mark来源：时间序列缺失值填充

import pandas as pd

def fill_source(source, start_time, end_time):
  """
  采用窗口长度为5的移动均值对缺失值进行填充
  @param source_df:
  @param start_time: 开始时间戳，str格式
  @param end_time: 结束时间，str格式
  @return:
  """
  if source is None or len(source) <= 0:
      return source
  # 转换起止时间格式
  start_time = datetime.strptime(start_time, time_format_sample_time)
  end_time = datetime.strptime(end_time, time_format_sample_time)
  # 缺失值填充
  source[column_name_time] = pd.to_datetime(source[column_name_time])
  helper = pd.DataFrame({column_name_time: pd.date_range(start_time, end_time, freq="H")})
  source = pd.merge(source, helper, on=column_name_time, how="outer").sort_values(column_name_time)
  # 线性插值
  source[column_name_data] = source[column_name_data].interpolate(method="linear")
  # 填补未计算出来的缺失值（method可以参考官方文档）
  source[column_name_data].fillna(method="backfill", inplace=True)

  return source
        
source = pd.DataFrame()
time_list = ["2022-01-04 04:00:00", "2022-01-04 06:00:00", "2022-01-04 00:00:00", "2022-01-04 02:00:00"]
data_list = [4, 6, 0, 2]
source[column_name_time] = time_list
source[column_name_data] = data_list
source = fill_source(source, "2022-01-03 23:00:00", "2022-01-04 07:00:00")