Introduction to Clean Code in Python
Tags: webdev, pyth2, python, developer
Published: 2024-03-07 17:40:26

 
1. **Introduction:**
- Worked with Python and Airflow for dynamically creating data pipelines based on configurations defined in a web application
- Shared insights about Clean Code techniques for diverse data engineering teams in an article series

2. **Technical debt, the silent evil:**
- Primary challenge revolved around accumulation of technical debt
- Technical debt occurs when coding decisions make the code harder to change in the future

3.** Why Clean Code?:**
- Motivation for embracing Clean Code was to mitigate technical debt and it offers additional benefits
- Quality code enhances estimation accuracy, facilitating steady and predictable delivery of features

4. **When Clean Code Might Not Be Appropriate:**
- Clean Code may not be the best choice for every project
- Not suitable for single-use scripts, proof of concepts, prototypes and legacy code slated for deprecation

5. **Code Formatting and Tools:**
- Importance of code formatting and layout in reaching a quality codebase
- Introduction to coding standard PEP-8 and its benefits for consistency and error identification

6.** Documentation Best Practices:**
- Importance of well-documented code and usage of tools like docstrings and annotations
- Clear guidelines for good documentation in Python using docstrings and annotations

7.** Docstrings:**
- Explanation of usage and importance of docstrings in documenting code logic
- The way to add docstrings to the codebase and use of automated documentation tools like sphinx

8. **Annotations:**
- Introduction to annotations as a tool for documenting code in Python
- Explanation of how annotations reduce possibility of errors and aid in automatic type checking

9. **Automatic code formatting with black:**
- Recommend using black for automatic code formatting.
- Black ensures consistent code layout and formatting, reducing git diffs and allowing more effective code reviews.

10. **Static type checking with mypy:**
- Mypy is recommended for static type checking.
- It helps detect bugs early in development but does not enforce type restrictions in Python.

11. **Generic types and type hints:**
- Type hinting with generic types like Any or Iterable can silently ignore errors.
- It's important to use more specific types to avoid such issues.

12. **Linters and code analysis with pylint:**
- Pylint is recommended for more advanced code analysis beyond PEP-8 standards.
- It can validate imports, detect code smells, and provide suggestions for refactoring.

13.** Automating checks using Makefiles:**
- Using Makefiles to automate the execution of tools is recommended.
- It allows the configuration of commands for easier availability and execution in CI builds.

14. **Conclusion:**
- Clean Code goes beyond formatting and layout; it's about setting a coding style standard and implementing automatic checks and good documentation practices.
- The next part of the series will focus on Python idioms and design principles.

15. **Alejandro Machado's Joining Date:**
- Alejandro Machado joined on Feb 28, 2024.
- A comment was made on Mar 7 saying 'That's great! Thanks!'