Book Image

Modern Python Cookbook - Second Edition

By : Steven F. Lott
Book Image

Modern Python Cookbook - Second Edition

By: Steven F. Lott

Overview of this book

Python is the preferred choice of developers, engineers, data scientists, and hobbyists everywhere. It is a great language that can power your applications and provide great speed, safety, and scalability. It can be used for simple scripting or sophisticated web applications. By exposing Python as a series of simple recipes, this book gives you insight into specific language features in a particular context. Having a tangible context helps make the language or a given standard library feature easier to understand. This book comes with 133 recipes on the latest version of Python 3.8. The recipes will benefit everyone, from beginners just starting out with Python to experts. You'll not only learn Python programming concepts but also how to build complex applications. The recipes will touch upon all necessary Python concepts related to data structures, object oriented programming, functional programming, and statistical programming. You will get acquainted with the nuances of Python syntax and how to effectively take advantage of it. By the end of this Python book, you will be equipped with knowledge of testing, web services, configuration, and application integration tips and tricks. You will be armed with the knowledge of how to create applications with flexible logging, powerful configuration, command-line options, automated unit tests, and good documentation.
Table of Contents (18 chapters)
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Debugging with f"{value=}" strings

One of the most important debugging and design tools available in Python is the print() function. There are some kinds of formatting options available; we looked at these in the Using features of the print() function recipe.

What if we want more flexible output? We have more flexibility with f"string" formatting.

Getting ready

Let's look at a multistep process that involves some moderately complex calculations. We'll compute the mean and standard deviation of some sample data. Given these values, we'll locate all items that are more than one standard deviation above the mean:

>>> import statistics
>>> size = [2353, 2889, 2195, 3094, 
... 725, 1099, 690, 1207, 926, 
... 758, 615, 521, 1320]
>>> mean_size = statistics.mean(size)
>>> std_size = statistics.stdev(size)
>>> sig1 = round(mean_size + std_size, 1)
>>> [x for x in size if x > sig1]