jupyter-live-kernel

To keep Python variables and state persistent across executions, you can use a Jupyter kernel environment. This allows iterative development by retaining variable values and state between separate code runs.

The best way to prototype algorithms iteratively without losing state is using a persistent Jupyter kernel. It maintains the Python environment between runs, enabling incremental testing and refinement of code.

Yes, this environment supports incremental testing for data science tasks. It provides a full-featured Jupyter kernel ideal for exploring data, prototyping algorithms, or building machine learning models through state persistence.

To execute Python code in a Jupyter kernel from the command line, run the execution script with a target notebook path and the code string. This sends the code directly to the persistent kernel environment.

Yes, you can use this for building machine learning models that require state persistence. The persistent Jupyter kernel allows you to incrementally test and refine model code across multiple executions.

Your Python session resets variables between executions because it lacks a persistent Jupyter kernel. Using an interactive environment with state persistence ensures variables remain available across code runs.