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# numpy.random.mtrand.RandomState.randn

`RandomState.``randn`([d1, ..., dn])

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

  If positive, int_like or int-convertible arguments are provided, randn generates an array of shape `(d1, ..., dn)`, filled with random floats sampled from a univariate “normal” (Gaussian) distribution of mean 0 and variance 1 (if any of the are floats, they are first converted to integers by truncation). A single float randomly sampled from the distribution is returned if no argument is provided.

  This is a convenience function. If you want an interface that takes a tuple as the first argument, use `numpy.random.standard_normal` instead.

  Parameters : | d1, ..., dn : n ints, optional The dimensions of the returned array, should be all positive.
  Returns : | Z : ndarray or float A `(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

  `random.standard_normal`
    Similar, but takes a tuple as its argument.

  Notes

  For random samples from , use:

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

  Examples

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

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

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

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