http://stackoverflow.com/questions/23709811/best-way-to-sum-group-value-counts-in-pandas/23712433?noredirect=1#comment36441762_23712433
any of these look palatable?
df.groupby('c',as_index='exclude').apply(....)df.groupby('c')['~c'].apply(...)df.groupby('c',filter='c')df.groupby('c',filter=True)
rather than doing a negated selection (df.columns-['c'])
df = pd.DataFrame.from_dict({'b1':['aa','ac','ac','ad'],\
'b2':['bb','bc','ad','cd'],\
'b3':['cc','cd','cc','ae'],\
'c' :['a','b','a','b']})
df.groupby('c')[df.columns - ['c']].apply(lambda x: x.unstack().value_counts())
Out[92]:
c
a cc 2
bb 1
ad 1
ac 1
aa 1
b cd 2
ad 1
ae 1
ac 1
bc 1
dtype: int64
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