dpnp.equal

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

Calculates equality results for each element x1_i of the input array x1 the respective element x2_i of the input array x2.

For full documentation refer to numpy.equal.

Parameters:

x1 ({dpnp.ndarray, usm_ndarray}) – First input array, expected to have numeric data type.
x2 ({dpnp.ndarray, usm_ndarray}) – Second input array, also expected to have numeric data type.
out ({None, dpnp.ndarray}, optional) – Output array to populate. Array have the correct shape and the expected data type.
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 result of element-wise equality comparison. 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. Otherwise NotImplementedError exception will be raised.

See also

dpnp.not_equal: Return (x1 != x2) element-wise.
dpnp.greater_equal: Return the truth value of (x1 >= x2) element-wise.
dpnp.less_equal: Return the truth value of (x1 =< x2) element-wise.
dpnp.greater: Return the truth value of (x1 > x2) element-wise.
dpnp.less: Return the truth value of (x1 < x2) element-wise.

Examples

>>> import dpnp as np
>>> x1 = np.array([0, 1, 3])
>>> x2 = np.arange(3)
>>> np.equal(x1, x2)
array([ True,  True, False])

What is compared are values, not types. So an int (1) and an array of length one can evaluate as True:

>>> np.equal(1, np.ones(1))
array([ True])

The == operator can be used as a shorthand for equal on dpnp.ndarray.

>>> a = np.array([2, 4, 6])
>>> b = np.array([2, 4, 2])
>>> a == b
array([ True,  True, False])