def neighbord_analysis(x_as, column=0):
    '\n\tGiven an array xas this function compute the distance between the elements the mean distance and the variance\n\t\n\tAuthor: Michele Monti\n\t\n\tArgs:\n\t\t\tx_as: the name of the list or data set that you want:\n\t\n\tKwargs:\n\t\tcolumn: is the column of the data set that you need to analyze\n\n\tReturns:\n\t\tmean_distance: the mean distance between neighbords, \n\t\tstd_dev: stdeviation of the distances between neighbords.\n\t\tdiff_neighbor: the difference between the first-neighbours in a list \n\t'
    x_as = np.array(x_as)
    correct_axis = x_as
    if (shape(x_as) > 1):
        correct_axis = x_as[:, column]
    diff_neighbor = [(itm - correct_axis[(idx - 1)]) for (idx, itm) in enumerate(correct_axis)][1:]
    mean_distance = np.mean(diff_neighbor)
    std_dev = np.std(diff_neighbor)
    return (diff_neighbor, mean_distance, std_dev)
Given an array xas this function compute the distance between the elements the mean distance and the variance

Author: Michele Monti

Args:
                x_as: the name of the list or data set that you want:

Kwargs:
        column: is the column of the data set that you need to analyze

Returns:
        mean_distance: the mean distance between neighbords, 
        std_dev: stdeviation of the distances between neighbords.
        diff_neighbor: the difference between the first-neighbours in a list
def neighbord_analysis(x_as, column=0):
    '\n\tGiven an array xas this function compute the distance between the elements the mean distance and the variance\n\t\n\tAuthor: Michele Monti\n\t\n\tArgs:\n\t\t\tx_as: the name of the list or data set that you want:\n\t\n\tKwargs:\n\t\tcolumn: is the column of the data set that you need to analyze\n\n\tReturns:\n\t\tmean_distance: the mean distance between neighbords, \n\t\tstd_dev: stdeviation of the distances between neighbords.\n\t\tdiff_neighbor: the difference between the first-neighbours in a list \n\t'
    x_as = np.array(x_as)
    correct_axis = x_as
    if (shape(x_as) > 1):
        correct_axis = x_as[:, column]
    diff_neighbor = [(itm - correct_axis[(idx - 1)]) for (idx, itm) in enumerate(correct_axis)][1:]
    mean_distance = np.mean(diff_neighbor)
    std_dev = np.std(diff_neighbor)
    return (diff_neighbor, mean_distance, std_dev)