# numpy.random.RandomState.randn

method

random.RandomState.randn(d0, d1, ..., dn)

  Return a sample (or samples) from the “standard normal” distribution.

  Note

  This is a convenience function for users porting code from Matlab, and wraps `standard_normal`. That function takes a tuple to specify the size of the output, which is consistent with other NumPy functions like `numpy.zeros` and `numpy.ones`.

  Note

  New code should use the `standard_normal` method of a `default_rng()` instance instead; please see the Quick Start.

  If positive int_like arguments are provided, `randn` generates an array of shape `(d0, d1, ..., dn)`, filled with random floats sampled from a univariate “normal” (Gaussian) distribution of mean 0 and variance 1. A single float randomly sampled from the distribution is returned if no argument is provided.

  Parameters
    d0, d1, …, dn int, optional

      The dimensions of the returned array, must be non-negative. If no argument is given a single Python float is returned.

  Returns
    Z ndarray or float

      A `(d0, d1, ..., dn)`-shaped array of floating-point samples from the standard normal distribution, or a single such float if no parameters were supplied.

  See also

  `standard_normal`

    Similar, but takes a tuple as its argument.

  `normal`

    Also accepts mu and sigma arguments.

  `random.Generator.standard_normal`

    which should be used for new code.

  Notes

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

  `sigma * np.random.randn(...) + mu`

  Examples

      >>> np.random.randn()
      2.1923875335537315  # random

  Two-by-four array of samples from N(3, 6.25):

      >>> 3 + 2.5 * np.random.randn(2, 4)
      array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random
             [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random

previous

numpy.random.RandomState.rand

next

numpy.random.RandomState.randint