What is Jupyter?
Who uses Jupyter?
Why developers like Jupyter?
Here are some stack decisions, common use cases and reviews by companies and developers who chose Jupyter in their tech stack.
Jupyter Anaconda Pandas IPython
A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.
The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead
When integrated with cloud platforms it's an easy way to code and test data science projects. The web-based nature makes it easy to transition between coding on your local machine or the cloud. Jupyter
I use Jupyter and JupyterLab specifically because it's such a great interface for Python, SQL, data exploration, and data viz. Documentation is built into the design of a notebook, and it's commonly used enough in people's workloads that not much educating is required.
I know some on G Suite like using CoLab, but it runs pretty slow and disconnects runtimes often.
Here's a fun little example to make it feel more real Jupyter/CoLab Example