method
Generator.
standard_normal
Draw samples from a standard Normal distribution (mean=0, stdev=1).
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.
(m, n, k)
m * n * k
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’.
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.
A floating-point array of shape size of drawn samples, or a single sample if size was not specified.
size
See also
normal
Equivalent function with additional loc and scale arguments for setting the mean and standard deviation.
loc
scale
Notes
For random samples from
System Message: WARNING/2 (N(\mu, \sigma^2))
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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
System Message: WARNING/2 (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