Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Functional Python Programming, 3rd edition
  • Table Of Contents Toc
  • Feedback & Rating feedback
Functional Python Programming, 3rd edition

Functional Python Programming, 3rd edition

By : Steven F. Lott
4.5 (28)
Buy this Book
close
close
Functional Python Programming, 3rd edition

Functional Python Programming, 3rd edition

4.5 (28)
By: Steven F. Lott
Buy this Book

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)
close
close
close
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
chevron up
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

14.3 Using multiprocessing pools and tasks

Python’s multiprocessing package introduces the concept of a Pool object. A Pool object contains a number of worker processes and expects these processes to be executed concurrently. This package allows OS scheduling and time slicing to interleave execution of multiple processes. The intention is to keep the overall system as busy as possible.

To make the most of this capability, we need to decompose our application into components for which non-strict, concurrent execution is beneficial. The overall application must be built from discrete tasks that can be processed in an indefinite order.

An application that gathers data from the internet through web scraping, for example, is often optimized through concurrent processing. A number of individual processes can be waiting for the data to download, while others are performing the scraping operation on data that’s been received. We can create a Pool object of several identical workers...

Unlock full access

Continue reading for free

A Packt free trial gives you instant online access to our library of over 7000 practical eBooks and videos, constantly updated with the latest in tech
Start a 7-day FREE trial
End of Section 4
Next Section
bookmark search playlist download
font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY