Data science task 5

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3ff87169",
   "metadata": {},
   "source": [
    "# AUTHOR : RAJIL JAIN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61e96016",
   "metadata": {},
   "source": [
    "# GRIP@The Sparks Foundation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2344116b",
   "metadata": {},
   "source": [
    "# Task - 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23a3cf08",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis - Sports"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb7da3bd",
   "metadata": {},
   "source": [
    "Perform **'EDA'** on dataset **'Indian Premier League (IPL)'** \n",
    "\n",
    "In these task, We will find out the most successful teams, player and factors contributing win or loss of a team. Suggest teams or players a company should endorse for its product.  \n",
    "\n",
    "Language:Python\n",
    "\n",
    "IDE:Jupyter Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2962e9ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import all  relevant libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7300b4f5",
   "metadata": {},
   "source": [
    "## Load data of \"Matches\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7537f993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>AY Dandekar</td>\n",
       "      <td>NJ Llong</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>Rising Pune Supergiant</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>A Nand Kishore</td>\n",
       "      <td>S Ravi</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <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": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import dataset\n",
    "\n",
    "df=pd.read_csv(\"D:\\Sparks Foundation\\Task 5\\matches.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "4a1f1bc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(756, 18)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f45b55a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 756 entries, 0 to 755\n",
      "Data columns (total 18 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   id               756 non-null    int64 \n",
      " 1   season           756 non-null    int64 \n",
      " 2   city             749 non-null    object\n",
      " 3   date             756 non-null    object\n",
      " 4   team1            756 non-null    object\n",
      " 5   team2            756 non-null    object\n",
      " 6   toss_winner      756 non-null    object\n",
      " 7   toss_decision    756 non-null    object\n",
      " 8   result           756 non-null    object\n",
      " 9   dl_applied       756 non-null    int64 \n",
      " 10  winner           752 non-null    object\n",
      " 11  win_by_runs      756 non-null    int64 \n",
      " 12  win_by_wickets   756 non-null    int64 \n",
      " 13  player_of_match  752 non-null    object\n",
      " 14  venue            756 non-null    object\n",
      " 15  umpire1          754 non-null    object\n",
      " 16  umpire2          754 non-null    object\n",
      " 17  umpire3          119 non-null    object\n",
      "dtypes: int64(5), object(13)\n",
      "memory usage: 106.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "87787814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>season</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "      <td>756.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1792.178571</td>\n",
       "      <td>2013.444444</td>\n",
       "      <td>0.025132</td>\n",
       "      <td>13.283069</td>\n",
       "      <td>3.350529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3464.478148</td>\n",
       "      <td>3.366895</td>\n",
       "      <td>0.156630</td>\n",
       "      <td>23.471144</td>\n",
       "      <td>3.387963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>189.750000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>378.500000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>567.250000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11415.000000</td>\n",
       "      <td>2019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>146.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id       season  dl_applied  win_by_runs  win_by_wickets\n",
       "count    756.000000   756.000000  756.000000   756.000000      756.000000\n",
       "mean    1792.178571  2013.444444    0.025132    13.283069        3.350529\n",
       "std     3464.478148     3.366895    0.156630    23.471144        3.387963\n",
       "min        1.000000  2008.000000    0.000000     0.000000        0.000000\n",
       "25%      189.750000  2011.000000    0.000000     0.000000        0.000000\n",
       "50%      378.500000  2013.000000    0.000000     0.000000        4.000000\n",
       "75%      567.250000  2016.000000    0.000000    19.000000        6.000000\n",
       "max    11415.000000  2019.000000    1.000000   146.000000       10.000000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d4dcb2fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sunrisers Hyderabad', 'Mumbai Indians', 'Gujarat Lions',\n",
       "       'Rising Pune Supergiant', 'Royal Challengers Bangalore',\n",
       "       'Kolkata Knight Riders', 'Delhi Daredevils', 'Kings XI Punjab',\n",
       "       'Chennai Super Kings', 'Rajasthan Royals', 'Deccan Chargers',\n",
       "       'Kochi Tuskers Kerala', 'Pune Warriors', 'Rising Pune Supergiants',\n",
       "       'Delhi Capitals'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['team1'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b8cb586e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Changing team names to their abbreviations\n",
    "\n",
    "teams = {'Sunrisers Hyderabad':'SRH', 'Mumbai Indians':'MI', 'Gujarat Lions':'GL',\n",
    "       'Rising Pune Supergiant':'RPS', 'Royal Challengers Bangalore':'RCB',\n",
    "       'Kolkata Knight Riders':'KKR', 'Delhi Daredevils':'DC', 'Kings XI Punjab':'KXIP',\n",
    "       'Chennai Super Kings':'CSK', 'Rajasthan Royals':'RR', 'Deccan Chargers':'DCH',\n",
    "       'Kochi Tuskers Kerala':'KTK', 'Pune Warriors':'PW', 'Rising Pune Supergiants':'RPS',\n",
    "       'Delhi Capitals':'DC'}\n",
    "\n",
    "#Delhi capitails & Delhi daredevils both teams are same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "39f2f4c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['team1'] = df['team1'].map(teams)\n",
    "df['team2'] = df['team2'].map(teams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b343fde9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>season</th>\n",
       "      <th>city</th>\n",
       "      <th>date</th>\n",
       "      <th>team1</th>\n",
       "      <th>team2</th>\n",
       "      <th>toss_winner</th>\n",
       "      <th>toss_decision</th>\n",
       "      <th>result</th>\n",
       "      <th>dl_applied</th>\n",
       "      <th>winner</th>\n",
       "      <th>win_by_runs</th>\n",
       "      <th>win_by_wickets</th>\n",
       "      <th>player_of_match</th>\n",
       "      <th>venue</th>\n",
       "      <th>umpire1</th>\n",
       "      <th>umpire2</th>\n",
       "      <th>umpire3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</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>MI</td>\n",
       "      <td>RPS</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>GL</td>\n",
       "      <td>KKR</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>RPS</td>\n",
       "      <td>KXIP</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>RCB</td>\n",
       "      <td>DC</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 team2                  toss_winner  \\\n",
       "0   1    2017  Hyderabad  2017-04-05   SRH   RCB  Royal Challengers Bangalore   \n",
       "1   2    2017       Pune  2017-04-06    MI   RPS       Rising Pune Supergiant   \n",
       "2   3    2017     Rajkot  2017-04-07    GL   KKR        Kolkata Knight Riders   \n",
       "3   4    2017     Indore  2017-04-08   RPS  KXIP              Kings XI Punjab   \n",
       "4   5    2017  Bangalore  2017-04-08   RCB    DC  Royal Challengers Bangalore   \n",
       "\n",
       "  toss_decision  result  dl_applied                       winner  win_by_runs  \\\n",
       "0         field  normal           0          Sunrisers Hyderabad           35   \n",
       "1         field  normal           0       Rising Pune Supergiant            0   \n",
       "2         field  normal           0        Kolkata Knight Riders            0   \n",
       "3         field  normal           0              Kings XI Punjab            0   \n",
       "4           bat  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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "4daec36e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "756"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total matches played in IPL till 2019\n",
    "\n",
    "df.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "66fd1f9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Rajiv Gandhi International Stadium, Uppal'\n",
      " 'Maharashtra Cricket Association Stadium'\n",
      " 'Saurashtra Cricket Association Stadium' 'Holkar Cricket Stadium'\n",
      " 'M Chinnaswamy Stadium' 'Wankhede Stadium' 'Eden Gardens'\n",
      " 'Feroz Shah Kotla' 'Punjab Cricket Association IS Bindra Stadium, Mohali'\n",
      " 'Green Park' 'Punjab Cricket Association Stadium, Mohali'\n",
      " 'Sawai Mansingh Stadium' 'MA Chidambaram Stadium, Chepauk'\n",
      " 'Dr DY Patil Sports Academy' 'Newlands' \"St George's Park\" 'Kingsmead'\n",
      " 'SuperSport Park' 'Buffalo Park' 'New Wanderers Stadium'\n",
      " 'De Beers Diamond Oval' 'OUTsurance Oval' 'Brabourne Stadium'\n",
      " 'Sardar Patel Stadium, Motera' 'Barabati Stadium'\n",
      " 'Vidarbha Cricket Association Stadium, Jamtha'\n",
      " 'Himachal Pradesh Cricket Association Stadium' 'Nehru Stadium'\n",
      " 'Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket Stadium'\n",
      " 'Subrata Roy Sahara Stadium'\n",
      " 'Shaheed Veer Narayan Singh International Stadium'\n",
      " 'JSCA International Stadium Complex' 'Sheikh Zayed Stadium'\n",
      " 'Sharjah Cricket Stadium' 'Dubai International Cricket Stadium'\n",
      " 'M. A. Chidambaram Stadium' 'Feroz Shah Kotla Ground'\n",
      " 'M. Chinnaswamy Stadium' 'Rajiv Gandhi Intl. Cricket Stadium'\n",
      " 'IS Bindra Stadium' 'ACA-VDCA Stadium']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total venues\n",
    "\n",
    "print(df['venue'].unique())\n",
    "df['venue'].nunique()\n",
    "\n",
    "# from nunique(), we get number of that particular operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "4efcd3dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Hyderabad' 'Pune' 'Rajkot' 'Indore' 'Bangalore' 'Mumbai' 'Kolkata'\n",
      " 'Delhi' 'Chandigarh' 'Kanpur' 'Jaipur' 'Chennai' 'Cape Town'\n",
      " 'Port Elizabeth' 'Durban' 'Centurion' 'East London' 'Johannesburg'\n",
      " 'Kimberley' 'Bloemfontein' 'Ahmedabad' 'Cuttack' 'Nagpur' 'Dharamsala'\n",
      " 'Kochi' 'Visakhapatnam' 'Raipur' 'Ranchi' 'Abu Dhabi' 'Sharjah' nan\n",
      " 'Mohali' 'Bengaluru']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cities where tournament played in past year \n",
    "\n",
    "print(df['city'].unique())\n",
    "df['city'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e0fd28a",
   "metadata": {},
   "source": [
    "## Finding venue hosted most IPL matches played "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "63602605",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABBcAAAIBCAYAAAAS41eNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAACSRElEQVR4nOzdebhd0/3H8ffHLOaZmKKDVquDJoaYaU2lhpipolpqaimlrSFRlB9atNRYQouY1UwMqXlIUFONFcQcJJFEEpHv74+1TrKzc8695959k3sTn9fznOecs/faa629z755sr57DYoIzMzMzMzMzMzaa7bOroCZmZmZmZmZzdwcXDAzMzMzMzOzShxcMDMzMzMzM7NKHFwwMzMzMzMzs0ocXDAzMzMzMzOzShxcMDMzMzMzM7NKHFwwMzOzLkfSo5ImSepe2PYTSSHphM6sWzMk7ZXrenidfRvmfWe1ksdQSaObKKup/Nor5/1sB+XVXVI/Sdu249jFc10GdURd2lj2AZL6zehyS3Vo6n7o4DKXkfSIpAn52s9R2t8/b++Vv0fhNUrSrZJ65H2D8vbFZ0C9f5jvsx5NpO2w+9vsi87BBTMzM+uKrgQEbF/YVvt81YyvTod6HtgVuKizK9IJugN9gW07uR5tdQCp3jM9SbO3IXkfYE3gGtI9+3kTxwzLaQcAWwB/b2sdO8APSb9Xj04o2+wLy8EFMzMz64quBgLYAUDS/MCmwAsR8bSk3pIeljRa0kuSds3peuQnkQ9Jui0/Pb1cSW3fzTnt4fn7Xvl73TyLJHXLT3EH5O+35/SzSzo457dJ4ZBvS3pC0seSfpW3fQO4AvhpzmPl/IR3RE53UKnMUyR9KOlxSUu3cM2Wl/RvSSMlnZqPXULSk7mOoyXdL+mbed86kp6WNE7SB5KuaCHvuSRdnOt4p6RuOY91cy+T0ZJekbRv3r6kpLvz9lE5zRLA4zm/PWvXXtJCki6S9L6k4ZLOlzRfzufbkp6RNAI4tIX61Z5AvyTpakmf5Dx3y+f2qqTv5XSL5CfuH+TXpZIWyfv6SXovX5NX8vH9gW8WyhhUp+zaE/y/Snozv9Yr7as93R8taWj+XOvhcoWk5/M12F7SZZLGSLpB0/YWODXn8ZCkFfK2ZSVdm++ftyWdrBxEUOrxMEbS3ySNBL5Vym9uSafn40ZI+pek5SVtCNR6w+wKnBwR0dJvkI2MiAFA7X5fs4ljivUZms/v7Hwv3yJpi3xN35G0eU73g/wbjcv3zQBJCyj9PR+Ys7tXUuT0a0i6L98b70vqUyi20f3d6N+ZRve32ReagwtmZmbW5UTEm8DDwLqSlgK2AuYBrpK0KHAzsDBwIjAU+Kek7xayWBO4D3iR1DBat6XymsyTiBgLPAGsJUm5nPlIDba1gYnAQ4VDNiU9uQ3gZElzlcqdA7gJ2AQ4EzgK+LSQZD5gCeBWoBfw8xZO4/vAdcCHwOG54Tkpb/sVcDLwHeCMnP4I4EukhtgfgOEt5P1V4APgkVzX7SUtBtwIrAgcDrwPnCdpY2B3YGPgT8BhwFPA7Pn8IP02uwL/zvXZA+gPXAjsk+sDcAmwCuk3WaWF+hXr+Tzpd9871+uv+TyPzWnOBPbM5V2cyz4zBxj6As8B+wL/JP1f+RzS03hynWt1q6cncB6wHNCvifrWrJPLWZzUM2cE8ACwDener5kPWCifQ2+m/Jb/ZMo9dCNwJKm3RU03Uq+R2u9UdBRwCHAn6R7ZCriMdB0vy2nOBQ5u8lxmVxr6UOtp9EaTxxXNl98fJvVCOAc4FVgy1xFgNPA34JekYN3O+fO/87kAHA/smv++bwW+CxwHnET626ipd3+39G9Co/vb7IstIvzyyy+//PLLL7+63IvUIA5gf6b0ZPgGsGX+XH79mtQNOoCHch6/zd/3KOy7Oe87PH/fq6U869Tr1LzvB8BYUmNmf1Lj47GcZq+c5o/5++35+/LAhvnzWaQn4gFcXaecoaRu6PMAa+V0f6+Trpbf5fn7ufn7eqQG5YOkhlTtnN7N6U7L+V9LamB+vcHvEMDb+fMu+fsxpEZoACfmfZvk76cW9j1AagxunNP0ytv7F/L/oM51f5rUiA7g/pzuy/n7oBbqOSx/PjF/34fU6AvgP3nf8Fq6/H1YrsOcwDvAW8D5wM+AbjnNs0C0cK/2z2Vsmr+PA14t7euVv48GhpbukxPy97fybzJXrnsAh5buh7ny9zeBj4D5S79v7XVj4bgAFmpQ98E537nz99r9Mj+Fv5FWzrt2buU6fFS4JoPytsVb+buffJ6kYFoAxxfOeWT+vBHwSqm8AXnfWfn7hvl77e/71Dbc3y39O1P3/vbLry/6a6puVmZmZmZdyNXAn0mBgW8Dz0XE85JWyvsvBf5RSD+08Pmj/D4xv8/OlPHitf//LFynzJbyrLmP1Oj6Nalhdh9pbPqKpIZ6Ub16tMWnETFOUjPH1yvrl6QeFacDt5HmeVgg7z8SuJ/0BHwf4HeSlouIEU3mXROldyLiZklrkQIOmwNHKg0X+bhB3d8l/c414wufVXpvyYj8/ll+HxkRn6dOJi1f+4j4TNJ3SE/cVyMFaTYEfkzh3FpRvE618ibfd5LmBuZtpd6fRsQESbXjmrlnBPyHdF/WjCx8HhMRI2lO0Ny1bmQoKSgwEvhvRLRnEsraNZj8O+b3z5lyPU4i9UjZhxSwuZIUiIPmf6+alu7vaf5NiIhX6t3fEXFXG8s1m6U4uGBmZmZdUkS8LelB0hN4SI0HSF2lPyL9p/5x0v9ntiJ1gX69hSzfIz1R7ilpJ1LX+JqW8nyllM+DpMbL5qSn9A8DR+d997XpJFP3/ZeAbZVWI3gPmBARHT0J3iLA+qTu+rWG2u9IjfjnSE+EVwIWZEpDtzUPk4IF+0h6kynBgVsl7UAagvFKzn8dUi+K/+U0q+Xx6wNJXc/3ArYmNZB7ApMi4n5JT5KGoPyGFATpCLcAP5H0f/n7ssClkhYg/Z4PkYJGu+U6k88TSQcAj0fE4zRvaH7fg9R1v8qw5NmAsyR9QPotr4+I0XkeiPVJfytvkYYBvcCUOS5acgvpmp8j6UXSdb4v59ueOo5ppZF9jKTa0J+bIuLB9hSSiTSUZIvS9loQawel+TseIg0X2k/Se6QgwtCIuKGFvBv+m5CHRtS7v82+0DzngpmZmXVlVxY+XwUQER+R/pP/CqlL8lGk4QlDW8ooIiaQhknMSRp//2BhX9N55rTPkRo2D5PGade6TD/QlpOLiImkRvVdpHHvfySNj+8ofyU1jHYmNaKLS+5NIo2j/zuwMnBsRDQ9Pj4iPiTV/Q1SD5Olgf0i4l7Stdue9PR/J9LveE1E/A+4PJd3OfB10nlfmNOdSZrHonYd9yY1ko8AXmvTmTd2COlp9D759Y+8bSKp98nJwF+Al5kSNDqTNFfB2cB+bSzvAtJvsDvpifinLSdv0RhgFPAL0n13SN7+Y9LcGgeRhrt8GXisyTz/SDq/LUgBp5tzftPLL0m9Zo4EVq+Qz+9JQbHfkeY8KLqMdN8cAJwZER+T5m74D2kujN/TSjuolX8T6t7fFc7FbJagiLb2GjIzMzMzMzMzm8I9F8zMzMzMzMysEgcXzMzMzMzMzKwSBxfMzMzMzMzMrBIHF8zMzMzMzMysEgcXzMzMzMzMzKySOTq7AmZm1j6LL7549OjRo7OrYWZmZmazsCFDhgyPiCVaS+fggpnZTKpHjx4MHjy4s6thZmZmZrMwSa83k87DIszMzMzMzMysEgcXzMzMzMzMzKwSBxfMzMzMzMzMrBIHF8zMzMzMzMysEgcXzMzMzMzMzKwSBxfMzMzMzMzMrBIHF8zMzMzMzMysEgcXzMzMzMzMzKwSBxfMzMzMzMzMrBJFRGfXwczM2kHdFezX2bUwMzMzs+kt+nZeu13SkIjo1Vo691wwMzMzMzMzs0ocXDAzMzMzMzOzShxcMDMzMzMzM7NKHFwwMzMzMzMzs0ocXDD7ApPUT1I0eP24lWOvkTRoBlW1Vubckg6V9LikTySNl/SypHMlfXM6l32apKHTswwzMzMzs5nVHJ1dATPrdCOBzetsf2VGV6QlkroBdwLfAv4KHA1MAFYFfg5sCSzfaRU0MzMzM/sCc3DBzCZGxCOdXYkmnAh8F1gzIp4rbL9X0tnAPlULkDRvRHxaNR8zMzMzsy8aD4sws1ZJWl7SrZI+lTRU0s8apFtV0i15yMInkq6WtHRh/4Z5yMWGed9oSf+TdEAr5XcD9gX+VgosABARkyLigkL6+SSdJelFSWMlvSbpbEkLlvINSb+WdIakD4Bn8vaFJV2e6/eOpKMa1GsFSQMkfZTLuUPS1wr7e+QydpJ0nqSRkoZJOk7SbIV0y0m6StL7+Rq/Kun4lq6JmZmZmVlX4p4LZoakaf4tiIiJeZ+AfwGLk3oHjAOOAxYFXi7k8RXgQWAw8GPSvy/HAzdJWiMiopD9BcAlwPnArsDZkgZHxGMNqtgTqA2LaEY3YHbgKOAD0nCJo4Crgc1KaX8D3AfswZSA68XAhsChwLvA4cCXgYmF810UeAD4EPgFMBb4LXCXpJVLPSBOAa4FdgC+DxwLPAdclfdfCsxLCqCMAL4EfL3JczUzMzMz63QOLpjZYsBn5Y2SVoqIocAWwGrAWhHxaN43BHiVQnAB6EtqiG8RERNyuqeBF4AfArcU0l4RESfkNIOAHwF9gEbBhe75/c1SHWej0AOrFhCJiA+A/Qvp5gBeAx6QtEJEvFHI5p2I2LmQ9pvAtsAuEXFl3nYv8AYwqnDcocB8wHcj4qOc7kFgKPBT4OxC2vsi4rD8eaCkzfP51oILawC7RsRN+fugBtfBzMzMzKxL8rAIMxsJrF7n9XbevwbwXi2wABARrwNDSvn8ALgemCRpjkKDfijQq5R2cg+EiPiMFKRYrom6Run7jaTAyGfAZ5JWre2QtIekJyWNzvsfyLtWLuVxa+n76vn9X4U6jgYGltL9IG8bVTjfT0jXpeH5Zs8z9fk+BZwkaS9JK9ACSftKGixpMGNbSmlmZmZmNuM4uGBmEyNicJ3XhLx/aeD9OseVty0OHEmhsZ9fX2LaVRxGlL5PAOZpoY61QEc5AHEIKRjwi+JGSduRhho8DOwIrAVsl3eXy3mv9H1p4JOIGFfaXu98d2ba892Itp/vzqThJKcDr0t6StL3qSMizo+IXhHRi271UpiZmZmZzXgeFmFmrXkXWLLO9iWB4rwCH5F6LlxYJ+3winUYQprTYFPgntrGiHgFQNL8pfQ7Ao9GxOSJIiVt0CDvcm+Id4EFJM1TCjCUr8FHpJ4T9SZe/KRBWfUrEPEWsFce5rEG0A+4MQ/h+LAteZmZmZmZdQb3XDCz1jwOLCVpzdqG3HX/e6V0dwPfBIbU6QUxtEoFImIsafLHAyWt0sQh8wLjS9t2b7K4x/P7NrUNOXixSSld7Xyfq3O+LzZZ1lTyqhePkCbM7Aas2J58zMzMzMxmNPdcMLM5JK1VZ/ub+Yn6rcB/gKslHUlqtB/HtMME+pEmZLxF0kWk3grLkhrl/SNiUMV6HkV6qv+wpLOA+0krVywL7Al8zpSeFANJK1AcBTxKmlCy7jCDsoh4TtKNwDl56cp3SCtKlGc4+DNpVYx7JP0VeAtYCtgAeCAirmimPEkLAXeQhnG8BMwNHEbqQfHfZvIwMzMzM+tsDi6Y2UKkuQnKjgFOiIiQtDWp58BFpKDCH0lBg8VriSPipRykOCGnnZfU4L4beKVqJSNirKSNgQOB3UjzLcxBWkHibuA7EfFqTn4eaa6HX5HmNhiYj3mkyeL2As4BzgBGk1Z+eJy0lGStPsPz+Z5ImithYVIg4gHg6Tac2jjgmVzX5UlBjEeATUvLWZqZmZmZdVmaeul5MzObWai7gv06uxZmZmZmNr1F385rt0saEhHl1dCm4TkXzMzMzMzMzKwSBxfMzMzMzMzMrBIHF8zMzMzMzMysEgcXzMzMzMzMzKwSrxZhZjaT6tm9J4P7Du7sapiZmZmZueeCmZmZmZmZmVXj4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVaKI6Ow6mJlZO6i7gv06uxZmZmZms67o6/aypCER0au1dO65YGZmZmZmZmaVOLhgZmZmZmZmZpU4uGBmZmZmZmZmlTi4YGZmZmZmZmaVOLhgMw1J/SSFpJcb7H857+/XRF5zSzpc0pOSxkgaK+lxSYdJmjen2SvnN38reQ2SdE27TsraRNK6kgZK+iD/bi9L6i9puUKaIyRt2IFlbpXvgx75e4/8fauOKsPMzMzMbGY3R2dXwKyNxgErSeoVEYNrGyWtDvTI+1uUgwd3At8CzgAeyLt6A0cCE4Ez21CnA4DP2pDe2kHSusAg4AZgH+BT4BvAbsCKwLCc9AjgrJx2eniHdK+8MJ3yNzMzMzOb6Ti4YDObMcATwC7A4ML2XYB7gJ5N5HEC8D1gzYh4trD9LklnA19vS4Ui4vm2pLd22x/4L7BjTFlDdyBwpiTNqEpExHjgkRlVnpmZmZnZzMDDImxmNADYqdagzO875e0tktQN2A84txRYACAiPoqIh0qbV8pd8cdIekFSn1KeUw2LyMM3hktaTdIjecjFk5LWKx03VNJpkg6VNEzSx5IGSFq4kGY+SWdJejHn85qksyUtWMprH0nPS/o0l/1vSd/M+/4t6fxC2s1yt/4/F7ZtL2lCvj5I+omkByR9lOt1r6RepTL7Sxosactc9lhJt0haVNJX8jFjcppvF467StKgOr9NP0nvSZqzvC9bGHi/EFiYrLZN0lBgMaBvPseoDZHIQ14elzQyl3OTpK+U6qBcj/clfSLpUqB8racZFpG/H1TnfIYXvteG2Xwv3zNjJT2Vv88n6eJct/9J2rXBNTAzMzMz65IcXLCZ0XXAUsC6+ft6wBJ5e2t6AvMBt7ehvMuBG4HtgJeBASqM8W+gG3AJcB6wPTAeuK7WeC/YCfg+sC9pSMZWwB9L+cwOHAVsARwDbAxcXUsgaX3gXOAfOc1PgYeAhXKS+0nXqGZ90vCR8rYnImJs/t4DuBTYkTTs4E3gfklfKtV/BeAPwNH5HNYGzicFegYAO5B6SA0o9C74O7C+pJUK5yBgT+CfEdFoiMkTwEaSjqlTj5rtgJG5jN759UTetxxpuMQ2wM9J1/UhSQsVjv8lcGw+hx1IQy9OaVBWe10CXEG6LwRck+v7di7zUeDSJu4xMzMzM7Muw8MibKYTESMk3U4aCnF/fr89Ikaq9d7xy+b3N9pQ5OkRcRGApCHAe6QgwLktHDMvcEhE3JOPewd4ktSILwY2PgO2jYiJOd038vkcABARH5CGA5D3zwG8BjwgaYWIeANYA3g6Ik4q5Htj4fP9wFGSlsj5rUdqzP5C0vwRMTpvu7t2QET8oVDmbKThB2sAPyYFE2oWBXpHxKs57beB3wB7RsSleZuAW0jDTf6b8xoG7AX0zflsRApoXNzwisKpwDq5/D/ka3oj8OeIeCnX+0lJE4FhETHV0IWIOLRwTrPnerxPCjZcmrcdCZwXEUfnpHdIGsiU+6YjnBYRl+R61K7NoIg4Km97jBRk+BFwTgeWa2ZmZmY23bjngs2sBgA7SJqb1BBrdUhEyTRd61tw5+SDIj4kNUhbe6o8gaknFKzNy1A+7t5aYKGQbsni0ABJeygNqxhNCkbUJqBcOb8/Bawm6XRJ60uaq1TGQ8DnwLr5eq0BXAh8CPTOQyy+QwpC1MpcRdL1kt7Lx34GfK1QZs3QWmAheyW/31Nn27IAETGJFET4SaE3w17A4HpDVWoiYhSpl8fapN4drwI/A56Q9L1GxxXOaS2l4S0fkibtHAvMXzin5YFlgH+VDm2mR0xb3F34PM31ioiRwAc0CGhI2jcPNRnM2HopzMzMzMxmPAcXbGZ1I6lheCJpmMNNTR73Vn5foQ1ljSh9nwDM08oxn+RGNAARMSF/LB9XL28BcwNI2o40POFh0hCFtUhd/yfnFRF3AXuTekUMAoYrzcswX97/CSkAsR4psPAp8DRThkusk8t8IJe5ACmgsjzw65xmdeA/Tda/vL3euV9MWuFho1ze9sBFtCKShyPiqIhYD+gFTCINF2lI0gr5nESac2OdfE7vF+q1dH5/v3R4+XtVIwqf612v2va691hEnB8RvSKiF+VBNmZmZmZmncTDImymFBFjJN0MHApcHRFjmjx0MGnFic2Au6ZX/TrQjsCjEXFAbYOkDcqJcjf7SyQtAfQBTgc+AX6bk9QCCR8CD0bEJEn3A9sCcwLPR8RHOW1vUg+LTSJi8nKLpbkJKomIoZLuIvVYWIkU6LyiHfk8lYctfKOVpJuT5q/Ypnav5CEmixbSvJvflywdW/5ez3ig3GNkkSaOMzMzMzObJbjngs3MziH1WGhp7oOpRMSnpEkW98/zG0xF0sKSendcFSubl9RwLdq9UeKI+CAiziMFE4rndx+wGvDD/Lm2bU3SUIP7C2nnze+Ty5W0NmlOhI70d1KPhQOAGyJiREuJJU3TyM/DKr5Mmgejpt5T/3lJPRyKQ1B2YuoA65ukAMM2pWP70LphwCqFes1Guq5mZmZmZl8I7rlgM62IGMTU8xo062jS8IAHJZ0OPJi3rwkcDJxMGobQFQwEzpZ0FGkVgR9SarRKOo70BH4QMJwURNiAKb0WIA15mJ00X8Fhedt/SHMprA6cUUj7CDAauEDSKaReDP2YMqSko9wA/A34HvC7JtJfmBvt15LmW1iENBzkO6QeHjUvAFvmST9HAy+S5jSYHbhY0t+BbwKHUxiOEBGf5/M9LS8heT8p+LEKrbseOFDSk8D/SHNBLNjyIWZmZmZmsw73XLAvnNx74Qek+Rr6kHo/3ESay+AUUs+GruI84E/Ar0gTC65IWhqy6HFSL4VzgTtIq0v0A86sJcirRLxAmsRwSN42iTTZI0yZJJKIeI/UWF+aNLnhIcAvmDL5YIeIiPHAbaQeA80MUfkbKVhwLGn+hPOABYDNIuKaQrrfkIa+3EK6Nj0j4hnSEIw1gZtJ13BH0rKVRWeQJov8BSmIMT9wRBN1O460POgJQH/SHBctrXxhZmZmZjZLUURbJs03M+sYec6D14GLIqLFCRmtPnVXsF9n18LMzMxs1hV93V6WNCQierWWzsMizGyGyktlfofUe2AxulZPETMzMzMzawcHF8xsRusOPEZa4nG/iBjWyfUxMzMzM7OKHFwwsxkqIoYC6ux6mJmZmZlZx/GEjmZmZmZmZmZWiXsumJnNpHp278ngvoM7uxpmZmZmZu65YGZmZmZmZmbVOLhgZmZmZmZmZpU4uGBmZmZmZmZmlTi4YGZmZmZmZmaVOLhgZmZmZmZmZpU4uGBmZmZmZmZmlTi4YGZmZmZmZmaVOLhgZmZmZmZmZpU4uGBmZmZmZmZmlTi4YGZmZmZmZmaVOLhgZmZmZmZmZpU4uGBmZmZmZmZmlTi4YGZmZmZmZmaVOLhgZmZmZmZmZpU4uGBmZmZmZmZmlTi4YGZmZmZmZmaVOLhgZmZmZmZmZpU4uGBmZmZmZmZmlTi4YGZmZmZmZmaVOLhgZmZmZmZmZpU4uGBmZmZmZmZmlSgiOrsOZmbWDuquYL/OroWZmZnZrCv6ur0saUhE9GotnXsumJmZmZmZmVklDi6YmZmZmZmZWSUOLpiZmZmZmZlZJQ4umJmZmZmZmVklDi6YTQeS9pEUkpYrbf+/vP3Hpe2b5O1rd3A9hko6rYX9PXK5W3VkuaUyBkvq30F57SVpiKRPJH0s6UlJfy7sX1JSP0k9OqK8nOdpkoaW6hCS5u+oMszMzMzMZnYOLphNHw/l93KwYG1gbIPt44Eh07leMy1JvwMuBO4A+gA/Af4FbF1ItiTQF+gxHatyC9Cb9DuamZmZmRkwR2dXwGwW9QLwESlocBWApDmBXkB/6gcXhkTE+BlYx5nNQcB5EfH7wrabJB03IysRER8AH8zIMs3MzMzMujr3XDCbDiIigIeZOoiwWn7/G7CqpAUAJM0GrAk8mL9vKWmgpPcljZL0iKRNi/nnrv/DJa2W94/NQwTWa6lekpaV9IKkuyR1K+zqJuk8SSMlDZN0XK5X8dhVJd2ShyR8IulqSUvXSfOgpHGS/iup2KugmG49Sf/O9f5Q0gW169GChYF3yxvztSYPhXgmb743D12o7ZtP0lmSXsxlvibpbEkLluq1sKTLJY2W9I6ko+rUfaphEZI2zN9XLaUbJOmawvf+eYjIlpKez/W4RdKikr4i6V5JY3Kab7dyLczMzMzMuhQHF8ymn4eA70qaN3/vTRr28CwwkhRQAPgmsBA5uACsBNwE7AFsn/O5TdI6pfy7AZcA5+V044HrSkGDyXLj+z7gVWCriCh26z8FGA3sAPwTODZ/rh37lVy/eYAfA3vlet8kSTnNvKQhC/MDuwEnAGcAK5TqsQ5wFylQsANwCPBD4OJ69S54AjhY0p6SFquz/x1g9/z5QNL17p2/dwNmB44CtgCOATYGri7lcXHefyiwL7ApsEsr9WqLFYA/AEfn/NcGzgcG5NcOpB5lA2rX1czMzMxsZuBhEWbTz4PAnMDqpEb92sDDERGSHsnf72JK74aHACLirFoGuffAvaSG/D5MCUAAzAscEhH35LTvAE8C6wO3FyuSgwP3AI8Du0bEhFJd74uIw/LngZI2J81rcFXe1pcUDNiidqykp0nDP35Imodgb9KcB2tGxLCcZijwQKmsk4GHImLnQv3eAu6WtGpEPDvNlUwOBG4gDSsJSf8FrgVOi4hRETE+1wng+Yh4pHZgHsqwf6G8OYDXgAckrRARb0j6JrAtsEtEXJnT3Qu8AYxqUKe2WhToHRGv5vy/DfwG2DMiLs3bRLqeXwf+20HlmpmZmZlNV+65YDb9PA5MZErwYG3SUAmAR0rbX84NYCQtJ+mS3OCeCHxGeoK+cin/CcCgwvfn8/typXRfIwU3HgB2rhNYALiz9P35Uj4/AK4HJkmao9A4H0qaRwJgDdK8EcNqB0XEg8D7te+5V0Vv4KpaPjmvB/J59qxTt1peTwOrkCZw/BsgUg+Ewc2s3CBpjzx0ZHQuqxb0qF3X1fP7vwpljgYGtpZ3GwytBRayV/L7PXW2LVsvA0n75qETgz2lpJmZmZl1FQ4umE0nedjBU8DaSktSLseUVSQeBtbKT6nXZsp8C7MBN+ZtxwIbkRq9t5GGJBR9EhGTCuXVggbldGsDywAXRsTEBtUdUfo+oZTP4sCRpEZ58fUlYPmcZmkKgYSC4rZFSMMT/lbKZzypl8fy0xxdEBHjI+KmiDgoIr4B/Az4KqlXR0OStgMuJV33HYG1gO3y7tp5Lk26puNaqH9VI0rfJ9TZ3uh3BCAizo+IXhHRi7oDYMzMzMzMZjwPizCbvh4kzQOwNumpdW1CwseABYANga+Q5jwgf16NNPxg8tCGwrwN7XExsCBwg6QfRMRj7cjjI1LPhQvr7Bue398ldeUvW7LweQQQQD/g1jpp325LpSLi75JOaVBu0Y7AoxFxQG2DpA1Kad4FFpA0TynAsCQtq6Wdq7R9EaZcGzMzMzOzWZp7LphNXw+RnvrvyZQhEUTEKOA54PC8qTaXQi2IMHlJSkkrAuXJHNvqF8DNpIkhv9WO4+8mzfswJCIGl15Dc5rHgZ65l0at7utQaJxHxBjSkJCv1clncEQ0DC5ImqaRL2kJ0mSY7+VNjZ76z0vhmma7l74/nt+3KeQ/P7BJozpltWEgqxSOW57WAx5mZmZmZrMM91wwm75qwyC2AH5V2vcw8HPgY6ZM3PcCqbH6J0nHkHo3HAe8VaUSETFJ0k9IEyDeKWm9iHilteMK+pF6W9wi6SLSE/llSQ3v/hExiNRD4uicph+pQX880z69P4I0eeMk4BrgE9IqClsCR0XESw3q8Iykf5Hmh3gfWJEUnBlLWjUD0uSLnwJ7ShoJfBYRg0nzJpydl5Z8lDQJ5fdL1+g5STcC5+QlKt8hTbbY4swGETFM0mDgeEljSUHb35N6e5iZmZmZfSG454LZdJQnN3yDNPngw6XdD9e2R0Tk9ONJqzRMJDW8jwdOAv7dAXWZCOwEPENq3Lc4v0Hp2JdI8xSMJS2deBsp6DGePAFhnmNiM2AMaVnFvsBhwOulvB4grWixBPAP0rKbRwBvMqUHQj1/AHoAfyEFGI4n9f5YIyJey3mPIwVsepKuWa03wnnAn0gBnutIgYnd6pSxV877DODvpB4bA1qoU82upN/5n8Afc11fbOI4MzMzM7NZgnKbxszMZjLqrmC/zq6FmZmZ2awr+rq9LGlIRPRqLZ17LpiZmZmZmZlZJQ4umJmZmZmZmVklDi6YmZmZmZmZWSUOLpiZmZmZmZlZJV6K0sxsJtWze08G9x3c2dUwMzMzM3PPBTMzMzMzMzOrxsEFMzMzMzMzM6vEwQUzMzMzMzMzq8TBBTMzMzMzMzOrxMEFMzMzMzMzM6vEwQUzMzMzMzMzq8TBBTMzMzMzMzOrxMEFMzMzMzMzM6vEwQUzMzMzMzMzq8TBBTMzMzMzMzOrxMEFMzMzMzMzM6vEwQUzMzMzMzMzq8TBBTMzMzMzMzOrxMEFMzMzMzMzM6vEwQUzMzMzMzMzq8TBBTMzMzMzMzOrxMEFMzMzMzMzM6vEwQUzMzMzMzMzq8TBBTMzMzMzMzOrxMEFMzMzMzMzM6tEEdHZdTAzs3ZQdwX7dXYtzMzMzOqLvm5rzgokDYmIXq2lc88FMzMzMzMzM6vEwQUzMzMzMzMzq8TBBTMzMzMzMzOrxMEFMzMzMzMzM6vEwQWzmYykfpKizuuuzq5bI5Lmk3S8pBclfSrpPUn/lrRPIc1e+Tzm74Dy+kka3s5jQ9JBpW1rSBop6XZJczeZzxGSNmwmfzMzMzOzmd0cnV0BM2uXkcDmdbZ1VdcCqwEnAM8CSwLrAz8E/t6J9WqVpO8CtwNDgO0iYnyThx4BnAUMmj41MzMzMzPrOhxcMJs5TYyIRzoqM0mzA7NHxISOyrOQ91eBzYCdIuLqwq4rJamjy+tIkr4JDASeA34UEZ92cpXMzMzMzLokD4swmwVJ+pmk5ySNl/S6pCNK+/tLGixpW0nPAeOANfO+gyS9nI99RdKhpWPrDckISXs1qM7C+f3d8o6IqLf48UqSBkoaI+kFSX1K5W+Z978vaZSkRyRt2uA6rJb3j5X0pKT1GtSx3rErA3cB/wN+GBFjCvsWl3SJpA9z3oMk9SrsHwosBvQtXJ8NG5TT9PmYmZmZmXVVDi6YzaQkzVF6KW//DXAOcAOwVf58fJ1x/j2AU4CTgC2A1yT9HPgrcCPwI+Bq4E+Sfls4rnfp9VcggFcaVPVFYAxwhqRNJc3TyqldnsvfDngZGCBpucL+lYCbgD2A7YGHgNskrVPKpxtwCXBeTjceuE5St1bKr5VxN/AOsFlEfFLafwOpN8bhwM6kf0vvlfSVvH870jCVvzPlOj3RQlnNnI+ZmZmZWZflYRFmM6fFgM9K2zaR9BjQFzghIo7L2wfmBvXRks6JiM8LefwgIp4CkDQb0A/oHxGH5TR3SloI+J2kMyJiXHE4hqSewM+B4yLigXoVjYhROWhxAXAH8JmkR4B/ABfW6b1wekRclPMfArxHCpKcm/M7q1D+bMC9wDeBfYAHC/nMCxwSEffktO8AT5Lmeri9Xl0Lfg18CqweESOKOyRtDqwDbBgR/87b7gGGAr8B9ouIJyVNBIa1NnylDedjZmZmZtZlueeC2cxpJLB66fUo6Qn5fMDVxV4NwD3AUkCxB8BbtcBCthzQndRboehKYEHgW8WNkpYAriMNHfhDS5WNiCuAFYGfAgOAlYHzSb0Uyu4sHPch8H6x3pKWy0MS3gImkoIsm+Y8iyYw9WSKzxfOszV3AXMDJ9eZF2IN4P1aYCHXcwxwM7BuE3lPpQ3nU0u/bx7SMpixbS3NzMzMzGz6cM8Fs5nTxIgYXN4oafH88bkGxy0PvJ4/v1fat0yD7bXvixbKmQO4itSA/3GDuROmkgMFFwMXS5qTNFxhb0knR8R/CklHlA6dAMyTy52NNGRiAeBY0lCMMaTgxpKl4z6JiEmF8ifkOEFrwzIA/kUa+nAWKbhRnLNimbyt7D0K16gZbTwfACLifFJgBnVXq9fdzMzMzGxGcHDBbNbyUX7fimmDBJDmP6gpN0zfye/lRu1SpbwBTiP1llgrItq8BGZEfCbpdGBv4OvAf1o5pOYrpCUtt4iIyUMbJM3b1jo0UcezJS0FHCPp3Yj4c971DvUb/ksx9TVqxgw7HzMzMzOz6cnDIsxmLQ+T5groHhGD67zKExMWDQPeBnYsbd8JGAU8AyBpD+BXwD4R8WxrFZK0QIPG8lfze70gSCO1fMYX8l+RNAdCh4uIY0m9BE6T9OO8+VFgSUnrF+rQDdgSKM47MbnHRQtm6PmYmZmZmU0v7rlgNguJiBGS+gFn5kbqfaQg4srARhGxXQvHTsrHnifpQ2AgsAGwP/D7iBgn6cukxvZtwOuS1ipk8WpEfFAn668BN0q6iLQSwljgu8BRwFNM3SBvzQukIMifJB1DGk5wHPBWG/Joq/2BJYCLJH0QEXdIegi4Mq+i8SFp1Yh5gVNLdd1S0u3AaODFOsGdzjgfMzMzM7MO1+6eC5KOlbRsR1bGzKqLiFOAfUnLS/4LuALYHbi/iWMvIPVK2I40QeGuwGERcXJOsjzpafwWpF4SxdeWDbJ9FbgQ2IS0QsStwC+Ai4DvR8TENpzbeKAPaeLDa4DjSUtp/rul46rI8zbsRgqMXCtpDWBbUvDlDNIEmAI2jojicpy/Ic2fcAvwONCzTt4z/HzMzMzMzKYHNTEPW/0DpUmk/xDfSn6S2cykbmZm1jHUXcF+nV0LMzMzs/qir5uHswJJQyKiV2vpqsy58AZpWMXWwE3AUEnHSOpeIU8zMzMzMzMzm8lUCS6sROoafT2pB8PyQD9SkOEGSVvUWR/ezMzMzMzMzGYx7Q4uRHJHRGxPCiz8HvgfU3oz3Ay85t4MZmZmZmZmZrO2DlmKMiLej4iTI+KrwA+Aq4DPgBVwbwYzMzMzMzOzWVq7J3RsNWNpMeAnpFnrvwbUCnqTNAHkeRHx4XQp3MzsC6BXr14xePDgzq6GmZmZmc3CZsSEjq1ZCFgSWIQUWFB+rUBabu01SYdMx/LNzMzMzMzMbAaYoyMzkzQHac32fYENmRJQeJe0zv3VwKakNe6/DPxJ0riIOLcj62FmZmZmZmZmM06H9FyQ9FVJpwJvAVcAG5OCCvcAOwLLR8SxEfFMRPyJNEzijznNwR1RBzMzMzMzMzPrHO3uuSBpLmAH4OfA+rXNwEdAf9KcCi/XOzYiJkk6hhRY+HJ762BmZmZmZmZmna/KsIi3SfMp1FZ/eBg4F7gqIsa3dnBEhKSPSctYmpmZmZmZmdlMqkpwYVFgNPBP4NyIeLodeRwGzF+hDmZmZmZmZmbWyaoEF/YHLouI0e3NICKurVC+mZmZmZmZmXUB7Q4uRMR5HVkRMzMzMzMzM5s5VVotQtKCklod1iBpfkkLVinLzMzMzMzMzLqmdgcXJPUBPgbObyL5P4GPJW3d3vLMzMzMzMzMrGuq0nNhx/z+9ybSXkBaVWKnCuWZmZmZmZmZWRdUJbiwGjAJeLCJtPfktN+rUJ6ZmZmZmZmZdUFVggvLAiMiYlxrCSPiU2BEPsbMzMzMzMzMZiFVlqIMoFsb0s+bjzEzMzMzMzOzWUiVngtvAvNI+lZrCSV9hxRceKtCeWZmZmZmZmbWBVUJLgwiTdJ4XBNp+5F6LdxboTwzMzMzMzMz64KqBBf+SpqkcRtJ/5S0VDmBpKUkXQ5sk9P+pUJ5ZmZmZmZmZtYFtXvOhYh4QdJRwEnArsAOkoYAr+ckKwK9CmUcHRHPV6msmZmZmZmZmXU9VSZ0JCL+T9Io4GRgAaA3sFberfw+CjgiIs6vUpaZmZmZmZmZdU2VggsAEXGOpCuAHYC1gaVJ8yu8CzwEXB0Ro6qWY2ZmZmZmZmZdU+XgAkBEjAAuzC8zMzMzMzMz+wJRRHR2HczMrB3UXcF+nV0LMzOzmUv0dfvHrC0kDYmIXq2lq7JahJmZmZmZmZlZ9WERkr4M7AR8G1gUmLOF5BER369appmZmZmZmZl1HZWCC5L6AkeTekColeSQJno0MzMzMzMzs1lIu4MLknYH+uavbwN35PeJHVAvMzMzMzMzM5tJVJlz4cD8fiPwpYjYJyKOiYjjWnp1QJ2R1E9SFF7vSrpZ0rfbkddeOY/58/ce+ftWHVTXL0k6X9JQSeMljZT0kKTDa2XOCJK2yufVI39v6jzztR7eSpqprmEb6rSppEPacsyM1Kh+kvpLGtwJVaqVv2G+3qu2km5VSTdIekfSp5JekzSgeJykfSVt24F1WzXXbcPCtpB0UEeV0RFauoblv5XOIGmQpGs6q3wzMzMzs7aqElxYlTTM4YCImNBB9WmLkUDv/DoEWBkYKGnRNuZzS85jbP7+Tv7+QNUKSloXeAroBZwIbArsBjwMHAscWbWMGeBCYLPplPempN+uq2pUv+OBvWZoTdpI0leAR4AFgYOALYGTgcVJ86PU7AtsO52r0xu4ejqXYWZmZmZmnajKnAsBjIqItzuqMm00MSIeyZ8fkTSU1GjfHLi82Uwi4gPgg8L38aRGWSWS5gWuzHltGRGfFXbfIuk0YPWq5UxvETEMGNbZ9WiGpHkiYtz0LiciXp3eZXSAvYHxwBb5nga4BzhPUjPzo3SYwt+pmZmZmZnNoqr0XHgB6CZp7o6qTEX/ye/L1zZI6i3pxtwtfIykp/JcERTStDgsIneBf7xcmKQDJY2VtECD+uwEdAcOLQUWAIiIdyLixnbW9VuSBuZ0L0jqU0qnPJzhfUmfSLqU9AS7nm6SzsvDNYZJOk7SbIW8Wh0WUVa4hjs1yltSP+AwYEVNGd7Sv5DHepL+na/xh5IuKF7rwrVYI3ch/xT4TRuu0ZZ5//uSRkl6RNKmxfNuVL96wyIkfVfS3bm+H0u6TNJSbbkmOd3XlYYuvJnzek7SIcU0TVoYGFEILEwWEZHLGgT0BPYsnONeed9PJD0g6aN8PvdKmmZtW0kH5LqOkXQTsEydNFMNi1AaInRaKU3577A2bOH7kv6V839ZaajK7JJOlTRc0luSft3Ga9MmhbpsqjT8aoykNyT9opSuv6TBkrbN99y4fA2/UUp3mKTH8z3wnqSblHqamJmZmZnNtKoEFy4kLTu5YwfVpaoV8vtrhW0rAg8C+wA/Aq4FLpa0axvyvRLoJWml0vadgVsj4pMGx60PvBURzzVZTlvqejlprovtgJeBAZKWK+z/JWnYxfnADsCnwCkNyj0FGJ3T/TMft0OTdW5NS3lfmM/jXaYMbzkeQNI6wF153w6koQk/BC6uU8YVwE15/82F7a1do5XycXsA2wMPAbflslusX5mkJYBBQDfSsJeDgQ1Iw3TmasM1AVgWeBE4IJ/TBcBxtH0IzRPAlySdWW7cFhxAChLeypRzvCXv6wFcSvr73g14E7hf0pdqB0vaBjibdN37AM8AF7Wxnq05jzREaTvgdeAa4CxggVyva4A/SVqzg8ut5+/A06RzvRU4R9POWbIi8GfSvbIbsBBwh6R5CmmWI53DNsDPgdmBhyQtNH2rb2ZmZmY2/bR7WEREXCBpS+Avkt6IiPs6sF5NkVSr/4qk/6w/BfyrUMcBhbQC7iP9x/7npEZpMwYCH5KCCSfnvJYF1iX1TmikO6lB1qjOuYrxeTvqenpEXJTTDgHeA7YCzpU0O6khel5EHJ3T3yFpIKnhWnZfRBxWO1dJm5MaT1e1cG7Naph3RAyT9A4wvk63+ZOBhyJi59oGSW8Bd0taNSKeLaT9S0ScWUj3nfyx4TUCiIizCsfMBtwLfJMU3HmwlfqV1c5xs4gYlfN8mTQkZnum/v1avN4RcTdwd85DpIZ1N9J9cFIr9Si6hDRnxC+BX0r6iNQgPjMiBueynpc0BvigfI4R8Yfa53x9BgJrAD8GavuOAm6PiP3z9ztyoOVnbahna/4REafmegwDngO+FhEb5213kf42+wCPdmC59dwWEb/Pn++Q9GXSUrzFoNbiwDYR8VCu3xDgVdIcHbV779Ba4vz3OhB4nxRsuHQ6n4OZmZmZ2XRRZSnKY0lDEdYD7pX0IOk/942e5ANTN1oqWgwoDjf4EFi92A1c0iKkp77bkBrWs+ddbzVbSERMlHQdheAC6WnuGKY85W14ePGLpMUpzO8ADCFN9tjWut5ZqN+Hkt4nBSIgDQtZhkKQJbsO+EFLeWXPM6UXSFVtzltSN9IT9INLgZgHSL93T6AYXGj0G7R0jci9GE4kXZNlgNo8BA+2VL8G1gDurAUWcpmPKs0Dsi5TBxdavCb5CffvgN3z9jkL++aIiKaWes3pdpZ0IrA1qSfNTsAukraNiBbvXUmrAH8E1gaWLOxauVYX4HukySKLrqNjgwt3Fz6/kt/vqW2IiEmS/kf9wFlHu770/TpScHX2WpAQeL8WWMj1ez0HGNYgBxckrUXq2fA9oDgB7crNVELSvqSJOFO/CDMzMzOzLqDKhI79mNJ4FqkRtU7D1FN0VHBhJKlhODvwHeA04HJJ60TEpJymP1D7j/zzwChgf1IDvi0GAD+XtHJEvEQKNNwYEZ+2cMzbpCfhRSOYMoljX6Yen96Wuo4ofZ8A1LpdL53f3y+lKX9vJq+q2pP3IqTf9G/5VbZ86ft7bS07P4m/kdS1/lhSo3UM6d5ckrZbhvREvew9pm48tliv7P9IjfPjSEMbRpDugaNzutFtqVhEPE3qyo/S0or3ASfQQmBMaW6LO3P9f00ajjCONFSkVtfFSb9Ts/dZe42ofYiICakzR4fcs7Ugzex19s1eSlNT71znIF2L9xqkqW1bBkDSCqRr+xiwH+nfiQmk36Opc4iI80lDnlB3RSvJzczMzMxmiCrBhfsoPZmfwSbWuncDjypN6FcbI35lfgK8FXBgRJxbO0htnxgP4N+kxsPOSpMjrkXrXdTvA34qaZWI+C9Mfpo8ONfjQ6Y0ODqyru/m93IjuT2N5s4wgnRf9SN14y8rr07SnnvwK8BqpJUUbq9tVFrhoz3eof71XYrUO6UtdgT+GhGT58jIw48qi4ihkq4mzbXQkt6kXh6bRMQLhXoUn5MPBz6nfffZOKA8F8UiTRzXkWo9iJZmymSwNcsAk0i9oYrqnetE0rVolKa2rRZ82pw0zGWbiBgDk3uBtHUJXTMzMzOzLqXKnAsbdmA9OsI/SXMNHEmahHFu0oSVxWESC5C6iLepQRoRn+dG2c6khtEI4PYWD0pj6P8I/FnS1vVWjCjosLqS5nl4l/S0u1jHPvWTd6ppnjhHxBhJj5DG1XdUL5eyWhCheL1XJPW8ebql+jXwKLC/pAVqE3xKWp00KeID7ahbsV6zA7u0MQ8kLRkR9Z6if5Wpe3vUO8d612dt0vkMgcnDhZ4k3WfnFo5t5j4bBqxS2rZpvYTT0ctM+Tu5o7RvG2BwnZ5J2wG3lb4PKQyJAFhS0tqFORdWIA1/qE1GOi8pcFHsFbET1QK9ZmZmZmadbpb5D21EhKQ/ApdJ+n5E3K20hOSxkkaR/kP/W9JwikbLMrbkStL48kOBGyJiQiv1+VTSLqTuzo9IOpe0CsA8wLeA75MmeiMiRnZUXXMg5BTgNKUlJO8nTSpYbsx1BS8ASyktf/gsMDwihgJHkCZvnERaDeAT0vwDWwJH5aEpVcsdRlpl4BjS8IjjmHZ+i0b1K/szaQjLHZL+D5ifND/HM6RVP9piIHCgpFeAj4ADScGntjomT255OfBfYD5Sw/9HwOGFdC8Am0najPSk/jXSRJSjgQvyvbQcqSdJ+fr8EbhO0jmk+Qg2ID2Zb831wF8l/R54nHR/locQtZukDUkTdG4UEYPqpclzNRwH/E1SkP5O5ySt8LAp6TqVbZHnsPg36VpuwrTDloYD/5R0NGmVluNIwyL65/33kIZdXCzp76TzPpxph3qYmZmZmc1UqixF2RVdSXoieUT+vhvwP9JwiTNJDb1Gs7G31kPgQVKvgGVIczC0KiLuJ3W/f4I0Zv4u4GpSY+pM0lCImrbUtTVnkBp+v8j5zM+Ua9KVXEVqdJ1CamT2A4iIB0gTEC4B/IO0ZOQRpOvfaI6FpuVJP/uQnh5fQ5rn4iRSo7HV+tXJ7wNgI1KvlitIyzPeTxpW0GIQqo6D87Fnk5Z1fJa2rRJRcxkpeHUY6Wn7pcA3gF0j4k+FdCeQgg9Xkc7xRxHxHml4xtKkiUEPId1LrxSOIyKuz/X9EXAD6V7fp4m6nU+6R3+Zyx2f69FRuuX3Fud/yEOQfkIa5nQdKRCzLLBVRNQbkvMzUi+EG5gyjOnGUprXScGCfqR/Jz4hrSIyLpf5DGnliDVJq0zsRrrWI9twfmZmZmZmXY4iqk+bIOnbwGakJSHnjYh9CvvmJDUSIyLeqVxYB5N0MHBqRHTUJIZm1olyj4T1I2KjDspvQ1JPiG+VlkEtp+sPrBoRvTqi3Gaou4L9ZlRpZmZms4bo6/mQzdpC0pBm/o9baVhEnuDtImDb2iZSD4Di08s5SROmLSLpOxFRb1b9GS6PZV8f2IO2T7pnZl3X2qShKmZmZmZmNoO0e1hE7pFwGymwMJY0ZnlcOV1EjCVNZjYbsEN7y5sOFiBN5DY7aS4FM5sFRMQmEXFTZ9fDzMzMzOyLpErPhX1IY5VfBTaIiLclNVqS71rSOOT1K5TXoSJiBNMuh2dmNpU8KaSaSLfXdK+MmZmZmVkXVSW4sCtpCMShEfF2K2mfJK2A8PUK5ZmZWUHP7j0Z3HdwZ1fDzMzMzKzSahHfIgUX7mwtYZ4xfySwWIXyzMzMzMzMzKwLqhJc6AZ80oal9uYkLf1nZmZmZmZmZrOQKsGF4cCCkuZvLaGklYD5gdaGT5iZmZmZmZnZTKZKcOHR/L5lE2kPzu/3VyjPzMzMzMzMzLqgKsGFi0gzqB8vqXujRJL2A35Fmp/h/ArlmZmZmZmZmVkX1O7VIiLiFknXAtsDgyVdDswLIGlfYEVgK2BVUhDigoh4tFF+ZmZmZmZmZjZzqrIUJcAewDhgd+DQwvZz8nttbfiLgAMrlmVmZmZmZmZmXVCVYRFExLiI2ANYH/gH8CrwKTABeAO4HNgwIn4WEV4pwszMzMzMzGwWVLXnAgAR8QDwQEfkZWZmZmZmZmYzl0o9F8zMzMzMzMzMHFwwMzMzMzMzs0raPSxC0k/ac1xEXNreMs3MzMzMzMys66ky50J/INp4TAAOLpiZmZmZmZnNQqoEF96g5eDCQsDC+fMYYHiFsszMzMzMzMysi2p3cCEierSWRtJXgaOBHYEjI+Kq9pZnZmZmZmZmZl1ThyxF2UhEvAzsKekz4FJJL0XEU9OzTDMzMzMzMzObsWbUahH9gLmA382g8szMzMzMzMxsBpkhwYWIGAaMADaYEeWZmZmZmZmZ2YwzXYdF1EiaB1gQ+GxGlGdmZmZmZmZmM86MGhaxdy7rrRlUnpmZmZmZmZnNIO3uuSBphVaSzAMsD2wP/JS0bOX17S3PzMzMzMzMzLomRUT7DpQ+b0ty4Dlg3YgY2a4CzcxsKuquYL/OroWZmc0qom/72gVmNmuTNCQierWWrsqwCDX5+h9wArCWAwtmZmZmZmZms54qEzqu1Mr+icDHETG2QhlmZmZmZmZm1sW1O7gQEa93ZEXMzMzMzMzMbOY0o1aLMDMzMzMzM7NZlIMLHUxSP0kh6eUG+1/O+/s12P9a3v+VNpa7vaR7JI2QNF7SS5L+LKl73t8j57tVE/Uf3kR5gyX1b0sdq5A0SNI1M6q8GUXS4pLOkvQ/SeMkvS3pDknbFtJsKumQDi53ePEe7MrXV9Lckg6X9KSkMZLGSnpc0mGS5s1p9sr39/ydXd+2avZv08zMzMysK6uyFOWxHVWJiPhDR+XVRYwDVpLUKyIG1zZKWh3okfdPQ1LvvB9gV+D4ZgqT9CfgEOBi4HRgFPAN4BekuTG2a0PdLwRuakN6aydJcwL3At2AE4FXgeWATYHvAzfkpJsCOwBnTMfqHAB8Nh3zb5ccPLgT+Bbp/B/Iu3oDR5LmdjmzUypnZmZmZmaTVZnQsR/QUevVzGrBhTHAE8AuwODC9l2Ae4CeDY7bNR/7LE0GFyT9CPg1sE9EXFTY9W9J55Mapk2LiGHAsLYcM7OTNG9EfNoJRW8IrAqsERGPF7b/U5JmZEUi4vkZWV4bnAB8D1gzIp4tbL9L0tnA1zunWmZmZmZmVlRlWMR9+TWKKctOvg08ll9vFbaPLKSv95oVDQB2qjUS8/tOefs0JM2e998IXASsIuk7TZRzKPBEKbAAQER8HhG3lTZ3k3SepJGShkk6TtLk+6DesAhJq0p6MHfb/6+krevUv7ekGyW9k7uuPyVp91KaWtf17+Vu+GNzuu9Jmk/Sxble/5O0a4PrtK+koZI+lXSLpGVL+0+W9Iyk0fn8LpO0dCnNUEl/knSMpGGke3iGnUPBwvn93fKOiIhcXj/gMGDFXG7UhqM0U9+cbn1J/8m/3xBJa9dJM9WwCEn9JQ0upZmm+37+fmi+nh8qDbc4PO/bM1+HEZIukjRPK9ejXKduwH7AuaXAQu0afRQRD5U2ryRpYL4eL0jqUyffbZSG9YyT9K6kU3Ivktr+fvk81pH0RE73lKR1S/n8RNIDkj6S9LGkeyX1KqWZZriJpA3zdVu1hXPfSNInkv7Y4kUyMzMzM+si2h1ciIgNgUeAhYArgK9FxPIR0Tu/VgBWBi4jNaIejoiN6r2qn0aXdB2wFFBrkKwHLJG317NRTj8AuIbURb3FxmluEK0N3N6Gep0CjCZ1s/8ncGz+3KiMeYE7gPmB3UhPks8AViglXRF4ENgH+BFwLXBxgwb2JaR7ZntS8Oka4O+k4NQOwKPApZKWKx3XGziY3FMD+DZThg7ULAn8EdiSNFTkS8A9KgRQst2ADUjDAXaegedQ9BQwCbhI0rqS6vUkuhC4nBSA6J1ftR4trdZXac6N24CPcr3OI/1NdmuhXm11GOn+2DXX9VRJpwB7Ab8Efg/sTvo92qInMB9tu78vJwXotgNeBgYUfwNJO5H+Bh8DtgaOA/YFTirl043093EusCMwAritFKjqAVya9+8GvAncL+lLbajvNCRtBtwCnBIRv6+Sl5mZmZnZjFJlzoXtgSOAv0XEQfXSRMQrwB6SRgJHShocEY0a17OUiBgh6XbSUIj78/vtETFS9Xu870pqwNweERMk3QnsIul3tafYdSwGzA280Yaq3RcRh+XPAyVtDvQBrmqQfm9Sg33NPGQCSUOZMvYdgIiY3CND6QTvI80f8HNSI7zotIi4pJD2FmBQRByVtz1Gagj/CDincNySQO+IeCOnex14QNLmEXF7rsdPC/WYHXiYNMxjXabtJbNVREye/2IGnUPxmr0s6TfAyaR7ZJykfwN/j4irc5phkt4BxkfEI6Xjm6nvIaQ5PraMiLE57RhSw7mjvBwR++W87yI1tn8OrBgRtV4hG5Ia/Ce3Id9ar5S23N+n13rxSBoCvAdsBZybr9GpwKURcUDtAEnjgbMlnRQRH+bN8wJHRcTlOc29uR6HAL+FqeeKycGrgcAawI9p51AvpV5BVwFHR8Rp7cnDzMzMzKwzVBkWcRBpzoV+TaStpakbhJiFDQB2kDQ3qaHZaEjEXKQG/vURMaFw7IqkJ9WtacvcF3eWvj9PapA2sgYwpBZYAIiIB4H3i4kkLSLpL7nB/1l+7UvqvVJ2d+HzK/n9nkL+I4EPmNK4rHmiFlgo1WONQj22kPRQDmhNZMr8EeV63F0MLMzAc5hKRPyZNOnmgaSJNNcErpJUfpI+jSbruwYwsBZYyK5vLe82mnwtImIS8BrpnhlVSPMKrVyLFrTr/s6BgveZcn+vTOpxc5WkOWov0u82D2n+i6LrC3mNZkrwAABJq0i6XtJ7wOek6/816t8vzdgeuBo4rKXAgtLQoMGSBjO2USozMzMzsxmrSnDh28DIiGh12cKcZgTQzBwCs5IbSd3FTyR17260CsMWpKEjt0paWNLCwCBgPC0PjfgwpykPUWjJiNL3CaSGVSNLUwokZOVt/UnDC04lTSK5OmnuiHp5F+swoc62RvVqVI9lYPJqHDeSAgp7kAIza+V05bzeq5PXjDiHaUTEWxHxt4jYidQQvh34jaTFWjm0mfpO8/vlQMPo1urVBiNK3yc02NamORdI87ZAx93fi+f3W5kSjPmMFAwBWL5w3Og6k3wW77UFSIGM5UnDdNYjXf//0PbzrNmaNHylxeBPRJwfEb0ioleHDm4xMzMzM6ugymoRcwNzS5o/P9VrSGnt+QVJDeEvjIgYI+lm0qSLV0fEmAZJawGEq+vs21HSIRHxeZ38P5P0ILAZcHSHVHpa71J/Rv4lax/yRH1bAQdGxLmF7VWCV/Us2WDbO/nzdqTeAjsXJkRcsUFeUz0Nn4Hn0KJ8z/wN2Bz4CimANI021PddStdNaaLE+VupyjhgrtK2RVo9gY41mLR6ymbAXR2Q30f5fV/gyTr7Xyt8nl/TriJSvNd6kwJBm0TEC7UEkhYq5dmW61ibT+ROSRsUhmiYmZmZmXV5VRpOL+bjmxnqcBAwez7mi+YcUo+Fc+vtlDQfaVz+FaRJHYuvX5Mmedy4hfzPAHpJ2rNO3rPlORWqeBzoWZoUbx2mbrDOTboXxhfSLEB6EtuRvidp8lPsQj0ey5vmBT4rzVExzeoJDcyoc5hM0qJ5Xoiyr+b3Wu+Kek/9m63v48AmOaBQs10T1RsG9NDUKzy0aVnTqnLD/jxgf0nfKO/PvXyaGTZU8yKpN0SPiBhc51VuzE++TjlAuglT32sw9fVfmzTJY9Ewpg3ONbqOo0iBFIA7JC3YxDmZmZmZmXUJVXou9AfOBE7Icwr8qdyDITdoDietSBDAxRXKmylFxCDSEIdGtiHNTH9mRDxa3JF7JRxF6tkwsEH+N0n6M/D33Nj+F6nL+9eBXwBDadts+2UXk3pF3KK0LOK8pNUKJg+HyZNUPg4cK2kUaQWE35KWIO3IBtIHuR59SY3t/yPNw1A7v4HAIZLOIAV01iZNrteqGXgORRsDJ0m6mBQEmESq82+BmyNiaE73ArCUpL2AZ4HhETG0yfqeQZrP4eZ8n3QHfgeUu/yX3UCalPBCpaUvVwN+2tIBbSUpgOMiol8LyY4mzXPwoKTTSatjQJqb4mDSBJEPN1NeREySdBjwj9xwv40UuPkSsC2wQ2Fuik+BE3NQ4W3Sv2Nzkf7Ng7RSzmjgAqWVMZYjzS1TG8pRcz2wT677LaSgYcOAX0R8KGkT0gSfN+fJSj2zgpmZmZl1eVV6LpxNGnM8Gyl48K7Smu6X5dcg0hjlvjnNXcDfKtZ3VrQrabb9R8s7IuIz0szxfXIAp668+sPOpCfel5Ma2YeRJtrbv0rlcsNmM1L39AGk3/Mw4PVS0t2A/5GW5juTtCzipVXKruMh0n13BmnZx2dJjcJaXW8FjiRNjHcjaanJrdqQ/4w4h6JHScGgnUhDYm7In09gyvKYkO6B/qRlRB9nygSprdY3It4Cfkiab+Ba0tKbP4aWpwKMiGdJwYTeTLmWe7f9FOsr9KSoN49GsR6fAj8gzVvShxQ0uonUq+AUUs+GpkXElaSA3ndJ1/w60jV5gilzZ0C6Pj/J+64lDWX4YUS8k/N5j7QqxtKk3/AQUjDvlUIeRMQtpKU4dyAFGlYEftVKHd8Bvk/qBXFdnvDVzMzMzKxLU+NVDps4OP2n92TSsIdaL4hahrX1Fj8nNQiPjIgv1JwLZlafpI1IwZTlS6tKdLrcQ+egiFi8tbSdTd0V7NfZtTAzs1lF9G1/u8DMZl2ShkREr9bSVRkWQV428deSTiU9mevFlLH475MmZLs2It6uUo6ZzXLWBi7saoEFMzMzMzNrn0rBhZrcjfevHZGXmc36IuLEzq6DmZmZmZl1nBm6zJ6ZWVcXEf1mhiERZmZmZmZdSYf0XJC0OGkW9BWBbhHxh47I18zMzMzMzMy6vqoTOs5BWg7wANIybQBExOyFNIuQZrSfF/h6YXk9MzOroFevXjF48ODOroaZmZmZzcKandCx6rCIq0lLsM0FPAdMLCeIiI9JyyPORVpmz8zMzMzMzMxmIe0OLkjahbRe/PtAr4j4NvBRg+RX5/eN2luemZmZmZmZmXVNVXou7A0E8JuIeLKVtI/ltN+oUJ6ZmZmZmZmZdUFVggur5fdrW0sYEWOBkcCSFcozMzMzMzMzsy6oSnBhIWBkRHzahrLaP3ukmZmZmZmZmXVJVYILHwMLSZqntYSSlgEWBN6rUJ6ZmZmZmZmZdUFVggtP5PdmJmn8aX5/uEJ5ZmZmZmZmZtYFVQkuXAYIOF7S/I0SSdocOIY0JOKSCuWZmZmZmZmZWRc0R4VjLwf2BdYDHpF0LjAXgKRNgB7Aj4AfkoIYN0XEHZVqa2ZmZmZmZmZdTruDCxERkrYFrgfWB84s7L698FnAXcDu7S3LzMzMzMzMzLquKsMiiIiPgY2BPYH7gQmkYIKAz0lzLOwFbB4RoyvV1MzMzMzMzMy6pCrDIgCIiEnAP4B/SJoNWBSYHfgwIiZWzd/MzMzMzMzMurZ2BxckvQZMAjaLiFdgcqBheAfVzczMzMzMzMxmAlV6LiwDTKgFFszMzMzMzMzsi6nKnAtvk+ZWMDMzMzMzM7MvsCrBhbuAbpJW66jKmJmZmZmZmdnMp0pw4WRgDHCWpG4dVB8zMzMzMzMzm8lUmXNhIrAfcB7wrKS/Ag8B75OWoawrIt6oUKaZmZmZmZmZdTFVgguvFT7PB5zWxDFRsUwzMzMzMzMz62KqNPTbM5mjJ4A0MzMzMzMzm8VUCS6s1GG1MDMzMzMzM7OZliKiuYTSJOCdiFi2zr5VgDkj4ukOrp+ZmTWg7gr26+xamJlZVxB9m/s/vZlZW0kaEhG9WkvX1p4LjYY13AMs0Y78zMzMzMzMzGwmV2UpyjLPp2BmZmZmZmb2BdSRwQUzMzMzMzMz+wJycMHMzMzMzMzMKnFwwWY6kvaSNETSJ5I+lvSkpD93dr1gct1C0vwtpNkwp5kkafk6+y/M+wdN18o2IKm/pMHTKe8e+dy2asexc0r6taRnJY2VNFzSo5J+W0izsqR+khbuwDpfU/wtcv7DOyp/MzMzM7NZgYMLNlOR9DvgQuAOoA/wE+BfwNadWa+CW4DewNgm0o4Bdi5ukDQX6bxGd3zVmnY8sFcnlt/IWcAfgMuArYB9gX8DPyqkWRnoCyw8HetxIbDZdMzfzMzMzGym49UdbGZzEHBeRPy+sO0mScd1VoWKIuID4IMmk98E7AKcVti2GTA7MAhYoEMr16SIeLUzym2JpG7A3sBREXFqYdd1kmboZLIRMQwYNiPLNDMzMzPr6trac2EpSZ+XX8CSAPX2lV4TO/4U7AtmYeDd8saImGpxZ0knS3pG0mhJwyRdJmnpwv7jJL1U+D6fpM8kPVHYtngeurBJ/t5b0o2S3pE0RtJTknYvldvqsIiCAUBPSV8pbNsFuAEYX8p3GUkXSfqfpE8lvSTphNzToZamNuRgJ0nnSRqZz/04SbMV0i0n6SpJ7+e8XpV0fGH/VMMiCuf0LUkD87m/IKlPqY6SdHzOd1Su7y752B6lc+/WUh3rmA+YkxZ+e0kbkgI2AK/lcoc2e/1yuuUl3ZrTDJX0s3J55WERjX7zfPxphe+D8hCLvSW9lu/Nf0iaW9Iakh7L2wZJWqGFa2FmZmZm1uW0NbigDniZVfEEcLCkPSUt1kK6JYE/AlsChwBfAu4pNGDvB74qaan8fW1gIvAdSQvmbesBk4CH8/cVgQeBfUhd8a8FLpa0azvP5X/AY8CuMPnp/NbAFXXSLg58BPwa2Bw4lfQk/6910p5CGlaxA/BP4Nj8ueZSYHnSsIItgBOBuZuo7+XAjcB2wMvAAEnLFfYfAvweODeX92muSz2t1XEquUfIm0A/SX0k1evV8QRweP7chzQ8Zbv8vdXrJ0mkITarkn7jXwO/yvl0lLWAPYGDgSOAnXIdLgDOBH5MulfP78AyzczMzMymu7YMi+gS3c7tC+9A0pP9/kBI+i+pkX9aRIyqJYqIn9Y+S5qdFCAYBqwL3Je/TyQFEK7J77eSGpJrA7fnbU9GxOic54BCnsr5LAf8nPoBgWYMIDVkjyfNIzAOuAuY6ol5RDzDlIYzkh4kzdlwkaSDI2JCIfl9EXFY/jxQ0uakxvZVedsawK4RUXvKP6jJup4eERfl8ocA7+U6n5uv8RHAuRFxbE5/p6SVSIGMstbqWM9epOt1LTBJ0pP5+18iYkJEjJL0Yk77ZEQMrR3Y5PXbAlgNWCsiHi2c56ukYEpHmB/YJiJG5vw3JN0/G0TEfXlbd+BsSd0iopm5O8zMzMzMOl3TwYWIcHDBOl1EPC1pFWBT0vwEGwPHALtI+l4tECBpi7z9m8CChSxWJjVsxygNgagFF9YHrmdKwOH2vO2+2oGSFiEF2bYBliXNjQDwVoVTugo4TdK3SEMiro2IiSpNI5CDGb8i9TZYCZinsHsF4JXC9ztLZTyf09Q8BZyUe37cExFvNFnXyflGxIeS3icFVyAFEJYm9WwoupHUaG+YV4M6TiMi7pH0ZeCHwA/y61RgW0nrR8SkRsc2ef3WAN6rBRZyma/nAENHGVwLLGSvABOAB0rbALoz9e8KgKR9SecBC3VgzczMzMzMKvBqETbTiYjxEXFTRBwUEd8gPeX/KqkHAJJWJzVqhwF7kHojrJUPLzYq7wfWy+Pu18zfa9sWAL6bv9f0J63ucCopuLE6cFEpz7aey1ukhuV+pEb4gAZJDyFN/Hg9KbixBqkXR/mcAEaUvk8opdkZGAycDryuNHfE95uobkv51uazKE9m2Whyy9bqWFdEfBIRV0bEz0nDB44H1mHqFSPqOYTWr9/SwPt1jq23rb1GlL5PAD4pBUZqvVDqXo+IOD8iekVEL7p1YM3MzMzMzCpwcMFmehHxd9J4+q/nTduRGrU7R8SNEfEIdSYCJAUOvgN8n9SgeypvWwPYiNQz4QEASfOQhgD0jYizIuKeiBhMx/wNDQD2z+dwX4M0OwLXRMRREXFnRDxO6tbfZhHxVkTsBSxGCry8C9zYyhwWrald3yVK28vfO0yeyLG2csTXW0pLc9fvXfLktCX1thWNy+9zlbYv0spxZmZmZmazDAcXbKYiaZqGnqQlSB3E38ub5gU+K60gsXv5OFIgQcBvgQfz0+NnSBMRHga8kCcShDTh4WwUVnHIvRu2rnRCydWkVQ5OaqFr/7yUVpCg/jk1LSIm5cDLcUA30oSV7fUmqXG+TWl7R1wfJM0paeE6u76a32u/faOn/s1cv8dJK+KsWSh3BeB7rVSvtizlKoXj1mTq4ThmZmZmZrO0tkzoaNYVPCPpX6Qx+++TGsSHA2OBS3KagcAhks4gNdrXJs3CP5WI+EjS86S5FX6Xt03Kk/1tSZrBv5Z2pKTHgWMljSKtIvFbYCQVG5ERMRzYtpVkA4FfSnqUNMHg7sBXWj5kWpIWAu4grRjxEilochgpMPDftuZXExGfSzoVOFXSB6RVNbYGvpWTNJwPoUkLAS9JugS4l3Tdv0b63d4iDXcAqE3ouJ+kAcDYPJljM9fvVuA/wNWSjiQFI46j9WERj+U6/EXSMcCipMktR7V4lJmZmZnZLMQ9F2xm8wegB/AXUoDheOA5YI2IeA0gIm4FjgS2J829sAFpSEM9tTkV7quz7YFS2t1Iy0deSlo28Nr8eUb4A2lFihPy+wTgl+3IZxypd8avSNfmElJgZtOI+LRiHU8HTgIOIF2bRUjLgUL1hvYo0vKVPUlBn9tJDfg7SKs7jIQ0ASMp2NSHFOCorYjR6vXLPV22Jk0ueVE+n7OYshRpXXmlie1IAZRrSMGa/YGPK5yvmZmZmdlMRVP3HDcz6ziSLgQ2iYgqQy6sAXVXsF9n18LMzLqC6Ov/05vZ9CFpSET0ai2dh0WYWYeQtCppJYqHSE/xtwD2JvUiMTMzMzOzWZiDC2bWUcYA6wIHAfMBr5MCC3/qzEqZmZmZmdn05+CCmXWIPOfFRp1dDzMzMzMzm/EcXDAzm0n17N6TwX0Hd3Y1zMzMzMy8WoSZmZmZmZmZVePggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVWiiOjsOpiZWTuou4L9OrsWZvZFFn39/0gzs1mdpCER0au1dO65YGZmZmZmZmaVOLhgZmZmZmZmZpU4uGBmZmZmZmZmlTi4YGZmZmZmZmaVdLnggqR+kqLwelvStZK+PJ3KGyTpmjYes2Gu26rtLHNuSYdLelLSGEljJT0u6TBJ87ZybI9c9lZtKK+/pMHtqWshjyXzb9OjjccNyvX9QZXyp6dcv4PaeMymkg6ps73ytW5jPeaU9GtJz+b7aLikRyX9tpBm5fzbLdyB5V4jaVDhez9Jwzsq/45S+Pfk5Qb7X877+7Ux373ycfN3UB2HF75X+vfFzMzMzKwzzNHZFWhgJLB5/vwl4HjgbknfjIgxHVzWAcBnHZxnQzl4cCfwLeAM4IG8qzdwJDAROLOFLN7JaV+YfrWsa0mgLzAIGNrMAZKWBdbLX3cF7poeFesAvYHX2njMpsAOpN+w6HigxQBRBzsL2B04EXgUWBhYC/gRcHJOszLpt+sPjJhO9bgQuGk65V3VOGAlSb0iYnLgR9LqQI+8vyt5gnRPvtrZFTEzMzMza1ZXDS5MjIhH8udHJL0B3A/8ELi6IwuKiOc7Mr8mnAB8D1gzIp4tbL9L0tnA1xsdKGmeiBgHPNIoTRezMyDgHqCPpP0jYkIn12kahXutI/KaYQ1CSd2AvYGjIuLUwq7rJGlG1QMgIoYBw2ZkmW0whtRg3wUo9irZhXRv9uyMSjUSEaOYef7GzczMzMyALjgsooEh+b0H1O/GXqdrca3b8rckDczDD16Q1Kd03FTDIiR9XdIASW/mbubPSTpEUr1r1V3SzTnvNyT9oqWTyI3B/YBzS4EFACLio4h4qFT/NXIdPwV+02hYhKSfS3pG0jhJ7+Vu6ws1qMdckq7Ldf5K3rZCPu+P8nnfIelreV8P4Jl8+L21ISstnWu2K6mR9H+kJ+pblOoxp6TTcj3GKw2BuV7SXHn/wpIuzNvH5XQXlPLYOA8DqJ3338pd1SUtJuk8Se/kdC8WhzSU7ydJW+Z75n1JoyQ9ImnTwv5+wGHAipoyfKd/3jfNsAhJ35V0d76uH0u6TNJShf2133SnXM+RkoZJOq7BfVczHzAn8G55R0REzntDpvQoeC2XMzTvW0bSRZL+J+lTSS9JOqF2/Qv1W17SrTnNUEk/K5fXwt9f+bcYKum0wvdB+V7dW9JrkkZL+ofS0KE1JD2Wtw2StEIL16I1A4CdpBR0ye875e3TyL/FM/m+fFPSiZLqBWNXUsv/vrR4LzUo28MizMzMzGymM7MEF3rk92kaUU24HLgR2A54GRggabkW0i8LvEgaLvFD4ALgONKQhbK/A08DfYBbgXPU8lwIPUkNwtvbUP8rSI3DHwI310sg6WjgPODfwLbA/qShJdOMB5c0D3A98B1gvYh4RdKipOEZXwN+QWp0zUfqTTEvaSjG7jmLA0ldtnu3VGmloEUvUuPtbuB9UrCh6Hc532OATYBDcr1nz/v/DKwLHApsBvwemBzUkPRN0rUcDmxP6vq/G1AMFs1LGsqxLWnIwg+BPwHdW6j+SqRrvkfO9yHgNknr5P0Xku6rdwvX4vgG12GJXH63XLeDgQ2AgeVGPHAKMJo03OKfwLH5c10R8QHwJtBPUh9JC9RJ9gRweP7cJ9d1u/x9ceAj4NekYUinknpC/LVQfwH/AlYF9slpf0Urv38brQXsSbo2R5Duv7+S/vbOBH5MGh51foUyrgOWIt1PkIbrLJG3TyU3/q8kXbttcl0OJw1BKWvt35fW7iUzMzMzs1lCVx0WQeEp4ZeAvwGf0L4x+6dHxEU5zyHAe8BWwLn1EkfE3aTGcK1h9QCpYfhz4KRS8tsi4vf58x1Kk04eTYMgAClwAfBGG+r/l4iYPAeDShMqKk3S93vgjIj4dWFXvUZTN1JDaDlg/Yh4K+86lBRM+G5EfJTTPkiaW+GnEXG2pKdz2uebHEawKzAJuCoiPpd0NbC3pPkK82asAVweEZcUjruq8HkN4OyIuLKw7Z+Fz8cArwNbR8Tnud4fAVdK6h0RDwM/Ab4JfC8insrH3dNSxSNiciMy9xy4N+exD/BgRAyT9A4wvolrcVh+3yx3d0dpcsFHSI3NKwpp74uIWvqBkjYnBQSK16RsL1IA51pgkqQn8/e/RMSEiBgl6cWc9smIGFo4z2eYEnio/eZjgIskHZyHsGwBrAasFRGP5nRDSPMB1J0ksR3mB7aJiJE5/w1Jf28bRMR9eVt34GxJ3SJibFsLiIgRkm4nDYW4P7/fHhEjNe0Ikj8AgyJiz/z99pzmJEkn5CEgNS3++9LavdTW8zAzMzMz66q6as+FxUiTLH5G6kXwJWDniHinHXndWfsQER+SnqA37LkgaZ7cHf0VYHyuw4mk7s/lYMz1pe/XAT0lzU7LmhlSUHNLK/t7kyYQvLiVdLUeE0uSGm1vFfb9ABgIjJI0Rz7PT0jDUXq1oa5FuwL/johab5MBpCDNNoU0TwF7STpC0rc1bSvvKdJQkAMkrVynjDWA62uBhexa0qSYtSfUG5Ma1U81W3FJy0m6RNJbOa/PSBM41qtDa9YA7qwFFgByI31ooY41d5a+P08L92rO6x7gy6TG8kWkv51TgXvU8pAKlBwi6XmlYTefAZcBcwO1IQhrAO/VAgu5zNeZMlSpIwyuBRayV4AJTJnstLYNWu5x0poBwA6S5ib1CJlmSET+2/0e087tciXp38tyj40W/33p4Huplue+kgZLGkybwyxmZmZmZtNHVw0ujARWJzVslwN6RMRt7cxrROn7BGCeFtL/H+lp7vmkLvSrkyZhpM5x79f5Pgepu3k9tQZ9W8aOv9fK/sXye2uBl+7A2qTGeDnPxUmTL35Wem0ELN+GugIg6TvAKsDNSvMmLAw8l+tYHBpxAnA2aQjKf4A3Jf2qsP8g4AbS8IAXlZYN3KWwfxlK1ycHGj4EFs2bFqP1a1Os+2yk3h1r53I3It0Dt9HyfdPINHXM3ivUsWZE6Xtr9yoAEfFJRFwZET9nyuoq65BWjGjJIcBppCDZNqRAwoF5X63cpZn2PqfBtvYaUfo+AfgkIiaVthXr1R43knpJnEgKttVb3WJx0jwW5d+s9r3p32w63EsARMT5EdErInrRrb25mJmZmZl1rK46LGJiccm4OsYD5fHqi3RQ2TsCf42IU2obJG3ZIO2Sdb5PJM0BUM9gUrfzzWh+iEdrvRw+zO/LtFAupC7sZwL9Jb0bEecU9n1EagTVmzfgkybrWVQLIPwpv4o2k7RonrxyHKnRdaykr5LmezhD0osRcXtEjAB+CfxS0rdJ4/Evk/R0XuXjHUq/QX7yvFg+J0jX5yttqPtXSMMAtoiIyXNj5Lkb2mOaOmZL0bFP/4E0kaOkU0lDRr5Omi+hkR2BayLiqNoGSd8opXmX+vVfEvi0hbxryztOr7/TNouIMZJuJg0DurrBsrbDSYG18jnXJuD8iOZ19L1kZmZmZtZlddWeC60ZRnoyDkx+Qvj9Dsp7XlLwopb37KQu5/VsV+f7kFI3/cki4lPSxIv712nE1VZHaOtEeQ+TGnl7tpYwIv5B6g1wlqQfF3bdTRoH/lxEDC69auP1m3pynIc27EIaW75R6bUb6anw9nXq9jKpx8h4YJprExFPA78h3bO15TofBbYrDUPpQwqa1brU3w2sloMTzag1/Ir3wIqkngBFTfUqyHXcrDjZoqTVSZOUPtDooGYorbaxcJ1dX83vtaftjX67qe71bPfS98eBpSStWSh3BdLQgZbU5iUo/p2uCSzYynHT2zmkHguN5lz5nBT02bG0ayfSHCIPt6GsZu8lMzMzM7OZXlftudCa64ED8+R1/wN+Rsc1WgbmvF8hPaU8kDQGvZ4tJJ1IWqWhD2nFg20apK05mtT9/EFJpzNlUrc1SbPln0wbGjB5orrjgRPz6gO35vpuCRxXmluBiDhHaXnAiyWNjogbSKsy/Jg0Tv+vpOEbS5FWNXggIq4gTUL5KbCnpJHAZw16l/QGVgSOjIhB5Z2Sfkfq2XCBpOtJDbknc947kO7J2iR+D5B+62dJPTh+Tur58VjO7oR87A2SziENofk/4I48mSPApaTf8E6lJSRfJM3gv3JE/LZO/V8gNYz/JOkYYAHSaiFv1Um3lKS9cv2GFydLLPgzafWOOyT9H6lb/smkpT2vrZO+LRYCXpJ0CSmYM5K04sfvcn1rc4LUAkT7SRoAjM2TOQ4k9Qp5lDRB4+5M28vjVtKQlaslHUlqKB9H68MiHst1+Eu+jouSep6MavGoNpA0CCAiNmz2mHxPDmolWV/S73UxaV6Gb5F69VxQmsyxNc3eS2ZmZmZmM72ZtefCcaQJ104A+pMm/mttQsOWFIceHEyaTf5s0gR5zzLtKhE1PyM9wb2BNEP8gRFxY4sFpd4LPyCN++5Deop6E6nXwymkng1tq3zESaQG7A9I3eDPAxamwZCGiDiVdE4DJG0SEcNJywG+AJxOmqTuFFLj9el8zDhS474nKZjyeIPq7EpqQDa6Dv8ENpC0DGlZvm1Jy/n9K+e9fSFo8TBpNYRrSCsmLE7qYj4s1+k50moGS5Im0zyBtPrC5OUbc703Jl3jP5DGux8BvN3g2own/S4Tc7nH52v171LSq0j33in5WvRrkN8HpF4b43LdzibdX5vk1RiqGJXL70latvF20rndQVrdYWSuw+ukXiF9SMGs2lwDf8h1ql23CaRhKMX6B7A1aXLJi0j3x1m0EgDL57Yd6Wn/NaRVM/YHPq5wvmXd6Ni5HwCIiDtJvW96ka7VIaThPQe1MZ9m7yUzMzMzs5meUtvhiysvHzc4Ivbr7LqYWXPyig+jgE0j4gvbWFd3Bf6Xy8w6UfT9Yv8/0szsi0DSkIhodRXBmbXnQmWSlpa0G/Bt0kSLZjbz6AU880UOLJiZmZmZdSVf2OACqdvz2cBlwD86uS5m1gYR8WAz0VMzMzMzM5sxZtYJHSuLiDOAMzq5GmZmZmZmZmYzvS9yzwUzMzMzMzMz6wBf2J4LZmYzu57dezK4r6eMMTMzM7PO554LZmZmZmZmZlaJgwtmZmZmZmZmVomDC2ZmZmZmZmZWiYMLZmZmZmZmZlaJgwtmZmZmZmZmVomDC2ZmZmZmZmZWiYMLZmZmZmZmZlaJgwtmZmZmZmZmVomDC2ZmZmZmZmZWiYMLZmZmZmZmZlaJgwtmZmZmZmZmVomDC2ZmZmZmZmZWiYMLZmZmZmZmZlaJgwtmZmZmZmZmVomDC2ZmZmZmZmZWiYMLZmZmZmZmZlaJgwtmZmZmZmZmVomDC2ZmZmZmZmZWiYMLZmZmZmZmZlaJgwtmZmZmZmZmVomDC2ZmZmZmZmZWiSKis+tgZmbtoO4K9uvsWpi1TfT1/zvMzMxmJpKGRESv1tK554KZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVTJLBRck9ZMUkl5usP/lvL9fG/PdKx83f4dUdDqR1CPXc6tW0m0q6ZAZVC0kfVfSlZLelTRB0tuSLpO0ehPH9pc0uA1lNXUNmshnJ0l7tfGYDXPZD1Qpe3rKfyPD23jMXPm475a2d8i1bmNdVpV0g6R3JH0q6TVJAyStWkizr6RtO7jMkLRhYVtIOqijyjAzMzMzm9nNUsGFbBywkqSpZrPMDdkeef8X3abAITOiIEl9gMeAxYBDgR8AhwELAXc2kcXxwF7Tq34t2Kkd5e6a39eWtELHVqfDXAhs1sZj5gL6At8tbX8H6A3MkGCKpK8AjwALAgcBWwInA4sD3y4k3RfYdjpXpzdw9XQuw8zMzMxspjFHZ1dgOhgDPAHsAhSfeO8C3AP07IxKNSJJwNwR0eWCHpJmB2aPiAntPL47cAlwBbBXTL3u6RUtPfGWNG9EfBoRr7an7BlN0pzADqR7bGPS/XZKp1aqjogYBgzroLzGkxr7M8rewHhgi1w2pOt9Xv47mmEiYkaet5mZmZlZlzcr9lwAGADsVGtw5Ped8vapSOot6cbczXqMpKck7d4g35UkDczpXshP5Yt5bZn3vy9plKRHJG1aStNP0nBJ60p6nNSTYkdJ80k6S9KLksbm7t5nS1qwdPw+kp7PXcKHS/q3pG+W6tlN0nmSRkoaJuk4SbPVyif1HFgxd+0OSf3zvv6SBkvaVtJzuW5rSlpG0kWS/pfLfUnSCZLmauV3+BnpqfdhpcACABFxc+G8hkr6k6RjJA0DRhXrVLoGK0q6Ip//WElPS9qtUSUkbSTpE0l/LGz7maTnJI2X9LqkIwr7+gPbAxsUrlG/Vs51U2BR4P+Ah5nSi6FYj3Ul3Z/vjVH5XtuxsH9rSUPy/fWxpEclbVDY303SX5SGl4yT9Hj5/srptpP0WP6tPpR0q6QV876phkU0ed99kt8vLlyPHqozLELS7LmMN/K1fa782xTus03ybzdG0gN17uOyhYERhcDCZLX7S9IgUgBxz0Jd98r7fpLL+Shf33tV6uGU0x0g6c1cr5uAZeqkmWpYRL5/TyulmWo4laYMm/m+pH/l/F9WGqY0u6RT8z39lqRft3ItzMzMzMy6lFk1uHAdsBSwbv6+HrBE3l62IvAgsA/wI+BaUiNqmsYhcDlwI7Ad8DIwQNJyhf0rATcBe5Aapw8Bt0lap5RPN9IT/QuBzUnDBroBswNHAVsAx5CegE/uei1pfeBc4B85zU9zGQuV8j8FGE16kv5P4Nj8mVzm5cC7pK7dvUlDD2p65ONPymW8Rup2/hHw61zfU0lPkf9a5xoVbQAMjohmx/jvlo85ANi5XgJJS5Ia76sDh5N+s78DyzdIvxlwC3BKRPw+b/sNcA5wA7BV/nx8obF4PHAv8CRTrtGFrdR9V+AD4G5ST43vSvp6oR4LAjcD/yPdGzuQfseF8/4vA9eQnsT/CNg9p1+0UMYFpOt+IukefBO4RVLtPkfSHqT7/FVSQG1v4CXS/V9Pq/dd/g5wQuF6vNMgvz/kvM4Htib9bV1W5+9pBdJ9dCLp2i0JXCm12APhCeBLks6U9I0GaQ4AXgBuLdT1lryvB3ApsCPpXnsTuF/Sl2oHS9oGOJt07fsAzwAXtVCn9jiPNJRkO+B10u9+FrBArtc1wJ8krdnB5ZqZmZmZTTez4rAIImKEpNtJXdPvz++3R8TIctslIib3ZsgNm/uA5YCfkxqJRadHxEU57RDgPVLj9Nyc11mFvGYjNVC/SQpcPFjIZ17g1xHxr1L++xeOn4PUsH9A0goR8QawBvB0RJxUOObGOpfgvog4LH8eKGlzUkPpqogYJukdYHyDrt2LAT+IiKcK24aRGvK1uj1IGn5ykaSDWxg2sSypgd4WW7UyRORQUjClZ0TUGrh310soaWvgKuDoiDgtb1uQNH/ACRFxXE46UFI34GhJ50TEq5I+AmZrpvu7pHmBbYB/RMTnkq4CTic1mvvmZCvneh8UEbWeAMU5J1YDPomI3xS23VooY5Wc394RcUnedgfwNCkgsFm+504Gro+IYmO+3j0CQER8QOv33eN596vF61H+W5K0KGkujxMi4oS8+Y4cgOvH1H9PiwLrRMTL+djZgOuBr5GCA/VcQuoh8kvgl/k3uhU4MyIG5/N5XtIY4IPybxcRfyjUdTZgIOlv6sekoAikwMjtEVG7JndIWoLUC6ej/CMiTs31GAY8B3wtIjbO2+4iBdf6AI92YLlmZmZmZtPNrNpzAdIQiB0kzU16SjzNkAgASYsodTV/Hfgsv/YlNQbLJjcGI+JD4H1SIKKW13KSLpH0FjAx57VpnbwCuK1OXfaQ9KSk0fnY2kR5teOfAlaTdLqk9dV4WEJ5osTni/VsxVulwAJKDlEejpHrdhkwN+kJdEumGQ7RgrubmHtiY1Ljr9GT85rtSU/fD6sFFrLewHzA1ZLmqL1IPQaWovnrVPQjYH7yPRYR7wGDmHpoxKuk3iSXS9pG0sKlPJ4BFsr3z6aS5ivtXx0QhR4FETEpf6/1XPga0B24uC2Vb+K+a9aqpJ4Q5YkOrwRWzo30mqG1wEL2fH5veP0jYmJE7Ax8hxRQGULqnfGwpC1bq5ykVSRdL+k94HPSuX6NfJ75PvgeUA761evxVEUxGPZKfr+ntiH/rv8jBeemobQaxmBJgxnbwTUzMzMzM2unWTm4cCOpwXciqTF5U4N0/UlPCU8lBQJWJ3WDnqdO2hGl7xNq6fKT0BuBtUnDEDbKed1WJ6+Py0/7JW1H6rL9MKnb9lqkbtPUjo+Iu0jd3NcnNV6HK42PLzdEG9azCe/V2XYIcBrpyfI2pKe9Bxbr1sBbtB58aK3sssVo3CW/aGvSUI7rS9sXz+/PMSWY9Bmplwk0GF7Ril1JdX9G0sI5cHAT8FVJPQEi4mNgE2BOUm+KDyTdUuuSHxEvkq7tl0hP44dLurzQIF8GGB0R5ebke6Q5NuYmXRto7voAzd13bVCbm6D8O9a+F4d4jCilqf09tFpmRDwdESdExKak4MA7pCEbDUlagBR0W540vGc90t/nfwplLk4aIvJ+6fDy96pG1D4U/h0YUUrT8G82Is6PiF4R0YtuHVwzMzMzM7N2miWHRQBExBhJN5O60V8dEWPKaSTNQxrWcGBEnFvY3p6gy1dIXdu3iIjbC3nNW696dbbtCDwaEQcUjt2gnCh3ib8kNzr7kLrffwL8th11rqdR3a6JiKMKdWs05r1oEHCUpEUj4qN2ll32IXUm2KvjYFIj8k5JG+SeJpACDpB+93rBjBebyHsySQuR5iqYu5B30a6kJ+y1FQY2z/fED4A/k+a/WCvvv4U0h8JCpGUWzyDNa7ELqQE9v6RupQDDUsDYiBgvqXaOzVyfmqbuuybVghpLkn6nYh2h/vWpJCKGSrqaNNdCS3qTekVsEhGTh13ka10znNSjYcnSseXv9YwjTV5atEgTx5mZmZmZzRJm5Z4LkCbqu4k8J0Idc5OuweTZ5/MTzq3bUVYtiFDMa0WgPJljS8eXZ8FvtGoFEfFBRJxHmlOimYZ+UVt6MrS5bgV/J/UKOK3ezma6stdxN2l+gaVaSTcK2Cx/vkNTVj94GPgU6B4Rg+u8avMhNHuN+pDuoz1JvVWKrzuBncuTFOYlNm8i9ZCZ5reLiJERcTmp10Vt/+Ok4EttYs7aHCE7MGUYw4uk3iJ7NlHvmmZ+22Z7FTwLjCUFLIp2Al7K8zu0m9JknvV8lakDRfV+u3p/n2uTJnkE0rAL0hwh25SO7UPrhgGrlLZNs5KHmZmZmdmsapbtuQAQEYNIT88b7R+ptBzksZJGAZNIPQBGAgs2Oq6BF0gNjD9JOoY08/txpMZeMwYCZ0s6ijSJ2w+B7xcTSDqO1LV8EOkp62qk1RXa2mvhBWAppSX6ngWGR8TQVur2S0mPkuYO2J3UU6NFEfF2LuOKPKnfRaTrsSzpafz6TN1VvhmnAz8hzfJ/ImnG/1WA+SLilFL5H0rahBSAuVnS5nmyz37AmTn4cx8pwLQysFFE1IYEvABsI2lb0u/6dkS8Xac+uwIvRMSl5R15gsNrgfVy0OqnpBUq3sjXYD/yWHtJ+5Gert8OvE1qMO9IGrJARPxX0hXAWTmvV0mTjn6dPCFjRExSWlLzMkmXkSZQDNI8FVfUJj0safW+i4gJkl4jLe/6LOkp/dPljCLiI0lnkCbGnAgMJjXMf0idpTnb4RhJ3yH19vgvabhTH9KcF4cX0r1ACkBtRupB8RrwCGnOiwsknULqxdCPaf8+/whcJ+kcUnBnA9IKKa25HvirpN+TAkHbkyZzNTMzMzP7QpilgwtN2o20NNylpIbIWaRJ6Q5q6aCy3C29D2kZu2tIDdITgQ1JE9215jzSePtfkZ66Dsx1K854/zhpmMcupODF66QG0pltqStpzP9GpCUnlyDNwr9XC+n/kNPVxrVfR5qxv9E8FpNFxLVKS+r9LtdzUdKSjfeQhga0SUR8oLS05ymkYQNzk5YFPalB+nckfZ8UYLhO0tYRcYqkt0nX8jBSY/kl0sSDNX8jBW8uInVvP450rSfLvSc2Js2xUc8tpLH0u+a6BqnxuiTpGtwM/D6nfZrUY+bPpGv0DmnpyWLePwf+L29bmDQJ5FYRUeu5QERcLmkcadWDa0irejySy6unmfsO4BekHih3ka75Sg3yO5Y0men+pOEQrwA/Lq7KUsFlpHlUDiMFZ8aSfrddS/mfQJrr4ypSkHDviOgvacd8Dv8i3TO/AI4oFhAR10s6mBSw25MUyNsHuKOVup0PfJn0dzE36d+TE0jX18zMzMxslqeItkzmb2ZmXYW6K9ivs2th1jbR1//vMDMzm5lIGhIRvVpLN6vPuWBmZmZmZmZm05mDC2ZmZmZmZmZWiYMLZmZmZmZmZlaJJ3Q0M5tJ9ezek8F96y0CYmZmZmY2Y7nngpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmZlV4uCCmZmZmZmZmVXi4IKZmZmZmZmZVeLggpmZmZmZmdn/t3fn8deV8/7HX2+FSoMGIUPySxLO8SM5xcn461SoVCpzZofDMc8SEpllylCSKBkjESJDiUoZG5AioqJBpfnz++Na273a93fY3+/e9/29v3k9H4/92Htd61prXfuz131/9/rs67qWxmJyQZIkSZIkjcXkgiRJkiRJGovJBUmSJEmSNBaTC5IkSZIkaSwmFyRJkiRJ0lhMLkiSJEmSpLGYXJAkSZIkSWMxuSBJkiRJksaSqlroNkiS5iEbpHj2QrdCi0m93r/5kiRpbpKcUlWbz1bPnguSJEmSJGksJhckSZIkSdJYTC5IkiRJkqSxmFyQJEmSJEljMbmgiUiyZ5JTkvw9ycVJTk3yrnns5+AkJy+LNvaOsU2SF05wf3snqd7jz0mOSvJvkzrGCG1YL8n7k5yd5Kokf0pyTJKd5rifh3Tv4V7LqKljSbJhkk8m+X33Pv+Q5MgkW/fq7JZkzwkec/UuJnv2ys5J8o5JHUOSJEla7EwuaGxJXgV8DDgG2Bl4MnAksMNCtmsG2wAvnPA+LwW27B4vBDYBvplknQkfZylJbg58B9gOeDOwLfBy4C/Aw5f18ZeXJGsDJwL3BF5Fe797ATfQ4j6wG7DnMm7OY4D9l/ExJEmSpEVj5YVugG4S/gf4cFW9ulf2lSRvWF4NSLJqVf1jeR1vCtdV1Ynd6xOTnAP8kHah/+llfOyHAPcCtqiqk3rlhybJMj72nCRZpaqumufmuwK3Bf69qi7olX98eb/Pqjp1eR5PkiRJWtHZc0GTcGvgz8OFVfXPG6pP190+yXFJPje8bZKdkpzRdX3/QZLNhtZXkhcneU+SC4Gfd+WPTPLNJBckuSzJiUm26W23N/ASYMPeMIaDu3VbJvlykvOTXJHktCRPmGdMfto936l37JW6IRS/T3J1kl8meXxv/fZJbkiy0dB73agr33GaY926e57tM9g0yeHdUIIru+O/MMlU/w+sl+SzSS7vhlo8d6hNs8aqGypTSbboPud/AC/r1r01yc+7/Z+X5FNJbjfN++u/z2uAv033PrvPchfgwb3Pd+9u3YznRq/duyQ5K8k/knwP2HSKOjcaFjHVeTx8zie5S7e8R5KPd204L8kTu/UvTxvOcmGS/ab5XCRJkqQVkl9eNQk/AZ6f5ClJ1p3A/jYE3gW8CXg8sBZwTJJVhuq9DLg98CTgBV3ZRsBXurJdgBOAryV5YLf+Y7SeBH9myTCGN/WOezzwdODRwOdpv4o/bh7v4c7d8+96ZW8EXgN8hDZk5HjgU739HwP8CXjK0L72BC4AvjrNsU6jDQ04KMmDkkzXI+kOwJnAc4HtgY8CbwBeMUXdj9ISJI8BjgM+kGSL3vq5xOow2meyPXBUV7Y+sC/wSNowkrsC357lgvonwC2BTya53zR130QbInIqSz7fj3XrZjs3SHJf4DPde9+5q3/EDG2aj/2A87s2fB/4RJJ3AlsATwPeQxvWstuEjytJkiQtMw6L0CQ8D/gScDBQSU6nXWy+o6oum8f+1gN2rKoTAJKcAvyWdpF9QK/e+VW1e3/Dqnr/4HV38fkd2hj9pwPHV9V5Sc4Hru4NYxhse3hv2wDfA+4IPJN2gTyj3kX9hsD7aRf9R3br1qFdRO9TVft09Y5Jckdgb+Cwqrq+++X9KUneUFXVteMpwKFVdd1Ux62qXyd5GfBW2sXqVUm+CxxYVZ/t1TsWOLb3/n4ArNa9v7cM7fawQTuTHEdLIOwM/Hgesdq/qt471Oan9bZfiTaE5DzgQd2+pnqfxyZ5Ny2OewB/T/JN4ENV9a2uzm+T/A242RSf74znRrfqlcBZwG5db4ivJbkFsA+T8+3BEKIkP6IN99gB2LSqrge+3vVSeQxw+PS7kSRJklYc9lzQ2KrqZ8A9aBdIHwQCvA44Ocnq89jlBYPEQrf/c4FTaL/s9h09vGGSOyb5RJI/AtcB19ImcNxktoMmWTvJ/knO7ba7FnjWKNsC6/a2+Q3wf4Gdq+rqbv29aBfynx3a7jPAJklu0y0fREtOPKRbfmi3/PGZDl5V76L9Mv882q/tDwCOSPLPpEGSVZK8IclvgKu7tr4Z2GiK3g7f6O37WuDXtOTBYF9zidVSPS6SbJfkhCSX0j6n87pVM8a6ql7c1XkZrUfFtsA3kjxnpu26Y45ybmwBfLk/nAT4wmz7nqNjBy+65NuFwHe7xMLAb2g9TZaS5FlJTk5yMldOuGWSJEnSPJlc0ERU1dVV9ZWq+p+q2gx4BnA32q/Cc3XBNGW3Hyr7S3+h+zX6y8BWtLsIPBS4P/A1YHhIxVQOBnYH3k676Lw/7WJ/lG0v7er/B/Bs4BbAp3td9wdt/8vQdoPldQCq6mzaRfNTu/KnAj+uql/O1oCq+mNVfbCqdqMlAr4OvKw3VGU/4KW0YRnbd+0d/CI//B4vGVq+ZqjOwYweq+HP6f60z+k82hCFLWlxm6odS6mq31TVO6pqB1ri5TRg364HxZTmcG7cjqXPv6nOx3FcMrR8zTRlU8aiqj5SVZtX1easNuGWSZIkSfPksAgtE1V1YJK3sWQyvMEdAm4xVHVt4KKhsvWn2OX6wPAFdg0tb0zrMbBdVX19UJhk1dna283n8CjgeVV1QK981ATcdVV1cvf6R93khYcAj6X1Tji/9z7+2tvutt1zf5LCjwEfTbvF5860CSjnpKquSPJB2i/7G3fHfCzwvqp626BekkfOdd/ziNXw5/QY2q/1u/cmYtxwru0AqKqLknycdlvI9Vk6eTMw6rnxZ5Y+/6Y6H4ddxdTntiRJkvQvwZ4LGluSpS6+um7+a7HkYm/Q7f0evTp3YoqZ+IH1k2zVq3dn4L504/1nMLhQHAxFGFy0PnCo3lS/Ct+S9u+hv+0atKEe83EoLRkymCzxF8CVtAv8vt2As6rqwl7ZF7o2Ht61acZx90nW6eYtGHa37nnwGazKjd/fSrS5C+Zq3FitClw7NPRg1rty9IaODLtb15ZLu+WpPt9Rz42TgB2GekHsPFvbaOf38Lm81J0oJEmSpJsqey5oEn6e5EjaOP0LaF3VX0q7mP4EQDeR4snAm5JcSbs4fTVT3FaQ1pPh0CSvBf5Bu6PBBbSu+DM5g3aR984krwPW6Lb94xT1bptkT9pF/0VVdU6Sk4C9klxGu/vCK2kXrGuOGId/6iZj3Jd2N4iHd5MRvgd4bZLrgJNpF63bA48b2vaqJJ+izZ9wWFVdMsvhHga8pfsF/6Su7Vt17T+qqs7p6n0TeF4358Lfuv3fch7v7dIxY/VN4IVdPL7StfWJI2z3lLTbXR5Cu5vDzYFH0O5+8aGqGvSOOQPYMclOtPPhT4x+buwH/Ig2X8WBtLkyRhna80Xg6d2Ek1+lDbvYdoTtJEmSpJsEey5oEt4I3IXWNf0btNsB/hLYoqr6t2J8HPB72q/6+3bbnTnF/s6lJSf2pv1q/3fgv3oXj1PqJk/cmTZZ3+e6drwF+O5Q1SNoiYq30S7G9+7KHw+cTbt4fS/tjheHzHTMWXyGNhHiy7vlvbr2/DftloxbA0/s33mh50vd80EjHOdHtLtS7EabMPJL3et9aPMiDDyfdjeJD3T7/QVL3yViVPOOVVUdTevRsQttHoQH04ZZzOZo2u0jn0l7v5+hJVaeD7yoV++DtPPwINrn+6xRz41uaMsetCEUXwJ24sYxnO49fZWWLNuVlmjYEPjfEd6TJEmSdJOQG/dMlrQi6Oar2A24a1XdsNDt0YopG6R49kK3QotJvd6/+ZIkaW6SnFJVm89Wz2ER0gokyd2BzWi9G95gYkGSJEnSYmByQVqxfBh4AG24wP4L3BZJkiRJGonJBWkFUlUPWeg2SJIkSdJcOaGjJEmSJEkaiz0XJGmRut8G9+Pk15+80M2QJEmS7LkgSZIkSZLGY3JBkiRJkiSNxeSCJEmSJEkai8kFSZIkSZI0FpMLkiRJkiRpLCYXJEmSJEnSWEwuSJIkSZKksZhckCRJkiRJYzG5IEmSJEmSxmJyQZIkSZIkjSVVtdBtkCTNQ5K/A2cudDtuItYDLlroRtwEGMfJMZaTYywnx1hOjrGcHGM5GTPFccOqus1sO1h5su2RJC1HZ1bV5gvdiJuCJCcby/EZx8kxlpNjLCfHWE6OsZwcYzkZk4ijwyIkSZIkSdJYTC5IkiRJkqSxmFyQpMXrIwvdgJsQYzkZxnFyjOXkGMvJMZaTYywnx1hOxthxdEJHSZIkSZI0FnsuSJIkSZKksZhckKRFJMlmSY5NcmWSPyV5Y5KVFrpdK7okGyf5cJKfJbk+yXFT1EmSVyf5Q5J/JPlekvss/9auuJI8NsmXk/wxyeVJTknyuCnqPTPJr5Nc1dV5+EK0d0WWZNckJyT5axenM5O8NsktenU8J+coyR26c7OSrN4rN5YjSLJnF7vhx3N6dYzlCJKsnOSV3f+FVyc5L8m7h+oYy1kkOW6ac7KSbNnVMY4jSrJHkp90/0/+MckhSTYYqjPveJpckKRFIsnawLeAAnYE3gi8BHjDQrZrkbgnsD1wJnDWNHVeCbwO2A94NHA58K0kt1suLVwcXkyLy4uAHYDvAJ9O8vxBhS7ZcABwCLAd8EvgqCT3Wv7NXaGtC3wbeAYtTgcBrwHe1avjOTl3b6fFaZixnJuHAVv2Hl/orTOWozkYeAHwDmAbWtz+MVTHWM7uudz4XNwS+CZwEXBSV8c4jiDJDsBhwAm075GvALYGvpqknxeYfzyryocPHz58LIIH8CrgYmDNXtnLgSv7ZT6mjN3Neq8/Bxw3tH4V4FJgr17ZrYALgX0Wuv0rygNYb4qyTwO/6y2fCRzUjz3wc+DQhW7/iv4A3gxcAsRzcl7x2xr4G/BSWhJ29a7cWI4ewz37sZtivbEcLY7bAtcCm81Qx1jOL7a36P6df8g4zjl2hwOnDJXt0P2bv8ck4mnPBUlaPLYDjqmqy3plhwOrAg9emCYtDlV1wyxVtgLWBI7obXMF8BVa3AVU1UVTFJ8KbACQ5K7AJtw4jjcAn8U4juKvtC/O4Dk5J2nDw95H69E1fJ4ay8kxlqN5GvDtqvrVDHWM5fxsC6xN+wUejONc3JyWOOi7pHtO9zxWPE0uSNLisSlwRr+gqn5P67mw6YK06KZjU+B64NdD5adjbGezJUuGmgxidcZQndOBdZLcZrm1apFIslKS1ZI8iNaF+kPVfirynJyb5wC3BD4wxTpjOXe/TXJdNxfIs3vlxnI0DwDOSvL+JJelzZP0haGx7cZyfvYAzgO+3y0bx9EdBPxnkicnWTPJJsA+3DgRNlY8TS5I0uKxNksyzH0Xd+s0f2sDl1fV9UPlFwOr9SfZ0xLdRI07Ae/sigbn4SVDVS8eWq8lruge3we+C7ysK/ecHFGSdYE3AS+uqmunqGIsR3c+baz1k2hjrU8EDkjyom69sRzN7WhDTO5Duxh+KnA/4ItJBr8QG8s5SrIarRv/EV0SFozjyKrqq7Tz8iO0HgxnAisBu/SqjRXPlSfWWkmS9C8jyV1o8y0cWVUHL2xrFrWtgNWALYC9gPfTJjDT6N4MnFhVRy90Qxa7qjoGOKZX9LUkqwCvTfLeBWrWYpTusWNV/RUgyfm0BOLDgGMXsG2L2aNp4/8Pm62ilpbkobQJl98LfA24LbA3Len1iCkSCnNmckGSFo+LgbWmKF+bJb8Ma34uBlZPstLQH9e1gSur6poFatcKKck6tC8m5wJP6K0anIdrcePeC2sPrVenqn7SvfxBkouATyR5J56TI0lyT9r49q2T3LorXq17XivJ9RjLcX0O2A24C8ZyVBcDZw8SC50fANcAm9GSC8Zy7vYAflNVJ/fKjOPo3gl8uapeMShIchptKOOOtLvCjBVPh0VI0uJxBkPj3ZLcifZFeniMu+bmDFrXwI2Hypea5+JfXdct9SjaxIOPqqore6sHsRoel7kp8LequnA5NHExGyQaNsJzclR3o01S9kPal+KLWTLvwnm0SR6N5Xiq92wsR3M6SybI6wswmGDYWM5BkrVoEwoO91owjqPbFDitX1BVZ9Jukfp/uqKx4mlyQZIWj68B/5VkjV7Z7rQ/Ct9dmCbdZJwAXAY8dlDQXUQ/mhZ3AUlWpt354W7AtlV1QX99VZ1Nm9yxH8ebdcvGcXYP7J5/h+fkqH4APHTosV+3bnvg7RjLce1KuwPHuRjLUR0F3DvJer2yrWmJsJ92y8Zybh5Dm7R1OLlgHEd3LnDffkGSe9DuOnZOVzRWPB0WIUmLxwG02eS/kGQ/4K60sXLvGro9pYZ0fxi37xbvAKyZZNdu+eiqujLJW4HXJbmYlp1/MS0J/77l3uAV1wdpcfxfYN1uIr2BU6vqato5eWiSc4DjgafQkhGPX75NXbEl+TrwLeCXtJm5Hwi8BPhMVf22q+M5OYvu9qjH9cu6+UAAvl9Vl3dlxnIEST4P/Bj4Ge3Xy927xwu628peZSxH8hHa3+uvJNkXWIOW9PpWVf0AoKqM5dzsAfy0qk7vFxrHOTkAeHeSP7FkzoW9aImFo2H8eJpckKRFoqou7mbnfz/tfsOXAO+mXcxpZuvTfnHvGyxvRPvD+lbaH89XAesCJwP/r6r+spzauBhs0z1PNbHbRsA5VXVYktWBV9Bmnf8lbfjEL5ZTGxeLk2izdt8FuA44m3buHdCr4zk5OcZyNGfS5rC4E60L/6+AJ1fVJ3t1jOUsquqyJA8D9gcOp821cCTwoqGqxnIEXQ+Qh9P+pkzFOI5mf9q5+N+02/deQuv99aqquqJXb97xzJK7eEiSJEmSJM2dcy5IkiRJkqSxmFyQJEmSJEljMbkgSZIkSZLGYnJBkiRJkiSNxeSCJEmSJEkai8kFSZIkSZI0FpMLkiRJkiRpLCYXJEmSNFFJPpqkkvw1yS3nsN2vu+2+vCzbJ0maPJMLkiRJmrQDu+d1gB1H2SDJg4GNh7aXJC0SJhckSZI0UVV1IvCrbvGpI242qPcX4KsTb5QkaZkyuSBJkqRlYdD7YJskd5ipYpI1gF27xUOq6rpl2jJJ0sSZXJAkSdKy8EngWtr3zT1nqbs7cKvu9UGDwiS3SbJPklOTXJrkqiRnJzkwyT2n2lGSh3TzNlS3vHGSg5L8IcnVSc7r5oSYMuGRZO9u++Oma+zwMaaps0aSVyb5YZK/dcf+Q5LDk2w5SzwkadExuSBJkqSJq6oLgcHEjHvOUn0wJOKEqjoDIMkjgLOA1wD3AVYFrgM2Ap4G/CTJk2faaZKHAqd2+1+L9t33DsAzgB/P1qNivpLchzYs5C3AfwBrAlcDd6QlUo5P8qplcWxJWigmFyRJkrSsDIZGbJxk66kqJLk7sFW/fpJ70xITtwY+CmwGrFpVqwMbAh8EbgEcmGTzGY7/eeDbwD2qak1a74jdgb8DG9Au/icqye2BY2iJhC8Am3dtXxO4LfAm4Hpg3yQ7Tfr4krRQTC5IkiRpWTkGOK97/bRp6gzKLweO6F6/h9ZT4S1V9ayqOr2qrgeoqt9X1fOA/YGVgdfOcPzTgMcMekNU1TVVdQStNwTArklWnvO7mtk+wPrAp6tql6o6paqu7Y5/QVXtBby8q7v3hI8tSQvG5IIkSZKWiaq6ATi4W9w1yer99UlWAp7ULR5RVZcnuQvwMNoQiHfMsPtDuudHdPuZyr5dG4Yd2T2vCtxtxjcxB0lWAR7fLe43Q9VB2/89yW0ndXxJWkiTztRKkiRJfR+n9RQYDEk4sLduO+D23etB+QO755sBv0oy3X4HCYVbAesCF0xR50fTbPun3ut1pjvAPNwPWKV7/Y0Z2t63Ie32m5K0qJlckCRJ0jJTVWd3d154KG0IRD+5MBgScUZVndC93qB7vhltjoJRrDbNsf8+Tfl1vQv/m494jFFs0Hs9VtslabExuSBJkqRl7UBacmGrJJtU1VlJbgM8qlt/UK/uoEfCX6rqdsuzkRPQH56xalVdtWAtkaTlzDkXJEmStKx9Hrikez3orfBEWq+B61gyBwHAn7vn9ZLcarm07sau655XmaHOWtOU/7n3esPJNEeSFgeTC5IkSVqmul/wP90tPrmbgPGp3fJRVdWfc+D47nkl2pwMy9vF3fOdZqjzgGnKTwKu6V4/emItkqRFwOSCJEmSlofBXAu3B14H3Ltb7g+JoKp+DRzXLb45yXS9BABIMskJGQF+2j1vkGSpJEKS9YFnTrVhVV3BkiTKK5LceaYDLYO2S9KCMbkgSZKkZa6qfgKc1i2+rns+Hzh6iurPBy4HNgFOTLJjd5tHAJLcIcmTkhzLzLd8nI8TgHO7159IsnmamyV5CC3xMdN36FfT7kaxHvDDrp1r9Np+myS7JPkicNiE2y5JC8bkgiRJkpaXQe+FwXfQT1TV9cOVquoXwLa0OQw2Bb4EXJ7koiRXAufR5ml42KQbWFU3AM8GrgXuThvqcDlwBfAd2oToz5th+/OBRwBn0e4ecQhwSZK/JrmcdsvMzwE74XdxSTch/ocmSZKk5eVTQP8OCgdNV7Gqjqf1XHgp8D3ahJC3Bq4HTgcOBZ4AvHDSjayqY4D/BI6izcGwEvAH4K3A/bjxxI1TbX868G+0JMU3gIuANYEAvwE+CzwL2G3SbZekhZKqWug2SJIkSZKkRcyeC5IkSZIkaSwmFyRJkiRJ0lhMLkiSJEmSpLGYXJAkSZIkSWMxuSBJkiRJksZickGSJEmSJI3F5IIkSZIkSRqLyQVJkiRJkjQWkwuSJEmSJGksJhckSZIkSdJYTC5IkiRJkqSx/H8C4gldZaDyaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.venue.value_counts().sort_values(ascending=True).tail(10).plot(kind='barh',figsize=(12,8),fontsize=15,color='green')\n",
    "plt.title('Venue which has hosted most number of IPL matches',fontsize=10,fontweight='bold')\n",
    "plt.xlabel('Venue',size=25)\n",
    "plt.ylabel('Frequency',size=25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dd9a8231",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['SRH' 'MI' 'GL' 'RPS' 'RCB' 'KKR' 'DC' 'KXIP' 'CSK' 'RR' 'DCH' 'KTK' 'PW']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total teams in IPL\n",
    "\n",
    "print(df['team1'].unique())\n",
    "df['team1'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cd41c33b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CH Gayle          21\n",
       "AB de Villiers    20\n",
       "DA Warner         17\n",
       "MS Dhoni          17\n",
       "RG Sharma         17\n",
       "Name: player_of_match, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most Man of the Match(MOM) awards\n",
    "\n",
    "MOM = df['player_of_match'].value_counts().head()\n",
    "MOM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5ec7ed0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013    76\n",
       "2012    74\n",
       "2011    73\n",
       "2010    60\n",
       "2014    60\n",
       "2016    60\n",
       "2018    60\n",
       "2019    60\n",
       "2015    59\n",
       "2017    59\n",
       "2008    58\n",
       "2009    57\n",
       "Name: season, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total match played across each season\n",
    "\n",
    "df['season'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "22ce0a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(df['season'])\n",
    "plt.title('Matches played each year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9678c133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season  toss_decision\n",
       "2008    field            32\n",
       "        bat              26\n",
       "2009    bat              35\n",
       "        field            22\n",
       "2010    bat              39\n",
       "        field            21\n",
       "2011    field            48\n",
       "        bat              25\n",
       "2012    bat              37\n",
       "        field            37\n",
       "2013    bat              45\n",
       "        field            31\n",
       "2014    field            41\n",
       "        bat              19\n",
       "2015    field            34\n",
       "        bat              25\n",
       "2016    field            49\n",
       "        bat              11\n",
       "2017    field            48\n",
       "        bat              11\n",
       "2018    field            50\n",
       "        bat              10\n",
       "2019    field            50\n",
       "        bat              10\n",
       "Name: toss_decision, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Toss decisions made across all seasons\n",
    "\n",
    "df.groupby('season')['toss_decision'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20d08c86",
   "metadata": {},
   "source": [
    "From above info, \n",
    "\n",
    "\n",
    "we found that **Maximum number of toss decision** made for **field** also particularly of season played in 2019 year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ef4bc191",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,7))\n",
    "sns.countplot(df['season'],hue=df['toss_decision'])\n",
    "plt.title('Toss decision taken in all seasons')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "446a9839",
   "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>team_name</th>\n",
       "      <th>matches_played</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MI</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RCB</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KKR</td>\n",
       "      <td>178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DC</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KXIP</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CSK</td>\n",
       "      <td>164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RR</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SRH</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DCH</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PW</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GL</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>RPS</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KTK</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   team_name  matches_played\n",
       "7         MI             187\n",
       "9        RCB             180\n",
       "4        KKR             178\n",
       "1         DC             177\n",
       "6       KXIP             176\n",
       "0        CSK             164\n",
       "11        RR             147\n",
       "12       SRH             108\n",
       "2        DCH              75\n",
       "8         PW              46\n",
       "3         GL              30\n",
       "10       RPS              30\n",
       "5        KTK              14"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total matches played by each team\n",
    "\n",
    "total_matches = ((df['team1']).value_counts()+(df['team2']).value_counts()).reset_index()\n",
    "total_matches.columns = ['team_name','matches_played']\n",
    "total_matches.sort_values('matches_played',ascending = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e4f2eff5",
   "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>team_name</th>\n",
       "      <th>wins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MI</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CSK</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>KKR</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RCB</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KXIP</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DC</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>RR</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SRH</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DCH</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RPS</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GL</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PW</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KTK</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   team_name  wins\n",
       "0         MI   109\n",
       "1        CSK   100\n",
       "2        KKR    92\n",
       "3        RCB    84\n",
       "4       KXIP    82\n",
       "5         DC    77\n",
       "6         RR    75\n",
       "7        SRH    58\n",
       "8        DCH    29\n",
       "9        RPS    15\n",
       "10        GL    13\n",
       "11        PW    12\n",
       "12       KTK     6"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Most winning teams \n",
    "\n",
    "df['winner'] = df['winner'].map(teams)\n",
    "winner = df['winner'].value_counts().reset_index()\n",
    "winner.columns = ['team_name','wins']\n",
    "winner.sort_values('wins',ascending=False)\n",
    "winner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8d38801b",
   "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>team_name</th>\n",
       "      <th>matches_played</th>\n",
       "      <th>wins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MI</td>\n",
       "      <td>187</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CSK</td>\n",
       "      <td>164</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KKR</td>\n",
       "      <td>178</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RCB</td>\n",
       "      <td>180</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KXIP</td>\n",
       "      <td>176</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DC</td>\n",
       "      <td>177</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RR</td>\n",
       "      <td>147</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SRH</td>\n",
       "      <td>108</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DCH</td>\n",
       "      <td>75</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>RPS</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GL</td>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PW</td>\n",
       "      <td>46</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KTK</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   team_name  matches_played  wins\n",
       "7         MI             187   109\n",
       "0        CSK             164   100\n",
       "4        KKR             178    92\n",
       "9        RCB             180    84\n",
       "6       KXIP             176    82\n",
       "1         DC             177    77\n",
       "11        RR             147    75\n",
       "12       SRH             108    58\n",
       "2        DCH              75    29\n",
       "10       RPS              30    15\n",
       "3         GL              30    13\n",
       "8         PW              46    12\n",
       "5        KTK              14     6"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Matches played VS matches wins\n",
    "\n",
    "result = total_matches.merge(winner,left_on = 'team_name',right_on = 'team_name',how = 'inner')\n",
    "result.sort_values('wins',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "480841aa",
   "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>team_name</th>\n",
       "      <th>matches_played</th>\n",
       "      <th>wins</th>\n",
       "      <th>%win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CSK</td>\n",
       "      <td>164</td>\n",
       "      <td>100</td>\n",
       "      <td>60.975610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MI</td>\n",
       "      <td>187</td>\n",
       "      <td>109</td>\n",
       "      <td>58.288770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SRH</td>\n",
       "      <td>108</td>\n",
       "      <td>58</td>\n",
       "      <td>53.703704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KKR</td>\n",
       "      <td>178</td>\n",
       "      <td>92</td>\n",
       "      <td>51.685393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RR</td>\n",
       "      <td>147</td>\n",
       "      <td>75</td>\n",
       "      <td>51.020408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>RPS</td>\n",
       "      <td>30</td>\n",
       "      <td>15</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RCB</td>\n",
       "      <td>180</td>\n",
       "      <td>84</td>\n",
       "      <td>46.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KXIP</td>\n",
       "      <td>176</td>\n",
       "      <td>82</td>\n",
       "      <td>46.590909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DC</td>\n",
       "      <td>177</td>\n",
       "      <td>77</td>\n",
       "      <td>43.502825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GL</td>\n",
       "      <td>30</td>\n",
       "      <td>13</td>\n",
       "      <td>43.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>KTK</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>42.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DCH</td>\n",
       "      <td>75</td>\n",
       "      <td>29</td>\n",
       "      <td>38.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PW</td>\n",
       "      <td>46</td>\n",
       "      <td>12</td>\n",
       "      <td>26.086957</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   team_name  matches_played  wins       %win\n",
       "0        CSK             164   100  60.975610\n",
       "7         MI             187   109  58.288770\n",
       "12       SRH             108    58  53.703704\n",
       "4        KKR             178    92  51.685393\n",
       "11        RR             147    75  51.020408\n",
       "10       RPS              30    15  50.000000\n",
       "9        RCB             180    84  46.666667\n",
       "6       KXIP             176    82  46.590909\n",
       "1         DC             177    77  43.502825\n",
       "3         GL              30    13  43.333333\n",
       "5        KTK              14     6  42.857143\n",
       "2        DCH              75    29  38.666667\n",
       "8         PW              46    12  26.086957"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['%win'] = (result['wins']/result['matches_played'])*100\n",
    "result.sort_values('%win',ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c7c3159",
   "metadata": {},
   "source": [
    " From above info, \n",
    " \n",
    " we can say that Mumbai Indians(MI) wins the most number of matches (109) but Chennai Super Kings(CSK) has highest %win also\n",
    " \n",
    " more Fanbase holds to same teams on top of table like **MI and CSK** has more number of followings because of their game and \n",
    " \n",
    " this insight justify companies to **endores** the team and player of that specific team.    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87e124e2",
   "metadata": {},
   "source": [
    "# Finding does toss decision affect the winning the match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "42713114",
   "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>team_name</th>\n",
       "      <th>toss_wins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MI</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KKR</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DC</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CSK</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RCB</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  team_name  toss_wins\n",
       "0        MI         98\n",
       "1       KKR         92\n",
       "2        DC         90\n",
       "3       CSK         89\n",
       "4       RCB         81"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Maximum toss winner team\n",
    "\n",
    "df['toss_winner'] = df['toss_winner'].map(teams)\n",
    "toss_winner = df['toss_winner'].value_counts().reset_index()\n",
    "toss_winner.columns = ['team_name','toss_wins']\n",
    "toss_winner.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "742d3da9",
   "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>team_name</th>\n",
       "      <th>matches_played</th>\n",
       "      <th>toss_wins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MI</td>\n",
       "      <td>187</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KKR</td>\n",
       "      <td>178</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DC</td>\n",
       "      <td>177</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CSK</td>\n",
       "      <td>164</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>KXIP</td>\n",
       "      <td>176</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  team_name  matches_played  toss_wins\n",
       "7        MI             187         98\n",
       "4       KKR             178         92\n",
       "1        DC             177         90\n",
       "0       CSK             164         89\n",
       "6      KXIP             176         81"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toss_win = total_matches.merge(toss_winner,left_on = 'team_name', right_on = 'team_name',how = 'inner')\n",
    "toss_win.sort_values('toss_wins',ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "dd24ef3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "field    463\n",
       "bat      293\n",
       "Name: toss_decision, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['toss_decision'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "256df67a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['toss_decision'].value_counts().plot(kind='bar',figsize=(10,7))\n",
    "plt.xlabel('Toss Decision')\n",
    "plt.ylabel('Counts')\n",
    "plt.xticks(rotation=360)\n",
    "plt.title('Decision taken by captains after winning tosses')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "143ec167",
   "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>index</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>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>Bangalore</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>RCB</td>\n",
       "      <td>DC</td>\n",
       "      <td>RCB</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>RCB</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2017</td>\n",
       "      <td>Indore</td>\n",
       "      <td>2017-04-10</td>\n",
       "      <td>RCB</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>RCB</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>AR Patel</td>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>AK Chaudhary</td>\n",
       "      <td>C Shamshuddin</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "      <td>2017</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>DC</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>DC</td>\n",
       "      <td>bat</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>DC</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "      <td>CJ Anderson</td>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>YC Barde</td>\n",
       "      <td>Nitin Menon</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  id  season       city        date team1 team2 toss_winner  \\\n",
       "0      4   5    2017  Bangalore  2017-04-08   RCB    DC         RCB   \n",
       "1      7   8    2017     Indore  2017-04-10   RCB  KXIP         RCB   \n",
       "2     14  15    2017      Delhi  2017-04-15    DC  KXIP          DC   \n",
       "\n",
       "  toss_decision  result  dl_applied winner  win_by_runs  win_by_wickets  \\\n",
       "0           bat  normal           0    RCB           15               0   \n",
       "1           bat  normal           0   KXIP            0               8   \n",
       "2           bat  normal           0     DC           51               0   \n",
       "\n",
       "  player_of_match                   venue       umpire1        umpire2 umpire3  \n",
       "0       KM Jadhav   M Chinnaswamy Stadium           NaN            NaN     NaN  \n",
       "1        AR Patel  Holkar Cricket Stadium  AK Chaudhary  C Shamshuddin     NaN  \n",
       "2     CJ Anderson        Feroz Shah Kotla      YC Barde    Nitin Menon     NaN  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bat_first = df[df['toss_decision'] == 'bat'].reset_index()\n",
    "bat_first.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "539ab65f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(293, 19)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bat_first.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7155c9d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "While batting first teams won: 131 matches\n"
     ]
    }
   ],
   "source": [
    "print('While batting first teams won:',(bat_first['win_by_runs'] > 1).sum(),'matches')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f9f59127",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "While batting first teams lost: 152 matches\n"
     ]
    }
   ],
   "source": [
    "print('While batting first teams lost:',(bat_first['win_by_wickets'] > 1).sum(),'matches')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a561743f",
   "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>index</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>SRH</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>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2017</td>\n",
       "      <td>Pune</td>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>MI</td>\n",
       "      <td>RPS</td>\n",
       "      <td>RPS</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>RPS</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>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2017</td>\n",
       "      <td>Rajkot</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>GL</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KKR</td>\n",
       "      <td>field</td>\n",
       "      <td>normal</td>\n",
       "      <td>0</td>\n",
       "      <td>KKR</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  id  season       city        date team1 team2 toss_winner  \\\n",
       "0      0   1    2017  Hyderabad  2017-04-05   SRH   RCB         RCB   \n",
       "1      1   2    2017       Pune  2017-04-06    MI   RPS         RPS   \n",
       "2      2   3    2017     Rajkot  2017-04-07    GL   KKR         KKR   \n",
       "\n",
       "  toss_decision  result  dl_applied winner  win_by_runs  win_by_wickets  \\\n",
       "0         field  normal           0    SRH           35               0   \n",
       "1         field  normal           0    RPS            0               7   \n",
       "2         field  normal           0    KKR            0              10   \n",
       "\n",
       "  player_of_match                                      venue         umpire1  \\\n",
       "0    Yuvraj Singh  Rajiv Gandhi International Stadium, Uppal     AY Dandekar   \n",
       "1       SPD Smith    Maharashtra Cricket Association Stadium  A Nand Kishore   \n",
       "2         CA Lynn     Saurashtra Cricket Association Stadium     Nitin Menon   \n",
       "\n",
       "     umpire2 umpire3  \n",
       "0   NJ Llong     NaN  \n",
       "1     S Ravi     NaN  \n",
       "2  CK Nandan     NaN  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "field_first = df[df['toss_decision']=='field'].reset_index()\n",
    "field_first.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0da32923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(463, 19)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "field_first.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "8966314f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "While bowling first teams won: 251 matches\n"
     ]
    }
   ],
   "source": [
    "print('While bowling first teams won:',(field_first['win_by_wickets'] > 1).sum(),'matches')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a08b7354",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "While bowling first teams lost: 196 matches\n"
     ]
    }
   ],
   "source": [
    "print('While bowling first teams lost:',(field_first['win_by_runs'] > 1).sum(),'matches')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe76acc",
   "metadata": {},
   "source": [
    "# Finding does playing ground affect the loss or win of team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "11ed605e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ground(df,team_name):\n",
    "    return df[df['winner']==team_name]['venue'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "2276cbe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Wankhede Stadium              42\n",
       "Eden Gardens                  10\n",
       "M Chinnaswamy Stadium          8\n",
       "Brabourne Stadium              6\n",
       "Dr DY Patil Sports Academy     5\n",
       "Name: venue, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground(df,'MI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "772a1294",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M Chinnaswamy Stadium    34\n",
       "Feroz Shah Kotla          6\n",
       "Eden Gardens              4\n",
       "Wankhede Stadium          3\n",
       "New Wanderers Stadium     3\n",
       "Name: venue, dtype: int64"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground(df,'RCB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "4ff8a301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MA Chidambaram Stadium, Chepauk    34\n",
       "Wankhede Stadium                    7\n",
       "M. A. Chidambaram Stadium           6\n",
       "Feroz Shah Kotla                    5\n",
       "M Chinnaswamy Stadium               5\n",
       "Name: venue, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ground(df,'CSK')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c26b3bdc",
   "metadata": {},
   "source": [
    "From above, we can say that ground can affect the win/loss of the team.\n",
    "And teams perform well at their home ground. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "484bd5e8",
   "metadata": {},
   "source": [
    "# Load the data of \"deliveries\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "14d6393a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning         batting_team                 bowling_team  over  \\\n",
       "0         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "1         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "2         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "3         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "4         1       1  Sunrisers Hyderabad  Royal Challengers Bangalore     1   \n",
       "\n",
       "   ball    batsman non_striker    bowler  is_super_over  ...  bye_runs  \\\n",
       "0     1  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "1     2  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "2     3  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "3     4  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "4     5  DA Warner    S Dhawan  TS Mills              0  ...         0   \n",
       "\n",
       "   legbye_runs  noball_runs  penalty_runs  batsman_runs  extra_runs  \\\n",
       "0            0            0             0             0           0   \n",
       "1            0            0             0             0           0   \n",
       "2            0            0             0             4           0   \n",
       "3            0            0             0             0           0   \n",
       "4            0            0             0             0           2   \n",
       "\n",
       "   total_runs  player_dismissed dismissal_kind fielder  \n",
       "0           0               NaN            NaN     NaN  \n",
       "1           0               NaN            NaN     NaN  \n",
       "2           4               NaN            NaN     NaN  \n",
       "3           0               NaN            NaN     NaN  \n",
       "4           2               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('D:\\Sparks Foundation\\Task 5\\deliveries.csv')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b0152ad1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2['batting_team']=df2['batting_team'].map(teams)\n",
    "df2['bowling_team']=df2['bowling_team'].map(teams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0809331d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   match_id  inning batting_team bowling_team  over  ball    batsman  \\\n",
       "0         1       1          SRH          RCB     1     1  DA Warner   \n",
       "1         1       1          SRH          RCB     1     2  DA Warner   \n",
       "2         1       1          SRH          RCB     1     3  DA Warner   \n",
       "3         1       1          SRH          RCB     1     4  DA Warner   \n",
       "4         1       1          SRH          RCB     1     5  DA Warner   \n",
       "\n",
       "  non_striker    bowler  is_super_over  ...  bye_runs  legbye_runs  \\\n",
       "0    S Dhawan  TS Mills              0  ...         0            0   \n",
       "1    S Dhawan  TS Mills              0  ...         0            0   \n",
       "2    S Dhawan  TS Mills              0  ...         0            0   \n",
       "3    S Dhawan  TS Mills              0  ...         0            0   \n",
       "4    S Dhawan  TS Mills              0  ...         0            0   \n",
       "\n",
       "   noball_runs  penalty_runs  batsman_runs  extra_runs  total_runs  \\\n",
       "0            0             0             0           0           0   \n",
       "1            0             0             0           0           0   \n",
       "2            0             0             4           0           4   \n",
       "3            0             0             0           0           0   \n",
       "4            0             0             0           2           2   \n",
       "\n",
       "   player_dismissed dismissal_kind fielder  \n",
       "0               NaN            NaN     NaN  \n",
       "1               NaN            NaN     NaN  \n",
       "2               NaN            NaN     NaN  \n",
       "3               NaN            NaN     NaN  \n",
       "4               NaN            NaN     NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0208f889",
   "metadata": {},
   "source": [
    "## Top 10 Batsman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7cbe0bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "batsman\n",
       "SK Raina          5651\n",
       "V Kohli           5616\n",
       "RG Sharma         5057\n",
       "DA Warner         4975\n",
       "S Dhawan          4876\n",
       "CH Gayle          4873\n",
       "RV Uthappa        4703\n",
       "MS Dhoni          4691\n",
       "AB de Villiers    4583\n",
       "G Gambhir         4485\n",
       "Name: total_runs, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_batsman = df2.groupby('batsman')['total_runs'].sum()\n",
    "top_batsman.sort_values(ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "88e9fd45",
   "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": [
    "top_batsman.sort_values(ascending = False).head(10).plot(kind='bar',color = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0b4f68c",
   "metadata": {},
   "source": [
    "These players are most successful batsman of IPL and companies more likely endores them. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "247bf47d",
   "metadata": {},
   "source": [
    "## Top highest individual score of batsman"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "b1e88c2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>batsman</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>batsman_runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6200</th>\n",
       "      <td>411</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>RCB</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>60</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>KKR</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8426</th>\n",
       "      <td>562</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>RCB</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10149</th>\n",
       "      <td>7935</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>DC</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9257</th>\n",
       "      <td>620</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>RCB</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5585</th>\n",
       "      <td>372</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>RCB</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3135</th>\n",
       "      <td>206</td>\n",
       "      <td>M Vijay</td>\n",
       "      <td>CSK</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>36</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>SRH</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7752</th>\n",
       "      <td>516</td>\n",
       "      <td>V Sehwag</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10417</th>\n",
       "      <td>7953</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>CSK</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       match_id         batsman batting_team  batsman_runs\n",
       "6200        411        CH Gayle          RCB           175\n",
       "900          60     BB McCullum          KKR           158\n",
       "8426        562  AB de Villiers          RCB           133\n",
       "10149      7935         RR Pant           DC           130\n",
       "9257        620  AB de Villiers          RCB           129\n",
       "5585        372        CH Gayle          RCB           128\n",
       "3135        206         M Vijay          CSK           127\n",
       "529          36       DA Warner          SRH           126\n",
       "7752        516        V Sehwag         KXIP           122\n",
       "10417      7953       SR Watson          CSK           121"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.groupby(['match_id','batsman','batting_team'])['batsman_runs'].sum().reset_index().sort_values('batsman_runs',\n",
    "ascending = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "5aead21a",
   "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": [
    "df2.groupby('batsman_runs')['batsman'].value_counts()[:10].plot(kind='barh')\n",
    "plt.title('Top highest scorer batsman')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0663c25",
   "metadata": {},
   "source": [
    "## Highest wicket taken by player"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "5c636860",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'caught', 'bowled', 'run out', 'lbw', 'caught and bowled',\n",
       "       'stumped', 'retired hurt', 'hit wicket', 'obstructing the field'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['dismissal_kind'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "5df18fff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# This dismissal ways only considered as bowler's wicket.\n",
    "\n",
    "dismissal_kinds = ['caught','bowled','lbw','caught and bowled','stumped','hit wicket']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "fd51a90c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>match_id</th>\n",
       "      <th>inning</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>over</th>\n",
       "      <th>ball</th>\n",
       "      <th>batsman</th>\n",
       "      <th>non_striker</th>\n",
       "      <th>bowler</th>\n",
       "      <th>is_super_over</th>\n",
       "      <th>...</th>\n",
       "      <th>bye_runs</th>\n",
       "      <th>legbye_runs</th>\n",
       "      <th>noball_runs</th>\n",
       "      <th>penalty_runs</th>\n",
       "      <th>batsman_runs</th>\n",
       "      <th>extra_runs</th>\n",
       "      <th>total_runs</th>\n",
       "      <th>player_dismissed</th>\n",
       "      <th>dismissal_kind</th>\n",
       "      <th>fielder</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>A Choudhary</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>caught</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>STR Binny</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>caught</td>\n",
       "      <td>Sachin Baby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>caught</td>\n",
       "      <td>Sachin Baby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>19</td>\n",
       "      <td>4</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>TS Mills</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>bowled</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>Rashid Khan</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Mandeep Singh</td>\n",
       "      <td>bowled</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     match_id  inning batting_team bowling_team  over  ball        batsman  \\\n",
       "11          1       1          SRH          RCB     2     5      DA Warner   \n",
       "64          1       1          SRH          RCB    11     3       S Dhawan   \n",
       "94          1       1          SRH          RCB    16     2   MC Henriques   \n",
       "116         1       1          SRH          RCB    19     4   Yuvraj Singh   \n",
       "160         1       2          RCB          SRH     6     4  Mandeep Singh   \n",
       "\n",
       "      non_striker       bowler  is_super_over  ...  bye_runs  legbye_runs  \\\n",
       "11       S Dhawan  A Choudhary              0  ...         0            0   \n",
       "64   MC Henriques    STR Binny              0  ...         0            0   \n",
       "94   Yuvraj Singh    YS Chahal              0  ...         0            0   \n",
       "116      DJ Hooda     TS Mills              0  ...         0            0   \n",
       "160      CH Gayle  Rashid Khan              0  ...         0            0   \n",
       "\n",
       "     noball_runs  penalty_runs  batsman_runs  extra_runs  total_runs  \\\n",
       "11             0             0             0           0           0   \n",
       "64             0             0             0           0           0   \n",
       "94             0             0             0           0           0   \n",
       "116            0             0             0           0           0   \n",
       "160            0             0             0           0           0   \n",
       "\n",
       "     player_dismissed dismissal_kind        fielder  \n",
       "11          DA Warner         caught  Mandeep Singh  \n",
       "64           S Dhawan         caught    Sachin Baby  \n",
       "94       MC Henriques         caught    Sachin Baby  \n",
       "116      Yuvraj Singh         bowled            NaN  \n",
       "160     Mandeep Singh         bowled            NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_wicket = df2[df2['dismissal_kind'].isin(dismissal_kinds)]\n",
    "highest_wicket.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "5b4b5b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SL Malinga         170\n",
       "A Mishra           156\n",
       "Harbhajan Singh    150\n",
       "PP Chawla          149\n",
       "DJ Bravo           147\n",
       "B Kumar            133\n",
       "R Ashwin           125\n",
       "SP Narine          122\n",
       "UT Yadav           119\n",
       "RA Jadeja          108\n",
       "Name: bowler, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highest_wicket['bowler'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "22e93ffd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 119,
     "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": [
    "highest_wicket['bowler'].value_counts().head(10).plot(kind='barh',color ='green')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c9c735",
   "metadata": {},
   "source": [
    "## Highest wicket taken by player in single match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "7dd68f7f",
   "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>bowler</th>\n",
       "      <th>bowling_team</th>\n",
       "      <th>batting_team</th>\n",
       "      <th>wickets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>579</th>\n",
       "      <td>83</td>\n",
       "      <td>Sohail Tanvir</td>\n",
       "      <td>RR</td>\n",
       "      <td>CSK</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4782</th>\n",
       "      <td>11310</td>\n",
       "      <td>A Joseph</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4109</th>\n",
       "      <td>616</td>\n",
       "      <td>A Zampa</td>\n",
       "      <td>RPS</td>\n",
       "      <td>SRH</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>119</td>\n",
       "      <td>A Kumble</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RR</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2103</th>\n",
       "      <td>313</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>CSK</td>\n",
       "      <td>DCH</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>13</td>\n",
       "      <td>AJ Tye</td>\n",
       "      <td>GL</td>\n",
       "      <td>RPS</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1731</th>\n",
       "      <td>257</td>\n",
       "      <td>Harbhajan Singh</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>625</th>\n",
       "      <td>91</td>\n",
       "      <td>L Balaji</td>\n",
       "      <td>CSK</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>43</td>\n",
       "      <td>JD Unadkat</td>\n",
       "      <td>RPS</td>\n",
       "      <td>SRH</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>19</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>SRH</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      match_id           bowler bowling_team batting_team  wickets\n",
       "579         83    Sohail Tanvir           RR          CSK        6\n",
       "4782     11310         A Joseph           MI          SRH        6\n",
       "4109       616          A Zampa          RPS          SRH        6\n",
       "795        119         A Kumble          RCB           RR        5\n",
       "2103       313        RA Jadeja          CSK          DCH        5\n",
       "84          13           AJ Tye           GL          RPS        5\n",
       "1731       257  Harbhajan Singh           MI          CSK        5\n",
       "625         91         L Balaji          CSK         KXIP        5\n",
       "299         43       JD Unadkat          RPS          SRH        5\n",
       "134         19          B Kumar          SRH         KXIP        5"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_bowling = highest_wicket.groupby(['match_id','bowler','bowling_team','batting_team'])['dismissal_kind'].count().reset_index().sort_values('dismissal_kind',ascending=False).head(10)\n",
    "best_bowling.rename(columns = {'dismissal_kind':'wickets'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86fe63a0",
   "metadata": {},
   "source": [
    "# Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53c4a7c7",
   "metadata": {},
   "source": [
    "From above Exploratory Data Analysis on **IPL** dataset, we can conclude that:\n",
    "    \n",
    "    1.CSK is most successful team of Indian Premier League (with highest %win of 61%) & MI is second on board.\n",
    "    \n",
    "    2.Mahendra Singh Dhoni is most successful captain of IPL.\n",
    "    \n",
    "    3.Toss result & ground can affects the win/loss of team.\n",
    "    \n",
    "    4.SK Raina(5651),V Kohli(5616),RG Sharma(5057) are top three batsman(all are indian citizen)\n",
    "    \n",
    "    5.SL Malinga(170),A Mishra(156),Harbajan Singh(150) are top three highest wicket takers of IPL.\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2b1222c",
   "metadata": {},
   "source": [
    "Thank You"
   ]
  }
 ],
 "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": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}