Python Best Practices to Follow in 2026

Python is a language that is constantly evolving, and the best practices that surround it evolve too. As we head into 2026, the tools and techniques that define a modern, professional Python workflow have solidified. If you're looking to write clean, maintainable, and robust Python code, these are the best practices you should be following.

1. Dependency Management: pyproject.toml is King

The era of juggling setup.py, requirements.txt, and other configuration files is over. The undisputed standard for managing project metadata and dependencies is the pyproject.toml file. This single file is the home for your project's dependencies, author information, and the configuration for almost all your development tools.

For managing your virtual environments and dependencies, Poetry and PDM have emerged as the leading, all-in-one tools. They handle the creation of virtual environments, dependency resolution (with a lock file), and packaging, all driven by the pyproject.toml file. While venv and pip are still fundamental, these higher-level tools provide a much more robust and integrated workflow.

2. Code Quality: Black, Ruff, and Mypy are Non-Negotiable

A modern Python project should have an automated code quality suite. The holy trinity of tools for this is:

• Black: The uncompromising code formatter. Black ends all arguments about code style by automatically formatting your code to a single, consistent standard. Just run it, and you're done.
• Ruff: This is the new superstar of the Python ecosystem. Ruff is an extremely fast linter and code formatter, written in Rust. It can check for hundreds of common errors, enforce best practices, and even automatically fix many of the issues it finds. It has replaced older tools like Flake8 and isort for most projects.
• Mypy: Static type checking is no longer optional for serious projects. Using type hints and running Mypy to check for type-related errors before you run your code is one of the most effective ways to prevent a whole class of common bugs.

These tools should be configured in your pyproject.toml and run automatically, ideally with a pre-commit hook.

3. Project Structure: The src/ Layout

For any project that is intended to be installable or that has a proper test suite, the src/ layout is the recommended standard. Placing your main package code inside a src/ directory provides a clean separation between your source code and other project files (like tests and documentation) and, most importantly, prevents a class of confusing import bugs.

4. Testing: pytest is the Standard

While Python's built-in unittest module is capable, pytest has been the community standard for years, and for good reason. Its simple, assertion-based syntax, powerful fixture model, and rich plugin ecosystem make it a joy to work with. For any new project, you should be writing your tests with pytest.

5. Embrace Modern Python Features

Python has added a number of powerful features in its recent versions. Don't be afraid to use them:

• Structural Pattern Matching (match-case): Introduced in Python 3.10, this is a powerful tool for control flow that is far more capable than a simple switch statement. It's excellent for handling complex conditional logic based on the structure of your data.
• asyncio for I/O-Bound Tasks: For applications that spend a lot of time waiting for network requests or database queries, asyncio is the standard way to handle concurrency and improve performance.
• Data Classes: For classes that are primarily used for storing data, the @dataclass decorator is a clean and concise way to automatically generate boilerplate methods like __init__ and __repr__.

6. Security: It's Everyone's Job

• Use a Dependency Scanner: Tools like pip-audit or GitHub's Dependabot should be part of your CI/CD pipeline. They automatically scan your dependencies for known security vulnerabilities and alert you when you need to upgrade.
• Environment Variables for Secrets: Never hardcode secrets like API keys or passwords in your code. Use environment variables (and a library like pydantic-settings to load them) to manage sensitive information.

By adopting these modern best practices, you'll be writing Python code that is not only functional but also clean, maintainable, secure, and easy for others to contribute to. The Python ecosystem has provided a powerful and standardized set of tools; in 2026, it's time to use them.