Pandas - cumsum() on groupby() over multiple axis

Question

I'm new to Pandas. I have a data set that is horse racing results. Example here:

RaceID  RaceDate    RaceMeet   Position Horse       Jockey          Trainer        RaceLength race win  HorseWinPercentage 
446252  01/01/2008  Southwell (AW)  1   clear reef  tom mclaughlin  jane chapple-hyam   3101    1   1   0
447019  14/01/2008  Southwell (AW)  5   clear reef  tom mclaughlin  jane chapple-hyam   2654    1   0   100
449057  21/02/2008  Southwell (AW)  2   clear reef  tom mclaughlin  jane chapple-hyam   3101    1   0   50
463805  26/08/2008  Chelmsford (AW) 6   clear reef  tom mclaughlin  jane chapple-hyam   3080    1   0   33.33333333
469220  27/11/2008  Chelmsford (AW) 3   clear reef  tom mclaughlin  jane chapple-hyam   3080    1   0   25
470195  11/12/2008  Chelmsford (AW) 5   clear reef  tom mclaughlin  jane chapple-hyam   3080    1   0   20
471052  26/12/2008  Wolhampton (AW) 1   clear reef  andrea atzeni   jane chapple-hyam   2690    1   1   16.66666667
471769  07/01/2009  Wolhampton (AW) 6   clear reef  ian mongan      jane chapple-hyam   2690    1   0   28.57142857
472137  13/01/2009  Chelmsford (AW) 2   clear reef  jamie spencer   jane chapple-hyam   3080    1   0   25
472213  20/01/2009  Southwell (AW)  5   clear reef  jamie spencer   jane chapple-hyam   2654    1   0   22.22222222
476595  25/03/2009  Kempton (AW)    4   clear reef  pat cosgrave    jane chapple-hyam   2639    1   0   20
477674  08/04/2009  Kempton (AW)    5   clear reef  pat cosgrave    jane chapple-hyam   2639    1   0   18.18181818
479098  21/04/2009  Kempton (AW)    3   clear reef  andrea atzeni   jane chapple-hyam   2639    1   0   16.66666667
492913  14/11/2009  Wolhampton (AW) 1   clear reef  andrea atzeni   jane chapple-hyam   3639    1   1   15.38461538
493720  25/11/2009  Kempton (AW)    3   clear reef  andrea atzeni   jane chapple-hyam   3518    1   0   21.42857143
495863  29/12/2009  Southwell (AW)  1   clear reef  shane kelly     jane chapple-hyam   3101    1   1   20

I want to be able to groupby() multiple axis to count up wins and create combination win percentages or results at specific track and lengths.

When I just need to groupby a single axis – it works great:

df['horse_win_count'] = df.groupby(['Horse'])['win'].cumsum()
df['horse_race_count'] = df.groupby(['Horse'])['race'].cumsum()
df['HorseWinPercentage2'] = df['horse_win_count'] / df['horse_race_count'] * 100
df['HorseWinPercentage'] = df.groupby('Horse')['HorseWinPercentage2'].shift(+1)

However when I need to groupby more than one axis I get some really weird results.

For example I was to create a win percentage for when a specific Jockey rides a specific Trainers' horse – groupby(['Jockey','Trainer']). Then I need to know the percentage as it changes for each individual row (race).

df['jt_win_count'] = df.groupby(['Jockey','Trainer'])['win'].cumsum()
df['jt_race_count'] = df.groupby(['Jockey','Trainer'])['race'].cumsum()
df['JTWinPercentage2'] = df['jt_win_count'] / df['jt_race_count'] * 100
df['JTWinPercentage'] = df.groupby(['Jockey','Trainer'])['JTWinPercentage2'].shift(+1)
df['JTWinPercentage'].fillna(0, inplace=True)

Or I want to count up the number of times a horse has won over that course and that distance. So I need to groupby(['Horse', 'RaceMeet','RaceLength']):

df['CD'] = df.groupby([‘RaceMeet’,’RaceLength’,’Horse’])[‘win’].cumsum()
df['CD'] = df.groupby(["RaceMeet","RaceLength","Horse"]).shift(+1)

I get results in the 10s of 1000s.

How can I groupby several axis, make a computation and shift the results back by one entry while grouped by several entries?

And even better can you explain why my code above doesn't work? Like I say I'm new to Pandas and keen to learn.

Cheers.

Answer 1

Question was already asked: Pandas DataFrame Groupby two columns and get counts and here python pandas groupby() result

I do not really know what your goal is though. I guess you should first add another column with the new parameter you want to group by. for example: df['jockeyTrainer']=df.loc['Jockey']+df.loc['Trainer'] Then you can use this to groupby. Or you follow the information in the links.