On Tue, Jan 08, 2019 at 04:25:17PM +0900, Stephen J. Turnbull wrote:
> Steven D'Aprano writes:
>
> > By definition, data containing Not A Number values isn't numeric :-)
>
> Unfortunately, that's just a joke, because in fact numeric functions
> produce NaNs.
I'm not sure if you're agreeing with
I'd like to see internal consistency across the central-tendency
statistics in the presence of NaNs. What happens now:
mean: the code appears to guarantee that a NaN will be returned if a
NaN is in the input.
median: as recently detailed, just about anything can happen,
depending on how undefi
On Mon, Jan 07, 2019 at 11:27:22AM +1100, Steven D'Aprano wrote:
[...]
> I propose adding a "nan_policy" keyword-only parameter to the relevant
> statistics functions (mean, median, variance etc), and defining the
> following policies:
I asked some heavy users of statistics software (not just
On Tue, Jan 8, 2019 at 11:57 PM Tim Peters wrote:
> I'd like to see internal consistency across the central-tendency
> statistics in the presence of NaNs. What happens now:
>
I think consistent NaN-poisoning would be excellent behavior. It will
always make sense for median (and its variants).
[David Mertz ]
> I think consistent NaN-poisoning would be excellent behavior. It will
> always make sense for median (and its variants).
>
>> >>> statistics.mode([2, 2, nan, nan, nan])
>> nan
>> >>> statistics.mode([2, 2, inf - inf, inf - inf, inf - inf])
>> 2
>
>
> But in the mode case, I'm not
