dpnp.floor_divide

dpnp.floor_divide = <DPNPBinaryFunc 'floor_divide'>

Rounds the result of dividing each element \(x1_i\) of the input array x1 by the respective element \(x2_i\) of the input array x2 to the greatest (i.e., closest to +infinity) 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 a real-valued data type.

x2{dpnp.ndarray, usm_ndarray, scalar}

Second input array, also expected to have a real-valued data type.

out{None, dpnp.ndarray, usm_ndarray, tuple of ndarray}, optional

Output array to populate. Array must have the correct shape and the expected data type. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

Default: None.

order{None, "C", "F", "A", "K"}, optional

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

Default: "K".

Returns:

outdpnp.ndarray

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.

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.divmod

Simultaneous floor division and remainder.

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.

Notes

At least one of x1 or x2 must be an array.

If x1.shape != x2.shape, they must be broadcastable to a common shape (which becomes the shape of the output).

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.])