Dashnow Harriet - Elegant sciPy: the art of Scientific Python

by Juan Nunez-Iglesias , Stfan van der Walt , and Harriet Dashnow

Copyright 2017 Juan Nunez-Iglesias, Stfan van der Walt, and Harriet Dashnow. All rights reserved.

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978-1-491-92287-3

[LSI]

Unlike the stereotypical wedding dress, it wasto use a technical termelegant, like a computer algorithm that achieves an impressive outcome with just a few lines of code.

Graeme Simsion, The Rosie Effect

Welcome to Elegant SciPy. Were going to spend rather a lot of time focusing on the SciPy bit of the title, so lets take a moment to reflect on the Elegant bit. There are plenty of manuals, tutorials, and documentation websites out there that describe the SciPy library. Elegant SciPy goes further. More than just teaching you how to write code that works, we will inspire you to write code that rocks!

In The Rosie Effect (hilarious book; go read its prequel The Rosie Project when youre done with Elegant SciPy), Graeme Simsion twists the conventions of the word elegant around. Most would use it to describe the visual simplicity, style, and grace of, say, the first iPhone. Instead Graeme Simsions hero, Don Tillman, uses a computer algorithm to define elegance. We hope that you will understand exactly what he means after reading this book; that you will read or write a piece of elegant code, and feel calmed in the glow of its beauty and grace. (Note: The authors may be prone to hyperbole.)

A good piece of code just feels right. When you look at it, its intent is clear, it is often concise (but not so concise as to be obscure), and it is efficient at executing the task at hand. For the authors, the joy of analyzing elegant code lies in the lessons hidden within, and the way it inspires us to be creative in how we approach new coding problems.

Ironically, creativity can also tempt us to show off cleverness at the expense of the reader, and write obtuse code that is hard to understand. PEP8 (the Python style guide) and PEP20 (the Zen of Python) remind us that code is read much more often than it is written and therefore readability counts.

The conciseness of elegant code comes through abstraction and the judicious use of functions, not just through packing in a bunch of nested function calls. It may take a minute or two to grok, but it should ultimately provide a crisp, ah-ha! moment of understanding. Once you know the various components of the code, its correctness should be obvious. This can be aided by clear variable and function names, and carefully crafted comments that explain the code, rather than merely describe it.

In the New York Times, software engineer J. Bradford Hipps recently argued that to write better code, [one should] read Virginia Woolf:

As a practice, software development is far more creative than algorithmic.

The developer stands before her source code editor in the same way the author confronts the blank page. [] They may also share a healthy impatience for the ways things have always been done and a generative desire to break conventions. When the module is finished or the pages complete, their quality is judged against many of the same standards: elegance, concision, cohesion; the discovery of symmetries where none were seen to exist. Yes, even beauty.

This is the position we take in this book.

Now that weve dealt with the elegant part of the title, lets come back to the SciPy.

Depending on context, SciPy can mean a software library, an ecosystem, or a community. Part of what makes SciPy great is that it has excellent online documentation and tutorials, rendering Just Another Reference book pointless; instead, Elegant SciPy wants to present the best code built with SciPy.

The code we have chosen highlights clever, elegant uses of advanced features of NumPy, SciPy, and related libraries. The beginning reader will learn to apply these libraries to real-world problems using beautiful code. And we use real scientific data to motivate our examples.

Like SciPy itself, we wanted Elegant SciPy to be driven by the community. Weve taken many of our examples from working code found in the wider scientific Python ecosystem, selecting them for their illustration of the principles of elegant code we outlined above.