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  • Book Overview & Buying Python Real-World Projects
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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

8.4 Summary

This chapter’s projects have shown examples of the following features of a data acquisition application:

  • Using the Pydantic module for crisp, complete definitions

  • Using JSON Schema to create an exportable language-independent definition that anyone can use

  • Creating test scenarios to use the formal schema definition

Having formalized schema definitions permits recording additional details about the data processing applications and the transformations applied to the data.

The docstrings for the class definitions become the descriptions in the schema. This permits writing details on data provenance and transformation that are exposed to all users of the data.

The JSON Schema standard permits recording examples of values. The Pydantic package has ways to include this metadata in field definitions, and class configuration objects. This can be helpful when explaining odd or unusual data encodings.

Further, for text fields, JSONSchema permits including a format attribute...

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