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 ``` 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
