jupyter-live-kernel

A stateful live Jupyter kernel keeps variables, objects, and imports alive across multiple code executions, allowing you to build Python state progressively without rewriting code each time.

You can inspect variables and DataFrames during an interactive Python notebook session by targeting an existing notebook path and using the operational toolkit to execute code and query the live Jupyter kernel state.

Yes, you need a running JupyterLab server to use a live kernel for interactive coding, as the environment requires REST-based session execution and notebook path targeting for stateful REPL workflows.

The best way to debug ML preprocessing steps incrementally is using a notebook-based REPL workflow, which allows you to execute small code snippets, inspect DataFrames, and keep context alive between runs.

Yes, you can edit notebook cells directly during data science exploration, as the stateful execution environment supports cell editing alongside code execution and variable inspection for incremental development.