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 Generator 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 the normal distribution with mean mu and standard deviation sigma, use:

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

Examples

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

Two-by-four array of samples from the normal distribution with mean 3 and standard deviation 2.5:

>>> 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