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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

11.4 Summary

In this chapter, we looked at two important parts of the data acquisition pipeline:

  • File formats and data persistence

  • The architecture of applications

There are many file formats available for Python data. It seems like newline delimited (ND) JSON is, perhaps, the best way to handle large files of complex records. It fits well with Pydantic’s capabilities, and the data can be processed readily by Jupyter Notebook applications.

The capability to retry a failed operation without losing existing data can be helpful when working with large data extractions and slow processing. It can be very helpful to be able to re-run the data acquisition without having to wait while previously processed data is processed again.

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