I'm working with two tables with the same columns. I need to merge them but replacing null values (identified ad 'N/A').
The first table is my reference. So in cases to different values (and is not 'N/A'), the result of the first table must prevail.
The expected result is one row with 'DistanciaPercorrida' equal to '3km' and 'TempoCorrida' equal to '4min 38s'.
SOLVE:
I used df.replace('?', np.NaN)
to transform 'N/A' to NaN value and after df1.set_index('Data').combine_first(df2.set_index('Data')).reset_index()
CodePudding user response:
Use combine_first
to fill NaN
values from your first dataframe with the values of the second:
>>> df1.set_index('Data').combine_first(df2.set_index('Data')).reset_index()
Data DistanciaPercorrida TempoCorrida
0 15 de ago 3km 4min 38s