Functional Python programming : use a functional approach to write succinct, expressive, and efficient Python code / Steven F. Lott ; foreword by Ricardo Banffy.

Format:

Book

Author/Creator:

Lott, Steven F., author.

Contributor:

B̀nffy, Ricardo, writer of foreword.

Series:

Expert insight.

Expert insight

Language:

English

Subjects (All):

Python (Computer program language).

Functional programming (Computer science).

Physical Description:

1 online resource (576 pages)

Edition:

Third edition.

Place of Publication:

Birmingham : Packt Publishing, [2022]

Summary:

Python isn't all about object-oriented programming. Discover a valuable way of thinking about code design through a function-first approach - and learn when you need to use it. Now with detailed exercises at the end of every chapter! Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn how, when, and why to adopt functional elements in your projects Explore the Python modules essential to functional programming, like itertools and functools Revised to cover new features of Python 3.10, exercises at the end of every chapter, and more Book Description 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. Starting from the fundamentals, this book shows you how to apply functional thinking and techniques in a range of scenarios, with examples centered around 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. What you will learn Use Python's libraries to avoid the complexities of state-changing classes Leverage built-in higher-order functions to avoid rewriting common algorithms Write generator functions to create lazy processing Design and implement decorators for functional composition Make use of Python type annotations to describe parameters and results of functions Apply functional programming to concurrency and web services Explore the PyMonad library for stateful simulations Who this book is for The functional paradigm is very useful for programmers working in data science, but any Python developer who wants to create more reliable, succinct, and expressive code will have much to learn from this book. No prior knowledge of functional programming is required to get started, though Python programming knowledge is assumed. A running Python environment is essential.

Contents:

Cover

Copyright

Contributors

Table of Contents

Preface

Chapter 1: Understanding Functional Programming

The functional style of programming

Comparing and contrasting procedural and functional styles

Using the functional paradigm

Using a functional hybrid

The stack of turtles

A classic example of functional programming

Exploratory data analysis

Summary

Exercises

Convert an imperative algorithm to functional code

Convert step-wise computation to functional code

Revise the sqrt() function

Data cleansing steps

(Advanced) Optimize this functional code

Chapter 2: Introducing Essential Functional Concepts

Functions as first-class objects

Pure functions

Higher-order functions

Immutable data

Strict and non-strict evaluation

Lazy and eager evaluation

Recursion instead of an explicit loop state

Functional type systems

Familiar territory

Learning some advanced concepts

Apply map() to a sequence of values

Function vs. lambda design question

Optimize a recursion

Chapter 3: Functions, Iterators, and Generators

Writing pure functions

Functions as first-class objects

Using strings

Using tuples and named tuples

Using generator expressions

Exploring the limitations of generators

Combining generator expressions

Cleaning raw data with generator functions

Applying generators to built-in collections

Generators for lists, dicts, and sets

Using stateful mappings

Using the bisect module to create a mapping

Using stateful sets

Rewrite the some_function() function

Alternative Mersenne class definition

Alternative algorithm implementations

Map and filter

Dictionary comprehension

Raw data cleanup

Chapter 4: Working with Collections

An overview of function varieties

Working with iterables

Parsing an XML file

Parsing a file at a higher level

Pairing up items from a sequence

Using the iter() function explicitly

Extending an iteration

Applying generator expressions to scalar functions

Using any() and all() as reductions

Using len() and sum() on collections

Using sums and counts for statistics

Using zip() to structure and flatten sequences

Unzipping a zipped sequence

Flattening sequences

Structuring flat sequences

Structuring flat sequences

an alternative approach

Using sorted() and reversed() to change the order

Using enumerate() to include a sequence number

Palindromic numbers

Hands of cards

Replace legs() with pairwise()

Chapter 5: Higher-Order Functions

Using max() and min() to find extrema

Using Python lambda forms

Lambdas and the lambda calculus

Using the map() function to apply a function to a collection

Working with lambda forms and map()

Using map() with multiple sequences

Notes:

OCLC-licensed vendor bibliographic record.

ISBN:

9781523151417

1523151412

9781803232577

1803232579

OCLC:

1357496464