Review, "The Python Workshop" (Packt)
Learning Python can be a blocker to success with DevSecOps. “The Python Workshop, 2nd ed” ( Packt , 2022) by Corey Wade , Mario Corchero Jiménez , Andrew Bird , Dr. Cher Han Lau , and Graham Lee covers every part of Python a new user could possibly hope to know. The book provides extensive code samples, small exercises to work through, and more detailed activities to explore coding options. Starting with basic Python fundamentals, the book works through advanced implementation with libraries and ends with thoughts about implementing ML structures, including data analytics. Again, anything a Python user could want, either from a new start point or a reference manual to keep on the desk. Recommend for anyone using Python.
No sections are provided in the reference; instead, chapters are listed from 1-13 with gradually advancing topics. The first four chapters cover the basics of developing in Python, the next five are about software development and the last four advanced data analytics. Starting with the fundamentals, the frame introduces Jupyter Notebook as one of the ways to sequentially implement Python. The standards for basic coding, assigning variables, using variables, and developing conditional statements are all included. These first four sections include 70 exercises and 13 activities to test individual abilities and ensure concepts are understood.
The middle sections are the meat of Python expressions. Classes, methods, and modules are all covered as well as how to import previous Python libraries from others to shortcut code. Also included is an excellent section on coding collaboratively through using Git and merge requests. Most texts skip this function, and the inclusion is a strong addition to the overall value. The authors briefly touch on other Python forms, such as Cython for using C+ wrappers and PyPy for Just-in-Time compilation through Python. Another 58 exercises are included, and ten more activities to ensure all the presented topics continue to be understood.
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The last section touches on the cutting edge of Python development by introducing machine learning and data analytics topics. The text references pandas and NumPy as the primary libraries to access these tools and provides a number of samples to best implement. Different machine-learning types like K-nearest neighbors, decision trees, random forests and naive Bayes are all presented and explained as methods to reach one’s ML goals. One strong point in reference books is this ability to not just stop at the basics but show the most advanced options possible. In personal development work, my company offers the toolbox, the blueprints to use the toolbox, and then shows what our artists can deliver with that toolbox. This reference follows a similar path. This last section offers 41 exercises and four activities to practice ML skills.
Though offering a clear path from beginning to end, the book moves fast. Covering all this material in an individual text is a lot, and the reader will most likely be using Stack Overflow and community groups at points if they become stuck on the various activities. An excellent reference, this book could easily have been split into 3 manuals of equivalent length to really dig into the various pieces. However, this does not detract from the book’s value as an initial reference or a continuing reference to Python’s capabilities.
Overall, “The Python Workshop, 2nd ed” (Packt, 2022) is an excellent reference and one I am happily including in the stack of manuals on my desk. I even found using the exercises and activities a good refresher for my own skills. Whether one is just starting their Python coding journey or looking for a refresher, I recommend purchasing and frequently referencing this work for anyone currently involved with Python at any level or just hoping to get started with this great language.
