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Module « scipy.stats.mstats »

Fonction zscore - module scipy.stats.mstats

Signature de la fonction zscore

def zscore(a, axis=0, ddof=0, nan_policy='propagate') 

Description

help(scipy.stats.mstats.zscore)

Compute the z score.

Compute the z score of each value in the sample, relative to the
sample mean and standard deviation.

Parameters
----------
a : array_like
    An array like object containing the sample data.
axis : int or None, optional
    Axis along which to operate. Default is 0. If None, compute over
    the whole array `a`.
ddof : int, optional
    Degrees of freedom correction in the calculation of the
    standard deviation. Default is 0.
nan_policy : {'propagate', 'raise', 'omit'}, optional
    Defines how to handle when input contains nan. 'propagate' returns nan,
    'raise' throws an error, 'omit' performs the calculations ignoring nan
    values. Default is 'propagate'.  Note that when the value is 'omit',
    nans in the input also propagate to the output, but they do not affect
    the z-scores computed for the non-nan values.

Returns
-------
zscore : array_like
    The z-scores, standardized by mean and standard deviation of
    input array `a`.

See Also
--------
numpy.mean : Arithmetic average
numpy.std : Arithmetic standard deviation
scipy.stats.gzscore : Geometric standard score

Notes
-----
This function preserves ndarray subclasses, and works also with
matrices and masked arrays (it uses `asanyarray` instead of
`asarray` for parameters).

References
----------
.. [1] "Standard score", *Wikipedia*,
       https://en.wikipedia.org/wiki/Standard_score.
.. [2] Huck, S. W., Cross, T. L., Clark, S. B, "Overcoming misconceptions
       about Z-scores", Teaching Statistics, vol. 8, pp. 38-40, 1986

Examples
--------
>>> import numpy as np
>>> a = np.array([ 0.7972,  0.0767,  0.4383,  0.7866,  0.8091,
...                0.1954,  0.6307,  0.6599,  0.1065,  0.0508])
>>> from scipy import stats
>>> stats.zscore(a)
array([ 1.1273, -1.247 , -0.0552,  1.0923,  1.1664, -0.8559,  0.5786,
        0.6748, -1.1488, -1.3324])

Computing along a specified axis, using n-1 degrees of freedom
(``ddof=1``) to calculate the standard deviation:

>>> b = np.array([[ 0.3148,  0.0478,  0.6243,  0.4608],
...               [ 0.7149,  0.0775,  0.6072,  0.9656],
...               [ 0.6341,  0.1403,  0.9759,  0.4064],
...               [ 0.5918,  0.6948,  0.904 ,  0.3721],
...               [ 0.0921,  0.2481,  0.1188,  0.1366]])
>>> stats.zscore(b, axis=1, ddof=1)
array([[-0.19264823, -1.28415119,  1.07259584,  0.40420358],
       [ 0.33048416, -1.37380874,  0.04251374,  1.00081084],
       [ 0.26796377, -1.12598418,  1.23283094, -0.37481053],
       [-0.22095197,  0.24468594,  1.19042819, -1.21416216],
       [-0.82780366,  1.4457416 , -0.43867764, -0.1792603 ]])

An example with ``nan_policy='omit'``:

>>> x = np.array([[25.11, 30.10, np.nan, 32.02, 43.15],
...               [14.95, 16.06, 121.25, 94.35, 29.81]])
>>> stats.zscore(x, axis=1, nan_policy='omit')
array([[-1.13490897, -0.37830299,         nan, -0.08718406,  1.60039602],
       [-0.91611681, -0.89090508,  1.4983032 ,  0.88731639, -0.5785977 ]])

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