numpy.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 type used by Python 2 for “short” integers 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 : int, optional If provided, the largest (signed) integer to be drawn from the distribution (see above for behavior if size : int or tuple of ints, optional Output shape. If the given shape is, e.g., 

Returns: 
out : int or ndarray of ints

See also
random.randint
random_integers
, only for the halfopen interval [low
, high
), and 0 is the lowest value if high
is omitted.To sample from N evenly spaced floatingpoint numbers between a and b, use:
a + (b  a) * (np.random.random_integers(N)  1) / (N  1.)
>>> np.random.random_integers(5) 4 >>> type(np.random.random_integers(5)) <type 'int'> >>> np.random.random_integers(5, size=(3,2)) array([[5, 4], [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 ])
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, normed=True) >>> plt.show()
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Licensed under the NumPy License.
https://docs.scipy.org/doc/numpy1.14.2/reference/generated/numpy.random.random_integers.html