pandas interpolation and extrapolation by timestamp id by id

Question

i want interpolation and extrapolation(Linear interpolation) by timestamp id by id.

timestamp starts 1383260400000, ends 1383343800000 and another id(from 1 to 2025) has same issues.

expected :

timestamp	id	strength
1383260400000	1	-0.3803901328171995
1383261000000	1	-0.42196042219455937
1383261600000	1	Linear interpolated data
1383262200000	1	Linear interpolated data
1383262800000	1	Linear interpolated data
1383263400000	1	Linear interpolated data
1383264000000	1	Linear interpolated data
1383264600000	1	Linear interpolated data
1383265200000	1	-0.460714706261982

and some are have start timestamp(1383260400000) or end timestamp(1383343800000).

timestamp	id	strength
1383260400000	3	Linear interpolated data
1383261000000	3	Linear interpolated data
1383261600000	3	Linear interpolated data
1383262200000	3	Linear interpolated data
1383262800000	3	some values
1383263400000	3	some values
....
1383343800000	3	Linear interpolated data

i want to extrapolate and interpolate missing values.

here is code other did :

def interpolation():
    # from 1383260400000 to 1383343800000
    r = pd.date_range(pd.to_datetime(1383260400000, unit='ms'),
                      pd.to_datetime(1383343800000, unit='ms'),
                      freq='10Min')
    # df3['strength'] = df3['strength'].replace(0, np.nan)
    ids = df3['id'].unique()
    mux = pd.MultiIndex.from_product([r, ids], names=['timestamp', 'id'])
    f = lambda x: x.interpolate(limit_area='inside', limited_direction='both')
    df4 = (df3.set_index(['timestamp', 'id'])
           .reindex(mux)
           .groupby('id')['strength']
           .transform(f).fillna(0)
           .reset_index())

    return df4