numpy.random.Generator.integers¶
method

Generator.
integers
(low, high=None, size=None, dtype='int64', endpoint=False)¶ Return random integers from low (inclusive) to high (exclusive), or if endpoint=True, low (inclusive) to high (inclusive). Replaces
RandomState.randint
(with endpoint=False) andRandomState.random_integers
(with endpoint=True)Return random integers from the “discrete uniform” distribution of the specified dtype. If high is None (the default), then results are from 0 to low.
Parameters:  low : int or arraylike of ints
Lowest (signed) integers to be drawn from the distribution (unless
high=None
, in which case this parameter is 0 and this value is used for high). high : int or arraylike of ints, optional
If provided, one above the largest (signed) integer to be drawn from the distribution (see above for behavior if
high=None
). If arraylike, must contain integer values size : int 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. dtype : {str, dtype}, optional
Desired dtype of the result. All dtypes are determined by their name, i.e., ‘int64’, ‘int’, etc, so byteorder is not available and a specific precision may have different C types depending on the platform. The default value is ‘np.int’.
 endpoint : bool, optional
If true, sample from the interval [low, high] instead of the default [low, high) Defaults to False
Returns:  out : int or ndarray of ints
sizeshaped array of random integers from the appropriate distribution, or a single such random int if size not provided.
Notes
When using broadcasting with uint64 dtypes, the maximum value (2**64) cannot be represented as a standard integer type. The high array (or low if high is None) must have object dtype, e.g., array([2**64]).
References
[1] Daniel Lemire., “Fast Random Integer Generation in an Interval”, ACM Transactions on Modeling and Computer Simulation 29 (1), 2019, http://arxiv.org/abs/1805.10941. Examples
>>> rng = np.random.default_rng() >>> rng.integers(2, size=10) array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0]) # random >>> rng.integers(1, size=10) array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
Generate a 2 x 4 array of ints between 0 and 4, inclusive:
>>> rng.integers(5, size=(2, 4)) array([[4, 0, 2, 1], [3, 2, 2, 0]]) # random
Generate a 1 x 3 array with 3 different upper bounds
>>> rng.integers(1, [3, 5, 10]) array([2, 2, 9]) # random
Generate a 1 by 3 array with 3 different lower bounds
>>> rng.integers([1, 5, 7], 10) array([9, 8, 7]) # random
Generate a 2 by 4 array using broadcasting with dtype of uint8
>>> rng.integers([1, 3, 5, 7], [[10], [20]], dtype=np.uint8) array([[ 8, 6, 9, 7], [ 1, 16, 9, 12]], dtype=uint8) # random