[Numpy-discussion] NumPy date/time types and the resolution concept

Francesc Alted faltet@pytables....
Thu Jul 17 03:10:13 CDT 2008


A Thursday 17 July 2008, Matt Knox escrigué:
> > Maybe you are right, but by providing many resolutions we are
> > trying to cope with the needs of people that are using them a lot. 
> > In particular, we are willing that the authors of the timseries
> > scikit can find on these new dtype a fair replacement of their Date
> > class (our proposal will be not so featured, but...).
>
> I think a basic date/time dtype for numpy would be a nice addition
> for general usage.
>
> Now as for the timeseries module using this dtype for most of the
> date-fu that goes on... that would be a bit more challenging. Unless
> all of the frequencies/resolutions currently supported in the
> timeseries scikit are supported with the new dtype, it is unlikely we
> would be able to replace our implementation. In particular, business
> day frequency (Monday - Friday) is of central importance for working
> with financial time series (which was my motivation for the original
> prototype of the module). But using plain integers for the DateArray
> class actually seems to work pretty well and I'm not sure a whole lot
> would be gained by using a date dtype.

Yeah, the business week.  We've pondered including this, but we are not 
sure about the differences of such a thing and a calendar week in terms 
of a time unit.  I see for sure its merits on the TimeSeries module, 
but I'm afraid that it would be non-sense in the context of a general 
date/time dtype.

Now that I think about it, maybe we should revise our initial intention 
of adding a quarter too, because ISO 8601 does not offer a way to print 
it nicely.  We can also opt by extending the ISO 8601 representation in 
order to allow the next sort of string representation:

In [35]: array([70, 72, 19], 'datetime64[Q]')
Out[35]: array([1988Q2, 1988Q4, 1975Q3], dtype="datetime64[Q]")

but, I don't know if this would innecessarily complicate things (apart 
of representing a departure from standards :-/).

> That being said, if someone creates a fork of the timeseries module
> using a new date dtype at it's core and it works amazingly well, then
> I'd probably get on board. I just think that may be difficult to do
> with a general purpose date dtype suitable for inclusion in the numpy
> core.

Yeah, I understand your reasons.  In fact, it is a pity that your 
requeriments diverge in some key points from our proposal for the 
general dtypes.  I have had a look at how you have integrated recarrays 
in your TimeSeries module, and I'm sure that by choosing a date/time 
dtype you would be able to reduce the complexity (and specially the 
efficiency too) of your code quite a few.

Cheers,

-- 
Francesc Alted


More information about the Numpy-discussion mailing list