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Home > Programming > Functional Programming > Functional Python Programming
Functional Python Programming
Functional Python Programming

Functional Python Programming: Discover the power of functional programming, generator functions, lazy evaluation, the built-in itertools library, and monads , Second Edition

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Functional Python Programming

Introducing Essential Functional Concepts

Most of the features of functional programming are already first-class parts of Python. Our goal in writing functional Python is to shift our focus away from imperative (procedural or object-oriented) techniques to as much of an extent as possible.

We'll look at each of the following functional programming topics:

  • First-class and higher-order functions, which are sometimes known as pure functions.
  • Immutable data.
  • Strict and non-strict evaluation. We can also call this eager versus lazy evaluation.
  • Recursion instead of an explicit loop state.
  • Functional type systems.

This should reiterate some concepts from the first chapter: firstly, that purely functional programming avoids the complexities of an explicit state maintained through variable assignments; secondly, that Python is not a purely functional language.

This book...

First-class functions

Functional programming is often succinct and expressive. One way to achieve it is by providing functions as arguments and return values for other functions. We'll look at numerous examples of manipulating functions.

For this to work, functions must be first-class objects in the runtime environment. In programming languages such as C, a function is not a runtime object. In Python, however, functions are objects that are created (usually) by the def statements and can be manipulated by other Python functions. We can also create a function as a callable object or by assigning lambda to a variable.

Here's how a function definition creates an object with attributes:

>>> def example(a, b, **kw):
...    return a*b
...
>>> type(example)
<class 'function'>
>>> example.__code__.co_varnames
('a&apos...

Immutable data

Since we're not using variables to track the state of a computation, our focus needs to stay on immutable objects. We can make extensive use of tuples and namedtuples to provide more complex data structures that are immutable.

The idea of immutable objects is not foreign to Python. There can be a performance advantage to using immutable tuples instead of more complex mutable objects. In some cases, the benefits come from rethinking the algorithm to avoid the costs of object mutation.

We will avoid class definitions almost entirely. It can seem like anathema to avoid objects in an Object-Oriented Programming (OOP) language. Functional programming simply doesn't need stateful objects. We'll see this throughout this book. There are reasons for defining callable objects; it is a tidy way to provide namespaces for closely related functions...

Strict and non-strict evaluation

Functional programming's efficiency stems, in part, from being able to defer a computation until it's required. The idea of lazy or non-strict evaluation is very helpful. To an extent, Python offers this feature.

In Python, the logical expression operators and, or, and if-then-else are all non-strict. We sometimes call them short-circuit operators because they don't need to evaluate all arguments to determine the resulting value.

The following command snippet shows the and operator's non-strict feature:

>>> 0 and print("right")
0
>>> True and print("right")
right

When we execute the first of the preceding command snippet, the left-hand side of the and operator is equivalent to False; the right-hand side is not evaluated. In the second example, when the left-hand side is equivalent to True...

Recursion instead of an explicit loop state

Functional programs don't rely on loops and the associated overhead of tracking the state of loops. Instead, functional programs try to rely on the much simpler approach of recursive functions. In some languages, the programs are written as recursions, but Tail-CallOptimization (TCO) in the compiler changes them to loops. We'll introduce some recursion here and examine it closely in Chapter 6, Recursions and Reductions.

We'll look at a simple iteration to test a number for being prime. A prime number is a natural number, evenly divisible by only 1 and itself. We can create a naïve and poorly-performing algorithm to determine whether a number has any factors between 2 and the number. This algorithm has the advantage of simplicity; it works acceptably for solving Project Euler problems. Read up on Miller-Rabin...

Functional type systems

Some functional programming languages, such as Haskell and Scala, are statically compiled, and depend on declared types for functions and their arguments. To provide the kind of flexibility Python already has, these languages have sophisticated type-matching rules so that a generic function can be written, which works for a variety of related types.

In Object-Oriented Python, we often use the class inheritance hierarchy instead of sophisticated function type matching. We rely on Python to dispatch an operator to a proper method based on simple name-matching rules.

Since Python already has the desired levels of flexibility, the type matching rules for a compiled functional language aren't relevant. Indeed, we could argue that the sophisticated type matching is a workaround imposed by static compilation. Python doesn't need this...

Familiar territory

One of the ideas that emerged from the previous list of topics is that most functional programming is already present in Python. Indeed, most functional programming is already a very typical and common part of OOP.

As a very specific example, a fluent Application Program Interface (API) is a very clear example of functional programming. If we take time to create a class with return self() in each method function, we can use it as follows:

some_object.foo().bar().yet_more()

We can just as easily write several closely related functions that work as follows:

yet_more(bar(foo(some_object)))

We've switched the syntax from traditional object-oriented suffix notation to a more functional prefix notation. Python uses both notations freely, often using a prefix version of a special method name. For example, the len() function is generally implemented by the __len__...

Learning some advanced concepts

We will set some more advanced concepts aside for consideration in later chapters. These concepts are part of the implementation of a purely functional language. Since Python isn't purely functional, our hybrid approach won't require deep consideration of these topics.

We will identify these up front for the benefit of folks who already know a functional language such as Haskell and are learning Python. The underlying concerns are present in all programming languages but we'll tackle them differently in Python. In many cases, we can and will drop into imperative programming rather than use a strictly functional approach.

The topics are as follows:

  • Referential transparency: When looking at lazy evaluation and the various kinds of optimizations that are possible in a compiled language, the idea of multiple routes to the same object...

First-class functions

Functional programming is often succinct and expressive. One way to achieve it is by providing functions as arguments and return values for other functions. We'll look at numerous examples of manipulating functions.

For this to work, functions must be first-class objects in the runtime environment. In programming languages such as C, a function is not a runtime object. In Python, however, functions are objects that are created (usually) by the def statements and can be manipulated by other Python functions. We can also create a function as a callable object or by assigning lambda to a variable.

Here's how a function definition creates an object with attributes:

>>> def example(a, b, **kw):
...    return a*b
...
>>> type(example)
<class 'function'>
>>> example.__code__.co_varnames
('a', 'b', 'kw')
>>> example.__code__.co_argcount
2

We've created an object, example, that is of the function() class. This object has numerous attributes...

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Key benefits

  • • Learn how to choose between imperative and functional approaches based on
  • expressiveness, clarity, and performance
  • • Get familiar with complex concepts such as monads, concurrency, and immutability
  • • Apply functional Python to common Exploratory Data Analysis (EDA) programming
  • problems

Description

If you’re a Python developer who wants to discover how to take the power of functional programming (FP) and bring it into your own programs, then this book is essential for you, even if you know next to nothing about the paradigm. Starting with a general overview of functional concepts, you’ll explore common functional features such as first-class and higher-order functions, pure functions, and more. You’ll see how these are accomplished in Python 3.6 to give you the core foundations you’ll build upon. After that, you’ll discover common functional optimizations for Python to help your apps reach even higher speeds. You’ll learn FP concepts such as lazy evaluation using Python’s generator functions and expressions. Moving forward, you’ll learn to design and implement decorators to create composite functions. You'll also explore data preparation techniques and data exploration in depth, and see how the Python standard library fits the functional programming model. Finally, to top off your journey into the world of functional Python, you’ll at look at the PyMonad project and some larger examples to put everything into perspective.

Who is this book for?

This book is for Python developers who would like to perform Functional programming with Python. Python Programming knowledge is assumed.

What you will learn

  • • Use Python s generator functions and generator expressions to work with collections in a non-strict (or lazy) manner
  • • Utilize Python library modules including itertools, functools, multiprocessing, and concurrent features to ensure efficient functional programs
  • • Use Python strings with object-oriented suffix notation and prefix notation
  • • Avoid stateful classes with families of tuples
  • • Design and implement decorators to create composite functions
  • • Use functions such as max(), min(), map(), filter(), and sorted()
  • • Write higher-order functions
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Publication date : Apr 13, 2018
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Category :
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Concepts :
Functional Programming

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Table of Contents

17 Chapters
Understanding Functional Programming Chevron down icon Chevron up icon
Understanding Functional Programming
Identifying a paradigm
Subdividing the procedural paradigm
A classic example of functional programming
Exploratory data analysis
Summary
Introducing Essential Functional Concepts Chevron down icon Chevron up icon
Introducing Essential Functional Concepts
First-class functions
Immutable data
Strict and non-strict evaluation
Recursion instead of an explicit loop state
Functional type systems
Familiar territory
Learning some advanced concepts
Summary
Functions, Iterators, and Generators Chevron down icon Chevron up icon
Functions, Iterators, and Generators
Writing pure functions
Functions as first-class objects
Using strings
Using tuples and named tuples
Cleaning raw data with generator functions
Using lists, dicts, and sets
Summary
Working with Collections Chevron down icon Chevron up icon
Working with Collections
An overview of function varieties
Working with iterables
Using zip() to structure and flatten sequences
Using reversed() to change the order
Using enumerate() to include a sequence number
Summary
Higher-Order Functions Chevron down icon Chevron up icon
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
Using map() with multiple sequences
Using the filter() function to pass or reject data
Using filter() to identify outliers
The iter() function with a sentinel value
Using sorted() to put data in order
Writing higher-order functions
Writing higher-order mappings and filters
Writing generator functions
Building higher-order functions with callables
Review of some design patterns
Summary
Recursions and Reductions Chevron down icon Chevron up icon
Recursions and Reductions
Simple numerical recursions
Group-by reduction from many items to fewer
Summary
Additional Tuple Techniques Chevron down icon Chevron up icon
Additional Tuple Techniques
Using tuples to collect data
Using named tuples to collect data
Building named tuples with functional constructors
Avoiding stateful classes by using families of tuples
Polymorphism and type-pattern matching
Summary
The Itertools Module Chevron down icon Chevron up icon
The Itertools Module
Working with the infinite iterators
Using the finite iterators
Cloning iterators with tee()
The itertools recipes
Summary
More Itertools Techniques Chevron down icon Chevron up icon
More Itertools Techniques
Enumerating the Cartesian product
Reducing a product
Permuting a collection of values
Generating all combinations
Recipes
Summary
The Functools Module Chevron down icon Chevron up icon
The Functools Module
Function tools
Memoizing previous results with lru_cache
Defining classes with total ordering
Applying partial arguments with partial()
Reducing sets of data with the reduce() function
Summary
Decorator Design Techniques Chevron down icon Chevron up icon
Decorator Design Techniques
Decorators as higher-order functions
Cross-cutting concerns
Composite design
Adding a parameter to a decorator
Implementing more complex decorators
Complex design considerations
Summary
The Multiprocessing and Threading Modules Chevron down icon Chevron up icon
The Multiprocessing and Threading Modules
Functional programming and concurrency
What concurrency really means
Using multiprocessing pools and tasks
Using a multiprocessing pool for concurrent processing
Summary
Conditional Expressions and the Operator Module Chevron down icon Chevron up icon
Conditional Expressions and the Operator Module
Evaluating conditional expressions
Using the operator module instead of lambdas
Starmapping with operators
Reducing with operator module functions
Summary
The PyMonad Library Chevron down icon Chevron up icon
The PyMonad Library
Downloading and installing
Functional composition and currying
Functional composition and the PyMonad * operator
Functors and applicative functors
Monad bind() function and the >> operator
Implementing simulation with monads
Additional PyMonad features
Summary
A Functional Approach to Web Services Chevron down icon Chevron up icon
A Functional Approach to Web Services
The HTTP request-response model
The WSGI standard
Defining web services as functions
Tracking usage
Summary
Optimizations and Improvements Chevron down icon Chevron up icon
Optimizations and Improvements
Memoization and caching
Specializing memoization
Tail recursion optimizations
Optimizing storage
Optimizing accuracy
Case study–making a chi-squared decision
Computing expected values and displaying a contingency table
Functional programming design patterns
Summary
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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(3 Ratings)
5 star 66.7%
4 star 0%
3 star 0%
2 star 0%
1 star 33.3%
Jerome3590 Sep 05, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Author does a great job of walking through both the how and why of python's functional programming capabilities. First programming book I've read that actually details the implementation of the code and what it's doing. I do not have a computer science background and the author's format and explanations for how the code works behind the scenes I found extremely insightful. This will be a book I routinely refer to. I will definitely check out Steven Lott's other books!
Amazon Verified review Amazon
Jonathan Davis Jan 09, 2021
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Having had experience with functional programming before, I was pleased that this book covered all the concepts and did so clearly.
Amazon Verified review Amazon
AmazonReviewer Mar 24, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
If this is functional programming, then it sucks. It makes everything difficult and more complex than it needs to be, which makes it hard to create-read-understand-maintain. I fail to see the point. It is not practical.
Amazon Verified review Amazon

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For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact [email protected] with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at [email protected] using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on [email protected] with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on [email protected] within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on [email protected] who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on [email protected] within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela