Create multiple pandas DataFrame columns from applying a function with multiple returns

Question

I'd like to apply a function with multiple returns to a pandas DataFrame and put the results in separate new columns in that DataFrame.

So given something like this:

import pandas as pd

df = pd.DataFrame(data = {'a': [1, 2, 3], 'b': [4, 5, 6]})

def add_subtract(a, b):
  return (a + b, a - b)

The goal is a single command that calls add_subtract on a and b to create two new columns in df: sum and difference.

I thought something like this might work:

(df['sum'], df['difference']) = df.apply(
    lambda row: add_subtract(row['a'], row['b']), axis=1)

But it yields this error:

----> 9 lambda row: add_subtract(row['a'], row['b']), axis=1)

ValueError: too many values to unpack (expected 2)

EDIT: In addition to the below answers, pandas apply function that returns multiple values to rows in pandas dataframe shows that the function can be modified to return a list or Series, i.e.:

def add_subtract_list(a, b):
  return [a + b, a - b]

df[['sum', 'difference']] = df.apply(
    lambda row: add_subtract_list(row['a'], row['b']), axis=1)

or

def add_subtract_series(a, b):
  return pd.Series((a + b, a - b))

df[['sum', 'difference']] = df.apply(
    lambda row: add_subtract_series(row['a'], row['b']), axis=1)

both work (the latter being equivalent to Wen's accepted answer).

Answer 1

Adding pd.Series

df[['sum', 'difference']] = df.apply(
    lambda row: pd.Series(add_subtract(row['a'], row['b'])), axis=1)
df

yields

   a  b  sum  difference
0  1  4    5          -3
1  2  5    7          -3
2  3  6    9          -3