Book Image

Mastering Object-Oriented Python - Second Edition

By : Steven F. Lott
Book Image

Mastering Object-Oriented Python - Second Edition

By: Steven F. Lott

Overview of this book

Object-oriented programming (OOP) is a relatively complex discipline to master, and it can be difficult to see how general principles apply to each language's unique features. With the help of the latest edition of Mastering Objected-Oriented Python, you'll be shown how to effectively implement OOP in Python, and even explore Python 3.x. Complete with practical examples, the book guides you through the advanced concepts of OOP in Python, and demonstrates how you can apply them to solve complex problems in OOP. You will learn how to create high-quality Python programs by exploring design alternatives and determining which design offers the best performance. Next, you'll work through special methods for handling simple object conversions and also learn about hashing and comparison of objects. As you cover later chapters, you'll discover how essential it is to locate the best algorithms and optimal data structures for developing robust solutions to programming problems with minimal computer processing. Finally, the book will assist you in leveraging various Python features by implementing object-oriented designs in your programs. By the end of this book, you will have learned a number of alternate approaches with different attributes to confidently solve programming problems in Python.
Table of Contents (25 chapters)
Free Chapter
1
Section 1: Tighter Integration Via Special Methods
11
Section 2: Object Serialization and Persistence
17
Section 3: Object-Oriented Testing and Debugging

Defining a new kind of sequence

A common requirement that we have when performing statistical analysis is to compute basic means, modes, and standard deviations on a collection of data. Our blackjack simulation will produce outcomes that must be analyzed statistically to see if we have actually invented a better strategy.

When we simulate the playing strategy for a game, we will develop some outcome data that will be a sequence of numbers that show the final result of playing the game several times with a given strategy.

We could accumulate the outcomes into a built-in list class. We can compute the mean via , where is the number of elements in :

def mean(outcomes: List[float]) -> float:
return sum(outcomes) / len(outcomes)

Standard deviation can be computed via :

def stdev(outcomes: List[float]) -> float:
n = float(len(outcomes))
return math.sqrt(
n ...