On Mon, Dec 30, 2019 at 3:32 AM Andrew Barnert via Python-ideas <
python-ideas@python.org> wrote:
> On Dec 29, 2019, at 23:50, Steven D'Aprano <st...@pearwood.info> wrote:
> >
> > On Sun, Dec 29, 2019 at 06:23:03PM -0800, Andrew Barnert via
> Python-ideas wrote:
> >
> >> Likewise, it’s even easier to write ignore-nan yourself than to write
> the DSU yourself:
> >>
> >> median = statistics.median(x for x in xs if not x.isnan())
> >
> > Try that with xs = [1, 10**400, 2] and come back to me.
>
> Presumably the end user (unlike the statistics module) knows what data
> they have.
No, Steven is right here. In Python we might very sensibly mix numeric
datatypes. But this means we need an `is_nan()` function like some
discussed in these threads, not rely on a method (and not the same behavior
as math.isnan()).
E.g.:
my_data = {'observation1': 10**400, # really big amount
'observation2': 1, # ordinary size
'observation3': 2.0, # ordinary size
'observation4': math.nan # missing data }
median = statistics.median_high(x for x in my_data if not is_nan(x))
The answer '2.0' is plainly right here, and there's no reason we shouldn't
provide it.
--
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from the bellies of the hungry; books from the hands of the
uneducated; technology from the underdeveloped; and putting
advocates of freedom in prisons. Intellectual property is
to the 21st century what the slave trade was to the 16th.
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