[Numpy-discussion] Matching 0-d arrays and NumPy scalars
Travis E. Oliphant
oliphant@enthought....
Thu Feb 21 09:03:16 CST 2008
Konrad Hinsen wrote:
> On 21.02.2008, at 08:41, Francesc Altet wrote:
>
>
>> Well, it seems like a non-intrusive modification, but I like the
>> scalars
>> to remain un-indexable, mainly because it would be useful to raise an
>> error when you are trying to index them. In fact, I thought that when
>> you want a kind of scalar but indexable, you should use a 0-d array.
>>
>
> I agree. In fact, I'd rather see NumPy scalars move towards Python
> scalars rather than towards NumPy arrays in behaviour.
A good balance should be sought. I agree that improvements are needed,
especially because much behavior is still just a side-effect of how
things were implemented rather than specifically intentional.
> In particular,
> their nasty habit of coercing everything they are combined with into
> arrays is still my #1 source of compatibility problems with porting
> code from Numeric to NumPy. I end up converting NumPy scalars to
> Python scalars explicitly in lots of places.
>
This bit, for example, comes from the fact that most of the math on
scalars still uses ufuncs for their implementation. The numpy scalars
could definitely use some improvements.
However, I think my proposal for limited indexing capabilities should be
considered separately from coercion behavior of NumPy scalars. NumPy
scalars are intentionally different from Python scalars, and I see this
difference growing due to where Python itself is going. For example,
the int/long unification is going to change the ability for numpy.int to
inherit from int. I could also forsee the Python float being an
instance of a Decimal object or some other infinite precision float at
some point which would prevent inheritance for the numpy.float object.
The legitimate question is *how* different should they really be in each
specific case.
-Travis
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