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References: Computational Thinking and Best Practices

  1. Computational Thinking — Wikipedia. https://en.wikipedia.org/wiki/Computational_thinking Explains the four pillars — decomposition, pattern recognition, abstraction, and algorithm design — that form the foundation of this chapter and underpin all problem-solving in computer science.

  2. Don't Repeat Yourself — Wikipedia. https://en.wikipedia.org/wiki/Don%27t_repeat_yourself Defines the DRY principle and explains why avoiding duplicated code makes programs easier to read, maintain, and debug — a central theme of this chapter's best-practices section.

  3. Abstraction (Computer Science) — Wikipedia. https://en.wikipedia.org/wiki/Abstraction_(computer_science) Covers how abstraction lets programmers hide complexity behind functions and objects, directly supporting the chapter's lessons on writing clean, modular Python code.

  4. Python Crash Course by Eric Matthes (No Starch Press, 2nd ed.). Chapters 8–9 demonstrate good function design and modularity in Python, reinforcing the DRY principle and readable code habits introduced in this chapter.

  5. Think Python by Allen B. Downey (O'Reilly, free online at greenteapress.com). Frames programming through computational thinking throughout, using decomposition and algorithmic problem-solving as the scaffolding for every Python concept it teaches.

  6. Python Tutorial: Functions — Python.org. https://docs.python.org/3/tutorial/controlflow.html#defining-functions The official Python tutorial section on defining and calling functions, essential reading for understanding how abstraction and modularity work in real Python programs.

  7. Python Best Practices — Real Python. https://realpython.com/python-code-quality/ A practical guide to writing clean, readable Python code covering naming conventions, documentation, and refactoring — directly aligned with this chapter's best-practices goals.

  8. Python Comments and Documentation — W3Schools. https://www.w3schools.com/python/python_comments.asp Beginner-friendly explanation of how and when to write comments and docstrings in Python, supporting the chapter's emphasis on code readability and meaningful documentation.

  9. Python Functions — Programiz. https://www.programiz.com/python-programming/function Clear, example-driven introduction to defining and reusing functions in Python, illustrating how decomposition and the DRY principle translate directly into working code.

  10. Python Programming Best Practices — GeeksforGeeks. https://www.geeksforgeeks.org/python-best-practices-for-writing-clean-code/ Covers naming conventions, code structure, and documentation habits that help students write professional-quality Python from the start, tying together all of this chapter's themes.