[{'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:company = \'Example Client\'\nlabels = [\'High Risk\', \'Suspect\', \'Non-Suspect\']\ntotal_counts = [df.highRisk.sum(), df.suspect.sum(), df.nonSuspect.sum()]\ntotal_counts = [1,2,3]\nlegend_labels = []\nfor i, l in enumerate(labels):\nlegend_labels.append("{}: {}".format(l, total_counts[i]))\ncolors = [\'red\', \'gold\', \'lightskyblue\']\nexplode = (0.2, 0.1, 0)  # explode 1st slice\nplt.pie(total_counts, explode=explode, labels=None, colors=colors,\nautopct=\'%1.1f%%\', shadow=True, startangle=100)\nplt.legend(legend_labels, title="Totals")\nplt.axis(\'equal\')\nfig = plt.gcf()\nfig.set_size_inches(8,8)\nplt.savefig(\'./{}.png\'.format(company))\nplt.show()'}, {'role': 'assistant', 'content': 'data_exploration'}]