# numpy.random.random_integers

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

  Random integers of type np.int_ between low and high, inclusive.

  Return random integers of type np.int_ from the “discrete uniform” distribution in the closed interval [low, high]. If high is None (the default), then results are from [1, low]. The np.int_ type translates to the C long integer type and its precision is platform dependent.

  This function has been deprecated. Use randint instead.

  Deprecated since version 1.11.0.

  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

  `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 # random
      >>> type(np.random.random_integers(5))
      <class 'numpy.int64'>
      >>> np.random.random_integers(5, size=(3,2))
      array([[5, 4], # random
             [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 \({0, 5/8, 10/8, 15/8, 20/8}\)):

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

  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, density=True)
      >>> plt.show()