# Compute the truth value of an array XOR another array element-wise based on conditions in Numpy

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To compute the truth value of an array XOR another array element-wise, use the numpy.logical_xor() method in Python Numpy. Return value is either True or False. We have set conditions here. Return value is the Boolean result of the logical XOR operation applied to the elements of x1 and x2; the shape is determined by broadcasting. This is a scalar if both x1 and x2 are scalars.

The 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.

## Steps

At first, import the required library −

import numpy as np

Creating two 2D numpy array using the array() method. We have inserted elements. The True is considered value 1. The False is considered value 0 −

arr1 = np.array([[True, 8, 7], [13, False, 11]])
arr2 = np.array([[False, 9, True], [19, 25, 6]])

Display the arrays −

print("Array 1...\n", arr1)
print("\nArray 2...\n", arr2)

Get the type of the arrays −

print("\nOur Array 1 type...\n", arr1.dtype)
print("\nOur Array 2 type...\n", arr2.dtype)

Get the dimensions of the Arrays −

print("\nOur Array 1 Dimensions...\n",arr1.ndim)
print("\nOur Array 2 Dimensions...\n",arr2.ndim)

Get the shape of the Arrays −

print("\nOur Array 1 Shape...\n",arr1.shape)
print("\nOur Array 2 Shape...\n",arr2.shape)

To compute the truth value of an array XOR another array element-wise, use the numpy.logical_xor() method. Return value is either True or False. We have set conditions here −

print("\nResult (XOR)...\n",np.logical_xor(arr1 > 10, arr2 < 15))

## Example

import numpy as np
import numpy.ma as ma

# Creating two 2D numpy array using the array() method
# We have inserted elements
# The True is considered value 1
# The False is considered value 0
arr1 = np.array([[True, 8, 7], [13, False, 11]])
arr2 = np.array([[False, 9, True], [19, 25, 6]])

# Display the arrays
print("Array 1...\n", arr1)
print("\nArray 2...\n", arr2)

# Get the type of the arrays
print("\nOur Array 1 type...\n", arr1.dtype)
print("\nOur Array 2 type...\n", arr2.dtype)

# Get the dimensions of the Arrays
print("\nOur Array 1 Dimensions...\n",arr1.ndim)
print("\nOur Array 2 Dimensions...\n",arr2.ndim)

# Get the shape of the Arrays
print("\nOur Array 1 Shape...\n",arr1.shape)
print("\nOur Array 2 Shape...\n",arr2.shape)

# To compute the truth value of an array XOR another array elementwise, use the numpy.logical_xor() method in Python Numpy
# Return value is either True or False
# We have set conditions here
print("\nResult (XOR)...\n",np.logical_xor(arr1 > 10, arr2 < 15))

## Output

Array 1...
[[ 1 8 7]
[13 0 11]]

Array 2...
[[ 0 9 1]
[19 25 6]]

Our Array 1 type...
int64

Our Array 2 type...
int64

Our Array 1 Dimensions...
2

Our Array 2 Dimensions...
2

Our Array 1 Shape...
(2, 3)

Our Array 2 Shape...
(2, 3)

Result (XOR)...
[[ True True True]
[ True False False]]