How to add a new column in pandas Dataframe if the string or object value of column 1 is repeated in three consecutive rows

Say, I have a dataframe like this,

import pandas as pd
df = pd.DataFrame({'ID': ['p1305', 'p1305', 'p1305', 'p1307', 'p1307', 'p1307', 'p1301', 'p1301', 'p1301', 'p1340', 'p1340', 'p1340','P569','P987','P569']})

I need to add a column y if the values in ID are the same for three consecutive rows, then add yes in column y. Otherwise, add no.

Here is what I have tried,

# create a rolling window of size 3
rolling = df['ID'].rolling(3)

# apply a custom function to the rolling window to check if all values are the same
df['y'] = rolling.apply(lambda x: 'Yes' if all(x == x[0]) else 'No')

However, the above code is throwing the following error,

DataError: No numeric types to aggregate

The final desired output would be:

  ID        y
0   p1305  Yes
1   p1305  Yes
2   p1305  Yes
3   p1307  Yes
4   p1307  Yes
5   p1307  Yes
6   p1301  Yes
7   p1301  Yes
8   p1301  Yes
9   p1340  Yes
10  P1340  Yes
11  P1340  Yes

Any suggestions or help are much appreciated!
Thanks

You need to trick the method and convert to a number first, for exampe using factorize (or a Categorical):

df['y'] = (
 pd.Series(pd.factorize(df['ID'])[0], index=df.index)
   .rolling(3, min_periods=1).apply(lambda s: s.iloc[1:].eq(s.iloc[0]).all())
   .astype(bool)
)

Output:

       ID      y
0   p1305   True
1   p1305   True
2   p1305   True
3   p1307  False
4   p1307  False
5   p1307   True
6   p1301  False
7   p1301  False
8   p1301   True
9   p1340  False
10  p1340  False
11  p1340   True

Another approach if you want True in all the rows of the group, would be to use:

group = df['ID'].ne(df['ID'].shift()).cumsum()
df['y'] = df.groupby(group)['ID'].transform('size').eq(3) # or .ge(3)

Output:

       ID     y
0   p1305  True
1   p1305  True
2   p1305  True
3   p1307  True
4   p1307  True
5   p1307  True
6   p1301  True
7   p1301  True
8   p1301  True
9   p1340  True
10  p1340  True
11  p1340  True