numpy.random.randn¶

numpy.random.
randn
(d0, d1, ..., dn)¶ 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(d0, d1, ..., dn)
, filled with random floats sampled from a univariate “normal” (Gaussian) distribution of mean 0 and variance 1 (if any of the d_i 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:  d0, d1, …, dn : int, optional
The dimensions of the returned array, should be all positive. If no argument is given a single Python float is returned.
Returns:  Z : ndarray or float
A
(d0, d1, ..., dn)
shaped array of floatingpoint samples from the standard normal distribution, or a single such float if no parameters were supplied.
See also
standard_normal
 Similar, but takes a tuple as its argument.
Notes
For random samples from N(\mu, \sigma^2), 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