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

Functional Python Programming: Create succinct and expressive implementations with functional programming in Python

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Functional Python Programming

Chapter 2. Introducing Some Functional Features

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 the maximum extent possible.

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

  • First-class and higher-order functions, which are also known as pure functions.
  • Immutable Data.
  • Strict and non-strict evaluation. We can also call this eager vs. 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 explicit state maintained via variable assignment. Secondly, that Python is not a purely functional language.

We don't offer a rigorous definition of functional programming. Instead, we'll locate some common features that are indisputably important...

First-class functions

Functional programming is often succinct and expressive. One way to achieve this 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...

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 it's 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 namespace for closely-related functions, and it supports a pleasant level of configurability.

We'll look at a common design pattern that works well with...

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. It's so helpful that Python already 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 preceding command snippet, the left-hand side of the and operator is equivalent to False; the right-hand side is not evaluated. When the left-hand side is equivalent to True, the right-hand side is evaluated.

Other parts of Python are strict. Outside the logical operators, an expression is evaluated eagerly...

Recursion instead of a 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-Call Optimization (TCO) by 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 if a number has any factors between two and the number. This algorithm has the advantage of simplicity; it works acceptably for solving Project Euler problems. Read up on Miller-Rabin primality tests for a much better algorithm.

We'll use the term coprime to mean that two numbers have only 1 as their...

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. In order 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 workaround because it's a dynamic language.

In some cases, we might have to resort to using isinstance(a, tuple) to detect...

Familiar territory

One of the ideas that emerge 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 Object-Oriented Programming.

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 class.__len__() special method.

Of course, the implementation of the class shown above might involve...

First-class functions

Functional programming is often succinct and expressive. One way to achieve this 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 class function(). This object has numerous attributes...

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 it's 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 namespace for closely-related functions, and it supports a pleasant level of configurability.

We'll look at a common design pattern that works well with immutable objects: the...

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This book is for developers who want to use Python to write programs that lean heavily on functional programming design patterns. You should be comfortable with Python programming, but no knowledge of functional programming paradigms is needed.
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Table of Contents

17 Chapters
1. Introducing Functional Programming Chevron down icon Chevron up icon
1. Introducing Functional Programming
Identifying a paradigm
Subdividing the procedural paradigm
A classic example of functional programming
Exploratory Data Analysis
Summary
2. Introducing Some Functional Features Chevron down icon Chevron up icon
2. Introducing Some Functional Features
First-class functions
Immutable data
Strict and non-strict evaluation
Recursion instead of a explicit loop state
Functional type systems
Familiar territory
Saving some advanced concepts
Summary
3. Functions, Iterators, and Generators Chevron down icon Chevron up icon
3. Functions, Iterators, and Generators
Writing pure functions
Functions as first-class objects
Using strings
Using tuples and namedtuples
Cleaning raw data with generator functions
Using lists, dicts, and sets
Summary
4. Working with Collections Chevron down icon Chevron up icon
4. 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
5. Higher-order Functions Chevron down icon Chevron up icon
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
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
Looking at some of the design patterns
Summary
6. Recursions and Reductions Chevron down icon Chevron up icon
6. Recursions and Reductions
Simple numerical recursions
Group-by reductions – from many to fewer
Summary
7. Additional Tuple Techniques Chevron down icon Chevron up icon
7. Additional Tuple Techniques
Using an immutable namedtuple as a record
Building namedtuples with functional constructors
Avoiding stateful classes by using families of tuples
Polymorphism and Pythonic pattern matching
Summary
8. The Itertools Module Chevron down icon Chevron up icon
8. The Itertools Module
Working with the infinite iterators
Using the finite iterators
Cloning iterators with tee()
The itertools recipes
Summary
9. More Itertools Techniques Chevron down icon Chevron up icon
9. More Itertools Techniques
Enumerating the Cartesian product
Reducing a product
Permuting a collection of values
Generating all combinations
Recipes
Summary
10. The Functools Module Chevron down icon Chevron up icon
10. 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 reduce()
Summary
11. Decorator Design Techniques Chevron down icon Chevron up icon
11. Decorator Design Techniques
Decorators as higher-order functions
Cross-cutting concerns
Composite design
Adding a parameter to a decorator
Implementing more complex descriptors
Recognizing design limitations
Summary
12. The Multiprocessing and Threading Modules Chevron down icon Chevron up icon
12. The Multiprocessing and Threading Modules
What concurrency really means
The boundary conditions
Sharing resources with process or threads
Where benefits will accrue
Using multiprocessing pools and tasks
Using a multiprocessing pool for concurrent processing
Summary
13. Conditional Expressions and the Operator Module Chevron down icon Chevron up icon
13. Conditional Expressions and the Operator Module
Evaluating conditional expressions
Using the operator module instead of lambdas
Starmapping with operators
Reducing with operators
Summary
14. The PyMonad Library Chevron down icon Chevron up icon
14. The PyMonad Library
Downloading and installing
Functional composition and currying
Functional composition and the PyMonad multiplication operator
Functors and applicative functors
Monad concepts, the bind() function and the Binary Right Shift operator
Implementing simulation with monads
Additional PyMonad features
Summary
15. A Functional Approach to Web Services Chevron down icon Chevron up icon
15. A Functional Approach to Web Services
The HTTP request-response model
The WSGI standard
Defining web services as functions
Tracking usage
Summary
16. Optimizations and Improvements Chevron down icon Chevron up icon
16. Optimizations and Improvements
Memoization and caching
Specializing memoization
Optimizing storage
Optimizing accuracy
Case study – making a chi-squared decision
Computing expected values and displaying a contingency table
Summary
Index Chevron down icon Chevron up icon
Index

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Matteo Mar 04, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book provides insight into applying functional programming to python using simple examples and without making the reader go mad with just code to look at. The book eventually devolves into quite heavy stuff so you should have a modicum of coding experience or you may not understand it after a while. The examples and code structures are easy to follow and possibly handy if you want to fine tune your python code. Good read if you want to learn new tricks.
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ajk251 Jul 20, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is, pound for pound, the best Python book I have (and, in my view, the best Python book Packt has published). Unlike some other books, this book generally avoids the general introductions into Python, material easily covered in a google search, or overly simplistic examples. It is well written, clear, and concise. I have learned a lot from every chapter.As a novice/intermediate programmer, I never put much thought into having a functional style - but reviewer JC puts it well - it improves your code. The book has introduces strategies and styles to solve common programming problems succinctly and efficiently. I think nearly every type of programmer can get something out of this book.At the risk of redundancy, some Python experience is important and this book is not for the faint of heart. Also, it can seem densely written and (at least in the digital versions) tricky to read some of the code segments.
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Konstantinos Passadis Mar 20, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Python is not a functional programming language but it is multiparadigm and as such it has a lot of functional features. In fact, it turns out that using those features you end up writing elegant, idiomatic and bug-free code. This is what this book is all about. The author assumes familiarity with python but none with functional programming is required.As the book progresses the reader is introduced to functional concepts (higher order functions, recursion, tail call optimization, lazy evaluation, lambdas, reductions, transformations, functional composition etc) as well as python tools and idioms (itertools, functools, decorator design pattern, designing concurrent processing etc.) via short and clearly explained examples. Towards the end the author even introduces "mystical" monads and applicative functors. In effect, by reading this book your achievement is twofold: 1. you get to understand functional concepts and see programming from a different way of thinking, which will make you a better programmer and 2. you raise your python skills to the next level - the way you code python will never be the same. 5 stars.
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Mr H Apr 19, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great practical book which shows how to apply functional techniques to your python code. This is not a book on pure functional programming since python is not a pure functional language. The author gives a lot of techniques on how to use the functional side of python in the way its meant to be used. Great techniques with tuples, iterators, generators and warp-unwrap are used throughout the book. This is a great book if you wish to learn a different angle to programming with python.
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Ptter May 17, 2017
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Great book, but not for beginners.
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About the author

Profile icon Steven F. Lott
Steven F. Lott
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Steven Lott has been programming since computers were large, expensive, and rare. Working for decades in high tech has given him exposure to a lot of ideas and techniques, some bad, but most are helpful to others. Since the 1990s, Steven has been engaged with Python, crafting an array of indispensable tools and applications. His profound expertise has led him to contribute significantly to Packt Publishing, penning notable titles like "Mastering Object-Oriented," "The Modern Python Cookbook," and "Functional Python Programming." A self-proclaimed technomad, Steven's unconventional lifestyle sees him residing on a boat, often anchored along the vibrant east coast of the US. He tries to live by the words “Don't come home until you have a story.”
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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
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

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