Hi all,
I would like to share the first formal draft of
NEP 50: Promotion rules for Python scalars
with everyone. The full text can be found here:
https://numpy.org/neps/nep-0050-scalar-promotion.html
NEP 50 is an attempt to remove value-based casting/promotion. We wish
to replace it
On Wed, Jun 1, 2022 at 5:51 PM Sebastian Berg
wrote:
>
> An important part of moving forward will be assessing the real world
> impact. To start that process, I have created a branch as a draft PR
> (at this time):
>
> https://github.com/numpy/numpy/pull/21626
>
> It is missing some parts, b
On Wed, 2022-06-01 at 20:23 +0200, Ralf Gommers wrote:
> On Wed, Jun 1, 2022 at 5:51 PM Sebastian Berg
>
> wrote:
>
> >
> > An important part of moving forward will be assessing the real
> > world
> > impact. To start that process, I have created a branch as a draft
> > PR
> > (at this time):
>
Hi all,
this has been discussed before, so mainly a brief announcement that we
merged a PR to add the `equal_nan` kwarg to `np.unique`.
If set to False, multiple `NaN`s will be reported multiple times (which
was the behavior prior to NumPy 1.21).
The keyword argument name was chosen to match tha
Hi all,
just another small API announcement, that I merged:
https://github.com/numpy/numpy/pull/16154
which adds `symbol="x"` to the polynomial classes. Ross' more detailed
explanation is copied below.
Cheers,
Sebastian
New attribute ``symbol`` added to polynomial classes
-
> For example, in NumPy:
>
>np.median(np.float32([1, 2, 3, 4]))
>
> did return a float64 before and will now return a float32. I assume
> because somewhere we write: `(np.float64(3) + np.float32(2)) / 2`.
Sorry, I missed this part of the discussion — I know the discussion centered
around P
On Wed, 2022-06-01 at 18:37 -0500, Juan Nunez-Iglesias wrote:
> > For example, in NumPy:
> >
> > np.median(np.float32([1, 2, 3, 4]))
> >
> > did return a float64 before and will now return a float32. I
> > assume
> > because somewhere we write: `(np.float64(3) + np.float32(2)) / 2`.
>
> Sorr
