["python","pandas","dataframe","group-by"]
Groupby and sum only one column

So I have a dataframe, df1, that looks like the following: A B C 1 foo 12 California 2 foo 22 California 3 bar 8 Rhode Island 4 bar 32 Rhode Island 5 baz 15 Ohio 6 baz 26 Ohio I want to group by column A and then sum column B while keeping the value in column C. Something like this: A B C 1 foo 34 California 2 bar 40 Rhode Island 3 baz 41 Ohio The issue is, when I say df.groupby('A').sum() column C gets removed, returning B A bar 40 baz 41 foo 34 How can I get around this and keep column C when I group and sum?
The only way to do this would be to include C in your groupby (the groupby function can accept a list). Give this a try: df.groupby(['A','C'])['B'].sum() One other thing to note, if you need to work with df after the aggregation you can also use the as_index=False option to return a dataframe object. This one gave me problems when I was first working with Pandas. Example: df.groupby(['A','C'], as_index=False)['B'].sum()