Book Image

Python Real-World Projects

By : Steven F. Lott
5 (1)
Book Image

Python Real-World Projects

5 (1)
By: Steven F. Lott

Overview of this book

In today's competitive job market, a project portfolio often outshines a traditional resume. Python Real-World Projects empowers you to get to grips with crucial Python concepts while building complete modules and applications. With two dozen meticulously designed projects to explore, this book will help you showcase your Python mastery and refine your skills. Tailored for beginners with a foundational understanding of class definitions, module creation, and Python's inherent data structures, this book is your gateway to programming excellence. You’ll learn how to harness the potential of the standard library and key external projects like JupyterLab, Pydantic, pytest, and requests. You’ll also gain experience with enterprise-oriented methodologies, including unit and acceptance testing, and an agile development approach. Additionally, you’ll dive into the software development lifecycle, starting with a minimum viable product and seamlessly expanding it to add innovative features. By the end of this book, you’ll be armed with a myriad of practical Python projects and all set to accelerate your career as a Python programmer.
Table of Contents (20 chapters)

13.5 Extras

Here are some ideas for the reader to add to these projects.

13.5.1 Use Seaborn for plotting

An alternative to the pyplot package is the Seaborn package. This package also provides statistical plotting functions. It provides a wider variety of styling options, permitting more colorful (and perhaps more informative) plots.

See for more information.

This module is based on matplotlib, making it compatible with JupyterLab.

Note that the Seaborn package can work directly with a list-of-dictionary structure. This matches the ND JSON format used for acquiring and cleaning the data.

Using a list-of-dictionary type suggests it might be better to avoid the analysis model structure, and stick with dictionaries created by the clean application. Doing this might sacrifice some model-specific processing and validation functionality.

On the other hand, the pydantic package offers a built-in dict() method that covers a sophisticated analysis model object into...