Copy-on-Write Compatibility Boundary in pandas 2.x: Opt‑in Mode Decision

Goal: assess the safety of enabling pandas 2.x copy‑on‑write mode as the default behavior for an existing codebase that is being upgraded from pandas 1.x.

Uncertainty stems from the fact that copy‑on‑write alters the semantics of item assignment, chained indexing, and inplace operations, and its interaction with downstream libraries (e.g., NumPy, PyArrow, scikit‑learn) is not fully documented. The pandas project has not committed to a release date for making the mode default, leaving the compatibility boundary unresolved.

What specific behavioral changes should be expected when copy‑on‑write is enabled for typical DataFrame workflows? How can teams verify that their existing pipelines, including third‑party extensions, remain compatible without introducing silent errors? Are there known libraries or patterns that break when the mode is turned on globally?