When implementing data processing operations, avoid unnecessary data transformations, copies, and conversions that can impact query performance. Consider these practices: 1. Prefer direct pattern matching over string conversions: ```rust // Avoid this: matches!(function, FunctionExpr::Range(f) if f.to_string() == "int_range") // Prefer this: matches!(function, FunctionExpr::Range(RangeFunction::IntRange { .. })) ``` 2. Avoid creating temporary data structures when existing ones can be reused: ```rust // Avoid duplicate data with unnecessary temp dataframes: let tmp_df = value_col.into_frame().hstack(pivot_df.get_columns()).unwrap(); // Instead, pass existing data directly: // Use pivot_df directly in subsequent operations ``` 3. Ensure schema is cleared after operations that modify DataFrame structure to prevent incorrect schema information from being cached: ```rust // After modifying DataFrame columns df.clear_schema(); ``` 4. Be precise about row handling in data processing operations, clearly distinguishing between rows scanned vs. rows read, especially important for operations like slicing, filtering, and maintaining correct row indices.