Python for Scientists

by John M. Stewart, Michael Mommert

The third edition of this practical introduction to Python has been thoroughly updated, with all code migrated to Jupyter notebooks. The notebooks are available online with executable versions of all of the book's content (and more). The text starts with a detailed introduction to the basics of the Python language, without assuming any prior knowledge. Building upon each other, the most important Python packages for numerical math (NumPy), symbolic math (SymPy), and plotting (Matplotlib) are introduced, with brand new chapters covering numerical methods (SciPy) and data handling (Pandas). Further new material includes guidelines for writing efficient Python code and publishing code for other users. Simple and concise code examples, revised for compatibility with Python 3, guide the reader and support the learning process throughout the book. Readers from all of the quantitative sciences, whatever their background, will be able to quickly acquire the skills needed for using Python effectively.

Genres: Programming, Nonfiction

300 pages, Paperback

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.