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The Python Workshop Second Edition

You're reading from   The Python Workshop Second Edition Write Python code to solve challenging real-world problems

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Product type Paperback
Published in Nov 2022
Publisher Packt
ISBN-13 9781804610619
Length 600 pages
Edition 2nd Edition
Languages
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Authors (5):
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Mario Corchero Jiménez Mario Corchero Jiménez
Author Profile Icon Mario Corchero Jiménez
Mario Corchero Jiménez
Andrew Bird Andrew Bird
Author Profile Icon Andrew Bird
Andrew Bird
Corey Wade Corey Wade
Author Profile Icon Corey Wade
Corey Wade
Graham Lee Graham Lee
Author Profile Icon Graham Lee
Graham Lee
Dr. Lau Cher Han Dr. Lau Cher Han
Author Profile Icon Dr. Lau Cher Han
Dr. Lau Cher Han
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Toc

Table of Contents (16) Chapters Close

Preface 1. Chapter 1: Python Fundamentals – Math, Strings, Conditionals, and Loops 2. Chapter 2: Python Data Structures FREE CHAPTER 3. Chapter 3: Executing Python – Programs, Algorithms, and Functions 4. Chapter 4: Extending Python, Files, Errors, and Graphs 5. Chapter 5: Constructing Python – Classes and Methods 6. Chapter 6: The Standard Library 7. Chapter 7: Becoming Pythonic 8. Chapter 8: Software Development 9. Chapter 9: Practical Python – Advanced Topics 10. Chapter 10: Data Analytics with pandas and NumPy 11. Chapter 11: Machine Learning 12. Chapter 12: Deep Learning with Python 13. Chapter 13: The Evolution of Python – Discovering New Python Features 14. Index 15. Other Books You May Enjoy

Using functools

The final module of the Standard Library you are going to look at allows constructs with a minimal amount of code. In this topic, you are going to see how to use lru_cache and partial.

Caching with functools.lru_cache

Often, you have a function that is heavy to compute, in which you just want to cache results. Many developers will create their own caching implementation by using a dictionary, but that is error-prone and adds unnecessary code to our project. The functools module comes with a decorator — that is, functools.lru_cache, which is provided exactly for these situations. It is a recently used cache, with max_size that is provided when the code is constructed. This means that you can specify a number of input values that you want to cache as a maximum to limit the memory this function can take, or it can grow indefinitely. Once you reach the maximum number of different inputs that we want to cache, the input that was the least recently used will...

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