On 19.04.2014 09:03, Andreas Hilboll wrote: > On 14.04.2014 20:59, Chris Barker wrote: >> On Fri, Apr 11, 2014 at 4:58 PM, Stephan Hoyer <sho...@gmail.com >> <mailto:sho...@gmail.com>> wrote: >> >> On Fri, Apr 11, 2014 at 3:56 PM, Charles R Harris >> <charlesr.har...@gmail.com <mailto:charlesr.har...@gmail.com>> wrote: >> >> Are we in a position to start looking at implementation? If so, >> it would be useful to have a collection of test cases, i.e., >> typical uses with specified results. That should also cover >> conversion from/(to?) datetime.datetime. >> >> >> yup -- tests are always good! >> >> Indeed, my personal wish-list for np.datetime64 is centered much >> more on robust conversion to/from native date objects, including >> comparison. >> >> >> A good use case. >> >> >> Here are some of my particular points of frustration (apologies for >> the thread jacking!): >> - NaT should have similar behavior to NaN when used for comparisons >> (i.e., comparisons should always be False). >> >> >> make sense. >> >> >> - You can't compare a datetime object to a datetime64 object. >> >> >> that would be nice to have. >> >> >> - datetime64 objects with high precision (e.g., ns) can't compare to >> datetime objects. >> >> >> That's a problem, but how do you think it should be handled? My thought >> is that it should round to microseconds, and then compare -- kind of >> like comparing float32 and float64... >> >> >> Pandas has a very nice wrapper around datetime64 arrays that solves >> most of these issues, but it would be nice to get much of that >> functionality in core numpy, >> >> >> yes -- it would -- but learning from pandas is certainly a good idea. >> >> >> from numpy import datetime64 >> from datetime import datetime >> >> print np.datetime64('NaT') < np.datetime64('2011-01-01') # this >> should not to true >> print datetime(2010, 1, 1) < np.datetime64('2011-01-01') # raises >> exception >> print np.datetime64('2011-01-01T00:00', 'ns') > datetime(2010, 1, 1) >> # another exception >> print np.datetime64('2011-01-01T00:00') > datetime(2010, 1, 1) # >> finally something works! >> >> >> now to get them into proper unit tests.... > > As one further suggestion, I think it would be nice if doing arithmetic > using np.datetime64 and datetime.timedelta objects would work: > > np.datetime64(2011,1,1) + datetime.timedelta(1) == > np.datetime64(2011,1,2) > > And of course, but this is probably in the loop anyways, > np.asarray([list_of_datetime.datetime_objects]) should work as expected.
One more wish / suggestion from my side (apologies if this isn't the place to make wishes): 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. Cheers, -- Andreas. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
