numpy.logical_xor() in Python

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
.