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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

Chapter 7
Data Inspection Features

There are three broad kinds of data domains: cardinal, ordinal, and nominal. The first project in this chapter will guide you through the inspection of cardinal data; values like weights, measures, and durations where the data is continuous, as well as counts where the data is discrete. The second project will guide reasoners through the inspection of ordinal data involving things like dates, where order matters, but the data isn’t a proper measurement; it’s more of a code or designator. The nominal data is a code that happens to use digits but doesn’t represent numeric values. The third project will cover the more complex case of matching keys between separate data sources.

An inspection notebook is required when looking at new data. It’s a great place to keep notes and lessons learned. It’s helpful when diagnosing problems that arise in a more mature analysis pipeline.

This chapter will cover a number of skills...

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