Groupby custom function based on other column values

January 10, 2023

I have a data frame that contains survey responses by country.

country=['Country A','Country A','Country A','Country B','Country B','Country B']
responses=['Agree','Neutral','Disagree','Agree','Neutral','Disagree']
num_respondents=[10,50,30,58,24,23]
example_df = pd.DataFrame({"Country": country, "Response": responses, "Count": num_respondents})

For each country, I want to compute the fraction (#Agree-#Disagree)/(Total Respondents). Is there a clean way to do this using groupby or another pandas function?

>Solution :

Maybe it helps:

example_df.groupby('Country').apply(lambda x: (sum(x['Count'][x['Response'] == 'Agree']) 
                                            - sum(x['Count'][x['Response'] == 'Disagree'])) 
                                              /sum(x['Count']))