numpy.logical_xor(arr1, arr2, out=None, where = True, casting = ‘same_kind’, order = ‘K’, dtype = None, ufunc ‘logical_xor’) : This is a logical function and it helps user to find out the truth value of arr1 XOR arr2 element-wise. Both the arrays must be of same shape.
Parameters :
arr1 : [array_like]Input array.
arr2 : [array_like]Input array.out : [ndarray, optional]Output array with same dimensions as Input array, placed with result.
**kwargs : allows you to pass keyword variable length of argument to a function. It is used when we want to handle named argument in a function.
where : [array_like, optional]True value means to calculate the universal functions(ufunc) at that position, False value means to leave the value in the output alone.
Return :
An array with Boolean results of arr1 XOR arr2 element-wise(of the same shape).
Code 1 : Working
# Python program explaining # logical_xor() function import numpy as np # input arr1 = [ 1 , 3 , False , 0 ] arr2 = [ 3 , 0 , True , False ] # output out_arr = np.logical_xor(arr1, arr2) print ( "Output Array : " , out_arr) |
Output :
Output Array : [False True True False]
Code 2 : Value Error if input array’s have different shapes
# Python program explaining # logical_xor() function import numpy as np # input arr1 = [ 8 , 2 , False , 4 ] arr2 = [ 3 , 0 , False , False , 8 ] # output out_arr = np.logical_xor(arr1, arr2) print ( "Output Array : " , out_arr) |
Output :
ValueError: operands could not be broadcast together with shapes (4,) (5,)
Code 3 : Can check condition
# Python program explaining # logical_xor() function import numpy as np # input arr1 = np.arange( 8 ) print ( "arr1 : " , arr1) print ( "\narr1>3 : \n" , arr1> 3 ) print ( "\narr1<6 : \n" , arr1< 6 ) print ( "\nXOR Value : \n" , np.logical_xor(arr1> 3 , arr1< 6 )) |
Output :
arr1 : [0 1 2 3 4 5 6 7] arr1>3 : [False False False False True True True True] arr1<6 : [ True True True True True True False False] XOR Value : [ True True True True False False True True]
References :
https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.logical_xor.html#numpy.logical_xor
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