使用多个isin子句的 pandas 索引 [英] pandas indexing using multiple isin clauses

问题描述

如果我想一次对多个列进行即时测试，则可以执行以下操作:

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], 'B': [1, 4, 7], 'C' : [10, 12, 18]})   
>>> mask = df[['A','B']].isin({'A': [1, 3], 'B': [4, 7, 12]}).all(axis=1)
>>> df = df[mask]

可行-是否有更简洁的解决方案?

That works--is there a more succinct solution?

推荐答案

TBH，您当前的方法对我来说还不错；我看不到使用isin或filter进行改进的方法，因为我看不到如何让isin仅使用字典中的列或filter来充当all

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.

但是，我不喜欢对列名进行硬编码，所以我可能将其写为

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

，或者如果需要需要使用.loc.

or with .loc if I needed a handle.

这篇关于使用多个isin子句的 pandas 索引的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！