Hello Sebastian, I rarely use NumPy scalars directly, but the repr change could have impact in assorted downstream projects' documentation. For clarity, this idea would not alter how NumPy arrays print, would it - since they already include the type information?
np.array([34.3, 10.1, -0.5], np.float32) array([34.3, 10.1, -0.5], dtype=float32) np.array([5, 10, 0], np.uint8) array([ 5, 10, 0], dtype=uint8)
Thanks, Peter On Thu, Sep 8, 2022 at 10:42 AM Sebastian Berg <sebastian@sipsolutions.net> wrote:
TL;DR: NumPy scalars representation is e.g. `34.3` instead of `float32(34.3)`. So the representation is missing the type information. What are your thoughts on changing that?
Hi all,
I am thinking about the next steps for NEP 50 (The NEP wants to fix the NumPy promotion rules, especially with respect to scalars):
https://numpy.org/neps/nep-0050-scalar-promotion.html
In relation to that, there was one point that Stéfan brought up previously.
The NumPy scalars (representation) currently print as numbers:
>>> np.float32(34.3) 34.3 >>> np.uint8(5) 5
That can already be confusing now. However, it gets more problematic if NEP 50 is introduced since the behavior between a Python `34.3` and `np.float32(34.3)` would differ more than it does now (please refer to the NEP).
The change would be that we should print as:
float64(34.3) (or similar?)
This Email is mainly to ask for any feedback or concern on such a change. I suspect we may have to write a very brief NEP about it.
If there is little concern, maybe we could move forward such a change promptly. Otherwise it could be moved forward together with NEP 50 and take effect in a "major" release [1].
Cheers,
Sebastian
[1] Note that for me, even a major release would hopefully not affect the majority of users or be very disruptive.
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