What is the bitwise_or() method in NumPy?

Overview

In NumPy, the bitwise_or() method is used to calculate bitwise OR between two input arrays, element-wise. It performs bitwise logical OR of underlying binary representation.

Syntax

Parameters

It takes the following argument values.

arr1: This is an integer and it handles boolean type arrays.
arr2: This is an integer and it handles boolean type arrays.
out: This is a memory location in which the result will be stored. It will be of the same dimension as arr1 or arr2. Its default value is None. If its value is None, a new array of the same dimensions will be initialised.

where: This is the value to replace the ufunc result when the condition returns True. Otherwise, the out array will retain its original output values. Its default value is True.

**kwargs: These are additional arguments.

Return value

if arr1 and arr2 are two scalars, it returns a scalar value. Otherwise, it returns Ndarray type objects only.

Example

In this code, we discuss numpy.bitwise_or() with multiple scenarios: performing OR operation between integers, Python Lists, and NumPy arrays.

# importing numpy library
import numpy as np
# performing logical OR between 16, 18
print("16 OR 18:", end=" ")
print(np.bitwise_or(16, 18))
# logical OR between a Python list [3,13] and 12
print("[3,13] OR 12:", end=" ")
print(np.bitwise_or([3,13], 12))
# logical OR between two lists of same size
print("[9,7] OR [8,35]:", end=" ")
print(np.bitwise_or([9,7], [8,35]))
# performing logical OR between two numpy arrays
print("np.array([2,7,255]) OR np.array([6,12,18]):", end=" ")
print(np.bitwise_or(np.array([2,7,255]), np.array([6,12,18])))

Explanation

Line 4–5: We perform a bitwise OR between 16 and 18, it will return 18.

Lines 7–8: We perform a bitwise OR between a list, [3,13] and 12. Hence, it will return a list, [15,12].
Line 10–11: It returns a bitwise OR as a list, [9 39], between two lists [9,7] and [8,35].

Line 13–14: It returns a bitwise OR as a NumPy array, [6 15 255], between two NumPy arrays, np.array([2,7,255]) and np.array([6,12,18].

Universal function implementation

NumPy also supports universal functions that implement C or Core Python operator, |.