How to merge/combine columns in pandas?

Question

I have a (example-) dataframe with 4 columns:

data = {\'A\': [\'a\', \'b\', \'c\', \'d\', \'e\', \'f\'],
    \'B\': [42, 52, np.nan, np.nan, np.nan, np.nan],         


        

Answer 1

The question as written asks for merge/combine as opposed to sum, so posting this to help folks who find this answer looking for help on coalescing with combine_first, which can be a bit tricky.

df2 = pd.concat([df["A"], 
             df["B"].combine_first(df["C"]).combine_first(df["D"])], 
            axis=1)
df2.rename(columns={"B":"E"}, inplace=True)
   A     E
0  a  42.0
1  b  52.0
2  c  31.0
3  d  2.0 
4  e  62.0
5  f  70.0

What's so tricky about that? in this case there's no problem - but let's say you were pulling the B, C and D values from different dataframes, in which the a,b,c,d,e,f labels were present, but not necessarily in the same order. combine_first() aligns on the index, so you'd need to tack a set_index() on to each of your df references.

df2 = pd.concat([df.set_index("A", drop=False)["A"], 
             df.set_index("A")["B"]\
             .combine_first(df.set_index("A")["C"])\
             .combine_first(df.set_index("A")["D"]).astype(int)], 
            axis=1).reset_index(drop=True)
df2.rename(columns={"B":"E"}, inplace=True)

   A   E
0  a  42
1  b  52
2  c  31
3  d  2 
4  e  62
5  f  70