def order(self, *columns): self.order_by.extend(columns) return self
@dataclass class WindowellExpression: partition_by: List[str] order_by: List[str] frame: Optional[WindowFrame] = None name: Optional[str] = None windowell expressions
def test_dynamic_boundary(self): self.df['threshold'] = [1, 2, 1, 3, 2] dynamic = DynamicBoundary(lambda df: df['threshold'].median()) self.assertEqual(dynamic.evaluate(self.df), 2) if == ' main ': unittest.main() 5. Performance Optimizations class OptimizedWindowellEngine(WindowellEngine): """Performance-optimized version""" def apply_window_optimized(self, df, window, agg_func, alias): """Use vectorized operations where possible""" window_expr = self.resolve_window(window) if not window_expr.order_by and not window_expr.frame: # Simple partition aggregate (fast path) return df.assign(** alias: df.groupby(window_expr.partition_by)[agg_func.__name__].transform(agg_func) ) # Use numba for JIT-compiled rolling windows if window_expr.frame and window_expr.frame.frame_type == 'rows': return self._numba_rolling_apply(df, window_expr, agg_func, alias) return super().apply_window(df, window, agg_func, alias) def order(self, *columns): self
@staticmethod def overlay(window1: WindowellExpression, window2: WindowellExpression): """Overlay windows: combine frame definitions""" return WindowellExpression( partition_by=window1.partition_by or window2.partition_by, order_by=window1.order_by or window2.order_by, frame=window2.frame if window2.frame else window1.frame ) class DynamicBoundary: """Compute frame boundaries dynamically from data""" def __init__(self, expression: Callable[[pd.DataFrame], int]): self.expression = expression alias) return super().apply_window(df