Inplace transformation pandas with groupby
Would it be possible to mutate DataFrame inplace with groupby statement? import pandas as pd dt = pd.DataFrame({ "LETTER": ["a", "b", "c", "a", "b"], "VALUE" : [10 , 12 , 13, 0, 15] }) def __add_new_col(dt_): dt_['NEW_COL'] = dt_['VALUE'] - dt_['VALUE'].mean() return dt_ pass dt.groupby("LETTER").apply(__add_new_col) LETTER VALUE NEW_COL 0 a 10 5.0 1 b 12 -1.5 2 c 13 0.0 3 a 0 -5.0 4 b 15 1.5 dt LETTER VALUE 0 a 10 1 b 12 2 c 13 3 a 0 4 b 15 In R data.table it is possible by using := operator e.g. dt[, col := ... , by ='LETTER'] I think you can use transform which return Series same length and