#### 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
Conditional Expressions and the Operator Module
A Functional Approach to Web Services
Optimizations and Improvements
Other Books You May Enjoy
Index

## Adding a parameter to a decorator

A common requirement is to customize a decorator with additional parameters. Rather than simply creating a composite

, we can do something a bit more complex. With parameterized decorators, we can create

. We've applied a parameter, c, as part of creating the wrapper,

. This parameterized composite function,

, can then be used with the actual data, x.

In Python syntax, we can write it as follows:

```@deco(arg)
def func(x):
something```

There are two steps to this. The first step applies the parameter to an abstract decorator to create a concrete decorator. Then the concrete decorator, the parameterized `deco(arg)` function, is applied to the base function definition to create the decorated function.

The effect is as follows:

```def func(x):
return something(x)
concrete_deco = deco(arg)
func= concrete_deco(func)```

We've done three things, and they are as follows:

1. Defined a function, `func()`.
2. Applied the abstract decorator, `deco()`, to its argument, `arg`, to create a concrete...