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

Composite design

The common mathematical notation for a composite function looks as follows:

The idea is that we can define a new function,

, that combines two other functions,

and

.

Python's multiple-line definition of a composition function can be done through the following code:

```@f_deco
def g(x):
something  ```

The resulting function can be essentially equivalent to

. The `f_deco()` decorator must define and return the composite function by merging an internal definition of `f()` with the provided `g()`

The implementation details show that Python actually provides a slightly more complex kind of composition. The structure of a wrapper makes it helpful to think of Python decorator composition as follows:

A decorator applied to some application function,

, will include a wrapper function,

, that has two parts. One portion of the wrapper,

, applies to the arguments of the wrapped function,

, and the other portion,

, applies to the result of the wrapped function.

Here's a more concrete idea, shown...