triggerZipFiles = glob.glob('*.zip')
  for triggerFiles in triggerZipFiles:
      with zipfile.ZipFile(triggerFiles, 'r') as myzip:
          for logfile in myzip.namelist():
              datacc = []
              zipcc = []
              csvout = '{}_US.csv'.format(logfile[:-4])
              f = myzip.open(logfile)
              contents = f.readlines()
              for line in contents:
                  try:
                      parsed = json.loads(line[:-2])
                      if "CC" in parsed['data']['weatherType'] and "US" in parsed['zipcodes']:
                          datacc.append(parsed['data'])
                          zipcc.append(parsed['zipcodes'])
                  except:
                      pass
              if len(datacc) > 0:
                  df = pd.concat([pd.DataFrame(zipcc), pd.DataFrame(datacc)], axis=1)
                  df = pd.concat((pd.Series((v, row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key'], row['key'], row['key'],
                    row['key'], row['key'], row['key'], row['key']), df.columns) for _,
                      row in df.iterrows() for v in row['US']), axis=1).T
                  df.to_csv(csvout, header=None, index=False)
              else:
                  pass
              print datetime.now().strftime('%Y/%m/%d %H:%M:%S') + ": Finished: {}".format(logfile)