Python Cookbook

Chapter 17. Algorithms

Introduction

Credit: Tim Peters, PythonLabs

Algorithm research is what drew me to Python—and I fell in love. It wasn’t love at first sight, but it was an attraction that grew into infatuation, which grew steadily into love. And that love shows no signs of fading. Why? I’ve worked in fields pushing the state of the art, and, in a paradoxical nutshell, Python code is easy to throw away!

When you’re trying to solve a problem that may not have been solved before, you may have some intuitions about how to proceed, but you rarely know in advance exactly what needs to be done. The only way to proceed is to try things, many things, everything you can think of, just to see what happens. Python eases this by minimizing the time and pain from conception to code: if your colleagues are using, for example, C or Java, it’s not unusual for you to try and discard six different approaches in Python while they’re still getting the bugs out of their first attempt.

In addition, you will have naturally grown classes and modules that capture key parts of the problem domain, simply because you find the need to keep reinventing them when starting over from scratch. A true C++ expert can give you a good run, but C++ is so complex that true experts are very rare. Moderate skill with Python is much easier to obtain, yet much more productive for research and prototyping than merely moderate C++ skill.

So if you’re in the research business—and every programmer who doesn’t know everything ...