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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

4.8 Summary

In this chapter, we saw detailed ways to use a number of built-in reductions.

We’ve used any() and all() to do essential logic processing. These are tidy examples of reductions using a simple operator, such as or or and. We’ve also looked at numeric reductions such as len() and sum(). We’ve applied these functions to create some higher-order statistical processing. We’ll return to these reductions in Chapter 6, Recursions and Reductions.

We’ve also looked at some of the built-in mappings. The zip() function merges multiple sequences. This leads us to look at using this in the context of structuring and flattening more complex data structures. As we’ll see in examples in later chapters, nested data is helpful in some situations and flat data is helpful in others. The enumerate() function maps an iterable to a sequence of two-tuples. Each two-tuple has the sequence number at index [0] and the original value at index [1].

The reversed...

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