dpnp.floor_divide

dpnp.floor_divide(x1, x2, out=None, where=True, order='K', dtype=None, subok=True, **kwargs)

Calculates the ratio for each element x1_i of the input array x1 with the respective element x2_i of the input array x2 to the greatest integer-value number that is not greater than the division result.

For full documentation refer to numpy.floor_divide.

Parameters:

  • x1 ({dpnp.ndarray, usm_ndarray, scalar}) -- First input array, expected to have numeric data type. Both inputs x1 and x2 can not be scalars at the same time.

  • x2 ({dpnp.ndarray, usm_ndarray, scalar}) -- Second input array, also expected to have numeric data type. Both inputs x1 and x2 can not be scalars at the same time. If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

  • out ({None, dpnp.ndarray, usm_ndarray}, optional) --

    Output array to populate. Array must have the correct shape and the expected data type.

    Default: None.

  • order ({None, "C", "F", "A", "K"}, optional) --

    Memory layout of the newly output array, if parameter out is None.

    Default: "K".

Returns:

out -- An array containing the result of element-wise floor of division. The data type of the returned array is determined by the Type Promotion Rules.

Return type:

dpnp.ndarray

Limitations

Parameters where and subok are supported with their default values. Keyword argument kwargs is currently unsupported. Otherwise NotImplementedError exception will be raised.

See also

dpnp.remainder

Remainder complementary to floor_divide.

dpnp.divide

Standard division.

dpnp.floor

Round a number to the nearest integer toward minus infinity.

dpnp.ceil

Round a number to the nearest integer toward infinity.

Examples

>>> import dpnp as np
>>> np.floor_divide(np.array([1, -1, -2, -9]), -2)
array([-1,  0,  1,  4])

>>> np.floor_divide(np.array([1., 2., 3., 4.]), 2.5)
array([ 0.,  0.,  1.,  1.])

The // operator can be used as a shorthand for floor_divide on dpnp.ndarray.

>>> x1 = np.array([1., 2., 3., 4.])
>>> x1 // 2.5
array([0., 0., 1., 1.])