[Numpy-discussion] Integers to integer powers, let's make a decision

[Nathaniel Smith]

On Mon, Jun 6, 2016 at 1:17 PM, Charles R Harris
<charlesr.harris at gmail.com> wrote:
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> On Mon, Jun 6, 2016 at 2:11 PM, Marten van Kerkwijk <m.h.vankerkwijk at gmail.com> wrote:
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>> Hi Chuck,
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>> I consider either proposal an improvement, but among the two I favour returning float for `**`, because, like for `/`, it ensures one gets closest to the (mathematically) true answer in most cases, and makes duck-typing that much easier -- I'd like to be able to do x** y without having to worry whether x and y are python scalars or numpy arrays of certain type.
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>> I do agree with Nathaniel that it would be good to check what actually breaks. Certainly, if anybody is up to making a PR that implements either suggestion, I'd gladly check whether it breaks anything in astropy.
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>> I  should add that I have no idea how to assuage the fear that new code would break with old versions of numpy, but on the other hand, I don't know its vailidity either, as it seems one either develops larger projects  for multiple versions and tests, or writes more scripty things for whatever the current versions are. Certainly, by this argument I better not start using the new `@` operator!
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>> I do think the argument that for division it was easier because there was `//` already available is a red herring: here one can use `np.power(a, b, dtype=...)` if one really needs to.
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> It looks to me like users want floats, while developers want the easy path of raising an error. Darn those users, they just make life sooo difficult...

I dunno, with my user hat on I'd be incredibly surprised / confused /
annoyed if an innocent-looking expression like

  np.arange(10) ** 2

started returning floats... having exact ints is a really nice feature
of Python/numpy as compared to R/Javascript, and while it's true that
int64 can overflow, there are also large powers that can be more
precisely represented as int64 than float.

-n

-- 
Nathaniel J. Smith -- https://vorpus.org