How does Pandas handle high-cardinality groupby performance issues?

High-cardinality groupby operations slow down due to large hash tables and memory overhead.

When grouping by high-cardinality columns (like UUIDs), Pandas must create large hash maps, increasing memory usage and reducing cache efficiency. This leads to slower aggregation and higher CPU overhead. Optimizations include converting to categorical dtype or reducing key dimensionality.