[Numpy-discussion] Matching 0-d arrays and NumPy scalars

[Travis E. Oliphant]

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