2

Suppose I have the following dataframe:

          a         b         c         d 
0  0.049531  0.408824  0.975756  0.658347
1  0.981644  0.520834  0.258911  0.639664
2  0.641042  0.534873  0.806442  0.066625
3  0.764057  0.063252  0.256748  0.045850

and I want only the subset of columns whose value in row 0 is creater than 0.5. I can do this:

df2 = df.T
myResult = df2[df2.iloc[:, 0] > 0.5].T

But this feels like a horrible hack. Is there a nicer way to do boolean indexing along columns? Somewhere I can specify an axis argument?

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  • I believe you've got the most elegant way out there. Commented Aug 12, 2014 at 20:11

3 Answers 3

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7

How about this?

df.loc[:, df.iloc[0, :] > 0.5]
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1 Comment

Yes, that is precisely what I was looking for.
1

Another method without using transpose is to create a boolean mask on whether the first row has values larger than 0.5 and then drop the NaN's with a threshold and then finally make a list of the df columns to filter the original df. This is pretty obfuscated though ;)

In [76]:

df[list(df[df.head(1)> 0.5].dropna(thresh=1, axis=1))]
Out[76]:
              c         d
index                    
0      0.975756  0.658347
1      0.258911  0.639664
2      0.806442  0.066625
3      0.256748  0.045850
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0

Another way of looking at your answer:

In [14]: df.T[df.T[0] > 0.5].T
Out[14]: 
          c        d 
0  0.975756  0.658347
1  0.258911  0.639664
2  0.806442  0.066625
3  0.256748  0.045850
Improve this answer

1 Comment

Triple transpose might not be as elegant as your answer.

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