Starting Jupyter Lab in Docker
A stable way to use Python with 4 snippets
Why working with Docker instead of just installing Python and Jupyter on your local machine?
If you work alone and only on one project, having docker could be more trouble than helping you. But if you are working on different projects with different needs, at one point you will have struggles. Spending a day or two on solving dependency problem when switching from one project to the other, is really not fun.
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At least, use virtual environment
Using virtual environments in Python is like creating a personal playground for your projects. Imagine you’re working on different projects that require different packages and dependencies. Without virtual environments, it could get messy.
It helps avoid conflicts between different projects. Each virtual environment is like a clean slate, isolating your project from the rest. This means you can have Project A using Python 3.7 and a specific library version, while Project B runs on Python 3.9 with different library…
