numpy.random.randint¶
-
numpy.random.
randint
(low, high=None, size=None, dtype=int)¶ Return random integers from low (inclusive) to high (exclusive).
Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high). If high is None (the default), then results are from [0, low).
Note
New code should use the
integers
method of adefault_rng()
instance instead; please see the Quick Start.- 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 one above the highest such integer).- 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 int.
New in version 1.11.0.
- Returns
- outint or ndarray of ints
size-shaped array of random integers from the appropriate distribution, or a single such random int if size not provided.
See also
random_integers
similar to
randint
, only for the closed interval [low, high], and 1 is the lowest value if high is omitted.Generator.integers
which should be used for new code.
Examples
>>> np.random.randint(2, size=10) array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0]) # random >>> np.random.randint(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:
>>> np.random.randint(5, size=(2, 4)) array([[4, 0, 2, 1], # random [3, 2, 2, 0]])
Generate a 1 x 3 array with 3 different upper bounds
>>> np.random.randint(1, [3, 5, 10]) array([2, 2, 9]) # random
Generate a 1 by 3 array with 3 different lower bounds
>>> np.random.randint([1, 5, 7], 10) array([9, 8, 7]) # random
Generate a 2 by 4 array using broadcasting with dtype of uint8
>>> np.random.randint([1, 3, 5, 7], [[10], [20]], dtype=np.uint8) array([[ 8, 6, 9, 7], # random [ 1, 16, 9, 12]], dtype=uint8)