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# 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. 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: | out : array_like Result.

  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], dtype=bool)