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)
19
Index

12.1 Description

In Chapter 8, Project 2.5: Schema and Metadata, we used Pydantic to generate a schema for the analysis data model. This schema provides a formal, language-independent definition of the available data. This can then be shared widely to describe the data and resolve questions or ambiguities about the data, the processing provenance, the meaning of coded values, internal relationships, and other topics.

This specification for the schema can be extended to create a complete specification for a RESTful API that provides the data that meets the schema. The purpose of this API is to allow multiple users — via the requests module — to query the API for the analytical data as well as the results of the analysis. This can help users to avoid working with out-of-date data. An organization creates large JupyterLab servers to facilitate doing analysis processing on machines far larger than an ordinary laptop.

Further, an API provides a handy wrapper around the...