Chapter 6: Testing, Deployment, and Best Practices
Writing correct code on your laptop is only the beginning. Professional Python work means proving behavior with automated tests, packaging projects so others can install and run them, and shipping safely to servers or the cloud—with configuration and observability that match how real teams operate.
In this chapter you will move from notebook scripts to behaviors that resemble production-grade teams: test suites anchored in unittest and pytest, mock boundaries responsibly, quantify coverage thoughtfully, ship installable distributions (including historic setup.py context), bake containers, survey cloud platforms responsibly, automate GitHub pipelines, profile slow paths deliberately, and close with PEP 8 habits that survive code review.
Series
- Series 19: Testing your Python applications — Articles 145–152 aligning with automated testing fundamentals through coverage measurement.
- Series 20: Packaging and deploying Python applications — Articles 153–160 bridging setuptools/Docker/cloud deployment/CI/CD, performance intuition, and idiomatic style.
How to use this chapter
Read Series 19 with your editor open: type the examples, break them on purpose, and watch tests fail and pass. Then work through Series 20 with a tiny CLI or library project of your own, even if it only prints/version-checks—getting an installable artifact and a runnable container is the real goal.