[Numpy-discussion] start of an array (tensor) and dataframe API standardization initiative

[YueCompl]

This is great!

I'm working on some Haskell based mmap shared array lib, with Python like surface language API. I would adhere to such standard very willingly.

A quick skim but I can't find dataframe related info, that's scheduled for the future? Will take Pandas as primary reference?

Thanks with best regards,
Compl


> On 2020-11-11, at 02:19, Ralf Gommers <ralf.gommers at gmail.com> wrote:
> 
> Hi all,
> 
> I'd like to share an update on this topic. The draft array API standard is now ready for wider review:
> 
> - Blog post: https://data-apis.org/blog/array_api_standard_release <https://data-apis.org/blog/array_api_standard_release/>
> - Array API standard document: https://data-apis.github.io/array-api/latest/ <https://data-apis.github.io/array-api/latest/>
> - Repo: https://github.com/data-apis/array-api/ <https://github.com/data-apis/array-api/>
> 
> It would be great if people - and in particular, NumPy maintainers - could have a look at it and see if that looks sensible from a NumPy perspective and whether the goals and benefits of adopting it are described clearly enough and are compelling.
> 
> I'm sure a NEP will be needed for proposing adoption of the standard once it is closer to completion, and work out what that means for interaction with the array protocol NEPs and/or NEP 37, and how an implementation would look. It's a bit early for that now, I'm thinking maybe by the end of the year. Some initial discussion now would be useful though, since it's easier to make changes now rather than when that API standard is already further along.
> 
> Cheers,
> Ralf
> 
> 
> On Mon, Aug 17, 2020 at 9:34 PM Ralf Gommers <ralf.gommers at gmail.com <mailto:ralf.gommers at gmail.com>> wrote:
> Hi all,
> 
> I'd like to share this announcement blog post about the creation of a consortium for array and dataframe API standardization here: https://data-apis.org/blog/announcing_the_consortium/ <https://data-apis.org/blog/announcing_the_consortium/>. It's still in the beginning stages, but starting to take shape. We have participation from one or more maintainers of most array and tensor libraries - NumPy, TensorFlow, PyTorch, MXNet, Dask, JAX, Xarray. Stephan Hoyer, Travis Oliphant and myself have been providing input from a NumPy perspective.
> 
> The effort is very much related to some of the interoperability work we've been doing in NumPy (e.g. it could provide an answer to what's described in https://numpy.org/neps/nep-0037-array-module.html#requesting-restricted-subsets-of-numpy-s-api <https://numpy.org/neps/nep-0037-array-module.html#requesting-restricted-subsets-of-numpy-s-api>).
> 
> At this point we're looking for feedback from maintainers at a high level (see the blog post for details).
> 
> Also important: the python-record-api tooling and data in its repo has very granular API usage data, of the kind we could really use when making decisions that impact backwards compatibility.
> 
> Cheers,
> Ralf
> 
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