Introduction

Error handling is where a lot of otherwise-good Java code quietly goes wrong. Exceptions get swallowed, null leaks across boundaries, and stack traces arrive with no context about what the program was trying to do.

Good error handling is not about catching everything. It is about being deliberate: fail fast on programmer errors, recover gracefully from expected failures, and never lose the information a future on-call engineer will need.

Introduction

Python’s error handling reads deceptively simply — try, except, done. But the difference between code that fails clearly and code that fails mysteriously comes down to a handful of habits: catching the right exception, preserving the original cause, and knowing when not to raise at all.

This post covers the practices that keep Python failures debuggable in production.

Prefer EAFP over Look-Before-You-Leap

Python idiom favors EAFP — “Easier to Ask Forgiveness than Permission.” Rather than checking whether an operation will succeed, attempt it and handle the failure: