[{'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:test_ids = data_test['id']\nNrows_train = data_train.shape[0]  \n\n# Store country names\nlabels = data_train['country_destination'].values\ndata_train1 = data_train.drop(['country_destination'], axis=1)\n\n# Combining the test and train data. If this is not done, the number of dummy variable columns do not match in test and train data.\n# Some items present in train data and are not present in test data. For example, browser type. \ndata_all = pd.concat((data_train1, data_test), axis = 0, ignore_index = True)\n\n# Dropping ids which are saved separately and date of first booking which is completely absent in the test data\ndata_all = data_all.drop(['id','date_first_booking'], axis=1)\n"}, {'role': 'assistant', 'content': 'data_preprocessing'}]