[Numpy-discussion] Output type of round is inconsistent with python built-in

[Ilhan Polat]

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.kern at gmail.com> wrote:

> On Wed, Feb 26, 2020 at 5:41 PM <josef.pktd at gmail.com> wrote:
>
>>
>>
>> On Wed, Feb 26, 2020 at 5:30 PM Ilhan Polat <ilhanpolat at 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.kern at gmail.com> wrote:
>>>
>>>> On Wed, Feb 26, 2020 at 3:19 PM Hameer Abbasi <
>>>> einstein.edison at 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 at python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/numpy-discussion/attachments/20200227/b1053c06/attachment.html>