# 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).

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.

Examples

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