Level Up in Python with Dependency Inversion and Entry Points

Python programmers don’t use Dependency Inversion (DI) enough. There are a number of reasons why not: <ul> <li>It’s tricky — it inverts certain intuitive understandings of code flow.</li> <li>Sometimes people do dependency injection and think they’ve done Dependency Inversion.</li> <li>The benefits of DI are easily visible in mature codebases, but implementing DI in new code adds a level of abstraction that many programmers feel is cumbersome. It gets YAGN I’d, when it shouldn’t.</li> </ul> But dependency Inversion DI could save the code you’re writing today from being obsolete in 2 years. What’s more, Python has a fantastic native feature called Entry Points (supported by most packaging tools) that allows you to get a lot of extensibility out of Dependency Inversion, easily. So here’s what I plan to cover in this post: <ul> <li>What is Dependency Inversion (and what does it look like in Python)?</li> <li>What are Python Entry Points?</li> <li>How Do These Two Things Go Together?</li> </ul> If you’re a Dependency Inversion Champ and feel like you have a SOLID handle on the concept (get it?), feel free to skim down to the Entry Points section. <h1>What is Dependency Inversion?</h1> <h2>Definitions</h2> There’s a standard definition of the Dependency Inversion principle. It’s absolutely correct, but when you’re first learning about DI, it feels cryptic. Lets get it out of the way: <a href="https://johndanielraines.medium.com/level-up-in-python-with-dependency-inversion-and-entry-points-c648a1b087ee">Visit Now</a>