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 bitwise XOR of two arrays elementwise.
Computes the bitwise 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 freshlyallocated 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 keywordonly 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 by00010001
. The bitwise XOR of 13 and 17 is therefore00011100
, 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])