On Tue, Feb 14, 2012 at 9:17 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. > > 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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