pandas indexing using multiple isin clauses

Question

If I want to do is-in testing on multiple columns at once, I can do:

>>> from pandas import DataFrame
>>> df = DataFrame({\'A\': [1, 2, 3]         


        

Answer 1

TBH, your current approach looks fine to me; I can't see a way with isin or filter to improve it, because I can't see how to get isin to use only the columns in the dictionary or filter to behave as an all.

I don't like hardcoding column names, though, so I'd probably write this as

>>> keep = {'A': [1, 3], 'B': [4, 7, 12]}
>>> df[df[list(keep)].isin(keep).all(axis=1)]
   A  B   C
2  3  7  18

or with .loc if I needed a handle.