numpy.setdiff1d(ar1, ar2, assume_unique=False)

numpy.ma.getdata(a, subok=True)

numpy.testing.assert_array_almost_equal(x, y, decimal=6, err_msg='', verbose=True)

MaskedArray.cumprod(axis=None, dtype=None, out=None)

ndarray.take(indices, axis=None, out=None, mode='raise') Return an array formed from the elements of a at the

ndarray.swapaxes(axis1, axis2) Return a view of the array with axis1 and axis2 interchanged

numpy.random.binomial(n, p, size=None) Draw samples from a binomial distribution. Samples are drawn from a binomial

numpy.random.power(a, size=None) Draws samples in [0, 1] from a power distribution with positive exponent a - 1. Also

numpy.random.dirichlet(alpha, size=None) Draw samples from the Dirichlet distribution. Draw size

generic.resize() Not implemented (virtual attribute) Class generic exists solely to derive numpy scalars from, and