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Functional Python Programming

Functional Python Programming

By : Steven F. Lott
4 (9)
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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
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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
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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
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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
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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
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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
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Index
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Index

Exploratory Data Analysis

Later in this book, we'll use the field of EDA as a source for concrete examples of functional programming. This field is rich with algorithms and approaches to working with complex datasets; functional programming is often a very good fit between the problem domain and automated solutions.

While details vary from author to author, there are several widely accepted stages of EDA. These include the following:

  • Data preparation: This might involve extraction and transformation for source applications. It might involve parsing a source data format and doing some kinds of data scrubbing to remove unusable or invalid data. This is an excellent application of functional design techniques.
  • Data exploration: This is a description of the available data. This usually involves the essential statistical functions. This is another excellent place to explore functional programming. We can describe our focus as univariate and bivariate statistics but that sounds too daunting and complex. What this really means is that we'll focus on mean, median, mode, and other related descriptive statistics. Data exploration may also involve data visualization. We'll skirt this issue because it doesn't involve very much functional programming. I'll suggest that you use a toolkit like SciPy.

    Visit the following link to get more information how SciPY works and its usage:

    https://www.packtpub.com/big-data-and-business-intelligence/learning-scipy-numerical-and-scientific-computing or https://www.packtpub.com/big-data-and-business-intelligence/learning-python-data-visualization

  • Data modeling and machine learning: This tends to be proscriptive as it involves extending a model to new data. We're going to skirt this because some of the models can become mathematically complex. If we spend too much time on these topics, we won't be able to focus on functional programming.
  • Evaluation and comparison: When there are alternative models, each must be evaluated to determine which is a better fit for the available data. This can involve ordinary descriptive statistics of model outputs. This can benefit from functional design techniques.

The goal of EDA is often to create a model that can be deployed as a decision support application. In many cases, a model might be a simple function. A simple functional programming approach can apply the model to new data and display results for human consumption.

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