On Fri, Apr 25, 2014 at 4:57 AM, Andreas Hilboll <li...@hilboll.de> wrote:
> Array-wide access to the individual datetime components should work, i.e.,
>
> datetime64array.year
>
> should yield an array of dtype int with the years. That would allow
> boolean indexing to filter data, like
>
> datetime64array[datetime64array.year == 2014]
>
> would yield all entries from 2014.
>
that would be nice, yes, but datetime64 doesn't support anything like that
at all -- i.e. array-wide or not access to the components. In this case,
you could kludge it with:
In [19]: datetimearray
Out[19]: array(['2014-02-03', '2013-03-08', '2012-03-07', '2014-04-06'],
dtype='datetime64[D]')
In [20]: datetimearray[datetimearray.astype('datetime64[Y]') ==
np.datetime64('2014')]
Out[20]: array(['2014-02-03', '2014-04-06'], dtype='datetime64[D]')
but that wouldn't work for months, for instance.
I think the current NEP should stick with simply fixing the timezone thing
-- no new functionality or consequence.
But:
Maybe it's time for a new NEP for what we want datetime64 to be in the
future -- maybe borrow from the blaze proposal cited earlier? Or wait and
see how that works out, then maybe port that code over to numpy?
In the meantime, a set of utilities that do the kind of things you're
looking for might make sense. You could do it as a ndarray subclass, and
add those sorts of methods, though ndarray subclasses do get messy....
-Chris
--
Christopher Barker, Ph.D.
Oceanographer
Emergency Response Division
NOAA/NOS/OR&R (206) 526-6959 voice
7600 Sand Point Way NE (206) 526-6329 fax
Seattle, WA 98115 (206) 526-6317 main reception
chris.bar...@noaa.gov
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