[Numpy-discussion] Why NaN?

[Keith Goodman]

On Tue, Aug 4, 2009 at 9:54 AM, Keith Goodman<kwgoodman@gmail.com> wrote:
> On Tue, Aug 4, 2009 at 9:46 AM, Gökhan Sever<gokhansever@gmail.com> wrote:
>> Hello,
>>
>> I know this has to have a very simple answer, but stuck at this very moment
>> and can't get a meaningful result out of np.mean()
>>
>>
>> In [121]: a = array([NaN, 4, NaN, 12])
>>
>> In [122]: b = array([NaN, 2, NaN, 3])
>>
>> In [123]: c = a/b
>>
>> In [124]: mean(c)
>> Out[124]: nan
>>
>> In [125]: mean a
>> --------> mean(a)
>> Out[125]: nan
>>
>> Further when I tried:
>>
>> In [138]: c
>> Out[138]: array([ NaN,   2.,  NaN,   4.])
>>
>> In [139]: np.where(c==NaN)
>> Out[139]: (array([], dtype=int32),)
>>
>>
>> In [141]: mask = [c != NaN]
>>
>> In [142]: mask
>> Out[142]: [array([ True,  True,  True,  True], dtype=bool)]
>>
>>
>> Any ideas?
>
>>> a = array([NaN, 4, NaN, 12])
>>> b = array([NaN, 2, NaN, 3])
>>> c = a/b
>>> from scipy import stats
>>> stats.nan [tab]
> stats.nanmean    stats.nanmedian  stats.nanstd
>>> stats.nanmean(c)
>   3.0
>>> stats.nanmean(a)
>   8.0
>>> c[isnan(c)]
>   array([ NaN,  NaN])

One more:

>> c[isfinite(c)].mean()
   3.0