[{'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:train = train.drop('QuoteNumber', axis=1) \ntest = test.drop('QuoteNumber', axis=1)\n\n# Lets play with some dates\ntrain['Date'] = pd.to_datetime(pd.Series(train['Original_Quote_Date']))\ntrain = train.drop('Original_Quote_Date', axis=1)\n\ntest['Date'] = pd.to_datetime(pd.Series(test['Original_Quote_Date']))\ntest = test.drop('Original_Quote_Date', axis=1)\n\ntrain['Year'] = train['Date'].apply(lambda x: int(str(x)[:4]))\ntrain['Month'] = train['Date'].apply(lambda x: int(str(x)[5:7]))\ntrain['weekday'] = train['Date'].dt.dayofweek\n\ntest['Year'] = test['Date'].apply(lambda x: int(str(x)[:4]))\ntest['Month'] = test['Date'].apply(lambda x: int(str(x)[5:7]))\ntest['weekday'] = test['Date'].dt.dayofweek\n\ntrain = train.drop('Date', axis=1)  \ntest = test.drop('Date', axis=1)"}, {'role': 'assistant', 'content': 'data_preprocessing'}]