jupyter-live-kernel

A live Jupyter kernel provides stateful Python execution where variables persist across commands. This enables iterative code development and incremental state building for data exploration without restarting the kernel between executions.

The best way to inspect DataFrames and explore APIs iteratively is through a stateful Python REPL via a live Jupyter kernel. This approach maintains persistent variables across executions, allowing you to build up state incrementally and interact with notebooks without restarting kernels.

You should use a live Jupyter kernel instead of standard code execution when you need stateful, iterative Python execution for ML prototyping. It is ideal for tasks requiring persistent state across executions, such as data exploration, API exploration, and building complex scripts step-by-step.

Start a live Jupyter kernel using the hamelnb script to start servers, create or attach notebooks, and execute code cells. This structured workflow enforces stateful execution, allowing you to run Python code iteratively across cells with persistent state maintained throughout the session.

Yes, you can attach an existing notebook to a live Jupyter kernel. The hamelnb script supports creating or attaching notebooks to a running server, enabling continued iterative code execution with persistent state across the session.

A stateful Python REPL via a live Jupyter kernel requires a structured workflow through the hamelnb script to manage servers and notebooks. It is designed for iterative, stateful tasks and depends on maintaining a persistent kernel session, which may not suit stateless or one-off execution workflows.