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Mastering Python

You're reading from   Mastering Python Master the art of writing beautiful and powerful Python by using all of the features that Python 3.5 offers

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Product type Paperback
Published in Apr 2016
Publisher Packt
ISBN-13 9781785289729
Length 486 pages
Edition 1st Edition
Languages
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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 (17) Chapters Close

Preface 1. Getting Started – One Environment per Project FREE CHAPTER 2. Pythonic Syntax, Common Pitfalls, and Style Guide 3. Containers and Collections – Storing Data the Right Way 4. Functional Programming – Readability Versus Brevity 5. Decorators – Enabling Code Reuse by Decorating 6. Generators and Coroutines – Infinity, One Step at a Time 7. Async IO – Multithreading without Threads 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. Multiprocessing – When a Single CPU Core Is Not Enough 14. Extensions in C/C++, System Calls, and C/C++ Libraries 15. Packaging – Creating Your Own Libraries or Applications Index

What are generators?

A generator, in its simplest form, is a function that returns elements one at a time instead of returning a collection of items. The most important advantage of this is that it requires very little memory and that it doesn't need to have a predefined size. Creating an endless generator (such as the itertools.count iterator discussed in Chapter 4, Functional Programming – Readability Versus Brevity) is actually quite easy, but it does come with a cost, of course. Not having the size of an object available makes certain patterns difficult to achieve.

The basic trick in writing generators (as functions) is using the yield statement. Let's use the itertools.count generator as an example and extend it with a stop variable:

>>> def count(start=0, step=1, stop=10):
...     n = start
...     while n <= stop:
...         yield n
...         n += step

>>> for x in count(10, 2.5, 20):
...     print(x)
10
12.5
15.0
17.5
20.0

Due to the potentially...

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