Further Reading: Functions — Writing Reusable Code

Function Design and Clean Code

Martin, R. C. (2008). Clean Code: A Handbook of Agile Software Craftsmanship. Prentice Hall. Chapter 3: "Functions." (Tier 1) The definitive guide to writing clean functions in professional software. Martin's rules — functions should be small, do one thing, have descriptive names, and have few arguments — are widely adopted industry standards. The examples are in Java, but the principles apply directly to Python.
Hunt, A. & Thomas, D. (1999). The Pragmatic Programmer. Addison-Wesley. Chapter 2: "A Pragmatic Approach." (Tier 1) Introduces the DRY principle ("Don't Repeat Yourself") and the concept of orthogonality — designing components that are independent of each other. Both ideas map directly to function design. The 20th Anniversary Edition (2019) updates the examples for modern development.

Lutz, M. (2013). Learning Python, 5th Edition. O'Reilly. Chapters 16-21. (Tier 1) The most comprehensive coverage of Python functions available. Covers everything from basic definitions to advanced topics like closures, decorators, and generator functions. Chapters 16 (basics) and 17 (scopes) are most relevant to this chapter.
Python Official Documentation: "Defining Functions" (Tier 1) https://docs.python.org/3/tutorial/controlflow.html#defining-functions Python's official tutorial on functions. Concise, accurate, and authoritative. The section on default argument values includes the mutable default gotcha.
Python Official Documentation: "More on Defining Functions" (Tier 1) https://docs.python.org/3/tutorial/controlflow.html#more-on-defining-functions Covers keyword arguments, *args, **kwargs, and lambda expressions. Goes slightly beyond what this chapter covers — good for readers who want to go deeper.

Abelson, H. & Sussman, G. J. (1996). Structure and Interpretation of Computer Programs (SICP). MIT Press. (Tier 1) The classic computer science textbook that treats functions (procedures) as the fundamental building block of programs. Chapter 1 builds up from simple functions to complex abstractions in a way that permanently changes how you think about programming. Available free online at mitpress.mit.edu.
Denning, P. J. & Tedre, M. (2019). Computational Thinking. MIT Press. (Tier 1) Places abstraction in the broader context of computational thinking. Shows how the ability to create and use abstractions — functions being the most fundamental — is what makes computer science a distinct intellectual discipline.

PEP 257 — Docstring Conventions (Tier 1) https://peps.python.org/pep-0257/ The official Python Enhancement Proposal that defines docstring conventions. Short but authoritative — this is the standard that Python's community follows.
Google Python Style Guide: "Docstrings" (Tier 2) https://google.github.io/styleguide/pyguide.html#38-comments-and-docstrings Google's internal Python style guide has an excellent section on docstrings that balances completeness with readability. Many open-source projects follow this style.

Sweigart, A. (2019). Automate the Boring Stuff with Python, 2nd Edition. No Starch Press. Chapter 3: "Functions." (Tier 1) A practical, example-heavy introduction to functions with good coverage of scope. Available free online at automatetheboringstuff.com. The scope explanation is particularly clear for beginners.

Ramalho, L. (2022). Fluent Python, 2nd Edition. O'Reilly. Chapter 7: "Functions as First-Class Objects." (Tier 1) Explores the idea that functions in Python are objects — you can pass them as arguments, return them from other functions, and store them in data structures. This goes well beyond the current chapter but previews powerful concepts you'll encounter later. Recommended after you're comfortable with Chapters 6-12 of this textbook.
Raymond Hettinger, "Transforming Code into Beautiful, Idiomatic Python." PyCon US 2013 talk. (Tier 2) A widely celebrated conference talk that demonstrates how to refactor Python code into clean, idiomatic patterns. Available on YouTube. Several examples involve extracting functions and improving function design.