Introduction
Python tooling has changed a lot in recent years. This guide covers the practices and tools worth setting up on a Python project: the development environment, code quality and testing, dependency management, newer language features and a few general habits.
Development Environment Setup
Tools
- VS Code or PyCharm: editors with good Python support
- Python 3.11 or newer: a supported release; 3.11 was noticeably faster than the versions before it
- Git: version control
# Setting up a new Python project
python -m venv venv
source venv/bin/activate # or venv\Scripts\activate on Windows
pip install --upgrade pip
Code Quality and Testing
Testing and Quality Tools
- Pytest: testing framework
- Black: code formatter
- Flake8: style guide enforcement
- Ruff: a newer, much faster linter and formatter that can replace both Black and Flake8
- MyPy: static type checking
# A function and its pytest test
def calculate_total(numbers: list[int]) -> int:
return sum(numbers)
def test_calculate_total():
assert calculate_total([1, 2, 3]) == 6
assert calculate_total([]) == 0
Dependency Management
Pin your dependencies so every install gets the same versions, and keep each project's packages separate. The usual tools:
Tools and Practices
- Poetry: dependency management and packaging, configured in pyproject.toml
- pip-tools: compiles a fully pinned requirements.txt from your top-level dependencies
- uv: a newer, fast tool that covers installing, locking and virtual environments in one command
- Virtual environments: one per project, so packages do not leak between projects
Modern Python Features
Type Hints
# Python 3.10+ syntax: "dict | None" replaces Optional[dict]
def get_user(user_id: int) -> dict | None:
...
Async/Await
import aiohttp
async def fetch_data(url: str) -> dict:
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
return await response.json()
Best Practices and Tips
Code Organization
- Use clear directory structure
- Follow package naming conventions
- Implement proper logging
Performance
- Use built-in data structures appropriately
- Implement caching when needed
- Profile code for optimizations
