`numpy.random:RandomState.random_integers`

Signature

def random_integers(low, high = None, size = None)

Summary

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

Extended Summary

Return random integers of type numpy.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 numpy.int_ type translates to the C long integer type and its precision is platform dependent.

This function has been deprecated. Use randint instead.

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.

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)

✗

np.int64(1)

type(np.random.random_integers(5))

✓

<class 'numpy.int64'>

np.random.random_integers(5, size=(3,2))

✗

array([[1, 2],
       [4, 2],
       [5, 2]])

Choose five random numbers from the set of five evenly-spaced numbers between 0 and 2.5, inclusive (*i.e.*, from the set :math:`{0, 5/8, 10/8, 15/8, 20/8}`):

2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.

✗

array([0.  , 1.25, 0.  , 0.  , 1.25])

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()

✓

Aliases

numpy.random.random_integers
numpy.random.RandomState.random_integers