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

[Robert Kern]

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
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