How to replace a value in a pandas dataframe with column name based on a condition?

Question

I have a dataframe that looks something like this:

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:

How can I do that?

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]})

Answer 1

A solution using where:

>>> dfz.where(dfz != 1, dfz.columns.to_series(), axis=1)
   A  B  C  D      E
0  A  B  C  D   22.0
1  0  0  0  0   15.0
2  0  0  0  0    NaN
3  A  B  C  D   10.0
4  0  0  3  0    NaN
5  0  B  C  0  557.0

Answer 2

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

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

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.