pandas 使用多个 isin 子句的熊猫索引
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pandas indexing using multiple isin clauses
提问by Michael K
If I want to do is-in testing on multiple columns at once, I can do:
如果我想一次对多个列进行 is-in 测试，我可以这样做：
>>> 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]
That works--is there a more succinct solution?
那行得通——有没有更简洁的解决方案？
回答by Zero
You could put both the isinconditions in &
你可以把这两个isin条件都放在&
df[df['A'].isin([1, 3]) & df['B'].isin([4, 7, 12])]
A B C
2 3 7 18
You could also use queryfunction like
您还可以使用query类似的功能
c_a = [1, 3]
c_b = [4, 7, 12]
df.query('(B in @c_b) & (A in @c_a)')
A B C
2 3 7 18
回答by DSM
TBH, your current approach looks fine to me; I can't see a way with isinor filterto improve it, because I can't see how to get isinto use only the columns in the dictionary or filterto behave as an all.
TBH，你目前的方法对我来说很好；我看不到使用isin或filter改进它的方法，因为我看不到如何isin只使用字典中的列或filter作为all.
I don't like hardcoding column names, though, so I'd probably write this as
不过，我不喜欢对列名进行硬编码，所以我可能会将其写为
>>> 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 .locif I needed a handle.
或者.loc如果我需要一个把手。
回答by EdChum
You could put both conditions in as a mask and use &:
您可以将这两个条件作为掩码并使用&：
In [12]:
df[(df['A'].isin([1,3])) & (df['B'].isin([4,7,12]))]
Out[12]:
A B C
2 3 7 18
Here the conditions require parentheses ()around them due to operator precedence
()由于运算符优先级，这里的条件需要括号
Slightly more readable is to use query:
稍微更具可读性的是使用query：
In [15]:
df.query('A in [1,3] and B in [4,7,12]')
Out[15]:
A B C
2 3 7 18
