[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:import requests\nfrom bs4 import BeautifulSoup\n\nreq = requests.get(\'https://www.worldometers.info/coronavirus/\')\nsoup = BeautifulSoup(req.text, "lxml")\n\ndf_country = soup.find(\'div\',attrs={"id" : "nav-tabContent"}).find(\'table\',attrs={"id" : "main_table_countries_today"}).find_all(\'tr\')\narrCountry = []\nfor i in range(1,len(df_country)-1):\n    tmp = df_country[i].find_all(\'td\')\n    if (tmp[0].string.find(\'<a\') == -1):\n        country = [tmp[0].string]\n    else:\n        country = [tmp[0].a.string] # Country\n    for j in range(1,7):\n        if (str(tmp[j].string) == \'None\' or str(tmp[j].string) == \' \'):\n            country = country + [0]\n        else:\n            country = country + [float(tmp[j].string.replace(\',\',\'\').replace(\'+\',\'\'))]\n    arrCountry.append(country)\ndf_worldinfor = pd.DataFrame(arrCountry)\ndf_worldinfor.columns = [\'Country\',\'Total Cases\',\'Cases\',\'Total Deaths\',\'Deaths\',\'Total Recovers\',\'Active Case\']\nfor i in range(0,len(df_worldinfor)):\n    df_worldinfor[\'Country\'].iloc[i] = df_worldinfor[\'Country\'].iloc[i].strip()\''}, {'role': 'assistant', 'content': 'helper_functions'}]