`numpy.random._generator:Generator.integers`

Summary

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)

Extended Summary

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 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).
high : int 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
size : int or tuple of ints, optional: Output 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.
dtype : dtype, optional: Desired dtype of the result. Byteorder must be native. The default value is np.int64.
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: size-shaped 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]).

Examples

rng = np.random.default_rng()

✓

rng.integers(2, size=10)

✗

rng.integers(1, size=10)

✓

Generate a 2 x 4 array of ints between 0 and 4, inclusive:

rng.integers(5, size=(2, 4))

✗

Generate a 1 x 3 array with 3 different upper bounds

rng.integers(1, [3, 5, 10])

✗

Generate a 1 by 3 array with 3 different lower bounds

rng.integers([1, 5, 7], 10)

✗

Generate a 2 by 4 array using broadcasting with dtype of uint8

rng.integers([1, 3, 5, 7], [[10], [20]], dtype=np.uint8)