Book Image

Functional Python Programming, 3rd edition - Third Edition

By : Steven F. Lott
Book Image

Functional Python Programming, 3rd edition - Third Edition

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.

Related Content you might be interested in

Current Title:

Small book image
Functional Python Programming, 3rd edition - Third Edition
Steven F. Lott
Dec, 2022 | 576 pages
Small book image
Modern Python Cookbook.
Steven F Lott
Jul, 2020 | 822 pages
Small book image
Python Object-Oriented Programming
Steven F Lott | Dusty Phillips
Jul, 2021 | 714 pages
Small book image
Python Real-World Projects
Steven F Lott
Sep, 2023 | 478 pages
Table of Contents (18 chapters)
Preface
Preface
Free Chapter
1
Chapter 1: Understanding Functional Programming
Chapter 1: Understanding Functional Programming
1.1 The functional style of programming
1.2 Comparing and contrasting procedural and functional styles
1.3 A classic example of functional programming
1.4 Exploratory data analysis
1.5 Summary
1.6 Exercises
Join our community Discord space
2
Chapter 2: Introducing Essential Functional Concepts
Chapter 2: Introducing Essential Functional Concepts
2.1 Functions as first-class objects
2.2 Immutable data
2.3 Strict and non-strict evaluation
2.4 Lazy and eager evaluation
2.5 Recursion instead of an explicit loop state
2.6 Functional type systems
2.7 Familiar territory
2.8 Learning some advanced concepts
2.9 Summary
2.10 Exercises
Join our community Discord space
3
Chapter 3: Functions, Iterators, and Generators
Chapter 3: Functions, Iterators, and Generators
3.1 Writing pure functions
3.2 Functions as first-class objects
3.3 Using strings
3.4 Using tuples and named tuples
3.5 Using generator expressions
3.6 Cleaning raw data with generator functions
3.7 Applying generators to built-in collections
3.8 Summary
3.9 Exercises
Join our community Discord space
4
Chapter 4: Working with Collections
Chapter 4: Working with Collections
4.1 An overview of function varieties
4.2 Working with iterables
4.3 Using any() and all() as reductions
4.4 Using len() and sum() on collections
4.5 Using zip() to structure and flatten sequences
4.6 Using sorted() and reversed() to change the order
4.7 Using enumerate() to include a sequence number
4.8 Summary
4.9 Exercises
Join our community Discord space
5
Chapter 5: Higher-Order Functions
Chapter 5: Higher-Order Functions
5.1 Using max() and min() to find extrema
5.2 Using the map() function to apply a function to a collection
5.3 Using the filter() function to pass or reject data
5.4 The iter() function with a sentinel value
5.5 Using sorted() to put data in order
5.6 Overview of writing higher-order functions
5.7 Writing higher-order mappings and filters
5.8 Building higher-order functions with callables
5.9 Review of some design patterns
5.10 Summary
5.11 Exercises
Join our community Discord space
6
Chapter 6: Recursions and Reductions
Chapter 6: Recursions and Reductions
6.1 Simple numerical recursions
6.2 Reductions and folding a collection from many items to one
6.3 Group-by reduction from many items to fewer
6.4 Summary
6.5 Exercises
Join our community Discord space
7
Chapter 7: Complex Stateless Objects
Chapter 7: Complex Stateless Objects
7.1 Using tuples to collect data
7.2 Using NamedTuple to collect data
7.3 Using frozen dataclasses to collect data
7.4 Complicated object initialization and property computations
7.5 Using pyrsistent to collect data
7.6 Avoiding stateful classes by using families of tuples
7.7 Polymorphism and type pattern matching
7.8 Summary
7.9 Exercises
Join our community Discord space
8
Chapter 8: The Itertools Module
Chapter 8: The Itertools Module
8.1 Working with the infinite iterators
8.2 Using the finite iterators
8.3 Cloning iterators with tee()
8.4 The itertools recipes
8.5 Summary
8.6 Exercises
Join our community Discord space
9
Chapter 9: Itertools for Combinatorics – Permutations and Combinations
Chapter 9: Itertools for Combinatorics – Permutations and Combinations
9.1 Enumerating the Cartesian product
9.2 Reducing a product
9.3 Performance improvements
9.4 Permuting a collection of values
9.5 Generating all combinations
9.6 Recipes
9.7 Summary
9.8 Exercises
Join our community Discord space
10
Chapter 10: The Functools Module
Chapter 10: The Functools Module
10.1 Function tools
10.2 Memoizing previous results with cache
10.3 Defining classes with total ordering
10.4 Applying partial arguments with partial()
10.5 Reducing sets of data with the reduce() function
10.6 Handling multiple types with singledispatch
10.7 Summary
10.8 Exercises
Join our community Discord space
11
Chapter 11: The Toolz Package
Chapter 11: The Toolz Package
11.1 The itertools star map function
11.2 Reducing with operator module functions
11.3 Using the toolz package
11.4 Summary
11.5 Exercises
12
Chapter 12: Decorator Design Techniques
Chapter 12: Decorator Design Techniques
12.1 Decorators as higher-order functions
12.2 Cross-cutting concerns
12.3 Composite design
12.4 Adding a parameter to a decorator
12.5 Implementing more complex decorators
12.6 Complicated design considerations
12.7 Summary
12.8 Exercises
13
Chapter 13: The PyMonad Library
Chapter 13: The PyMonad Library
13.1 Downloading and installing
13.2 Functional composition and currying
13.3 Functors – making everything a function
13.4 Monad bind() function
13.5 Implementing simulation with monads
13.6 Additional PyMonad features
13.7 Summary
13.8 Exercises
Join our community Discord space
14
Chapter 14: The Multiprocessing, Threading, and Concurrent.Futures Modules
Chapter 14: The Multiprocessing, Threading, and Concurrent.Futures Modules
14.1 Functional programming and concurrency
14.2 What concurrency really means
14.3 Using multiprocessing pools and tasks
14.4 Using a multiprocessing pool for concurrent processing
14.5 Summary
14.6 Exercises
15
Chapter 15: A Functional Approach to Web Services
Chapter 15: A Functional Approach to Web Services
15.1 The HTTP request-response model
15.2 The WSGI standard
15.3 Defining web services as functions
15.4 Tracking usage
15.5 Summary
15.6 Exercises
Join our community Discord space
16
Other Books You Might Enjoy
Other Books You Might Enjoy
17
Index
Index

3.4 Using tuples and named tuples

Since Python tuples are immutable objects, they’re another excellent example of objects suitable for functional programming. A Python tuple has very few methods, so almost everything is done using prefix syntax. There are a number of use cases for tuples, particularly when working with list-of-tuple, tuple-of-tuple, and generator-of-tuple constructs.

The typing.NamedTuple class adds an essential feature to a tuple: names to use instead of cryptic index numbers. We can exploit named tuples to create objects that are accretions of data. This allows us to write pure functions based on stateless objects, yet keep data bound into tidy object-like packages. The collections.namedtuple() can also be used to define an immutable class of objects. This lacks a mechanism for providing type hints, making it less desirable than the typing.NamedTuple class.

The decision to use a tuple or typing.NamedTuple object is entirely a matter of convenience. As an example...

Previous Section
End of Section 5
Next Section