jupyter-live-kernel

To maintain stateful Python execution in JupyterLab, you need a live kernel that preserves variables across executions. This allows variables to persist across multiple code runs, which is useful for iterative data science tasks and intermediate result inspection.

Stateful Python execution keeps variables and objects in memory between separate code executions. You need it for ML experimentation and data science workflows to build complex code step-by-step and inspect intermediate results without reloading state.

Yes, you need both hamelnb and JupyterLab to run a live Python kernel. These dependencies are required to maintain state across multiple executions and utilize JupyterLab's features for data science and ML experimentation.

The best way to explore APIs iteratively in a Python notebook is using stateful execution with a live Jupyter kernel. This approach allows you to inspect intermediate results and maintain state across multiple executions during API exploration.

Yes, you can use stateful Python execution with a live Jupyter kernel for step-by-step data science workflows. It is ideal for building complex code incrementally and inspecting intermediate results during iterative data science tasks.