Module « scipy.stats.mstats »

Fonction gmean - module scipy.stats.mstats

Signature de la fonction gmean

def gmean(a, axis=0, dtype=None, weights=None) 

Description

gmean.__doc__

Compute the geometric mean along the specified axis.

    Return the geometric average of the array elements.
    That is:  n-th root of (x1 * x2 * ... * xn)

    Parameters
    ----------
    a : array_like
        Input array or object that can be converted to an array.
    axis : int or None, optional
        Axis along which the geometric mean is computed. Default is 0.
        If None, compute over the whole array `a`.
    dtype : dtype, optional
        Type of the returned array and of the accumulator in which the
        elements are summed. If dtype is not specified, it defaults to the
        dtype of a, unless a has an integer dtype with a precision less than
        that of the default platform integer. In that case, the default
        platform integer is used.
    weights : array_like, optional
        The weights array can either be 1-D (in which case its length must be
        the size of `a` along the given `axis`) or of the same shape as `a`.
        Default is None, which gives each value a weight of 1.0.

    Returns
    -------
    gmean : ndarray
        See `dtype` parameter above.

    See Also
    --------
    numpy.mean : Arithmetic average
    numpy.average : Weighted average
    hmean : Harmonic mean

    Notes
    -----
    The geometric average is computed over a single dimension of the input
    array, axis=0 by default, or all values in the array if axis=None.
    float64 intermediate and return values are used for integer inputs.

    Use masked arrays to ignore any non-finite values in the input or that
    arise in the calculations such as Not a Number and infinity because masked
    arrays automatically mask any non-finite values.

    References
    ----------
    .. [1] "Weighted Geometric Mean", *Wikipedia*, https://en.wikipedia.org/wiki/Weighted_geometric_mean.

    Examples
    --------
    >>> from scipy.stats import gmean
    >>> gmean([1, 4])
    2.0
    >>> gmean([1, 2, 3, 4, 5, 6, 7])
    3.3800151591412964