> I was playing with this though and was a little surprised to find > NumPy allows things like this: > > >>> a = np.array([1, 2, 3]) > >>> a[:] = np.array([[[5, 6, 7]]]) > >>> a > array([5, 6, 7])
Thanks Aaron for this example! I hadn't seen this before, and indeed the suggested PR doesn't intercept this case. Perhaps that's something we should consider deprecating as well. I'll add a comment to the PR; let's take it from there. Cheers, Nico On Thu, Sep 16, 2021 at 2:00 AM Aaron Meurer <asmeu...@gmail.com> wrote: > > Presumably this also changes int(), bool(), and complex() in the same way. > > The array API standard (and numpy.array_api) only requires float(), > bool(), and int() (and soon complex()) for dimension 0 arrays (the > standard does not have scalars), in part because of this NumPy issue > https://data-apis.org/array-api/latest/API_specification/array_object.html#float-self. > > On Wed, Sep 15, 2021 at 6:18 AM Nico Schlömer <nico.schloe...@gmail.com> > wrote: > > > > Hi everyone, > > > > This is seeking input on PR [1] which I've worked on with @eric-wieser > > and @seberg. It deprecates > > ``` > > float(x) > > ``` > > if `x` is an array of ndim > 0. (It works with all arrays of size 1 > > right now.) This aligns the behavior of float() on ndarrays with > > float() on lists which already fails today. It also deprecates the > > implicit conversion to float in assignment expressions like > > ``` > > a = np.array([1, 2, 3]) > > a[0] = [5] # deprecated, should be a[0] = 5 > > This already gives a ValueError in NumPy 1.21.1. Do you mean a[0] = > np.array([5]) is deprecated? > > I was playing with this though and was a little surprised to find > NumPy allows things like this: > > >>> a = np.array([1, 2, 3]) > >>> a[:] = np.array([[[5, 6, 7]]]) > >>> a > array([5, 6, 7]) > > Array assignment allows some sort of reverse broadcasting? Given this > behavior, it seems to me that a[0] = np.array([5]) actually should > work. Or is the idea that this entire behavior would be deprecated? > > Aaron Meurer > > > ``` > > In general, the PR makes numpy a tad bit stricter on how it treats > > scalars vs. single-item arrays. > > > > The change also prevents the #1 wrong usage of float(), namely for > > extracting the scalar value from an array. One should rather use > > `x[0]` or `x.item()` to that which doesn't convert the value to a > > Python float. > > > > To estimate the impact of the PR, I looked at major numpy dependents > > like matplotlib, scipy, pandas etc., and of course numpy itself. > > Except scipy, all projects were virtually clean to start with. Scipy > > needed some changes for all tests to pass without warning, and all of > > the changes were improvements. In particular, the deprecation > > motivates users to use actual scalars when scalars are needed, e.g., > > in the case of scipy, as the return value of a goal functional. > > > > It'd be great if you could try the branch against your own project and > > let us know (here or in the PR) about and problems that you might > > have. > > > > Thanks! > > Nico > > > > [1] https://github.com/numpy/numpy/pull/10615 > > [2] https://github.com/numpy/numpy/issues/10404 > > _______________________________________________ > > NumPy-Discussion mailing list > > NumPy-Discussion@python.org > > https://mail.python.org/mailman/listinfo/numpy-discussion > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
