This document provides an overview of the NumPy library in Python for working with multidimensional arrays and numerical data. It outlines key NumPy concepts like NumPy arrays, array operations, broadcasting, sorting, and image processing that will be covered, with the goal of teaching readers how to effectively use NumPy for data analysis and scientific computing. NumPy arrays can store elements of the same type and size, and provide significant performance benefits over regular Python lists for numerical work.

1. Table of contents
 Highlights
 Introduction
 Here’s how we’ll learn NumPy:
 What is the NumPy library in Python?
 Python Lists vs NumPy Arrays – What’s the Difference?
 Creating a NumPy Array
 The Shape and Reshaping of NumPy Arrays
 Expanding and Squeezing a NumPy array
 Indexing and Slicing of NumPy array
 Stacking and Concatenating NumPy arrays
 Broadcasting in NumPy arrays – A class apart!
 NumPy Ufuncs – The secret of its success!
 Maths with NumPy arrays
 Sorting in NumPy arrays
 NumPy arrays and Images
 End Notes
INTRODUCTION
NumPy is one of the most fundamental libraries in Python and perhaps the
most useful of them all. NumPy handles large datasets effectively and
efficiently.
NumPy stands for Numerical Python and is one of the most useful scientific
libraries in Python programming. It provides support for large
multidimensional array objects and various tools to work with them. NumPy
arrays are called ndarray or N-dimensional arrays and they store
elements of the same type and size
Numpy installation
pip install numpy
import numpy as np