def standard_deviation(x):
    """
    Return a numpy array of column standard deviation

    Parameters
    ----------
    x : ndarray
        A numpy array instance

    Returns
    -------
    ndarray
        A 1 x n numpy array instance of column standard deviation

    Examples
    --------
    >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    >>> np.testing.assert_array_almost_equal(
    ...     standard_deviation(a),
    ...     [0.816496, 0.816496, 0.816496])
    >>> a = np.array([1, 2, 3])
    >>> np.testing.assert_array_almost_equal(
    ...     standard_deviation(a),
    ...     0.816496)

    """
    if x.ndim > 1 and len(x[0]) > 1:
        return np.std(x, axis=1)
    return np.std(x)
def standard_deviation ( x ) : if x . ndim > 1 and len ( x [ 0 ] ) > 1 : return np . std ( x , axis = 1 ) return np . std ( x )
Return a numpy array of column standard deviation

    Parameters
    ----------
    x : ndarray
        A numpy array instance

    Returns
    -------
    ndarray
        A 1 x n numpy array instance of column standard deviation

    Examples
    --------
    >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
    >>> np.testing.assert_array_almost_equal(
    ...     standard_deviation(a),
    ...     [0.816496, 0.816496, 0.816496])
    >>> a = np.array([1, 2, 3])
    >>> np.testing.assert_array_almost_equal(
    ...     standard_deviation(a),
    ...     0.816496)