[{'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:for col in dfSummary.columns:\ndfSummary[col] = dfSummary[col].astype(str)\nscl = [[0.0, \'rgb(242,240,247)\'],[0.2, \'rgb(218,218,235)\'],[0.4, \'rgb(188,189,220)\'],\\\n[0.6, \'rgb(158,154,200)\'],[0.8, \'rgb(117,107,177)\'],[1.0, \'rgb(84,39,143)\']]\ndfSummary[\'text\'] = dfSummary[\'State\'] + \'<br>\' +\\\n\'Avg Loan Amount ($ USD): \'+dfSummary[\'Avg Loan Amount\']+\'<br>\'+\\\n\'Number of loans: \'+dfSummary[\'Accepted Loans\']+\'<br>\'+\\\n\'Most Likely Term: \'+dfSummary[\'Most Likely Term\']+\'<br>\'+\\\n\'Most Likely Grade: \'+dfSummary[\'Most Likely Grade\']+\'<br>\'+\\\n\'Avg EMP Length: \'+dfSummary[\'Avg EMP Length\']\ndata = [ dict(\ntype=\'choropleth\',\ncolorscale = scl,\nautocolorscale = True,\nlocations = dfSummary[\'State\'],\nz = dfSummary[\'Accepted Loans\'], #.astype(int),\nlocationmode = \'USA-states\',\ntext = dfSummary[\'text\'],\nmarker = dict(\nline = dict (\ncolor = \'rgb(255,255,255)\',\nwidth = 2\n) ),\ncolorbar = dict(\ntitle = "Number of loans")\n) ]\nlayout = dict(\ntitle = \'Total number of defaulted loans by state <br> (Hover over state for other metrics)\',\ngeo = dict(\nscope=\'usa\',\nprojection=dict( type=\'albers usa\' ),\nshowlakes = True,\nlakecolor = \'rgb(255, 255, 255)\'),\n)\nfig = dict( data=data, layout=layout )\niplot( fig, filename=\'d3-cloropleth-map\' )'}, {'role': 'assistant', 'content': 'data_exploration'}]