It's not about what I want but this changes the output of round. In my example I didn't use any arrays but a scalar type which looks like will upcasted.
On Wed, Feb 26, 2020, 23:04 Robert Kern <robert.k...@gmail.com> wrote: > On Wed, Feb 26, 2020 at 5:41 PM <josef.p...@gmail.com> wrote: > >> >> >> On Wed, Feb 26, 2020 at 5:30 PM Ilhan Polat <ilhanpo...@gmail.com> wrote: >> >>> Does this mean that np.round(np.float32(5)) return a 64 bit upcasted int? >>> >>> That would be really awkward for many reasons pandas frame size being >>> bloated just by rounding for an example. Or numpy array size growing for no >>> apparent reason >>> >>> I am not really sure if I understand why LSP should hold in this case to >>> be honest. Rounding is an operation specific for the number instance and >>> not for the generic class. >>> >>> >>> >>> >>> On Wed, Feb 26, 2020, 21:38 Robert Kern <robert.k...@gmail.com> wrote: >>> >>>> On Wed, Feb 26, 2020 at 3:19 PM Hameer Abbasi < >>>> einstein.edi...@gmail.com> wrote: >>>> >>>>> >>>>> There still remains the question, do we return Python ints or np.int64 >>>>> s? >>>>> >>>>> - Python ints have the advantage of not overflowing. >>>>> - If we decide to add __round__ to arrays in the future, Python ints >>>>> may become inconsistent with our design, as such a method will return >>>>> an >>>>> int64 array. >>>>> >>>>> >>>>> >>>>> This was issue was discussed in the weekly triage meeting today, and >>>>> the following plan of action was proposed: >>>>> >>>>> - change scalar floats to return integers for __round__ (which >>>>> integer type was not discussed, I propose np.int64) >>>>> - not change anything else: not 0d arrays and not other numpy >>>>> functionality >>>>> >>>>> >> I think making numerical behavior different between arrays and numpy >> scalars with the same dtype, will create many happy debugging hours. >> > > round(some_ndarray) isn't implemented, so there is no difference to worry > about. > > If you want the float->float rounding, use np.around(). That function > should continue to behave like it currently does for both arrays and > scalars. > > -- > Robert Kern > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion >
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