5. Higher-order Functions | Functional Python Programming

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Functional Python Programming

By : Steven F. Lott

4 (9)

Functional Python Programming

4 (9)

By: Steven F. Lott

Overview of this book

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Introducing Functional Programming

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

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

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

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

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

6. Recursions and Reductions

Simple numerical recursions

Group-by reductions – from many to fewer

Summary

7. Additional Tuple Techniques

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

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

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

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

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

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

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

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

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

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

Index

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Using the filter() function to pass or reject data

The job of the filter() function is to use and apply a decision function called a predicate to each value in a collection. A decision of True means that the value is passed; otherwise, the value is rejected. The itertools module includes filterfalse() as variations on this theme. Refer to Chapter 8, The Itertools Module to understand the usage of the itertools module's filterfalse() function.

We might apply this to our trip data to create a subset of legs that are over 50 nautical miles long, as follows:

long= list(filter(lambda leg: dist(leg) >= 50, trip)))

The predicate lambda will be True for long legs, which will be passed. Short legs will be rejected. The output is the 14 legs that pass this distance test.

This kind of processing clearly segregates the filter rule (lambda leg: dist(leg) >= 50) from any other processing that creates the trip object or analyzes the long legs.

For another simple example, look at the following code...

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