Windowell Expressions Guide

def partition(self, *columns): self.partition_by.extend(columns) return 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) windowell expressions

def rows_between(self, start_offset: int, start_type: str, end_offset: int, end_type: str): self.frame = WindowFrame( start=(start_offset, FrameBound(start_type)), end=(end_offset, FrameBound(end_type)), frame_type="rows" ) return self def partition(self, *columns): self

def __init__(self): self.window_registry: dict[str, WindowellExpression] = {} alias) return super().apply_window(df

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)