numpy.random.Generator.standard_normal¶

method

random.Generator.standard_normal(size=None, dtype=np.float64, 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.
dtypedtype, optional: Desired dtype of the result, only float64 and float32 are supported. Byteorder must be native. The default value is np.float64.
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

numpy.random.Generator.standard_gamma numpy.random.Generator.standard_t