numpy.random.standard_normal#
- random.standard_normal(size=None)#
Draw samples from a standard Normal distribution (mean=0, stdev=1).
Note
New code should use the
standard_normal
method of aGenerator
instance instead; please see the Quick start.- 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.
- 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.random.Generator.standard_normal
which should be used for new code.
Notes
For random samples from the normal distribution with mean
mu
and standard deviationsigma
, use one of:mu + sigma * np.random.standard_normal(size=...) np.random.normal(mu, sigma, size=...)
Examples
>>> np.random.standard_normal() 2.1923875335537315 #random
>>> s = np.random.standard_normal(8000) >>> s array([ 0.6888893 , 0.78096262, -0.89086505, ..., 0.49876311, # random -0.38672696, -0.4685006 ]) # random >>> s.shape (8000,) >>> s = np.random.standard_normal(size=(3, 4, 2)) >>> s.shape (3, 4, 2)
Two-by-four array of samples from the normal distribution with mean 3 and standard deviation 2.5:
>>> 3 + 2.5 * np.random.standard_normal(size=(2, 4)) array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], # random [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) # random