jupyter-live-kernel

To keep Python variables persistent across multiple code executions, you need a stateful REPL environment like a live Jupyter kernel. This allows variables, imports, and objects to persist across multiple interactive code execution steps without re-running entire scripts.

The best way to perform iterative data exploration without reloading datasets is using a stateful Python REPL. It allows you to load a dataset once and perform multiple sequential transformations or visualizations in a persistent Jupyter kernel environment.

Yes, you need a running Jupyter server and the uv package manager to use a live kernel. The skill requires these dependencies to interface with the live Jupyter kernel via REST API for interactive code execution.

A live Jupyter kernel provides stateful execution for interactive exploration, whereas a standard stateless Python script loses variable state after execution. The persistent kernel is ideal for inspecting DataFrames and testing APIs incrementally without re-running scripts.

Yes, you can test APIs interactively and inspect complex logic using a persistent Python REPL. The stateful Jupyter kernel facilitates interactive API testing and incremental data science workflows by maintaining variable state between execution steps.