def build(self, name: Optional[str] = None): return WindowellExpression( partition_by=self.partition_by, order_by=self.order_by, frame=self.frame, name=name ) 3.1 Window Composition class WindowComposer: """Compose multiple window expressions""" @staticmethod def chain(window1: WindowellExpression, window2: WindowellExpression): """Chain windows: apply window2 on window1's result""" return WindowellExpression( partition_by=window1.partition_by + window2.partition_by, order_by=window1.order_by + window2.order_by, frame=window2.frame or window1.frame )
def range_between(self, start: int, end: int): self.frame = WindowFrame( start=(start, FrameBound.PRECEDING), end=(end, FrameBound.FOLLOWING), frame_type="range" ) return self windowell expressions
def partition(self, *columns): self.partition_by.extend(columns) return self order_by=window1.order_by + window2.order_by
def test_window_composition(self): w1 = WindowellBuilder().partition('product').build() w2 = WindowellBuilder().order('date').rows_between(1, 'preceding', 1, 'following').build() composed = WindowComposer.chain(w1, w2) self.assertEqual(composed.partition_by, ['product']) self.assertEqual(composed.order_by, ['date']) end: int): self.frame = WindowFrame( start=(start
def define_window(self, name: str, expression: WindowellExpression): """Register a named window template""" self.window_registry[name] = expression return self
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)