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

11.2 Overall approach

For reference see Chapter 9, Project 3.1: Data Cleaning Base Application, specifically Approach. This suggests that the clean module should have minimal changes from the earlier version.

A cleaning application will have several separate views of the data. There are at least four viewpoints:

  • The source data. This is the original data as managed by the upstream applications. In an enterprise context, this may be a transactional database with business records that are precious and part of day-to-day operations. The data model reflects considerations of those day-to-day operations.

  • Data acquisition interim data, usually in a text-centric format. We’ve suggested using ND JSON for this because it allows a tidy dictionary-like collection of name-value pairs, and supports quite complex Python data structures. In some cases, we may perform some summarization of this raw data to standardize scores. This data may be used to diagnose and debug problems with upstream...