[{'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:# skew_col = skewed_data[skewed_data > 10].index\n\n# # \n# fig = plt.figure(figsize=(10, 8))\n# for i in range(len(skew_col)):\n#     ax = fig.add_subplot(2, 3, i+1)\n#     try:\n#         sns.distplot(combined_df[skew_col[i]], fit=norm, ax=ax)\n#     except:\n#         # kdekde=False\n#         sns.distplot(combined_df[skew_col[i]], fit=norm, kde=False, ax=ax)\n# plt.show()\n\n# # \n# for i in range(len(skew_col)):\n#     combined_df[skew_col[i]] = np.log1p(combined_df[skew_col[i]])\n    \n#     # \n# # \n# fig = plt.figure(figsize=(10, 8))\n# for i in range(len(skew_col)):\n#     ax = fig.add_subplot(2, 3, i+1)\n#     try:\n#         sns.distplot(combined_df[skew_col[i]], fit=norm, ax=ax)\n#     except:\n#         # kdekde=False\n#         sns.distplot(combined_df[skew_col[i]], fit=norm, kde=False, ax=ax)\n# plt.show()'}, {'role': 'assistant', 'content': 'helper_functions'}]