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

Identifying the bottlenecks


In the previous section, we saw how a different choice of input parameters degrades the application runtime. Now, we need some way to accurately measure the execution time and find out the performance bottlenecks or the time consuming blocks of the code.

Measuring the execution time

Let's start by monitoring the time taken by the application. To do this, we will use Python's built-in time module. The time.perf_counter function is a performance counter that returns a clock with the highest available resolution. This function can be used to determine the time interval or the system-wide time difference between the two consecutive calls to the function.

Tip

The time.perf_counter function is available in Python versions 3.3 onwards. If you have an older version of Python (for example, version 2.7), use time.clock() instead. On Unix, time.clock() returns a floating point number within seconds that represents the processor time. On Windows, it returns the elapsed wall-clock...