jupyter-live-kernel

A stateful Python REPL keeps Python variables, imports, and objects persistent across multiple code executions by interfacing with a live Jupyter kernel. This mimics a notebook environment, allowing incremental data exploration without re-running scripts.

Iterative data exploration is best handled using a stateful Python environment connected to a live Jupyter kernel. You can load a dataset once and perform multiple sequential transformations and visualizations without reloading the data each time.

Yes, a stateful Python REPL requires a running JupyterLab server to function. It interfaces with the live Jupyter kernel via REST API and also requires the uv package manager to manage the execution environment.

Yes, you can inspect DataFrames and test APIs incrementally in a stateful Python REPL. It facilitates interactive exploration by maintaining variable states, allowing you to test logic and inspect objects without executing entire scripts repeatedly.

A live Jupyter kernel connection works for exploratory data analysis by interfacing with the kernel via REST API. This provides a persistent environment where variables and objects survive across executions, enabling complex debugging and incremental code development.

Python scripts lose variable state because they execute statelessly. To maintain state during exploratory data analysis, you need a stateful Python REPL that interfaces with a live Jupyter kernel, allowing variables and objects to persist across multiple runs.