How to count all positive and negative values in a pandas groupby?

Question

Let\'s assume we have a table:

df = pd.DataFrame({\'A\' : [\'foo\', \'bar\', \'foo\', \'bar\', \'foo\', \'bar\', \'foo\', \'foo\'],
                   \'B\' : [\         


        

Answer 1

You could do this as a one line apply (the first column being negative, the second positive):

In [11]: df.groupby('A').C.apply(lambda x: pd.Series([(x < 0).sum(), (x >= 0).sum()])).unstack()
Out[111]: 
     0  1
A        
bar  2  1
foo  2  3

[2 rows x 2 columns]

However, I think a neater way is to use a dummy column and use value_counts:

In [21]: df['C_sign'] = np.sign(df.C)

In [22]: df.groupby('A').C_sign.value_counts()
Out[22]: 
A      
bar  -1    2
      1    1
foo   1    3
     -1    2
dtype: int64

In [23]: df.groupby('A').C_sign.value_counts().unstack()
Out[23]: 
     -1   1
A          
bar   2   1
foo   2   3

[2 rows x 2 columns]