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Module « scipy.stats »

Fonction variation - module scipy.stats

Signature de la fonction variation

def variation(a, axis=0, nan_policy='propagate', ddof=0, *, keepdims=False) 

Description

help(scipy.stats.variation)

    


Compute the coefficient of variation.

The coefficient of variation is the standard deviation divided by the
mean.  This function is equivalent to::

    np.std(x, axis=axis, ddof=ddof) / np.mean(x)

The default for ``ddof`` is 0, but many definitions of the coefficient
of variation use the square root of the unbiased sample variance
for the sample standard deviation, which corresponds to ``ddof=1``.

The function does not take the absolute value of the mean of the data,
so the return value is negative if the mean is negative.

Parameters
----------
a : array_like
    Input array.
axis : int or None, default: 0
    If an int, the axis of the input along which to compute the statistic.
    The statistic of each axis-slice (e.g. row) of the input will appear in a
    corresponding element of the output.
    If ``None``, the input will be raveled before computing the statistic.
nan_policy : {'propagate', 'omit', 'raise'}
    Defines how to handle input NaNs.
    
    - ``propagate``: if a NaN is present in the axis slice (e.g. row) along
      which the  statistic is computed, the corresponding entry of the output
      will be NaN.
    - ``omit``: NaNs will be omitted when performing the calculation.
      If insufficient data remains in the axis slice along which the
      statistic is computed, the corresponding entry of the output will be
      NaN.
    - ``raise``: if a NaN is present, a ``ValueError`` will be raised.
ddof : int, optional
    Gives the "Delta Degrees Of Freedom" used when computing the
    standard deviation.  The divisor used in the calculation of the
    standard deviation is ``N - ddof``, where ``N`` is the number of
    elements.  `ddof` must be less than ``N``; if it isn't, the result
    will be ``nan`` or ``inf``, depending on ``N`` and the values in
    the array.  By default `ddof` is zero for backwards compatibility,
    but it is recommended to use ``ddof=1`` to ensure that the sample
    standard deviation is computed as the square root of the unbiased
    sample variance.
keepdims : bool, default: False
    If this is set to True, the axes which are reduced are left
    in the result as dimensions with size one. With this option,
    the result will broadcast correctly against the input array.

Returns
-------
variation : ndarray
    The calculated variation along the requested axis.

Notes
-----
There are several edge cases that are handled without generating a
warning:

* If both the mean and the standard deviation are zero, ``nan``
  is returned.
* If the mean is zero and the standard deviation is nonzero, ``inf``
  is returned.
* If the input has length zero (either because the array has zero
  length, or all the input values are ``nan`` and ``nan_policy`` is
  ``'omit'``), ``nan`` is returned.
* If the input contains ``inf``, ``nan`` is returned.

Beginning in SciPy 1.9, ``np.matrix`` inputs (not recommended for new
code) are converted to ``np.ndarray`` before the calculation is performed. In
this case, the output will be a scalar or ``np.ndarray`` of appropriate shape
rather than a 2D ``np.matrix``. Similarly, while masked elements of masked
arrays are ignored, the output will be a scalar or ``np.ndarray`` rather than a
masked array with ``mask=False``.

References
----------
.. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
   Probability and Statistics Tables and Formulae. Chapman & Hall: New
   York. 2000.

Examples
--------
>>> import numpy as np
>>> from scipy.stats import variation
>>> variation([1, 2, 3, 4, 5], ddof=1)
0.5270462766947299

Compute the variation along a given dimension of an array that contains
a few ``nan`` values:

>>> x = np.array([[  10.0, np.nan, 11.0, 19.0, 23.0, 29.0, 98.0],
...               [  29.0,   30.0, 32.0, 33.0, 35.0, 56.0, 57.0],
...               [np.nan, np.nan, 12.0, 13.0, 16.0, 16.0, 17.0]])
>>> variation(x, axis=1, ddof=1, nan_policy='omit')
array([1.05109361, 0.31428986, 0.146483  ])

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