Introduction to NumPy bitwise_xor
The bitwise XOR of two array elements is compute using the numpy.bitwise_xor() function. The bit-wise XOR of the underlying binary representation of the numbers in the input arrays is compute by this function.
Syntax:
numpy.bitwise_xor(arr1, arr2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, ufunc ‘bitwise_xor’)
Parameters:
- arr1: [array_like] Input array.
- arr2: [array_like] Input array.
- out: [ndarray, optional] A location in 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 return.
- **kwargs: This allows you to pass keyword variable length of argument to a function. It is use when we want to handle a 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: [ndarray or scalar] Result. This is a scalar if both x1 and x2 are scalars.
Code Examples of NumPy bitwise_xor
Example 01: When inputs are numbers
# welcome to softhunt.net
# Python program explaining
# bitwise_xor() function
import numpy as np
num1 = 5
num2 = 15
print ("Input number1 : ", num1)
print ("Input number2 : ", num2)
ans = np.bitwise_xor(num1, num2)
print ("bitwise_xor of 5 and 15 : ", ans)Output:
Input number1 : 5 Input number2 : 15 bitwise_xor of 5 and 15 : 10
Example 01: When inputs are arrays
# welcome to softhunt.net
# Python program explaining
# bitwise_xor() function
import numpy as np
array1 = [3, 4, 54]
array2 = [23, 2, 3]
print ("Input array1 : ", array1)
print ("Input array2 : ", array2)
ans = np.bitwise_xor(array1, array2)
print ("Output array after bitwise_xor: ", ans)Output:
Input array1 : [3, 4, 54] Input array2 : [23, 2, 3] Output array after bitwise_xor: [20 6 53]
Example 03: When inputs are Boolean
# welcome to softhunt.net
# Python program explaining
# bitwise_xor() function
import numpy as np
bool1 = [True, False, False, True, False, True]
bool2 = [False, True, False, True, True, False]
print ("Input array1 : ", bool1)
print ("Input array2 : ", bool2)
ans = np.bitwise_xor(bool1, bool2)
print ("Output array after bitwise_xor: ", ans)Output:
Input array1 : [True, False, False, True, False, True] Input array2 : [False, True, False, True, True, False] Output array after bitwise_xor: [ True True False False True True]
FAQs
What is the operator in NumPy?
A multidimensional array is the most fundamental type in NumPy. Unless you specify the number of dimensions and type. NumPy array assignments are normally kept as n-dimensional arrays with the minimal type necessary to retain the objects in order. Because NumPy performs operations element-by-element, multiplying 2D arrays with * is an element-by-element multiplication rather than a matrix multiplication. For traditional matrix multiplication, the @ operator can be use.
What is backslash used for in Python?
In Python strings, the backslash “\” is a special character, also called the “escape” character. It is use in representing certain whitespace characters: “\t” is a tab, “\n” is a new line, and “\r” is a carriage return.
Conclusion
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