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

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).

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

Usually apply + serise function to whole dataframe will slowing down the whole process , Additional Reading : Link

df.mask(df.eq('None')).stack().str.get_dummies().sum(level=0)
Out[165]: 
   C1  C2  C3
0   1   0   0
1   1   1   0
2   2   0   0
3   1   1   1

Or you can do with Counter

from  collections import Counter

pd.DataFrame([ Counter(x) for x in df.values]).drop('None',1)
Out[170]: 
   C1   C2   C3
0   1  NaN  NaN
1   1  1.0  NaN
2   2  NaN  NaN
3   1  1.0  1.0