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  • Functional Python Programming
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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

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
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Free Chapter
1
1. Introducing Functional Programming
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1. Introducing Functional Programming
Identifying a paradigm
Subdividing the procedural paradigm
A classic example of functional programming
Exploratory Data Analysis
Summary
2
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
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
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
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
6. Recursions and Reductions
6. Recursions and Reductions
Simple numerical recursions
Group-by reductions – from many to fewer
Summary
7
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
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
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
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
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
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
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
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
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
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
17
Index
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.

End of Section 5
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