On Fri, Jun 10, 2011 at 6:35 PM, Charles R Harris
<charlesr.har...@gmail.com> wrote:
> On Fri, Jun 10, 2011 at 5:19 PM, Olivier Delalleau <sh...@keba.be> wrote:
>> But isn't it a bug if numpy.dtype('i') != numpy.dtype('l') on a 32 bit
>> computer where both are int32?
>>
>
> Maybe yes, maybe no ;) They have different descriptors, so from numpy's
> perspective they are different, but at the hardware/precision level they are
> the same. It's more of a decision as to what != means in this case. Since
> numpy started as Numeric with only the c types the current behavior is
> consistent, but that doesn't mean it shouldn't change at some point.
Maybe this is the same question, but are you maybe yes, maybe no on this too:
>>> type(np.sum([1, 2, 3], dtype=np.int32)) == np.int32
False
Ben, what happens if you put an axis in there? Like
>>> np.sum([[1, 2, 3], [4,5,6]], axis=0).dtype == np.int32
Just wondering if this is another different-dtype-for-different-axis case.
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