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

[Travis E. Oliphant]

> Travis,
>
> You have been getting mostly objections so far;
I wouldn't characterize it that way, but yes 2 people have pushed back a 
bit, although one not directly speaking to the proposed behavior.  

The issue is that [] notation does more than just "select from a 
container" for NumPy arrays.   In particular, it is used to reshape an 
array to more dimensions:  [..., newaxis]

A common pattern is to reduce over a dimension and then re-shape the 
result so that it can be combined with the un-reduced object.  
Broadcasting makes this work if the dimension being reduced along is the 
first dimension.  But, broadcasting is not enough if you want the 
reduction dimension to be arbitrary:

Thus,

y = add.reduce(x, axis=-1)  produces an N-1 array if x is 2-d and a 
numpy scalar if x is 1-d.

Suppose y needs to be subtracted from x. 

If x is 2-d, then

 >>> x - y[...,newaxis]

is the needed code. But, if x is 1-d,  then

 >>> x - y[..., newaxis]

returns an error  and a check must be done to handle the case 
separately.  If y[..., newaxis]  worked and produced a 1-d array when y 
was a numpy scalar, this could be avoided.


-Travis O.