# 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
    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]).

  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

previous

numpy.random.Generator.bit_generator

next

numpy.random.Generator.random