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Tutorials›Python›Debugging, Logging, and Profiling
Testing & Debugging · Chapter 27

Debugging, Logging, and Profiling

Find why code is wrong before trying to make it faster.

Why this matters

Good engineering means understanding failures and performance with evidence rather than guessing.

Start with the idea

Debugging traces incorrect behavior, logging records useful runtime context, and profiling measures where time or resources are actually spent.

IdeaUnderstand the purpose
→
CodeRun the smallest example
→
PracticeChange it yourself
SubjectVision learning pattern: understand the idea before memorizing syntax.

Small working example

Run this example first. Do not change several things at once; confirm the basic behavior, then experiment.

Python 
import logging

logging.basicConfig(level=logging.INFO)
user_id = 42
logging.info("processing user %s", user_id)
What to notice

The example is intentionally small. Focus on the chapter’s main idea before adding extra syntax or framework code.

Common beginner mistake

Mistake

Adding many print statements that expose sensitive values or remain in production code forever.

Better approach

Use structured, intentional logging and avoid secrets or personal data that are not required for diagnosis.

Quick recap

  • Debug with evidence.
  • Logs should be useful and safe.
  • Profile before optimizing.

Try it yourself

These are deliberately small. If you can complete them without copying the example, you are ready to continue.

  1. Add one useful log around a function call.
  2. Use a debugger or traceback to inspect an intentional error.