Windowell Expressions May 2026

@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

I'll help you develop a feature. Since "Windowell" isn't a standard term, I'll assume you're referring to window functions with well-expressions (possibly for a data processing, SQL-like engine, or streaming analytics system). windowell expressions

def __init__(self): self.window_registry: dict[str, WindowellExpression] = {} @staticmethod def overlay(window1: WindowellExpression

def test_named_window(self): weekly = WindowellBuilder()\ .partition('product')\ .order('date')\ .rows_between(3, 'preceding', 0, 'current_row')\ .build('weekly_sales') self.engine.define_window('weekly_sales', weekly) result = self.engine.apply_window( self.df, 'weekly_sales', lambda x: x['sales'].mean(), 'moving_avg' ) self.assertIn('moving_avg', result.columns) self.assertEqual(len(result), 5) order_by=window1.order_by or window2.order_by

@dataclass class WindowFrame: start: tuple[int, FrameBound] # (offset, bound_type) end: tuple[int, FrameBound] frame_type: str = "rows" # rows, range, groups