Applying a custom groupby aggregate function to find average of Numpy Array

Question

I am having a pandas DataFrame where B contains NumPy list of fixed size.

|------|---------------|-------|
|  A   |       B       |   C   |
|------|--------         


        

Answer 1

Dummy data

size,list_size = 10,5
data = [{'C':random.randint(95,100), 
         'B':[random.randint(0,10) for i in range(list_size)]} for j in range(size)]
df = pd.DataFrame(data)

Custom Aggregation Using numpy

unique_C = df.C.unique()
data_calculated  = []
axis = 0

for c in unique_C:
    arr = np.reshape(np.hstack(df[df.C==c]['B']),(-1,list_size))
    mean, std = arr.mean(axis=axis), arr.std(axis=axis)  # other aggergation can also be added
    data_calculated.append(dict(C=t,B_mean=mean, B_std=std))
new_df = pd.DataFrame(data_calculated)