Adding a list to two columns of a dataframe in Pandas?

Replace:

    df['C'], df['D'] = df.apply(lambda row: generate_list(row), axis=1) 

With:

    df[['C','D']] = pd.DataFrame(df.apply(lambda row: generate_list(row), axis=1).tolist(), index= df.index)
    return df

Gives you:

A   B   C   D
0   0   4   0   12
1   2   15  4   45
2   3   6   6   18

Because apply() function can't return a list or tuple, it just return a Series or a DataFrame, so it's OK when you add only one new column but not for more than one new columns. For your purpose, you can use concat function like this:

import pandas as pd 

def generate_list(row): 
    return [row['A']*2, row['B']*3] 

def main(): 
    data = { 
            'A':[0, 2, 3],  
            'B':[4, 15, 6]} 
      
    df = pd.DataFrame(data) 
   
    # applying function to each row in 
    df = pd.concat([df, df.apply(lambda row: generate_list(row), axis=1, result_type='expand').rename(columns={0:'C',1:'D'})],axis=1)
    print(df) 
       
if __name__ == '__main__': 
    main() 

The result of above code is:

   A   B  C   D
0  0   4  0  12
1  2  15  4  45
2  3   6  6  18

Is that what you want?

I try some experiments, like:

data = {'A':[0, 2, 3],  
        'B':[4, 15, 6]} 
df = pd.DataFrame(data) 
def generate_list(row): 
    return [row['A']*2, row['B']*3] 

obj = df.apply(lambda row: generate_list(row), axis=1)

    0    [0.0, 12.0]
    1    [4.0, 45.0]
    2    [6.0, 18.0]
    dtype: object

obj_accept = np.array(obj.sum()).reshape(-1,2)

    array([[ 0., 12.],
           [ 4., 45.],
           [ 6., 18.]])

# it's worked
df[['C', 'D']] = obj_accept

# also work
df[['C', 'D']] = np.array(obj.tolist())

# also work
df[['C', 'D']] = obj.tolist()

# not worked
df[['C', 'D']] = obj

the shapes:

obj.values.shape              # (3,)
df[['C', 'D']].shape          # (3, 2)
np.array(obj.tolist()).shape  # (3, 2)

some weird result:

df = pd.DataFrame({'A':[0, 2, 3], 'B':[4, 15, 6]} ) 
df.loc[:1, ['C', 'D']] = obj.loc[:1]
print(obj.loc[:1])

    0    [0.0, 12.0]
    1    [4.0, 45.0]
    dtype: object

print(df)

       A   B     C     D
    0  0   4   0.0   4.0
    1  2  15  12.0  45.0
    2  3   6   NaN   NaN