ipl-srh-dc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "%matplotlib inline\n",
    "import missingno as msg\n",
    "import math\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('deliveries.csv')\n",
    "df1 = pd.read_csv('matches.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>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",
       "  </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",
       "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",
       "                         team2                  toss_winner toss_decision  \\\n",
       "0  Royal Challengers Bangalore  Royal Challengers Bangalore         field   \n",
       "1       Rising Pune Supergiant       Rising Pune Supergiant         field   \n",
       "2        Kolkata Knight Riders        Kolkata Knight Riders         field   \n",
       "3              Kings XI Punjab              Kings XI Punjab         field   \n",
       "4             Delhi Daredevils  Royal Challengers Bangalore           bat   \n",
       "\n",
       "   result  dl_applied                       winner  win_by_runs  \\\n",
       "0  normal           0          Sunrisers Hyderabad           35   \n",
       "1  normal           0       Rising Pune Supergiant            0   \n",
       "2  normal           0        Kolkata Knight Riders            0   \n",
       "3  normal           0              Kings XI Punjab            0   \n",
       "4  normal           0  Royal Challengers Bangalore           15   \n",
       "\n",
       "   win_by_wickets player_of_match                                      venue  \\\n",
       "0               0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal   \n",
       "1               7       SPD Smith    Maharashtra Cricket Association Stadium   \n",
       "2              10         CA Lynn     Saurashtra Cricket Association Stadium   \n",
       "3               6      GJ Maxwell                     Holkar Cricket Stadium   \n",
       "4               0       KM Jadhav                      M Chinnaswamy Stadium   \n",
       "\n",
       "          umpire1        umpire2 umpire3  \n",
       "0     AY Dandekar       NJ Llong     NaN  \n",
       "1  A Nand Kishore         S Ravi     NaN  \n",
       "2     Nitin Menon      CK Nandan     NaN  \n",
       "3    AK Chaudhary  C Shamshuddin     NaN  \n",
       "4             NaN            NaN     NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['match_id', 'inning', 'batting_team', 'bowling_team', 'over', 'ball',\n",
       "       'batsman', 'non_striker', 'bowler', 'is_super_over', 'wide_runs',\n",
       "       'bye_runs', 'legbye_runs', 'noball_runs', 'penalty_runs',\n",
       "       'batsman_runs', 'extra_runs', 'total_runs', 'player_dismissed',\n",
       "       'dismissal_kind', 'fielder'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "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": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 1 - win ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['batting_team'] = df['batting_team'].replace({\"Delhi Daredevils\":\"Delhi Capitals\"})\n",
    "df['bowling_team'] = df['bowling_team'].replace({\"Delhi Daredevils\":\"Delhi Capitals\"})\n",
    "\n",
    "df1['team1'] = df1['team1'].replace({\"Delhi Daredevils\":\"Delhi Capitals\"})\n",
    "df1['team2'] = df1['team2'].replace({\"Delhi Daredevils\":\"Delhi Capitals\"})\n",
    "df1['winner'] = df1['winner'].replace({\"Delhi Daredevils\":\"Delhi Capitals\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>20</th>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</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>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KS Williamson</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>CB Gaffaney</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>2017</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2017-05-02</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>YC Barde</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>394</td>\n",
       "      <td>2013</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2013-04-12</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>Aleem Dar</td>\n",
       "      <td>Subroto Das</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>428</td>\n",
       "      <td>2013</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2013-05-04</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>DJG Sammy</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>Asad Rauf</td>\n",
       "      <td>S Asnani</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>468</th>\n",
       "      <td>469</td>\n",
       "      <td>2014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-04-25</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</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>4</td>\n",
       "      <td>0</td>\n",
       "      <td>AJ Finch</td>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>M Erasmus</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>489</td>\n",
       "      <td>2014</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2014-05-10</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>DW Steyn</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>RM Deshpande</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>531</td>\n",
       "      <td>2015</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>2015-04-18</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>JP Duminy</td>\n",
       "      <td>Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...</td>\n",
       "      <td>PG Pathak</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>561</td>\n",
       "      <td>2015</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>2015-05-09</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</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>6</td>\n",
       "      <td>0</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>VA Kulkarni</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>617</th>\n",
       "      <td>618</td>\n",
       "      <td>2016</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>CH Morris</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>K Bharatan</td>\n",
       "      <td>M Erasmus</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>627</th>\n",
       "      <td>628</td>\n",
       "      <td>2016</td>\n",
       "      <td>Raipur</td>\n",
       "      <td>2016-05-20</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>KK Nair</td>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>BNJ Oxenford</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>7929</td>\n",
       "      <td>2018</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>05/05/18</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Daredevils</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>Rashid Khan</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>Bruce Oxenford</td>\n",
       "      <td>O Nandan</td>\n",
       "      <td>Virender Kumar Sharma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>7935</td>\n",
       "      <td>2018</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>10/05/18</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>Anil Dandekar</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>711</th>\n",
       "      <td>11152</td>\n",
       "      <td>2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>04/04/19</td>\n",
       "      <td>Delhi Capitals</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>5</td>\n",
       "      <td>J Bairstow</td>\n",
       "      <td>Feroz Shah Kotla Ground</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>KN Anantapadmanabhan</td>\n",
       "      <td>S Ravi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>11321</td>\n",
       "      <td>2019</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>14/04/19</td>\n",
       "      <td>Delhi Capitals</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>Delhi Capitals</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>Rajiv Gandhi Intl. Cricket Stadium</td>\n",
       "      <td>Anil Chaudhary</td>\n",
       "      <td>Bruce Oxenford</td>\n",
       "      <td>Chris Gaffaney</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  season           city        date                team1  \\\n",
       "20      21    2017      Hyderabad  2017-04-19  Sunrisers Hyderabad   \n",
       "38      39    2017          Delhi  2017-05-02  Sunrisers Hyderabad   \n",
       "393    394    2013          Delhi  2013-04-12       Delhi Capitals   \n",
       "427    428    2013      Hyderabad  2013-05-04       Delhi Capitals   \n",
       "468    469    2014            NaN  2014-04-25  Sunrisers Hyderabad   \n",
       "488    489    2014          Delhi  2014-05-10       Delhi Capitals   \n",
       "530    531    2015  Visakhapatnam  2015-04-18       Delhi Capitals   \n",
       "560    561    2015         Raipur  2015-05-09  Sunrisers Hyderabad   \n",
       "617    618    2016      Hyderabad  2016-05-12  Sunrisers Hyderabad   \n",
       "627    628    2016         Raipur  2016-05-20  Sunrisers Hyderabad   \n",
       "671   7929    2018      Hyderabad    05/05/18       Delhi Capitals   \n",
       "677   7935    2018          Delhi    10/05/18       Delhi Capitals   \n",
       "711  11152    2019          Delhi    04/04/19       Delhi Capitals   \n",
       "725  11321    2019      Hyderabad    14/04/19       Delhi Capitals   \n",
       "753  11413    2019  Visakhapatnam    08/05/19  Sunrisers Hyderabad   \n",
       "\n",
       "                   team2          toss_winner toss_decision  result  \\\n",
       "20        Delhi Capitals  Sunrisers Hyderabad           bat  normal   \n",
       "38        Delhi Capitals     Delhi Daredevils         field  normal   \n",
       "393  Sunrisers Hyderabad     Delhi Daredevils           bat  normal   \n",
       "427  Sunrisers Hyderabad     Delhi Daredevils           bat  normal   \n",
       "468       Delhi Capitals  Sunrisers Hyderabad           bat  normal   \n",
       "488  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "530  Sunrisers Hyderabad     Delhi Daredevils           bat  normal   \n",
       "560       Delhi Capitals  Sunrisers Hyderabad           bat  normal   \n",
       "617       Delhi Capitals     Delhi Daredevils         field  normal   \n",
       "627       Delhi Capitals     Delhi Daredevils         field  normal   \n",
       "671  Sunrisers Hyderabad     Delhi Daredevils           bat  normal   \n",
       "677  Sunrisers Hyderabad     Delhi Daredevils           bat  normal   \n",
       "711  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "725  Sunrisers Hyderabad  Sunrisers Hyderabad         field  normal   \n",
       "753       Delhi Capitals       Delhi Capitals         field  normal   \n",
       "\n",
       "     dl_applied               winner  win_by_runs  win_by_wickets  \\\n",
       "20            0  Sunrisers Hyderabad           15               0   \n",
       "38            0       Delhi Capitals            0               6   \n",
       "393           0  Sunrisers Hyderabad            0               3   \n",
       "427           0  Sunrisers Hyderabad            0               6   \n",
       "468           0  Sunrisers Hyderabad            4               0   \n",
       "488           1  Sunrisers Hyderabad            0               8   \n",
       "530           0       Delhi Capitals            4               0   \n",
       "560           0  Sunrisers Hyderabad            6               0   \n",
       "617           0       Delhi Capitals            0               7   \n",
       "627           0       Delhi Capitals            0               6   \n",
       "671           0  Sunrisers Hyderabad            0               7   \n",
       "677           0  Sunrisers Hyderabad            0               9   \n",
       "711           0  Sunrisers Hyderabad            0               5   \n",
       "725           0       Delhi Capitals           39               0   \n",
       "753           0       Delhi Capitals            0               2   \n",
       "\n",
       "    player_of_match                                              venue  \\\n",
       "20    KS Williamson          Rajiv Gandhi International Stadium, Uppal   \n",
       "38   Mohammed Shami                                   Feroz Shah Kotla   \n",
       "393        A Mishra                                   Feroz Shah Kotla   \n",
       "427       DJG Sammy          Rajiv Gandhi International Stadium, Uppal   \n",
       "468        AJ Finch                Dubai International Cricket Stadium   \n",
       "488        DW Steyn                                   Feroz Shah Kotla   \n",
       "530       JP Duminy  Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...   \n",
       "560    MC Henriques   Shaheed Veer Narayan Singh International Stadium   \n",
       "617       CH Morris          Rajiv Gandhi International Stadium, Uppal   \n",
       "627         KK Nair   Shaheed Veer Narayan Singh International Stadium   \n",
       "671     Rashid Khan          Rajiv Gandhi International Stadium, Uppal   \n",
       "677        S Dhawan                                   Feroz Shah Kotla   \n",
       "711      J Bairstow                            Feroz Shah Kotla Ground   \n",
       "725          K Paul                 Rajiv Gandhi Intl. Cricket Stadium   \n",
       "753         RR Pant                                   ACA-VDCA Stadium   \n",
       "\n",
       "            umpire1               umpire2                umpire3  \n",
       "20      CB Gaffaney              NJ Llong                    NaN  \n",
       "38         YC Barde           Nitin Menon                    NaN  \n",
       "393       Aleem Dar           Subroto Das                    NaN  \n",
       "427       Asad Rauf              S Asnani                    NaN  \n",
       "468       M Erasmus                S Ravi                    NaN  \n",
       "488    RM Deshpande          BNJ Oxenford                    NaN  \n",
       "530       PG Pathak                S Ravi                    NaN  \n",
       "560     VA Kulkarni                S Ravi                    NaN  \n",
       "617      K Bharatan             M Erasmus                    NaN  \n",
       "627  A Nand Kishore          BNJ Oxenford                    NaN  \n",
       "671  Bruce Oxenford              O Nandan  Virender Kumar Sharma  \n",
       "677   C Shamshuddin         Anil Dandekar                 S Ravi  \n",
       "711   C Shamshuddin  KN Anantapadmanabhan                 S Ravi  \n",
       "725  Anil Chaudhary        Bruce Oxenford         Chris Gaffaney  \n",
       "753             NaN                   NaN                    NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "both_team = df1[((df1['team1'] == 'Delhi Capitals') & (df1['team2'] == 'Sunrisers Hyderabad')) | ((df1['team1'] == 'Sunrisers Hyderabad') & (df1['team2'] == 'Delhi Capitals'))]\n",
    "both_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sunrisers Hyderabad    9\n",
      "Delhi Capitals         6\n",
      "Name: winner, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sb.countplot(both_team['winner'])\n",
    "plt.grid()\n",
    "plt.show()\n",
    "print(both_team['winner'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# last 3 seasons\n",
    "recent = both_team[both_team['season'] >= 2017]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sunrisers Hyderabad    4\n",
      "Delhi Capitals         3\n",
      "Name: winner, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize  =(7,7))\n",
    "sb.countplot(recent['winner'])\n",
    "plt.grid()\n",
    "plt.show()\n",
    "print(recent['winner'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toss_decision  toss_winner        \n",
       "bat            Delhi Daredevils       5\n",
       "               Sunrisers Hyderabad    3\n",
       "field          Delhi Capitals         1\n",
       "               Delhi Daredevils       3\n",
       "               Sunrisers Hyderabad    3\n",
       "Name: winner, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "both_team.groupby([ 'toss_decision' , 'toss_winner'])['winner'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toss_decision  toss_winner        \n",
       "bat            Delhi Daredevils       2\n",
       "               Sunrisers Hyderabad    1\n",
       "field          Delhi Capitals         1\n",
       "               Delhi Daredevils       1\n",
       "               Sunrisers Hyderabad    2\n",
       "Name: winner, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recent.groupby([ 'toss_decision' , 'toss_winner'])['winner'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Take Aways:\n",
    "### 1 - Sunrisers have won 9 matches and DC 6\n",
    "### 2 -In the past 3 years , SRH has won 4 and DC 3 match\n",
    "### 3 - In the last face -off , SRH has won the match\n",
    "### 4 -The match is going to be held in Dubai International Stadium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 1 ans - DC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "###################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 2 -Total runs scored ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "        match_id  inning         batting_team         bowling_team  over  \\\n",
       "4810          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4811          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4812          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4813          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4814          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "...          ...     ...                  ...                  ...   ...   \n",
       "178586     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178587     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178588     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178589     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178590     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "\n",
       "        ball    batsman non_striker   bowler  is_super_over  ...  bye_runs  \\\n",
       "4810       1  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "4811       2   S Dhawan   DA Warner  J Yadav              0  ...         0   \n",
       "4812       3   S Dhawan   DA Warner  J Yadav              0  ...         0   \n",
       "4813       4  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "4814       5  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "...      ...        ...         ...      ...            ...  ...       ...   \n",
       "178586     2   A Mishra      K Paul  K Ahmed              0  ...         0   \n",
       "178587     3     K Paul    A Mishra  K Ahmed              0  ...         0   \n",
       "178588     4     K Paul    A Mishra  K Ahmed              0  ...         0   \n",
       "178589     5   A Mishra      K Paul  K Ahmed              0  ...         0   \n",
       "178590     6     K Paul    TA Boult  K Ahmed              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "4810              0            1             0             1           1   \n",
       "4811              0            0             0             4           0   \n",
       "4812              0            0             0             1           0   \n",
       "4813              0            0             0             0           0   \n",
       "4814              0            0             0             1           0   \n",
       "...             ...          ...           ...           ...         ...   \n",
       "178586            0            0             0             1           0   \n",
       "178587            0            0             0             0           0   \n",
       "178588            0            0             0             1           0   \n",
       "178589            0            0             0             0           0   \n",
       "178590            0            0             0             4           0   \n",
       "\n",
       "        total_runs  player_dismissed         dismissal_kind fielder  \n",
       "4810             2               NaN                    NaN     NaN  \n",
       "4811             4               NaN                    NaN     NaN  \n",
       "4812             1               NaN                    NaN     NaN  \n",
       "4813             0               NaN                    NaN     NaN  \n",
       "4814             1               NaN                    NaN     NaN  \n",
       "...            ...               ...                    ...     ...  \n",
       "178586           1               NaN                    NaN     NaN  \n",
       "178587           0               NaN                    NaN     NaN  \n",
       "178588           1               NaN                    NaN     NaN  \n",
       "178589           0          A Mishra  obstructing the field     NaN  \n",
       "178590           4               NaN                    NaN     NaN  \n",
       "\n",
       "[3512 rows x 21 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df_team = df[(((df['batting_team'] == 'Sunrisers Hyderabad')  & (df['bowling_team'] == 'Delhi Capitals')) | ((df['batting_team'] == 'Delhi Capitals')  & (df['bowling_team'] == 'Sunrisers Hyderabad')))]\n",
    "main_df_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>SV Samson</td>\n",
       "      <td>B Kumar</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>4937</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>SV Samson</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178586</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>K Ahmed</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>178587</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Ahmed</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>178588</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Ahmed</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>178589</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>K Ahmed</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>A Mishra</td>\n",
       "      <td>obstructing the field</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178590</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>TA Boult</td>\n",
       "      <td>K Ahmed</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>1823 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning    batting_team         bowling_team  over  ball  \\\n",
       "4933          21       2  Delhi Capitals  Sunrisers Hyderabad     1     1   \n",
       "4934          21       2  Delhi Capitals  Sunrisers Hyderabad     1     2   \n",
       "4935          21       2  Delhi Capitals  Sunrisers Hyderabad     1     3   \n",
       "4936          21       2  Delhi Capitals  Sunrisers Hyderabad     1     4   \n",
       "4937          21       2  Delhi Capitals  Sunrisers Hyderabad     1     5   \n",
       "...          ...     ...             ...                  ...   ...   ...   \n",
       "178586     11413       2  Delhi Capitals  Sunrisers Hyderabad    20     2   \n",
       "178587     11413       2  Delhi Capitals  Sunrisers Hyderabad    20     3   \n",
       "178588     11413       2  Delhi Capitals  Sunrisers Hyderabad    20     4   \n",
       "178589     11413       2  Delhi Capitals  Sunrisers Hyderabad    20     5   \n",
       "178590     11413       2  Delhi Capitals  Sunrisers Hyderabad    20     6   \n",
       "\n",
       "            batsman  non_striker   bowler  is_super_over  ...  bye_runs  \\\n",
       "4933      SV Samson  SW Billings  B Kumar              0  ...         0   \n",
       "4934      SV Samson  SW Billings  B Kumar              0  ...         0   \n",
       "4935    SW Billings    SV Samson  B Kumar              0  ...         0   \n",
       "4936    SW Billings    SV Samson  B Kumar              0  ...         0   \n",
       "4937    SW Billings    SV Samson  B Kumar              0  ...         0   \n",
       "...             ...          ...      ...            ...  ...       ...   \n",
       "178586     A Mishra       K Paul  K Ahmed              0  ...         0   \n",
       "178587       K Paul     A Mishra  K Ahmed              0  ...         0   \n",
       "178588       K Paul     A Mishra  K Ahmed              0  ...         0   \n",
       "178589     A Mishra       K Paul  K Ahmed              0  ...         0   \n",
       "178590       K Paul     TA Boult  K Ahmed              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "4933              0            0             0             0           0   \n",
       "4934              1            0             0             0           1   \n",
       "4935              0            0             0             0           0   \n",
       "4936              0            0             0             0           0   \n",
       "4937              0            0             0             0           0   \n",
       "...             ...          ...           ...           ...         ...   \n",
       "178586            0            0             0             1           0   \n",
       "178587            0            0             0             0           0   \n",
       "178588            0            0             0             1           0   \n",
       "178589            0            0             0             0           0   \n",
       "178590            0            0             0             4           0   \n",
       "\n",
       "        total_runs  player_dismissed         dismissal_kind fielder  \n",
       "4933             0               NaN                    NaN     NaN  \n",
       "4934             1               NaN                    NaN     NaN  \n",
       "4935             0               NaN                    NaN     NaN  \n",
       "4936             0               NaN                    NaN     NaN  \n",
       "4937             0               NaN                    NaN     NaN  \n",
       "...            ...               ...                    ...     ...  \n",
       "178586           1               NaN                    NaN     NaN  \n",
       "178587           0               NaN                    NaN     NaN  \n",
       "178588           1               NaN                    NaN     NaN  \n",
       "178589           0          A Mishra  obstructing the field     NaN  \n",
       "178590           4               NaN                    NaN     NaN  \n",
       "\n",
       "[1823 rows x 21 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_dc = main_df_team[main_df_team['batting_team'] == 'Delhi Capitals']\n",
    "only_dc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_score = only_dc.pivot_table(index = ['match_id'] , values = ['total_runs' , 'batsman_runs'] , aggfunc=['count', 'sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">count</th>\n",
       "      <th colspan=\"2\" halign=\"left\">sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>batsman_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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>169</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>186</td>\n",
       "      <td>189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>125</td>\n",
       "      <td>125</td>\n",
       "      <td>107</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>117</td>\n",
       "      <td>117</td>\n",
       "      <td>74</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>469</th>\n",
       "      <td>123</td>\n",
       "      <td>123</td>\n",
       "      <td>174</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>138</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>158</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>153</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>147</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>628</th>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>147</td>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7929</th>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>163</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7935</th>\n",
       "      <td>121</td>\n",
       "      <td>121</td>\n",
       "      <td>187</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11152</th>\n",
       "      <td>122</td>\n",
       "      <td>122</td>\n",
       "      <td>129</td>\n",
       "      <td>134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11321</th>\n",
       "      <td>124</td>\n",
       "      <td>124</td>\n",
       "      <td>155</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11413</th>\n",
       "      <td>124</td>\n",
       "      <td>124</td>\n",
       "      <td>165</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count                     sum           \n",
       "         batsman_runs total_runs batsman_runs total_runs\n",
       "match_id                                                \n",
       "21                122        122          169        176\n",
       "39                117        117          186        189\n",
       "394               125        125          107        114\n",
       "428               117        117           74         80\n",
       "469               123        123          174        180\n",
       "489               121        121          138        143\n",
       "531               126        126          158        167\n",
       "561               121        121          153        157\n",
       "618               112        112          147        150\n",
       "628               126        126          147        161\n",
       "7929              122        122          163        166\n",
       "7935              121        121          187        190\n",
       "11152             122        122          129        134\n",
       "11321             124        124          155        164\n",
       "11413             124        124          165        170"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['match_id', '('count', 'batsman_runs')', '('count', 'total_runs')',\n",
       "       '('sum', 'batsman_runs')', '('sum', 'total_runs')'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_score = pd.DataFrame(dc_score.to_records())\n",
    "dc_score.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CoUAvItGn+4Jdm37LJyE1Z8amrX2jvHGxhz+sKmG6boczLre99996CC6+Oqvh3rIsj4zkBH6r2vcyDxToRST67PkauBLh5k/7bfqbo3btrg9sLA7862z8E0hfBLv/OfC+V0lOcLNj1UJ+d6qDKa9Kw8rcUqAXkaiSNN4Lh38MVX8CmYV+2794soN1xVmU56cH/sUSU+Dmh+HCS3ZOwCy8Z10hl4YmONTYO6vriARKgV5EosqizldhasLRbL53eIIDDb2883qV8Jyq+aSdC/D614O/BrBj1UIS3YbfntTyvcwtBXoRiSoFHa9AcfWMVfB8Xj3XhdeCd6wpCP4LpubAhg/ByWfsY3CDlJWSyM1L8/jtiXad7CZzSoFeRKJHx0kyhy7CxvscNX/pVCf5GUlsKLn+kbWObbwPPKOzftTuPesKqe8e4XxnfB1dK/NLgV5EosfRn2LhgnUf9NvUM+Vl19ku7li5CJeTIjkzKd0CuRVw5MlZXebd0ysLWr6XuaRALyLRweuFY0/Rs6AaMhb6bX6wsY/+0UneuWYW+/M+xsCGD9uP2fW3BH2ZwuwUNpRm8/JplcOVuaNALyLRof73MNBCe+EOR81fPt1Jgstw64r80Hz9DR8CLDj+9Kwus315Pkea+hga94RmXCJ+KNCLSHQ4+jNIyqQ7b6uj5i+f7mBrxQKyUkJ0YlzeMnsJ/8hPZ3WZ7cvy8Xgt9l3sCc24RPxQoBeRyDcxAid/DWvvwetO9tu8uXeEsx1DvGM2j9Vdz4YPQ+cJaD8W9CU2L8klye1SOVyZMwr0IhL5zjwLE4Ow8cOOmr8yvQce8kC/7oPgSoCjwc/qU5PcVC/JYff57hAOTOTGFOhFJPId/SlklcKSWx01f+l0J+V5aSxdmBHacaTnwYr3wLGnZ3V87bZl+ZxsG6B3eCKEgxO5PgV6EYlsQ11w/iXY8Mfg8v8ja2TCw54L3bxj9SyK5Mxkw4dgsA0u7gr6EtuX5wHwep1m9RJ+CvQiEtnOPAvWFKy/11HzPee7mfB4Q79s77NyJyRn28mBQdpQmkN6klv79DInFOhFJLKdfR6yF0PBOkfNd53tIi3JzdaKBeEZT2IKrP4D+xeQqcngLuF2sbViAXu0Ty9zQIFeRCLX5ChceAVW3WUXrXFgz4VL3FSxgKSEMP54W7UTxvqhcW/Ql9i+PJ+6S8O09Y+GcGAib6dALyKR6+Krdo35le911Ly9f4wLXcNsWxaiIjk3suwd4E6yVxuCdMsye59es3oJNwV6EYlcZ5+HpAwov81R89fr7D1vXxANm+TpMZ15FoI8iW5NYRa5aYnsuaBAL+GlQC8ikcmy4OwLsOxOSPBfJAfs2XFOWiJri7LCPDjs5fueOrh0LqjuLpfhlmV57LlwScfWSlgp0ItIZGo/CgMtdpa7A5ZlsedCN7cszZv9aXVOrLzLfj37XNCX2LYsn7b+Meq7R0I0KJG3U6AXkch05nnA2AVqHGjqGaWlb5Rt4V6298kpg4JKODObQG+Pdfd5PWYn4aNALyKR6exzUFrj6EhagN0XfPvzYU7Eu9qqndD0BowEd0BNRX46RdkpKpwjYaVALyKRZ7AdWg9dWR53YM+FbhZlJrNsYXoYB3aNVXeB5YVzvw2quzGGmvIFHGzoDfHARK5QoBeRyHP2BfvVYaC3LIvXL1xi27I8jMPn7UOiaBNkFNjZ90HavDiHtv4xWvv0PL2EhwK9iESes89DdpnjanjnOoe4NDTBtuVzuGwPdu39lXfB+ZfBE9wBNZuX2BX8DmhWL2GiQC8ikcVXDW+l82p4vmS2OUvEu9qqnfYRug2vBdV9dVEmqYluBXoJGwV6EYks9a9NV8MLbH9+8YI0SnPTwjiwG6i4AxJSgs6+T3S72FiWzcFGBXoJDwV6EYksdbXgToby7Y6aT3kt9tZ1z89sHiApDZbuCDohD2DzklxOtA4wMuEJ2bBEfBToRSSy1O2Csq2QmOqo+YnWfgbHPOEvezuTZe+A3nr7vyBsXpLLlNfiaHN/SIclAgr0IhJJhi9BxzFYeofjLr5a8fMa6Cumx1u3K6jum8pyASXkSXgo0ItI5Lj4qv1ascNxl30Xe1i6MJ1FmSnhGZMTC1dBRqG97RCE3PQkli1M1/P0EhYK9CISOS7uguQsKN7kqLnXa7G/oZct04+ozRtj7H36i7vA6w3qEpuX5HKgsRevVwfcSGgp0ItI5KjbBUu2gzvBUfNznUP0j06ypWKeAz3Y2w0j3dB5Iqjum5fk0jcySd2l4RAPTOKdAr2IRIa+Rui9CBW3O+6yr96uMb+lPDdco3Lu8j59bVDdfYVztHwvoaZALyKRwZfIFkAi3r76HhZlJrN4wTw8P3+t7BLIXxl0oF+an05OWqIS8iTkFOhFJDJcfBXSF8KitY677K/vZUv5grmtbz+TijugYU9Q5XBdLkP1YnufXiSUFOhFZP5Zlp3IVnG747K3LX32+fM1kbBs77N0B0yOQPO+oLpvXpLL+c4h+kaCq5svcj0K9CIy/7rOwFDHlX1uB/Zf3p+PgEQ8n/JbwbiCXr6vXmz/0nKosS+Eg5J4p0AvIvPvYnD78xnJCawpygrToIKQmmM/GngxuMI5G8uycbsM+xt6QjwwiWcK9CIy/+p2Qc4SyC133GV/fS/VS3JxuyJkf95n6Q5o3g9jAwF3TUtKYHVhJkeaVApXQkeBXkTm15THPrEugNl8/8gkZzoG2bIkgvbnfSruAGsKGnYH1X1DaTZHm/uwLBXOkdBQoBeR+dV2BMb7A9qfP9DYg2VBTSTtz/uU3WQfWxtk3fsNpTkMjHlo6B4J8cAkXinQi8j8qv+9/RpAoZw3L/aS6DZUleWEaVCzkJgCi28JOiGvsiQbgKMtWr6X0HAU6I0xdxljzhhjzhtjHr3O+8YY87Xp948aY6qveu97xphOY8zxa/p8xRhzerr9L40xEfgvVkTCrnEv5C2HjEWOu+yv72F9STapSe4wDmwWym+FrlMwEnhS3arCTJISXBxtUua9hIbfQG+McQPfAHYCa4H7jTHXVrTYCayY/u9B4FtXvfd94K7rXPpFYL1lWRuAs8DnAx28iEQ5rxcaX4fFNzvuMjFln9u+NRKX7X2WbLNfG18PuGui28XaoizN6CVknMzotwLnLcuqsyxrAngSuOeaNvcAP7Rse4EcY0wRgGVZrwJv+7XWsqzfWpblmf5wL1Aa7DchIlHq0hkY64PF2xx3udjvZWLKG5n78z7F1eBOsqvkBWFjaTbHW/qZ0kl2EgJOjogqAZqu+rgZuMlBmxKgzeE4Pgn89HpvGGMexF4loKCggNraWoeXjA9DQ0O6J/NE9372ilueYyWwt93FmMN7ebxjFDCMNZ+ktvNUOIc3K1UZy3Edf4GDye8OuG/i0CQjE1M8+ewrlGRERiqV/r5HLyeB/noPqV77a6aTNte/uDF/C3iA/7je+5ZlfRv4NkBNTY21Y8cOJ5eNG7W1teiezA/d+xD4+b9DRgE333Wf49K3/3zweZYuTOHu9+wI79hma2onvPZVdtxSA8kZAXUt6RjkO8deJaVoJTs2R8Zip/6+Ry8nvyo2A2VXfVwKtAbR5m2MMR8H3g98xNJDoyLxp3GvnaHuMMhblsX5vik2lUXg8/PXWrzNfp4+iLr3SxdmkJbk5mizEvJk9pwE+n3ACmNMhTEmCbgPeOaaNs8AH5vOvr8Z6Lcsa8Zle2PMXcBfAx+wLEsPjIrEm74m6G+yA71DjT0jDE5A9ZIoeEinbKtd9z6IhDy3y7C+OFsJeRISfgP9dMLcI8ALwCngZ5ZlnTDGPGSMeWi62bNAHXAe+Ffg077+xpifAK8Dq4wxzcaYP5t+6+tAJvCiMeawMeaJUH1TIhIFGvfar0ucB/qD00e4+g5/iWgpWVCwPuiEvA2l2ZxsHWByyhvigUm8cbJHj2VZz2IH86s/98RVf7aAz9yg7/03+Pxy58MUkZjTuAeSMu1g6NChxj5S3LCyIDOMAwuhJdvgwA/s8+kTkgLqWlmazbjHy9mOQdYVZ4dpgBIPIiOdU0TiT8Pr9vK2y3nRm4ONvVRkuyLvIJsbWbINPKPQdjjgrhtL7e2Jo81avpfZUaAXkbk30mNXjgtg2X5kwsOptkGW50RoNbzr8eUfBLF8vyQvjayUBAV6mTUFehGZe01v2K8BFMo52mwXkFmWE0U/tjIW2eV9g0jIM8awoTSHYy3KvJfZiaJ/MSISMxpfB1cilFT7bzvtUKMd8KJqRg/2rL5xr13uN0CVpdmcbhtkbHIqDAOTeKFALyJzr+F1O8gnpjrucrCxl4r8dDKSomR/3mfJdrvMb1fgVfw2lGTj8Vqcbh8Mw8AkXijQi8jcmhyF1kMBPT9vWRaHGnvZtDgKnp+/1pLg9+k3lPkS8rR8L8FToBeRudVyALyTAQX6pp5RLg1NRMfz89fKWQKZxUEF+uLsFPLSk5SQJ7OiQC8ic8tXKKdsq+Muh5qiqFDOtYyxZ/WNr0OAlb6NMawtzuJk60CYBifxQIFeROZW8z7IXwlpzo+ZPdjQS1qSm5UFgR0OEzHKbobBNuhvDrjruuJsznUOMuFRhTwJjgK9iMwdy7IDfanz2TzAwcY+NpbmkOCO0h9ZZVvs1+Y3A+66rjiLySmLsx1KyJPgROm/GhGJSj11MNJ9JfA5MDoxxam2geg4yOZGCtZDQio0BX6S3driLABOtmn5XoKjQC8ic8d3ZGup80B/rKUfj9eKzv15H3ciFG8KakZfkZdOWpJb+/QSNAV6EZk7TW/aB9ksXO24i+/EuqqyKJ7Rg72K0XYUJscC6uZyGdYUZXGiVZn3EhwFehGZO837oHRzQAfZHGnqY/GCNPIyksM4sDlQutV+rLDtSMBd101n3nu9gWXti4ACvYjMlYlh6DgR0LI9wOGmvuifzcOVxwmDWL5fW5TF8MQUjT0jIR6UxAMFehGZGy0HwZoKKOO+Y2CMtv6x2Aj0GYvs4jlNwWTe2+fRn9A+vQRBgV5E5sblRLwax10ON9mlXzfGQqAHe1bfvC/gwjkrCjJIcBnt00tQFOhFZG4077OPbA2gUM7hpj4S3YZ104+YRb3SrUEVzklJdLN8UYYesZOgKNCLSPgFWSjncGMfa4qySEmMsqNpb8S3mtEc3PP0WrqXYCjQi0j49dbDcFdAy/ZTXoujzXZFvJhRWGkXzgki0K8rzqZrcJzOwcAezxNRoBeR8PMFtgAOsrnQNcTwxFRsJOL5+ArnBJGQt7ZoukKeZvUSIAV6EQm/5n2QmA6L1jrucrjRTsSrisYz6GdStsV+lj7Awjm+UrhavpdAKdCLSPg1vQkl1QEVyjnU1EdmSgIVeelhHNg8CLJwTnZqImULUjWjl4Ap0ItIeE2MQMfxgJbtwa6IV1WWg8tlwjSweTKLwjnrirL1iJ0ETIFeRMKr7TB4PQFl3I9OTHGmYzC29ud9ZlE4Z21xFvXdIwyNe8IwMIlVCvQiEl6+gBZAxv2xln6mvFZsZdxfzVc4J0C+egKn9Dy9BECBXkTCq+UA5JZDer7jLkeaYjQRz6d0i104Z6A1oG6XS+G2aPlenFOgF5HwajkIJc5n82BXxCvNTSU/2k+su5GSzfZry4GAuhVkJbMgPUkV8iQgCvQiEj6D7TDQfCWwOXS4qS926ttfT8F6cCUGHOiNMawpyuR0+2CYBiaxSIFeRMLHF8gCCPSdg2O09I2yKZYDfWIKFK4PONADrC7M4kz7IFM6m14cUqAXkfBpOQCuBCja4LjLkSZ7/zkmM+6vVrIZWg6B1xtQt9WFmYx7vNR3D4dpYBJrFOhFJHxaDkDBOkhMddzlSFMfbpe5nHgWs4qrYWIQus8F1G3NdCnc021avhdnFOhFJDy83ulEvMD2548097GqIJPUpBg5se5GgkzIW74oA7fL6BE7cUyBXkTCo/s8jA8EFOgty+JIrCfi+eSvgKTMgAN9SqKbpfnpnG5XoBdnFOhFJDwuJ+I5f7SuvnuEgTEPVWUxvmwPdt3/4ip71SNAq4uyOKWle3FIgV5EwqNlvz1jzV/huIuvUM6GWK2Id62SzdB+DDzjAXVbU5RJS98oA2OTYRqYxBIFehEJj4TavJcAACAASURBVJYD9ow1gBPrDjf1kZroZsWijDAOLIKUbLZPsms/HlC3NYVKyBPnFOhFJPQmx+zgFUQiXmVJNgnuOPnRFGRC3uqiTADt04sjcfKvSUTmVPsxe6YawEE2Ex4vJ1oH2BgP+/M+WcWQURhwoC/MSiE7NVH79OKIAr2IhF4QFfHOdgwy4fHGR8a9jzHThXOCLYWrGb34p0AvIqHXcgAyi+wZq0OHpxPxYvZo2hspqbaL5oz2BdTNVwrXq1K44ocCvYiEXsuBwPfnm/pYkJ5Eaa7zKnoxwXefWg8F1G1NUSYjE1M09oyEYVASSxToRSS0Rnqg50JQiXgbS7MxxoRpYBGqeJP9GmhCni/zXsv34ocCvYiEVut0AZgAAv3QuIdznUPxtT/vk5oDeSsCLpyzsiATl0EJeeKXAr2IhFbLQcDYz9A7dLylH8siPgM9TCfk7QfL+X57apKbcpXCFQcU6EUktFoOQP5KSHH+mNyReE3E8ymphqEOGGgNqNuaQpXCFf8U6EUkdCxr+sS66oC6HWnuo2xBKgvSk8I0sAjn26dvOxxQt9WFmTT2jDA07gnDoCRWOAr0xpi7jDFnjDHnjTGPXud9Y4z52vT7R40x1Ve99z1jTKcx5vg1fRYYY140xpybfs2d/bcjIvNqoAWGO+2z1gNwpKk/fmfzAAXrwbiDyLy3E/LOtGtWLzfmN9AbY9zAN4CdwFrgfmPM2mua7QRWTP/3IPCtq977PnDXdS79KPCSZVkrgJemPxaRaOZLKAtgRt81OE5L3yhV8bo/D5CUBovWBhzoVQpXnHAyo98KnLcsq86yrAngSeCea9rcA/zQsu0FcowxRQCWZb0K9FznuvcAP5j+8w+APwzmGxCRCNJyAFwJ9gzVoaPN0/vz8RzowU5ebD0UUEJeSU4qmSkJnGpToJcbcxLoS4Cmqz5unv5coG2uVWBZVhvA9OsiB2MRkUjWetAO8okpjrscaerDZWBdcVYYBxYFijfBSDf0N/lvO80Yw6qCTM62D4VxYBLtEhy0uV71imt/5XTSJijGmAextwMoKCigtrY2FJeNGUNDQ7on80T3/hqWl1sb99NRcDvnArgvLx8doyTDxZt7XnPUPlbve+aAl83A8Rf/nUsLtznv5x3njTYPr7zySliLDcXqfY8HTgJ9M1B21celwLXPgDhpc60OY0yRZVlt08v8nddrZFnWt4FvA9TU1Fg7duxwMOT4UVtbi+7J/NC9v8alc7BrhJKa91NSvcNRF8uy+O+7XmTn+kJ27NjgqE/M3nfPLXD486zPnYAAvr/G5Hpe+fUJVlffTFF2+MoHx+x9jwNOlu73ASuMMRXGmCTgPuCZa9o8A3xsOvv+ZqDftyw/g2eAj0//+ePArwMYt4hEmiAS8Rq6R+gfndT+PEBCMhSsCzghb1WBLyFPmfdyfX4DvWVZHuAR4AXgFPAzy7JOGGMeMsY8NN3sWaAOOA/8K/BpX39jzE+A14FVxphmY8yfTb/1JeDdxphzwLunPxaRaNV6EBLTIH+V4y5HmuO8UM61ijcFnJDnq3mvR+zkRpws3WNZ1rPYwfzqzz1x1Z8t4DM36Hv/DT7fDbzT8UhFJLK1HICijeB29GMFsI+mTUl0sbIgI4wDiyLFm+DAv0HvRViw1FGX7LRECrNSFOjlhlQZT0Rmb2oS2o8FfGLd0eZ+KkuySXDrRxFw1Ul2gR1ws6owU0v3ckP61yUis9d5EjxjVwKVA5NTXo639LNBy/ZXLFoD7uTAC+cUZnKhcwjPlDdMA5NopkAvIrMXRCLemfZBxj1eJeJdzZ0IhZXQGljN+1WFmUxMeanvHg7TwCSaKdCLyOy1HoTUXMitcNzFl4hXpRn9WxVvsg+38Tqfna8qVOa93JgCvYjMXsshO0AFULDlSFMfuWmJlC0I37PfUal4E0wMQfd5x12WLczA7TJKyJPrUqAXkdmZGLH36IM5sa4sJ6zV3KKSL88hgH36lEQ35XlpmtHLdSnQi8jstB8FayqgjPvhcQ/nOgf1/Pz15K+06xEEnJCXpRm9XJcCvYjMThCJeMdb+vFaxPfRtDfiToDCDYFXyCvMpLFnhOFxT5gGJtFKgV5EZqf1IGQWQ2ah4y6+RLwNpdnhGlV0K95kr5RMOQ/avoS8c506yU7eSoFeRGan5WBAs3mw9+dLc1PJy0gO06CiXPEmmByBS2cdd1k9HejPtOtsenkrBXoRCd5oL/RcCKhQDtilb/X8/AyKq+zXNufP05flppGW5FZCnryNAr2IBM+3jxxAIl7X4DgtfaN6fn4mecshMT2gwjkul2FFQaYS8uRtFOhFJHi+RLwAZvRHtT/vn8sNRRsCmtEDrCrIUKCXt1GgF5HgtR6CBcsg1fns/HBTH26XoVKBfmZFVfZBQd4px11WFWbRPTxB1+B4GAcm0UaBXkSCF0Qi3uGmPlYWZJKW5Pw427hUXDWLhDzN6uUKBXoRCc5AGwy2BlQRz+u1ONLUR1WZZvN+FU0n5AWwT3+l5r0y7+UKBXoRCU6rr1CO80S8i93DDIx5VCjHifwVdkJeAPv0+RnJ5GckcbZDM3q5QoFeRILTchCM2z5W1aHDjdMn1pXlhmtUscPlDurI2pUFmZzpUNEcuUKBXkSC03oQFq2FpDTHXQ439ZGe5Gb5oowwDiyGFFfZFfICSMhbWZDJuY5BvF4rjAOTaKJALyKBs6zpRLzAC+VsKM3B7dKJdY4U+RLyzjnusrowk5GJKVr6RsM4MIkmCvQiErieOhjrC2h/fmxyilNtA1Qt1v68Y0FUyFt5OSFP+/RiU6AXkcD5KuIFkHF/onUAj9fS0bSBCOLI2hXT2yJKyBMfBXoRCVzLQUhIgUVrHHc53GQn4m3SjN65IBLyMlMSKclJ1bP0cpkCvYgErvWgfWa6O9Fxl8NNfRRlp1CQlRLGgcWgosAT8lYXqua9XKFALyKBmfLYM8yAK+L16vn5YBQHnpC3sjCTC11DTHi8YRyYRAsFehEJTNdp8IwGlIjXPTROU8+oAn0wigJPyFtVkInHa1HfPRymQUk0UaAXkcD4KuIFkIh3pNlXKEeBPmD5KyEhNaB9+pUFqnkvVyjQi0hgWg5CcjYsWOq4y+HGPlwG1peoxn3A3Al2Ql4AM/pli9Jxu4wCvQAK9CISqJYDdqEcl/MfH4emT6xLT9aJdUEproI25wl5yQluKvLTOaNH7AQFehEJxOQodJwIaH/ed2KdHqubhaIqmByG7vOOu6wqyNSz9AIo0ItIINqOgjUVUKCv14l1s3e5Qt4Rx11WFmTS2DPCyIQnTIOSaKFALyLOtRywXwMI9Id0Yt3s5a8KOCFvVWEGlgXndJJd3FOgFxHnWg5AVglkFjrucqipl4zkBJ1YNxvuBChcH9gjdoVZANqnFwV6EQlAy4GAC+UcbOijqkwn1s1a0cbphDxnRXAWL0gjOcHFWWXexz0FehFxZqQHei8GtGw/MuHhdPsA1UrEm72iKpgYtE8OdMDtMqwoyNCMXhToRcShy/vzNY67HGnqx2vBpsXan5+1YI6sLVDNe1GgFxGnWg4A5krAceBgYy+ginghsXA1uJMDLoXbOThO7/BEGAcmkU6BXkScaTlgB5vkTMddDjX2sTQ/ndz0pDAOLE64E6FgXYCZ9/b/Kz1PH98U6EXEP8uaTsRzvj9vWRaHGnu1bB9Kvgp5luWouS/Qa58+vinQi4h/fQ0w0h1Qxn1TzyjdwxNUL9GyfcgUVcF4v+OEvMKsFDJTErRPH+cU6EXEvyAK5fj25zepUE7oFG20Xx1WyDPGqBSuKNCLiAMtB+1EsIJ1jrscbOwlLcl9eflYQmDRWnAnBVg4x868txwu90vsUaAXEf9aDtizSXei4y6HGvvYWKpCOSGVkGQH+wAT8gbGPLQPjIVxYBLJFOhFZGZTHjuwBLBsPzoxxam2Ae3Ph0PRRnvp3uEMfWXBdEKe9unjlgK9iMys8yR4RgMK9Eeb+/B4Le3Ph0NxFYz12QmSDqwq0CN28U6BXkRmdjkRz3nG/aEm+8Q6nUEfBkXTBYscLt/npiexKDOZ05rRxy0FehGZWcsBSM2FBUsddznY0Et5Xhp5GclhHFicKlgHroSAzqZfVajM+3imQC8iM2s5AMXVYJwl1VmWxaGmPhXKCZeEZFi0JuBSuOc6hpjyKvM+HjkK9MaYu4wxZ4wx540xj17nfWOM+dr0+0eNMdX++hpjqowxe40xh40x+40xW0PzLYlIyIwNQOcpKHP+z7O5d5SuwXGdWBdORVX20r3ThLzCTMY9Xhq6h8M8MIlEfgO9McYNfAPYCawF7jfGrL2m2U5gxfR/DwLfctD3y8DfW5ZVBXxh+mMRiSStBwELSp2fWHe5UI5m9OFTXAWjPdDf5Kj5atW8j2tOZvRbgfOWZdVZljUBPAncc02be4AfWra9QI4xpshPXwvImv5zNtA6y+9FREKteZ/9GkDG/f56u1DOahXKCR9fQp7DffrlizIwBs60D4VxUBKpnAT6EuDqXxubpz/npM1Mff8H8BVjTBPwGPB558MWkTnRvB/yV9rJeA7tb+hl0+IcEtxKAQqbgnVg3I4z79OSEli8II0zHQNhHphEogQHba6XgXPtxtCN2szU92Hg/7As6+fGmA8B3wXe9bYvbsyD2NsBFBQUUFtb62DI8WNoaEj3ZJ7E/L23LLZd3EN3Xg1nHH6fox6L020jfGBZYtjuTczfd4dq0soYP/Eyx9y3OWqf5x7nUF1H0PdO9z16OQn0zUDZVR+X8vZl9hu1SZqh78eB/z7956eA71zvi1uW9W3g2wA1NTXWjh07HAw5ftTW1qJ7Mj9i/t731MGuAYpq7qaoZoejLq+e7cLiTe7dsYnbViwMy7Bi/r471XcrGWefZ8cddzh6IuLAxBm+WXuBm7ffRkqiO+Avp/sevZysre0DVhhjKowxScB9wDPXtHkG+Nh09v3NQL9lWW1++rYCd0z/+R3AuVl+LyISSs377dfSLY677G/oxWWUiDcniqtg5BIMtDhqvrIgkymvxYUu7dPHG78zesuyPMaYR4AXADfwPcuyThhjHpp+/wngWeB9wHlgBPjETH2nL/3nwD8bYxKAMaaX50UkQjTvg8R0WLjGcZcDDT2sLswiI9nJYqHMytUV8rJL/Ta/OvN+XXF2OEcmEcbRv0bLsp7FDuZXf+6Jq/5sAZ9x2nf6868BzlN5RWRuNe+zy966nQVtz5SXQ4193LvZf9CREChcbyfktR2GNe/327w8P51Et1HmfRxSWqyIvN3kKLQfC2jZ/nT7ICMTU2xeomX7OZGYCgtXO868T3S7WLYwQ8/SxyEFehF5u7Yj4PUEtj9f3wNATfmCcI1KrlVcZc/oHVbIW1WYqeNq45ACvYi8na9QTgAV8fY19FKUnUJJTmqYBiVvU1QFw10w4Kze2MqCTFr6RhkcmwzzwCSSKNCLyNs174OcJZCxyFFzy7I4UN+rZfu5VuyrkOds+f5KQp726eOJAr2IvF3z/oCW7Vv6RmkfGKNGgX5uFawH43K8T7+ywA70Wr6PLwr0IvJW/S32s9kBBPoDDfZBNtqfn2NJaZC/yvGMvjQ3lfQkN2faVQo3nijQi8hbtQRRKKe+l3QdZDM/ip0fWWuMYWVhJqc1o48rCvQi8lbN+8CdDIWVjrvYB9nk6iCb+VBUBcOdMNjmqPnqwizOdAxiOczUl+inf5Ui8lZN+6BoAyQkOWo+ODbJmfYBJeLNl+KrKuQ5sLowk76RSToHx8M4KIkkCvQicoVnHFoPQdlNjrscbOzDa0FNuQL9vCistBPyHO7Tr5reXtHyffxQoBeRK9qOwNR4QIH+zYvduF2Gah1kMz+S0iF/ZUAzekAJeXFEgV5Ermjca78uvtlxlzcv9rC+JJt0HWQzf4qqHM/oc9KSKMhK5nSbZvTxQoFeRK5oegNyKxwXyhmbnOJIUz83V+ixunlVXAVDHTDgLCFvVWGWlu7jiAK9iNgsy57RBzCbP9TYx8SUl60K9PPr8pG1hxw1X12YyfmuITxT3jAOSiKFAr2I2HrqYORSgPvzPRijQjnzLtCEvIJMJjxe6ruHwzwwiQQK9CJia3rDfg0k0Nd3s6Ywi+zUxDANShxJzrAr5DlMyFPmfXxRoBcRW+NeSMm2zzh3YMLj5UBDr5btI0XxJnvp3kEhnOWLMnC7jGrexwkFehGxNb0BpVvB5ezHwrGWfsYmvdy8VIE+IhRPV8hzcGRtSqKb8rw0zejjhAK9iMBID3SdhsXOl+3fuNgNwBbtz0eG4k32q9OEvKIszejjhAK9iNj17QHKAnt+fvmiDPIyksM0KAlIwXowbueBviCTxp4Rhsc9YR6YzDcFehGxl+2NG0qqHTWf8lrsr+/lJu3PR46kNFi0JuBSuGc7NKuPdQr0IgKNb9gH2SSlO2p+snWAoXGPEvEiTXGV44S81YVZAFq+jwMK9CLxbmoSWg4EtGzv25+/qSIvXKOSYBRvgpFu6G/y27Q0N5W0JLcS8uKAAr1IvGs7Cp7RgBLx3rzYw5K8NAqzU8I4MAlYkfOEPJfLsLIgk9M63CbmKdCLxLum6YNsHM7ovV6LN+t72Kps+8hTsA5cCQGVwj3TPojlYKlfopcCvUi8a3oDshdDVpGj5uc6h+gbmeSmpVq2jziJKbBobUAV8npHJukaHA/zwGQ+KdCLxDPLgobXA1q233PhEoAy7iNVABXyVAo3PijQi8Sz7gt2NbUl2x132XOhm8UL0ihbkBbGgUnQijfBWB/01vtt6su81z59bFOgF4lnDa/Zr+W3OmrumfKyt66b7cu1bB+xAqiQtyA9iYKsZE63aUYfyxToReJZ/W5IXwR5yx01P946wOCYh1uW5Yd5YBK0RWvBneQ4IW9NURantHQf0xToReKVZUHDbliyDYxx1MW3P3+LEvEiV0KSnX3vsELe6sIszncOMuHxhnlgMl8U6EXiVV8DDLQ4XrYH2HO+m1UFmSzMVH37iFa8CVqPgNd/8F5TlMnklEXdpaE5GJjMBwV6kXjVsMd+XbLNUfNxzxT76nvYpv35yFe8Ccb7ofei36ZriuyEvFNtSsiLVQr0IvGqfjek5sLCNY6aH2zoY9zjZZv25yOfLyGv5aDfpkvz00lyuzilhLyYpUAvEq8aXrMfq3M5+zHw+oVLuAzctFTPz0e8hashIRVa/Qf6BLeLFQUZmtHHMAV6kXjU32I/Z+1w2R5g94VuKktzyEpJDN+4JDTcifZphA5m9DCdea8ZfcxSoBeJR5f3550Vyhka93CkqY/ty7Q/HzWKq6HtCEx5/DZdXZjJpaFxlcKNUQr0IvGoYTckZ0FhpaPm+y724PFa2p+PJiXV9qmEXaf9Nl1bpAp5sUyBXiQeNeyGxTeDy+2o+Z4Ll0hyu6gpzw3zwCRkiqvtVwf79KuVeR/TFOhF4s1QF1w6G1B9+93nu6lekkNKorNfDCQCLFgKKdnQcsB/U5XCjWkK9CLxpmG3/eow0PcOT3CybYDtWraPLi6X/ZhdAAl5JzWjj0kK9CLxpmE3JKZDcZWj5q+dt8vebluuQB91iquh8yRMjvlturowiwtdQyqFG4MU6EXiTf1uKNtiP4LlwKtnu8hOTaSqLCfMA5OQK6kGrwfaj/lt6iuFe6FLpXBjjQK9SDwZ6oLOE1Bxu6PmlmXx6rkubl2Rj9vl7OAbiSC+hDwH+/RrlHkfsxToReLJxV32a8UOR83PdAzSMTDOHSsWhm9MEj5ZxZBR6CjzXqVwY5cCvUg8ubgLkrMd78/vOtMFwO0rFeijkjH28r2DhDyVwo1dCvQi8aSuFipuc/z8/KvnulhVkElhdkp4xyXhU1wN3edgrN9vU5XCjU0K9CLxouci9DXC0h2Omg+Pe9h3sZc7Vmk2H9VKpk+yaz3kt+maoiyVwo1BjgK9MeYuY8wZY8x5Y8yj13nfGGO+Nv3+UWNMtZO+xpi/nH7vhDHmy7P/dkTkhupq7delOxw131vXzcSUl9u1Px/dLifk+V++X1OYCSghL9b4DfTGGDfwDWAnsBa43xiz9ppmO4EV0/89CHzLX19jzJ3APcAGy7LWAY+F4hsSkRuoq4XMYshb7qj5q2e7SE10q+xttEtbALkVjhLyfJn3J1sV6GOJkxn9VuC8ZVl1lmVNAE9iB+ir3QP80LLtBXKMMUV++j4MfMmyrHEAy7I6Q/D9iMj1eL1w8VV7Nm+cPSa362wXNy9doLK3saCkGlr8L93npidRnJ3CCQX6mOIk0JcATVd93Dz9OSdtZuq7ErjNGPOGMWaXMWZLIAMXkQB0HIPRHlh6h6PmDd3D1HePcIey7WNDcTUMNMNgh9+ma4tVCjfWJDhoc71f/y2HbWbqmwDkAjcDW4CfGWOWWpb1lmsbYx7E3g6goKCA2tpaB0OOH0NDQ7on8ySa7n1Z4y9ZBuxpT2Sit9Zv+5caJwFI6btIbW1DeAcXoGi675Eiu8+wCTj2wg/ozt86Y9v0iQkudE7ywkuvkOy+8iNc9z16OQn0zUDZVR+XAq0O2yTN0LcZ+MV0YH/TGOMF8oGuqy9sWda3gW8D1NTUWDt27HAw5PhRW1uL7sn8iKp7/6OvQf4qtr33jxw1//cf7KdswQAfft+dGIdL/XMlqu57pJjYAkf+bypzx8HPvRtf2M6vLxxg4Yoqqhdfyc/QfY9eTpbu9wErjDEVxpgk4D7gmWvaPAN8bDr7/mag37KsNj99fwW8A8AYsxL7l4JLs/6OROStPOPQ+LrjbPsJj5fXL1zi9hULIy7IS5CS0qFgLbTs99t0rRLyYo7fGb1lWR5jzCPAC4Ab+J5lWSeMMQ9Nv/8E8CzwPuA8MAJ8Yqa+05f+HvA9Y8xxYAL4+LXL9iISAs37YHLEcaDfX9/D8MSU9udjTUkNHP+5nZjpuvEcrzQ3lezURCXkxRAnS/dYlvUsdjC/+nNPXPVnC/iM077Tn58APhrIYEUkCHW7wLig3Nn58y+e6iApwcWtK3QsbUwp3QIH/s2ukrdw1Q2bGWNYW5TFyVb/lfQkOqgynkisq6uFks2Qku23qWVZvHSqk+3L8khLcjQPkGhRWmO/Nu/z23RdcRan2wfxTOls+ligQC8Sy0Z77X3ZpTscNT/fOURjzwjvWlsQ1mHJPMhbYR9o1Oxgn744i3GPl7pLw3MwMAk3BXqRWHbhFbC8sOI9jpq/eMp+zvqdqxXoY47LNV04x3+gX1dsr/6c0PJ9TFCgF4ll516E1Fx76d6Bl051sr4kS6fVxarSGug4ARMzz9SXLUwnKcHFiRYl5MUCBXqRWOX1wvkXYdk7HR1L2z00zsHGXt61RrP5mFVSY6/wtB6esVmC28XqwkxVyIsRCvQisar9CAx3wYp3O2r+8ulOLAsF+ljmS8hztHyfxYnWAfTUc/RToBeJVedeBIw9o3fgpVOdFGalsK44K7zjkvmTng+55Y4y79cWZ9M/OklL32j4xyVhpUAvEqvO/dZOvsrwX/hmbHKKV8918c41i1QNL9aV1EDzAb/NVCEvdijQi8Si4W77Marlzpbt99Z1MzIxpWX7eFC6BQZbob9lxmZrijIxBlXIiwEK9CKx6MLLgOX4sbqXTnWSmujmlmV54R2XzD+H+/RpSQkszU9XoI8BCvQisej8i5CWB8Wb/Da1q+F1cNuKfFIS/WfnS5QrrAR3kqPCOeuKszmlzPuop0AvEmu8U3D+d7D8XTMeXuJzonWA1v4xLdvHi4RkKNwALQ726YuzaOkbpXd4Yg4GJuGiQC8Sa1oPwUi34/35Z4+14XYZlb2NJ6U19t+TKc+MzXxPYOh5+uimQC8Sa3yP1S33/1idZVk8e6yNbcvyWJCeFP6xSWQoqbGPLu48OWMzXync4y0qhRvNFOhFYs2539qZ1WkL/DY93T5IffcIO9cXzcHAJGI4PMluQXoSJTmpHFWgj2oK9CKxZLDdXpJ1mG3/3LE2XAbes07L9nEltxzSFzoqnLOhNFsz+iinQC8SS07/F2DB6j9w1PzZ4+3cVJFHfkZyeMclkcUYKLsJmt7w27SyNJuG7hGGJ1UKN1op0IvEktO/gQVLYdEav03PdgxyvnOI91UWzsHAJOKUbYWeOhjqmrFZZYm9T1/f752LUUkYKNCLxIrRXrj4Kqx+vz1j8+PZY20YA+9dr0Afl8pusl+b35yx2eVAPzAV7hFJmCjQi8SKs78FrwfW3O2o+XPH2tlSvoBFmTp7Pi4VVYEr0e/yfU5aEosXpHFRM/qopUAvEitO/ydkFNqPTvlxvnOIMx2DvE+z+fiVmALFVdA084we7Fl9w4ACfbRSoBeJBZOjcP4lOwnPQTW854+3AXCXHquLb2U3QctB8Mxc+a6yNJuuUUsV8qKUAr1ILLjwsl0AZc37HTX/r2PtbF6SS2G2lu3jWtlNMDUObUdmbObbpz+mx+yikgK9SCw49RtIyYby2/w2resa4lTbADu1bC9lW+1XP/v064sV6KOZAr1ItJuahLPPwcq7wJ3ot/mvDrdiDNy9sXgOBicRLbMQcpb4DfTZaYkUpBmONSvQRyMFepFo17DbfrRutf9le8uy+NWhFrYvy6cgS8v2wpXCOdbMBXGWZLk0o49SCvQi0e7UbyAhxdEhNgcb+2jsGeEPN5XMwcAkKpRthaEO6GucsVlFtpuWvlG6h8bnaGASKgr0ItHM67XL3i57JySl+23+q0MtpCS6eK9q24uPr3COn8fsyrPscKFZffRRoBeJZk1vwGArrP2A36YTHi+/OdrKu9cWkpnify9f4sSitZCU4Xeffokv0GufPuoo0ItEs2NPQUKqo0NsXj3bRe/IJB/Usr1czZ0AJZv9Bvq0RMPS/HTN6KOQAr1ItJqahBO/hFU7ITnTb/NfHm4hLz2JW1fkz8HgJKqU3QQdx2F8GNx6/gAAIABJREFUaMZmlaXZCvRRSIFeJFpdeAVGe6Dyj/02HRib5HcnO7h7YzGJbv2zl2uU3QSWF1r2z9issiSbtv4xugaVkBdN9C9eJFodewpScmD5u/w2ff54O+Mer7Lt5fpKawADjTMv31+pkNc3B4OSUFGgF4lGE8N2tv26P4SEJL/Nf3WohYr8dDaWZs/B4CTqpOZAwTpo3DNjs/Ul2bgMHG7S8n00UaAXiUZnnoPJYUfL9i19o7xe1809VcUYB+fUS5xasg2a9tm5HzeQnpzAyoJMDjX2zuHAZLYU6EWi0bGnILMYFm/z2/Sn+5oAuHdzabhHJdFsyTb7l8e2ozM227Q4lyNNfXi9M1fSk8ihQC8SbUZ64PzvoPKP/B5JO+W1eGp/E7evWEhpbtocDVCiku+XxobdMzbbVJbDwJiHukvDczAoCQUFepFoc/JX4PVA5Yf8Nt11tpO2/jHu31o2BwOTqJZZAAuWQcPM+/SbFucAcLhJCXnRQoFeJNocexryV0Fhpd+mP3mzifyMZN65RiVvxYEl26Dxdbu08g0sW5hBZnKC9umjiAK9SDTpbbCXViv/GPwk1nUMjPHy6U7u3VyqZ+fFmSXbYKwPuk7dsInLZdhYlqMZfRTRv36RaHLoR4CBjff5bfrU/iamvBb3bdGyvTi0xLdPP/PyfVVZDqfbBxmdmJqDQclsKdCLRIspDxz6d7tATs7Mwdvrtfjp/iZuWZpHeb7/U+1EAMhZAlkljvbpp7yWyuFGCQV6kWhx/kUYbIPNH/fbdPeFSzT1jHKfkvAkEMbA4lvsQG/d+PG5qjI7IU/79NFBgV4kWhz8IaQvgpV3+W365JtN5KQl8t51hXMwMIkpS7bBUDv01N2wSV5GMosXpGmfPkoo0ItEg4E2OPsCbPoIuGc+S75zcIzfnmzng5tKSUl0z9EAJWYs2W6/OtinP9SoQB8NFOhFosHhfwdrCqo/5rfpf+xtZHLK4qM3L56DgUnMWbgK0vLsx+xmsGlxDu0DY7T1j87RwCRYCvQikc7rhYM/gorbYcHSGZuOe6b4jzcaeMfqRSxdmDFHA5SYcnmffuYKeb59+sOa1Uc8BXqRSHexFvoaoNp/Et5/Hmnj0tAEn9heHvZhSQxbsg1662Gg9YZN1hZnkeR2aZ8+CjgK9MaYu4wxZ4wx540xj17nfWOM+dr0+0eNMdUB9P2sMcYyxuTP7lsRiVEHfgCpC2DN3TM2syyLf9t9kRWLMrh1uf45ySw4eJ4+OcHN2uIs7dNHAb+B3hjjBr4B7ATWAvcbY9Ze02wnsGL6vweBbznpa4wpA94NNM76OxGJRUOd9rnzG++HhOQZm+6r7+VE6wCf2F6h42hldgoqISkT6l+bsdmmxTkcbeljcurGJXNl/jmZ0W8FzluWVWdZ1gTwJHDPNW3uAX5o2fYCOcaYIgd9Hwf+CtB5hyLXs++74J2Emk/4bfpvuy+Sk5bIf9tUMgcDk5jmTrBn9fW/n7FZVVkOY5NezrQPztHAJBgJDtqUAE1XfdwM3OSgTclMfY0xHwBaLMs6MtPswxjzIPYqAQUFBdTW1joYcvwYGhrSPZkn4b73rqkJbt77LQbytnD8eAvQcsO2XSNenj8+ys6KRN7YM/MP52inv/Nzo9RbwvLuF3j9+acZT8m/7n2fGLFn8k/+7k3etWTmxz5l/jgJ9NeLwtfOwG/U5rqfN8akAX8LvMffF7cs69vAtwFqamqsHTt2+OsSV2pra9E9mR9hv/cHfwiT/eS//wvsqLh9xqb/89lTuFwX+b8+fBvFOanhG1ME0N/5OdKeBxe+xy2Fk1C147r33bIs/tfhl+lLzGXHjur/v737Dq+qyh4+/t1ppJBeIUASkkAooYYioYsIWGAoioqKwmBBHZ2x64z+7OPoOI4i2FAcUREFpQkqRbp0CJ0khBBIIL2Sevf7x4m+Ae69REjuTVmf58kTcs8+cd3tSVbOPnuvbf77CLurzdB9GlCzjmYb4MKpmJbaWHo9EogA9iqlUqpf36WUkjJeQoBRfnTLLGMr2vBBVpsWlVXy1bZURnUNafJJXthQUBdjEujx9RabKKXoE+HH9pQctJWSucK+apPotwPRSqkIpZQLMBlYckGbJcAd1bPv+wP5Wut0S+dqrRO01kFa63CtdTjGHwS9tNYZdfXGhGjUklZD5mG46oFLbkf7+dYTFJRW8udB1tfYC/GHODhAxCAj0VtJ4n3CfTlTUEZarhTOaagumei11pXAA8Aq4BDwtdb6gFLqXqXUvdXNVgDJQCLwIXC/tXPr/F0I0dRsmQUtQ6DLeKvNSiuq+GjDcQZFB/xewESIOhMxGArSrNa97xPuB8D2lBxbRSX+oNo8o0drvQIjmdd8bU6Nf2tgZm3PNdMmvDZxCNEsnDkISWvg6n+Ak4vVpgu2nySrqIyZw3raKDjRrEQMMT4f/wUwP2LUIdgTT1cntqfkMr5XG9vFJmpNKuMJ0dBsnQXO7tDb+pK68koT7/+SRFyYL/0i/GwUnGhW/KPAs7XV5/SODoq4MF+5o2/AJNEL0ZAUnoF9X0OPW8HdevL+bvcpTueXMnN4lBTIEfVDKWP4/vgG0JaL4sSF+5F4toic4nIbBidqSxK9EA3JprfBVAX977farLLKxHvrEuka6sXQDoE2Ck40SxGDoSQLj2LLBUz7Vo8o7ZC7+gZJEr0QDUVhBuz4GLrdDP6RVpsuT0gnJbuEmUPlbl7Us+oaDr65+yw2iQ31xsXRgR0ncm0VlfgDJNEL0VBs/A9UVcCQx6w2M5k0761NIiqoJdd2kdITop75tAW/9vjkWU70rs6OdG/rLc/pGyhJ9EI0BAXpsGMu9LjlknvOL913miNnCnlweBQODnI3L2wgYjA+eQegqtJik7hwPxLS8jlXXmXDwERtSKIXoiHY+BboKhhs/W6+vNLEmz8epVMrL27o1tpGwYlmL2IwTlUlkL7XYpO+4X5UmrTsT98ASaIXwt7yT8HOT42Z9r7hVpsu2J5Kak4Jj4/qKHfzwnbCq/daOL7OYpNeYb4oJYVzGiJJ9ELY28Z/G3fzgx612qy4rJK3VyfSN8JPZtoL22oZSJFHOCSttdjE282ZjsGekugbIEn0QthT3kljl7qeU8A3zGrTTzYdJ6uojCdGxchMe2FzOX69IHUrlFnee75PuB+7TuRSWWV5zb2wPUn0QtjTz8+Dcrjk3XxucTnv/5LMNZ2D6R3ma5vYhKghx68nmCqM4jkW9Inwo7i8isMZlv8YELYniV4Ie0n9FfZ/AwMeMpYwWfHeukSKyyt5/NqONgpOiPPle3cCZw9I/Nlim99KMW9JyrZVWKIWJNELYQ8mE6x8wqgjPvBhq01PZBczb8sJJvRqQ3Swp40CFOJ82sEZ2g+BxJ8sblsb7OVKZKAHm5KybBydsEYSvRD2sPdLOL0bRjwPLh5Wm76w9CDODorH5G5e2FvU1ZCXCtlJFpvERwWw7XgO5ZXynL6hkEQvhK2VFcLq/4PQOIidZLXp6kNnWH34LA+P6ECQl6uNAhTCgsirjc9Whu8HRAZQUl7F3jRZT99QSKIXwtY2/BuKzsDof4KD5R/B0ooqXlh2kKiglkyND7ddfEJY4hdhbF1rJdH3b++HUrA5UZ7TNxSS6IWwpZzjsGWWsXFNmzirTT9cn8yJ7BKev6ELzo7yoyoaiKgRkLIRKs6ZPezj7kLX1t7ynL4Bkd8eQtiK1rD0IXB0MZ7NW5GWW8KsdYmMiQ1hYHSATcITolaiRkDlOTix2WKTAVH+7E7NpaTccm18YTuS6IWwlZ2fwPH1MPJF8LJep/6lZYcAeOa6zraITIjaC4sHxxaQuNpikwGRAVRUabanyLa1DYEkeiFsIe8k/PgPY2/v3lOtNl25P52VBzJ4cHg0oT5utolPiNpycYfweKvP6fuE++LsqNicKMP3DYEkeiHqm9aw9C+gTXDjO2ClfG1OcTnPfrefrqFezBhsfbtaIewmagRkHTGW2pnh7uJEz3a+bJbCOQ2CJHoh6tue+ZC02nguf4nd6Z5bcoD8cxW8Mam7TMATDVfUCOOz1eF7f/afzievpNxGQQlL5DeJEPWp4DSsfBraDYA+0602Xbk/naV7T/PQ8GhiQrxsFKAQlyGgA3i3hWM/WWwSHxWA1rA1We7q7U0SvRD1paoSvp1ubAQy9l2ra+ZrDtnfOzTShkEKcRmUgg6jIGkNlJeYbdK9jQ/uLo5skvX0dieJXoj6svYlOLEJrn8L/C0nb601f/9+vwzZi8YlZoyxzC55ndnDLk4O9An3k/X0DYD8RhGiPhxdBRvfgl53QvfJVpt+tf0ky/el8/CIDjJkLxqPsIHQwguOLLfYJD7Kn+TMYjLyS20YmLiQJHoh6lpeKiyaASGxMPp1q00PnM7nuSUHGBQdwH1DZMheNCJOLhA9Eo6sBFOV2SYDowIBWH8s05aRiQtIoheiLlWWw8KpxlK6SfPA2fJGNIWlFcycvws/dxf+c3MPHBwsL7sTokGKGQMlWZC23ezhTq08CfZqwdrDZ20cmKhJEr0QdUVrWPYInNoJY2dd8rn8k98mcDL3HO/c2hP/li1sGKgQdSTqGnBwhsPLzB5WSjGsYxAbjmXJtrV2JIleiLqy7jXY8zkMfhw632i16f+2nmB5QjqPXduRPuF+NgpQiDrm6gURg+DwCuMPXTOGxQRRVFbJjhM5Ng5O/EYSvRB1Yddn8Mtr0OM2GPa01aYbj2XxwtKDXB0TxIxBUv1ONHIdx0BOEmQdNXt4YFQALo4OMnxvR5LohbhSR3+EpQ9D5NVww9tWS9weySjkvs93EhnYkrcmy3N50QR0HGN8Pmx+9r1HCyf6tfdjjSR6u5FEL8SVSNtpTL4L7gI3zQNHZ4tNzxaUcven23FzcWTuXX3wcrXcVohGwzsUWveEIyssNhnaMYikzGJSs80X1xH1SxK9EJfJK/8Q/G8ceATAbQuhhafFtiXllUybt4PcknLmTu0ju9KJpqXjdcbM+8IMs4eHxwQBsPaI3NXbgyR6IS7H8fV03/s8eATCXT+AZ4jFpmWVVTzwxW4OnM7n3Vt70jXU23ZxCmELMdXD90d+MHs4IsCDiAAPSfR2IoleiD/q2M8wfxKlrkFGkvcOtdi0rLKK+z/fxZrDZ3lpXCzDY4JtGKgQNhLU2diZ0cIyO4ChHQPZkpTNuXLzxXVE/ZFEL8QfcWAxfHULBESzp8fL4Gk5cZdWVHHv/3ay+vBZXhrXlVv7tbNhoELYkFLQeZxR977Y/CY2w2OCKKs0sVlq39ucJHohasNkgjUvGRPvWvWAO5dS4WK5Ln1pRRX3fr6TtUcyeeVPsUzpH2a7WIWwh64TwFQJh5aYPdw3wg93F0cZvrcDSfRCXEppASy4Ddb/C3pOganLwM3XYvP8cxVMn7eDdUcyeW18rNzJi+YhJBb8o2H/t2YPt3ByZGBUAGsPZ6ItFNcR9UMSvRDWZB2Dj0YYu9GN/hfc+C44WS5Xm5JVzPj3NrE1OZs3JnVncl9J8qKZUMq4q0/ZaHH2/bCYIE7lnePImUIbB9e8SaIXwhyTCbbOhjmDoDgT7vgO+s2wWgxna3I2497bRHZxOZ9P78fE3m1sGLAQDUDXCYA25rKYcXWnIJSCHxLM/yEg6ockeiEulJMM866HlU8adbzv2wwRgy0211rz+dYTTPnoV/w9XPh+Zjz92/vbMGAhGojADsYQvoXh+yBPV/qG+7EiId3GgTVvkuiF+E1lGWx+F2bHQ0aCsQPdrV+DVyuLp5wtLGX6vB08+91+4qMCWDwznjB/DxsGLUQD03WCUTwnN8Xs4eu6teLY2SKOyfC9zUiiF0JrSPgG3u0DPz4DYfFw/xZj4p2VofodGZVc+9Z6NiZm8Y/rO/PJVClrKwRdxhufLQzfj+oSglKwQobvbUYSvWi+tIaktfDhcPh2mlHCdsoimPINeFt+vp6WW8KDX+7m3T1ltPF1Z/lDA7l7YIRsUCMEgG8YtOljefjey5U+YTJ8b0tO9g5ACJurLDPu4Le+B2f2g2drGPsedJ8MDo4WTysoreC9tUnM3XQcBYyLcuZfdw3A2VH+XhbiPF0nwsonIPOo8dz+AmNiQ3h+6UESzxYRFdTSDgE2L/IbSjQfWYmw5mV4qyt8f79xRz92Fjy0G3reZjHJF5RW8OH6ZIa8vpY5vyRxfbdWrH10KOOiXCTJC2FOl3GAsnhXPzrWmPfyg9zV20St7uiVUqOAtwFH4COt9WsXHFfVx8cAJcBUrfUua+cqpf4F3ACUA0nAXVrrvLp4U0L8rvCM8axw3wI4vQtQEDUCrpoJ7YdafQafklXMp5tTWLjjJMXlVQyI9OfpMZ1+35TmqE3egBCNkGeIsWJl3wIY+uRFP2fBXq7EhfmyPCGdB6+OtlOQzcclE71SyhGYBVwDpAHblVJLtNYHazQbDURXf/QDZgP9LnHuT8BTWutKpdQ/gaeAJ+rurYlmyWSCjL1GgZujq6qTOxDSDUa+bMwItjKLPr+kglUHMli67zQbE7NwclDc0K01d8VHENtGdp0TotZ6TIHFM4wCOhGDLjo8JrYVLyw7SHJmEe0DZfi+PtXmjr4vkKi1TgZQSn0FjAVqJvqxwGfaqGu4VSnlo5RqBYRbOldr/WON87cCE6/0zYhmSGtj3fvxX+D4BkjZYBS4QRkTgoY/CzE3QFCMxW9xMqeETYlZrDqQwcbELCqqNO383HlweDRT+rUjyMvVdu9HiKai842w4jHY/T+ziX50bAgvLDvID/szmDksyg4BNh+1SfShwMkaX6dh3LVfqk1oLc8FuBtYUItYRHOnNWQegROb4MRm46PwtHHMsxVEDof2wyD6GvAIMHO6Ji33HLtSc/n1eA6bErM4kV0CQKiPG3fFR3B9t1bEhnqjrAzrCyEuwdkNuk2C3Z/D6NfBzee8w6283egd5svyfemS6OtZbRK9ud92F+5IYKnNJc9VSj0DVALzzf7HlZoBzAAIDg5m3bp1lwi3eSkqKmrafaI1budO4ZubgE+e8eFSUQBAmYsv+d5dyAu5kVzfbpxza208C8wDtu8HoLhCk5Jv4nh+Fcn5JpLyTeSXGZegqyPE+DkyMMaFzgGOtPZQKHWGnMQz/JJ46dCafN83UNLv9nE5/d7S1Jm4ylKOLnqF06FjLjrewa2CL0+U89XyNYR4yMTW+lKbRJ8GtK3xdRvgdC3buFg7Vyl1J3A9cLW2sJ2R1voD4AOAuLg4PXTo0FqE3HysW7eOJtcnZUXGvtbHVsGxn2vcsbeGTmMgfCCEDaCFX3uClCKo+rTyShMH0wvYnZrLnpN57D2ZR0r13TpAuL87V3f2pWc7H3q28yUmxBOnK5g13yT7vhGQfrePy+v3oXDqUzoUbqHD0NcvOtq5oJQFr67mpFNrJg+1/HhNXJnaJPrtQLRSKgI4BUwGbr2gzRLggepn8P2AfK11ulIq09K51bPxnwCGaK1LEM1baT4cWmYsx0nZAFXl4OIJkcOM4fiIweDX/rzZu2WVVexJzWNLcjabk7LZczKP8koTAMFeLejR1odJcW3p3saHrqFe+Li72OvdCdF89boDVjwKp/dA6x7nHQrycmVoxyC+3XmKv17TEUcpOlUvLpnoq2fFPwCswlgiN1drfUApdW/18TnACoyldYkYy+vusnZu9bd+F2gB/FT9LHSr1vreunxzooGrqoCjK2Hf18YM+aoy8AmDvjMgeiS0uwqczk/OeSXl/HzoLCv3Z7AxMZPSCpOxO2Zrb+7oH0avMOOOvZW3m53elBDiPLET4cdnjUl5FyR6gEm923Df4V1sOJbJ0I5BZr6BuFK1WkevtV6Bkcxrvjanxr81MLO251a/LrMvmquiTNj1KWyfawzLewRC76kQOwnaxF205ra0oorl+9JZvPsUW5KzqTJpWnu7cnNcW+KjAugX4Y+3u9SYF6JBcvOFzmNh30K45kVwcT/v8NWdgvF1d2bhjjRJ9PVESuAK28k6Bhv+Dfu/MYbm2w+D69407t4dL74Uj54p5ItfU1m0K42C0krC/d25Z3B7RnUNkVnxQjQmPW83iuccWmKUmq7BxcmBcT1Dmb81ldzicnw95BFbXZNEL+pfTjL88rrxg+7kBr3uNIbnzdTABtiRksPbq4+x4VgWLo4OjOoawq392tEvwk+SuxCNUfhAY47NznkXJXqASb3b8smmFL7fc4qp8RF2CLBpk0Qv6k9hBqx9GXbPB0dn6H8/DHzE7Pp2gF+Ts3l79TE2J2Xj7+HCY9d2ZHKftvi3bGHjwIUQdUopiLvbeFZvZlJe59ZedA31YuHONEn09UASvah7VRWw7QNY+6oxwa7PdBj0V6P+tRnJmUW8uOwga49kEujZgmev68St/drh7iKXpxBNRq87YN1rxq6R4z+46PCk3m15bskB9p/K/30/CVE35DepqFspG2H5o5B5yHj2Puo18I8027SorJJ31yTy8cZkWjg58vSYGO64KhxXZ8tbxQohGilXbyPZb/sARjwPXq3POzy2R2teXn6Ib3amSaKvY1KKSNSNskJY8hB8eh1UFMPkL+HWry0m+ZX7Mxj+xjrm/JLE2B6hrHl0CDMGR0qSF6Ip63cPaJOR7C/g4+7CNV2C+W7PKcoqq+wQXNMliV5cuZSNMHuAsU42/i8wcxvEjDG7BWx+SQWPLNjDvZ/vJNCzBYvuH8Abk7oT5CkbxwjR5PmGQ8z1sGOuUQHzApP7tCWvpIJle2Wf+rokiV5cvopSWPUMfHo9KEe4ayVc84KxmYUZ646cZeR/fmHp3tM8PCKa72bG06udr42DFkLY1VUPGJUw93550aGBUQFEB7Xko43HsVAVXVwGSfTi8mQnwccjYMu70Gca3LcJ2pnbmNCoQf9/Sw8w9ZPteLs5s/j+eB4e0QHnK6gzL4RopNr2hdA4Y1Ke6fwheqUU0wZGcCi9gC3J2XYKsOmR37Tijzv4PXwwFPJOwi0LjKI3Lh5mm6blljDp/S18simFu+LDWfLAQGLbyEQbIZotpeCqmUZ9jaMrLzo8rmco/h4ufLzhuB2Ca5ok0YvaqyyHH56Er++AgGi4dwN0HGWx+epDZ7juvxtJPlvE7Nt68dwNXWSynRACOt0I3m1h87sXHXJ1dmRK/zBWHz5LUubFz/HFHyeJXtROUSZ8diP8Ohv63Wc8j/dpZ7apyaR566ejTJu3g1AfN5Y+OJDRsa1sHLAQosFydDKe1aduhuRfLjo8pX8YLk4OfLJJ7urrgiR6cWnp++DDYUZFqwkfw+jXLtpV7jcl5ZXM/GIXb68+xoRebVh0/wDCA8wP6wshmrHeU8ErFNa8BBdMvAv0bMGfeoTyzc40covL7RNfEyKJXlh3YDF8PNJY+3r3SmPLSQvSckuYMHsLqw5k8Ox1nXhjUjcZqhdCmOfsCoMfg7RtcOyniw5PGxRBaYWJL7al2iG4pkUSvTBPa6OE7cKp0Ko7zFhndi/p3+w8kcvYdzeRllvC3Kl9mD6ovWxAI4SwrucUY239mhcvuqvvEOzJoOgA5m1OkQI6V0gSvbhYZRksvgd+eQ163AZ3LoGWlveJXpGQzi0fbsXT1YnvZsbLntJCiNpxdIahT0HGPji09KLD9wyO5GxhGQu2n7RDcE2HJHpxvpIc+GycsaXs8L/D2FngZH73OK01H6xP4v75u4gN9WbR/fFEBra0ccBCiEYtdhIEdDB2urxgXX18lD/92/vx39WJlJRX2inAxk8Svfj/spPgoxFwaidMnAuDHzVbxhagssrE37/fzysrDnNdbCvmT++Hn4f5CXpCCGGRgyMMexoyD8P+b887pJTisWtjyCoq45NNKfaJrwmQRC8MJ7fBx9dAaR7cuRS6TrDY9Fx5Ffd+vpPPt6Zyz5D2vHNLT5l0J4S4fJ3GQnAsrH3FeHRYQ+8wX0Z0CuL9X5LIL6mwU4CNmyR6YTwbm3eDsY3ktJ8slrIFyC4q45YPt7L68FleHNuFp0Z3wsFBJt0JIa6AgwOMfAFyj8Om/150+G8jO1JYVsn765PsEFzjJ4m+ufv1fVhwO4TEGknewrayAKnZJUycs4VD6QXMvq03t18Vbrs4hRBNW+Rw6DwONrwBuSnnHerUyosbu7fmk00pnC0stU98jZgk+ubKZDJ2nvvhcYi5Du5YAh4BFpsnpOUzfvYmcorLmT+9H6O6htgwWCFEs3DtK8ZOmD88cdGhR0Z0oKLKxKw1iXYIrHGTRN8cVZTCN1ONnef6zoCbPgMXd4vN1x05y80fbKGFkyPf3ncVceF+totVCNF8eIfC0CeNzW4OrzjvUHiABzf1acsX21KlBv4fJIm+uSnJgc/GGjvQjXwZRr9uzHq1YOGOk0ybt4Nwfw8W3z+AqCBPGwYrhGh2+t8HgZ2Mu/rykvMOPTKiA27Ojjy1KAGTSfarry1J9M1JTrKxfO70bpg0DwY8YHH5nNaad9cc47Fv9jEg0p8F9/QnyMvVxgELIZodR2dj6+v8VON5fQ2Bni14ekwnth3PYeFOKaJTW5Lom4vUX40kfy7HqHTXZZzFphVVJp5alMAbPx5lfM9QPr6zD56uzjYMVgjRrIXHQ7fJsOltOLXrvEM3xbWlb4Qfr6w4TGZhmYVvIGqSRN8cJHxTvXzOB6b9DO36W2xaWFrB3Z9u56vtJ3lweBRv3tQdFye5TIQQNjbqVWgZDN9Og7LC3192cFC88qdYzpVX8eKyg3YMsPGQ3+BNmdaw7p/GD0qbOJj+MwREWWx+Ou8ck+ZsYUtSNq9P6MbfRnaUjWmEEPbh7gfjPzCW2q14/LxDUUEtuX9YJEv2nmbdkbP2ia8RkUTfVFWcg0UzYN0r0P0WuH2x8YNjwb60PP703iZO5Z7jk7v6cFOftjYMVgghzAgfCIMehb1fwL6F5x26b2gkkYHMAuE0AAANtklEQVQePLN4PwWlUjHPGkn0TVF+GswdBQkLjY1pxs22uDENwNK9p5k0ZwtODg4svO8qBkUH2jBYIYSwYsgT0LYfLHsEco7//nILJ0den9iNjIJSHl+4D61lFr4lkuibmtSt8MFQY4OaW760ujGNyaR588cjPPjlbrq18eb7B+KJCfGybbxCCGGNoxNM+AiUg/EYskYt/N5hfjw5KoaVBzL4eONxK9+keZNE31RoDTs+gU+vhxae8OfV0HG0xeZFZZXcN38n76xJ5Oa4tsyf3p+Alpbv+oUQwm582sHYd4ydNRffa1T2rDZ9UATXdgnm1R8Osz0lx45BNlyS6JuCsiJYfA8sexgiBsOf10BgR4vNj2QUcuO7G/n50Fn+fn1nXpsQKzPrhRANW+exMOL/4MAi+Pm5319WSvGvSd1p6+vGzPm7ZMmdGfLbvZFzL06FD4fBvq9h2DNw20Jw87XYfvHuNMbN2kRhaSXzp/dj2sAImVkvhGgc4v8CfabD5v/Ctg9/f9nL1Zn3butN/rkKHvxyF+WVJivfpPmRRN9YaQ2759N759/gXB7c8T0MedxiOdvSiiqeXpzAIwv2EtvGm+UPDqR/e38bBy2EEFdAKRj1T+gw2tiQq0Y9/M6tvXh1fCxbk3N4eMFuKqsk2f/Gyd4BiMtQnAVL/wKHl1Hg0xXfaYvAM9hi8/2n8nl4wR4SzxZxz+D2PHZtR5wc5W88IUQj5OgEEz825iN9czfc/DlEjwBgfK825BSX89LyQ7g5J/Cvid1wcJARS0n0jc3hFbD0ISjNh2teZG95F4ZaSPJVJs3765N466ej+Lq78NndfRncQZbOCSEaORcP4zHl/8bBl5Nh/PvQdQIA0we1p7isird+Poq7iyMvjO3S7B9PSqJvLIqz4ae/w575EBJr7B8f3BnWrTPbPDmziCe/TWBbSg5jYkN4eVwsvh4uto1ZCCHqi0cATF0OX0yGb6YZjzD7TAPgoaujKCmv5P31ybi7OPLk6Jhmnewl0Td0JpOR3H/6B5QVwKC/wZAnwcl80i6rrGLOumRmrU3E1dmBNyd1Z3yv0GZ9kQshmihXb7h9ESycCsv/amzaNehRlFI8OTqGkvIq3l+fzJmCUl6b0A1XZ8tbcjdlkugbsjMHjYs3dQu0uwqu+7dxF2/BlqRsnvkugeTMYm7s3ppnr+9EkKdsLSuEaMKc3Yzn9N/PhDUvQcZ+uPG/KFdvXhjbhWCvFrzx41FSc0p4//Y4Aj2bX70QSfQNUf4pWPeqcSfv6gNjZ0H3W8HB/AS6pMwiXl95mFUHztDWz415d/dliDyLF0I0F47OMG4OBHWG1S/A6d0w6RNUaG8eGB5N+8CW/PXrPYybtYmP7oyjU6vmVQFUpl43JOdyjSH6d3rBvgXQ7154cCf0nGI2yWcWlvHZgTJGvrWeTYnZ/O2aDvz48BBJ8kKI5sfBAQY+DHf9ANoEH18LW2aBycSY2FYsvGcAlSYTf3pvEx9tSKbK1Hxq48sdfUNQkA6/zoEdc419l7vdDMOeBt8ws81P553jow3H+XJbKuWVVdzWP4yHro6WErZCCNGuH9yzHpY8CKuehoRvYPTrxLbtw9IHBvLUogReWn6IFQnpvD6xO1FBLe0dcb2TRG9PmUdg8zvG3bup0ijxOOhRCOlqtnni2ULm/JLMd7tPoYGxPVrT1yOHydeZby+EEM2Su5/x3H7f18Yo6ccjoPstBI14no/ujOP7Pad5fukBxvx3Aw8Oi2LaoAjcXZpuOmy676yhKi+Gg9/Drv9B6mZwcoVed8BVM8Gv/UXNSyuq+GF/Ol9uO8m24zm4OjswpX8Y0wdF0MbXnXUWltcJIUSzphR0vxlixsCGN41h/ENLUXF3Ma7ffcQ/MoTnluznzZ+OMm9LCvcOiWRK/7AmOTNfEr0tVFVAykY4+B3sX2Qsk/OLhKufM5K8R8D5zU2aHSk5rEhI57s9p8k/V0GYvzuPj+rIzXFt8ZcheiGEqJ0WnjDieeh5O6x92Uj4W2cT2HUi7139ADvir+Ktn4/y0vJDfLA+mRmD2zOxdxt83JtO3RFJ9PWlNB+Ob4DDy+DID1CaB87uxvB8z9shbMB5+8SXVlSxPSWHVQcyWLn/DFlFZbg4OTCyczC39m1H//b+UspRCCEul38kTJxr3GBtnQ27PoN9XxHXuifzu05gV79h/HNzIS8tP8TrK48wskswN/dpS3xkQKP/3SuJvq6U5ht7JR9fb3yc3m3M/HT1gY5joNMNEDnMWPOJkdgPphewJSmbTYlZ7DiRS3mlCTdnR4bHBDGqawjDYoJo2UL+FwkhRJ3xDYPRr8HQJ2DPF5CwEH58ll4oFrS7ijODh/B9fhSzj5xh2b50QrxcGRYTyNCOQcRHBTTK38m1ilgpNQp4G3AEPtJav3bBcVV9fAxQAkzVWu+ydq5Syg9YAIQDKcBNWuvcK39L9UxrKDgNWUfg7GE4vctI6tmJxnEHJwiNMybVRQyGdv0pqIDEs0Uc25NJwql89qXlcyi9gIoqY3lHTIgnd/QPIz4qgP7t/XFzaXrPiIQQokFx8zXmRl01E7KTYP+3cOA7gre9ygzgz64+ZLSKY0t5e37YG8ST29pQ5OhNz3a+9GznQ8+2PvRo60uId8MvSnbJRK+UcgRmAdcAacB2pdQSrfXBGs1GA9HVH/2A2UC/S5z7JLBaa/2aUurJ6q+fqLu3dpmqKqEkG4rOQGEG5KdCXirknYTcFMg6BuWFvzfXnq2pCulOQdR4znp24WiLTpwocOBkTgknE89xPGs9GQWlv7dv2cKJbm28mT6oPd3beBMX7ifL4oQQwp78I41tvoc8DoVnIGUDKnkdrVI2MD73Z8YrwBUKXIJIymrDwbQAtpuCWKiDOeMWhXtQBJGBLYkM9KBjiCeDohtWLZPa3NH3BRK11skASqmvgLFAzUQ/FvhMa62BrUopH6VUK4y7dUvnjgWGVp8/D1iHLRP9oWXG8/NzucZmCOdyjTrJxVnABYUUHJzBuw2ppkCS3UeQ4h7KEVNr9pe3IjHPnXOZVTUaG3f2gZ4tCPVxY0CkP9HBnkQHtSQ6uCVtfd0b/fMeIYRosjyDIXai8QFQkgMZCZC+F6+MffTMTqRH9lZUWQEAm3wm8h89jVUHMsgpLqdrqFejTPShwMkaX6dh3LVfqk3oJc4N1lqnA2it05VSQX8g7iuXk2TMhHfzMYZwAjsaay9bBoNHoPG5ZTD4tDM+Ozjw/KfbSckuxsfNGR93F6LcnOnn4UKgZ4vfP1p5u9HG161JLtEQQohmx90P2g8xPqoprY2bw5xk4l29iQ+IBiCnuJy8knJ7RWpRbRK9udvPC2sHWmpTm3Ot/8eVmgHMqP6ySCl15I+c3wwEAFn2DqKZkr63D+l3+5B+t4+OV/oNapPo04C2Nb5uA5yuZRsXK+eeUUq1qr6bbwWcNfcf11p/AHxQizibJaXUDq11nL3jaI6k7+1D+t0+pN/tQym140q/R202tdkORCulIpRSLsBkYMkFbZYAdyhDfyC/elje2rlLgDur/30n8P0VvhchhBBCXOCSd/Ra60ql1APAKowlcnO11geUUvdWH58DrMBYWpeIsbzuLmvnVn/r14CvlVLTgFRgUp2+MyGEEEKgjInyorFSSs2ofrwhbEz63j6k3+1D+t0+6qLfJdELIYQQTVhtntELIYQQopGSRN/IKKVSlFIJSqk9v83GVEr5KaV+Ukodq/7sa+84Gzul1Fyl1Fml1P4ar1nsZ6XUU0qpRKXUEaXUtfaJuvGz0O/PK6VOVV/ze5RSY2ock36vA0qptkqptUqpQ0qpA0qpv1S/Ltd8PbLS73V6zcvQfSOjlEoB4rTWWTVeex3IqVFO2Fdrbf9ywo2YUmowUIRR8bFr9Wtm+1kp1Rn4EqOKZGvgZ6CD1rrKwrcXFljo9+eBIq31Gxe0lX6vI9VLnFtprXcppTyBncA4YCpyzdcbK/1+E3V4zcsdfdMwFqOMMNWfx9kxliZBa70eyLngZUv9PBb4SmtdprU+jrH6pK9NAm1iLPS7JdLvdURrnf7bRmRa60LgEEZlU7nm65GVfrfksvpdEn3jo4EflVI7q6sGwgXlhAHblhNuPiz1s6US0KLuPKCU2lc9tP/b8LH0ez1QSoUDPYFfkWveZi7od6jDa14SfeMTr7XuhbFj4MzqoU5hX1dc6llYNRuIBHoA6cCb1a9Lv9cxpVRL4FvgYa11gbWmZl6Tvr9MZvq9Tq95SfSNjNb6dPXns8BijGGbM9XPen575mO2nLC4Ypb6uTZlosVl0lqf0VpXaa1NwIf8/6FK6fc6pJRyxkg287XWi6pflmu+npnr97q+5iXRNyJKKY/qCRsopTyAkcB+pJywrVjq5yXAZKVUC6VUBBANbLNDfE3Sb4mm2p8wrnmQfq8zSikFfAwc0lr/u8YhuebrkaV+r+trvjab2oiGIxhYbFwbOAFfaK1XKqW2I+WE65RS6ktgKBCglEoDnsNC2ebqktBfAweBSmCmzD6+PBb6fahSqgfGEGUKcA9Iv9exeOB2IEEptaf6taeRa76+Wer3W+rympfldUIIIUQTJkP3QgghRBMmiV4IIYRowiTRCyGEEE2YJHohhBCiCZNEL4QQQjRhkuiFEEKIJkwSvRBCCNGESaIXQgghmrD/B1kImt+kz0BKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sb.kdeplot(dc_score[\"('sum\\', \\'total_runs\\')\"] , label = 'total')\n",
    "sb.kdeplot(dc_score[\"('sum\\', \\'batsman_runs\\')\"] , label = 'batsman')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sb.distplot(dc_score[\"('sum\\', \\'total_runs\\')\"] , label = 'total')\n",
    "sb.distplot(dc_score[\"('sum\\', \\'batsman_runs\\')\"] , label = 'batsman')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JPjWH9Z0Xqmp/kp9L8roxxsMrLjKnyRjj9jHGc8YYe8YYezK7oXv5GOMrMafT/dckVydJVb04ydMye6N0c3rcHyX5ken01Un+cDq9mDParFcrJfnFJHcmuSPJf8js1Vo7k/xeZn8Iv5fkWfN+VdVmfyT5YGbPCz+a2Y3b3/9Oc8ns4bgvJPmDTK+GW4SPM8xpKbPnXT4zffxbc3rynE67/O5Mr8I1pyf9fXpakv843UbdmuTqRZ7TGWb0qiS3ZPaK299P8opFnpF3IgKABu9EBAANAgoADQIKAA0CCgANAgoADQIKAA0CysKoqh3Te5xuq6o9VXV0g6/v7rVuU1WXV9U/XMP2e6rq761xuycd0myjrZxrVX1fVf36Zq8BNouAskjektlbtZ2c90JWcXmSswY0yZ4kZw3ouZiOkLTuxhi3J3luVT1vI/YP8yagLJKfzONvi3gy06GaquovVdWnpoMEf7aqXnT6PbjpYNT/Yjp9tKreW1Ufr6rPV9Urq+rD0wHQ/+WK6/vaGtZ0apt3JnnhtIb31Mx7anYA+tur6g0rtvvhabvrpnX+76q6dfr4obUMoqreVFW/WVW/leSmqrqqqv77ist/pareNJ2+u6p+cdr/7VX1vdP5PzKt4zNV9emqumzlXCe/leQn1rImuNBsyL884XxTVU9L8oIxxt1JMsa4N8nfni7+B0neN8b4jWm7bUmuOMsu/3SM8eqqOphZlF+RWTi+UFXvHWMcH2O88jvvIlmxzTuS/OUxxsum9f6dJC9L8tLM3tD8/1bVx6ft3jbGeO203SVJ/voY49tV9aLM3n5t3xpGkiR/Jcn3jzEeqKqrzrLt18cYL58eZn5bkrdOn//RGOMTVfVnknx7jPHNPD7XJLl5WvO717gmuGC4B8qi2JXkwTNc9n+S/LOq+rkkzx9jPLKG/X10+nx7ks+NMe4bswMxfzFPPCpF16uSfHCMcXKMcX+SjyVZLcjfleTfVdXtmR0d4yXncB2/M8ZY7eDbqzl1BI5bMnsYOUk+keRfV9U/TnL5ePzg5it9NbNDGMKWI6AsikeSPH21C8YY/ynJ66ZtbqyqqzM7vujK34/Tv/fE9PmxFadPfb0ej+ysdnzF1VyX5P7M7qnuy+wN0dfqoRWn1/rznsz0840x3pnZPdEdST556qHd0zw9s7nCliOgLIQxxjeSbKuqJ0W0ql6Q5ItjjF/O7J7l92cWpedU1c6qujizY462VdWdZ9nkm0kuW/H1x5O8YXrF8LOTvDqzw0Odvt0zktw3xngsyTWZPfzc8aUkL5kOR/WMzA45+B1V1QvH7HBp78rsodrVAvrizI5uAluO50BZJDdl9tDo7552/huS/FRVPZrkK0l+aYzxaFX9UmaHbLors0PxtVTVrpzlHuUY43hVfWJ64dJvJ3l7Zs9R3pZkJHn7GOMrVXU8yXJV3Zbk15P8myT/par+bpL/lSfeq1yzMca9VfWhJJ/N7DB6n17Dt/1sVb0ms3ul/29a9+lek+R/dNYE5zuHM2NhVNWVSf7JGOOaTb7e12b2AqZf3szrnbfpnvvHkrzqDM+PwgVNQFkoVfWWJIfP0/8LuqVMrwrePcY4Ou+1wEYQUNjCqurHkrzrtLPvGmP8rXmsB7YSAQWABq/CBYAGAQWABgEFgAYBBYAGAQWAhv8PYhUslQ6aMj0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sb.boxplot(dc_score[\"('sum\\', \\'total_runs\\')\"])\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize = (8,8))\n",
    "sb.boxplot(dc_score[\"('sum\\', \\'batsman_runs\\')\"])\n",
    "\n",
    "plt.grid()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.plot(dc_score[\"('sum\\', \\'batsman_runs\\')\"] , label = 'bat')\n",
    "plt.plot(dc_score[\"('sum\\', \\'total_runs\\')\"] , label = 'total')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     15.000000\n",
      "mean     150.133333\n",
      "std       29.666881\n",
      "min       74.000000\n",
      "25%      142.500000\n",
      "50%      155.000000\n",
      "75%      167.000000\n",
      "max      187.000000\n",
      "Name: ('sum', 'batsman_runs'), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(dc_score[\"('sum\\', \\'batsman_runs\\')\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     15.000000\n",
      "mean     156.066667\n",
      "std       29.224419\n",
      "min       80.000000\n",
      "25%      146.500000\n",
      "50%      164.000000\n",
      "75%      173.000000\n",
      "max      190.000000\n",
      "Name: ('sum', 'total_runs'), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(dc_score[\"('sum\\', \\'total_runs\\')\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dc = 150 minimum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets check srh score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>4810</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J 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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4811</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J 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>4812</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J 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>4813</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J 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>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>4814</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J 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>...</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>178462</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>178463</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>Mohammad Nabi</td>\n",
       "      <td>caught</td>\n",
       "      <td>AR Patel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178464</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>K Paul</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>178465</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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>Rashid Khan</td>\n",
       "      <td>caught</td>\n",
       "      <td>RR Pant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178466</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>Basil Thampi</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1689 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team    bowling_team  over  ball  \\\n",
       "4810          21       1  Sunrisers Hyderabad  Delhi Capitals     1     1   \n",
       "4811          21       1  Sunrisers Hyderabad  Delhi Capitals     1     2   \n",
       "4812          21       1  Sunrisers Hyderabad  Delhi Capitals     1     3   \n",
       "4813          21       1  Sunrisers Hyderabad  Delhi Capitals     1     4   \n",
       "4814          21       1  Sunrisers Hyderabad  Delhi Capitals     1     5   \n",
       "...          ...     ...                  ...             ...   ...   ...   \n",
       "178462     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     5   \n",
       "178463     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     6   \n",
       "178464     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     7   \n",
       "178465     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     8   \n",
       "178466     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     9   \n",
       "\n",
       "              batsman  non_striker   bowler  is_super_over  ...  bye_runs  \\\n",
       "4810        DA Warner     S Dhawan  J Yadav              0  ...         0   \n",
       "4811         S Dhawan    DA Warner  J Yadav              0  ...         0   \n",
       "4812         S Dhawan    DA Warner  J Yadav              0  ...         0   \n",
       "4813        DA Warner     S Dhawan  J Yadav              0  ...         0   \n",
       "4814        DA Warner     S Dhawan  J Yadav              0  ...         0   \n",
       "...               ...          ...      ...            ...  ...       ...   \n",
       "178462  Mohammad Nabi     DJ Hooda   K Paul              0  ...         0   \n",
       "178463  Mohammad Nabi     DJ Hooda   K Paul              0  ...         0   \n",
       "178464       DJ Hooda  Rashid Khan   K Paul              0  ...         0   \n",
       "178465    Rashid Khan      B Kumar   K Paul              0  ...         0   \n",
       "178466   Basil Thampi      B Kumar   K Paul              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "4810              0            1             0             1           1   \n",
       "4811              0            0             0             4           0   \n",
       "4812              0            0             0             1           0   \n",
       "4813              0            0             0             0           0   \n",
       "4814              0            0             0             1           0   \n",
       "...             ...          ...           ...           ...         ...   \n",
       "178462            0            0             0             0           0   \n",
       "178463            0            0             0             0           0   \n",
       "178464            0            0             0             1           0   \n",
       "178465            0            0             0             0           0   \n",
       "178466            0            0             0             1           0   \n",
       "\n",
       "        total_runs  player_dismissed dismissal_kind   fielder  \n",
       "4810             2               NaN            NaN       NaN  \n",
       "4811             4               NaN            NaN       NaN  \n",
       "4812             1               NaN            NaN       NaN  \n",
       "4813             0               NaN            NaN       NaN  \n",
       "4814             1               NaN            NaN       NaN  \n",
       "...            ...               ...            ...       ...  \n",
       "178462           0               NaN            NaN       NaN  \n",
       "178463           0     Mohammad Nabi         caught  AR Patel  \n",
       "178464           1               NaN            NaN       NaN  \n",
       "178465           0       Rashid Khan         caught   RR Pant  \n",
       "178466           1               NaN            NaN       NaN  \n",
       "\n",
       "[1689 rows x 21 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_srh = main_df_team[main_df_team['batting_team'] == 'Sunrisers Hyderabad']\n",
    "only_srh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'match_id', '('count', 'batsman_runs')',\n",
       "       '('count', 'total_runs')', '('sum', 'batsman_runs')',\n",
       "       '('sum', 'total_runs')'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srh_score = only_srh.pivot_table(index = ['match_id'] , values = ['total_runs' , 'batsman_runs'] , aggfunc=['count', 'sum'])\n",
    "srh_score = pd.DataFrame(dc_score.to_records())\n",
    "srh_score.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sb.kdeplot(srh_score[\"('sum\\', \\'total_runs\\')\"] , label = 'total')\n",
    "sb.kdeplot(srh_score[\"('sum\\', \\'batsman_runs\\')\"] , label = 'batsman')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sb.distplot(srh_score[\"('sum\\', \\'total_runs\\')\"] , label = 'total')\n",
    "sb.distplot(srh_score[\"('sum\\', \\'batsman_runs\\')\"] , label = 'batsman')\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JPjWH9Z0Xqmp/kp9L8roxxsMrLjKnyRjj9jHGc8YYe8YYezK7oXv5GOMrMafT/dckVydJVb04ydMye6N0c3rcHyX5ken01Un+cDq9mDParFcrJfnFJHcmuSPJf8js1Vo7k/xeZn8Iv5fkWfN+VdVmfyT5YGbPCz+a2Y3b3/9Oc8ns4bgvJPmDTK+GW4SPM8xpKbPnXT4zffxbc3rynE67/O5Mr8I1pyf9fXpakv843UbdmuTqRZ7TGWb0qiS3ZPaK299P8opFnpF3IgKABu9EBAANAgoADQIKAA0CCgANAgoADQIKAA0CysKoqh3Te5xuq6o9VXV0g6/v7rVuU1WXV9U/XMP2e6rq761xuycd0myjrZxrVX1fVf36Zq8BNouAskjektlbtZ2c90JWcXmSswY0yZ4kZw3ouZiOkLTuxhi3J3luVT1vI/YP8yagLJKfzONvi3gy06GaquovVdWnpoMEf7aqXnT6PbjpYNT/Yjp9tKreW1Ufr6rPV9Urq+rD0wHQ/+WK6/vaGtZ0apt3JnnhtIb31Mx7anYA+tur6g0rtvvhabvrpnX+76q6dfr4obUMoqreVFW/WVW/leSmqrqqqv77ist/pareNJ2+u6p+cdr/7VX1vdP5PzKt4zNV9emqumzlXCe/leQn1rImuNBsyL884XxTVU9L8oIxxt1JMsa4N8nfni7+B0neN8b4jWm7bUmuOMsu/3SM8eqqOphZlF+RWTi+UFXvHWMcH2O88jvvIlmxzTuS/OUxxsum9f6dJC9L8tLM3tD8/1bVx6ft3jbGeO203SVJ/voY49tV9aLM3n5t3xpGkiR/Jcn3jzEeqKqrzrLt18cYL58eZn5bkrdOn//RGOMTVfVnknx7jPHNPD7XJLl5WvO717gmuGC4B8qi2JXkwTNc9n+S/LOq+rkkzx9jPLKG/X10+nx7ks+NMe4bswMxfzFPPCpF16uSfHCMcXKMcX+SjyVZLcjfleTfVdXtmR0d4yXncB2/M8ZY7eDbqzl1BI5bMnsYOUk+keRfV9U/TnL5ePzg5it9NbNDGMKWI6AsikeSPH21C8YY/ynJ66ZtbqyqqzM7vujK34/Tv/fE9PmxFadPfb0ej+ysdnzF1VyX5P7M7qnuy+wN0dfqoRWn1/rznsz0840x3pnZPdEdST556qHd0zw9s7nCliOgLIQxxjeSbKuqJ0W0ql6Q5ItjjF/O7J7l92cWpedU1c6qujizY462VdWdZ9nkm0kuW/H1x5O8YXrF8LOTvDqzw0Odvt0zktw3xngsyTWZPfzc8aUkL5kOR/WMzA45+B1V1QvH7HBp78rsodrVAvrizI5uAluO50BZJDdl9tDo7552/huS/FRVPZrkK0l+aYzxaFX9UmaHbLors0PxtVTVrpzlHuUY43hVfWJ64dJvJ3l7Zs9R3pZkJHn7GOMrVXU8yXJV3Zbk15P8myT/par+bpL/lSfeq1yzMca9VfWhJJ/N7DB6n17Dt/1sVb0ms3ul/29a9+lek+R/dNYE5zuHM2NhVNWVSf7JGOOaTb7e12b2AqZf3szrnbfpnvvHkrzqDM+PwgVNQFkoVfWWJIfP0/8LuqVMrwrePcY4Ou+1wEYQUNjCqurHkrzrtLPvGmP8rXmsB7YSAQWABq/CBYAGAQWABgEFgAYBBYAGAQWAhv8PYhUslQ6aMj0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sb.boxplot(srh_score[\"('sum\\', \\'total_runs\\')\"])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,8))\n",
    "sb.boxplot(srh_score[\"('sum\\', \\'batsman_runs\\')\"])\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     15.000000\n",
      "mean     156.066667\n",
      "std       29.224419\n",
      "min       80.000000\n",
      "25%      146.500000\n",
      "50%      164.000000\n",
      "75%      173.000000\n",
      "max      190.000000\n",
      "Name: ('sum', 'total_runs'), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(srh_score[\"('sum\\', \\'total_runs\\')\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     15.000000\n",
      "mean     150.133333\n",
      "std       29.666881\n",
      "min       74.000000\n",
      "25%      142.500000\n",
      "50%      155.000000\n",
      "75%      167.000000\n",
      "max      187.000000\n",
      "Name: ('sum', 'batsman_runs'), dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(srh_score[\"('sum\\', \\'batsman_runs\\')\"].describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# srh - 150 min"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Take Aways;\n",
    "### 1 - The median score of DC and SRH is 150 and 155 respectively\n",
    "### 2 -Since ,the stadium is dubai interntional stadium , bowlers have an adv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 2 ans - Option 1 (< 320)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "###############################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 3 - Total Catch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4810                        NaN\n",
       "4811                        NaN\n",
       "4812                        NaN\n",
       "4813                        NaN\n",
       "4814                        NaN\n",
       "                  ...          \n",
       "178586                      NaN\n",
       "178587                      NaN\n",
       "178588                      NaN\n",
       "178589    obstructing the field\n",
       "178590                      NaN\n",
       "Name: dismissal_kind, Length: 3512, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>4810</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J 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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4811</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J 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>4812</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J 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>4813</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J 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>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>4814</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J 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>...</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>178462</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>178463</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>Mohammad Nabi</td>\n",
       "      <td>caught</td>\n",
       "      <td>AR Patel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178464</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>K Paul</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>178465</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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>Rashid Khan</td>\n",
       "      <td>caught</td>\n",
       "      <td>RR Pant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178466</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>Basil Thampi</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>1689 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team    bowling_team  over  ball  \\\n",
       "4810          21       1  Sunrisers Hyderabad  Delhi Capitals     1     1   \n",
       "4811          21       1  Sunrisers Hyderabad  Delhi Capitals     1     2   \n",
       "4812          21       1  Sunrisers Hyderabad  Delhi Capitals     1     3   \n",
       "4813          21       1  Sunrisers Hyderabad  Delhi Capitals     1     4   \n",
       "4814          21       1  Sunrisers Hyderabad  Delhi Capitals     1     5   \n",
       "...          ...     ...                  ...             ...   ...   ...   \n",
       "178462     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     5   \n",
       "178463     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     6   \n",
       "178464     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     7   \n",
       "178465     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     8   \n",
       "178466     11413       1  Sunrisers Hyderabad  Delhi Capitals    20     9   \n",
       "\n",
       "              batsman  non_striker   bowler  is_super_over  ...  bye_runs  \\\n",
       "4810        DA Warner     S Dhawan  J Yadav              0  ...         0   \n",
       "4811         S Dhawan    DA Warner  J Yadav              0  ...         0   \n",
       "4812         S Dhawan    DA Warner  J Yadav              0  ...         0   \n",
       "4813        DA Warner     S Dhawan  J Yadav              0  ...         0   \n",
       "4814        DA Warner     S Dhawan  J Yadav              0  ...         0   \n",
       "...               ...          ...      ...            ...  ...       ...   \n",
       "178462  Mohammad Nabi     DJ Hooda   K Paul              0  ...         0   \n",
       "178463  Mohammad Nabi     DJ Hooda   K Paul              0  ...         0   \n",
       "178464       DJ Hooda  Rashid Khan   K Paul              0  ...         0   \n",
       "178465    Rashid Khan      B Kumar   K Paul              0  ...         0   \n",
       "178466   Basil Thampi      B Kumar   K Paul              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "4810              0            1             0             1           1   \n",
       "4811              0            0             0             4           0   \n",
       "4812              0            0             0             1           0   \n",
       "4813              0            0             0             0           0   \n",
       "4814              0            0             0             1           0   \n",
       "...             ...          ...           ...           ...         ...   \n",
       "178462            0            0             0             0           0   \n",
       "178463            0            0             0             0           0   \n",
       "178464            0            0             0             1           0   \n",
       "178465            0            0             0             0           0   \n",
       "178466            0            0             0             1           0   \n",
       "\n",
       "        total_runs  player_dismissed dismissal_kind   fielder  \n",
       "4810             2               NaN            NaN       NaN  \n",
       "4811             4               NaN            NaN       NaN  \n",
       "4812             1               NaN            NaN       NaN  \n",
       "4813             0               NaN            NaN       NaN  \n",
       "4814             1               NaN            NaN       NaN  \n",
       "...            ...               ...            ...       ...  \n",
       "178462           0               NaN            NaN       NaN  \n",
       "178463           0     Mohammad Nabi         caught  AR Patel  \n",
       "178464           1               NaN            NaN       NaN  \n",
       "178465           0       Rashid Khan         caught   RR Pant  \n",
       "178466           1               NaN            NaN       NaN  \n",
       "\n",
       "[1689 rows x 21 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df_team['dismissal_kind'] = main_df_team['dismissal_kind'].replace({\"caught and bowled\" : \"caught\"})\n",
    "srh_wicket_type = main_df_team[main_df_team['batting_team'] == 'Sunrisers Hyderabad']\n",
    "srh_wicket_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (7,7))\n",
    "sb.countplot(srh_wicket_type['dismissal_kind'])\n",
    "plt.xticks(rotation = 90)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_wicket_type = main_df_team.pivot_table(index = ['match_id'] , values = ['dismissal_kind'] , aggfunc = ['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>('count', 'dismissal_kind')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>394</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>428</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>469</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>489</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>531</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>561</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>618</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>628</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7929</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7935</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11152</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11321</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11413</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  ('count', 'dismissal_kind')\n",
       "0         21                            9\n",
       "1         39                            7\n",
       "2        394                           15\n",
       "3        428                           14\n",
       "4        469                            5\n",
       "5        489                            9\n",
       "6        531                           12\n",
       "7        561                            8\n",
       "8        618                           11\n",
       "9        628                           11\n",
       "10      7929                            8\n",
       "11      7935                            6\n",
       "12     11152                           13\n",
       "13     11321                           17\n",
       "14     11413                           14"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_wicket_type = pd.DataFrame(total_wicket_type.to_records())\n",
    "total_wicket_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c14fad60>"
      ]
     },
     "execution_count": 49,
     "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": [
    "sb.kdeplot(total_wicket_type[\"('count\\', \\'dismissal_kind\\')\"] , label = 'per_match')\n",
    "sb.kdeplot(srh_all_wicket_type[\"('count\\', \\'dismissal_kind\\')\"] , label = 'total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c14c6f40>"
      ]
     },
     "execution_count": 50,
     "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": [
    "sb.distplot(total_wicket_type[\"('count\\', \\'dismissal_kind\\')\"] , label = 'per_match')\n",
    "sb.distplot(srh_all_wicket_type[\"('count\\', \\'dismissal_kind\\')\"] , label = 'total')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    15.00000\n",
       "mean     10.60000\n",
       "std       3.54159\n",
       "min       5.00000\n",
       "25%       8.00000\n",
       "50%      11.00000\n",
       "75%      13.50000\n",
       "max      17.00000\n",
       "Name: ('count', 'dismissal_kind'), dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_wicket_type[\"('count\\', \\'dismissal_kind\\')\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230bef3fb80>"
      ]
     },
     "execution_count": 40,
     "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": [
    "sb.boxplot(total_wicket_type[\"('count\\', \\'dismissal_kind\\')\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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",
       "    <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>178462</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>178463</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>Mohammad Nabi</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>K Paul</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>Mohammad Nabi</td>\n",
       "      <td>caught</td>\n",
       "      <td>AR Patel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178464</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>7</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>K Paul</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>178465</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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>Rashid Khan</td>\n",
       "      <td>caught</td>\n",
       "      <td>RR Pant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178466</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>20</td>\n",
       "      <td>9</td>\n",
       "      <td>Basil Thampi</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>K Paul</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>12908 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team                 bowling_team  \\\n",
       "0              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "1              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "2              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "3              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "4              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "...          ...     ...                  ...                          ...   \n",
       "178462     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178463     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178464     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178465     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178466     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "\n",
       "        over  ball        batsman  non_striker    bowler  is_super_over  ...  \\\n",
       "0          1     1      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "1          1     2      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "2          1     3      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "3          1     4      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "4          1     5      DA Warner     S Dhawan  TS Mills              0  ...   \n",
       "...      ...   ...            ...          ...       ...            ...  ...   \n",
       "178462    20     5  Mohammad Nabi     DJ Hooda    K Paul              0  ...   \n",
       "178463    20     6  Mohammad Nabi     DJ Hooda    K Paul              0  ...   \n",
       "178464    20     7       DJ Hooda  Rashid Khan    K Paul              0  ...   \n",
       "178465    20     8    Rashid Khan      B Kumar    K Paul              0  ...   \n",
       "178466    20     9   Basil Thampi      B Kumar    K Paul              0  ...   \n",
       "\n",
       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "0              0            0            0             0             0   \n",
       "1              0            0            0             0             0   \n",
       "2              0            0            0             0             4   \n",
       "3              0            0            0             0             0   \n",
       "4              0            0            0             0             0   \n",
       "...          ...          ...          ...           ...           ...   \n",
       "178462         0            0            0             0             0   \n",
       "178463         0            0            0             0             0   \n",
       "178464         0            0            0             0             1   \n",
       "178465         0            0            0             0             0   \n",
       "178466         0            0            0             0             1   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind   fielder  \n",
       "0                0           0               NaN            NaN       NaN  \n",
       "1                0           0               NaN            NaN       NaN  \n",
       "2                0           4               NaN            NaN       NaN  \n",
       "3                0           0               NaN            NaN       NaN  \n",
       "4                2           2               NaN            NaN       NaN  \n",
       "...            ...         ...               ...            ...       ...  \n",
       "178462           0           0               NaN            NaN       NaN  \n",
       "178463           0           0     Mohammad Nabi         caught  AR Patel  \n",
       "178464           0           1               NaN            NaN       NaN  \n",
       "178465           0           0       Rashid Khan         caught   RR Pant  \n",
       "178466           0           1               NaN            NaN       NaN  \n",
       "\n",
       "[12908 rows x 21 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['dismissal_kind'] = df['dismissal_kind'].replace({\"caught and bowled\" : \"caught\"})\n",
    "srh_w_all = df[(((df['batting_team'] == 'Sunrisers Hyderabad')))]\n",
    "srh_w_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>('count', 'dismissal_kind')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>11336</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>11339</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>11342</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>11345</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>11413</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>108 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id  ('count', 'dismissal_kind')\n",
       "0           1                            4\n",
       "1           6                            1\n",
       "2          10                            8\n",
       "3          14                            6\n",
       "4          19                            6\n",
       "..        ...                          ...\n",
       "103     11336                            8\n",
       "104     11339                            6\n",
       "105     11342                            6\n",
       "106     11345                            7\n",
       "107     11413                            7\n",
       "\n",
       "[108 rows x 2 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srh_all_wicket_type = srh_w_all.pivot_table(index = ['match_id'] , values = ['dismissal_kind'] , aggfunc = ['count'])\n",
    "srh_all_wicket_type = pd.DataFrame(srh_all_wicket_type.to_records())\n",
    "srh_all_wicket_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230bf454820>"
      ]
     },
     "execution_count": 44,
     "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": [
    "sb.kdeplot(srh_all_wicket_type[\"('count\\', \\'dismissal_kind\\')\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230bf4ab850>"
      ]
     },
     "execution_count": 45,
     "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": [
    "sb.distplot(srh_all_wicket_type[\"('count\\', \\'dismissal_kind\\')\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c14c3820>"
      ]
     },
     "execution_count": 46,
     "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": [
    "sb.boxplot(srh_all_wicket_type[\"('count\\', \\'dismissal_kind\\')\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    108.000000\n",
       "mean       5.564815\n",
       "std        2.435229\n",
       "min        0.000000\n",
       "25%        3.750000\n",
       "50%        6.000000\n",
       "75%        7.000000\n",
       "max       10.000000\n",
       "Name: ('count', 'dismissal_kind'), dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srh_all_wicket_type[\"('count\\', \\'dismissal_kind\\')\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>('count', 'dismissal_kind')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>11337</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>11341</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>11344</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>11413</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>11414</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>177 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id  ('count', 'dismissal_kind')\n",
       "0           5                            9\n",
       "1           9                            4\n",
       "2          15                            6\n",
       "3          18                            7\n",
       "4          21                            5\n",
       "..        ...                          ...\n",
       "172     11337                            5\n",
       "173     11341                           10\n",
       "174     11344                            5\n",
       "175     11413                            7\n",
       "176     11414                            9\n",
       "\n",
       "[177 rows x 2 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df['dismissal_kind'] = df['dismissal_kind'].replace({\"caught and bowled\" : \"caught\"})\n",
    "dc_w_all = df[(((df['batting_team'] == 'Delhi Capitals')))]\n",
    "dc_w_all\n",
    "dc_all_wicket_type = dc_w_all.pivot_table(index = ['match_id'] , values = ['dismissal_kind'] , aggfunc = ['count'])\n",
    "dc_all_wicket_type = pd.DataFrame(dc_all_wicket_type.to_records())\n",
    "dc_all_wicket_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c15cb4f0>"
      ]
     },
     "execution_count": 53,
     "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": [
    "sb.distplot(dc_all_wicket_type[\"('count\\', \\'dismissal_kind\\')\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c1983ee0>"
      ]
     },
     "execution_count": 54,
     "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": [
    "sb.kdeplot(dc_all_wicket_type[\"('count\\', \\'dismissal_kind\\')\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c19d2910>"
      ]
     },
     "execution_count": 55,
     "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": [
    "sb.kdeplot(dc_all_wicket_type[\"('count\\', \\'dismissal_kind\\')\"] , label = 'total_dc')\n",
    "sb.kdeplot(srh_all_wicket_type[\"('count\\', \\'dismissal_kind\\')\"] , label = 'total_srh')\n",
    "sb.kdeplot(total_wicket_type[\"('count\\', \\'dismissal_kind\\')\"] , label = 'per_match')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Take Aways:\n",
    "### 1 - Generally , the wickets taken by catch is around 4-5\n",
    "### 2 - Dc sometimes give less wicket via catch \n",
    "### 3 - The total generally lies around 8-10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 3 ans - (8-12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 4 - Score made by srh compared to dc  , more or less ?"
   ]
  },
  {
   "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>4810</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J 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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4811</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J 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>4812</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J 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>4813</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J 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>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>4814</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J 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>...</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>178586</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>K Ahmed</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>178587</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Ahmed</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>178588</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Ahmed</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>178589</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>K Ahmed</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>A Mishra</td>\n",
       "      <td>obstructing the field</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178590</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>TA Boult</td>\n",
       "      <td>K Ahmed</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>3512 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team         bowling_team  over  \\\n",
       "4810          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4811          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4812          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4813          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4814          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "...          ...     ...                  ...                  ...   ...   \n",
       "178586     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178587     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178588     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178589     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178590     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "\n",
       "        ball    batsman non_striker   bowler  is_super_over  ...  bye_runs  \\\n",
       "4810       1  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "4811       2   S Dhawan   DA Warner  J Yadav              0  ...         0   \n",
       "4812       3   S Dhawan   DA Warner  J Yadav              0  ...         0   \n",
       "4813       4  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "4814       5  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "...      ...        ...         ...      ...            ...  ...       ...   \n",
       "178586     2   A Mishra      K Paul  K Ahmed              0  ...         0   \n",
       "178587     3     K Paul    A Mishra  K Ahmed              0  ...         0   \n",
       "178588     4     K Paul    A Mishra  K Ahmed              0  ...         0   \n",
       "178589     5   A Mishra      K Paul  K Ahmed              0  ...         0   \n",
       "178590     6     K Paul    TA Boult  K Ahmed              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "4810              0            1             0             1           1   \n",
       "4811              0            0             0             4           0   \n",
       "4812              0            0             0             1           0   \n",
       "4813              0            0             0             0           0   \n",
       "4814              0            0             0             1           0   \n",
       "...             ...          ...           ...           ...         ...   \n",
       "178586            0            0             0             1           0   \n",
       "178587            0            0             0             0           0   \n",
       "178588            0            0             0             1           0   \n",
       "178589            0            0             0             0           0   \n",
       "178590            0            0             0             4           0   \n",
       "\n",
       "        total_runs  player_dismissed         dismissal_kind fielder  \n",
       "4810             2               NaN                    NaN     NaN  \n",
       "4811             4               NaN                    NaN     NaN  \n",
       "4812             1               NaN                    NaN     NaN  \n",
       "4813             0               NaN                    NaN     NaN  \n",
       "4814             1               NaN                    NaN     NaN  \n",
       "...            ...               ...                    ...     ...  \n",
       "178586           1               NaN                    NaN     NaN  \n",
       "178587           0               NaN                    NaN     NaN  \n",
       "178588           1               NaN                    NaN     NaN  \n",
       "178589           0          A Mishra  obstructing the field     NaN  \n",
       "178590           4               NaN                    NaN     NaN  \n",
       "\n",
       "[3512 rows x 21 columns]"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
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       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4933</th>\n",
       "      <td>21</td>\n",
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       "      <td>Delhi Capitals</td>\n",
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       "      <th>4934</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
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       "      <td>SV Samson</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4935</th>\n",
       "      <td>21</td>\n",
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       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>SV Samson</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4936</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>SV Samson</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4937</th>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>SV Samson</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>178493</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>P Shaw</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>178494</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>P Shaw</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178495</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>P Shaw</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>B Kumar</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>178496</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>P Shaw</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>B Kumar</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>178497</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>P Shaw</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>B Kumar</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>462 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning    batting_team         bowling_team  over  ball  \\\n",
       "4933          21       2  Delhi Capitals  Sunrisers Hyderabad     1     1   \n",
       "4934          21       2  Delhi Capitals  Sunrisers Hyderabad     1     2   \n",
       "4935          21       2  Delhi Capitals  Sunrisers Hyderabad     1     3   \n",
       "4936          21       2  Delhi Capitals  Sunrisers Hyderabad     1     4   \n",
       "4937          21       2  Delhi Capitals  Sunrisers Hyderabad     1     5   \n",
       "...          ...     ...             ...                  ...   ...   ...   \n",
       "178493     11413       2  Delhi Capitals  Sunrisers Hyderabad     5     3   \n",
       "178494     11413       2  Delhi Capitals  Sunrisers Hyderabad     5     4   \n",
       "178495     11413       2  Delhi Capitals  Sunrisers Hyderabad     5     5   \n",
       "178496     11413       2  Delhi Capitals  Sunrisers Hyderabad     5     6   \n",
       "178497     11413       2  Delhi Capitals  Sunrisers Hyderabad     5     7   \n",
       "\n",
       "            batsman  non_striker   bowler  is_super_over  ...  bye_runs  \\\n",
       "4933      SV Samson  SW Billings  B Kumar              0  ...         0   \n",
       "4934      SV Samson  SW Billings  B Kumar              0  ...         0   \n",
       "4935    SW Billings    SV Samson  B Kumar              0  ...         0   \n",
       "4936    SW Billings    SV Samson  B Kumar              0  ...         0   \n",
       "4937    SW Billings    SV Samson  B Kumar              0  ...         0   \n",
       "...             ...          ...      ...            ...  ...       ...   \n",
       "178493       P Shaw     S Dhawan  B Kumar              0  ...         0   \n",
       "178494       P Shaw     S Dhawan  B Kumar              0  ...         0   \n",
       "178495       P Shaw     S Dhawan  B Kumar              0  ...         0   \n",
       "178496       P Shaw     S Dhawan  B Kumar              0  ...         0   \n",
       "178497       P Shaw     S Dhawan  B Kumar              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "4933              0            0             0             0           0   \n",
       "4934              1            0             0             0           1   \n",
       "4935              0            0             0             0           0   \n",
       "4936              0            0             0             0           0   \n",
       "4937              0            0             0             0           0   \n",
       "...             ...          ...           ...           ...         ...   \n",
       "178493            0            0             0             4           0   \n",
       "178494            0            0             0             6           0   \n",
       "178495            0            0             0             4           0   \n",
       "178496            0            0             0             0           0   \n",
       "178497            0            0             0             0           0   \n",
       "\n",
       "        total_runs  player_dismissed dismissal_kind fielder  \n",
       "4933             0               NaN            NaN     NaN  \n",
       "4934             1               NaN            NaN     NaN  \n",
       "4935             0               NaN            NaN     NaN  \n",
       "4936             0               NaN            NaN     NaN  \n",
       "4937             0               NaN            NaN     NaN  \n",
       "...            ...               ...            ...     ...  \n",
       "178493           4               NaN            NaN     NaN  \n",
       "178494           6               NaN            NaN     NaN  \n",
       "178495           4               NaN            NaN     NaN  \n",
       "178496           0               NaN            NaN     NaN  \n",
       "178497           0               NaN            NaN     NaN  \n",
       "\n",
       "[462 rows x 21 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_6 = main_df_team[(main_df_team['batting_team'] == \"Delhi Capitals\") & (main_df_team['over'] <6)]\n",
    "dc_6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_6 = dc_6.pivot_table(aggfunc = ['count','sum'] , index = ['match_id'] , values=  ['batsman_runs','total_runs'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_6 = pd.DataFrame(dc_6.to_records())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>('count', 'batsman_runs')</th>\n",
       "      <th>('count', 'total_runs')</th>\n",
       "      <th>('sum', 'batsman_runs')</th>\n",
       "      <th>('sum', 'total_runs')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>46</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>394</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>428</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>469</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>35</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>489</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>35</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>531</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>25</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>561</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>618</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>628</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>28</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7929</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7935</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11152</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11321</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>39</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11413</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>49</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  ('count', 'batsman_runs')  ('count', 'total_runs')  \\\n",
       "0         21                         30                       30   \n",
       "1         39                         30                       30   \n",
       "2        394                         30                       30   \n",
       "3        428                         31                       31   \n",
       "4        469                         31                       31   \n",
       "5        489                         31                       31   \n",
       "6        531                         32                       32   \n",
       "7        561                         30                       30   \n",
       "8        618                         30                       30   \n",
       "9        628                         34                       34   \n",
       "10      7929                         31                       31   \n",
       "11      7935                         30                       30   \n",
       "12     11152                         30                       30   \n",
       "13     11321                         31                       31   \n",
       "14     11413                         31                       31   \n",
       "\n",
       "    ('sum', 'batsman_runs')  ('sum', 'total_runs')  \n",
       "0                        46                     47  \n",
       "1                        46                     46  \n",
       "2                        26                     28  \n",
       "3                        20                     23  \n",
       "4                        35                     37  \n",
       "5                        35                     37  \n",
       "6                        25                     28  \n",
       "7                        33                     34  \n",
       "8                        41                     41  \n",
       "9                        28                     36  \n",
       "10                       40                     41  \n",
       "11                       23                     24  \n",
       "12                       27                     28  \n",
       "13                       39                     41  \n",
       "14                       49                     50  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c1a6ebe0>"
      ]
     },
     "execution_count": 65,
     "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": [
    "sb.kdeplot(dc_6[\"('sum\\', \\'total_runs\\')\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    15.000000\n",
       "mean     36.066667\n",
       "std       8.463929\n",
       "min      23.000000\n",
       "25%      28.000000\n",
       "50%      37.000000\n",
       "75%      41.000000\n",
       "max      50.000000\n",
       "Name: ('sum', 'total_runs'), dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_6[\"('sum\\', \\'total_runs\\')\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "srh_6 = main_df_team[(main_df_team['batting_team'] == \"Sunrisers Hyderabad\") & (main_df_team['over'] <6)]\n",
    "\n",
    "srh_6 = srh_6.pivot_table(aggfunc = ['count','sum'] , index = ['match_id'] , values=  ['batsman_runs','total_runs'])\n",
    "srh_6 = pd.DataFrame(srh_6.to_records())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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>('count', 'batsman_runs')</th>\n",
       "      <th>('count', 'total_runs')</th>\n",
       "      <th>('sum', 'batsman_runs')</th>\n",
       "      <th>('sum', 'total_runs')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>394</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>428</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>469</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>43</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>489</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>531</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>561</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>618</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>628</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7929</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>34</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7935</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>38</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11152</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>48</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11321</td>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>36</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11413</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>44</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  ('count', 'batsman_runs')  ('count', 'total_runs')  \\\n",
       "0         21                         31                       31   \n",
       "1         39                         30                       30   \n",
       "2        394                         31                       31   \n",
       "3        428                         30                       30   \n",
       "4        469                         32                       32   \n",
       "5        489                         30                       30   \n",
       "6        531                         31                       31   \n",
       "7        561                         31                       31   \n",
       "8        618                         31                       31   \n",
       "9        628                         30                       30   \n",
       "10      7929                         31                       31   \n",
       "11      7935                         30                       30   \n",
       "12     11152                         31                       31   \n",
       "13     11321                         31                       31   \n",
       "14     11413                         30                       30   \n",
       "\n",
       "    ('sum', 'batsman_runs')  ('sum', 'total_runs')  \n",
       "0                        30                     31  \n",
       "1                        49                     49  \n",
       "2                        24                     25  \n",
       "3                        33                     33  \n",
       "4                        43                     47  \n",
       "5                        33                     44  \n",
       "6                        28                     32  \n",
       "7                        31                     32  \n",
       "8                        42                     43  \n",
       "9                        42                     42  \n",
       "10                       34                     36  \n",
       "11                       38                     39  \n",
       "12                       48                     57  \n",
       "13                       36                     37  \n",
       "14                       44                     44  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srh_6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c14f5820>"
      ]
     },
     "execution_count": 69,
     "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": [
    "sb.kdeplot(srh_6[\"('sum\\', \\'total_runs\\')\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    15.0000\n",
       "mean     39.4000\n",
       "std       8.3135\n",
       "min      25.0000\n",
       "25%      32.5000\n",
       "50%      39.0000\n",
       "75%      44.0000\n",
       "max      57.0000\n",
       "Name: ('sum', 'total_runs'), dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srh_6[\"('sum\\', \\'total_runs\\')\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230bea27190>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LSChQ4vzybwOMB/4K4HxGkO48fiqQ4HKrVatTXOrgvY2HuTwqhF5hVQzj3P8tfDHXmnR1/Yt1Xq6x3kQMgtg7YPMbMHw2tK/mYbbTkC5BDI4M5J0Nh5g1siturkx+U9Xy9PSke/eLXFS0YNV2ARljSoG5wEogEVhkjNktInNEZI6z2DIgBUgGXgcedG6PANaKyE6sQLLKGHPuSv9vIrLLuW8M8Cu7PpRqeZYnpJOZW8Q9l1bxh5q2FRbdYeXnn/YeeLiYF6ixXPlb8PCBb/9Yo8PuvKQbKVl5rE8+UT/tUq2KNKd+r7i4OBMfH9/YzVCNYMpLP5JbUMK3j46+8Mr3xH4rB7+Xr5XZM6BD4zSypr77P6u76u4V0HWUS4cUlZZx6TNrGBzZjjfvGlbPDVQthYhsMcbEVdyuM4FVk7f1yCl2HD3NXZd2u/DL/9RheHeK1d1z++Lm8+UP1iIyARHwzZMuDwv19nBnWlxn1iZlknGmsJ4bqFo6DQCqyXv7x0ME+HhwU2yFh1u5x60v/+KzMGsJhNRiEfjG5OULY38HaVtgr+tjIG4eGonDwOdb0+qxcao10ACgmrTsvGKWJ6Rz89BI/LzLjVnIPQ7vXA9nM+H2z62FWJqjwTOhfRSsfRpczPXeI9SfuK5BfLLlaLMfuqgalwYA1aQt2ZZGSZlhxrByaRxy0uCtq+FMmpXiIfKCrs3mw80drpxnzVjes9jlw26JiyQlK4+tR07XY+NUS6cBQDVZxhgWxR9lcGQgfTo4h36eOmytt5uXZV35d7u0cRtph/43Qmg/a16Ao8ylQ64Z1JE2nu58uuVo9YWVqoIGANVk7UrLYe/xXKadW+0rfQe8OcFK7jZribUIe0vg5gZjnrAWj3ExRYS/tweTB3bgyx3pFBS7FjSUqkgDgGqyPt58FB9PN64b3BGSv7W6fdy94J5vIHJoYzfPXn2vs9aO/e6vLq8ZcMvQzpwtKmXF7vTqCytVCQ0AqkkqKC5j6fZjXN2/A213LIAPp0Nwd2ucf1jfxm6e/dzcYPTjkJ0Ce5a4dMiI7sF0Dm7Dp1tS67lxqqXSAKCapBW70ykpyuOJ4n/C8t9A1ES4axm0jWjsptWfPtdASB/44TmXRgS5uQlTYzrx04GTZOqcAFULGgBUk7T+p5/40vdPhKQsgTFPwvQPwKdtYzerfrm5weWPWiOC9q1w6ZDrYzpa2a93ajeQqjkNAKppMYacH17jz5kPEel2Crl1EYz+jfXl2BoMuNlaueyHZ12aHdwrLIB+EW35cmfFBL1KVa+V/FWpZiH3OHw0k8DVv2azow9Zs9ZB74mN3aqG5e4Blz5izQ4++L1Lh1w/uCPbjpzmaHZ+PTdOtTQaAFTjMwZ2fAwvjYCUtbzhex/PhT1Nl649GrtljSPmNvAPh/V/d6n4tYOs5yJLd+hdgKoZDQCqceWkwUczYPFsCO3DkWkr+XP2WK6LcX1RixbH0wdGzIGUdXB8V7XFOwf7EtulHV9qAFA1pAFANQ5jIP4teHkkpHwHVz0Ndy/ns8O+iGCN/W/N4u4GTz/46V8uFb9+cEf2Hs9lX0ZuPTdMtSQaAFTDy0mF92+0Fm6PGAwP/gSjHsKIG0t3HGNUj/aEt3V9XdMWqU0QxM6ChE+tu6RqXD0oAjeBpdv1LkC5TgOAajjGwPaP4OVRcGQTXPMc3LEUgq2+/l1pORw8kcf1rf3q/5yRvwDjgJ9frbZoWIAPo3q25+td6ZohVLnMpQAgIpNEJElEkkVkXiX7RURecO7fKSKxzu0+IvKziOwQkd0i8qdyxwSLyCoR2e/8GWTfx1JNTnEeLPkFLJkD4f3hF+th2H3nDe/8YvsxPN2FyQNa8GSvmgjqBtFTIP5tKKq+a2fygAgOnsgjSbuBlIuqDQAi4g68BEwGooGZIhJdodhkIMr5mg3Md24vAsYaYwYDMcAkERnp3DcPWG2MiQJWO9+rlihzL7w2BnYshNHz4K6v/33Vf47DYVi2K53RvUMJ9PVspIY2QZf8FxTlwLb3qy06sX84IrBs1/EGaJhqCVy5AxgOJBtjUowxxcBCYEqFMlOAd41lI9BORCKc7886y3g6X6bcMe84f38HuKEuH0Q1UQfWWhk8C7LhjiVW1ks39wuK7Ug9TXpOIVcP1Kv/83QaCp1HwKZXq00VHRbgw7CuwaxI0FnByjWuBIBOQPmk46nObS6VERF3EdkOZAKrjDGbnGXCjTHpAM6fYZWdXERmi0i8iMRnZWW50FzVZGx7Hz64GQIjYfY66HFllUWXJxzH010Y1y+8gRrXjIyYA6cOwv5vqi06aUAH9mWc5UDW2WrLKuVKAJBKtlV8ylRlGWNMmTEmBogEhotIjdbuM8a8ZoyJM8bEhYaG1uRQ1Zh+ehG+eAi6XQ73rLCCQBWMMSxPSOfSXiEEttHunwv0uw4COsLG+dUWnTSgAwArErQbSFXPlQCQCnQu9z4SqDjWrNoyxpjTwDpgknNThohEADh/ZrrcatW0bXwFvvkd9J9qLdnoE3jR4ruPneFodgGTnV9eqgJ3Txh+Hxz8DjL2XLRox3ZtiOncjuXaDaRc4EoA2AxEiUh3EfECZgBLK5RZCtzhHA00EsgxxqSLSKiItAMQkTbAeGBvuWPudP5+J/BFHT+LagriF8CKx6HvtXDj69aXVzWWJ6Tj7iZMiNYAUKWhd4OHj0tDQicP6EBC2hnNDaSqVW0AMMaUAnOBlUAisMgYs1tE5ojIHGexZUAKkAy8Djzo3B4BrBWRnViBZJUx5ivnvmeACSKyH5jgfK+as71fw1ePQu9JcPNbLn35G2NYvus4I3sEE+zn1QCNbKZ8g2HQNCtnUn72RYueG0ar3UCqOh6uFDLGLMP6ki+/7ZVyvxvgoUqO2wkMqaLOk8C4mjRWNWEZe+Dz2dBxCNzyNni49mW+L+MsKSfyuPuy7vXbvpZg+GzY+i5s/8AaHlqFLu19iY5oy4rdx7n/ilaaUE+5RGcCq7rLz4aFM8HLH2Z8AJ5tXD50eUI6InBVfx39U60OA6HzSNj8ZrUrhl3VvwNbj5wiK7eogRqnmiMNAKpuHA747F44cwymvw9ta5bGYUXCceK6BhEW0Mpz/7hq+P3WkNADqy9abGL/cIyB1YkZDdQw1RxpAFB18/OrcGANTP4bdB5Wo0OPZuez93guV/XXh78u63c9+IXB5jcuWqxvhwAig9rwzR4NAKpqGgBU7WUlwbd/tB76Dr2rxoef+3KaEK3dPy7z8IKhd8K+lXDqUJXFRISJ0R1Yn3yCvKLShmufalY0AKjaKSuxHvp6+sJ1L4BUNhfw4lbtOU7vcH+6tverhwa2YEPvBnGzhtxexMT+4RSXOvh+n86gV5XTAKBqZ/3zkL4drvsHBNT8Cv5UXjGbD51ioo79r7nATtBnMmx9D0oKqywW1zWIdr6e2g2kqqQBQNXcqcPww3PWTN/oinkBXbNmbyZlDqPdP7UVd4+VYC/xyyqLeLi7Ma5vOGv2ZlJSdvFRQ6p10gCgam7lb60uiIlP1bqKVXsyCG/rzcBOF08ToarQY4y1XsCWty5abGL/cHIKSth88OKTx1TrpAFA1Uzyatj7FVzxmNUVUQuFJWV8ty+LCdHhuLnV/NmBwlpIZ+hdcPhH62F8FS6PCsHbw027gVSlNAAo15UWw/LHrcVcRs2tdTU/Jp+goKRMc//UVczt4OYJ8VXfBfh6eXB5VAir9mToUpHqAhoAlOu2vAUn98OkZ8DDu9bVrNqTgb+3ByN7BNvYuFbIP9RKFb3jQygpqLLYhOhw0k4XsPe4LhWpzqcBQLmmOB++f9bK7x81sdbVOByGbxMzGd07FG+PC1cGUzUUdzcU5sDuxVUWGdvXWiryW+0GUhVoAFCu2fwG5GXCmCdrNeb/nJ1pOZw4W8S4fpUuAKdqqtvl0L4XbHmnyiKhAd7EdG7Ht5oWQlWgAUBVryjXGvffcxx0HVWnqlYnZuAmMKaPBgBbiEDsHXB0I2TurbLY+H7h7EjNIeNM1fMGVOujAUBVb+Mr1pjzsU/WuarViZkM7RpEkOb+t8/gW62HwVvfrbLIufkWqxN14T31HxoA1MUV5ljr+/a5BjoNrVNVx04XsCf9jC78bjf/UOh7Nez4CEorT/8cFeZPl2Bf7QZS59EAoC5uy9tQlAOjf13nqlbvta4+x2v/v/1i77Tu0vZ+VeluEWF8v3BNDqfO41IAEJFJIpIkIskiMq+S/SIiLzj37xSRWOf2ziKyVkQSRWS3iDxc7pg/ikiaiGx3vq6272MpW5QWw8b50P0Ka6WvOlqdmEHX9r70DPW3oXHqPD3GQGCXiz4MHh8dRnGpgx/2n2jAhqmmrNoAICLuwEvAZCAamCki0RWKTQainK/ZwHzn9lLg/xlj+gEjgYcqHPu8MSbG+TpvyUnVBOz6BHLT4ZKHqy9bjfziUn46cJJxfcOROowiUlVwc4PYWXDwO8hOqbTIsG7BtPXxYJUOB1VOrtwBDAeSjTEpxphiYCFQMQPYFOBdY9kItBORCGNMujFmK4AxJhdrUfna5Q9QDcsYq+8/rD/0qvvSzT/sP0FxqUO7f+pTzG1WjqZtH1S629PdjTF9w1ibZCXiU8qVANAJOFrufSoXfolXW0ZEumEtEL+p3Oa5zi6jBSISVNnJRWS2iMSLSHxWluY1bzD7V0FWorX4uA1X7GsSMwnw9mBYd539W28CO1lDdbd/CI6ySouM7xdOdl4x246cauDGqabIlQBQ2V9/xcuHi5YREX/gM+ARY8wZ5+b5QE8gBkgHnqvs5MaY14wxccaYuNDQUBeaq2zx0wsQ0BEG3FTnqhwOw5qkTK7oE4qnu447qFexsyD3GBxYW+nu0X1C8XATvtXhoArXAkAq0Lnc+0jgmKtlRMQT68v/A2PM5+cKGGMyjDFlxhgH8DpWV5NqCjL3wqEfrAXIPeo+Xj/hWA5ZuUWM66vdP/Wu92TwbQ/bKp8T0NbHk5E92utwUAW4FgA2A1Ei0l1EvIAZwNIKZZYCdzhHA40Ecowx6WI97XsTSDTG/L38ASISUe7tVCCh1p9C2Sv+TXD3smaY2mB1YiYicKXO/q1/Hl4waDrsXQZ5JystMr5fGMmZZzl4Iq+BG6eammoDgDGmFJgLrMR6iLvIGLNbROaIyBxnsWVACpCMdTX/oHP7pcAsYGwlwz3/JiK7RGQnMAb4lW2fStVe0VnY/pG12pdfiC1Vrk3KJLZLEME6+7dhDJkFjhLYtajS3ecm4q3Wu4BWz8OVQs4hmssqbHul3O8GeKiS49ZT+fMBjDGzatRS1TB2LYLiXBh2ny3VZZ4pZGdqDr++qo8t9SkXhEdDx1hrzeARcy54iN852Je+HQJYtSeD+y7v0UiNVE2BPpFT/2EMbH4TOgyEyGG2VLk2yXrYOFb7/xvWkNshczcc21bp7gnR4cQfPsWpvOIGbphqSjQAqP84ugkyEqyrf5sma61OzKRjoA99OwTYUp9y0YCbwN3bGhJaifH9wilzGNbt09FArZkGAPUf8W+Bd1sYeIst1RWVlrE++QRj+4Xp7N+G1qYd9LvWms1dSYK4gZ0CCQvw1lnBrZwGAGUpzIE9X8DAm8HLz5YqN6Vkk19cpt0/jSXmVig8DUnLL9jl5iaMjw7nu6QsCksqnzSmWj4NAMqS8DmUFlh9xzZZszcTbw83RvWwZzSRqqEeY6zJfNsrTw0xITqcvOIyNhyofLioavk0ACjLtvchzDl6xAbGGFbvzeCyXiG08dK1fxuFmzsMngHJ30Lu8Qt2X9KzPX5e7nyj3UCtlgYABZmJkBZvXf3b1FefnHmWo9kFjNXkb40r5jYwDtix8IJd3h7uXNknjG8TM3BocrhWSQOAsq7+3TysGaQ2Obf4i/b/N7KQXtB5hDUayFz4JT+xfzhZuUVsTz3dCI1TjU0DQGtXVgI7P4bek2yb+QtW9s/oiLZEBLaxrU5VS4NnwokkOLb1gl1X9gnDw034Zrd2A7VGGgBau/2rIC/L1oe/p/OLiT+czTjt/mka+k91zgn46IJdgW2s5HCr9lz4jEC1fBoAWrudC8E3BHpNsK3K7/Zl4TDa/dNktGkHfa+BhE8rnRMwITqcA/mxwVAAACAASURBVFl5HMg62wiNU41JA0BrVnAaklZYY//dXUoL5ZLViZm09/NicGQ72+pUdRRzKxScgn0rL9g1IdpKDqfdQK2PBoDWLHEplBXBoGm2VVla5mBdUiZj+obh5qazf5uMHmPAPxx2XNgN1LFdGwZ2CmTlbu0Gam00ALRmOxdB+162jf0H2HL4FGcKS3Xxl6bG3cMK9Pu/gbwTF+yeNKAD24+eJj2noBEapxqLBoDW6vRRa9WvQdNtG/sP1vBPT3fhsiid/dvkDL4VHKVWfqAKJg3oAMDKBL0LaE00ALRWCZ9aP21K/HbOt4kZjOzRngAfT1vrVTYIj4aIwZVOCusZ6k/vcH+WawBoVTQAtEbGwI6PrQlCwd1tqzYl6ywpWXna/dOUDZ4J6dut2d8VTBoQweZD2Zw4e+FIIdUyuRQARGSSiCSJSLKIzKtkv4jIC879O0Uk1rm9s4isFZFEEdktIg+XOyZYRFaJyH7nzyD7Ppa6qMw9kJVo+9X/6kRr9u+5JQdVEzTgZhD3Su8CJvXvgMOgKaJbkWoDgIi4Ay8Bk4FoYKaIRFcoNhmIcr5mA/Od20uB/2eM6QeMBB4qd+w8YLUxJgpY7XyvGkLCZ9aXQPQNtlb7bWIGfTsE0DnY19Z6lY38QyFqgjUAwHF+Guh+EQF0be+r3UCtiCt3AMOBZGNMijGmGFgITKlQZgrwrrFsBNqJSIQxJt0YsxXAGJOLtah8p3LHvOP8/R3A3m8jVTljrADQY7T1ZWATa/bvKZ392xwMngG5x+Dg9+dtFhEmDejAT8knyCkoaaTGqYbkSgDoBBwt9z6V/3yJu1xGRLoBQ4BNzk3hxph0AOfPSr85RGS2iMSLSHxWVpYLzVUXdWwrnDpkdQXYaF1SFmUOw3jt/mn6ek8G78Aqu4FKHYbVidoN1Bq4EgAqGyNYMa3gRcuIiD/wGfCIMeaM680DY8xrxpg4Y0xcaKh9V6ytVsLn4O5lpQaw0beJGYT4e+vs3+bA0wcGTLUmAhadn/4hpnM7Ogb6sGxXeiM1TjUkVwJAKtC53PtI4JirZUTEE+vL/wNjzOflymSISISzTASgq1PXN4fDCgC9Jlj5YWxSXOrgu31ZjNPZv83HoBlQkg+JX563WUS4ZlAE3+3LIidfu4FaOlcCwGYgSkS6i4gXMANYWqHMUuAO52igkUCOMSZdrJXA3wQSjTF/r+SYO52/3wl8UetPoVxzZIPV9zvgRlur3Xwom9zCUu3/b066jIR2Xa1kgBVcO6gjJWWGlZohtMWrNgAYY0qBucBKrIe4i4wxu0VkjojMcRZbBqQAycDrwIPO7ZcCs4CxIrLd+braue8ZYIKI7AcmON+r+pTwGXj6Qp/Jtla7ak8GXh5uOvu3ORGxHganfAc5aeftGhQZSJdgX77aqd1ALZ1LKSCNMcuwvuTLb3ul3O8GeKiS49ZT+fMBjDEngXE1aayqg7JS2PMF9L4KvPxsq9YYw6o9GVwRFYKvl30ZRVUDGDQdvvurlRriskf+vflcN9Br36eQnVdMsJ9XIzZS1SedCdxaHF4P+Segv73dP3vSz5B2uoCJ0R1srVc1gPY9IXK4NRqownKR1w6KoMxhWJ6gdwEtmQaA1mL3YvD0syYB2eib3RmIoIu/N1eDZ1izwo/vPG9zdERbeoT68dUODQAtmQaA1qCsFPYstfr+Pe1do/ebPRnEdQ0ixN/b1npVA+k/1RoWXGFOgIhw7aCObDp4kszcwkZqnKpvGgBag0PfQ0E29Ld3svXR7HwS089o909z5hsMUROt5wBlpeftum5QBA4Dy/RhcIulAaA12L0YvPyh13hbqz2XNOzckoKqmRo8E/Ky4MCa8zZHhQfQL6Iti7dXnPajWgoNAC1dWYk12adeun+O0yc8gG4h9o0qUo0gaiK0Cap0TsBNsZ3YcfQ0yZm6YHxLpAGgpTv4nbUYeP+ptlZ7Kq+Ynw9m69V/S+DhBQNugr1fQ2HOebuuj+mIm8DibamN1DhVnzQAtHS7F4NXAPS0d8rF6r2ZOAxM7K8BoEUYNANKC63BAuWEBfhwRe9QFm9Nw+GomAJMNXcaAFqyshLrqq7PJCsBmI1WJBwnItCHgZ0Cba1XNZLIOAjuWWmG0BtjIzmWU8jGgycboWGqPmkAaMnqqfvnbFEp3+/PYtKADoiNC8qrRnQuNcTh9XD6yHm7JkaHE+Dtwedb06o4WDVXGgBast1L6qX7Z83eTIpLHUweEGFrvaqRDZpm/dz58XmbfTzduXpgBMt3pZNfXFrJgaq50gDQUpWVwN6v6qn7J53QAG+GdtVlnFuUoG7Q9dJKU0PcGNuJvOIyVu7WDKEtiQaAlurg91b3j83r/hYUl7F2bxZX9Q/HXXP/tzyDZ8DJZEjbct7mYd2C6drel4U/H63iQNUcaQBoqfYscU7+srf757t9mRSUlGn3T0sVPQU8fGDHR+dtdnMTZgzrwqaD2TonoAXRANASlZVA4lfQe5Ltk7+WJxwnyNeTEd2Dba1XNRE+gdZyoQmfQWnRebtuHhqJh5uw8OcjVRysmhsNAC3RoR/qJfdPUWkZqxMzmRjdAQ93/afTYg2+1eo+3P/NeZtDA7y5qn8HPt2aSmFJWSM1TtlJ/4pbot1L6iX3z/r9JzhbVMqkgZr8rUXrcSX4h1c6J2Dm8C6czi/Rh8EthEsBQEQmiUiSiCSLyLxK9ouIvODcv1NEYsvtWyAimSKSUOGYP4pIWiVLRaq6qMfcP1/vTKetjweX9tSlH1s0dw8YeAvsWwl550/+uqRne7q29+WDTdoN1BJUGwBExB14CZgMRAMzRSS6QrHJQJTzNRuYX27f28CkKqp/3hgT43wtq6KMqolz3T82j/4pLCnjmz0ZTB4QgZeH3ji2eINngqPEehZQzrmHwT/rw+AWwZW/5OFAsjEmxRhTDCwEplQoMwV411g2Au1EJALAGPM9kG1no9VF1FPq53VJmZwtKuW6wR1trVc1UR0GQIeBsOPDC3bdEheJp7vw3oZDDd4sZS9XAkAnoPzg31TntpqWqcxcZ5fRAhGpdFaRiMwWkXgRic/KynKhylbs390/V9s++evLHemE+HsxsoeO/mk1Ym6DY9sgY895m0P8vblucEc+2ZJKTkFJIzVO2cGVAFDZbJ+KaQFdKVPRfKAnEAOkA89VVsgY85oxJs4YExcaGlpdW1u3f+f+sbf752xRKav3ZnD1wAgd/dOaDLwF3DwqvQu497Lu5BeX6ZDQZs6Vv+ZUoHO595FAxSWCXClzHmNMhjGmzBjjAF7H6mpSdVFPuX9WJ2ZQWOLQ7p/Wxi8Eoq6CHR9fsFxk/46BjOrRnrd/OkRJmaORGqjqypUAsBmIEpHuIuIFzACWViizFLjDORpoJJBjjLnoQqLnnhE4TQUSqiqrXPDv3D+T66H75xgRgT4M7aK5f1qdmFshLxMOrL5g172XdSc9p5DlCToktLmqNgAYY0qBucBKIBFYZIzZLSJzRGSOs9gyIAVIxrqaf/Dc8SLyEbAB6CMiqSJyr3PX30Rkl4jsBMYAv7LrQ7VKKevqJfVzTn4J3+3L4tpBEbhp7p/WJ2oi+LaH7R9csGts3zC6h/jx5vqDGKOLxTRHHq4Ucg7RXFZh2yvlfjfAQ1UcO7OK7bNcb6aqVsJn1jR+m3P/rNidTkmZ4dpB2v3TKnl4wcBpEP8m5GeD738GAbi5CXdf2o3//mI3Ww6fIq6bDhBobvSJXktQUmit/NX3OvDwtrXqz7em0T3Ej0GRuvJXqxVzK5QVw65PL9h189BIgv28+Nfa5EZomKorDQAtQfK3UHQGBtxoa7VHs/PZdDCbG4d00pW/WrOIQdBhEGx774Jdvl4e3Hd5d9YlZbH96OlGaJyqCw0ALUHCZ1Y/bffRtlb7xXZrCcAbhrgypUO1aENmwfGdkL7jgl13jOpGO19PXli9vxEapupCA0BzV5wH+1ZYedzdXXqk4xJjDJ9vTWN492A6B/vaVq9qpgbeDO7esO3Ch8H+3h7cd1l31uzNZGeq3gU0JxoAmrt9K6AkHwbcZGu124+eJuVEHjfF6tW/wnr42+9aa73gksILdt95STcC23jywmp9FtCcaABo7hI+h4AI6DLK1moXb0vD28ONyQN15S/lNOR2KDwNSV9fsCvAx5N7L+vOt4kZJKTlNELjVG1oAGjOzi3a0X8quLnbVm1xqYOlO44xsX8H2vp42lavaua6j4bAzrDt/Up333Wp9SzgL8sTdV5AM6EBoDnbs9QanjfwFlurXbM3k9P5JdyoD39VeW7u1pDQA2vh1OELdrf18eThcVH8mHySdUmauLE50ADQnO36BNr3go5DbK12UfxRwgK8uTxKF35RFQxxzt+s4i7gthFd6R7ix1PLEinVHEFNngaA5ionDQ6tt2Zp2jhGPz2ngHVJmdwSF6mZP9WF2nW21prY9t4FCeIAvDzceHxSX5Izz/Jx/NFKKlBNif6FN1cJnwLGGp5no0WbU3EYmB7XxdZ6VQsy9C7ITYfkVZXuvqp/OMO7BfP8qn3kFup6AU2ZBoDmaucn0GkotO9pW5VlDsOi+KNc1iuELu117L+qQu+rwL8DbHm70t0iwpPX9OPE2WL+8a1ODmvKNAA0R5mJkLHL6v6x0frkE6SdLmD6sM7VF1atl7snDLnNGoGWk1ZpkcGd23HriC689eNBHRbahGkAaI52LgJxsz33z8KfjxDk68nE/uG21qtaoNg7wDiqfBgM8PikvgT7efPE57v0gXATpQGguXGUwY6F1oM4/zDbqs3KLWLVngxuio3E28O+OQWqhQrqBj3HwtZ3Kn0YDBDYxpM/XBfNrrQc3t1w4bBR1fg0ADQ3Kesg95g1HttGi+KPUuowzBiu3T/KRcPugzNpVjqSKlw7KIIr+4Ty3DdJpJ0uaMDGKVdoAGhudnwEPu2g92Tbqiwtc/DBxsNc0rM9vcICbKtXtXBRV0HbSNj8epVFRIT/nTIAgEc/3k6ZQ2cINyUuBQARmSQiSSKSLCLzKtkvIvKCc/9OEYktt2+BiGSKSEKFY4JFZJWI7Hf+1AVnq1OYA4lfWonfbFz3d9WeDI7lFHLnJd1sq1O1Au4eEHeXdVd6ourRPp2DffnD9f3ZdDCbN35IabDmqepVGwBExB14CZgMRAMzRSS6QrHJQJTzNRuYX27f28CkSqqeB6w2xkQBq53v1cXsXgylhRBzm63VvrPhEJ3atWF8P334q2oo9k5w84TNb1602C1DI5nUvwPPfpPE7mM6KqipcOUOYDiQbIxJMcYUAwuBKRXKTAHeNZaNQDsRiQAwxnwPZFdS7xTgHefv7wA31OYDtCrbP4SQPtAptvqyLtp7/AwbU7KZNaor7rrou6op/zBrLYrtH1prU1RBRHj6xoEE+XrxyMLtFJaUNWAjVVVcCQCdgPJzulOd22papqJwY0w6gPNnpUNaRGS2iMSLSHxWVitOMHUiGY5ugpiZtqZ+eOenw3h7uDE9Th/+qloadh8U5VjDky8i2M+LZ28ZzP7Mszy5OEEzhjYBrgSAyr5tKv6fc6VMrRhjXjPGxBlj4kJDQ+2osnna+g6IOwyaYVuVOfklLNmWxg0xnQjy87KtXtXKdBkJHQbCplehmi/1K3qH8stxUXy2NZUPfz7SQA1UVXElAKQC5S8PI4FjtShTUca5biLnz0wX2tI6lRbB9g+gz2Roa98CLR/+fISCkjJ9+KvqRgRGPghZiZCyttriD4+LYnTvUP64dDfbjpxqgAaqqrgSADYDUSLSXUS8gBnA0gpllgJ3OEcDjQRyznXvXMRS4E7n73cCX9Sg3a3L3q8g/yQMvdu2KgtLyljw40EujwohumNb2+pVrdSAm8AvDDa8XG1RdzfhnzNiCG/rw4MfbCUrt6gBGqgqU20AMMaUAnOBlUAisMgYs1tE5ojIHGexZUAKkAy8Djx47ngR+QjYAPQRkVQRude56xlggojsByY436vKbHkbArtAzzG2Vbl4WxpZuUXMGW1fMjnVinl4W88CkldB1r5qi7fz9eKV24dyKr+Y+9+N14fCjUSa04OYuLg4Ex8f39jNaFgnD8CLsTD2d3DFr22pssxhGP/37/DzdufLuZchNj5UVq3Y2Sx4vr+VKO7a5106ZEVCOr/4YCtXD4jgxZlDcNORaPVCRLYYY+IqbteZwE3dlreth78xt9tW5ao9xzl4Io85o3vql7+yj38oDLoFtn8E+ZWN/L7QpAERzJvUl693pfPcqqR6bqCqSANAU1ZSaPvDX2MM879LoUuwL5MH2PdAWSnAehhcWgCb33D5kNlX9GDm8C68tPYA72/UpHENSQNAU5bwmfXwd9h9tlW54cBJdhw9zf1X9NCJX8p+4f2tHEEb5190Ylh5Vr6g/ozrG8bvv0hg2a7qxo8ou2gAaKqMgU2vQGhf6HGlTVUanv92H+FtvbllaKQtdSp1gct+BQXZF10roCIPdzf+dWssQ7sE8cjC7fyYfKIeG6jO0QDQVB3ZAMd3wogHbJv5uz75BJsPnWLumF74eGrOf1VPuo6CLqPgpxehzPU1gdt4ufPmncPoEerH7Hfj2apzBOqdBoCmauN8K+2zTTN/jTH8fdU+Ogb6ME2XfFT17bJfQc5RqxuzBgJ9PXnnnuGEBHhz14KfdTnJeqYBoCk6fcSa/DX0TvCyZ3H2dfuy2HbkNHPHRumKX6r+RU2EsP6w/nlw1Gw5yPC2Pnxw3wgCfDyZ9eYmko7n1lMjlQaApujn1wGBYffbUp0xhudX7SMyqA03a9+/aggicPmjkLUX9iyu8eGRQb58cN8IPN3duO2NTSRnnq2HRioNAE1NwWlr7H+/66CdPV01KxKOszM1h1+OjcLLQ/+XqwbSfyqE9oN1z1hrWddQtxA/Prx/JAAzX9/IgSwNAnbTb4OmZvMbUHTGunqyQXGpg2dW7KV3uD83xlaXoVspG7m5w5Xz4MS+Gj8LOKdXmD8f3T8CYwwzX9MgYDcNAE1JcT5sfBl6TYCIwbZU+e6GQxw+mc9vr+6Hh7v+71YNrN/1ED7AugsoK61VFVHhAXx4/0jKHBoE7KbfCE3J1netiV+X/z9bqjuVV8wLq/dzRe9QruxT6Xo7StUvNze48gnIPgA7P651Nb3DA/ho9kgcxjDjtY0kZ+qDYTtoAGgqSoutcdNdLrHGUdvgn6v3c7aolCev7mdLfUrVSt9rICIG1v3FSm9SS73DA/jo/pEYAzNe28T+DA0CdaUBoKnYuRDOpNp29Z+cmcv7Gw8zfVgX+nQIsKVOpWpFBCb8jzUvYNP8OlUVFR7AwtkjcROY8dpG9h4/Y1MjWycNAE1BSSGs+yt0jIVe4+pcnTGGJxcn4Ovlzv+b2NuGBipVRz1GQ+9J8MPfIa9uaR56hfmzcPZIPN3dmPnaRp0sVgcaAJqC+Detq//xf7Ql7cOnW1LZdDCbJ67uR4i/d53rU8oWE/7HShC3ru5rP/UI9efjB0bi6+XBra9vZMfR0zY0sPXRANDYCs/A989CjzHWVVIdZecV8/SyROK6BjE9TlM+qCYktA8MvQviF7i0alh1urb3Y+HskQT6enL7G5uIP+TaGgTqP1wKACIySUSSRCRZROZVsl9E5AXn/p0iElvdsSLyRxFJE5HtztfV9nykZmbDv6zMieP+25bqnl6WSG5hKU/fOFBXV1JNz5VPgJcfLP+NlfG2jjoH+7LogVGEBHhzx4Kf+emAZhGtiWoDgIi4Ay8Bk4FoYKaIRFcoNhmIcr5mA/NdPPZ5Y0yM87Wsrh+m2TmbCT/9C6JvgE6x1Zevxvf7svh0Syr3X9GD3uH64Fc1Qf6h1sVOytpaTw6rKCKwDR8/MJLIoDbc/dZm1iVl2lJva+DKHcBwINkYk2KMKQYWAlMqlJkCvGssG4F2IhLh4rGt16o/QFkxjP19nas6lVfMY5/sICrMn4fHRdnQOKXqSdw90HEIrPytlfrEBmEBPiycPYqeof7c/248KxJ0URlXuBIAOgFHy71PdW5zpUx1x851dhktEJGgyk4uIrNFJF5E4rOyslxobjNxZCPs+BAumQshvepUlTGGJ5fs4lR+Mc9Pj9Fc/6ppc3O3Fo3Py4I1f7at2mA/Lz66fyQDOgXy0Ifb+Hxrqm11t1SuBIDKOpIrdt5VVeZix84HegIxQDrwXGUnN8a8ZoyJM8bEhYaGutDcZqCsFL5+DNpGwhW/rnN1i7elsWzXcX41oTcDOgXa0ECl6lnHITB8tpX76sgm26oN9PXk/XtHMKJ7MI8u2sF7usbwRbkSAFKB8sNJIoFjLpap8lhjTIYxpswY4wBex+ouah3iF0DGLrjqKeuBWB0cOpHHH77YzbBuQTxwRU+bGqhUAxj7Oyvj7eLZUGRffh8/bw8W3DXMWmN4SQL/WrMfY8MD55bIlQCwGYgSke4i4gXMAJZWKLMUuMM5GmgkkGOMSb/Ysc5nBOdMBRLq+Fmah5xUWPO/1rDP6Lo9DskvLmXO+1twcxP+Pi1GF3lXzYt3AEx9FU4dhm+etLVqH093Xpk1lKlDOvHsN/v489eJOBwaBCryqK6AMaZUROYCKwF3YIExZreIzHHufwVYBlwNJAP5wN0XO9ZZ9d9EJAarS+gQ8ICdH6xJcjhgyYNWbvRrn6/TpC9jDE98voukjFzevns4nYPtWTlMqQbV9RK49Jfw4z+h92ToM8m2qj3d3XjulsEEtvHkzfUHOZVfzF9vGoSnZsX9N2lOt0ZxcXEmPj6+sZtRe5teg+W/huv+aU2IqYMF6w/yP1/t4ddX9eGhMXV7iKxUoyotgtfHwtkMeOB7aNvR1uqNMby4Jpm/r9rHFb1Defm2WPy9q732bVFEZIsxJq7idg2FDeXEflj131au/9g761TV2qRMnlqWyMTocH4xWvv9VTPn4Q03vWmth7HoDisg2EhE+OW4KP5600B+TD7BjNc2kJlb+6ykLYkGgIZQUgCf3gOePjDlX3Xq+tlx9DQPvr+Vvh0CeG7aYJ3tq1qGsL5ww8uQuhlWPFEvp5g+rAtv3BHHgcw8pr70ky42jwaA+mcMfPkwHN9lPfAK6FDrqg6eyOOetzcTEuDFW3cPI8DH08aGKtXI+t8Al/zSSo649b16OcWYvmF8/MBISsoc3DT/J9a28lnDGgDq26ZXrZWQxvwWel9V62qOnS7gjgWbMMA7dw8nLMDHvjYq1VSM+wP0uBK+egT2f1svpxgU2Y4v5l5Kl2Bf7n17MwvWH2y1w0Q1ANSnlHXWdPc+18Dlj9W6mqPZ+Ux7dQOn80t4665h9Aj1t6+NSjUl7h4w7T0I6weLZkHqlno5TURgGz6ZM4rx/cL5n6/28KuPt1NQXFYv52rKNADUl9R4+OhWKwXu1FestVFr4eCJPKa9uoHcwlI+vG8kgzu3s7mhSjUxPm3hts/ALxQ+uBmykurlNH7eHrxy+1Aem9ibL3YcY+rLP3LoRF69nKup0gBQHzL2wPs3WZkPZy22/kHXwo6jp5n26gaKSx18dP9IBkZqmgfVSgSEW387bh7w1mRI31Evp3FzE+aOjeLtu4dz/Ewh1764niXb0urlXE2RBgC7ZSbCezeAZxu444taP/T9cscxpr26AR9PNz5+YCTRHWsXRJRqttr3hHtWgEcbePs6W3MGVTS6dyhf/ddl9O0QwCMfb+fRj7eTW1hSb+drKjQA2OnwT7DgKkBg1hII6lbjKsochr9/k8R/fbSNQZGBLHnwUnqFaW5/1UqdCwJ+IdaF1Z4v6u1UkUG+LJw9kkfGR7FkexqT/vED3+9rQRmIK6EBwC57voB3bwC/MLhvlTWuuYZST+Uz87WNvLAmmVuGRvL+fSNor2v6qtauXWcrCIT3tyaKrf5fK51KPfBwd+OR8b35ZM4ovD3duGPBzzz2yQ5O5xfXy/kam6aCqKvSYlj9J2tpx8jhcOvH4BtcoyqMMSzZnsZ/L9mNAf5nSn+mDumE2LBAvFItRmkRLHsMtr4LPcfBlJegbUT1x9VSYUkZL67ZzyvfpdDWx4NHJ/Zh5rDOeDTDXEJVpYLQAFAX2Qfhs3shbQsMux8m/tma7VsD+zJy+cMXu9mQcpKhXYP4x/QYTeymVFWMgS1vwYrfgocXTP4/GDStTrPrq5OYfoY/fbmbjSnZ9AkP4Imr+zK6d2izukDTAGCn0iL48QX44Vlw94YpL9Y4tXNmbiHz1x3gvQ2H8fP24NdX9WHm8C6a0lkpV5w8AEt+AUc3WXcDE/8M4RWXKrePMYaVu4/z1LJEjmYXMKRLOx4eF9VsAoEGADs4yiBxqdUHmX3AWsz9qqchsOIKmVXLOFPIK98d4MNNRygpczB9WGd+fVVfgv286rHhSrVAjjL4+TVY9xcoyoWY26wV9oK61tspi0sdfLollZfWJpN2uoDoiLbcdUk3ro/p2KSXYtUAUBelRbB7MfzwHJzYByF9YNJfoNc4lw53OAw/HTjJB5sOs2pPBgaYOqQTc8f0oltI3VYEU6rVy8+G75+1goEps+7GR82FTkPrrWuouNTB51tTeevHQyRl5BLk68nUIZHcMKQjAzsFNrm7Ag0ANWUMHN8J2z+CnQuh4BSE9YcrHrP+gbldPNo7HIZtR0+zfFc6yxOOk3a6gCBfT24eGsmskd3o0l77+ZWyVU4a/PwqxL8FRWcgtC8Mmg4Db4Z2XerllMYYNqZk897GQ3y7J5PiMgc9QvyYNKADY/uGMaRLUJPo1tUA4Ir8bKtPMXk1JC2HM6ng7gV9r4Ehs6xlHKtI6VDmMBw8cZafD57ipwMn2JhykhNni/Fyd+OyqBCuH9yRSQM6NOnbRKVahKJc2PUJ7PgYjm60toX2g6gJ0GM0dIqDNvanVMnJL2F5QjpLtqex+dApyhyGdr6ejOzenrhuQQzvHkzfDm3x8mj4UUR1CgAiMgn47w0ImgAAByhJREFUJ9ayjm8YY56psF+c+6/GWhLyLmPM1osdKyLBwMdAN6wlIacZY05drB22BYCyEjh9BLJTIHMPZOy2pppn7bX2e7SBnmOhz2ToczX4tf/3ocWlDjLOFHL4ZD4pJ85yIPMsiem57D6WQ54zmVR4W28u7RnC6D6hjOkbRltN26xU48hOgb3LYP831kRNRwkgVo6uDoOspHNh0RDc3bpL8Gxjy2lz8kv4ITmLtXuz+PnQSY5mFwDg6S5EhQUQ3bEtPUP96R7iS7cQPzq1a1Ov6d1rHQBExB3YB0wAUrEWep9pjNlTrszVwH9hBYARwD+NMSMudqyI/A3INsY8IyLzgCBjzOMXa0utA8D2DyFpGeQehzPpkHsMjOM/+wM6QocB0HkESd4DiC/pzqliN84UlnIqr5iTecWcOFtExplCMnOLKP+fzN/bg6hwfwZ1CmRgZDuGdGlHjxC/JtcHqFSrV3TWGrJ99Gdr4ZmM3dZdfnn+4Vb6Fv8OVjI63yBoEwz9roOQqFqfOj2ngPhDp9h97Ax70s+w59gZTvz/9s41xKoqiuO/vzovnXFGHbVJJ3RCrYh8BGJYZhah5ONTYBBJBRIUKBRRfQmDoL5EfSrETKNSzF6DRCVWEEUPNcOZ1BK1FKfxkc6oqWWsPuxtcx3ncUfHO3dz1w8u5+x9zz53/e+ZOWufdfde++SFK59VlAzgqspSqstLGFZezLBBxVSWFTG4rIiK0gHMGD+cmspLc1CdOYBsFsacCuw2sz3xRGuBBcDPGccsAN604E2+lVQlqYbQu++s7QJgZmy/GvgS6NIBXDLH9sGR3eHCjr0NKmtDmoahY0OcMGPi1pr6RlZ9sxuA0qJ+VJYVUV1eQnV5CeNHVjCqqoyrq0qpHTKQa0eUM6KixG/2jpMCJeUhBFR3e1vdmRY4/Eu4RxzbCy374UQznGiC5oYQFj53OtwnLsMB1FSWMW9iGfMmtq133HrmH3478hd7j56i6fhpmlrOcPD4aY6e+pvGg60cPXmWE2fP/d/hXP3Q1Et2AJ2RjQMYBezPKB8g9PK7O2ZUN21HmlkTgJk1SRrR0YdLWgwsjsWTkno7N2w1cKSXz5lrXEN+4Bryg97XsOyeXj1dFlykYeaLl3W+DsfGZuMAOureto8bdXZMNm27xMyWA8t70qYnSNrc0aNRSriG/MA15AeuIXuy+Tn6AFCbUR4NHMzymK7aNscwEXFb2ItzOo7j5JhsHMAPwDhJYyUVAwuB+nbH1AMPKDANaInhna7a1gOL4v4i4MrleXUcx3EuotsQkJmdk/QY8ClhKOdKM2uU9Eh8/zXgY8IIoN2EYaAPdtU2nvoFYJ2kh4HfgXt7VVn2XLHwUg5xDfmBa8gPXEOWJDURzHEcx+k90kts7TiO4/QK7gAcx3EKlIJyAJJWSjokqSGjbqikjZJ+jdshfWljd0iqlfSFpB2SGiUtifVJ6JBUKul7ST9F+5fF+iTsz0RSf0k/StoQy0lpkLRP0nZJ2yRtjnWpaaiStF7Szvg/cUtKGiRNiN//+VerpKW50lBQDgBYBcxuV/cUsMnMxgGbYjmfOQc8bmbXA9OARyXdQDo6zgKzzGwiMAmYHUeOpWJ/JkuAHRnlFDXcYWaTMsacp6bhFeATM7sOmEi4HsloMLNd8fufBNxMGETzAbnSYGYF9SKkp2jIKO8CauJ+DbCrr23soZ6PCLmWktMBDAS2EmaHJ2U/YU7LJmAWsCHFvyVCEsbqdnXJaAAGA3uJg1lS1NDO7ruBr3OpodCeADrigpQUQIcpKfIRSWOAycB3JKQjhk62ESb/bTSzpOyPvAw8CWRkFUxOgwGfSdoSU65AWhrqgMPAGzEUt0LSINLSkMlCYE3cz4kGdwCJIqkceA9YamatfW1PTzCzfy088o4Gpkq6sa9t6gmS5gKHzGxLX9tymUw3synAHEIocUZfG9RDBgBTgFfNbDJwijwO93RFnCg7H3g3l5/rDiDBlBSSigg3/7fN7P1YnZwOMztOyAI7m7Tsnw7Ml7QPWAvMkvQWaWnAzA7G7SFC3HkqaWk4AByIT5AA6wkOISUN55kDbDWz5ljOiQZ3AImlpFDIPf06sMPMXsp4KwkdkoZLqor7ZcBdwE4SsR/AzJ42s9FmNobw2P65md1PQhokDZJUcX6fEH9uICENZvYHsF/ShFh1JyHVfDIaMriPtvAP5EhDQc0ElrSGsAZBNdAMPAt8CKwDriGmpDCzP/vKxu6QdCvwFbCdtvjzM4TfAfJeh6SbCOs/9Cd0QNaZ2XOShpGA/e2RNBN4wszmpqRBUh2h1w8hlPKOmT2fkgYASZOAFUAxsIeQhqYfaWkYSEibX2dmLbEuJ9ehoByA4ziO04aHgBzHcQoUdwCO4zgFijsAx3GcAsUdgOM4ToHiDsBxHKdAcQfgOI5ToLgDcBzHKVD+A4mguV0LEnqEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.kdeplot(srh_6[\"('sum\\', \\'total_runs\\')\"] , label = 'srh')\n",
    "sb.kdeplot(dc_6[\"('sum\\', \\'total_runs\\')\"] , label = 'dc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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",
       "    <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>178364</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>MJ Guptill</td>\n",
       "      <td>AR Patel</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>178365</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>MJ Guptill</td>\n",
       "      <td>AR Patel</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>178366</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>MJ Guptill</td>\n",
       "      <td>AR Patel</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>178367</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>MJ Guptill</td>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>AR Patel</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>178368</th>\n",
       "      <td>11413</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>MJ Guptill</td>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>AR Patel</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>3370 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team                 bowling_team  \\\n",
       "0              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "1              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "2              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "3              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "4              1       1  Sunrisers Hyderabad  Royal Challengers Bangalore   \n",
       "...          ...     ...                  ...                          ...   \n",
       "178364     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178365     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178366     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178367     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "178368     11413       1  Sunrisers Hyderabad               Delhi Capitals   \n",
       "\n",
       "        over  ball     batsman non_striker    bowler  is_super_over  ...  \\\n",
       "0          1     1   DA Warner    S Dhawan  TS Mills              0  ...   \n",
       "1          1     2   DA Warner    S Dhawan  TS Mills              0  ...   \n",
       "2          1     3   DA Warner    S Dhawan  TS Mills              0  ...   \n",
       "3          1     4   DA Warner    S Dhawan  TS Mills              0  ...   \n",
       "4          1     5   DA Warner    S Dhawan  TS Mills              0  ...   \n",
       "...      ...   ...         ...         ...       ...            ...  ...   \n",
       "178364     5     2   MK Pandey  MJ Guptill  AR Patel              0  ...   \n",
       "178365     5     3   MK Pandey  MJ Guptill  AR Patel              0  ...   \n",
       "178366     5     4   MK Pandey  MJ Guptill  AR Patel              0  ...   \n",
       "178367     5     5  MJ Guptill   MK Pandey  AR Patel              0  ...   \n",
       "178368     5     6  MJ Guptill   MK Pandey  AR Patel              0  ...   \n",
       "\n",
       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "0              0            0            0             0             0   \n",
       "1              0            0            0             0             0   \n",
       "2              0            0            0             0             4   \n",
       "3              0            0            0             0             0   \n",
       "4              0            0            0             0             0   \n",
       "...          ...          ...          ...           ...           ...   \n",
       "178364         0            0            0             0             0   \n",
       "178365         0            0            0             0             0   \n",
       "178366         0            0            0             0             1   \n",
       "178367         0            0            0             0             6   \n",
       "178368         0            0            0             0             0   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
       "0                0           0               NaN            NaN     NaN  \n",
       "1                0           0               NaN            NaN     NaN  \n",
       "2                0           4               NaN            NaN     NaN  \n",
       "3                0           0               NaN            NaN     NaN  \n",
       "4                2           2               NaN            NaN     NaN  \n",
       "...            ...         ...               ...            ...     ...  \n",
       "178364           0           0               NaN            NaN     NaN  \n",
       "178365           0           0               NaN            NaN     NaN  \n",
       "178366           0           1               NaN            NaN     NaN  \n",
       "178367           0           6               NaN            NaN     NaN  \n",
       "178368           0           0               NaN            NaN     NaN  \n",
       "\n",
       "[3370 rows x 21 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srh_all = df[(df['batting_team'] == 'Sunrisers Hyderabad') & (df['over'] <6)]\n",
    "srh_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "srh_6 = srh_all.pivot_table(aggfunc = ['count','sum'] , index = ['match_id'] , values=  ['batsman_runs','total_runs'])\n",
    "srh_6 = pd.DataFrame(srh_6.to_records())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c2221970>"
      ]
     },
     "execution_count": 75,
     "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": [
    "sb.kdeplot(srh_6[\"('sum\\', \\'total_runs\\')\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    108.000000\n",
       "mean      39.759259\n",
       "std       11.354007\n",
       "min       17.000000\n",
       "25%       32.000000\n",
       "50%       39.000000\n",
       "75%       47.000000\n",
       "max       78.000000\n",
       "Name: ('sum', 'total_runs'), dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srh_6[\"('sum\\', \\'total_runs\\')\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>1084</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>AP Tare</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>B Stanlake</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>1085</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>AP Tare</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>B Stanlake</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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1086</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>AP Tare</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>B Stanlake</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>1087</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AP Tare</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>B Stanlake</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>1088</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>AP Tare</td>\n",
       "      <td>B Stanlake</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178617</th>\n",
       "      <td>11414</td>\n",
       "      <td>1</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>C Munro</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178618</th>\n",
       "      <td>11414</td>\n",
       "      <td>1</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>C Munro</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178619</th>\n",
       "      <td>11414</td>\n",
       "      <td>1</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>C Munro</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178620</th>\n",
       "      <td>11414</td>\n",
       "      <td>1</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>C Munro</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178621</th>\n",
       "      <td>11414</td>\n",
       "      <td>1</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>C Munro</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5535 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning    batting_team                 bowling_team  over  \\\n",
       "1084           5       2  Delhi Capitals  Royal Challengers Bangalore     1   \n",
       "1085           5       2  Delhi Capitals  Royal Challengers Bangalore     1   \n",
       "1086           5       2  Delhi Capitals  Royal Challengers Bangalore     1   \n",
       "1087           5       2  Delhi Capitals  Royal Challengers Bangalore     1   \n",
       "1088           5       2  Delhi Capitals  Royal Challengers Bangalore     1   \n",
       "...          ...     ...             ...                          ...   ...   \n",
       "178617     11414       1  Delhi Capitals          Chennai Super Kings     5   \n",
       "178618     11414       1  Delhi Capitals          Chennai Super Kings     5   \n",
       "178619     11414       1  Delhi Capitals          Chennai Super Kings     5   \n",
       "178620     11414       1  Delhi Capitals          Chennai Super Kings     5   \n",
       "178621     11414       1  Delhi Capitals          Chennai Super Kings     5   \n",
       "\n",
       "        ball      batsman  non_striker      bowler  is_super_over  ...  \\\n",
       "1084       1      AP Tare  SW Billings  B Stanlake              0  ...   \n",
       "1085       2      AP Tare  SW Billings  B Stanlake              0  ...   \n",
       "1086       3      AP Tare  SW Billings  B Stanlake              0  ...   \n",
       "1087       4      AP Tare  SW Billings  B Stanlake              0  ...   \n",
       "1088       5  SW Billings      AP Tare  B Stanlake              0  ...   \n",
       "...      ...          ...          ...         ...            ...  ...   \n",
       "178617     2      C Munro     S Dhawan   DL Chahar              0  ...   \n",
       "178618     3     S Dhawan      C Munro   DL Chahar              0  ...   \n",
       "178619     4     S Dhawan      C Munro   DL Chahar              0  ...   \n",
       "178620     5      C Munro     S Dhawan   DL Chahar              0  ...   \n",
       "178621     6      C Munro     S Dhawan   DL Chahar              0  ...   \n",
       "\n",
       "        bye_runs  legbye_runs  noball_runs  penalty_runs  batsman_runs  \\\n",
       "1084           0            0            0             0             0   \n",
       "1085           0            0            0             0             0   \n",
       "1086           0            0            0             0             0   \n",
       "1087           0            0            0             0             1   \n",
       "1088           0            0            0             0             0   \n",
       "...          ...          ...          ...           ...           ...   \n",
       "178617         0            0            0             0             1   \n",
       "178618         0            0            0             0             0   \n",
       "178619         0            0            0             0             1   \n",
       "178620         0            0            0             0             0   \n",
       "178621         0            0            0             0             1   \n",
       "\n",
       "        extra_runs  total_runs  player_dismissed dismissal_kind fielder  \n",
       "1084             0           0               NaN            NaN     NaN  \n",
       "1085             1           1               NaN            NaN     NaN  \n",
       "1086             0           0               NaN            NaN     NaN  \n",
       "1087             0           1               NaN            NaN     NaN  \n",
       "1088             0           0               NaN            NaN     NaN  \n",
       "...            ...         ...               ...            ...     ...  \n",
       "178617           0           1               NaN            NaN     NaN  \n",
       "178618           0           0               NaN            NaN     NaN  \n",
       "178619           0           1               NaN            NaN     NaN  \n",
       "178620           0           0               NaN            NaN     NaN  \n",
       "178621           0           1               NaN            NaN     NaN  \n",
       "\n",
       "[5535 rows x 21 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_all = df[(df['batting_team'] == 'Delhi Capitals') & (df['over'] <6)]\n",
    "dc_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "dc_6 = dc_all.pivot_table(aggfunc = ['count','sum'] , index = ['match_id'] , values=  ['batsman_runs','total_runs'])\n",
    "dc_6 = pd.DataFrame(dc_6.to_records())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c216fee0>"
      ]
     },
     "execution_count": 79,
     "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": [
    "sb.kdeplot(dc_6[\"('sum\\', \\'total_runs\\')\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    177.000000\n",
       "mean      38.954802\n",
       "std        9.721590\n",
       "min       13.000000\n",
       "25%       33.000000\n",
       "50%       40.000000\n",
       "75%       46.000000\n",
       "max       60.000000\n",
       "Name: ('sum', 'total_runs'), dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc_6[\"('sum\\', \\'total_runs\\')\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c1dba400>"
      ]
     },
     "execution_count": 81,
     "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": [
    "sb.kdeplot(dc_6[\"('sum\\', \\'total_runs\\')\"] ,label = 'dc')\n",
    "sb.kdeplot(srh_6[\"('sum\\', \\'total_runs\\')\"] , label = 'srh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c1dd92b0>"
      ]
     },
     "execution_count": 82,
     "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": [
    "sb.distplot(dc_6[\"('sum\\', \\'total_runs\\')\"] ,label = 'dc')\n",
    "sb.distplot(srh_6[\"('sum\\', \\'total_runs\\')\"] , label = 'srh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Take Aways:\n",
    "### 1 - Generally SRH score more than DC\n",
    "### 2 -There is not much of a difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 4 ans - (6-14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#################################################################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 5 - Total Extras ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>4810</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J 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>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4811</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J 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>4812</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>J 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>4813</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J 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>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>4814</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>J 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>...</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>178586</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>K Ahmed</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",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178587</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "      <td>K Paul</td>\n",
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       "      <td>...</td>\n",
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       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>178589</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>K Paul</td>\n",
       "      <td>K Ahmed</td>\n",
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       "      <td>obstructing the field</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178590</th>\n",
       "      <td>11413</td>\n",
       "      <td>2</td>\n",
       "      <td>Delhi Capitals</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3512 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  inning         batting_team         bowling_team  over  \\\n",
       "4810          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4811          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4812          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4813          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "4814          21       1  Sunrisers Hyderabad       Delhi Capitals     1   \n",
       "...          ...     ...                  ...                  ...   ...   \n",
       "178586     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178587     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178588     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178589     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "178590     11413       2       Delhi Capitals  Sunrisers Hyderabad    20   \n",
       "\n",
       "        ball    batsman non_striker   bowler  is_super_over  ...  bye_runs  \\\n",
       "4810       1  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "4811       2   S Dhawan   DA Warner  J Yadav              0  ...         0   \n",
       "4812       3   S Dhawan   DA Warner  J Yadav              0  ...         0   \n",
       "4813       4  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "4814       5  DA Warner    S Dhawan  J Yadav              0  ...         0   \n",
       "...      ...        ...         ...      ...            ...  ...       ...   \n",
       "178586     2   A Mishra      K Paul  K Ahmed              0  ...         0   \n",
       "178587     3     K Paul    A Mishra  K Ahmed              0  ...         0   \n",
       "178588     4     K Paul    A Mishra  K Ahmed              0  ...         0   \n",
       "178589     5   A Mishra      K Paul  K Ahmed              0  ...         0   \n",
       "178590     6     K Paul    TA Boult  K Ahmed              0  ...         0   \n",
       "\n",
       "        legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "4810              0            1             0             1           1   \n",
       "4811              0            0             0             4           0   \n",
       "4812              0            0             0             1           0   \n",
       "4813              0            0             0             0           0   \n",
       "4814              0            0             0             1           0   \n",
       "...             ...          ...           ...           ...         ...   \n",
       "178586            0            0             0             1           0   \n",
       "178587            0            0             0             0           0   \n",
       "178588            0            0             0             1           0   \n",
       "178589            0            0             0             0           0   \n",
       "178590            0            0             0             4           0   \n",
       "\n",
       "        total_runs  player_dismissed         dismissal_kind fielder  \n",
       "4810             2               NaN                    NaN     NaN  \n",
       "4811             4               NaN                    NaN     NaN  \n",
       "4812             1               NaN                    NaN     NaN  \n",
       "4813             0               NaN                    NaN     NaN  \n",
       "4814             1               NaN                    NaN     NaN  \n",
       "...            ...               ...                    ...     ...  \n",
       "178586           1               NaN                    NaN     NaN  \n",
       "178587           0               NaN                    NaN     NaN  \n",
       "178588           1               NaN                    NaN     NaN  \n",
       "178589           0          A Mishra  obstructing the field     NaN  \n",
       "178590           4               NaN                    NaN     NaN  \n",
       "\n",
       "[3512 rows x 21 columns]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['match_id', 'inning', 'batting_team', 'bowling_team', 'over', 'ball',\n",
       "       'batsman', 'non_striker', 'bowler', 'is_super_over', 'wide_runs',\n",
       "       'bye_runs', 'legbye_runs', 'noball_runs', 'penalty_runs',\n",
       "       'batsman_runs', 'extra_runs', 'total_runs', 'player_dismissed',\n",
       "       'dismissal_kind', 'fielder'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_df_team.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['match_id' , 'wide_runs',\n",
    "       'bye_runs', 'legbye_runs', 'noball_runs', 'penalty_runs' , 'extra_runs']\n",
    "extra = main_df_team[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        match_id  wide_runs  bye_runs  legbye_runs  noball_runs  penalty_runs  \\\n",
       "4810          21          0         0            0            1             0   \n",
       "4811          21          0         0            0            0             0   \n",
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       "178589     11413          0         0            0            0             0   \n",
       "178590     11413          0         0            0            0             0   \n",
       "\n",
       "        extra_runs  \n",
       "4810             1  \n",
       "4811             0  \n",
       "4812             0  \n",
       "4813             0  \n",
       "4814             0  \n",
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       "178589           0  \n",
       "178590           0  \n",
       "\n",
       "[3512 rows x 7 columns]"
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     "execution_count": 120,
     "metadata": {},
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    "extra"
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      "text/plain": [
       "        match_id  wide_runs  bye_runs  legbye_runs  noball_runs  penalty_runs  \\\n",
       "4810          21          0         0            0            1             0   \n",
       "4811          21          0         0            0            0             0   \n",
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       "\n",
       "        extra_runs  \n",
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       "\n",
       "[3512 rows x 7 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra = extra.replace(list(range(1,15)), 1)\n",
    "extra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = extra.pivot_table(index = ['match_id'] , values=  ['wide_runs'] , aggfunc = ['sum'])\n",
    "b = extra.pivot_table(index = ['match_id'] , values=  ['bye_runs'] , aggfunc = ['sum'])\n",
    "lb = extra.pivot_table(index = ['match_id'] , values=  ['legbye_runs'] , aggfunc = ['sum'])\n",
    "nb = extra.pivot_table(index = ['match_id'] , values=  ['noball_runs'] , aggfunc = ['sum'])\n",
    "pr = extra.pivot_table(index = ['match_id'] , values=  ['penalty_runs'] , aggfunc = ['sum'])\n",
    "ex = extra.pivot_table(index = ['match_id'] , values=  ['extra_runs'] , aggfunc = ['sum'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = pd.DataFrame(w.to_records())\n",
    "b = pd.DataFrame(b.to_records())\n",
    "lb = pd.DataFrame(lb.to_records())\n",
    "nb = pd.DataFrame(nb.to_records())\n",
    "pr = pd.DataFrame(pr.to_records())\n",
    "ex = pd.DataFrame(ex.to_records())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>('sum', 'wide_runs')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>394</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>428</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>469</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>489</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>531</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>561</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>618</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>628</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7929</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7935</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11152</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11321</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11413</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  ('sum', 'wide_runs')\n",
       "0         21                     3\n",
       "1         39                     4\n",
       "2        394                     5\n",
       "3        428                     4\n",
       "4        469                     4\n",
       "5        489                     4\n",
       "6        531                     7\n",
       "7        561                     4\n",
       "8        618                     6\n",
       "9        628                     7\n",
       "10      7929                     6\n",
       "11      7935                     2\n",
       "12     11152                     5\n",
       "13     11321                     6\n",
       "14     11413                    10"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = pd.merge(w , b , how = 'inner' , on = 'match_id')\n",
    "w = pd.merge(w , lb , how = 'inner' , on = 'match_id')\n",
    "w = pd.merge(w , nb , how = 'inner' , on = 'match_id')\n",
    "w = pd.merge(w , pr , how = 'inner' , on = 'match_id')\n",
    "w = pd.merge(w , ex , how = 'inner' , on = 'match_id')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>('sum', 'wide_runs')</th>\n",
       "      <th>('sum', 'bye_runs')</th>\n",
       "      <th>('sum', 'legbye_runs')</th>\n",
       "      <th>('sum', 'noball_runs')</th>\n",
       "      <th>('sum', 'penalty_runs')</th>\n",
       "      <th>('sum', 'extra_runs')</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>428</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>469</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>489</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>531</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>561</td>\n",
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       "      <td>0</td>\n",
       "      <td>8</td>\n",
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       "      <th>8</th>\n",
       "      <td>618</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>628</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7929</td>\n",
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       "      <th>11</th>\n",
       "      <td>7935</td>\n",
       "      <td>2</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
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       "      <th>12</th>\n",
       "      <td>11152</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "      <th>13</th>\n",
       "      <td>11321</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11413</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
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       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  ('sum', 'wide_runs')  ('sum', 'bye_runs')  \\\n",
       "0         21                     3                    3   \n",
       "1         39                     4                    0   \n",
       "2        394                     5                    0   \n",
       "3        428                     4                    1   \n",
       "4        469                     4                    0   \n",
       "5        489                     4                    1   \n",
       "6        531                     7                    2   \n",
       "7        561                     4                    0   \n",
       "8        618                     6                    0   \n",
       "9        628                     7                    1   \n",
       "10      7929                     6                    0   \n",
       "11      7935                     2                    0   \n",
       "12     11152                     5                    1   \n",
       "13     11321                     6                    0   \n",
       "14     11413                    10                    1   \n",
       "\n",
       "    ('sum', 'legbye_runs')  ('sum', 'noball_runs')  ('sum', 'penalty_runs')  \\\n",
       "0                        3                       2                        0   \n",
       "1                        3                       1                        0   \n",
       "2                        3                       2                        0   \n",
       "3                        1                       1                        0   \n",
       "4                        6                       1                        0   \n",
       "5                        4                       1                        0   \n",
       "6                        3                       0                        0   \n",
       "7                        4                       0                        0   \n",
       "8                        3                       2                        0   \n",
       "9                        5                       0                        0   \n",
       "10                       1                       1                        0   \n",
       "11                       3                       0                        0   \n",
       "12                       4                       0                        0   \n",
       "13                       5                       2                        0   \n",
       "14                       2                       1                        0   \n",
       "\n",
       "    ('sum', 'extra_runs')  \n",
       "0                      11  \n",
       "1                       8  \n",
       "2                      10  \n",
       "3                       7  \n",
       "4                      11  \n",
       "5                      10  \n",
       "6                      12  \n",
       "7                       8  \n",
       "8                      11  \n",
       "9                      13  \n",
       "10                      8  \n",
       "11                      5  \n",
       "12                     10  \n",
       "13                     13  \n",
       "14                     14  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "w['sum'] = w[\"('sum\\', \\'bye_runs\\')\"]+w[\"('sum\\', \\'extra_runs\\')\"]+w[\"('sum\\', \\'legbye_runs\\')\"]+w[\"('sum\\', \\'noball_runs\\')\"] + w[\"('sum\\', \\'penalty_runs\\')\"] + w[\"('sum\\', \\'wide_runs\\')\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>sum</th>\n",
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       "      <th>4</th>\n",
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       "      <td>22</td>\n",
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       "      <th>5</th>\n",
       "      <td>489</td>\n",
       "      <td>4</td>\n",
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       "      <td>10</td>\n",
       "      <td>20</td>\n",
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       "      <th>6</th>\n",
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       "      <td>3</td>\n",
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       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>561</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>618</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>628</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7929</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7935</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11152</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11321</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>11413</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    match_id  ('sum', 'wide_runs')  ('sum', 'bye_runs')  \\\n",
       "0         21                     3                    3   \n",
       "1         39                     4                    0   \n",
       "2        394                     5                    0   \n",
       "3        428                     4                    1   \n",
       "4        469                     4                    0   \n",
       "5        489                     4                    1   \n",
       "6        531                     7                    2   \n",
       "7        561                     4                    0   \n",
       "8        618                     6                    0   \n",
       "9        628                     7                    1   \n",
       "10      7929                     6                    0   \n",
       "11      7935                     2                    0   \n",
       "12     11152                     5                    1   \n",
       "13     11321                     6                    0   \n",
       "14     11413                    10                    1   \n",
       "\n",
       "    ('sum', 'legbye_runs')  ('sum', 'noball_runs')  ('sum', 'penalty_runs')  \\\n",
       "0                        3                       2                        0   \n",
       "1                        3                       1                        0   \n",
       "2                        3                       2                        0   \n",
       "3                        1                       1                        0   \n",
       "4                        6                       1                        0   \n",
       "5                        4                       1                        0   \n",
       "6                        3                       0                        0   \n",
       "7                        4                       0                        0   \n",
       "8                        3                       2                        0   \n",
       "9                        5                       0                        0   \n",
       "10                       1                       1                        0   \n",
       "11                       3                       0                        0   \n",
       "12                       4                       0                        0   \n",
       "13                       5                       2                        0   \n",
       "14                       2                       1                        0   \n",
       "\n",
       "    ('sum', 'extra_runs')  sum  \n",
       "0                      11   22  \n",
       "1                       8   16  \n",
       "2                      10   20  \n",
       "3                       7   14  \n",
       "4                      11   22  \n",
       "5                      10   20  \n",
       "6                      12   24  \n",
       "7                       8   16  \n",
       "8                      11   22  \n",
       "9                      13   26  \n",
       "10                      8   16  \n",
       "11                      5   10  \n",
       "12                     10   20  \n",
       "13                     13   26  \n",
       "14                     14   28  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c2a75be0>"
      ]
     },
     "execution_count": 131,
     "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": [
    "sb.kdeplot(w['sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['match_id' , 'wide_runs',\n",
    "       'bye_runs', 'legbye_runs', 'noball_runs', 'penalty_runs' , 'extra_runs']\n",
    "extra = df[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "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>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>extra_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179073</th>\n",
       "      <td>11415</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>179074</th>\n",
       "      <td>11415</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179075</th>\n",
       "      <td>11415</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>179076</th>\n",
       "      <td>11415</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>179077</th>\n",
       "      <td>11415</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>179078 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        match_id  wide_runs  bye_runs  legbye_runs  noball_runs  penalty_runs  \\\n",
       "0              1          0         0            0            0             0   \n",
       "1              1          0         0            0            0             0   \n",
       "2              1          0         0            0            0             0   \n",
       "3              1          0         0            0            0             0   \n",
       "4              1          1         0            0            0             0   \n",
       "...          ...        ...       ...          ...          ...           ...   \n",
       "179073     11415          0         0            0            0             0   \n",
       "179074     11415          0         0            0            0             0   \n",
       "179075     11415          0         0            0            0             0   \n",
       "179076     11415          0         0            0            0             0   \n",
       "179077     11415          0         0            0            0             0   \n",
       "\n",
       "        extra_runs  \n",
       "0                0  \n",
       "1                0  \n",
       "2                0  \n",
       "3                0  \n",
       "4                1  \n",
       "...            ...  \n",
       "179073           0  \n",
       "179074           0  \n",
       "179075           0  \n",
       "179076           0  \n",
       "179077           0  \n",
       "\n",
       "[179078 rows x 7 columns]"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra = extra.replace(list(range(1,15)), 1)\n",
    "extra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = extra.pivot_table(index = ['match_id'] , values=  ['wide_runs'] , aggfunc = ['sum'])\n",
    "b = extra.pivot_table(index = ['match_id'] , values=  ['bye_runs'] , aggfunc = ['sum'])\n",
    "lb = extra.pivot_table(index = ['match_id'] , values=  ['legbye_runs'] , aggfunc = ['sum'])\n",
    "nb = extra.pivot_table(index = ['match_id'] , values=  ['noball_runs'] , aggfunc = ['sum'])\n",
    "pr = extra.pivot_table(index = ['match_id'] , values=  ['penalty_runs'] , aggfunc = ['sum'])\n",
    "ex = extra.pivot_table(index = ['match_id'] , values=  ['extra_runs'] , aggfunc = ['sum'])\n",
    "\n",
    "w = pd.DataFrame(w.to_records())\n",
    "b = pd.DataFrame(b.to_records())\n",
    "lb = pd.DataFrame(lb.to_records())\n",
    "nb = pd.DataFrame(nb.to_records())\n",
    "pr = pd.DataFrame(pr.to_records())\n",
    "ex = pd.DataFrame(ex.to_records())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = pd.merge(w , b , how = 'inner' , on = 'match_id')\n",
    "w = pd.merge(w , lb , how = 'inner' , on = 'match_id')\n",
    "w = pd.merge(w , nb , how = 'inner' , on = 'match_id')\n",
    "w = pd.merge(w , pr , how = 'inner' , on = 'match_id')\n",
    "w = pd.merge(w , ex , how = 'inner' , on = 'match_id')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "w['sum'] = w[\"('sum\\', \\'bye_runs\\')\"]+w[\"('sum\\', \\'extra_runs\\')\"]+w[\"('sum\\', \\'legbye_runs\\')\"]+w[\"('sum\\', \\'noball_runs\\')\"] + w[\"('sum\\', \\'penalty_runs\\')\"] + w[\"('sum\\', \\'wide_runs\\')\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>('sum', 'wide_runs')</th>\n",
       "      <th>('sum', 'bye_runs')</th>\n",
       "      <th>('sum', 'legbye_runs')</th>\n",
       "      <th>('sum', 'noball_runs')</th>\n",
       "      <th>('sum', 'penalty_runs')</th>\n",
       "      <th>('sum', 'extra_runs')</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>121</td>\n",
       "      <td>6</td>\n",
       "      <td>47</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>183</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>20</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>738</th>\n",
       "      <td>11347</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>739</th>\n",
       "      <td>11412</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>11413</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>741</th>\n",
       "      <td>11414</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>742</th>\n",
       "      <td>11415</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>743 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id  ('sum', 'wide_runs')  ('sum', 'bye_runs')  \\\n",
       "0           1                   121                    6   \n",
       "1          15                     5                    0   \n",
       "2          16                    12                    0   \n",
       "3          17                    10                    2   \n",
       "4          18                     6                    1   \n",
       "..        ...                   ...                  ...   \n",
       "738     11347                     9                    0   \n",
       "739     11412                     3                    0   \n",
       "740     11413                    10                    1   \n",
       "741     11414                     4                    0   \n",
       "742     11415                     7                    2   \n",
       "\n",
       "     ('sum', 'legbye_runs')  ('sum', 'noball_runs')  ('sum', 'penalty_runs')  \\\n",
       "0                        47                       9                        0   \n",
       "1                         3                       3                        0   \n",
       "2                         4                       2                        0   \n",
       "3                         4                       0                        0   \n",
       "4                         3                       0                        0   \n",
       "..                      ...                     ...                      ...   \n",
       "738                       2                       2                        0   \n",
       "739                       2                       3                        0   \n",
       "740                       2                       1                        0   \n",
       "741                       4                       1                        0   \n",
       "742                       0                       0                        0   \n",
       "\n",
       "     ('sum', 'extra_runs')  sum  \n",
       "0                      183  366  \n",
       "1                       11   22  \n",
       "2                       18   36  \n",
       "3                       16   32  \n",
       "4                       10   20  \n",
       "..                     ...  ...  \n",
       "738                     13   26  \n",
       "739                      8   16  \n",
       "740                     14   28  \n",
       "741                      9   18  \n",
       "742                      9   18  \n",
       "\n",
       "[743 rows x 8 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x230c2f2f280>"
      ]
     },
     "execution_count": 138,
     "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": [
    "sb.kdeplot(w['sum'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    743.000000\n",
       "mean      25.672948\n",
       "std       15.033723\n",
       "min        4.000000\n",
       "25%       20.000000\n",
       "50%       24.000000\n",
       "75%       30.000000\n",
       "max      366.000000\n",
       "Name: sum, dtype: float64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w['sum'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Take Aways:\n",
    "### 1 = Generally , there are around 10 count\n",
    "### 2 - But while taking values , it ranges more\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ques 5 ans  - (0-8)"
   ]
  },
  {
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}