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 looked at three optimization techniques. The first technique involves finding the right algorithm and data structure. This has more impact on performance than any other single design or programming decision. Using the right algorithm can easily reduce runtimes from minutes to fractions of a second. Changing a poorly used sequence to a properly used mapping, for example, may reduce runtime by a factor of 200.

We should generally optimize all of our recursions to be loops. This will be faster in Python and it won't be stopped by the call stack limit that Python imposes. There are many examples of how recursions are flattened into loops in other chapters, primarily, Chapter 6, Recursions and Reductions. Additionally, we may be able to improve performance in two other ways. First, we can apply memoization to cache results. For numeric calculations, this can have a large impact; for collections, the impact may be less. Secondly, replacing large materialized data objects...