On Tue, Apr 1, 2014 at 9:51 PM, Ralf Gommers <[email protected]> wrote: > > > > On Tue, Apr 1, 2014 at 10:08 PM, Nathaniel Smith <[email protected]> wrote: >> >> On Tue, Apr 1, 2014 at 9:02 PM, Sturla Molden <[email protected]> >> wrote: >> > Haslwanter Thomas <[email protected]> wrote: >> > >> >> Personally I cannot think of many applications where it would be >> >> desired >> >> to calculate the standard deviation with ddof=0. In addition, I feel >> >> that >> >> there should be consistency between standard modules such as numpy, >> >> scipy, and pandas. >> > >> > ddof=0 is the maxiumum likelihood estimate. It is also needed in >> > Bayesian >> > estimation. >> >> It's true, but the counter-arguments are also strong. And regardless >> of whether ddof=1 or ddof=0 is better, surely the same one is better >> for both numpy and scipy. > > If we could still choose here without any costs, obviously that's true. This > particular ship sailed a long time ago though. By the way, there isn't even > a `scipy.stats.std`, so we're comparing with differently named functions > (nanstd for example).
Presumably nanstd is a lot less heavily used than std, and presumably people expect 'nanstd' to be a 'nan' version of 'std' -- what do you think of changing nanstd to ddof=0 to match numpy? (With appropriate FutureWarning transition, etc.) -- Nathaniel J. Smith Postdoctoral researcher - Informatics - University of Edinburgh http://vorpus.org _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
