[Numpy-discussion] fixing up datetime
Wes McKinney
wesmckinn@gmail....
Thu Jun 9 15:17:48 CDT 2011
On Wed, Jun 8, 2011 at 8:53 PM, Mark Wiebe <mwwiebe@gmail.com> wrote:
> On Wed, Jun 8, 2011 at 4:57 AM, Wes McKinney <wesmckinn@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')
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
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