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

Chapter 3. Using NumPy Arrays

The beauty of NumPy Arrays is that you can use array indexing and slicing to quickly access your data or perform a computation while keeping the efficiency as the C arrays. There are also plenty of mathematical operations that are supported. In this chapter, we will take an in-depth look at using NumPy Arrays. After this chapter, you will feel comfortable using NumPy Arrays and the bulk of their functionality.

Here is a list of topics that will be covered in this chapter:

  • Basic operations and the attributes of NumPy Arrays
  • Universal functions (ufuncs) and helper functions
  • Broadcasting rules and shape manipulation
  • Masking NumPy Arrays