numpy.random.RandomState.random_integers¶
method

RandomState.
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
 lowint
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
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
sizeshaped 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 halfopen interval [low, high), and 0 is the lowest value if high is omitted.
Notes
To sample from N evenly spaced floatingpoint 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 evenlyspaced 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()