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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

Generator examples

Now that you know how generators can be created, let’s look at a few useful generators and examples of how to use them.

Before you start writing a generator for your project, always make sure to look at the Python itertools module. It features a host of useful generators that cover a vast array of use cases. The following sections show some custom generators and a few of the most useful generators in the standard library.

These generators work on all iterables, not just generators. So, you could also apply them to a list, tuple, string, or other kinds of iterables.

Breaking an iterable up into chunks/groups

When executing large amounts of queries in a database or when running tasks via multiple processes, it is often more efficient to chunk the operations. Having a single huge operation could result in out-of-memory issues; having many tiny operations can be slow due to start-up/teardown sequences.

To make things more efficient...

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