jupyter-live-kernel

A stateful Python REPL leverages a live Jupyter kernel to maintain variables across executions. This allows you to build up state incrementally and iterate on complex code without losing previous variable definitions during data science tasks.

Yes, JupyterLab supports stateful Python code execution by hosting a live kernel. This integration provides a notebook-like interface suitable for incremental development, machine learning experimentation, and complex data science workflows.

A live Jupyter kernel is a persistent Python process that retains memory and variable states between code submissions. It works by executing code incrementally, allowing iterative exploration where each command builds upon the previous state without re-running the entire script.

Yes, you need to install JupyterLab and set up a Python environment to run this stateful Python REPL. JupyterLab is a required dependency that provides the server and interface infrastructure needed to host the live kernel.

To execute code and inspect variables, you start a JupyterLab server and send Python commands to the live kernel. The kernel processes the code, persists the state, and allows you to query current variables and edit cells for ongoing experimentation.

You should use a stateful Python REPL when you need to perform iterative exploration, data science, or machine learning tasks that require building complex state incrementally. Standard scripts are less suitable for this as they lose variable state after each execution.