Optimize data transformations

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

1. Ensure schema is cleared after operations that modify DataFrame structure to prevent incorrect schema information from being cached:

   // After modifying DataFrame columns
   df.clear_schema();
   

2. 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.