pandas 替换特定列上的值

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问题描述

我知道这两个类似的问题:

I am aware of these two similar questions:

我使用了另一种替代值的方法，我认为这应该是最干净的方法.但这是行不通的.我知道如何解决它，但我想了解为什么它不起作用:

I used a different approach for substituting values from which I think it should be the cleanest one. But it does not work. I know how to work around it, but I would like to understand why it does not work:

In [108]: df=pd.DataFrame([[1, 2, 8],[3, 4, 8], [5, 1, 8]], columns=['A', 'B', 'C'])

In [109]: df
Out[109]:
   A  B  C
0  1  2  8
1  3  4  8
2  5  1  8

In [110]: df.loc[:, ['A', 'B']].replace([1, 3, 2], [3, 6, 7], inplace=True)

In [111]: df
Out[111]:
   A  B  C
0  1  2  8
1  3  4  8
2  5  1  8

In [112]: df.loc[:, 'A'].replace([1, 3, 2], [3, 6, 7], inplace=True)

In [113]: df
Out[113]:
   A  B  C
0  3  2  8
1  6  4  8
2  5  1  8

如果仅切一列In [112]，则其工作方式与切几列In [110]的工作原理不同.据我了解.loc方法，它返回一个视图，而不是副本.在我的逻辑中，这意味着在切片上进行就地更改应该更改整个DataFrame.这就是在行In [110]发生的情况.

If I slice only one column In [112] it works different to slicing several columns In [110]. As I understand the .loc method it returns a view and not a copy. In my logic this means that making an inplace change on the slice should change the whole DataFrame. This is what happens at line In [110].

推荐答案

以下是开发人员之一的回答: https://github.com/pydata/pandas/issues/11984

Here is the answer by one of the developers: https://github.com/pydata/pandas/issues/11984

理想情况下，这应该显示SettingWithCopyWarning，但是我认为这很难检测到.

This should ideally show a SettingWithCopyWarning, but I think this is quite difficult to detect.

绝对不要执行这种类型的链式就地设置.这简直是不好的做法.

You should NEVER do this type of chained inplace setting. It is simply bad practice.

惯用语是:

In [7]: df[['A','B']] = df[['A','B']].replace([1, 3, 2], [3, 6, 7])

In [8]: df
Out[8]:
   A  B  C
0  3  7  8
1  6  4  8
2  5  3  8

(您也可以使用df.loc[:,['A','B']]，但如上所述更加清晰.

(you can do with df.loc[:,['A','B']] as well, but more clear as above.

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