`numpy.random:RandomState.standard_normal`

Signature

def standard_normal(size = None)

Draw samples from a standard Normal distribution (mean=0, stdev=1).

size : int 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.

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

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=...)

np.random.standard_normal()

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-1.4605549319534663

s = np.random.standard_normal(8000)

✓

✗

array([ 0.72207177, -0.431767  , -0.97062797, ..., -0.76863801,
        2.21979488, -1.12869481], shape=(8000,))

s.shape
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))

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array([[6.49528847, 0.51320294, 4.90665838, 2.60873479],
       [2.10450704, 5.5719306 , 1.26342294, 3.55496605]])