Sum all columns with a wildcard name search using Python Pandas

Question

I have a dataframe in python pandas with several columns taken from a CSV file.

For instance, data =:

Day P1S1 P1S2 P1S3 P2S1 P2S2 P2S3
1   1    2    2    3    1    2
2   2    2    3    5    4    2

And what I need is to get the sum of all columns which name starts with P1... something like P1* with a wildcard.

Something like the following which gives an error:

P1Sum = data["P1*"]

Is there any why to do this with pandas?

Solution 1

I found the answer.

Using the data, dataframe from the question:

from pandas import *

P1Channels = data.filter(regex="P1")
P1Sum = P1Channels.sum(axis=1)

Solution 2

List comprehensions on columns allow more filters in the if condition:

In [1]: df = pd.DataFrame(np.arange(15).reshape(5, 3), columns=['P1S1', 'P1S2', 'P2S1'])

In [2]: df
Out[2]: 
   P1S1  P1S2  P2S1
0     0     1     2
1     3     4     5
2     6     7     8
3     9    10    11
4    12    13    14

In [3]: df.loc[:, [x for x in df.columns if x.startswith('P1')]].sum(axis=1)
Out[3]: 
0     1
1     7
2    13
3    19
4    25
dtype: int64

Solution 3

Thanks for the tip jbssm, for anyone else looking for a sum total, I ended up adding .sum() at the end, so:

P1Sum= P1Channels.sum(axis=1).sum()

Comments

[Brian Larsen]

I find using fnmatch in the standard lib gives a lot of power in these "if" list comprehensions.