# randomgen.mtrand.RandomState.randint¶

- RandomState.randint(
*low*,*high=None*,*size=None*,*dtype='l'*)¶ 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).

- Parameters
**low**int or array-like of intsLowest (signed) integers to be drawn from the distribution (unless

`high=None`

, in which case this parameter is one above the*highest*such integer).**high**int or array-like of ints, optionalIf 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, optionalOutput 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, optionalDesired dtype of the result. All dtypes are determined by their name, i.e., ‘int64’, ‘int’, etc, so byteorder is not available and a specific precision may have different C types depending on the platform. The default value is ‘int’.

New in version 1.11.0.

- Returns
**out**int or ndarray of intssize-shaped array of random integers from the appropriate distribution, or a single such random int if size not provided.

See also

`random.random_integers`

similar to randint, only for the closed interval [low, high], and 1 is the lowest value if high is omitted. In particular, this other one is the one to use to generate uniformly distributed discrete non-integers.

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)