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Don't Install Python for Data Science. Use Docker Instead!
A beginner's step-by-step guide to start using Docker Containers for Data Science development and avoid complex Python environment managers.
Due to changes in Medium.com policy concerning non-members reads, implemented in Sep 2023, this post is now freely available on geocorner.net: https://www.geocorner.net/post/don-t-install-python-for-data-science-use-docker-instead
Introduction
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 ‘Why You Should Use Devcontainers for Your Geospatial Development’ on Towards Data Science publication, where…
