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)
, thenm * 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
andfloat32
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 ifsize
was not specified.
See also
normal
Equivalent function with additional
loc
andscale
arguments for setting the mean and standard deviation.
Notes
For random samples from \(N(\mu, \sigma^2)\), use one of:
mu + sigma * rng.standard_normal(size=...) rng.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