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)
Other Books You May Enjoy

Using dataclasses to simplify working with CSV files

One commonly used data format is known as CSV. Python's csv module has a very handy DictReader class definition. When a file contains a one-row header, the header row's values become keys that are used for all the subsequent rows. This allows a great deal of flexibility in the logical layout of the data. For example, the column ordering doesn't matter, since each column's data is identified by a name taken from the header row.

This leads to dictionary-based references to a column's data. We're forced to write, for example, row['lat'] or row['date'] to refer to data in specific columns. While this isn't horrible, it would be much nicer to use syntax like or to refer to column values.

Additionally, we often have derived values that should – perhaps – be properties of a class definition instead of a separate function. This can properly encapsulate...