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Functional Python Programming, 3rd edition

Functional Python Programming, 3rd edition

By : Steven F. Lott
4.5 (28)
Buy this Book
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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)
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Preface
Preface
Free Chapter
1
Chapter 1: Understanding Functional Programming
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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Other Books You Might Enjoy
Other Books You Might Enjoy
17
Index
Index

1.4 Exploratory data analysis

Later in this book, we’ll use the field of exploratory data analysis as a source for concrete examples of functional programming. This field is rich with algorithms and approaches to working with complex datasets; functional programming is often a very good fit between the problem domain and automated solutions.

While details vary from author to author, there are several widely accepted stages of EDA. These include the following:

  • Data preparation: This might involve extraction and transformation for source applications. It might involve parsing a source data format and doing some kind of data scrubbing to remove unusable or invalid data. This is an excellent application of functional design techniques.

David Mertz’s superb book Cleaning Data for Effective Data Science( https://www.packtpub.com/product/cleaning-data-for-effective-data-science/9781801071291) provides additional information on data cleaning. This is a crucial subject for all data science and analytical work.

  • Data exploration: This is a description of the available data. This usually involves the essential statistical functions. This is another excellent place to explore functional programming. We can describe our focus as univariate and bivariate statistics, but that sounds too daunting and complex. What this really means is that we’ll focus on mean, median, mode, and other related descriptive statistics. Data exploration may also involve data visualization. We’ll skirt this issue because it doesn’t involve very much functional programming.

For more information on Python visualization, see Interactive Data Visualization with Python, https://www.packtpub.com/product/interactive-data-visualization-with-python-second-edition/9781800200944. See https://www.projectpro.io/article/python-data-visualization-libraries/543 for some additional visualization libraries.

  • Data modeling and machine learning: This tends to be prescriptive as it involves extending a model to new data. We’re going to skirt around this because some of the models can become mathematically complex. If we spend too much time on these topics, we won’t be able to focus on functional programming.

  • Evaluation and comparison: When there are alternative models, each must be evaluated to determine which is a better fit for the available data. This can involve ordinary descriptive statistics of model outputs, which can benefit from functional design techniques.

One goal of EDA is often to create a model that can be deployed as a decision support application. In many cases, a model might be a simple function. A functional programming approach can apply the model to new data and display results for human consumption.

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