[Python-ideas] Users of statistics software, what quantile functionality would be useful for you?
Stephan Hoyer
shoyer at gmail.com
Sat Apr 27 19:21:09 EDT 2019
On Sat, Apr 27, 2019 at 6:10 AM Steven D'Aprano <steve at pearwood.info> wrote:
> The statistics module is soon to get a quantile function.
>
> For those of you who use statistics software (whether in Python, or
> using other languages) and specifically use quantiles, what sort of
> functionality would be useful to you?
>
> For example:
>
> - evenly-spaced quantiles (say, at 0.25, 0.5, 0.75)?
> - unevenly-spaced quantiles (0.25, 0.8, 0.9, 0.995)?
>
If I'm interested in multiple quantiles, they are usually unevenly spaced.
Something like [0.8, 0.9, 0.95, 0.99, 0.999] would be pretty typical if I'm
not sure what the right threshold for an "outlier" is.
> - one quantile at a time?
>
Yes, this is also quite common, once I know what threshold I care about.
> - any specific definition?
>
NumPy's quantile function has an "interpolation" option for controlling the
quantile definition. But in years of calculating quantiles for data
analysis, I've never used it.
> - quantiles of a distribution?
>
Yes, rarely -- though the only example that comes to mind is quantiles for
a Normal distribution. (scipy.stats supports this use-case well.)
> - anything else?
>
The flexibility of calculating either one or multiple quantiles with
np.quantile() is pretty convenient. But this might make for a more dynamic
type signature that you'd like for the standard library, e.g.,
T = TypeVar('T')
@overload
def quantile(data: Iterable[T], threshold: float) -> T: ...
@overload
def quantile(data: Iterable[T], threshold: Sequence[float]) -> List[T]: ...
> Thanks in advance.
>
>
> --
> Steven
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