Python Best Practices

Overview

This repository provides a comprehensive guide to writing clean, efficient, and professional Python code. By following these best practices, you will improve code maintainability, readability, and performance.

Topics Covered

Below are key areas that are often overlooked but are crucial for writing professional Python code:

1. Code Style & Conventions

• PEP 8: Python's official style guide for formatting code.
• PEP 257: Docstring conventions for better documentation.
• Type Hinting: Using PEP 484 and PEP 526 for type safety.
• Naming Conventions: Best practices for variables, functions, and classes.

2. Pythonic Code (Writing Efficient Code)

• List comprehensions & generator expressions for concise code.
• enumerate() and zip() for cleaner loops.
• Using any() and all() to simplify conditionals.
• Context managers (with statement) to manage resources efficiently.
• F-strings (f"") over format() for better readability.

3. Error Handling & Debugging

• Proper try/except usage to handle exceptions gracefully.
• Logging (logging module) instead of print debugging.
• Using pdb for interactive debugging.

4. Object-Oriented Programming (OOP) Best Practices

• Understanding __init__ and self correctly.
• Using @staticmethod and @classmethod appropriately.
• Encapsulation: _protected vs __private attributes.
• Leveraging magic (dunder) methods like __repr__, __str__, and __eq__.

5. Functional Programming Concepts

• Using map(), filter(), and reduce() effectively.
• When to use lambda functions.
• Understanding closures and decorators.

6. Memory Management & Performance

• Avoiding unnecessary list copies ([:], .copy()).
• Understanding is vs ==.
• Choosing between deepcopy and copy.
• Using generators for large datasets.
• Lazy evaluation techniques (itertools, yield).

7. Working with Files & OS

• Proper file handling (with open() as f:).
• Using pathlib over os.path.
• Storing sensitive values in environment variables.

8. Python Modules & Best Practices

• Structuring Python projects properly.
• Writing reusable modules.
• Managing virtual environments (venv / conda).
• Handling dependencies (requirements.txt, pipenv).

9. Testing in Python

• Writing unit tests using unittest and pytest.
• Mocking external dependencies.
• Understanding Test-Driven Development (TDD).

10. Python’s Standard Library & Hidden Gems

• collections: defaultdict, Counter, namedtuple.
• itertools: permutations, combinations.
• functools: lru_cache, partial, reduce.
• dataclasses for clean and concise class definitions.
• argparse for building CLI tools.

Getting Started

1. Clone the repository:

   git clone https://github.com/Volcann/Python-coding-standards.git

2. Navigate to the project folder:

   cd python-best-practices