numpy.random.standard_normal — NumPy v2.2 Manual (original) (raw)

random.standard_normal(size=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.

Returns:

outfloat or ndarray

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

Notes

For random samples from the normal distribution with mean mu and standard deviation sigma, 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