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# numpy.random.random_integers

`numpy.random.``random_integers`(low, high=None, size=None)

  Return random integers between low and high, inclusive.

  Return random integers from the “discrete uniform” distribution in the closed interval [low, high]. If high is None (the default), then results are from [1, low].

  Parameters: | low : int Lowest (signed) integer to be drawn from the distribution (unless `high=None`, in which case this parameter is the highest such integer). high : int, optional If provided, the largest (signed) integer to be drawn from the distribution (see above for behavior if `high=None`). 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.
  Returns: | out : int or ndarray of ints size-shaped array of random integers from the appropriate distribution, or a single such random int if size not provided.

  See also

  `random.randint`
    Similar to `random_integers`, only for the half-open interval [low, high), and 0 is the lowest value if high is omitted.

  Notes

  To sample from N evenly spaced floating-point numbers between a and b, use:

      a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)

  Examples

      >>> np.random.random_integers(5)
      4
      >>> type(np.random.random_integers(5))
      <type 'int'>
      >>> np.random.random_integers(5, size=(3.,2.))
      array([[5, 4],
             [3, 3],
             [4, 5]])

  Choose five random numbers from the set of five evenly-spaced numbers between 0 and 2.5, inclusive (i.e., from the set ):

      >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.
      array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ])

  Roll two six sided dice 1000 times and sum the results:

      >>> d1 = np.random.random_integers(1, 6, 1000)
      >>> d2 = np.random.random_integers(1, 6, 1000)
      >>> dsums = d1 + d2

  Display results as a histogram:

      >>> import matplotlib.pyplot as plt
      >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)
      >>> plt.show()

  (Source code, png, pdf)