numpy.random.Generator.standard_normal — NumPy v2.3.dev0 Manual (original) (raw)
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 and float32 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 if size was not specified.
See also
Equivalent function with additional loc and scale arguments for setting the mean and standard deviation.
Notes
For random samples from the normal distribution with mean mu and standard deviation sigma, 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 the normal distribution with mean 3 and standard deviation 2.5:
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