如何根据条件用列名替换pandas数据框中的值?

问题描述：

我有一个看起来像这样的数据框:

I have a dataframe that looks something like this:

我想用列名替换A:D范围内的所有1，以便最终结果应类似于:

I want to replace all 1's in the range A:D with the name of the column, so that the final result should resemble:

我该怎么做?

您可以使用以下方法重新创建我的数据框:

You can recreate my dataframe with this:

dfz = pd.DataFrame({'A' : [1,0,0,1,0,0],
                    'B' : [1,0,0,1,0,1],
                    'C' : [1,0,0,1,3,1],
                    'D' : [1,0,0,1,0,0],
                    'E' : [22.0,15.0,None,10.,None,557.0]})

答

一种方法是使用

One way could be to use replace and pass in a Series mapping column labels to values (those same labels in this case):

>>> dfz.loc[:, 'A':'D'].replace(1, pd.Series(dfz.columns, dfz.columns))
   A  B  C  D
0  A  B  C  D
1  0  0  0  0
2  0  0  0  0
3  A  B  C  D
4  0  0  3  0
5  0  B  C  0

要使更改永久生效，请将返回的DataFrame分配回dfz.loc[:, 'A':'D'].

To make the change permanent, you'd assign the returned DataFrame back to dfz.loc[:, 'A':'D'].

除了解决方案外，请记住，将数字和字符串类型混合在列中可能会失去很多性能优势，这是有用的，因为熊猫*使用通用的对象" dtype来保存值.

Solutions aside, it's useful to keep in mind that you may lose a lot of performance benefits when you mix numeric and string types in columns, as pandas is forced to use the generic 'object' dtype to hold the values.

如何根据条件用列名替换pandas数据框中的值?

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