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

Parameters

x1, x2 : array_like

Logical XOR is applied to the elements of x1 and :None:None:`x2`. 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 :None:None:`out` array will be set to the ufunc result. Elsewhere, the :None:None:`out` array will retain its original value. Note that if an uninitialized :None:None:`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 <ufuncs.kwargs> .

Returns

y : bool or ndarray of bool

Boolean result of the logical XOR operation applied to the elements of x1 and :None:None:`x2`; the shape is determined by broadcasting. This is a scalar if both x1 and :None:None:`x2` are scalars.

Compute the truth value of x1 XOR x2, element-wise.

See Also

bitwise_xor

logical_and

logical_not

logical_or

Examples

This example is valid syntax, but we were not able to check execution

>>> np.logical_xor(True, False)
True

This example is valid syntax, but we were not able to check execution

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

This example is valid syntax, but we were not able to check execution

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

Simple example showing support of broadcasting

This example is valid syntax, but we were not able to check execution

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

See :

Back References

The following pages refer to to this document either explicitly or contain code examples using this.

numpy.ma.core.logical_and numpy.ma.core.logical_not numpy.ma.core.logical_or numpy.ma.core.bitwise_xor

Local connectivity graph

Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.

Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)

SVG is more flexible but power hungry; and does not scale well to 50 + nodes.

All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them

---

GitHub : /numpy/ma/core.py#None
type: <class 'numpy.ma.core._MaskedBinaryOperation'>
Commit: