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 wrapsstandard_normal. That function takes atuple to specify the size of the output, which is consistent withother NumPy functions likenumpy.zeros andnumpy.ones.

Note

New code should use thestandard_normalmethod of aGenerator instance instead;please see theQuick start.

If positive int_like arguments are provided,randn generates an arrayof shape(d0,d1,...,dn), filledwith random floats sampled from a univariate “normal” (Gaussian)distribution of mean 0 and variance 1. A single float randomly sampledfrom 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 fromthe standard normal distribution, or a single such float ifno 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 meanmu andstandard deviationsigma, use:

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

Examples

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

Two-by-four array of samples from the normal distribution withmean 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

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