How to replace a value in a pandas dataframe with column name based on a condition?
samthebrand
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]})
Alex Riley
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.
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