[{'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:%matplotlib inline\nimport matplotlib.pyplot as plt\nbp = TripsDF.boxplot(column='tip_amount', by='payment_type', grid=False, figsize=(8,4))\nfor i in [0,1,2]:\ny = TripsDF.tip_amount[TripsDF.payment_type==i].dropna()\nx = [i] * len(y)\nbp2 = TripsDF.boxplot(widths=0.5, sym='',column='tip_amount', by='fare_amount', figsize = (20,5), grid=False)\nfor i in [0,1,2]:\ny=TripsDF.tip_amount[TripsDF.fare_amount==i].dropna()\nx = [i] * len(y)\nplt.xticks(x,TripsDF.fare_amount)"}, {'role': 'assistant', 'content': "['helper_functions', 'data_exploration']"}]