On Wed, Jun 8, 2011 at 8:53 PM, Mark Wiebe <[email protected]> wrote: > On Wed, Jun 8, 2011 at 4:57 AM, Wes McKinney <[email protected]> wrote: >> >> <snip> >> >> >> So in summary, w.r.t. time series data and datetime, the only things I >> care about from a datetime / pandas point of view: >> >> - Ability to easily define custom timedeltas > > Can you elaborate on this a bit? I'm guessing you're not referring to the > timedelta64 in NumPy, which is simply an integer with an associated unit.
I guess what I am thinking of may not need to be available at the NumPy level. As an example, suppose you connected a timedelta (DateOffset, in pandas parlance) to a set of holidays, so when you say: date + BusinessDay(1, calendar='US') then you have some custom logic which knows how to describe the result of that. Some things along these lines have already been discussed-- but this is the basic way that the date offsets work in pandas, i.e. subclasses of DateOffset which implement custom logic. Probably too high level for NumPy. >> >> - Generate datetime objects, or some equivalent, which can be used to >> back pandas data structures > > Do you mean mechanisms to generate sequences of datetime's? I'm fixing up > arange to work reasonably with datetimes at the moment. Yes, that will be very nice. >> >> - (possible now??) Ability to have a set of frequency-naive dates >> (possibly not in order). > > This should work just as well as if you have an arbitrary set of integers > specifying the locations of the sample points. > Cheers, > Mark Cool (!). >> >> This last point actually matters. Suppose you wanted to get the worst >> 5-performing days in the S&P 500 index: >> >> In [7]: spx.index >> Out[7]: >> <class 'pandas.core.daterange.DateRange'> >> offset: <1 BusinessDay>, tzinfo: None >> [1999-12-31 00:00:00, ..., 2011-05-10 00:00:00] >> length: 2963 >> >> # but this is OK >> In [8]: spx.order()[:5] >> Out[8]: >> 2008-10-15 00:00:00 -0.0903497960942 >> 2008-12-01 00:00:00 -0.0892952780505 >> 2008-09-29 00:00:00 -0.0878970494885 >> 2008-10-09 00:00:00 -0.0761670761671 >> 2008-11-20 00:00:00 -0.0671229140321 >> >> - W >> _______________________________________________ >> NumPy-Discussion mailing list >> [email protected] >> http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
