CODEWITHSUNDEEP
pythonpandas
MorganM
MorganM

Reputation: 311

dataframe boolean selection along columns instead of row

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?

Upvotes: 2

Views: 1635

Answers (3)

chrisb
chrisb

Reputation: 52276

How about this?

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

Upvotes: 7

EdChum
EdChum

Reputation: 394159

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

Upvotes: 1

ericmjl
ericmjl

Reputation: 14704

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

Upvotes: 0

Related Questions