numpy.random.randint¶

numpy.random.
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 “halfopen” interval [low, high). If high is None (the default), then results are from [0, low).
Parameters: low : int
Lowest (signed) integer to be drawn from the distribution (unless
high=None
, in which case this parameter is one above the highest such integer).high : int, optional
If provided, one above the largest (signed) integer to be drawn from the distribution (see above for behavior if
high=None
).size : int 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.dtype : dtype, optional
Desired 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 ‘np.int’.
New in version 1.11.0.
Returns: out : int or ndarray of ints
sizeshaped 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 nonintegers.
Examples
>>> np.random.randint(2, size=10) array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0]) >>> 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], [3, 2, 2, 0]])