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

Chapter 16. Optimizations and Improvements

In this chapter, we'll look at some optimizations that we can make to create high-performance functional programs. We will look at the following topics:

  • We'll expand on using the@lru_cache decorator from Chapter 10, The Functools Module. We have a number of ways to implement the memoization algorithm.
  • We'll also discuss how to write our own decorators. More importantly, we'll see how to use a Callable object to cache memoized results.
  • We'll also look at some optimization techniques that were presented in Chapter 6, Recursions and Reductions. We'll review the general approach to tail recursion optimization. For some algorithms, we can combine memoization with a recursive implementation and achieve good performance. For other algorithms, memoization isn't really very helpful and we have to look elsewhere for performance improvements.
  • We'll look at an extensive case study in optimizing accuracy by using the Fraction class.

In most cases, small changes to...