Pandas merge columns, but not the 'key' column

Question

This may seem like a stupid question, but this has been bugging me for some time.

df1:

imp_type    value
1           abc
2           def
3           ghi
         


        

Answer 1

I agree it would be nice if one of the columns were dropped. Of course, then there is the question of what to name the remaining column.

Anyway, here is a workaround. Simply rename one of the columns so that the joined column(s) have the same name:

In [23]: df1 = pd.DataFrame({'imp_type':[1,2,3], 'value':['abc','def','ghi']})

In [27]: df2 = pd.DataFrame({'id':[1,2,3], 'value2':[123,345,567]})

In [28]: df2.columns = ['imp_type','value2']

In [29]: df1.merge(df2, on='imp_type')
Out[29]: 
   imp_type value  value2
0         1   abc     123
1         2   def     345
2         3   ghi     567

Renaming the columns is a bit of a pain, especially (as DSM points out) compared to .drop('id', 1). However, if you can arrange for the joined columns to have the same name from the very beginning, then df1.merge(df2, on='imp_type') would be easiest.