Functional Python Programming

Book Image

Functional Python Programming

By : Steven F. Lott, Steven F. Lott

Book Image

Functional Python Programming

By: Steven F. Lott, Steven F. Lott

Overview of this book

Functional Python Programming

Functional Python Programming

Credits

About the Author

About the Author

About the Reviewers

About the Reviewers

www.PacktPub.com

www.PacktPub.com

Preface

Free Chapter

Introducing Functional Programming

Introducing Functional Programming

Identifying a paradigm

Subdividing the procedural paradigm

A classic example of functional programming

Exploratory Data Analysis

Introducing Some Functional Features

Introducing Some Functional Features

First-class functions

Strict and non-strict evaluation

Recursion instead of a explicit loop state

Functional type systems

Familiar territory

Saving some advanced concepts

Functions, Iterators, and Generators

Functions, Iterators, and Generators

Writing pure functions

Functions as first-class objects

Using tuples and namedtuples

Cleaning raw data with generator functions

Using lists, dicts, and sets

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

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

Recursions and Reductions

Recursions and Reductions

Simple numerical recursions

Group-by reductions – from many to fewer

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

The Itertools Module

The Itertools Module

Working with the infinite iterators

Using the finite iterators

Cloning iterators with tee()

The itertools recipes

More Itertools Techniques

More Itertools Techniques

Enumerating the Cartesian product

Reducing a product

Permuting a collection of values

Generating all combinations

The Functools Module

The Functools Module

Memoizing previous results with lru_cache

Defining classes with total ordering

Applying partial arguments with partial()

Reducing sets of data with reduce()

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

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

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

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

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

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

Index

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Using zip() to structure and flatten sequences

The zip() function interleaves values from several iterators or sequences. It will create n tuples from the values in each of the n input iterables or sequences. We used it in the previous section to interleave data points from two sets of samples, creating two tuples.

Note

The zip() function is a generator. It does not materialize a resulting collection.

The following is an example that shows what the zip() function does:

>>> xi= [1.47, 1.50, 1.52, 1.55, 1.57, 1.60, 1.63, 1.65,... 1.68, 1.70, 1.73, 1.75, 1.78, 1.80, 1.83,] 
>>> yi= [52.21, 53.12, 54.48, 55.84, 57.20, 58.57, 59.93, 61.29,... 63.11, 64.47, 66.28, 68.10, 69.92, 72.19, 74.46,] 
>>> zip( xi, yi )
<zip object at 0x101d62ab8>
>>> list(zip( xi, yi ))
[(1.47, 52.21), (1.5, 53.12), (1.52, 54.48), (1.55, 55.84), (1.57, 57.2), (1.6, 58.57), (1.63, 59.93), (1.65, 61.29), (1.68, 63.11), (1.7, 64.47), (1.73, 66.28), (1.75, 68.1), (1.78, 69.92), (1.8...