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
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
@dataclass class WindowellExpression: partition_by: List[str] order_by: List[str] frame: Optional[WindowFrame] = None name: Optional[str] = None window_spec: str | WindowellExpression) ->
def define_window(self, name: str, expression: WindowellExpression): """Register a named window template""" self.window_registry[name] = expression return self window: WindowellExpression | str
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)
def order(self, *columns): self.order_by.extend(columns) return self
@dataclass class WindowFrame: start: tuple[int, FrameBound] # (offset, bound_type) end: tuple[int, FrameBound] frame_type: str = "rows" # rows, range, groups