For what it's worth, these are fairly widely used functions.  From a user standpoint, I'd gently argue against deprecating them. Documenting the inconsistency with scalars  seems like a less invasive approach.

In particular ascontiguousarray is a very common check to make when working with C libraries or low-level file formats.  A significant advantage over asarray(..., order='C') is readability.  It makes the intention very clear.  Similarly, asfortranarray is quite readable for folks that aren't deeply familiar with numpy. 

Given that the use-cases they're primarily used for are likely to be read by developers working in other languages (i.e. ascontiguousarray gets used at a lot of "boundaries" with other systems), keeping function names that make intention very clear is important.

Just my $0.02, anyway.  Cheers,
-Joe

On Thu, Oct 25, 2018 at 3:17 PM Alex Rogozhnikov <alex.rogozhnikov@yandex.ru> wrote:
Dear numpy community,
 
I'm planning to depreciate np.asfortranarray and np.ascontiguousarray
functions due to their misbehavior on scalar (0-D tensors) with PR #12244.
 
Current behavior (converting scalars to 1-d array with single element)
- is unexpected and contradicts to documentation
- probably, can't be changed without breaking external code
- I believe, this was a cause for poor support of 0-d arrays in mxnet.
- both functions are easily replaced with asarray(..., order='...'), which has expected behavior
 
There is no timeline for removal - we just need to discourage from using this functions in new code.
 
Function naming may be related to how numpy treats 0-d tensors specially,  
and those probably should not be called arrays.
https://www.numpy.org/neps/nep-0027-zero-rank-arrarys.html
However, as a user I never thought about 0-d arrays being special and being "not arrays".
 
 
Please see original discussion at github for more details
https://github.com/numpy/numpy/issues/5300
 
Your comments welcome,
Alex Rogozhnikov
 
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