On Wed, Nov 30, 2022 at 10:39 AM Sebastian Berg wrote:

Hi all,

there is a discussion about how `round(array)` should behave in:

https://github.com/numpy/numpy/issues/6248

There is some discussion about object arrays which should probably be fixed for `around()` in that ago.

Otherwise, the is the question what to do about the fact that:

* round(np.float64(2.**64)) -> 18446744073709551616 (a Python int) * round(np.array([2., 3., 2.**64]) can only return float or integer

The NumPy `np.round`/`np.around` (same function) functions always return the same dtype.

We can either ignore that discrepancy, or opt to raise an error, so that:

>>> round(np.array([2., 3.5])) TypeError: Rounding a NumPy float array cannot return integers, use `round(arr, ndigits=0)` or `round(arr, 0) to indicate that a float result is desired.

In the call today, I think we leaned a bit towards ignoring it, but if I read Aaron correctly, he prefers the error and it may be the conservative choice.

I guess I weakly prefer it, but I mostly just suggested it because it is the conservative option. Making ndigits=None just match ndigits=0 (so that round() works like around()) is also fine IMO. I don't think it would be too surprising for most people to have round() work that way on arrays. What I feel more strongly about is that there shouldn't be distinct behavior for scalars vs. arrays, and the output dtype shouldn't be value-based. Aaron Meurer

We could of course do other things (i.e. return an integer `intp` array) but that would probably require a hard error for overflows (which is different from `np.rint(arr).astype(np.intp)`.

Are there any small or big opinions on this? It seems useful to enable `round()` and is a bit of a shame to get caught up on the detail, but I am not sure what the right choice is :).

- Sebastian

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