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Functional Python Programming, 3rd edition

Functional Python Programming, 3rd edition

By : Steven F. Lott
4.5 (28)
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Functional Python Programming, 3rd edition

Functional Python Programming, 3rd edition

4.5 (28)
By: Steven F. Lott

Overview of this book

Not enough developers understand the benefits of functional programming, or even what it is. Author Steven Lott demystifies the approach, teaching you how to improve the way you code in Python and make gains in memory use and performance. If you’re a leetcoder preparing for coding interviews, this book is for you. Starting from the fundamentals, this book shows you how to apply functional thinking and techniques in a range of scenarios, with Python 3.10+ examples focused on mathematical and statistical algorithms, data cleaning, and exploratory data analysis. You'll learn how to use generator expressions, list comprehensions, and decorators to your advantage. You don't have to abandon object-oriented design completely, though – you'll also see how Python's native object orientation is used in conjunction with functional programming techniques. By the end of this book, you'll be well-versed in the essential functional programming features of Python and understand why and when functional thinking helps. You'll also have all the tools you need to pursue any additional functional topics that are not part of the Python language.
Table of Contents (18 chapters)
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Preface
16
Other Books You Might Enjoy
17
Index

10.2 Memoizing previous results with cache

The @cache and @lru_cache decorators transform a given function into a function that might perform more quickly. LRU means Least Recently Used—a finite pool of recently used items is retained. Items not recently used are discarded to keep the pool to a bounded size. The @cache has no storage management and requires a little bit of consideration to be sure it won’t consume all available memory.

Since these are decorators, we can apply one of them to any function that might benefit from caching previous results. We can use it as follows:

from functools import lru_cache 
 
@lru_cache(128) 
def fibc(n: int) -> int: 
    if n == 0: return 0 
    if n == 1: return 1 
    return fibc(n-1) + fibc(n-2)

This is an example based on Chapter 6, Recursions and Reductions. We’ve applied the @lru_cache decorator to the naive Fibonacci number...

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