jupyter-notebook

To maintain variable state across multiple Python code executions, you need a stateful execution environment like a persistent Jupyter kernel. This allows variables, imports, and objects to persist, enabling incremental development and iterative data science without re-running previous code.

The best way to perform iterative data science with persistent variables in Python is using a stateful Jupyter notebook environment. It leverages a live JupyterLab kernel for state management, notebook cell manipulation, and structured code execution across complex analytical workflows.

Yes, you need a running JupyterLab server to manage notebook cells programmatically. The environment uses a live Jupyter kernel and requires a Jupyter REST API integration script to interface with JupyterLab for executing code and inspecting live variables.

A stateful Python REPL helps debug complex machine learning pipelines by keeping the environment state intact between execution steps. You can incrementally run code, inspect live variables, and manage notebook cells without losing loaded data or imported objects during exploration.

Yes, you can use uv to manage Python dependencies when running a stateful Jupyter kernel. The environment requires both the uv dependency manager and JupyterLab to provide a persistent execution environment for iterative data science and software exploration.