On Wed, Nov 21, 2012 at 7:45 PM, <[email protected]> wrote: > On Wed, Nov 21, 2012 at 9:22 PM, Olivier Delalleau <[email protected]> wrote: > > Current behavior looks sensible to me. I personally would prefer no > warning > > but I think it makes sense to have one as it can be helpful to detect > issues > > faster. > > I agree that nan should be the correct answer. > (I gave up trying to define a default for 0/0 in scipy.stats ttests.) > > some funnier cases > > >>> np.var([1], ddof=1) > 0.0 >
This one is a nan in development. > >>> np.var([1], ddof=5) > -0 > >>> np.var([1,2], ddof=5) > -0.16666666666666666 > >>> np.std([1,2], ddof=5) > nan > > These still do this. Also In [10]: var([], ddof=1) Out[10]: -0 Which suggests that the nan is pretty much an accidental byproduct of division by zero. I think it might make sense to have a definite policy for these corner cases. <snip> Chuck
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