为什么大 pandas 中的.loc切片包含stop，这与典型的python切片相反? [英] Why is .loc slicing in pandas inclusive of stop, contrary to typical python slicing?

问题描述

我正在切片pandas数据框，并且与numpy和普通python切片相比，我似乎正在使用.loc获得意外切片.请参见下面的示例.

I am slicing a pandas dataframe and I seem to be getting unexpected slices using .loc, at least as compared to numpy and ordinary python slicing. See the example below.

>>> import pandas as pd
>>> a = pd.DataFrame([[0,1,2],[3,4,5],[4,5,6],[9,10,11],[34,2,1]])
>>> a
    0   1   2
0   0   1   2
1   3   4   5
2   4   5   6
3   9  10  11
4  34   2   1
>>> a.loc[1:3, :]
   0   1   2
1  3   4   5
2  4   5   6
3  9  10  11
>>> a.values[1:3, :]
array([[3, 4, 5],
       [4, 5, 6]])

有趣的是，这仅在.loc而不是.iloc上发生.

Interestingly, this only happens with .loc, not .iloc.

>>> a.iloc[1:3, :]
   0  1  2
1  3  4  5
2  4  5  6

因此，.loc似乎包含终止索引，而numpy和.iloc则不包含.

Thus, .loc appears to be inclusive of the terminating index, but numpy and .iloc are not.

通过评论，看来这不是一个错误，我们得到了很好的警告.但是为什么会这样呢?

By the comments, it seems this is not a bug and we are well warned. But why is it the case?

推荐答案

记住.loc是基于主要标签的索引.使用non-RangeIndex时，包含stop端点的决定变得更加明显:

Remember .loc is primarily label based indexing. The decision to include the stop endpoint becomes far more obvious when working with a non-RangeIndex:

df = pd.DataFrame([1,2,3,4], index=list('achz'))
#   0
#a  1
#c  2
#h  3
#z  4

如果我想选择'a'和'h'(包括)之间的所有行，我只会了解'a'和'h'.为了与其他python切片保持一致，您还需要知道'h'之后是哪个索引，在本例中为'z'，但可能是任何索引.

If I want to select all rows between 'a' and 'h' (inclusive) I only know about 'a' and 'h'. In order to be consistent with other python slicing, you'd need to also know what index follows 'h', which in this case is 'z' but could have been anything.

文档的隐藏部分也解释了这种设计选择端点是包容的

There's also a section of the documents hidden away that explains this design choice Endpoints are Inclusive

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