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

[Charles R Harris]

On Tue, Feb 14, 2012 at 9:17 PM, Benjamin Root <ben.root at 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.
>
> 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 works in the development branch,

In [1]: arange(0,3,1, dtype="datetime64[D]")
Out[1]: array(['1970-01-01', '1970-01-02', '1970-01-03'],
dtype='datetime64[D]')

but linspace is more complicated in that it might not be possible to
subdivide an interval into reasonable datetime64 units

In [4]: a = datetime64(0, 'D')

In [5]: b = datetime64(1, 'D')

In [6]: linspace(a, b, 5)
Out[6]: array(['1970-01-01', '1970-01-01', '1970-01-01', '1970-01-01',
'1970-01-02'], dtype='datetime64[D]')

Looks like a project for somebody. There is probably a lot of work along
that line to be done.

Chuck
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