Comparing ID, Type day by day Pandas

Question

I have the following df:

index, date, ID, type   
0, 2022-04-04, 1, A  
1, 2022-04-04, 2, B 
2, 2022-04-03, 1, A
3, 2022-04-03, 2, A 

I want to have the following df (that adds a new column('change') that is a True/False if the type of the ID change of the actual date vs the date before):

index, date, ID, type, change   
0, 2022-04-04, 1, A, False (False because type didnt change from one day to the other: A-->A)
1, 2022-04-04, 2, B, True  (True because type change from one day to the other: A-->B)
2, 2022-04-03, 1, A, ---   (no information of the day before)
3, 2022-04-03, 2, A, ---   (no information of the day before)

I need a code that generates the new column "change" with the True/False per date.

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EDIT: This is my real df (150.000 ID per date, 3 types - Enabled, Not eligible, Eligible (not enabled) and not the same number of ID per date (some ID are activated/desactivated)):

index, date, ID, type
2022-03-14,262199,Enabled
2022-03-14,267695,Enabled
2022-03-14,262708,Not eligible
2022-03-14,233,Enabled
2022-03-14,263652,Not eligible
2022-03-14,267908,Not eligible
2022-03-14,263992,Enabled
2022-03-14,267973,Not eligible
2022-03-14,1996,Enabled
2022-03-14,268216,Eligible (not enabled)
....

So is the same idea of code showed up in an easy way but with 3 types of Id and not a fixed amount of ID per date.

Answer 1

IIUC use:

df['change'] = df['type'].ne(df.groupby('ID')['type'].shift(-1).fillna(df['type']))
print (df)
         date  ID type  change
0  2022-04-04   1    A   False
1  2022-04-04   2    B    True
2  2022-04-03   1    A   False
3  2022-04-03   2    A   False