numpy.logical_xor#

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

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

Parameters:
x1, x2array_like

Logical XOR is applied to the elements ofx1 andx2.Ifx1.shape!=x2.shape, they must be broadcastable to a commonshape (which becomes the shape of the output).

outndarray, None, or tuple of ndarray and None, optional

A location into which the result is stored. If provided, it must havea shape that the inputs broadcast to. If not provided or None,a freshly-allocated array is returned. A tuple (possible only as akeyword argument) must have length equal to the number of outputs.

wherearray_like, optional

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

**kwargs

For other keyword-only arguments, see theufunc docs.

Returns:
ybool or ndarray of bool

Boolean result of the logical XOR operation applied to the elementsofx1 andx2; the shape is determined by broadcasting.This is a scalar if bothx1 andx2 are scalars.

Examples

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

Simple example showing support of broadcasting

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