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

Summary


In this chapter, we've looked at two kinds of decorators: the simple decorator with no arguments and parameterized decorators. We've seen how decorators involve an indirect composition between functions: the decorator wraps a function (defined inside the decorator) around another function.

Using the functools.wraps() decorator assures that our decorators will properly copy attributes from the function being wrapped. This should be a piece of every decorator we write.

In the next chapter, we'll look at the multiprocessing and multithreading techniques that are available to us. These packages become particularly helpful in a functional programming context. When we eliminate a complex shared state and design around non-strict processing, we can leverage parallelism to improve the performance.