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

Summary


With this chapter, we end the series of chapters focused on performance improvements. Let's first summarize what you learned in this chapter. We started with a basic introduction to the NumPy library and saw how to leverage it to further speed up the Gold Hunt application. In particular, we used the array (numpy.ndarray) data structure and other functionalities, such as numpy.random.uniform and numpy.einsum to achieve the speedup. The final optimization pass involved parallelizing the code. The chapter briefly introduced you to the basics of parallel processing. We used functionality from Python's multiprocessing.Pool class to further trim down the application runtime.

Finally, let's summarize the three performance chapters together. We started by profiling the code to identify the performance bottlenecks and learned about the big O notation. We gradually addressed these bottlenecks to improve the application performance. This was accomplished by several means, ranging from changing...