jupyter-live-kernel

You can execute Python code incrementally using a live Jupyter kernel, which preserves variables, imports, and objects across multiple runs. This eliminates the need to re-run entire scripts for small code changes during iterative data analysis.

You can inspect live DataFrame variables by discovering running Jupyter servers and querying the active kernel via the command line. The hamelnb utility script enables checking computed values and variable states without opening the notebook interface.

Yes, a running JupyterLab server is required to manage live kernel sessions. You also need the uv package manager and the hamelnb utility script to execute code, inspect variables, and edit notebook contents programmatically.

The best way to avoid reloading large datasets is stateful iterative Python execution via a live Jupyter kernel. By loading data once, you can run exploratory queries, adjust filters, and check results instantly without re-running the costly data load step.

Stateless code execution wipes variables between runs, causing the loss of imported libraries and intermediate results. Using a persistent Jupyter kernel retains this state, making incremental API exploration and complex code debugging more efficient.

Yes, you can edit notebook cells and run full notebook validations programmatically. The skill's live kernel management capabilities allow direct modification and validation of notebook contents from the command line.