Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Compute the bit-wise AND of two boolean arrays element-wise in Numpy
To compute the bit-wise AND of two arrays element-wise, use the numpy.bitwise_and() method in Python Numpy.
Computes the bit-wise AND of the underlying binary representation of the integers in the input arrays. This ufunc implements the C/Python operator &.
The 1st and 2nd parameter are the arrays, only integer and boolean types are handled. If x1.shape != x2.shape, they must be broadcastable to a common shape.
The where parameter is the condition broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.
Steps
At first, import the required library −
import numpy as np
Creating two numpy boolean arrays using the array() method −
arr1 = np.array([[True, False, False], [True, False, True]]) arr2 = np.array([[False, True, False], [False, False, False]])
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 −
print("
Result...
",np.bitwise_and(arr1, arr2))Example
import numpy as np
# Creating two numpy boolean arrays using the array() method
arr1 = np.array([[True, False, False],
[True, False, True]])
arr2 = np.array([[False, True, False],
[False, False, False]])
# 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))Output
Array 1... [[ True False False] [ True False True]] Array 2... [[False True False] [False False False]] Our Array 1 type... bool Our Array 2 type... bool Our Array 1 Dimensions... 2 Our Array 2 Dimensions... 2 Our Array 1 Shape... (2, 3) Our Array 2 Shape... (2, 3) Result... [[False False False] [False False False]]
346 Views
