Return a sample (or samples) from the “standard normal” distribution.
If positive, int_like or intconvertible arguments are provided, randn generates an array of shape (d1, ..., dn), filled with random floats sampled from a univariate “normal” (Gaussian) distribution of mean 0 and variance 1 (if any of the are floats, they are first converted to integers by truncation). A single float randomly sampled from the distribution is returned if no argument is provided.
This is a convenience function. If you want an interface that takes a tuple as the first argument, use numpy.random.standard_normal instead.
Parameters:  d1, ..., dn : n ints, optional


Returns:  Z : ndarray or float

See also
Notes
For random samples from , use:
sigma * np.random.randn(...) + mu
Examples
>>> np.random.randn()
2.1923875335537315 #random
Twobyfour array of samples from N(3, 6.25):
>>> 2.5 * np.random.randn(2, 4) + 3
array([[4.49401501, 4.00950034, 1.81814867, 7.29718677], #random
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random