jupyter-live-kernel

You can keep Python variables persistent across executions by using a stateful REPL backed by a live Jupyter kernel. This retains your environment state, enabling incremental data exploration and iterative machine learning experimentation without restarting.

Yes, you can run multi-line Python code interactively without restarting the kernel environment. The live Jupyter kernel supports multi-line code execution and variable inspection, allowing you to build up complex state over time for API exploration and debugging.

Yes, you need a running Jupyter server and an active Python kernel to use a stateful Python REPL. Starting a headless Jupyter kernel allows you to execute code against a persistent notebook and maintain variables across multiple runs.

The best way to prototype and debug data frames iteratively in Python is using a live Jupyter kernel. It provides a stateful environment where variables persist across executions, integrating with notebook-style workflows for interactive data science tasks.

Your Python REPL loses variables between executions because it lacks a persistent stateful kernel. A live Jupyter kernel solves this by retaining variables across executions, allowing you to incrementally build up state for complex data science and ML workflows.