On Thu, Jan 25, 2018 at 1:49 PM, Marten van Kerkwijk <
m.h.vankerkw...@gmail.com> wrote:
> On Thu, Jan 25, 2018 at 1:16 PM, Stefan van der Walt
> <stef...@berkeley.edu> wrote:
> > On Mon, 22 Jan 2018 10:11:08 -0500, Marten van Kerkwijk wrote:
> >>
> >> I think on the consistency argument is perhaps the most important:
> >> views are very powerful and in many ways one *counts* on them
> >> happening, especially in working with large arrays.
> >
> >
> > I had the same gut feeling, but the fancy indexing example made me
> > pause:
> >
> > In [9]: x = np.arange(12, dtype=float).reshape((3, 4))
> >
> > In [10]: p = x[[0, 1]] # copy of data
> >
> > Then:
> >
> > In [11]: x = np.array([(0, 1), (2, 3)], dtype=[('a', int), ('b', int)])
> >
> > In [12]: p = x[['a', 'b']] # copy of data, but proposal will change that
>
What does this do?
p = x[['a', 'b']].copy()
My impression is that the problems with the view are because the padded
view doesn't behave like a "standard" dtype or array, i.e. the follow-up
behavior is the problematic part.
Josef
> >
> > We're not doing the same kind of indexing here exactly (in one case we
> > grab elements, in the other parts of elements), but the view behavior
> > may still break the "mental expectation".
>
> A bit off-topic, but maybe this is another argument to just allow
> `x['a', 'b']` -- I never understood why a tuple was not the
> appropriate iterable for getting multiple items from a record.
>
> -- Marten
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