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Mastering Python 2E

You're reading from   Mastering Python 2E Write powerful and efficient code using the full range of Python's capabilities

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Product type Paperback
Published in May 2022
Last Updated in May 2022
Publisher Packt
ISBN-13 9781800207721
Length 710 pages
Edition 2nd Edition
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Author (1):
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Rick Hattem Rick Hattem
Author Profile Icon Rick Hattem
Rick Hattem
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Table of Contents (21) Chapters Close

Preface 1. Getting Started – One Environment per Project FREE CHAPTER 2. Interactive Python Interpreters 3. Pythonic Syntax and Common Pitfalls 4. Pythonic Design Patterns 5. Functional Programming – Readability Versus Brevity 6. Decorators – Enabling Code Reuse by Decorating 7. Generators and Coroutines – Infinity, One Step at a Time 8. Metaclasses – Making Classes (Not Instances) Smarter 9. Documentation – How to Use Sphinx and reStructuredText 10. Testing and Logging – Preparing for Bugs 11. Debugging – Solving the Bugs 12. Performance – Tracking and Reducing Your Memory and CPU Usage 13. asyncio – Multithreading without Threads 14. Multiprocessing – When a Single CPU Core Is Not Enough 15. Scientific Python and Plotting 16. Artificial Intelligence 17. Extensions in C/C++, System Calls, and C/C++ Libraries 18. Packaging – Creating Your Own Libraries or Applications 19. Other Books You May Enjoy
20. Index

Functional Programming – Readability Versus Brevity

This chapter will show you some of the cool tricks that functional programming in Python gives you, and it will explain some of the limitations of Python’s implementation. For learning and entertainment, we will also briefly discuss the mathematical equivalent using lambda calculus, using the Y combinator as an example.

The last few paragraphs will list and explain the usage of the functools and itertools libraries. If you are familiar with these libraries, feel free to skip them, but note that some of these will be used heavily in the later chapters about decorators (Chapter 6), generators (Chapter 7), and performance (Chapter 12).

These are the topics covered in this chapter:

  • The theory behind functional programming
  • list, dict, and set comprehensions
  • lambda functions
  • functools (partial and reduce)
  • itertools (accumulate, chain, dropwhile, starmap, and so on)

First...

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