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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

10.6 Handling multiple types with singledispatch

We’ll often have functions which have similar semantics but distinct implementations based on the type of data presented. We might have a function that works for either a subclass of NamedTuple, or TypedDict. The syntax for working with these objects is distinct, and we can’t use a single, generic Python function.

We have the following choices for working with data of distinct types:

  • Use the match statement with a case clause for each distinct type.

  • Use the @singledispatch decorator to define a number of closely-related functions. This will create the necessary type-matching match statement for us.

A small example arises when working with US postal data and spreadsheets. It’s common for a US postal ZIP code to be misinterpreted as an integer (or float) value. The town of Andover, MA, for example, has a postal code of 01810. A spreadsheet might misinterpret this as an integer, 1810, dropping the leading zero.

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