dpnp.subtract

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

Calculates the difference between each element x1_i of the input array x1 and the respective element x2_i of the input array x2.

For full documentation refer to numpy.subtract.

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 ({"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 element-wise differences. 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.

Notes

Equivalent to x1 - x2 in terms of array broadcasting.

Examples

>>> import dpnp as np
>>> np.subtract(dp.array([4, 3]), np.array([2, 7]))
array([ 2, -4])

>>> x1 = np.arange(9.0).reshape((3, 3))
>>> x2 = np.arange(3.0)
>>> np.subtract(x1, x2)
array([[ 0.,  0.,  0.],
       [ 3.,  3.,  3.],
       [ 6.,  6.,  6.]])

The - operator can be used as a shorthand for subtract on dpnp.ndarray.

>>> x1 - x2
array([[ 0.,  0.,  0.],
       [ 3.,  3.,  3.],
       [ 6.,  6.,  6.]])