Python 使用 or 语句在多个条件下对 Pandas 进行切片/选择
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/42082385/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Pandas slicing/selecting with multiple conditions with or statement
提问by jtorca
When I select by chaining different conditions with "AND" the selection works fine. When I select by chaining conditions with "OR" the selection throws an error.
当我通过用“AND”链接不同的条件进行选择时，选择工作正常。当我通过使用“OR”链接条件进行选择时，选择会引发错误。
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame([[1,4,3],[2,3,5],[4,5,6],[3,2,5]],
... columns=['a', 'b', 'c'])
>>> df
a b c
0 1 4 3
1 2 3 5
2 4 5 6
3 3 2 5
>>> df.loc[(df.a != 1) & (df.b < 5)]
a b c
1 2 3 5
3 3 2 5
>>> df.loc[(df.a != 1) or (df.b < 5)]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3/dist-packages/pandas/core/generic.py", line 731, in __nonzero__
.format(self.__class__.__name__))
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
I would expect it to return the whole dataframe as all rows meet this condition.
我希望它返回整个数据帧，因为所有行都满足此条件。
回答by Steve Barnes
The important thing to note is that &is not identical to andthey are different things so the "or" equivalent to to &is |
需要注意的重要一点是，&不等于and他们是不同的东西，因此“或”等同于对&IS|
Normally both &and |are bitwiselogical operators rather than the python "logical" operators.
通常，&和|都是按位逻辑运算符，而不是python“逻辑”运算符。
In pandas these operators are overloaded for Seriesoperation.
在熊猫中，这些运算符被重载以进行Series操作。
In [1]: import pandas as pd
In [2]: import numpy as np
In [3]: df = pd.DataFrame([[1,4,3],[2,3,5],[4,5,6],[3,2,5]], columns=['a', 'b',
...: 'c'])
In [4]: df
Out[4]:
a b c
0 1 4 3
1 2 3 5
2 4 5 6
3 3 2 5
In [5]: df.loc[(df.a != 1) & (df.b < 5)]
Out[5]:
a b c
1 2 3 5
3 3 2 5
In [6]: df.loc[(df.a != 1) | (df.b < 5)]
Out[6]:
a b c
0 1 4 3
1 2 3 5
2 4 5 6
3 3 2 5
