[Numpy-discussion] Implicit conversion of python datetime to numpy datetime64?
Benjamin Root
ben.root@ou....
Tue Feb 14 23:37:26 CST 2012
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 python datetime and datetime64 would allow numpy
to piggy-back on established functionality in the python system libraries,
allowing for focus to be given to extended features.
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