groupby date using other start time than midnight - pandas

Question

I am aggregating some data by date.
for dt,group in df.groupby(df.timestamp.dt.date):
# do stuff
Now, I would like to do the same, but without using midnight as time offset.
Still, I would like to use groupby, but e.g. in 6AM-6AM bins.
Is there any better solution than a dummy column?
unfortunately, resample as discussed in
Resample daily pandas timeseries with start at time other than midnight
Resample hourly TimeSeries with certain starting hour
does not work, as I do need to apply any resampling/aggregation function

Answer 1

You can, for example, subtract the offset before grouping:
for dt, group in df.groupby(df.timestamp.sub(pd.to_timedelta('6H')).dt.date):
# do stuff

Answer 2

There's a base argument for resample or pd.Grouper that is meant to handle this situation. There are many ways to accomplish this, pick whichever you feel is more clear.
'1D' frequency with base=0.25
'24h' frequency with base=6
'1440min' frequency with base=360
Code
df = pd.DataFrame({'timestamp': pd.date_range('2010-01-01', freq='10min', periods=200)})
df.resample(on='timestamp', rule='1D', base=0.25).timestamp.agg(['min', 'max'])
#df.resample(on='timestamp', rule='24h', base=6).timestamp.agg(['min', 'max'])
#df.resample(on='timestamp', rule=f'{60*24}min', base=60*6).timestmap.agg(['min', 'max'])
min max
timestamp
2009-12-31 06:00:00 2010-01-01 00:00:00 2010-01-01 05:50:00 #[Dec31 6AM - Jan1 6AM)
2010-01-01 06:00:00 2010-01-01 06:00:00 2010-01-02 05:50:00 #[Jan1 6AM - Jan2 6AM)
2010-01-02 06:00:00 2010-01-02 06:00:00 2010-01-02 09:10:00 #[Jan2 6AM - Jan3 6AM)
For completeness, resample is a convenience method and is in all ways the same as groupby. If for some reason you absolutely cannot use resample you could do:
for dt, gp in df.groupby(pd.Grouper(key='timestamp', freq='24h', base=6)):
...
which is equivalent to
for dt, gp in df.resample(on='timestamp', rule='24h', base=6):
...