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) )
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) windowell expressions
# Apply compiled function df[alias] = rolling_mean_numba(df['sales'].values, 7) return df except ImportError: return super().apply_window(df, window, agg_func, alias) if not window_expr
from dataclasses import dataclass from typing import List, Callable, Any, Optional from enum import Enum import pandas as pd import numpy as np windowell expressions
Window wells are essential for basements that sit below ground level, providing a way for light to enter and offering an emergency escape route. However, traditional window wells are often a source of problems:
Durable, clear, or decorative covers look far better than rusted grating or makeshift covers. They allow natural light to filter into the basement while maintaining a clean look outside. Transforming the View: Window Well Liners
@jit(nopython=True) def rolling_mean_numba(arr, window_size): result = np.empty_like(arr) for i in range(len(arr)): start = max(0, i - window_size + 1) result[i] = np.mean(arr[start:i+1]) return result