Notebook Cells vs Python Scripts for Large-Scale DataFrame Processing in DataSpell

When processing large pandas DataFrames in DataSpell, the choice between cell-based notebook execution and integrated Python scripts involves a trade-off between interactive exploration and execution stability. The notebook editor offers inline visualization and Scientific Mode's Variable View for inspecting dataframe state, but documented behavior includes synchronization lag between the notebook's visual state and the underlying kernel memory during interrupted executions.

Integrated Python scripts avoid some notebook-specific overhead and may be easier to refactor for production, yet they sacrifice the immediate cell-level feedback that makes iterative data cleaning practical. The IDE supports shared kernel sessions across .ipynb and .py files, including the Convert to Python Script feature, but resource consumption remains a concern when multiple heavy kernels run alongside the IDE's indexing processes.

Assuming current DataSpell releases with standard Conda or Virtualenv environments, which approach better balances responsiveness and memory stability for large-scale processing?

Does the Variable View tool window accurately reflect kernel memory after an interrupted cell execution involving large DataFrames?

At what point does converting a notebook to a Python script become necessary to avoid synchronization lag during heavy computation?

How does the shared kernel session manage memory cleanup when switching between .ipynb and .py files mid-workflow?