NumPy Cookbook - Second Edition

Over 90 fascinating recipes to learn and perform mathematical, scientific, and engineering Python computations with NumPy

About This Book

• Perform high-performance calculations with clean and efficient NumPy code
• Simplify large data sets by analysing them with statistical functions
• A solution-based guide packed with engaging recipes to execute complex linear algebra and mathematical computations

Who This Book Is For

If you are a Python developer with some experience of working on scientific, mathematical, and statistical applications and want to gain an expert understanding of NumPy programming in relation to science, math, and finance using practical recipes, then this book is for you.

What You Will Learn

• Learn advanced indexing and linear algebra
• Deal with missing stock price data using masked arrays
• Explore everything you need to know about image processing
• Dive into broadcasting and histograms
• Profile NumPy code and visualize the results
• Speed up your code with Cython
• Use universal functions and interoperability features
• Analyze your performance using Quality Assurance
• Learn about exploratory and predictive data analysis with NumPy

In Detail

NumPy has the ability to give you speed and high productivity. High performance calculations can be done easily with clean and efficient code, and it allows you to execute complex algebraic and mathematical computations in no time.

This book will give you a solid foundation in NumPy arrays and universal functions. Starting with the installation and configuration of IPython, you'll learn about advanced indexing and array concepts along with commonly used yet effective functions. You will then cover practical concepts such as image processing, special arrays, and universal functions. You will also learn about plotting with Matplotlib and the related SciPy project with the help of examples. At the end of the book, you will study how to explore atmospheric pressure and its related techniques. By the time you finish this book, you'll be able to write clean and fast code with NumPy.

Ivan Idris

Ivan Idris has an MSc in experimental physics. His graduation thesis had a strong emphasis on applied computer science. After graduating, he worked for several companies as a Java developer, data warehouse developer, and QA analyst. His main professional interests are business intelligence, big data, and cloud computing. Ivan enjoys writing clean, testable code and interesting technical articles. He is the author of NumPy Beginner's Guide, NumPy Cookbook, Python Data Analysis, and Learning NumPy, all by Packt Publishing. You can find more information about him and a few NumPy examples at http://ivanidris.net/wordpress/.