[{'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:f, (ax1, ax2, ax3) = plt.subplots(1, 3, sharey=True, figsize=(9, 3))\nsns.barplot(x='Term', y='LoanAmt', data=dfLoanData,\nestimator=lambda x: len(x) / (numberOfNonDefaults + numberOfDefaults) * 100,\nax=ax1, order=sorted(dfLoanData['Term'].unique()), palette='deep')\nsns.barplot(x='Term', y='LoanAmt', data=dfLoanData[dfLoanData['DefaultStatus'] == 'Good-Loan'],\nestimator=lambda x: len(x) / numberOfNonDefaults * 100,\nax=ax2, order=sorted(dfLoanData['Term'].unique()), palette='deep')\nsns.barplot(x='Term', y='LoanAmt', data=dfLoanData[dfLoanData['DefaultStatus'] == 'Defaulted-Loan'],\nestimator=lambda x: len(x) / numberOfDefaults * 100,\nax=ax3, order=sorted(dfLoanData['Term'].unique()), palette='deep')\nax1.set_title('Overall')\nax2.set_title('Timely Payments')\nax3.set_title('Defaulted Loans')\nax1.set_ylabel('Percentage')\nax2.set_ylabel('')\nax3.set_ylabel('')\nplt.tight_layout()\nplt.show()"}, {'role': 'assistant', 'content': 'data_exploration'}]