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.
Lowest (signed) integer to be drawn from the distribution (unless
high=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
- sizeint or tuple of ints, optional
Output shape. If the given shape is, e.g.,
(m, n, k), then
m * n * ksamples are drawn. Default is None, in which case a single value is returned.
- 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.
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.)
>>> 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 ):
>>> 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()