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Fonction skew - module scipy.stats

Signature de la fonction skew

def skew(a, axis=0, bias=True, nan_policy='propagate') 

Description

skew.__doc__

Compute the sample skewness of a data set.

    For normally distributed data, the skewness should be about zero. For
    unimodal continuous distributions, a skewness value greater than zero means
    that there is more weight in the right tail of the distribution. The
    function `skewtest` can be used to determine if the skewness value
    is close enough to zero, statistically speaking.

    Parameters
    ----------
    a : ndarray
        Input array.
    axis : int or None, optional
        Axis along which skewness is calculated. Default is 0.
        If None, compute over the whole array `a`.
    bias : bool, optional
        If False, then the calculations are corrected for statistical bias.
    nan_policy : {'propagate', 'raise', 'omit'}, optional
        Defines how to handle when input contains nan.
        The following options are available (default is 'propagate'):

          * 'propagate': returns nan
          * 'raise': throws an error
          * 'omit': performs the calculations ignoring nan values

    Returns
    -------
    skewness : ndarray
        The skewness of values along an axis, returning 0 where all values are
        equal.

    Notes
    -----
    The sample skewness is computed as the Fisher-Pearson coefficient
    of skewness, i.e.

    .. math::

        g_1=\frac{m_3}{m_2^{3/2}}

    where

    .. math::

        m_i=\frac{1}{N}\sum_{n=1}^N(x[n]-\bar{x})^i

    is the biased sample :math:`i\texttt{th}` central moment, and
    :math:`\bar{x}` is
    the sample mean.  If ``bias`` is False, the calculations are
    corrected for bias and the value computed is the adjusted
    Fisher-Pearson standardized moment coefficient, i.e.

    .. math::

        G_1=\frac{k_3}{k_2^{3/2}}=
            \frac{\sqrt{N(N-1)}}{N-2}\frac{m_3}{m_2^{3/2}}.

    References
    ----------
    .. [1] Zwillinger, D. and Kokoska, S. (2000). CRC Standard
       Probability and Statistics Tables and Formulae. Chapman & Hall: New
       York. 2000.
       Section 2.2.24.1

    Examples
    --------
    >>> from scipy.stats import skew
    >>> skew([1, 2, 3, 4, 5])
    0.0
    >>> skew([2, 8, 0, 4, 1, 9, 9, 0])
    0.2650554122698573