Shortcut for calculating differences of multiple columns

Question

I calculated the differences of mutliple columns based on Country and Year. The origin dataset is (only a subset, I've got all countries):

Country	Year	Continent	Inhabitants	Score1	Score2	Score3	Score4
Brazil	2021	South America	int	6.1	7.2	4.2	9.2
Brazil	2020	South America	int	6.9	7.0	4.9	7.2
Brazil	2019	South America	int	5.6	3.4	2.5	8.4
Germany	2021	Europe	int	5.6	3.4	2.5	8.4
Germany	2020	Europe	int	5.6	3.4	2.5	8.4
Germany	2019	Europe	int	5.6	3.4	2.5	8.4
Japan	2021	Asia	int	5.6	3.4	2.5	8.4
Japan	2020	Asia	int	5.6	3.4	2.5	8.4
Japan	2019	Asia	int	5.6	3.4	2.5	8.4

I figured it out to calculate for each Score-column the differences with:

df['diff1'] = df.groupby(['Country'])['Score1'].diff()
df['diff2'] = df.groupby(['Country'])['Score2'].diff()
df['diff3'] = df.groupby(['Country'])['Score3'].diff()
df['diff4'] = df.groupby(['Country'])['Score4'].diff()

Is there an easier way to solve it instead of apply this code four times (for every column separately)? With a loop maybe?

Answer 1

df.groupby('Country')[['Score1', 'Score2', 'Score3' ,'Score4']].diff()