Frequency tables in pandas (like plyr in R)

Question

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.

Answer 1

I finally decided to use apply.

I am posting what I came up with hoping that it can be useful for others.

From what I understand from Wes' book "Python for Data analysis"

• apply is more flexible than agg and transform because you can define your own function.
• the only requirement is that the functions returns a pandas object or a scalar value.
• the inner mechanics: the function is called on each piece of the grouped object abd results are glued together using pandas.concat
• One needs to "hard-code" structure you want at the end

Here is what I came up with

def ZahlOccurence_0(x):
      return pd.Series({'All': len(x['StudentID']),
                       'Part': sum(x['Participated'] == 'yes'),
                       'Pass' :  sum(x['Passed'] == 'yes')})

when I run it :

 d1.groupby('ExamenYear').apply(ZahlOccurence_0)

I get the correct results

            All  Part  Pass
ExamenYear                 
2007          3     2     2
2008          4     3     3
2009          3     3     2

This approach would also allow me to combine frequencies with other stats

import numpy as np
d1['testValue'] = np.random.randn(len(d1))

def ZahlOccurence_1(x):
    return pd.Series({'All': len(x['StudentID']),
        'Part': sum(x['Participated'] == 'yes'),
        'Pass' :  sum(x['Passed'] == 'yes'),
        'test' : x['testValue'].mean()})


d1.groupby('ExamenYear').apply(ZahlOccurence_1)


            All  Part  Pass      test
ExamenYear                           
2007          3     2     2  0.358702
2008          4     3     3  1.004504
2009          3     3     2  0.521511

I hope someone else will find this useful

Answer 2

You may use pandas crosstab function, which by default computes a frequency table of two or more variables. For example,

> import pandas as pd
> pd.crosstab(d1['ExamenYear'], d1['Passed'])
Passed      no  yes
ExamenYear         
2007         1    2
2008         1    3
2009         1    2

Use the margins=True option if you also want to see the subtotal of each row and column.

> pd.crosstab(d1['ExamenYear'], d1['Participated'], margins=True)
Participated  no  yes  All
ExamenYear                
2007           1    2    3
2008           1    3    4
2009           0    3    3
All            2    8   10

Answer 3

This:

d1.groupby('ExamenYear').agg({'Participated': len, 
                              'Passed': lambda x: sum(x == 'yes')})

doesn't look way more awkward than the R solution, IMHO.