numpy.random.randn#

random.randn(d0, d1, ..., dn)#

Return a sample (or samples) from the “standard normal” distribution.

Note

This is a convenience function for users porting code from Matlab, and wraps standard_normal. That function takes a tuple to specify the size of the output, which is consistent with other NumPy functions like numpy.zeros and numpy.ones.

Note

New code should use the standard_normal method of a default_rng() instance instead; please see the Quick Start.

If positive int_like 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. A single float randomly sampled from the distribution is returned if no argument is provided.

Parameters

d0, d1, …, dnint, optional: The dimensions of the returned array, must be non-negative. If no argument is given a single Python float is returned.

Returns

Zndarray or float: A (d0, d1, ..., dn)-shaped array of floating-point 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.
normal: Also accepts mu and sigma arguments.
random.Generator.standard_normal: which should be used for new code.

Notes

For random samples from \(N(\mu, \sigma^2)\), use:

sigma * np.random.randn(...) + mu

Examples

>>> np.random.randn()
2.1923875335537315  # random

Two-by-four array of samples from N(3, 6.25):

>>> 3 + 2.5 * np.random.randn(2, 4)
array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random
       [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random

numpy.random.randint

numpy.random.random