How does Pandas optimize categorical encoding in memory and computation?

Categorical encoding replaces repeated strings with integer codes for efficiency.

Pandas categorical dtype maps unique values to integer codes stored in a compact array. A separate category table stores the mapping. This reduces memory usage drastically for repeated values and speeds up operations like groupby, sorting, and comparisons by replacing string operations with integer arithmetic.