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):
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 )
def partition(self, *columns): self.partition_by.extend(columns) 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 __init__(self): self.window_registry: dict[str, WindowellExpression] = {}
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)
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 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
Zimbabwe
