[Numpy-discussion] FeatureRequest: support for array construction from iterators
Juan Nunez-Iglesias
jni.soma at gmail.com
Sat Dec 12 18:02:06 EST 2015
Hey Nathaniel,
Fascinating! Thanks for the primer! I didn't know that it would check dtype
of values in the whole array. In that case, I would agree that it would be
bad to infer it magically from just the first value, and this can be left
to the users.
Thanks!
Juan.
On Sat, Dec 12, 2015 at 7:00 PM, Nathaniel Smith <njs at pobox.com> wrote:
> On Fri, Dec 11, 2015 at 11:32 PM, Juan Nunez-Iglesias
> <jni.soma at gmail.com> wrote:
> > Nathaniel,
> >
> >> IMO this is better than making np.array(iter) internally call list(iter)
> >> or equivalent
> >
> > Yeah but that's not the only option:
> >
> > from itertools import chain
> > def fromiter_awesome_edition(iterable):
> > elem = next(iterable)
> > dtype = whatever_numpy_does_to_infer_dtypes_from_lists(elem)
> > return np.fromiter(chain([elem], iterable), dtype=dtype)
> >
> > I think this would be a huge win for usability. Always getting tripped
> up by
> > the dtype requirement. I can submit a PR if people like this pattern.
>
> This isn't the semantics of np.array, though -- np.array will look at
> the whole input and try to find a common dtype, so this can't be the
> implementation for np.array(iter). E.g. try np.array([1, 1.0])
>
> I can see an argument for making the dtype= argument to fromiter
> optional, with a warning in the docs that it will guess based on the
> first element and that you should specify it if you don't want that.
> It seems potentially a bit error prone (in the sense that it might
> make it easier to end up with code that works great when you test it
> but then breaks later when something unexpected happens), but maybe
> the usability outweighs that. I don't use fromiter myself so I don't
> have a strong opinion.
>
> > btw, I think np.array(['f', 'o', 'o']) would be exactly the expected
> result
> > for np.array('foo'), but I guess that's just me.
>
> In general np.array(thing_that_can_go_inside_an_array) returns a
> zero-dimensional (scalar) array -- np.array(1), np.array(True), etc.
> all work like this, so I'd expect np.array("foo") to do the same.
>
> -n
>
> --
> Nathaniel J. Smith -- http://vorpus.org
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