Windowell Expressions _verified_ May 2026

def rows_between(self, start_offset: int, start_type: str, end_offset: int, end_type: str): self.frame = WindowFrame( start=(start_offset, FrameBound(start_type)), end=(end_offset, FrameBound(end_type)), frame_type="rows" ) return self

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 ) windowell expressions

def apply_window(self, df: pd.DataFrame, window: WindowellExpression | str, agg_func: Callable[[pd.Series], Any], alias: str = "window_result") -> pd.DataFrame: """Apply window function to DataFrame""" # Resolve window expression window_expr = self.resolve_window(window) # Build pandas window if window_expr.partition_by: grouped = df.groupby(window_expr.partition_by) else: # Create dummy group for non-partitioned window grouped = [(None, df)] result_dfs = [] results = [] for _, group in grouped: if window_expr.order_by: group = group.sort_values(window_expr.order_by) # Apply frame if specified if window_expr.frame: group = self._apply_frame(group, window_expr) # Compute rolling aggregation if window_expr.order_by: result = agg_func(group).rolling( window=len(group), # Simplified - real impl would use frame min_periods=1 ).mean() # Placeholder - should be generic else: result = agg_func(group) group[alias] = result results.append(group) return pd.concat(results, ignore_index=True) frame_type="rows" ) return self def build(self