new_contents: import pandas as pd
  import pymysql
  
  import os
  import json
  
  __location__ = os.path.join(os.getcwd(), os.path.dirname(os.path.realpath(__file__)))
  
  with open(os.path.join(__location__, "config.json"), 'r') as config:
      conf = json.load(config)
      HOST = conf['host']
      USER = conf['user']
      PASS = conf['pass']
      DATABASE = conf['database']
      CHARSET = conf['charset']
  
  connection = pymysql.connect(host=HOST, user=USER, password=PASS, db=DATABASE, charset=CHARSET, cursorclass=pymysql.cursors.DictCursor)
  
  sql = "SELECT location, usr_count_json FROM loc_week_count_reshape"
  data = pd.read_sql(sql, connection)
  data['usr_count_json'] = data['usr_count_json'].map(lambda row: json.dumps(list(pd.Series(json.loads(row)) / pd.Series([22] * 24 + [9] * 24))))
  
  with connection.cursor() as cursor:
      update_sql = "UPDATE loc_week_count_reshape SET usr_count_normalize=%s WHERE location=%s;" % ('"%s"', '"%s"')
  
      for row in data.iterrows():
          cursor.execute(update_sql % (row[1]['usr_count_json'], row[1]['location']))
  
  connection.commit()
  connection.close()
  
subject: normalize data by time length