我想我最好通过一个例子来说明我想要实现的目标。假设我有这个数据框：
time 0 2013-01-01 12:56:00 1 2013-01-01 12:00:12 2 2013-01-01 10:34:28 3 2013-01-01 09:34:54 4 2013-01-01 08:34:55 5 2013-01-01 16:35:19 6 2013-01-01 16:35:30
给定间隔T，我想为每一行计数在该间隔内“打开”了多少个寄存器。例如，考虑到T = 2hours，这将是输出：
time count 0 2013-01-01 12:56:00 1 # 12:56-2 = 10:56 -> 1 register between [10:56, 12:56) 1 2013-01-01 12:00:12 1 2 2013-01-01 10:34:28 2 # 10:34:28-2 = 8:34:28 -> 2 registers between [8:34:28, 10:34:28) 3 2013-01-01 09:34:54 1 4 2013-01-01 08:34:55 0 5 2013-01-01 16:35:19 0 6 2013-01-01 16:35:30 1
我不知道如何使用熊猫获得此结果。例如，如果我仅考虑dt.hour的前身，则对于T等于1，我可以每小时创建一个列数，然后将其移位1，然后将结果相加count[i]+ count[i-1]。但是我不知道是否可以将其推广到所需的输出。
count[i]+ count[i-1]
这里的想法是将所有寄存器打开时间标记为+1，并将所有寄存器关闭时间标记为-1。然后按时间排序，并在+/- 1值上执行累加总和，以在给定时间打开计数。
# initialize interval start times as 1, end times as -1 start_times= df.assign(time=df['time'] - pd.Timedelta(hours=2), count=1) all_times = start_times.append(df.assign(count=-1), ignore_index=True) # sort by time and perform a cumulative sum get the count of overlaps at a given time # (subtract 1 since you don't want to include the current value in the overlap) all_times = all_times.sort_values(by='time') all_times['count'] = all_times['count'].cumsum() - 1 # reassign to the original dataframe, keeping only the original times df['count'] = all_times['count']
结果输出：
time count 0 2013-01-01 12:56:00 1 1 2013-01-01 12:00:12 1 2 2013-01-01 10:34:28 2 3 2013-01-01 09:34:54 1 4 2013-01-01 08:34:55 0 5 2013-01-01 16:35:19 0 6 2013-01-01 16:35:30 1
