On Tuesday, February 14, 2012, Mark Wiebe <[email protected]> wrote: > On Tue, Feb 14, 2012 at 9:37 PM, Benjamin Root <[email protected]> wrote: > > On Tuesday, February 14, 2012, Mark Wiebe <[email protected]> wrote: >> On Tue, Feb 14, 2012 at 8:17 PM, Benjamin Root <[email protected]> wrote: >>> >>> Just a thought I had. Right now, I can pass a list of python ints or floats into np.array() and get a numpy array with a sensible dtype. Is there any reason why we can't do the same for python's datetime? Right now, it is very easy for me to make a list comprehension of datetime objects using strptime(), but it is very awkward to make a numpy array out of it. >> >> I would consider this a bug, it's not behaving sensibly at present. Here's what it does for me: >> >> In [20]: np.array([datetime.datetime.strptime(date, "%m/%d/%y") for date in ["02/03/12", >> >> ...: "07/22/98", "12/12/12"]], dtype="M8") > > Well, I guess it would be nice if I didn't even have to provide the dtype (I.e., inferred from the datetime type, since we aren't talking about strings). But I hadn't noticed the above, I was just making object arrays. > >> >> --------------------------------------------------------------------------- >> >> TypeError Traceback (most recent call last) >> >> C:\Python27\Scripts\<ipython-input-20-d3b7b5392190> in <module>() >> >> 1 np.array([datetime.datetime.strptime(date, "%m/%d/%y") for date in ["02/03/12", >> >> ----> 2 "07/22/98", "12/12/12"]], dtype="M8") >> >> TypeError: Cannot cast datetime.datetime object from metadata [us] to [D] according to the rule 'same_kind' >> >> In [21]: np.array([datetime.datetime.strptime(date, "%m/%d/%y") for date in ["02/03/12", >> >> ...: "07/22/98", "12/12/12"]], dtype="M8[us]") >> >> Out[21]: >> >> array(['2012-02-02T16:00:00.000000-0800', >> >> '1998-07-21T17:00:00.000000-0700', '2012-12-11T16:00:00.000000-0800'], dtype='datetime64[us]') >> >> In [22]: np.array([datetime.datetime.strptime(date, "%m/%d/%y") for date in ["02/03/12", >> >> ...: "07/22/98", "12/12/12"]], dtype="M8[us]").astype("M8[D]") >> >> Out[22]: array(['2012-02-03', '1998-07-22', '2012-12-12'], dtype='datetime64[D]') >>> >>> The only barrier I can think of are those who have already built code around a object dtype array of datetime objects. >>> >>> Thoughts? >>> Ben Root >>> >>> P.S. - what ever happened to arange() and linspace() for datetime64? >> >> arange definitely works: >> In[28] np.arange('2011-03-02', '2011-04-01', dtype='M8') >> Out[28]: >> array(['2011-03-02', '2011-03-03', '2011-03-04', '2011-03-05', >> '2011-03-06', '2011-03-07', '2011-03-08', '2011-03-09', >> '2011-03-10', '2011-03-11', '2011-03-12', '2011-03-13', >> '2011-03-14', '2011-03-15', '2011-03-16', '2011-03-17', >> '2011-03-18', '2011-03-19', '2011-03-20', '2011-03-21', >> '2011-03-22', '2011-03-23', '2011-03-24', '2011-03-25', >> '2011-03-26', '2011-03-27', '2011-03-28', '2011-03-29', >> '2011-03-30', '2011-03-31'], dtype='datetime64[D]') >> I didn't get to implementing linspace. I did look at it, but the current code didn't make it a trivial thing to put in. >> -Mark > > Sorry, I wasn't clear about arange, I meant that it would be nice if it could take python datetimes as arguments (and timedelat for the step?) because that is much more intuitive than remembering the exact dtype code and string format. > > I see it as the numpy datetime64 type could take three types for it's constructor: another datetime64, python datetime, and The standard unambiguous datetime string. I should be able to use these interchangeably in numpy. The same would be true for timedelta64. > > Easy interchange between pyth > > Ben Walsh actually implemented this and the code is in a pull request here: > https://github.com/numpy/numpy/pull/111 > This didn't go in, because the datetime properties don't exist on the arrays after you convert them to datetime64, so there could be some unintuitive consequences from that. When Martin implemented the quaternion dtype, we discussed the possibility that dtypes could expose properties that show up on the array object, and if this were implemented I think the conversion and compatibility between python datetime and datetime64 could be made quite natural. > -Mark >
Actually, at first glance, I don't see why this shouldn't go ahead as-is. If I know I am getting datetime64, then I should expect to lose the features of the datetime object, right. Sure, it would be nice if it kept those attributes, but keeping them would provide an inconsistent interface in the case of a numpy array created from datetime objects and one created from datetime64 objects (unless I misunderstood) I will read through the pull request more closely and comment further. Ben Root
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