Book Image

Mastering Python 2E - Second Edition

By : Rick van Hattem
5 (1)
Book Image

Mastering Python 2E - Second Edition

5 (1)
By: Rick van Hattem

Overview of this book

Even if you find writing Python code easy, writing code that is efficient, maintainable, and reusable is not so straightforward. Many of Python’s capabilities are underutilized even by more experienced programmers. Mastering Python, Second Edition, is an authoritative guide to understanding advanced Python programming so you can write the highest quality code. This new edition has been extensively revised and updated with exercises, four new chapters and updates up to Python 3.10. Revisit important basics, including Pythonic style and syntax and functional programming. Avoid common mistakes made by programmers of all experience levels. Make smart decisions about the best testing and debugging tools to use, optimize your code’s performance across multiple machines and Python versions, and deploy often-forgotten Python features to your advantage. Get fully up to speed with asyncio and stretch the language even further by accessing C functions with simple Python calls. Finally, turn your new-and-improved code into packages and share them with the wider Python community. If you are a Python programmer wanting to improve your code quality and readability, this Python book will make you confident in writing high-quality scripts and taking on bigger challenges
Table of Contents (21 chapters)
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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...