numpy.random.RandomState.randint#
method
- random.RandomState.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 aGenerator
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 long.
New in version 1.11.0.
Warning
This function defaults to the C-long dtype, which is 32bit on windows and otherwise 64bit on 64bit platforms (and 32bit on 32bit ones). Since NumPy 2.0, NumPy’s default integer is 32bit on 32bit platforms and 64bit on 64bit platforms. Which corresponds to np.intp. (dtype=int is not the same as in most NumPy functions.)
- Returns:
See also
random_integers
similar to
randint
, only for the closed interval [low, high], and 1 is the lowest value if high is omitted.random.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)