On Thu, Jun 9, 2011 at 3:17 PM, Wes McKinney <wesmck...@gmail.com> wrote:
> On Wed, Jun 8, 2011 at 8:53 PM, Mark Wiebe <mwwi...@gmail.com> wrote: > > On Wed, Jun 8, 2011 at 4:57 AM, Wes McKinney <wesmck...@gmail.com> > 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') > If you have a moment of time, can you comment on the business day api I proposed, and possibly try out the busday_offset function I've begun? It doesn't do holidays yet, but the weekmask seems to work. -Mark > > 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 > >> NumPy-Discussion@scipy.org > >> http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@scipy.org > > http://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion >
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