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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

15.2 Approach

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

  • Context: For this project, a context diagram would show a user creating analytical reports. You may find it helpful to draw this diagram.

  • Containers: There only seems to be one container: the user’s personal computer.

  • Components: We’ll address the components below.

  • Code: We’ll touch on this to provide some suggested directions.

The heart of this application is a module to summarize data in a way that lets us test whether it fits the expectations of a model. The statistical model is a simplified reflection of the underlying real-world processes that created the source data. The model’s simplifications include assumptions about events, measurements, internal state changes, and other details of the processing being observed.

For very simple cases — like Anscombe’s Quartet data — there are only two variables, which leaves...

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