You can use the between_time function directly:
ts.between_time(datetime.time(18), datetime.time(9), include_start=False, include_end=False)
Original answer:
You can use the indexer_between_time Index method.
For example, to include those times between 9am and 6pm (inclusive):
ts.ix[ts.index.indexer_between_time(datetime.time(9), datetime.time(18))]
to do the opposite and exclude those times between 6pm and 9am (exclusive):
ts.ix[ts.index.indexer_between_time(datetime.time(18), datetime.time(9),
include_start=False, include_end=False)]
Note: indexer_between_time's arguments include_start and include_end are by default True, setting include_start to False means that datetimes whose time-part is precisely start_time (the first argument), in this case 6pm, will not be included.
Example:
In [1]: rng = pd.date_range('1/1/2000', periods=24, freq='H')
In [2]: ts = pd.Series(pd.np.random.randn(len(rng)), index=rng)
In [3]: ts.ix[ts.index.indexer_between_time(datetime.time(10), datetime.time(14))]
Out[3]:
2000-01-01 10:00:00 1.312561
2000-01-01 11:00:00 -1.308502
2000-01-01 12:00:00 -0.515339
2000-01-01 13:00:00 1.536540
2000-01-01 14:00:00 0.108617
Note: the same syntax (using ix) works for a DataFrame:
In [4]: df = pd.DataFrame(ts)
In [5]: df.ix[df.index.indexer_between_time(datetime.time(10), datetime.time(14))]
Out[5]:
0
2000-01-03 10:00:00 1.312561
2000-01-03 11:00:00 -1.308502
2000-01-03 12:00:00 -0.515339
2000-01-03 13:00:00 1.536540
2000-01-03 14:00:00 0.108617
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