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Fonction bitwise_xor - module numpy

Signature de la fonction bitwise_xor

Description

bitwise_xor.__doc__

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

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
    :ref:`ufunc docs <ufuncs.kwargs>`.

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])