#### 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
Free Chapter
Understanding Functional Programming
Introducing Essential Functional Concepts
Functions, Iterators, and Generators
Working with Collections
Recursions and Reductions
Additional Tuple Techniques
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

## An overview of function varieties

We need to distinguish between two broad species of functions, as follows:

• Scalar functions: They apply to individual values and compute an individual result. Functions such as `abs()`, `pow()`, and the entire `math` module are examples of scalar functions.
• Collection functions: They work with iterable collections.

We can further subdivide the collection functions into three subspecies:

• Reduction: This uses a function to fold values in the collection together, resulting in a single final value. For example, if we fold (`+`) operations into a sequence of integers, this will compute the sum. This can be also be called an aggregate function, as it produces a single aggregate value for an input collection.
• Mapping: This applies a scalar function to each individual item of a collection; the result is a collection of the same size.
• Filter: This applies a scalar function to all items of a collection to reject some items and pass others. The result is a subset of the input.

We...