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# Frequency tables in pandas (like plyr in R)

My problem is how to calculate frequencies on multiple variables in pandas .
I have from this dataframe :

``d1 = pd.DataFrame( {'StudentID': ["x1", "x10", "x2","x3", "x4", "x5", "x6",   "x7",     "x8", "x9"],                        'StudentGender' : ['F', 'M', 'F', 'M', 'F', 'M', 'F', 'M', 'M', 'M'],                  'ExamenYear': ['2007','2007','2007','2008','2008','2008','2008','2009','2009','2009'],                  'Exam': ['algebra', 'stats', 'bio', 'algebra', 'algebra', 'stats', 'stats', 'algebra', 'bio', 'bio'],                  'Participated': ['no','yes','yes','yes','no','yes','yes','yes','yes','yes'],                   'Passed': ['no','yes','yes','yes','no','yes','yes','yes','no','yes']},                   columns = ['StudentID', 'StudentGender', 'ExamenYear', 'Exam', 'Participated', 'Passed']) ``

To the following result

``             Participated  OfWhichpassed  ExamenYear                              2007                   3              2 2008                   4              3 2009                   3              2 ``

(1) One possibility I tried is to compute two dataframe and bind them

``t1 = d1.pivot_table(values="StudentID", rows=['ExamenYear'], cols = ['Participated'], aggfunc = len) t2 = d1.pivot_table(values="StudentID", rows=['ExamenYear'], cols = ['Passed'], aggfunc = len) tx = pd.concat([t1, t2] , axis = 1) Res1 = tx['yes'] ``

(2) The second possibility is to use an aggregation function .

``import collections dg = d1.groupby('ExamenYear') Res2 = dg.agg({'Participated': len,'Passed': lambda x : collections.Counter(x == 'yes')[True]})  Res2.columns = ['Participated', 'OfWhichpassed'] ``

Both ways are awckward to say the least.
How is this done properly in pandas ?

P.S: I also tried value_counts instead of collections.Counter but could not get it to work

For reference: Few months ago, I asked similar question for R here and plyr could help

—- UPDATE ——

user DSM is right. there was a mistake in the desired table result.

(1) The code for option one is

`` t1 = d1.pivot_table(values="StudentID", rows=['ExamenYear'], aggfunc = len)  t2 = d1.pivot_table(values="StudentID", rows=['ExamenYear'], cols = ['Participated'], aggfunc = len)  t3 = d1.pivot_table(values="StudentID", rows=['ExamenYear'], cols = ['Passed'], aggfunc = len)  Res1 = pd.DataFrame( {'All': t1,                        'OfWhichParticipated': t2['yes'],                      'OfWhichPassed': t3['yes']}) ``

It will produce the result

``             All  OfWhichParticipated  OfWhichPassed ExamenYear                                          2007          3                    2              2 2008          4                    3              3 2009          3                    3              2 ``

(2) For Option 2, thanks to user herrfz, I figured out how to use value_count and the code will be

``Res2 = d1.groupby('ExamenYear').agg({'StudentID': len,                                  'Participated': lambda x: x.value_counts()['yes'],                                  'Passed': lambda x: x.value_counts()['yes']}) Res2.columns = ['All', 'OfWgichParticipated', 'OfWhichPassed'] ``

which will produce the same result as Res1

My question remains though:

Using Option 2, will it be possible to use the same Variable twice (for another operation ?) can one pass a custom name for the resulting variable ?

—- A NEW UPDATE —-

I have finally decided to use apply which I understand is more flexible.