Python 根据阈值和组大小在 DataFrame 中添加列

我有一个带有x和y坐标的DataFrame，其中索引表示时间戳.我们可以假设它是一个每个时间步长都在移动的物体.连续时间戳之间的距离预计会增加.然而，如果距离没有增加一定的门槛，我认为这是一个潜在的"等待"位置. 我用了潜在这个词，因为数据是相当嘈杂的，单一的‘等待’条件不足以真正确定物体没有移动.因此，我需要至少3个或更多连续的‘等待’条件，才能确定物体确实没有移动.

我想要检测这些等待的位置，并相应地在一个新的专栏中标记它们.

Example :
                    x         y
timestamp                       
2023-07-01 00:00:00   1         5
2023-07-01 00:01:00   2         6
2023-07-01 00:02:00   3         7
2023-07-01 00:03:00   4         8
2023-07-01 00:04:00   4         8
2023-07-01 00:05:00   5         9
2023-07-01 00:06:00   6         9
2023-07-01 00:07:00   7        10
2023-07-01 00:08:00   7        10
2023-07-01 00:09:00   7        10
2023-07-01 00:10:00   7        10
2023-07-01 00:11:00   8        11
2023-07-01 00:12:00   9        11

为了计算距离，我已经将数据帧移位1，并计算出距离:

                    x         y  distance  
timestamp                                                    
2023-07-01 00:00:00   1         5       NaN   
2023-07-01 00:01:00   2         6  1.414214    
2023-07-01 00:02:00   3         7  1.414214   
2023-07-01 00:03:00   4         8  1.414214   
2023-07-01 00:04:00   4         8  0.000000   
2023-07-01 00:05:00   5         9  1.414214  
2023-07-01 00:06:00   6         9  1.000000   
2023-07-01 00:07:00   7        10  1.414214   
2023-07-01 00:08:00   7        10  0.000000   
2023-07-01 00:09:00   7        10  0.000000   
2023-07-01 00:10:00   7        10  0.000000   
2023-07-01 00:11:00   8        11  1.414214   
2023-07-01 00:12:00   9        11  1.000000    

现在，假设距离小于1，则可能是等待位置:

                    x         y  distance  condition_fulfilled  
timestamp                                                    
2023-07-01 00:00:00   1         5       NaN    NaN    
2023-07-01 00:01:00   2         6  1.414214    False    
2023-07-01 00:02:00   3         7  1.414214    False    
2023-07-01 00:03:00   4         8  1.414214    False    
2023-07-01 00:04:00   4         8  0.000000    True   
2023-07-01 00:05:00   5         9  1.414214    False   
2023-07-01 00:06:00   6         9  1.000000    False   
2023-07-01 00:07:00   7        10  1.414214    False   
2023-07-01 00:08:00   7        10  0.000000    True   
2023-07-01 00:09:00   7        10  0.000000    True   
2023-07-01 00:10:00   7        10  0.000000    True    
2023-07-01 00:11:00   8        11  1.414214    False    
2023-07-01 00:12:00   9        11  1.000000    False    

由于我要求至少3个连续满足的条件，因此预期输出将为:

                    x         y  distance    status  
timestamp                                                    
2023-07-01 00:00:00   1         5       NaN    moving    
2023-07-01 00:01:00   2         6  1.414214    moving    
2023-07-01 00:02:00   3         7  1.414214    moving    
2023-07-01 00:03:00   4         8  1.414214    moving    
2023-07-01 00:04:00   4         8  0.000000    moving   
2023-07-01 00:05:00   5         9  1.414214    moving   
2023-07-01 00:06:00   6         9  1.000000    moving   
2023-07-01 00:07:00   7        10  1.414214    moving   
2023-07-01 00:08:00   7        10  0.000000    waiting   
2023-07-01 00:09:00   7        10  0.000000    waiting   
2023-07-01 00:10:00   7        10  0.000000    waiting    
2023-07-01 00:11:00   8        11  1.414214    moving    
2023-07-01 00:12:00   9        11  1.000000    moving