BallByBall_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "import warnings\n",
    "import seaborn as sns\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Ball_by_Ball.csv')\n",
    "player = pd.read_csv('Player.csv')\n",
    "team = pd.read_csv('Team.csv')\n",
    "match = pd.read_csv('Match.csv')\n",
    "season = pd.read_csv('season.csv')\n",
    "player_m = pd.read_csv('Player_Match.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Match_Id</th>\n",
       "      <th>Player_Id</th>\n",
       "      <th>Team_Id</th>\n",
       "      <th>Is_Keeper</th>\n",
       "      <th>Is_Captain</th>\n",
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       "      <th>0</th>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   Match_Id  Player_Id  Team_Id  Is_Keeper  Is_Captain\n",
       "0    335987          1        1          0           1\n",
       "1    335987          2        1          0           0\n",
       "2    335987          3        1          0           0\n",
       "3    335987          4        1          0           0\n",
       "4    335987          5        1          0           0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "player_m.head()\n",
    "#data['Dissimal_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Match_Id</th>\n",
       "      <th>Innings_Id</th>\n",
       "      <th>Over_Id</th>\n",
       "      <th>Ball_Id</th>\n",
       "      <th>Team_Batting_Id</th>\n",
       "      <th>Team_Bowling_Id</th>\n",
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       "      <th>Striker_Batting_Position</th>\n",
       "      <th>Non_Striker_Id</th>\n",
       "      <th>Bowler_Id</th>\n",
       "      <th>Batsman_Scored</th>\n",
       "      <th>Extra_Type</th>\n",
       "      <th>Extra_Runs</th>\n",
       "      <th>Player_dissimal_Id</th>\n",
       "      <th>Dissimal_Type</th>\n",
       "      <th>Fielder_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>335987</td>\n",
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       "      <td>caught</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
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       "      <td></td>\n",
       "      <td>4</td>\n",
       "      <td>caught</td>\n",
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       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6</td>\n",
       "      <td>bowled</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>335987</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>8</td>\n",
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       "      <td>0</td>\n",
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       "      <td></td>\n",
       "      <td>8</td>\n",
       "      <td>bowled</td>\n",
       "      <td></td>\n",
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      ],
      "text/plain": [
       "     Match_Id  Innings_Id  Over_Id  Ball_Id  Team_Batting_Id  Team_Bowling_Id  \\\n",
       "33     335987           1        6        2                1                2   \n",
       "74     335987           1       13        1                1                2   \n",
       "106    335987           1       18        1                1                2   \n",
       "131    335987           2        2        1                2                1   \n",
       "138    335987           2        3        2                2                1   \n",
       "\n",
       "     Striker_Id  Striker_Batting_Position  Non_Striker_Id  Bowler_Id  \\\n",
       "33            1                         1               2         15   \n",
       "74            3                         3               2          9   \n",
       "106           4                         4               2         13   \n",
       "131           6                         1               7         84   \n",
       "138           8                         3               7        106   \n",
       "\n",
       "    Batsman_Scored Extra_Type Extra_Runs Player_dissimal_Id Dissimal_Type  \\\n",
       "33               0                                        1        caught   \n",
       "74               0                                        3        caught   \n",
       "106              0                                        4        caught   \n",
       "131              0                                        6        bowled   \n",
       "138              0                                        8        bowled   \n",
       "\n",
       "    Fielder_Id  \n",
       "33           9  \n",
       "74          14  \n",
       "106         10  \n",
       "131             \n",
       "138             "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Maximum Wickets\n",
    "Wickets = data[(data['Dissimal_Type'] != \" \") & (data['Dissimal_Type'] != \"run out\") & (data['Dissimal_Type'] != \"hit wicket\") & (data['Dissimal_Type'] != \"retired hurt\") & (data['Dissimal_Type'] != \"obstructing the field\") ]\n",
    "Wickets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' ', 'caught', 'bowled', 'run out', 'lbw', 'retired hurt',\n",
       "       'stumped', 'caught and bowled', 'hit wicket',\n",
       "       'obstructing the field'], dtype=object)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Dissimal_Type'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
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       "      <td></td>\n",
       "      <td>6</td>\n",
       "      <td>bowled</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>335987</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
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       "      <td></td>\n",
       "      <td>8</td>\n",
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      ],
      "text/plain": [
       "     Match_Id  Innings_Id  Over_Id  Ball_Id  Team_Batting_Id  Team_Bowling_Id  \\\n",
       "33     335987           1        6        2                1                2   \n",
       "74     335987           1       13        1                1                2   \n",
       "106    335987           1       18        1                1                2   \n",
       "131    335987           2        2        1                2                1   \n",
       "138    335987           2        3        2                2                1   \n",
       "\n",
       "     Striker_Id  Striker_Batting_Position  Non_Striker_Id  Bowler_Id  \\\n",
       "33            1                         1               2         15   \n",
       "74            3                         3               2          9   \n",
       "106           4                         4               2         13   \n",
       "131           6                         1               7         84   \n",
       "138           8                         3               7        106   \n",
       "\n",
       "    Batsman_Scored Extra_Type Extra_Runs Player_dissimal_Id Dissimal_Type  \\\n",
       "33               0                                        1        caught   \n",
       "74               0                                        3        caught   \n",
       "106              0                                        4        caught   \n",
       "131              0                                        6        bowled   \n",
       "138              0                                        8        bowled   \n",
       "\n",
       "    Fielder_Id  \n",
       "33           9  \n",
       "74          14  \n",
       "106         10  \n",
       "131             \n",
       "138             "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Wickets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>335987</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>106</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Match_Id  Innings_Id  Over_Id  Ball_Id  Team_Batting_Id  Team_Bowling_Id  \\\n",
       "33     335987           1        6        2                1                2   \n",
       "74     335987           1       13        1                1                2   \n",
       "106    335987           1       18        1                1                2   \n",
       "131    335987           2        2        1                2                1   \n",
       "138    335987           2        3        2                2                1   \n",
       "\n",
       "     Striker_Id  Striker_Batting_Position  Non_Striker_Id  Bowler_Id  \\\n",
       "33            1                         1               2         15   \n",
       "74            3                         3               2          9   \n",
       "106           4                         4               2         13   \n",
       "131           6                         1               7         84   \n",
       "138           8                         3               7        106   \n",
       "\n",
       "    Batsman_Scored Extra_Type Extra_Runs Player_dissimal_Id  Dissimal_Type  \\\n",
       "33               0                                        1              1   \n",
       "74               0                                        3              1   \n",
       "106              0                                        4              1   \n",
       "131              0                                        6              1   \n",
       "138              0                                        8              1   \n",
       "\n",
       "    Fielder_Id  \n",
       "33           9  \n",
       "74          14  \n",
       "106         10  \n",
       "131             \n",
       "138             "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Wickets['Dissimal_Type'].value_counts()\n",
    "Wickets['Dissimal_Type'] = Wickets['Dissimal_Type'] = 1\n",
    "Wickets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bowler_Id\n",
       "194    120\n",
       "50     107\n",
       "67     101\n",
       "136    100\n",
       "71      98\n",
       "Name: Dissimal_Type, dtype: int64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Total = Wickets.groupby('Bowler_Id')['Dissimal_Type'].sum()\n",
    "Total.nlargest(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>Player_Id</th>\n",
       "      <th>Wickets_Taken</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player_Id  Wickets_Taken\n",
       "0          1              8\n",
       "1          4              5\n",
       "2          5              2\n",
       "3          8              4\n",
       "4          9             44"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records = pd.DataFrame({'Player_Id':Total.index,'Wickets_Taken':Total.values})\n",
    "records.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>Player_Id</th>\n",
       "      <th>Wickets_Taken</th>\n",
       "      <th>Player_Name</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Batting_Hand</th>\n",
       "      <th>Bowling_Skill</th>\n",
       "      <th>Country</th>\n",
       "      <th>Is_Umpire</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>194</td>\n",
       "      <td>120</td>\n",
       "      <td>SL Malinga</td>\n",
       "      <td>28-Aug-83</td>\n",
       "      <td>Right_Hand</td>\n",
       "      <td>Right-arm fast</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>50</td>\n",
       "      <td>107</td>\n",
       "      <td>Harbhajan Singh</td>\n",
       "      <td>03-Jul-80</td>\n",
       "      <td>Right_Hand</td>\n",
       "      <td>Right-arm offbreak</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>67</td>\n",
       "      <td>101</td>\n",
       "      <td>PP Chawla</td>\n",
       "      <td>24-Dec-88</td>\n",
       "      <td>Left_Hand</td>\n",
       "      <td>Legbreak</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>136</td>\n",
       "      <td>100</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>24-Nov-82</td>\n",
       "      <td>Right_Hand</td>\n",
       "      <td>Legbreak</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>71</td>\n",
       "      <td>98</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>07-Oct-83</td>\n",
       "      <td>Right_Hand</td>\n",
       "      <td>Right-arm medium-fast</td>\n",
       "      <td>West Indies</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Player_Id  Wickets_Taken      Player_Name        DOB Batting_Hand  \\\n",
       "121        194            120       SL Malinga  28-Aug-83   Right_Hand   \n",
       "28          50            107  Harbhajan Singh  03-Jul-80   Right_Hand   \n",
       "39          67            101        PP Chawla  24-Dec-88    Left_Hand   \n",
       "81         136            100         A Mishra  24-Nov-82   Right_Hand   \n",
       "42          71             98         DJ Bravo  07-Oct-83   Right_Hand   \n",
       "\n",
       "             Bowling_Skill      Country  Is_Umpire  Unnamed: 7  \n",
       "121         Right-arm fast    Sri Lanka          0         NaN  \n",
       "28      Right-arm offbreak        India          0         NaN  \n",
       "39                Legbreak        India          0         NaN  \n",
       "81                Legbreak        India          0         NaN  \n",
       "42   Right-arm medium-fast  West Indies          0         NaN  "
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergeResultw = pd.merge(records, player, on='Player_Id',how='left').sort_values(by='Wickets_Taken', ascending=False)\n",
    "mergeResultw.nlargest(5,'Wickets_Taken')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iilx55ZXp2rVrNt988yxfvrzl/uXLl68WltamU6dOLbubXrfkiukbfN7WvO+ow940AwAAAMDG6o2bdlpTkW9tu+666zJ9+vRMmzYtH/rQh5IkO+64Y+69996sWLEiy5Yty6OPPpqPfexjlRgHAAAAgLegzXckNTY25pvf/Ga23XbbHHfccUmS3XbbLccff3zGjBmT0aNHp7m5OSeeeGI6darsR9QAAAAAKFfT3Nzc3N5DrI+FCxdWxUfbAAAAAN4p1tRb1qQiH20DAAAAYOMnJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEpDbQ3NCwSa0LAAAAbBrq2nuAd6Kaurr8bcolFV93638/vuJrAgAAAJsOO5IAAAAAKCIkAQAAAFBESAIAAACgiJAEAAAAQBEhCQAAAIAiQhIAAAAARYQkAAAAAIoISQAAAAAUEZIAAAAAKCIkAQAAAFBESAIAAACgiJAEAAAAQBEhCQAAAIAiQhIAAAAARYQkAAAAAIoISQAAAAAUEZIAAAAAKCIkAQAAAFBESAIAAACgiJAEAAAAQBEhCQAAAIAiQhIAAAAARYQkAAAAAIoISQAAAAAUEZIAAAAAKCIkAQAAAFBESAIAAACgiJAEAAAAQBEhCQAAAIAiQtImorlh1Sa1LgAAALDh1bX3AFRGTV3HPH3ZSRVf9wPHTKr4mgAAAEDbaLMdSQ888EDGjBmTJFm0aFEOOeSQjB49OuPHj09TU1OS5NJLL83BBx+cUaNG5fe//31bjQIAAADABtAmIWnq1Kk566yzsmLFiiTJ+eefnxNOOCFXXXVVmpubM2fOnCxYsCB33XVXZs2alUmTJuXcc89ti1EAAAAA2EDaJCRtt912mTx5csvtBQsWZPfdd0+S9O/fP/Pmzcu9996bvn37pqamJh/4wAfS2NiY559/vi3GAQAAAGADaJNrJA0aNChPPvlky+3m5ubU1NQkSTp37pxly5blpZdeynve856Wx7x+vGvXrq2ee8WKFVm4cGHL7Z49e27g6cu8cYZ/1F4zJWufqxpn+vAO/5z6d21W4WmSla++nEf/sqji6wIAAMDGriIX2+7Q4f/f+LR8+fJsscUW2XzzzbN8+fLVjnfp0mWd5+rUqVO7RpHXVcMMa1KNc7U208OXHVDBSV7z8WOuq8rfEwAAALSX1jbMvFGbXWz7jT7xiU9k/vz5SZK5c+dm1113zac//encfvvtaWpqytNPP52mpqZ17kYCAAAAoP1UZEfSaaedlrPPPjuTJk1K9+7dM2jQoNTW1mbXXXfNyJEj09TUlHHjxlViFAAAAADeojYLSd26dcvMmTOTJDvssEOmT5/+psccd9xxOe6449pqBAAAAAA2oIp8tA0AAACAjZ+QBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGS4A2aGlZuUusCAADA+qhr7wGgmnSoq88dV+5X8XX7fOWGiq8JAAAA68uOJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQk2Ao0NKzeJNQEAAKhude09ALButXX1+eX396nomoP+7aaKrgcAAED1q1hIWrVqVU4//fQ89dRT6dChQ77xjW+krq4up59+empqavLRj34048ePT4cONkkBAAAAVKOKhaTf/OY3aWhoyE9/+tP89re/zcUXX5xVq1blhBNOSO/evTNu3LjMmTMnn//85ys1EgAAAADroWLbf3bYYYc0NjamqakpL730Uurq6rJgwYLsvvvuSZL+/ftn3rx5lRoHAAAAgPVUsR1Jm222WZ566qnsvffeWbp0aaZMmZK77747NTU1SZLOnTtn2bJl6zzPihUrsnDhwpbbPXv2bLOZW/PGGf5Re82UrH0uM61uY5opqc73OQAAAJueioWkH/3oR+nbt2/Gjh2bZ555JocffnhWrVrVcv/y5cuzxRZbrPM8nTp1atf/2X9dNcywJtU4l5nKmAkAAID2UrqRoGIfbdtiiy3SpUuXJMmWW26ZhoaGfOITn8j8+fOTJHPnzs2uu+5aqXEAAAAAWE8V25H0pS99KWeccUZGjx6dVatW5cQTT0yvXr1y9tlnZ9KkSenevXsGDRpUqXEAAAAAWE8VC0mdO3fOd77znTcdnz59eqVGAAAAAOBtqNhH24B3lsaGlZvUugAAAFRwRxLwzlJbV5+rfzi44usefMTNFV8TAACA19iRBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSgHeMhsaVm9S6AAAAlVbX3gMAbCh1tfX54X99oeLrHnH4/1R8TQAAgPZgRxIAAAAARYQkAAAAAIoISQAAAAAUEZIAAAAAKCIkAbQh3yQHAAC8k/jWNoA2VFdbn0tmDKr4uscf+suKrwkAALzz2ZEEAAAAQBEhCQAAAIAiQhIAAAAARYQkgE3Mqna6EHd7rQsAAGw4LrYNsInpWFufc2ZW/gLg54xwAXAAANjY2ZEEAAAAQBEhCQAAAIAiQhIAAAAARYQkAKpCe1yM2wXAAQBg/bjYNgBVoWNtfY64dnBF1/zh0Jsruh4AAGzs7EgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCgLVY2bhqk1oXAADWpa69BwCAalVf2zF7X3dMxdf97wMuq/iaAABQwo4kAAAAAIoISQAAAAAUEZIAAAAAKCIkAQAAAFBESAKAjcjKxoZNal0AAKqLb20DgI1IfW1d9rn2vIqve9PQsyq+JgAA1ceOJAAAAACKCEkAAAAAFBGSAAAAAChSdI2kK6+8MlOnTk2nTp1ajt1+++1tNhQAsPFY2diQ+trKX3axtXWrcSYAgHeCov/SueGGG3Lrrbemc+fObT0PALCRqa+ty76zL6n4ujcOO36t99XX1mXfa75XwWlec+NBR1Z8TQCASir6aFu3bt1EJAAAAIBNXNGOpMbGxuy///7p0aNHampqkiQTJkxo08EAAAAAqC5FIelLX/pSG48BAAAAQLUr+mjbjjvumHvuuSc33XRTli9fnm7durX1XAAA7zgrGxs3iTUBgHeuoh1JZ555Zvr06ZN58+blgAMOyBlnnJFp06a19WwAAO8o9bW12e/qGRVd84aDD63oegDAO1vRjqTnn38+I0eOTMeOHbPrrrum0d9sAQAAAGxyikJSc3NzFi1alCRZvHhxamtr23QoAAAAAKpP8UfbTjrppDz22GM55phjcs4557TxWAAAAABUm6KQ1K1bt1xzzTUttx944IE2GwgAgMpZ2diY+nbYbd7autU4EwDwmqKQdOyxx+bKK69MbW1tJk+enFtuuSXXXXddW88GAEAbq6+tzZCrZ1d83esPHrbW++pra3PA1TdXcJrXXHfw4IqvCQAbm6KQdNhhh+WYY47Jiy++mN69e2fWrFltPRcAAAAAVabVi20/8cQTeeKJJ9KjR4/stttu6dy5c4YPH57FixdXaj4AAAAAqkSrO5JOO+201NTUpLm5+U3HZsyY0ebDAQAAAFA9Wg1JV1111RqPr1q1qk2GAQCAarSysSn1ta1u5n9HrQsAa1N0jaRZs2blRz/6URoaGtLc3Jyampr88pe/bOvZAACgKtTXdsjQa26v+LrXHtS34msCQGuK/nrjJz/5Sb73ve+lT58+Oeecc/LRj360recCAAAAoMoUhaStttoq2267bV555ZXsscceWbp0aVvPBQAAAECVKQpJm2++eebMmZPktY+5/f3vf2/ToQAAAACoPkUh6bzzzss222yTE088MY888kjGjx/f1nMBAAAAUGVaDUnnnXdekqRLly7p1atX3v/+9+ess85Knz59KjIcAAAAANWj1ZD0xz/+sVJzAAAAAFDl6lq7c/HixfnZz362xvtGjhzZJgMBAAAAUJ1aDUmrVq3KkiVLKjULAAAAAFWs1ZD0wQ9+MMcee2ylZgEAAACgirV6jaRtttmmUnMAAAAAUOVaDUkXXnhhq08+5phjNugwAAAAAFSvVkPSurz44osbag4AAAAAqtzbCkk1NTUbag4AAAAAqtzbCkkAAAAAbDqEJAAAAACKvK2QtOWWW26oOQAAAACockUh6U9/+lPuu+++PPDAAzn88MNzxx13JEkmT57cpsMBAAAAUD2KQtL48eNTX1+fK664IieeeGIuvfTStp4LAAAAgCpTFJLq6ury0Y9+NKtWrcpOO+2UxsbGtp4LAAAAgCpTFJJqamoyduzY9O/fPzfddFPe/e53t/VcAAAAAFSZupIHXXTRRXnwwQfTv3//zJ8/PxdffHFbzwUAAABAlWl1R1JjY2NWrlyZcePGZY899mj5aNtxxx1XqfkAAAAAqBKt7ki65pprMmXKlDz33HMZPHhwmpubU1tbm1122aVS8wEAAABQJVoNSSNGjMiIESNy9dVX5+CDD67UTAAAAABUoaJrJPXt2zcnn3xyli5dmkGDBqVHjx751Kc+1dazAQAAa7GysSn1tUXfnfOOWBeA6lAUksaNG5cjjjgil19+eXbdddecfvrpmTlzZlvPBgAArEV9bYcMv+b3FV931kE7VnxNAKpH0V8lrFixIn369ElNTU26d++eTp06vaXFvvvd72bkyJEZNmxYZs2alUWLFuWQQw7J6NGjM378+DQ1Nb2l8wIAAADQ9opCUn19fW677bY0NTXl/vsJL69SAAAgAElEQVTvT319/XovNH/+/Nx33335yU9+kmnTpuXZZ5/N+eefnxNOOCFXXXVVmpubM2fOnPU+LwAAAACVURSSvvGNb2T27NlZunRpfvCDH+Tcc89d74Vuv/32fOxjH8sxxxyTf//3f8/AgQOzYMGC7L777kmS/v37Z968eet9XgAAAAAqo+gaSTU1Nbnoootabt94443p1q3bei20dOnSPP3005kyZUqefPLJHHXUUWlubk5NTU2SpHPnzlm2bNk6z7NixYosXLiw5XbPnj3Xa44N5Y0z/KP2milZ+1xmWt3GNFPiff6PNqbXz0yrq8b3VDXOlGxcr5+ZVleN76lqnCnZuF4/M62utfcUAO9sRSHp+OOPz5QpU1JXV5fx48dn2bJl2Xfffddrofe85z3p3r176uvrW66z9Oyzz7bcv3z58myxxRbrPE+nTp3a9V+ar6uGGdakGucyUxkzlavGucxUxkzlqnEuM5UxU7lqnMtMZapxJgDentK/JCj6aNuZZ56Zo48+OqNHj84ee+yRqVOnrvdAu+yyS2677bY0Nzdn8eLFeeWVV9KnT5/Mnz8/STJ37tzsuuuu631eAAAAACqj1R1Jt99+e8uf+/Tpk3nz5uX9739/br/99vTt23e9Ftprr71y99135+CDD05zc3PGjRuXbt265eyzz86kSZPSvXv3DBo06K39FAAAAAC0uVZD0o033rja7R122KHl2PqGpCQ59dRT33Rs+vTp630eAAAAACqv1ZB0/vnnJ0mef/75LFy4MHvuuWemT5+e/fffvyLDAQAAG5dVjc3pWFvzjl8TYFNVdLHtsWPHZuTIkUmSLbfcMqecckq++93vtulgAADAxqdjbU2Ov/aJiq55ydAPVXQ9gE1Z0cW2X3nllQwePDhJMmTIkLz88sttOhQAAAAA1acoJHXs2DG//e1v89JLL+WOO+5IbW1tW88FAAAAQJUpCknnnXdeZsyYkeHDh+eqq67K17/+9baeCwAAAIAq0+o1khoaGlJXV5dtt902F198caVmAgAAAKAKtRqSTjzxxEyePDmDBw9OTc1r34LQ3NycmpqazJkzpyIDAgAAAFAdWg1JS5cuzec///n07t07/fr1y5577pktt9yyUrMBAAAAUEVaDUnTp0/PypUrc9999+Wuu+7KrFmzkiS77bZbjj766IoMCAAAAEB1aDUkJUl9fX0++clP5oUXXsjy5cuzYMGC/OEPf6jEbAAAAABUkVZD0g9/+MPceuutWbZsWfr06ZOBAwdm7Nix6dixY6XmAwAAAKBKtBqSLrvssvTr1y9f/epXs9tuuwlIAADARqehsTl1tTWbzLoAbanVkHTHHXfknnvuydy5czNp0qS8733vS//+/TNgwIB84AMfqNSMAAAAb1ldbU0uu3Zxxdc9Zug2FV8ToK21GpI6duyYPn36pE+fPkmSuXPn5rvf/W6+/vWvZ+HChRUZEAAAAIDq0GpIevDBB3PvvffmnnvuyWOPPZaPf/zjOfDAAzNx4sRKzQcAAABAlWg1JF144YXp27dvjjrqqHziE59ITY3P9wIAAABsqloNSf/1X/9VqTkAAAA2GY2Nzalthwtxt9e6wDtHqyEJAACADa+2tiazr36u4usOO/ifKr4m8M7Sob0HAAAAAGDjICQBAAAAUERIAgAAAKCIkAQAAEAaG5s3qXWBt8bFtgEAAEhtbU1umbGk4uvudej7Kr4m8NbZkQQAAABAESEJAAAAgCJCEgAAAABFhCQAAACqUlND+1yIe13rtsdc7fW7gH/kYtsAAABUpQ51Nbnve3+r+Lo7H7l1q/d3qKvJXy9+tkLTvGb7E95f0fVgbexIAgAAAKCIkAQAAABAESEJAAAAgCJCEgAAAABFhCQAAAAAighJAAAAABQRkgAAAAAoIiQBAAAAUERIAgAAAKCIkAQAAAAbueaGpk1qXdpPXXsPAAAAALw9NXUd8uyFf674uu8/+SMVX5P2ZUcSAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAsME1NzRV3brNDY0VnKRs3WqcqTV1G3gOAAAAgNTUdcjii++t+LrbnLDLWu+rqavN4kturdww/882xw9c6301dbX522XXV26Y/2frY4a8pefZkQQAAABAESEJAAAAgCJCEgAAAABFhCQAAAAAighJAAAAABQRkgAAAAAoIiQBAAAAUERIAgAAAKCIkAQAAABAESEJAAAAgCJCEgAAAABFhCQAAAAAighJAAAAABQRkgAAAAAoIiQBAAAAUERIAgAAAKCIkAQAAABAESEJAAAAgCJCEgAAAABFhCQAAAAAighJAAAAABQRkgAAAAAoIiQBAAAAUERIAgAAAKCIkAQAAABAESEJAAAAgCJCEgAAAABFhCQAAAAAighJAAAAABQRkgAAAAAoIiQBAAAAUERIAgAAAKCIkAQAAABAESEJAAAAgCJCEgAAAABFhCQAAAAAighJAAAAABQRkgAAAAAoIiQBAAAAUERIAgAAAKCIkAQAAABAESEJAAAAgCJCEgAAAABFhCQAAAAAighJAAAAABQRkgAAAAAoIiQBAAAAUKTiIenvf/97BgwYkEcffTSLFi3KIYccktGjR2f8+PFpamqq9DgAAAAAFKpoSFq1alXGjRuXd73rXUmS888/PyeccEKuuuqqNDc3Z86cOZUcBwAAAID1UNGQ9O1vfzujRo3K1ltvnSRZsGBBdt999yRJ//79M2/evEqOAwAAAMB6qKvUQrNnz07Xrl3Tr1+/XHnllUmS5ubm1NTUJEk6d+6cZcuWrfM8K1asyMKFC1tu9+zZs20GXoc3zvCP2mumZO1zmWl1G9NMiff5P9qYXj8zra4a31PVOFOycb1+ZlpdNb6nqnGmZON6/cy0ump8T1XjTMnG9fqZaXXV+J6qxpmSjev1M9PqWntPrU3FQtI111yTmpqa3HHHHVm4cGFOO+20PP/88y33L1++PFtsscU6z9OpU6d2/SW/rhpmWJNqnMtMZcxUrhrnMlMZM5WrxrnMVMZM5apxLjOVMVO5apzLTGXMVK4a5zJTmTfOVBqVKhaSZsyY0fLnMWPG5JxzzsnEiRMzf/789O7dO3Pnzs1nPvOZSo0DAAAAwHqq+Le2vdFpp52WyZMnZ+TIkVm1alUGDRrUnuMAAAAA0IqK7Uh6o2nTprX8efr06e0xAgAAAADrqV13JAEAAACw8RCSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAEWEJAAAAACKCEkAAAAAFBGSAAAAACgiJAEAAABQREgCAAAAoIiQBAAAAEARIQkAAACAIkISAAAAAP8fe3ceV3Pe/4//cdpkiaGUrbFGslwMiUsha2Np7DK2QZMt2ScpFImZhDG2sYYihBlTyC7ZYuwGJftaTUJJm/P7o995X+ekrjnX9/O5er3m43H/Z3L63K7z+Jx35708X6/X86UXFpKIiIiIiIiIiEgvLCQREREREREREZFeWEgiIiIiIiIiIiK9sJBERERERERERER6YSGJiIiIiIiIiIj0wkISERERERERERHphYUkIiIiIiIiIiLSCwtJRERERERERESkFxaSiIiIiIiIiIhILywkERERERERERGRXlhIIiIiIiIiIiIivbCQREREREREREREemEhiYiIiIiIiIiI9MJCEhERERERERER6YWFJCIiIiIiIiIi0gsLSUREREREREREpBcWkoiIiIiIiIiISC9GJfVGubm5mDVrFp4+fYqcnByMGzcO9erVw8yZM6FSqWBjY4O5c+fCwIC1LSIiIiIiIiIiGZVYIWnfvn347LPPEBwcjFevXqFPnz6wtbXF5MmT4eDggDlz5uDo0aPo0qVLSUUiIiIiIiIiIqL/QIkVklxcXNCtWzfl34aGhrh58yZatWoFAGjXrh1Onz79l4Wk7Oxs3Lp1S/l3w4YN/zuB/4J2hsJEZQKKz8VMuv5OmQD+nRf2dzp+zKRLxr8pGTMBf6/jx0y6ZPybkjET8Pc6fsykS8a/KRkzAX+v48dMumT8m5IxE/D3On7MpOvf/U0Vp8QKSWXLlgUAZGRkwMvLC5MnT8b3338PlUql/P7t27d/+b9TqlQpoR+yhgwZiiJjLmbSDzPpT8ZczKQfZtKfjLmYST/MpD8ZczGTfphJfzLmYib9MJP+ZMzFTPrRzqRvUalEGxI9f/4cw4cPx1dffYVevXrp9EPKzMxE+fLlSzIOERERERERERH9B0qskJSamopRo0ZhxowZ6N+/PwDAzs4O58+fBwDExsaiZcuWJRWHiIiIiIiIiIj+QyVWSFqzZg3evHmDVatWYdiwYRg2bBgmT56Mn376CYMGDUJubq5ODyUiIiIiIiIiIpJLifVI8vPzg5+f30evh4WFlVQEIiIiIiIiIiL6HyjRHklERERERERERPT3xUISERERERERERHphYUkIiIiIiIiIiLSCwtJRERERERERESkFxaSiIiIiIiIiIhILywkERERERERERGRXlhIIiIiIiIiIiIivbCQREREREREREREemEhiYiIiIiIiIiI9MJCEhERERERERER6YWFJCIiIiIiIiIi0gsLSUREREREREREpBcWkoiIiIiIiIiISC8sJBERERERERERkV5YSCIiIiIiIiIiIr2wkERERERERERERHphIYmIiIiIiIiIiPTCQhIREREREREREemFhSQiIiIiIiIiItILC0lERERERERERKQXFpKIiIiIiIiIiEgvLCQREREREREREZFeWEgiIiIiIiIiIiK9sJBERERERERERER6YSGJiIiIiIiIiIj0wkISERERERERERHphYUkIiIiIiIiIiLSCwtJRERERERERESkFxaSiIiIiIiIiIhILywkERERERERERGRXlhIIiIiIiIiIiIivbCQREREREREREREemEhiYiIiIiIiIiI9MJCEhERERERERER6YWFJCIiIiIiIiIi0gsLSUREREREREREpBcWkoiIiIiIiIiISC8sJBERERERERERkV5YSCIiIiIiIiIiIr2wkERERERERERERHphIYmIiIiIiIiIiPTCQhIREREREREREemFhSQiIiIiIiIiItILC0lERERERERERKQXFpKIiIiIiIiIiEgvLCQREREREREREZFeWEgiIiIiIiIiIiK9sJBERERERERERER6YSGJiIiIiIiIiIj0wkISERERERERERHphYUkIiIiIiIiIiLSCwtJRERERERERESkFxaSiIiIiIiIiIhILywkERERERERERGRXlhIIiIiIiIiIiIivbCQREREREREREREemEhiYiIiIiIiIiI9MJCEhERERERERER6YWFJCIiIiIiIiIi0gsLSUREREREREREpBcWkoiIiIiIiIiISC8sJBERERERERERkV5YSCIiIiIiIiIiIr2wkERERERERERERHphIYmIiIiIiIiIiPTCQhIREREREREREemFhSQiIiIiIiIiItILC0lERERERERERKQXFpKIiIiIiIiIiEgvLCQREREREREREZFeWEgiIiIiIiIiIiK9sJBERERERERERER6YSGJiIiIiIiIiIj0wkISERERERERERHphYUkIiIiIiIiIiLSCwtJRERERERERESkFxaSiIiIiIiIiIhILywkERERERERERGRXlhIIiIiIiIiIiIivbCQREREREREREREemEhiYiIiIiIiIiI9MJCEhERERERERER6YWFJCIiIiIiIiIi0gsLSUREREREREREpBcWkoiIiIiIiIiISC8sJBERERERERERkV5YSCIiIiIiIiIiIr2wkERERERERERERHphIYmIiIiIiIiIiPTCQhIREREREREREemFhSQiIiIiIiIiItILC0lERERERERERKQXFpKIiIiIiIiIiEgvLCQREREREREREZFeWEgiIiIiIiIiIiK9GIkO8OHDB/j7++POnTswMTFBYGAgatasKToWEREREREREREVInxG0pEjR5CTk4MdO3Zg2rRpWLRokehIRERERERERERUBOGFpN9//x1OTk4AgGbNmuHGjRuCExERERERERERUVFUarVaLTKAr68vunbtivbt2wMAOnTogCNHjsDIqOhVd1euXEGpUqVKMiIRERERERER0f9p2dnZaNas2V/+3wnvkVSuXDlkZmYq//7w4UOxRSQAev0/RURERERERERE//uEL2374osvEBsbC6BgtlH9+vUFJyIiIiIiIiIioqIIX9qm2bUtISEBarUaQUFBqFu3rshIRERERERERERUBOGFJCIiIiIiIiIi+nsQvrSNiIiIiIiIiIj+HlhIIiIiIiIiIiIivbCQREREREREREREejESHYDo/1Vubi6MjY1Fx5AePyf6v+rPP/9Edna28u9q1aoJTAOcO3cOrVu3Fprh70S246dWq6FSqYRmKEymTC9evECVKlVw//79j35Xu3ZtAYmKx+vex/5Ox08WGRkZePr0KaytrVGmTBnRcej/gOTkZOTl5UGtViM5ORnNmzcXHUlK/O79PXxSzbYfPnyIgwcPIjc3F0DBl3nevHlCM61YsQJhYWEwMvpXTS8uLk5gIuDKlSvYs2ePzue0YcMGoZkAYPv27QgNDVVOwEZGRjh06JDQTPn5+YiIiMDdu3dRq1YtDB48GCYmJkIzyfg5PX/+HFFRUToPjZ6engITAenp6YiLi9O5oI8ZM0ZoJkDOz+rJkyeIiYlBVlaW8proTP7+/oiNjYWlpaXysB0RESE005AhQxAeHi40g8bUqVOLLUCEhISUcJqPyXj8Ro0ahY0bNwrNUJhMmRYuXAgfHx8MGzZM53WVSoUtW7YISlVAxuseUFAIvH79us753N7eXkiWoKAgzJo1S8rjl5eXh+vXr+tcj3v27Ck008GDB7FmzRrk5+fDxcUFKpUK48ePF5LF0dGx2N+JfGaQ+Toj4zOfj48Prl69iqysLGRlZeHzzz/Hzp07hWaS8ZlPpu+etqNHjyI8PFw5T6Wnp+O3334TkiUkJKTY797UqVNLLMcnNSPJ29sbzs7OuHTpEiwtLfHu3TvRkXD8+HGcOHECpqamoqMoAgMD8c033yAmJgb169dHTk6O6EgAgF27dmHr1q1YvXo1XFxcsHnzZtGRMHv2bJiZmaFt27aIj4+Hn58ffvjhB6GZZPycJk2ahDZt2qBq1aqioyi8vLxQq1YtJCQkoFSpUihdurToSADk/KymTZsGJycnWFhYiI6iuHbtGo4cOQIDA3lWaKtUKkyYMAG1a9dWcpXkBV2bm5ubkPfVl4zHz8zMDEeOHNE5fqJnasiUycfHBwCwdetWIe//78h43QOAiRMn4s8//1TO5yqVSlghadasWQDkPH6enp7Izc1FcnIy8vPzYWlpKbyQFBoaip07d2L06NEYP348+vXrJ+xhVvQAc3Fkvs7I+Mx3//59REdHY86cOZgyZQomTZokOpKUz3wyffe0rVy5ErNnz0ZERAQcHBxw5swZYVnq1Kkj7L21fVKFJFNTU4wZMwYPHjzAwoUL8fXXX4uOBHNzc53ZSDIoX748evbsidOnT2PixIkYOnSo6EgAgIoVK8LS0hKZmZlwcHDA8uXLRUfCw4cPlRkInTt3luKiKuPnVLZsWUyZMkV0jI/MmzcPPj4+WLBgAYYMGSI6DgA5PytTU1PhM5AKq1mzJrKzs6UpAAJAv379REdQtGrVCkDB9PB169YhJSUFHTp0QIMGDQQnKyDj8UtLS9MpQMgwU0PGTL/88gvWrl2rM8vm6NGjAhPJed0DgNTUVOEz7TS8vLywfPnyIme3mJiYoFu3bvD29haQrOA8FRYWBl9fX8yePRsjR44UkkObgYEBTExMoFKpoFKppDhXyTZ7RObrjIzPfGXLloVKpcK7d+9QqVIl5TiKJOMzn4zfPaDgOtO8eXNERESgb9++2LNnj7Asffr0AVAwm3Pv3r14/vw5HBwcYGNjU6I55Kpg/Jep1WqkpKTg3bt3ePfuHV6/fi0si2Y6aGpqKvr06aMceJVKJXw6qEqlQmJiIrKysnDv3j2kpKQIzaOhGZnVLIFIS0sTHQnZ2dnIyspC6dKl8f79e+Tn54uOJOXnZGNjg+joaDRs2FCZiil6pB/41/HTXNhlINNnpemlYWFhgaioKNjZ2QnPpPH8+XM4OzujZs2aACDF0qhevXp9tDxDtFmzZqFdu3a4cOECLCws4Ovri7CwMNGxpDx+hWdqyDAyK2OmdevWYfXq1VLNmpTxugcUnCdfvnwJKysr0VGU4lpRs1tyc3MxYsSIko6k0AyoZmVlwdTUVIoH7JYtW2Lq1Kl4+fIl5syZgyZNmoiOJOXsEUDO64xMz3wajRo1woYNG2BpaYkpU6YgLy9PdCQpn/lk/O4BgLGxMS5cuIC8vDycOnVKis9q7ty5sLS0xJkzZ9C4cWN4e3tj3bp1Jfb+n1SPpAsXLiAxMRFWVlbw8/ND7969hY2+xMfHF/s7TYVflMTEROVzWrBgAVxdXfHNN98IzQQUjHg8evQIFhYW2LhxI5ydneHg4CA00759+7BixQrY2Njg7t278PLyQo8ePYRmkvFzkrEnQ0xMDB48eIBKlSrhp59+QosWLbB06VKhmQC5PqvCWTRkOH5Pnz796LXq1asLSPIvY8eO/Wh5RmhoqNBMw4cPx5YtW5T/ytLHScbjFxERgU2bNimFQGNjY8TExDBTIWPHjsWaNWuEZiis8HWvY8eOwu+lAKBbt254/PgxKlWqpLwmeplScbNacnJyhPV4DA8Px6tXr2BiYoIjR46gdOnSUixPjI2NRUJCAurWrQtnZ2fRcZSeaT4+Pli4cCGGDh0qvGADyHmdkemZT1tmZiZKlSqF2NhY/OMf/4C5ubnQPLI+88n23QOAly9f4t69e6hcuTJ+/PFHfPnll+jevbvQTMOGDcPWrVuV/7q5uZXooNwnNSPJ3t5eWZveqVMnoVk0NzgrVqzQed3Y2BjPnz9H9+7dS3zHEc3IRs2aNZVRYtEjxNq8vLyUpqMzZ84UnKaAo6Mj2rVrh8ePH6NGjRqoWLGi6EhSfk4y9mTo1q2b8vOXX36JcuXKCUzzLzLNQJDxuGkYGhoiKCgISUlJqFWrltK/RSQZl2cAQFJSEoCCXZtk6UmUl5cnXSPUnTt3StdnR8ZMpqamcHd315k1KaoXmIZarcbz58/x4MEDNG3aFKmpqULzaISFhaFy5cqiY+goblaLyI1CtJeWt2/fHrVq1RKWRSMjIwMZGRmwsLDA69ev8csvv6B3795CM8k4e0RDtuuMTM98GteuXUN0dLSyLDg2Nhb+/v5CsuTl5cHIyEjKZz4Zv3tAwez8V69e4d27d3B3d5diR9X8/HykpaVBpVIhIyOjxL97n1QhycnJCWlpaahYsSLS09NhYmICCwsLzJ07F23bthWS6c6dOyhVqhRatmyJq1ev4vnz56hcuTLi4uIQHBxcolk0nfGBgpsyDZVKJbz/ASBX01GN0aNH4/PPP8fAgQOlKCIBcn1O/64ng+hR2V27diE0NBTv379XXpPh71zGGQgynjv9/PwwePBg2NvbIz4+Hr6+vsIfsmVcnuHn54dZs2YhKSkJXl5ewm5aC5OxEaqMfXZkzOTo6AhDQ0PRMXSMGjUKdevWRfny5QEU3LeIHikGCq6BlSpVQv/+/dG+fXspHrBl6omyatUqjB8/vsjdv4yNjdGhQwe4uLgIyTZ+/HhYWlrqNEoXbebMmUhMTMSwYcMwffp0DB48WHQkAHJdZzp27KhzrIyMjJCXl4dSpUph//79wnIBBde9b7/9VjlPic4SEhLy0bOfDM98Mn73AMDDwwM5OTk615nCE0JK2uTJkzF48GCkpKRg0KBB8PX1LdH3/6QKSfb29vD09ESdOnXw6NEjrFixAhMmTMCMGTOEPQy9efNGefhxc3PDqFGjEBwcLOTicOzYMQDA5cuX0bx58xJ//78iY9PRvXv34vr169izZw9CQkLQpUsXjBs3TmgmmT4nzUPP7t27pegRoW379u1Yu3atdKPFMs5AkPHcmZ2drYwydu7cGZs2bRKSQ1uXLl2wYsUK2NraYuDAgShbtqzoSKhWrRp27Nih/Pv3338XmOZfZGyEKmOfHRkz7d+/X5n1KgszMzMsWrRIdIyPbN++HUlJSYiMjMTq1avRpk0b9O/fH9bW1sIyyTSrpWPHjgCK3v0rNzcXwcHBwgpJarUaixcvFvLexX+B07IAACAASURBVLGxsYG5uTnev3+Pn376SZoH7KdPn+pcZ/bv3w87OzshWQ4ePAi1Wo2AgAC4ubmhadOm+OOPP7Bt2zYhebTVrFkTffv2FR0DAJR+vMuWLUPTpk2V18+fPy8qkkLG7x5QcN8pw1JSbc2bN0dMTIwy2Pv48eMSff9PqpD04sULZbu8zz//HM+fP0fNmjWFjqy9ffsWaWlpqFSpEl69eoW3b98iNzdXZ5ZESdu9ezfmz5+P5s2bo2vXrrC3t5diFE3WZTY2NjZo1qwZHj16hIsXL4qOI+XnNGvWLOTk5MDZ2RldunQRehOtUbFiReE9WYoi4wwEGc+d+fn5uHPnDho0aIA7d+5IcUMt4/KMCRMmYO3atTA0NMSPP/6IuLg47N27V3QsKRuhBgYG4tGjR5g2bRo2btyIgIAA0ZGkzGRmZoajR4+iVq1awme9ajg6OmL79u2oV6+e8ppmWYtolpaWsLa2xs2bN5GQkIAFCxagYcOGwrb+lmlWi62tLQDAzs7uo12/atasic8++0xYtgYNGuDq1ato2LCh8prI5X8AMHv2bJw9exYWFhbK7BGRy5GOHz+OS5cuITo6GpcvXwYAfPjwAUePHhU2I1BzjB4/fqwUSOzs7JTNQ0Tq1q0bpkyZgrp16yqvidoR9+LFi7h79y5CQ0OVZfgfPnxAeHg4oqKihGTSkPG7BxQ0AT916pTO8atWrZrARMC0adOwfPlyVKpUSVnRUJKrGD6pQlLlypWxePFiNG/eHJcvX4aFhQVOnz5d4r2ItE2cOBEDBw5EuXLl8O7dO/j5+WHTpk3o37+/sEyBgYEACk4ywcHBePjwIc6dOycsj8aKFSsQHh6u8/AqenmUj48Prl69im7duiEgIAA1atQQmgeQ83PasGEDMjIyEBsbi+nTpyM7Oxu//PKLkCwhISFQqVTIycnB6NGjdXYiE93nA5BzBoKM507NVPrk5GRYWVlh/vz5wrJoHDt2DHv27NHZFr0kd88oyogRIzB+/Hi8efMGjo6O2Llzp9A8Gp6enjh8+DBcXV3RqVMnKfofyNhnR8ZMaWlpOk3kZZgdfPHiReTk5ODChQtKJhkKSZMmTUJiYiJcXV0RHByszMzt27dviReSZO6DWdyuX40aNRKWKT4+HseOHYNKpZJmyc+dO3dw+PBhKQZOgIJCYHp6OkqVKqUUk1UqlfBNZ4CCeynNbJvLly9LMXC4bds2dOnSRYqlbeXLl0dqaipycnKUWYkqlQozZswQnOxf3z0NGb57APDnn38iKChIZ2mb6HNomzZtMGPGDLx9+xZmZmYlfo/3Se3alp2djR07diApKQn169dH//798ccff8Da2hoWFhbCcn348AFpaWkwNzeX4uKwefNmnD17Fmlpafjiiy/g6OhYZI+bktavXz+Eh4fD1NRUdBTFsWPH0KFDBylmbGnI+DkdOXIEZ86cwdWrV1GtWjU4Ojpi0KBBQrL8u9kYffr0KcEkRZNx9yFZz50az58/l2Ir8m7dumHevHmoUKGC8ppmxL2kaY++Hjp0COfOncOcOXMAiJ89IqsBAwZ81GdHdBN3GTMVJnK3L41vvvlG+A6JRTl9+nSRy3+zs7NRqlSpEs2i6R1T+LZfhoc0GXf9+vXXX/HVV18JzVCYp6cnFi1aJM3mIBofPnzQuQ9OTk6GpaWlwETAu3fvsHfvXiQmJqJu3br4+uuvhfd2c3d3x/r164VmKOzly5c6rSdyc3OFDhLKTJZdEgHdjXi2bt2Ks2fPYtWqVQBKdvbWJzUjycTEBM2aNVOmyl27dk34iNXp06cRGhqqM4ItemQvNjYWb9++RdeuXeHo6CjsQagwc3NzpZmtLCpVqgR/f/+PttEVScbPafHixShVqhQ8PDzg5OQkdDSmXr16aNKkSZGztM6fPw87OzuYmZkJSFZArVbj0aNHSEhIQIMGDfDs2TNhWTRkPHdu2bIFpqamePPmDfbs2QMnJyfhD9g2NjZwcHAQmkFDUzTSfnCcM2eOFLNHgIKZk2FhYTrnKtEzJ2XssyNjJhk3BLCxsUF0dLTOTnIyFEwrV66Mr7/+Gm/fvkWvXr1gY2MDZ2fnEi8iAdAZ4ZeRbLt+7dq1S5pC0qBBg6BSqfDnn3+ia9euSnsAGWZEAAXn823btimtOWrVqoXo6GihmUqXLo0mTZqgfv36AIBLly4Jv2+pWLEi5syZozMTXtSgqsbx48elOZ/PmzcPc+bMUf7etcnwd16/fn1cuXJFp/+XqEGUojbI0rxWkgMDcj1t/pd5enri1atXqFq1qjJNVfRJZeHChZg1axaqVKkiNIe2DRs2IDs7G+fOncOCBQtw//59oTf4mt08UlNT0adPH9jY2AAouIBqmsWJUtw2uiIU9TlpTjKiP6eDBw/iyZMniIuLg6enJ96/fy9sic25c+fQpEmTIm9ycnNzce/ePezZs0dAsgITJkxA9erVlZk+MsxSlPHcGR0dja1bt8Ld3R3R0dEYMWKE0DxAwRbDgwYNUvpJAQXneBE0vdLWr18Pd3d3IRn+nePHj+PEiRNSzZyUsc+OjJlk3BDg9u3buH37tvJvWQqmCxYswMKFC+Hn54f+/fvD3d0dzs7OQjMdPXpUeehXq9VIT0/Hb7/9JjSTr6+vzq5fc+fOFZoHKBjx7927t84OuKLupZYsWSLkffUVGxuL2NhYBAUFYeTIkVL0cps4cSLS0tKkum/RLCmVYYmyhkzn8/HjxwMAgoKCpLo30Lhw4QJOnDghxXJXzcCA6JmTn1Qh6c8//5SioqmtatWq+Oc//yk6ho5Dhw7h5MmT+OOPP9C4cWN8++23QvMUtZuHLGTaRlfmz+nmzZs4efIkzpw5A1NTU3z55ZfCsmj+nsePH48XL16gatWqOr2t1q5dKyoagIKRBVHFh+LIeO5UqVRISUmBhYUFVCqVFM2aNYUtkTPaCouNjcXIkSOFT+kvTMaZkzL22ZExk4wbArRv317KgilQ8PCoUqlQqVIlKXZyXLlyJWbPno2IiAg4ODjg9OnToiOhQYMGOrt+yWD69OmiIyg0/X0ePnyIgwcP6syCnzdvnshoAIDPPvsMJiYmyMzMRM2aNZGVlSU6ElJTU6W7b7l//77wgd3CZDqfawZQ/fz8sH37dmE5iuPu7i7NLEUN0TMn5bqL+y+rXbv2R2tBRTM3N5dumuPFixfRp08fBAYGSjEbQtMfJjk5GW/evIGBgQHWr1+PYcOGCU4m1za6ms8pIyPjo91PRFu1ahW6du2KVatWCW8ymJmZiWnTpiE9PR3Vq1fHgwcPYG5ujiVLlqBcuXLw8PAQkkszm83a2hqXL1/WaTIquv+IjOdOBwcHDB06FCEhIQgKCkLXrl1FR4KFhYWwnWqK8+rVKzg5OaFGjRpQqVTCl0LIPHPy3bt30vXZkTGTjBsCyFowrVChAiIiIpCVlYXo6Gjh1z+g4MGxefPmiIiIQN++fYXOwNX0bdIwMjJCXl4eTExMcODAAWG5gIJlLHFxccqSn+TkZOH9Cr29veHs7IxLly7B0tIS7969E5pHo0qVKoiMjETp0qUREhKCjIwM0ZGkvG/Jzc3F7du3Ubt2beXvXvT9nYzn8zJlyiAoKEhnNqDoZ2NAfNGmKNozJzX3eCV5L/VJNdvu2rUrnjx5gkqVKimvie7JsGLFio9eE7UVpMaDBw8QExMj3YjHyJEjMWbMGGzbtg3dunVDRESE8K3uExMTkZiYCCsrKyxYsACurq745ptvhGby8vJCu3btsGfPHkyfPh1LliwR3hzu8ePHiImJwfv375XXRP2dz5s3D02bNtXZJWrXrl24fv260L/zwo1QZZg6qyHjufP69eto0qQJgIIL6ZUrV4Tf5Ht5eSEzM1OqnQCfPn360Wsid6+Jj48v9neij9+CBQuUXmCy9NmRMZOMGwL06tULf/75pzQFU42MjAysWbMGCQkJqFu3LsaOHavTjF+EsWPHYvTo0YiIiEDv3r0RGBgorCdKTk4O1Go1AgIC4ObmhqZNm+KPP/7Atm3blB2ERRk+fDhq1aqFhIQElCpVCqVLl8aaNWuEZtI0lffx8cHChQvx9ddfY9u2bUIzAQXNtp8/f44KFSpg7969aNOmjc5yXBFkvG/p1asXMjMzlX/LcH8n4/lcxmdjABg4cCBycnKkWO6qUdQ9VUkev09qRtKhQ4dER1C8ePECVapUkWKLzMJmzpwp5YhHXl4e7O3tsWbNGvTo0UOKi2dkZKTS4FfkqJ629PR09O/fH/v27cMXX3zx0Q4tIkyfPh1OTk5S7PB1+/ZtpRGxxoABAxAZGSkoUYFff/21yCVRmgakIsl07rx48SLu3r2L0NBQjBw5EkDBTWx4eDiioqKEZhPd+6QohoaGCAoKQlJSEmrVqiW8IXmLFi2Qn5+PqVOnYunSpVCr1fjw4QM8PDyE97SRsc+OjJlu3LiB1q1bAyjY3n7hwoXCHzxEP+AXZ8uWLTpLpEJCQjBt2jSBiYCAgADcu3cP48aNw48//ggvLy9hWTSzMR4/foymTZsCAOzs7HR2nRRp3rx58PHxwYIFCzBkyBDRcaBWq5GSkoJ3797h3bt3wpd0p6SkYOPGjShTpgxGjx6NMmXKSLFaAJDrvkVDdC+yosi4wYunpyeSk5N1ZgPKQKblrhp2dnZYuXKlco+n6TNVUj6JQtKqVaswfvx4ZUq9NlGVxE2bNsHHx0fZRUd7FoLom0RTU1OMGTMGDx48UEY8ZJCbm4uFCxeiZcuWOHfuHPLz80VHQlJSEt68eSPFdHVtsu1+YmpqKsVoAoBi+7KIXhLh4eGB0NBQnd189u3bhx9++EHYKJqM587y5csjNTUVOTk5ylJSlUqFGTNmCMmjTbvXliz8/PwwePBg2NvbIz4+Hr6+vkKbae7evRtr1qxBamoqXFxcoFarYWhoiBYtWgjLpCF6hmtRZMz0448/omzZssjPz4efnx9cXV1FR0JeXp5UvWN27dqFyMhIJCUlITY2FkBBwTs3N1d4IcnKygpWVla4dOkShg4dKsVOk2ZmZli2bBmaNm2Ky5cvC501qS07OxtZWVlQqVRSDKp6enri8OHDcHV1RadOnXRmVovg7e2NLl264PXr1wgODpaiSbqM9y0aMja6l3GDl1mzZuHKlSvIysrC+/fvYW1tLWyDHm0yLnedNWsW7O3t4erqivj4eMycObNEB1Y+iUJSx44dAcjVjFgzKqy5SdT0/ilXrpzIWADkG/HQWLRoEU6fPo0BAwbgyJEjCA4OFh0JSUlJaN26NSpWrKicfEVPnZVp9xPNqKKFhQWioqJ0lvyIWp7x2Wef6SyLAgqWSYlebuDi4oJx48bh559/hlqtxrx583Dr1i2hD5Iynjvr16+P+vXrY8CAATq9DzQPkCJpmkOq1WrcvXsX1atXF94YOTs7G506dQIAdO7cGZs2bRKaZ+DAgRg4cCAiIyPRv39/oVkKK9yvxczMDL/88ovARHJmWrlyJcaPH4+cnBz8+OOPqFu3rtA8gHy9Y7766iu0adMGP//8M8aOHQsAMDAwgLm5ubBMR44cwdy5c2FlZQUXFxf89ttvKF++PBo3bix8puLixYuxd+9exMbGom7dupgyZYrQPAAwZMgQbN68GW3btkX79u2lKHbb29sr1xTNeV2kvLw8DB48GACEt3XQkPG+RUPGRvcybvBy7949REdHY86cOZgyZQomTZokOhKAgvYFhZe7ivbq1StlFmDDhg1LfJnyJ1FIunr1Kq5evVrk70RVEm/evAlfX1/s2rULJ06cwNy5c2FmZgZvb2/lJCiKbCMemof+J0+eoGbNmoiPj0f58uXx8OFDWFtbC812/Phxoe9fFJl2P9FeQqadSeTMu++++w7jxo2Dg4MDrK2t8eTJE5w9exarV68WkkdjxIgRyM3NxYQJE5CamgoHBwdERETA2NhYWKYGDRrgyJEj6Ny5M96+fYtVq1bBxMQEY8aMEZZJ4/jx49i0aZMyMmRsbCysz4eG9hbNOTk5mDx5ssA0BfLz83Hnzh00aNAAd+7ckWK0ESh4IPr555+lmUECAAcPHgRQcGN948YN5d8iyZQpJCREZyDg1KlT+PXXXwGI7wUm20xqExMTZGRkYP78+cjNzcXOnTthYmKCfv36Ccu0evVqxMTE4O3bt+jduzeOHTuGMmXKKIUAkUxMTGBnZ4f69etDrVZj//796Nmzp5AsmvNlt27dlNe+/PJLKQZ6V6xYgbCwMJ2Z1SIHL7WvJx8+fBCWQ5utrS1u376Ns2fP4tWrV6hSpQpcXFxQq1Yt0dGkanQv8wYvZcuWVWYBVqpUSYqBQg3ZlrtmZ2cjJSUFlStXRmpqaol/Dz+JQpLInbSKs3TpUixatAjGxsZYunQp1q5di1q1asHd3V14IUm2EY+zZ8+iSZMmiI6O/uh3jo6OAhIV7KSzY8cOVK9eHc2aNYO3tzfy8vIwc+ZMnZNxSRo2bFixD4miijZFzaZ5/vw5qlatKiBNgRo1aiAyMhInTpxQ+jJMmTIFZcqUEZZJw93dHfn5+Th79iy8vb1Fx0FISAgePnwIZ2dnzJ8/H6VLl4aVlRX8/f3xww8/CM22c+dObN26FatXr4aLi4vQ5VpFyc/Px+PHj0XHgJ+fH2bNmoXk5GRYWVlh/vz5oiMBkG8GCaB789yiRQudwqAoMmWqU6eO8nPt2rWFT+nXJttM6k2bNmH//v3Yvn07vv/+ezx79gzVqlVDUFAQ/Pz8hGQqU6YMypUrh3LlysHGxgZly5YFIP6hESgYwMzNzUVycjLy8/NhaWkprJAUGBiIFy9ewN7eHk5OTmjbtq007QuOHz+OEydOwNTUVHQUAEBWVhYePHiADx8+4P3793jw4IHSqkPUrPMDBw5g3bp1cHNzQ+PGjfHs2TN4eXnBy8sLnTt3FpJJw9jYGBcuXEBeXh5OnTol9PnUxcVFaa1y7tw55XUZGoA3atQIGzZsgKWlJaZMmYK8vDyhebTJttx10qRJcHNzQ7ly5ZCZmVni93ifRCFJuzeLLM271Go1bG1t8fLlS2RlZaFx48YAIEU/G9lGPDTbscs09XLmzJmoXbs2Hj58iODgYIwZMwZVq1ZFYGCgsrylpAUEBOj8+/bt2wgKChJ2M6Zty5YtMDU1xZs3b7Bnzx44OTkJnUpfqlQpndFGGWhG+9VqNR48eIDx48cru56IGu2/efOmMuvnxIkTOHnyJEqXLi3FCHbFihVhaWmJzMxMODg4YPny5aIj6RS28/LyMHz4cIFpCtjZ2WH37t2iY3xEthkkgO6Mm5SUFCmuxzJlqly5srD3/iuyzaSOjY1FREQEVCoVoqKiEBMTgwoVKghdbqM90CTD37a2jIwMhIWFwdfXF7Nnz1Y2UhBh69atyMnJweXLlxEfH6/0ZmnZsiUmTJggLBcAmJubF9vnUYRSpUph9uzZH/0sctb5li1bEBYWpjM42KdPH4wbN054Ialwo3uRS7aOHTsm7L3/ytSpU5GRkQFTU1PExsYqjfhFGzJkCEJDQ6Va7tq2bVscPXoUaWlpOjsUlhR5zkYlQKbmXZqpZ6dOnUKbNm0AFEwz1N4WUhTZRjxsbW1RoUKFIpf4iCpwpaamKg+urq6uSq+P9evXC8kD/Gu0WK1WY+3atfjll1+wZMkSKUaNo6OjsXXrVri7uyM6OhojRowQHUk62qP9sqwH1zQgv3btGurXr6+sB5dhmrGZmRmOHDmibPOdlpYmLEtubi6MjY0/Oh+JzFS4t4420aONgHwzSADd76CtrS3atWsnME0BmTIVNStYQ9TsYA3ZZlIbGBjA0NAQN2/ehLW1tdKDT+QuqpcuXVKOU3p6uvKzDN89TXEkKysLpqamwq8xJiYmaNSoEV6/fo3MzEzcvHkTt27dEpZH0zg6NTUVffr0gY2NDYCCgo3IBtIybgZgZGT00QzzcuXKCd9QBSjoF5qcnKz0tRG51Dw1NRVr1qxB9erV0a5dO4wbNw55eXkICAiAk5OTkEzF9f+Li4sTPjgAQKrlrv9uoLIki7ifVCFJpuZdbdq0gZubG168eIHVq1fj0aNH8Pf3R/fu3YVl0pBtxMPb2xuxsbGwtraGq6srWrZsKTqSzufz2WefKT+L3knuwYMH8Pb2RoMGDRAZGalMXRdNpVIhJSUFFhYWUKlUUty4yqZPnz6iI3zE0NAQcXFx2Lt3L7p27QoAOHPmjBTT/AMDA/Ho0SNMmzYNGzduhL+/v7AsU6ZMwfLly3VG+S9cuIAZM2bgxIkTQjJ17NgRN27cwD//+U/06tVLml2QNGSaQfLbb7+hV69eOt/B7OxsBAQEICgoiJn+fzLNCi5MtpnUQMFmE3v27FHaFSQmJgqdCXTjxg1h7/1XunTpghUrVsDW1hYDBw4U+oC2adMmnDhxAm/fvkWbNm3QoUMHTJs2TWi/QhkbR8uquOKMDD2cvLy88ObNG2V2p0qlErYhx3fffQcXFxe8fv0aQ4YMwdKlS1G1alV4e3sLKyRpdpzWUKvV2LNnD0xNTaUoJO3atQuhoaF4//698pqogbkyZcrg0aNH+PLLL9G5c2edHZ9LkjzVghIgU/MuDw8PdOrUCZUqVULFihXx6NEjDB48GF26dBGWSdYRj5EjR2LkyJG4d+8e9u3bh59++gnNmzeHq6urzmhtSXr58iV27NgBtVqt87PI5ZJhYWHYtGkTfHx8lFFrTTM90T0QHBwcMHToUISEhCAoKEjosrKkpCQsW7YMZcuWxfTp05UtT+ljvr6+WLJkCapXr47Bgwfj1KlTCA4OxrJly4Rl8vT0RLt27eDk5AQ7OzsABUtNRapevTpmzpyp9I1avXo1du/ejcWLFwvL5Ofnhw8fPiAuLg6rV6/G69ev0blzZ3z55ZfCzwcZGRlo3LixNDNI1q1bh7JlyyoP/Pfv34eXlxeaNWvGTEXQnn2Unp4Oa2trHDhwQGAi+WZST5o0Cd999x2qV6+OqVOnIj4+HjNmzMCPP/4oOpqUtJvWtm/fHjVr1hSWZeXKlXBycsKYMWNgb28vtICk0apVK+zYsQP9+vWDkZERLl68iMTERCmWmcvm7t27mDZtms5rarX6oyKFCK9evcK2bdtExwBQMDAxcOBAAAWbOmhWx4jsF6p93B4+fIiZM2eiQ4cOmDVrlrBM2rZv3461a9dKscx7zZo1eP36Nfbv34+QkBBUrlwZvXr1Uo5jSVGpRc6zLWFLlixBhQoVkJqaihcvXuDx48eIjIwUHUsa8fHxxf5OhuVRGi9evMCiRYtw9OhRXL9+XUiGFStWFPs77Z5cJUm7SbtmREatVkvROE9bTk4ODh8+jB49egh5/6FDh8LDwwOvX79GXFwcvv/+eyE56P/N+fPnER8fj/j4eGRmZqJVq1ZwcnKCvb290AJJYGAg3r9/j5cvX6JMmTKYP3++FDO3NNLT0+Hv749jx47h2rVrwnKEhYVh48aNMDIywuzZs4WNfGpLSUmBu7s7fHx8kJqaipCQEMycOVNowVvGTEV5+vQpVqxYIXy2koeHB1atWiXVbGptOTk5UKlUUhQlZKIZwNRmbm6O0aNHw8rKSkim3NxcXLx4EbGxsbhw4QIqV66Mdu3aoX379qhWrZqQTCtWrEBCQgK+//57lC5dGk+ePMGiRYvQsGFD4X2bZCPzs8z06dMxbdo0oRvOaAwfPlxZAlXcz6KEh4dj8+bN8PHxgbOzs9As2kaPHo0NGzaIjlGkp0+fIjg4GL///jtOnTpVYu/7SRWSAOg07/rHP/4Bc3Nz0ZGkIuuIR3p6Og4cOKCMenbv3h0uLi46y8pIbsnJyYiIiMDu3btha2uLn3/+WUgO7YvkN998g9DQUCE5Chs0aBB8fHw+mnEwduxYrFmzRlAqueXk5CA2NhYbN27EH3/8gStXrgjNM3fuXDx+/BgbN24UmkPjw4cPOH36NKKjo3Hr1i20a9cOvXr1Qv369YVlcnNzw5YtW5CRkYHvvvtOaF85bS9fvsSoUaNgamqKlStXokqVKqIjSZmpKIMGDcKOHTuEvLemEHH//n3k5uZKM5P67+Thw4fCZgAV9dCfnJyM3bt3Y9OmTQISfSw2NhY///wzLl26JKxP0oABA7Bz506doltubi7c3NyEbqYgYw9TGWlmcebk5ODdu3eoUKGCcixFfU7Ozs7o1asX1Go1oqKilJ+jo6OFNeJ++fIlfHx8UKFCBfj7+yu95UTT7JZ6+fJlmJiYwM7OTjl+ojbD0dC07Tl27Bhq166NXr16lWjxTc6hm/9lxc0e+eOPP4TNHpGRZsTD1dUVRkZGqFKlCkJDQ5GWliZ0xMPDwwMvXryAi4sLAgMDpb2ZpqLFx8cjLCwMt27dgoGBASIiIqQYjQHkWDOv8erVK3h7e8Pd3R0DBgxQXpehAb9MPnz4gEuXLuH48eM4e/YsypUrhw4dOmDOnDnCMmkeom1tbXHy5EkEBgYqD7SDBg0SkikgIAAXLlxAq1atMHDgQHzxxRdCchRmYmICExMT4cvLteXk5KBixYpYuXIlJk2apLP7iahZbjJm0tCeRZKcnCx0QI69Y/7npk2bJmx2fnEzRMLDw0s4yb9cv34dv//+Oy5evIh79+7B1tYWvXv3RnBwsLBMZcqU+WjmlrGxsfA+mDL2MJWRjEU1Ly+vIn+eOHGiiDgAgJ49e8LY2BitW7fGvHnzdH4ncmCgdu3aOv+Vwfr16xETEwNzc3P06NED27ZtUzbEKUmfRCEpLCwM5cuXR48ePVClShWhu2YU9vz5c0RFRSE7O1t5TVRx6+TJkzojHjVq1MDSpUvh5uYmtJB09+5dAEBkZKQy8iLjki0ZvXr1Crt27YKHh4eQ9+/bty/q1KkDNzc3tG7dGh4eHsKLSOnp6YiLi4NarVaWt2mI3HmoXfkMhQAAIABJREFUSpUqWLp0Kby8vHDz5k3MmTMHBgYGQnf1AICEhASULl0a1tbWQnNotGnTBq1bt0aPHj0wbtw4oU1ZNVJSUpSfNTs4ar8mwvbt2/HZZ5/h0KFDOHTokM7vZLmpleVa7OLiorMcWHMNFnmNkTGThnbxplSpUmjUqJGwLPb29jh69Cg6d+6Mt2/fYtWqVTAxMcGYMWOEZQIKttZu3bq10H4j+pLlewgUrBoYN24catWqJSzD4sWL4ejoiHHjxunMPBDJ1NQUjx8/1rkOP378WHg2GXuYyuj169dYuXIlZs6ciaSkJMycORMmJiYICgoSVpyQcYOXlStXio5QpD59+uD27duwtbVFTk4Odu3aBRMTE/Tr109YpsWLF+Pzzz+HgYEBwsLCdIrvERERJZbjkygkxcXF4dSpU4iKisKtW7fQtWtXdOvWTXglHyhoyNimTRvhD9eAvCMeoqZY/juFH8RUKhXMzc1ha2srKJGua9euITw8HHFxcUJ7ajRp0gSXL19GbGwsrKyshN/0AECjRo2Ubazt7Ox0trQWvYW1ubk5Nm/ejMDAQAwbNgw//fST0DxLly7F+fPnkZOTg+HDh0uxa8aoUaMQFxeHrVu3IikpCe3bt1eabosi48zW27dvi45QJE0jVLVa/VFTVFEjjjJeY2TMtG7dOnz77bc6s0jS0tLg4eEhbDlnSEgIHj58CGdnZ8yfPx+lS5eGlZUV/P39leb3Ihw6dAjff/89qlSpAicnJzg5OaFBgwbC8vw7MlyXNcqWLYvZs2cLXX67efNmYe9dnOnTp2P8+PFo06YNrK2t8ezZM6l6PNapUweTJ09Weph+9dVXwnqY5ufn4+jRo6hatSpq166NRYsWIS8vD56enqhRo4aQTP7+/mjevDkAYP78+Rg6dCjq16+PwMBAaXvuiCC6h1VxNm3ahP3792P79u344Ycf8OzZM1SrVg1BQUHw8/MTkkn0gJLGJ1FIMjIygrOzM5ydnZGZmYnDhw9j2rRpKF26NJYuXSo0W9myZTFlyhShGTRkHfGQkXbxQSM5ORn16tWDj4+PgEQFSyGio6MRHh4OExMTZGRk4OjRo0J3sQkICMD79+9x4MABzJ49G4mJidi2bRu6d+8urL/VwoULkZGRAUNDQyHTQIujGRU2MjKCv78/du/eLbw/2fnz5xEREYF3797B09NTikLSmDFjMGbMGLx9+xZxcXEICwtDYmIi6tWrJ7zhL/017R3/uCzp7yU2NhZmZmbKcbt48SJmzJghbPkmANy8eRObNm1CXl4eTpw4gZMnT6J06dLCz52LFi0CADx58gTx8fEIDQ3Fo0ePULNmTQQFBQnJVFRja7VajcePHwvJUxSVSiW0iCQrGxsbbNu2DUePHkVycjIaNWqECRMmSDEjt6gepv7+/sLyBAQEIDMzE5mZmUhLS4OjoyOqVq2KWbNmCWsi/ebNGwwfPhwZGRm4c+cOevfuDZVKhaysLCF56D8TGxuLiIgIqFQqREVFISYmBhUqVBB6D1O9enVh763tkygkabt58yYuXbqEZ8+eoW3btqLjwMbGBtHR0WjYsKFygRc1zVH2EQ9topdsFffAOmzYsBJO8i8dO3ZEz549sXjxYtSqVQvu7u5SbIVsamqKPn36oE+fPkhKSkJkZCS++uornDx5Ukie8PBwbNiwQapdowDdNeoA0K9fP9jY2Og8eJc0TS+WMmXKIC8vT1iOojx9+hR//vkn3r17B2NjYxgYGIiORHqQdcSR/tqaNWvw7bffwtTUVGmIvGzZMvzjH/8QlsnQ0BBAwSzc+vXrK4MDsvTfys7OxuvXr5GZmQlDQ0OhD/7FPfSwoKuf7OxslCpVStj7m5mZSTGYo03GHqYJCQmIiIhAfn4+unfvrtxbFTUAXNIuXLiAli1bKs97IgtJeXl5OHbsGMqXL4/WrVsDAFJTUxEYGCj0vlNGBgYGMDQ0xM2bN2Ftba00AZdpWbAon0Qh6dq1a4iOjsaZM2fQrFkz9OzZEwEBAVLMtLl165bOLhAqlUpYxVzmEQ8NWZZsFZaTk4OoqCihN6/Dhw9HVFQUnj59iv79+0t5gqtbty68vb2F7nLw22+/4eDBg8quUbIUkuzt7T96rWnTptLsACYLDw8PJCQkoGHDhmjbti0mTpyIunXrio4FoGC21IABA+Ds7Kw84MokLy8PMTEx6NGjh+goUsvJyQEgvqm1bDMny5Yti7Vr12LUqFEwNjbG7t27hd8fGBoaIi4uDnv37kXXrl0BAGfOnEH58uWF5goMDMT58+dRvXp1tGvXDjNmzBDeZ+7vUsRNSEhAWFjYR812S8q8efM+2rwhKSkJkydPxm+//SYkk6y0e5hGRkZCpVIJ72GqOW8bGhrCyspKeV3k5iqWlpZYsmQJ4uLiMH78eGRkZGD9+vVCl7tOnz4dhoaGSElJwd27d1GjRg34+vpi+PDhwjIlJSVh2bJlKFu2LKZPnw4LCwthWQq7f/8+9uzZg44dOwIAEhMTOYAJQKWW8Wnzf5mtrS3q1q0LJycnGBsb6xSQRG/bV1hOTo7wm1fZFLVka+fOnVLMttHIzs7GsmXLMGjQIKFNIoGCXdJ27dqF2NhY9O/fH1999RWnimsZPny4UqwdMWKElP0QZNGiRQvY2Ngo/Ww0P6tUqhJt5qft4sWLaNasGYyM5BsHSUpKwu7du3H69Gk4OjpiwIABws8HQMGy24iICERGRqJhw4b4+eefRUf6iMjR/vv37+P7779HjRo10K1bN6XnlY+Pj7AZAGFhYdi4cSOMjIzg5+eHdu3aCcmh7f79+wAKlmnMmDEDM2bMQL169QCIm0n96NEjLFmyBNWrV8fkyZNx7tw5BAcHY9myZUKb/Xbv3h2ff/45OnXqhHbt2uk80NLH8vPzcejQIYSHhyM1NRUDBgzA6NGjhWSZPHkyatasqbSd2LdvH4KDgzFjxgy4uroKyUT6c3V1xXfffQe1Wo3g4GDl58WLF+PXX38Vkik7Oxu7d+9G9erV0b59e1y5cgVRUVGYOnWqsIb8ffv2xZ49e5CTk4N+/frB2NgYwcHBQgfmhg4dCg8PD2UjHFlWxFy7dg3z589H9erVsWDBAty8eRMzZszAjz/+iGbNmgnJ9O/aqJRkm4dPopC0d+/eYn8numt9RESEsr5frVbD2NgYMTExQjPJxtHRET179oSbm5uyZGv9+vWiY+H27duIiYnBq1evUKVKFbi4uEjx0Kjx5s0b/Prrr9i9ezd++eUX0XGkoV1I0v6ZPvb06dNifyfL+mwZpaWlYcGCBYiJiYG9vT2mTp2KJk2alHiO+Ph4hIWF4datWzAwMEBoaKjwjR1kHO0fPHgwPD098fr1a/j6+mLv3r2oVKkS3N3dsXPnTiGZ3NzcsGXLFmXmpAzXvOKWboucSS2zx48f4+TJk4iNjUV6ejpatWqF9u3bFzn79FOVkpKCHTt24Ndff0WzZs3w8OFDYd85jfz8fEyePBkNGzbEixcvkJCQgODgYKEzyrKzsxEREYHhw4fj5cuXCAoKgomJCby9vVG5cmVhuWR5mNVWVKZXr17h9OnTwhqAy0j7/rdHjx4IDw8X1ru0qEzffPMNQkNDheYpTk5ODlQqFYyNjYVl6NWrF96/fw9XV1c0b95cZxVKSa60kG9I979AdLHo39m5cye2bt2K1atXw8XFhbMjiiDjkq0DBw5g3bp1cHNzQ+PGjfHs2TN4eXnBy8sLnTt3Fh0PAFC+fHkMGzZMaN8mGddgy7hrlKysrKykO34yO3nyJPbu3Yt79+7B1dUVs2bNQl5eHr799lvs27evRLP07dsXderUgZubG1q3bg0PDw/hRSSgoMi2dOnSIkf7RTEyMlJ6Jm7ZskUZEBC5dbuJiQlMTExQqVIlafr9bN26VXSEvxVra2sMHToUvXv3xpkzZ7B582aEh4fj8uXLoqNJo2vXrhg2bBj27t2LcuXKwd3dXXQkGBoaYunSpfD09MT79++xbds24UtYAgMDUaZMGXz48AEBAQFo0qQJbGxs4O/vL3TL9Bs3bhT7MCuKdgHr2rVrCAsLw/Xr19G/f3+BqeRmbm4uvIhUmMiliH9FhpVDv/32GxISErBv3z6sXbsW9vb2cHV1Rc2aNUs0xydRSJJZxYoVYWlpiczMTDg4OGD58uXCssi6NtXDwwMeHh7Kkq0bN24gODhY6JKtLVu2ICwsTOdBo0+fPhg3bpw0hSQZyLgGm7tG6U/G41eckydPon379kIz7Nu3D19//fVHvUg0S6VKUpMmTXD58mXExsbCyspKip6AQEGxdvLkyVi1apUy2r9t2zaho/3an432DaIsN7IyPJzRf+bgwYO4ePEiLl26BAMDA7Rp0waenp5o0aKF6GhSWbBgASIjIzFixAj069dPiqJpXFwcAGDAgAFYsGABtmzZoizhdHR0FJLp2bNn2LBhA7Kzs/H7779j+fLlMDY2Ft5DUZaHWW2adhjbtm2DsbGxFDsYy0jGQdX09HTExcVBrVYry9s0RH33ZFa/fn1Mnz4dQEEj95CQELx48aJEZ3WykCSYmZkZjhw5ovQcSUtLE5Zl7ty5ytrU4OBgadamarRq1QqtWrVSlmx99913wpZsGRkZfTRaXa5cOSkb7Ir06NGjj9Zgb9myRega7L9Lw1EZyHj89uzZgyVLlsDU1BTLly+HtbU1/Pz8cO/ePeGFpEWLFuHGjRu4cOEC1Go1kpOT0bNnT6UJcEkKCAjA+/fvceDAAcyePRuJiYnYtm0bunfvLnTkUcbR/qJuqNVqNZKSkqTKpMGZk/KLjY2Fo6MjJkyYgIoVK4qOI63u3buje/fuePLkCSIjI/H48WNMnjwZX331FZydnYVk0t7dy8HBAXfu3MGdO3cAiHuY1RS7L126hCZNmihLarKzs4Xk0SbDw6w2zQ7GwcHBUu1gDADv379HREQE7t+/DxsbGwwaNEjY8igZB1UbNWqkfP/s7Ox0vossJBUtIyMDhw8fRlRUFLKyskq8j9sn0SOpODNmzEBwcLDQDBkZGXj06BEsLCywceNGODs7w8HBQUiWv8vaVBkU11uHPXd0ybgGm/Qn4/Hr1asXwsLCkJKSgkWLFiE5ORmdOnXC+PHjha5XB4CxY8ciNzcXycnJyM/Ph6WlpTTn0aSkJERGRmL//v04efKksByaEcbs7GwsWLAAw4cPFz7aHx8fX+zvRBWeZcxUWGJiIoyNjYX2BrS1tUWFChWK/O5rj2aLIHsfRRl9+PABJ06cQGRkJFatWiU6DoCCfpMGBgZCdyj08vJC27ZtERMTg549e6J3797YvXs3Tpw4IXRpm0bhh9nu3btj6NChQrKsXbsWUVFRqFmzJvr3748tW7Zgw4YNQrIUNn78eNSuXRvNmzfHpUuXkJycjMWLF4uOJS0ZvnsAMGjQoI9mdYveeObAgQOIjo7Gs2fP0LVrV/Ts2RM1atQo8Ryf9IwkzQ4kIly/fh1NmjTBlStXABT0jXB0dERubi7Onz8POzs7mJmZCcsny5R+WRUeIQYgfARbdjKuwSb9yXL8PvvsM1SoUAEVKlRAUlIS/P39hc9E0sjIyEBYWBh8fX0xe/ZsjBw5UliWwtc3Q0NDjBs3TvhOpTKO9stSmNEmY6bTp0/D19cXhw8fRmRkJDZs2IBKlSphwIABGDBggJBM3t7eiI2NhbW1NVxdXdGyZUshOQo7ePAg1q5dq9NHceLEiZg0aRKXv2sp3EfRwMAATZs2Fba7FgDcvHkTvr6+2LVrF44fPw5/f3+YmZnB29tb2fq7pA0bNgwnTpxAp06d0KdPH5w7dw7Hjh3DvHnzhOTRKPwwGxAQIORhVpuM7TA00tPTlX6AnTt3xtdffy00j2xk/O4BwJIlS4S9d3GmTJmCOnXqwNbWFgkJCVi6dKnyu5KctfxJF5JEOnv2LJo0aaJzU62Rm5uLe/fuYc+ePSWaiWtT9Vdco2FZpofKgssz/t5kPH7ao0LVqlWTpogEFCx5BYCsrCyYmpoK7fdReGc0oGDnmr59+2LUqFECEhUQtZMP/c9t2LABO3fuhLGxMdatW4dNmzahatWqGDZsmLBC0siRIzFy5Ejcu3cP+/btw08//YTmzZvD1dUVderUEZIJADZv3sw+inqQsQ/f0qVLsWjRIhgbG2PZsmVYu3atskRK1MPs7Nmz4ebmhiFDhgAA2rRpgzZt2gjJok2Wh9miyNQOQ6NevXr4/fff0aJFC9y5cwfVqlVDbu7/1969B0VVv3Ec/xwERFQEM7yUomANadlNy00d08ZkqMwsFSwszbFmSsdRu3hrFi+ZFeOli4WkJXkrkUqQirKiy3jpPlZqooUmKImXWQQEd39/OOzkT7EN1O+Bfb/+Ws7OsJ/ZnXNm9znf7/NUyuPx2KKBs2l2PPcke04ptsvuF78oJJ1tebPH45HL5TKQ5pSxY8dKOvNL9cGDBxUZGanU1NSLnom9qb6z491iO7LjHmz4zo6f35EjR/T111/L7XbL5XLZquA9YMAAvfzyy4qNjdWwYcOMLsc+24Qtt9utpKQko4Wksy0Rr2ZqiTh8Y1mWIiMjtXfvXgUFBXkb6tqhN2B0dLQmTJigoqIiPffcc7r77ruNjvqmj6Jv7NiHz+PxKDY2VgcOHFBZWZmuvvpqSTLay23dunWaN2+eHn74YT333HO69NJLjWX5J7v8mD0XO0wwrvbdd9/pq6++UlBQkPdG08CBA2VZlj799FPD6cyz47lnV3b5HeoXhaSzrfqRpOuvv/4iJznTokWLtHLlSlVWVqq8vFwdO3ZUdna2t9B0Mc2dO1cul0uNGjVSkyZNLvrro+Gxy4UOtWPHz69r167KysqSZL+Cd/XdYknq27evrfqhnDx5Ut99953Ky8uN5rDjEnH4pqqqSlVVVfr888+959qxY8dUVlZmNNeRI0eUk5OjnJwcSacaODudTqOZaiqW0jbgdNXF9uDgYLndbi1dutT4Furqz+jLL7/0rvo5ceKESktLjWUKDQ1VcnKytm7dqsTERF177bXe50yu/LHjdwQ7q/6+cuTIEbVo0cI201Ttwo7nHs7NLwpJ/7/qxy7Nu6RTkz3y8vL07LPPatSoUUpOTjaWZcWKFXrjjTcUGBioGTNmqE+fPsayAMDZ2Pl6/ttvv2nNmjWnTdKxy1auiooKvf3225o+fbrRHHZcIg7f3HPPPYqPj9fJkyf15ptvaufOnZo8ebLRbUhjx45VUVGR4uLiNHv2bLVp08ZYln+ij+J/Z5c+fA6HQwkJCSoqKtLixYtVUFAgp9Op+Ph4o7ny8/OVkpKim266SYMHDzaaBbWzdetWJScn6+TJk4qLi1O7du2MbQu2I7uee6iZX0xts2vzLkkaM2aM0tLSvBPkEhMTtWrVKiNZEhIStHz5crlcLj355JNKS0szkgMAamLn6/ndd9+tBx544LQfsxTk0ZC4XC6FhoYqICBAxcXFKi4uVpcuXYzl+f9z3rIs7zQdk1tF6sPUPTu45ZZb5HA45PF4tGnTptP6/phcaZOfn6+WLVsqIiJCBQUF2rFjhwYMGGAsT2pqqlavXq1nnnlGt956q7EcqJv7779fr7zyisaNG6e0tDQlJiZe9H64dme3cw/n5hcrkuzavEuS2rRpo7Vr16pJkyZKSUkx2rcpODhYwcHBatmypdEmsQBQEztfz1u1asXdRTRo+/btO2OkvUkbN240+vo1oVjkGzv24ZN0Wo+mDh06qEOHDgbTSNu2bVNGRoYiIiKM5kDdBAQEKDw8XJZlqXHjxmratKnpSLZjt3MP5+YXhSQ7N++aOXOmd1l2ZmZmjdPALjY/WKgGoB6y8/X8sssuU2pqqq666ipv7wPTfZuA8yUnJ0dLliyx1Uj7KVOm1PicXbaVomYU3HyzaNEi0xFwHnTo0EEpKSk6fPiwUlNT1a5dO9ORgDrxi0KSnZt37d+/Xx999JG3WWVOTo4ef/xxI1nsOOobAP7JztfzyspK7dmzR3v27PEeo5CEhmL58uW2G2m/bds2lZeXa9CgQbr++uu5CQbAtpKTk/Xuu++qe/fuCg0N1axZs0xHAurELwpJdm7eNWnSJPXp00etWrUyHcW2S4wBoJodr+dVVVUKDAw0OiwBuNDsONJ+/fr12rlzpz744AOlpqaqR48eGjRokKKiooxlAoCzsSxLgYGBioiI0BVXXCGXy6WWLVuajgXUml8025bs27zrwQcf1FtvvWU6BgDUG3a7nk+aNEkpKSnq37+/d0ubHRr+AufTyJEjtXz5cp+Pm7B161alp6erqKhI77zzjuk4AOA1bdo0RUZG6ptvvtEjjzyiVatWacmSJaZjAbXmFyuSJPs176re+tCqVStlZWWpS5cu3h8gnTp1MhkNAGzNbtfz++67T5J9G/8C54OdR9q7XC7l5uYqKytLZWVlGjRokOlIAHCagoICzZkzR99++6369++v1NRU05GAOvGbQpLdPPPMM97Ha9as8T62LMs2d/YAAP8uPT1ds2bN0pAhQzRkyBCWqqNBqmkYiMmt8Dk5OcrOztb+/ft1++23Kzk5WZdffrmxPABQk5MnT6qkpESWZcnlctliSAhQF36ztc3OSkpK9NdffykqKkphYWGm4wAA/qNDhw7pvffe0/r169WxY0cNHz7c2wwcwIURGxur6OhoxcbGSpJ3ZbfEkBAA9rJlyxbNmDFDxcXFatu2raZOnapevXqZjgXUGoUkwzIyMrRkyRLFxMRo9+7dGjdunC2agAMAauenn35SZmamtmzZog0bNpiOAzRYW7ZsqfE5RssDsKOSkhJFREScVvgG6iO2thm2atUqvf/++2rcuLGOHz+uBx98kEISANRTP/zwgzIzM/Xjjz9q4MCBpuMADRrFIgB2l5SUVGPRiHYmqM8oJBkWHh6uwMBTH0NISAhb2wCgntm7d6/ef/99ZWVlKTo6WkOHDpXT6aT/AQAAfi45OVmS9Morr+i2227TjTfeqJ9//lmfffaZ4WRA3VBIMmTixImyLEslJSUaMmSIrr32Wv36668KCQkxHQ0A8B+MGjVK9913n9566y21bt3adBwAAGAT0dHRkqS///7bu+tkwIABSk9PNxkLqDMKSYZUTzk5duyYdxXSnXfeaTISAKAWcnNz6XUAAADO6d1331W3bt30ww8/qEmTJqbjAHVCs23DEhMTtWrVKtMxAAAAAAAXQHFxsZYuXapdu3YpJiZG48ePV2hoqOlYQK1RSDLs0UcflcPhUKdOnbz9NHr37m04FQAAAADgfDl06JAqKiq8f7dr185gGqBu2NpmWEREhLZv367t27d7j1FIAoD6o6qqShs3blRYWJh69uwp6VQvhNmzZ2vBggWG0wEAANOcTqfy8vIUGRkpj8cjy7K0evVq07GAWqOQZNjcuXNP+/vgwYOGkgAAamPy5Mlq1KiRiouLtWvXLl1++eWaNm2aRo4caToaAACwgZ9//lmffPIJE13RYFBIMmzRokVauXKlKisrVV5ero4dOyo7O9t0LACAjwoKCrRu3TqdOHFC9957r4KCgrR8+XLFxMSYjgYAAGwgKipKFRUVNNlGg0FJ1LC8vDzl5eXprrvu0oYNGxgdDQD1TLNmzSRJwcHBcrvdWrp0KUUkAADgVVhYqH79+mn48OEaPny4d4I3UF+xIsmw8PBwBQcHq7S0VFFRUSorKzMdCQBQS5dcconCw8NNxwAAADaSkpJiOgJwXlFIMqxNmzZau3atmjRpopSUFLlcLtORAAD/wa5duzRp0iR5PB7v42p8cQQAAFVVVfrwww9VWVkp6VRf3JkzZxpOBdSe5fF4PKZD+DO3263CwkK1aNFCmZmZcjgc6ty5s+lYAAAfbdmypcbnbrrppouYBAAA2FFCQoL69eunzZs3KzIyUsePH9eiRYtMxwJqjRVJhrz33ntnHGvevLm2bdtGIQkA6hGKRQAA4FxCQkL0yCOP6I8//tDcuXM1YsQI05GAOqGQZEh+fr73cXZ2tu644w5JkmVZpiIBAAAAAM4zj8ej4uJilZaW6vjx4zp69KjpSECdsLXNBpKSkpSenm46BgAAAADgPNu6dat+//13tW7dWtOnT9fgwYP11FNPmY4F1BorkmyAVUgAAAAA0DD16NFDPXr00LFjx5Sbm6tmzZqZjgTUSYDpAAAAAAAANDS//PKLBg8erMrKSn388ceKi4vTvffeq40bN5qOBtQJW9sMmThxoizLksfj0aZNm+RwOLzPMS4aAAAAAOq3MWPGaPLkyYqNjVV8fLxeeOEFRUVFacyYMVq9erXpeECtsbXNkISEhLM+BgAAAADUfx6PR7GxsTpw4IDKysrUtWtXSVJAABuDUL9RSDKEcdEAAAAA0HC53W5J0pdffundgXLixAmVlpaajAXUGYUkAAAAAADOM4fDoYSEBBUVFWnx4sUqKCiQ0+lUfHy86WhAndAjCQAAAACACyA/P18tW7ZURESECgoKtGPHDg0YMMB0LKBOKCQBAAAAAADAJ3T5AgAAAAAAgE8oJAEAAAAAAMAnFJIAAECDt3nzZjkcDiUlJSkpKUnDhg1Tenq6kpKSlJ+ff9EydO/eXYWFhd5jL774otatW3dRXh8AAOB8YGobAADwCz179tT8+fMlnRq/HBcXp+bNm1/UDEFBQZoyZYqWLVsmy7Iu6msDAACcDxSSAACA33G5XAoICFCjRo0kSUVFRXI6naqoqNCRI0f02GOPKSYmRk888YTWrl0rSZowYYJGjx6t8vJyzZ8/X40aNVL79u01c+ZMrV+/XhkZGXK73Ro/frwcDsdZX7dnz55yu91asWKFHnjggdOeS0lJ0bZt21RaWqqYmBjNnTtXL730kv78808dPnxYR48e1YgRI/Txxx9rz549mjdvnq677jqlp6crKyvk4VuPAAADD0lEQVRLlmUpPj5eI0eOvLBvHgAA8GsUkgAAgF/YtGmTkpKSZFmWgoKCNGPGDKWlpUmSdu/erVGjRunmm2/W999/r5deeknLli1TSEiIdu3apVatWmnfvn265pprFBcXp5UrV+qSSy7RggULlJmZqcDAQIWFhWnx4sX/msPpdGro0KHq3bu395jL5VJYWJiWLVsmt9utO+64QwcOHJAkhYSE6I033lBqaqq++OILvfbaa8rIyFB2draaNWumDRs2aOXKlbIsSw899JB69+6t6OjoC/MmAgAAv0chCQAA+IV/bm2rVl1IuvTSS7V48WKtXbtWlmWpqqpKkjR06FCtW7dO7dq106BBg1RSUqKDBw9qwoQJkqTy8nL16tVLHTp0UKdOnXzKERERoalTp+rpp5/WDTfcIElq3LixSkpKNHHiRIWGhur48eOqrKyUJHXp0kWS1Lx5c3Xu3FmS1KJFC1VUVGjnzp3av3+/HnroIUnS0aNHVVBQQCEJAABcMBSSAACA31u4cKGGDh2qvn37KiMjQ5mZmZKkuLg4LV26VOHh4Vq4cKHCwsLUpk0bvfrqq2revLk+/fRThYaGqrCwUAEBvs8w6d+/v3Jzc5WZmaknnnhCeXl5Kiws1IIFC1RSUqLc3Fx5PB5JOmcvpejoaHXu3FlpaWmyLEtvvvmmrrzyyrq9GQAAAOdAIQkAAPi9uLg4zZkzR6+//rratm2rw4cPSzq1UqhHjx4qKSlReHi4JGnatGkaO3asPB6PmjZtqueff/60SWy+mjZtmjZt2iRJ6tatm1599VUNGzZMwcHBat++vQ4ePPiv/yM2NlYOh0OJiYk6ceKEunXrptatW//nLAAAAL6yPNW3uwAAAHAGp9OpgQMH1thAGwAAwJ+wIgkAAKAGo0ePVmRk5H8qIjmdTuXn559xfMmSJQoJCTmf8QAAAC46ViQBAAAAAADAJ753hQQAAAAAAIBfo5AEAAAAAAAAn1BIAgAAAAAAgE8oJAEAAAAAAMAnFJIAAAAAAADgEwpJAAAAAAAA8Mn/AC8Y5dvalrk2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Wickets_Graph\n",
    "plt.figure(figsize=(20,12))\n",
    "sns.barplot(y='Wickets_Taken', x=\"Player_Name\", data=mergeResultw.head(30))\n",
    "plt.title('Player_Name vs Max_Wickets')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4934"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Wickets['Dissimal_Type'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>Innings_Id</th>\n",
       "      <th>Over_Id</th>\n",
       "      <th>Ball_Id</th>\n",
       "      <th>Team_Batting_Id</th>\n",
       "      <th>Team_Bowling_Id</th>\n",
       "      <th>Striker_Id</th>\n",
       "      <th>Striker_Batting_Position</th>\n",
       "      <th>Non_Striker_Id</th>\n",
       "      <th>Bowler_Id</th>\n",
       "      <th>Batsman_Scored</th>\n",
       "      <th>Extra_Type</th>\n",
       "      <th>Extra_Runs</th>\n",
       "      <th>Player_dissimal_Id</th>\n",
       "      <th>Dissimal_Type</th>\n",
       "      <th>Fielder_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Match_Id  Innings_Id  Over_Id  Ball_Id  Team_Batting_Id  Team_Bowling_Id  \\\n",
       "10    335987           1        2        4                1                2   \n",
       "20    335987           1        4        2                1                2   \n",
       "25    335987           1        4        7                1                2   \n",
       "60    335987           1       10        5                1                2   \n",
       "65    335987           1       11        4                1                2   \n",
       "\n",
       "    Striker_Id  Striker_Batting_Position  Non_Striker_Id  Bowler_Id  \\\n",
       "10           2                         2               1         15   \n",
       "20           2                         2               1         13   \n",
       "25           2                         2               1         13   \n",
       "60           2                         2               3         16   \n",
       "65           3                         3               2          9   \n",
       "\n",
       "   Batsman_Scored Extra_Type Extra_Runs Player_dissimal_Id Dissimal_Type  \\\n",
       "10              6                                                          \n",
       "20              6                                                          \n",
       "25              6                                                          \n",
       "60              6                                                          \n",
       "65              6                                                          \n",
       "\n",
       "   Fielder_Id  \n",
       "10             \n",
       "20             \n",
       "25             \n",
       "60             \n",
       "65             "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Maximum Sixes\n",
    "maxsix = data[data['Batsman_Scored'] == \"6\"]\n",
    "maxsix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Striker_Id\n",
       "162    252\n",
       "57     164\n",
       "21     161\n",
       "8      148\n",
       "31     143\n",
       "Name: Batsman_Scored, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msixes = maxsix.groupby('Striker_Id')['Batsman_Scored'].count()\n",
    "msixes.nlargest(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2b6941a0f60>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 448.75x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "records = pd.DataFrame({'Player_Id':msixes.index,'Maximum_Sixes':msixes.values}).sort_values(by='Maximum_Sixes', ascending=False).head(50)\n",
    "mergeResults = pd.merge(records, player, on='Player_Id',how='left')\n",
    "mergeResults.nlargest(5,'Maximum_Sixes')\n",
    "\n",
    "season_wise = data.loc[:,('Match_Id','Striker_Id','Batsman_Scored')]\n",
    "season_wise = season_wise[season_wise['Batsman_Scored']=='6']\n",
    "data_season = match.loc[:,('Match_Id','Season_Id')]\n",
    "season_wise = season_wise.merge(data_season, on='Match_Id')\n",
    "season_sixes = season_wise.groupby(['Season_Id','Striker_Id'])['Batsman_Scored'].count().reset_index()\n",
    "data_player = player.loc[:,('Player_Id','Player_Name')].rename(columns={'Player_Id':'Striker_Id'})\n",
    "\n",
    "season_sixes = season_sixes.merge(data_player, on='Striker_Id').rename(columns={'Batsman_Scored':'Season_Sixes'})\n",
    "season_sixes = season_sixes.sort_values(by=['Season_Id','Season_Sixes'], ascending=[True,False]).drop('Striker_Id',axis=1)\n",
    "season_sixes = season_sixes.loc[(season_sixes.Player_Name=='SK Raina') | (season_sixes.Player_Name=='MS Dhoni') | (season_sixes.Player_Name=='V Kohli') | (season_sixes.Player_Name=='RG Sharma') | (season_sixes.Player_Name=='CH Gayle'),:]\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.lineplot(x='Player_Name',y='Maximum_Sixes',data=mergeResults)\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "sns.catplot(x=\"Season_Id\", y=\"Season_Sixes\",kind='bar',hue=\"Player_Name\",data=season_sixes)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Innings_Id</th>\n",
       "      <th>Over_Id</th>\n",
       "      <th>Ball_Id</th>\n",
       "      <th>Team_Batting_Id</th>\n",
       "      <th>Team_Bowling_Id</th>\n",
       "      <th>Striker_Id</th>\n",
       "      <th>Striker_Batting_Position</th>\n",
       "      <th>Non_Striker_Id</th>\n",
       "      <th>Bowler_Id</th>\n",
       "      <th>Batsman_Scored</th>\n",
       "      <th>Extra_Type</th>\n",
       "      <th>Extra_Runs</th>\n",
       "      <th>Player_dissimal_Id</th>\n",
       "      <th>Dissimal_Type</th>\n",
       "      <th>Fielder_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Match_Id  Innings_Id  Over_Id  Ball_Id  Team_Batting_Id  Team_Bowling_Id  \\\n",
       "8     335987           1        2        2                1                2   \n",
       "9     335987           1        2        3                1                2   \n",
       "11    335987           1        2        5                1                2   \n",
       "16    335987           1        3        4                1                2   \n",
       "22    335987           1        4        4                1                2   \n",
       "\n",
       "    Striker_Id  Striker_Batting_Position  Non_Striker_Id  Bowler_Id  \\\n",
       "8            2                         2               1         15   \n",
       "9            2                         2               1         15   \n",
       "11           2                         2               1         15   \n",
       "16           2                         2               1         14   \n",
       "22           1                         1               2         13   \n",
       "\n",
       "   Batsman_Scored Extra_Type Extra_Runs Player_dissimal_Id Dissimal_Type  \\\n",
       "8               4                                                          \n",
       "9               4                                                          \n",
       "11              4                                                          \n",
       "16              4                                                          \n",
       "22              4                                                          \n",
       "\n",
       "   Fielder_Id  \n",
       "8              \n",
       "9              \n",
       "11             \n",
       "16             \n",
       "22             "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Maximum Fours\n",
    "maxfour = data[data['Batsman_Scored'] == '4']\n",
    "maxfour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Striker_Id\n",
       "40    422\n",
       "8     360\n",
       "21    360\n",
       "42    348\n",
       "46    341\n",
       "Name: Batsman_Scored, dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maxf = maxfour.groupby('Striker_Id')['Batsman_Scored'].count()\n",
    "maxf.nlargest(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "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>Player_Id</th>\n",
       "      <th>Maximum_Fours</th>\n",
       "      <th>Player_Name</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Batting_Hand</th>\n",
       "      <th>Bowling_Skill</th>\n",
       "      <th>Country</th>\n",
       "      <th>Is_Umpire</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>422</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>14-Oct-81</td>\n",
       "      <td>Left_Hand</td>\n",
       "      <td>Legbreak</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>360</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>05-Nov-88</td>\n",
       "      <td>Right_Hand</td>\n",
       "      <td>Right-arm medium</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>360</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>27-Nov-86</td>\n",
       "      <td>Left_Hand</td>\n",
       "      <td>Right-arm offbreak</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>348</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>05-Dec-85</td>\n",
       "      <td>Left_Hand</td>\n",
       "      <td>Right-arm offbreak</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>341</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>11-Nov-85</td>\n",
       "      <td>Right_Hand</td>\n",
       "      <td>Right-arm medium</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player_Id  Maximum_Fours Player_Name        DOB Batting_Hand  \\\n",
       "0         40            422   G Gambhir  14-Oct-81    Left_Hand   \n",
       "1          8            360     V Kohli  05-Nov-88   Right_Hand   \n",
       "2         21            360    SK Raina  27-Nov-86    Left_Hand   \n",
       "3         42            348    S Dhawan  05-Dec-85    Left_Hand   \n",
       "4         46            341  RV Uthappa  11-Nov-85   Right_Hand   \n",
       "\n",
       "        Bowling_Skill Country  Is_Umpire  Unnamed: 7  \n",
       "0            Legbreak   India          0         NaN  \n",
       "1    Right-arm medium   India          0         NaN  \n",
       "2  Right-arm offbreak   India          0         NaN  \n",
       "3  Right-arm offbreak   India          0         NaN  \n",
       "4    Right-arm medium   India          0         NaN  "
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records = pd.DataFrame({'Player_Id':maxf.index,'Maximum_Fours':maxf.values}).sort_values(by='Maximum_Fours', ascending=False).head(50)\n",
    "mergeResultf = pd.merge(records, player, on='Player_Id',how='left')\n",
    "mergeResultf.nlargest(5,'Maximum_Fours')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0,\n",
       "  1,\n",
       "  2,\n",
       "  3,\n",
       "  4,\n",
       "  5,\n",
       "  6,\n",
       "  7,\n",
       "  8,\n",
       "  9,\n",
       "  10,\n",
       "  11,\n",
       "  12,\n",
       "  13,\n",
       "  14,\n",
       "  15,\n",
       "  16,\n",
       "  17,\n",
       "  18,\n",
       "  19,\n",
       "  20,\n",
       "  21,\n",
       "  22,\n",
       "  23,\n",
       "  24,\n",
       "  25,\n",
       "  26,\n",
       "  27,\n",
       "  28,\n",
       "  29,\n",
       "  30,\n",
       "  31,\n",
       "  32,\n",
       "  33,\n",
       "  34,\n",
       "  35,\n",
       "  36,\n",
       "  37,\n",
       "  38,\n",
       "  39,\n",
       "  40,\n",
       "  41,\n",
       "  42,\n",
       "  43,\n",
       "  44,\n",
       "  45,\n",
       "  46,\n",
       "  47,\n",
       "  48,\n",
       "  49],\n",
       " <a list of 50 Text xticklabel objects>)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.lineplot(x='Player_Name',y='Maximum_Fours',data=mergeResultf)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0', '4', '6', '1', '2', 'Do_nothing', '5', '3', ' '], dtype=object)"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Maximum Runs\n",
    "data['Batsman_Scored'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>Innings_Id</th>\n",
       "      <th>Over_Id</th>\n",
       "      <th>Ball_Id</th>\n",
       "      <th>Team_Batting_Id</th>\n",
       "      <th>Team_Bowling_Id</th>\n",
       "      <th>Striker_Id</th>\n",
       "      <th>Striker_Batting_Position</th>\n",
       "      <th>Non_Striker_Id</th>\n",
       "      <th>Bowler_Id</th>\n",
       "      <th>Batsman_Scored</th>\n",
       "      <th>Extra_Type</th>\n",
       "      <th>Extra_Runs</th>\n",
       "      <th>Player_dissimal_Id</th>\n",
       "      <th>Dissimal_Type</th>\n",
       "      <th>Fielder_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Match_Id  Innings_Id  Over_Id  Ball_Id  Team_Batting_Id  Team_Bowling_Id  \\\n",
       "8     335987           1        2        2                1                2   \n",
       "9     335987           1        2        3                1                2   \n",
       "10    335987           1        2        4                1                2   \n",
       "11    335987           1        2        5                1                2   \n",
       "16    335987           1        3        4                1                2   \n",
       "\n",
       "    Striker_Id  Striker_Batting_Position  Non_Striker_Id  Bowler_Id  \\\n",
       "8            2                         2               1         15   \n",
       "9            2                         2               1         15   \n",
       "10           2                         2               1         15   \n",
       "11           2                         2               1         15   \n",
       "16           2                         2               1         14   \n",
       "\n",
       "   Batsman_Scored Extra_Type Extra_Runs Player_dissimal_Id Dissimal_Type  \\\n",
       "8               4                                                          \n",
       "9               4                                                          \n",
       "10              6                                                          \n",
       "11              4                                                          \n",
       "16              4                                                          \n",
       "\n",
       "   Fielder_Id  \n",
       "8              \n",
       "9              \n",
       "10             \n",
       "11             \n",
       "16             "
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned = data[(data['Batsman_Scored'] != '0') & (data['Batsman_Scored'] != 'Do_nothing') & (data['Batsman_Scored'] != ' ')]\n",
    "cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Striker_Id\n",
       "1    1349\n",
       "2    2435\n",
       "3      91\n",
       "4    1322\n",
       "5      64\n",
       "Name: Batsman_Scored, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned['Batsman_Scored'] = cleaned['Batsman_Scored'].apply(lambda x: int(x))\n",
    "maxruns = cleaned.groupby('Striker_Id')['Batsman_Scored'].sum()\n",
    "maxruns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>Player_Id</th>\n",
       "      <th>Maximum_Runs</th>\n",
       "      <th>Player_Name</th>\n",
       "      <th>DOB</th>\n",
       "      <th>Batting_Hand</th>\n",
       "      <th>Bowling_Skill</th>\n",
       "      <th>Country</th>\n",
       "      <th>Is_Umpire</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>4106</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>27-Nov-86</td>\n",
       "      <td>Left_Hand</td>\n",
       "      <td>Right-arm offbreak</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>4105</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>05-Nov-88</td>\n",
       "      <td>Right_Hand</td>\n",
       "      <td>Right-arm medium</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57</td>\n",
       "      <td>3874</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>30-Apr-87</td>\n",
       "      <td>Right_Hand</td>\n",
       "      <td>Right-arm offbreak</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>3634</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>14-Oct-81</td>\n",
       "      <td>Left_Hand</td>\n",
       "      <td>Legbreak</td>\n",
       "      <td>India</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162</td>\n",
       "      <td>3447</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>21-Sep-79</td>\n",
       "      <td>Left_Hand</td>\n",
       "      <td>Right-arm offbreak</td>\n",
       "      <td>West Indies</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Player_Id  Maximum_Runs Player_Name        DOB Batting_Hand  \\\n",
       "0         21          4106    SK Raina  27-Nov-86    Left_Hand   \n",
       "1          8          4105     V Kohli  05-Nov-88   Right_Hand   \n",
       "2         57          3874   RG Sharma  30-Apr-87   Right_Hand   \n",
       "3         40          3634   G Gambhir  14-Oct-81    Left_Hand   \n",
       "4        162          3447    CH Gayle  21-Sep-79    Left_Hand   \n",
       "\n",
       "        Bowling_Skill      Country  Is_Umpire  Unnamed: 7  \n",
       "0  Right-arm offbreak        India          0         NaN  \n",
       "1    Right-arm medium        India          0         NaN  \n",
       "2  Right-arm offbreak        India          0         NaN  \n",
       "3            Legbreak        India          0         NaN  \n",
       "4  Right-arm offbreak  West Indies          0         NaN  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records = pd.DataFrame({'Player_Id':maxruns.index,'Maximum_Runs':maxruns.values}).sort_values(by='Maximum_Runs',ascending=False).head(50)\n",
    "mergeResultr = pd.merge(records, player, on='Player_Id',how='left')\n",
    "mergeResultr.nlargest(5,'Maximum_Runs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "        34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49]),\n",
       " <a list of 50 Text xticklabel objects>)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H1a9fP40aNUoPPfSQhg0b5l6vsLBQmzdv1rJly3TgwAGlpKRoxYoVyszMVGpqqrp166aMjAzl5eWpR48e/m4TAAAAAEzJ71NsvXr10hNPPOH+3Wq1aufOnVq3bp2GDBmi9PR0lZWVacuWLYqLi5PFYlHr1q3lcDhUUlKiwsJCde3aVZIUHx+vDRs2+LtFABe5U15cY+hNDQAAQEPk95nAZs2aSZLKyso0ZswYpaamqrKyUgMGDNA111yjefPmac6cOQoPD1fz5s1r1JWWlsrlcslisdRYVpeKigoVFRUpJibGq56LiorcP/s6hrf1wTIG26J2vT/GYFv4/3FMW2rslNJp931Y43EAAACYlaEQ6HQ65XK5tHXrVnXu3Fl2+9lv0nDgwAGNGjVKgwcP1j333KPjx48rIiJCktSjRw9Nnz5dv//971VeXu6uKS8vV3h4eI3r/8rLy9115xMaGurTgaUvtQ1tjGDoIVjGCIYegmWMYOjBH2P4owcAAIBgZOTDbo9PB501a5aWLl2qF198UfPmzdOUKVPOut6RI0c0bNgwPfnkk0pKSpIkDR8+XNu3b5ckbdy4UVdffbW6dOmigoICOZ1O7d+/X06nUy1atFCnTp20adMmSVJ+fr5iY2M9fjAAAAAAgPPzeCZwy5YtevLJJzV06FAtXLhQDz744FnXmz9/vo4fP665c+dq7ty5kqQJEyZoxowZatSokSIjIzV9+nSFhYUpNjZWycnJcjqdysjIkCSlpaVpypQpysrKUnR0tBISjN9FEAAAAABwdh6HQKfTqe3bt6tNmzaqrKxUSUnJWdebPHmyJk+eXGt5bm5urWUpKSlKSUmpsSwqKko5OTmetgUAAAAAMMDj00H79Omj6dOna9iwYZo1a5YeeOCBQPYFAAAAAAgAj2cChwwZoiFDhkiSJk2aFLCGAAAAAACB43EIXLVqlRYsWKCKigr3sry8vIA0BQCBdspRqUbWs9/h2NOaSkel7AbH8KYGAADAnzwOga+++qrmzZunSy+9NJD9AEC9aGS1K3VFL0M1f+n/QY3f7Va77nvb2BhL+3xQ90oAAAAB5HEIbNu2rdq3bx/IXgAAAAAAAeZxCGzcuLEefvhhxcTEyGKxSJLGjh0bsMYAAAAAAP7ncQi89dZbA9kHAAAAAKAeeBwC27RpE8g+AAAAAAD1wOMQuGTJEkmSy+XSt99+q8suu0w33nhjwBoDAAAAAPifxyEwKyvL/XNlZaVSU1MD0hAAAAAAIHBCvClyOBzau3evv3sBAAAAAASYxzOBcXFx7p+rqqr0wAMPBKQhADCLSscp2a2N6q0OAABAMhACCwoKavx+8uRJvzcDAGZitzbS/3t7uOG69/u85v7Zm0BIiAQAwNzqDIH79u3T66+/roiICI0YMUJNmjTR+vXr9cwzz+jjjz+ujx4BAOdgtzbSXavSDNW813dmjd8rHVWyWz3+TNDrGgAAEBzq3IOPGzdO/fr10/79+/XSSy+pUaNG+uijj5SZmVkf/QEAAsxutemulc8Yqnmv3+QAdQMAAAKtzhBosViUnJwsSbrjjjt044036u2331ZoaGjAmwMAAAAA+FedIdBm+79VmjdvrmeffVYWiyWgTQEAAAAAAqPOr4g4M/CFhYURAAEAAADgIlbnTOA///lP99dDHDt2rMZXRfz8jqEAAAAAgOBWZwjcuXPnef/9iy++0G9/+1u/NQQAAAAACJw6Twety+zZs/3RBwAAAACgHvgcAl0ulz/6AAAAAADUA59DIDeKAQAAAICLh88hEACASkdVvdQAAADf1XljmLpwOigAwG616e63jF0jvubecQHqBgAAnI/PM4H33HOPP/oAAAAAANQDj2cCX3jhBS1fvrzGNYAFBQW67777AtIYAAAAAMD/PA6B69ev19q1a2W32wPZDwAAAAAggDw+HTQmJkYVFRWB7AUAYFLe3iSGm8sAAGCcxzOBHTt2VFxcnCIjI+VyuWSxWJSXlxfI3gAAJnH6xjLZhuvW3Ds6AN0AANCweRwC33vvPeXl5SkiIiKQ/QAAAAAAAsjjENi6dWs1adKEawIBAEGp0lElu9XYNx95UwMAwMXO4z1fcXGxevToobZt20qSLBaLcnNzA9YYAABG2K023b1igaGaNf0fCVA3AAAEL0NfEQEAAAAAuLh5HAJXrlxZa9no0VyQDwAAAAAXE49DYGRkpCTJ5XLpX//6l5xOZ8CaAgAAAAAEhschcODAgTV+f/jhh/3eDAAAAAAgsDwOgbt373b/fPjwYR04cCAgDQEAAAAAAsfjEJiRkeH+OTQ0VE899VRAGgIAAAAABI7HIXDhwoWB7AMAgAuO7xoEAJiBoa+IWLFiRY1lBQUFfm8IAIALxW61qfeK/zJU827/PwSkFwAAAsXjELhu3Tr94x//kN1uD2Q/AAAAAIAA8jgEdurUSRUVFXWGwFOnTik9PV379u1TZWWlHn/8cf3mN7/RhAkTZLFY1LFjR02dOlUhISHKzs7WunXrZLPZlJ6ers6dO2vPnj1nXRcAAAAA4DuPQ2DHjh0VFxenyMhIuVwuWSwW5eXl1Vpv9erVat68uWbNmqWjR4+qX79+uuqqq5Samqpu3bopIyNDeXl5at26tTZv3qxly5bpwIEDSklJ0YoVK5SZmVlr3R49evj1QQMAAACAWXkcAt977z3l5eUpIiLivOv16tVLCQkJ7t+tVqsKCwvVtWtXSVJ8fLw+++wzRUVFKS4uThaLRa1bt5bD4VBJSclZ1yUEAgAAAIB/eBwCW7durSZNmtR5OmizZs0kSWVlZRozZoxSU1M1c+ZMWSwW97+XlpaqrKxMzZs3r1FXWlrqnmU8c1ldKioqVFRUpJiYGE8fTg1FRUXun30dw9v6YBmDbVG73h9jsC0a3uPwxxhsi4a5LQAACHYeh8Di4mL16NFDbdu2lSRZLBbl5uaedd0DBw5o1KhRGjx4sO655x7NmjXL/W/l5eWKiIhQWFiYysvLaywPDw+vcf1f9bp1CQ0N9ekAwpfahjZGMPQQLGMEQw/BMkYw9OCPMYKhh2AZIxh6CJYx/NlDpcMhu9VquN7bOgAAqhn5QNLQV0R44siRIxo2bJgyMjJ08803Szp9U5lNmzapW7duys/P10033aR27dpp1qxZGj58uIqLi+V0OtWiRYuzrgsAwMXAbrWq9/JFhuveTRoSgG4AADi7OkPgsmXLNGDAAOXm5rpP06w2duzYWuvPnz9fx48f19y5czV37lxJ0qRJk/TMM88oKytL0dHRSkhIkNVqVWxsrJKTk+V0OpWRkSFJSktL05QpU2qsCwCAWTCbCAAItDpDYKtWrSRJ0dHRNZb/PBBWmzx5siZPnlxreU5OTq1lKSkpSklJqbEsKirqrOsCAGAGp2cTlxquezfpvgB0AwBoiOoMgd27d5cknTx5UoMGDZJ0+rsAs7KyAtsZAADwijezgswkAoB5eHxN4BdffKH/+Z//0cMPP6ypU6cqPj4+kH0BAAAv2a1W3bN8laGad5L6BqgbAECwCal7ldOeffZZ2e123XvvvRowYIBGjx4dyL4AAAAAAAHgcQgcN26cSktLlZubq+XLl+uVV14JZF8AAOACqnQ46qUGAFD/PD4dtEuXLhoy5PQtrHNycvT8888HrCkAAHBh2a1W9Vn+nqGat5PuClA3AAB/8jgEJicna+vWraqqqpLL5dK1114byL4AAAAAAAHgcQgcPXq0Tp06pUOHDsnhcKhly5bq3bt3IHsDAAAAAPiZx9cElpWV6bXXXlPnzp311ltvqaKiIpB9AQAAAAACwOMQaP3f7w46ceKEGjdurMrKyoA1BQAAAAAIDI9DYM+ePTVnzhxdddVVSk5OVnh4eCD7AgAAAAAEQJ3XBE6cONH9s9PpVEhIiFq2bCmbzePLCQEAAAAAQaLOJLdz506dPHlSiYmJuv766+VyueqjLwAAAABAANR5Oug777yjOXPmqKKiQgsWLNC2bdvUrl07de/evT76AwAAAAD4kUfndF5xxRUaP368JOnzzz/X7NmzVVxcrKVLlwa0OQAAcHGqdDhk/9+bytVHHQDAcx5f2FdWVqaPP/5Y7777rk6cOKHExMRA9gUAAC5idqtVfZd/YrhuVdKdAegGAHCmOkPg+++/rzVr1mj//v3q2bOnnn76abVp06Y+egMAACZW6XDKbvX4RuY+1wGAWdQZAv/4xz8qOjpaV111lb7++mu98MIL7n+bPXt2QJsDAADmZbeGqN+KAsN1K/vHBaAbAGg46gyBb7zxRn30AQAAAACoB3WGwK5du9ZHHwAAAACAesAJ8wAAAABgIoRAAAAAADARQiAAAAAAmAghEAAAAABMhBAIAAAAACZCCAQAAAAAEyEEAgAAAICJEAIBAAAAwEQIgQAAoMGqdDjrpQYALia2C90AAABAoNitIeq/4n8M1azoHxugbgAgODATCAAAAAAmQggEAAAAABMhBAIAAACAiRACAQAAAMBECIEAAAAAYCKEQAAAAAAwEUIgAAAAAJgIIRAAAAAATIQQCAAAAAAmQggEAAA4j0qHs15qAKC+2C50AwAAAMHMbg3RfSv+Zahmaf9OAeoGAHzHTCAAAAAAmAghEAAAAABMhBAIAAAAACYSsBD4xRdfaOjQoZKkwsJCde/eXUOHDtXQoUP13nvvSZKys7OVlJSkgQMHavv27ZKkPXv2aNCgQRo8eLCmTp0qp5MLqwEAAADAXwJyY5hXX31Vq1evVpMmTSRJ//rXv/TQQw9p2LBh7nUKCwu1efNmLVu2TAcOHFBKSopWrFihzMxMpaamqlu3bsrIyFBeXp569OgRiDYBAAAAwHQCMhPYrl07vfzyy+7fd+7cqXXr1mnIkCFKT09XWVmZtmzZori4OFksFrVu3VoOh0MlJSUqLCxU165dJUnx8fHasGFDIFoEAAAAAFMKyExgQkKCvv/+e/fvnTt31oABA3TNNddo3rx5mjNnjsLDw9W8eXP3Os2aNVNpaalcLpcsFkuNZXWpqKhQUVGRYmJivOq3qKjI/bOvY3hbHyxjsC1q1/tjDLZFw3sc/hiDbdHwtgWv9eB4HP4Yw9+vEQAIJvXyPYE9evRQRESE++fp06fr97//vcrLy93rlJeXKzw8XCEhITWWVdedT2hoqE87Cl9qG9oYwdBDsIwRDD0EyxjB0IM/xgiGHoJljGDoIVjGCIYegmWMYOghWMYIhh4AwAgjHzzVy91Bhw8f7r7xy8aNG3X11VerS5cuKigokNPp1P79++V0OtWiRQt16tRJmzZtkiTl5+crNja2PloEAAAIiFMOl891/hgDAKrVy0zgtGnTNH36dDVq1EiRkZGaPn26wsLCFBsbq+TkZDmdTmVkZEiS0tLSNGXKFGVlZSk6OloJCQn10SIAAEBANLJa9NBb/zFc9/q97WqMMW3lfsNjTOvX2nANgIYvYCGwTZs2Wrp0qSTp6quvVm5ubq11UlJSlJKSUmNZVFSUcnJyAtUWAAAAAJgaXxYPAAAAACZCCAQAADCBKi+uD/SmBkDwq5drAgEAAHBh2awWzVl50FDNqH6XBKgbABcSM4EAAAAAYCKEQAAAAAAwEUIgAAAAPOLw4hpBb2oABBbXBAIAAMAjVqtFi1YcNlQzpP+vA9QNAG8xEwgAAAAAJkIIBAAAAAATIQQCAAAAgIkQAgEAAFAvvL1JDDeXAfyLG8MAAACgXlitFq1edsRwXeKAyAB0A5gXM4EAAAAAYCKEQAAAAFw0/HFKqdOLMX5e46zyYgwvaoBA4HRQAAAAXDSsVos+WWzsuwol6c7B//d9hSFWiza8YWyM3z1Q8/sOQ2wWbf3rIUNjXP9wS0PrA4HCTCAAAABQz7ydFWQ2Ef7ATCAAAABQz0JsFn0156DhuitHXeL+2VXlksVmMVTvTQ0aHkIgAAAAcBGy2CzaO7vYUE3bca0C1A0uJpwOCgAAAAAmQggEAAAATMpV5ayXGgQXTgcFAAAATMpiC1HxrO8M1bR6skNAekH9YSYQAAAAgNeYTbz4MBMIAAAAwGsWW4iKs/5lqKbV2E4B6gaeYCYQAAAAwAXj7awgs4neYyYQAAAAwAVjsYXOI5jXAAAgAElEQVTo4F+2GK67JPWGAHRjDswEAgAAAICJEAIBAAAAwEQIgQAAAABgIoRAAAAAADARQiAAAAAAmAghEAAAAMBFzR9fWO+qcngxhvGaYMBXRAAAAAC4qFlsITr44meGai554pafjWHVoZf/YWiMlil3GFo/WDATCAAAAAAmQggEAAAAABMhBAIAAACAiRACAQAAAMAPLpaby3BjGAAAAADwA4vNqkPZ7xmqaTn6rgB1c27MBAIAAACAiRACAQAAAMBECIEAAAAAYCKEQAAAAAAwEUIgAAAAAAQBb+8UarQuYHcH/eKLL/T8889r4cKF2rNnjyZMmCCLxaKOHTtq6tSpCgkJUXZ2ttatWyebzab09HR17tz5nOsCAAAAQENmsVl1aM5bhutajrrX0PoBSVevvvqqJk+erIqKCklSZmamUlNTtXjxYrlcLuXl5amwsFCbN2/WsmXLlJWVpaeffvqc6wIAAAAA/CMgIbBdu3Z6+eWX3b8XFhaqa9eukqT4+Hht2LBBW7ZsUVxcnCwWi1q3bi2Hw6GSkpKzrgsAAAAA8I+AnA6akJCg77//3v27y+WSxWKRJDVr1kylpaUqKytT8+bN3etULz/bunWpqKhQUVGRYmJivOq3qKjI/bOvY3hbHyxjsC1q1/tjDLZFw3sc/hiDbdHwtgWv9eB4HP4Yg21Ru94fY7AtGt7j8McYbAv/bAsjAnZN4JnOvKavvLxcERERCgsLU3l5eY3l4eHhZ123LqGhoT5tMH9s7IYyRjD0ECxjBEMPwTJGMPTgjzGCoYdgGSMYegiWMYKhh2AZIxh6CJYxgqGHYBkjGHoIljGCoQd/jBEMPQTLGMHQg7/GMKJe7rjSqVMnbdq0SZKUn5+v2NhYdenSRQUFBXI6ndq/f7+cTqdatGhx1nUBAAAAAP5RLzOBaWlpmjJlirKyshQdHa2EhARZrVbFxsYqOTlZTqdTGRkZ51wXAAAAAOAfAQuBbdq00dKlSyVJUVFRysnJqbVOSkqKUlJSaiw717oAAAAAAN/xBXwAAAAAYCKEQAAAAAAwEUIgAAAAAJgIIRAAAAAATIQQCAAAAAAmQggEAAAAABMhBAIAAACAiRACAQAAAMBECIEAAAAAYCKEQAAAAAAwEUIgAAAAAJgIIRAAAAAATIQQCAAAAAAmQggEAAAAABMhBAIAAACAiRACAQAAAMBECIEAAAAAYCKEQAAAAAAwEUIgAAAAAJgIIRAAAAAATIQQCAAAAAAmQggEAAAAABMhBAIAAACAiRACAQAAAMBECIEAAAAAYCKEQAAAAAAwEUIgAAAAAJgIIRAAAAAATIQQCAAAAAAmQggEAAAAABMhBAIAAACAiRACAQAAAMBECIEAAAAAYCKEQAAAAAAwEUIgAAAAAJgIIRAAAAAATIQQCAAAAAAmQggEAAAAABMhBAIAAACAiRACAQAAAMBECIEAAAAAYCKEQAAAAAAwEUIgAAAAAJiIrT7/WN++fRUeHi5JatOmjZKTk/XnP/9ZVqtVcXFxGj16tJxOp6ZNm6avvvpKdrtdzzzzjNq3b1+fbQIAAABAg1VvIbCiokKStHDhQveyPn366OWXX1bbtm31yCOPqLCwUPv27VNlZaXefPNNbdu2Tc8++6zmzZtXX20CAAAAQINWbyHwyy+/1IkTJzRs2DBVVVUpJSVFlZWVateunSQpLi5OGzdu1OHDh9W9e3dJ0nXXXaedO3fWV4sAAAAA0ODVWwhs3Lixhg8frgEDBui7777TiBEjFBER4f73Zs2aae/evSorK1NYWJh7udVqVVVVlWy2c7daUVGhoqIixcTEeNVbUVGR+2dfx/C2PljGYFvUrvfHGGyLhvc4/DEG26LhbQte68HxOPwxBtuidr0/xmBbNLzH4Y8x2Bb+2RZG1FsIjIqKUvv27WWxWBQVFaXw8HAdO3bM/e/l5eWKiIjQyZMnVV5e7l7udDrPGwAlKTQ01KcN5o+N3VDGCIYegmWMYOghWMYIhh78MUYw9BAsYwRDD8EyRjD0ECxjBEMPwTJGMPQQLGMEQw/BMkYw9OCPMYKhh2AZIxh68NcYRtTb3UGXL1+uZ599VpJ08OBBnThxQk2bNtV//vMfuVwuFRQUKDY2Vl26dFF+fr4kadu2bbriiivqq0UAAAAAaPDqbSYwKSlJEydO1KBBg2SxWDRjxgyFhIRo/PjxcjgciouL029/+1tde+21+uyzzzRw4EC5XC7NmDGjvloEAAAAgAav3kKg3W7X7Nmzay1funRpjd9DQkL0pz/9qb7aAgAAAABT4cviAQAAAMBECIEAAAAAYCKEQAAAAAAwEUIgAAAAAJgIIRAAAAAATIQQCAAAAAAmQggEAAAAABMhBAIAAACAiRACAQAAAMBECIEAAAAAYCKEQAAAAAAwEUIgAAAAAJgIIRAAAAAATIQQCAAAAAAmQggEAAAAABMhBAIAAACAiRACAQAAAMBECIEAAAAAYCKEQAAAAAAwEUIgAAAAAJgIIRAAAAAATIQQCAAAAAAmQggEAAAAABMhBAIAAACAiRACAQAAAMBECIEAAAAAYCKEQAAAAAAwEUIgAAAAAJgIIRAAAAAATIQQCAAAAAAmQggEAAAAABMhBAIAAACAiRACAQAAAMBECIEAAAAAYCKEQAAAAAAwEUIgAAAAAJgIIRAAAAAATIQQCAAAAAAmQggEAAAAABMhBAIAAACAiRACAQAAAMBECIEAAAAAYCK2C93A2TidTk2bNk1fffWV7Ha7nnnmGbVv3/5CtwUAAAAAF72gnAn85JNPVFlZqTfffFPjxo3Ts88+e6FbAgAAAIAGIShD4JYtW9S9e3dJ0nXXXaedO3de4I4AAAAAoGGwuFwu14Vu4ucmTZqknj176tZbb5Uk3Xbbbfrkk09ks5397NVt27YpNDS0PlsEAAAAgKBRUVGh6667zqN1g/KawLCwMJWXl7t/dzqd5wyAkjx+sAAAAABgdkF5OmiXLl2Un58v6fQs3xVXXHGBOwIAAACAhiEoTwetvjvo119/LZfLpRkzZujyyy+/0G0BAAAAwEUvKEMgAAAAACAwgvJ0UAAAAABAYBACAQAAAMBECIEAAAAAYCKEwHp26NAhQ+uPGzcuQJ3Uv9WrV3tVV1xcLEnavXt3rf984XQ6far31Q8//KD9+/e7/7sQvvvuO61fv17FxcXy9vJgXx/HwYMHa/y+Zs0aQ/XTp09XUVGR4b8bCGVlZSovL9eqVav0448/Gqp1OBzasWOHPv/8c/d/Ru3Zs8dwTTA583n08/8uhLlz59b4ffbs2YbHOHXqlE89XOjn99ned719//3b3/6mkpISn/opKyvTV199pZ9++smncS40b/eH52L02MKf/LEf8cWRI0fq/W+eja+v9WolJSU+vff5sh/yJ18ex4cffqiqqqoAdWZMMByrBUpQfk+gP+zZs0cffPCB+0V56NAh/elPf/KodsyYMXrppZcUFxdX698KCgoM9fHiiy9qyZIlOnXqlE6ePKkOHToYOsitrKzUl19+qaioKFksFkmS3W431ENZWZleffVVHT58WLfddpuuvPJKtW/f3tAYvmzPakuXLlViYqKhGkl6/fXXNXHiRGVkZNRYbrFY9MYbbxga6/3335fT6VRlZaWee+45Pfzwwxo+fLihMcrKypSfn6/Kykr3sr59+xoaY9q0acrPz1fLli3lcrlksViUm5vrcX3Pnj3lcDjcv9tsNl166aV68skndfXVV3s0Rk5Ojj7++GP9+OOP6tu3r/7zn//U2saBfhzS6dfb/PnzZbPZNHXqVJWWluruu+/2uP7WW2/V/PnzdfDgQSUmJioxMVFhYWGGenjhhRe0fPly92tMMv5af+qpp3TLLbdo69atcjqd+vjjjzVnzhyP68eMGaPjx4/r17/+taTTz+8bb7zRUA8TJkzQkiVLDNX83KpVq2otM/r8Li0t1WeffaaTJ08aGuOPf/yjJOnYsWMqLy9Xx44d9e233yoyMlIrV6401EN2drZycnJqfMesp/9Ply1bpuXLl2vXrl3urytyOByqqqoy/MHcvffeq5tuukkDBgzw6uuOfHl+T5w48Zz/lpmZ6dEY53pP8Ob9t0mTJho5cqRatmyp/v37Kz4+vsZrri4ffPCB5s+fL4fDoV69eslisWjkyJGGevDHvqyqqko7duxQVVWVXC6XDh06pN69exsaw9v9YTVfjy2qe/j73/+ukydPut+/8/LyDI3hj/3Io48+qgEDBuj222+X1Wo1VCtJKSkpatGihZKSknTrrbcqJMT4/IY/nhe+vtYlacqUKdq4caMiIyO92qf6uh+STr/X5ebm6ttvv1WHDh00aNAgw8edvj6OHTt2aM6cObrllluUlJTk1TcEzJ8/X3/961/VuHFj9zKj+3V/HOP4eszocrm0Y8cOVVRUuJcZPTY4lwYbAtPS0nT77bfrn//8p1q2bGnoU8OXXnpJkvEny9l8+umnys/P14wZM/TQQw/p6aefNlS/e/fuGjs5b96k09PTFR8fr88//1yRkZGaNGmScnJyDI3hy/asVllZqb59+yoqKsr9Ju3JJ+vVBzILFy40/Dd/7m9/+5sWLFigsWPHav369Ro2bJjhEFh9EHPppZdKkqGDmGrbt2/XJ5984tXOSpJuuukm9erVS7Gxsdq6dauWLVum/v3765lnnvE4BKxZs0aLFy/WAw88oD/84Q/q37+/4T58fRySNGnSJI0cOVJlZWV68MEHlZSUZKg+Pj5e8fHxKikp0Z///Gc999xz6tWrl1JSUnTZZZd5NMb69eu1du1awzu6M+3bt099+vTR8uXLtXDhQj344IOG6o8eParFixd7/fclqWnTppo5c6aio6Pdz0uj23PXrl2STu94ioqK1Lx5c8MhcNSoUbrssssUGRkpyfPXyJtvvumunzlzpsLCwvTTTz9p7Nixhv6+JK1du1br1q2rcQDgqT59+ujmm2/WK6+8oscee0ySFBISol/96leGx3r77bf16aefKjs7W0ePHlViYqLuuusuNWvWzKN6X57fd911lyRpyZIluv7669WlSxft2LFDO3bs8Lj/M993S0tLtW/fPrVt29bj/s80aNAgDRo0SN98843mz5+vqVOnqn///nrwwQcVERFRZ/1//dd/aenSpRo+fLhGjhyp/v37Gw6B/tiXjR49WqdOndKhQ4fkcDjUsmVLwyHQ2/1hNV+PLSQpNzdXCxYscH/w5A1/7EeeeuoprVixQi+//LLi4uI0YMAAdejQweP6JUuWaNeuXVq+fLnmzZunm2++WUlJSWrbtq3HY/jjeeHra12SvvrqK3388cdeHVdIvu+HpNMBLjw8XLfccos2b96syZMn67nnnjM0hq+PY/z48Ro7dqzy8/P1l7/8RYcPH9Z9992nxMTEGh/snc/777+vTz/9VE2aNPGqB8k/xzi+HjOmpKTohx9+qFFPCKxD48aN9eijj+q7775TZmamBg8e7HHt2LFjz/k/yejpQM2bN5fdbld5ebnat2+vEydOGKp/9913Da1/NseOHVNSUpJWr16tLl26eHW6hi/bs9r48eMN15xp1apVWrBgQY1PQ4wG4uqD/GbNmrn/vxjlcrn0/PPPG647U/v27VVRUeH1m9Pu3bv1u9/9TpLUrVs3zZ07VzfffLOys7M9HqP6eeDtDLPk2+M480OWm2++WRs2bFCrVq1UUFBw1ln4c9m1a5feeustrV27Vl27dtXixYtVVVWllJQUvfXWWx6NERMTo4qKCp9C4KlTp/Tee+/pN7/5jUpKSnTs2DFD9a1bt9aBAwfcb/TeuPbaayWdPhCQvPuA4syZLpfLpUcffdTwGC6Xy+OZprMpLi52z3Y1bdrUq1PdfvWrX3l8sPBzdrtdbdq00cSJE3X8+HHZbDa9+eab6tu3r8cfLFQLCQlRfHy8JLkPzFasWKF+/fopOTm5znpfnt/du3eXdPpsihEjRkiSbrjhBj300EOGHoN0+vSsefPm+TQLd/z4ca1Zs0Zvv/22wsPDNWnSJFVVVWnkyJEefTAZEhIiu90ui8Uii8Xi1fuOP/ZlZWVlysnJ0aRJkzRlyhSvtqev+0Nfjy0k6Ze//KXh5/PP+WM/cvnll+upp55yf9DRu3dv3XjjjRo7dqz7Pa0uLVu2VNu2bVVYWKivv/5af/7znxUTE6MnnnjCo3p/PC98fa1XP47y8nLDZ7NU83U/JJ2eFV20aJEk6c4779TAgQMNj+Hr43C5XCooKNCqVau0b98+JSYmqqSkRKNHj9b8+fM9GuOyyy7z6kPAM/l6rCb5fsx45MgRw7OPnmqwIdDlcunw4cP66aef9NNPPxk6L9qbJ/y5tGrVSsuXL1eTJk00e/ZslZWVeVT3pz/9SRkZGUpOTq51IOfNk6H60/3i4mKvPtGo3p7l5eWGt2e1Tp061Tot1YhXX31V8+bN8+kguW3bturfv7+mTJmi7Oxsde7c2fAYV155pb744gvFxMS4lxnd8R04cEC33367+7Rco6cY2O1296f7W7duld1u186dO2ucIlqX3r17a8iQIdq/f79GjBihO++809Bj8PVx/PzUpaioKPcyIyFw0qRJSk5OVkpKSo03fCOfSHfs2FFxcXE1Tl0x+gHDww8/rDVr1mjixIlauHChUlNTPaqrfqyVlZX64IMP9Itf/ML9mjd6NkJqaqpKSkrcpzT9/HpLT5x5ysrhw4f1/fffG65t27attm7dWuPUZCOvkbi4ON1///265pprtH37dvXp08fj2uoP8Y4cOaJ+/fqpY8eO7u1p9EO88ePH695779VHH32k3/zmN8rIyNBrr71maIznnntOeXl56tq1q0aMGKHOnTvL6XTq3nvv9ejA0B/P759++kkbN27Utddeq61bt3p17dLrr7/u8yxcUlKSEhMT9cILL9R4H//yyy89qo+NjdXYsWN18OBBZWRkeBwQzuTLsUG16g8XTpw4ocaNG3u1PX3dH3p7bCFJWVlZkk6/XocPH65OnTq5XyNGZ93vvvtun/cj69ev18qVK/Xvf/9biYmJSk9PV1VVlUaMGOHRtZNPPPGEvvnmGyUmJmrWrFm65JJLJJ0+PdPTEOiP54Uvr/XqY70ffvhBPXv2dM9iGj028HY/dKaKigqdOHFCTZo00cmTJw0dV/jrcfTs2VOxsbEaOnSobrjhBvfy6mNZT5w6dUr33HOP+9Rci8VieB/g67Ga5PsxY1RUlA4ePOh+XvtTg/2y+M8//1zffPONLrnkEk2ePFl9+/ZVWlqaoTG+//57ffjhhzU+YRs9erShMZxOpw4cOKBf/OIXWrlypX73u995dG7zkSNHFBkZ6f5E/0xGP7n76quvlJGRoV27dik6OlpTp071+Lqxav7YnmPGjFF8fLzeeustjR8/XllZWYZOS33sscc8/gToXP77v/9b1157rZo1a6bDhw97dRpMYmJijR2uN4HB1/+vR48e1fz587Vr1y5dccUVGjFihLZv3642bdoYOnd+165d+vrrrxUVFaWrrrrK47pqvjyOqqoq2Wy2GqGjmtFQfejQoRrX51x//fWG6pOSkjR//vwap6R52oM/H4evpkyZos2bN+vkyZM6efKk2rZtq+XLlxsa44477pDFYpHL5VLjxo01fPhwjwPHmbVn8uY18s033+ibb75RdHS0oefm5s2bz/lvXbt2NdTD/fff7z6l6o033tDQoUMNn5a+dOlS3X333bVOCfv+++/Vpk0bj8bYsGGDvv/+e3Xu3FlRUVEKDQ011MOuXbv04osv6ttvv9Xll1+ujIwMw+99gwcPdp/298Ybb2jIkCHu2QJPuVwu7dmzR3v27NGVV16pSy65xPBsdX5+vr7++mtdfvnluv322w3VSqf3Zd9++61atmzp9b5s0aJFOnr0qOx2uz755BM1adJEf//73w2N4ev+0Ol0qri4WBEREYaOLSSd8/pai8Vi+NRv6f/2I9HR0YbDrHT67IPk5ORar8+PPvpIPXv2rLP+XGePVFRUePxa8ccxztKlS9W7d281bdq0xnJPXutn7kurP4isrKyU3W73aJ/qz/3Q6tWrlZ2d7b4me8yYMR5fp1/9OE6dOqVGjRq5l//444/q1KmTxz1kZ2cbPt7+ubPtC4zuA/xxDO7rMWNCQoL27t2rX/7yl15/QHwuDTYE+kNycrK6d+/uvq5FMj5LuHfvXq1du7bGKYzVp+V4YseOHVq5cmWNIOrLaVa+KCkp0d69e9W+fXs1b97ccH31wYO3BxGpqakqKytTTEyM159aenPgEgj+uAjd1+Bz4MABvfvuuzWem0bfdIuLizVjxgzt2rVLHTp00MSJEz0+sB03bpxmz57tDg6SvJqFS09P17Zt23TixAmdOHFC7dq109KlSw09jjFjxigzM9Or65x+/jjOPD3KyON44IEHavzeqFEjtWrVSo8//rjH23TgwIFasmSJpk6dqtTUVD3xxBN+uZbWFw6Hw+ObPSxbtkwDBgzQ7Nmza4UDT1/rDodDDodDY8eO1QsvvCCXyyWn06lHHnnE8I1MkpOT1bNnTx05ckT9+/fXU0895fEpxtX279+vNWvWeP06y8rKUnFxsXbt2qX7779fn376qXsmx1OZmZnnvUmMp33s27dPO3fuVLdu3dS0aVNNmDDB0Bi+3kTEHzfleu211wxfB34+X331lTp06GA4mPu6P/THh9TVZxxVe+qppwxf+/Xz51X1+9aQIUP0i1/84ry15zuQNXI2SLDs14uLi/X888/r6NGjSkhI0JVXXqnf/va3hsZYunSpvv32W6Wnp2vYsGFKTEz06Dnur/2QdPpYLyQkRHv37lWbNm30y1/+0uPaw4cPq6ysTGlpaXruuefc779paWmGPpB84IEH9Prrr3t1o6Bqx44dU0FBQY3jJKOXN/jjWO3Mfs480ycYNNjTQVetWqVXXnmlxs7C6AuhcePGPn8SMXLkSPXs2dOji97PZtq0abr//vtrBFFPne9N1OinCIsWLdLf//539ydDI0eONHSKVjVfTku99dZbDf+9n7NYLBo1alSNi/GNBsm8vDwtXrxYp06dksvl0rFjx/TOO+8YGsPXi9AnTpyoL774QidOnHDP+BgNPk888YRuvvlmn06vnTx5sgYNGqQbb7xRmzdv1qRJkzz+RLz6tIx//OMfXv99Sfr3v/+tNWvWKCMjQ3/84x89Pv3nTMXFxerRo4dXp67463Fcdtll6tKli2644QZt27ZNa9eu1XXXXWdomzZr1kwWi0U//fSTWrRoYeg0taFDh55z5+TLXXhnzZql4cOHe3Tg3apVK0lSdHS0ob93phUrVmj+/Pk6cuSIevXqJZfLJavVWuOUIk+lpaXpk08+0eOPP6533nlH06ZNMzxGamqqT6+zLVu2aNGiRRo6dKj69evn1d1fd+3apePHj3u9H5LkvklDTEyMoqOjdccddxgew9ebiPjjplzr16/XH/7wB68OLufOnauRI0ee9b4BjRo10m233aZevXp5PJ4v+8Nx48bV+pDaU4sWLdK8efP0448/6qOPPnIv9+YOjBUVFWr7/9l787iatv9//HlwqluSJtdQZEoXdbnmeWiQKdeQQhmT4RrKGCXScCOEFOWqRKZShoSLUOYyRYYGQ5EGKinVadjfP85v7/c5Kfbae1/3Pvw+z8fDw2lY66z22Xut1/B8PV+6uujZsycePnyIR48eQUNDA6tXr/4mc+draqYkTqAQ5zofNWEarq6umDVrFgICAtCzZ084OTkRn8uHDx9mzp7AwEDY2NiwcgLrOodIAnCymDNnDlq3bo3JkycTOYAA8PDhQ+zfvx8vX77EunXrAEhrJUk+T0DKdho0aBB0dHSYGmAuyuN6enpITU2FoqIip7o+IQSDEhMT4ebmxtRTt2zZEpaWlqzHP3/+HGvXrkVubi60tLTg5eVFlFX9Gn5YJ3Dv3r3Ys2cPp4OX7n+kpaWFmJgYOb5827ZtieZq0aIFFi9eTLwGGo0bN8b48eM5jaU3MK60R1lERETg9OnTUFRURFlZGWxsbIidQBcXF6xduxYZGRlYsmQJ1q9fTzR+7NixOHr0qJxsMSm4KJfVhr+/P9atW4cjR46gT58+uH79OvEcfIvQX758ydvxUVFRYST5uaKiogLGxsYApAXkISEhxHNERERg//79chFtkoANH8eHhq+vL/EYGnXV7dIgObSys7OZLH+7du1w+vRpWFpa4uTJk6znMDAwQGhoKLS0tLBixQqia0GrC/r7+8PY2Bg9evRAcnIyLl++zHoOGrIqvFeuXGGtwksLmVy4cAFWVlbELQQAYPLkyZg8eTIiIyOJlVFp5OTkoHnz5lBXV4elpSU+fPjACDGRgu9zVl1djYqKCohEIlRXV3Oq6c7IyECfPn2goaFBTCeqnVnt27cvampqmAwWCfiKiAghysXHuKQd37oYQZWVlfDx8WHtBDo7O/M6D/kEqadNm4Zp06Zhz549jPotVxQUFDCZ6UGDBmH27NlwcHDAtGnTvjlWKFaTEOc6HzVhGhUVFejXrx92796Ndu3aEWeHAanDRI8Ti8XE+x/XAJwsoqOj8ejRI0RFRWHr1q0wNTXFggULWI01MTGBiYkJrl69yitwz7f0h8bGjRuxZs0aeHp6srona0MIwaDt27fj4MGDWLx4MebPn48pU6YQOYEeHh7w9PSEgYEBnj59Cjc3N8GEYn5YJ1BXV5e4Fx4NWXoELVkOcOuLNGzYMGzZsgUdOnRgvscmqkMf0KqqqtizZw+6dOnCbAakEZUlS5bw7qGjqanJRJSUlJQ40UH19fXlricpXF1d0aRJE16yxWPHjkV0dDTevXuHPn36oGPHjsTrUFdXR/fu3XHkyBFMmDCBmB4G8C9CF8Lx6dixI86cOQA3BkUAACAASURBVCNHryUNclRXV+P58+fo1KkTnj9/zikyf/jwYQQGBnIOVHTp0gX79u1Ds2bN4OjoyKnBbF01MmwNLFJqXn2orKxEQkICunfvjnv37qGqqgpZWVlEqn8rV67Ep0+foKSkhCtXrhAJZ9DZt/fv3zOtBUxNTTnRSfmq8C5YsADR0dHYtm0bTExMMGnSJNYBPZpS+vr16y8+G7bZAdnepLWpVaRnAN/nbMaMGZgwYQIKCgpgaWmJmTNnEr0/AE6OPA0hM6t8xaiEEOXiY1zStal1ibqQlkl06tSJ03koZJDa2toaMTExvOhyJSUlyMjIQPv27ZGRkYHS0lIUFhYSZUxkbZqioiLo6uri7NmzrMePHTv2i76NpOCjJkxDQUEBCQkJqKmpwYMHDzjVhBsbG2Pq1KkwMjJCSkoKccadawCuNjp27Ihu3bohMzMTSUlJrMfR2fKTJ09+IepDIspSVVUlCA2TFrmh7SVSCCEY1KBBAzRt2hQikQiKiorEZScURTF7zy+//ML7PpXFD+sEKikpwc7OjlP9mKzRU1hYyPCiNTQ0iNcRGxuLdu3aMbQPtkYyTZNQVVVlCulpkDqBQvTQoSgKv//+O7p3744nT57INU1m+2DzbfEghGzx+vXr0axZM9y4cQNdu3bF6tWrsXfvXqI5xGIxEhMTUVVVhYSEBOTn5xOvY9GiRbhw4QIsLCxgbGxMXNdS2/EhUe+i8fTpUzx9+pT5mouBS2d38/Ly8PPPP8Pd3Z14HXxlypctW4bS0lIoKioiPj6euAYDAEOpoigKT548QU1NDeuxXzPkSChJ3t7e2Lx5M7y8vKCvrw8vLy88ePCAqJYrJCSEkatv27YtFi5cyClIERERASMjI9y/f58ThYavCq+hoSEMDQ3x8eNHbNiwAaampnj8+DGrsUJQSulrPmTIENjZ2XGeB+D/nI0cORL9+/fH69evoaurS0zPAoAHDx4gKipKzqBiq3IqRGaVho2NDfr168dZjOrOnTtydDcu9U5CGJf19d5lI7jGt0xDyCC1EHQ5V1dXrFy5Enl5eWjRogVcXV0RGxtLlGGU/bvfvn1L1OoI4Ne3sS41YYCbkqS7uzs2bdqEwsJCBAcHc6KPL1y4EMOGDcPLly/x+++/Ez8jdBaRTxssutxkxIgRcHNzY12TDnw9W04CIWiY06ZNQ2hoKAYMGIAhQ4ZwClzxtdUAoHXr1ti6dSuKiooQFBSEli1bEo1v1KgRLl++jJ49eyIxMVFQwbkfVhimrsg+Ka3y7Nmz2L59O9q3b4+0tDQsWrSImAI5Z84cYknx2nj27BlevXqFjh07cuLsA9Imv6dPn8a5c+egoqICiqKIeujQKkt1Kf+xVVsaPXo0AgIC5CL6JDfzpEmTcODAAUa22NbWFhEREazHA2DU/ej/ra2tidPqubm5ePHiBbS1tbFjxw6Ym5uzVs4SCpWVlZBIJIzjY2RkxKk2RBa0EhlXkPa4o7M0dIsLUpnyr4mIiMViDBgwAL/99hvBX/A/2NnZ4a+//mL1u/Up7QHkew5fLF++HH379kVpaSkiIyOxfv164qay+fn5CA4OZpQklyxZ8oXa3bfAV4U3KSkJUVFRePToEczNzTFx4kTGuWOLT58+4c6dO3JBJzrDyRZCiBPwRWJiIsrKykBRFNzd3bF06VKMHTuWaI5JkyZh5syZOH/+PPT19fHq1StiA5ePQMLXjHoulEY+AgvW1tYYNmwYbt++zRiXO3fuJJqDr6jLfwX0+mXpclxqToWGlZUVUZbUxsbmi76NbP8OIdWEASk7hqIoPHjwAEZGRpzaR/ERbHNycsKdO3ewbt06pKSkID8/n6H6s0VcXByGDh3Kq0E6X1HEmTNnIjQ0FGvWrGFomIcOHeK8npKSEs49C/miqqoKERERjKrx5MmTie6Lt2/fYtOmTXjx4gXTU5Nvf08aP1wm8NGjRzA0NORdAwcAoaGhiIqKgoqKCkpKSjBjxgxiJ7Bly5YIDAyUM3BJMnm7d+9GfHw8DA0NERoaCnNzc2I6kBA9dNq3b4/du3czzuj8+fO/qfxVG3wouoD0wBo3bhwjTsOl1rK6uhoFBQUQiUQoKSnhtMn5+/vDzMwMbdu2hZ+fH/F4gH8RupWVFdq2bQszMzMMHjyYUwT3yJEjCAkJYSg0YrEY58+fJ5ojLCwMSkpKKC4uRlRUFAYNGsQ6c0VTl2pTmNgadl/L+FRVVWH9+vWsBXtoihUgdYLevXvHahzwP0evqqqKFyVpz549+Ouvv+RqUkiFCXx8fODo6IiioiIcP36cU01KZWUlbG1tma+LioqInUA/Pz/GKOayF+/fvx+TJ0+Gp6cnZyW12bNno0OHDlBVVQUgva9InUA+9WNLlizBzp0769zvST5XHx8fbNmyBW5ubjh8+DAcHByIncAmTZpgzJgxuH79OhYvXgwbGxui8QC/yDwdoLp48SJ0dHTw22+/4dGjR0TPGcBfYAEQpsYH4C7qUpeoDA02jvnX7isFBQWMGDGCqLUBV7qcrKozIM1WVFVVQUFBgYjKCchfk7y8PGhqahKNp4M0XPo20o5eXUqSpE6gj48PdHV1kZ2djZSUFGhpaWHTpk1Ec/AVbPP29kZpaSlUVFRgaGjIKTisoaGBDRs2cGIO0OAriigEDTMiIgKhoaEoLy9nvkfKHOBjqyUmJjKvO3TowJSFPXz4kCg426pVK0bp+sGDB4L4NzR+OCeQbohbl+oUKY1SJBIx3N3GjRtzMqiqqqrw6tUrvHr1itM6rl69ikOHDqFBgwaoqqrC1KlTiZ3AyZMnY8CAAV98nyTi5+DggFGjRmHSpEm4e/cuVq1ahcDAQKJ18KHoAtJeK4MHD+YkW0zDwcEBU6ZMQX5+PqysrLB27VriOcaNG4e4uDjs2rULbdq0gZmZGSOOwhZ8i9CjoqKQkZGBS5cuYdasWdDU1IS/vz/RHMeOHcOBAwewe/dumJubE/e5AqS05QMHDjANamfMmMF6LO081SVTzoZyQdff1LUhDhw4EM2aNWO9Ftn3V1RUxKpVq1iPpcGHkgRImQcJCQmcHPqpU6cyz1RlZSWePHmC2bNnAwBxhsLR0REikQg1NTV48+YN2rRpQ5wd4KvWt23bNjx+/BhJSUmMQUZyLQEplZ6v8ASf+jE6u8S3n5OioiJTr6StrV1nH7BvQSQSIS0tDWVlZXjx4gUnCjsf54mmhl24cIGhyFlYWDDUZbbgK7AACGNc8hF14UuT+9p9VVlZSbQH86HLnTt3DhRFwc3NDdbW1jAyMsKTJ084ZWtkr4mioiK6du1KNN7MzAy7du2CgYEBJk+ezCnjIwQ19u7du1i5ciXDNCL5LGjwFZISgjng4eEhxxzgsufwFUUUgoZ5+PBhBAUF8XKa+Nhq9LmZmZmJyspKGBoa4smTJ1BRUSGqtRciuFAffjgn0N7eHsCXqlNcCoVbt24Nb29v9OzZE0lJSWjdujXxHE2aNOHVn0lDQwNlZWVQUVFBZWUlp7pEFRUVuLq6fhHVIXVqaTVOAwMDnDt3jngdAwcO5EWrunLlCg4fPiwnlEFaA9G7d2+cP38eBQUFco03SdCjRw/o6enBwMAA4eHhcHNzI3YC+RahP3v2DNevX8ft27cBcJP2VldXR7NmzVBaWoo+ffoQU6IAqXGZn58PLS0tiEQiIoOKlikvKiriJFNOUw7rC/iQKJMJ0UuvpKTkC0oSCVq1asU5KODt7c1pXF2QpWEVFxcT9XGjwVetb/HixbwcakB6Dxw+fFhOlIuUGltQUMC7T2tcXByioqLkaFEkdciNGzfGrFmzMHXqVISHh3PKEDg5OSEtLQ22trZYsWIFJ2VlIZynwsJCZGZmonXr1njx4oVcA2U24CuwAEiNy4sXL/IyLrmKugBAaWkphg0bVud4tpmn8+fP4+DBg8jOzkbLli0xbdo0mJubQywWIzQ0lPVaRowYwbweOXIkkfNE09mysrKYmt/OnTvLsSrYojYl89atW2jevDlGjRol13C8PpiamjJBvyFDhnBmHPFVkqypqUFycjJ0dHQgkUhQUFBAPAdfIan/CnOAqygijby8PIwbNw4qKirE9hUNvnoDAD9bjS55sbe3R0BAABo1aoTq6mrGT2ELIYIL9eGHcwJp7Ny5k+nlVl5eDj09va/2pKkLXl5eOHr0KG7cuIH27dtjxYoVxOvg2p+Jlp3/8OED03Q0IyODkyqnEFGddu3a4dSpU+jTpw9SUlLQtGlTZrNnu0HFxsYiODiY+L1p7NixA2vWrOFEb6CzTXXJ+ZPWBI4bNw4NGjTA2LFjsXHjRujr67MeW1cROr0ekhqdadOmQVdXF46OjpxlmFVVVXHx4kWG4sblwOrTpw9sbGywdetWeHl5wczMjPVYvjLlpaWlcm0VuEDIXpr0QcGFkgRIo/hjx45l7icSYYKv1SVyaR9CQ1VVFZmZmcTj+Kr18XWoAWldoUQiYSg5IpGI2Ank06eVxqZNm7Bx40Zi+jyNHTt2IDMzEx06dEBaWhpR5ove69u0acMYxlylxYWIzK9duxbLli1Dbm4utLW14ePjQzSer8ACIFXXo+8DY2Nj3L17l/VYISiQRUVFAMApGwtIBdbOnj2LDRs2QFdXF69evYKPjw9KS0sxceJEolqj69evIzQ0VC5AQRpYVVVVxfbt2xkhKS5G9/Pnz6GoqMj0Gnz37h20tbVx7do1VvfImjVrIJFIMGzYMJiamnIOpvFVkhw3bhzc3d3h5eUFHx8fTJ8+nXgOvkJS/xXmAFdRRBqZmZmwt7eHqqoqzMzMMHz4cNb2L+18SSQSzJkzh1hvgP49IWw1QP5Zp0uSSCBEcKE+/LDCMJMmTcKhQ4fg5eWFWbNmwc3NjbUDcvr06S8iJxUVFXBzc4OXlxfROoYNG4acnBzi/kxv376t92ekm+zs2bMRHBzMFNjSRdQkkK0RkgXJBrV06VJYWFhAT0+PoYiRRLjoQmEueP/+PbS0tOq8rqTXMyYmBteuXcO7d+9gYGCAgQMHMv3NvgWhitCrqqpw9+5dXLt2DcnJydDU1CRuVVBSUoLMzExoaWkhODgYw4cPJ66BoGtwAemG++DBA+I5pkyZwkmMIDQ0FCdOnIC2tjasrKwwfPhwXoXsfBEeHo6ioiKIxWJcvHgRysrKRPdrXfcG22v5NYEkUrocHSihKAoFBQXo378/sbDA/Pnzv8jkkVyLGTNmYP/+/Vi2bBm2bduGSZMmITIykmgNfPaL2uvgg0WLFhGrHcoiLS2NqV/etm0b5s+fj379+rEaSzsttY95UlXNkpISNGzYkBNFTkjwFVgApJ9pUFAQGjZsiB07duDatWtfDaLIQiKR1EuB9PDwYDVHdnZ2vT9j49ROmzYNISEhcn93aWkp5syZQ+zgjxkzBmvXrpUTXSJV1f38+TOio6ORnp6Odu3aYerUqcSMn9rPGW2zkJwNJSUliI+PR1hYGMrLy3HixAmiNZw/fx6vX7+Guro6/Pz80KNHD179Y0mF0oTCggUL8OHDB0ydOhWlpaW4ffs2McsnLS0NaWlp+Pnnn+Hp6QkLCwviMiQhRBEBqY3h4eGBlJQU1grRQoi1CSkYFB4ejrCwMOjr6yM9PR1LlizByJEjicafOHECXl5eOHbsGPT19YnP9frww2YCmzZtysjjtmnThqjX1t69e6GiosJI3b58+RJLlixBt27diNfBtT8T7ZgkJyfjzJkzcpE6UtlhIaI6telylZWVrGgasigoKJAzytg6kDRtRiwWY926dXI9E62srFi99/79++uNRJHUKgHSg9PMzAy3b99GUFAQYmNjkZCQwGpsjx495BovUxSFmpoa2NvbE0X7iouLkZOTg+zsbJSXl3OKiFMUhczMTKSmpqJTp05fNU5qIykpCenp6QgNDWWyNDU1NQgPD0dMTAzROtTU1LB//365+jE2dbMzZ87EzJkzmaa2vr6+MDU1xeTJk1lfD74iDcD/noVp06aBoiiIRCIMGTKEdfb/8uXLjCR4bbA9bOgD4d69e6x+/2vw9vZmDExFRUVOWTC+mTwzMzP4+/vzqvHhQ6sSsk+rsbExrKys5Ixrkuz1+vXr4ezsDD8/Pzg6OsLHx4e1EyjbToErDh48iODgYDRq1Ajr1q1jHfCqC1zbBAklsABIHY6FCxeiuLgYAwcOxLFjx1iPFYICOXz4cLRq1YqpU5LtQcnGiWvYsOEXjq+KigqnUosWLVqgf//+xONkQZeV0GdZdXU18Vo+ffqEgoICaGhooLCwEJ8+fWJYXGxw8eJF3LhxAw8fPkTLli2Jn1GAHzWWBh+hNKGEpGSZA6mpqZychcjISGbdXFoMAfxFEb28vPDw4UOoq6tjzJgxRCUPtKP3+fNnFBcXo2HDhjh27BgRe0EoWw2Q3lvjxo3DixcvOLWbo1lTgLQeWUj8sE5g8+bNERkZiZ9++glbt24lqj3Yt28f7OzsoKysjPfv32Pr1q1wcnKS2yTYgk9/JkCqyDZ37lzOCkuAMPUgQihJ1nYk2dIUaKeV7v/2/v17ovcF+PUMq4358+cjOzsbAwcOhKOjI1EbAqEaL9vZ2cHExAQLFiyQ49yT4I8//kCrVq0YI5+ErtGkSRO8f/8eEomE+XxEIhFWrlxJvA51dXU8e/YMz549Y75HcljQPeUkEgn8/f1hbm6O5ORkVmP5ijQAUiGVnTt3okGDBsw1/PjxI+bNm4crV658czxfepgsQkNDmcxPeno6dHR0EBQURDTHkiVL0LdvX1haWnKup+BLjW3fvj369OnDONRcanxq31MkrAUh+7TSwkm0SikpGjVqhI4dO6KyshLdunUj6glaF/2dBtusUUxMDM6dO4eSkhKsWrWKlxO4d+9e7N69mzhDIoTAgmz5Qu/evXHr1i1YWFjgzZs3xE3W+VAgd+7cidjYWFRUVMDc3BxmZmZEGdaamhpG/ZFGSUkJp16xmpqacHV1lTPU2QZWaaxbtw5NmjTBwIEDcefOHbi4uGDz5s1EcyxevJgJ9nz+/BkuLi4ICQlh3Zdyy5YtUFRUhL29PQYNGkRkL2VlZcHb2xs7duzA/fv34eDgAGVlZfj4+BAH/vkIpQklJFVYWIg9e/agsLAQI0aMQFlZGXHvXK5lTLLgK4pYXl4ORUVFtGjRAi1btiQSeqOxYsUKTJgwAX///Tc6dOgAV1dX1va3ULYaIL2/NTQ0MGnSJGLRIwDw9fXF8ePH5b7H9z5hQP2gqK6upt68eUN9+vSJCgsLo9LT04nG5+TkUKNGjaImTJhAvXv3jvM6Jk6cSJ0+fZpatGgRtXPnTmrZsmVE4+fNm8f5vWWRk5NDvX37lsrOzuY0fvz48VRubi61YcMG6tatW9SCBQuI5zh8+DBlZmZGDR8+nBo2bBhlZmbGemxFRQV148YN6syZM9Tdu3ep6upqovdOSEio9x8pnj59Kve1RCIhniMiIoJ4jCw+fvxIxcbGUtHR0cw/UtjY2PBaA0VJ7ytZcLkWtZGbm0v0+9nZ2dSePXuocePGUcuWLaNu3LhB/J6y15H0enp5eVErV65kvg4ICKCMjY2pxMRE1nN8+PCBeX358mXq+vXrrMfWh4qKCmrhwoXE46qrq6krV65QixcvpmxsbKhjx45RJSUlRHMcPHiQ8vPzowIDAylLS0tqxowZROOnTp1K9Ptfw8ePH6lPnz5xGvvhwwfq2rVrFEVR1IEDB6iPHz8SzzF37lxO701j+vTp1KJFi6jQ0FDqzJkz1MyZM1mPffPmTb3/2MLW1lZuLXzA9zybO3cuVVlZSVEURVVVVVGzZ89mPdbGxqbOf7J/H1uUlpZSBw8epNavX0+FhYURn0cURVHFxcXUsWPHqHnz5lGrVq2i4uPjWY2LjY2lZs6cST1+/JgqLi6mnj59Ss2aNYs6ffo08Rr8/Py++EeK2s+qlZUV8RwUJd138vPzqZqaGk7js7KyqMOHD1O2traUpaUl63Hz58+nLl68SFEURU2YMIG6du0alZOTw+l8tLKyot68eUM5OjpSFEVRFhYWrMcuXryYeX3lyhXi96Yxd+5c6saNG5SNjQ2VkZFBdC1oDB06lPrll1+o/v37UwMGDKAGDBjAeT2pqanUy5cvOY9/+PAhZWNjQ3Xp0oV47LRp06iamhrmGefymfK11Wikp6dT3t7elKWlJbVt2zYqMzOT9VgLCwuqoqJCkHXUxg+XCayqqkJcXByaNGmCvn37AgDMzc3h6emJ7du3s5pDIpFAXV0d/v7+WLp0KUNTAMiamwP8VZZGjBgBR0dHOcVEto1D09PTsXHjRoSFhWHGjBlQU1NDbm4u3N3diaO5QihJcm1J8PTpUyxbtgxdunSBpqYmzp49i4yMDPj5+bFWkvyaKBBpZP/+/ftYunQpkxVt1KiRnLolG/Tq1QuBgYGcGi8D0nuAaxaPzsDq6uri/v376NKlC/Mz0vv78uXLvDPEXEWcoqKiEB0djaKiIkycOBEhISGc2oYA/+v5RVEUnj59iqZNm7KmjqxZswYeHh5wcXFBbm4ulJWVERUVxTqKevr0aSY7EBgYiISEBGhra+Phw4dYsGABp78HkGYMsrKyiMc1aNAAgwcPBiClBR04cADHjx/H+PHjWWcJZJX1hgwZAj09PaI18GkxkZKSAmdnZ0RERODKlStYv349VFVVsXr1aobizxbLly9n/mY1NTWsXLmSU2scruIEgDQK/OjRIwwePBi3b98mqlOiM1Q5OTnw8vJCRkYG9PT0OCtWUzwlBPi2CeIjsEBnDD99+iSXlSURhqGhoKCAzp07Q19fHxRFITY2llMLE0tLS3To0AEhISFYs2YNq+j+yJEj0aRJE/j5+SErKwvNmzeHra0thg0bxvq9c3Jy0Lx5c4wePZpozXWBFlP56aefUF5ezikjyVegJiUlBVevXsWNGzegpKREVG8lkUhgbGyMwsJC5OTkMO20ampq2P8B/x/4CKUVFhYyr/ft28dZ8K2iogL9+vXD7t270a5dO06tzbiWMQHSz9LZ2RkXLlzA8ePH8ddff0FDQwOWlpZE1NTg4GAkJCSgvLwcQ4YMIS6DAqSlGsHBwejSpQvS09NRWlpKPAdfW41Gs2bNoKuri5SUFKSmpsLT0xO//PILK+G2zp07o6Kigtg+Y4MfzglcsWIFGjZsiPz8fIYO5ezsTKTSZG5uzhxQFEUxThdpMT09hk893qFDh2BqasopLb9lyxaGnqetrY0DBw7g9evXcHFxIXYChVCS5OpIbtmyBf7+/nKUztTUVHh7e7OWWt+4cSPEYjEnpazaiIiI4N1fj0/jZUB6X3JVxaTvb4qicOvWLeb7XO5vIXoNxsfHIz4+Xk7EiQ1u374NBwcHTvSM2li+fDnzmqIozJs3j2i8i4sL1q9fj+rqauzYsYNo7PHjx3Hy5EmIxWIcOXIEUVFR0NLSgrW1NbETOGTIEOazraio4CTtvXnzZly6dAm9e/fG3LlzYWRkhJqaGkyYMOGbTuDXnAuS+5VPiwlfX194e3tDLBbD19cXQUFB0NPTg52dHbETWFZWBnNzcwBSxVOS+jEaJMZ5XYiPjwcAnDx5EoDUgCdV5nRxccGUKVPQq1cv3LlzB87Ozqyf1fT0dCxfvpyhGMs+K6QqeVwNWxqTJk3C6NGj5QQWSLFo0SLOwjCyc/BpYfLs2TPExMQgPj4enTt3hqWlJesgNQAMGDCgzt6/bEE7na6urnK2DqkSJQBMnz4d48aNQ8eOHZGens6pN9yff/75hUANCQICAmBmZoaAgADOFMabN28yiYOamhp8+vSJeA5HR0emx5+hoSGxbgINPsEWBQUFJCQkoKamBg8ePCByHD5//oyjR4+iVatW6NatG1avXo2qqio4OTnJBYu/hn379uHYsWMQi8UICgpCSEgIWrRoAVtbWyInsGHDhvjzzz853xOA1M66ePEiFixYgNOnT3NyJPnaaoBUFDEtLQ0WFhbw8fHBzz//DACYMGECKyewY8eOGDhwILS0tJjnlNRWqw8/nBOYmZmJqKgoSCQSTJw4EWKxGGFhYUR91IQopqfBtx5PTU2NuKcIjbKyMka5kY58tmnTBlVVVcRzeXh4IDMzE8uXL0dwcDCxWiC9Bi6OZHl5+Rc1ffr6+kR1Rk5OTti6desXDj6Xh0mIrCjXxstCZPGEvL+FuBZcRZyEapYKyNen5ufn482bN6zH0sJFBgYGuHr1Kjw8PNCxY0cA7OprGjZsCGVlZaSnp0NDQ4OpfeCidCr72XLtyamnp4eoqCi5mqMGDRqwUrh8/PgxysvLYWFhge7du3MyZj58+ICqqioUFRWhefPmRCI7gPS5NjAwQG5uLsrKypgaDC7XUywW4/r16/j111/x6NEjTtdUR0eHeIws+GSpaVRUVDC9tkxMTBASEsJ6rKxzwreGlm/rkPbt2+P48eOcBRYAfsIwNPgIH9HZt9GjR2Pz5s1Mpub169fEtYlcQQdrhgwZAjs7O15zWVhYYPDgwcjKyoKOjg4nNgZXgRpaVGvQoEEoLy+Xa9PBlrXQsWNHLF++HI8fP4a7uzvy8vKwbds2xiEkga2t7ResHBKnurKyktkzZV+TOHLu7u7YtGkTCgsLERwcTOT4ODk5oW3btnj9+jV8fHwwb948tGjRAh4eHqxVWkUiEZo1a4asrCyIxWKmnpvt3hkQEICFCxfi4cOHX9T2sw060fW/6urqsLS0xIcPHzgLIHG11WQxefLkOoM2bK9pbGwsLl26xKtGsz78cE4greikoKCAmpoaBAcHc+qtJxQ6duwITU1NlJeXw8/Pj7hXirq6OufCbVlqRUBAAPOaS+NLioeSJI3ajiTbzam+zYOErkEXmAvhAAmRFaU4Nl4WMot36dIlhoZJURSKiopw+vRpojmEuBZ8IPMKaQAAIABJREFURJyEgux1pel7bCGb3afvM5KMf3V1NUpKSnDu3DmGhpmTk0MUrHFxcal3b3F3d2c9DyBVJD148OAX9Bc2zszp06eRmpqKU6dOISgoCL169YKFhQVrYZdbt25hw4YNGDFiBDQ1NZGSkgI/Pz/8+eefrDO+9L6QkJDAqGhKJBJOVCAPDw9s2rQJHh4e6NChAycaEH3Q05m0Vq1aESla8s1SA9J77Pnz5+jUqROeP39OdA6RyqF/DXwzaH5+fggPD2eUOUkgpDAMH+Ej2nG9efMms4dzzcLxRXx8PGbNmsUpuCFU1h/gLlBDn5tcxOJorF69GvHx8Zg3bx709fXx/PlzGBgY1NsW62ugg+MURSElJUVOmOpbePv2LcM6oCiKESNhe7bTgUwNDQ3OAdL3798zgVwLCwvmPPvrr79Yz1FVVYWqqipcuXKFKbUpLi5mHdyl2Rp8Ak6urq51fp/LM8bVVpOFiooKXF1dvxCIZEvVbdmyJX766af/o4OSQlNT8191AAGpctbNmzfl0rgkvXxo44nLJtesWTMkJyfLHZjJycmMNDUJ+ChJLlq0CIMHD8agQYPQuXNnANKIE1vk5uYy2RYapFHkAwcOwN3dHRMmTMCECRM4RZBpCJEV5dp4ubYTW1RUBDU1NeLgAgD4+/tj3bp1OHLkCPr06YPr168Tz8HVsZfFxo0b8e7dO5ibmyM6OpqIFiUU6OtaVVVFHCRhW6NbH2bNmgULCwtoaWlh9+7dSE5OhoODA9atW8d6DhMTEwDSrOSvv/6K3377DY8ePUJKSgrxepycnHjRX/T19bFixQoAUmn/rVu3Iicnh1XGxd/fH+Hh4dDU1GS+N3v2bKxatYp1z79+/frB2toaOTk52L17NzIzM7FhwwaMGjWK9d9AG1QtWrTgfT/K9u+USCRwcHAgGs8nS03DxcUFa9euRX5+Ppo1a8a6p53Q4Ns6hE+taF2GIU2HJDUMTU1NsWvXLk4tTNiqmdaHVatWYfLkyejZsyeveQBpHdqgQYOgo6MDkUhEZJ/IPk8+Pj6clKFp0AEmUjsnNzcXAL89mFYgptGpUyd06tSJ01yyjCU6a80WfAPUsoFMrmwn2bNP1nYmqfMcP348Ro0aherqaoSGhiI1NRUrVqxgXZK1Z88ebN++nVfwie8zJguutposPDw8MHPmTJw/fx76+vrEZUk5OTkwNTWFrq4uAPbtZNjgh3MChahfiIyMxJgxY6CkpMR7Pc+fP8eFCxc4GemA9AbMy8vjRJ9ZuXIlFi5ciL59+6JNmzbIysrCzZs3sWfPHuJ18KlBs7W1xZ07d7Bq1SqUlpaid+/eGDRoEHr16sUqsjF27Ng6MyskEeSAgAB8+PABJ06cwOzZs6GnpwcrKyvW/bZkwTcrWlJSgq5duzLZAJqmRYLExES4ubmhuroa5ubmaNmyJXE/IHV1dXTv3h1HjhzBhAkTiPoB8XXsAalc/Ny5c9GgQQNGwGL06NFYsWIFgoODvzleiEh0Tk4OHBwcEBgYCDU1NZw7dw5hYWHw8/NjePv/NIYMGSJnAIjFYhw7doyoP9/QoUMBSKlH8+fPByDN4JAa2YAw9JeSkhJcuHABMTExKCsrg4WFBatxFEXJOYAAiKXB7e3tYWxsDA0NDairqyMzMxNTpkyBqakp6zmEMKjqQnV1NbFYD58sNSD9LNq2bUtkkLJBbm4u8TPCt3UIn1rR+gxDLnXitYWPuLQw4QozMzP89ddf2LhxIyZOnIjff/8dampqnObiYgvQkNUVCAoK4tU6hKudc/36dU6Z8X8KssHq/Px8TuwDrhCC5UQH3CmKkntNYnf+/vvvMDExgbKyMho0aIC8vDx4e3szdsK3wIVNVBtC9VwUwlYD+AtEkoiBkeKHcwKFqF94/vw5AgMDMWDAAFhZWeGXX37hvB66XopL41EAWLt2LR48eICysjKUl5dDV1eXdQ2Drq4uIiIiEBcXhzdv3qBr165YunQplJWVWb+/EDVoffr0QZ8+fZj54uPj4e/vjydPnuDBgwffHM8300JDU1MTc+bMwZw5c/Dw4UNER0fD3d0dsbGxRPPwyYoK1Xh5+/btOHjwIBYvXoz58+djypQpxE6gWCxGYmIiqqqqkJCQQERh5OvYA1IqkqqqKvOcJiUlMVFuNhCi/mz9+vWws7NjjKgxY8agUaNGWL9+PS8DiQ/48P5LS0uRmJgIQ0ND3L9/X44SzhZ86C9nz57FmTNnkJ2dDTMzM7i5uRHVxNVXt0eq1CdbA966dWu0bt2aaLyQdbOyhkhVVRVR7zC+axGy0TuNW7duITw8HPfu3SNmD5iamsLf359TBg3gX1MI8Ot5u2zZsi/2e/pcESJozAYmJiYwMTHB+/fvceLECcycORMdOnSAlZUV6+zg1+p7uZy3XIPcNLjaOUVFRfUa9VwaxvOF7BmqoKDwXVkt586dw59//gklJSX4+PhwokzLBtxlX5Mq38o+182aNSMK5GVlZcmxJ2TBNusvRM9FIfdOvgKRVVVVOHfuHG+F0rrwwzmBQtQvODs7Y9WqVbh06RJ8fX1RXFyMiRMnYsyYMaybutJNej98+AAzMzPOadwXL17gzJkzcHV1haOjIyslIVkoKSkR0aBqQ4gatJqaGty7dw+XL1/GzZs30bhxYwwdOrRe3vY/jfv37yM6OhoPHjzAiBEjiMfzyYoK1Xi5QYMGaNq0KUQiERQVFeVEPNjCzc0NL168wIIFC7Bjxw6ie4uvYw9Io9Bz586FkpIS8vLycPz4cfj6+rJubMu3/gyQOk00lZIGqcppYmJivT8jqf0SAp6envD19UV6ejratWvHKYK4aNEiXLx4kRP9xdHREe3atYOBgQFSU1Pl3p8NE6MuA4CiKE4USD7YuHEjXF1d62y2TkrD4WqI1CUyQYMtfVGo/ebz58+Ijo7G4cOHkZ+fj3Xr1hErgwLSDBqdUeWSQeNbUwjwUzSuK7Ccl5cHJycnIrGd2khNTcXBgweJDDstLS3Y2dnB1tYWAQEBmDVrFh49esR6LABcvHgROjo6DH383bt3nNbPF1ztnIKCgnrbCbF1AvPz8xEcHAxlZWXMmTOHKEhOg2YDTZgwgXisUAgNDcWpU6dQXFwMT09PTkFMoQLufKCkpCSIQNLBgwdx9uxZFBYWMu1QSJgEQu2dAH+BSCEUSuvDD+cECgWxWAxzc3OYm5sjLy8PYWFhGDp0KG7fvs1qfH2RDFKoqKhAJBLh8+fP0NDQIKbP8AUdha5dW8j2OgDSGp2+ffti9OjRWLBgAeesKB9kZWXh5MmTiImJQbt27WBpaYkNGzYQKQYKkRVVUFCAgoIC78+ydevW2Lp1K4qKihAUFISWLVsSjS8oKICqqipDh925cydrpSpAGMdeRUUFQUFBmD17NsRiMY4fP058b/CpPwPql+ImySrKXrfbt2+jb9++jKH7vZ3ADh06wN/fn9ccvXr1kqO/PH/+nPVYvsIW9Un+c5Gd54OFCxcC4L+P8zFEZEUmnJycOIk9CLHfuLu749atWzAxMYG/vz/c3d2JHa87d+7A29sbKioqcHd3h56eHqe6K741hQA/ReP6gszh4eHE66iursbff/+N8PBwvH//npjJkZSUhJMnT+Lu3bswMTFh1VuVBu3MXrhwganjtrCwILqesk5WUVGR3NekgQ+udk7btm05B2RprF69Gqampvj48SN8fHywfv164jkcHR2ZYHlGRgY6dOhArAFRV8BJU1MTS5YsYfWsKCgoQE1NDWpqaqxFWP6L0NLSwvjx43nN4efnh/z8fHh6ekJLSwtv3rxBSEgIcnNzmb39WxDKVgOkApG0YjhJ2Q0NIUo06sP/b5zAiooK4qaZFRUVuHDhAk6cOIHS0lKiwmdtbW0cPnwY06dPR15eHjw9PaGgoIDVq1cTraFLly7Yt28fmjVrBkdHR06NWPkgKSkJGRkZCAkJYQ6ImpoahIeHIyYmhtUcs2fPxrVr13DgwAFkZGRgyJAhrPnhNJ49ewYDAwNUVlbi2LFjUFBQwMSJE1k7cbNmzcKkSZOwf/9+zrVeQipzAvx6Abm5uSEiIgI9evSAsrIykQJkYGAgIiMjUV1dDU9PT7Rp0waOjo5o3Lgx681FCMeeVutzdnbGypUrcfPmTXTo0AEAiCKBXOvPAMDIyAhhYWFyResHDhwgMlBlHQVbW1viDMnXsk5isRgmJiasKYRBQUHYu3ev3F7H1iBLSEjA5s2boaamhj///BMtWrTAtm3bEBsbiytXrrCagy8Tg+/hD0ibgUdGRqJJkyYYP348GjRogOfPn2P9+vWsDTItLS3cuHED/fv3x+bNm1FYWAiRSIRVq1axXgdfQ0RWZOKnn376ok0OKbjuN3fv3kWXLl3w66+/QldXlxP1z9fXFz4+PigqKsK2bds4tZIB/qcUzbWmEBBG0ZhGSUkJFixYAD09PdZj8vPzcfToUZw8eRLdunWDRCLBuXPnWI/fuXMnYmJioKenB0tLS6xfv56T4jcgFYbJzMxE69at8eLFCyJlZj5Uu9rgaudwbYMji6qqKiYzM3PmTE5zyNYC2trachImqSvglJubC3d3dxw8eJBoLj62hRA4ePAgYmNjmTY/o0aNYpRGvwW6pQ8fXLt2Te4zMTAwwJ9//onp06ezdgJlwfV6ygZGPn78KFe7S/L80CUapaWlnBVK64OI+rfvFoFBG1SyyMjIgIODA2v5+9u3b+PEiRO4ffs2jI2NYWlpCX19faJ1rFu3DsrKyli1ahUWLVoEQ0NDdOzYESdOnCCO1JeUlEBJSQnx8fEwMjJiLRghBO8/NTUVf//9N6Kiohiqg0gkQteuXYkb/3769AnXrl1DQkIC0tLS0KFDB1ZRvJCQEMTGxuLw4cPw9vZGdnY2k/lycXFh9d6yAg//Jvr3749+/foxjqSsMA1b54F2iCUSCY4dOwZFRUUih9jCwgKRkZEoLCzEsmXL8P79e8ydO5f1Jg1IHUl6E+vfvz8nx74+CW62an2168/GjBlD3JNNIpHA09MTly5dQrNmzZjeYU5OTpxqfKZPn06cDXv//j20tLTw9u3bL35WWVmJFStWIDIyktVcFhYWOHz4MCd6sIWFBXbs2IG3b98iMjISHz58QIsWLeDs7MxZeOLfwOzZs9G1a1e8e/cObdq0gZaWFnbt2gUnJyfWGayAgACkpaXB19cXkydPxuLFi5GUlASJRMI6kGdlZfWFqnFNTQ2mT59ObNRxua8AYfYbALh37x4iIiJw9+5dUBSFPXv2EPXelV3/zJkzWSu91kZ4eDgKCwuhoKCAixcvQllZmXiukpISZGZmQktLC8HBwRg+fDjn4AVFUUhLSyOyD7p37w5bW1vY29ujcePGsLOzI5Lg37lzJywtLdGiRQsuS5ZDUlISvL29kZubC21tbfj4+BB9rkKCq53DFzNmzGAowVyfM1kIMYcs6tpH6sKwYcMwduxYUBSFmJgYjB07lvkZ21q62o6JSCSCpqYmDAwMWK+XDn7Nnj2bOdeCg4PRpk0bTg4YF9TniMt+1t+CUHvnt9bEBomJiUhLS8PPP/8MFxcX/P7778QJpfrww2UCCwoK4OvrC0dHRwDAqVOniOWL/fz8YGVlBTc3N859ObKzs7Fv3z5UVFTg7t272LlzJ8RiMSvVQxp0JDogIICJRP/222+sx8tuoqGhoZyiXPr6+tDX18fkyZOJVfpq4+3bt/jw4QM+f/4MsVjM2mmJj4/HkSNHIBKJEBMTg/Pnz0NNTY1I+EcIB1AikcDX1xd///03KioqoKKigtGjR2PhwoWsI7F8hYtkHeLNmzczDrGXlxdrh1hNTQ0KCgr4+eefkZubix07dshRW9lg3rx5mDdvHuPYHzx4kMixB/jLOPOtPwOklA83Nze4uLjgw4cP0NLSQkFBwXcTeQD+95zWV/xNEjTS0dHh5AACUkGatm3bom3btnB2dsYff/zBWqTnv4TS0lIsW7aM6bXVqlUrnDx58gvV0a/h5s2bjHOhqKiIQYMGoX///kSUvbrOjgYNGrDOXsiqVlIUxal5tFCN3n/77Tf89ttvKCkpwalTp5jzlAu1iVTkRxampqbMOcRVlfPixYsApAFOAwMD5OXlISkpiVPLBZFIRBwg9vT0RGRkJGbMmIGJEycSZzN1dXXrLccgla/v2bOnXIDpe5ebAPztHL74/PkzXr16hZqaGpSXl+PVq1fMcyZEbRpXFBcXY/z48azrNGWp9PXR6r+FuijFeXl56NChw1fVuGVROwvXqVMnXlk4LqjP3iPJeQm1d35rTWxAl2gUFxfjwoULgpZU/XBO4NatW+Hg4ICAgADk5OQgNTUVhw4dYoRZ2IA0SlsX6A/83r17MDQ0hFgsBgDWan10JLp///5ISkpiItGBgYGsIwCyN+6ZM2c43cjDhw9n/haRSAQlJSUYGhpixYoVrI0qe3t7pKam4pdffsGAAQOwePFiomgjbTilpKRAV1eXyUp87yT2pk2boK2tjdjYWCgqKqKkpAR//fUXNm3aBGdnZ1Zz8KXLCe0Qt2jRgtgBlAVXx14ICBltDQwMRElJCZycnODh4YGuXbvC3t6e1ditW7cyNOHawiZsI7BA/cXfJPTl6upqWFhYoFOnTsznvHnzZlZjZT+7Fi1acHIAjx8/ztS8paWlMXUQu3btYsU+qKqqQlxcHJo0aYK+ffsCkGZKPTw8WCvt0Q4SLZq0e/du4lIA4H9UM5qK27BhQ6iqqrIez9cQoenn9BjS5tGAcI3eS0pK0LBhQ4YyPnXqVDx9+pT1+Lqk52mwaQpOY82aNZBIJBg2bBhMTU05BWvOnDmDsrIydO/eHcnJyaioqECjRo3QuXNnrF27lng+UowaNQqjRo3CmzdvEBkZiaysLDg4OGDcuHEYNmzYN8e/ePFC7muKohAVFQUlJSViJ7C2UmqjRo3w999/E83BB0LYOXyhpKTE9GRVVFRkXpP0j5S9n/nc3zSSk5MRHh6OT58+sR4vBJW+vgBufaydusA3+CUE7t27V6cwEAmNUqi9kw9SUlLg7OyMiIgIXL58GRs2bICqqipWr16N4cOHC/IeP5wT2LBhQ/j6+mLRokUoLy/HoUOHvqthSkNZWRlHjx7F+fPnMWbMGNTU1OD48eOsKRxCRKJlwTUKUbtWobS0FFevXoWLiwt2797Nag57e3t069aNc90CIK0fi4qKYm78tLQ0Tp9rSUkJ9u7di/z8fAwdOhSdOnViHU1OSUmRqylq3LgxHBwciDZIvhDCIa7d/4fLgcXXsRcCQm7ScXFxTFZj586dsLa2Zu0EytZqcY3AAsIUf3OtaQGkdVZ0RLympoZTRPzkyZOME+ju7s4YUXfu3GE1fsWKFWjYsCHy8/ORnp4OHR0dODs7s240DMjvdU2bNuXkAFZWVkIikUBBQYFRj5VIJEQ12XwNESHbVPCBrFS6i4sLBg8eDABErZPqk54nxb59+1BSUoL4+HisXLkS5eXlOHHiBNEcVVVVCAsLQ4MGDVBTU4O5c+di3759nKP9XJQ9AWnW3sHBAUuWLMGVK1dw7NgxVk6gbO/j169fw8nJCUOHDuXkwPJRSuUrZALwt3NOnz4tR3sEpM/phg0b4OXlxWoOFxcXzs3hacjez1zvb4lEgjNnziA8PBwKCgrMff49WSl1rSkmJoYoQ8w3+FVRUYEjR45g+vTpyM3NhZeXF6Onoa2tzWqOx48fs17vPwlZeu3Hjx/lvmajXuvr6wtvb2+IxWJs374de/fuRZs2bWBnZ/d/TmB9oC+ypaUlPD09ERYWxohNfM++MRs2bMC+fftgbGyM8ePH49atW4iLiyM6KPhGooVA7aiOgoICfv/9d9bqiwA40WxksXTpUqxatQqtWrXCsmXLcOfOHaxcuRI7duwgnmvt2rUYPHgwEhMToaWlBWdnZ9aZXzqbWxtCUE1JhIv4OsRCGGRCOPY0srKyUFhYiJ9//vm7NWivDZFIxBj9srQ7NujWrdsXc2loaBD3++PTn4+GkZERgoODkZeXhyFDhhAZN0JExGWvW32vv4bMzExERUVBIpFg4sSJEIvFCAsLIwowpKSkwNraGhRFIT09nXlNotQ3duxYrF27FuvWrYOamhqKi4vh5eVFpIr5XzFE6gJJo/faUum0E0gCoaTnL168iBs3buDhw4do2bIlpzO9qKgIVVVVUFBQQFVVFfOckTSN56vs+ezZM5w/f55RjTU3N2cdVKURHh6O/fv3Y82aNaycx7rARylVKCETPnbO3r17oaKiwpyFL1++xJIlS77Yk78GDw8P5OTkoFevXhg0aBAGDBhAvHcLcX8PHz4cY8aMwZYtW6Cnpwc7O7t/1QEE/lfz6u3tzXoM3+CXh4cHlJWVUVNTAzc3N0ZPY8OGDbyVr4UAia0mS6/t3Lmz3Nds9i6KomBgYIDc3FyUlZUxrC0hE1s/nBMoe5H79OmD58+fMxLn39MJ1NDQkKtD7Nevn1xh6bcgRCSajtTxMYbqA5cm1FxhZGSEiIgI5utu3brh4sWL9TplX0NRUREmTZqEU6dO4bfffiOmlNblIJDMwVe4SAiHWIgDi69jDwBv3ryBg4MDxGIxNDU1kZ2djZ9++gm+vr68609JYW1tjbFjx0JfXx8vXryAnZ0d67Gurq7Mc0ajsLAQEyZMwOzZs1nPw6c/Hw1nZ2f069cPN27cwLhx47B27VrWtZd8azQB+YBIfa+/BrrWQUFBATU1NQgODkbTpk2J1nDq1Cmi368L06ZNg0gkgo2NDT5+/AgVFRVMmzZNkNqQfxNcGr0LKZXOF1u2bIGioiLs7e0xaNAgYmMdAKZOnYqxY8eiY8eOzLO+Z88eVn3A+Cp7AlJBq71798La2hpdu3ZFdnY2lixZgiVLlnzRs7Qu5ObmYs2aNVBTU0NERAQv0SY+SqmtWrWq83skzjRfO2ffvn2ws7ODsrIy3r9/j61bt8LJyYmo/++BAwcgkUhw//593Llzhwlw9+zZE3/88Qfrefhi+vTpiImJwdu3bzFp0iTO5S51ZcZJzpLaAQorKysi9Vu+wS8h9DSEgBAik3zbl9D10wkJCYz/IJFIUFpaymteWfxwTmDti15cXIwGDRr8K73p+ECISLQQvQppGX8aEokE58+fJ9oU6gIJheZrBclcHrKMjAwAQE5ODlFE5e3btzA3N5f7HqnqKF/hIiEd4n8b3t7ecHJyknMor1+/jo0bN35V2fafgKWlJYyNjZGVlQVdXV1oaGiwHluX81RTUwNbW1siJzA5ORlz5swBIO3PxwUFBQWwsrLCmTNn0LNnz+/eUqaoqAjXr19HTU0NQ3+hKIpTVlNTU5PYAQSkhmhdmRbSPYuuffu3kJOTg+bNm3/x/Tt37hBRoYVq9A78+9Lz586dw5s3b3Dt2jWm5IOElQJIn3UTExOmNYK6ujqqq6tZ1SyZmZnB1tYW0dHRjLInKcLCwnDw4EG5puTjx4/HggULWDmBY8aMgVgsRt++fb84P0k/Vw8PD2RmZmL58uUIDg5megZyAS1kQtKih6+do62tzfSaVVJSwuHDh+t8Zr4FBQUFdOnSBR8/fkRpaSlSUlKIal6FgL29Pezt7XHnzh1ERETg8ePH8PHxwbhx44jEh2j7hqIoPH36FE2bNmXtBPINUNDvv2PHDigrK2PFihXEKq989TSEghAik3zRr18/WFtbIycnB7t370ZmZiY2bNiAUaNGCfYeP5wT+D0KKb8HhIhEt2rVCkePHsXEiRPRqFEjJCUlIS0tjemJwwa1IyFKSkro0qUL08iYBFwpNI8fP0Z5eTksLCzQvXt3XoaIs7Mz1q5di4yMDCxZsoSoMawQNTpCCBfJgqt67T+Fq1evsm4dUlBQ8EVGccCAAdi7d+8/sbQ6IXSAAZDe53fv3kV5eTnRuKtXr2LmzJm8CugpisLr168BSDMG37MYH5D2+6L7h8rSX9i2D0lPT8fy5csZ9oJs/RNbA7cuQ2bx4sVYunQpa0Pmv4C5c+di//79cgGJgIAAHDt2jHXfRiEavQvxmQhRuwVIz/erV6/ixo0bUFJSwsiRI1mPpfH06VMcPXpUzqhk+6zzVfYEgEaNGsk5gIA0A872WRWKEldQUIAmTZqgc+fOOH36NPT19XmpciopKeHkyZNEAXe+do5EIoG6ujr8/f2xdOlSFBQUMM8L27MxJCQEV65cwadPn9CvXz8MHToUy5cvJwqsCtFagUbv3r3Ru3dvFBcX4+TJk1i1ahVR3avs80lRFObNm8d6LN8ABQCsX78e9vb2+PjxI3x8fLBp0ybW7w/w19MAUGcNOR2wZ1vaILStxgX29vYwNjaGhoYG1NXVkZmZiSlTpsDU1FSw9/jhnMDahZRBQUEMv/p7O4F0dJFWIBOJREQ0Or6RaD8/P6SlpcHCwgKNGjVC8+bNERoaioKCAtY0ByEoYnwpNKdPn0ZqaipOnTqFoKAg9OrVCxYWFpzkwTt16sSq584/hf+CcFFiYmK9P+vVqxerOaKiorBt2zYoKSlh586d0NXVhYuLC168eMHaCayvnpCPhDwphAww0KioqMDBgwdZt+ygUVhYiEGDBkFHRwcikYgTbdvZ2RnLli3Dixcv8McffxAFOWSVPbmCLxNDCFluIQwZIXD27Fl4e3tDSUkJPj4+MDIyIhr/xx9/MI4g3S9SQUGBqC2DEI3ehfhMhKjdAqROsJmZGQICAjhRQQHAyckJNjY2nDJGfJU9gfqp0Wz3PSEEscLDwxEaGgolJSX06NEDr1+/hqamJm7cuIEtW7awmuPly5fYtGkTdHR0MGLECKbMYM2aNUT0Qz52Dq2gS+/b9BpIFHT9/f0xaNAgzJs3D7169eLEqhGitUJtNGnSBLa2tsTCc7J03Pz8fLx584b1WL4BCkA+lch7AAAeX0lEQVRar0bXDR8/fpz1OBpC6GkoKysjMzMTI0eOhImJCWd16H/KViNp2SZbD9+6dWu0bt1akDXQ+OGcwNqFlF27dgUgbCElGyQlJcHLywtRUVFYu3Yt9PX18fLlS8yZM4c4EssV8fHxOHbsGHPo6OjowNfXF9bW1t+V6y4EhUZfXx8rVqwAIHVitm7dipycHNZUINlWF4B0s6PFAc6ePUu8Hq7gK1wUGRmJMWPG8CoYt7W1RevWrWFoaAjgfxQvkUjE2gkMCQnBmTNnkJ+fD29vb+Tl5cHY2Ji1AQFIqYO1I6hcqYNcIWSAgYaysjKRwAKNXbt2cRbaoTOwBgYGnA5eQF7Zkyv4MjGEMHCFMGQA6f1J01E/fPjACP6wxf79+3Hq1CkUFxfD09MTe/bsIXp/c3NzVFdXY9asWSguLsb06dMxbdo0ojlOnDjBNHr39vYGRVHIyMggEtoR4jMRonYLkNIXr1+/jri4OEbdmCTTAUj7cnJV2aZRW9kzIiKCtRNYO5sKgPlcvheio6Nx9uxZfP78GaNGjcKVK1fQqFEj2NjYsJ5j7dq1WLRoET5+/Ah7e3tER0dDQ0MDdnZ2nOqZuUAIds7NmzeRlJSE+Ph4bNu2Ddra2hg8eDCGDBmCli1bsppDiNYKQkHWMVZSUmJKDNiAb4BCiHG19TS6dOkCHx8fogzznj178PHjR8TGxmLr1q3Q1tbG2LFjiXQ5/kmRyZiYGF5K3kLih3MCv0chJRvs3r2bMQTV1dWxbds25OfnY/Hixd/NCVRWVv7ioRaLxZybSXOFEBQaQNre4cKFC4iJiUFZWRlR7cG5c+dAURTc3NxgbW0NIyMjPHnyBIcOHeK0FoBbvenRo0eZ3+ciXPT8+XMEBgZiwIABsLKyIpJpp3H8+HHExMQgJSUFffv2xdixY4kpDk2bNoWamhrU1NSQkZGBDRs2sM4A0ujSpUudEVS21EGhwDfAIBTmz5+PYcOGwdLSkrhJ8b59+4ivf23UbpQsC7br+S8wMYQwZO7cuYPVq1fjxIkTUFNTw7Nnz+Di4gIfHx/WbA4FBQXmGSkrK2P93rIYPXo0qqqqEBERwdlxEbLRO1cIVbu1dOlS6OnpITU1FYqKivjpp5+I52jVqhWCgoLwyy+/MPcKW6Oudh/LBg0awMjICCdPnmT9/vX1u/yeokM//fQTGjVqhCZNmqBt27ZM8IkkUNKoUSMMGDAAgDT7Ttfc1g7A/JMQgs4vFovlhPvi4+MRGBiIjRs3cq4L5NJaQSjwcYyFCFDQwV06oEvaFkGoki41NTVMmTIFU6ZMwdu3b+Hj44PVq1cjISGB1fh/UmSSLeOopKTkH9cz+eGcwO9RSMkGlZWV0NHRAfA/A0pbW5tTjU5ubi6qq6shEomIeNFKSkqM0AWNrKwsIkrQjh07YGlpyToiVhf4UmjOnj2LM2fOIDs7G2ZmZnBzc2OuLVvQ9QFZWVkMLatz585fCN98DUJsTmlpabC2tuYcBXJ2dsaqVatw6dIl+Pr6ori4GBMnTsSYMWNYG0RdunRBly5dQFEUbt26hd27d+P9+/cYPnw4a0NE9h5q2bIlJwdk6dKl9QpffG/wCTAIhZMnTyIuLg7e3t6oqKjAhAkTWK+Doqh6W1uwrY15+fIlXF1dv5iDtEXEv83EEMKQ2b59Ow4cOMAoLw4YMADBwcFwdnbmFDjiQjNetmwZE9HPzMzE1KlTmQw121o8WXEZ2UbvJP3ghIAQtVs0Nm7ciDVr1sDT05M4MwpIz+aXL1/K7f1sjToh+lj+F5pQA/JK1/RrkkCJ7Bkg+xmSZn/4ZNyFoPM/evQId+/eRVJSEl68eAEDAwP8/vvv8PHxIZ6LBpfWCnxha2tbr23Hdv8WIkAhG9zl0hZByN54L168wJkzZxAXF4e2bdsSUUr5KnsCZG1n6oKFhQX69esHKysr4nICtvjhnMDvUUjJBrJF57I3HlsHLD09HRs3bkRYWBhmzJgBNTU1pgcPGylrQHpgLVy4EP369YOuri6ys7Nx7do1okJdNTU1LFy4ENra2rCyssLw4cM5G3Rcm+M6OjqiXbt2MDAwQGpqKnx9fZmfkaqhqaqqYvv27TAyMsKDBw/qlLmuD0JsTlFRUdi0aRPmzJkDb29v1s1PZSEWi2Fubg5zc3Pk5eUhLCwMQ4cOxe3bt4nmEYlE6N69Oz58+IATJ04gIiKC9WYvqwJZUlJCHO0DhBG+4AshAgxCQUFBAebm5tDS0kJYWBh2797N2gl8+PAhzM3N5dRq6ddsa2MMDAxYGwv14b/AxBDCkGnYsOEX90Hbtm2J9r6srCxs27YNFEUxr2ksW7bsm+Nl18s1S/S1Z4zuy/Y9IETtFo2KigqUlZVBJBLh8+fPxGupbdzl5eWxHitEH8v/Amila/rzqL13sEFdgkGkwRa+GXch6PxbtmzBwIEDsWDBAnTu3Jlz31++rRX4ghbr8/f3h7GxMXr06IHk5GRcvnyZ9RxCBCj41oUL0Rvvr7/+wvnz56GpqYnRo0fj0KFDxKwBusVaXWBbq1973yPF33//jbi4OPj7++P9+/eYOHEiLCwsBM0Oiqh/W/P5B8WaNWtgYmIiJ/V++fJlxMXFwd3d/Zvj58+fjz/++AOGhoawtbXFgQMH8Pr1a7i4uBCJtXz69AmXLl1CXl4eWrZsiaFDh3K6gR49eoSoqCjcuXMHpqammDx5MlF2kI9k+9cyQ6SbFi2XnpaWhnbt2mHatGmss7Nz5szBvn37kJubC2tra2ZznTp1KnF2IDExEWvWrMGvv/7KfI/Eoa2oqMCFCxdw4sQJlJaWYuLEiZg0aRKrsZWVlYiPj0dMTAxevXrFNKkloSAKQcM5d+4c9u7d+4XwhaenJ1HtFR8YGBgwAQZAPkjDVUafK3bt2oVz586hc+fOsLS0ZF2fCYDZI/igrjkKCwsREREBe3t7VnMEBQUhLi6OYWKoqKhgw4YN6NOnD3Ht1r8JGxsbhIWFyRke1dXVmDp1Kmthqejo6Hp/Nn78+G+Of/LkSZ3U6IsXL7IWuPkvPGNC4vz583j16hU0NDTg5+eHHj16yAUF2WDnzp04dOgQKisrUV5eDj09vTpp6XVh+vTpTKBk9OjRCA8P59TG5EeAEOfy1KlTsXnzZrmAy8uXLzln3BMTE3HgwIHvTueXVSSme95GREQQtVYQCjNmzJDL9Mves98DfBlTs2bNQkhICCIjI3Hv3j14eXlBIpHA0tKSNe3awMCAaf8CyJ/rbB24t2/f1vszkuSBUMjPz0d0dDTi4uLQoUMHeHh4CDLvD5cJ/K9g5cqVWLhwIY4fP442bdogKysL79+/Zy0OUFZWxgh3qKqqAgDatGmDqqoqonWoqqoKUqRtaGgIQ0NDSCQS+Pv7w9zcHMnJyazG8u09IyR9RllZmROFCBAuy5GRkYGtW7eid+/exJ/N7du3ceLECdy+fRvGxsZYtWoVUQ8hAOjfvz+aNWuG0aNHY8KECRCJRHj79i3evn3LOosnRD9OIYQv+OJ7Ho7fgpqaGg4dOsRZ9ZAvduzYwbxOTk5GeHg4rl27RiTe8V9hYvCFhYUFli1bhvnz50NHRwc5OTnw9/cnKiuorXxJ09zYfr7e3t7M/UkbRoD0nmVrWP4XnjFAuFYsrVq1Yu7HkSNHcgpoxsfHIz4+Hl5eXpg1axandkcA9z6WPwqEOJeFyLgD/z6d/7+iSEwjIiICRkZGuH//Pqe6WT7gWxcuREkXKbugLgjh6AnZgkpNTQ2tWrWCuro6keLrt/B/TuA/BA0NDRw5cgTJycl48+YNTE1NiXrwyNJJAwICmNdc1QP54t27dzh16hTOnj2L9u3bIzAwkPXY/9oGyRVCbE5BQUE4cuQIXF1dMXToUOI1/L/27jSoqbOLA/g/IggRARWRRV6LaOuotZsFHOl0cUYR3EYFoRWq2Dq1WrFatBW1oF1EQFxGbK2AlWUYFaIV1GptcS2L+sGlo47guCAIloADHUUI7wcnaUCpCbnk3iT/36fMjfdySs01557nOWfLli2YOXMm4uLiOj0jcOzYsZDJZLh9+zZu374N4EnF5/Tp07h48aJO1xBq87YQjS8MIZX9OQBQX1//VFKqXjb3PKtWrTL459vb20OhUCArKws2NjZoaGjAsWPH9O5E29UtrY0hJCQEPXv2xHfffYeamhq4u7tjypQpqKqq0vkaq1evfmopkFKpxLRp0xAZGfnc87XP0374p+/iHbE/Y4Bwo1hSU1NRUVGByZMnd/qLvpOTE2xsbNDY2IiBAwfq1bRHiJmJ9C/1PsT2FXddG6pIZTm/UB2JhZCYmIi0tDQcOXIE3t7eelfKDWXovnAhHiQmJCR0uC3AmIS475WWlkKhUODs2bMYO3YsoqOjMWjQIMFiZBLYRbS7iKmThPv37+Obb77R6S+ni4sLLly40GYz6IULFzq1h8wQeXl5UCgUqKurw/Tp05Genq4psetKSjdIQwhxc7p06RJyc3P1/h2qZWZmduo8bdqb1S9cuIDMzExcvHhR5+WkgDBdIIVofGFOnJ2dATz5R/Svv/7Sq8GCvtXgZ1EvC05MTNT8vzRkFImpCwoKQlBQkOYzEh8fr1dV9FnLc1UqFcLDw3VKArWXMHX0+nmk8hkTahRLcnIy6uvrkZ+fj6ioKPTp0wchISHw9fXV+Rqurq7Yu3cv7OzskJSUhIaGBp3PFWJmohR1ZiWHEJ5VcU9JScGECRN0Ol/IfgGGEHq0giH69euH5cuXG/3nqgmxYsrQB4m1tbV6/fmuYuh9b+zYsRg4cCBmzJiBuLi4Ts2wfB4mgV3E0C5i6uWkfn5+muWkf/75p96zpgxVXFyMzz//XK8qZntSuEG2tLTg2LFjcHNzg5eXF9atW4fm5mYsXLhQryeHht6cOjM/TmhNTU0oKChAdnY2rK2tO1XxEaILpBCNL8xJ+99BZ+ZpGiIiIgL5+fmoqKjAjBkzOl2tMQdCfEbaa2lpwblz5/Dw4UOd/rx2x9f2r3Ulpc+YUKNY7t+/j7t370KpVMLb2xuHDx/Gvn37dF5etWbNGlRWViIgIAAKhUKvSomUVg4YQqiVHIbSrrhXV1fDw8MD06dP13l1jVSW80th9qNUSKFDf/tGXNp0acolJEPuezt37mzT3b8rHtYwCewihnYR8/T0xJ49e/D777/jzp07GDFiBKKioow6gwd4Mpz3jz/+QFFREfz8/ADoV9EEpHGDjIuLQ2NjIxobG1FbWwt/f3+4ublhxYoVkvmHxFjUFZ+EhIROV3yEeNonhS9UQj0cEIJ2y/qamhpUVlYafE31EHldzJs3D/PmzUNJSQn27NmDS5cuISEhAVOmTBGk0mhKhPiMtPfo0SNkZmZi5cqVOv359h0c1VVIfSqBUviMaTN071ZwcDBsbW0RHByMqKgozZJ4XQdiq5vqODg4YOvWrbCxsTFo/JGpErINv6HUFXfg3y+5upLK328pzH6UCinsC7e1tdV71m5X6ux978GDB5g6dWqXPqxhEthF1Jm6jY0NVCoV0tLS9N5Ebmtra/T5hu1FR0cbPBdJCjfIa9euIScnBy0tLQgMDMSiRYsAQOeucOZEiIqPFJ72CUEKDweuXr2Kl156CatXr9Ycs7W1xbJly3S+Rl5eHjZs2ABbW1ts3rwZnp6eWLlyJcrLy/We4ejj4wMfHx88ePAA+/fvx7Jly7Bv3z69rmHquqIqKpfL9VoJYMjQZ6kRau+WOilvLzU19bnnJiYm4ubNm3jnnXewdu1a2NnZoX///oiNjcX69ev1jsWUCdGGXwjaFcnCwkJ8/fXX6NWrF5YtW9ams7rUSSEZTUlJwaeffgrgydgTFxcX0WIRe1+4s7OzTh2Yu5qh9z1jPKxhEmgEptxFTIi5SFK4QaqfGFtZWaF///6a42Ks2RebEBUfKTztE4IUHg5ERUUhNDTUoBEP6enpKCgoQE1NDdatW4fq6mqMHTsWiYmJnb6mg4MDwsPDER4e3ulrmCopVEWF7CwnNqH2bpWVlWHt2rWapbF1dXU4cOCATudevnwZ6enpaG5uxvHjx1FYWAg7OzuEhYXp/d9j6qQwzxNo+yU3OTm5zd5yU0oCpaCoqEiTBH7xxRcWt8JJm3p7itgMve8Z42ENk8AuYi5dxISoaEpBXV0dTp06pfnioH5dX18vdmiiMbTiI/bTPiFI4eFAXl4e4uPjMXfuXKxbt65TzZ+cnJzg6OgIR0dHlJWVITY2Vu8KID1NzKqoUB01pUCoL6Rbt27FqlWrkJOTA19fX5w5c0bnc9WNyC5cuIAhQ4ZoWufr2onSnEhlJYcQe8vpCe37gynfK4QgZmMcbYbe94zxsIZJYBcxxy5iplzRHD58uKa6o36tVCpRXl4ucmTis+SKT/sHAmI8HJDL5YiLi0NpaSnCwsLwyiuvaN7T9YGR9j4xd3d3JoACE+MzIlRHTSkQajVI79698dprryEnJwfTpk1DXl6ezudaWVnh1KlTUCgUGDduHADgzJkzos3lFJNUVnJIpSJpDjrbQZi6jqH3PWM8rGES2EWksARSCOZS0dRePtXZsQhkfrQfDgwbNqzNa2MqKytDUlISfHx8MHXqVL3Pr6urw+nTp6FSqdDQ0IBTp05p3vP39xcyVDIioTpqmgtra2uUlpaiubkZJ0+eRE1Njc7nxsTEYMOGDfDw8EBYWBhOnjwpmXliYpDCSg6pVCTNweXLlxEaGqr5rqZ+LZPJkJOTI3Z41AnGeFgja7X0ujH9p5KSkg7fM6VE91kt33fv3m3Rc9DoaU1NTQD+XSZqDNu3b0dOTg5Wr16Nd955p1PXMKf9Y9RW+85ygYGBmDVrlthhieLevXsoLy9Hv379sGnTJgQEBGg6S5JpKisra/Ml9+rVqya3t1wKKioqOnzPw8PDiJGQKWESSBbB398fEydORGhoqGbj+Y4dO8QOi0R248YNxMfHY8CAARg/fjwWLlwI4ElS1ZmKXGcsWrQIcXFx6N27t2DXFGv4MwmnfWe5iRMnGn1siVRoj08Bnix369Onj0Uu5SQiEgqTQLII27dvR35+PgYOHIgZM2Zg165dOrUVJ/MWFhaGhQsXor6+HjExMVAoFOjTpw8++ugjk1pyJ5XhzyScoUOHajrLAW33+ZjSUnwhPGsvplKpxLRp0xAZGSlCREREpo97AskiSKHlO0lP9+7dMWbMGABPOnmpZ5DJ5XIRo9Jf+3lC2q3WmQSaJktu8d7es8anqFQqhIeH65UE1tXVaZqb/f3335qKIhGRJWISSBaFg7BJm3Z1RXsfoKnNj2SrdfNjSnuuja2lpQXnzp3Dw4cPdT6npKQEy5cvx759++Do6IgrV65g5cqVSEhIwKhRo7owWiIiaWISSBbJksci0L+e1f22tbUVZWVlYoemF7ZaJ0vy6NEjZGZmYuXKlTqfs3HjRmRkZMDR0REAMGbMGKSlpSEmJgbZ2dldFSoRkWQxCSQii9XRPE9Tm+3JVutkSeRyOTZv3qzXOVZWVk811vHy8mK1nIgsFpNAIrJY5rLkTirDn4mkqrW1FSqVqk3S19LSgsePH4sYFRGReNgdlIiIiMza7t27cebMGXzyyScYMGAAqqqqkJKSgpEjR2L27Nlih0dEZHRMAomIiEjSCgsLUVBQgLq6Ori6uiIwMFCz/1VXBw8eRE5ODqqrq+Hh4YHp06dzyTQRWSwmgURERCRZWVlZOHHiBCIiItC3b1/cvXsXu3btwoQJEzBz5kydrlFVVQVXV9enjpeUlJjNsnAiIn0wCSQiIiLJCg0NRVZWFqysrDTHmpqaEBkZiczMTJ2uMWnSJPz8889t5gKmpKRg9+7dKCwsFDpkIiLJY1ssIiIikixra+s2CSDwZK5n+2P/ZcGCBfj444/R0NAApVKJuXPn4uLFi8jLyxM6XCIik8DuoERERCRZMpnsmcf1WcgUEBCAlpYWzJkzBw8ePEBERAQ++OADoUIkIjI5TAKJiIhIsi5fvvzU7M7W1laUlZXpdZ2goCA0Nzdjz549CA4OFjJEIiKTwz2BREREJFkVFRUdvufh4aHTNZYsWQKZTIbW1lacPXsWLi4uGDhwIAAgKSlJkDiJiEwJK4FEREQkWaWlpR2+p2sSqF1JbF9VJCKyRKwEEhERkWSNGDEC7u7uePfdd9GjR482ewGXLl0qYmRERKaLSSARERFJVm1tLQoKClBYWAg3NzdMmjQJvr6+YodFRGTSmAQSERGRSbh16xZ++eUXnD9/HsOHD2clkIiokzgnkIiIiExCt27dYG1tjYaGBty8eVPscIiITBYrgURERCRZNTU1OHToEA4fPgw7OzsEBQVh3LhxsLe3Fzs0IiKTxSSQiIiIJGvYsGHw8vLChAkT4Ozs3GZ4/MyZM0WMjIjIdHFEBBEREUnW/PnzNYnf/fv3RY6GiMg8sBJIREREknbt2jXY2dnB09NT7FCIiMwCK4FEREQkWcnJySguLkZTUxM+/PBDTJkyReyQiIhMHruDEhERkWQVFxcjJycHmZmZ2L9/v9jhEBGZBSaBREREJFk2NjYAALlcjubmZpGjISIyD0wCiYiIiIiILAgbwxAREZFkvfHGGxgyZAhaW1tx/fp1zWuZTIacnByxwyMiMklMAomIiEiyKioqOnzPw8PDiJEQEZkPJoFEREQkaVeuXMGvv/4KpVIJV1dXBAQE4IUXXhA7LCIik8U9gURERCRZhw4dwooVK+Dm5oa33noLPXv2xGeffYbffvtN7NCIiEwWK4FEREQkWWFhYUhNTYVcLtcca2howPz585GRkSFiZEREpouVQCIiIpKs7t27t0kAAcDe3h5WVlYiRUREZPqYBBIREZFkyWSyZx5XqVRGjoSIyHx0FzsAIiIioo5cv34dS5cubXOstbUVZWVlIkVERGT6uCeQiIiIJKukpKTD93x8fIwYCRGR+WASSEREREREZEG4J5CIiIiIiMiCMAkkIiIiIiKyIEwCiYjIrBQXF2P06NEIDw9HeHg4QkJCkJGRgfDwcKM1EykuLsaoUaNQWVmpOZaYmIi8vDyj/HwiIqL/wu6gRERkdvz8/JCcnAwAaGpqQkBAAHr16mXUGKytrfHVV18hPT29wzEHREREYmASSEREZq2hoQHdunXTDBevqqpCbGwsHj16hLq6OixYsADe3t6Ijo7G3r17AQCLFy9GZGQkHj58iOTkZFhZWcHT0xNr1qzBgQMHkJubC5VKhUWLFmH06NHP/Ll+fn5QqVTIysrCrFmz2ryXlJSES5cuobGxEd7e3vj++++xZcsW3Lx5E0qlEvX19Xj//fdx5MgR3LhxA/Hx8Xj11VeRkZGB/Px8yGQyBAYGIiIiomt/eUREZJaYBBIRkdkpKipCeHg4ZDIZrK2tsWrVKuzYsQMAUF5ejjlz5sDX1xfnz5/Hli1bkJ6eDltbW1y/fh3Ozs64c+cOXn75ZQQEBCA7Oxt9+/bFxo0boVAo0L17dzg4OGDbtm3PjSM2NhbBwcHw9/fXHGtoaICDgwPS09OhUqkQFBSEe/fuAQBsbW2RmpqK7du34/jx4/jhhx+Qm5uLgoIC2Nvb4+DBg8jOzoZMJsPs2bPh7++PQYMGdc0vkYiIzBaTQCIiMjvay0HV1Elgv379sG3bNuzduxcymQzNzc0AgODgYOTl5cHd3R2TJ09GbW0tqqursXjxYgDAw4cPMWbMGPzvf/+Dl5eXTnH07t0bK1aswJdffonXX38dANCjRw/U1tZiyZIlkMvl+Oeff/D48WMAwLBhwwAAvXr1wuDBgwEAjo6OePToEa5du4a7d+9i9uzZAID6+nrcunWLSSAREemNSSAREVmUTZs2ITg4GG+//TZyc3OhUCgAAAEBAUhLS4OTkxM2bdoEBwcHuLq6IiUlBb169cKxY8cgl8tRWVmJbt1076v23nvv4ejRo1AoFIiOjsaJEydQWVmJjRs3ora2FkePHoV6ZO9/7R0cNGgQBg8ejB07dkAmk2Hnzp148cUXDftlEBGRRWISSEREFiUgIADffvstfvzxR7i5uUGpVAJ4UqF78803UVtbCycnJwBATEwM5s2bh9bWVvTs2RPr169v0/FTVzExMSgqKgIAjBw5EikpKQgJCYGNjQ08PT1RXV393GsMHToUo0ePRlhYGJqamjBy5Ej0799f71iIiIhkrerHj0RERBYuNjYW48eP77DZCxERkTlgJZCIiAhAZGQkXFxc9EoAY2Njnzl78KeffoKtra2Q4REREQmGlUAiIiIiIiILovvOdiIiIiIiIjJ5TAKJiIiIiIgsCJNAIiIiIiIiC8IkkIiIiIiIyIIwCSQiIiIiIrIgTAKJiIiIiIgsyP8BRoz7GTCt0qwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(15,8))\n",
    "sns.barplot(x='Player_Name',y='Maximum_Runs',data=mergeResult)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "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>Innings_Id</th>\n",
       "      <th>Over_Id</th>\n",
       "      <th>Ball_Id</th>\n",
       "      <th>Team_Batting_Id</th>\n",
       "      <th>Team_Bowling_Id</th>\n",
       "      <th>Striker_Id</th>\n",
       "      <th>Striker_Batting_Position</th>\n",
       "      <th>Non_Striker_Id</th>\n",
       "      <th>Bowler_Id</th>\n",
       "      <th>Batsman_Scored</th>\n",
       "      <th>Extra_Type</th>\n",
       "      <th>Extra_Runs</th>\n",
       "      <th>Player_dissimal_Id</th>\n",
       "      <th>Dissimal_Type</th>\n",
       "      <th>Fielder_Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>335987</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Match_Id  Innings_Id  Over_Id  Ball_Id  Team_Batting_Id  Team_Bowling_Id  \\\n",
       "8     335987           1        2        2                1                2   \n",
       "9     335987           1        2        3                1                2   \n",
       "10    335987           1        2        4                1                2   \n",
       "11    335987           1        2        5                1                2   \n",
       "16    335987           1        3        4                1                2   \n",
       "\n",
       "    Striker_Id  Striker_Batting_Position  Non_Striker_Id  Bowler_Id  \\\n",
       "8            2                         2               1         15   \n",
       "9            2                         2               1         15   \n",
       "10           2                         2               1         15   \n",
       "11           2                         2               1         15   \n",
       "16           2                         2               1         14   \n",
       "\n",
       "   Batsman_Scored Extra_Type Extra_Runs Player_dissimal_Id Dissimal_Type  \\\n",
       "8               4                                                          \n",
       "9               4                                                          \n",
       "10              6                                                          \n",
       "11              4                                                          \n",
       "16              4                                                          \n",
       "\n",
       "   Fielder_Id  \n",
       "8              \n",
       "9              \n",
       "10             \n",
       "11             \n",
       "16             "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Maximum Runs by Team\n",
    "teamss = data[(data['Batsman_Scored'] != '0') & (data['Batsman_Scored'] != 'Do_nothing') & (data['Batsman_Scored'] != ' ')]\n",
    "teamss.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Team_Batting_Id\n",
       "1     18209\n",
       "2     20464\n",
       "3     19940\n",
       "4     19720\n",
       "5     16658\n",
       "6     18532\n",
       "7     20662\n",
       "8     10881\n",
       "9      1758\n",
       "10     6040\n",
       "11     8806\n",
       "12     1962\n",
       "13     2318\n",
       "Name: Batsman_Scored, dtype: int64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "teamss['Batsman_Scored'] = teamss['Batsman_Scored'].apply(lambda x: int(x))\n",
    "maxruns_team = teamss.groupby('Team_Batting_Id')['Batsman_Scored'].sum()\n",
    "maxruns_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team_Id</th>\n",
       "      <th>Maximum_Runs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>18209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>19940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>19720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>16658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>18532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>10881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>6040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>8806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>2318</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Team_Id  Maximum_Runs\n",
       "0         1         18209\n",
       "1         2         20464\n",
       "2         3         19940\n",
       "3         4         19720\n",
       "4         5         16658\n",
       "5         6         18532\n",
       "6         7         20662\n",
       "7         8         10881\n",
       "8         9          1758\n",
       "9        10          6040\n",
       "10       11          8806\n",
       "11       12          1962\n",
       "12       13          2318"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "records = pd.DataFrame({'Team_Id':maxruns_team.index,'Maximum_Runs':maxruns_team.values})\n",
    "records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Team_Id</th>\n",
       "      <th>Maximum_Runs</th>\n",
       "      <th>Team_Name</th>\n",
       "      <th>Team_Short_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>18209</td>\n",
       "      <td>Kolkata Knight Riders</td>\n",
       "      <td>KKR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20464</td>\n",
       "      <td>Royal Challengers Bangalore</td>\n",
       "      <td>RCB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>19940</td>\n",
       "      <td>Chennai Super Kings</td>\n",
       "      <td>CSK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>19720</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>KXIP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>16658</td>\n",
       "      <td>Rajasthan Royals</td>\n",
       "      <td>RR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>18532</td>\n",
       "      <td>Delhi Daredevils</td>\n",
       "      <td>DD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20662</td>\n",
       "      <td>Mumbai Indians</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>10881</td>\n",
       "      <td>Deccan Chargers</td>\n",
       "      <td>DC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>1758</td>\n",
       "      <td>Kochi Tuskers Kerala</td>\n",
       "      <td>KTK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>6040</td>\n",
       "      <td>Pune Warriors</td>\n",
       "      <td>PW</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>8806</td>\n",
       "      <td>Sunrisers Hyderabad</td>\n",
       "      <td>SRH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>1962</td>\n",
       "      <td>Rising Pune Supergiants</td>\n",
       "      <td>RPS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>2318</td>\n",
       "      <td>Gujarat Lions</td>\n",
       "      <td>GL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Team_Id  Maximum_Runs                    Team_Name Team_Short_Code\n",
       "0         1         18209        Kolkata Knight Riders             KKR\n",
       "1         2         20464  Royal Challengers Bangalore             RCB\n",
       "2         3         19940          Chennai Super Kings             CSK\n",
       "3         4         19720              Kings XI Punjab            KXIP\n",
       "4         5         16658             Rajasthan Royals              RR\n",
       "5         6         18532             Delhi Daredevils              DD\n",
       "6         7         20662               Mumbai Indians              MI\n",
       "7         8         10881              Deccan Chargers              DC\n",
       "8         9          1758         Kochi Tuskers Kerala             KTK\n",
       "9        10          6040                Pune Warriors              PW\n",
       "10       11          8806          Sunrisers Hyderabad             SRH\n",
       "11       12          1962      Rising Pune Supergiants             RPS\n",
       "12       13          2318                Gujarat Lions              GL"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mergeResulttr = pd.merge(records, team, on='Team_Id',how='left')\n",
    "mergeResulttr.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b695e68cc0>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(25,15))\n",
    "sns.barplot(x=\"Team_Name\", y='Maximum_Runs',\n",
    "               data=mergeResulttr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "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>Venue_Name</th>\n",
       "      <th>Total_Matches</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Barabati Stadium</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brabourne Stadium</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Buffalo Park</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>De Beers Diamond Oval</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dr DY Patil Sports Academy</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Dubai International Cricket Stadium</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Eden Gardens</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Feroz Shah Kotla</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Green Park</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Himachal Pradesh Cricket Association Stadium</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Holkar Cricket Stadium</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>JSCA International Stadium Complex</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Kingsmead</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>M Chinnaswamy Stadium</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>MA Chidambaram Stadium, Chepauk</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Maharashtra Cricket Association Stadium</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nehru Stadium</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>New Wanderers Stadium</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Newlands</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>OUTsurance Oval</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Punjab Cricket Association IS Bindra Stadium, ...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Rajiv Gandhi International Stadium, Uppal</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Sardar Patel Stadium, Motera</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Saurashtra Cricket Association Stadium</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Sawai Mansingh Stadium</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Shaheed Veer Narayan Singh International Stadium</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Sharjah Cricket Stadium</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Sheikh Zayed Stadium</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>St George's Park</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Subrata Roy Sahara Stadium</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>SuperSport Park</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Vidarbha Cricket Association Stadium, Jamtha</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Wankhede Stadium</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           Venue_Name  Total_Matches\n",
       "0                                    Barabati Stadium              7\n",
       "1                                   Brabourne Stadium             11\n",
       "2                                        Buffalo Park              3\n",
       "3                               De Beers Diamond Oval              3\n",
       "4                          Dr DY Patil Sports Academy             17\n",
       "5   Dr. Y.S. Rajasekhara Reddy ACA-VDCA Cricket St...             11\n",
       "6                 Dubai International Cricket Stadium              7\n",
       "7                                        Eden Gardens             54\n",
       "8                                    Feroz Shah Kotla             53\n",
       "9                                          Green Park              2\n",
       "10       Himachal Pradesh Cricket Association Stadium              9\n",
       "11                             Holkar Cricket Stadium              2\n",
       "12                 JSCA International Stadium Complex              7\n",
       "13                                          Kingsmead             15\n",
       "14                              M Chinnaswamy Stadium             58\n",
       "15                    MA Chidambaram Stadium, Chepauk             48\n",
       "16            Maharashtra Cricket Association Stadium              8\n",
       "17                                      Nehru Stadium              5\n",
       "18                              New Wanderers Stadium              8\n",
       "19                                           Newlands              7\n",
       "20                                    OUTsurance Oval              2\n",
       "21  Punjab Cricket Association IS Bindra Stadium, ...              7\n",
       "22         Punjab Cricket Association Stadium, Mohali             35\n",
       "23          Rajiv Gandhi International Stadium, Uppal             41\n",
       "24                       Sardar Patel Stadium, Motera             12\n",
       "25             Saurashtra Cricket Association Stadium              5\n",
       "26                             Sawai Mansingh Stadium             33\n",
       "27   Shaheed Veer Narayan Singh International Stadium              6\n",
       "28                            Sharjah Cricket Stadium              6\n",
       "29                               Sheikh Zayed Stadium              7\n",
       "30                                   St George's Park              7\n",
       "31                         Subrata Roy Sahara Stadium             17\n",
       "32                                    SuperSport Park             12\n",
       "33       Vidarbha Cricket Association Stadium, Jamtha              3\n",
       "34                                   Wankhede Stadium             49"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total Matches in Each Stadium\n",
    "most_matches = match.groupby('Venue_Name')['Match_Id'].count()\n",
    "most_matches.nlargest(5)\n",
    "most_matches = pd.DataFrame({'Venue_Name':most_matches.index,'Total_Matches':most_matches.values})\n",
    "most_matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0,\n",
       "  1,\n",
       "  2,\n",
       "  3,\n",
       "  4,\n",
       "  5,\n",
       "  6,\n",
       "  7,\n",
       "  8,\n",
       "  9,\n",
       "  10,\n",
       "  11,\n",
       "  12,\n",
       "  13,\n",
       "  14,\n",
       "  15,\n",
       "  16,\n",
       "  17,\n",
       "  18,\n",
       "  19,\n",
       "  20,\n",
       "  21,\n",
       "  22,\n",
       "  23,\n",
       "  24,\n",
       "  25,\n",
       "  26,\n",
       "  27,\n",
       "  28,\n",
       "  29,\n",
       "  30,\n",
       "  31,\n",
       "  32,\n",
       "  33,\n",
       "  34],\n",
       " <a list of 35 Text xticklabel objects>)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('whitegrid')\n",
    "plt.figure(figsize=(20,8))\n",
    "sns.lineplot(x=\"Venue_Name\", y='Total_Matches',\n",
    "               data=most_matches)\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Kings XI Punjab - Punjab Cricket Association Stadium, Mohali - 18'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Match Winning in Each Stadium Most By Team\n",
    "match_winner_each_std = match.loc[:,('Venue_Name','Match_Winner_Id')]\n",
    "\n",
    "team['Team_Id'] = team['Team_Id'].astype(float)\n",
    "\n",
    "team_data = team.loc[:,('Team_Id','Team_Name')]\n",
    "\n",
    "match_winner_each_std=match_winner_each_std.rename(index=str,columns={'Match_Winner_Id':'Team_Id'})\n",
    "\n",
    "team_winners = match_winner_each_std.merge(team_data, on=\"Team_Id\")\n",
    "team_winner_count = team_winners.groupby(['Venue_Name','Team_Name'])['Team_Id'].count()\n",
    "team_winner_count.nlargest(5)\n",
    "\n",
    "final_result = pd.DataFrame(team_winner_count)\n",
    "team_lucky = final_result.sort_values(by='Team_Name')\n",
    "\n",
    "team_lucky=team_lucky.rename(index=str,columns={'Team_Id':'Matches Wins'})\n",
    "\n",
    "#Find Lucky Teams\n",
    "lucky_punjab = team_lucky[team_lucky.index.get_level_values('Team_Name') == 'Kings XI Punjab'].sort_values(by='Matches Wins', ascending=False)['Matches Wins']\n",
    "\n",
    "most_match_in_best = team_lucky[team_lucky.index.get_level_values('Team_Name') == 'Kings XI Punjab'].sort_values(by='Matches Wins', ascending=False)['Matches Wins'][0]\n",
    "best_stadium_punjab = lucky_punjab.index.get_level_values('Venue_Name')[0]\n",
    "\n",
    "top_five_best = lucky_punjab.index.get_level_values('Venue_Name')[:5]\n",
    "\n",
    "\"Kings XI Punjab - \"+str(best_stadium_punjab)+\" - \"+str(most_match_in_best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Venue_Name</th>\n",
       "      <th>Team_Name</th>\n",
       "      <th>Matches Wins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>Punjab Cricket Association Stadium, Mohali</td>\n",
       "      <td>Kings XI Punjab</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Venue_Name        Team_Name  Matches Wins\n",
       "86  Punjab Cricket Association Stadium, Mohali  Kings XI Punjab            18"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datas = pd.DataFrame(team_lucky.to_records())\n",
    "datas[datas['Team_Name']=='Kings XI Punjab'].nlargest(1,'Matches Wins')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>Man_Of_The_Match_Id</th>\n",
       "      <th>Player_Name</th>\n",
       "      <th>most_man_of_the_match_awards</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>162.0</td>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31.0</td>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>110.0</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>187.0</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>57.0</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>21.0</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19.0</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>40.0</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20.0</td>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>85.0</td>\n",
       "      <td>AM Rahane</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Man_Of_The_Match_Id     Player_Name  most_man_of_the_match_awards\n",
       "90                162.0        CH Gayle                            17\n",
       "21                 31.0       YK Pathan                            16\n",
       "67                110.0  AB de Villiers                            15\n",
       "98                187.0       DA Warner                            14\n",
       "36                 57.0       RG Sharma                            13\n",
       "14                 21.0        SK Raina                            13\n",
       "12                 19.0      MEK Hussey                            12\n",
       "26                 40.0       G Gambhir                            12\n",
       "13                 20.0        MS Dhoni                            12\n",
       "52                 85.0       AM Rahane                            12"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Man of the Match\n",
    "match['Man_Of_The_Match_Id']=match['Man_Of_The_Match_Id'].astype(str).astype(float)\n",
    "a=match.loc[:,('Match_Id','Man_Of_The_Match_Id')]\n",
    "\n",
    "player['Player_Id']=player['Player_Id'].astype(float)\n",
    "b=player.loc[:,('Player_Id','Player_Name')]\n",
    "\n",
    "c=b.rename(index=str,columns={'Player_Id':'Man_Of_The_Match_Id'})\n",
    "\n",
    "d=a.merge(c,on='Man_Of_The_Match_Id')\n",
    "\n",
    "e=d.sort_values(by='Man_Of_The_Match_Id')\n",
    "\n",
    "l=e.groupby('Man_Of_The_Match_Id')['Player_Name'].count()\n",
    "\n",
    "f=c.merge(l,on='Man_Of_The_Match_Id')\n",
    "h=f.rename(columns={'Player_Name_x':'Player_Name','Player_Name_y':'most_man_of_the_match_awards'})\n",
    "h.sort_values(by='most_man_of_the_match_awards',ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season_Year</th>\n",
       "      <th>Orange_Cap_Id</th>\n",
       "      <th>Player_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>100</td>\n",
       "      <td>SE Marsh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>18</td>\n",
       "      <td>ML Hayden</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>133</td>\n",
       "      <td>SR Tendulkar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>162</td>\n",
       "      <td>CH Gayle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>162</td>\n",
       "      <td>CH Gayle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>19</td>\n",
       "      <td>MEK Hussey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014</td>\n",
       "      <td>46</td>\n",
       "      <td>RV Uthappa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015</td>\n",
       "      <td>187</td>\n",
       "      <td>DA Warner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>V Kohli</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season_Year  Orange_Cap_Id   Player_Name\n",
       "0         2008            100      SE Marsh\n",
       "1         2009             18     ML Hayden\n",
       "2         2010            133  SR Tendulkar\n",
       "3         2011            162      CH Gayle\n",
       "4         2012            162      CH Gayle\n",
       "5         2013             19    MEK Hussey\n",
       "6         2014             46    RV Uthappa\n",
       "7         2015            187     DA Warner\n",
       "8         2016              8       V Kohli"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Purple Cap Id\n",
    "datap = season.loc[:,('Season_Year','Orange_Cap_Id')]\n",
    "orangecap_player = player.loc[:,('Player_Id','Player_Name')]\n",
    "orangecap_player = orangecap_player.rename(columns={'Player_Id':'Orange_Cap_Id'})\n",
    "final = datap.merge(orangecap_player, on=\"Orange_Cap_Id\")\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season_Year</th>\n",
       "      <th>Purple_Cap_Id</th>\n",
       "      <th>Player_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>102</td>\n",
       "      <td>Sohail Tanvir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2009</td>\n",
       "      <td>61</td>\n",
       "      <td>RP Singh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>131</td>\n",
       "      <td>PP Ojha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011</td>\n",
       "      <td>194</td>\n",
       "      <td>SL Malinga</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012</td>\n",
       "      <td>190</td>\n",
       "      <td>M Morkel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013</td>\n",
       "      <td>71</td>\n",
       "      <td>DJ Bravo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>71</td>\n",
       "      <td>DJ Bravo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2014</td>\n",
       "      <td>364</td>\n",
       "      <td>MM Sharma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>299</td>\n",
       "      <td>B Kumar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season_Year  Purple_Cap_Id    Player_Name\n",
       "0         2008            102  Sohail Tanvir\n",
       "1         2009             61       RP Singh\n",
       "2         2010            131        PP Ojha\n",
       "3         2011            194     SL Malinga\n",
       "4         2012            190       M Morkel\n",
       "5         2013             71       DJ Bravo\n",
       "6         2015             71       DJ Bravo\n",
       "7         2014            364      MM Sharma\n",
       "8         2016            299        B Kumar"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Purple Cap Id\n",
    "datao = season.loc[:,('Season_Year','Purple_Cap_Id')]\n",
    "purplecap_player = player.loc[:,('Player_Id','Player_Name')]\n",
    "purplecap_player = purplecap_player.rename(columns={'Player_Id':'Purple_Cap_Id'})\n",
    "final = datao.merge(purplecap_player, on=\"Purple_Cap_Id\")\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season_Year</th>\n",
       "      <th>Man_of_the_Series_Id</th>\n",
       "      <th>Player_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>32</td>\n",
       "      <td>SR Watson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013</td>\n",
       "      <td>32</td>\n",
       "      <td>SR Watson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2009</td>\n",
       "      <td>53</td>\n",
       "      <td>AC Gilchrist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>133</td>\n",
       "      <td>SR Tendulkar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011</td>\n",
       "      <td>162</td>\n",
       "      <td>CH Gayle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2012</td>\n",
       "      <td>315</td>\n",
       "      <td>SP Narine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2014</td>\n",
       "      <td>305</td>\n",
       "      <td>GJ Maxwell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015</td>\n",
       "      <td>334</td>\n",
       "      <td>AD Russell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>V Kohli</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season_Year  Man_of_the_Series_Id   Player_Name\n",
       "0         2008                    32     SR Watson\n",
       "1         2013                    32     SR Watson\n",
       "2         2009                    53  AC Gilchrist\n",
       "3         2010                   133  SR Tendulkar\n",
       "4         2011                   162      CH Gayle\n",
       "5         2012                   315     SP Narine\n",
       "6         2014                   305    GJ Maxwell\n",
       "7         2015                   334    AD Russell\n",
       "8         2016                     8       V Kohli"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Man of the Series\n",
    "datas = season.loc[:,('Season_Year','Man_of_the_Series_Id')]\n",
    "manofseries = player.loc[:,('Player_Id','Player_Name')]\n",
    "manofseries= manofseries.rename(columns={'Player_Id':'Man_of_the_Series_Id'})\n",
    "final = datas.merge(manofseries, on=\"Man_of_the_Series_Id\")\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Match_Id</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name</th>\n",
       "      <th>Opponent_Team_Name</th>\n",
       "      <th>Winner_Team</th>\n",
       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335987</td>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>RCB</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KKR</td>\n",
       "      <td>by runs</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>335988</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>CSK</td>\n",
       "      <td>CSK</td>\n",
       "      <td>by runs</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>335989</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>DD</td>\n",
       "      <td>RR</td>\n",
       "      <td>DD</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>335990</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>MI</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>335991</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>KKR</td>\n",
       "      <td>DC</td>\n",
       "      <td>KKR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>335992</td>\n",
       "      <td>21-Apr-08</td>\n",
       "      <td>RR</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>RR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>335993</td>\n",
       "      <td>22-Apr-08</td>\n",
       "      <td>DC</td>\n",
       "      <td>DD</td>\n",
       "      <td>DD</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>335994</td>\n",
       "      <td>23-Apr-08</td>\n",
       "      <td>CSK</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>by runs</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>335995</td>\n",
       "      <td>24-Apr-08</td>\n",
       "      <td>DC</td>\n",
       "      <td>RR</td>\n",
       "      <td>RR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>335996</td>\n",
       "      <td>25-Apr-08</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>MI</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by runs</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>335997</td>\n",
       "      <td>26-Apr-08</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RR</td>\n",
       "      <td>RR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>335998</td>\n",
       "      <td>26-Apr-08</td>\n",
       "      <td>CSK</td>\n",
       "      <td>KKR</td>\n",
       "      <td>CSK</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>335999</td>\n",
       "      <td>27-Apr-08</td>\n",
       "      <td>MI</td>\n",
       "      <td>DC</td>\n",
       "      <td>DC</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>336000</td>\n",
       "      <td>27-Apr-08</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>DD</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>336001</td>\n",
       "      <td>28-Apr-08</td>\n",
       "      <td>RCB</td>\n",
       "      <td>CSK</td>\n",
       "      <td>CSK</td>\n",
       "      <td>by runs</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>336002</td>\n",
       "      <td>29-Apr-08</td>\n",
       "      <td>KKR</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>336003</td>\n",
       "      <td>30-Apr-08</td>\n",
       "      <td>DD</td>\n",
       "      <td>RCB</td>\n",
       "      <td>DD</td>\n",
       "      <td>by runs</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>336004</td>\n",
       "      <td>1-May-08</td>\n",
       "      <td>DC</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>336005</td>\n",
       "      <td>1-May-08</td>\n",
       "      <td>RR</td>\n",
       "      <td>KKR</td>\n",
       "      <td>RR</td>\n",
       "      <td>by runs</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>336006</td>\n",
       "      <td>2-May-08</td>\n",
       "      <td>CSK</td>\n",
       "      <td>DD</td>\n",
       "      <td>DD</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>336007</td>\n",
       "      <td>25-May-08</td>\n",
       "      <td>DC</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>336008</td>\n",
       "      <td>3-May-08</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by runs</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>336009</td>\n",
       "      <td>4-May-08</td>\n",
       "      <td>MI</td>\n",
       "      <td>DD</td>\n",
       "      <td>MI</td>\n",
       "      <td>by runs</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>336010</td>\n",
       "      <td>4-May-08</td>\n",
       "      <td>RR</td>\n",
       "      <td>CSK</td>\n",
       "      <td>RR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>336011</td>\n",
       "      <td>5-May-08</td>\n",
       "      <td>RCB</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>336012</td>\n",
       "      <td>6-May-08</td>\n",
       "      <td>CSK</td>\n",
       "      <td>DC</td>\n",
       "      <td>DC</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>336013</td>\n",
       "      <td>7-May-08</td>\n",
       "      <td>MI</td>\n",
       "      <td>RR</td>\n",
       "      <td>MI</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>336014</td>\n",
       "      <td>8-May-08</td>\n",
       "      <td>DD</td>\n",
       "      <td>CSK</td>\n",
       "      <td>CSK</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>336015</td>\n",
       "      <td>8-May-08</td>\n",
       "      <td>KKR</td>\n",
       "      <td>RCB</td>\n",
       "      <td>KKR</td>\n",
       "      <td>by runs</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>336016</td>\n",
       "      <td>9-May-08</td>\n",
       "      <td>RR</td>\n",
       "      <td>DC</td>\n",
       "      <td>RR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>980966</td>\n",
       "      <td>3-May-16</td>\n",
       "      <td>GL</td>\n",
       "      <td>DD</td>\n",
       "      <td>DD</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>980968</td>\n",
       "      <td>4-May-16</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KKR</td>\n",
       "      <td>by runs</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>546</th>\n",
       "      <td>980970</td>\n",
       "      <td>5-May-16</td>\n",
       "      <td>DD</td>\n",
       "      <td>RPS</td>\n",
       "      <td>RPS</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>547</th>\n",
       "      <td>980972</td>\n",
       "      <td>6-May-16</td>\n",
       "      <td>SRH</td>\n",
       "      <td>GL</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>980974</td>\n",
       "      <td>7-May-16</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RPS</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>980976</td>\n",
       "      <td>7-May-16</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>DD</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by runs</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>980978</td>\n",
       "      <td>8-May-16</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by runs</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>980980</td>\n",
       "      <td>8-May-16</td>\n",
       "      <td>KKR</td>\n",
       "      <td>GL</td>\n",
       "      <td>GL</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>980982</td>\n",
       "      <td>9-May-16</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by runs</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>553</th>\n",
       "      <td>980984</td>\n",
       "      <td>10-May-16</td>\n",
       "      <td>RPS</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by runs</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>980986</td>\n",
       "      <td>11-May-16</td>\n",
       "      <td>RCB</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>980988</td>\n",
       "      <td>12-May-16</td>\n",
       "      <td>SRH</td>\n",
       "      <td>DD</td>\n",
       "      <td>DD</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>980990</td>\n",
       "      <td>13-May-16</td>\n",
       "      <td>MI</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>980992</td>\n",
       "      <td>14-May-16</td>\n",
       "      <td>RCB</td>\n",
       "      <td>GL</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by runs</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>980994</td>\n",
       "      <td>14-May-16</td>\n",
       "      <td>KKR</td>\n",
       "      <td>RPS</td>\n",
       "      <td>KKR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>980996</td>\n",
       "      <td>15-May-16</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>980998</td>\n",
       "      <td>15-May-16</td>\n",
       "      <td>MI</td>\n",
       "      <td>DD</td>\n",
       "      <td>MI</td>\n",
       "      <td>by runs</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>981000</td>\n",
       "      <td>16-May-16</td>\n",
       "      <td>KKR</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>981002</td>\n",
       "      <td>17-May-16</td>\n",
       "      <td>RPS</td>\n",
       "      <td>DD</td>\n",
       "      <td>RPS</td>\n",
       "      <td>by runs</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>563</th>\n",
       "      <td>981004</td>\n",
       "      <td>18-May-16</td>\n",
       "      <td>RCB</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by runs</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>981006</td>\n",
       "      <td>19-May-16</td>\n",
       "      <td>GL</td>\n",
       "      <td>KKR</td>\n",
       "      <td>GL</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>981008</td>\n",
       "      <td>20-May-16</td>\n",
       "      <td>DD</td>\n",
       "      <td>SRH</td>\n",
       "      <td>DD</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>981010</td>\n",
       "      <td>21-May-16</td>\n",
       "      <td>RPS</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>RPS</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>981012</td>\n",
       "      <td>21-May-16</td>\n",
       "      <td>GL</td>\n",
       "      <td>MI</td>\n",
       "      <td>GL</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>981014</td>\n",
       "      <td>22-May-16</td>\n",
       "      <td>KKR</td>\n",
       "      <td>SRH</td>\n",
       "      <td>KKR</td>\n",
       "      <td>by runs</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>981016</td>\n",
       "      <td>22-May-16</td>\n",
       "      <td>DD</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>981018</td>\n",
       "      <td>24-May-16</td>\n",
       "      <td>GL</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>981020</td>\n",
       "      <td>25-May-16</td>\n",
       "      <td>SRH</td>\n",
       "      <td>KKR</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by runs</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>981022</td>\n",
       "      <td>27-May-16</td>\n",
       "      <td>GL</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>981024</td>\n",
       "      <td>29-May-16</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by runs</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>574 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Match_Id Match_Date Team_Name Opponent_Team_Name Winner_Team    Win_Type  \\\n",
       "0      335987  18-Apr-08       RCB                KKR         KKR     by runs   \n",
       "1      335988  19-Apr-08      KXIP                CSK         CSK     by runs   \n",
       "2      335989  19-Apr-08        DD                 RR          DD  by wickets   \n",
       "3      335990  20-Apr-08        MI                RCB         RCB  by wickets   \n",
       "4      335991  20-Apr-08       KKR                 DC         KKR  by wickets   \n",
       "5      335992  21-Apr-08        RR               KXIP          RR  by wickets   \n",
       "6      335993  22-Apr-08        DC                 DD          DD  by wickets   \n",
       "7      335994  23-Apr-08       CSK                 MI         CSK     by runs   \n",
       "8      335995  24-Apr-08        DC                 RR          RR  by wickets   \n",
       "9      335996  25-Apr-08      KXIP                 MI        KXIP     by runs   \n",
       "10     335997  26-Apr-08       RCB                 RR          RR  by wickets   \n",
       "11     335998  26-Apr-08       CSK                KKR         CSK  by wickets   \n",
       "12     335999  27-Apr-08        MI                 DC          DC  by wickets   \n",
       "13     336000  27-Apr-08      KXIP                 DD        KXIP  by wickets   \n",
       "14     336001  28-Apr-08       RCB                CSK         CSK     by runs   \n",
       "15     336002  29-Apr-08       KKR                 MI          MI  by wickets   \n",
       "16     336003  30-Apr-08        DD                RCB          DD     by runs   \n",
       "17     336004   1-May-08        DC               KXIP        KXIP  by wickets   \n",
       "18     336005   1-May-08        RR                KKR          RR     by runs   \n",
       "19     336006   2-May-08       CSK                 DD          DD  by wickets   \n",
       "20     336007  25-May-08        DC                RCB         RCB  by wickets   \n",
       "21     336008   3-May-08      KXIP                KKR        KXIP     by runs   \n",
       "22     336009   4-May-08        MI                 DD          MI     by runs   \n",
       "23     336010   4-May-08        RR                CSK          RR  by wickets   \n",
       "24     336011   5-May-08       RCB               KXIP        KXIP  by wickets   \n",
       "25     336012   6-May-08       CSK                 DC          DC  by wickets   \n",
       "26     336013   7-May-08        MI                 RR          MI  by wickets   \n",
       "27     336014   8-May-08        DD                CSK         CSK  by wickets   \n",
       "28     336015   8-May-08       KKR                RCB         KKR     by runs   \n",
       "29     336016   9-May-08        RR                 DC          RR  by wickets   \n",
       "..        ...        ...       ...                ...         ...         ...   \n",
       "544    980966   3-May-16        GL                 DD          DD  by wickets   \n",
       "545    980968   4-May-16       KKR               KXIP         KKR     by runs   \n",
       "546    980970   5-May-16        DD                RPS         RPS  by wickets   \n",
       "547    980972   6-May-16       SRH                 GL         SRH  by wickets   \n",
       "548    980974   7-May-16       RCB                RPS         RCB  by wickets   \n",
       "549    980976   7-May-16      KXIP                 DD        KXIP     by runs   \n",
       "550    980978   8-May-16        MI                SRH         SRH     by runs   \n",
       "551    980980   8-May-16       KKR                 GL          GL  by wickets   \n",
       "552    980982   9-May-16      KXIP                RCB         RCB     by runs   \n",
       "553    980984  10-May-16       RPS                SRH         SRH     by runs   \n",
       "554    980986  11-May-16       RCB                 MI          MI  by wickets   \n",
       "555    980988  12-May-16       SRH                 DD          DD  by wickets   \n",
       "556    980990  13-May-16        MI               KXIP        KXIP  by wickets   \n",
       "557    980992  14-May-16       RCB                 GL         RCB     by runs   \n",
       "558    980994  14-May-16       KKR                RPS         KKR  by wickets   \n",
       "559    980996  15-May-16      KXIP                SRH         SRH  by wickets   \n",
       "560    980998  15-May-16        MI                 DD          MI     by runs   \n",
       "561    981000  16-May-16       KKR                RCB         RCB  by wickets   \n",
       "562    981002  17-May-16       RPS                 DD         RPS     by runs   \n",
       "563    981004  18-May-16       RCB               KXIP         RCB     by runs   \n",
       "564    981006  19-May-16        GL                KKR          GL  by wickets   \n",
       "565    981008  20-May-16        DD                SRH          DD  by wickets   \n",
       "566    981010  21-May-16       RPS               KXIP         RPS  by wickets   \n",
       "567    981012  21-May-16        GL                 MI          GL  by wickets   \n",
       "568    981014  22-May-16       KKR                SRH         KKR     by runs   \n",
       "569    981016  22-May-16        DD                RCB         RCB  by wickets   \n",
       "570    981018  24-May-16        GL                RCB         RCB  by wickets   \n",
       "571    981020  25-May-16       SRH                KKR         SRH     by runs   \n",
       "572    981022  27-May-16        GL                SRH         SRH  by wickets   \n",
       "573    981024  29-May-16       RCB                SRH         SRH     by runs   \n",
       "\n",
       "     Won_By  \n",
       "0     140.0  \n",
       "1      33.0  \n",
       "2       9.0  \n",
       "3       5.0  \n",
       "4       5.0  \n",
       "5       6.0  \n",
       "6       9.0  \n",
       "7       6.0  \n",
       "8       3.0  \n",
       "9      66.0  \n",
       "10      7.0  \n",
       "11      9.0  \n",
       "12     10.0  \n",
       "13      4.0  \n",
       "14     13.0  \n",
       "15      7.0  \n",
       "16     10.0  \n",
       "17      7.0  \n",
       "18     45.0  \n",
       "19      8.0  \n",
       "20      5.0  \n",
       "21      9.0  \n",
       "22     29.0  \n",
       "23      8.0  \n",
       "24      6.0  \n",
       "25      7.0  \n",
       "26      7.0  \n",
       "27      4.0  \n",
       "28      5.0  \n",
       "29      8.0  \n",
       "..      ...  \n",
       "544     8.0  \n",
       "545     7.0  \n",
       "546     7.0  \n",
       "547     5.0  \n",
       "548     7.0  \n",
       "549     9.0  \n",
       "550    85.0  \n",
       "551     5.0  \n",
       "552     1.0  \n",
       "553     4.0  \n",
       "554     6.0  \n",
       "555     7.0  \n",
       "556     7.0  \n",
       "557   144.0  \n",
       "558     8.0  \n",
       "559     7.0  \n",
       "560    80.0  \n",
       "561     9.0  \n",
       "562    19.0  \n",
       "563    82.0  \n",
       "564     6.0  \n",
       "565     6.0  \n",
       "566     4.0  \n",
       "567     6.0  \n",
       "568    22.0  \n",
       "569     6.0  \n",
       "570     4.0  \n",
       "571    22.0  \n",
       "572     4.0  \n",
       "573     8.0  \n",
       "\n",
       "[574 rows x 7 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Math Winnings of Each Team\n",
    "match_data = match.loc[:,('Match_Id','Match_Date','Team_Name_Id','Opponent_Team_Id','Match_Winner_Id','Win_Type','Won_By')]\n",
    "team_data = team.loc[:,('Team_Id','Team_Short_Code')]\n",
    "team_data = team_data.rename(columns={'Team_Id':'Team_Name_Id'})\n",
    "\n",
    "result = match_data.merge(team_data, on='Team_Name_Id')\n",
    "\n",
    "result = result.drop('Team_Name_Id', axis=1)\n",
    "\n",
    "result=result.rename(columns={'Opponent_Team_Id':'Team_Name_Id','Team_Short_Code':'Team_Name'})\n",
    "result=result.merge(team_data,on='Team_Name_Id')\n",
    "\n",
    "result=result.drop('Team_Name_Id',axis=1)\n",
    "\n",
    "final_res = result.rename(columns={'Match_Winner_Id':'Team_Name_Id','Team_Short_Code':'Opponent_Team_Name'})\n",
    "\n",
    "final_res = final_res.merge(team_data,on='Team_Name_Id')\n",
    "\n",
    "final_res = final_res.drop('Team_Name_Id',axis=1)\n",
    "\n",
    "winner_team = final_res.rename(columns={'Team_Short_Code':'Winner_Team'})\n",
    "winner_team = winner_team.sort_values(by='Match_Id').reset_index()\n",
    "winner_team = winner_team.drop('index',axis=1)\n",
    "winner_team = winner_team[['Match_Id','Match_Date','Team_Name','Opponent_Team_Name','Winner_Team','Win_Type','Won_By']]\n",
    "winner_team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Match_Id</th>\n",
       "      <th>Man_of_the_match</th>\n",
       "      <th>Match_Date</th>\n",
       "      <th>Team_Name</th>\n",
       "      <th>Opponent_Team_Name</th>\n",
       "      <th>Winner_Team</th>\n",
       "      <th>Win_Type</th>\n",
       "      <th>Won_By</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335987</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>RCB</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KKR</td>\n",
       "      <td>by runs</td>\n",
       "      <td>140.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>335988</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>CSK</td>\n",
       "      <td>CSK</td>\n",
       "      <td>by runs</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>335989</td>\n",
       "      <td>MF Maharoof</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>DD</td>\n",
       "      <td>RR</td>\n",
       "      <td>DD</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>335990</td>\n",
       "      <td>MV Boucher</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>MI</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>335991</td>\n",
       "      <td>DJ Hussey</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>KKR</td>\n",
       "      <td>DC</td>\n",
       "      <td>KKR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>335992</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>21-Apr-08</td>\n",
       "      <td>RR</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>RR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>335993</td>\n",
       "      <td>V Sehwag</td>\n",
       "      <td>22-Apr-08</td>\n",
       "      <td>DC</td>\n",
       "      <td>DD</td>\n",
       "      <td>DD</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>335994</td>\n",
       "      <td>ML Hayden</td>\n",
       "      <td>23-Apr-08</td>\n",
       "      <td>CSK</td>\n",
       "      <td>MI</td>\n",
       "      <td>CSK</td>\n",
       "      <td>by runs</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>335995</td>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>24-Apr-08</td>\n",
       "      <td>DC</td>\n",
       "      <td>RR</td>\n",
       "      <td>RR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>335996</td>\n",
       "      <td>KC Sangakkara</td>\n",
       "      <td>25-Apr-08</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>MI</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by runs</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>335997</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>26-Apr-08</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RR</td>\n",
       "      <td>RR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>335998</td>\n",
       "      <td>JDP Oram</td>\n",
       "      <td>26-Apr-08</td>\n",
       "      <td>CSK</td>\n",
       "      <td>KKR</td>\n",
       "      <td>CSK</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>335999</td>\n",
       "      <td>AC Gilchrist</td>\n",
       "      <td>27-Apr-08</td>\n",
       "      <td>MI</td>\n",
       "      <td>DC</td>\n",
       "      <td>DC</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>336000</td>\n",
       "      <td>SM Katich</td>\n",
       "      <td>27-Apr-08</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>DD</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>336001</td>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>28-Apr-08</td>\n",
       "      <td>RCB</td>\n",
       "      <td>CSK</td>\n",
       "      <td>CSK</td>\n",
       "      <td>by runs</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>336002</td>\n",
       "      <td>ST Jayasuriya</td>\n",
       "      <td>29-Apr-08</td>\n",
       "      <td>KKR</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>336003</td>\n",
       "      <td>GD McGrath</td>\n",
       "      <td>30-Apr-08</td>\n",
       "      <td>DD</td>\n",
       "      <td>RCB</td>\n",
       "      <td>DD</td>\n",
       "      <td>by runs</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>336004</td>\n",
       "      <td>SE Marsh</td>\n",
       "      <td>1-May-08</td>\n",
       "      <td>DC</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>336005</td>\n",
       "      <td>SA Asnodkar</td>\n",
       "      <td>1-May-08</td>\n",
       "      <td>RR</td>\n",
       "      <td>KKR</td>\n",
       "      <td>RR</td>\n",
       "      <td>by runs</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>336006</td>\n",
       "      <td>V Sehwag</td>\n",
       "      <td>2-May-08</td>\n",
       "      <td>CSK</td>\n",
       "      <td>DD</td>\n",
       "      <td>DD</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>336007</td>\n",
       "      <td>R Vinay Kumar</td>\n",
       "      <td>25-May-08</td>\n",
       "      <td>DC</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>336008</td>\n",
       "      <td>IK Pathan</td>\n",
       "      <td>3-May-08</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by runs</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>336009</td>\n",
       "      <td>SM Pollock</td>\n",
       "      <td>4-May-08</td>\n",
       "      <td>MI</td>\n",
       "      <td>DD</td>\n",
       "      <td>MI</td>\n",
       "      <td>by runs</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>336010</td>\n",
       "      <td>Sohail Tanvir</td>\n",
       "      <td>4-May-08</td>\n",
       "      <td>RR</td>\n",
       "      <td>CSK</td>\n",
       "      <td>RR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>336011</td>\n",
       "      <td>S Sreesanth</td>\n",
       "      <td>5-May-08</td>\n",
       "      <td>RCB</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>336012</td>\n",
       "      <td>AC Gilchrist</td>\n",
       "      <td>6-May-08</td>\n",
       "      <td>CSK</td>\n",
       "      <td>DC</td>\n",
       "      <td>DC</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>336013</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>7-May-08</td>\n",
       "      <td>MI</td>\n",
       "      <td>RR</td>\n",
       "      <td>MI</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>336014</td>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>8-May-08</td>\n",
       "      <td>DD</td>\n",
       "      <td>CSK</td>\n",
       "      <td>CSK</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>336015</td>\n",
       "      <td>SC Ganguly</td>\n",
       "      <td>8-May-08</td>\n",
       "      <td>KKR</td>\n",
       "      <td>RCB</td>\n",
       "      <td>KKR</td>\n",
       "      <td>by runs</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>336016</td>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>9-May-08</td>\n",
       "      <td>RR</td>\n",
       "      <td>DC</td>\n",
       "      <td>RR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>980966</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>3-May-16</td>\n",
       "      <td>GL</td>\n",
       "      <td>DD</td>\n",
       "      <td>DD</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>980968</td>\n",
       "      <td>AD Russell</td>\n",
       "      <td>4-May-16</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KKR</td>\n",
       "      <td>by runs</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>980970</td>\n",
       "      <td>AM Rahane</td>\n",
       "      <td>5-May-16</td>\n",
       "      <td>DD</td>\n",
       "      <td>RPS</td>\n",
       "      <td>RPS</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>527</th>\n",
       "      <td>980972</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>6-May-16</td>\n",
       "      <td>SRH</td>\n",
       "      <td>GL</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>980974</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>7-May-16</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RPS</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>980976</td>\n",
       "      <td>MP Stoinis</td>\n",
       "      <td>7-May-16</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>DD</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by runs</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>980978</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>8-May-16</td>\n",
       "      <td>MI</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by runs</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>980980</td>\n",
       "      <td>P Kumar</td>\n",
       "      <td>8-May-16</td>\n",
       "      <td>KKR</td>\n",
       "      <td>GL</td>\n",
       "      <td>GL</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>980982</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>9-May-16</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by runs</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>980984</td>\n",
       "      <td>A Zampa</td>\n",
       "      <td>10-May-16</td>\n",
       "      <td>RPS</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by runs</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>570</th>\n",
       "      <td>980986</td>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>11-May-16</td>\n",
       "      <td>RCB</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>980988</td>\n",
       "      <td>CH Morris</td>\n",
       "      <td>12-May-16</td>\n",
       "      <td>SRH</td>\n",
       "      <td>DD</td>\n",
       "      <td>DD</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>980990</td>\n",
       "      <td>MP Stoinis</td>\n",
       "      <td>13-May-16</td>\n",
       "      <td>MI</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>980992</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>14-May-16</td>\n",
       "      <td>RCB</td>\n",
       "      <td>GL</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by runs</td>\n",
       "      <td>144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>980994</td>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>14-May-16</td>\n",
       "      <td>KKR</td>\n",
       "      <td>RPS</td>\n",
       "      <td>KKR</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>980996</td>\n",
       "      <td>HM Amla</td>\n",
       "      <td>15-May-16</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>980998</td>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>15-May-16</td>\n",
       "      <td>MI</td>\n",
       "      <td>DD</td>\n",
       "      <td>MI</td>\n",
       "      <td>by runs</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>981000</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>16-May-16</td>\n",
       "      <td>KKR</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>562</th>\n",
       "      <td>981002</td>\n",
       "      <td>AB Dinda</td>\n",
       "      <td>17-May-16</td>\n",
       "      <td>RPS</td>\n",
       "      <td>DD</td>\n",
       "      <td>RPS</td>\n",
       "      <td>by runs</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>981004</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>18-May-16</td>\n",
       "      <td>RCB</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by runs</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>981006</td>\n",
       "      <td>DR Smith</td>\n",
       "      <td>19-May-16</td>\n",
       "      <td>GL</td>\n",
       "      <td>KKR</td>\n",
       "      <td>GL</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>981008</td>\n",
       "      <td>KK Nair</td>\n",
       "      <td>20-May-16</td>\n",
       "      <td>DD</td>\n",
       "      <td>SRH</td>\n",
       "      <td>DD</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>981010</td>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>21-May-16</td>\n",
       "      <td>RPS</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>RPS</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>981012</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>21-May-16</td>\n",
       "      <td>GL</td>\n",
       "      <td>MI</td>\n",
       "      <td>GL</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>981014</td>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>22-May-16</td>\n",
       "      <td>KKR</td>\n",
       "      <td>SRH</td>\n",
       "      <td>KKR</td>\n",
       "      <td>by runs</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>981016</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>22-May-16</td>\n",
       "      <td>DD</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>981018</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>24-May-16</td>\n",
       "      <td>GL</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>981020</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>25-May-16</td>\n",
       "      <td>SRH</td>\n",
       "      <td>KKR</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by runs</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>981022</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>27-May-16</td>\n",
       "      <td>GL</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by wickets</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>981024</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>29-May-16</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "      <td>SRH</td>\n",
       "      <td>by runs</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>574 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Match_Id Man_of_the_match Match_Date Team_Name Opponent_Team_Name  \\\n",
       "0      335987      BB McCullum  18-Apr-08       RCB                KKR   \n",
       "5      335988       MEK Hussey  19-Apr-08      KXIP                CSK   \n",
       "17     335989      MF Maharoof  19-Apr-08        DD                 RR   \n",
       "18     335990       MV Boucher  20-Apr-08        MI                RCB   \n",
       "20     335991        DJ Hussey  20-Apr-08       KKR                 DC   \n",
       "22     335992        SR Watson  21-Apr-08        RR               KXIP   \n",
       "32     335993         V Sehwag  22-Apr-08        DC                 DD   \n",
       "43     335994        ML Hayden  23-Apr-08       CSK                 MI   \n",
       "47     335995        YK Pathan  24-Apr-08        DC                 RR   \n",
       "63     335996    KC Sangakkara  25-Apr-08      KXIP                 MI   \n",
       "23     335997        SR Watson  26-Apr-08       RCB                 RR   \n",
       "68     335998         JDP Oram  26-Apr-08       CSK                KKR   \n",
       "69     335999     AC Gilchrist  27-Apr-08        MI                 DC   \n",
       "76     336000        SM Katich  27-Apr-08      KXIP                 DD   \n",
       "77     336001         MS Dhoni  28-Apr-08       RCB                CSK   \n",
       "89     336002    ST Jayasuriya  29-Apr-08       KKR                 MI   \n",
       "91     336003       GD McGrath  30-Apr-08        DD                RCB   \n",
       "92     336004         SE Marsh   1-May-08        DC               KXIP   \n",
       "101    336005      SA Asnodkar   1-May-08        RR                KKR   \n",
       "33     336006         V Sehwag   2-May-08       CSK                 DD   \n",
       "102    336007    R Vinay Kumar  25-May-08        DC                RCB   \n",
       "105    336008        IK Pathan   3-May-08      KXIP                KKR   \n",
       "107    336009       SM Pollock   4-May-08        MI                 DD   \n",
       "109    336010    Sohail Tanvir   4-May-08        RR                CSK   \n",
       "111    336011      S Sreesanth   5-May-08       RCB               KXIP   \n",
       "70     336012     AC Gilchrist   6-May-08       CSK                 DC   \n",
       "112    336013          A Nehra   7-May-08        MI                 RR   \n",
       "78     336014         MS Dhoni   8-May-08        DD                CSK   \n",
       "118    336015       SC Ganguly   8-May-08       KKR                RCB   \n",
       "48     336016        YK Pathan   9-May-08        RR                 DC   \n",
       "..        ...              ...        ...       ...                ...   \n",
       "566    980966          RR Pant   3-May-16        GL                 DD   \n",
       "544    980968       AD Russell   4-May-16       KKR               KXIP   \n",
       "440    980970        AM Rahane   5-May-16        DD                RPS   \n",
       "527    980972          B Kumar   6-May-16       SRH                 GL   \n",
       "406    980974          V Kohli   7-May-16       RCB                RPS   \n",
       "567    980976       MP Stoinis   7-May-16      KXIP                 DD   \n",
       "117    980978          A Nehra   8-May-16        MI                SRH   \n",
       "159    980980          P Kumar   8-May-16       KKR                 GL   \n",
       "31     980982        SR Watson   9-May-16      KXIP                RCB   \n",
       "569    980984          A Zampa  10-May-16       RPS                SRH   \n",
       "570    980986        KH Pandya  11-May-16       RCB                 MI   \n",
       "564    980988        CH Morris  12-May-16       SRH                 DD   \n",
       "568    980990       MP Stoinis  13-May-16        MI               KXIP   \n",
       "218    980992   AB de Villiers  14-May-16       RCB                 GL   \n",
       "61     980994        YK Pathan  14-May-16       KKR                RPS   \n",
       "572    980996          HM Amla  15-May-16      KXIP                SRH   \n",
       "571    980998        KH Pandya  15-May-16        MI                 DD   \n",
       "407    981000          V Kohli  16-May-16       KKR                RCB   \n",
       "562    981002         AB Dinda  17-May-16       RPS                 DD   \n",
       "408    981004          V Kohli  18-May-16       RCB               KXIP   \n",
       "280    981006         DR Smith  19-May-16        GL                KKR   \n",
       "531    981008          KK Nair  20-May-16        DD                SRH   \n",
       "88     981010         MS Dhoni  21-May-16       RPS               KXIP   \n",
       "173    981012         SK Raina  21-May-16        GL                 MI   \n",
       "62     981014        YK Pathan  22-May-16       KKR                SRH   \n",
       "409    981016          V Kohli  22-May-16        DD                RCB   \n",
       "219    981018   AB de Villiers  24-May-16        GL                RCB   \n",
       "555    981020     MC Henriques  25-May-16       SRH                KKR   \n",
       "339    981022        DA Warner  27-May-16        GL                SRH   \n",
       "573    981024      BCJ Cutting  29-May-16       RCB                SRH   \n",
       "\n",
       "    Winner_Team    Win_Type  Won_By  \n",
       "0           KKR     by runs   140.0  \n",
       "5           CSK     by runs    33.0  \n",
       "17           DD  by wickets     9.0  \n",
       "18          RCB  by wickets     5.0  \n",
       "20          KKR  by wickets     5.0  \n",
       "22           RR  by wickets     6.0  \n",
       "32           DD  by wickets     9.0  \n",
       "43          CSK     by runs     6.0  \n",
       "47           RR  by wickets     3.0  \n",
       "63         KXIP     by runs    66.0  \n",
       "23           RR  by wickets     7.0  \n",
       "68          CSK  by wickets     9.0  \n",
       "69           DC  by wickets    10.0  \n",
       "76         KXIP  by wickets     4.0  \n",
       "77          CSK     by runs    13.0  \n",
       "89           MI  by wickets     7.0  \n",
       "91           DD     by runs    10.0  \n",
       "92         KXIP  by wickets     7.0  \n",
       "101          RR     by runs    45.0  \n",
       "33           DD  by wickets     8.0  \n",
       "102         RCB  by wickets     5.0  \n",
       "105        KXIP     by runs     9.0  \n",
       "107          MI     by runs    29.0  \n",
       "109          RR  by wickets     8.0  \n",
       "111        KXIP  by wickets     6.0  \n",
       "70           DC  by wickets     7.0  \n",
       "112          MI  by wickets     7.0  \n",
       "78          CSK  by wickets     4.0  \n",
       "118         KKR     by runs     5.0  \n",
       "48           RR  by wickets     8.0  \n",
       "..          ...         ...     ...  \n",
       "566          DD  by wickets     8.0  \n",
       "544         KKR     by runs     7.0  \n",
       "440         RPS  by wickets     7.0  \n",
       "527         SRH  by wickets     5.0  \n",
       "406         RCB  by wickets     7.0  \n",
       "567        KXIP     by runs     9.0  \n",
       "117         SRH     by runs    85.0  \n",
       "159          GL  by wickets     5.0  \n",
       "31          RCB     by runs     1.0  \n",
       "569         SRH     by runs     4.0  \n",
       "570          MI  by wickets     6.0  \n",
       "564          DD  by wickets     7.0  \n",
       "568        KXIP  by wickets     7.0  \n",
       "218         RCB     by runs   144.0  \n",
       "61          KKR  by wickets     8.0  \n",
       "572         SRH  by wickets     7.0  \n",
       "571          MI     by runs    80.0  \n",
       "407         RCB  by wickets     9.0  \n",
       "562         RPS     by runs    19.0  \n",
       "408         RCB     by runs    82.0  \n",
       "280          GL  by wickets     6.0  \n",
       "531          DD  by wickets     6.0  \n",
       "88          RPS  by wickets     4.0  \n",
       "173          GL  by wickets     6.0  \n",
       "62          KKR     by runs    22.0  \n",
       "409         RCB  by wickets     6.0  \n",
       "219         RCB  by wickets     4.0  \n",
       "555         SRH     by runs    22.0  \n",
       "339         SRH  by wickets     4.0  \n",
       "573         SRH     by runs     8.0  \n",
       "\n",
       "[574 rows x 8 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Man of the Match\n",
    "mom=match.loc[:,('Match_Id','Man_Of_The_Match_Id')]\n",
    "plr=player.loc[:,('Player_Id','Player_Name')]\n",
    "plr=plr.rename(columns={'Player_Id':'Man_Of_The_Match_Id'})\n",
    "\n",
    "mom=mom.merge(plr,on='Man_Of_The_Match_Id')\n",
    "\n",
    "man_of_match=mom.merge(winner_team,on='Match_Id')\n",
    "\n",
    "man_of_match=man_of_match.sort_values(by='Match_Id')\n",
    "\n",
    "man_of_match=man_of_match.drop(columns='Man_Of_The_Match_Id').rename(columns={'Player_Name':'Man_of_the_match'})\n",
    "\n",
    "man_of_match\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Team_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>RR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>DC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CSK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>CSK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>KKR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7</td>\n",
       "      <td>KKR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>SRH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Season_Id Team_Name\n",
       "0          1        RR\n",
       "1          2        DC\n",
       "2          3       CSK\n",
       "3          4       CSK\n",
       "4          5       KKR\n",
       "5          7       KKR\n",
       "6          6        MI\n",
       "7          8        MI\n",
       "8          9       SRH"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Season Wise Winner\n",
    "season_data = match.loc[:,('Season_Id','Match_Winner_Id')]\n",
    "win = season_data.groupby('Season_Id').tail(1)\n",
    "\n",
    "team_data=team.loc[:,('Team_Id','Team_Short_Code')]\n",
    "team_data = team_data.rename(columns = {'Team_Id':'Match_Winner_Id','Team_Short_Code':'Team_Name'})\n",
    "win.merge(team_data, on='Match_Winner_Id').drop('Match_Winner_Id', axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>Player_Name</th>\n",
       "      <th>Max_score_get</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Harbhajan Singh</td>\n",
       "      <td>3030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>PP Chawla</td>\n",
       "      <td>3021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P Kumar</td>\n",
       "      <td>2966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>A Mishra</td>\n",
       "      <td>2846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>R Vinay Kumar</td>\n",
       "      <td>2830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Player_Name  Max_score_get\n",
       "29  Harbhajan Singh           3030\n",
       "40        PP Chawla           3021\n",
       "8           P Kumar           2966\n",
       "86         A Mishra           2846\n",
       "50    R Vinay Kumar           2830"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total no of scores get by a bowler in all seasons\n",
    "cleaned['Batsman_Scored'] = cleaned['Batsman_Scored'].apply(lambda x: int(x))\n",
    "max_score=cleaned.groupby('Bowler_Id')['Batsman_Scored'].sum()\n",
    "max_score=pd.DataFrame(max_score)\n",
    "\n",
    "nw_plyr=player.rename(columns={'Player_Id':'Bowler_Id'})\n",
    "#max_score.rename(columns={:'Player_Id'})\n",
    "\n",
    "max_score_bowler=nw_plyr.merge(max_score, on='Bowler_Id').sort_values(by='Batsman_Scored',ascending=False)\n",
    "max_score_bowler=max_score_bowler.rename(columns={'Batsman_Scored':'Max_score_get'})\n",
    "\n",
    "max_score_bowler.drop('Bowler_Id',axis=1)\n",
    "max_score_bowler[['Player_Name','Max_score_get']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Player_Name</th>\n",
       "      <th>Batsman_Scored</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>V Kohli</td>\n",
       "      <td>1191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>1166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>1089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>CH Gayle</td>\n",
       "      <td>1088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>1081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Player_Name  Batsman_Scored\n",
       "7       V Kohli            1191\n",
       "39    G Gambhir            1166\n",
       "45   RV Uthappa            1089\n",
       "159    CH Gayle            1088\n",
       "56    RG Sharma            1081"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#total no of dot balls by players in all seasons\n",
    "\n",
    "zeros=data[data.Batsman_Scored == '0']\n",
    "\n",
    "zeros=zeros.groupby('Striker_Id')['Batsman_Scored'].count()\n",
    "max_zero=pd.DataFrame(zeros)\n",
    "\n",
    "max_zero=max_zero.sort_values(by='Batsman_Scored',ascending=False)\n",
    "#max_zero1=max_zero.rename(columns={'Striker_Id':'Player_Id'})\n",
    "#max_zero1\n",
    "player_name=player.loc[:,('Player_Id','Player_Name')]\n",
    "player_name=player_name.rename(columns={'Player_Id':'Striker_Id'})\n",
    "player_name1=player_name.merge(max_zero,on='Striker_Id').sort_values(by='Batsman_Scored',ascending=False)\n",
    "\n",
    "player_name1.drop('Striker_Id',axis=1).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Match_Date</th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Team_Name</th>\n",
       "      <th>Opponent_Team_Name</th>\n",
       "      <th>Match_Winner_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>392195</td>\n",
       "      <td>23-Apr-09</td>\n",
       "      <td>2</td>\n",
       "      <td>KKR</td>\n",
       "      <td>RR</td>\n",
       "      <td>RR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>419126</td>\n",
       "      <td>21-Mar-10</td>\n",
       "      <td>3</td>\n",
       "      <td>CSK</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>598009</td>\n",
       "      <td>7-Apr-13</td>\n",
       "      <td>6</td>\n",
       "      <td>SRH</td>\n",
       "      <td>RCB</td>\n",
       "      <td>SRH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>598022</td>\n",
       "      <td>16-Apr-13</td>\n",
       "      <td>6</td>\n",
       "      <td>RCB</td>\n",
       "      <td>DD</td>\n",
       "      <td>RCB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>729320</td>\n",
       "      <td>29-Apr-14</td>\n",
       "      <td>7</td>\n",
       "      <td>KKR</td>\n",
       "      <td>RR</td>\n",
       "      <td>RR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>829746</td>\n",
       "      <td>21-Apr-15</td>\n",
       "      <td>8</td>\n",
       "      <td>RR</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>KXIP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Match_Id Match_Date  Season_Id Team_Name Opponent_Team_Name  \\\n",
       "0    392195  23-Apr-09          2       KKR                 RR   \n",
       "2    419126  21-Mar-10          3       CSK               KXIP   \n",
       "4    598009   7-Apr-13          6       SRH                RCB   \n",
       "5    598022  16-Apr-13          6       RCB                 DD   \n",
       "1    729320  29-Apr-14          7       KKR                 RR   \n",
       "3    829746  21-Apr-15          8        RR               KXIP   \n",
       "\n",
       "  Match_Winner_Name  \n",
       "0                RR  \n",
       "2              KXIP  \n",
       "4               SRH  \n",
       "5               RCB  \n",
       "1                RR  \n",
       "3              KXIP  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Super_Over_Winners\n",
    "super_over = match.loc[(match.IS_Superover == 1), ('Match_Id','Match_Date','Season_Id','Team_Name_Id','Opponent_Team_Id','Match_Winner_Id','IS_Superover')]\n",
    "team_names = team.loc[:,('Team_Id','Team_Short_Code')]\n",
    "team_names = team_names.rename(columns={'Team_Id':'Team_Name_Id'})\n",
    "super_data = super_over.merge(team_names, on='Team_Name_Id').drop('Team_Name_Id', axis=1)\n",
    "super_data = super_data.rename(columns={'Team_Short_Code':'Team_Name','Opponent_Team_Id':'Team_Name_Id'})\n",
    "super_data = super_data.merge(team_names, on='Team_Name_Id').drop('Team_Name_Id', axis=1)\n",
    "super_data = super_data.rename(columns={'Team_Short_Code':'Opponent_Team_Name','Match_Winner_Id':'Team_Name_Id'})\n",
    "super_data = super_data.merge(team_names, on='Team_Name_Id').drop('Team_Name_Id', axis=1)\n",
    "super_data = super_data.rename(columns={'Team_Short_Code':'Match_Winner_Name'})\n",
    "super_data.drop('IS_Superover', axis=1).sort_values(by='Season_Id', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Match_Date</th>\n",
       "      <th>Season_Id</th>\n",
       "      <th>Team_Name</th>\n",
       "      <th>Opponent_Team_Name</th>\n",
       "      <th>Match_Winner_Name</th>\n",
       "      <th>Toss_Winner_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>335987</td>\n",
       "      <td>18-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>RCB</td>\n",
       "      <td>KKR</td>\n",
       "      <td>KKR</td>\n",
       "      <td>RCB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>335988</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>KXIP</td>\n",
       "      <td>CSK</td>\n",
       "      <td>CSK</td>\n",
       "      <td>CSK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>335989</td>\n",
       "      <td>19-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>DD</td>\n",
       "      <td>RR</td>\n",
       "      <td>DD</td>\n",
       "      <td>RR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>335990</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>MI</td>\n",
       "      <td>RCB</td>\n",
       "      <td>RCB</td>\n",
       "      <td>MI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>335991</td>\n",
       "      <td>20-Apr-08</td>\n",
       "      <td>1</td>\n",
       "      <td>KKR</td>\n",
       "      <td>DC</td>\n",
       "      <td>KKR</td>\n",
       "      <td>DC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Match_Id Match_Date  Season_Id Team_Name Opponent_Team_Name  \\\n",
       "0      335987  18-Apr-08          1       RCB                KKR   \n",
       "403    335988  19-Apr-08          1      KXIP                CSK   \n",
       "477    335989  19-Apr-08          1        DD                 RR   \n",
       "261    335990  20-Apr-08          1        MI                RCB   \n",
       "332    335991  20-Apr-08          1       KKR                 DC   \n",
       "\n",
       "    Match_Winner_Name Toss_Winner_Name  \n",
       "0                 KKR              RCB  \n",
       "403               CSK              CSK  \n",
       "477                DD               RR  \n",
       "261               RCB               MI  \n",
       "332               KKR               DC  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Toss_Winners\n",
    "toss_s = match.loc[:, ('Match_Id','Match_Date','Season_Id','Team_Name_Id','Opponent_Team_Id','Match_Winner_Id','Toss_Winner_Id')]\n",
    "team_names = team.loc[:,('Team_Id','Team_Short_Code')]\n",
    "team_names = team_names.rename(columns={'Team_Id':'Team_Name_Id'})\n",
    "toss_data = toss_s.merge(team_names, on='Team_Name_Id').drop('Team_Name_Id', axis=1)\n",
    "toss_data = toss_data.rename(columns={'Team_Short_Code':'Team_Name','Opponent_Team_Id':'Team_Name_Id'})\n",
    "toss_data = toss_data.merge(team_names, on='Team_Name_Id').drop('Team_Name_Id', axis=1)\n",
    "toss_data = toss_data.rename(columns={'Team_Short_Code':'Opponent_Team_Name','Match_Winner_Id':'Team_Name_Id'})\n",
    "toss_data = toss_data.merge(team_names, on='Team_Name_Id').drop('Team_Name_Id', axis=1)\n",
    "toss_data = toss_data.rename(columns={'Team_Short_Code':'Match_Winner_Name','Toss_Winner_Id':'Team_Name_Id'})\n",
    "toss_data = toss_data.merge(team_names, on='Team_Name_Id').drop('Team_Name_Id', axis=1)\n",
    "toss_data = toss_data.rename(columns={'Team_Short_Code':'Toss_Winner_Name'})\n",
    "toss_data.sort_values(by='Match_Id', ascending=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_Id</th>\n",
       "      <th>Player_Name</th>\n",
       "      <th>Match_Winner_Id</th>\n",
       "      <th>Total_Matches_as_Captain</th>\n",
       "      <th>Win_Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>20.0</td>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>83</td>\n",
       "      <td>142</td>\n",
       "      <td>58.450704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40.0</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>61</td>\n",
       "      <td>107</td>\n",
       "      <td>57.009346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.0</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>37</td>\n",
       "      <td>71</td>\n",
       "      <td>52.112676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>53.0</td>\n",
       "      <td>AC Gilchrist</td>\n",
       "      <td>35</td>\n",
       "      <td>74</td>\n",
       "      <td>47.297297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>57.0</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>34</td>\n",
       "      <td>58</td>\n",
       "      <td>58.620690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Player_Id   Player_Name  Match_Winner_Id  Total_Matches_as_Captain  \\\n",
       "8        20.0      MS Dhoni               83                       142   \n",
       "14       40.0     G Gambhir               61                       107   \n",
       "5         8.0       V Kohli               37                        71   \n",
       "20       53.0  AC Gilchrist               35                        74   \n",
       "22       57.0     RG Sharma               34                        58   \n",
       "\n",
       "    Win_Percentage  \n",
       "8        58.450704  \n",
       "14       57.009346  \n",
       "5        52.112676  \n",
       "20       47.297297  \n",
       "22       58.620690  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Wins as Captain \n",
    "\n",
    "captain_data=player_m.loc[(player_m.Is_Captain== 1),('Match_Id','Player_Id','Is_Captain','Team_Id')]\n",
    "\n",
    "match_data = match.loc[:,('Match_Id','Season_Id','Team_Name_Id','Opponent_Team_Id','Match_Winner_Id')]\n",
    "final_data = match_data.merge(captain_data, on='Match_Id', how='left')\n",
    "final_data = final_data.loc[(final_data.Match_Winner_Id== final_data.Team_Id)]\n",
    "final_data = final_data.groupby('Player_Id')['Match_Winner_Id'].count()\n",
    "\n",
    "total_match_as_cap = captain_data.groupby('Player_Id')['Match_Id'].count()\n",
    "total_match_as_cap = pd.DataFrame(total_match_as_cap)\n",
    "total_match_as_cap = total_match_as_cap.rename(columns={'Match_Id':'Total_Matches_as_Captain'})\n",
    "\n",
    "captain_win = pd.DataFrame(final_data)\n",
    "player_data = player.loc[:,('Player_Id','Player_Name')]\n",
    "captain_win = player_data.merge(captain_win,on='Player_Id')\n",
    "captain_win = captain_win.merge(total_match_as_cap, on='Player_Id')\n",
    "captain_win['Win_Percentage'] = ( captain_win['Match_Winner_Id'].values / captain_win['Total_Matches_as_Captain'].values) * 100\n",
    "captain_win.sort_values(by='Match_Winner_Id', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Matches</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Season_Id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Total_Matches\n",
       "Season_Id               \n",
       "1                     58\n",
       "2                     57\n",
       "3                     60\n",
       "4                     73\n",
       "5                     74\n",
       "6                     76\n",
       "7                     60\n",
       "8                     59\n",
       "9                     60"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total Matches in All Seasons\n",
    "total_matches = match.loc[:,('Match_Id','Season_Id')]\n",
    "total_matches = total_matches.groupby('Season_Id')['Match_Id'].count()\n",
    "total_matches = pd.DataFrame(total_matches)\n",
    "total_matches.rename(columns={'Match_Id':'Total_Matches'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Player_Name</th>\n",
       "      <th>Dissimal_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SK Raina</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Player_Name  Dissimal_Type\n",
       "81       KD Karthik             72\n",
       "101  AB de Villiers             72\n",
       "18         SK Raina             67\n",
       "42       RV Uthappa             58\n",
       "17         MS Dhoni             51"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Max catches\n",
    "catch = data.loc[data.Dissimal_Type=='caught',('Dissimal_Type','Fielder_Id')]\n",
    "\n",
    "catch['Fielder_Id']=catch['Fielder_Id'].astype(str).astype(float)\n",
    "catch=catch.groupby('Fielder_Id')['Dissimal_Type'].count()\n",
    "\n",
    "#catch.info()\n",
    "\n",
    "player_c = player.loc[:,('Player_Id','Player_Name')]\n",
    "#plr.info()\n",
    "\n",
    "plr = player_c.rename (columns={'Player_Id':'Fielder_Id'})\n",
    "\n",
    "catches=plr.merge(catch,on='Fielder_Id')\n",
    "catches.sort_values(by='Dissimal_Type',ascending=False).drop('Fielder_Id',axis=1).head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>KKR</th>\n",
       "      <th>RCB</th>\n",
       "      <th>CSK</th>\n",
       "      <th>KXIP</th>\n",
       "      <th>RR</th>\n",
       "      <th>DD</th>\n",
       "      <th>MI</th>\n",
       "      <th>DC</th>\n",
       "      <th>KTK</th>\n",
       "      <th>PW</th>\n",
       "      <th>SRH</th>\n",
       "      <th>RPS</th>\n",
       "      <th>GL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SC Ganguly</td>\n",
       "      <td>R Dravid</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>DJ Hussey</td>\n",
       "      <td>R Dravid</td>\n",
       "      <td>Z Khan</td>\n",
       "      <td>RT Ponting</td>\n",
       "      <td>CL White</td>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>SC Ganguly</td>\n",
       "      <td>CL White</td>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>BB McCullum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BB McCullum</td>\n",
       "      <td>W Jaffer</td>\n",
       "      <td>DJ Hussey</td>\n",
       "      <td>P Kumar</td>\n",
       "      <td>JDP Oram</td>\n",
       "      <td>JR Hopes</td>\n",
       "      <td>P Kumar</td>\n",
       "      <td>PA Patel</td>\n",
       "      <td>B Akhil</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>P Kumar</td>\n",
       "      <td>IK Pathan</td>\n",
       "      <td>P Kumar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RT Ponting</td>\n",
       "      <td>V Kohli</td>\n",
       "      <td>PA Patel</td>\n",
       "      <td>K Goel</td>\n",
       "      <td>T Kohli</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>Z Khan</td>\n",
       "      <td>KC Sangakkara</td>\n",
       "      <td>PA Patel</td>\n",
       "      <td>M Rawat</td>\n",
       "      <td>PA Patel</td>\n",
       "      <td>RP Singh</td>\n",
       "      <td>SK Raina</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DJ Hussey</td>\n",
       "      <td>JH Kallis</td>\n",
       "      <td>ML Hayden</td>\n",
       "      <td>JR Hopes</td>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>IK Pathan</td>\n",
       "      <td>PA Patel</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>KC Sangakkara</td>\n",
       "      <td>SS Tiwary</td>\n",
       "      <td>RA Jadeja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mohammad Hafeez</td>\n",
       "      <td>CL White</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>KC Sangakkara</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>MEK Hussey</td>\n",
       "      <td>AC Gilchrist</td>\n",
       "      <td>VVS Laxman</td>\n",
       "      <td>AM Nayar</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>I Sharma</td>\n",
       "      <td>DJ Bravo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JH Kallis</td>\n",
       "      <td>MV Boucher</td>\n",
       "      <td>MS Dhoni</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>M Kaif</td>\n",
       "      <td>V Sehwag</td>\n",
       "      <td>JDP Oram</td>\n",
       "      <td>Y Venugopal Rao</td>\n",
       "      <td>RP Singh</td>\n",
       "      <td>LRPL Taylor</td>\n",
       "      <td>IK Pathan</td>\n",
       "      <td>AM Rahane</td>\n",
       "      <td>DS Kulkarni</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MV Boucher</td>\n",
       "      <td>B Akhil</td>\n",
       "      <td>SK Raina</td>\n",
       "      <td>SM Katich</td>\n",
       "      <td>DS Lehmann</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>VVS Laxman</td>\n",
       "      <td>DPMD Jayawardene</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>S Dhawan</td>\n",
       "      <td>R Bhatia</td>\n",
       "      <td>KD Karthik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>YK Pathan</td>\n",
       "      <td>AA Noffke</td>\n",
       "      <td>JDP Oram</td>\n",
       "      <td>IK Pathan</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>LRPL Taylor</td>\n",
       "      <td>L Ronchi</td>\n",
       "      <td>A Symonds</td>\n",
       "      <td>S Sreesanth</td>\n",
       "      <td>GC Smith</td>\n",
       "      <td>Y Venugopal Rao</td>\n",
       "      <td>AB Dinda</td>\n",
       "      <td>DW Steyn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>G Gambhir</td>\n",
       "      <td>P Kumar</td>\n",
       "      <td>S Badrinath</td>\n",
       "      <td>T Kohli</td>\n",
       "      <td>M Rawat</td>\n",
       "      <td>Y Venugopal Rao</td>\n",
       "      <td>ST Jayasuriya</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>R Vinay Kumar</td>\n",
       "      <td>M Kartik</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>JA Morkel</td>\n",
       "      <td>PJ Sangwan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>Z Khan</td>\n",
       "      <td>RA Jadeja</td>\n",
       "      <td>M Kaif</td>\n",
       "      <td>D Salunkhe</td>\n",
       "      <td>LR Shukla</td>\n",
       "      <td>DJ Thornely</td>\n",
       "      <td>SB Styris</td>\n",
       "      <td>BJ Hodge</td>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>I Sharma</td>\n",
       "      <td>KP Pietersen</td>\n",
       "      <td>DR Smith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SB Bangar</td>\n",
       "      <td>SB Joshi</td>\n",
       "      <td>SB Styris</td>\n",
       "      <td>V Sehwag</td>\n",
       "      <td>SK Warne</td>\n",
       "      <td>DPMD Jayawardene</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>SB Bangar</td>\n",
       "      <td>M Muralitharan</td>\n",
       "      <td>AB Dinda</td>\n",
       "      <td>DW Steyn</td>\n",
       "      <td>GJ Bailey</td>\n",
       "      <td>SB Jakati</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>WP Saha</td>\n",
       "      <td>PA Patel</td>\n",
       "      <td>WP Saha</td>\n",
       "      <td>AM Nayar</td>\n",
       "      <td>SK Trivedi</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>PR Shah</td>\n",
       "      <td>WPUJC Vaas</td>\n",
       "      <td>RR Powar</td>\n",
       "      <td>JD Ryder</td>\n",
       "      <td>DB Ravi Teja</td>\n",
       "      <td>R Ashwin</td>\n",
       "      <td>S Ladda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LR Shukla</td>\n",
       "      <td>Yuvraj Singh</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>AC Gilchrist</td>\n",
       "      <td>PR Shah</td>\n",
       "      <td>SS Tiwary</td>\n",
       "      <td>AM Nayar</td>\n",
       "      <td>RP Singh</td>\n",
       "      <td>OA Shah</td>\n",
       "      <td>Kamran Khan</td>\n",
       "      <td>S Anirudha</td>\n",
       "      <td>MR Marsh</td>\n",
       "      <td>AJ Finch</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>B Lee</td>\n",
       "      <td>SR Watson</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>WP Saha</td>\n",
       "      <td>AM Nayar</td>\n",
       "      <td>AB Agarkar</td>\n",
       "      <td>SM Pollock</td>\n",
       "      <td>S Sohal</td>\n",
       "      <td>KM Jadhav</td>\n",
       "      <td>M Manhas</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>NLTC Perera</td>\n",
       "      <td>JP Faulkner</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PP Chawla</td>\n",
       "      <td>M Kaif</td>\n",
       "      <td>VY Mahesh</td>\n",
       "      <td>DPMD Jayawardene</td>\n",
       "      <td>LRPL Taylor</td>\n",
       "      <td>Shoaib Malik</td>\n",
       "      <td>Harbhajan Singh</td>\n",
       "      <td>Shahid Afridi</td>\n",
       "      <td>RV Gomez</td>\n",
       "      <td>BAW Mendis</td>\n",
       "      <td>RS Bopara</td>\n",
       "      <td>F du Plessis</td>\n",
       "      <td>PV Tambe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>R Vinay Kumar</td>\n",
       "      <td>RV Uthappa</td>\n",
       "      <td>SP Fleming</td>\n",
       "      <td>S Sohal</td>\n",
       "      <td>Kamran Akmal</td>\n",
       "      <td>MK Tiwary</td>\n",
       "      <td>A Symonds</td>\n",
       "      <td>I Sharma</td>\n",
       "      <td>NLTC Perera</td>\n",
       "      <td>TL Suman</td>\n",
       "      <td>MC Henriques</td>\n",
       "      <td>SPD Smith</td>\n",
       "      <td>Ishan Kishan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AB Agarkar</td>\n",
       "      <td>S Chanderpaul</td>\n",
       "      <td>S Vidyut</td>\n",
       "      <td>B Lee</td>\n",
       "      <td>GC Smith</td>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>RG Sharma</td>\n",
       "      <td>B Chipli</td>\n",
       "      <td>M Klinger</td>\n",
       "      <td>AD Mathews</td>\n",
       "      <td>KV Sharma</td>\n",
       "      <td>Ankit Sharma</td>\n",
       "      <td>AD Nath</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>M Kartik</td>\n",
       "      <td>LRPL Taylor</td>\n",
       "      <td>JA Morkel</td>\n",
       "      <td>PP Chawla</td>\n",
       "      <td>Pankaj Singh</td>\n",
       "      <td>R Bhatia</td>\n",
       "      <td>RP Singh</td>\n",
       "      <td>DW Steyn</td>\n",
       "      <td>P Parameswaran</td>\n",
       "      <td>AA Jhunjhunwala</td>\n",
       "      <td>NV Ojha</td>\n",
       "      <td>M Ashwin</td>\n",
       "      <td>S Kaushik</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>I Sharma</td>\n",
       "      <td>RP Singh</td>\n",
       "      <td>Joginder Sharma</td>\n",
       "      <td>WA Mota</td>\n",
       "      <td>S Sreesanth</td>\n",
       "      <td>MF Maharoof</td>\n",
       "      <td>DJ Bravo</td>\n",
       "      <td>HH Gibbs</td>\n",
       "      <td>Y Gnaneswara Rao</td>\n",
       "      <td>MD Mishra</td>\n",
       "      <td>DA Warner</td>\n",
       "      <td>PSP Handscomb</td>\n",
       "      <td>ER Dwivedi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MK Tiwary</td>\n",
       "      <td>Pankaj Singh</td>\n",
       "      <td>MS Gony</td>\n",
       "      <td>RR Sarwan</td>\n",
       "      <td>DS Kulkarni</td>\n",
       "      <td>VY Mahesh</td>\n",
       "      <td>MA Khote</td>\n",
       "      <td>DNT Zoysa</td>\n",
       "      <td>P Prasanth</td>\n",
       "      <td>R Sharma</td>\n",
       "      <td>AP Tare</td>\n",
       "      <td>UT Khawaja</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>R Bhatia</td>\n",
       "      <td>SS Tiwary</td>\n",
       "      <td>M Muralitharan</td>\n",
       "      <td>S Sreesanth</td>\n",
       "      <td>AM Rahane</td>\n",
       "      <td>B Chipli</td>\n",
       "      <td>A Nehra</td>\n",
       "      <td>D Kalyankrishna</td>\n",
       "      <td>None</td>\n",
       "      <td>MR Marsh</td>\n",
       "      <td>EJG Morgan</td>\n",
       "      <td>A Zampa</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>DB Das</td>\n",
       "      <td>R Vinay Kumar</td>\n",
       "      <td>M Ntini</td>\n",
       "      <td>VRV Singh</td>\n",
       "      <td>R Bhatia</td>\n",
       "      <td>AB Dinda</td>\n",
       "      <td>SS Tiwary</td>\n",
       "      <td>LPC Silva</td>\n",
       "      <td>None</td>\n",
       "      <td>AJ Finch</td>\n",
       "      <td>Bipul Sharma</td>\n",
       "      <td>SM Boland</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>M Kartik</td>\n",
       "      <td>S Anirudha</td>\n",
       "      <td>M Kartik</td>\n",
       "      <td>SA Asnodkar</td>\n",
       "      <td>JA Morkel</td>\n",
       "      <td>DS Kulkarni</td>\n",
       "      <td>DB Ravi Teja</td>\n",
       "      <td>None</td>\n",
       "      <td>RV Gomez</td>\n",
       "      <td>AJ Finch</td>\n",
       "      <td>DL Chahar</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Salman Butt</td>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>CK Kapugedera</td>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>Sohail Tanvir</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>R Vinay Kumar</td>\n",
       "      <td>MS Gony</td>\n",
       "      <td>None</td>\n",
       "      <td>WD Parnell</td>\n",
       "      <td>NLTC Perera</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>BJ Hodge</td>\n",
       "      <td>B Chipli</td>\n",
       "      <td>DR Smith</td>\n",
       "      <td>TM Srivastava</td>\n",
       "      <td>BJ Hodge</td>\n",
       "      <td>PJ Sangwan</td>\n",
       "      <td>AM Rahane</td>\n",
       "      <td>PP Ojha</td>\n",
       "      <td>None</td>\n",
       "      <td>TD Paine</td>\n",
       "      <td>B Kumar</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Umar Gul</td>\n",
       "      <td>DW Steyn</td>\n",
       "      <td>L Balaji</td>\n",
       "      <td>SE Marsh</td>\n",
       "      <td>MM Patel</td>\n",
       "      <td>Mohammad Asif</td>\n",
       "      <td>KD Karthik</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>None</td>\n",
       "      <td>SB Wagh</td>\n",
       "      <td>Ankit Sharma</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>AB Dinda</td>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>A Mukund</td>\n",
       "      <td>MS Gony</td>\n",
       "      <td>SP Goswami</td>\n",
       "      <td>GD McGrath</td>\n",
       "      <td>MK Pandey</td>\n",
       "      <td>PM Sarvesh Kumar</td>\n",
       "      <td>None</td>\n",
       "      <td>AC Thomas</td>\n",
       "      <td>A Ashish Reddy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>PJ Sangwan</td>\n",
       "      <td>AB Dinda</td>\n",
       "      <td>A Flintoff</td>\n",
       "      <td>U Kaul</td>\n",
       "      <td>AD Mascarenhas</td>\n",
       "      <td>TM Dilshan</td>\n",
       "      <td>HH Gibbs</td>\n",
       "      <td>DP Vijaykumar</td>\n",
       "      <td>None</td>\n",
       "      <td>NL McCullum</td>\n",
       "      <td>BB Samantray</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>A Chopra</td>\n",
       "      <td>JA Morkel</td>\n",
       "      <td>T Thushara</td>\n",
       "      <td>AD Mascarenhas</td>\n",
       "      <td>NK Patel</td>\n",
       "      <td>A Mishra</td>\n",
       "      <td>YV Takawale</td>\n",
       "      <td>H Das</td>\n",
       "      <td>None</td>\n",
       "      <td>JE Taylor</td>\n",
       "      <td>PA Reddy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>T Taibu</td>\n",
       "      <td>AB de Villiers</td>\n",
       "      <td>M Manhas</td>\n",
       "      <td>LA Pomersbach</td>\n",
       "      <td>Iqbal Abdulla</td>\n",
       "      <td>JP Duminy</td>\n",
       "      <td>RR Raje</td>\n",
       "      <td>DR Smith</td>\n",
       "      <td>None</td>\n",
       "      <td>AG Murtaza</td>\n",
       "      <td>GH Vihari</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>JD Unadkat</td>\n",
       "      <td>R Ninan</td>\n",
       "      <td>None</td>\n",
       "      <td>Shivam Sharma</td>\n",
       "      <td>GB Hogg</td>\n",
       "      <td>CH Morris</td>\n",
       "      <td>TG Southee</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Mohammed Shami</td>\n",
       "      <td>S Aravind</td>\n",
       "      <td>None</td>\n",
       "      <td>BE Hendricks</td>\n",
       "      <td>A Chandila</td>\n",
       "      <td>SV Samson</td>\n",
       "      <td>BR Dunk</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>SMSM Senanayake</td>\n",
       "      <td>J Syed Mohammad</td>\n",
       "      <td>None</td>\n",
       "      <td>Karanveer Singh</td>\n",
       "      <td>KK Nair</td>\n",
       "      <td>BJ Rohrer</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>M de Lange</td>\n",
       "      <td>P Parameswaran</td>\n",
       "      <td>None</td>\n",
       "      <td>Anureet Singh</td>\n",
       "      <td>MDKJ Perera</td>\n",
       "      <td>Q de Kock</td>\n",
       "      <td>LMP Simmons</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>JO Holder</td>\n",
       "      <td>VR Aaron</td>\n",
       "      <td>None</td>\n",
       "      <td>SN Thakur</td>\n",
       "      <td>R Shukla</td>\n",
       "      <td>CM Gautam</td>\n",
       "      <td>S Gopal</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Anureet Singh</td>\n",
       "      <td>ND Doshi</td>\n",
       "      <td>None</td>\n",
       "      <td>MP Stoinis</td>\n",
       "      <td>CH Morris</td>\n",
       "      <td>S Kaul</td>\n",
       "      <td>M de Lange</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>PJ Cummins</td>\n",
       "      <td>S Rana</td>\n",
       "      <td>None</td>\n",
       "      <td>P Sahu</td>\n",
       "      <td>SV Samson</td>\n",
       "      <td>NM Coulter-Nile</td>\n",
       "      <td>J Suchith</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>C Munro</td>\n",
       "      <td>HV Patel</td>\n",
       "      <td>None</td>\n",
       "      <td>KJ Abbott</td>\n",
       "      <td>KW Richardson</td>\n",
       "      <td>JDS Neesham</td>\n",
       "      <td>HH Pandya</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>KC Cariappa</td>\n",
       "      <td>RR Bhatkal</td>\n",
       "      <td>None</td>\n",
       "      <td>NS Naik</td>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>Imran Tahir</td>\n",
       "      <td>MJ McClenaghan</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>SS Sarkar</td>\n",
       "      <td>KK Nair</td>\n",
       "      <td>None</td>\n",
       "      <td>KC Cariappa</td>\n",
       "      <td>PV Tambe</td>\n",
       "      <td>SS Iyer</td>\n",
       "      <td>JC Buttler</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>MB Parmar</td>\n",
       "      <td>JD Unadkat</td>\n",
       "      <td>None</td>\n",
       "      <td>Swapnil Singh</td>\n",
       "      <td>TG Southee</td>\n",
       "      <td>DJ Muthuswami</td>\n",
       "      <td>KH Pandya</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>JW Hastings</td>\n",
       "      <td>R Rampaul</td>\n",
       "      <td>None</td>\n",
       "      <td>HM Amla</td>\n",
       "      <td>BCJ Cutting</td>\n",
       "      <td>CR Brathwaite</td>\n",
       "      <td>MJ Guptill</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Kuldeep Yadav</td>\n",
       "      <td>KL Rahul</td>\n",
       "      <td>None</td>\n",
       "      <td>F Behardien</td>\n",
       "      <td>R Tewatia</td>\n",
       "      <td>RR Pant</td>\n",
       "      <td>N Rana</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>None</td>\n",
       "      <td>DJG Sammy</td>\n",
       "      <td>None</td>\n",
       "      <td>Gagandeep Singh</td>\n",
       "      <td>DJ Hooda</td>\n",
       "      <td>SW Billings</td>\n",
       "      <td>A Nel</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>None</td>\n",
       "      <td>KW Richardson</td>\n",
       "      <td>None</td>\n",
       "      <td>RR Bose</td>\n",
       "      <td>BB Sran</td>\n",
       "      <td>J Yadav</td>\n",
       "      <td>RA Shaikh</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>None</td>\n",
       "      <td>Parvez Rasool</td>\n",
       "      <td>None</td>\n",
       "      <td>MG Neser</td>\n",
       "      <td>S Badree</td>\n",
       "      <td>B Geeves</td>\n",
       "      <td>RW Price</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>None</td>\n",
       "      <td>Sachin Baby</td>\n",
       "      <td>None</td>\n",
       "      <td>P Dharmani</td>\n",
       "      <td>None</td>\n",
       "      <td>AM Salvi</td>\n",
       "      <td>P Suyal</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>None</td>\n",
       "      <td>NJ Maddinson</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>GS Sandhu</td>\n",
       "      <td>K Santokie</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>None</td>\n",
       "      <td>MA Starc</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>RV Pawar</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>None</td>\n",
       "      <td>RR Rossouw</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>None</td>\n",
       "      <td>YS Chahal</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>None</td>\n",
       "      <td>VH Zol</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>None</td>\n",
       "      <td>SA Abbott</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>None</td>\n",
       "      <td>D Wiese</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>None</td>\n",
       "      <td>SN Khan</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>None</td>\n",
       "      <td>TM Head</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>None</td>\n",
       "      <td>CJ Jordan</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>None</td>\n",
       "      <td>AA Kazi</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>None</td>\n",
       "      <td>AF Milne</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>None</td>\n",
       "      <td>T Shamsi</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                KKR              RCB              CSK              KXIP  \\\n",
       "0        SC Ganguly         R Dravid      BB McCullum         DJ Hussey   \n",
       "1       BB McCullum         W Jaffer        DJ Hussey           P Kumar   \n",
       "2        RT Ponting          V Kohli         PA Patel            K Goel   \n",
       "3         DJ Hussey        JH Kallis        ML Hayden          JR Hopes   \n",
       "4   Mohammad Hafeez         CL White       MEK Hussey     KC Sangakkara   \n",
       "5         JH Kallis       MV Boucher         MS Dhoni      Yuvraj Singh   \n",
       "6        MV Boucher          B Akhil         SK Raina         SM Katich   \n",
       "7         YK Pathan        AA Noffke         JDP Oram         IK Pathan   \n",
       "8         G Gambhir          P Kumar      S Badrinath           T Kohli   \n",
       "9        RV Uthappa           Z Khan        RA Jadeja            M Kaif   \n",
       "10        SB Bangar         SB Joshi        SB Styris          V Sehwag   \n",
       "11          WP Saha         PA Patel          WP Saha          AM Nayar   \n",
       "12        LR Shukla     Yuvraj Singh         DJ Bravo      AC Gilchrist   \n",
       "13            B Lee        SR Watson          A Nehra           WP Saha   \n",
       "14        PP Chawla           M Kaif        VY Mahesh  DPMD Jayawardene   \n",
       "15    R Vinay Kumar       RV Uthappa       SP Fleming           S Sohal   \n",
       "16       AB Agarkar    S Chanderpaul         S Vidyut             B Lee   \n",
       "17         M Kartik      LRPL Taylor        JA Morkel         PP Chawla   \n",
       "18         I Sharma         RP Singh  Joginder Sharma           WA Mota   \n",
       "19        MK Tiwary     Pankaj Singh          MS Gony         RR Sarwan   \n",
       "20         R Bhatia        SS Tiwary   M Muralitharan       S Sreesanth   \n",
       "21           DB Das    R Vinay Kumar          M Ntini         VRV Singh   \n",
       "22        MK Pandey         M Kartik       S Anirudha          M Kartik   \n",
       "23      Salman Butt       KD Karthik    CK Kapugedera        KD Karthik   \n",
       "24         BJ Hodge         B Chipli         DR Smith     TM Srivastava   \n",
       "25         Umar Gul         DW Steyn         L Balaji          SE Marsh   \n",
       "26         AB Dinda        MK Pandey         A Mukund           MS Gony   \n",
       "27       PJ Sangwan         AB Dinda       A Flintoff            U Kaul   \n",
       "28         A Chopra        JA Morkel       T Thushara    AD Mascarenhas   \n",
       "29          T Taibu   AB de Villiers         M Manhas     LA Pomersbach   \n",
       "..              ...              ...              ...               ...   \n",
       "70       JD Unadkat          R Ninan             None     Shivam Sharma   \n",
       "71   Mohammed Shami        S Aravind             None      BE Hendricks   \n",
       "72  SMSM Senanayake  J Syed Mohammad             None   Karanveer Singh   \n",
       "73       M de Lange   P Parameswaran             None     Anureet Singh   \n",
       "74        JO Holder         VR Aaron             None         SN Thakur   \n",
       "75    Anureet Singh         ND Doshi             None        MP Stoinis   \n",
       "76       PJ Cummins           S Rana             None            P Sahu   \n",
       "77          C Munro         HV Patel             None         KJ Abbott   \n",
       "78      KC Cariappa       RR Bhatkal             None           NS Naik   \n",
       "79        SS Sarkar          KK Nair             None       KC Cariappa   \n",
       "80        MB Parmar       JD Unadkat             None     Swapnil Singh   \n",
       "81      JW Hastings        R Rampaul             None           HM Amla   \n",
       "82    Kuldeep Yadav         KL Rahul             None       F Behardien   \n",
       "83             None        DJG Sammy             None   Gagandeep Singh   \n",
       "84             None    KW Richardson             None           RR Bose   \n",
       "85             None    Parvez Rasool             None          MG Neser   \n",
       "86             None      Sachin Baby             None        P Dharmani   \n",
       "87             None     NJ Maddinson             None              None   \n",
       "88             None         MA Starc             None              None   \n",
       "89             None       RR Rossouw             None              None   \n",
       "90             None        YS Chahal             None              None   \n",
       "91             None           VH Zol             None              None   \n",
       "92             None        SA Abbott             None              None   \n",
       "93             None          D Wiese             None              None   \n",
       "94             None          SN Khan             None              None   \n",
       "95             None          TM Head             None              None   \n",
       "96             None        CJ Jordan             None              None   \n",
       "97             None          AA Kazi             None              None   \n",
       "98             None         AF Milne             None              None   \n",
       "99             None         T Shamsi             None              None   \n",
       "\n",
       "                RR                DD               MI                DC  \\\n",
       "0         R Dravid            Z Khan       RT Ponting          CL White   \n",
       "1         JDP Oram          JR Hopes          P Kumar          PA Patel   \n",
       "2          T Kohli      Yuvraj Singh           Z Khan     KC Sangakkara   \n",
       "3        YK Pathan         IK Pathan         PA Patel          S Dhawan   \n",
       "4        SR Watson         G Gambhir       MEK Hussey      AC Gilchrist   \n",
       "5           M Kaif          V Sehwag         JDP Oram   Y Venugopal Rao   \n",
       "6       DS Lehmann          S Dhawan         S Dhawan        VVS Laxman   \n",
       "7        RA Jadeja       LRPL Taylor         L Ronchi         A Symonds   \n",
       "8          M Rawat   Y Venugopal Rao    ST Jayasuriya         RG Sharma   \n",
       "9       D Salunkhe         LR Shukla      DJ Thornely         SB Styris   \n",
       "10        SK Warne  DPMD Jayawardene       RV Uthappa         SB Bangar   \n",
       "11      SK Trivedi           A Nehra          PR Shah        WPUJC Vaas   \n",
       "12         PR Shah         SS Tiwary         AM Nayar          RP Singh   \n",
       "13        AM Nayar        AB Agarkar       SM Pollock           S Sohal   \n",
       "14     LRPL Taylor      Shoaib Malik  Harbhajan Singh     Shahid Afridi   \n",
       "15    Kamran Akmal         MK Tiwary        A Symonds          I Sharma   \n",
       "16        GC Smith        KD Karthik        RG Sharma          B Chipli   \n",
       "17    Pankaj Singh          R Bhatia         RP Singh          DW Steyn   \n",
       "18     S Sreesanth       MF Maharoof         DJ Bravo          HH Gibbs   \n",
       "19     DS Kulkarni         VY Mahesh         MA Khote         DNT Zoysa   \n",
       "20       AM Rahane          B Chipli          A Nehra   D Kalyankrishna   \n",
       "21        R Bhatia          AB Dinda        SS Tiwary         LPC Silva   \n",
       "22     SA Asnodkar         JA Morkel      DS Kulkarni      DB Ravi Teja   \n",
       "23   Sohail Tanvir    AB de Villiers    R Vinay Kumar           MS Gony   \n",
       "24        BJ Hodge        PJ Sangwan        AM Rahane           PP Ojha   \n",
       "25        MM Patel     Mohammad Asif       KD Karthik          A Mishra   \n",
       "26      SP Goswami        GD McGrath        MK Pandey  PM Sarvesh Kumar   \n",
       "27  AD Mascarenhas        TM Dilshan         HH Gibbs     DP Vijaykumar   \n",
       "28        NK Patel          A Mishra      YV Takawale             H Das   \n",
       "29   Iqbal Abdulla         JP Duminy          RR Raje          DR Smith   \n",
       "..             ...               ...              ...               ...   \n",
       "70         GB Hogg         CH Morris       TG Southee              None   \n",
       "71      A Chandila         SV Samson          BR Dunk              None   \n",
       "72         KK Nair         BJ Rohrer        YS Chahal              None   \n",
       "73     MDKJ Perera         Q de Kock      LMP Simmons              None   \n",
       "74        R Shukla         CM Gautam          S Gopal              None   \n",
       "75       CH Morris            S Kaul       M de Lange              None   \n",
       "76       SV Samson   NM Coulter-Nile        J Suchith              None   \n",
       "77   KW Richardson       JDS Neesham        HH Pandya              None   \n",
       "78     Sachin Baby       Imran Tahir   MJ McClenaghan              None   \n",
       "79        PV Tambe           SS Iyer       JC Buttler              None   \n",
       "80      TG Southee     DJ Muthuswami        KH Pandya              None   \n",
       "81     BCJ Cutting     CR Brathwaite       MJ Guptill              None   \n",
       "82       R Tewatia           RR Pant           N Rana              None   \n",
       "83        DJ Hooda       SW Billings            A Nel              None   \n",
       "84         BB Sran           J Yadav        RA Shaikh              None   \n",
       "85        S Badree          B Geeves         RW Price              None   \n",
       "86            None          AM Salvi          P Suyal              None   \n",
       "87            None         GS Sandhu       K Santokie              None   \n",
       "88            None              None         RV Pawar              None   \n",
       "89            None              None             None              None   \n",
       "90            None              None             None              None   \n",
       "91            None              None             None              None   \n",
       "92            None              None             None              None   \n",
       "93            None              None             None              None   \n",
       "94            None              None             None              None   \n",
       "95            None              None             None              None   \n",
       "96            None              None             None              None   \n",
       "97            None              None             None              None   \n",
       "98            None              None             None              None   \n",
       "99            None              None             None              None   \n",
       "\n",
       "                 KTK               PW              SRH            RPS  \\\n",
       "0        BB McCullum       SC Ganguly         CL White       MS Dhoni   \n",
       "1            B Akhil     Yuvraj Singh          P Kumar      IK Pathan   \n",
       "2           PA Patel          M Rawat         PA Patel       RP Singh   \n",
       "3          RA Jadeja       RV Uthappa    KC Sangakkara      SS Tiwary   \n",
       "4         VVS Laxman         AM Nayar     Yuvraj Singh       I Sharma   \n",
       "5           RP Singh      LRPL Taylor        IK Pathan      AM Rahane   \n",
       "6   DPMD Jayawardene          A Nehra         S Dhawan       R Bhatia   \n",
       "7        S Sreesanth         GC Smith  Y Venugopal Rao       AB Dinda   \n",
       "8      R Vinay Kumar         M Kartik          A Nehra      JA Morkel   \n",
       "9           BJ Hodge        MK Pandey         I Sharma   KP Pietersen   \n",
       "10    M Muralitharan         AB Dinda         DW Steyn      GJ Bailey   \n",
       "11          RR Powar         JD Ryder     DB Ravi Teja       R Ashwin   \n",
       "12           OA Shah      Kamran Khan       S Anirudha       MR Marsh   \n",
       "13         KM Jadhav         M Manhas         A Mishra    NLTC Perera   \n",
       "14          RV Gomez       BAW Mendis        RS Bopara   F du Plessis   \n",
       "15       NLTC Perera         TL Suman     MC Henriques      SPD Smith   \n",
       "16         M Klinger       AD Mathews        KV Sharma   Ankit Sharma   \n",
       "17    P Parameswaran  AA Jhunjhunwala          NV Ojha       M Ashwin   \n",
       "18  Y Gnaneswara Rao        MD Mishra        DA Warner  PSP Handscomb   \n",
       "19        P Prasanth         R Sharma          AP Tare     UT Khawaja   \n",
       "20              None         MR Marsh       EJG Morgan        A Zampa   \n",
       "21              None         AJ Finch     Bipul Sharma      SM Boland   \n",
       "22              None         RV Gomez         AJ Finch      DL Chahar   \n",
       "23              None       WD Parnell      NLTC Perera           None   \n",
       "24              None         TD Paine          B Kumar           None   \n",
       "25              None          SB Wagh     Ankit Sharma           None   \n",
       "26              None        AC Thomas   A Ashish Reddy           None   \n",
       "27              None      NL McCullum     BB Samantray           None   \n",
       "28              None        JE Taylor         PA Reddy           None   \n",
       "29              None       AG Murtaza        GH Vihari           None   \n",
       "..               ...              ...              ...            ...   \n",
       "70              None             None             None           None   \n",
       "71              None             None             None           None   \n",
       "72              None             None             None           None   \n",
       "73              None             None             None           None   \n",
       "74              None             None             None           None   \n",
       "75              None             None             None           None   \n",
       "76              None             None             None           None   \n",
       "77              None             None             None           None   \n",
       "78              None             None             None           None   \n",
       "79              None             None             None           None   \n",
       "80              None             None             None           None   \n",
       "81              None             None             None           None   \n",
       "82              None             None             None           None   \n",
       "83              None             None             None           None   \n",
       "84              None             None             None           None   \n",
       "85              None             None             None           None   \n",
       "86              None             None             None           None   \n",
       "87              None             None             None           None   \n",
       "88              None             None             None           None   \n",
       "89              None             None             None           None   \n",
       "90              None             None             None           None   \n",
       "91              None             None             None           None   \n",
       "92              None             None             None           None   \n",
       "93              None             None             None           None   \n",
       "94              None             None             None           None   \n",
       "95              None             None             None           None   \n",
       "96              None             None             None           None   \n",
       "97              None             None             None           None   \n",
       "98              None             None             None           None   \n",
       "99              None             None             None           None   \n",
       "\n",
       "              GL  \n",
       "0    BB McCullum  \n",
       "1        P Kumar  \n",
       "2       SK Raina  \n",
       "3      RA Jadeja  \n",
       "4       DJ Bravo  \n",
       "5    DS Kulkarni  \n",
       "6     KD Karthik  \n",
       "7       DW Steyn  \n",
       "8     PJ Sangwan  \n",
       "9       DR Smith  \n",
       "10     SB Jakati  \n",
       "11       S Ladda  \n",
       "12      AJ Finch  \n",
       "13   JP Faulkner  \n",
       "14      PV Tambe  \n",
       "15  Ishan Kishan  \n",
       "16       AD Nath  \n",
       "17     S Kaushik  \n",
       "18    ER Dwivedi  \n",
       "19          None  \n",
       "20          None  \n",
       "21          None  \n",
       "22          None  \n",
       "23          None  \n",
       "24          None  \n",
       "25          None  \n",
       "26          None  \n",
       "27          None  \n",
       "28          None  \n",
       "29          None  \n",
       "..           ...  \n",
       "70          None  \n",
       "71          None  \n",
       "72          None  \n",
       "73          None  \n",
       "74          None  \n",
       "75          None  \n",
       "76          None  \n",
       "77          None  \n",
       "78          None  \n",
       "79          None  \n",
       "80          None  \n",
       "81          None  \n",
       "82          None  \n",
       "83          None  \n",
       "84          None  \n",
       "85          None  \n",
       "86          None  \n",
       "87          None  \n",
       "88          None  \n",
       "89          None  \n",
       "90          None  \n",
       "91          None  \n",
       "92          None  \n",
       "93          None  \n",
       "94          None  \n",
       "95          None  \n",
       "96          None  \n",
       "97          None  \n",
       "98          None  \n",
       "99          None  \n",
       "\n",
       "[100 rows x 13 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#All Players Of Teams till 2016\n",
    "playerss = player_m.loc[:,('Player_Id','Team_Id')].sort_values(by='Team_Id')\n",
    "playerss['Player_Id'] = playerss['Player_Id'].astype(float)\n",
    "playerss\n",
    "\n",
    "player_data = player.loc[:,('Player_Id','Player_Name')]\n",
    "player_data = player_data.merge(playerss,on='Player_Id')\n",
    "playera = player_data.groupby('Team_Id')['Player_Name'].unique()\n",
    "\n",
    "t1=pd.Series(playera[1])\n",
    "t2=pd.Series(playera[2])\n",
    "t3=pd.Series(playera[3])\n",
    "t4=pd.Series(playera[4])\n",
    "t5=pd.Series(playera[5])\n",
    "t6=pd.Series(playera[6])\n",
    "t7=pd.Series(playera[7])\n",
    "t8=pd.Series(playera[8])\n",
    "t9=pd.Series(playera[9])\n",
    "t10=pd.Series(playera[10])\n",
    "t11=pd.Series(playera[11])\n",
    "t12=pd.Series(playera[12])\n",
    "t13=pd.Series(playera[13])\n",
    "\n",
    "#b=pd.DataFrame({'Player_Id': a.index, 'Max_fours':a.values})\n",
    "dict1={'KKR':t1.values,'RCB':t2.values,'CSK':t3.values,'KXIP':t4.values,'RR':t5.values,'DD':t6.values,'MI':t7.values,'DC':t8.values,'KTK':t9.values,'PW':t10.values,'SRH':t11.values,'RPS':t12.values,'GL':t13.values}\n",
    "df = pd.DataFrame.from_dict(dict1, orient='index')\n",
    "df.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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