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Module « scipy.stats »

Fonction scoreatpercentile - module scipy.stats

Signature de la fonction scoreatpercentile

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

Description

help(scipy.stats.scoreatpercentile)

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
----------
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 `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).

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.

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

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