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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

To get the most out of this book

This book presumes some familiarity with Python 3 and the general concept of application development. Because a project is a complete unit of work, it will go beyond the Python programming language. This book will often challenge you to learn more about specific Python tools and packages, including pytest, mypy, tox, and many others.

Most of these projects use exploratory data analysis (EDA) as a problem domain to show the value of functional programming. Some familiarity with basic probability and statistics will help with this. There are only a few examples that move into more serious data science.

Python 3.11 is expected. For data science purposes, it’s often helpful to start with the conda tool to create and manage virtual environments. It’s not required, however, and you should be able to use any available Python.

Additional packages are generally installed with pip. The command looks like this:

% python -m pip install pytext mypy tox beautifulsoup4

Complete the extras

Each chapter includes a number of “extras” that help you to extend the concepts in the chapter. The extra projects often explore design alternatives and generally lead you to create additional, more complete solutions to the given problem.

In many cases, the extras section will need even more unit test cases to confirm they actually solve the problem. Expanding the core test cases of the chapter to include the extra features is an important software development skill.

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/PacktPublishing/Python-Real-World-Projects. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

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