[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify code snippets into one of the following categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']."}, {'role': 'user', 'content': 'Identify the single most appropriate category to which the provided code belongs. Provide your response as a single category label without any additional information, commentary, or personal opinions.\\nPython Code:rets = tech_rets.dropna()\ntext = list(rets.mean().index.values)\nfig = go.Figure()\nfig[\'data\'].append(go.Scatter(x = rets.mean(),\ny = rets.std(),\nmode = \'markers+text\',\nmarker = dict(color = \'orange\', size = 10, opacity = 0.7,\nline = dict(color = \'darkorange\', width = 1))))\nfig[\'layout\'].update(title = "Standard Deviation (Risk) vs. Expected Return",\nxaxis = dict(title = "Expected Return",\nrange = (0, 0.002)),\nyaxis = dict(title = "Standard Deviation (Risk)",\nrange = (0.005, 0.02)),\nannotations=[dict(showarrow=True,\narrowhead=2,\narrowsize = 2,\narrowwidth = 1,\nx = x,\ny = y,\ntext = stock,\nxref=\'x\',\nyref=\'y\',\nax=20,\nbordercolor=\'#c7c7c7\',\nborderwidth=2,\nborderpad=4,\nbgcolor=\'#ff7f0e\',\nopacity=0.8,\nay=-40) for x,y,stock in zip(rets.mean(), rets.std(), text)])\nplotly.offline.iplot(fig)'}, {'role': 'assistant', 'content': 'data_preprocessing'}]