Python, a high-level language with easy-to-read syntax, is highly flexible, which makes it an ideal language to learn and use. For science andR&D, a few extra packages are used to streamline the development process and obtain goals with the fewest steps possible. Among the best of these are SciPy and NumPy. This book gives a brief overview of different tools in these two scientific packages, in order to jump start their use in the reader’s own research projects.
NumPy and SciPy are the bread-and-butter Python extensions for numerical arrays and advanced data analysis. Hence, knowing what tools they contain and how to use them will make any programmer’s life more enjoyable. This book will cover their uses, ranging from simple array creation to machine learning.
Learn the basics of SciPy and NymPy quickly. With this concise introduction, you’ll cut through the complexity of online documentation and discover how easily you can get up to speed with these Python libraries. You’ll also understand why they’re powerful enough for many of today’s leading scientists and engineers.
This overview shows you how to use NumPy for numerical processing, including array indexing, math operations, and loading and saving data. You’ll learn how SciPy helps you work with advanced mathematical functions such as optimization, interpolation, integration, clustering, statistics, and other tools that take scientific programming to a whole new level. This book also introduces add-on SciKits packages that focus on advanced imaging algorithms and machine learning.