This is documentation for an old release of NumPy (version 1.19). Read this page Search for this page in the documentation of the latest stable release (version 2.2).

numpy.random.RandomState.randn

method

RandomState.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; see random-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.

Generator.standard_normal

which should be used for new code.

Notes

For random samples from , 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