numpy.random.RandomState.random_integers — NumPy v2.6.dev0 Manual
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method
random.RandomState.random_integers(low, high=None, size=None)
Random integers of type
between low and high, inclusive.
Return random integers of type
from the “discrete uniform” distribution in the closed interval [low, high]. If high is None (the default), then results are from [1, low]. The
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:lowintLowest (signed) integer to be drawn from the distribution (unless high=None, in which case this parameter is the highest such integer).
highint, optionalIf provided, the largest (signed) integer to be drawn from the distribution (see above for behavior if high=None).
sizeint or tuple of ints, optionalOutput 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:outint or ndarray of ints
-shaped array of random integers from the appropriate distribution, or a single such random int if
not provided.
See also
Similar to
, 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 ]) # randomRoll 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+d2Display results as a histogram:
>>> importmatplotlib.pyplotasplt>>> count,bins,ignored=plt.hist(dsums,11,density=True)>>> plt.show()
