Book Image

NumPy Essentials

By : Leo (Liang-Huan) Chin, Tanmay Dutta, Shane Holloway
Book Image

NumPy Essentials

By: Leo (Liang-Huan) Chin, Tanmay Dutta, Shane Holloway

Overview of this book

In today’s world of science and technology, it’s all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy gives you both the speed and high productivity you need. This book will walk you through NumPy using clear, step-by-step examples and just the right amount of theory. We will guide you through wider applications of NumPy in scientific computing and will then focus on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples. You will then learn about different NumPy modules while performing mathematical operations such as calculating the Fourier Transform; solving linear systems of equations, interpolation, extrapolation, regression, and curve fitting; and evaluating integrals and derivatives. We will also introduce you to using Cython with NumPy arrays and writing extension modules for NumPy code using the C API. This book will give you exposure to the vast NumPy library and help you build efficient, high-speed programs using a wide range of mathematical features.
Table of Contents (16 chapters)
NumPy Essentials
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface

Summary


In this chapter, we covered the basic operations of NumPy and its ufuncs. We took a look at the huge difference between NumPy operations and Python looping. We also took a look at how broadcasting works and what we should avoid. We tried to understand the concept of masking as well.

The best way to use NumPy Arrays is to eliminate loops as much as you can and use ufuncs in NumPy instead. Keep in mind the broadcasting rules and use them with care. Using slicing and indexing with masking makes your code more efficient. Most importantly, have fun while using it.

In the next few chapters, we will cover the core libs of NumPy, including date/time and a file I/O to help you extend your NumPy experience.