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

Chapter 6. Recursions and Reductions

In previous chapters, we've looked at several related kinds of processing designs; some of them are as follows:

  • Mapping and filtering, which creates collections from collections
  • Reductions that create a scalar value from a collection

The distinction is exemplified by functions such as map() and filter() that accomplish the first kind of collection processing. There are several specialized reduction functions, which include min(), max(), len(), and sum(). There's a general-purpose reduction function as well, functools.reduce().

We'll also consider a collections.Counter() function as a kind of reduction operator. It doesn't produce a single scalar value per se, but it does create a new organization of the data that eliminates some of the original structure. At heart, it's a kind of count-group-by operation that has more in common with a counting reduction than with a mapping.

In this chapter, we'll look at reduction functions in more detail. From a purely functional...