Pyenv is a tool for managing different Python versions.
Whether you’re upgrading a project to a newer Python release or switching between projects with different requirements, pyenv makes the process simple and hassle-free.

Introducing the factory pattern in your code can significantly improve its structure—but it also makes unit testing a bit trickier.
We’ll walk through two different mocking strategies, compare their pros and cons, and help you write more robust tests!

With mock objects, we can simulate database operations without actually connecting to a real database.
This means we don’t need to prepare a dedicated test database or worry about cleaning it up after tests.

After covering the basic tools, this article demonstrates how to use mocks effectively in Pytest.
Mocks allow us to replace the logic of real functions to simulate different scenarios and write more precise unit tests.
We’ll walk through examples of mocking functions, simulating errors, and even handling external dependencies like Redis.

Writing unit tests is a well-known best practice in software development, but actually getting started often feels overwhelming.
Where do you begin? What tools should you use? And how do you use them?
For beginners, these first steps can easily become roadblocks.

This is my first time using Python in a work environment, so I’m documenting the environment setup. Many of these packages are new to me, not having used them when I was self-learning a few years ago. I guess that reveals how long it’s been since I touched Python!

Do you know about TTRPG? One of the most well-known is probably Dungeons & Dragons (D&D).
After watching hololive EN play, I thought, “Let’s create a dice rolling bot and give it a try.”