Software Engineering for Data Scientists

Chapter 1. What Is Good Code?

This book aims to help you write better code. But first, what makes code “good”? There are a number of ways to think about this: the best code could be the code that runs fastest. Or it could be easiest to read. Another possible definition is that good code is easy to maintain. That is, if the project changes, it should be easy to go back to the code and change it to reflect the new requirements. The requirements for your code will change frequently because of updates to the business problem you’re solving, new research directions, or updates elsewhere in the codebase.

In addition, your code shouldn’t be complex, and it shouldn’t break if it gets an unexpected input. It should be easy to add a simple new feature to your code; if this is hard it suggests your code is not well written. In this chapter, I’ll introduce aspects of good code and show examples for each. I’ll divide these into five categories: simplicity, modularity, readability, performance, and robustness.

Why Good Code Matters

Good code is especially important when your data science code integrates with a larger system. This could be putting a machine learning model into production, writing packages for wider distribution, or building tools for other data scientists. It’s most useful for larger codebases that will be run repeatedly. As your project grows in size and complexity, the value of good code will increase.

Sometimes, the code you write will be a one-off, a prototype that needs ...