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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

2.5 Summarize and analyze

Summarizing data in a useful form is more art than technology. It can be difficult to know how best to present information to people to help them make more valuable, or helpful decisions.

There are a few projects to capture the essence of summaries and initial analysis:

  • Project 4.1: ”A Data Dashboard”. This notebook will show a number of visual analysis techniques.

  • Project 4.2: ”A Published Report”. A notebook can be saved as a PDF file, creating a report that’s easily shared.

The initial work of summarizing and creating shared, published reports sets the stage for more formal, automated reporting. The next set of projects builds modules that provide deeper and more sophisticated statistical models.

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