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


In this chapter, we've looked at a number of functions in the functools module. This library module provides a number of functions that help us create sophisticated functions and classes.

We've looked at the @lru_cache function as a way to boost certain types of applications with frequent re-calculations of the same values. This decorator is of tremendous value for certain kinds of functions that take the integer or the string argument values. It can reduce processing by simply implementing memoization.

We looked at the @total_ordering function as a decorator to help us build objects that support rich ordering comparisons. This is at the fringe of functional programming, but is very helpful when creating new kinds of numbers.

The partial() function creates a new function with the partial application of argument values. As an alternative, we can build a lambda with similar features. The use case for this is ambiguous.

We also looked at the reduce() function as a higher-order function...