On Fri, Apr 11, 2014 at 3:56 PM, Charles R Harris <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. > Indeed, my personal wish-list for np.datetime64 is centered much more on robust conversion to/from native date objects, including comparison. 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). - You can't compare a datetime object to a datetime64 object. - datetime64 objects with high precision (e.g., ns) can't compare to datetime objects. 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, since I don't always want to store my values in a 1-dimensional array + hash-table (the pandas Index): http://pandas.pydata.org/pandas-docs/stable/timeseries.html Here's code which reproduces all of the above: 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!
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