可能不是最短的解决方案，但绝对有效且快速：
1.整形
1.创建遮罩
1.应用遮罩并获得结果：

import numpy as np
array = np.array([0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0])
array
groups = array.reshape(-1, 4)  # group every 4 elements into new columns
groups
mask = groups.sum(axis=1)>0  # identify groups with at least one '1'
mask
np.logical_or(groups.T, mask).T.astype(int).flatten()
# swap rows and columns in groups, apply mask, swap back, 
# replace True/False with 1/0 and restore original shape

返回（在Jupyter笔记本中）：

array([0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0])
array([[0, 0, 0, 0],
       [1, 1, 1, 1],
       [0, 0, 1, 0],
       [0, 0, 0, 0]])
array([False,  True,  True, False])
array([0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0])