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 “half-open” 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 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), then m * 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
size-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]) >>> 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]])