#### 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

## Working with the infinite iterators

The `itertools` module provides a number of functions that we can use to enhance or enrich an iterable source of data. We'll look at the following three functions:

• `count()`: This is an unlimited version of the `range()` function
• `cycle()`: This will reiterate a cycle of values
• `repeat()`: This can repeat a single value an indefinite number of times

Our goal is to understand how these various iterator functions can be used in generator expressions and with generator functions.

### Counting with count()

The built-in `range()` function is defined by an upper limit: the lower limit and step values are optional. The `count()` function, on the other hand, has a start and optional step, but no upper limit.

This function can be thought of as the primitive basis for a function such as `enumerate()`. We can define the `enumerate()` function in terms of `zip()` and `count()` functions, as follows:

`enumerate = lambda x, start=0: zip(count(start), x)`

The `enumerate()` function behaves as if it's a `zip...`