Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, and clearly explains how to apply the best practices from software engineering to data science.

Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics that are often missing from introductory data science or coding classes, including how to:

Understand data structures and object-oriented programming

Clearly and skillfully document your code

Package and share your code

Integrate data science code with a larger code base

Learn how to write APIs

Create secure code

Apply best practices to common tasks such as testing, error handling, and logging

Work more effectively with software engineers

Write more efficient, maintainable, and robust code in Python

Put your data science projects into production

And more

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