Pandas: check a sequence in one column for each unique value in another column

Question

I have a table that looks like this:

Date	Unique id	Indicator
2018	1	1
2019	1	0
2020	1	0
2020	2	1
2018	2	0
2019	2	1
2020	2	1
2021	2	1

For each value in "Unique id" I want to check whether "Indicator" match a special sequence of values (say, [1,1,1])

I have tried using df.groupby(['Unique id'])['Indicator].isin([1,1,1]), but the groupby method has no isin method.

The desired output would be:

Unique id	Sequence match
1	False
2	True

Answer 1

You can do rolling().apply() like this:

def check_pattern(s, pattern):
    return s.eq(pattern).all()

(df.groupby('Unique id', group_keys=False)['Indicator'].rolling(3)
   .apply(check_pattern, kwargs={'pattern':(1,1,1)})
   .groupby('Unique id').max().eq(1)
)

Output:

Unique id
1    False
2     True
Name: Indicator, dtype: bool