NumPy参考 >例行程序 >Random sampling (numpy.random) >Legacy Generator (RandomState) > 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 a default_rng()
instance instead; see random-quick-start.
Lowest (signed) integers to be drawn from the distribution (unless
high=None
, in which case this parameter is one above the
highest such integer).
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
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.
Desired dtype of the result. Byteorder must be native. The default value is int.
New in version 1.11.0.
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)