Python Code Quality: Write Better, Ship Faster
Code quality is not about perfection -- it is about writing code that other developers (including future you) can read, modify, and trust. In Python, this means following established idioms, using type hints effectively, structuring projects for maintainability, and knowing where to optimize for performance.
This collection covers the practices that elevate Python code from 'it works' to 'it works well': code review processes, naming conventions, type hinting, architecture patterns, and performance optimization.
Tutorials marked with the cert badge include a final exam that awards a certificate of completion you can download and share.
How to Do Python Code Review
Effective code review practices, what to look for, and how to give constructive feedback.
Python Code Review Tools
Linters, formatters, type checkers, and CI integration for automated code quality.
Python Code Review Exercises
Practice exercises for identifying issues and suggesting improvements in Python code.
Improving Python Readability and Style
Practical techniques for writing clear, maintainable Python that follows community standards.
Examples of Good Python Code to Learn From
Real-world examples of well-written Python code and what makes them effective.
Understanding Pythonic Code
What Pythonic means and the principles behind writing idiomatic Python.
Embracing Pythonic Idioms
Before-and-after examples transforming common patterns into clean Pythonic code.
Python Naming and Type Hints
Naming conventions, type annotation syntax, and using mypy for static type checking.
Python Type Hinting Improvements
Recent type hinting additions: union syntax, TypeAlias, ParamSpec, and TypeVarTuple.
Python Lazy Annotations
PEP 563 and PEP 649: deferred evaluation of annotations and forward references.
Python Architecture Best Practices
Structuring Python projects for testability, modularity, and long-term maintainability.
Python Separation of Concerns and Modularity
Designing clean module boundaries, dependency management, and avoiding tight coupling.
Python Big O Notation
Understanding algorithmic complexity and choosing efficient data structures and algorithms.
Python Speed Optimization Tips
Profiling, benchmarking, and practical techniques for making Python code faster.