[Numpy-discussion] big-bangs versus incremental improvements (was: Re: SciPy 2014 BoF NumPy Participation)

[Todd]

On 5 Jun 2014 14:28, "David Cournapeau" <cournape at gmail.com> wrote:
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> On Thu, Jun 5, 2014 at 9:44 AM, Todd <toddrjen at gmail.com> wrote:
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>> On 5 Jun 2014 02:57, "Nathaniel Smith" <njs at pobox.com> wrote:
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>> > On Wed, Jun 4, 2014 at 7:18 AM, Travis Oliphant <travis at continuum.io>
wrote:
>> > And numpy will be much harder to replace than numeric --
>> > numeric wasn't the most-imported package in the pythonverse ;-).
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>> If numpy is really such a core part of  python ecosystem, does it really
make sense to keep it as a stand-alone package?  Rather than thinking about
a numpy 2, might it be better to be focusing on getting ndarray and dtype
to a level of quality where acceptance upstream might be plausible?
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> There has been discussions about integrating numpy a long time ago (can't
find a reference right now), and the consensus was that this was possible
in its current shape nor advisable. The situation has not changed.
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> Putting something in the stdlib means it basically cannot change anymore:
API compatibility requirements would be stronger than what we provide even
now. NumPy is also a large codebase which would need some major clean up to
be accepted, etc...
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> David

I am not suggesting merging all of numpy, only ndarray and dtype (which I
know is a huge job itself).  And perhaps not even all of what us currently
included in those, some methods could be split out to their own functions.

And any numpy 2.0 would also imply a major code cleanup.  So although
ndarray and dtype are certainly not ready for such a thing right now, if
you are talking about numpy 2.0 already, perhaps part of that discussion
could involve a plan to get the code into a state where such a move might
be plausible.  Even if the merge doesn't actually happen, having the code
at that quality level would still be a good thing.

I agree that the relationship between numpy and python has not changed very
much in the last few years, but I think the scientific computing landscape
is changing.  The latter issue is where my primary concern lies.
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