Continuous learning is part of AKABI’s DNA. Every year, all the collaborators have the opportunity to register for some training.
At the start of the year, I had the privilege of guiding my colleagues in the wonderful world of Python.
My challenge was the profile variety. From developers to data analysts. From Python beginner to veteran. But ultimately, Python’s versatility retains its greatest strength, and this training day was filled with exchanges of points of view.
The beginning of the journey
We began with an immersion into the fundamentals of the Python language, exploring its elegant and simple syntax. Then we explored the different data types and some of the greatest built-in tools provided by this language. We continued with some more advanced concepts like the generators and the decorators. The latter has attracted a lot of attention.
We also discussed several automated tools to improve the code quality and avoid issues due to the dynamic typing of Python. First, the duo iSort/Black reformat all the files with the same rules across the developers. Then the famous PyLint for static analysis. Of course, I had to talk about Ruff who does the same but much faster! The last tool presented was MyPy which, thanks to the annotations, allows us to have a type checking (I know it’s a bit against the DuckTyping but it can save your production environment).
We ended this introduction with an introspection exercise. A practical case where we must be able to retrieve a catalog of error classes to automatically generate documentation. This exercise has helped the consultant to understand the limitations of an interpreted language.
“Testing is doubting” but not for developers
After a short break, we dived into the fascinating world of testing. There, we were out of the comfort zone of the data analyst. We start with the well-known unit-test framework. It was brief since I don’t like its approach (more like Java than Python). Then I explain, in detail, the power of Pytest, its simple syntax and the flexibility provided by the fixtures.
Then I was able to share my enthusiasm about the Locust framework that I recently discovered. This tool is so great for perf-testing APIs. And best, we can write our scenario in Python.
Some web frameworks
After lunch and a small recap of the morning, I introduced some frameworks largely used to build APIs.
We start with the validation of user inputs with both Schemas and Pydantic.
With this new knowledge, we were able to discover Flask. The simplicity and easiness of implementing a web service have surprised our .Net developers.
In order to introduce the FastAPI framework, we talked about the GIL. The Global Interpreter Lock allows only one thread to execute code at a time, even on multi-core CPUs. This limits true parallelism. The discussion was mainly focused on how to deal with this limitation thanks to the asynchronous paradigm. We coded some examples of code with Asyncio to better understand this less-known paradigm. Thanks to these foundations, we were able to explore the main functions of FastAPI. Its elegant approach seduced some of the audience.
Data exploration
To end our journey, we explored the world of data analysis. We obviously discussed the famous Numpy and Pandas which are essential tools.
To improve the visualization of our analysis, we used the essential MatPlotLib. And for the first time, we leave the IDE for the data scientist IDE, aka Jupiter notebook.
After this busy day, we were able to discuss our different needs specific to our profiles. Some were even interested in a sequel in the form of a hackathon.
Testimonials
I am very happy to have attended this Python coding camp. I went there to expand my knowledge and refresh my Python skills a bit, but I found it very interesting.
The fact that we reviewed all the most commonly used Python libraries makes this training a real asset for my future projects.
If a Python project were ever to be proposed to me, I now have a better understanding to navigate towards one solution or another.
— Valentin Gesquiere
I had the opportunity to attend Simon’s Python course.
As a data engineer using data-oriented Python, I was looking forward to learning more about the language and its many uses.
After a quick refresher on the basics of the language, Simon went through the many uses of Python, including web frameworks, unit-test frameworks, libraries used in the data domain, and pre-commit tools to improve code quality.
It was really interesting to have an expert able to answer our many questions and show live examples.
I’ve come away from this course with lots of ideas for projects to try out.
— Pierre-Yves Richer
Author: Simon OLBREGTS, Software Craftmanship Practice Leader at AKABI