jupyter-live-kernel

To keep Python variables alive across executions, you need stateful execution against a live Jupyter kernel, which preserves imports, objects, and variables between iterative code steps so you can inspect intermediate results without restarting.

Yes, you can run iterative data science code in a Jupyter notebook without losing state by executing against a live kernel that persists variables and outputs between steps, enabling incremental refinement for pipelines like data-cleaning tasks.

To inspect and edit notebook cells during stateful Python execution, use a notebook-centric workflow with compact JSON operations to inspect notebooks, edit cells, list variables, and restart-plus-run-all for end-to-end verification of your analysis session.

Yes, stateful Python code for API exploration requires an active JupyterLab server along with the hamelnb CLI workflow, enabling you to probe APIs and preserve session objects across iterative testing steps.

The best way to prototype ML models iteratively without restarting is stateful execution in a live Jupyter kernel, which removes the friction of stateless one-off runs by keeping your imports, DataFrames, and objects active until analysis is complete.

Python execution loses variables between steps because it runs in a stateless environment, whereas a live Jupyter kernel provides stateful execution that preserves variables, imports, and outputs across incremental code changes for complex analysis.