openers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "openers=['rohit','shikhar','kl','ajinkya']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "open_0=pd.read_csv('dataset/'+openers[0]+'.csv')\n",
    "open_1=pd.read_csv('dataset/'+openers[1]+'.csv')\n",
    "open_2=pd.read_csv('dataset/'+openers[2]+'.csv')\n",
    "open_3=pd.read_csv('dataset/'+openers[3]+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Versus</th>\n",
       "      <th>Ground</th>\n",
       "      <th>How Dismissed</th>\n",
       "      <th>Runs</th>\n",
       "      <th>S/R</th>\n",
       "      <th>catches</th>\n",
       "      <th>aggregate</th>\n",
       "      <th>catches_wk</th>\n",
       "      <th>stumping_wk</th>\n",
       "      <th>agg_wk</th>\n",
       "      <th>age</th>\n",
       "      <th>who_dismissed</th>\n",
       "      <th>played_city</th>\n",
       "      <th>played_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-06-23</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>Civil Service Cricket Club</td>\n",
       "      <td>did not bat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>Belfast</td>\n",
       "      <td>Ireland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-06-26</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Civil Service Cricket Club</td>\n",
       "      <td>b J H Kallis</td>\n",
       "      <td>8.0</td>\n",
       "      <td>88.89</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20</td>\n",
       "      <td>J H Kallis</td>\n",
       "      <td>Belfast</td>\n",
       "      <td>Ireland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Versus                      Ground How Dismissed  Runs  \\\n",
       "0  2007-06-23       Ireland  Civil Service Cricket Club   did not bat   NaN   \n",
       "1  2007-06-26  South Africa  Civil Service Cricket Club  b J H Kallis   8.0   \n",
       "\n",
       "     S/R  catches  aggregate  catches_wk  stumping_wk  agg_wk  age  \\\n",
       "0   0.00        1          1         0.0          0.0     0.0   20   \n",
       "1  88.89        1          2         0.0          0.0     0.0   20   \n",
       "\n",
       "  who_dismissed played_city played_country  \n",
       "0             0     Belfast        Ireland  \n",
       "1    J H Kallis     Belfast        Ireland  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open_0.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Versus</th>\n",
       "      <th>Ground</th>\n",
       "      <th>How Dismissed</th>\n",
       "      <th>Runs</th>\n",
       "      <th>S/R</th>\n",
       "      <th>catches</th>\n",
       "      <th>aggregate</th>\n",
       "      <th>catches_wk</th>\n",
       "      <th>stumping_wk</th>\n",
       "      <th>agg_wk</th>\n",
       "      <th>age</th>\n",
       "      <th>who_dismissed</th>\n",
       "      <th>played_city</th>\n",
       "      <th>played_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-10-20</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Dr YS Rajasekhara Reddy Cricket Stadium</td>\n",
       "      <td>b C J McKay</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>C J McKay</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-11-24</td>\n",
       "      <td>West Indies</td>\n",
       "      <td>Dr YS Rajasekhara Reddy Cricket Stadium</td>\n",
       "      <td>lbw b V Permaul</td>\n",
       "      <td>35</td>\n",
       "      <td>94.59</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27</td>\n",
       "      <td>V Permaul</td>\n",
       "      <td>Visakhapatnam</td>\n",
       "      <td>India</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Versus                                   Ground  \\\n",
       "0  2010-10-20    Australia  Dr YS Rajasekhara Reddy Cricket Stadium   \n",
       "1  2013-11-24  West Indies  Dr YS Rajasekhara Reddy Cricket Stadium   \n",
       "\n",
       "     How Dismissed  Runs    S/R  catches  aggregate  catches_wk  stumping_wk  \\\n",
       "0      b C J McKay     0   0.00        0          0         0.0          0.0   \n",
       "1  lbw b V Permaul    35  94.59        1         11         0.0          0.0   \n",
       "\n",
       "   agg_wk  age who_dismissed    played_city played_country  \n",
       "0     0.0   24     C J McKay  Visakhapatnam          India  \n",
       "1     0.0   27     V Permaul  Visakhapatnam          India  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open_1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Versus</th>\n",
       "      <th>Ground</th>\n",
       "      <th>How Dismissed</th>\n",
       "      <th>Runs</th>\n",
       "      <th>S/R</th>\n",
       "      <th>catches</th>\n",
       "      <th>aggregate</th>\n",
       "      <th>catches_wk</th>\n",
       "      <th>stumping_wk</th>\n",
       "      <th>agg_wk</th>\n",
       "      <th>age</th>\n",
       "      <th>who_dismissed</th>\n",
       "      <th>played_city</th>\n",
       "      <th>played_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-11-06</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Harare Sports Club</td>\n",
       "      <td>not out</td>\n",
       "      <td>100.0</td>\n",
       "      <td>86.96</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>Harare</td>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-06-13</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Harare Sports Club</td>\n",
       "      <td>b C J Chibhabha</td>\n",
       "      <td>33.0</td>\n",
       "      <td>66.00</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>C J Chibhabha</td>\n",
       "      <td>Harare</td>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date    Versus              Ground    How Dismissed   Runs    S/R  \\\n",
       "0  2016-11-06  Zimbabwe  Harare Sports Club          not out  100.0  86.96   \n",
       "1  2016-06-13  Zimbabwe  Harare Sports Club  b C J Chibhabha   33.0  66.00   \n",
       "\n",
       "   catches  aggregate  catches_wk  stumping_wk  agg_wk  age  who_dismissed  \\\n",
       "0        1          1         0.0          0.0     0.0   24              0   \n",
       "1        0          1         0.0          0.0     0.0   24  C J Chibhabha   \n",
       "\n",
       "  played_city played_country  \n",
       "0      Harare       Zimbabwe  \n",
       "1      Harare       Zimbabwe  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open_2.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Versus</th>\n",
       "      <th>Ground</th>\n",
       "      <th>How Dismissed</th>\n",
       "      <th>Runs</th>\n",
       "      <th>S/R</th>\n",
       "      <th>catches</th>\n",
       "      <th>aggregate</th>\n",
       "      <th>catches_wk</th>\n",
       "      <th>stumping_wk</th>\n",
       "      <th>agg_wk</th>\n",
       "      <th>age</th>\n",
       "      <th>who_dismissed</th>\n",
       "      <th>played_city</th>\n",
       "      <th>played_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-03-09</td>\n",
       "      <td>England</td>\n",
       "      <td>Riverside Ground</td>\n",
       "      <td>c S R Patel b S C J Broad</td>\n",
       "      <td>40.0</td>\n",
       "      <td>90.91</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>S C J Broad</td>\n",
       "      <td>Chester-Le-Street</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-09-09</td>\n",
       "      <td>England</td>\n",
       "      <td>Kennington Oval</td>\n",
       "      <td>c I J L Trott b J M Anderson</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23</td>\n",
       "      <td>J M Anderson</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Versus            Ground                 How Dismissed  Runs  \\\n",
       "0  2011-03-09  England  Riverside Ground     c S R Patel b S C J Broad  40.0   \n",
       "1  2011-09-09  England   Kennington Oval  c I J L Trott b J M Anderson   0.0   \n",
       "\n",
       "     S/R  catches  aggregate  catches_wk  stumping_wk  agg_wk  age  \\\n",
       "0  90.91        0          0         0.0          0.0     0.0   23   \n",
       "1   0.00        0          0         0.0          0.0     0.0   23   \n",
       "\n",
       "  who_dismissed        played_city played_country  \n",
       "0   S C J Broad  Chester-Le-Street        England  \n",
       "1  J M Anderson             London        England  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open_3.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creates df for games played in england\n",
    "def in_england(df):\n",
    "    df_eng=df[df['played_country']=='England']\n",
    "    runs=df_eng['Runs'].sum()\n",
    "    avg=runs/df_eng['Runs'].count()\n",
    "    avg_srate=(df_eng['S/R'].sum())/df_eng['S/R'].count()\n",
    "    return(df_eng,runs,avg,avg_srate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "opener_0_eng,opener_0_eng_runs,opener_0_eng_avg,opener_0_eng_srate=in_england(open_0)\n",
    "opener_1_eng,opener_1_eng_runs,opener_1_eng_avg,opener_1_eng_srate=in_england(open_1)\n",
    "opener_2_eng,opener_2_eng_runs,opener_2_eng_avg,opener_2_eng_srate=in_england(open_2)\n",
    "opener_3_eng,opener_3_eng_runs,opener_3_eng_avg,opener_3_eng_srate=in_england(open_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Find all of the wicket takers for particular batsman\n",
    "def wicket_takers(df):\n",
    "    s=df['who_dismissed'].value_counts()\n",
    "    return(np.array(s.index),s.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "opener_0_eng_bowl,opener_0_eng_times=wicket_takers(opener_0_eng)\n",
    "opener_1_eng_bowl,opener_1_eng_times=wicket_takers(opener_1_eng)\n",
    "opener_2_eng_bowl,opener_2_eng_times=wicket_takers(opener_2_eng)\n",
    "opener_3_eng_bowl,opener_3_eng_times=wicket_takers(opener_3_eng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array([0,1,2,3])\n",
    "scores_in_eng=[opener_0_eng_runs,opener_1_eng_runs,opener_2_eng_runs,opener_3_eng_runs]\n",
    "avg_in_eng=[opener_0_eng_avg,opener_1_eng_avg,opener_2_eng_avg,opener_3_eng_avg]\n",
    "srate_in_eng=[opener_0_eng_srate,opener_1_eng_srate,opener_2_eng_srate,opener_3_eng_srate]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opener's total run in england"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11819d310>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1182021d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(x, openers)\n",
    "plt.bar(x,scores_in_eng)\n",
    "plt.title(\"opener's total run in england\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1475aa90>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a147712d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='player',y='Runs', data=all_openers,inner=None,color='lightgray')\n",
    "sns.stripplot(x='player',y='Runs',data=all_openers,jitter=True,size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/onion8/miniconda3/lib/python2.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/Users/onion8/miniconda3/lib/python2.7/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "/Users/onion8/miniconda3/lib/python2.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/Users/onion8/miniconda3/lib/python2.7/site-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "opener_0_eng['player']=openers[0]\n",
    "opener_1_eng['player']=openers[1]\n",
    "opener_2_eng['player']=openers[2]\n",
    "opener_3_eng['player']=openers[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_openers=pd.concat([opener_0_eng, opener_1_eng,opener_2_eng,opener_3_eng], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>Date</th>\n",
       "      <th>Versus</th>\n",
       "      <th>Ground</th>\n",
       "      <th>How Dismissed</th>\n",
       "      <th>Runs</th>\n",
       "      <th>S/R</th>\n",
       "      <th>catches</th>\n",
       "      <th>aggregate</th>\n",
       "      <th>catches_wk</th>\n",
       "      <th>stumping_wk</th>\n",
       "      <th>agg_wk</th>\n",
       "      <th>age</th>\n",
       "      <th>who_dismissed</th>\n",
       "      <th>played_city</th>\n",
       "      <th>played_country</th>\n",
       "      <th>player</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-03-09</td>\n",
       "      <td>England</td>\n",
       "      <td>Riverside Ground</td>\n",
       "      <td>retired hurt</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>England</td>\n",
       "      <td>rohit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-11-06</td>\n",
       "      <td>West Indies</td>\n",
       "      <td>Kennington Oval</td>\n",
       "      <td>c †J Charles b S P Narine</td>\n",
       "      <td>52.0</td>\n",
       "      <td>92.86</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26</td>\n",
       "      <td>S P Narine</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>rohit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       Versus            Ground              How Dismissed  Runs  \\\n",
       "0  2011-03-09      England  Riverside Ground               retired hurt   0.0   \n",
       "1  2013-11-06  West Indies   Kennington Oval  c †J Charles b S P Narine  52.0   \n",
       "\n",
       "     S/R  catches  aggregate  catches_wk  stumping_wk  agg_wk  age  \\\n",
       "0   0.00        0         25         0.0          0.0     0.0   24   \n",
       "1  92.86        0         32         0.0          0.0     0.0   26   \n",
       "\n",
       "  who_dismissed played_city played_country player  \n",
       "0             0           0        England  rohit  \n",
       "1    S P Narine      London        England  rohit  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_openers.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a146241d0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a144b2250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Ground',y='Runs', data=two_openers,inner=None,color='lightgray')\n",
    "sns.stripplot(x='Ground',y='Runs',data=two_openers,jitter=True,size=5,hue='player')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opener's avg in england"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x118385bd0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118202a90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(x, openers)\n",
    "plt.bar(x,avg_in_eng)\n",
    "plt.title(\"opener's avg in england\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.violinplot(x='player',y=(('Runs')/len()), data=all_openers,inner=None,color='lightgray')\n",
    "sns.stripplot(x='player',y='Runs',data=all_openers,jitter=True,size=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opener's S/R in england"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1184ade10>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1183f32d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(x, openers)\n",
    "plt.bar(x,srate_in_eng)\n",
    "plt.title(\"opener's S/R in england\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plots all the wicket takers for a particular batsman\n",
    "def wickets_taken(bowlers,times,i,j):\n",
    "    x=np.arange(len(bowlers))\n",
    "    plt.xticks(x, bowlers)\n",
    "    plt.bar(x,times)\n",
    "    plt.tick_params(axis='x', which='major', labelsize=j)\n",
    "    plt.tight_layout()\n",
    "    plt.title(openers[i]+\" wicket taken by\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a14eeeb50>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a14ea5150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Ground',y='S/R', data=all_openers,inner=None,color='lightgray')\n",
    "sns.stripplot(x='Ground',y='S/R',data=all_openers,jitter=True,size=5,hue='player')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bowlers who got Rohit out in eng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1184ad290>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wickets_taken(opener_0_eng_bowl,opener_0_eng_times,0,7)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bowlers who got Shikhar out in eng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1185c3a90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wickets_taken(opener_1_eng_bowl,opener_1_eng_times,1,7)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bowlers who got KL out in eng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118786bd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wickets_taken(opener_2_eng_bowl,opener_2_eng_times,2,7)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bowlers who got Rahane out in eng\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11882b750>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wickets_taken(opener_3_eng_bowl,opener_3_eng_times,3,7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#finds favt ground for the batsman depending on their scores\n",
    "def favt_grnd(df):\n",
    "    grp=df.groupby('Ground')\n",
    "    ru=(([grp['Runs'].sum()/grp['Runs'].count()]))\n",
    "    s= (max(ru).sort_values(ascending=False))\n",
    "    return(s.index[0],s[0])\n",
    " \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "opener_0_favt_grnd,opener_0_favt_avg=favt_grnd(opener_0_eng)\n",
    "opener_1_favt_grnd,opener_1_favt_avg=favt_grnd(opener_1_eng)\n",
    "opener_2_favt_grnd,opener_2_favt_avg=favt_grnd(opener_2_eng)\n",
    "opener_3_favt_grnd,opener_3_favt_avg=favt_grnd(opener_3_eng)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Favt grounds in England for the openers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a14ea5310>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1516bf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Ground',y='Runs', data=all_openers,inner=None,color='lightgray')\n",
    "sns.stripplot(x='Ground',y='Runs',data=all_openers,jitter=True,size=5,hue='player')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rohit loves Edgbaston in england as he scores there at an avg of 60.25\n"
     ]
    }
   ],
   "source": [
    "print(openers[0]+' loves '+opener_0_favt_grnd+' in england as he scores there at an avg of '+ str(opener_0_favt_avg ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shikhar loves Kennington Oval in england as he scores there at an avg of 81.5\n"
     ]
    }
   ],
   "source": [
    "print(openers[1]+' loves '+opener_1_favt_grnd+' in england as he scores there at an avg of '+ str(opener_1_favt_avg ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kl loves Lord's in england as he scores there at an avg of 0.0\n"
     ]
    }
   ],
   "source": [
    "print(openers[2]+' loves '+opener_2_favt_grnd+' in england as he scores there at an avg of '+ str(opener_2_favt_avg ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ajinkya loves Edgbaston in england as he scores there at an avg of 106.0\n"
     ]
    }
   ],
   "source": [
    "print(openers[3]+' loves '+opener_3_favt_grnd+' in england as he scores there at an avg of '+ str(opener_3_favt_avg ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance analysis of openers overseas against top 5 teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "top5_teams = ['England','New Zealand','South Africa','Australia','Pakistan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creates df for a batsman against top5 teams  overseas\n",
    "def against_top_5(df):\n",
    "    df=(df[(df['Versus'].isin(top5_teams))])\n",
    "    df=(df[(df['played_country']!=('India'))])\n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "open_0_top_5=against_top_5(open_0)\n",
    "open_1_top_5=against_top_5(open_1)\n",
    "open_2_top_5=against_top_5(open_2)\n",
    "open_3_top_5=against_top_5(open_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "opener_0_top5_bowl,opener_0_top5_times=wicket_takers(open_0_top_5)\n",
    "opener_1_top5_bowl,opener_1_top5_times=wicket_takers(open_1_top_5)\n",
    "opener_2_top5_bowl,opener_2_top5_times=wicket_takers(open_2_top_5)\n",
    "opener_3_top5_bowl,opener_3_top5_times=wicket_takers(open_3_top_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "open_0_top_5['player']=openers[0]\n",
    "open_1_top_5['player']=openers[1]\n",
    "open_2_top_5['player']=openers[2]\n",
    "open_3_top_5['player']=openers[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_openers_top5=pd.concat([open_0_top_5, open_1_top_5,open_2_top_5,open_3_top_5], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bowlers who got Rohit out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118923250>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wickets_taken(opener_0_top5_bowl,opener_0_top5_times,0,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rohit should take care of ['M Morkel' 'K Rabada'] as they got him [5 2] times respectively\n"
     ]
    }
   ],
   "source": [
    "print(openers[0]+' should take care of '+str(opener_0_top5_bowl[1:3])+' as they got him '+str(opener_0_top5_times[1:3])+' times respectively')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bowlers who got Shikhar out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118a50bd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wickets_taken(opener_1_top5_bowl,opener_1_top5_times,1,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shikhar should take care of ['T A Boult' 'J W Hastings'] as they got him [3 2] times respectively\n"
     ]
    }
   ],
   "source": [
    "print(openers[1]+' should take care of '+str(opener_1_top5_bowl[1:3])+' as they got him '+str(opener_1_top5_times[1:3])+' times respectively')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bowlers who got Kl out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118e4da90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wickets_taken(opener_2_top5_bowl,opener_2_top5_times,2,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kl should take care of L E Plunkett as he got him 1 times\n"
     ]
    }
   ],
   "source": [
    "print(openers[2]+' should take care of '+str(opener_2_top5_bowl[0])+' as he got him '+str(opener_2_top5_times[0])+' times')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bowlers who got Rahane out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118bf24d0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "wickets_taken(opener_3_top5_bowl,opener_3_top5_times,3,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ajinkya should take care of ['S C J Broad' 'C J Anderson' 'J M Anderson' 'G S Sandhu' 'K D Mills'] as they got him [2 2 2 1 1] times respectively\n"
     ]
    }
   ],
   "source": [
    "print(openers[3]+' should take care of '+str(opener_3_top5_bowl[0:5])+' as they got him '+str(opener_3_top5_times[0:5])+' times respectively')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#finds properties wrt top5 teams overseas\n",
    "def out_top5(df):\n",
    "    \n",
    "    runs=df['Runs'].sum()\n",
    "    avg=runs/df['Runs'].count()\n",
    "    avg_srate=(df['S/R'].sum())/df['S/R'].count()\n",
    "    return(runs,avg,avg_srate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "opener_0_top5_runs,opener_0_top5_avg,opener_0_top5_srate=out_top5(open_0_top_5)\n",
    "opener_1_top5_runs,opener_1_top5_avg,opener_1_top5_srate=out_top5(open_1_top_5)\n",
    "opener_2_top5_runs,opener_2_top5_avg,opener_2_top5_srate=out_top5(open_2_top_5)\n",
    "opener_3_top5_runs,opener_3_top5_avg,opener_3_top5_srate=out_top5(open_3_top_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.array([0,1,2,3])\n",
    "scores=[opener_0_top5_runs,opener_1_top5_runs,opener_2_top5_runs,opener_3_top5_runs]\n",
    "avg=[opener_0_top5_avg,opener_1_top5_avg,opener_2_top5_avg,opener_3_top5_avg]\n",
    "srate=[opener_0_top5_srate,opener_1_top5_srate,opener_2_top5_srate,opener_3_top5_srate]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opener's total run against top5 overseas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1183f3c50>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118b6fa10>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(x, openers)\n",
    "plt.bar(x,scores)\n",
    "plt.title(\"opener's total run against top5 overseas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a13c07290>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a14879810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='player',y='Runs', data=all_openers_top5,inner=None,color='lightgray')\n",
    "sns.stripplot(x='player',y='Runs',data=all_openers_top5,jitter=True,size=5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opener's avg against top5 overseas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1187e3050>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d73390>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(x, openers)\n",
    "plt.bar(x,avg)\n",
    "plt.title(\"opener's avg against top5 overseas\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opener's S/R against top5 overseas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11868b690>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118934190>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks(x, openers)\n",
    "plt.bar(x,srate)\n",
    "plt.title(\"opener's S/R against top5 overseas\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a13b6ae90>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a14e559d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='player',y='S/R', data=all_openers_top5,inner=None,color='lightgray')\n",
    "sns.stripplot(x='player',y='S/R',data=all_openers_top5,jitter=True,size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#finds favt_team out of top5\n",
    "def favt_team(df):\n",
    "    grp=df.groupby('Versus')\n",
    "    ru=(([grp['Runs'].sum()/grp['Runs'].count()]))\n",
    "    s= (max(ru).sort_values(ascending=False))\n",
    "    return(s.index[0],s[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "opener_0_favt_team,opener_0_favtt_avg=favt_team(open_0_top_5)\n",
    "opener_1_favt_team,opener_1_favtt_avg=favt_team(open_1_top_5)\n",
    "opener_2_favt_team,opener_2_favtt_avg=favt_team(open_2_top_5)\n",
    "opener_3_favt_team,opener_3_favtt_avg=favt_team(open_3_top_5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rohit loves Pakistan  overseas as he scores at an avg of 40.77777777777778 against them\n"
     ]
    }
   ],
   "source": [
    "print(openers[0]+' loves '+opener_0_favt_team+'  overseas as he scores at an avg of '+ str(opener_0_favtt_avg ) + ' against them')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shikhar loves South Africa  overseas  as he scores at an avg of 63.857142857142854 against them\n"
     ]
    }
   ],
   "source": [
    "print(openers[1]+' loves '+opener_1_favt_team+'  overseas  as he scores at an avg of '+ str(opener_1_favtt_avg )+ ' against them')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kl loves England  overseas  as he scores at an avg of 0.0 against them\n"
     ]
    }
   ],
   "source": [
    "print(openers[2]+' loves '+opener_2_favt_team+'  overseas  as he scores at an avg of '+ str(opener_2_favtt_avg )+ ' against them')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ajinkya loves Australia  overseas  as he scores at an avg of 38.25 against them\n"
     ]
    }
   ],
   "source": [
    "print(openers[3]+' loves '+opener_3_favt_team+'  overseas  as he scores at an avg of '+ str(opener_3_favtt_avg )+ ' against them')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}