Python Keep other columns when using sum() with groupby

Question

I have a pandas dataframe below:

    df      name    value1    value2  otherstuff1 otherstuff2  0   Jack       1         1       1.19        2.39      1   Jack       1         2       1.19        2.39 2   Luke       0         1       1.08        1.08   3   Mark       0         1       3.45        3.45 4   Luke       1         0       1.08        1.08 

Same "name" will have the same value for otherstuff1 and otherstuff2.

I'm trying to groupby by column 'name' and sum column 'value1' and sum column 'value2' (Not sum value1 with value2!!! But sum them individually in each column)

Expecting to get result below:

    newdf      name    value1    value2  otherstuff1 otherstuff2  0   Jack       2         3       1.19        2.39      1   Luke       1         1       1.08        1.08   2   Mark       0         1       3.45        3.45 

I've tried

newdf = df.groupby(['name'], as_index = False).sum() 

which groupsby name and sums up both value1 and value2 columns correctly but end up dropping column otherstuff1 and otherstuff2.

Please help. Thank you guys so much!

Answer 1

You should specify what pandas must do with the other columns. In your case, I think you want to keep one row, regardless of its position within the group.

This could be done with agg on a group. agg accepts a parameter that specifies what operation should be performed for each column.

df.groupby(['name'], as_index=False).agg({'value1': 'sum', 'value2': 'sum', 'otherstuff1': 'first', 'otherstuff2': 'first'}) 

Answer 2

Something like ?(Assuming you have same otherstuff1 and otherstuff2 under the same name )

df.groupby(['name','otherstuff1','otherstuff2'],as_index=False).sum() Out[121]:     name  otherstuff1  otherstuff2  value1  value2 0  Jack         1.19         2.39       2       3 1  Luke         1.08         1.08       1       1 2  Mark         3.45         3.45       0       1