[{'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:intoxdf = wholedf[(wholedf[\'Call Type\'] == "Domestic Disturbance")]\nhourdf = intoxdf[[\'timeonly\']].groupby(intoxdf[\'Date\'].dt.hour, as_index=False).count()\nhourdf[\'Hour\'] = ["12AM","1AM","2AM","3AM","4AM","5AM","6AM","7AM","8AM","9AM","10AM","11AM","NOON","1PM","2PM","3PM","4PM","5PM","6PM","7PM","8PM","9PM","10PM","11PM"]\nhourdf.columns = [\'Count\',\'Hour\']\nhourdf\nsns.barplot(x=\'Hour\', y="Count", data=hourdf, palette="PuBuGn_d")\nplt.title("Domestic Disturbance - Count By Hour")\nplt.xlabel("Hour")\nplt.ylabel("Count")\nplt.xticks(rotation=90)\nplt.show()'}, {'role': 'assistant', 'content': "['data_exploration']"}]