# numpy.logical_xor¶

`numpy.``logical_xor`(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'logical_xor'>

Compute the truth value of x1 XOR x2, element-wise.

Parameters: x1, x2 : array_like Logical XOR is applied to the elements of x1 and x2. They must be broadcastable to the same shape. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see the ufunc docs. y : bool or ndarray of bool Boolean result of the logical XOR operation applied to the elements of x1 and x2; the shape is determined by whether or not broadcasting of one or both arrays was required. This is a scalar if both x1 and x2 are scalars.

Examples

```>>> np.logical_xor(True, False)
True
>>> np.logical_xor([True, True, False, False], [True, False, True, False])
array([False,  True,  True, False])
```
```>>> x = np.arange(5)
>>> np.logical_xor(x < 1, x > 3)
array([ True, False, False, False,  True])
```

Simple example showing support of broadcasting

```>>> np.logical_xor(0, np.eye(2))
array([[ True, False],
[False,  True]])
```

#### Previous topic

numpy.logical_not

numpy.allclose