Book Image

Functional Python Programming - Second Edition

By : Steven F. Lott
Book Image

Functional Python Programming - Second Edition

By: Steven F. Lott

Overview of this book

If you’re a Python developer who wants to discover how to take the power of functional programming (FP) and bring it into your own programs, then this book is essential for you, even if you know next to nothing about the paradigm. Starting with a general overview of functional concepts, you’ll explore common functional features such as first-class and higher-order functions, pure functions, and more. You’ll see how these are accomplished in Python 3.6 to give you the core foundations you’ll build upon. After that, you’ll discover common functional optimizations for Python to help your apps reach even higher speeds. You’ll learn FP concepts such as lazy evaluation using Python’s generator functions and expressions. Moving forward, you’ll learn to design and implement decorators to create composite functions. You'll also explore data preparation techniques and data exploration in depth, and see how the Python standard library fits the functional programming model. Finally, to top off your journey into the world of functional Python, you’ll at look at the PyMonad project and some larger examples to put everything into perspective.
Table of Contents (22 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Using strings


Since Python strings are immutable, they're an excellent example of functional programming objects. A Python str object has a number of methods, all of which produce a new string as the result. These methods are pure functions with no side effects.

The syntax for str method functions is postfix, where most functions are prefix. This means that complex string operations can be hard to read when they're co-mingled with conventional functions. For example, in this expression, len(variable.title()), the title() method is in postfix notation and the len() function is in prefix notation.

When scraping data from a web page, we may have a function to clean the data. This could apply a number of transformations to a string to clean up the punctuation and return a Decimal object for use by the rest of the application. This will involve a mixture of prefix and postfix syntax.

It could look like the following command snippet:

from decimal import *
from typing import Text, Optional
def clean_decimal...