Python 处理数据间隔插入：CPU 使用率数据填充

您可以使用以下代码处理数据：

import datetime

data = [{'cpu_manage_usage': '11', 'cpu_usage': '4', 'cpu_dp_avg_usage': '1', 'coming_time': '2023-9-22, 18:21:51'},
        {'cpu_manage_usage': '11', 'cpu_usage': '4', 'cpu_dp_avg_usage': '1', 'coming_time': '2023-9-22, 18:22:51'},
        {'cpu_manage_usage': '11', 'cpu_usage': '4', 'cpu_dp_avg_usage': '1', 'coming_time': '2023-9-22, 18:23:51'},
        {'cpu_manage_usage': '11', 'cpu_usage': '4', 'cpu_dp_avg_usage': '1', 'coming_time': '2023-9-22, 18:27:51'},
        {'cpu_manage_usage': '11', 'cpu_usage': '4', 'cpu_dp_avg_usage': '1', 'coming_time': '2023-9-22, 18:28:51'}]

new_data = []
for i in range(len(data)-1):
    new_data.append(data[i])
    current_time = datetime.datetime.strptime(data[i]['coming_time'], "%Y-%m-%d, %H:%M:%S")
    next_time = datetime.datetime.strptime(data[i+1]['coming_time'], "%Y-%m-%d, %H:%M:%S")
    time_diff = (next_time - current_time).total_seconds()
    if time_diff > 120:
        diff_minutes = int(time_diff / 60) - 1
        for j in range(diff_minutes):
            new_time = current_time + datetime.timedelta(minutes=j+1)
            new_data.append({'cpu_manage_usage': '0', 'cpu_usage': '0', 'cpu_dp_avg_usage': '0', 'coming_time': new_time.strftime("%Y-%m-%d, %H:%M:%S")})

new_data.append(data[-1])

这样，new_data中的数据就是您所期望得到的结果。