Numpy bitwise_and() Function

The NumPy bitwise_and() function performs a bitwise AND operation on corresponding elements of two arrays. This function compares the binary representations of the input elements by setting each bit in the result to 1 if both corresponding bits in the input arrays are 1 otherwise, the bit is set to 0.

This operation is element-wise which means processes each pair of elements from the input arrays independently. The bitwise_and() supports broadcasting, allowing it to handle arrays of different shapes by aligning them according to broadcasting rules.

This function is useful for binary data manipulation and low-level data processing. Below are the results of bitwise AND operation of bit combinations 1 and 0 −

• Bitwise AND of 0 and 0: 0
• Bitwise AND of 0 and 1: 0
• Bitwise AND of 1 and 0: 0
• Bitwise AND of 1 and 1: 1

Syntax

Following is the syntax of Numpy bitwise_and() function −

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

Parameters

Following are the Parameters of Numpy bitwise_and() function −

• x1: First input array or scalar.
• x2: Second input array or scalar. Must be broadcastable to the shape of x1.
• out(Optional): A location into which the result is stored. If provided then it must have a shape that matches the broadcasted output of x1 and x2.
• where(Optional): A condition to determine where the operation is performed. The result is computed where this condition is True.
• **kwargs: Additional keyword arguments.

Return value

This function returns the array with the result of bitwise AND operation.

Example 1

Following is the basic example of Numpy bitwise_and() function. Here this example shows how to compute the bitwise AND of two numbers −

import numpy as np 

# Print binary equivalents of 13 and 17
print('Binary equivalents of 13 and 17:')
a, b = 13, 17
print(bin(a), bin(b))
print('\n')  

# Print bitwise AND of 13 and 17
print('Bitwise AND of 13 and 17:')
print(np.bitwise_and(a, b))

Below is the output of bitwise_and() function applied on numbers 13 and 17 −

Binary equivalents of 13 and 17:
0b1101 0b10001

Bitwise AND of 13 and 17:
1

Example 2

Here in this example we show how to create two 2D NumPy arrays by displaying their properties and computing their element-wise bitwise AND−

import numpy as np

# Creating two numpy arrays using the array() method
# We have inserted elements of int type
arr1 = np.array([[49, 6, 61],
                 [82, 69, 29]])
arr2 = np.array([[40, 60, 61],
                 [81, 55, 32]])

# Display the arrays
print("Array 1:", arr1)
print("Array 2:", arr2)

# Get the type of the arrays
print("Our Array 1 type:", arr1.dtype)
print("Our Array 2 type:", arr2.dtype)

# Get the dimensions of the Arrays
print("Our Array 1 Dimensions:",arr1.ndim)
print("Our Array 2 Dimensions:",arr2.ndim)

# Get the shape of the Arrays
print("Our Array 1 Shape:",arr1.shape)
print("Our Array 2 Shape:",arr2.shape)

# To compute the bit-wise AND of two arrays element-wise, use the numpy.bitwise_and() method in Python Numpy
print("Result:",np.bitwise_and(arr1, arr2))

Following is the output for the above example −

Array 1: [[49  6 61]
 [82 69 29]]
Array 2: [[40 60 61]
 [81 55 32]]
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: [[32  4 61]
 [80  5  0]]