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

Optimizing Gold Hunt – Part two


The previous section served as a short introduction to NumPy. Recall that, in earlier chapters, we gradually improved the runtime performance of the game. The last recorded timing was the one obtained with optimization pass three. We successfully reduced the total runtime down to nearly 44 seconds from the original time of about 106 seconds. NumPy supports vectorized calculation routines such as element-wise multiplication. It internally uses efficient C loops that help run such operations faster. Let's leverage NumPy capabilities to speed up the Gold Hunt game even further.

Gold Hunt optimization – pass four

It is now time to resume the optimization operation for the Gold Hunt problem. Let's start with optimization pass four. We will focus our attention once again on the function, generate_random_numbers. As a refresher, the cProfiler output of the last optimization run reported the total time as ~ 2.6 seconds and a cumulative time, which includes the time...