numpy.random.random_integers — NumPy v2.3.dev0 Manual (original) (raw)
random.random_integers(low, high=None, size=None)#
Random integers of type numpy.int_ between low and high, inclusive.
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.
Deprecated since version 1.11.0.
Parameters:
lowint
Lowest (signed) integer to be drawn from the distribution (unlesshigh=None, in which case this parameter is the highest such integer).
highint, optional
If provided, the largest (signed) integer to be drawn from the distribution (see above for behavior if high=None).
sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g., (m, n, k), thenm * n * k samples are drawn. Default is None, in which case a single value is returned.
Returns:
outint 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
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()
