[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:# Merge train and test, exclude overlap\ndates_overlap = [\'2020-03-19\',\'2020-03-20\',\'2020-03-21\',\'2020-03-22\',\'2020-03-23\', \'2020-03-24\', \'2020-03-25\', \n                 \'2020-03-26\', \'2020-03-27\', \'2020-03-28\', \'2020-03-29\', \'2020-03-30\', \'2020-03-31\']\ntrain2 = train.loc[~train[\'Date\'].isin(dates_overlap)]\nall_data = pd.concat([train2, test], axis = 0, sort=False)\n\n# Double check that there are no informed ConfirmedCases and Fatalities after 2020-03-11\nall_data.loc[all_data[\'Date\'] >= \'2020-03-19\', \'ConfirmedCases\'] = np.nan\nall_data.loc[all_data[\'Date\'] >= \'2020-03-19\', \'Fatalities\'] = np.nan\nall_data[\'Date\'] = pd.to_datetime(all_data[\'Date\'])\n\n# Create date columns\nle = preprocessing.LabelEncoder()\nall_data[\'Day_num\'] = le.fit_transform(all_data.Date)\nall_data[\'Day\'] = all_data[\'Date\'].dt.day\nall_data[\'Month\'] = all_data[\'Date\'].dt.month\nall_data[\'Year\'] = all_data[\'Date\'].dt.year\n\n# Fill null values given that we merged train-test datasets\nall_data[\'Province_State\'].fillna("None", inplace=True)\nall_data[\'ConfirmedCases\'].fillna(0, inplace=True)\nall_data[\'Fatalities\'].fillna(0, inplace=True)\nall_data[\'Id\'].fillna(-1, inplace=True)\nall_data[\'ForecastId\'].fillna(-1, inplace=True)\n\ndisplay(all_data)\ndisplay(all_data.loc[all_data[\'Date\'] == \'2020-03-19\'])\''}, {'role': 'assistant', 'content': "['data_preprocessing']"}]