jupyter-live-kernel

To maintain variable state during Python data exploration, you need a stateful REPL environment like a live Jupyter kernel, which persists variables, imports, and objects across multiple sequential code execution steps without restarting.

You can run iterative data analysis by executing code against an active Jupyter notebook, which allows you to load a dataset once and perform multiple sequential transformations or visualizations incrementally without restarting the environment.

Yes, you need a running JupyterLab server and the uv package manager to interface with the live Jupyter kernel via its REST API for persistent state execution.

The best way to incrementally debug complex logic is using a persistent Jupyter kernel environment, which facilitates interactive exploration and incremental debugging by retaining object state between execution steps.

Your Python REPL loses variable state because it uses stateless code execution, whereas a persistent Jupyter kernel provides stateful execution to maintain variables and objects across complex data exploration steps.