Numpy-scalars vs Numpy 0-d arrays: copy or not copy?

[Sebastien Bardeau]

Hi!

I am confused with Numpy behavior with its scalar or 0-d arrays objects:

 >>> numpy.__version__
'1.0rc2'
 >>> a = numpy.array((1,2,3))
 >>> b = a[:2]
 >>> b += 1
 >>> b
array([2, 3])
 >>> a
array([2, 3, 3])
 >>> type(b)
<type 'numpy.ndarray'>

To this point all is ok for me: subarrays share (by default) memory with 
their parent array. But:

 >>> c = a[2]
 >>> c += 1
 >>> c
4
 >>> a
array([2, 3, 3])
 >>> type(c)
<type 'numpy.int32'>
 >>> id(c)
169457808
 >>> c += 1
 >>> id(c)
169737448

That's really confusing, because slices (from __getslice__ method) are 
not copies (they share memory), and items (single elements from 
__getitem__ ) are copies to one of the scalar objects provided by Numpy. 
I can understand that numpy.scalars do not provide inplace operations 
(like Python standard scalars, they are immutable), so I'd like to use 
0-d Numpy.ndarrays. But:

 >>> d = numpy.array(a[2],copy=False)
 >>> d += 1
 >>> d
array(4)
 >>> a
array([2, 3, 3])
 >>> type(d)
<type 'numpy.ndarray'>
 >>> d.shape
()
 >>> id(d)
169621280
 >>> d += 1
 >>> id(d)
169621280

This is not a solution because d is a copy since construction time...
My question is: is there a way to get a single element of an array into 
a 0-d array which shares memory with its parent array?

Thx for your help,

Sebastien


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