[Numpy-discussion] field names on numpy arrays
Wed Jun 3 16:03:49 CDT 2009
On Wed, Jun 3, 2009 at 15:26, <email@example.com> wrote:
> 2009/6/3 Stéfan van der Walt <firstname.lastname@example.org>:
>> Hi Jon
>> 2009/6/3 D2Hitman <email@example.com>:
>>> I understand record arrays such as:
>>> a_array =
>>> do this with field names.
>>> a_array['a'] = array([ 0., 1.], dtype=float32)
>>> however i seem to lose simple operations such as multiplication (a_array*2)
>>> or powers (a_array**2).
>> As a workaround, you can have two views on your data:
>> n : x
>> array([(0.0, 1.0, 2.0, 3.0, 4.0), (1.0, 2.0, 3.0, 4.0, 5.0)],
>> dtype=[('a', '<f4'), ('b', '<f4'), ('c', '<f4'), ('d', '<f4'),
>> ('e', '<f4')])
>> In : x = x_dict.view(np.float32)
>> In : x**2
>> Out: array([ 0., 1., 4., 9., 16., 1., 4., 9., 16.,
>> 25.], dtype=float32)
>> Then you can manipulate the same data using two different "interfaces".
> Why does it not preserve "shape", to do e.g. np.mean by axis?
It does preserve the shape. The input and output are both 1D. If you
need a different shape (e.g. re-interpreting the record as another
axis), you need to reshape it yourself. numpy can't guess what you
"I have come to believe that the whole world is an enigma, a harmless
enigma that is made terrible by our own mad attempt to interpret it as
though it had an underlying truth."
-- Umberto Eco
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