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  • Book Overview & Buying Python Real-World Projects
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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

11.1 Description

In the previous chapters, particularly those starting with Chapter 9, Project 3.1: Data Cleaning Base Application, the question of ”persistence” was dealt with casually. The previous chapters all wrote the cleaned samples into a file in ND JSON format. This saved delving into the alternatives and the various choices available. It’s time to review the previous projects and consider the choice of file format for persistence.

What’s important is the overall flow of data from acquisition to analysis. The conceptual flow of data is shown in Figure 11.1.

Figure 11.1: Data Analysis Pipeline
Figure 11.1: Data Analysis Pipeline

This differs from the diagram shown in Chapter 2, Overview of the Projects, where the stages were not quite as well defined. Some experience with acquiring and cleaning data helps to clarify the considerations around saving and working with data.

The diagram shows a few of the many choices for persisting interim data. A more complete list of...

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