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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

10.7 Extras

Here are some ideas for you to add to these projects.

10.7.1 Hypothesis testing

The computations for mean, variance, standard deviation, and standardized Z-scores involve floating-point values. In some cases, the ordinary truncation errors of float values can introduce significant numeric instability. For the most part, the choice of a proper algorithm can ensure results are useful.

In addition to basic algorithm design, additional testing is sometimes helpful. For numeric algorithms, the Hypothesis package is particularly helpful. See https://hypothesis.readthedocs.io/en/latest/.

Looking specifically at Project 3.5: Standardize data to common codes and ranges, the Approach section suggests a way to compute the variance. This class definition is an excellent example of a design that can be tested effectively by the Hypothesis module to confirm that the results of providing a sequence of three known values produces the expected results for the count, sum, mean, variance...

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