{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idseasoncitydateteam1team2toss_winnertoss_decisionresultdl_appliedwinnerwin_by_runswin_by_wicketsplayer_of_matchvenueumpire1umpire2umpire3
012017Hyderabad4/5/2017Sunrisers HyderabadRoyal Challengers BangaloreRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ LlongNaN
122017Pune4/6/2017Mumbai IndiansRising Pune SupergiantRising Pune Supergiantfieldnormal0Rising Pune Supergiant07SPD SmithMaharashtra Cricket Association StadiumA Nand KishoreS RaviNaN
232017Rajkot4/7/2017Gujarat LionsKolkata Knight RidersKolkata Knight Ridersfieldnormal0Kolkata Knight Riders010CA LynnSaurashtra Cricket Association StadiumNitin MenonCK NandanNaN
342017Indore4/8/2017Rising Pune SupergiantKings XI PunjabKings XI Punjabfieldnormal0Kings XI Punjab06GJ MaxwellHolkar Cricket StadiumAK ChaudharyC ShamshuddinNaN
452017Bangalore4/8/2017Royal Challengers BangaloreDelhi DaredevilsRoyal Challengers Bangalorebatnormal0Royal Challengers Bangalore150KM JadhavM Chinnaswamy StadiumNaNNaNNaN
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" ], "text/plain": [ " id season city date team1 \\\n", "0 1 2017 Hyderabad 4/5/2017 Sunrisers Hyderabad \n", "1 2 2017 Pune 4/6/2017 Mumbai Indians \n", "2 3 2017 Rajkot 4/7/2017 Gujarat Lions \n", "3 4 2017 Indore 4/8/2017 Rising Pune Supergiant \n", "4 5 2017 Bangalore 4/8/2017 Royal Challengers Bangalore \n", "\n", " team2 toss_winner toss_decision \\\n", "0 Royal Challengers Bangalore Royal Challengers Bangalore field \n", "1 Rising Pune Supergiant Rising Pune Supergiant field \n", "2 Kolkata Knight Riders Kolkata Knight Riders field \n", "3 Kings XI Punjab Kings XI Punjab field \n", "4 Delhi Daredevils Royal Challengers Bangalore bat \n", "\n", " result dl_applied winner win_by_runs \\\n", "0 normal 0 Sunrisers Hyderabad 35 \n", "1 normal 0 Rising Pune Supergiant 0 \n", "2 normal 0 Kolkata Knight Riders 0 \n", "3 normal 0 Kings XI Punjab 0 \n", "4 normal 0 Royal Challengers Bangalore 15 \n", "\n", " win_by_wickets player_of_match venue \\\n", "0 0 Yuvraj Singh Rajiv Gandhi International Stadium, Uppal \n", "1 7 SPD Smith Maharashtra Cricket Association Stadium \n", "2 10 CA Lynn Saurashtra Cricket Association Stadium \n", "3 6 GJ Maxwell Holkar Cricket Stadium \n", "4 0 KM Jadhav M Chinnaswamy Stadium \n", "\n", " umpire1 umpire2 umpire3 \n", "0 AY Dandekar NJ Llong NaN \n", "1 A Nand Kishore S Ravi NaN \n", "2 Nitin Menon CK Nandan NaN \n", "3 AK Chaudhary C Shamshuddin NaN \n", "4 NaN NaN NaN " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df=pd.read_csv('F:\\Data Science projects\\EDA\\IPL/matches.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "id 0\n", "season 0\n", "city 7\n", "date 0\n", "team1 0\n", "team2 0\n", "toss_winner 0\n", "toss_decision 0\n", "result 0\n", "dl_applied 0\n", "winner 3\n", "win_by_runs 0\n", "win_by_wickets 0\n", "player_of_match 3\n", "venue 0\n", "umpire1 1\n", "umpire2 1\n", "umpire3 636\n", "dtype: int64" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(636, 18)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "df.drop(['umpire3'],axis=1,inplace=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['id', 'season', 'city', 'date', 'team1', 'team2', 'toss_winner',\n", " 'toss_decision', 'result', 'dl_applied', 'winner', 'win_by_runs',\n", " 'win_by_wickets', 'player_of_match', 'venue', 'umpire1', 'umpire2'],\n", " dtype='object')" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "Teams={\n", " 'Royal Challengers Bangalore':'RCB', \n", " 'Sunrisers Hyderabad':'SRH',\n", " 'Rising Pune Supergiant':'RPS',\n", " 'Mumbai Indians':'MI',\n", " 'Kolkata Knight Riders':'KKR', \n", " 'Gujarat Lions':'GL',\n", " 'Kings XI Punjab':'KXIP',\n", " 'Delhi Daredevils':'DD',\n", " 'Chennai Super Kings':'CSK',\n", " 'Rajasthan Royals':'RR',\n", " 'Deccan Chargers':'DC',\n", " 'Kochi Tuskers Kerala':'KTK',\n", " 'Pune Warriors':'PW',\n", " 'Rising Pune Supergiants':'RPS'\n", "}" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "df['team1']=df['team1'].map(Teams)\n", "df['team2']=df['team2'].map(Teams)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idseasoncitydateteam1team2toss_winnertoss_decisionresultdl_appliedwinnerwin_by_runswin_by_wicketsplayer_of_matchvenueumpire1umpire2
012017Hyderabad4/5/2017SRHRCBRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ Llong
122017Pune4/6/2017MIRPSRising Pune Supergiantfieldnormal0Rising Pune Supergiant07SPD SmithMaharashtra Cricket Association StadiumA Nand KishoreS Ravi
232017Rajkot4/7/2017GLKKRKolkata Knight Ridersfieldnormal0Kolkata Knight Riders010CA LynnSaurashtra Cricket Association StadiumNitin MenonCK Nandan
342017Indore4/8/2017RPSKXIPKings XI Punjabfieldnormal0Kings XI Punjab06GJ MaxwellHolkar Cricket StadiumAK ChaudharyC Shamshuddin
452017Bangalore4/8/2017RCBDDRoyal Challengers Bangalorebatnormal0Royal Challengers Bangalore150KM JadhavM Chinnaswamy StadiumNaNNaN
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" ], "text/plain": [ " id season city date team1 team2 toss_winner \\\n", "0 1 2017 Hyderabad 4/5/2017 SRH RCB Royal Challengers Bangalore \n", "1 2 2017 Pune 4/6/2017 MI RPS Rising Pune Supergiant \n", "2 3 2017 Rajkot 4/7/2017 GL KKR Kolkata Knight Riders \n", "3 4 2017 Indore 4/8/2017 RPS KXIP Kings XI Punjab \n", "4 5 2017 Bangalore 4/8/2017 RCB DD Royal Challengers Bangalore \n", "\n", " toss_decision result dl_applied winner win_by_runs \\\n", "0 field normal 0 Sunrisers Hyderabad 35 \n", "1 field normal 0 Rising Pune Supergiant 0 \n", "2 field normal 0 Kolkata Knight Riders 0 \n", "3 field normal 0 Kings XI Punjab 0 \n", "4 bat normal 0 Royal Challengers Bangalore 15 \n", "\n", " win_by_wickets player_of_match venue \\\n", "0 0 Yuvraj Singh Rajiv Gandhi International Stadium, Uppal \n", "1 7 SPD Smith Maharashtra Cricket Association Stadium \n", "2 10 CA Lynn Saurashtra Cricket Association Stadium \n", "3 6 GJ Maxwell Holkar Cricket Stadium \n", "4 0 KM Jadhav M Chinnaswamy Stadium \n", "\n", " umpire1 umpire2 \n", "0 AY Dandekar NJ Llong \n", "1 A Nand Kishore S Ravi \n", "2 Nitin Menon CK Nandan \n", "3 AK Chaudhary C Shamshuddin \n", "4 NaN NaN " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Some Basic Analysis" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total Matches Played: 636\n", " \n", " Venues Played At: ['Hyderabad' 'Pune' 'Rajkot' 'Indore' 'Bangalore' 'Mumbai' 'Kolkata'\n", " 'Delhi' 'Chandigarh' 'Kanpur' 'Jaipur' 'Chennai' 'Cape Town'\n", " 'Port Elizabeth' 'Durban' 'Centurion' 'East London' 'Johannesburg'\n", " 'Kimberley' 'Bloemfontein' 'Ahmedabad' 'Cuttack' 'Nagpur' 'Dharamsala'\n", " 'Kochi' 'Visakhapatnam' 'Raipur' 'Ranchi' 'Abu Dhabi' 'Sharjah' nan]\n", " \n", " Teams : ['SRH' 'MI' 'GL' 'RPS' 'RCB' 'KKR' 'DD' 'KXIP' 'CSK' 'RR' 'DC' 'KTK' 'PW']\n" ] } ], "source": [ "print('Total Matches Played:',df.shape[0])\n", "print(' \\n Venues Played At:',df['city'].unique()) \n", "print(' \\n Teams :',df['team1'].unique())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### most man of the match awards" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CH Gayle 18\n", "YK Pathan 16\n", "AB de Villiers 15\n", "DA Warner 15\n", "SK Raina 14\n", " ..\n", "DL Vettori 1\n", "AC Voges 1\n", "JDP Oram 1\n", "LH Ferguson 1\n", "BCJ Cutting 1\n", "Name: player_of_match, Length: 201, dtype: int64\n" ] } ], "source": [ "print(df['player_of_match'].value_counts())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### maximum win by runs" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idseasoncitydateteam1team2toss_winnertoss_decisionresultdl_appliedwinnerwin_by_runswin_by_wicketsplayer_of_matchvenueumpire1umpire2
43442017Delhi5/6/2017MIDDDelhi Daredevilsfieldnormal0Mumbai Indians1460LMP SimmonsFeroz Shah KotlaNitin MenonCK Nandan
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" ], "text/plain": [ " id season city date team1 team2 toss_winner toss_decision \\\n", "43 44 2017 Delhi 5/6/2017 MI DD Delhi Daredevils field \n", "\n", " result dl_applied winner win_by_runs win_by_wickets \\\n", "43 normal 0 Mumbai Indians 146 0 \n", "\n", " player_of_match venue umpire1 umpire2 \n", "43 LMP Simmons Feroz Shah Kotla Nitin Menon CK Nandan " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['win_by_runs']==df['win_by_runs'].max()]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Mumbai Indians(MI) defeated Delhi Daredevils(DD) with the highest run difference" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### highest wins by wickets" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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idseasoncitydateteam1team2toss_winnertoss_decisionresultdl_appliedwinnerwin_by_runswin_by_wicketsplayer_of_matchvenueumpire1umpire2
232017Rajkot4/7/2017GLKKRKolkata Knight Ridersfieldnormal0Kolkata Knight Riders010CA LynnSaurashtra Cricket Association StadiumNitin MenonCK Nandan
34352017Chandigarh4/30/2017DDKXIPKings XI Punjabfieldnormal0Kings XI Punjab010Sandeep SharmaPunjab Cricket Association IS Bindra Stadium, ...YC BardeCK Nandan
71722008Mumbai4/27/2008MIDCDeccan Chargersfieldnormal0Deccan Chargers010AC GilchristDr DY Patil Sports AcademyAsad RaufSL Shastri
1191202009Cape Town4/19/2009KXIPDDDelhi Daredevilsfieldnormal1Delhi Daredevils010DL VettoriNewlandsMR BensonSD Ranade
1831842010Bangalore3/18/2010RRRCBRoyal Challengers Bangalorefieldnormal0Royal Challengers Bangalore010JH KallisM Chinnaswamy StadiumK HariharanDJ Harper
2982992011Mumbai5/20/2011MIRRMumbai Indiansbatnormal0Rajasthan Royals010SR WatsonWankhede StadiumRE KoertzenPR Reiffel
3763772012Jaipur5/20/2012RRMIRajasthan Royalsbatnormal0Mumbai Indians010DR SmithSawai Mansingh StadiumHDPK DharmasenaC Shamshuddin
3903912013Chandigarh4/10/2013KXIPCSKChennai Super Kingsfieldnormal0Chennai Super Kings010MEK HusseyPunjab Cricket Association Stadium, MohaliAleem DarC Shamshuddin
5425432015Delhi4/26/2015DDRCBRoyal Challengers Bangalorefieldnormal0Royal Challengers Bangalore010VR AaronFeroz Shah KotlaM ErasmusS Ravi
5905912016Rajkot4/21/2016GLSRHSunrisers Hyderabadfieldnormal0Sunrisers Hyderabad010B KumarSaurashtra Cricket Association StadiumK BharatanHDPK Dharmasena
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" ], "text/plain": [ " id season city date team1 team2 \\\n", "2 3 2017 Rajkot 4/7/2017 GL KKR \n", "34 35 2017 Chandigarh 4/30/2017 DD KXIP \n", "71 72 2008 Mumbai 4/27/2008 MI DC \n", "119 120 2009 Cape Town 4/19/2009 KXIP DD \n", "183 184 2010 Bangalore 3/18/2010 RR RCB \n", "298 299 2011 Mumbai 5/20/2011 MI RR \n", "376 377 2012 Jaipur 5/20/2012 RR MI \n", "390 391 2013 Chandigarh 4/10/2013 KXIP CSK \n", "542 543 2015 Delhi 4/26/2015 DD RCB \n", "590 591 2016 Rajkot 4/21/2016 GL SRH \n", "\n", " toss_winner toss_decision result dl_applied \\\n", "2 Kolkata Knight Riders field normal 0 \n", "34 Kings XI Punjab field normal 0 \n", "71 Deccan Chargers field normal 0 \n", "119 Delhi Daredevils field normal 1 \n", "183 Royal Challengers Bangalore field normal 0 \n", "298 Mumbai Indians bat normal 0 \n", "376 Rajasthan Royals bat normal 0 \n", "390 Chennai Super Kings field normal 0 \n", "542 Royal Challengers Bangalore field normal 0 \n", "590 Sunrisers Hyderabad field normal 0 \n", "\n", " winner win_by_runs win_by_wickets player_of_match \\\n", "2 Kolkata Knight Riders 0 10 CA Lynn \n", "34 Kings XI Punjab 0 10 Sandeep Sharma \n", "71 Deccan Chargers 0 10 AC Gilchrist \n", "119 Delhi Daredevils 0 10 DL Vettori \n", "183 Royal Challengers Bangalore 0 10 JH Kallis \n", "298 Rajasthan Royals 0 10 SR Watson \n", "376 Mumbai Indians 0 10 DR Smith \n", "390 Chennai Super Kings 0 10 MEK Hussey \n", "542 Royal Challengers Bangalore 0 10 VR Aaron \n", "590 Sunrisers Hyderabad 0 10 B Kumar \n", "\n", " venue umpire1 \\\n", "2 Saurashtra Cricket Association Stadium Nitin Menon \n", "34 Punjab Cricket Association IS Bindra Stadium, ... YC Barde \n", "71 Dr DY Patil Sports Academy Asad Rauf \n", "119 Newlands MR Benson \n", "183 M Chinnaswamy Stadium K Hariharan \n", "298 Wankhede Stadium RE Koertzen \n", "376 Sawai Mansingh Stadium HDPK Dharmasena \n", "390 Punjab Cricket Association Stadium, Mohali Aleem Dar \n", "542 Feroz Shah Kotla M Erasmus \n", "590 Saurashtra Cricket Association Stadium K Bharatan \n", "\n", " umpire2 \n", "2 CK Nandan \n", "34 CK Nandan \n", "71 SL Shastri \n", "119 SD Ranade \n", "183 DJ Harper \n", "298 PR Reiffel \n", "376 C Shamshuddin \n", "390 C Shamshuddin \n", "542 S Ravi \n", "590 HDPK Dharmasena " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df['win_by_wickets']==df['win_by_wickets'].max()]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Toss Decisions across Seasons" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(x='season',hue='toss_decision',data=df)\n", "plt.savefig('F:\\Data Science projects\\EDA\\IPL/IPL_1.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### In some seasons, the probablity that toss winners opt for batting is high while In 2016, the majority of toss winners opted for fielding" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Maximum Toss Winners" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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bWTAt00vKH3nZKT/lp/wKTNuJmSRJkswumYmZJEnSUVKBJ0mSdJRU4B1D0lqSHjjb85jLSNpQ0razPY/pImkVSevN9jzmEpLWmMq5lWWkFLikvSXdozx+u6QTJG1fUf46klYpjx8gaQ9Jq1WUf7Ck9RQcJel8SU+uKP8ZwIXAKeV4O0lVIn8krSnpkPKZf0PS6yWtWUN2kd/1z/7MIv+ewEXA0ZI+UkHuJZIunujWfObLxjmmzH8d4HLgSkn/UVF+q59/GWM3SW+S9J+9W0XZHyzzX03S6ZL+IOkFteQDP53iuZVipBQ48A7bt0raBXgKsAj4dEX5PwTWlLQpcDrwEuALFeW/1PafgScD84v8wyvKfydRwuBPALYvBBZUkv1F4F+BI4BPAA8GvlRJNnT/s1+/yH8WcLTtRwBPrCB3d+AZxEX5FOD55fY94PgK8ntsU+a/V5G9OfDCivJb/fwlfQbYF3gtkUS4N7BFLfnAk8v8dydyXB4ANL7ASfoXSY8A1pL0cEnbl9vjgLWbym+SidkGd5b73YBP2z5R0jsrypft2yQdABxh+4OSLqgpv9w/nfiRXyRpWMbqdLnD9i11RS7jgbYf1nd8hqSLKsrv+me/qqRNgH2At9USavtXAJJ2tr1z31OHSvox8O5KQ61Wdjx7AZ+wfbukmiFobX/+j7a9raSLbb9L0oeBEyrK7+0Gnw4ca/uPlab/FODFRKJj/47tVuCtTYWP2gr8t5I+S/xIvldsRDXnKEk7ESuc75ZzNS9iSySdSnwJvl/MQXdVlH+ppOcB8yRtLekI4CeVZF8gacfegaRHAT+uJLuI7PRn/y4i5+EXts+TdD/gqory1yk7TwAkPRpYp6L8zwDXFpk/lLQF8OeK8tv+/P9W7m+TdB/gdmDLivK/LennwELgdEnzgb83FWp7ke3HAy+2/fi+2x62m1+AbI/MjdhSPAvYuhxvQmxtasl/DJEt+uZyfD/g4xXlrwJsD2xQju8FbFv583kfkQV7HvBeYM2GMi8BLgauIH5w1wLXlMeXVpz7Y7v62ROJaq+vNdcJxngEYVu/ttwuBLav+NnsM3BOwKpd+PyLvHcAGwDPBn4PXA+8p6L8NYANgXnleB1g48ryn0esuv+zd2sqd+QSeRR1xjemb3Vm+9eV5B5uu5rjpk/2pI5W2+dXGGMe8H3bNeyu/XIntSO6bPFHlZn47Ms4ZzhWUq2iiA6R7Vsqy/2h7cfUlDlkjE0Ju3T/b/eHLYyzBrFwqfYZSTrf9vYrOtdA/inALcASxkzF2P5wE7kjZQOX9FrgMOAGxrZfBhqHbNm+szgT2mCyf4KBJzQdoMz/Nknr1/ziDipoSfcGqkWf9Mn9NstXq7wFWAx81vZ0t6utf/aFn0j6BPA14K/LBqh0gYCIsiAcyWv27K+2a9nAT5P0Rpaf/x9rCJf0AcLJeDljCsqE87qJ3GdN8hxuaIaQ9C/AphQnI2O2/PWo4GTsYzPbT60oDxixVHpJvyBqit/UkvwPA1sDX2f8l7imM6Q1JB0H7Aicxvj5H1RB9h6EMrwPcCOxkrrC9r82lV3kf4yITji2nNqX2AqvBaxnu2ZERHUknTHktG1XuUCUKIu1gccDnwOeA/zM9gGV5F8z5LRt36+S/CsJk8k/asjrk3v0JE/b9ksbyt+fcDIuJBYTPW4FvlBLN0g6knDeX1JD3jK5I6bAzwCeZPuOluQP+zLU+BI8wfYPJlotVPwS7D+B/EUVZF9ErFb/z/bDJT0eeK7tA1fw1qnKX24L3zsn6bIaFwpJDyHa+y3bQdj+YlO5M0GJrti2735d4ATbVWOp20LSycDetv8y23OZDpKebfsbLcq/HLg/4V/6B7HSt+1G1oWRMqEAVwNnSvou8UcCYLtxwkSR85IacobwWOAHRDzvcsNSKdzJ9iJF6d4HlFNX2r69hmzgdts3KbL0VrF9RtkW12K+pM17/gxJmxNdSQD+2VS4pMOAxxEK/HtEr9azifj2xkjaGHg/cB/bT1P0f93J9lE15LN8lMVNVIyyKCGE/0448gHOJExXtb4/twEXSjqd8b/dxrvDHv0mpj75tUxM3ykRXgsYb8OvJf9pleSMY9QU+K/LbfVyq4qkBxCJQRvbfogiHXoP2+9tItf2YeXhy2zfOemLG1CC/xcRUQoC7itp/0qOoj+VVd8Pga9IuhGouRN6A3C2pF8Sc98SeJUiM7DxDoIwOTwMuMD2S4rC/VwFuT2+ABzNWAz4/yPsybUU+HckbQD8N3A+ceH/30qyIb73qwGfKscvLOdeVkn+SbTYD2AiE1PFIU5kzMlY1QwE4+L96/qYaoXJdOEGnEVkMl7Qd65mqNyviTq/u1LMU5Xnv4RIuOkdPwBYUkn2OkS43KrA/sBBwL0qz38NQsluR8PwxyGyf9b3Ga1HXCQuqyj/vHLf/925sPb/uO9zWr+yzIumcq7hGKsDDym31SrLvnjgfl3g1Iryq+mBCeTvQeQN/JWxMN3G38+RWoGX4Pk3sfw2qVYkwdq2fzaQYVVzlflAwozyauAoSd8Bvmr77EryV7O9rCm07f+nSvVEbP+177DGingYj2Bsi7ptiSKoZaNeXFaw/0so8b9Qd4X2V0n3okTSlKSnmmFsawKvAnYpY5wt6dOefnTOIHdK2sr2L8t496MvnK0pLe8OYSypphUTExFl9FBXdjL28R4iAGGcj6mp0JFS4MBXiG3p7sAriZXg0ory/yBpK8Z+hM8hEgKqYPtvwHHAcZI2BD5GrPrnVRpisaSjGKtR8nxCWU0bSWfb3kXSrQxpSk38UP7b9qeGPLcy43wJ2IpIUOkPM6uiwG2/qjz8TIm5Xc92tWJQwCGEiWArRYr7fGIbX4svEpEPR5Tj5xL/570ryf8PojzC1YSC3YKoV1KLDxNJd1fCMnPlscRFuwbfbtnEtAvw4hKtU83J2EcrPqZRi0JZYvsRPU98OXeW7cdWkn8/wsTxaOBmYivzAtvX1pBfxngsESL3NCJb8muu5N0uCQyvJr5sIuzVn3Ll0K2BMe8F/MR2oxK2kq4gCiq18oWTdDxhoz7Zds0U7v4xViV2WaKuAxlJF3l8LZqh5xqOsQZj8/95ze9N/292snPTlL0KsKPtn5TjNhJ5hia0uVIim6T/I+rQ/BfhvL8R2MH2oxvJHTEFfo7tHSV9H/g40eX+eNtbVR5nHWAV27dWlnsNscI8DjhpwCwx8miCLFhJm9hutFOR9HXgoKZyJpH/RGJFuSMR5/8F2z+vKH9YiOgtwCW2b6wg/wvAZ2yfU44fBezft7OYrtyZCnH9PLEq7t8drupKkV+Sfmp7pxqyVjDOOCejK2SBF7nrEGYgEZ/N+sBX3DDnZdQU+O7Aj4D7ElvJ9YB32W7k3ZZ0yGTPu1KYoqT1HCUpqyLpEoabNwCotMoZmgVbawtZYvy3I+zS/WFme9SQ3zfO+oT54W3Ab4ht9pebrpZLaOtOQC+h53HAOYQj+d22p1V6t+9/uxqxOv51Od4CuNz2QxrO+122D2srB6JvnFZ3h5LeRdTsOaGNXZxaTmRri5FS4G1RYoQhfiA7MBbu9Azgh7arhFK1FaY40fauR41tntrPgh1qBrN9VsUx7gW8gAiR+x3hU9kFeKjtxzWU/W0iTPSGcrwxY2F4P5yuop2h/+0qwHNsH9dU1mxRfDTrEP6TvzFmo67SWUgtJbJN4mOqMv+RUOCS3uSoD30EQ1aarpQMoCh3+eye6URR8vLrrlSjQNJZhLPos7YfXs5d2nQVNROo5SzYtpF0AvAgYgv/hX5TjaTFthc2lH+J7Yf2HYswnzxE0gW9/3dTWtzCt1LMStJxtveZaJdY0QnYKr3vSFHkD7d9l6Sf2X7kbM9tMkYlCuWKcr940lc1Z3PGZ/39k3odbaDlMMUSunYE0S1ndSK65a+VViGtZMG2vQLp4xO2fzDsiabKu/CjEhb69XL8bKKu9jqUDklNmGgLT4TU1qCtYlYHl/vdG8qZlHLBfD6wpe33SLovsIntWqGirSayKVrxDXJrY9PeKKzAZwpJbyOaRXyTUCbPBI6z/f5K8k8GXkOs6rcvYYoH2K6SRitpMbAfoUQWAi8C7m+7cYeYPjPTOGy/q6nsScZco6KNdDBV/CzCKVgzUuTZjNl4zwa+Ucse29YWvk9+q8WsJhjzxx7fZaiJrE8Tvpkn2H5wCdM91fYOleS34mTsk38t4du7uYyxARHCfCPwctvTCgceCQWu4aVGl1HT0aWoH/1v5fCHtqu19Wo7TLFvm9cfZvmTpqFIbSLpHbbfM+T8ekSkzuMqjfM5whHYS0J6IXBnDf9GsSFf3KYprM0tfJn/3ra/1nymKzXub2zft5Ks88uiaJm5qnaYZZsoSgF80/b3y/GTgacSEWsfs/2o6cgdFRPKh8r9s4B/Ab5cjp9LZHY1ohcdUrYx1/bLlHTPCttIAGxfDTyxrTBFIgttdaJo0AeJK3iVtlvFBj7Mhtk0C/bfJL2vf5egqMH8fer2NNxh4Mf8A1Xq6VmU6UXqK8bVAq1t4cv8X02YT2aSmqvD2xVhrr0kvPlUaNk2gya+hbZf2Tuwfaqk99s+pETwTIuRUOC9SARJ7xlwtHxbUo1U3GMIG90ShvyTiPZe02aiMEWNFeWvEqZIrCrnEWaa1xNbsmdXkv3GvsdrFrk1FMgewPGSPlK+rFsDJxPZnZ+tIL9Hq6niRHu/yyT9jPE25Fq7wz2J6IrXM7aFr1UJD1qygWvihgsiar3X4uOE6fPekt5HZMG+valQ27uU+3s0lbUC/ijpzcBXy/G+wM3lojTtC9FImFB6KLL1disrWSRtCXzP9oNnd2aTM1NhijONKmXBFvv0V4lGtDsBr7P9zaZyB8bYlcjEHJcqbntYI4bpyG8tDFIttcsbGKMVG7gmb7iAGybySNrM9nXl8YMoheKA0wn/z7ebyO8bpxUnY5/8jYg8i17j6rOJC/QtwOa2fzEtuSOmwJ9K2JCvLqcWAK/o2Y1aGO+BwBttv7ySvLbDFIeFavXakr23icNl4Au8ClHD4uNunkLf252sRhQq+xF9bbYq7k56ySTLUsWJbeuPa8lvE0knAS905V6YXUfR6ecpg34kSS8F3uZKWdptORmL7HnAItsvqDDVcYyECaWH7VPKFvtB5VSVeg2KhJoPESFa3yJC8T4FPIrJeyquLG2HKZ5MmAWOKcf7EV+2W4h61cMaSkyV/i/oHYQDtkY7r/6t6ceHnGtE+XHsQ/Q1PNn2xYqM3k8TW/ha8dlthnBCREBcIql6u7we6mbHotcT5p+n274KQNKhhJmpSo2kwilM7GTs6Ypp4ehnO1/S6rYbNy/pZ6RW4ACSHs3yXTEafckknUv8oH9K/FPeRCjBd7heuc6ZCFNcLiyrd04DiSZzBUUNkfsSKfqPAn5F1EN5i+1vVRxnWAjn1rbfWkl+a+3yivyhHYts16yo2ArFPPZZohjUywgz5e62b644xnLJXn2RQRfa3q6h/M8C2xPm1f4LdKMd6EitwNVeydE1bH+hPL6yOHMOdeXuObbfV2LBe2GKL6kZpgisK+lRts8FkPRIorA9TNPhqCiadCTxuV8CvNT2FZO/a6RYSDTTvUtRU/sPhG3097UHsv0LSfPK9+ZoST+pKHuRpLUIe+iVK3zDytNqxyINiekfdm462D5d0ouJNnA/AXatufAqtOJk7ON35bYKFXegI6XAiR9jGyVH15T0cMLcAFHsf1uVMBHb51cca23gz7aPLtumLW0PcyBNh5cBny/hZgL+DLyshC3+1zRlfpKIQPkhETHyUeApFeY6U/zTpXys7b9L+n9tKG9aDOEEkPQMwsy3OrClpO2IIlm1olz+Vi5ydyhi8G+kYfTVAD8lVpgrOrdS9IX3iehUtCtwY/nt1gzzex7hZOzt2s4u53omuka4JMRJWscVq5SOlAlFLZUcLTHOE+EKsc69cQ4jLkIPtP0AReeQrw+aPSqMsz7xv6uRwn2+7e0nOq6BWiymJOk2oOfBF7GT+AXULcivKDp1A6FgX0+E+X1qutEDQ+QvITIxz/RYoko1s5ikTwFvJcxAbyAWMRdWiBL5F8L/8GVC4fUWSesRmbAPmnva1zUAACAASURBVOi9o0KbTsa+MXYi+qeua3tzSQ8jAjQalQsetRX4RsDlJda2WslR249vOrEp8kzCaXZ+Gfd3JRKlCiXK4tkUH0FfnHmTeOENBmJ5xx27Qr3osvJ7DeEQqs2MhJh6rCrg34E2ygvcYfsWja+j03h1JWkvoiFHWx2LngK8GNgM6Lfn3kpcMEaeNp2MffR2tieVMS+S1Li42Kgp8HfO9gQa8k/bltTLFqu2xS600Tn7LMZHr/Qfm3rZkq0kkrhSx5QVIWln4vu5BeMd7LXMEJdKeh4wr0RiHUTYe5vyAuCTZafy4yLzx5WUd8/JukjSs12p89QscS3w4xLOWc3J2I/t3wxcoBv74EbKhNJ1ioLaGngSYZN+KXCM7SMmfePU5XeiNO0w2kokmSkk/ZwwnSyh74fXJPZ+QP7aRBOKJxNmiO8D76nlrFMkxe1E1OnZiQh5Pc/202vIL2PsxvINyWtmk7aGWi7mpmj59xHgE0SU1EFEnsJ+jeSOggLXxA11azsqWqM4VTYjYtiX/Qhtn1ZxjCOBI9xe5+xkAiSd62kWHBoVFJmMOxNKfEfgxlrmRUWxprWBxxPRLc8Bfma7Ri5Bbzfbc8Q+gPidneyK1SZ749R0MvbJ3Yhocv5EIhLl+8DBTRcAI6HAZwpJp9vedUXnGshfYrtWF+5h8i8H7k8k2bTRObs1ygrzECJM7sBiJnig7e/M8tQmRVG9EiISYR5hUur3zzSKYFLLlTglvZVYcc8HriTawJ1DVFesFkarUiGz735dov3ZkyvJX0KE525IzH8xcJvt51eS34qTsW1GzQbeCiU+eG1gI0Ud4X5P+X0qDnWOpB1sn1dRZj9V6orPEkcT5ode6dvriKSYKgp8iI26d3FraqIZzNTtT/YwETnShFYrcRIJR38hPuefAOe6nXT9nqnnthJ9dROwZUX5sn2bpAOIXegHJdXMsWjFydhDUVztY8TOx0SI5etd6j5NlzmhwIFXAK8jlPUSxhT4n4k46Fo8HniFpF8RjpCqK+Sew04DbbeaoImryfXGrOXE3Mr2vpKeW+T+rReHX4mjGGKjbkrbEUxuuRKn7Qcp6tw8msjEPLSsji8iolMmLUa1Enxb0gbAfxNRWCYaStdCZZX8fMZKPFTVX204Gfs4htA1zyzH+wHH0iBFH+aIArf9MeBjkl5by6E4Aa2ukNVO263J6qfUjEL5pyLTsBehsxX1ImkAbrF9ckV5wLJiXLfYPmrg/GuBebY/Wmmo+ZLu5/GVOOfXEFwifb5TwgcfQXQtegXhZG+swEuc/+klL+EbitZza1Ze6R8MvIWoV3JZWdFWqTRZ+I2ijIdLwtZBjLV6rIFsf6nv+MsltLax0KYyOoVaqLUyZIy2GtO22narTSQ9iajfvA1wKuFMe7HtMyvJP5x2bNSXAtsPxgeXmPzzKiYKDavEeaDtUxvK3YNYfe9MXOgvI0wpPyFW4EubyO8b56e2d6oha4jsecDhtv+jDflljFacjH3yDyd6p36VWMTsS2SWfhKmH047EgpcM9QVQxPUWnG9rvdDV8i2qzSmVQtttyQdAbzVA92DSsTCJ1yxRrWkexE2QAHn2P5DRdnDVmN2wyzbybIhJ3tummOtQf1KnCdQYr+BJW0lqkh6F3Ax4bisrlQk/aDp/3I2mSCMtse0fTUjYULxzHXFaKvWSo/3EApq3Aq5ovw22m79nqjv8Q7bx5RokXcSld/e3FD2II8lCtqbqA9eralDm7ZqSRvbvmHwXOUxFgOfB451xSp7tif1cVTkEKI2zB2Ses2Ba4YAX1CSbL7O+ESbKia+tpyMPWzXdOguYyRW4IO0aIJopdZKn/zWGtMW+esQbbdWoWLn7GJv/QRRJe0+RMr7e23f1mzG48b4FBECeWw5tS/wS9uvrjhG9UQSSS8i7KFvoJRIIOzIHwQ+6XrlXu8PvIT4XBYTtulTW1xsdAoN7/xj2y+tJP8cwpzR+37uB7y2Vux/+R4tR1Pz7UiswHu05KTrj7W9By3UWumjt0L+EZUb0wL0JRjcJem7wE2VfuA9GasSF4crairvwmOBh/TmK2kRUb62ChMlkjSVa/uLkpYS7a8eQnxWlwGH1XSaOopivU3SO4j+rZ8n/s+fJ7qWV2m83SYlRHdrxl9Aa/S0bdyabQq04mTsY4e+x2sSVRXPp2mpbNsjcyNCm+5F1CyG+DEeWUHuYye7VZz/OoQCXBXYn1i53auC3B2JWsgnEMWyLiVMHzcCT20o++1E9b59y/GmwPFETZRtKn42JwBb9B1vQZgLasm/eOB+XWIFO+vf65X4G7YF/odIuPk4EWL2BqJqYA35axHJU23M/WXEBflmIjrkb8APKsp/ANEH89K+z+rtFeUfDhxKOI+3IJq+vAO4J3DPFj6v9YGTGstp45/Z4I9aXO4vAlYpj3822/NaiflvQFxpdwDWr/m5EOn5e5cfyI7l/IN6F7sGsj8G3GPI+acRK/Faf8NZwG3lQnQmYcf8PyJxovkXORJUILL07kN4+K+a7e/ESsx/SVFQzyMakPQ/d0IF+c8oF4ZryvF2NT73PvmXECvLC8vxg4CvVf7+PLL/+95T5pXkXzPJ7eoW/t+r1fh9jZQJhZZNEEOiXGCsKfAbPE2HRYkbPZJw/F1DOHC2kPRN4JVu7vlf1SWcTNK7bZ8DYPvnTXNhbB88wfmTJf2gkfDx/GdFWcP4zpBEkmodZ2aAvSf6/rmOI/KdhAI8s8i8UNKCCnJ7/N3RUKPXiefniqbhtVjb9s8Gvu81zZOtOBl7aHzJhHlEGeTG5ZVHTYHvSaTkvo4xJ13NamYfIdoaHUMo2f2I9OUrCZvj46Yp9+3EFfW+Ht+R/pPENuwdjWY9vqXT3waea83J5QphbH2yzqolawL57ykP20okaYWSKNR7vNzzrlfOdFi98ZpcVy6g3yJKB99M/NZq8YeS/NXzoTyH6IpUhbacjH18qO/xHcCvbF/XVOjIRaEoOnw8kvhHneeK7bE0pKKcpHNs7yjpItsPm6bcS4FHesDxV3YT57hhCVhJdzKWmr8WYYqgHK9pe7Um8meCgd3P6sQFr2ZX91aTtDTQTKNPftMol/4ypq8gmvcuw/XKmR5FmGgOJf6Og4DVbL+yhvyBsR5LLL5OqbD77Mm8H7HLfTRhRrwGeL4r1YMv+RA9ljkZXbnpc8mFeAzwa9tLmsobqRW4pJcRW+0fEMrpiGIy+HylIe6StA/hpIOIVOjR5Ep216DyBrD9F5XmDk2wPa+pjNnGAzH+ik4xVcIri7y2GmL3aKOZxjgFLWmvWgp7CK8l6o3/gwiV+z6Rt9AISevZ/rOi3kqPXnTROpLudJ2qhxvafmIJpV3F9q2KPqJVFLjt1/YfK9oWfmmCl0+Zshs81PalkjYhzHuLga0kHemmpRhqG+cbGvavpC9qg4hIubKi/PsB3yY6ly8tj+9PrGp3aSD3IqLM5T2H3C6a7c91in/DwUR1RhGFoc4HntzymOdUlHUFZUfZ0lyrOcwmGeP82f4eTGPO3yn31xBlAAYdgEuB99f4bICH9h3vR3Fct/R3VXEyApf1PX4r8MXy+B6UiKkmt5FagRMlRvtTum8FflNLuMNJNFHxprMbiF6f8VUOxw3bQO5M8lLbH5P0FKKI0ksoySQ1hGt81cNViKzYmp/NpYQ/o5UkLeAnkh7qjjbTUDRBeCPLm4Aapafb3r3cD3UCljoml9K8P+ZzgOMlPZ/I5n0REZlVhbacjEB/w4ldKRUaHTuIu4a/ZeqMhALvc+T8FjhX0onEh7knFZIxJL3JUT/4CIYoDTeshWJ7QZP3jwi9i8/TgaMd9ZBrerz6L5x3ELWu92wqdAaTtHYBXlxqWlRrpiHpEsa+k/eX1OtVWbtZx9eBzxCROTXLpAKgCWpnOxJ5Gjeetn21pP0IJ+lviN3hoEO/Ca04GYkqh68lFqfbA6cAKCpzNvZdjYQCJ358AL8stx4nVpLfKwu5uJK8WUHttpVaIulUogj/W0oUTeMVQg+3l0n3oRW/pAptlQrevSW5g9xh+9Mtyu+vFLgm4d9YQsOGFwMXOAiz5DxioUetC5zH6rL3nIx/J5RuUw4gIumeSCTL/amc35EapXyLPSbpAGqxrZSipvN2RNLCn8oXeVM37F4u6eXAmbavKiv6o4goiF8R5WQblXvtG+cDtt+8onMVxmmlTk/bSHonkbn7TcbvUFpJ0Zd0X+CDthsVc5O0xWTPu2EUymRORiILvFa991YYKQUuaSHhKd+C8Xa6WjWXW7EDzhSSzre9fdmSrVXMQhfYfngF2dsPOX0LsZWcdsJECbF8uO3bJT2PSA1/MlES4DDb/zZd2QPjnG97+4FzF1f87rRaKrhtNLycqd285dxE44lw0lUpt1tiwK+z/Q9JjyNS6b/Yt6KdrtzLev9DRf/QB9l+UdmB/riiCasVRsWE0uMrxFbsEipu3/toxQ44QSjVMiqucqT22kp9irDRXUzYXx9SHt9L0is9/cYCd/SZeHYnfnQ3Af8n6YNNJy3p34FXEWFZ/buFexA1sGvRdqngVpnIyViLAf9Sbzd3UcUhvgEsVFRtPIoowXAM4bNpQqtOxrYZNQW+1PZJLcpvyw54DKGclhBf4n7nn4nwxRq02VbqWuAA25cBSNqGuJi+hyhENV0FflfZmt5M/EDe1/fcWtOe7RjHACcD/0UkqfS4tbJ54HbbN0laRdIqts+Q9IGK8pehqOp336bmqwGZbWca9vuX7iAKldW8gN5l+44SzfRR20eoTlPjVp2MbTNqCvwwSZ8jMsb67XS1+jJ+W9KrqGwHXFEoVQ1KONYz+qMqSlhklW5CxNbxsj7Zl0t6ePH+N5H7n8SPex5RPKl3gXgsY+3Dpo0jXf4WSW8Hft+/xZbUeIvdRxvNNJYh6UxgD+I3eSGwVNJZtg+Z9I1Tp51ypgVXqos+CbcrGmK/iLGIphoKtlUnY49ivv00sLHth0jaFtjD9nsbyR0xG/iXiciKyxgzodj1ira3bgeUtCnL2/Cr1ERWi22lJH0N+CPRsw+iscBGwAuBs23vMNF7pyB7VaLi4c1959Yhvn9/mf6sx41xIRFbvoDIMjyJKJ3adIvdk99KM40++RcU08zLiNX3YTVt+EPGWx/4UtMwS0l7ApvZ/mQ5PpexZsxvtv31ZjNdNs42wCuBn9o+VtGEZF/bh9eQ3zaSziJ2tJ/t+awkXeqGZTZGbQX+sFpOj34krWb79mEr5GKGqDXOBwjFdznj07mrKHDabSv1YsKW/DrCBHQ24fC9najLPm2KE/TmgXN/neDl06WtLXaPewPX2/47sKhssTcGqihwYNViatqHcOS3zW1E84WmvInIiuyxBrHaX4dYwVZR4LYvp2+3afsaooZ3V2ilmuKoKfBzJG1T/lk1OUnSnl6+s/i2xEptQaVx9iJWfdVqZQxwT0Jh9K/CTdioG1GSIj5cboNUWSW3TFtb7B5fJwop9biznJv2zmSAdxE7h7Ntn1cWFldVkj2YabgKsA11Mg1Xt92fLX122ZXcVHYtVSi752FJeK1E0bRAK9UUR02B7wLsXzvbjXAunizpGS5Fp4qd9MtEyngtriaURisKvMVkGCTtTNSMHjT/dOUH8hJii/0+29eULfaXK8pftX8BYPufijrwjSn+jfv2f8+Lf+PZNeQX2so03LD/wHZ/G7L51GNh3+M1ieYmQ6O+RpRXE9UUHyTpt0SdmBc0FTpqNvChQftNg/WL7LcBTyUy6p5CtK56lu3G2Zl9IVSbAg9jeSdsFUdjW46QIvvnwOuJi92yEMumNl5JD3IU9x8WZ44rJfK0jaTTgCN6UVLF9nuQ7V0ryT/DdiNT1QRy1yQubPcnwnOPahLXP0T+V4hErf8dOP8K4HFumMizgrHPtr1LJVmt/bYGxllWTbGKvBFT4JsPO+96XekPIWouC3i6o5FsDbn7T/Z8LQ99W46QIme5Wuk1UJTMPFDSsHBH13LKStqaCCXchvGZklV2EGX7+xUikQci7OxFFb9D7yMco19jvH+j0QWuOKdvJ7pcPY1YeQ/twjRN+fcm6pP8g4hqAXgEYQvfy/YNlcbpXwD0iqH9u6dZw3+I/NZ+W0VWO/XkR0yB9+oeiPgRbkmUk63VlV7AzkQT32WNIpp64mcKSefZ3kF92ZeSLrS9XQXZhxOhficwfvfQlRXy2cBhxM7qGYRJRbYPm/SNKz/OukVulRVUn9xWLnCSLukFBpRooJ95IGO1BpKeAPR+p5fZrtKOT9Kptp888Pn0iqF9yPaVlcZp7bdVZJ3CWD35/h3uMJ/TlBkpG/hgBEq56r6igugPTfC4Clq+4M44KoaCtdlWqrf67rc1mobFiPqR9BCWXyHXSiRZy/bpklRMbu+U9CNCqTdG0vuJ2h5/KscbEn1U315Dfhvmk8KyTMMSpdPKIEVh1+yh2mN+kd/W59Oj1ZZtRKjlUyvKA0ZsBT4MDalxMWpMZLvvUcOGX8YZ1lbqBbavrSG/TRStwx5HKPDvEdv5s12pZZWkHxOFvo4nFMlvgcNtV2msqyE1Z2p+NyVtDLwfuI/tp5W4551sH9VQbq8dHzCuJV8vQKBaS7s2kHQ1Ec46lEohtK3/tiQdSfhQqtaTHykFrr4Gr4Sda3uiQ89TZmlKK01R5lvb/r8SK7xqC9vtao4QSS+w/eWBz34ZrtRUt+xSHgZcYPthRWF9zvZEDTZWVv4ORNngDYj0//WJFfM5leRfDOzQCxEt/9vFTc17ffJPJuKm31Y+n1WJz6p6XkSXkHQTUVZ6aLMUV0ry6xuvqpOxT+7lhCO5aoTdSJlQGKsLDmHn+i5RxKYTKEqnHkiEN20FbEYUz6oVqXDIwDEUu5rtC6cpthere48hz9W8uvfqmN8haT2iol+1EEXb55WHf6FuaGiPLwOnSzqa+FxeCtRMH9/I9nGS3gLLzB3VGy90kF/VVtLDGHQy9kxNTZ2MfbRST36kFLjba+g6U7yaKGR/LoCjBva9K8pfWG7fLse7AecBr5T0ddvTqe73XRj+2SuaxtZisaQNiGpvSwhFW6Pb0kdtv24gUaWHifIAn226EneU7r2YqJkh4D22v99E5gB/VdRg79lgdyQuznOddoz2y9NK0+o+WjF1jIQJRZEePiG1okQkzQfezPKOtFqhbOfafpTG6lqsSjSqrVWT+vvAs13qh5SIiOOBZxKr8G2mIfNK4CmDtj5JLwHebnurxhNffswFwHquUG1P0iNsL1EUxxrGRoSyXenPZshY/eaxtYF51eJ5w2F/BFHG91LCefecGp9Rl5H0ENuXzsA41UIGJ5DfSoTdqKzAdyL63B1LrF7buup+hYiz3Y1Ibtif6Jpdi7MUReHXkvQkorbIt1fwnpVhc6C/HMDtwBa2/yZpuquG1wOnSXq67asAyjb+ecBESnFaaKDQl6THuGGhL9tLyv1Zk4z7z4memypDzGObUtE8Zvv8chF6IPH9v9J1WuV1mplQ3oVWm1a3FWE3KivwecCTiAL52xLb+mPdV9600jhLbD9CfVXeFCU7qygqRVuyA4iOMyJqW3zOlT5kSe8gVtu9XqHPIGq5fJho/zSt1mqSdgU+S9RyeRlR32N391UPbIomKPRVcXfVdiLPhRTzWF+c8CWDP8wG8vcGTnE0Eng74cB/b1fi8LtOW07GFYzZOIppJFbgtu8kCqmfUpwJzwXOlPRu20dUHKq3orle0m7A7whHYxWKk+5bwLds11zZ9+S/p0Qr7Ex8wV7psVIA0+6LWeKnXwycCfwE2NVRda8mbRf6OpqxRJ7HUxJ5Ksr/h6P+CbAsKabm6ucdtr8uaRei1MOHiNTu6tmxyVDaaloNTBhh11hHjIQCh2Ve4N0I5b0A+DgVquwN8F5FHeQ3EPbG9YjyqY1Q/KoPA15DKA2VCIIjKnqxe1xAXHh6ZojN3aDUgKRbGbPNrUGYBG4sf1PNOOFWC33RciIP7ZvHeruS3YBP2z5R0Yh4TiPpONv7aPlkudor5LZNEa1E2I2EApe0iHDenAy8q0W7180uHVwoNa4VVfia8jpiVbyDo05xLzHg05Jeb/t/KoyBovXTYcANxA9exBdv2l9i28PCB9vgNuBCSa0U+gL+XkxYV0l6DZHIUzMC6FDCPHYJYbv8HtFbtRa/lfRZIsrlA2VBs0pF+V2lV7dl95bH+S5DnIyMlQeYNpIeTjSpucz2FU3ljZM9IjbwuxjLFht2la2yChxmc6phh1I0DniS7T8MnJ8PnDqYwddgnF8Aj3KlLjAziSYo+OV6hb6GJfJ8wPa5NeSXMXpp3dXNYyWq5anAJSX8dBPgoZ5+M+mkAT0no+1GjkZJ/0mUjV1CmMP+ywOVG5swEitw262uNBSd3B8NzB+wRa1HFHBqymqDyhvihy6pZlOB39DR2OBainoS+eMSeYqNel9KTP50ads8Jmk9238mVn1nlnP3JHYpjUsd311QdFr6ALGrEi2XAihRQTWadewLbGf7thLnfwqRC1GFkVDgM8DqwLrE39tvMvgzUKMWx2Rhao1D2Pq4mnDufpfxZogq6e5touUbRvR+gI2iRBRZna8mwvpOAk4rx28ELiJCR5vQtnnsGMI8sISxLXwPUzFbteN8kGjqXdUE0aMtJyPwd5cmMrZvKma+aoyECWWmkLSFKxWWGpDbXzBo3FPAmrarrMIVBaGWowsZrGqvYcSJRPGhnxIO2A2JC/bBnn55gX75rZvHyir/vk2c0Xd3JP3Ydg1/1UTy+39bvXK132gajSXpT4z1xBVRcG1Z7kPTMNq5psDnE01Y/5UWMjFnCknruH5T4FZRew0j+utdzwP+AGxeMUNywgy9mtl7vRyFGrLuThTTCUR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idseasoncitydateteam1team2toss_winnertoss_decisionresultdl_appliedwinnerwin_by_runswin_by_wicketsplayer_of_matchvenueumpire1umpire2
012017Hyderabad4/5/2017SRHRCBRoyal Challengers Bangalorefieldnormal0Sunrisers Hyderabad350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ Llong
122017Pune4/6/2017MIRPSRising Pune Supergiantfieldnormal0Rising Pune Supergiant07SPD SmithMaharashtra Cricket Association StadiumA Nand KishoreS Ravi
232017Rajkot4/7/2017GLKKRKolkata Knight Ridersfieldnormal0Kolkata Knight Riders010CA LynnSaurashtra Cricket Association StadiumNitin MenonCK Nandan
342017Indore4/8/2017RPSKXIPKings XI Punjabfieldnormal0Kings XI Punjab06GJ MaxwellHolkar Cricket StadiumAK ChaudharyC Shamshuddin
452017Bangalore4/8/2017RCBDDRoyal Challengers Bangalorebatnormal0Royal Challengers Bangalore150KM JadhavM Chinnaswamy StadiumNaNNaN
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" ], "text/plain": [ " id season city date team1 team2 toss_winner \\\n", "0 1 2017 Hyderabad 4/5/2017 SRH RCB Royal Challengers Bangalore \n", "1 2 2017 Pune 4/6/2017 MI RPS Rising Pune Supergiant \n", "2 3 2017 Rajkot 4/7/2017 GL KKR Kolkata Knight Riders \n", "3 4 2017 Indore 4/8/2017 RPS KXIP Kings XI Punjab \n", "4 5 2017 Bangalore 4/8/2017 RCB DD Royal Challengers Bangalore \n", "\n", " toss_decision result dl_applied winner win_by_runs \\\n", "0 field normal 0 Sunrisers Hyderabad 35 \n", "1 field normal 0 Rising Pune Supergiant 0 \n", "2 field normal 0 Kolkata Knight Riders 0 \n", "3 field normal 0 Kings XI Punjab 0 \n", "4 bat normal 0 Royal Challengers Bangalore 15 \n", "\n", " win_by_wickets player_of_match venue \\\n", "0 0 Yuvraj Singh Rajiv Gandhi International Stadium, Uppal \n", "1 7 SPD Smith Maharashtra Cricket Association Stadium \n", "2 10 CA Lynn Saurashtra Cricket Association Stadium \n", "3 6 GJ Maxwell Holkar Cricket Stadium \n", "4 0 KM Jadhav M Chinnaswamy Stadium \n", "\n", " umpire1 umpire2 \n", "0 AY Dandekar NJ Llong \n", "1 A Nand Kishore S Ravi \n", "2 Nitin Menon CK Nandan \n", "3 AK Chaudhary C Shamshuddin \n", "4 NaN NaN " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 RCB\n", "1 RPS\n", "2 KKR\n", "3 KXIP\n", "4 DD\n", " ... \n", "631 RCB\n", "632 RCB\n", "633 KKR\n", "634 SRH\n", "635 RCB\n", "Name: team2, Length: 636, dtype: object" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['team2']" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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team1team2
0SRHRCB
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631DDRCB
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" ], "text/plain": [ " team1 team2\n", "0 SRH RCB\n", "1 MI RPS\n", "2 GL KKR\n", "3 RPS KXIP\n", "4 RCB DD\n", ".. ... ...\n", "631 DD RCB\n", "632 GL RCB\n", "633 SRH KKR\n", "634 GL SRH\n", "635 SRH RCB\n", "\n", "[636 rows x 2 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "matches_played_byteams=pd.concat([df['team1'],df['team2']],axis=1)\n", "matches_played_byteams" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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team_nameMatches_played
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1DC75
2DD147
3GL30
4KKR148
5KTK14
6KXIP148
7MI157
8PW46
9RCB152
10RPS30
11RR118
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" ], "text/plain": [ " team_name Matches_played\n", "0 CSK 131\n", "1 DC 75\n", "2 DD 147\n", "3 GL 30\n", "4 KKR 148\n", "5 KTK 14\n", "6 KXIP 148\n", "7 MI 157\n", "8 PW 46\n", "9 RCB 152\n", "10 RPS 30\n", "11 RR 118\n", "12 SRH 76" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "teams=(matches_played_byteams['team1'].value_counts()+matches_played_byteams['team2'].value_counts()).reset_index()\n", "teams.columns=['team_name','Matches_played']\n", "teams" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "df['winner']=df['winner'].map(Teams)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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team_namewins
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1CSK79
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5RR63
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" ], "text/plain": [ " team_name wins\n", "0 MI 92\n", "1 CSK 79\n", "2 KKR 77\n", "3 RCB 73\n", "4 KXIP 70\n", "5 RR 63\n", "6 DD 62\n", "7 SRH 42\n", "8 DC 29\n", "9 RPS 15\n", "10 GL 13\n", "11 PW 12\n", "12 KTK 6" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wins=pd.DataFrame(df['winner'].value_counts()).reset_index()\n", "wins.columns=['team_name','wins']\n", "wins" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "player=teams.merge(wins,left_on='team_name',right_on='team_name',how='inner')" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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teammatches_playedwins
0CSK13179
1DC7529
2DD14762
3GL3013
4KKR14877
5KTK146
6KXIP14870
7MI15792
8PW4612
9RCB15273
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" ], "text/plain": [ " team matches_played wins\n", "0 CSK 131 79\n", "1 DC 75 29\n", "2 DD 147 62\n", "3 GL 30 13\n", "4 KKR 148 77\n", "5 KTK 14 6\n", "6 KXIP 148 70\n", "7 MI 157 92\n", "8 PW 46 12\n", "9 RCB 152 73\n", "10 RPS 30 15\n", "11 RR 118 63\n", "12 SRH 76 42" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "player.columns=['team','matches_played','wins']\n", "player" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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teammatches_playedwins%win
0CSK1317960.305344
1DC752938.666667
2DD1476242.176871
3GL301343.333333
4KKR1487752.027027
5KTK14642.857143
6KXIP1487047.297297
7MI1579258.598726
8PW461226.086957
9RCB1527348.026316
10RPS301550.000000
11RR1186353.389831
12SRH764255.263158
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" ], "text/plain": [ " team matches_played wins %win\n", "0 CSK 131 79 60.305344\n", "1 DC 75 29 38.666667\n", "2 DD 147 62 42.176871\n", "3 GL 30 13 43.333333\n", "4 KKR 148 77 52.027027\n", "5 KTK 14 6 42.857143\n", "6 KXIP 148 70 47.297297\n", "7 MI 157 92 58.598726\n", "8 PW 46 12 26.086957\n", "9 RCB 152 73 48.026316\n", "10 RPS 30 15 50.000000\n", "11 RR 118 63 53.389831\n", "12 SRH 76 42 55.263158" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "player['%win']=(player['wins']/player['matches_played'])*100\n", "player" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly.offline as py\n", "py.init_notebook_mode(connected=True)\n", "import plotly.graph_objs as go" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "trace1 = go.Bar(\n", " x=player['team'],\n", " y=player['matches_played'],\n", " name='Total Matches'\n", ")\n", "trace2 = go.Bar(\n", " x=player.team,\n", " y=player['wins'],\n", " name='Matches Won'\n", ")\n", "\n" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "linkText": "Export to plot.ly", "plotlyServerURL": "https://plot.ly", "showLink": false }, "data": [ { "name": "Total Matches", "type": "bar", "x": [ "CSK", "DC", "DD", "GL", "KKR", "KTK", "KXIP", "MI", "PW", "RCB", "RPS", "RR", "SRH" ], "y": [ 131, 75, 147, 30, 148, 14, 148, 157, 46, 152, 30, 118, 76 ] }, { "name": "Matches Won", "type": "bar", "x": [ "CSK", "DC", "DD", "GL", "KKR", "KTK", "KXIP", "MI", "PW", "RCB", "RPS", "RR", "SRH" ], "y": [ 79, 29, 62, 13, 77, 6, 70, 92, 12, 73, 15, 63, 42 ] } ], "layout": { "template": { "data": { "bar": [ { "error_x": { "color": "#2a3f5f" }, "error_y": { "color": "#2a3f5f" }, "marker": { "line": { "color": "#E5ECF6", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = [trace1, trace2]\n", "py.iplot(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Matches played across each season" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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idseasoncitydateteam1team2toss_winnertoss_decisionresultdl_appliedwinnerwin_by_runswin_by_wicketsplayer_of_matchvenueumpire1umpire2
012017Hyderabad4/5/2017SRHRCBRoyal Challengers Bangalorefieldnormal0SRH350Yuvraj SinghRajiv Gandhi International Stadium, UppalAY DandekarNJ Llong
122017Pune4/6/2017MIRPSRising Pune Supergiantfieldnormal0RPS07SPD SmithMaharashtra Cricket Association StadiumA Nand KishoreS Ravi
232017Rajkot4/7/2017GLKKRKolkata Knight Ridersfieldnormal0KKR010CA LynnSaurashtra Cricket Association StadiumNitin MenonCK Nandan
342017Indore4/8/2017RPSKXIPKings XI Punjabfieldnormal0KXIP06GJ MaxwellHolkar Cricket StadiumAK ChaudharyC Shamshuddin
452017Bangalore4/8/2017RCBDDRoyal Challengers Bangalorebatnormal0RCB150KM JadhavM Chinnaswamy StadiumNaNNaN
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" ], "text/plain": [ " id season city date team1 team2 toss_winner \\\n", "0 1 2017 Hyderabad 4/5/2017 SRH RCB Royal Challengers Bangalore \n", "1 2 2017 Pune 4/6/2017 MI RPS Rising Pune Supergiant \n", "2 3 2017 Rajkot 4/7/2017 GL KKR Kolkata Knight Riders \n", "3 4 2017 Indore 4/8/2017 RPS KXIP Kings XI Punjab \n", "4 5 2017 Bangalore 4/8/2017 RCB DD Royal Challengers Bangalore \n", "\n", " toss_decision result dl_applied winner win_by_runs win_by_wickets \\\n", "0 field normal 0 SRH 35 0 \n", "1 field normal 0 RPS 0 7 \n", "2 field normal 0 KKR 0 10 \n", "3 field normal 0 KXIP 0 6 \n", "4 bat normal 0 RCB 15 0 \n", "\n", " player_of_match venue umpire1 \\\n", "0 Yuvraj Singh Rajiv Gandhi International Stadium, Uppal AY Dandekar \n", "1 SPD Smith Maharashtra Cricket Association Stadium A Nand Kishore \n", "2 CA Lynn Saurashtra Cricket Association Stadium Nitin Menon \n", "3 GJ Maxwell Holkar Cricket Stadium AK Chaudhary \n", "4 KM Jadhav M Chinnaswamy Stadium NaN \n", "\n", " umpire2 \n", "0 NJ Llong \n", "1 S Ravi \n", "2 CK Nandan \n", "3 C Shamshuddin \n", "4 NaN " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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match_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowleris_super_over...bye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielder
011Sunrisers HyderabadRoyal Challengers Bangalore11DA WarnerS DhawanTS Mills0...0000000NaNNaNNaN
111Sunrisers HyderabadRoyal Challengers Bangalore12DA WarnerS DhawanTS Mills0...0000000NaNNaNNaN
211Sunrisers HyderabadRoyal Challengers Bangalore13DA WarnerS DhawanTS Mills0...0000404NaNNaNNaN
311Sunrisers HyderabadRoyal Challengers Bangalore14DA WarnerS DhawanTS Mills0...0000000NaNNaNNaN
411Sunrisers HyderabadRoyal Challengers Bangalore15DA WarnerS DhawanTS Mills0...0000022NaNNaNNaN
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5 rows × 21 columns

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" ], "text/plain": [ " match_id inning batting_team bowling_team over \\\n", "0 1 1 Sunrisers Hyderabad Royal Challengers Bangalore 1 \n", "1 1 1 Sunrisers Hyderabad Royal Challengers Bangalore 1 \n", "2 1 1 Sunrisers Hyderabad Royal Challengers Bangalore 1 \n", "3 1 1 Sunrisers Hyderabad Royal Challengers Bangalore 1 \n", "4 1 1 Sunrisers Hyderabad Royal Challengers Bangalore 1 \n", "\n", " ball batsman non_striker bowler is_super_over ... bye_runs \\\n", "0 1 DA Warner S Dhawan TS Mills 0 ... 0 \n", "1 2 DA Warner S Dhawan TS Mills 0 ... 0 \n", "2 3 DA Warner S Dhawan TS Mills 0 ... 0 \n", "3 4 DA Warner S Dhawan TS Mills 0 ... 0 \n", "4 5 DA Warner S Dhawan TS Mills 0 ... 0 \n", "\n", " legbye_runs noball_runs penalty_runs batsman_runs extra_runs \\\n", "0 0 0 0 0 0 \n", "1 0 0 0 0 0 \n", "2 0 0 0 4 0 \n", "3 0 0 0 0 0 \n", "4 0 0 0 0 2 \n", "\n", " total_runs player_dismissed dismissal_kind fielder \n", "0 0 NaN NaN NaN \n", "1 0 NaN NaN NaN \n", "2 4 NaN NaN NaN \n", "3 0 NaN NaN NaN \n", "4 2 NaN NaN NaN \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df2=pd.read_csv('F:\\Data Science projects\\EDA\\IPL/deliveries.csv')\n", "df2.head()\n" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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seasonmatch_idinningbatting_teambowling_teamoverballbatsmannon_strikerbowler...bye_runslegbye_runsnoball_runspenalty_runsbatsman_runsextra_runstotal_runsplayer_dismisseddismissal_kindfielder
0201711Sunrisers HyderabadRoyal Challengers Bangalore11DA WarnerS DhawanTS Mills...0000000NaNNaNNaN
1201711Sunrisers HyderabadRoyal Challengers Bangalore12DA WarnerS DhawanTS Mills...0000000NaNNaNNaN
2201711Sunrisers HyderabadRoyal Challengers Bangalore13DA WarnerS DhawanTS Mills...0000404NaNNaNNaN
3201711Sunrisers HyderabadRoyal Challengers Bangalore14DA WarnerS DhawanTS Mills...0000000NaNNaNNaN
4201711Sunrisers HyderabadRoyal Challengers Bangalore15DA WarnerS DhawanTS Mills...0000022NaNNaNNaN
..................................................................
15045520166362Royal Challengers BangaloreSunrisers Hyderabad202Sachin BabyCJ JordanB Kumar...0000202NaNNaNNaN
15045620166362Royal Challengers BangaloreSunrisers Hyderabad203Sachin BabyCJ JordanB Kumar...0000000CJ Jordanrun outNV Ojha
15045720166362Royal Challengers BangaloreSunrisers Hyderabad204Iqbal AbdullaSachin BabyB Kumar...0100011NaNNaNNaN
15045820166362Royal Challengers BangaloreSunrisers Hyderabad205Sachin BabyIqbal AbdullaB Kumar...0000101NaNNaNNaN
15045920166362Royal Challengers BangaloreSunrisers Hyderabad206Iqbal AbdullaSachin BabyB Kumar...0000404NaNNaNNaN
\n", "

150460 rows × 22 columns

\n", "
" ], "text/plain": [ " season match_id inning batting_team \\\n", "0 2017 1 1 Sunrisers Hyderabad \n", "1 2017 1 1 Sunrisers Hyderabad \n", "2 2017 1 1 Sunrisers Hyderabad \n", "3 2017 1 1 Sunrisers Hyderabad \n", "4 2017 1 1 Sunrisers Hyderabad \n", "... ... ... ... ... \n", "150455 2016 636 2 Royal Challengers Bangalore \n", "150456 2016 636 2 Royal Challengers Bangalore \n", "150457 2016 636 2 Royal Challengers Bangalore \n", "150458 2016 636 2 Royal Challengers Bangalore \n", "150459 2016 636 2 Royal Challengers Bangalore \n", "\n", " bowling_team over ball batsman non_striker \\\n", "0 Royal Challengers Bangalore 1 1 DA Warner S Dhawan \n", "1 Royal Challengers Bangalore 1 2 DA Warner S Dhawan \n", "2 Royal Challengers Bangalore 1 3 DA Warner S Dhawan \n", "3 Royal Challengers Bangalore 1 4 DA Warner S Dhawan \n", "4 Royal Challengers Bangalore 1 5 DA Warner S Dhawan \n", "... ... ... ... ... ... \n", "150455 Sunrisers Hyderabad 20 2 Sachin Baby CJ Jordan \n", "150456 Sunrisers Hyderabad 20 3 Sachin Baby CJ Jordan \n", "150457 Sunrisers Hyderabad 20 4 Iqbal Abdulla Sachin Baby \n", "150458 Sunrisers Hyderabad 20 5 Sachin Baby Iqbal Abdulla \n", "150459 Sunrisers Hyderabad 20 6 Iqbal Abdulla Sachin Baby \n", "\n", " bowler ... bye_runs legbye_runs noball_runs penalty_runs \\\n", "0 TS Mills ... 0 0 0 0 \n", "1 TS Mills ... 0 0 0 0 \n", "2 TS Mills ... 0 0 0 0 \n", "3 TS Mills ... 0 0 0 0 \n", "4 TS Mills ... 0 0 0 0 \n", "... ... ... ... ... ... ... \n", "150455 B Kumar ... 0 0 0 0 \n", "150456 B Kumar ... 0 0 0 0 \n", "150457 B Kumar ... 0 1 0 0 \n", "150458 B Kumar ... 0 0 0 0 \n", "150459 B Kumar ... 0 0 0 0 \n", "\n", " batsman_runs extra_runs total_runs player_dismissed \\\n", "0 0 0 0 NaN \n", "1 0 0 0 NaN \n", "2 4 0 4 NaN \n", "3 0 0 0 NaN \n", "4 0 2 2 NaN \n", "... ... ... ... ... \n", "150455 2 0 2 NaN \n", "150456 0 0 0 CJ Jordan \n", "150457 0 1 1 NaN \n", "150458 1 0 1 NaN \n", "150459 4 0 4 NaN \n", "\n", " dismissal_kind fielder \n", "0 NaN NaN \n", "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "... ... ... \n", "150455 NaN NaN \n", "150456 run out NV Ojha \n", "150457 NaN NaN \n", "150458 NaN NaN \n", "150459 NaN NaN \n", "\n", "[150460 rows x 22 columns]" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "season=df[['id','season']].merge(df2, left_on = 'id', right_on = 'match_id', how = 'left').drop('id', axis = 1)\n", "season" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "season=season.groupby(['season'])['total_runs'].sum().reset_index()\n", "season.set_index('season').plot()\n", "plt.title('Total Runs Across the Seasons')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### There was a decline in total runs from 2008 to 2009.But there after there was a increase in runs in every season until 2013, but from next season there was a slump in the total runs. But the number of matches are not equal in all seasons. We should check the average runs per match in each season" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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seasonmatches
0200858
1200957
2201060
3201173
4201274
5201376
6201460
7201559
8201660
9201759
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" ], "text/plain": [ " season matches\n", "0 2008 58\n", "1 2009 57\n", "2 2010 60\n", "3 2011 73\n", "4 2012 74\n", "5 2013 76\n", "6 2014 60\n", "7 2015 59\n", "8 2016 60\n", "9 2017 59" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "avg_runs=df.groupby(['season'])['id'].count().reset_index().rename(columns={'id':'matches'})\n", "avg_runs" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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seasontotal_runs
0200817937
1200916353
2201018883
3201121154
4201222453
5201322602
6201418931
7201518353
8201618862
9201718786
\n", "
" ], "text/plain": [ " season total_runs\n", "0 2008 17937\n", "1 2009 16353\n", "2 2010 18883\n", "3 2011 21154\n", "4 2012 22453\n", "5 2013 22602\n", "6 2014 18931\n", "7 2015 18353\n", "8 2016 18862\n", "9 2017 18786" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "season" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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seasonmatchestotal_runs
020085817937
120095716353
220106018883
320117321154
420127422453
520137622602
620146018931
720155918353
820166018862
920175918786
\n", "
" ], "text/plain": [ " season matches total_runs\n", "0 2008 58 17937\n", "1 2009 57 16353\n", "2 2010 60 18883\n", "3 2011 73 21154\n", "4 2012 74 22453\n", "5 2013 76 22602\n", "6 2014 60 18931\n", "7 2015 59 18353\n", "8 2016 60 18862\n", "9 2017 59 18786" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final=pd.concat([avg_runs,season.iloc[:,1]],axis=1)\n", "final" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "final['per_match_runs']=final['total_runs']/final['matches']\n", "final.set_index('season',inplace=True)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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matchestotal_runsper_match_runs
season
20085817937309.258621
20095716353286.894737
20106018883314.716667
20117321154289.780822
20127422453303.418919
20137622602297.394737
20146018931315.516667
20155918353311.067797
20166018862314.366667
20175918786318.406780
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" ], "text/plain": [ " matches total_runs per_match_runs\n", "season \n", "2008 58 17937 309.258621\n", "2009 57 16353 286.894737\n", "2010 60 18883 314.716667\n", "2011 73 21154 289.780822\n", "2012 74 22453 303.418919\n", "2013 76 22602 297.394737\n", "2014 60 18931 315.516667\n", "2015 59 18353 311.067797\n", "2016 60 18862 314.366667\n", "2017 59 18786 318.406780" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "final['per_match_runs'].plot()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### most lucky grounds for teams" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "def lucky(df,team_name):\n", " return df[df['winner']==team_name]['venue'].value_counts().nlargest(5)" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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K6twZOHBTc1mluXRWc9Y53t9FF886XzvecdtfnutaBsBxlKHHrRi1NvQBDny77/pqwD50dOcg24cDSHq07T/Vrqey/m2yVgNeDnR1HPgOfddXA54DXAx0McCPAV5DS2bodq4JRdIKwP/Y3r12LXNN0jOBo4E1bG8iaVvgzbbfVrm0gSDpXNu71q6jNklrA8fb3qt2LXNN0g/blA1dOAMfawtgk9pFVPJvlDVQTgWwfWkz0alzJPVPo1+Bcka+ZqVyBs09lPdJF7Vq1NrQB7ikuyltnWp+/hZ4X9WiKrL9K0n9h5bUqqWyT/RdXwzcQFlioHMkncZof8AKwNbAyfUqqmp1SnA/v+/YwA4jHPoAt52zqlG/krQz4GYj6ncBV1WuqQrbu9WuYYB8vO/6YuBG2zfXKqYm2/vXruGRGNo2cElb2b56zFflh3R0Dez1gU8Bz6V8IzkDOLBLE3skvdr2VySNO/MwQyq7TdKTKEssb2D7qc1eAnvZ/nDl0sY1zGfg7wXexNJflXsMtKajYqY06368qnYdlT26+dn5b2a9Ttu+ZsaH7qIMnVurUmk1fRE4GPgCgO3LmjbxgQzwoT0Dj1GSPs04Y557Ojp9POJhJP3M9g796+RLWmh7u9q1jWdoz8AlvXSy+we1V3mWLGh+7kLpoPpqc/vlwEVVKqpE0r9Pdn+XPsymWv+8i2uhALdL2pzmhEfSy4Bb6pY0saE9A+9bA+WxwM7AD5vbuwE/sj1pwA+jZi2U59v+c3N7ZeCMLnXo9a2BMu6Hme13Vymsgr7ZqKIMrf1Dc30dyiqem1UsrwpJTwSOomTGH4DrgVfbvqFmXRMZ2jPwXm+ypG8DW9u+pbm9IfDZmrVV9HhK22/vzGqN5lhn9BarkvQ6YLe+D7PPUzp1O6MX0M2//VTb321uv4DS0d05tq8Dnivp0cAKtu+uXdNkhjbA+8zrhXfjVuBJtYqp7AjgkuZMHOBZwGH1yqmq8x9mfXaw/ZbeDdvfk/ShmgXNtYlGJfXmTAzq6KQuBPiPJH0fOJHydfGVwMBtTjrbmiUEfkHZgb63C/0htn9br6qq8mE26nZJ/wR8hfIeeTXQmaGljd6opC0pa8P0NoHZEzinSkXTMLRt4P2aDs2/am6eY/ubNeupRdJ5tp9Zu45BIelxjH6YXdDVD7OmM/MDQG9ZhXOAw7vYiSnpDOD/9JpOJK0JfM32HnUrG18nAjwKSYcDlwGnOP/jkbQuZc2P1XrHbA/s2VbMPklXA9vavr+5vSpwqe2t6lY2vqFvQpG0E/Bp4MmUNbBXBP7U0UkK76FMZFks6T46PGFD0hspW8xtBCwEdgLOo4MTvCSNAP8APIWlP8w691oAxwMXSvompTlpHwZ4Wd0VahcwBz4D7AtcQ1mo5o2UQO8c22vaXsH2KrbXam53LrwbB1LaOm9shlFuDyyqW1I1JwBXA5sBh1MW9vpZzYJqsf0R4PWUIYR3APvb/pe6VU1s6M/AAWz/UtKKtpcAx0r6ae2aakmzwUPus32fJCSt2qybs2Xtoip5jO2jJR1o+2zgbEln1y6qFtsXSfoVzXtE0ia2b6pc1ri6EOD3NCvvLZT0/yizqh49xXOGUpoNlnKzpHWA/wbOlPQHOrpTE/Dn5uctkl5EeR02qlhPNZL2oqyf9HjgNsoEp6spzUsDZ+g7MSVtShn7vQrwbmBt4LO2r61aWAWSLqc0G5xveztJW1FGG7yicmlVSXoW5e/ie72JPV0i6cXAj4GNKc2LawGH2T6tamEVSLqUckLzP7a3l7QbsK/tAyqXNq4utIHvbfs+23fZPtz2e4AX1y6qkvts3wc81GxAGffaOZKO7123fbbtUyn7IXbRH2zfafsK27vZfjqjE5y65s/N8sorSFrB9lnAQC5kBd0I8P3GOfa6uS5iQIxtNvgW3W02WOorsaQVgadXqqW28Tr1O9nRD9whaQ3KWPgTJH2KssnFQBraNnBJ+wJ/B2wm6dS+u9aie7PMALC9T3P1sGYG4trA6RVLmnOSDgXeD6wu6a7eYeAByiJGndFscr0zMDJmKvlalOG2XfQS4F5Kc+urKO+RD1ataBJDG+DATykdluuz9KYOd1Mms3SWpCdQVlkDeLBmLXPN9keBj0r6qO1Da9dT2SqUNWBWYukNLu4CXlalovoeC9zSNDUeJ2l1YAMG9KRv6DsxeyQ9hjJV+CbbXVsD+1BgZdsfbG7fRBnjugpwXBNqndB0at9h+87m9m7A3pSxz5+1/UDF8qqQtKntG5vr61Jen24EwxiSFgA79/4OmhFsP7G9Q93Kxje0beCSvi3pqc31DYErKAP0j5d0UNXi5t7LWfpbyO9sb0NpB35RnZKqOZlmGKmk7YCvATdROqr+o2Jdc07SPzd7x94oaVVJPwSuBW6V1MnlZIGV+j/Em+urVKxnUkMb4MBmtq9oru8PnGl7T8riRa+vV1Ydtv/Ud/NTzbEllNmpXbK67V7H7auBY2x/gvI3smO9sqp4BWWFSiid/SsAI5SVGQd29uEsW9SMBQdA0kuA2yvWM6lhDvD+8bzPAb4L0Kwy1ql2X2CNZvcdAGx/CR5aqKdrU+nVd3134AcAtrv2NwHwQF9Tyd8AJ9peYvsqhrt/bDJvAd4v6aZmNub7gIEcAw7D/T/pV5LeCdwMPI1mtEXTKbHyZE8cQl8HviDpHbbvAWh2HPlMc1+X/FDSyZQO7nVpttprmtm61v59f9PMeCtlq8G/77vvUXVKqquZ4LdTM5RQtu+WtAOlaWngDPMZ+BsobbyvA15h+47m+E7AsRM9aUj9X8q04JskXSTpIkqn3a3NfV1yEHAK5d+/a9/My8cB/1irqEoOpHyAXw0caft6AEkvBC6pWdgA2AQ4WNL/Ap+rXcxEOjMKJR769vEXzc1f2r63Zj0Rg6QZobRvc1kMbArMH9QNjSEBHhFBs0Lp2sBJwEm2r5F0fW/j50E1zE0oERHTtYgymWkDykgcKBs6DLQEeHSSpBc3Gz1HYPslwF8CFwOHS7oeWFfSQA8tHfomFElPonRCbGD7qZK2Afay/eHKpVXRTKPflL4RSF3c0EHSV4BnAt8Ajm2GznWSpEcB7wU2sf0mSVsAW9r+duXSqpH0WMo4+X2BjW1vXLmkcXUhwM8GDga+YHv75tgVtp9at7K5J+ljlD/KnwNLmsO2vdfEzxpektaivEH3p3xdPpYyFvruqoXNMUlfBS4CXtuc5KwOnGd7YJdRnUv9Sw0MmmEeB97zKNsXSv3zNwZ3echZtjflzOr+2oUMAtt3SfoGZTbqQZQNbA+W9O+2u7Sc6ua2X9Gs4IntezXmDdNlgxre0I028NslbU7TISHpZZRJHF10Hd2bxDQuSXs2O4//kPKa7Gj7BcC2LD2hpQseaM66e++RzYF8yLdAF87A305Z53krSb+mLKP66rolVXMPZW/QH9D3BrX9rnolVfNyyuSVpdr/bd8jqWtr5XyAMlN5Y0knALvQ3U1PWmXo28B7mqnjK3StfbOfpPF2J8L2cXNdy6Bo2sH7O3Q7uZVYs9zyTpS1Ys63PbALOM2mZuPzD1M2dTid8o3sINtfqVrYBIY2wMfsMPIwtj85V7UMkuar8ia2fzHlg4eYpAOAD1HeqL03gW0/sV5V9WR0UiFpYbPh9z6UPqN3A2fZ3rZyaeMa5iaU3g4jW1J2Yu9tq7YnZb+7zpG0J/BxyvrGmzXrYX+wo6NQDgae0tUzzX59o5OuZHSlTtPN90mvj+iFlBFJvx/k/tyhDXDbhwNIOgN4Wq/pRNJhlEX8u+gwyprXPwKwvVDSQE8VnkXXUvoEIqOT+p0m6WrKN7O3SRoB7qtc04SGNsD7bMLSy4Q+AMyrU0p1i23fOeaMYjjb0KZ2KPBTSReQDt3e6KQEeOnQ/Rhwl+0lku4BBvYbahcC/HjgwmbImCljfb9ct6RqrpD0d8CKzWy7d1E2f+6iL1CGEF5O9zb4GCujk0adZ/tpvRu2/yTpx5Q9BQbO0Ae47Y9I+h7wV82h/W13da3jd1LWvL4fOBH4PqUjr4sW2560o7tDTmW0j6iTJD0OeAKwuqTtGd25aS0GeHOLoR2F0k/SrsAWto9t2rTW6C1eH90k6SPAjcBpLH3W2alhhJJWBL5vu6ubGAMPDbF9HTAf+BmjAX4XcJztUyqVNqmhD3BJH6D8T9nS9pMkPR74mu1dKpc2ZySdxiRt3V0chdKsNjdWJ4cRSjoVeI3tO2vXUlOzOuW+tk+oXct0DX0TCqXNe3vKMpHY/o2kNSd/ytD5ePPzpZStw3qTEvalbC3WOYO+UP8cuw+4XNKZwJ96B7vWBm77QUlvBhLgA+QB25bUW+fh0bULmmu2zwaQ9CHbf91312mSujjWF4BmQ9+tgdV6x2x3sYP7O80l4ExJfw98laU/zAayaa0LAX6ypC8A60h6E/B64IuVa6plRNITbV8H0IwBH5niOUOpaVp7NiXAvwu8ADiXDo5Q6vJSCuPorYPz9r5jBgayaW3o28ABJD0PeD6lY+L7ts+sXFIVkvagLOx1XXNoHvBm29+vVlQlki6nrHNxie1tJW0A/KftPSuXNuea/oCHBUEX+wPapgtn4DSB3cnQ7mf79Gb891bNoas7PPvu3qbNc3GzoNVtDOhZ1hyY33d9NcpKjetVqqUqSSsDbwV6TY0/omwG8+dqRU1i6NcDl/RSSddIulPSXZLulnRX7brmkqR/6Lu5l+1Lm8v9kv6lWmF1LZC0DqU57SJKJ/eFdUuqw/bv+i6/tv1vwO6166rkc8DTgf9oLk9vjg2koW9CkfRLYM+O73l4cW92Wf/18W53kaR5wFq2L6tcShWS+v//r0A5I3/roK7AN5skXTr23z3esUHRhSaUW7sc3g1NcH28250g6cvAj4Ef2766dj2VfaLv+mLK0NK/rVNKdUskbW77WgBJT2R0/9iBM7QBLumlzdUFzaat/83SM+4GcmbVLPEE18e73RVfAnYFPt28SRcC59j+VNWqKrC9W+0aBsjBwFmSrqOc3GxK2fR6IA1tE4qkYye527Y7s22WpCWUMa2ibODbW0ZVwGq2O7lPZjONfAdgN+AtlI7NrSZ/1vCRtDZlFb5ex93ZlHXiOzkzU9KqlH0ExIB39A9tgPdIWm/sIHxJm2UtlG5rVt57NHAepSnlXNu31a2qDknfAK4AeuPBXwNsa/ulEz9rOElaDXgb5duZKX8bn7c9kGuCdyHAfwK8wPZdze0nU9ZCeWrdyqImSUdSRhjcD/yEsvvMebbvrVpYBb1txKY61gWSTgbuZunlJta1/fJ6VU1saNvA+/wLZcr4iyhfi74MvKpuSVGb7XcDSFqD0sZ5LGWdmFVr1lXJvZJ2tX0ugKRdKDvSdNGWY0acnCXp0mrVTGHoA9z2d5rB+WdQ9snc2/Y1lcuKyiS9g7JG/NMpy8oeQ/m63EVvBY5r2sIF/B7Yr25J1VwiaSfb5wNIegblG9pAGtomFEmfZukRFrtTppDfAN1baS2WJulgSrPJRbYX165nEDQzUuk1N3aRpKso39Rvag5tAlxF2bXJtrepVdt4hvkMfMGY2xdVqSIG1SnAzbYXS3o2sA3wZdt31C1r7kl6DGUUyq6AJZ1LGYXyu7qVVbFH7QIeiaE9A4+YjKSFlBmH8yhby51Kaf98Yc26amjWAT+H0Y67VwHP7tIuPZIeBfy5t+aJpC2BFwI3DvKckS6shbKLpDMl/a+k6yRd3wzSj257sGk62Qf4t6ZTc8PKNdWynu0P2b6+uXwYWKd2UXPsdMqHOZL+gjK89InA2yV9tGJdkxrmJpSeo4F3U5pQBnZKbMy5P0val9JZ11tCtpMTmigjLV4JnNzcfhnd2+Bh3b7BDfsBJ9p+p6RVKNlxaL3SJtaFAL/T9vdqFxEDZ3/K7MuP2L6+2dziK1M8Z6hIupvS0S/gPYz++1cA/khpF++KsQMe/hXA9gOSHqxT0tSGvg1c0hHAipROq/61UC6uVlREDBRJXwF+C/waOATYzPY9zZLDZw/qaoRdCPCzxjls211d7ziAZmOLj/LwPTE7uamDpHWBLVj6tejMfqmSVgcOpPSDHGP70ub4zsDmto+vWd9Ehj7AI4j/t3gAAAcfSURBVMbTDJX7AHAkpQ18f8r7oUvNBgBIeiMlvDairMq4E2VZgZzkDLhOBHgzjf4pLH128cF6FUVtki6y/XRJl9v+y+bYj23/Ve3a5lqzP+gOwPm2t5O0FXC47VdULi2mMPSdmJI+DzyKsmTof1J62Du5dVYs5T5JKwDXNNPqfw08tnJNtdxn+z5JSFrV9tXNOOgYcEM/DhzY2fZrgT/YPhx4JrBx5ZqivoMoH+zvoqyH8hq6u/7HzU1n3X8DZ0r6FvCbyjXFNAx9E4qkC2w/Q9L5wEuB3wFX2N6icmkRA0fSs4C1gdNtP1C7nrki6TQm2Z3K9l5zWM60DX0TCvDt5uziXyk7j5uyE3l0mKQnUbbP2pS+90HXOu6aZqTLeuvj2z67ckm1fLx2ActiaM/AJR1EWQbykt5qc81WSat1dauoGNWs8fx5xszQtd25Rc8knQAcavumKR8cA2WYz8A3Aj4FbCXpMuCnlEA/r2pVMSgW2/5c7SIGxIbAlZIupOydCgxus8FskHSy7b9tRuQ87Kx20JaR7RnaM/CeZi2D+cDOlA7MZwJ32N66amFRlaTDgNuAb7L0DN3fT/ScYdW0ez9Ml5pTJG1o+xZJm453v+0b57qm6ehCgK9NCe1dmp/rAJfb3r9qYVGVpPE2tXZXZ2L2SFof+J2HPRgmIOljtt831bFBMbQBLukoyuSdu4ELgPMpExX+ULWwiAEhaSfgCMoWah8CjgfWpwwvfq3t0yuWV4Wki20/bcyxywa1CWWY28A3oWxQew1lksbNQOd2W4mJNetczGPpUShfrlbQ3PsM8H7KsMEfAi+wfX4zE/NEyhrZnSDprcDbgCc2fWY9a5I9MeuQJMpZ+M7N5amUs43zurjmRYySdDywOWXtj94oFHdpr1RJC21v11y/yvaT++67xPb29aqbW01T67qUBc4O6bvr7kHuFxnmM3CadrwrJN0B3NlcXgzsSLfWOo6Hmw9s3dW23kb/Otf3jrmvU69LM7T4TmBfAEmPpaydtIakNQZ1iOXQBrikd1HOuncB/szoEMJjgMsrlhaD4QrgccAttQupaFtJd1E2dFi9uU5ze7WJnza8JO0JfBJ4PGWU0qaUXemfUrOuiQxtgFPaNr8OvNt2l9+kMb71gZ83Y5/7hxF2Zuyz7RVr1zCAPkxZTvd/bG8vaTeas/JBNNRt4BETydjnGI+kBbbnNzN1t7f9oKQLbe9Yu7bxDPMZeMSEEtQxgTskrQGcA5wg6TZgceWaJpQz8OgUSefa3rVvQ9+H7qL0e69VqbQYAJIeBdxHGQv/KsoQyxNs/65qYRNIgEdE50l6BnAUZWjp5cAbbP+8blVTS4BHZ0laEdiApSfyDORwsZhdkhYAh1KaTvYC3mj7b+pWNbUEeHSSpHdS5gLcyuh4aA/qlOmYXWOn0I83pX4QpRMzuupAYMtBbduMObeOpJdOdNv2KRVqmlLOwKOTJJ0FPK+32Ud0m6RjJ7nbtl8/Z8U8Agnw6BRJ72muPgXYEvgOS0/k+WSNuiKWRZpQomvWbH7e1FxWaS4RrZMz8OgUSasDa9q+bczxDYA7bd9Xp7KIR26F2gVEzLFPAbuOc/y5wJFzXEsMCEmtnMCVM/DoFEk/n2g/VElX2h7IVedidkm6FvhH2yfVruWRyBl4dI0muS/vh+7aHXiFpDMl/UXtYqYrnZjRNbdJ2tH2hf0HJe0ALKpUU1TW7Dq/j6Q9gJ9I+hl9G14M6jLDCfDomoOBkyV9CbioOTYfeC3wylpFRX2StgT+Afgx8FmW3rFoIKUNPDqn2S7r7ZQ9UgGuBD4zdmRKdIekIyhroLzX9vdq1zNdOQOPzmmCOnuiRr8llA0c7pc0AmB74JvUcgYeEZ0nScA/A++kdGaLspHDp21/sGZtk0mve0REWdxsV2AH2+vZXhd4BrCLpHfXLW1iOQOPiM6TdAllcbPbxxwfAc6wvX2dyiaXNvDoFEmnTnb/oA4Xi1m38tjwhtIOLmnlGgVNRwI8uuaZwK+AE4ELmHxiT3THA8t4X1VpQolOabZRex6wL7ANZTnZE21fWbWwqErSEuBP490FrGZ7IM/CE+DRWZJWpQT5vwIftP3pyiVFPCJpQonOaYL7RZTwngf8OzCQW2ZFTCZn4NEpko6jzMD8HnCS7SsqlxSxzBLg0SmSHmS0rbP/j1+UvQ9buS50dFMCPCKChzq4v2/7ubVrma7MxIyIAGwvAe6RtHbtWqYrnZgREaPuAy6XdCZ9wwptv6teSRNLgEdEjPpOc2mFtIFHRPRp03KyaQOPiM5TcZik24Grgf+VtEjSP9eubTIJ8IgIOAjYhbKc7GOynGxEREu0dTnZnIFHREyynCwwkAtZQQI8IgKynGxERDtlOdmIiJhTaUKJiGipBHhEREslwCMiWioBHhHRUgnwiIiW+v/wn1uAYEgd1wAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "lucky(df,'MI').plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### comparison between 2 teams on the basis of their wins" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "def comparison(team1,team2):\n", " compare=df[((df['team1']==team1)|(df['team2']==team1))&((df['team1']==team2)|(df['team2']==team2))]\n", " sns.countplot(x='season', hue='winner',data=compare)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "comparison('MI','CSK')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Here we can see that MI and CSK have played against each other in more than 2 matches, and MI dominates" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }