# numpy.bitwise_and() – The NumPy bitwise_and Python Function

## Introduction to NumPy bitwise_and

The bitwise AND of two array elements are compute using the numpy.bitwise_and() function. The bit-wise AND of the underlying binary representation of the numbers in the input arrays. Are compute by this function.

Syntax:

`numpy.bitwise_and(arr1, arr2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, ufunc ‘bitwise_and’)`

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 provide or None, a freshly-allocate array is return.
• **kwargs: allows you to pass keyword variable length of argument to a function. It is use when we want to handle named arguments 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_and

Example 01: When inputs are numbers

``````# welcome to softhunt.net
# Python program explaining
# bitwise_and() function

import numpy as np
num1 = 5
num2 = 15

print ("Input number1 : ", num1)
print ("Input number2 : ", num2)

ans = np.bitwise_and(num1, num2)
print ("bitwise_and of 05 and 15 : ", ans)``````

Output:

```Input number1 :  5
Input number2 :  15
bitwise_and of 05 and 15 :  5```

Example 02: When inputs are arrays

``````# welcome to softhunt.net
# Python program explaining
# bitwise_and() function

import numpy as np

array1 = [3, 4,54]
array2 = [23, 2, 3]

print ("Input array1 : ", array1)
print ("Input array2 : ", array2)

ans = np.bitwise_and(array1, array2)
print ("Output array after bitwise_and: ", ans)``````

Output:

```Input array1 :  [3, 4, 54]
Input array2 :  [23, 2, 3]
Output array after bitwise_and:  [3 0 2]```

Example 03: When inputs are Boolean

``````# welcome to softhunt.net
# Python program explaining
# bitwise_and() 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_and(bool1, bool2)
print ("Output array after bitwise_and: ", ans)``````

Output:

```Input array1 :  [True, False, False, True, False, True]
Input array2 :  [False, True, False, True, True, False]
Output array after bitwise_and:  [False False False  True False False]```

## FAQs

### What is the cv2 bitwise_and in Python?

cv2.bitwise_and() is a function. That, as the name implies, does bitwise AND processing. The pixel value of the output picture is the AND of the values for each pixel of the input images src1 and src2.

### What is meant by bitwise operation?

Working with individual bits, which are the smallest units of data in a computer, is call as bitwise operations. Each bit has a binary value of either 0 or 1. Despite the fact that computers can manipulate bits. They typically store data and execute instructions in bit multiples known as bytes. The majority of programming languages work with 8-bit, 16-bit, or 32-bit groups.

Characters that indicate operations to be done on single bits are known as bitwise operators. A bitwise operation compares the positions of individual bits in two-bit patterns of equal length.

## Conclusion

That’s all for this article. If you have any confusion contact us through our website or email us at softhunt.n[email protected] or by using LinkedIn. And make sure you check out our NumPy tutorials.

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