Book Image

The Python Workshop

By : Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade
Book Image

The Python Workshop

By: Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade

Overview of this book

Have you always wanted to learn Python, but never quite known how to start? More applications than we realize are being developed using Python because it is easy to learn, read, and write. You can now start learning the language quickly and effectively with the help of this interactive tutorial. The Python Workshop starts by showing you how to correctly apply Python syntax to write simple programs, and how to use appropriate Python structures to store and retrieve data. You'll see how to handle files, deal with errors, and use classes and methods to write concise, reusable, and efficient code. As you advance, you'll understand how to use the standard library, debug code to troubleshoot problems, and write unit tests to validate application behavior. You'll gain insights into using the pandas and NumPy libraries for analyzing data, and the graphical libraries of Matplotlib and Seaborn to create impactful data visualizations. By focusing on entry-level data science, you'll build your practical Python skills in a way that mirrors real-world development. Finally, you'll discover the key steps in building and using simple machine learning algorithms. By the end of this Python book, you'll have the knowledge, skills and confidence to creatively tackle your own ambitious projects with Python.
Table of Contents (13 chapters)

Profiling

Having exhausted the minimum-effort options for improving your code's performance, it's time to actually put some work in if you need to go faster. There's no recipe to follow to write fast code: if there were, you could have taught you that in Chapters 1-8 and there wouldn't need to be a section on performance now. And, of course, speed isn't the only performance goal: you might want to reduce memory use or increase the number of simultaneous operations that can be in-flight. But programmers often use "performance" as a synonym for "reducing time to completion," and that's what you'll investigate here.

Improving performance is a scientific process: you observe how your code behaves, hypothesize about a potential improvement, make the change, and then observe it again and check that you really did improve things. Good tool support exists for the observation steps in this process, and you'll look at one such tool...