Urgh. That's a nasty dilemma. I propose that the default should be return
NAN, since that's what you'd expect if you did the super-naive arithmetic
version (e.g. mean(x, y, z) = (x+y+z)/3).

On Mon, Aug 23, 2021 at 8:55 PM Steven D'Aprano <[email protected]> wrote:

> At the moment, the handling of NANs in the statistics module is
> implementation dependent. In practice, that *usually* means that if your
> data has a NAN in it, the result you get will probably be a NAN.
>
>     >>> statistics.mean([1, 2, float('nan'), 4])
>     nan
>
> But there are unfortunate exceptions to this:
>
>     >>> statistics.median([1, 2, float('nan'), 4])
>     nan
>     >>> statistics.median([float('nan'), 1, 2, 4])
>     1.5
>
> I've spoken to users of other statistics packages and languages, such as
> R, and I cannot find any consensus on what the "right" behaviour should
> be for NANs except "not that!".
>
> So I propose that statistics functions gain a keyword only parameter to
> specify the desired behaviour when a NAN is found:
>
> - raise an exception
>
> - return NAN
>
> - ignore it (filter out NANs)
>
> which seem to be the three most common preference. (It seems to be
> split roughly equally between the three.)
>
> Thoughts? Objections?
>
> Does anyone have any strong feelings about what should be the default?
>
>
> --
> Steve
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