Count occurrences of items in Series in each row of a DataFrame

Question

I have a pandas.DataFrame that looks like this.

COL1    COL2    COL3
C1      None    None
C1      C2      None
C1      C1      None
C1      C2           


        

Answer 1

You could apply value_counts:

In [11]: df.apply(pd.Series.value_counts, axis=1)
Out[11]: 
   C1  C2  C3  None
0   1 NaN NaN     2
1   1   1 NaN     1
2   2 NaN NaN     1
3   1   1   1   NaN

So you can fill the NaN and applend just the base values you want:

In [12]: df.apply(pd.Series.value_counts, axis=1)[['C1', 'C2', 'C3']].fillna(0)
Out[12]: 
   C1  C2  C3
0   1   0   0
1   1   1   0
2   2   0   0
3   1   1   1

Note: there's an open issue to have a value_counts method directly for a DataFrame (which I think should be introduced by pandas 0.15).