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Functional Python Programming

By : Steven F. Lott, Steven F. Lott
Book Image

Functional Python Programming

By: Steven F. Lott, Steven F. Lott

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Functional Python Programming
Steven F. Lott | Steven F. Lott
Jan, 2015 | 360 pages
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Table of Contents (23 chapters)
Functional Python Programming
Functional Python Programming
Credits
Credits
About the Author
About the Author
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Preface
Preface
Free Chapter
1
Introducing Functional Programming
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
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
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
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
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
Recursions and Reductions
Simple numerical recursions
Group-by reductions – from many to fewer
Summary
7
Additional Tuple Techniques
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
The Itertools Module
Working with the infinite iterators
Using the finite iterators
Cloning iterators with tee()
The itertools recipes
Summary
9
More Itertools Techniques
More Itertools Techniques
Enumerating the Cartesian product
Reducing a product
Permuting a collection of values
Generating all combinations
Recipes
Summary
10
The Functools Module
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
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
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
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
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
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
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
Index

Polymorphism and Pythonic pattern matching

Some functional programming languages offer clever approaches to working with statically typed function definitions. The issue is that many functions we'd like to write are entirely generic with respect to data type. For example, most of our statistical functions are identical for integer or floating-point numbers, as long as division returns a value that is a subclass of numbers.Real (for example, Decimal, Fraction, or float). In order to make a single generic definition work for multiple data types, sophisticated type or pattern-matching rules are used by the compiler.

Instead of the (possibly) complex features of statically typed functional languages, Python changes the issue using dynamic selection of the final implementation of an operator based on the data types being used. This means that a compiler doesn't certify that our functions are expecting and producing the proper data types. We generally rely on unit testing for this.

In Python, we...

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