Book Image

Functional Python Programming. - Second Edition

Book Image

Functional Python Programming. - Second Edition

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.
Table of Contents (22 chapters)
Title Page
Packt Upsell
Contributors
Preface
Index

Avoiding stateful classes by using families of tuples


In several previous examples, we've shown the idea of Wrap-Unwrap design patterns that allow us to work with anonymous and named tuples. The point of this kind of design is to use immutable objects that wrap other immutable objects instead of mutable instance variables.

A common statistical measure of correlation between two sets of data is the Spearman rank correlation. This compares the rankings of two variables. Rather than trying to compare values, which might have different scales, we'll compare the relative orders. For more information, visit: http://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient.

Computing the Spearman rank correlation requires assigning a rank value to each observation. It seems like we should be able to use enumerate(sorted()) to do this. Given two sets of possibly correlated data, we can transform each set into a sequence of rank values and compute a measure of correlation.

We'll apply the Wrap...