Windowell Expressions [FHD 2026]

def range_between(self, start: int, end: int): self.frame = WindowFrame( start=(start, FrameBound.PRECEDING), end=(end, FrameBound.FOLLOWING), frame_type="range" ) return self

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) windowell expressions

def _apply_frame(self, df: pd.DataFrame, window: WindowellExpression) -> pd.DataFrame: """Apply frame boundaries (simplified implementation)""" # Real implementation would handle ROWS BETWEEN X PRECEDING AND Y FOLLOWING frame = window.frame if frame.frame_type == "rows" and frame.start[1] == FrameBound.PRECEDING: # Rolling window logic return df.assign( _row_num=np.arange(len(df)), _window_start=lambda x: x._row_num - frame.start[0] ) return df class WindowellBuilder: """Fluent API for building window expressions""" def range_between(self, start: int, end: int): self