Scipy sparse matrix variance and standard deviation

vars_stds.py

import numpy as np

def vars(a, axis=None):
    """ Variance of sparse matrix a
    var = mean(a**2) - mean(a)**2
    """
    a_squared = a.copy()
    a_squared.data **= 2
    return a_squared.mean(axis) - np.square(a.mean(axis))

def stds(a, axis=None):
    """ Standard deviation of sparse matrix a
    std = sqrt(var(a))
    """
    return np.sqrt(vars(a, axis))