mode.chained_assignment: Balancing Warnings and Noise in Pandas

In pandas, the option mode.chained_assignment controls whether a UserWarning is emitted, an exception is raised, or the detection is disabled for chained assignment patterns. When set to 'warn', the library emits a warning each time it heuristically detects a chained assignment, but the warning may be filtered out by IDE or logging configurations, producing silent bugs. Setting the option to None suppresses all warnings, which can hide legitimate data corruption. The detection algorithm is heuristic and has evolved across pandas releases, leading to inconsistent behavior on identical code.

Given this, how can developers configure mode.chained_assignment to emit warnings only for genuinely unsafe patterns while suppressing benign cases that are already safe? What specific heuristics does pandas use to flag a chained assignment, and is there a way to customize or extend them? Finally, how stable is the detection logic across major pandas releases, and can we pin the behavior to a particular version to avoid regressions?