Classe « RandomState »

Méthode numpy.random.RandomState.randint

Signature de la méthode randint

Description

randint.__doc__

        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; please see the :ref:`random-quick-start`.

        Parameters
        ----------
        low : int 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).
        high : int 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
        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. Byteorder must be native.
            The default value is int.

            .. versionadded:: 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_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)