jupyter-live-kernel

To maintain Python state across multiple executions, you need a persistent REPL environment backed by a live Jupyter kernel. This approach keeps variables, imports, and objects alive between code runs, preventing state loss during iterative data science workflows.

Yes, a running JupyterLab server is required. The live kernel skill coordinates kernel sessions and notebook operations through JupyterLab, utilizing the uv package manager to manage the environment and execute code within an active notebook session.

The best way to refine data analysis without reloading large datasets is using a persistent Python REPL. By loading data once into a live Jupyter kernel, you can iteratively execute analysis code while maintaining variable state across multiple tool calls.

A live Jupyter kernel integrates by executing code, inspecting variables, and managing notebook cells programmatically. This facilitates API exploration and complex debugging by maintaining your Python state within an active notebook session.

Yes, you can use the uv package manager to coordinate kernel sessions. Combined with JupyterLab, uv manages the persistent Python REPL environment required for iterative code execution and state management in data science workflows.