Counting specific values in rows in CSV - python-2.7

Question

I have a csv file:
A D1 B D2 C D3 E
1 action 0.5 action 0.35 null a
2 0 0.75 0 0.45 action b
3 action 1 action 0.85 action c
I want to count the number of 'action' keyword in each row and make a new column giving the output. So the output file would be something like this.
A D1 B D2 C D3 E TotalAction
1 action 0.5 action 0.35 null a 2
2 0 0.75 0 0.45 action b 1
3 action 1 action 0.85 action c 3
What is the best way to go forward using Pandas? thanks

Answer 1

You could use apply across the rows with str.contains for that keyword:
In [21]: df.apply(lambda x: x.str.contains('action').sum(), axis=1)
Out[21]:
0 2
1 1
2 3
dtype: int64
df['TotalAction'] = df.apply(lambda x: x.str.contains('action').sum(), axis=1)
In [23]: df
Out[23]:
A D1 B D2 C D3 E TotalAction
0 1 action 0.50 action 0.35 null a 2
1 2 0 0.75 0 0.45 action b 1
2 3 action 1.00 action 0.85 action c 3
EDIT
Although you could do it easier and faster with isin and then sum across the rows:
In [45]: df.isin(['action']).sum(axis=1)
Out[45]:
0 2
1 1
2 3
dtype: int64
Note: You need to wrap your string keyword into list.

Answer 2

you can use select_dtypes (for selecting only string columns) in conjunction with .sum(axis=1):
In [95]: df['TotalAction'] = (df.select_dtypes(include=[object]) == 'action').sum(axis=1)
In [96]: df
Out[96]:
A D1 B D2 C D3 E TotalAction
0 1 action 0.50 action 0.35 null a 2
1 2 0 0.75 0 0.45 action b 1
2 3 action 1.00 action 0.85 action c 3
Timing against 30K rows DF:
In [3]: df = pd.concat([df] * 10**4, ignore_index=True)
In [6]: df.shape
Out[6]: (30000, 7)
In [4]: %timeit df.apply(lambda x: x.str.contains('action').sum(), axis=1)
1 loop, best of 3: 7.89 s per loop
In [5]: %timeit (df.select_dtypes(include=[object]) == 'action').sum(axis=1)
100 loops, best of 3: 7.08 ms per loop
In [7]: %timeit df.isin(['action']).sum(axis=1)
10 loops, best of 3: 22.8 ms per loop
Conclusion: apply(...) is 1114 times slower compared to select_dtypes() method
Explanation:
In [92]: df.select_dtypes(include=[object])
Out[92]:
D1 D2 D3 E
0 action action null a
1 0 0 action b
2 action action action c
In [93]: df.select_dtypes(include=[object]) == 'action'
Out[93]:
D1 D2 D3 E
0 True True False False
1 False False True False
2 True True True False
In [94]: (df.select_dtypes(include=[object]) == 'action').sum(axis=1)
Out[94]:
0 2
1 1
2 3
dtype: int64