scipy.stats.

scoreatpercentile#

scipy.stats.scoreatpercentile(a, per, limit=(), interpolation_method='fraction', axis=None)[source]#

Calculate the score at a given percentile of the input sequence.

For example, the score at per=50 is the median. If the desired quantile lies between two data points, we interpolate between them, according to the value of interpolation. If the parameter limit is provided, it should be a tuple (lower, upper) of two values.

Parameters:

aarray_like

A 1-D array of values from which to extract score.

perarray_like

Percentile(s) at which to extract score. Values should be in range [0,100].

limittuple, optional

Tuple of two scalars, the lower and upper limits within which to compute the percentile. Values of a outside this (closed) interval will be ignored.

interpolation_method{‘fraction’, ‘lower’, ‘higher’}, optional

Specifies the interpolation method to use, when the desired quantile lies between two data points i and j The following options are available (default is ‘fraction’):

‘fraction’: i + (j - i) * fraction where fraction is the fractional part of the index surrounded by i and j

‘lower’: i

‘higher’: j

axisint, optional

Axis along which the percentiles are computed. Default is None. If None, compute over the whole array a.

Returns:

scorefloat or ndarray: Score at percentile(s).

See also

percentileofscore, numpy.percentile

Notes

This function will become obsolete in the future. For NumPy 1.9 and higher, numpy.percentile provides all the functionality that scoreatpercentile provides. And it’s significantly faster. Therefore it’s recommended to use numpy.percentile for users that have numpy >= 1.9.

scoreatpercentile has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	⛔
PyTorch	⛔	⛔
JAX	⛔	⛔
Dask	⛔	n/a

See Support for the array API standard for more information.

Examples

>>> import numpy as np
>>> from scipy import stats
>>> a = np.arange(100)
>>> stats.scoreatpercentile(a, 50)
49.5