Elegant SciPy: The Art of Scientific Python 1st Edition
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From the Publisher
From the Preface
Who Is This Book For?
Elegant SciPy is intended to inspire you to take your Python to the next level. You will learn SciPy by example, from the very best code.
Before starting, you should at least have seen Python, and know about variables, functions, loops, and maybe a bit of NumPy. You might have even honed your Python skills with advanced material, such as Fluent Python. If this doesn’t describe you, you should start with some beginner Python tutorials, such as Software Carpentry, before continuing with this book.
But perhaps you don’t know whether the 'SciPy stack' is a library or a menu item from the International House of Pancakes, and you aren’t sure about best practices. Perhaps you are a scientist who has read some Python tutorials online, and have downloaded some analysis scripts from another lab or a previous member of your own lab, and have fiddled with them. And you might think that you are more or less alone when you learn to code SciPy. You are not.
As we progress, we will teach you how to use the internet as your reference. And we will point you to the mailing lists, repositories, and conferences where you will meet like-minded scientists who are a little further in their journey than you.
This is a book that you will read once, but may return to for inspiration (and maybe to admire some elegant code snippets!).
The NumPy and SciPy libraries make up the core of the Scientific Python ecosystem. The SciPy software library implements a set of functions for processing scientific data, such as statistics, signal processing, image processing, and function optimization. SciPy is built on top of NumPy, the Python numerical array computation library. Building on NumPy and SciPy, an entire ecosystem of apps and libraries has grown dramatically over the past few years, spanning a broad spectrum of disciplines that includes astronomy, biology, meteorology and climate science, and materials science, among others.
This growth shows no sign of abating. In 2014, Thomas Robitaille and Chris Beaumont documented Python’s growing use in astronomy. Here’s what we found when we updated their plot in the second half of 2016 (pic to the left). It is clear that SciPy and related libraries will be driving much of scientific data analysis for years to come.
As another example, the Software Carpentry organization, which teaches computational skills to scientists, most often using Python, currently cannot keep up with demand.
About the Author
Stéfan van der Walt is an assistant researcher at the Berkeley Institute for Data Science at the University of California, Berkeley and a senior lecturer in applied mathematics at Stellenbosch University, South Africa. He has been involved in the development of scientific open-source software for more than a decade, and enjoys teaching Python at workshops and conferences. Stéfan is the founder of scikit-image and a contributor to numpy, scipy, and dipy.
- Item Weight : 1.07 pounds
- Paperback : 280 pages
- ISBN-10 : 9781491922873
- ISBN-13 : 978-1491922873
- Product Dimensions : 6.9 x 0.5 x 9.1 inches
- Publisher : O'Reilly Media; 1st Edition (September 5, 2017)
- Language: : English
- ASIN : 1491922877
- Best Sellers Rank: #317,914 in Books (See Top 100 in Books)
- Customer Reviews:
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What impresses me is that the book can be automatically built, from its github repository. This includes the book's sample code, which is executed and tested during the book build. That means no bugs or typos in the code. As all developers must experience, it is second nature to read programming books while mentally scanning for syntax errors or semantics that don't look quite right. None of that here. A big tick for the automate-everything mindset.
(The authors have excellent taste in comedy novels ( The Rosie Project: A Novel and The Rosie Effect: A Novel ) both of which I've read. An incisive insight into the engineering personality. If you can handle it.)