567        HDPK Dharmasena  \n",
+       "602        HDPK Dharmasena  \n",
+       "635           BNJ Oxenford  \n",
+       "674  Virender Kumar Sharma  \n",
+       "706   KN Anantapadmanabhan  \n",
+       "749         Anil Chaudhary  >"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RCB_SRH.describe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No of matches that takes place between RCB and SRH: 14\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"No of matches that takes place between RCB and SRH:\",len(RCB_SRH))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Below plot gives the details of the match that takes place between RCB and SRH"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.PairGrid at 0x1d9212bfe08>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 900x900 with 30 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.pairplot(RCB_SRH)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Now Time to answer the questions that were asked in the widhya ipl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Royal Challengers Bangalore    8\n",
+       "Sunrisers Hyderabad            6\n",
+       "Name: toss_winner, dtype: int64"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RCB_SRH['toss_winner'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sometimes Toss plays an crucial role in deciding the winner.RCB win the Toss 8 times and SRH win it 6 times"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d9224ab588>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_style('whitegrid')\n",
+    "RCB_SRH['toss_winner'].value_counts().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "No of times toss win by both teams:\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "Royal Challengers Bangalore    8\n",
+       "Sunrisers Hyderabad            6\n",
+       "Name: toss_winner, dtype: int64"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(\"No of times toss win by both teams:\")\n",
+    "RCB_SRH['toss_winner'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# From 14 matches Royal challengers Bangalore win 2 Times by Batting First and 4 times by Batting Second whereas Sunrises Hyderabad  win 3 times by batting first and 5 times by batting second."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d92258d808>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.setrecursionlimit(10000) \n",
+    "sns.set({'figure.figsize':(15,5)})\n",
+    "sns.countplot(x='toss_decision',hue='winner',data=RCB_SRH,palette='RdBu_r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Sunrisers Hyderabad            8\n",
+       "Royal Challengers Bangalore    6\n",
+       "Name: winner, dtype: int64"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "RCB_SRH['winner'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# It looks like MI and SRH produces some Tough competition.SRH(win-8,loss-6),RCB(win-6,loss-8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d9225b9208>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "RCB_SRH['winner'].value_counts().plot(kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Complete details RCB_SRH using sweetviz library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      ":FEATURES DONE:                    |                     | [  0%]   00:06  -> (00:00 left)\n",
+      ":PAIRWISE DONE:                    |█████████████████████| [100%]   00:00  -> (00:00 left)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Creating Associations graph... DONE!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import sweetviz \n",
+    "my_report=sweetviz.analyze([RCB_SRH,\"RCB_SRH\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_report.show_html()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "From 14 matches Both teams win 7 times each\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"From 14 matches SRH win 8 times\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "values=[8,6]\n",
+    "label=['SRH','RCB']\n",
+    "plt.pie(x=values,labels=label,autopct='%.2f')\n",
+    "plt.title(\"Win percentage of Both teams from 14 matches\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Now it's time to move into deliveries data sets"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Deliveries=pd.read_csv(\"deliveries.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "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": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Deliveries.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "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>179073</th>\n",
+       "      <td>11415</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>20</td>\n",
+       "      <td>2</td>\n",
+       "      <td>RA Jadeja</td>\n",
+       "      <td>SR Watson</td>\n",
+       "      <td>SL Malinga</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>179074</th>\n",
+       "      <td>11415</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>SR Watson</td>\n",
+       "      <td>RA Jadeja</td>\n",
+       "      <td>SL Malinga</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>179075</th>\n",
+       "      <td>11415</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>20</td>\n",
+       "      <td>4</td>\n",
+       "      <td>SR Watson</td>\n",
+       "      <td>RA Jadeja</td>\n",
+       "      <td>SL Malinga</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>SR Watson</td>\n",
+       "      <td>run out</td>\n",
+       "      <td>KH Pandya</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>179076</th>\n",
+       "      <td>11415</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>20</td>\n",
+       "      <td>5</td>\n",
+       "      <td>SN Thakur</td>\n",
+       "      <td>RA Jadeja</td>\n",
+       "      <td>SL Malinga</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>179077</th>\n",
+       "      <td>11415</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Chennai Super Kings</td>\n",
+       "      <td>Mumbai Indians</td>\n",
+       "      <td>20</td>\n",
+       "      <td>6</td>\n",
+       "      <td>SN Thakur</td>\n",
+       "      <td>RA Jadeja</td>\n",
+       "      <td>SL Malinga</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>SN Thakur</td>\n",
+       "      <td>lbw</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  \\\n",
+       "179073     11415       2  Chennai Super Kings  Mumbai Indians    20     2   \n",
+       "179074     11415       2  Chennai Super Kings  Mumbai Indians    20     3   \n",
+       "179075     11415       2  Chennai Super Kings  Mumbai Indians    20     4   \n",
+       "179076     11415       2  Chennai Super Kings  Mumbai Indians    20     5   \n",
+       "179077     11415       2  Chennai Super Kings  Mumbai Indians    20     6   \n",
+       "\n",
+       "          batsman non_striker      bowler  is_super_over  ...  bye_runs  \\\n",
+       "179073  RA Jadeja   SR Watson  SL Malinga              0  ...         0   \n",
+       "179074  SR Watson   RA Jadeja  SL Malinga              0  ...         0   \n",
+       "179075  SR Watson   RA Jadeja  SL Malinga              0  ...         0   \n",
+       "179076  SN Thakur   RA Jadeja  SL Malinga              0  ...         0   \n",
+       "179077  SN Thakur   RA Jadeja  SL Malinga              0  ...         0   \n",
+       "\n",
+       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
+       "179073            0            0             0             1           0   \n",
+       "179074            0            0             0             2           0   \n",
+       "179075            0            0             0             1           0   \n",
+       "179076            0            0             0             2           0   \n",
+       "179077            0            0             0             0           0   \n",
+       "\n",
+       "        total_runs  player_dismissed dismissal_kind    fielder  \n",
+       "179073           1               NaN            NaN        NaN  \n",
+       "179074           2               NaN            NaN        NaN  \n",
+       "179075           1         SR Watson        run out  KH Pandya  \n",
+       "179076           2               NaN            NaN        NaN  \n",
+       "179077           0         SN Thakur            lbw        NaN  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Deliveries.tail(5)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 179078 entries, 0 to 179077\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column            Non-Null Count   Dtype \n",
+      "---  ------            --------------   ----- \n",
+      " 0   match_id          179078 non-null  int64 \n",
+      " 1   inning            179078 non-null  int64 \n",
+      " 2   batting_team      179078 non-null  object\n",
+      " 3   bowling_team      179078 non-null  object\n",
+      " 4   over              179078 non-null  int64 \n",
+      " 5   ball              179078 non-null  int64 \n",
+      " 6   batsman           179078 non-null  object\n",
+      " 7   non_striker       179078 non-null  object\n",
+      " 8   bowler            179078 non-null  object\n",
+      " 9   is_super_over     179078 non-null  int64 \n",
+      " 10  wide_runs         179078 non-null  int64 \n",
+      " 11  bye_runs          179078 non-null  int64 \n",
+      " 12  legbye_runs       179078 non-null  int64 \n",
+      " 13  noball_runs       179078 non-null  int64 \n",
+      " 14  penalty_runs      179078 non-null  int64 \n",
+      " 15  batsman_runs      179078 non-null  int64 \n",
+      " 16  extra_runs        179078 non-null  int64 \n",
+      " 17  total_runs        179078 non-null  int64 \n",
+      " 18  player_dismissed  8834 non-null    object\n",
+      " 19  dismissal_kind    8834 non-null    object\n",
+      " 20  fielder           6448 non-null    object\n",
+      "dtypes: int64(13), object(8)\n",
+      "memory usage: 28.7+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "Deliveries.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Filtering the match details that only takes place between Rajastan Royals and Sunrises Hyderabad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Bangalore_Hyderabad1=Deliveries.loc[(Deliveries['batting_team' ] =='Royal Challengers Bangalore' ) & (Deliveries['bowling_team'] == 'Sunrisers Hyderabad')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Bangalore_Hyderabad2=Deliveries.loc[(Deliveries['bowling_team' ] =='Royal Challengers Bangalore' ) & (Deliveries['batting_team'] == 'Sunrisers Hyderabad')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Bangalore_Hyderabad=Bangalore_Hyderabad1.append(Bangalore_Hyderabad2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>match_id</th>\n",
+       "      <th>inning</th>\n",
+       "      <th>batting_team</th>\n",
+       "      <th>bowling_team</th>\n",
+       "      <th>over</th>\n",
+       "      <th>ball</th>\n",
+       "      <th>batsman</th>\n",
+       "      <th>non_striker</th>\n",
+       "      <th>bowler</th>\n",
+       "      <th>is_super_over</th>\n",
+       "      <th>...</th>\n",
+       "      <th>bye_runs</th>\n",
+       "      <th>legbye_runs</th>\n",
+       "      <th>noball_runs</th>\n",
+       "      <th>penalty_runs</th>\n",
+       "      <th>batsman_runs</th>\n",
+       "      <th>extra_runs</th>\n",
+       "      <th>total_runs</th>\n",
+       "      <th>player_dismissed</th>\n",
+       "      <th>dismissal_kind</th>\n",
+       "      <th>fielder</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>CH Gayle</td>\n",
+       "      <td>Mandeep Singh</td>\n",
+       "      <td>A Nehra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Mandeep Singh</td>\n",
+       "      <td>CH Gayle</td>\n",
+       "      <td>A Nehra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Mandeep Singh</td>\n",
+       "      <td>CH Gayle</td>\n",
+       "      <td>A Nehra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mandeep Singh</td>\n",
+       "      <td>CH Gayle</td>\n",
+       "      <td>A Nehra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>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>129</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Mandeep Singh</td>\n",
+       "      <td>CH Gayle</td>\n",
+       "      <td>A Nehra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>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>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177511</th>\n",
+       "      <td>11345</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>KS Williamson</td>\n",
+       "      <td>B Kumar</td>\n",
+       "      <td>UT Yadav</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>177512</th>\n",
+       "      <td>11345</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>20</td>\n",
+       "      <td>4</td>\n",
+       "      <td>KS Williamson</td>\n",
+       "      <td>B Kumar</td>\n",
+       "      <td>UT Yadav</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>177513</th>\n",
+       "      <td>11345</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>20</td>\n",
+       "      <td>5</td>\n",
+       "      <td>KS Williamson</td>\n",
+       "      <td>B Kumar</td>\n",
+       "      <td>UT Yadav</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>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177514</th>\n",
+       "      <td>11345</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>20</td>\n",
+       "      <td>6</td>\n",
+       "      <td>KS Williamson</td>\n",
+       "      <td>B Kumar</td>\n",
+       "      <td>UT Yadav</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>177515</th>\n",
+       "      <td>11345</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>20</td>\n",
+       "      <td>7</td>\n",
+       "      <td>B Kumar</td>\n",
+       "      <td>KS Williamson</td>\n",
+       "      <td>UT Yadav</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>3311 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        match_id  inning                 batting_team  \\\n",
+       "125            1       2  Royal Challengers Bangalore   \n",
+       "126            1       2  Royal Challengers Bangalore   \n",
+       "127            1       2  Royal Challengers Bangalore   \n",
+       "128            1       2  Royal Challengers Bangalore   \n",
+       "129            1       2  Royal Challengers Bangalore   \n",
+       "...          ...     ...                          ...   \n",
+       "177511     11345       1          Sunrisers Hyderabad   \n",
+       "177512     11345       1          Sunrisers Hyderabad   \n",
+       "177513     11345       1          Sunrisers Hyderabad   \n",
+       "177514     11345       1          Sunrisers Hyderabad   \n",
+       "177515     11345       1          Sunrisers Hyderabad   \n",
+       "\n",
+       "                       bowling_team  over  ball        batsman    non_striker  \\\n",
+       "125             Sunrisers Hyderabad     1     1       CH Gayle  Mandeep Singh   \n",
+       "126             Sunrisers Hyderabad     1     2  Mandeep Singh       CH Gayle   \n",
+       "127             Sunrisers Hyderabad     1     3  Mandeep Singh       CH Gayle   \n",
+       "128             Sunrisers Hyderabad     1     4  Mandeep Singh       CH Gayle   \n",
+       "129             Sunrisers Hyderabad     1     5  Mandeep Singh       CH Gayle   \n",
+       "...                             ...   ...   ...            ...            ...   \n",
+       "177511  Royal Challengers Bangalore    20     3  KS Williamson        B Kumar   \n",
+       "177512  Royal Challengers Bangalore    20     4  KS Williamson        B Kumar   \n",
+       "177513  Royal Challengers Bangalore    20     5  KS Williamson        B Kumar   \n",
+       "177514  Royal Challengers Bangalore    20     6  KS Williamson        B Kumar   \n",
+       "177515  Royal Challengers Bangalore    20     7        B Kumar  KS Williamson   \n",
+       "\n",
+       "          bowler  is_super_over  ...  bye_runs  legbye_runs  noball_runs  \\\n",
+       "125      A Nehra              0  ...         0            0            0   \n",
+       "126      A Nehra              0  ...         0            0            0   \n",
+       "127      A Nehra              0  ...         0            0            0   \n",
+       "128      A Nehra              0  ...         0            0            0   \n",
+       "129      A Nehra              0  ...         0            0            0   \n",
+       "...          ...            ...  ...       ...          ...          ...   \n",
+       "177511  UT Yadav              0  ...         0            0            0   \n",
+       "177512  UT Yadav              0  ...         0            0            0   \n",
+       "177513  UT Yadav              0  ...         0            0            1   \n",
+       "177514  UT Yadav              0  ...         0            0            0   \n",
+       "177515  UT Yadav              0  ...         0            0            0   \n",
+       "\n",
+       "        penalty_runs  batsman_runs  extra_runs  total_runs  player_dismissed  \\\n",
+       "125                0             1           0           1               NaN   \n",
+       "126                0             0           0           0               NaN   \n",
+       "127                0             0           0           0               NaN   \n",
+       "128                0             2           0           2               NaN   \n",
+       "129                0             4           0           4               NaN   \n",
+       "...              ...           ...         ...         ...               ...   \n",
+       "177511             0             6           0           6               NaN   \n",
+       "177512             0             4           0           4               NaN   \n",
+       "177513             0             3           1           4               NaN   \n",
+       "177514             0             1           0           1               NaN   \n",
+       "177515             0             4           0           4               NaN   \n",
+       "\n",
+       "       dismissal_kind fielder  \n",
+       "125               NaN     NaN  \n",
+       "126               NaN     NaN  \n",
+       "127               NaN     NaN  \n",
+       "128               NaN     NaN  \n",
+       "129               NaN     NaN  \n",
+       "...               ...     ...  \n",
+       "177511            NaN     NaN  \n",
+       "177512            NaN     NaN  \n",
+       "177513            NaN     NaN  \n",
+       "177514            NaN     NaN  \n",
+       "177515            NaN     NaN  \n",
+       "\n",
+       "[3311 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Bangalore_Hyderabad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "388      261\n",
+       "481      250\n",
+       "636      250\n",
+       "580      250\n",
+       "7932     249\n",
+       "1        248\n",
+       "603      246\n",
+       "11345    246\n",
+       "7944     245\n",
+       "503      244\n",
+       "11147    244\n",
+       "525      237\n",
+       "432      235\n",
+       "568      106\n",
+       "Name: match_id, dtype: int64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Bangalore_Hyderabad['match_id'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 3311 entries, 125 to 177515\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column            Non-Null Count  Dtype \n",
+      "---  ------            --------------  ----- \n",
+      " 0   match_id          3311 non-null   int64 \n",
+      " 1   inning            3311 non-null   int64 \n",
+      " 2   batting_team      3311 non-null   object\n",
+      " 3   bowling_team      3311 non-null   object\n",
+      " 4   over              3311 non-null   int64 \n",
+      " 5   ball              3311 non-null   int64 \n",
+      " 6   batsman           3311 non-null   object\n",
+      " 7   non_striker       3311 non-null   object\n",
+      " 8   bowler            3311 non-null   object\n",
+      " 9   is_super_over     3311 non-null   int64 \n",
+      " 10  wide_runs         3311 non-null   int64 \n",
+      " 11  bye_runs          3311 non-null   int64 \n",
+      " 12  legbye_runs       3311 non-null   int64 \n",
+      " 13  noball_runs       3311 non-null   int64 \n",
+      " 14  penalty_runs      3311 non-null   int64 \n",
+      " 15  batsman_runs      3311 non-null   int64 \n",
+      " 16  extra_runs        3311 non-null   int64 \n",
+      " 17  total_runs        3311 non-null   int64 \n",
+      " 18  player_dismissed  162 non-null    object\n",
+      " 19  dismissal_kind    162 non-null    object\n",
+      " 20  fielder           125 non-null    object\n",
+      "dtypes: int64(13), object(8)\n",
+      "memory usage: 569.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "Bangalore_Hyderabad.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method NDFrame.describe of         match_id  inning                 batting_team  \\\n",
+       "125            1       2  Royal Challengers Bangalore   \n",
+       "126            1       2  Royal Challengers Bangalore   \n",
+       "127            1       2  Royal Challengers Bangalore   \n",
+       "128            1       2  Royal Challengers Bangalore   \n",
+       "129            1       2  Royal Challengers Bangalore   \n",
+       "...          ...     ...                          ...   \n",
+       "177511     11345       1          Sunrisers Hyderabad   \n",
+       "177512     11345       1          Sunrisers Hyderabad   \n",
+       "177513     11345       1          Sunrisers Hyderabad   \n",
+       "177514     11345       1          Sunrisers Hyderabad   \n",
+       "177515     11345       1          Sunrisers Hyderabad   \n",
+       "\n",
+       "                       bowling_team  over  ball        batsman    non_striker  \\\n",
+       "125             Sunrisers Hyderabad     1     1       CH Gayle  Mandeep Singh   \n",
+       "126             Sunrisers Hyderabad     1     2  Mandeep Singh       CH Gayle   \n",
+       "127             Sunrisers Hyderabad     1     3  Mandeep Singh       CH Gayle   \n",
+       "128             Sunrisers Hyderabad     1     4  Mandeep Singh       CH Gayle   \n",
+       "129             Sunrisers Hyderabad     1     5  Mandeep Singh       CH Gayle   \n",
+       "...                             ...   ...   ...            ...            ...   \n",
+       "177511  Royal Challengers Bangalore    20     3  KS Williamson        B Kumar   \n",
+       "177512  Royal Challengers Bangalore    20     4  KS Williamson        B Kumar   \n",
+       "177513  Royal Challengers Bangalore    20     5  KS Williamson        B Kumar   \n",
+       "177514  Royal Challengers Bangalore    20     6  KS Williamson        B Kumar   \n",
+       "177515  Royal Challengers Bangalore    20     7        B Kumar  KS Williamson   \n",
+       "\n",
+       "          bowler  is_super_over  ...  bye_runs  legbye_runs  noball_runs  \\\n",
+       "125      A Nehra              0  ...         0            0            0   \n",
+       "126      A Nehra              0  ...         0            0            0   \n",
+       "127      A Nehra              0  ...         0            0            0   \n",
+       "128      A Nehra              0  ...         0            0            0   \n",
+       "129      A Nehra              0  ...         0            0            0   \n",
+       "...          ...            ...  ...       ...          ...          ...   \n",
+       "177511  UT Yadav              0  ...         0            0            0   \n",
+       "177512  UT Yadav              0  ...         0            0            0   \n",
+       "177513  UT Yadav              0  ...         0            0            1   \n",
+       "177514  UT Yadav              0  ...         0            0            0   \n",
+       "177515  UT Yadav              0  ...         0            0            0   \n",
+       "\n",
+       "        penalty_runs  batsman_runs  extra_runs  total_runs  player_dismissed  \\\n",
+       "125                0             1           0           1               NaN   \n",
+       "126                0             0           0           0               NaN   \n",
+       "127                0             0           0           0               NaN   \n",
+       "128                0             2           0           2               NaN   \n",
+       "129                0             4           0           4               NaN   \n",
+       "...              ...           ...         ...         ...               ...   \n",
+       "177511             0             6           0           6               NaN   \n",
+       "177512             0             4           0           4               NaN   \n",
+       "177513             0             3           1           4               NaN   \n",
+       "177514             0             1           0           1               NaN   \n",
+       "177515             0             4           0           4               NaN   \n",
+       "\n",
+       "       dismissal_kind fielder  \n",
+       "125               NaN     NaN  \n",
+       "126               NaN     NaN  \n",
+       "127               NaN     NaN  \n",
+       "128               NaN     NaN  \n",
+       "129               NaN     NaN  \n",
+       "...               ...     ...  \n",
+       "177511            NaN     NaN  \n",
+       "177512            NaN     NaN  \n",
+       "177513            NaN     NaN  \n",
+       "177514            NaN     NaN  \n",
+       "177515            NaN     NaN  \n",
+       "\n",
+       "[3311 rows x 21 columns]>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Bangalore_Hyderabad.describe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Check For any Null values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "match_id               0\n",
+       "inning                 0\n",
+       "batting_team           0\n",
+       "bowling_team           0\n",
+       "over                   0\n",
+       "ball                   0\n",
+       "batsman                0\n",
+       "non_striker            0\n",
+       "bowler                 0\n",
+       "is_super_over          0\n",
+       "wide_runs              0\n",
+       "bye_runs               0\n",
+       "legbye_runs            0\n",
+       "noball_runs            0\n",
+       "penalty_runs           0\n",
+       "batsman_runs           0\n",
+       "extra_runs             0\n",
+       "total_runs             0\n",
+       "player_dismissed    3149\n",
+       "dismissal_kind      3149\n",
+       "fielder             3186\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Bangalore_Hyderabad.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Looks like we have huge null(NaN) values in  player_dismissed,dismissal_kind,fielder columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d924e13888>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(Bangalore_Hyderabad.isnull(),yticklabels=False,cbar=False,cmap='PiYG')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Replace the NaN values to 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Bangalore_Hyderabad.fillna(0,inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Now it's clear"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d926eb4988>"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.heatmap(Bangalore_Hyderabad.isnull(),yticklabels=False,cbar=False,cmap='PiYG')                      "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# # Let's try covariance to find relation among features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "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>over</th>\n",
+       "      <th>ball</th>\n",
+       "      <th>is_super_over</th>\n",
+       "      <th>wide_runs</th>\n",
+       "      <th>bye_runs</th>\n",
+       "      <th>legbye_runs</th>\n",
+       "      <th>noball_runs</th>\n",
+       "      <th>penalty_runs</th>\n",
+       "      <th>batsman_runs</th>\n",
+       "      <th>extra_runs</th>\n",
+       "      <th>total_runs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>match_id</th>\n",
+       "      <td>1.821493e+07</td>\n",
+       "      <td>9.210334</td>\n",
+       "      <td>828.250262</td>\n",
+       "      <td>-60.284818</td>\n",
+       "      <td>-10.949065</td>\n",
+       "      <td>-35.023821</td>\n",
+       "      <td>5.479878</td>\n",
+       "      <td>-13.622060</td>\n",
+       "      <td>-5.237585</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>95.257017</td>\n",
+       "      <td>-48.403588</td>\n",
+       "      <td>46.853429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>inning</th>\n",
+       "      <td>9.210334e+00</td>\n",
+       "      <td>0.265169</td>\n",
+       "      <td>-0.128177</td>\n",
+       "      <td>-0.003881</td>\n",
+       "      <td>0.007718</td>\n",
+       "      <td>-0.000920</td>\n",
+       "      <td>0.000919</td>\n",
+       "      <td>0.001115</td>\n",
+       "      <td>-0.000440</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.016152</td>\n",
+       "      <td>0.000675</td>\n",
+       "      <td>-0.015478</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>over</th>\n",
+       "      <td>8.282503e+02</td>\n",
+       "      <td>-0.128177</td>\n",
+       "      <td>33.206685</td>\n",
+       "      <td>-0.009684</td>\n",
+       "      <td>-0.036110</td>\n",
+       "      <td>-0.007686</td>\n",
+       "      <td>0.007453</td>\n",
+       "      <td>0.026682</td>\n",
+       "      <td>0.001352</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.797394</td>\n",
+       "      <td>0.027802</td>\n",
+       "      <td>0.825195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ball</th>\n",
+       "      <td>-6.028482e+01</td>\n",
+       "      <td>-0.003881</td>\n",
+       "      <td>-0.009684</td>\n",
+       "      <td>3.277442</td>\n",
+       "      <td>0.000578</td>\n",
+       "      <td>-0.008309</td>\n",
+       "      <td>-0.000141</td>\n",
+       "      <td>-0.003692</td>\n",
+       "      <td>-0.000631</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.088686</td>\n",
+       "      <td>-0.012773</td>\n",
+       "      <td>-0.101459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>is_super_over</th>\n",
+       "      <td>-1.094906e+01</td>\n",
+       "      <td>0.007718</td>\n",
+       "      <td>-0.036110</td>\n",
+       "      <td>0.000578</td>\n",
+       "      <td>0.003912</td>\n",
+       "      <td>-0.000155</td>\n",
+       "      <td>-0.000014</td>\n",
+       "      <td>-0.000065</td>\n",
+       "      <td>0.000287</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.004795</td>\n",
+       "      <td>0.000052</td>\n",
+       "      <td>0.004847</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wide_runs</th>\n",
+       "      <td>-3.502382e+01</td>\n",
+       "      <td>-0.000920</td>\n",
+       "      <td>-0.007686</td>\n",
+       "      <td>-0.008309</td>\n",
+       "      <td>-0.000155</td>\n",
+       "      <td>0.071848</td>\n",
+       "      <td>-0.000143</td>\n",
+       "      <td>-0.000657</td>\n",
+       "      <td>-0.000155</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.047333</td>\n",
+       "      <td>0.070892</td>\n",
+       "      <td>0.023559</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bye_runs</th>\n",
+       "      <td>5.479878e+00</td>\n",
+       "      <td>0.000919</td>\n",
+       "      <td>0.007453</td>\n",
+       "      <td>-0.000141</td>\n",
+       "      <td>-0.000014</td>\n",
+       "      <td>-0.000143</td>\n",
+       "      <td>0.007238</td>\n",
+       "      <td>-0.000060</td>\n",
+       "      <td>-0.000014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000081</td>\n",
+       "      <td>0.007020</td>\n",
+       "      <td>0.007100</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>legbye_runs</th>\n",
+       "      <td>-1.362206e+01</td>\n",
+       "      <td>0.001115</td>\n",
+       "      <td>0.026682</td>\n",
+       "      <td>-0.003692</td>\n",
+       "      <td>-0.000065</td>\n",
+       "      <td>-0.000657</td>\n",
+       "      <td>-0.000060</td>\n",
+       "      <td>0.025404</td>\n",
+       "      <td>-0.000065</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.019545</td>\n",
+       "      <td>0.024621</td>\n",
+       "      <td>0.005076</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>noball_runs</th>\n",
+       "      <td>-5.237585e+00</td>\n",
+       "      <td>-0.000440</td>\n",
+       "      <td>0.001352</td>\n",
+       "      <td>-0.000631</td>\n",
+       "      <td>0.000287</td>\n",
+       "      <td>-0.000155</td>\n",
+       "      <td>-0.000014</td>\n",
+       "      <td>-0.000065</td>\n",
+       "      <td>0.003912</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.002680</td>\n",
+       "      <td>0.003677</td>\n",
+       "      <td>0.006358</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>penalty_runs</th>\n",
+       "      <td>0.000000e+00</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>batsman_runs</th>\n",
+       "      <td>9.525702e+01</td>\n",
+       "      <td>-0.016152</td>\n",
+       "      <td>0.797394</td>\n",
+       "      <td>-0.088686</td>\n",
+       "      <td>0.004795</td>\n",
+       "      <td>-0.047333</td>\n",
+       "      <td>0.000081</td>\n",
+       "      <td>-0.019545</td>\n",
+       "      <td>0.002680</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.988175</td>\n",
+       "      <td>-0.064117</td>\n",
+       "      <td>2.924057</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>extra_runs</th>\n",
+       "      <td>-4.840359e+01</td>\n",
+       "      <td>0.000675</td>\n",
+       "      <td>0.027802</td>\n",
+       "      <td>-0.012773</td>\n",
+       "      <td>0.000052</td>\n",
+       "      <td>0.070892</td>\n",
+       "      <td>0.007020</td>\n",
+       "      <td>0.024621</td>\n",
+       "      <td>0.003677</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.064117</td>\n",
+       "      <td>0.106210</td>\n",
+       "      <td>0.042093</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_runs</th>\n",
+       "      <td>4.685343e+01</td>\n",
+       "      <td>-0.015478</td>\n",
+       "      <td>0.825195</td>\n",
+       "      <td>-0.101459</td>\n",
+       "      <td>0.004847</td>\n",
+       "      <td>0.023559</td>\n",
+       "      <td>0.007100</td>\n",
+       "      <td>0.005076</td>\n",
+       "      <td>0.006358</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.924057</td>\n",
+       "      <td>0.042093</td>\n",
+       "      <td>2.966150</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   match_id    inning        over       ball  is_super_over  \\\n",
+       "match_id       1.821493e+07  9.210334  828.250262 -60.284818     -10.949065   \n",
+       "inning         9.210334e+00  0.265169   -0.128177  -0.003881       0.007718   \n",
+       "over           8.282503e+02 -0.128177   33.206685  -0.009684      -0.036110   \n",
+       "ball          -6.028482e+01 -0.003881   -0.009684   3.277442       0.000578   \n",
+       "is_super_over -1.094906e+01  0.007718   -0.036110   0.000578       0.003912   \n",
+       "wide_runs     -3.502382e+01 -0.000920   -0.007686  -0.008309      -0.000155   \n",
+       "bye_runs       5.479878e+00  0.000919    0.007453  -0.000141      -0.000014   \n",
+       "legbye_runs   -1.362206e+01  0.001115    0.026682  -0.003692      -0.000065   \n",
+       "noball_runs   -5.237585e+00 -0.000440    0.001352  -0.000631       0.000287   \n",
+       "penalty_runs   0.000000e+00  0.000000    0.000000   0.000000       0.000000   \n",
+       "batsman_runs   9.525702e+01 -0.016152    0.797394  -0.088686       0.004795   \n",
+       "extra_runs    -4.840359e+01  0.000675    0.027802  -0.012773       0.000052   \n",
+       "total_runs     4.685343e+01 -0.015478    0.825195  -0.101459       0.004847   \n",
+       "\n",
+       "               wide_runs  bye_runs  legbye_runs  noball_runs  penalty_runs  \\\n",
+       "match_id      -35.023821  5.479878   -13.622060    -5.237585           0.0   \n",
+       "inning         -0.000920  0.000919     0.001115    -0.000440           0.0   \n",
+       "over           -0.007686  0.007453     0.026682     0.001352           0.0   \n",
+       "ball           -0.008309 -0.000141    -0.003692    -0.000631           0.0   \n",
+       "is_super_over  -0.000155 -0.000014    -0.000065     0.000287           0.0   \n",
+       "wide_runs       0.071848 -0.000143    -0.000657    -0.000155           0.0   \n",
+       "bye_runs       -0.000143  0.007238    -0.000060    -0.000014           0.0   \n",
+       "legbye_runs    -0.000657 -0.000060     0.025404    -0.000065           0.0   \n",
+       "noball_runs    -0.000155 -0.000014    -0.000065     0.003912           0.0   \n",
+       "penalty_runs    0.000000  0.000000     0.000000     0.000000           0.0   \n",
+       "batsman_runs   -0.047333  0.000081    -0.019545     0.002680           0.0   \n",
+       "extra_runs      0.070892  0.007020     0.024621     0.003677           0.0   \n",
+       "total_runs      0.023559  0.007100     0.005076     0.006358           0.0   \n",
+       "\n",
+       "               batsman_runs  extra_runs  total_runs  \n",
+       "match_id          95.257017  -48.403588   46.853429  \n",
+       "inning            -0.016152    0.000675   -0.015478  \n",
+       "over               0.797394    0.027802    0.825195  \n",
+       "ball              -0.088686   -0.012773   -0.101459  \n",
+       "is_super_over      0.004795    0.000052    0.004847  \n",
+       "wide_runs         -0.047333    0.070892    0.023559  \n",
+       "bye_runs           0.000081    0.007020    0.007100  \n",
+       "legbye_runs       -0.019545    0.024621    0.005076  \n",
+       "noball_runs        0.002680    0.003677    0.006358  \n",
+       "penalty_runs       0.000000    0.000000    0.000000  \n",
+       "batsman_runs       2.988175   -0.064117    2.924057  \n",
+       "extra_runs        -0.064117    0.106210    0.042093  \n",
+       "total_runs         2.924057    0.042093    2.966150  "
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Bangalore_Hyderabad.cov()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# it's diffult to interpret covariance.Let's try correlation.Correlation clearly mentions using discrete values 1=positively correlated,-1=Negatively correlated,0=No correlation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>match_id</th>\n",
+       "      <th>inning</th>\n",
+       "      <th>over</th>\n",
+       "      <th>ball</th>\n",
+       "      <th>is_super_over</th>\n",
+       "      <th>wide_runs</th>\n",
+       "      <th>bye_runs</th>\n",
+       "      <th>legbye_runs</th>\n",
+       "      <th>noball_runs</th>\n",
+       "      <th>penalty_runs</th>\n",
+       "      <th>batsman_runs</th>\n",
+       "      <th>extra_runs</th>\n",
+       "      <th>total_runs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>match_id</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.004191</td>\n",
+       "      <td>0.033677</td>\n",
+       "      <td>-0.007802</td>\n",
+       "      <td>-0.041017</td>\n",
+       "      <td>-0.030616</td>\n",
+       "      <td>0.015092</td>\n",
+       "      <td>-0.020025</td>\n",
+       "      <td>-0.019621</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.012912</td>\n",
+       "      <td>-0.034800</td>\n",
+       "      <td>0.006374</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>inning</th>\n",
+       "      <td>0.004191</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.043195</td>\n",
+       "      <td>-0.004163</td>\n",
+       "      <td>0.239616</td>\n",
+       "      <td>-0.006665</td>\n",
+       "      <td>0.020976</td>\n",
+       "      <td>0.013587</td>\n",
+       "      <td>-0.013647</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.018145</td>\n",
+       "      <td>0.004020</td>\n",
+       "      <td>-0.017452</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>over</th>\n",
+       "      <td>0.033677</td>\n",
+       "      <td>-0.043195</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.000928</td>\n",
+       "      <td>-0.100187</td>\n",
+       "      <td>-0.004976</td>\n",
+       "      <td>0.015203</td>\n",
+       "      <td>0.029051</td>\n",
+       "      <td>0.003751</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.080049</td>\n",
+       "      <td>0.014804</td>\n",
+       "      <td>0.083147</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ball</th>\n",
+       "      <td>-0.007802</td>\n",
+       "      <td>-0.004163</td>\n",
+       "      <td>-0.000928</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.005101</td>\n",
+       "      <td>-0.017124</td>\n",
+       "      <td>-0.000914</td>\n",
+       "      <td>-0.012794</td>\n",
+       "      <td>-0.005571</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.028339</td>\n",
+       "      <td>-0.021649</td>\n",
+       "      <td>-0.032541</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>is_super_over</th>\n",
+       "      <td>-0.041017</td>\n",
+       "      <td>0.239616</td>\n",
+       "      <td>-0.100187</td>\n",
+       "      <td>0.005101</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.009269</td>\n",
+       "      <td>-0.002675</td>\n",
+       "      <td>-0.006544</td>\n",
+       "      <td>0.073285</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.044351</td>\n",
+       "      <td>0.002543</td>\n",
+       "      <td>0.044997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wide_runs</th>\n",
+       "      <td>-0.030616</td>\n",
+       "      <td>-0.006665</td>\n",
+       "      <td>-0.004976</td>\n",
+       "      <td>-0.017124</td>\n",
+       "      <td>-0.009269</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.006290</td>\n",
+       "      <td>-0.015388</td>\n",
+       "      <td>-0.009269</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.102153</td>\n",
+       "      <td>0.811534</td>\n",
+       "      <td>0.051033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bye_runs</th>\n",
+       "      <td>0.015092</td>\n",
+       "      <td>0.020976</td>\n",
+       "      <td>0.015203</td>\n",
+       "      <td>-0.000914</td>\n",
+       "      <td>-0.002675</td>\n",
+       "      <td>-0.006290</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.004441</td>\n",
+       "      <td>-0.002675</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000548</td>\n",
+       "      <td>0.253186</td>\n",
+       "      <td>0.048460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>legbye_runs</th>\n",
+       "      <td>-0.020025</td>\n",
+       "      <td>0.013587</td>\n",
+       "      <td>0.029051</td>\n",
+       "      <td>-0.012794</td>\n",
+       "      <td>-0.006544</td>\n",
+       "      <td>-0.015388</td>\n",
+       "      <td>-0.004441</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.006544</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.070939</td>\n",
+       "      <td>0.473994</td>\n",
+       "      <td>0.018491</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>noball_runs</th>\n",
+       "      <td>-0.019621</td>\n",
+       "      <td>-0.013647</td>\n",
+       "      <td>0.003751</td>\n",
+       "      <td>-0.005571</td>\n",
+       "      <td>0.073285</td>\n",
+       "      <td>-0.009269</td>\n",
+       "      <td>-0.002675</td>\n",
+       "      <td>-0.006544</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.024791</td>\n",
+       "      <td>0.180398</td>\n",
+       "      <td>0.059020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>penalty_runs</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>batsman_runs</th>\n",
+       "      <td>0.012912</td>\n",
+       "      <td>-0.018145</td>\n",
+       "      <td>0.080049</td>\n",
+       "      <td>-0.028339</td>\n",
+       "      <td>0.044351</td>\n",
+       "      <td>-0.102153</td>\n",
+       "      <td>0.000548</td>\n",
+       "      <td>-0.070939</td>\n",
+       "      <td>0.024791</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>-0.113812</td>\n",
+       "      <td>0.982169</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>extra_runs</th>\n",
+       "      <td>-0.034800</td>\n",
+       "      <td>0.004020</td>\n",
+       "      <td>0.014804</td>\n",
+       "      <td>-0.021649</td>\n",
+       "      <td>0.002543</td>\n",
+       "      <td>0.811534</td>\n",
+       "      <td>0.253186</td>\n",
+       "      <td>0.473994</td>\n",
+       "      <td>0.180398</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-0.113812</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.074994</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>total_runs</th>\n",
+       "      <td>0.006374</td>\n",
+       "      <td>-0.017452</td>\n",
+       "      <td>0.083147</td>\n",
+       "      <td>-0.032541</td>\n",
+       "      <td>0.044997</td>\n",
+       "      <td>0.051033</td>\n",
+       "      <td>0.048460</td>\n",
+       "      <td>0.018491</td>\n",
+       "      <td>0.059020</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.982169</td>\n",
+       "      <td>0.074994</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               match_id    inning      over      ball  is_super_over  \\\n",
+       "match_id       1.000000  0.004191  0.033677 -0.007802      -0.041017   \n",
+       "inning         0.004191  1.000000 -0.043195 -0.004163       0.239616   \n",
+       "over           0.033677 -0.043195  1.000000 -0.000928      -0.100187   \n",
+       "ball          -0.007802 -0.004163 -0.000928  1.000000       0.005101   \n",
+       "is_super_over -0.041017  0.239616 -0.100187  0.005101       1.000000   \n",
+       "wide_runs     -0.030616 -0.006665 -0.004976 -0.017124      -0.009269   \n",
+       "bye_runs       0.015092  0.020976  0.015203 -0.000914      -0.002675   \n",
+       "legbye_runs   -0.020025  0.013587  0.029051 -0.012794      -0.006544   \n",
+       "noball_runs   -0.019621 -0.013647  0.003751 -0.005571       0.073285   \n",
+       "penalty_runs        NaN       NaN       NaN       NaN            NaN   \n",
+       "batsman_runs   0.012912 -0.018145  0.080049 -0.028339       0.044351   \n",
+       "extra_runs    -0.034800  0.004020  0.014804 -0.021649       0.002543   \n",
+       "total_runs     0.006374 -0.017452  0.083147 -0.032541       0.044997   \n",
+       "\n",
+       "               wide_runs  bye_runs  legbye_runs  noball_runs  penalty_runs  \\\n",
+       "match_id       -0.030616  0.015092    -0.020025    -0.019621           NaN   \n",
+       "inning         -0.006665  0.020976     0.013587    -0.013647           NaN   \n",
+       "over           -0.004976  0.015203     0.029051     0.003751           NaN   \n",
+       "ball           -0.017124 -0.000914    -0.012794    -0.005571           NaN   \n",
+       "is_super_over  -0.009269 -0.002675    -0.006544     0.073285           NaN   \n",
+       "wide_runs       1.000000 -0.006290    -0.015388    -0.009269           NaN   \n",
+       "bye_runs       -0.006290  1.000000    -0.004441    -0.002675           NaN   \n",
+       "legbye_runs    -0.015388 -0.004441     1.000000    -0.006544           NaN   \n",
+       "noball_runs    -0.009269 -0.002675    -0.006544     1.000000           NaN   \n",
+       "penalty_runs         NaN       NaN          NaN          NaN           NaN   \n",
+       "batsman_runs   -0.102153  0.000548    -0.070939     0.024791           NaN   \n",
+       "extra_runs      0.811534  0.253186     0.473994     0.180398           NaN   \n",
+       "total_runs      0.051033  0.048460     0.018491     0.059020           NaN   \n",
+       "\n",
+       "               batsman_runs  extra_runs  total_runs  \n",
+       "match_id           0.012912   -0.034800    0.006374  \n",
+       "inning            -0.018145    0.004020   -0.017452  \n",
+       "over               0.080049    0.014804    0.083147  \n",
+       "ball              -0.028339   -0.021649   -0.032541  \n",
+       "is_super_over      0.044351    0.002543    0.044997  \n",
+       "wide_runs         -0.102153    0.811534    0.051033  \n",
+       "bye_runs           0.000548    0.253186    0.048460  \n",
+       "legbye_runs       -0.070939    0.473994    0.018491  \n",
+       "noball_runs        0.024791    0.180398    0.059020  \n",
+       "penalty_runs            NaN         NaN         NaN  \n",
+       "batsman_runs       1.000000   -0.113812    0.982169  \n",
+       "extra_runs        -0.113812    1.000000    0.074994  \n",
+       "total_runs         0.982169    0.074994    1.000000  "
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Bangalore_Hyderabad.corr()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# # Ball by Ball Details of RCB VS SRH From 14 matches "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>match_id</th>\n",
+       "      <th>inning</th>\n",
+       "      <th>batting_team</th>\n",
+       "      <th>bowling_team</th>\n",
+       "      <th>over</th>\n",
+       "      <th>ball</th>\n",
+       "      <th>batsman</th>\n",
+       "      <th>non_striker</th>\n",
+       "      <th>bowler</th>\n",
+       "      <th>is_super_over</th>\n",
+       "      <th>...</th>\n",
+       "      <th>bye_runs</th>\n",
+       "      <th>legbye_runs</th>\n",
+       "      <th>noball_runs</th>\n",
+       "      <th>penalty_runs</th>\n",
+       "      <th>batsman_runs</th>\n",
+       "      <th>extra_runs</th>\n",
+       "      <th>total_runs</th>\n",
+       "      <th>player_dismissed</th>\n",
+       "      <th>dismissal_kind</th>\n",
+       "      <th>fielder</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>CH Gayle</td>\n",
+       "      <td>Mandeep Singh</td>\n",
+       "      <td>A Nehra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Mandeep Singh</td>\n",
+       "      <td>CH Gayle</td>\n",
+       "      <td>A Nehra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Mandeep Singh</td>\n",
+       "      <td>CH Gayle</td>\n",
+       "      <td>A Nehra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>Mandeep Singh</td>\n",
+       "      <td>CH Gayle</td>\n",
+       "      <td>A Nehra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Mandeep Singh</td>\n",
+       "      <td>CH Gayle</td>\n",
+       "      <td>A Nehra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177511</th>\n",
+       "      <td>11345</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>KS Williamson</td>\n",
+       "      <td>B Kumar</td>\n",
+       "      <td>UT Yadav</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>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177512</th>\n",
+       "      <td>11345</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>20</td>\n",
+       "      <td>4</td>\n",
+       "      <td>KS Williamson</td>\n",
+       "      <td>B Kumar</td>\n",
+       "      <td>UT Yadav</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>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177513</th>\n",
+       "      <td>11345</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>20</td>\n",
+       "      <td>5</td>\n",
+       "      <td>KS Williamson</td>\n",
+       "      <td>B Kumar</td>\n",
+       "      <td>UT Yadav</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>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177514</th>\n",
+       "      <td>11345</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>20</td>\n",
+       "      <td>6</td>\n",
+       "      <td>KS Williamson</td>\n",
+       "      <td>B Kumar</td>\n",
+       "      <td>UT Yadav</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>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177515</th>\n",
+       "      <td>11345</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Sunrisers Hyderabad</td>\n",
+       "      <td>Royal Challengers Bangalore</td>\n",
+       "      <td>20</td>\n",
+       "      <td>7</td>\n",
+       "      <td>B Kumar</td>\n",
+       "      <td>KS Williamson</td>\n",
+       "      <td>UT Yadav</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>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3311 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        match_id  inning                 batting_team  \\\n",
+       "125            1       2  Royal Challengers Bangalore   \n",
+       "126            1       2  Royal Challengers Bangalore   \n",
+       "127            1       2  Royal Challengers Bangalore   \n",
+       "128            1       2  Royal Challengers Bangalore   \n",
+       "129            1       2  Royal Challengers Bangalore   \n",
+       "...          ...     ...                          ...   \n",
+       "177511     11345       1          Sunrisers Hyderabad   \n",
+       "177512     11345       1          Sunrisers Hyderabad   \n",
+       "177513     11345       1          Sunrisers Hyderabad   \n",
+       "177514     11345       1          Sunrisers Hyderabad   \n",
+       "177515     11345       1          Sunrisers Hyderabad   \n",
+       "\n",
+       "                       bowling_team  over  ball        batsman    non_striker  \\\n",
+       "125             Sunrisers Hyderabad     1     1       CH Gayle  Mandeep Singh   \n",
+       "126             Sunrisers Hyderabad     1     2  Mandeep Singh       CH Gayle   \n",
+       "127             Sunrisers Hyderabad     1     3  Mandeep Singh       CH Gayle   \n",
+       "128             Sunrisers Hyderabad     1     4  Mandeep Singh       CH Gayle   \n",
+       "129             Sunrisers Hyderabad     1     5  Mandeep Singh       CH Gayle   \n",
+       "...                             ...   ...   ...            ...            ...   \n",
+       "177511  Royal Challengers Bangalore    20     3  KS Williamson        B Kumar   \n",
+       "177512  Royal Challengers Bangalore    20     4  KS Williamson        B Kumar   \n",
+       "177513  Royal Challengers Bangalore    20     5  KS Williamson        B Kumar   \n",
+       "177514  Royal Challengers Bangalore    20     6  KS Williamson        B Kumar   \n",
+       "177515  Royal Challengers Bangalore    20     7        B Kumar  KS Williamson   \n",
+       "\n",
+       "          bowler  is_super_over  ...  bye_runs  legbye_runs  noball_runs  \\\n",
+       "125      A Nehra              0  ...         0            0            0   \n",
+       "126      A Nehra              0  ...         0            0            0   \n",
+       "127      A Nehra              0  ...         0            0            0   \n",
+       "128      A Nehra              0  ...         0            0            0   \n",
+       "129      A Nehra              0  ...         0            0            0   \n",
+       "...          ...            ...  ...       ...          ...          ...   \n",
+       "177511  UT Yadav              0  ...         0            0            0   \n",
+       "177512  UT Yadav              0  ...         0            0            0   \n",
+       "177513  UT Yadav              0  ...         0            0            1   \n",
+       "177514  UT Yadav              0  ...         0            0            0   \n",
+       "177515  UT Yadav              0  ...         0            0            0   \n",
+       "\n",
+       "        penalty_runs  batsman_runs  extra_runs  total_runs  player_dismissed  \\\n",
+       "125                0             1           0           1                 0   \n",
+       "126                0             0           0           0                 0   \n",
+       "127                0             0           0           0                 0   \n",
+       "128                0             2           0           2                 0   \n",
+       "129                0             4           0           4                 0   \n",
+       "...              ...           ...         ...         ...               ...   \n",
+       "177511             0             6           0           6                 0   \n",
+       "177512             0             4           0           4                 0   \n",
+       "177513             0             3           1           4                 0   \n",
+       "177514             0             1           0           1                 0   \n",
+       "177515             0             4           0           4                 0   \n",
+       "\n",
+       "       dismissal_kind fielder  \n",
+       "125                 0       0  \n",
+       "126                 0       0  \n",
+       "127                 0       0  \n",
+       "128                 0       0  \n",
+       "129                 0       0  \n",
+       "...               ...     ...  \n",
+       "177511              0       0  \n",
+       "177512              0       0  \n",
+       "177513              0       0  \n",
+       "177514              0       0  \n",
+       "177515              0       0  \n",
+       "\n",
+       "[3311 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Bangalore_Hyderabad"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Complete_Details=Bangalore_Hyderabad.groupby('match_id')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>inning</th>\n",
+       "      <th>over</th>\n",
+       "      <th>ball</th>\n",
+       "      <th>is_super_over</th>\n",
+       "      <th>wide_runs</th>\n",
+       "      <th>bye_runs</th>\n",
+       "      <th>legbye_runs</th>\n",
+       "      <th>noball_runs</th>\n",
+       "      <th>penalty_runs</th>\n",
+       "      <th>batsman_runs</th>\n",
+       "      <th>extra_runs</th>\n",
+       "      <th>total_runs</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>match_id</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>371</td>\n",
+       "      <td>2571</td>\n",
+       "      <td>900</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>366</td>\n",
+       "      <td>13</td>\n",
+       "      <td>379</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>388</th>\n",
+       "      <td>418</td>\n",
+       "      <td>2587</td>\n",
+       "      <td>945</td>\n",
+       "      <td>13</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>270</td>\n",
+       "      <td>25</td>\n",
+       "      <td>295</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>432</th>\n",
+       "      <td>345</td>\n",
+       "      <td>2390</td>\n",
+       "      <td>852</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>313</td>\n",
+       "      <td>10</td>\n",
+       "      <td>323</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>481</th>\n",
+       "      <td>373</td>\n",
+       "      <td>2596</td>\n",
+       "      <td>913</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>298</td>\n",
+       "      <td>15</td>\n",
+       "      <td>313</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>503</th>\n",
+       "      <td>365</td>\n",
+       "      <td>2561</td>\n",
+       "      <td>872</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>307</td>\n",
+       "      <td>14</td>\n",
+       "      <td>321</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>525</th>\n",
+       "      <td>348</td>\n",
+       "      <td>2343</td>\n",
+       "      <td>877</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>306</td>\n",
+       "      <td>32</td>\n",
+       "      <td>338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>568</th>\n",
+       "      <td>142</td>\n",
+       "      <td>541</td>\n",
+       "      <td>387</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>210</td>\n",
+       "      <td>8</td>\n",
+       "      <td>218</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>580</th>\n",
+       "      <td>375</td>\n",
+       "      <td>2628</td>\n",
+       "      <td>910</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>391</td>\n",
+       "      <td>18</td>\n",
+       "      <td>409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>603</th>\n",
+       "      <td>368</td>\n",
+       "      <td>2580</td>\n",
+       "      <td>883</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>365</td>\n",
+       "      <td>8</td>\n",
+       "      <td>373</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>636</th>\n",
+       "      <td>374</td>\n",
+       "      <td>2615</td>\n",
+       "      <td>912</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>1</td>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>385</td>\n",
+       "      <td>23</td>\n",
+       "      <td>408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7932</th>\n",
+       "      <td>372</td>\n",
+       "      <td>2604</td>\n",
+       "      <td>903</td>\n",
+       "      <td>0</td>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>287</td>\n",
+       "      <td>14</td>\n",
+       "      <td>301</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7944</th>\n",
+       "      <td>368</td>\n",
+       "      <td>2600</td>\n",
+       "      <td>875</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>422</td>\n",
+       "      <td>9</td>\n",
+       "      <td>431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11147</th>\n",
+       "      <td>367</td>\n",
+       "      <td>2578</td>\n",
+       "      <td>870</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>344</td>\n",
+       "      <td>9</td>\n",
+       "      <td>353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11345</th>\n",
+       "      <td>369</td>\n",
+       "      <td>2559</td>\n",
+       "      <td>894</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>353</td>\n",
+       "      <td>13</td>\n",
+       "      <td>366</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          inning  over  ball  is_super_over  wide_runs  bye_runs  legbye_runs  \\\n",
+       "match_id                                                                        \n",
+       "1            371  2571   900              0          9         0            2   \n",
+       "388          418  2587   945             13         13         1           10   \n",
+       "432          345  2390   852              0          8         0            1   \n",
+       "481          373  2596   913              0         10         0            4   \n",
+       "503          365  2561   872              0          7         3            4   \n",
+       "525          348  2343   877              0         21         0            9   \n",
+       "568          142   541   387              0          4         0            2   \n",
+       "580          375  2628   910              0         13         1            3   \n",
+       "603          368  2580   883              0          5         1            1   \n",
+       "636          374  2615   912              0         15         1            7   \n",
+       "7932         372  2604   903              0          8         0            5   \n",
+       "7944         368  2600   875              0          5         1            3   \n",
+       "11147        367  2578   870              0          4         4            1   \n",
+       "11345        369  2559   894              0          9         0            3   \n",
+       "\n",
+       "          noball_runs  penalty_runs  batsman_runs  extra_runs  total_runs  \n",
+       "match_id                                                                   \n",
+       "1                   2             0           366          13         379  \n",
+       "388                 1             0           270          25         295  \n",
+       "432                 1             0           313          10         323  \n",
+       "481                 1             0           298          15         313  \n",
+       "503                 0             0           307          14         321  \n",
+       "525                 2             0           306          32         338  \n",
+       "568                 2             0           210           8         218  \n",
+       "580                 1             0           391          18         409  \n",
+       "603                 1             0           365           8         373  \n",
+       "636                 0             0           385          23         408  \n",
+       "7932                1             0           287          14         301  \n",
+       "7944                0             0           422           9         431  \n",
+       "11147               0             0           344           9         353  \n",
+       "11345               1             0           353          13         366  "
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Complete_Details.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# From the correlation plot we can clearly see that extra runs,wide runs ,bye runs are correlated and apart from that batsman runs column and total_runs columns are highly correlated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1d926b72408>"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10,5))\n",
+    "sns.heatmap(Bangalore_Hyderabad.corr(),yticklabels=True,annot=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How many wickets ll fall in total during the powerplay for both teams?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Bangalore_Hyderabad_PP=Bangalore_Hyderabad.loc[(Bangalore_Hyderabad['over']<=6)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                    1026\n",
+       "caught                 24\n",
+       "bowled                  8\n",
+       "lbw                     2\n",
+       "caught and bowled       1\n",
+       "stumped                 1\n",
+       "run out                 1\n",
+       "Name: dismissal_kind, dtype: int64"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Bangalore_Hyderabad_PP['dismissal_kind'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Bangalore_Hyderabad['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average number of wickets fall in total during the power play for both teams 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average number of wickets fall in total during the power play for both teams\",37//14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# what ll be sandeep sharma's Economy rate and how many boundaries ll he give away?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sandeep_sharmas_Economy_rate=Bangalore_Hyderabad.loc[(Bangalore_Hyderabad['bowler']=='Sandeep Sharma')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sandeep_sharmas_Economy_Rate=sandeep_sharmas_Economy_rate.loc[(sandeep_sharmas_Economy_rate['noball_runs']==0)&(sandeep_sharmas_Economy_rate['wide_runs']==0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "58"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sandeep_sharmas_Economy_Rate['batsman_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    20\n",
+       "0    18\n",
+       "4     5\n",
+       "2     3\n",
+       "6     2\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sandeep_sharmas_Economy_Rate['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(sandeep_sharmas_Economy_Rate['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1     12\n",
+       "15     6\n",
+       "14     6\n",
+       "13     6\n",
+       "10     6\n",
+       "8      6\n",
+       "3      6\n",
+       "Name: over, dtype: int64"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sandeep_sharmas_Economy_Rate['over'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6    8\n",
+       "5    8\n",
+       "4    8\n",
+       "3    8\n",
+       "2    7\n",
+       "1    7\n",
+       "7    2\n",
+       "Name: ball, dtype: int64"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sandeep_sharmas_Economy_Rate['ball'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average economy of Sandeep sharma against RCB in a single match 7\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average economy of Sandeep sharma against RCB in a single match\",58//8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average Boundaries given by Sandeep sharma against RCB in a single match 4.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average Boundaries given by Sandeep sharma against RCB in a single match\",8/2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# which batsman ll have highest runs scored off boundaries-runs scored off 1's,2's,3's to total runs ?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Virat_kholi_Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Virat_kholi_Ratio=Bangalore_Hyderabad.loc[(Bangalore_Hyderabad['batsman']=='V Kohli')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0    143\n",
+       "1    130\n",
+       "4     50\n",
+       "2     25\n",
+       "6     21\n",
+       "3      1\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Virat_kholi_Ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "509"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Virat_kholi_Ratio['batsman_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ratio of virat kholi is 0.28487229862475444\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Ratio of virat kholi is\",((326-181)/509))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AB_de_Villiers_Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "AB_de_Villiers_Ratio=Bangalore_Hyderabad.loc[(Bangalore_Hyderabad['batsman']=='AB de Villiers')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    100\n",
+       "0     74\n",
+       "4     35\n",
+       "6     21\n",
+       "2     13\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "AB_de_Villiers_Ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "392"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "AB_de_Villiers_Ratio['batsman_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ratio of AB de Villiers is 0.35714285714285715\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Ratio of AB de Villiers is\",((266-126)/392))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Manish_Pandey_Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Manish_Pandey_Ratio=Bangalore_Hyderabad.loc[(Bangalore_Hyderabad['batsman']=='MK Pandey')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    26\n",
+       "0    19\n",
+       "4     8\n",
+       "2     5\n",
+       "6     2\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Manish_Pandey_Ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "80"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Manish_Pandey_Ratio['batsman_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ratio of Manish Pandey is 0.1\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Ratio of Manish Pandey is\",((44-36)/80))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Saha_Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Saha_Ratio=Bangalore_Hyderabad.loc[(Bangalore_Hyderabad['batsman']=='WP Saha')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    6\n",
+       "4    4\n",
+       "0    4\n",
+       "6    1\n",
+       "2    1\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Saha_Ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Saha_Ratio['batsman_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ratio of Wriddhiman Saha is 0.4666666666666667\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Ratio of Wriddhiman Saha is\",((22-8)/30))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Warner_Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Warner_Ratio=Bangalore_Hyderabad.loc[(Bangalore_Hyderabad['batsman']=='DA Warner')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    124\n",
+       "0    104\n",
+       "4     46\n",
+       "2     33\n",
+       "6     31\n",
+       "3      1\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Warner_Ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "563"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Warner_Ratio['batsman_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ratio of David Warner is 0.31438721136767317\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Ratio of David Warner is\",((370-193)/563))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Williamson_Ratio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Williamson_Ratio=Bangalore_Hyderabad.loc[(Bangalore_Hyderabad['batsman']=='KS Williamson')]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    90\n",
+       "0    42\n",
+       "4    24\n",
+       "6    11\n",
+       "2     5\n",
+       "3     2\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Williamson_Ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "268"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Williamson_Ratio['batsman_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ratio of Williamson is 0.208955223880597\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Ratio of Williamson is\",((162-106)/268))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ratio of David Warner is 0.31438721136767317\n",
+      "Ratio of Williamson is 0.208955223880597\n",
+      "Ratio of Wriddhiman Saha is 0.4666666666666667\n",
+      "Ratio of Manish Pandey is 0.1\n",
+      "Ratio of virat kholi is 0.28487229862475444\n",
+      "Ratio of AB de Villiers is 0.35714285714285715\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Ratio of David Warner is\",((370-193)/563))\n",
+    "print(\"Ratio of Williamson is\",((162-106)/268))\n",
+    "print(\"Ratio of Wriddhiman Saha is\",((22-8)/30))\n",
+    "print(\"Ratio of Manish Pandey is\",((44-36)/80))\n",
+    "print(\"Ratio of virat kholi is\",((326-181)/509))\n",
+    "print(\"Ratio of AB de Villiers is\",((266-126)/392))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# How many batsman from both teams ll have strike rate more than 130 and hit more than 3 boundaries(atleast 4's)?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Virat Kholi Strike Rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Virat_kholi_ratio=Virat_kholi_Ratio.loc[(Virat_kholi_Ratio['noball_runs']==0)&(Virat_kholi_Ratio['wide_runs']==0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "509"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Virat_kholi_ratio['batsman_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    130\n",
+       "0    129\n",
+       "4     50\n",
+       "2     25\n",
+       "6     21\n",
+       "3      1\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Virat_kholi_ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5    66\n",
+       "2    58\n",
+       "6    57\n",
+       "1    56\n",
+       "4    54\n",
+       "3    52\n",
+       "7    10\n",
+       "8     3\n",
+       "Name: ball, dtype: int64"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Virat_kholi_ratio['ball'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Virat_kholi_ratio['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average strike rate of virat kholi is 142.97752808988764\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average strike rate of virat kholi is\",(509/356)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average boundaries(4's) hit by virat kholi in a single match 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average boundaries(4's) hit by virat kholi in a single match\",50//13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AB_de_Villiers Strike Rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "AB_de_Villiers_ratio=AB_de_Villiers_Ratio.loc[(AB_de_Villiers_Ratio['noball_runs']==0)&(AB_de_Villiers_Ratio['wide_runs']==0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "389"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "AB_de_Villiers_ratio['batsman_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    97\n",
+       "0    67\n",
+       "4    35\n",
+       "6    21\n",
+       "2    13\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "AB_de_Villiers_ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4    44\n",
+       "5    39\n",
+       "2    39\n",
+       "1    39\n",
+       "3    34\n",
+       "6    33\n",
+       "7     5\n",
+       "Name: ball, dtype: int64"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "AB_de_Villiers_ratio['ball'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(AB_de_Villiers_ratio['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average strike rate of AB de Villiers is 166.95278969957081\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average strike rate of AB de Villiers is\",(389/233)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average boundaries(4's) hit by AB de Villiers in a single match 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average boundaries(4's) hit by AB de Villiers in a single match\",35//12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Manish_Pandey_Strike rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Manish_Pandey_ratio=Manish_Pandey_Ratio.loc[(Manish_Pandey_Ratio['noball_runs']==0)&(Manish_Pandey_Ratio['wide_runs']==0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "77"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Manish_Pandey_ratio['batsman_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    23\n",
+       "0    19\n",
+       "4     8\n",
+       "2     5\n",
+       "6     2\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Manish_Pandey_ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2    13\n",
+       "3    10\n",
+       "6     9\n",
+       "5     8\n",
+       "1     8\n",
+       "4     6\n",
+       "7     3\n",
+       "Name: ball, dtype: int64"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Manish_Pandey_ratio['ball'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Manish_Pandey_ratio['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average strike rate of Manish Pandey  is 135.08771929824562\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average strike rate of Manish Pandey  is\",(77/57)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average boundaries(4's) hit by Manish Pandey in a single match 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average boundaries(4's) hit by Manish Pandey in a single match\",8//3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# wriddhiman Saha Strike Rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Saha_ratio=Saha_Ratio.loc[(Saha_Ratio['noball_runs']==0)&(Saha_Ratio['wide_runs']==0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Saha_ratio['batsman_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    6\n",
+       "4    4\n",
+       "0    4\n",
+       "6    1\n",
+       "2    1\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Saha_ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    4\n",
+       "6    3\n",
+       "4    3\n",
+       "1    3\n",
+       "5    2\n",
+       "2    1\n",
+       "Name: ball, dtype: int64"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Saha_ratio['ball'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Saha_ratio['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average strike rate of Saha is 187.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average strike rate of Saha is\",(30/16)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average boundaries(4's) hit by Saha in a single match 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average boundaries(4's) hit by Saha in a single match\",4//2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# David Warner Strike Rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Warner_ratio=Warner_Ratio.loc[(Warner_Ratio['noball_runs']==0)&(Warner_Ratio['wide_runs']==0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "558"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Warner_ratio['batsman_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    123\n",
+       "0     93\n",
+       "4     45\n",
+       "2     33\n",
+       "6     31\n",
+       "3      1\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 184,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Warner_ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2    59\n",
+       "3    58\n",
+       "4    55\n",
+       "1    54\n",
+       "5    52\n",
+       "6    40\n",
+       "7     5\n",
+       "8     3\n",
+       "Name: ball, dtype: int64"
+      ]
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Warner_ratio['ball'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Warner_ratio['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average strike rate of Warner is 171.16564417177915\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average strike rate of Warner is\",(558/326)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average boundaries(4's) hit by Warner in a single match 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average boundaries(4's) hit by Warner in a single match\",45//9)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Williamson Strike Rate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Williamson_ratio=Williamson_Ratio.loc[(Williamson_Ratio['noball_runs']==0)&(Williamson_Ratio['wide_runs']==0)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "261"
+      ]
+     },
+     "execution_count": 192,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Williamson_ratio['batsman_runs'].sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    86\n",
+       "0    42\n",
+       "4    24\n",
+       "6    11\n",
+       "2     5\n",
+       "3     1\n",
+       "Name: batsman_runs, dtype: int64"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Williamson_ratio['batsman_runs'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6    30\n",
+       "1    30\n",
+       "4    29\n",
+       "2    26\n",
+       "3    25\n",
+       "5    24\n",
+       "7     5\n",
+       "Name: ball, dtype: int64"
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Williamson_ratio['ball'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(Williamson_ratio['match_id'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average strike rate of Williamson is 154.4378698224852\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average strike rate of Williamson is\",(261/169)*100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average boundaries(4's) hit by Williamson  in a single match 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average boundaries(4's) hit by Williamson  in a single match\",24//5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average strike rate of virat kholi is 142.97752808988764\n",
+      "Average boundaries(4's) hit by virat kholi in a single match: 3\n",
+      "Average strike rate of AB de Villiers is 166.95278969957081\n",
+      "Average boundaries(4's) hit by AB de Villiers in a single match: 2\n",
+      "Average strike rate of Manish_Pandey 135.08771929824562\n",
+      "Average boundaries(4's) hit by Manish_Pandey_ratio in a single match: 2\n",
+      "Average strike rate of Saha is 187.5\n",
+      "Average boundaries(4's) hit by Saha in a single match: 2\n",
+      "Average strike rate of Warner is 171.16564417177915\n",
+      "Average boundaries(4's) hit by Warner in a single match: 5\n",
+      "Average strike rate of Williamson is 154.4378698224852\n",
+      "Average boundaries(4's) hit by Williamson in a single match: 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average strike rate of virat kholi is\",(509/356)*100)\n",
+    "print(\"Average boundaries(4's) hit by virat kholi in a single match:\",50//13)\n",
+    "print(\"Average strike rate of AB de Villiers is\",(389/233)*100)\n",
+    "print(\"Average boundaries(4's) hit by AB de Villiers in a single match:\",35//12)\n",
+    "print(\"Average strike rate of Manish_Pandey\",(77/57)*100)\n",
+    "print(\"Average boundaries(4's) hit by Manish_Pandey_ratio in a single match:\",8//3)\n",
+    "print(\"Average strike rate of Saha is\",(30/16)*100)\n",
+    "print(\"Average boundaries(4's) hit by Saha in a single match:\",4//2)\n",
+    "print(\"Average strike rate of Warner is\",(558/326)*100)\n",
+    "print(\"Average boundaries(4's) hit by Warner in a single match:\",45//9)\n",
+    "print(\"Average strike rate of Williamson is\",(261/169)*100)\n",
+    "print(\"Average boundaries(4's) hit by Williamson in a single match:\",24//5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}