Python for Scientists

by John M. Stewart

Python is a free, open source, easy-to-use software tool that offers a significant alternative to proprietary packages such as MATLAB® and Mathematica®. This book covers everything the working scientist needs to know to start using Python effectively. The author explains scientific Python from scratch, showing how easy it is to implement and test non-trivial mathematical algorithms and guiding the reader through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the program's capabilities. In particular, readers are shown how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. Instead of exercises the book contains useful snippets of tested code which the reader can adapt to handle problems in their own field, allowing students and researchers with little computer expertise to get up and running as soon as possible.

Genres: Programming, Nonfiction

392 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.