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

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]]))

If you want to use your function as such, here is a one liner:

df.update(df.B.apply(lambda x: pd.Series(dict(zip(['C','D'],f(x))))), overwrite=False)

In [350]: df
Out[350]:
   A  B   C   D
2  1  O   s   h
4  2  P  Pk  Pn
7  3  Q  Qk  Qn
9  4  R   h   m

You can also do:

df1 = df.copy()

df[['C','D']] = df.apply(lambda x: pd.Series([x['B'] + 'k', x['B'] + 'n']), axis=1)

df1.update(df, overwrite=False)

I have a more easy way to do it, if the table is not so big.

def f(row): #row is the value of row. 
    if row['C']=='':
        row['C']=row['B']+'k'
    if row['D']=='':
        row['D']=row['B']+'n'
    return row
df=df.apply(f,axis=1)

I found this super confusing but eventually figured out a way of achieving this that didn't hurt my brain. Here it is, sorry if it doesn't match the example well...

dataframe with no index

# function to do the calcs
def f(row):
    my_a = row['a'] # row is a Series, my_a is a scalar string

    if my_a == 'a':  # dummy logic to calc new values based on the row values
        return [1, 2] # return 2 values to update 2 columns
    else:
        return [4, 5]

# simple test frame
input = pd.DataFrame.from_dict({
    'a': ['a', 'd'],
    'b': ['b', 'e'],
    'c': ['c', 'f'],
    'x': [0, 0],
    'y': [0, 0]
})

# apply the function to update the x and y columns with the returned values
input[['x','y']] = input.apply(f, axis=1)

dataframe with an index

if your dataframe has an index.. you need to be a bit more explicit when you are doing the apply to ensure that "list-like results will be turned into columns"...

def f(row): # function to do the calcs
    my_a = row['a'] # row is a Series, my_a is a scalar string
    my_index = row.name # you might also want to use the index value in the calcs

    if my_a == 'a': # dummy logic to calc new values based on the row values
        return [1, 2] # return 2 values to update 2 columns
    else:
        return [4, 5]

input = pd.DataFrame.from_dict({
    'an_index': ['indx1', 'indx2'],
    'a': ['a', 'd'],
    'b': ['b', 'e'],
    'c': ['c', 'f'],
    'x': [0, 0],
    'y': [0, 0]
}).set_index(['an_index'])

# apply the function to update the x and y columns with the returned values
input[['x','y']] = input.apply(f, axis=1, result_type='expand')

simply by doing the following

df.C.loc[df.C.isnull()] = df.B.loc[df.C.isnull()] + 'k'

df.D.loc[df.D.isnull()] = df.B.loc[df.D.isnull()] + 'n'

check this link indexing-view-versus-copy if you want to know why I've use loc