Python for Scientists

by John M. Stewart

Scientific Python is a significant public domain alternative to expensive proprietary software packages. This book teaches from scratch everything the working scientist needs to know using copious, downloadable, useful and adaptable code snippets. Readers will discover how easy it is to implement and test non-trivial mathematical algorithms and will be guided through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the language's capabilities. The author also shows how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. In this new edition, several chapters have been re-written to reflect the IPython notebook style. With an extended index, an entirely new chapter discussing SymPy and a substantial increase in the number of code snippets, researchers and research students will be able to quickly acquire all the skills needed for using Python effectively.

Genres: Programming, Nonfiction

274 pages, Kindle Edition

First published May 5, 2014

Reviews

[Sergey Antopolskiy · Rating 4 out of 5]

This book would be useful for any scientist, who wants to switch from another data analysis/processing software (MATLAB, Matematica, Statistica, etc) to Python (which I personally did and highly recommend). First of all, it is by no means a comprehensive guide to Python. This book is short and, in my opinion, well written and easy to understand. It covers the basics of iPython (which is, quote: "Python on steroids"), and gives some tips-and-tricks for how to use it efficiently and jump right in. There are many self-sufficient code snippets, which show you various aspects of Python programming; these are very helpful (although it would be amazing if I didn't have to type them all in by hand, some of them are rather lengthy; it would be a great improvement if the snippets were available as a download from the book's website).

First, the book cover the basics of Python programming, including data types, very numerical package numpy, functions, classes, and all that sweet stuff.

Then it covers basics of graphics and visualization (both 2D and 3D).

The last third is more technical, and I only skimmed through. It would be most helpful if you do a lot of hardcore math-related computations (I don't), e.g. differential equations. The book covers them quite extensively.