Book Image

Learning Python Application Development

By : Ninad Sathaye
Book Image

Learning Python Application Development

By: Ninad Sathaye

Overview of this book

Python is one of the most widely used dynamic programming languages, supported by a rich set of libraries and frameworks that enable rapid development. But fast paced development often comes with its own baggage that could bring down the quality, performance, and extensibility of an application. This book will show you ways to handle such problems and write better Python applications. From the basics of simple command-line applications, develop your skills all the way to designing efficient and advanced Python apps. Guided by a light-hearted fantasy learning theme, overcome the real-world problems of complex Python development with practical solutions. Beginning with a focus on robustness, packaging, and releasing application code, you’ll move on to focus on improving application lifetime by making code extensible, reusable, and readable. Get to grips with Python refactoring, design patterns and best practices. Techniques to identify the bottlenecks and improve performance are covered in a series of chapters devoted to performance, before closing with a look at developing Python GUIs.
Table of Contents (18 chapters)
Learning Python Application Development
Credits
Disclaimers
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

This is how the chapter is organized


We will start with first part of the performance improvements for the Gold Hunt scenario. The aim is to provide you with a practical example of how to approach the problem and gradually cut down the runtime. The following chart shows a preview of what will be accomplished by the end of this chapter—this is the same chart shown in the previous chapter. The application runtime is about to be cut down by more than 50%!

The second half of this book will show you many ways to improve the application speed. For this discussion, we will use generic examples, as not all techniques can be applied directly to the Gold Hunt scenario. The second half will serve as a handy reference for performance improvements.

Tip

The Python wiki has documented several performance improvement tips. Some of these will be covered here. Refer to https://wiki.python.org/moin/PythonSpeed/PerformanceTips for further details.