yorkpy takes a hat-trick, bowls out Intl. T20s, BBL and Natwest T20!!!

“Dear, dear! How queer everything is to-day! And yesterday things went on just as usual. I wonder if I’ve been changed in the night? Let me think: was I the same when I got up this morning? I almost think I can remember feeling a little different. But if I’m not the same, the next question is ’Who in the world am I? Ah, that’s the great puzzle!”

             Alice's adventures  in Wonderland, Lewis Carroll

1. Introduction

In this post, yorkpy clean bowls the following T20 formats namely International T20s, Big Bash League and Natwest T20 Blast. I take yorkpy on a spin through these T20 leagues. In the post below,I choose a random set of about 10-12 of the overall 63 functions that yorkpy has, and execute them for each of the different T20 leagues – Intl T20s, BBL and Natwest T20s. yorkpy, is the python avatar of my R package yorkr, see Introducing cricket package yorkr: Part 1- Beaten by sheer pace!

There were a couple of new functions that needed to be added for each of the T20 leagues – Intl T20, BBL and Natwest T20 to take into account the different teams in each of these leagues. Further some bugs were also ironed out in tje latest version of yorkpy. yorkpy uses data from Cricsheet . The match data is in the form of YAML files. yorkpy converts these YAML files to dataframes. YAML files are very detailed and include a ball-by-ball account of the match.

– You can clone/fork the latest code for yorkpy from github yorkpy
– This post has also been published in RPubs at yorkpy takes a hat-trick
– You can download the PDF version of this post at yorkpy takes a hat-trick

The data for IPL, Intl. T20, BBL and Natwest T20 have already been converted into pandas dataframes and saved as CSVs. You can download the converted files from Github at [allYorkpyT20Data])(https://github.com/tvganesh/allYorkpyT20Data)

yorkpy has the following 4 main classes of functions

A.Functions analyzing individual T20 match (Class 1)

This was demonstrated in Pitching yorkpy . short of good length to IPL – Part 1 The functions deal with individual T20 matches. The functions are

  1. convertYaml2PandasDataframeT20()
  2. convertAllYaml2PandasDataframesT20()
  3. teamBattingScorecardMatch()
  4. teamBatsmenPartnershipMatch()
  5. teamBatsmenVsBowlersMatch()
  6. teamBowlingScorecardMatch()
  7. teamBowlingWicketKindMatch()
  8. teamBowlingWicketRunsMatch()
  9. teamBowlingWicketMatch()
  10. teamBowlersVsBatsmenMatch()
  11. matchWormChart()

B. Functions that analyze all matches between 2 T20 teams (Class 2

Pitching yorkpy.on the middle and outside off-stump to IPL – Part 2 included functions that analyze head-to-head confrontation between any 2 T20 teams The functions are

  1. getAllMatchesBetweenTeams()
  2. saveAllMatchesBetween2IPLTeams()
  3. getAllMatchesBetweenTeams()
  4. saveAllMatchesBetween2IPLTeams()
  5. teamBatsmenPartnershiOppnAllMatches()
  6. teamBatsmenPartnershipOppnAllMatchesChart()
  7. teamBatsmenVsBowlersOppnAllMatches()
  8. teamBattingScorecardOppnAllMatches()
  9. teamBowlingScorecardOppnAllMatches()
  10. teamBowlingWicketKindOppositionAllMatches()
  11. teamBowlersVsBatsmenOppnAllMatches()
  12. plotWinLossBetweenTeams()
  13. plotWinsByRunOrWickets() 23.plotWinsbyTossDecision()

C. Functions that analyze the performance of a T20 team against all other teams (Class 3)

The post Pitching yorkpy.swinging away from the leg stump to IPL – Part 3 is based on Class C set of functions shown below

  1. getAllMatchesAllOpposition()
  2. saveAllMatchesAllOppositionIPLT20(dir1)
  3. getAllMatchesAllOpposition()
  4. saveAllMatchesAllOppositionIPLT20()
  5. teamBatsmenPartnershiAllOppnAllMatches()
  6. teamBatsmenPartnershipAllOppnAllMatchesChart()
  7. teamBatsmenVsBowlersAllOppnAllMatches()
  8. teamBattingScorecardAllOppnAllMatches()
  9. teamBowlingScorecardAllOppnAllMatches()
  10. teamBowlingWicketKindAllOppnAllMatches()
  11. teamBowlersVsBatsmenAllOppnAllMatches()
  12. plotWinLossByTeamAllOpposition()
  13. plotWinsByRunOrWicketsAllOpposition()
  14. plotWinsbyTossDecisionAllOpposition()

D. Functions that analyze performances of T20 batsmen and bowlers (Class 4)

These set of functions analyze individual batsmen and bowlers and have been used in Pitching yorkpy . in the block hole – Part 4 The functions are

  1. getTeamBattingDetails()
  2. getBatsmanDetails()
  3. batsmanRunsVsDeliveries()
  4. batsmanFoursSixes()
  5. batsmanDismissals()
  6. batsmanRunsVsStrikeRate()
  7. batsmanMovingAverage()
  8. batsmanCumulativeAverageRuns()
  9. batsmanCumulativeStrikeRate()
  10. batsmanRunsAgainstOpposition()
  11. batsmanRunsVenue
  12. getTeamBowlingDetails()
  13. getBowlerWicketDetails()
  14. bowlerMeanEconomyRate()
  15. bowlerMeanRunsConceded()
  16. bowlerMovingAverage()
  17. bowlerCumulativeAvgWickets()
  18. bowlerCumulativeAvgEconRate()
  19. bowlerWicketPlot()
  20. bowlerWicketsAgainstOpposition()
  21. bowlerWicketsVenue()

Additional new functions were added to handle Intl T20s, Big Bash League and Natwest T20 Blast, since the teams are different. They are

59. saveAllMatchesBetween2IntlT20s()
60. saveAllMatchesAllOppositionIntlT20()
61. saveAllMatchesBetween2BBLTeams()
62 saveAllMatchesAllOppositionBBLT20()
63. saveAllMatchesBetween2NWBTeams()
64. saveAllMatchesAllOppositionNWBT20()

All other functions can be used as is! You can get the help of any function in yorkpy using

import yorkpy.analytics as yka
help(yka.teamBatsmenPartnershiOppnAllMatches)
## Help on function teamBatsmenPartnershiOppnAllMatches in module yorkpy.analytics:
## 
## teamBatsmenPartnershiOppnAllMatches(matches, theTeam, report='summary', top=5)
##     Team batting partnership against a opposition all IPL matches
##     
##     Description
##     
##     This function computes the performance of batsmen against all bowlers of an oppositions in 
##     all matches. This function returns a dataframe
##     
##     Usage
##     
##     teamBatsmenPartnershiOppnAllMatches(matches,theTeam,report="summary")
##     Arguments
##     
##     matches     
##     All the matches of the team against the oppositions
##     theTeam     
##     The team for which the the batting partnerships are sought
##     report      
##     If the report="summary" then the list of top batsmen with the highest partnerships 
##     is displayed. If report="detailed" then the detailed break up of partnership is returned 
##     as a dataframe
##     top
##     The number of players to be displayed from the top
##     Value
##     
##     partnerships The data frame of the partnerships
##     
##     Note
##     
##     Maintainer: Tinniam V Ganesh tvganesh.85@gmail.com
##     
##     Author(s)
##     
##     Tinniam V Ganesh
##     
##     References
##     
##     http://cricsheet.org/
##     https://gigadom.wordpress.com/
##     
##     
##     See Also
##     
##     teamBatsmenVsBowlersOppnAllMatchesPlot
##     teamBatsmenPartnershipOppnAllMatchesChart

As I mentioned above I will be randomly choosing a set of 12 functions from Class 1,2,3,4 for each of the T20 leagues (Intl T20, BBL and NWB T20) for analysis

2. International T20s

The following functions were added for handling Intl. T20s

  1. saveAllMatchesBetween2IntlT20s()
  2. saveAllMatchesAllOppositionIntlT20()

To handle the countries in Intl. T20s below

Afghanistan, Australia, Bangladesh, Bermuda, Canada, England,Hong Kong,India, Ireland, Kenya, Nepal, Netherlands, “New Zealand, Oman,Pakistan,Scotland,South Africa, Sri Lanka, United Arab Emirates,West Indies, Zimbabwe

import os
#os.chdir('C:\\software\\cricket-package\\yorkpyT20\\t20s')
#import yorkpy.analytics as yka
#1.  Convert all YAML files to dataframes and CSV
#yka.convertAllYaml2PandasDataframesT20(".", "..\\data1")
#dir1='C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches'
#2. Save all matches between 2 T20 teams
#yka.saveAllMatchesBetween2IntlT20s(dir1)
#3. Save all matches between a T20 team and all other teams
#dir1='C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches'
#yka.saveAllMatchesAllOppositionIntlT20(dir1)
#4. Get batting details
#dir1='C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches
#yka.getTeamBattingDetails("Afghanistan",dir=dir1, save=True)
#yka.getTeamBattingDetails("Australia",dir=dir1,save=True)
#yka.getTeamBattingDetails("Bangladesh",dir=dir1,save=True)
#...
#5. Get bowling details
#dir1='C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches
#yka.getTeamBowlingDetails("Afghanistan",dir=dir1, save=True)
#yka.getTeamBowlingDetails("Australia",dir=dir1,save=True)
#yka.getTeamBowlingDetails("Bangladesh",dir=dir1,save=True)
# ...

Once the data is converted you can use the yorkpy functions. The data has been converted for Intl T20 and is available at Github at IntlT20

To use the yorkpy functions for a new league we need to initial convert the YAML files into appropriate format for processing by yorkpy functions

This will create the necessary files which are are used in the functions below

2.2 2.1 Intl. T20 – Team score card  (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches"
path=os.path.join(dir1,".\\India-New Zealand-2007-09-16.csv")
ind_nz=pd.read_csv(path)
scorecard,extras=yka.teamBattingScorecardMatch(ind_nz,"India")
print(scorecard)
##             batsman  runs  balls  4s  6s          SR
## 0         G Gambhir    51     34   5   2  150.000000
## 1          V Sehwag    40     18   6   2  222.222222
## 2        RV Uthappa     0      2   0   0    0.000000
## 3          MS Dhoni    24     20   2   0  120.000000
## 4      Yuvraj Singh     5      7   0   0   71.428571
## 5        KD Karthik    17     12   3   0  141.666667
## 6         IK Pathan    11     10   2   0  110.000000
## 7        AB Agarkar     1      2   0   0   50.000000
## 8   Harbhajan Singh     7      6   1   0  116.666667
## 9       S Sreesanth    19     10   4   0  190.000000
## 10         RP Singh     1      1   0   0  100.000000
print(extras)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    370      6        0        8     0        0      14

2.2 Intl. T20 -Team batsmen partnership (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches"
path=os.path.join(dir1,".\\South Africa-Australia-2009-03-27.csv")
sa_aus=pd.read_csv(path)
yka.teamBatsmenPartnershipMatch(sa_aus,'Australia','New Zealand',plot=True)

2.3 Intl. T20 -Team bowling scorecard match (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches"
path=os.path.join(dir1,".\\Sri Lanka-West Indies-2012-09-28.csv")
sl_wi=pd.read_csv(path)
a=yka.teamBowlingScorecardMatch(sl_wi,'Sri Lanka')
print(a)
##          bowler  overs  runs  maidens  wicket  econrate
## 0    A Mohammed      2    13        0       0       6.5
## 1  SA Campbelle      1     8        0       1       8.0
## 2     SC Selman      1     3        0       0       3.0
## 3      SF Daley      2     5        0       1       2.5
## 4     SR Taylor      2     4        0       1       2.0
## 5     TD Smartt      2    17        0       0       8.5

2.4 Intl. T20 -Match Worm chart (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-Matches"
path=os.path.join(dir1,".\\England-India-2012-09-29.csv")
eng_ind=pd.read_csv(path)
yka.matchWormChart(eng_ind,"England", "India")

path=os.path.join(dir1,".\\Bangladesh-Ireland-2015-12-05.csv")
ban_ire=pd.read_csv(path)
yka.matchWormChart(ban_ire,"Bangladesh", "Ireland")

2.5 Intl. T20 -Team Batting partnerships all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"India-England-allMatches.csv")
dc_mi_matches = pd.read_csv(path)
theTeam='India'
m=yka.teamBatsmenPartnershiOppnAllMatches(dc_mi_matches,theTeam,report="detailed", top=4)
print(m)
##      batsman  totalPartnershipRuns    non_striker  partnershipRuns
## 0   SK Raina                   265      G Gambhir                2
## 1   SK Raina                   265       KL Rahul               40
## 2   SK Raina                   265      MK Tiwary               24
## 3   SK Raina                   265       MS Dhoni              124
## 4   SK Raina                   265        P Kumar                0
## 5   SK Raina                   265      PP Chawla                4
## 6   SK Raina                   265       R Ashwin                1
## 7   SK Raina                   265      RG Sharma               16
## 8   SK Raina                   265        V Kohli               47
## 9   SK Raina                   265   Yuvraj Singh                7
## 10  MS Dhoni                   264       A Mishra                1
## 11  MS Dhoni                   264      AT Rayudu               18
## 12  MS Dhoni                   264      HH Pandya                8
## 13  MS Dhoni                   264      IK Pathan                2
## 14  MS Dhoni                   264      JJ Bumrah                2
## 15  MS Dhoni                   264      MK Pandey                3
## 16  MS Dhoni                   264  Parvez Rasool               21
## 17  MS Dhoni                   264       R Ashwin               11
## 18  MS Dhoni                   264      RA Jadeja               11
## 19  MS Dhoni                   264      RG Sharma                9
## 20  MS Dhoni                   264        RR Pant                6
## 21  MS Dhoni                   264     RV Uthappa                5
## 22  MS Dhoni                   264       SK Raina               98
## 23  MS Dhoni                   264      YK Pathan               36
## 24  MS Dhoni                   264   Yuvraj Singh               33
## 25   V Kohli                   236      AM Rahane                3
## 26   V Kohli                   236      G Gambhir               78
## 27   V Kohli                   236       KL Rahul               46
## 28   V Kohli                   236      RG Sharma                2
## 29   V Kohli                   236     RV Uthappa                4
## 30   V Kohli                   236       S Dhawan               45
## 31   V Kohli                   236       SK Raina               48
## 32   V Kohli                   236   Yuvraj Singh               10
## 33     M Raj                   176       A Sharma                2
## 34     M Raj                   176         H Kaur               18
## 35     M Raj                   176      J Goswami                6
## 36     M Raj                   176        KV Jain                5
## 37     M Raj                   176       L Kumari                5
## 38     M Raj                   176    N Niranjana                3
## 39     M Raj                   176       N Tanwar               17
## 40     M Raj                   176        PG Raut               41
## 41     M Raj                   176     R Malhotra                5
## 42     M Raj                   176     S Mandhana                8
## 43     M Raj                   176         S Naik               10
## 44     M Raj                   176       S Pandey               19
## 45     M Raj                   176       SK Naidu               37

2.6 Intl. T20 -Team Batsmen vs Bowlers all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"Ireland-Netherlands-allMatches.csv")
ire_nl_matches = pd.read_csv(path)
yka.teamBatsmenVsBowlersOppnAllMatches(ire_nl_matches,'Ireland',"Netherlands",plot=True,top=3,runsScored=10)

2.7 Intl. T20 -Team Bowling scorecard all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\IntlT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"Bangladesh-Nepal-allMatches.csv")
bang_nep_matches = pd.read_csv(path)
scorecard=yka.teamBowlingScorecardOppnAllMatches(bang_nep_matches,'Bangladesh',"Nepal")
print(scorecard)
##         bowler  overs  runs  maidens  wicket   econrate
## 0      B Regmi      3    14        0       1   4.666667
## 3   SP Gauchan      4    40        0       1  10.000000
## 1   JK Mukhiya      2    16        0       0   8.000000
## 2     P Khadka      3    23        0       0   7.666667
## 4    Sagar Pun      1    16        0       0  16.000000
## 5  Sompal Kami      2    21        0       0  10.500000

2.8 Intl. T20 -Team Batsmen vs Bowlers all Oppositions (Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\\IntlT20-allMatchesAllOpposition\\"
path=os.path.join(dir1,"Australia-allMatchesAllOpposition.csv")
aus_matches = pd.read_csv(path)
yka.teamBatsmenVsBowlersAllOppnAllMatches(aus_matches,"Australia",plot=True,top=3,runsScored=40)

2.9 Intl. T20 -Wins vs Losses of a team against all other teams (Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\\IntlT20-allMatchesAllOpposition\\"
path=os.path.join(dir1,"South Africa-allMatchesAllOpposition.csv")
sa_matches = pd.read_csv(path)
team1='South Africa'
yka.plotWinLossByTeamAllOpposition(sa_matches,team1,plot="detailed")

2.10 Intl. T20 -Batsmen analysis (Class 4)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\\IntlT20-BattingBowlingDetails\\"
# Rohit Sharma
name="RG Sharma"
team='India'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeAverageRuns(df,name)

# MJ Guptill
name="MJ Guptill"
team='New Zealand'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeStrikeRate(df,name)

2.11 Intl. T20 -Bowler analysis (Class 4)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyT20\\\IntlT20-BattingBowlingDetails\\"
# Shakib Al Hasan
name="Shakib Al Hasan"
team='Bangladesh'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerMeanEconomyRate(df,name)

# Rashid Khan
name="SL Malinga"
team='Sri Lanka'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsAgainstOpposition(df,name)

3. Big Bash League

The following functions for added to handle BBL teams

  1. saveAllMatchesBetween2BBLTeams()
  2. saveAllMatchesAllOppositionBBLT20

The BBL teams are included are Adelaide Strikers, Brisbane Heat, Hobart Hurricanes, Melbourne Renegades, Perth Scorchers, Sydney Sixers, Sydney Thunder

To use the yorkpy functions first the YAML files have to be converted into pandas dataframe and then saved as CSV as shown below

import os
import yorkpy.analytics as yka
os.chdir('C:\\software\\cricket-package\\yorkpyBBL\\bbl')
#1. Convert all YAML files to dataframes and save as CSV
#yka.convertAllYaml2PandasDataframesT20(".", "..\\BBLT20-Matches")
#2. Save all matches between 2 BBL teams
dir1='C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches'
#yka.saveAllMatchesBetween2BBLTeams(dir1)
#3. Save T20 matches between a BBL team and all other teams
dir1='C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches'
#yka.saveAllMatchesAllOppositionBBLT20(dir1)
#4. Get the batting details
dir1='C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches'
#yka.getTeamBattingDetails("Adelaide Strikers",dir=dir1, save=True)
#yka.getTeamBattingDetails("Brisbane Heat",dir=dir1,save=True)
#yka.getTeamBattingDetails("Hobart Hurricanes",dir=dir1,save=True)
#...
# Get the bowling details
dir1='C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches'
#yka.getTeamBowlingDetails("Adelaide Strikers",dir=dir1, save=True)
#yka.getTeamBowlingDetails("Brisbane Heat",dir=dir1,save=True)
#yka.getTeamBowlingDetails("Hobart Hurricanes",dir=dir1,save=True)
#...

The functions below perform analysis on the generated files from above. The YAML files have already been converted and are available at Github at BBL

3.1 Big Bash League – Team score card (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches"
path=os.path.join(dir1,".\\Adelaide Strikers-Brisbane Heat-2012-12-13.csv")
as_bh=pd.read_csv(path)
scorecard,extras=yka.teamBattingScorecardMatch(as_bh,"Brisbane Heat")
print(scorecard)
##          batsman  runs  balls  4s  6s          SR
## 0  LA Pomersbach    65     42   8   2  154.761905
## 1       JR Hopes     1      2   0   0   50.000000
## 2       JA Burns    37     31   2   2  119.354839
## 3   DT Christian    12     15   0   0   80.000000
## 4    NLTC Perera    12      4   0   2  300.000000
## 5        CA Lynn    19     18   1   1  105.555556
## 6    BCJ Cutting    13      5   0   2  260.000000
## 7     PJ Forrest    12      8   0   1  150.000000
## 8     CD Hartley     5      2   1   0  250.000000
print(extras)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    371     10        2        5     0        0      17

3.2 Big Bash League -Team batsmen vs Bowlers (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches"
path=os.path.join(dir1,".\\Hobart Hurricanes-Melbourne Renegades-2012-01-18.csv")
hh_mr=pd.read_csv(path)
yka.teamBatsmenVsBowlersMatch(hh_mr,'Hobart Hurricanes','Melbourne Renegades',plot=True)

3.3 Big Bash League -Team bowling scorecard match (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches"
path=os.path.join(dir1,".\\Melbourne Stars-Sydney Thunder-2016-01-24.csv")
ms_st=pd.read_csv(path)
a=yka.teamBowlingScorecardMatch(ms_st,'Sydney Thunder')
print(a)
##           bowler  overs  runs  maidens  wicket   econrate
## 0        A Zampa      4    32        0       2   8.000000
## 1  BW Hilfenhaus      2    21        0       0  10.500000
## 2      DJ Hussey      1     9        0       1   9.000000
## 3     DJ Worrall      3    42        0       0  14.000000
## 4      EP Gulbis      2    19        0       0   9.500000
## 5        MA Beer      3    25        0       1   8.333333
## 6     MP Stoinis      4    30        0       3   7.500000

3.4 Big Bash League – Match Worm chart (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-Matches"
path=os.path.join(dir1,".\\Sydney Sixers-Melbourne Stars-2011-12-27.csv")
ss_ms=pd.read_csv(path)
yka.matchWormChart(ss_ms,"Melbourne Stars", "Sydney Sixers")

path=os.path.join(dir1,".\\Hobart Hurricanes-Brisbane Heat-2015-01-02.csv")
hh_bh=pd.read_csv(path)
yka.matchWormChart(hh_bh,"Hobart Hurricanes", "Brisbane Heat")

3.5 Big Bash League -Team Batting partnerships all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"Brisbane Heat-Adelaide Strikers-allMatches.csv")
bh_as_matches = pd.read_csv(path)
yka.teamBatsmenPartnershipOppnAllMatchesChart(bh_as_matches,"Brisbane Heat","Adelaide Strikers",plot=True, top=4, partnershipRuns=20)

3.6 Big Bash League -Team Bowling wicket kind all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"Sydney Sixers-Perth Scorchers-allMatches.csv")
ss_ps_matches = pd.read_csv(path)
yka.teamBowlingWicketKindOppositionAllMatches(ss_ps_matches,'Perth Scorchers','Sydney Sixers',plot=True,top=5,wickets=1)

3.7 Big Bash League -Team Bowling scorecard all teams (Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-allMatchesAllOpposition"
path=os.path.join(dir1,"Hobart Hurricanes-allMatchesAllOpposition.csv")
hh_matches = pd.read_csv(path)
scorecard=yka.teamBowlingScorecardAllOppnAllMatches(hh_matches,"Hobart Hurricanes")
print(scorecard)
##              bowler  overs  runs  maidens  wicket   econrate
## 16            B Lee     20   132        0       9   6.600000
## 30         CJ McKay     13   110        0       9   8.461538
## 88    NJ Rimmington     16   103        1       9   6.437500
## 67      JW Hastings     15    88        0       8   5.866667
## 63      JP Faulkner     15   146        0       7   9.733333
## 27        CJ Gannon     17   147        1       7   8.647059
## 93          NM Lyon      8    51        0       7   6.375000
## 20      BCJ Cutting     27   226        0       7   8.370370
## 48          GB Hogg     22   167        0       7   7.590909
## 107       SM Boland     12    96        0       7   8.000000
## 15       B Laughlin     13    99        0       7   7.615385
## 87      MT Steketee     15   134        0       5   8.933333
## 121    Yasir Arafat      9    48        0       4   5.333333
## 96       PJ Cummins      8    83        0       4  10.375000
## 46      Fawad Ahmed     11    64        0       4   5.818182
## 76          MA Beer     12    63        0       4   5.250000
## 108     SNJ O'Keefe     15   104        0       4   6.933333
## 75   M Muralitharan      7    31        0       4   4.428571
## 10           AJ Tye     16   127        0       4   7.937500
## 52          J Botha     13    94        0       4   7.230769
## 56     JL Pattinson      7    71        0       4  10.142857
## 62   JP Behrendorff     16   119        0       4   7.437500
## 3           AC Agar     12    87        0       4   7.250000
## 24     BM Edmondson      4    40        0       4  10.000000
## 37        DJ Hussey      8    47        0       3   5.875000
## 49       GJ Maxwell      8    65        0       3   8.125000
## 84       MN Samuels      4    22        0       3   5.500000
## 81         MG Neser      5    54        0       3  10.800000
## 44     DT Christian      9   114        0       3  12.666667
## 50        GS Sandhu      7    51        0       3   7.285714
## ..              ...    ...   ...      ...     ...        ...
## 43        DP Nannes      8    58        0       1   7.250000
## 51         IA Moran      4    25        0       1   6.250000
## 55         JK Lalor     10    82        0       1   8.200000
## 54        JH Kallis      3    18        0       1   6.000000
## 73   LR Butterworth      4    25        0       1   6.250000
## 4      AC McDermott      2    28        0       1  14.000000
## 70         LA Doran      4    38        0       1   9.500000
## 69    KW Richardson      6    44        0       1   7.333333
## 119     WD Sheridan      2     6        0       0   3.000000
## 2       AB McDonald      1    15        0       0  15.000000
## 115      TD Andrews      3    23        0       0   7.666667
## 11          AK Heal      4    33        0       0   8.250000
## 7        AD Russell      4    40        0       0  10.000000
## 8          AJ Finch      2    15        0       0   7.500000
## 9         AJ Turner      3    28        0       0   9.333333
## 60        JM Mennie      1    20        0       0  20.000000
## 18        BA Stokes      1     9        0       0   9.000000
## 26         CH Gayle      1    16        0       0  16.000000
## 28         CJ Green      4    44        0       0  11.000000
## 95   PD Collingwood      2    20        0       0  10.000000
## 31       CJ Simmons      4    21        0       0   5.250000
## 59       JM Holland      3    34        0       0  11.333333
## 36         DJ Bravo      6    64        0       0  10.666667
## 38     DJ Pattinson      2    16        0       0   8.000000
## 41       DJ Worrall      8    90        0       0  11.250000
## 72      LN O'Connor      6    56        0       0   9.333333
## 71        LJ Wright      3    27        0       0   9.000000
## 68       KA Pollard      1     7        0       0   7.000000
## 58       JM Herrick      4    23        0       0   5.750000
## 92       NM Hauritz      5    42        0       0   8.400000
## 
## [122 rows x 6 columns]

3.8 Big Bash League -Plot wins vs losses against all teams(Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-allMatchesAllOpposition"
path=os.path.join(dir1,"Sydney Sixers-allMatchesAllOpposition.csv")
ss_matches = pd.read_csv(path)
yka.plotWinLossByTeamAllOpposition(ss_matches,'Sydney Sixers')

3.9 Big Bash League -Wins vs losses by toss decision (Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-allMatchesAllOpposition"
path=os.path.join(dir1,"Adelaide Strikers-allMatchesAllOpposition.csv")
as_matches = pd.read_csv(path)
yka.plotWinsByRunOrWicketsAllOpposition(as_matches,'Adelaide Strikers')

3.10 Big Bash League -Batsmen Analysis (Class 4)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-BattingBowlingDetails"
# CA Lynn
name="CA Lynn"
team='Brisbane Heat'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsVsStrikeRate(df,name)

# UT Khawaja
name="UT Khawaja"
team='Sydney Thunder'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsAgainstOpposition(df,name)

3.11Big Bash League – Bowler analysis (Class 4)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyBBL\\BBLT20-BattingBowlingDetails"
# CJ McKay
name="CJ McKay"
team='Sydney Thunder'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgWickets(df,name)

# AU Rashid
name="AU Rashid"
team='Adelaide Strikers'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgEconRate(df,name)

4. Natwest T20 Blast

The following functions for added to handle Natwest T20 teams

  1. saveAllMatchesBetween2NWBTeams()
  2. saveAllMatchesAllOppositionNWBT20

The Natwest teams are
Derbyshire, Durham, Essex, Glamorgan, Gloucestershire, Hampshire, Kent,Lancashire, Leicestershire, Middlesex,Northamptonshire, Nottinghamshire, Somerset, Surrey, Sussex, Warwickshire, Worcestershire,Yorkshire

In order to perform analysis with yorkpy, the YAML data has to be converted to pandas dataframe and saves as CSV as shown

#import os
#import yorkpy.analytics as yka
#os.chdir('C:\\software\\cricket-package\\yorkpyNWB\\nwb')
#1. Convert YAML to dataframes and save as CSV
#yka.convertAllYaml2PandasDataframesT20(".", "..\\NWBT20-Matches")
#2. Save all matches between 2 NWBT20 teams
#dir1='C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-Matches'
#yka.saveAllMatchesBetween2NWBTeams(dir1)
#3. Save all matches between a NWB T20 team and all other teams
#dir1='C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-Matches'
#yka.saveAllMatchesAllOppositionNWBT20(dir1)
#4. Compute the batting details
dir1='C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-Matches'
#yka.getTeamBattingDetails("Derbyshire",dir=dir1, save=True)
#yka.getTeamBattingDetails("Durham",dir=dir1,save=True)
#yka.getTeamBattingDetails("Essex",dir=dir1,save=True)
#..
#5. Compute bowling details
dir1='C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-Matches'
#yka.getTeamBowlingDetails("Derbyshire",dir=dir1, save=True)
#yka.getTeamBowlingDetails("Durham",dir=dir1,save=True)
#yka.getTeamBowlingDetails("Essex",dir=dir1,save=True)
#...

Once the data is converted all yorkpy functions can be used. This has already been done and is available at github NWB

4.1 Natwest T20 Blast – Team score card (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\\yorkpyNWB\\NWBT20-Matches"
path=os.path.join(dir1,".\\Durham-Yorkshire-2016-08-20.csv")
d_y=pd.read_csv(path)
scorecard,extras=yka.teamBattingScorecardMatch(d_y,"Durham")
print(scorecard)
##           batsman  runs  balls  4s  6s          SR
## 0     MD Stoneman    25     20   4   0  125.000000
## 1     KK Jennings    11     13   1   0   84.615385
## 2       BA Stokes    56     37   4   3  151.351351
## 3   MJ Richardson    29     23   4   1  126.086957
## 4     JTA Burnham    17     15   1   1  113.333333
## 5      RD Pringle    10      9   1   0  111.111111
## 6  PD Collingwood     2      3   0   0   66.666667
## 7        U Arshad     1      1   0   0  100.000000
print(extras)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    305      2        0        5     0        0       7

4.2 Natwest T20 Blast -Team batsmen vs Bowlers (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\\yorkpyNWB\\NWBT20-Matches"
path=os.path.join(dir1,".\\Derbyshire-Lancashire-2016-07-13.csv")
d_l=pd.read_csv(path)
yka.teamBatsmenVsBowlersMatch(d_l,'Lancashire','Derbyshire',plot=True)

4.3 Natwest T20 Blast -Team bowling scorecard match (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\\yorkpyNWB\\NWBT20-Matches"
path=os.path.join(dir1,".\\Essex-Surrey-2016-05-20.csv")
e_s=pd.read_csv(path)
a=yka.teamBowlingScorecardMatch(e_s,'Essex')
print(a)
##           bowler  overs  runs  maidens  wicket   econrate
## 0  Azhar Mahmood      3    38        0       4  12.666667
## 1       GJ Batty      4    33        0       1   8.250000
## 2       JE Burke      1    18        0       0  18.000000
## 3     MW Pillans      3    28        0       0   9.333333
## 4      SM Curran      4    23        0       2   5.750000
## 5      TK Curran      4    21        0       3   5.250000

4.4 Natwest T20 Blast -Match Worm chart (Class 1)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\\yorkpyNWB\\NWBT20-Matches"
path=os.path.join(dir1,".\\Gloucestershire-Glamorgan-2016-06-10.csv")
ss_ms=pd.read_csv(path)
yka.matchWormChart(ss_ms,"Gloucestershire", "Glamorgan")

path=os.path.join(dir1,".\\Leicestershire-Northamptonshire-2016-05-20.csv")
hh_bh=pd.read_csv(path)
yka.matchWormChart(hh_bh,"Northamptonshire", "Leicestershire")

4.5 Natwest T20 Blast -Team Batting partnerships all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"Hampshire-Sussex-allMatches.csv")
h_s_matches = pd.read_csv(path)
yka.teamBatsmenPartnershipOppnAllMatchesChart(h_s_matches,"Hampshire","Sussex",plot=True, top=4, partnershipRuns=10)

4.6 Natwest T20 Blast -Team Bowling wicket kind all matches 2 teams (Class 2)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-allMatchesBetween2Teams"
path=os.path.join(dir1,"Kent-Somerset-allMatches.csv")
k_s_matches = pd.read_csv(path)
yka.teamBowlersVsBatsmenOppnAllMatches(k_s_matches,'Kent','Somerset',plot=True,
top=5,runsConceded=10)

4.7 Natwest T20 Blast -Team Bowling scorecard all teams (Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-allMatchesAllOpposition"
path=os.path.join(dir1,"Middlesex-allMatchesAllOpposition.csv")
m_matches = pd.read_csv(path)
scorecard=yka.teamBowlingScorecardAllOppnAllMatches(m_matches,"Middlesex")
print(scorecard)
##               bowler  overs  runs  maidens  wicket   econrate
## 1             AJ Tye      8    75        0       6   9.375000
## 5         BAC Howell      8    41        0       5   5.125000
## 26         GR Napier      7    65        0       5   9.285714
## 15        DI Stevens      4    31        0       4   7.750000
## 19       DW Lawrence      6    37        0       4   6.166667
## 32       JW Dernbach      4    33        0       3   8.250000
## 7          BTJ Wheal      4    43        0       3  10.750000
## 18         DR Briggs      4    24        0       3   6.000000
## 50     RK Kleinveldt      4    24        0       3   6.000000
## 46         R McLaren      7    59        0       3   8.428571
## 47         R Rampaul      3    21        0       3   7.000000
## 34         L Gregory      6    51        0       2   8.500000
## 33   KMDN Kulasekara      2    24        0       2  12.000000
## 40          MG Hogan      3    17        0       2   5.666667
## 43        MTC Waller      4    31        0       2   7.750000
## 49        RJ Gleeson      4    20        0       2   5.000000
## 48  RE van der Merwe      5    24        0       2   4.800000
## 51  RN ten Doeschate      4    32        0       2   8.000000
## 53        S Prasanna      4    20        0       2   5.000000
## 56           SW Tait      3    17        0       2   5.666667
## 57     Shahid Afridi      8    55        0       2   6.875000
## 59  T van der Gugten      3    13        1       2   4.333333
## 64          TS Mills      3    34        0       2  11.333333
## 65          WAT Beer      4    23        0       2   5.750000
## 31          JH Davey      4    28        0       2   7.000000
## 68         ZS Ansari      3    16        0       2   5.333333
## 25         GM Andrew      3    19        0       2   6.333333
## 23          GJ Batty      6    55        0       2   9.166667
## 16          DJ Bravo      3    27        0       2   9.000000
## 41          MR Quinn      6    65        0       1  10.833333
## ..               ...    ...   ...      ...     ...        ...
## 24     GL van Buuren      7    49        0       1   7.000000
## 37           MD Hunn      3    35        0       1  11.666667
## 36        LC Norwell      6    62        0       1  10.333333
## 29       JC Tredwell      4    35        0       1   8.750000
## 35         LA Dawson      6    53        0       1   8.833333
## 62           TL Best      4    51        0       0  12.750000
## 58         T Westley      2    12        0       0   6.000000
## 4         Azharullah      3    24        0       0   8.000000
## 60     TD Groenewald      1    21        0       0  21.000000
## 61         TK Curran      4    35        0       0   8.750000
## 38         MD Taylor      3    30        0       0  10.000000
## 30        JG Myburgh      1     5        0       0   5.000000
## 8          C Overton      2    18        0       0   9.000000
## 2        Ashar Zaidi      1     5        0       0   5.000000
## 66          WR Smith      2    25        0       0  12.500000
## 28         J Overton      2    24        0       0  12.000000
## 6          BJ Taylor      1     6        0       0   6.000000
## 22          GG White      4    31        0       0   7.750000
## 55          SP Crook      1     9        0       0   9.000000
## 39        ME Claydon      4    40        0       0  10.000000
## 52         RS Bopara      4    32        0       0   8.000000
## 10           CD Nash      2    19        0       0   9.500000
## 11         CH Morris      4    36        0       0   9.000000
## 12         DA Cosker      3    32        0       0  10.666667
## 13      DA Griffiths      4    39        0       0   9.750000
## 45          PD Trego      1    11        0       0  11.000000
## 44   PA van Meekeren      2    19        0       0   9.500000
## 42          MS Crane      2    25        0       0  12.500000
## 20        FK Cowdrey      1    19        0       0  19.000000
## 14        DD Masters      2    16        0       0   8.000000
## 
## [69 rows x 6 columns]

4.8 Natwest T20 Blast -Plot wins vs losses against all teams(Class 3)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-allMatchesAllOpposition"
path=os.path.join(dir1,"Warwickshire-allMatchesAllOpposition.csv")
w_matches = pd.read_csv(path)
yka.plotWinLossByTeamAllOpposition(w_matches,'Warwickshire')

4.9 Natwest T20 Blast -Batsmen Analysis (Class 4)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-BattingBowlingDetails"
# M Klinger
name="M Klinger"
team='Gloucestershire'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanRunsAgainstOpposition(df,name)

# CA Ingram
name="CA Ingram"
team='Glamorgan'
df=yka.getBatsmanDetails(team,name,dir=dir1)
yka.batsmanCumulativeStrikeRate(df,name)

4.11 Natwest T20 Blast -Bowler analysis (Class 4)

import os
import pandas as pd
import yorkpy.analytics as yka
dir1="C:\\software\\cricket-package\\yorkpyNWB\\NWBT20-BattingBowlingDetails"
# BAC Howell
name="BAC Howell"
team='Gloucestershire'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerCumulativeAvgEconRate(df,name)

# GR Napier
name="GR Napier"
team='Essex'
df=yka.getBowlerWicketDetails(team,name,dir=dir1)
yka.bowlerWicketsVenue(df,name)

Note: yorkpy will work for all T20 leagues which are in YAML format as specified in Cricsheet.

You can clone/fork the latest code for yorkpy from github yorkpy

The data for IPL, Intl. T20, BBL and Natwest T20 have already been converted into pandas dataframes and saved as CSVs. You can download the converted files from Github at [allYorkpyT20Data])(https://github.com/tvganesh/allYorkpyT20Data)

Conclusion This post shows the kind of detailed analysis that can be performed with yorkpy. In fact with all the converted data it should be possible to also train a Machine Learning model, which I will probably keep for another day. You could go ahead and use the data in other innovative ways. Do keep me posted if you do!!

Important note: Do check out my other posts using yorkpy at yorkpy-posts

Have fun with yorkpy!!

See also
1. Take 4+: Presentations on ‘Elements of Neural Networks and Deep Learning’ – Parts 1-8
2. My book ‘Practical Machine Learning in R and Python: Third edition’ on Amazon
3. Hand detection through Haartraining: A hands-on approach
4.My book ‘Deep Learning from first principles:Second Edition’ now on Amazon
5. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
6. The 3rd paperback & kindle editions of my books on Cricket, now on Amazon

To see all posts click Index of posts

Pitching yorkpy … short of good length to IPL – Part 1

I fear not the man who has practiced 10,000 kicks once, but I fear the man who has practiced one kick 10,000 times.
Bruce Lee

I’ve missed more than 9000 shots in my career. I’ve lost almost 300 games. 26 times, I’ve been trusted to take the game winning shot and missed. I’ve failed over and over and over again in my life. And that is why I succeed.
Michael Jordan

Man, it doesn’t matter where you come in to bat, the score is still zero
Viv Richards

Introduction

“If cricketr is to cricpy, then yorkr is to _____?”. Yes, you guessed it right, it is yorkpy. In this post, I introduce my 2nd python package, yorkpy, which is a python clone of my R package yorkr. This package is based on data from Cricsheet. yorkpy currently handles IPL T20 matches.

When I created cricpy, the python avatar, of my R package cricketr, see Introducing cricpy:A python package to analyze performances of cricketers, I had decided that I should avoid doing a python avatar of my R package yorkr (see Introducing cricket package yorkr: Part 1- Beaten by sheer pace!) , as it was more involved, and required the parsing of match data available as yaml files.

Just out of curiosity, I tried the python package ‘yaml’ to read the match data, and lo and behold, I was sucked into the developing the package and so, yorkpy was born. Of course, it goes without saying that, usually when I am in the thick of developing something, I occasionally wonder, why I am doing it, for whom and for what purpose? Maybe it is the joy of ideation, the problem-solving,  the programmer’s high, for sharing my ideas etc. Anyway, whatever be the reason, I hope you enjoy this post and also find yorkpy useful.

You can clone/download the code at Github yorkpy
This post has been published to RPubs at yorkpy-Part1
You can download this post as PDF at IPLT20-yorkpy-part1

Note: If you would like to do a similar analysis for a different set of batsman and bowlers, you can clone/download my skeleton yorkpy-template from Github (which is the R Markdown file I have used for the analysis below).

The IPL T20 functions in yorkpy are

2. Install the package using ‘pip install’

import pandas as pd
import yorkpy.analytics as yka
#pip install yorkpy

3. Load a yaml file from Cricsheet

There are 2 functions that can be to convert the IPL Twenty20 yaml files to pandas dataframeare

  1. convertYaml2PandasDataframeT20
  2. convertAllYaml2PandasDataframesT20

Note 1: While I have already converted the IPL T20 files, you will need to use these functions for future IPL matches

4. Convert and save IPL T20 yaml file to pandas dataframe

This function will convert a IPL T20 IPL yaml file, in the format as specified in Cricsheet to pandas dataframe. This will be saved as as CSV file in the target directory. The name of the file wil have the following format team1-team2-date.csv. The IPL T20 zip file can be downloaded from Indian Premier League matches.  An example of how a yaml file can be converted to a dataframe and saved is shown below.

import pandas as pd
import yorkpy.analytics as yka
#convertYaml2PandasDataframe(".\\1082593.yaml","..\ipl", ..\\data")

5. Convert and save all IPL T20 yaml files to dataframes

This function will convert all IPL T20 yaml files from a source directory to dataframes, and save it in the target directory, with the names as mentioned above. Since I have already done this, I will not be executing this again. You can download the zip of all the converted RData files from Github at yorkpyData

import pandas as pd
import yorkpy.analytics as yka
#convertAllYaml2PandasDataframes("..\\ipl", "..\\data")

You can download the the zip of the files and use it directly in the functions as follows.For the analysis below I chosen a set of random IPL matches

The randomly selected IPL T20 matches are

  • Chennai Super Kings vs Kings Xi Punjab, 2014-05-30
  • Deccan Chargers vs Delhi Daredevils, 2012-05-10
  • Gujarat Lions vs Mumbai Indians, 2017-04-29
  • Kolkata Knight Riders vs Rajasthan Royals, 2010-04-17
  • Rising Pune Supergiants vs Royal Challengers Bangalore, 2017-04-29

6. Team batting scorecard

The function below computes the batting score card of a team in an IPL match. The scorecard gives the balls faced, the runs scored, 4s, 6s and strike rate. The example below is based on the CSK KXIP match on 30 May 2014.

You can check against the actual scores in this match Chennai Super Kings-Kings XI Punjab-2014-05-30

import pandas as pd
import yorkpy.analytics as yka
csk_kxip=pd.read_csv(".\\Chennai Super Kings-Kings XI Punjab-2014-05-30.csv")
scorecard,extras=yka.teamBattingScorecardMatch(csk_kxip,"Chennai Super Kings")
print(scorecard)
##         batsman  runs  balls  4s  6s          SR
## 0      DR Smith     7     12   0   0   58.333333
## 1  F du Plessis     0      1   0   0    0.000000
## 2      SK Raina    87     26  12   6  334.615385
## 3   BB McCullum    11     16   0   0   68.750000
## 4     RA Jadeja    27     22   2   1  122.727273
## 5     DJ Hussey     1      3   0   0   33.333333
## 6      MS Dhoni    42     34   3   3  123.529412
## 7      R Ashwin    10     11   0   0   90.909091
## 8     MM Sharma     1      3   0   0   33.333333
print(extras)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    428     14        3        5     5        0      27
print("\n\n")
scorecard1,extras1=yka.teamBattingScorecardMatch(csk_kxip,"Kings XI Punjab")
print(scorecard1)
##       batsman  runs  balls  4s  6s          SR
## 0    V Sehwag   122     62  12   8  196.774194
## 1     M Vohra    34     33   1   2  103.030303
## 2  GJ Maxwell    13      8   1   1  162.500000
## 3   DA Miller    38     19   5   1  200.000000
## 4   GJ Bailey     1      2   0   0   50.000000
## 5     WP Saha     6      4   0   1  150.000000
## 6  MG Johnson     1      1   0   0  100.000000
print(extras1)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    428     14        3        5     5        0      27

Let’s take another random match between Gujarat Lions and Mumbai Indian on 29 Apr 2017 Gujarat Lions-Mumbai Indians-2017-04-29

import pandas as pd
gl_mi=pd.read_csv(".\\Gujarat Lions-Mumbai Indians-2017-04-29.csv")
import yorkpy.analytics as yka
scorecard,extras=yka.teamBattingScorecardMatch(gl_mi,"Gujarat Lions")
print(scorecard)
##          batsman  runs  balls  4s  6s          SR
## 0   Ishan Kishan    48     38   6   2  126.315789
## 1    BB McCullum     6      4   1   0  150.000000
## 2       SK Raina     1      3   0   0   33.333333
## 3       AJ Finch     0      3   0   0    0.000000
## 4     KD Karthik     2      9   0   0   22.222222
## 5      RA Jadeja    28     22   2   1  127.272727
## 6    JP Faulkner    21     29   2   0   72.413793
## 7      IK Pathan     2      3   0   0   66.666667
## 8         AJ Tye    25     12   2   2  208.333333
## 9   Basil Thampi     2      4   0   0   50.000000
## 10    Ankit Soni     7      2   0   1  350.000000
print(extras)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    306      8        3        1     0        0      12
print("\n\n")
scorecard1,extras1=yka.teamBattingScorecardMatch(gl_mi,"Mumbai Indians")
print(scorecard1)
##             batsman  runs  balls  4s  6s          SR
## 0          PA Patel    70     45   9   1  155.555556
## 1        JC Buttler     9      7   2   0  128.571429
## 2            N Rana    19     16   1   1  118.750000
## 3         RG Sharma     5     13   0   0   38.461538
## 4        KA Pollard    15     11   2   0  136.363636
## 5         KH Pandya    29     20   2   1  145.000000
## 6         HH Pandya     4      5   0   0   80.000000
## 7   Harbhajan Singh     0      1   0   0    0.000000
## 8    MJ McClenaghan     1      1   0   0  100.000000
## 9         JJ Bumrah     0      1   0   0    0.000000
## 10       SL Malinga     0      1   0   0    0.000000
print(extras1)
##    total  wides  noballs  legbyes  byes  penalty  extras
## 0    306      8        3        1     0        0      12

7. Plot the team batting partnerships

The functions below plot the team batting partnership in the match. It shows what the partnership were in the mtach

Note: Many of the plots include an additional parameters plot which is either True or False. The default value is plot=True. When plot=True the plot will be displayed. When plot=False the data frame will be returned to the user. The user can use this to create an interactive chart using one of the packages like rcharts, ggvis,googleVis or plotly.

import pandas as pd
import yorkpy.analytics as yka
dc_dd=pd.read_csv(".\\Deccan Chargers-Delhi Daredevils-2012-05-10.csv")
yka.teamBatsmenPartnershipMatch(dc_dd,'Deccan Chargers','Delhi Daredevils')

yka.teamBatsmenPartnershipMatch(dc_dd,'Delhi Daredevils','Deccan Chargers',plot=True)
# Print partnerships as a dataframe

rps_rcb=pd.read_csv(".\\Rising Pune Supergiant-Royal Challengers Bangalore-2017-04-29.csv")
m=yka.teamBatsmenPartnershipMatch(rps_rcb,'Royal Challengers Bangalore','Rising Pune Supergiant',plot=False)
print(m)
##            batsman     non_striker  runs
## 0   AB de Villiers         V Kohli     3
## 1         AF Milne         V Kohli     5
## 2        KM Jadhav         V Kohli     7
## 3           P Negi         V Kohli     3
## 4        S Aravind         V Kohli     0
## 5        S Aravind       YS Chahal     8
## 6         S Badree         V Kohli     2
## 7        STR Binny         V Kohli     1
## 8      Sachin Baby         V Kohli     2
## 9          TM Head         V Kohli     2
## 10         V Kohli  AB de Villiers    17
## 11         V Kohli        AF Milne     5
## 12         V Kohli       KM Jadhav     4
## 13         V Kohli          P Negi     9
## 14         V Kohli       S Aravind     2
## 15         V Kohli        S Badree     8
## 16         V Kohli     Sachin Baby     1
## 17         V Kohli         TM Head     9
## 18       YS Chahal       S Aravind     4

8. Batsmen vs Bowler

The function below computes and plots the performances of the batsmen vs the bowlers. As before the plot parameter can be set to True or False. By default it is plot=True

import pandas as pd
import yorkpy.analytics as yka
gl_mi=pd.read_csv(".\\Gujarat Lions-Mumbai Indians-2017-04-29.csv")
yka.teamBatsmenVsBowlersMatch(gl_mi,"Gujarat Lions","Mumbai Indians", plot=True)
# Print 

csk_kxip=pd.read_csv(".\\Chennai Super Kings-Kings XI Punjab-2014-05-30.csv")
m=yka.teamBatsmenVsBowlersMatch(csk_kxip,'Chennai Super Kings','Kings XI Punjab',plot=False)
print(m)
##          batsman           bowler  runs
## 0    BB McCullum         AR Patel     4
## 1    BB McCullum       GJ Maxwell     1
## 2    BB McCullum  Karanveer Singh     6
## 3      DJ Hussey          P Awana     1
## 4       DR Smith       MG Johnson     7
## 5       DR Smith          P Awana     0
## 6       DR Smith   Sandeep Sharma     0
## 7   F du Plessis       MG Johnson     0
## 8      MM Sharma         AR Patel     0
## 9      MM Sharma       MG Johnson     0
## 10     MM Sharma          P Awana     1
## 11      MS Dhoni         AR Patel    12
## 12      MS Dhoni  Karanveer Singh     2
## 13      MS Dhoni       MG Johnson    11
## 14      MS Dhoni          P Awana    15
## 15      MS Dhoni   Sandeep Sharma     2
## 16      R Ashwin         AR Patel     1
## 17      R Ashwin  Karanveer Singh     4
## 18      R Ashwin       MG Johnson     1
## 19      R Ashwin          P Awana     1
## 20      R Ashwin   Sandeep Sharma     3
## 21     RA Jadeja         AR Patel     5
## 22     RA Jadeja       GJ Maxwell     3
## 23     RA Jadeja  Karanveer Singh    19
## 24     RA Jadeja          P Awana     0
## 25      SK Raina       MG Johnson    21
## 26      SK Raina          P Awana    40
## 27      SK Raina   Sandeep Sharma    26

9. Bowling Scorecard

This function provides the bowling performance, the number of overs bowled, maidens, runs conceded. wickets taken and economy rate for the IPL match

import pandas as pd
import yorkpy.analytics as yka
dc_dd=pd.read_csv(".\\Deccan Chargers-Delhi Daredevils-2012-05-10.csv")
a=yka.teamBowlingScorecardMatch(dc_dd,'Deccan Chargers')
print(a)
##        bowler  overs  runs  maidens  wicket  econrate
## 0  AD Russell      4    39        0       0      9.75
## 1   IK Pathan      4    46        0       1     11.50
## 2    M Morkel      4    32        0       1      8.00
## 3    S Nadeem      4    39        0       0      9.75
## 4    VR Aaron      4    30        0       2      7.50
rps_rcb=pd.read_csv(".\\Rising Pune Supergiant-Royal Challengers Bangalore-2017-04-29.csv")
b=yka.teamBowlingScorecardMatch(rps_rcb,'Royal Challengers Bangalore')
print(b)
##               bowler  overs  runs  maidens  wicket  econrate
## 0          DL Chahar      2    18        0       0      9.00
## 1       DT Christian      4    25        0       1      6.25
## 2        Imran Tahir      4    18        0       3      4.50
## 3         JD Unadkat      4    19        0       1      4.75
## 4        LH Ferguson      4     7        1       3      1.75
## 5  Washington Sundar      2     7        0       1      3.50

10. Wicket Kind

The plots below provide the kind of wicket taken by the bowler (caught, bowled, lbw etc.) for the IPL match

import pandas as pd
import yorkpy.analytics as yka
kkr_rr=pd.read_csv(".\\Kolkata Knight Riders-Rajasthan Royals-2010-04-17.csv")
yka.teamBowlingWicketKindMatch(kkr_rr,'Kolkata Knight Riders','Rajasthan Royals')

csk_kxip=pd.read_csv(".\\Chennai Super Kings-Kings XI Punjab-2014-05-30.csv")
m = yka.teamBowlingWicketKindMatch(csk_kxip,'Chennai Super Kings','Kings-Kings XI Punjab',plot=False)
print(m)
##             bowler     kind  player_out
## 0         AR Patel  run out           1
## 1         AR Patel  stumped           1
## 2  Karanveer Singh  run out           1
## 3       MG Johnson   caught           1
## 4          P Awana   caught           2
## 5   Sandeep Sharma   bowled           1

11. Wicket vs Runs conceded

The plots below provide the wickets taken and the runs conceded by the bowler in the IPL T20 match

import pandas as pd
import yorkpy.analytics as yka
dc_dd=pd.read_csv(".\\Deccan Chargers-Delhi Daredevils-2012-05-10.csv")
yka.teamBowlingWicketMatch(dc_dd,"Deccan Chargers", "Delhi Daredevils",plot=True)

print("\n\n")
rps_rcb=pd.read_csv(".\\Rising Pune Supergiant-Royal Challengers Bangalore-2017-04-29.csv")
a=yka.teamBowlingWicketMatch(rps_rcb,"Royal Challengers Bangalore", "Rising Pune Supergiant",plot=False)
print(a)
##               bowler      player_out  kind
## 0       DT Christian         V Kohli     1
## 1        Imran Tahir        AF Milne     1
## 2        Imran Tahir          P Negi     1
## 3        Imran Tahir        S Badree     1
## 4         JD Unadkat         TM Head     1
## 5        LH Ferguson  AB de Villiers     1
## 6        LH Ferguson       KM Jadhav     1
## 7        LH Ferguson       STR Binny     1
## 8  Washington Sundar     Sachin Baby     1

12. Bowler Vs Batsmen

The functions compute and display how the different bowlers of the IPL team performed against the batting opposition.

import pandas as pd
import yorkpy.analytics as yka
csk_kxip=pd.read_csv(".\\Chennai Super Kings-Kings XI Punjab-2014-05-30.csv")
yka.teamBowlersVsBatsmenMatch(csk_kxip,"Chennai Super Kings","Kings XI Punjab")

print("\n\n")
kkr_rr=pd.read_csv(".\\Kolkata Knight Riders-Rajasthan Royals-2010-04-17.csv")
m =yka.teamBowlersVsBatsmenMatch(kkr_rr,"Rajasthan Royals","Kolkata Knight Riders",plot=False)
print(m)
##        batsman      bowler  runs
## 0     AC Voges    AB Dinda     1
## 1     AC Voges  JD Unadkat     1
## 2     AC Voges   LR Shukla     1
## 3     AC Voges    M Kartik     5
## 4     AJ Finch    AB Dinda     3
## 5     AJ Finch  JD Unadkat     3
## 6     AJ Finch   LR Shukla    13
## 7     AJ Finch    M Kartik     2
## 8     AJ Finch     SE Bond     0
## 9      AS Raut    AB Dinda     1
## 10     AS Raut  JD Unadkat     1
## 11    FY Fazal    AB Dinda     1
## 12    FY Fazal   LR Shukla     3
## 13    FY Fazal    M Kartik     3
## 14    FY Fazal     SE Bond     6
## 15     NV Ojha    AB Dinda    10
## 16     NV Ojha  JD Unadkat     5
## 17     NV Ojha   LR Shukla     0
## 18     NV Ojha    M Kartik     1
## 19     NV Ojha     SE Bond     2
## 20     P Dogra  JD Unadkat     2
## 21     P Dogra   LR Shukla     5
## 22     P Dogra    M Kartik     1
## 23     P Dogra     SE Bond     0
## 24  SK Trivedi    AB Dinda     4
## 25    SK Warne    AB Dinda     2
## 26    SK Warne    M Kartik     1
## 27    SK Warne     SE Bond     0
## 28   SR Watson    AB Dinda     2
## 29   SR Watson  JD Unadkat    13
## 30   SR Watson   LR Shukla     1
## 31   SR Watson    M Kartik    18
## 32   SR Watson     SE Bond    10
## 33   YK Pathan  JD Unadkat     1
## 34   YK Pathan   LR Shukla     7

13. Match worm chart

The plots below provide the match worm graph for the IPL Twenty 20 matches

import pandas as pd
import yorkpy.analytics as yka
dc_dd=pd.read_csv(".\\Deccan Chargers-Delhi Daredevils-2012-05-10.csv")
yka.matchWormChart(dc_dd,"Deccan Chargers", "Delhi Daredevils")

gl_mi=pd.read_csv(".\\Gujarat Lions-Mumbai Indians-2017-04-29.csv")
yka.matchWormChart(gl_mi,"Mumbai Indians","Gujarat Lions")

Feel free to clone/download the code from Github yorkpy

Conclusion

This post included all functions between 2 IPL teams from the package yorkpy for IPL Twenty20 matches. As mentioned above the yaml match files have been already converted to dataframes and are available for download from Github at yorkpyData

After having used Python and R for analytics, Machine Learning and Deep Learning, I have now realized that neither language is superior or inferior. Both have, some good packages and some that are not so well suited.

To be continued. Watch this space!

Important note: Do check out my other posts using yorkpy at yorkpy-posts

You may also like
1.My book ‘Deep Learning from first principles:Second Edition’ now on Amazon
2.My book ‘Practical Machine Learning in R and Python: Second edition’ on Amazon
2. Cricpy takes a swing at the ODIs
3. Introducing cricket package yorkr: Part 1- Beaten by sheer pace!
4. Big Data-1: Move into the big league:Graduate from Python to Pyspark
5. Simulating an Edge Shape in Android

To see all posts click Index of posts

Big Data-2: Move into the big league:Graduate from R to SparkR

This post is a continuation of my earlier post Big Data-1: Move into the big league:Graduate from Python to Pyspark. While the earlier post discussed parallel constructs in Python and Pyspark, this post elaborates similar and key constructs in R and SparkR. While this post just focuses on the programming part of R and SparkR it is essential to understand and fully grasp the concept of Spark, RDD and how data is distributed across the clusters. This post like the earlier post shows how if you already have a good handle of R, you can easily graduate to Big Data with SparkR

Note 1: This notebook has also been published at Databricks community site Big Data-2: Move into the big league:Graduate from R to SparkR

Note 2: You can download this RMarkdown file from Github at Big Data- Python to Pyspark and R to SparkR
1a. Read CSV- R

Note: To upload the CSV to databricks see the video Upload Flat File to Databricks Table

# Read CSV file
tendulkar= read.csv("/dbfs/FileStore/tables/tendulkar.csv",stringsAsFactors = FALSE,na.strings=c(NA,"-"))
#Check the dimensions of the dataframe
dim(tendulkar)
[1] 347  12
1b. Read CSV – SparkR
# Load the SparkR library
library(SparkR)
# Initiate a SparkR session
sparkR.session()
tendulkar1 <- read.df("/FileStore/tables/tendulkar.csv", 
                header = "true", 
                delimiter = ",", 
                source = "csv", 
                inferSchema = "true", 
                na.strings = "")

# Check the dimensions of the dataframe
dim(tendulkar1)
[1] 347  12
2a. Data frame shape – R
# Get the shape of the dataframe in R
dim(tendulkar)
[1] 347  12
2b. Dataframe shape – SparkR

The same ‘dim’ command works in SparkR too!

dim(tendulkar1)
[1] 347  12
3a . Dataframe columns – R
# Get the names
names(tendulkar) # Also colnames(tendulkar)
 [1] "Runs"       "Mins"       "BF"         "X4s"        "X6s"       
 [6] "SR"         "Pos"        "Dismissal"  "Inns"       "Opposition"
[11] "Ground"     "Start.Date"
3b. Dataframe columns – SparkR
names(tendulkar1)
 [1] "Runs"       "Mins"       "BF"         "4s"         "6s"        
 [6] "SR"         "Pos"        "Dismissal"  "Inns"       "Opposition"
[11] "Ground"     "Start Date"
4a. Rename columns – R
names(tendulkar)=c('Runs','Minutes','BallsFaced','Fours','Sixes','StrikeRate','Position','Dismissal','Innings','Opposition','Ground','StartDate')
names(tendulkar)
 [1] "Runs"       "Minutes"    "BallsFaced" "Fours"      "Sixes"     
 [6] "StrikeRate" "Position"   "Dismissal"  "Innings"    "Opposition"
[11] "Ground"     "StartDate"
4b. Rename columns – SparkR
names(tendulkar1)=c('Runs','Minutes','BallsFaced','Fours','Sixes','StrikeRate','Position','Dismissal','Innings','Opposition','Ground','StartDate')
names(tendulkar1)
 [1] "Runs"       "Minutes"    "BallsFaced" "Fours"      "Sixes"     
 [6] "StrikeRate" "Position"   "Dismissal"  "Innings"    "Opposition"
[11] "Ground"     "StartDate"
5a. Summary – R
summary(tendulkar)
     Runs              Minutes        BallsFaced         Fours       
 Length:347         Min.   :  1.0   Min.   :  0.00   Min.   : 0.000  
 Class :character   1st Qu.: 33.0   1st Qu.: 22.00   1st Qu.: 1.000  
 Mode  :character   Median : 82.0   Median : 58.50   Median : 4.000  
                    Mean   :125.5   Mean   : 89.75   Mean   : 6.274  
                    3rd Qu.:181.0   3rd Qu.:133.25   3rd Qu.: 9.000  
                    Max.   :613.0   Max.   :436.00   Max.   :35.000  
                    NA's   :18      NA's   :19       NA's   :19      
     Sixes          StrikeRate        Position     Dismissal        
 Min.   :0.0000   Min.   :  0.00   Min.   :2.00   Length:347        
 1st Qu.:0.0000   1st Qu.: 38.09   1st Qu.:4.00   Class :character  
 Median :0.0000   Median : 52.25   Median :4.00   Mode  :character  
 Mean   :0.2097   Mean   : 51.79   Mean   :4.24                     
 3rd Qu.:0.0000   3rd Qu.: 65.09   3rd Qu.:4.00                     
 Max.   :4.0000   Max.   :166.66   Max.   :7.00                     
 NA's   :18       NA's   :20       NA's   :18                       
    Innings       Opposition           Ground           StartDate        
 Min.   :1.000   Length:347         Length:347         Length:347        
 1st Qu.:1.000   Class :character   Class :character   Class :character  
 Median :2.000   Mode  :character   Mode  :character   Mode  :character  
 Mean   :2.376                                                           
 3rd Qu.:3.000                                                           
 Max.   :4.000                                                           
 NA's   :1
5b. Summary – SparkR
summary(tendulkar1)
SparkDataFrame[summary:string, Runs:string, Minutes:string, BallsFaced:string, Fours:string, Sixes:string, StrikeRate:string, Position:string, Dismissal:string, Innings:string, Opposition:string, Ground:string, StartDate:string]
6a. Displaying details of dataframe with str() – R
str(tendulkar)
'data.frame':	347 obs. of  12 variables:
 $ Runs      : chr  "15" "DNB" "59" "8" ...
 $ Minutes   : int  28 NA 254 24 124 74 193 1 50 324 ...
 $ BallsFaced: int  24 NA 172 16 90 51 134 1 44 266 ...
 $ Fours     : int  2 NA 4 1 5 5 6 0 3 5 ...
 $ Sixes     : int  0 NA 0 0 0 0 0 0 0 0 ...
 $ StrikeRate: num  62.5 NA 34.3 50 45.5 ...
 $ Position  : int  6 NA 6 6 7 6 6 6 6 6 ...
 $ Dismissal : chr  "bowled" NA "lbw" "run out" ...
 $ Innings   : int  2 4 1 3 1 1 3 2 3 1 ...
 $ Opposition: chr  "v Pakistan" "v Pakistan" "v Pakistan" "v Pakistan" ...
 $ Ground    : chr  "Karachi" "Karachi" "Faisalabad" "Faisalabad" ...
 $ StartDate : chr  "15-Nov-89" "15-Nov-89" "23-Nov-89" "23-Nov-89" ...
6b. Displaying details of dataframe with str() – SparkR
str(tendulkar1)
'SparkDataFrame': 12 variables:
 $ Runs      : chr "15" "DNB" "59" "8" "41" "35"
 $ Minutes   : chr "28" "-" "254" "24" "124" "74"
 $ BallsFaced: chr "24" "-" "172" "16" "90" "51"
 $ Fours     : chr "2" "-" "4" "1" "5" "5"
 $ Sixes     : chr "0" "-" "0" "0" "0" "0"
 $ StrikeRate: chr "62.5" "-" "34.3" "50" "45.55" "68.62"
 $ Position  : chr "6" "-" "6" "6" "7" "6"
 $ Dismissal : chr "bowled" "-" "lbw" "run out" "bowled" "lbw"
 $ Innings   : chr "2" "4" "1" "3" "1" "1"
 $ Opposition: chr "v Pakistan" "v Pakistan" "v Pakistan" "v Pakistan" "v Pakistan" "v Pakistan"
 $ Ground    : chr "Karachi" "Karachi" "Faisalabad" "Faisalabad" "Lahore" "Sialkot"
 $ StartDate : chr "15-Nov-89" "15-Nov-89" "23-Nov-89" "23-Nov-89" "1-Dec-89" "9-Dec-89"
7a. Head & tail -R
print(head(tendulkar),3)
print(tail(tendulkar),3)
 Runs Minutes BallsFaced Fours Sixes StrikeRate Position Dismissal Innings
1   15      28         24     2     0      62.50        6    bowled       2
2  DNB      NA         NA    NA    NA         NA       NA             4
3   59     254        172     4     0      34.30        6       lbw       1
4    8      24         16     1     0      50.00        6   run out       3
5   41     124         90     5     0      45.55        7    bowled       1
6   35      74         51     5     0      68.62        6       lbw       1
  Opposition     Ground StartDate
1 v Pakistan    Karachi 15-Nov-89
2 v Pakistan    Karachi 15-Nov-89
3 v Pakistan Faisalabad 23-Nov-89
4 v Pakistan Faisalabad 23-Nov-89
5 v Pakistan     Lahore  1-Dec-89
6 v Pakistan    Sialkot  9-Dec-89
    Runs Minutes BallsFaced Fours Sixes StrikeRate Position Dismissal Innings
342   37     125         81     5     0      45.67        4    caught       2
343   21      71         23     2     0      91.30        4   run out       4
344   32      99         53     5     0      60.37        4       lbw       2
345    1       8          5     0     0      20.00        4       lbw       4
346   10      41         24     2     0      41.66        4       lbw       2
347   74     150        118    12     0      62.71        4    caught       2
       Opposition  Ground StartDate
342   v Australia  Mohali 14-Mar-13
343   v Australia  Mohali 14-Mar-13
344   v Australia   Delhi 22-Mar-13
345   v Australia   Delhi 22-Mar-13
346 v West Indies Kolkata  6-Nov-13
347 v West Indies  Mumbai 14-Nov-13
7b. Head – SparkR
head(tendulkar1,3)
  Runs Minutes BallsFaced Fours Sixes StrikeRate Position Dismissal Innings
1   15      28         24     2     0       62.5        6    bowled       2
2  DNB       -          -     -     -          -        -         -       4
3   59     254        172     4     0       34.3        6       lbw       1
  Opposition     Ground StartDate
1 v Pakistan    Karachi 15-Nov-89
2 v Pakistan    Karachi 15-Nov-89
3 v Pakistan Faisalabad 23-Nov-89
8a. Determining the column types with sapply -R
sapply(tendulkar,class)
       Runs     Minutes  BallsFaced       Fours       Sixes  StrikeRate 
"character"   "integer"   "integer"   "integer"   "integer"   "numeric" 
   Position   Dismissal     Innings  Opposition      Ground   StartDate 
  "integer" "character"   "integer" "character" "character" "character"
8b. Determining the column types with printSchema – SparkR
printSchema(tendulkar1)
root
 |-- Runs: string (nullable = true)
 |-- Minutes: string (nullable = true)
 |-- BallsFaced: string (nullable = true)
 |-- Fours: string (nullable = true)
 |-- Sixes: string (nullable = true)
 |-- StrikeRate: string (nullable = true)
 |-- Position: string (nullable = true)
 |-- Dismissal: string (nullable = true)
 |-- Innings: string (nullable = true)
 |-- Opposition: string (nullable = true)
 |-- Ground: string (nullable = true)
 |-- StartDate: string (nullable = true)
9a. Selecting columns – R
library(dplyr)
df=select(tendulkar,Runs,BallsFaced,Minutes)
head(df,5)
  Runs BallsFaced Minutes
1   15         24      28
2  DNB         NA      NA
3   59        172     254
4    8         16      24
5   41         90     124
9b. Selecting columns – SparkR
library(SparkR)
Sys.setenv(SPARK_HOME="/usr/hdp/2.6.0.3-8/spark")
.libPaths(c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"), .libPaths()))
# Initiate a SparkR session
sparkR.session()
tendulkar1 <- read.df("/FileStore/tables/tendulkar.csv", 
                header = "true", 
                delimiter = ",", 
                source = "csv", 
                inferSchema = "true", 
                na.strings = "")
df=SparkR::select(tendulkar1, "Runs", "BF","Mins")
head(SparkR::collect(df))
  Runs  BF Mins
1   15  24   28
2  DNB   -    -
3   59 172  254
4    8  16   24
5   41  90  124
6   35  51   74
10a. Filter rows by criteria – R
library(dplyr)
df=tendulkar %>% filter(Runs > 50)
head(df,5)
  Runs Minutes BallsFaced Fours Sixes StrikeRate Position Dismissal Innings
1  DNB      NA         NA    NA    NA         NA       NA             4
2   59     254        172     4     0      34.30        6       lbw       1
3    8      24         16     1     0      50.00        6   run out       3
4   57     193        134     6     0      42.53        6    caught       3
5   88     324        266     5     0      33.08        6    caught       1
     Opposition     Ground StartDate
1    v Pakistan    Karachi 15-Nov-89
2    v Pakistan Faisalabad 23-Nov-89
3    v Pakistan Faisalabad 23-Nov-89
4    v Pakistan    Sialkot  9-Dec-89
5 v New Zealand     Napier  9-Feb-90
10b. Filter rows by criteria – SparkR
df=SparkR::filter(tendulkar1, tendulkar1$Runs > 50)
head(SparkR::collect(df))
  Runs Mins  BF 4s 6s    SR Pos Dismissal Inns     Opposition       Ground
1   59  254 172  4  0  34.3   6       lbw    1     v Pakistan   Faisalabad
2   57  193 134  6  0 42.53   6    caught    3     v Pakistan      Sialkot
3   88  324 266  5  0 33.08   6    caught    1  v New Zealand       Napier
4   68  216 136  8  0    50   6    caught    2      v England   Manchester
5  114  228 161 16  0  70.8   4    caught    2    v Australia        Perth
6  111  373 270 19  0 41.11   4    caught    2 v South Africa Johannesburg
  Start Date
1  23-Nov-89
2   9-Dec-89
3   9-Feb-90
4   9-Aug-90
5   1-Feb-92
6  26-Nov-92
11a. Unique values -R
unique(tendulkar$Runs)
  [1] "15"   "DNB"  "59"   "8"    "41"   "35"   "57"   "0"    "24"   "88"  
 [11] "5"    "10"   "27"   "68"   "119*" "21"   "11"   "16"   "7"    "40"  
 [21] "148*" "6"    "17"   "114"  "111"  "1"    "73"   "50"   "9*"   "165" 
 [31] "78"   "62"   "TDNB" "28"   "104*" "71"   "142"  "96"   "43"   "11*" 
 [41] "34"   "85"   "179"  "54"   "4"    "0*"   "52*"  "2"    "122"  "31"  
 [51] "177"  "74"   "42"   "18"   "61"   "36"   "169"  "9"    "15*"  "92"  
 [61] "83"   "143"  "139"  "23"   "148"  "13"   "155*" "79"   "47"   "113" 
 [71] "67"   "136"  "29"   "53"   "124*" "126*" "44*"  "217"  "116"  "52"  
 [81] "45"   "97"   "20"   "39"   "201*" "76"   "65"   "126"  "36*"  "69"  
 [91] "155"  "22*"  "103"  "26"   "90"   "176"  "117"  "86"   "12"   "193" 
[101] "16*"  "51"   "32"   "55"   "37"   "44"   "241*" "60*"  "194*" "3"   
[111] "32*"  "248*" "94"   "22"   "109"  "19"   "14"   "28*"  "63"   "64"  
[121] "101"  "122*" "91"   "82"   "56*"  "154*" "153"  "49"   "10*"  "103*"
[131] "160"  "100*" "105*" "100"  "106"  "84"   "203"  "98"   "38"   "214" 
[141] "53*"  "111*" "146"  "14*"  "56"   "80"   "25"   "81"   "13*"
11b. Unique values – SparkR
head(SparkR::distinct(tendulkar1[,"Runs"]),5)
  Runs
1 119*
2    7
3   51
4  169
5  32*
12a. Aggregate – Mean, min and max – R
library(dplyr)
library(magrittr)
a <- tendulkar$Runs != "DNB"
tendulkar <- tendulkar[a,]
dim(tendulkar)

# Remove rows with 'TDNB'
c <- tendulkar$Runs != "TDNB"
tendulkar <- tendulkar[c,]

# Remove rows with absent
d <- tendulkar$Runs != "absent"
tendulkar <- tendulkar[d,]
dim(tendulkar)

# Remove the "* indicating not out
tendulkar$Runs <- as.numeric(gsub("\\*","",tendulkar$Runs))
c <- complete.cases(tendulkar)

#Subset the rows which are complete
tendulkar <- tendulkar[c,]
print(dim(tendulkar))
df <-tendulkar %>%  group_by(Ground) %>% summarise(meanRuns= mean(Runs), minRuns=min(Runs), maxRuns=max(Runs)) 
#names(tendulkar)
head(df)
[1] 327  12
# A tibble: 6 x 4
  Ground       meanRuns minRuns maxRuns
                   
1 Adelaide        32.6       0.    153.
2 Ahmedabad       40.1       4.    217.
3 Auckland         5.00      5.      5.
4 Bangalore       57.9       4.    214.
5 Birmingham      46.8       1.    122.
6 Bloemfontein    85.0      15.    155.
12b. Aggregate- Mean, Min, Max – SparkR
sparkR.session()

tendulkar1 <- read.df("/FileStore/tables/tendulkar.csv", 
                header = "true", 
                delimiter = ",", 
                source = "csv", 
                inferSchema = "true", 
                na.strings = "")

print(dim(tendulkar1))
tendulkar1 <-SparkR::filter(tendulkar1,tendulkar1$Runs != "DNB")
print(dim(tendulkar1))
tendulkar1<-SparkR::filter(tendulkar1,tendulkar1$Runs != "TDNB")
print(dim(tendulkar1))
tendulkar1<-SparkR::filter(tendulkar1,tendulkar1$Runs != "absent")
print(dim(tendulkar1))

# Cast the string type Runs to double
withColumn(tendulkar1, "Runs", cast(tendulkar1$Runs, "double"))
head(SparkR::distinct(tendulkar1[,"Runs"]),20)
# Remove the "* indicating not out
tendulkar1$Runs=SparkR::regexp_replace(tendulkar1$Runs, "\\*", "")
head(SparkR::distinct(tendulkar1[,"Runs"]),20)
df=SparkR::summarize(SparkR::groupBy(tendulkar1, tendulkar1$Ground), mean = mean(tendulkar1$Runs), minRuns=min(tendulkar1$Runs),maxRuns=max(tendulkar1$Runs))
head(df,20)
[1] 347  12
[1] 330  12
[1] 329  12
[1] 329  12
          Ground       mean minRuns maxRuns
1      Bangalore  54.312500       0      96
2       Adelaide  32.600000       0      61
3  Colombo (PSS)  37.200000      14      71
4   Christchurch  12.000000       0      24
5       Auckland   5.000000       5       5
6        Chennai  60.625000       0      81
7      Centurion  73.500000     111      36
8       Brisbane   7.666667       0       7
9     Birmingham  46.750000       1      40
10     Ahmedabad  40.125000     100       8
11 Colombo (RPS) 143.000000     143     143
12    Chittagong  57.800000     101      36
13     Cape Town  69.857143      14       9
14    Bridgetown  26.000000       0      92
15      Bulawayo  55.000000      36      74
16         Delhi  39.947368       0      76
17    Chandigarh  11.000000      11      11
18  Bloemfontein  85.000000      15     155
19 Colombo (SSC)  77.555556     104       8
20       Cuttack   2.000000       2       2
13a Using SQL with SparkR
sparkR.session()
tendulkar1 <- read.df("/FileStore/tables/tendulkar.csv", 
                header = "true", 
                delimiter = ",", 
                source = "csv", 
                inferSchema = "true", 
                na.strings = "")

# Register this SparkDataFrame as a temporary view.
createOrReplaceTempView(tendulkar1, "tendulkar2")

# SQL statements can be run by using the sql method
df=SparkR::sql("SELECT * FROM tendulkar2 WHERE Ground='Karachi'")

head(df)

  Runs Mins BF 4s 6s    SR Pos Dismissal Inns Opposition  Ground Start Date
1   15   28 24  2  0  62.5   6    bowled    2 v Pakistan Karachi  15-Nov-89
2  DNB    -  -  -  -     -   -         -    4 v Pakistan Karachi  15-Nov-89
3   23   49 29  5  0 79.31   4    bowled    2 v Pakistan Karachi  29-Jan-06
4   26   74 47  5  0 55.31   4    bowled    4 v Pakistan Karachi  29-Jan-06
Conclusion

This post discusses some of the key constructs in R and SparkR and how one can transition from R to SparkR fairly easily. I will be adding more constructs later. Do check back!

You may also like
1. Exploring Quantum Gate operations with QCSimulator
2. Deep Learning from first principles in Python, R and Octave – Part 4
3. A Bluemix recipe with MongoDB and Node.js
4. Practical Machine Learning with R and Python – Part 5
5. Introducing cricketr! : An R package to analyze performances of cricketers

To see all posts click Index of posts

Big Data-1: Move into the big league:Graduate from Python to Pyspark

This post discusses similar constructs in Python and Pyspark. As in my earlier post R vs Python: Different similarities and similar differences the focus is on the key and common constructs to highlight the similarities.

Important Note:You can also access this notebook at databricks public site  Big Data-1: Move into the big league:Graduate from Python to Pyspark (the formatting here is much better!!).

For this notebook I have used Databricks community edition

You can download the notebook from Github at Big Data-1:PythontoPysparkAndRtoSparkR

Hope you found this useful!

Note: There are still a few more important constructs which I will be adding to this post.

Also see
1. My book “Deep Learning from first principles” now on Amazon
2. My book ‘Practical Machine Learning in R and Python: Second edition’ on Amazon
3. Re-introducing cricketr! : An R package to analyze performances of cricketers
4. GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and tables
5. Deblurring with OpenCV: Weiner filter reloaded
6. Design Principles of Scalable, Distributed Systems

My 2 video presentations on ‘Essential Python for Datascience’

Here, in this post I include 2 sessions on ‘Essential Python for Datascience’. These 2 presentations cover the most important features of the Python language with which you can hit the ground running in datascience. All  the related material for these sessions can be cloned/downloaded from Github at ‘EssentialPythonForDatascience

1. Essential Python for Datascience -1
In this  video presentation I cover basic data types like tuples,lists, dictionaries. How to get the type of a variable, subsetting and numpy arrays. Some basic operations on numpy arrays, slicing is also covered

2. Essential Python for Datascience -2
In the 2nd part I cover Pandas, pandas Series, dataframes, how to subset dataframes using iloc,loc, selection of specific columns, filtering dataframes by criteria etc. Other operations include group_by, apply,agg. Lastly I also touch upon matplotlib.

This is no means an exhaustive coverage of the multitude of features available in Python but can provide as a good starting point for those venturing into datascience with Python.

Good luck with Python!

Also see
1. My 3 video presentations on “Essential R”
2. Neural Networks: The mechanics of backpropagation
3. Introducing QCSimulator: A 5-qubit quantum computing simulator in R
4. Deblurring with OpenCV: Weiner filter reloaded
5. GooglyPlus: yorkr analyzes IPL players, teams, matches with plots and table

To see all posts see Index of posts

R vs Python: Different similarities and similar differences

A debate about which language is better suited for Datascience, R or Python, can set off diehard fans of these languages into a tizzy. This post tries to look at some of the different similarities and similar differences between these languages. To a large extent the ease or difficulty in learning R or Python is subjective. I have heard that R has a steeper learning curve than Python and also vice versa. This probably depends on the degree of familiarity with the languuge To a large extent both R an Python do the same thing in just slightly different ways and syntaxes. The ease or the difficulty in the R/Python construct’s largely is in the ‘eyes of the beholder’ nay, programmer’ we could say.  I include my own experience with the languages below.

Check out my compact and minimal book  “Practical Machine Learning with R and Python:Third edition- Machine Learning in stereo”  available in Amazon in paperback($12.99) and kindle($8.99) versions. My book includes implementations of key ML algorithms and associated measures and metrics. The book is ideal for anybody who is familiar with the concepts and would like a quick reference to the different ML algorithms that can be applied to problems and how to select the best model. Pick your copy today!!

1. R data types

R has the following data types

  1.  Character
  2. Integer
  3. Numeric
  4. Logical
  5. Complex
  6. Raw

Python has several data types

  1. Int
  2. float
  3. Long
  4. Complex and so on

2. R Vector vs Python List

A common data type in R is the vector. Python has a similar data type, the list

# R vectors
a<-c(4,5,1,3,4,5)
print(a[3])
## [1] 1
print(a[3:4]) # R does not always need the explicit print. 
## [1] 1 3
#R type of variable
print(class(a))
## [1] "numeric"
# Length of a
print(length(a))
## [1] 6
# Python lists
a=[4,5,1,3,4,5] # 
print(a[2]) # Some python IDEs require the explicit print
print(a[2:5])
print(type(a))
# Length of a
print(len(a))
## 1
## [1, 3, 4]
## 
## 6

2a. Other data types – Python

Python also has certain other data types like the tuple, dictionary etc as shown below. R does not have as many of the data types, nevertheless we can do everything that Python does in R

# Python tuple
b = (4,5,7,8)
print(b)


#Python dictionary
c={'name':'Ganesh','age':54,'Work':'Professional'}
print(c)
#Print type of variable c
## (4, 5, 7, 8)
## {'name': 'Ganesh', 'age': 54, 'Work': 'Professional'}

2.Type of Variable

To know the type of the variable in R we use ‘class’, In Python the corresponding command is ‘type’

#R - Type of variable
a<-c(4,5,1,3,4,5)
print(class(a))
## [1] "numeric"
#Python - Print type of tuple a
a=[4,5,1,3,4,5]
print(type(a))
b=(4,3,"the",2)
print(type(b))
## 
## 

3. Length

To know length in R, use length()

#R - Length of vector
# Length of a
a<-c(4,5,1,3,4,5)
print(length(a))
## [1] 6

To know the length of a list,tuple or dict we can use len()

# Python - Length of list , tuple etc
# Length of a
a=[4,5,1,3,4,5]
print(len(a))
# Length of b
b = (4,5,7,8)
print(len(b))
## 6
## 4

4. Accessing help

To access help in R we use the ‘?’ or the ‘help’ function

#R - Help - To be done in R console or RStudio
#?sapply
#help(sapply)

Help in python on any topic involves

#Python help - This can be done on a (I)Python console
#help(len)
#?len

5. Subsetting

The key difference between R and Python with regards to subsetting is that in R the index starts at 1. In Python it starts at 0, much like C,C++ or Java To subset a vector in R we use

#R - Subset
a<-c(4,5,1,3,4,8,12,18,1)
print(a[3])
## [1] 1
# To print a range or a slice. Print from the 3rd to the 5th element
print(a[3:6])
## [1] 1 3 4 8

Python also uses indices. The difference in Python is that the index starts from 0/

#Python - Subset
a=[4,5,1,3,4,8,12,18,1]
# Print the 4th element (starts from 0)
print(a[3])

# Print a slice from 4 to 6th element
print(a[3:6])
## 3
## [3, 4, 8]

6. Operations on vectors in R and operation on lists in Python

In R we can do many operations on vectors for e.g. element by element addition, subtraction, exponentation,product etc. as show

#R - Operations on vectors
a<- c(5,2,3,1,7)
b<- c(1,5,4,6,8)

#Element wise Addition
print(a+b)
## [1]  6  7  7  7 15
#Element wise subtraction
print(a-b)
## [1]  4 -3 -1 -5 -1
#Element wise product
print(a*b)
## [1]  5 10 12  6 56
# Exponentiating the elements of a vector
print(a^2)
## [1] 25  4  9  1 49

In Python to do this on lists we need to use the ‘map’ and the ‘lambda’ function as follows

# Python - Operations on list
a =[5,2,3,1,7]
b =[1,5,4,6,8]

#Element wise addition with map & lambda
print(list(map(lambda x,y: x+y,a,b)))
#Element wise subtraction
print(list(map(lambda x,y: x-y,a,b)))
#Element wise product
print(list(map(lambda x,y: x*y,a,b)))
# Exponentiating the elements of a list
print(list(map(lambda x: x**2,a)))
## [6, 7, 7, 7, 15]
## [4, -3, -1, -5, -1]
## [5, 10, 12, 6, 56]
## [25, 4, 9, 1, 49]

However if we create ndarrays from lists then we can do the element wise addition,subtraction,product, etc. like R. Numpy is really a powerful module with many, many functions for matrix manipulations

import numpy as np
a =[5,2,3,1,7]
b =[1,5,4,6,8]
a=np.array(a)
b=np.array(b)
#Element wise addition
print(a+b)
#Element wise subtraction
print(a-b)
#Element wise product
print(a*b)
# Exponentiating the elements of a list
print(a**2)
## [ 6  7  7  7 15]
## [ 4 -3 -1 -5 -1]
## [ 5 10 12  6 56]
## [25  4  9  1 49]

7. Getting the index of element

To determine the index of an element which satisifies a specific logical condition in R use ‘which’. In the code below the index of element which is equal to 1 is 4

# R - Which
a<- c(5,2,3,1,7)
print(which(a == 1))
## [1] 4

In Python array we can use np.where to get the same effect. The index will be 3 as the index starts from 0

# Python - np.where
import numpy as np
a =[5,2,3,1,7]
a=np.array(a)
print(np.where(a==1))
## (array([3], dtype=int64),)

8. Data frames

R, by default comes with a set of in-built datasets. There are some datasets which come with the SkiKit- Learn package

# R 
# To check built datasets use
#data() - In R console or in R Studio
#iris - Don't print to console

We can use the in-built data sets that come with Scikit package

#Python
import sklearn as sklearn
import pandas as pd
from sklearn import datasets
# This creates a Sklearn bunch
data = datasets.load_iris()
# Convert to Pandas dataframe
iris = pd.DataFrame(data.data, columns=data.feature_names)

9. Working with dataframes

With R you can work with dataframes directly. For more complex dataframe operations in R there are convenient packages like dplyr, reshape2 etc. For Python we need to use the Pandas package. Pandas is quite comprehensive in the list of things we can do with data frames The most common operations on a dataframe are

  • Check the size of the dataframe
  • Take a look at the top 5 or bottom 5 rows of dataframe
  • Check the content of the dataframe

a.Size

In R use dim()

#R - Size
dim(iris)
## [1] 150   5

For Python use .shape

#Python - size
import sklearn as sklearn
import pandas as pd
from sklearn import datasets
data = datasets.load_iris()
# Convert to Pandas dataframe
iris = pd.DataFrame(data.data, columns=data.feature_names)
iris.shape

b. Top & bottom 5 rows of dataframe

To know the top and bottom rows of a data frame we use head() & tail as shown below for R and Python

#R 
head(iris,5)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
tail(iris,5)
##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 146          6.7         3.0          5.2         2.3 virginica
## 147          6.3         2.5          5.0         1.9 virginica
## 148          6.5         3.0          5.2         2.0 virginica
## 149          6.2         3.4          5.4         2.3 virginica
## 150          5.9         3.0          5.1         1.8 virginica
#Python
import sklearn as sklearn
import pandas as pd
from sklearn import datasets
data = datasets.load_iris()
# Convert to Pandas dataframe
iris = pd.DataFrame(data.data, columns=data.feature_names)
print(iris.head(5))
print(iris.tail(5))
##    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
## 0                5.1               3.5                1.4               0.2
## 1                4.9               3.0                1.4               0.2
## 2                4.7               3.2                1.3               0.2
## 3                4.6               3.1                1.5               0.2
## 4                5.0               3.6                1.4               0.2
##      sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
## 145                6.7               3.0                5.2               2.3
## 146                6.3               2.5                5.0               1.9
## 147                6.5               3.0                5.2               2.0
## 148                6.2               3.4                5.4               2.3
## 149                5.9               3.0                5.1               1.8

c. Check the content of the dataframe

#R
summary(iris)
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
## 
str(iris)
## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
#Python
import sklearn as sklearn
import pandas as pd
from sklearn import datasets
data = datasets.load_iris()
# Convert to Pandas dataframe
iris = pd.DataFrame(data.data, columns=data.feature_names)
print(iris.info())
## 
## RangeIndex: 150 entries, 0 to 149
## Data columns (total 4 columns):
## sepal length (cm)    150 non-null float64
## sepal width (cm)     150 non-null float64
## petal length (cm)    150 non-null float64
## petal width (cm)     150 non-null float64
## dtypes: float64(4)
## memory usage: 4.8 KB
## None

d. Check column names

#R
names(iris)
## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width" 
## [5] "Species"
colnames(iris)
## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width" 
## [5] "Species"
#Python
import sklearn as sklearn
import pandas as pd
from sklearn import datasets
data = datasets.load_iris()
# Convert to Pandas dataframe
iris = pd.DataFrame(data.data, columns=data.feature_names)
#Get column names
print(iris.columns)
## Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',
##        'petal width (cm)'],
##       dtype='object')

e. Rename columns

In R we can assign a vector to column names

#R
colnames(iris) <- c("lengthOfSepal","widthOfSepal","lengthOfPetal","widthOfPetal","Species")
colnames(iris)
## [1] "lengthOfSepal" "widthOfSepal"  "lengthOfPetal" "widthOfPetal" 
## [5] "Species"

In Python we can assign a list to s.columns

#Python
import sklearn as sklearn
import pandas as pd
from sklearn import datasets
data = datasets.load_iris()
# Convert to Pandas dataframe
iris = pd.DataFrame(data.data, columns=data.feature_names)
iris.columns = ["lengthOfSepal","widthOfSepal","lengthOfPetal","widthOfPetal"]
print(iris.columns)
## Index(['lengthOfSepal', 'widthOfSepal', 'lengthOfPetal', 'widthOfPetal'], dtype='object')

f.Details of dataframe

#Python
import sklearn as sklearn
import pandas as pd
from sklearn import datasets
data = datasets.load_iris()
# Convert to Pandas dataframe
iris = pd.DataFrame(data.data, columns=data.feature_names)
print(iris.info())
## 
## RangeIndex: 150 entries, 0 to 149
## Data columns (total 4 columns):
## sepal length (cm)    150 non-null float64
## sepal width (cm)     150 non-null float64
## petal length (cm)    150 non-null float64
## petal width (cm)     150 non-null float64
## dtypes: float64(4)
## memory usage: 4.8 KB
## None

g. Subsetting dataframes

# R
#To subset a dataframe 'df' in R we use df[row,column] or df[row vector,column vector]
#df[row,column]
iris[3,4]
## [1] 0.2
#df[row vector, column vector]
iris[2:5,1:3]
##   lengthOfSepal widthOfSepal lengthOfPetal
## 2           4.9          3.0           1.4
## 3           4.7          3.2           1.3
## 4           4.6          3.1           1.5
## 5           5.0          3.6           1.4
#If we omit the row vector, then it implies all rows or if we omit the column vector
# then implies all columns for that row
iris[2:5,]
##   lengthOfSepal widthOfSepal lengthOfPetal widthOfPetal Species
## 2           4.9          3.0           1.4          0.2  setosa
## 3           4.7          3.2           1.3          0.2  setosa
## 4           4.6          3.1           1.5          0.2  setosa
## 5           5.0          3.6           1.4          0.2  setosa
# In R we can all specific columns by column names
iris$Sepal.Length[2:5]
## NULL
#Python
# To select an entire row we use .iloc. The index can be used with the ':'. If 
# .iloc[start row: end row]. If start row is omitted then it implies the beginning of
# data frame, if end row is omitted then it implies all rows till end
#Python
import sklearn as sklearn
import pandas as pd
from sklearn import datasets
data = datasets.load_iris()
# Convert to Pandas dataframe
iris = pd.DataFrame(data.data, columns=data.feature_names)
print(iris.iloc[3])
print(iris[:5])
# In python we can select columns by column name as follows
print(iris['sepal length (cm)'][2:6])
#If you want to select more than 2 columns then you must use the double '[[]]' since the 
# index is a list itself
print(iris[['sepal length (cm)','sepal width (cm)']][4:7])
## sepal length (cm)    4.6
## sepal width (cm)     3.1
## petal length (cm)    1.5
## petal width (cm)     0.2
## Name: 3, dtype: float64
##    sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
## 0                5.1               3.5                1.4               0.2
## 1                4.9               3.0                1.4               0.2
## 2                4.7               3.2                1.3               0.2
## 3                4.6               3.1                1.5               0.2
## 4                5.0               3.6                1.4               0.2
## 2    4.7
## 3    4.6
## 4    5.0
## 5    5.4
## Name: sepal length (cm), dtype: float64
##    sepal length (cm)  sepal width (cm)
## 4                5.0               3.6
## 5                5.4               3.9
## 6                4.6               3.4

h. Computing Mean, Standard deviation

#R 
#Mean
mean(iris$lengthOfSepal)
## [1] 5.843333
#Standard deviation
sd(iris$widthOfSepal)
## [1] 0.4358663
#Python
#Mean
import sklearn as sklearn
import pandas as pd
from sklearn import datasets
data = datasets.load_iris()
# Convert to Pandas dataframe
iris = pd.DataFrame(data.data, columns=data.feature_names)
# Convert to Pandas dataframe
print(iris['sepal length (cm)'].mean())
#Standard deviation
print(iris['sepal width (cm)'].std())
## 5.843333333333335
## 0.4335943113621737

i. Boxplot

Boxplot can be produced in R using baseplot

#R
boxplot(iris$lengthOfSepal)

Matplotlib is a popular package in Python for plots

#Python
import sklearn as sklearn
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import datasets
data = datasets.load_iris()
# Convert to Pandas dataframe
iris = pd.DataFrame(data.data, columns=data.feature_names)
img=plt.boxplot(iris['sepal length (cm)'])
plt.show(img)

j.Scatter plot

#R
plot(iris$widthOfSepal,iris$lengthOfSepal)

#Python
import matplotlib.pyplot as plt
import sklearn as sklearn
import pandas as pd
from sklearn import datasets
data = datasets.load_iris()
# Convert to Pandas dataframe
iris = pd.DataFrame(data.data, columns=data.feature_names)
img=plt.scatter(iris['sepal width (cm)'],iris['sepal length (cm)'])
#plt.show(img)

k. Read from csv file

#R
tendulkar= read.csv("tendulkar.csv",stringsAsFactors = FALSE,na.strings=c(NA,"-"))
#Dimensions of dataframe
dim(tendulkar)
## [1] 347  13
names(tendulkar)
##  [1] "X"          "Runs"       "Mins"       "BF"         "X4s"       
##  [6] "X6s"        "SR"         "Pos"        "Dismissal"  "Inns"      
## [11] "Opposition" "Ground"     "Start.Date"

Use pandas.read_csv() for Python

#Python
import pandas as pd
#Read csv
tendulkar= pd.read_csv("tendulkar.csv",na_values=["-"])
print(tendulkar.shape)
print(tendulkar.columns)
## (347, 13)
## Index(['Unnamed: 0', 'Runs', 'Mins', 'BF', '4s', '6s', 'SR', 'Pos',
##        'Dismissal', 'Inns', 'Opposition', 'Ground', 'Start Date'],
##       dtype='object')

l. Clean the dataframe in R and Python.

The following steps are done for R and Python
1.Remove rows with ‘DNB’
2.Remove rows with ‘TDNB’
3.Remove rows with absent
4.Remove the “*” indicating not out
5.Remove incomplete rows with NA for R or NaN in Python
6.Do a scatter plot

#R
# Remove rows with 'DNB'
a <- tendulkar$Runs != "DNB"
tendulkar <- tendulkar[a,]
dim(tendulkar)
## [1] 330  13
# Remove rows with 'TDNB'
b <- tendulkar$Runs != "TDNB"
tendulkar <- tendulkar[b,]

# Remove rows with absent
c <- tendulkar$Runs != "absent"
tendulkar <- tendulkar[c,]
dim(tendulkar)
## [1] 329  13
# Remove the "* indicating not out
tendulkar$Runs <- as.numeric(gsub("\\*","",tendulkar$Runs))
dim(tendulkar)
## [1] 329  13
# Select only complete rows - complete.cases()
c <- complete.cases(tendulkar)
#Subset the rows which are complete
tendulkar <- tendulkar[c,]
dim(tendulkar)
## [1] 327  13
# Do some base plotting - Scatter plot
plot(tendulkar$BF,tendulkar$Runs)

#Python 
import pandas as pd
import matplotlib.pyplot as plt
#Read csv
tendulkar= pd.read_csv("tendulkar.csv",na_values=["-"])
print(tendulkar.shape)
# Remove rows with 'DNB'
a=tendulkar.Runs !="DNB"
tendulkar=tendulkar[a]
print(tendulkar.shape)
# Remove rows with 'TDNB'
b=tendulkar.Runs !="TDNB"
tendulkar=tendulkar[b]
print(tendulkar.shape)
# Remove rows with absent
c= tendulkar.Runs != "absent"
tendulkar=tendulkar[c]
print(tendulkar.shape)
# Remove the "* indicating not out
tendulkar.Runs= tendulkar.Runs.str.replace(r"[*]","")
#Select only complete rows - dropna()
tendulkar=tendulkar.dropna()
print(tendulkar.shape)
tendulkar.Runs = tendulkar.Runs.astype(int)
tendulkar.BF = tendulkar.BF.astype(int)
#Scatter plot
plt.scatter(tendulkar.BF,tendulkar.Runs)
## (347, 13)
## (330, 13)
## (329, 13)
## (329, 13)
## (327, 13)

m.Chaining operations on dataframes

To chain a set of operations we need to use an R package like dplyr. Pandas does this The following operations are done on tendulkar data frame by dplyr for R and Pandas for Python below

  1. Group by ground
  2. Compute average runs in each ground
  3. Arrange in descending order
#R
library(dplyr)
tendulkar1 <- tendulkar %>% group_by(Ground) %>% summarise(meanRuns= mean(Runs)) %>%
         arrange(desc(meanRuns))
head(tendulkar1,10)
## # A tibble: 10 × 2
##           Ground  meanRuns
##                 
## 1         Multan 194.00000
## 2          Leeds 193.00000
## 3  Colombo (RPS) 143.00000
## 4        Lucknow 142.00000
## 5          Dhaka 132.75000
## 6     Manchester  93.50000
## 7         Sydney  87.22222
## 8   Bloemfontein  85.00000
## 9     Georgetown  81.00000
## 10 Colombo (SSC)  77.55556
#Python
import pandas as pd
#Read csv
tendulkar= pd.read_csv("tendulkar.csv",na_values=["-"])
print(tendulkar.shape)
# Remove rows with 'DNB'
a=tendulkar.Runs !="DNB"
tendulkar=tendulkar[a]
# Remove rows with 'TDNB'
b=tendulkar.Runs !="TDNB"
tendulkar=tendulkar[b]
# Remove rows with absent
c= tendulkar.Runs != "absent"
tendulkar=tendulkar[c]
# Remove the "* indicating not out
tendulkar.Runs= tendulkar.Runs.str.replace(r"[*]","")

#Select only complete rows - dropna()
tendulkar=tendulkar.dropna()
tendulkar.Runs = tendulkar.Runs.astype(int)
tendulkar.BF = tendulkar.BF.astype(int)
tendulkar1= tendulkar.groupby('Ground').mean()['Runs'].sort_values(ascending=False)
print(tendulkar1.head(10))
## (347, 13)
## Ground
## Multan           194.000000
## Leeds            193.000000
## Colombo (RPS)    143.000000
## Lucknow          142.000000
## Dhaka            132.750000
## Manchester        93.500000
## Sydney            87.222222
## Bloemfontein      85.000000
## Georgetown        81.000000
## Colombo (SSC)     77.555556
## Name: Runs, dtype: float64

9. Functions

product <- function(a,b){
  c<- a*b
  c
}
product(5,7)
## [1] 35
def product(a,b):
  c = a*b
  return c
  
print(product(5,7))
## 35

Conclusion

Personally, I took to R, much like a ‘duck takes to water’. I found the R syntax very simple and mostly intuitive. R packages like dplyr, ggplot2, reshape2, make the language quite irrestible. R is weakly typed and has only numeric and character types as opposed to the full fledged data types in Python.

Python, has too many bells and whistles, which can be a little bewildering to the novice. It is possible that they may be useful as one becomes more experienced with the language. Also I found that installing Python packages sometimes gives errors with Python versions 2.7 or 3.6. This will leave you scrambling to google to find how to fix these problems. These can be quite frustrating. R on the other hand makes installing R packages a breeze.

Anyway, this is my current opinion, and like all opinions, may change in the course of time. Let’s see!

I may write a follow up post with more advanced features of R and Python. So do keep checking! Long live R! Viva la Python!

Note: This post was created using RStudio’s RMarkdown which allows you to embed R and Python code snippets. It works perfectly, except that matplotlib’s pyplot does not display.

Also see
1. My book ‘Deep Learning from first principles:Second Edition’ now on Amazon
2.  Dabbling with Wiener filter using OpenCV
3. My book ‘Practical Machine Learning in R and Python: Third edition’ on Amazon
4. Design Principles of Scalable, Distributed Systems
5. Re-introducing cricketr! : An R package to analyze performances of cricketers
6. Natural language processing: What would Shakespeare say?
7. Brewing a potion with Bluemix, PostgreSQL, Node.js in the cloud
8. Simulating an Edge Shape in Android
To see all posts click Index of posts