[Numpy-discussion] numpy.nansum() behavior in 1.3.0

Keith Goodman kwgoodman@gmail....
Mon Jun 1 13:45:06 CDT 2009


On Mon, Jun 1, 2009 at 11:34 AM,  <josef.pktd@gmail.com> wrote:
> On Mon, Jun 1, 2009 at 2:26 PM, Keith Goodman <kwgoodman@gmail.com> wrote:
>> On Mon, Jun 1, 2009 at 11:16 AM,  <josef.pktd@gmail.com> wrote:
>>> On Mon, Jun 1, 2009 at 1:43 PM, Keith Goodman <kwgoodman@gmail.com> wrote:
>>>> On Mon, Jun 1, 2009 at 9:55 AM, Michael Hearne <mhearne@usgs.gov> wrote:
>>>>> A question (and possibly a bug):
>>>>>
>>>>> What should be returned when I do:
>>>>>
>>>>> numpy.nansum([])
>>>>>
>>>>> In my copy of numpy 1.1.1, I get 0.0.  This is what I would expect to
>>>>> see.
>>>>> However, this behavior seems to have changed in 1.3.0, in which I get
>>>>> nan.
>>>>
>>>> Here's a weird one. This is in numpy 1.2:
>>>>
>>>>>> np.sum(9)
>>>>   9
>>>>>> np.sum(9.0)
>>>>   9.0
>>>>>> np.nansum(9)
>>>>   9
>>>>>> np.nansum(9.0)
>>>> ---------------------------------------------------------------------------
>>>> IndexError: 0-d arrays can't be indexed.
>>>
>>> wrong argument in isnan, I think
>>>
>>> Josef
>>>
>>> In file: C:\Programs\Python25\Lib\site-packages\numpy\lib\function_base.py
>>>
>>> def _nanop(op, fill, a, axis=None):
>>>    """
>>> ...
>>>
>>>    """
>>>    y = array(a,subok=True)
>>> -    mask = isnan(a)
>>> +   mask = isnan(y)
>>>    if mask.all():
>>>        return np.nan
>>>
>>>    if not issubclass(y.dtype.type, np.integer):
>>>        y[mask] = fill
>>>
>>>    return op(y, axis=axis)
>>
>> The problem I came across, np.nansum(float), is caused by this line
>>
>> y[mask] = fill
>>
>> when y is 0-d.
>
> if mask is an array then this works, my initial solution was mask =
> np.array(mask).  then I thought it works also the other way, but it
> doesn't. I shouldn't have changed my mind
>
>>>> y
> array(9.0)
>>>> y[np.array(np.isnan(9))] = 0
>>>> y
> array(9.0)
>>>> y[np.isnan(np.array(9))] = 0
> Traceback (most recent call last):
>  File "<pyshell#45>", line 1, in <module>
>    y[np.isnan(np.array(9))] = 0
> IndexError: 0-d arrays can't be indexed.

Nice!

Are you going to make the change?


More information about the Numpy-discussion mailing list