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

## Introduction to NumPy bitwise_or

The bitwise OR of two array elements are computed using the numpy.bitwise_or() function. The bit-wise OR of the underlying binary representation of the numbers in the input arrays are computed by this function.

Syntax:

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

Parameters:

• arr1: [array_like] Input array.
• arr2: [array_like] Input array.
• out: [ndarray, optional] A location into which the result is stored.
• If provided, it must have a shape that the inputs broadcast to.
• If not provides or None, a freshly-allocated array is return.
• **kwargs: allows you to pass keyword variable length of argument to a function. It is use when we want to handle a name 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_or

Example 01: When inputs are numbers

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

import numpy as np
num1 = 5
num2 = 15

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

ans = np.bitwise_or(num1, num2)
print ("bitwise_or of 5 and 15 : ", ans)``````

Output:

```Input number1 :  5
Input number2 :  15
bitwise_or of 5 and 15 :  15```

Example 02: When inputs are arrays

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

import numpy as np

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

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

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

Output:

```Input array1 :  [3, 4, 54]
Input array2 :  [23, 2, 3]
Output array after bitwise_or:  [23  6 55]```

Example 03: When inputs are Boolean

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

np = np.bitwise_or(bool1, bool2)
print ("Output array after bitwise_or: ", np)``````

Output:

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

## FAQs

### What is Arr Numpy?

Arr is the short form of an array – A NumPy array is a grid of identical-type items index by a tuple of nonnegative integers. The array’s rank is the number of dimensions; the shape is a tuple of numbers indicating the array’s size along each dimension.

### How does NumPy store data?

Numpy arrays are store in a single block of memory that is contiguous (continuous). Memory divides into two categories: dimensions and strides. When traversing an array, strides are the number of bytes you must step in each dimension.

## Conclusion

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

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