Efficient way to add columns of pandas dataframe with a function that returns two values using apply

Question

I have some code that fills two lists using a function, called function, that returns two values. The function requires some parameters that are located within a row of the dataframe. Then I append the lists to a new column in my dataframe.

data = [[45, 'F', 'Jill', 'USA'], [87, 'm', 'Jeff', 'Poland'], [99, 'M', 'Tim', 'Peru']]

df = pd.DataFrame(data, ['Age', 'Sex', 'Name', 'Location']


new_column1 = []
new_column2 = []

for member in tqdm(range(len(df))):
        list1, list2 = (function(df['Age'][member], df['Sex'][member], df['Name'][member], df['Location'][member]))
        
        new_column1.append(list1)
        new_column2.append(list2)

I am wondering if there is a faster way to do this using apply. I threw in tqdm because everyone likes to know how long they have to wait. For what it's worth, the output of the function is a float and a list.

Is there a better way to do this? I get the feeling that this is a little basic and I want something elegant and efficient. Is there a way to do this using apply? I would like to eventually use the swifter package at some point.

Update

I do not understand why this doesn't work.

df[['New_column1', 'New_column2']] = df[['Age', 'Sex', 'Name', 'Location']].swifter.applymap(function)

I am getting an error that the function is missing 3 required positional arguments: 'Sex', 'Name', and 'Location'.

Answer 1

Is this what you are looking for?

import pandas as pd
df = pd.DataFrame({'Name':['Alice','Bob'],'Age':[20,19],'Sex':['F','M'],'Location':['Berlin','San Sebastian']})

nested_list = df.values.tolist() #each pd row into list 

list1, list2 = map(list, nested_list)# nested list into separate lists

print(df,'\n')
>>    Name  Age Sex       Location
>>0  Alice   20   F         Berlin
>>1    Bob   19   M  San Sebastian 


print(f'{list1=}')
>>list1=['Alice', 20, 'F', 'Berlin']

print(f'{list2=}')
>>list2=['Bob', 19, 'M', 'San Sebastian']

Edit

import pandas as pd
import swifter

df = pd.DataFrame({'Name':['Alice','Bob'],'Age':[20,19],'Sex':['F','M'],'Location':['Berlin','San Sebastian']})

list1, list2 = map(list, df.swifter.apply(list,axis = 1))

print(df,'\n')

print(f'{list1=}')

print(f'{list2=}')