Count occurrences of items in Series in each row of a DataFrame
Andy's answer is spot on.
I'm adding this answer, if C1,C2...Cn list is huge and we want to view only subset of them.
dff = df.copy()
dff['C1']=(df == 'C1').T.sum()
dff['C2']=(df == 'C2').T.sum()
dff['C3']=(df == 'C3').T.sum()
dff
COL1 COL2 COL3 C1 C2 C3
0 C1 None None 1 0 0
1 C1 C2 None 1 1 0
2 C1 C1 None 2 0 0
3 C1 C2 C3 1 1 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).