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Understanding Functional Programming
Functional programming defines a computation using expressions and evaluation; often, they are encapsulated in function definitions. It de-emphasizes or avoids the complexity of state change and mutable objects. This tends to create programs that are more succinct and expressive. In this chapter, we’ll introduce some of the techniques that characterize functional programming. We’ll identify some of the ways to map these features to Python. Finally, we’ll also address some ways in which the benefits of functional programming accrue when we use these design patterns to build Python applications.
This book doesn’t contain a tutorial introduction to the Python language. We assume the reader knows some Python. In many cases, if the reader knows a functional programming language, then that knowledge can be applied to Python via the examples in this book. For background information on Python, see Python in a Nutshell, 4th Edition, or any of the Python introductions from Packt Publishing.
Python has a broad variety of programming features, including numerous ways to support functional programming. As we will see throughout this book, Python is not a purely functional programming language; instead, it relies on a mixture of features. We’ll see that the language offers enough of the right kinds of features to provide the benefits of functional programming. It also retains all the optimization power of an imperative programming language. Further, we can mix the object-oriented and functional features to make use of the best aspects of both paradigms.
We’ll also look at a problem domain that we’ll use for many of the examples in this book. We’ll try to stick closely to Exploratory Data Analysis (EDA). For more information, see https://www.itl.nist.gov/div898/handbook/eda/eda.htm. The idea of ”exploratory” means doing data collection followed by analysis, with a goal of inferring what model would be appropriate to describe the data. This is a helpful domain because many of the algorithms are good examples of functional programming. Furthermore, the benefits of functional programming accrue rapidly when exploring data to locate trends and relationships.
Our goal is to establish some essential principles of functional programming. The more serious Python code will begin in Chapter 2, Introducing Essential Functional Concepts.
In this chapter, we’ll focus on the following topics:
Comparing and contrasting the functional paradigm with other ways of designing software. We’ll look at how Python’s approach can be called a ”hybrid” between functional programming and object-oriented programming.
We’ll look in depth at a specific example extracted from the functional programming literature.
We’ll conclude with an overview of EDA and why this discipline seems to provide numerous examples of functional programming.
We’ll focus on Python 3.10 features in this book. This includes the new match statement.
Throughout this book, we’ll include Python 3 type hints in the examples. Type hints can help a reader visualize the essential purpose behind a function definition. Type hints are analyzed with the mypy tool. As with unit testing, mypy can be part of a tool chain to produce high-quality software.
