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

[Damian Eads]

In MATLAB, scalars are 1x1 arrays, and thus they can be indexed. There 
have been situations in my use of Numpy when I would have liked to index 
scalars to make my code more general.

It's not a very pressing issue for me but it is an interesting issue. 
Whenever I index an array with a sequence or slice I'm guaranteed to get 
another array out. This consistency is nice.

In [1]: A=numpy.random.rand(10)

In [2]: A[range(0,1)]
Out[2]: array([ 0.88109759])

In [3]: A[slice(0,1)]
Out[3]: array([ 0.88109759])

In [3]: A[[0]]
Out[3]: array([ 0.88109759])

However, when I index an array with an integer, I can get either a 
sequence or a scalar out.

In [4]: c1=A[0]
Out[4]: 0.88109759

In [5]: B=numpy.random.rand(5,5)

In [5]: c2=B[0]
Out[5]: array([ 0.81589633,  0.9762584 ,  0.72666631,  0.12700816, 
0.40653243])

Although c1 and c2 were derived by integer-indexing two different arrays 
of doubles, one is a sequence and the other is a scalar. This lack of 
consistency might be confusing to some people, and I'd imagine it 
occasionally results in programming errors.

Damian

Travis E. Oliphant wrote:
> Hi everybody,
> 
> In writing some generic code, I've encountered situations where it would 
> reduce code complexity to allow NumPy scalars to be "indexed" in the 
> same number of limited ways, that 0-d arrays support.
> 
> For example, 0-d arrays can be indexed with
> 
>     * Boolean masks
>     * Ellipses x[...]  and x[..., newaxis]
>     * Empty tuple x[()]
> 
> I think that numpy scalars should also be indexable in these particular 
> cases as well (read-only of course,  i.e. no setting of the value would 
> be possible).
> 
> This is an easy change to implement, and I don't think it would cause 
> any backward compatibility issues.
> 
> Any opinions from the list?
> 
> 
> Best regards,
> 
> -Travis O.