jupyter-live-kernel

A stateful Python REPL environment via a live Jupyter kernel keeps variables, imports, and objects persistent across executions for iterative data science exploration. You build up experiments incrementally without re-running everything from scratch.

Notebook-aware execution against a live Jupyter kernel allows iterative machine learning prototyping while preserving DataFrame state. You execute code against a notebook, inspect variables, and modify cells to refine your approach without losing context.

You run a stateful Jupyter kernel by starting or reusing a local Jupyter server, then executing Python snippets in a scratch notebook. The kernel maintains variable persistence for incremental API inspection and returns updated state with each execution.

Yes, a working uv and JupyterLab setup is required along with a running Jupyter server. The live kernel relies on notebook session execution with REST-based kernel sessions to provide stateful Python REPL workflows.

Yes, notebook-aware execution supports editing and executing cells interactively. You can inspect variables, modify notebook cells to refine your approach, and execute code against a persistent kernel state for incremental analysis.

Running one-off code repeatedly loses variable state and imports between executions, forcing you to re-run everything from scratch. A stateful Python environment solves this by keeping context between steps so you can build up experiments incrementally.