Resample daily pandas timeseries with start at time other than midnight
pandas, python
Solution
The `base` keyword can do the trick (see docs):
s.resample('24h', base=5)
Eg:
In [35]: idx = pd.date_range('2012-01-01 00:00:00', freq='5min', periods=24*12*3)
In [36]: s = pd.Series(np.arange(len(idx)), index=idx)
In [38]: s.resample('24h', base=5)
Out[38]:
2011-12-31 05:00:00 29.5
2012-01-01 05:00:00 203.5
2012-01-02 05:00:00 491.5
2012-01-03 05:00:00 749.5
Freq: 24H, dtype: float64
Problem
I have a pandas timeseries of 10-min freqency data and need to find the maximum value in each 24-hour period. However, this 24-hour period needs to start each day at 5AM - not the default midnight which pandas assumes. I've been checking out `DateOffset` but so far am drawing blanks. I might have expected something akin to `pandas.tseries.offsets.Week(weekday=n)`, e.g. `pandas.tseries.offsets.Week(hour=5)`, but this is not supported as far as I can tell. I can do a nasty work around by `shift`ing the data first, but it's unintuitive and even coming back to the same code after just a week I have problems wrapping my head around the shift direction! Any more elegant ideas would be much appreciated.