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

Chapter 8. The Itertools Module

Functional programming emphasizes stateless objects. In Python, this leads us to work with generator expressions, generator functions, and iterables, instead of large, mutable, collection objects. In this chapter, we'll look at elements of the itertools library. This library has numerous functions to help us work with iterable sequences of objects, as well as collection objects.

We introduced iterator functions in Chapter 3, Functions, Iterators, and Generators. In this chapter, we'll expand on that superficial introduction. We used some related functions in Chapter 5, Higher-Order Functions.


These functions behave as if they are proper, lazy, Python iterables. Some of them create intermediate objects, however; this leads to them consuming a large amount of memory. Since implementations may change with Python releases, we can't provide function-by-function advice here. If you have performance or memory issues, ensure that you check the implementation. "Use...