jupyter-live-kernel

A stateful Python REPL maintains Python variables, imports, and objects across multiple code executions, mimicking a Jupyter notebook environment. This allows you to load a dataset once and run sequential analysis steps without re-processing data or re-importing libraries each time.

You can execute Python code iteratively by interfacing with a live Jupyter kernel via the Jupyter REST API. This provides a persistent environment for incremental code development and interactive exploration directly through a running JupyterLab server.

Yes, you need the uv package manager to interface with the kernel via the Jupyter REST API. Along with uv, a running JupyterLab server is required to establish the persistent environment for stateful code execution.

Using a stateful Python REPL is the best way to inspect DataFrames and debug complex logic step-by-step. It provides persistent variable management and incremental code execution, ensuring your data objects remain available across multiple interactive analysis steps.

Python code loses its state during exploratory data analysis due to the limitation of stateless code execution. A live Jupyter kernel solves this by providing a persistent, stateful environment that retains variables and objects across multiple sequential execution steps.

Yes, you can use jupyter-live-kernel to test API calls and debug complex logic incrementally. It facilitates persistent variable management across multiple agent steps, allowing interactive exploration without losing the established session state.