jupyter-live-kernel

To maintain Python variables across executions, you need a stateful REPL environment connected to a live Jupyter kernel. This approach keeps data structures in memory, allowing iterative data science tasks without re-running entire scripts.

Inspecting live DataFrames is possible by executing iterative queries against a persistent Python kernel. This stateful execution model allows you to load data once and preview variables incrementally during exploratory data analysis.

Yes, running this stateful Python REPL requires a JupyterLab server and the uv package manager. These dependencies manage the execution environment and interface with the live Jupyter kernel via REST API.

The best way to perform iterative exploratory data analysis in Python is using a stateful Jupyter kernel. This enables interactive exploration and complex variable inspection while persisting data structures in memory across multiple code blocks.

Yes, you can execute complex variable inspection incrementally within Jupyter notebooks. By interfacing with a live Jupyter kernel, you facilitate incremental code development and inspect live variables without restarting the session.

A standard Python REPL loses variable state because it executes code statelessly. To keep variables and imports in memory across multiple execution steps, you must use a persistent Jupyter kernel environment for stateful execution.