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Mastering Python 2E

Mastering Python 2E - Second Edition

By : Rick Hattem
4.6 (65)
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Mastering Python 2E

Mastering Python 2E

4.6 (65)
By: Rick 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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19
Other Books You May Enjoy
20
Index

Summary

This chapter showed you some of the places where decorators can be used to make our code simpler and add some fairly complex behavior to very simple functions. Truthfully, most decorators are more complex than the regular function would have been by simply adding the functionality directly, but the added advantage of applying the same pattern to many functions and classes is generally well worth it.

Decorators have so many uses to make your functions and classes smarter and more convenient to use:

  • Debugging
  • Validation
  • Argument convenience (pre-filling or converting arguments)
  • Output convenience (converting the output to a specific type)

The most important takeaway of this chapter should be to never forget functools.wraps when wrapping a function. Debugging decorated functions can be rather difficult because of (unexpected) behavior modification, but losing attributes as well can make that problem much worse.

The next chapter will...

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Mastering Python 2E
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