Apply a function to one column and assign the output to two pandas dataframe columns.

pandas-one-in-two-out-apply.py

import pandas as pd

# MAKE UP SOME DATA
data = {'id': [1, 2, 3, 4, 5],
        'First Name': ['Mary', 'Harry', 'Larry', 'Fairy', 'Dairy',],
        'Birth Year': [1930,1940,1950,1960,1970],
        'Favorite Color': ['Blue', 'Red', 'Green', 'Pink', 'Orange']}

# CREATE A DATAFRAME
df = pd.DataFrame.from_dict(data)

# DEFINE A FUNCION WITH ONE INPUT AND TWO OUTPUTS
def generation(birth_year):
    age = 2022 - birth_year
    if 0 < age < 50:
        age_group = 'Young'
    elif 50 < age < 60:
        age_group = 'Young at Heart'
    elif 60 < age < 70:
        age_group = 'Arnold Palmer'
    elif 70 < age < 80:
        age_group = 'Old'
    elif 80 < age < 90:
        age_group = "Still Kickin'"
    else:
        age_group = "Ancient"
    return age, age_group

# APPLY THE FUNCTION TO ONE COLUMN, ASSIGNING THE OUTPUT TO TWO NEW COLUMNS
df[['Age','Age Group']] = df.apply(lambda x: generation(x['Birth Year']), axis=1, result_type='expand')

df

Example Output:

                        input1                    output1          output2
| id | First Name | Birth Year | Favorite Color |     Age | Age Group      |
| 1  | Mary       | 1930       | Blue           |      92 | Ancient        |
| 2  | Harry      | 1940       | Red            |      82 | Still Kickin'  |
| 3  | Larry      | 1950       | Green          |      72 | Old            |
| 4  | Fairy      | 1960       | Pink           |      62 | Arnold Palmer  |
| 5  | Dairy      | 1970       | Orange         |      52 | Young at Heart |