(1/4) Setting A Dockerized Python Environment — The Hard Way
I create a (relatively) short tutorial about setting up a dockerized 🐳 Python 🐍 environment on the command line (CLI). Generally, I don't advocate anyone to set their Python development workflow via the CLI. There are better tools to work with Python and Docker, such as VScode with the Dev Containers extension. 🧵👇🏼
(2/4) Rather, there is a great learning experience here of the core Docker commands and functionalities. The goal here is to see how you can take an official Python image and customize it to your needs.
(3/4) This includes the following:
✅ Running Python base image in interactive mode on the CLI
✅ Create a Dockerfile and add new components to the base image
✅ Install Python libraries and tools such as CLI text editor
✅ Expose a bash terminal and enable the editing of Python scripts
✅ Use volumes to transfer the container from an ephemeral mode to a persistent
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