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Modern Python Cookbook

You're reading from   Modern Python Cookbook 130+ updated recipes for modern Python 3.12 with new techniques and tools

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835466384
Length 818 pages
Edition 3rd Edition
Languages
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Author (1):
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Steven F. Lott Steven F. Lott
Author Profile Icon Steven F. Lott
Steven F. Lott
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Toc

Table of Contents (20) Chapters Close

Preface 1. Chapter 1 Numbers, Strings, and Tuples FREE CHAPTER 2. Chapter 2 Statements and Syntax 3. Chapter 3 Function Definitions 4. Chapter 4 Built-In Data Structures Part 1: Lists and Sets 5. Chapter 5 Built-In Data Structures Part 2: Dictionaries 6. Chapter 6 User Inputs and Outputs 7. Chapter 7 Basics of Classes and Objects 8. Chapter 8 More Advanced Class Design 9. Chapter 9 Functional Programming Features 10. Chapter 10 Working with Type Matching and Annotations 11. Chapter 11 Input/Output, Physical Format, and Logical Layout 12. Chapter 12 Graphics and Visualization with Jupyter Lab 13. Chapter 13 Application Integration: Configuration 14. Chapter 14 Application Integration: Combination 15. Chapter 15 Testing 16. Chapter 16 Dependencies and Virtual Environments 17. Chapter 17 Documentation and Style 18. Other Books You May Enjoy
19. Index

9.1 Writing generator functions with the yield statement

A generator function is often designed to apply some kind of transformation to each item of a collection. Generators can create data, too. A generator is called lazy because the values it yields must be consumed by a client; values are not computed until a client attempts to consume them. Client operations like the list() function or a for statement are common examples of consumers. Each time a function like list() demands a value, the generator function must yield a value using the yield statement.

In contrast, an ordinary function can be called eager. Without the yield statement, a function will compute the entire result and return it via the return statement.

A lazy approach is very helpful in cases where we can’t fit an entire collection in memory. For example, analyzing gigantic web log files can be done in small doses rather than by creating a vast in-memory collection.

In the language...

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