jupyter-live-kernel

You need a stateful Python REPL via a live Jupyter kernel to maintain persistent variables across multiple code executions. This enables iterative data science exploration, allowing you to build complex code step-by-step and inspect DataFrames without losing state.

Using a live Jupyter kernel is the best way to explore APIs and build complex Python code step-by-step. It provides Jupyter-like debugging and experimentation with persistent state across runs, ideal for ML experiments and data-science exploration.

Yes, you need uv, JupyterLab, and a running Jupyter server to operate a stateful Python REPL. These dependencies are required to activate and manage the live Jupyter kernel for persistent Python execution.

Use a live Jupyter kernel instead of standard code execution when you need to build up state incrementally. It is ideal for data-science exploration, ML experiments, API exploration, and building complex workflows where variables must persist across runs.

Yes, you can inspect DataFrames and debug Python code iteratively across runs with a live Jupyter kernel. It provides a stateful Python REPL that enables Jupyter-like debugging and experimentation for incremental data analysis.