numpy.random.Generator.integers#
method
- random.Generator.integers(low, high=None, size=None, dtype=np.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) and RandomState.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:
- lowint or array-like 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).- highint or array-like 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 array-like, must contain integer values- 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.- dtypedtype, optional
Desired dtype of the result. Byteorder must be native. The default value is np.int64.
- endpointbool, optional
If true, sample from the interval [low, high] instead of the default [low, high) Defaults to False
- Returns:
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, https://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