Don't Install Python for Data Science. Use Docker Instead!

Docker containers provide a lightweight and efficient way to package and deploy applications, making it easier to move them between different environments, such as development, testing, and production. However, while Docker is widely used for deployment, it has been underutilized by developers for their day-to-day work. Many developers still rely on traditional local development environments that can be difficult to set up, maintain, and share with others. This can lead to issues with version conflicts, dependencies, and different operating systems, which can slow down the development process and make it harder to collaborate with others. A few months ago, I wrote an article entitled <a href="https://medium.com/towards-data-science/why-you-should-use-devcontainers-for-your-geospatial-development-600f42c7b7e1" rel="noopener">‘Why You Should Use Devcontainers for Your Geospatial Development’ </a>on Towards Data Science publication, where I discussed the advantages of using VSCode’s Devcontainers for geospatial development over traditional local Python environments. Most of this was motivated by the complex dependencies that exist in specialized geospatial libraries. Since then, I have continued to develop using Devcontainers for most of my work, but I recently had to reformat my notebook, which gave me the opportunity to start fresh. After installing Windows 11 and Ubuntu 22.04, I decided not to install anything related to Python, such as Conda, VirtualEnv, or Miniconda. Instead, I opted for Git and Docker, and as I will show here, we don’t even need VSCode, although I recommend it as a full-fledged IDE if you are doing more than just data analysis. <a href="https://betterprogramming.pub/dont-install-python-for-data-science-use-docker-instead-bb61c585febc">Read More</a>