[{'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:def get_Promo2Active(df):\n    months_map = {v:i+1 for i, v in enumerate(["Jan", "Feb", "Mar", "Apr", "May", "Jun", \n                                               "Jul", "Aug", "Sept", "Oct", "Nov", "Dec"])}\n    def is_Promo2_active(row):\n        if row.Promo2 == 0:\n            return 0\n        \n        current_week, current_month, current_year = row.Date.week, row.Date.month, row.Date.year\n        start_week, start_year = row.Promo2SinceWeek, row.Promo2SinceYear\n        active_months = set([months_map[m] for m in row.PromoInterval.split(",")])\n        has_started = (current_year == start_year and current_week >= start_week) or current_year > start_year\n        return int(has_started and current_month in active_months)\n                        \n    return df.apply(is_Promo2_active, axis=1)'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]