Resolved: conditional filtering on rows rather than columns

In this post, we will see how to resolve conditional filtering on rows rather than columns

Question:

Given a table

	col_0	col_1	col_2
0	a_00	a_01	a_02
1	a_10	nan	a_12
2	a_20	a_21	a_22

If I am returning all rows such col_1 does not contain nan, then it can be easily done by df[df['col_1'].notnull()], which returns

	col_0	col_1	col_2
0	a_00	a_01	a_02
2	a_20	a_21	a_22

If I would like to return all columns such that its 1-th row does not contain nan, what should I do? The following is the result that I want:

	col_0	col_2
0	a_00	a_02
1	a_10	a_12
2	a_20	a_22

I can transpose dataframe, remove rows on transposed dataframe, and transpose back, but it would become inefficient if dataframe is huge. I also tried

df.loc[df.loc[0].notnull()]

but the code gives me an error. Any ideas?

Best Answer:

Boolean indexing with loc along columns axis

df.loc[:, df.iloc[1].notna()]

Result

  col_0 col_2
0  a_00  a_02
1  a_10  a_12
2  a_20  a_22

If you have better answer, please add a comment about this, thank you!

Source: Stackoverflow.com

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