Pandas – drop records if on specific column value is equal to previous record

I have a dataframe like following (sorted by id, time and status):

I need to remove remove repeats in status sequences but only for cases when status repeats in next to each other records. Meaning that id 111 can have multiple statuses == ‘A’, but I need to remove second ‘A’ if previous status in time also was ‘A’.
So new table should be like this (remove second line with same status):

So in the end we have a unique sequences of statuses for each id without repeats.

Appreciate any help because I stuck with some very slow and blunt approach comparing each row with previous, but dataset is very huge, > 5M records.

>Solution :

Get difference of multiple columns shifted values and get rows by boolean indexing:

out = df[df[['id','status']].ne(df[['id','status']].shift()).any(axis=1)]
print (out)
    id status         timestamp
0  111      A  29.08.2023 12:39
2  111      B  29.08.2023 12:47
3  111      C  29.08.2023 12:50
4  111      A  29.08.2023 12:50
5  112      A  29.08.2023 12:50
6  112      B  29.08.2023 13:09
7  112      C  29.08.2023 13:40
8  112      B  29.08.2023 13:50
9  112      A  29.08.2023 13:55