[{'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:cr_counts = pd.DataFrame(df["mode"].value_counts())\ntop10 = df\ntmp2 = pd.DataFrame(top10.groupby([times.day,"mode"]).size(), columns = [\'count\'])\ntmp2.reset_index(inplace=True)\ntmp2 = tmp2.replace({25:"Monday",\n26:"Tuesday",\n27:"Wednesday",\n28:"Thursday",\n29:"Friday",\n30:"Saturday",\n31:"Sunday",})\ntmp2 = tmp2.pivot(index="level_0", columns = "mode", values = \'count\')\ntmp2["total"] = tmp2.sum(axis=1)\ntmp2 = tmp2.sort_values(by = "total", ascending = False)\ndel tmp2["total"]\ntmp2.plot(kind = \'barh\', stacked = True)\nplt.legend(loc=\'center left\', bbox_to_anchor=(1.0, 0.5))\nplt.ylabel("")\nplt.tight_layout()\nplt.show()'}, {'role': 'assistant', 'content': "['data_exploration']"}]