Pandas resample timeseries data to 15 mins and 45 mins - using multi-index or column

Starting from your second last dataframe (after using weather.reset_index(Station, inplace=True)):

                       Station  Pressure  Temp   Hum

parsed_time
2018-04-15 14:15:00 Bow 1012.0 20.0 87.0
2018-04-15 14:45:00 Bow 1013.0 20.0 87.0
2018-04-15 15:15:00 Bow 1012.0 21.0 87.0
2018-04-15 15:45:00 Bow 1014.0 22.0 86.0
2018-04-15 16:00:00 Bow 1015.0 22.0 86.0
2018-04-15 16:01:00 Bow 1012.0 25.0 86.0
2018-04-15 16:02:00 Bow 1012.0 25.0 86.0
2018-04-15 14:15:00 Stratford 1011.0 18.0 87.0
2018-04-15 14:45:00 Stratford 1011.0 18.0 87.0
2018-04-15 15:15:00 Stratford 1012.0 18.0 87.0
2018-04-15 15:45:00 Stratford 1014.0 19.0 86.0
2018-04-15 16:00:00 Stratford 1014.0 19.0 86.0
2018-04-15 16:01:00 Stratford 1015.0 19.0 86.0
2018-04-15 16:02:00 Stratford 1016.0 20.0 86.0
2018-04-15 16:04:00 Stratford 1016.0 20.0 86.0
you could use a combination of groupby and resample:

res = weather.groupby('Station').resample('30min').mean().reset_index('Station')
By default, resample chooses the bin intervals [16:00, 16:30) and [16:30, 17:00). As you already noticed, the time index is resampled without an offset, but you can add it back afterwards using DateOffset:

res.index = res.index + pd.DateOffset(minutes=15)
which yields:

                       Station  Pressure  Temp   Hum

parsed_time
2018-04-15 14:15:00 Bow 1012.00 20.0 87.0
2018-04-15 14:45:00 Bow 1013.00 20.0 87.0
2018-04-15 15:15:00 Bow 1012.00 21.0 87.0
2018-04-15 15:45:00 Bow 1014.00 22.0 86.0
2018-04-15 16:15:00 Bow 1013.00 24.0 86.0
2018-04-15 14:15:00 Stratford 1011.00 18.0 87.0
2018-04-15 14:45:00 Stratford 1011.00 18.0 87.0
2018-04-15 15:15:00 Stratford 1012.00 18.0 87.0
2018-04-15 15:45:00 Stratford 1014.00 19.0 86.0
2018-04-15 16:15:00 Stratford 1015.25 19.5 86.0
Alternatively, you could specifiy the offset directly in the resample method:

weather.groupby('Station').resample('30min', loffset=pd.Timedelta('15min')).mean()