To me, adding enums as attributes of the `np.copy` function seems like a pretty good idea. This trick might resolve the only relatively important issue with Enums. Then, the benefits of Enum might outweigh the disadvantage of uncommon of usage of Enums in NumPy APIs. As an end user, I would like Enums rather than strings as the former would provide fixed number of choices (hence, easy debugging) as compared to the latter (in which case, infinite choices for passing strings and the code may work silently, imagine, passing, `if_neded` instead of `if_needed` and it working perfectly fine (silently). This thing has happened to me while using another library.
Why not both? The definition of the enum might live in a proper namespace location, but I see no reason why `np.copy.IF_NEEDED = np.flags.CopyFlgs.IF_NEEDED` can't be done (I mean, adding the enum members as attributes to the `np.copy()` function). Seems perfectly reasonable to me, and reads pretty nicely, too. It isn't like we are dropping support for the booleans, so those are still around for easy typing.
Ben Root
On Wed, Jun 23, 2021, at 18:01, Juan Nunez-Iglesias wrote:
> Personally I was a fan of the Enum approach. People dislike it because
> it is not “Pythonic”, but imho that is an accident of history because
> Enums only appeared (iirc) in Python 3.4. In fact, they are the right
> data structure for this particular problem, so for my money we should
> *make it* Pythonic by starting to use it everywhere where we have a
> finite list of choices.
The enum definitely feels like the right abstraction. But the resulting API is clunky because of naming and top-level scarcity.
Hence the suggestion to tag it onto np.copy, but there is an argument to be made for consistency by placing all enums under np.flags or similar.
Still, np.flags.copy.IF_NEEDED gets long.
Stéfan
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