#### 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.
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Understanding Functional Programming
Introducing Essential Functional Concepts
Functions, Iterators, and Generators
Working with Collections
Recursions and Reductions
The Itertools Module
More Itertools Techniques
The Functools Module
Decorator Design Techniques
The Multiprocessing and Threading Modules
Conditional Expressions and the Operator Module
A Functional Approach to Web Services
Optimizations and Improvements
Other Books You May Enjoy
Index

## Summary

In this chapter, we saw detailed ways to use a number of built-in reductions.

We've used `any()` and `all()` to do essential logic processing. These are tidy examples of reductions using a simple operator, such as `or` or `and`.

We've also looked at numeric reductions such as, `len()` and `sum()`. We've applied these functions to create some higher-order statistical processing. We'll return to these reductions in Chapter 6, Recursions and Reductions.

We've also looked at some of the built-in mappings.

The `zip()` function merges multiple sequences. This leads us to look at using this in the context of structuring and flattening more complex data structures. As we'll see in examples in later chapters, nested data is helpful in some situations and flat data is helpful in others.

The `enumerate()` function maps an iterable to a sequence of two-tuples. Each two-tuple has the sequence number at index `[0]` and the original value at index `[1]`.

The `reversed()` function iterates over the items in a sequence object...