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# 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.
  Returns: | 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.

  See also

  `logical_and`, `logical_or`, `logical_not`, `bitwise_xor`

  Examples

      >>> np.logical_xor(True, False)
      True
      >>> np.logical_xor([True, True, False, False], [True, False, True, False])
      array([False,  True,  True, False], dtype=bool)

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

  Simple example showing support of broadcasting

      >>> np.logical_xor(0, np.eye(2))
      array([[ True, False],
             [False,  True]], dtype=bool)