The behavior of your sort function is not any of the desirable options. Moving NaNs to the end is not the widely used Panda style of removing them; I cannot think of any situation where that behavior would be useful... even though I've read the Illiad.
Other than wastefully creating an eager list, your filter is the same as I suggested for the one behavior. In general, passing a sort function, while powerful, is terrible API design for the non-experts who are main users of statistics module. On Thu, Dec 26, 2019, 10:42 PM Marco Sulla via Python-ideas < python-ideas@python.org> wrote: > Oh my... Mertz, listen to me, you don't need a parameter. You only need a > key function to pass to `sorted()` > > If you use this key function: > > https://mail.python.org/archives/list/python-ideas@python.org/message/M3DEOZLA63Z5OIF6H6ZCEXK36GQMLVVA/ > > in my median() function: > > https://mail.python.org/archives/list/python-ideas@python.org/message/KN6BSMJRVCPSQW32DTWQHTGZ5E3E5KK2/ > > you can simply do: > > ``` > median(iterable, key=iliadSort) > ``` > > and you have "poisoned" your data. > > If you want to use the sorted iterable again later in the code, you can > just do: > > ``` > sorted_it = sorted(iterable, key=iliadSort) > median(sorted_it, sort_fn=None) > ``` > > I prefer this approach, since this way you can avoid potentially > re-sorting of your data. > > For the same reason, if you want to remove the NaNs, it's better to create > an iterable apart instead of creating it on the fly, because you can > reutilize it: > > ``` > filtered_it = [x for x in iterable if not math.isnan(x)] > median(filtered_it) > ``` > > If you want to raise an error, you can use another key function: > > ``` > class NanError(ValueError): > pass > > def alertNan(x): > if math.isnan(x): > raise NanError("There a NaN in my dish!") > > return x > > ``` > > and then use it: > > ``` > median(iterable, key=alertNan) > ``` > > But if you absolutely want a nan parameter, you can create a wrapper for > `sorted`: > > ``` > def nansorted(iterable, on_nan="poison", **kwargs): > if on_nan == "poison": > return sorted(iterable, key=iliadSort, **kwargs) > > if on_nan == "remove": > new_iterable = [x for x in iterable if not math.isnan(x)] > return sorted(new_iterable, **kwargs) > > if on_nan == "raise": > return sorted(iterable, key=alertNan, **kwargs) > > raise ValueError(f"Unsupported on_nan parameter value: {on_nan}") > ``` > > and then use it in my `median()`: > > ``` > median(iterable, sort_fn=nansorted, on_nan="raise") > ``` > > or, as before > > ``` > sorted_it = nansorted(iterable, on_nan="poison") > median(sorted_it, sort_fn=None) > ``` >
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