#### 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

## Writing pure functions

A function with no side effects fits the pure mathematical abstraction of a function: there are no global changes to variables. If we avoid the `global` statement, we will almost meet this threshold. To be pure, a function should also avoid changing the state mutable objects.

Here's an example of a pure function:

```def m(n: int) -> int:
return 2**n-1```

This result depends only on the parameter, n. There are no changes to global variables and the function doesn't update any mutable data structures.

Any references to values in the Python global namespace (using a free variable) is something we can rework into a proper parameter. In most cases, it's quite easy. Here is an example that depends on a free variable:

```def some_function(a: float, b: float, t: float) -> float:
We can refactor this function to turn the `global_adjustment` variable into a proper parameter. We would need to change each reference to this function, which may have a large...