This is going to need to be heavily documented with doctests. Also, just to clarify, are we talking about a ValueError for doing a nansum on an empty array as well, or will that now return a zero?
Ben Root On Mon, Jul 15, 2013 at 9:52 AM, Charles R Harris <charlesr.har...@gmail.com > wrote: > > > On Sun, Jul 14, 2013 at 3:35 PM, Charles R Harris < > charlesr.har...@gmail.com> wrote: > >> >> >> On Sun, Jul 14, 2013 at 2:55 PM, Warren Weckesser < >> warren.weckes...@gmail.com> wrote: >> >>> On 7/14/13, Charles R Harris <charlesr.har...@gmail.com> wrote: >>> > Some corner cases in the mean, var, std. >>> > >>> > *Empty arrays* >>> > >>> > I think these cases should either raise an error or just return nan. >>> > Warnings seem ineffective to me as they are only issued once by >>> default. >>> > >>> > In [3]: ones(0).mean() >>> > >>> /home/charris/.local/lib/python2.7/site-packages/numpy/core/_methods.py:61: >>> > RuntimeWarning: invalid value encountered in double_scalars >>> > ret = ret / float(rcount) >>> > Out[3]: nan >>> > >>> > In [4]: ones(0).var() >>> > >>> /home/charris/.local/lib/python2.7/site-packages/numpy/core/_methods.py:76: >>> > RuntimeWarning: invalid value encountered in true_divide >>> > out=arrmean, casting='unsafe', subok=False) >>> > >>> /home/charris/.local/lib/python2.7/site-packages/numpy/core/_methods.py:100: >>> > RuntimeWarning: invalid value encountered in double_scalars >>> > ret = ret / float(rcount) >>> > Out[4]: nan >>> > >>> > In [5]: ones(0).std() >>> > >>> /home/charris/.local/lib/python2.7/site-packages/numpy/core/_methods.py:76: >>> > RuntimeWarning: invalid value encountered in true_divide >>> > out=arrmean, casting='unsafe', subok=False) >>> > >>> /home/charris/.local/lib/python2.7/site-packages/numpy/core/_methods.py:100: >>> > RuntimeWarning: invalid value encountered in double_scalars >>> > ret = ret / float(rcount) >>> > Out[5]: nan >>> > >>> > *ddof >= number of elements* >>> > >>> > I think these should just raise errors. The results for ddof >= >>> #elements >>> > is happenstance, and certainly negative numbers should never be >>> returned. >>> > >>> > In [6]: ones(2).var(ddof=2) >>> > >>> /home/charris/.local/lib/python2.7/site-packages/numpy/core/_methods.py:100: >>> > RuntimeWarning: invalid value encountered in double_scalars >>> > ret = ret / float(rcount) >>> > Out[6]: nan >>> > >>> > In [7]: ones(2).var(ddof=3) >>> > Out[7]: -0.0 >>> > * >>> > nansum* >>> > >>> > Currently returns nan for empty arrays. I suspect it should return nan >>> for >>> > slices that are all nan, but 0 for empty slices. That would make it >>> > consistent with sum in the empty case. >>> > >>> >>> >>> For nansum, I would expect 0 even in the case of all nans. The point >>> of these functions is to simply ignore nans, correct? So I would aim >>> for this behaviour: nanfunc(x) behaves the same as func(x[~isnan(x)]) >>> >>> >> Agreed, although that changes current behavior. What about the other >> cases? >> >> > Looks like there isn't much interest in the topic, so I'll just go ahead > with the following choices: > > Non-NaN case > > 1) Empty array -> ValueError > > The current behavior with stats is an accident, i.e., the nan arises from > 0/0. I like to think that in this case the result is any number, rather > than not a number, so *the* value is simply not defined. So in this case > raise a ValueError for empty array. > > 2) ddof >= n -> ValueError > > If the number of elements, n, is not zero and ddof >= n, raise a > ValueError for the ddof value. > > Nan case > > 1) Empty array -> Value Error > 2) Empty slice -> NaN > 3) For slice ddof >= n -> Nan > > Chuck > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > >
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