NumPy

numpy.random.Generator.standard_normal

method

Generator.standard_normal(size=None, dtype='d', out=None)

Draw samples from a standard Normal distribution (mean=0, stdev=1).

Parameters
sizeint or tuple of ints, optional

Output shape. If the given shape is, e.g., (m, n, k), then m * n * k samples are drawn. Default is None, in which case a single value is returned.

dtype{str, dtype}, optional

Desired dtype of the result, either ‘d’ (or ‘float64’) or ‘f’ (or ‘float32’). All dtypes are determined by their name. The default value is ‘d’.

outndarray, optional

Alternative output array in which to place the result. If size is not None, it must have the same shape as the provided size and must match the type of the output values.

Returns
outfloat or ndarray

A floating-point array of shape size of drawn samples, or a single sample if size was not specified.

See also

normal

Equivalent function with additional loc and scale arguments for setting the mean and standard deviation.

Notes

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

mu + sigma * gen.standard_normal(size=...)
gen.normal(mu, sigma, size=...)

Examples

>>> rng = np.random.default_rng()
>>> rng.standard_normal()
2.1923875335537315 #random
>>> s = rng.standard_normal(8000)
>>> s
array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311,  # random
       -0.38672696, -0.4685006 ])                                # random
>>> s.shape
(8000,)
>>> s = rng.standard_normal(size=(3, 4, 2))
>>> s.shape
(3, 4, 2)

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

>>> 3 + 2.5 * rng.standard_normal(size=(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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