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)

Performance and Profiling

Python is not often thought of as a high-performance language, though it really should be. The simplicity of the language and the power of its standard library mean that the time from idea to result can be much shorter than in other languages with better runtime performance.

But we have to be honest. Python is not among the fastest-running programming languages in the world, and sometimes that's important. For instance, if you're writing a web server application, you need to be able to handle as many network requests as are being made, and with timeliness that satisfies the users making the requests.

Alternatively, if you're writing a scientific simulation or a deep learning inference engine, then the simulation or training time can completely dwarf the programmer time (which is your time) spent writing the code. In any situation, reducing the time spent running your application can decrease the cost, whether measured in dollars on your...