More flexible binning/aggregation of time series data
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I have TimeSeries that has observations at the second level:


mport pandas as pd
import numpy as np
rng = pd.date_range('2012-01-01', '2012-01-10', freq='T')
s = pd.TimeSeries(np.random.randn(12961)*100, index=rng)


I now want to resample the data to daily at 17:59:59, so that I have the sum of positions at 17:59:59 for all days in range.

If I didn't care about the time I could just do:

s1 = s.resample('D',how='sum')

Does anyone have a suggestion?```
``````
