[{'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:### Group one-hot encoded variables of a category into one single variable\ncols = df_train.columns\nr,c = df_train.shape\n\n# Create a new dataframe with r rows, one column for each encoded category, and target in the end\nnew_data = pd.DataFrame(index= np.arange(0,r), columns=[\'Wilderness_Area\', \'Soil_Type\', \'Cover_Type\'])\n\n# Make an entry in data for each r for category_id, target_value\nfor i in range(0,r):\n    p = 0;\n    q = 0;\n    # Category1_range\n    for j in range(10,14):\n        if (df_train.iloc[i,j] == 1):\n            p = j-9 # category_class\n            break\n    # Category2_range\n    for k in range(14,54):\n        if (df_train.iloc[i,k] == 1):\n            q = k-13 # category_class\n            break\n            # Make an entry in data for each r\n    new_data.iloc[i] = [p,q,df_train.iloc[i, c-1]]\n    \n# plot for category1\nsns.countplot(x = \'Wilderness_Area\', hue = \'Cover_Type\', data = new_data)\nplt.show()\n\n# Plot for category2\nplt.rc("figure", figsize = (25,10))\nsns.countplot(x=\'Soil_Type\', hue = \'Cover_Type\', data= new_data)\nplt.show()\''}, {'role': 'assistant', 'content': 'result_visualization'}]