python - Pandas: Aggregate differently based on group -

so let's have data follow:

patient_id  lab_type  value 1           food       10 1           food       8 2           food       3 2           food       5 1           shot       4 1           shot       10 2           shot       2 2           shot       4 

then group things such groupby(['patient_id', 'lab_type'])

after that, i'd aggregate on value different each lab_type. on food i'd aggregate using mean , on shot i'd aggregate using sum.

the final data should this:

  patient_id  lab_type  value   1           food       9 (10 + 8 / 2)   2           food       4 (3 + 5 / 2)   1           shot       14 (10 + 4)   2           shot       6 (2 + 4) 

on food i'd aggregate using mean , on shot i'd aggregate using sum.

just use .apply , pass custom function:

def calc(g):     if g.iloc[0].lab_type == 'shot':         return sum(g.value)     else:         return np.mean(g.value) result = df.groupby(['patient_id', 'lab_type']).apply(calc) 

here calc receives per-group dataframe shown in panda's split-apply-combine. result want:

patient_id  lab_type 1           food         9             shot        14 2           food         4             shot         6 dtype: float64