Module « scipy.stats »

Fonction describe - module scipy.stats

Signature de la fonction describe

def describe(a, axis=0, ddof=1, bias=True, nan_policy='propagate') 

Description

describe.__doc__

Compute several descriptive statistics of the passed array.

    Parameters
    ----------
    a : array_like
        Input data.
    axis : int or None, optional
        Axis along which statistics are calculated. Default is 0.
        If None, compute over the whole array `a`.
    ddof : int, optional
        Delta degrees of freedom (only for variance).  Default is 1.
    bias : bool, optional
        If False, then the skewness and kurtosis 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
    -------
    nobs : int or ndarray of ints
        Number of observations (length of data along `axis`).
        When 'omit' is chosen as nan_policy, the length along each axis
        slice is counted separately.
    minmax: tuple of ndarrays or floats
        Minimum and maximum value of `a` along the given axis.
    mean : ndarray or float
        Arithmetic mean of `a` along the given axis.
    variance : ndarray or float
        Unbiased variance of `a` along the given axis; denominator is number
        of observations minus one.
    skewness : ndarray or float
        Skewness of `a` along the given axis, based on moment calculations
        with denominator equal to the number of observations, i.e. no degrees
        of freedom correction.
    kurtosis : ndarray or float
        Kurtosis (Fisher) of `a` along the given axis.  The kurtosis is
        normalized so that it is zero for the normal distribution.  No
        degrees of freedom are used.

    See Also
    --------
    skew, kurtosis

    Examples
    --------
    >>> from scipy import stats
    >>> a = np.arange(10)
    >>> stats.describe(a)
    DescribeResult(nobs=10, minmax=(0, 9), mean=4.5,
                   variance=9.166666666666666, skewness=0.0,
                   kurtosis=-1.2242424242424244)
    >>> b = [[1, 2], [3, 4]]
    >>> stats.describe(b)
    DescribeResult(nobs=2, minmax=(array([1, 2]), array([3, 4])),
                   mean=array([2., 3.]), variance=array([2., 2.]),
                   skewness=array([0., 0.]), kurtosis=array([-2., -2.]))