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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

7.8 Summary

In this chapter, we looked at different ways to use NamedTuple subclasses to implement more complex data structures. The essential features of a NamedTuple are a good fit with functional design. They can be created with a creation function and accessed by position as well as name.

Similarly, we looked at frozen dataclasses as an alternative to NamedTuple objects. The use of a dataclass seems slightly superior to a NamedTuple subclass because a dataclass doesn’t also behave like a sequence of attribute values.

We looked at how immutable objects can be used instead of stateful object definitions. The core technique for replacing state changes is to wrap objects in larger objects that contain derived values.

We also looked at ways to handle multiple data types in Python. For most arithmetic operations, Python’s internal method dispatch locates proper implementations. To work with collections, however, we might want to handle iterators and sequences slightly differently...

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