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Functional Python Programming

Functional Python Programming

By : Steven F. Lott
4 (9)
Buy this Book
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Functional Python Programming

Functional Python Programming

4 (9)
By: Steven F. Lott
Buy this Book

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.
Table of Contents (18 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
Free Chapter
1
1. Introducing Functional Programming
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1. Introducing Functional Programming
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Identifying a paradigm
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Subdividing the procedural paradigm
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A classic example of functional programming
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Exploratory Data Analysis
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Summary
2
2. Introducing Some Functional Features
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2. Introducing Some Functional Features
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First-class functions
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Immutable data
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Strict and non-strict evaluation
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Recursion instead of a explicit loop state
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Functional type systems
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Familiar territory
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Saving some advanced concepts
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Summary
3
3. Functions, Iterators, and Generators
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3. Functions, Iterators, and Generators
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Writing pure functions
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Functions as first-class objects
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Using strings
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Using tuples and namedtuples
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Cleaning raw data with generator functions
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Using lists, dicts, and sets
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Summary
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4. Working with Collections
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4. Working with Collections
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An overview of function varieties
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Working with iterables
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Using zip() to structure and flatten sequences
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Using reversed() to change the order
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Using enumerate() to include a sequence number
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Summary
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5. Higher-order Functions
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5. Higher-order Functions
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Using max() and min() to find extrema
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Using Python lambda forms
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Lambdas and the lambda calculus
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Using the map() function to apply a function to a collection
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Using map() with multiple sequences
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Using the filter() function to pass or reject data
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Using filter() to identify outliers
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The iter() function with a sentinel value
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Using sorted() to put data in order
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Writing higher-order functions
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Writing higher-order mappings and filters
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Writing generator functions
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Building higher-order functions with Callables
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Looking at some of the design patterns
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Summary
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6. Recursions and Reductions
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6. Recursions and Reductions
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Simple numerical recursions
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Group-by reductions – from many to fewer
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Summary
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7. Additional Tuple Techniques
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7. Additional Tuple Techniques
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Using an immutable namedtuple as a record
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Building namedtuples with functional constructors
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Avoiding stateful classes by using families of tuples
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Polymorphism and Pythonic pattern matching
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Summary
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8. The Itertools Module
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8. The Itertools Module
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Working with the infinite iterators
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Using the finite iterators
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Cloning iterators with tee()
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The itertools recipes
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Summary
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9. More Itertools Techniques
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9. More Itertools Techniques
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Enumerating the Cartesian product
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Reducing a product
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Permuting a collection of values
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Generating all combinations
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Recipes
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Summary
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10. The Functools Module
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10. The Functools Module
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Function tools
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Memoizing previous results with lru_cache
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Defining classes with total ordering
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Applying partial arguments with partial()
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Reducing sets of data with reduce()
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Summary
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11. Decorator Design Techniques
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11. Decorator Design Techniques
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Decorators as higher-order functions
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Cross-cutting concerns
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Composite design
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Adding a parameter to a decorator
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Implementing more complex descriptors
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Recognizing design limitations
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Summary
12
12. The Multiprocessing and Threading Modules
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12. The Multiprocessing and Threading Modules
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What concurrency really means
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The boundary conditions
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Sharing resources with process or threads
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Where benefits will accrue
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Using multiprocessing pools and tasks
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Using a multiprocessing pool for concurrent processing
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Summary
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13. Conditional Expressions and the Operator Module
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13. Conditional Expressions and the Operator Module
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Evaluating conditional expressions
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Using the operator module instead of lambdas
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Starmapping with operators
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Reducing with operators
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Summary
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14. The PyMonad Library
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14. The PyMonad Library
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Downloading and installing
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Functional composition and currying
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Functional composition and the PyMonad multiplication operator
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Functors and applicative functors
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Monad concepts, the bind() function and the Binary Right Shift operator
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Implementing simulation with monads
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Additional PyMonad features
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Summary
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15. A Functional Approach to Web Services
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15. A Functional Approach to Web Services
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The HTTP request-response model
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The WSGI standard
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Defining web services as functions
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Tracking usage
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Summary
16
16. Optimizations and Improvements
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16. Optimizations and Improvements
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Memoization and caching
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Specializing memoization
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Optimizing storage
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Optimizing accuracy
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Case study – making a chi-squared decision
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Computing expected values and displaying a contingency table
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Summary
17
Index
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Index

Memoizing previous results with lru_cache

The lru_cache decorator transforms a given function into a function that might perform more quickly. The LRU means Least Recently Used: a finite pool of recently used items is retained. Items not frequently used are discarded to keep the pool to a bounded size.

Since this is a decorator, we can apply it to any function that might benefit from caching previous results. We might use it as follows:

from functools import lru_cache
@lru_cache(128)
def fibc(n):
    """Fibonacci numbers with naive recursion and caching
    >>> fibc(20)
    6765
    >>> fibc(1)
    1
    """
    if n == 0: return 0
    if n == 1: return 1
    return fibc(n-1) + fibc(n-2)

This is an example based on Chapter 6, Recursions and Reductions. We've applied the @lru_cache decorator to the naïve Fibonacci number calculation. Because of this decoration, each call to the fibc(n) function will now be checked against a cache maintained by the decorator. If the argument...

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