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Python Real-World Projects

Python Real-World Projects

By : Steven F. Lott
4.4 (5)
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Python Real-World Projects

Python Real-World Projects

4.4 (5)
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)
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19
Index

8.1 Description

Data validation is a common requirement when moving data between applications. It is extremely helpful to have a clear definition of what constitutes valid data. It helps even more when the definition exists outside a particular programming language or platform.

We can use the JSON Schema (https://json-schema.org) to define a schema that applies to the intermediate documents created by the acquisition process. Using JSON Schema enables the confident and reliable use of the JSON data format.

The JSON Schema definition can be shared and reused within separate Python projects and with non-Python environments, as well. It allows us to build data quality checks into the acquisition pipeline to positively affirm the data really fit the requirements for analysis and processing.

Additional metadata provided with a schema often includes the provenance of the data and details on how attribute values are derived. This isn’t a formal part of a JSON Schema, but we can add some...

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