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

13.2 Overall approach

We’ll take some guidance from the C4 model ( https://c4model.com) when looking at our approach:

  • Context: For this project, the context diagram has two use cases: the acquire-to-clean process and this analysis notebook.

  • Containers: There’s one container for analysis application: the user’s personal computer.

  • Components: The software components include the existing analysis models that provide handy definitions for the Python objects.

  • Code: The code is scattered in two places: supporting modules as well as the notebook itself.

A context diagram for this application is shown in Figure 13.1.

Figure 13.1: Context diagram
Figure 13.1: Context diagram

The analyst will often need to share their analytical results with stakeholders. An initial notebook might provide confirmation that some data does not conform to the null hypothesis, suggesting an interesting relationship that deserves deeper exploration. This could be part of justifying a budget allocation to do more...