Windowell Expressions __full__ May 2026

def define_window(self, name: str, expression: WindowellExpression): """Register a named window template""" self.window_registry[name] = expression return self

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 resolve_window(self, window_spec: str | WindowellExpression) -> WindowellExpression: """Resolve window reference with optional overrides""" if isinstance(window_spec, str): base = self.window_registry.get(window_spec) if not base: raise ValueError(f"Undefined window: window_spec") return base return window_spec windowell expressions

def __init__(self): self.partition_by = [] self.order_by = [] self.frame = None

def __init__(self): self.window_registry: dict[str, WindowellExpression] = {} end: int): self.frame = WindowFrame( start=(start

def evaluate(self, df: pd.DataFrame) -> int: return self.expression(df) dynamic_window = WindowellBuilder() .partition("category") .order("timestamp") .rows_between( DynamicBoundary(lambda df: df['lag'].max()), "preceding", 0, "current_row" ).build() 4. Testing Suite import unittest class TestWindowellExpressions(unittest.TestCase):

@dataclass class WindowellExpression: partition_by: List[str] order_by: List[str] frame: Optional[WindowFrame] = None name: Optional[str] = None frame_type="range" ) return self def resolve_window(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)