Sum Grouped Pandas Dataframe By Single Column

I have a Pandas dataframe: test=pd.DataFrame(columns=['GroupID','Sample','SampleMeta','Value']) test.loc[0,:]='1','S1','S1_meta',1 test.loc[1,:]='1','S1','S1_meta',1 test.loc[2,:]=

Solution 1:

Well, you can include SampleMeta as part of the groupby:

print test.groupby(['GroupID','Sample','SampleMeta']).sum()

                           Value
GroupID Sample SampleMeta       
1       S1     S1_meta         2
2       S2     S2_meta         1

If you don't want SampleMeta as part of the index when done you could modify it as follows:

print test.groupby(['GroupID','Sample','SampleMeta']).sum().reset_index(level=2)

               SampleMeta  Value
GroupID Sample                  
1       S1        S1_meta      2
2       S2        S2_meta      1

This will only work right if there is no variation within SampleMeta for ['GroupID','Sample']. Of course, If there was variation within ['GroupID','Sample'] then you probably to exclude SampleMeta from the groupby/sum entirely:

print test.groupby(['GroupID','Sample'])['Value'].sum()

GroupID  Sample
1        S1        2
2        S2        1