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

[Robert Kern]

Your example used np.round(), not the builtin round(). np.round() is not
changing. If you want the dtype of the output to be the dtype of the input,
you can certainly keep using np.round() (or its canonical spelling,
np.around()).

On Thu, Feb 27, 2020, 12:05 AM Ilhan Polat <ilhanpolat at gmail.com> wrote:

> 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.int64s?
>>>>>>
>>>>>>    - 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
>>
> _______________________________________________
> 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/d1f0ca69/attachment-0001.html>