jupyter-live-kernel

A stateful Python REPL coupled with a live Jupyter kernel maintains persistent variables across executions. It enables iterative data science exploration and API testing by keeping your session state intact until you shut it down.

Yes, a stateful Python REPL supports iterative API testing across sessions. By coupling with a live Jupyter kernel, it preserves your environment and variables, allowing you to run sequential API calls and explore responses without losing state.

Setting up a live Jupyter kernel for stateful Python execution requires uv, JupyterLab, a running kernel, and the hamelnb script. These components manage your sessions and maintain persistent state for interactive experimentation.

A live Jupyter kernel provides persistent variables across executions, unlike a standard Python REPL that resets state. This stateful environment is better suited for iterative data science exploration, allowing you to build complex workflows across multiple interactions.

You should avoid a stateful Jupyter kernel session for isolated or production-grade Python development. Because it maintains persistent state across executions, it risks variable contamination and unexpected side effects if you need clean, independent runs.