# 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)`, then `m * 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.

`normal`

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
```