Pandas Dataframe: How to update multiple columns by applying a function?

Question

I have a Dataframe df like this:

   A   B   C    D
2  1   O   s    h
4  2   P    
7  3   Q
9  4   R   h    m

I have a function f to calculate C and D based on B for a row:

def f(p): #p is the value of column B for a row. 
     return p+'k', p+'n'

How can I populate the missing values for row 4&7 by applying the function f to the Dataframe?

The expected outcome is like below:

   A   B   C    D
2  1   O   s    h
4  2   P   Pk   Pn
7  3   Q   Qk   Qn
9  4   R   h    m

The function f has to be used as the real function is very complicated. Also, the function only needs to be applied to the rows missing C and D

Answer 1

Maybe there is a more elegant way, but I would do in this way:

df['C'] = df['B'].apply(lambda x: f(x)[0])
df['D'] = df['B'].apply(lambda x: f(x)[1])

Applying the function to the columns and get the first and the second value of the outputs. It returns:

   A  B   C   D
0  1  O  Ok  On
1  2  P  Pk  Pn
2  3  Q  Qk  Qn
3  4  R  Rk  Rn

EDIT:

In a more concise way, thanks to this answer:

df[['C','D']] = df['B'].apply(lambda x: pd.Series([f(x)[0],f(x)[1]]))