You have a strongly ordered `NotNan` struct that wraps a float that's guaranteed to not be NaN, and an `OrderedFloat` that consideres all NaN equal, and greater than non-NaN values.
These are basically the special-cases you'd need to handle yourself anyway, and probably one of the approaches you'd end up taking.
I am not saying we do not need NaNs (I would even love to see them in integers, see: https://news.ycombinator.com/item?id=45174074), but I would prefer if we had less of them in floats with clear sorting rules.
But NaN could be defined to be smaller or higher than any other value.
Well, there are multiple NaN. And NaN isn't actually the only weirdness; there's also -0, and we have -0 == 0. I think equality for floating point is anyway weird, so then why not just define -0 < 0.
How should an algorithm specify that it should sort by insertion order instead of memory address order if the sort key is NaN for multiple records?
That's the default in SQL Relational Algebra IIRC?
Well each programming language has a "sort" method that sorts arrays. Should this method throw an exception in case of NaN? I think the NaN rules were the wrong decision. Because of these rules, everywhere there are floating point numbers, the libraries have to have special code for NaN, even if they don't care about NaN. Otherwise there might be ugly bugs, like sorting running into endless loops, data loss, etc. But well, it can't be changed now.
The best description of the decision is probably [1], where Stephen Canon (former member of the IEEE-754 committee if I understand correctly) explains the reasoning.
[1] https://stackoverflow.com/questions/1565164/what-is-the-rati...
There's probably no good way to standardize how to fill when values are null or nan. How else could this be solved without adding special cases for NaN?
In a language with type annotations we indicate whether a type is Optional:
def sum(a: float|None, b: Optional[float]) -> None|float :
def sum(a: float|np.nan|None, b: Optional[float|np.nan]) -> None|float|np.nan :