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scoreatpercentile(a, per, limit=(), interpolation_method='fraction', axis=None)

For example, the score at :None:None:`per=50` is the median. If the desired quantile lies between two data points, we interpolate between them, according to the value of :None:None:`interpolation`. If the parameter :None:None:`limit` is provided, it should be a tuple (lower, upper) of two values.

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.

Parameters

a : array_like

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

per : array_like

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

limit : tuple, 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 :None:None:`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

axis : int, optional

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

Returns

score : float or ndarray: Score at percentile(s).

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

Examples

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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

scipy.linalg._decomp_svd.svdvals scipy.stats._stats_py.scoreatpercentile scipy.sparse._construct.random scipy.interpolate._rbfinterp.RBFInterpolator scipy.sparse.linalg._eigen._svds.svds

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