jupyter-live-kernel

A stateful Python REPL keeps variables, imports, and objects alive between executions by connecting to a live Jupyter kernel. This allows you to build up results incrementally without restarting the session.

Iterative data exploration is best handled by a stateful Python REPL connected to a live Jupyter kernel. It lets you validate intermediate results and converge on a solution within a single session.

To start a stateful Python REPL, you need uv, JupyterLab, and a running kernel. You initialize the session using the hamelnb script and then begin executing Python code to build state incrementally.

Yes, running this stateful Python REPL requires uv, JupyterLab, a running kernel, and the hamelnb script to manage sessions and execute code for stateful exploration.

Yes, a live Jupyter kernel provides stateful execution ideal for API exploration and iterative code development. It enables stepwise refinement by keeping objects and variables alive across runs.

A standard Python REPL loses state because it restarts after each execution. Connecting to a live Jupyter kernel creates a persistent session that keeps variables, imports, and objects alive between runs.