[Numpy-discussion] Implicit conversion of python datetime to numpy datetime64?

Benjamin Root ben.root@ou....
Wed Feb 15 08:29:28 CST 2012


On Tuesday, February 14, 2012, Mark Wiebe <mwwiebe@gmail.com> wrote:
> On Tue, Feb 14, 2012 at 9:37 PM, Benjamin Root <ben.root@ou.edu> wrote:
>
> On Tuesday, February 14, 2012, Mark Wiebe <mwwiebe@gmail.com> wrote:
>> On Tue, Feb 14, 2012 at 8:17 PM, Benjamin Root <ben.root@ou.edu> 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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