docstr: return data that meets the minimum requirements of tsdata.
docstr_tok: ["return","data","that","meets","the","minimum","requirements","of","tsdata","."]
code: def preprocess_network_traffic(self):
      """
      return data that meets the minimum requirements of tsdata.
      """
      _is_first_columns = True
      pattern = r"%Start.*?\((.*?)\)\n%%End.*?\((.*?)\)\n%Avg.*?\s(\d+\.\w+).*?\n%total:\s(\d+)"
      columns_name = ["StartTime", "EndTime", "AvgRate", "total"]
  
      if not os.path.exists(self.final_file_path):
          import re
  
          for val in DATASET_NAME[self.name]:
              with open(os.path.join(self.dir_path, val), "r") as f:
                  content = f.read()
                  result = re.findall(pattern, content, re.DOTALL)
              net_temp_df = pd.DataFrame(
                  columns=columns_name, data=result
              )
              net_temp_df.to_csv(
                  self.final_file_path,
                  mode="a",
                  header=_is_first_columns,
                  index=False,
                  chunksize=1024,
              )
              _is_first_columns = False
  
      net_raw_df = pd.read_csv(self.final_file_path)
      net_raw_df.AvgRate.str[-4:].unique()
      self.df = pd.DataFrame(pd.to_datetime(net_raw_df.StartTime))
      self.df["AvgRate"] = net_raw_df.AvgRate.apply(
          lambda x: float(x[:-4])
          if x.endswith("Mbps")
          else float(x[:-4]) * 1000
      )
      self.df["total"] = net_raw_df["total"]
      return self
  
file_path: analytics-zoo/pyzoo/zoo/chronos/data/utils/public_dataset.py
imports: import os
  import requests
  import tqdm
  import pandas
  from zoo.chronos.data.tsdataset import TSDataset
label_desc: Use image node_id 3 for calling the PublicDataset obj's underlying member method code with example usage: obj.preprocess_network_traffic() and returns: self