jupyter-live-kernel

You can keep Python variables persistent across executions by using a stateful REPL via a live Jupyter kernel. This approach retains context and variables between runs, enabling iterative data science exploration without re-executing previous code blocks.

A live Jupyter kernel is used for iterative ML experimentation and data exploration by maintaining a persistent Python state. It allows you to probe APIs and test complex code workflows while retaining variables, imports, and context across multiple execution steps.

Yes, a running Jupyter server and the hamelnb tooling are required to use this stateful Python REPL. These uv-based commands manage servers, notebooks, and code execution, requiring a configured environment to execute code and inspect results.

Yes, you can use a persistent Python REPL for API probing and complex code development. The live Jupyter kernel maintains stateful context across runs, allowing you to iteratively test API responses and build complex logic without losing variable assignments or imported libraries.

The best way to run stateful Python code without losing context is using a live Jupyter kernel. It provides a persistent environment where variables and imports remain accessible across multiple runs, supporting iterative workflows like ML prototyping and data exploration.

Your Python execution context resets between runs because standard environments often lack persistent state. Using a stateful REPL via a live Jupyter kernel solves this by retaining variables and imports across executions, ensuring continuous context for iterative exploration.