# 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
    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.

    dtype dtype, optional

      Desired dtype of the result, only `float64` and `float32` are supported. Byteorder must be native. The default value is np.float64.

    out ndarray, 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
    out float or ndarray

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

  See also

  `normal`

    Equivalent function with additional `loc` and `scale` arguments for setting the mean and standard deviation.

  Notes

  For random samples from \(N(\mu, \sigma^2)\), 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 \(N(3, 6.25)\):

      >>> 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

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