[Python] How to split a DataFrame on missing blocks of data?

I have a Dataframe with a time series index with several thousand rows. Unfortunately some datapoints are missing it looks like this:

A
2023-03-18 05:00:00 3.0
2023-03-18 06:00:00 4.0
2023-03-18 07:00:00 24.4
2023-03-18 12:00:00 5.6
2023-03-18 13:00:00 3.4
2023-03-18 15:00:00 4.5
2023-03-18 20:00:00 8.8
2023-03-18 21:00:00 3.2


I want my data to be hourly and thus interpolate the missing data points. This can be done with .reindex() and .interpolate() like this:

start, end = df.index.min(), df.index.max()
new_index = pd.DatetimeIndex(pd.date_range(start, end, freq="1h"))
df = df.reindex(new_index)

df = df.interpolate(method="time")
df


which gives:

A
2023-03-18 05:00:00 3.00
2023-03-18 06:00:00 4.00
2023-03-18 07:00:00 24.40
2023-03-18 08:00:00 20.64
2023-03-18 09:00:00 16.88
2023-03-18 10:00:00 13.12
2023-03-18 11:00:00 9.36
2023-03-18 12:00:00 5.60
2023-03-18 13:00:00 3.40
2023-03-18 14:00:00 3.95
2023-03-18 15:00:00 4.50
2023-03-18 16:00:00 5.36
2023-03-18 17:00:00 6.22
2023-03-18 18:00:00 7.08
2023-03-18 19:00:00 7.94
2023-03-18 20:00:00 8.80
2023-03-18 21:00:00 3.20


QUESTION
However, I don't want to interpolate if the gaps are too large e.g. if they are longer than 3 hours. At these large gaps I want to split the DataFrame by rows i.e. so that I have multiple DataFrames in the end (, as a list or whatever). How can I do this?

The result should look something like this - 3 DataFrames:

A
2023-03-18 05:00:00 3.00
2023-03-18 06:00:00 4.00
2023-03-18 07:00:00 24.40


A
2023-03-18 12:00:00 5.60
2023-03-18 13:00:00 3.40
2023-03-18 14:00:00 3.95
2023-03-18 15:00:00 4.50


A
2023-03-18 20:00:00 8.80
2023-03-18 21:00:00 3.20

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