Book Image

Modern Python Cookbook - Second Edition

By : Steven F. Lott
Book Image

Modern Python Cookbook - Second Edition

By: Steven F. Lott

Overview of this book

Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great language that can power your applications and provide great speed, safety, and scalability. It can be used for simple scripting or sophisticated web applications. By exposing Python as a series of simple recipes, this book gives you insight into specific language features in a particular context. Having a tangible context helps make the language or a given standard library feature easier to understand. This book comes with 133 recipes on the latest version of Python 3.8. The recipes will benefit everyone, from beginners just starting out with Python to experts. You'll not only learn Python programming concepts but also how to build complex applications. The recipes will touch upon all necessary Python concepts related to data structures, object oriented programming, functional programming, and statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively take advantage of it. By the end of this Python book, you will be equipped with knowledge of testing, web services, configuration, and application integration tips and tricks. You will be armed with the knowledge of how to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation.
Table of Contents (18 chapters)
16
Other Books You May Enjoy
17
Index

Using properties for lazy attributes

In the Designing classes with lots of processing recipe, we defined a class that eagerly computed a number of attributes of the data in a collection. The idea there was to compute the values as soon as possible, so that the attributes would have no further computational cost.

We described this as eager processing, since the work was done as soon as possible. The other approach is lazy processing, where the work is done as late as possible.

What if we have values that are used rarely, and are very expensive to compute? What can we do to minimize the up-front computation, and only compute values when they are truly needed?

Getting ready...

Let's say we've collected data using a Counter object. For more information on the various collections, see Chapter 4, Built-In Data Structures Part 1: Lists and Sets; specifically the Using set methods and operators and Avoiding mutable default values for function parameters recipes....