# numpy.bitwise_xor

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

  Compute the bit-wise XOR of two arrays element-wise.

  Computes the bit-wise XOR of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator `^`.

  Parameters:
    x1, x2 array_like

      Only integer and boolean types are handled. If `x1.shape != x2.shape`, they must be broadcastable to a common shape (which becomes the shape of the output).

    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

      This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default `out=None`, locations within it where the condition is False will remain uninitialized.

    **kwargs

      For other keyword-only arguments, see the ufunc docs.

  Returns:
    out ndarray or scalar

      Result. This is a scalar if both x1 and x2 are scalars.

  See also

  `logical_xor`
  `bitwise_and`
  `bitwise_or`
  `binary_repr`

    Return the binary representation of the input number as a string.

  Examples

  The number 13 is represented by `00001101`. Likewise, 17 is represented by `00010001`. The bit-wise XOR of 13 and 17 is therefore `00011100`, or 28:

      >>> np.bitwise_xor(13, 17)
      28
      >>> np.binary_repr(28)
      '11100'

      >>> np.bitwise_xor(31, 5)
      26
      >>> np.bitwise_xor([31,3], 5)
      array([26,  6])

      >>> np.bitwise_xor([31,3], [5,6])
      array([26,  5])
      >>> np.bitwise_xor([True, True], [False, True])
      array([ True, False])

  The `^` operator can be used as a shorthand for `np.bitwise_xor` on ndarrays.

      >>> x1 = np.array([True, True])
      >>> x2 = np.array([False, True])
      >>> x1 ^ x2
      array([ True, False])