Book Image

Functional Python Programming - Second Edition

By : Steven F. Lott
Book Image

Functional Python Programming - Second Edition

By: Steven F. Lott

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

Case study–making a chi-squared decision

We'll look at a common statistical decision. The decision is described in detail at

This is a chi-squared decision on whether or not data is distributed randomly. To make this decision, we'll need to compute an expected distribution and compare the observed data to our expectations. A significant difference means there's something that needs further investigation. An insignificant difference means we can use the null hypothesis that there's nothing more to study: the differences are simply random variation.

We'll show how we can process the data with Python. We'll start with some backstory—some details that are not part of the case study, but often feature an Exploratory Data Analysis (EDA) application. We need to gather the raw data and produce a useful summary that we can analyze.

Within the production quality assurance operations, silicon wafer defect data is collected in a database....