When building applications with Python, you’ll often run into dependency conflicts, version mismatches, and the like. With Docker, you can package applications—along with the required dependencies, runtime, and config—into a single portable artifact called the image. Which you can then use to spin up a Docker container that runs the app.

So whether it is a simple Python application or a data science application, Docker makes managing dependencies simpler. This is especially helpful in data science projects where you need different libraries and specific versions of these libraries for your application to work without errors. With Docker you can have isolated, consistent, and reproducible environments for all your applications.