Book Image

Functional Python Programming. - Second Edition

Book Image

Functional Python Programming. - Second Edition

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

Memoization and caching


As we saw in Chapter 10, The Functools Module, many algorithms can benefit from memoization. We'll start with a review of some previous examples to characterize the kinds of functions that can be helped with memoization.

In Chapter 6, Recursions and Reductions, we looked at a few common kinds of recursions. The simplest kind of recursion is a tail recursion with arguments that can be easily matched to values in a cache. If the arguments are integers, strings, or materialized collections, then we can compare arguments quickly to determine if the cache has a previously computed result.

We can see from these examples that integer numeric calculations, such as computing factorial or locating a Fibonacci number, will be obviously improved. Locating prime factors and raising integers to powers are more examples of numeric algorithms that apply to integer values.

When we looked at the recursive version of a Fibonacci number,

, calculator, we saw that it contained two tail-call...