#### 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

## Optimizing accuracy

In a few cases, we need to optimize the accuracy of a calculation. This can be challenging and may require some fairly advanced math to determine the limits on the accuracy of a given approach.

An interesting thing we can do in Python is replace floating point approximations with a `fractions.Fraction` value. For some applications, this can create more accurate answers than floating point, because more bits are used for the numerator and denominator than a floating point mantissa.

It's important to use `decimal.Decimal` values to work with currency. It's a common error to use a `float` value. When using a `float` value, additional noise bits are introduced because of the mismatch between `Decimal` values provided as input and the binary approximation used by floating point values. Using `Decimal` values prevents the introduction of tiny inaccuracies.

In many cases, we can make small changes to a Python application to switch from `float` values to `Fraction` or `Decimal` values. When working...