[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:# P1\n# PP\ndistinct_cities = train.loc[:, "City"].unique()\n\n# Pcity\nmeans = []\nfor i in range(len(num_list)):\n    temp = []\n    for city in distinct_cities:\n        temp.append(train.loc[train.City == city, num_list[i]].mean())  \n    means.append(temp)\n    \ncity_pvars = pd.DataFrame(columns=["city_var", "means"])\nfor i in range(37):\n    for j in range(len(distinct_cities)):\n        city_pvars.loc[i+37*j] = ["P"+str(i+1), means[i][j]]\n\nprint(city_pvars)            \n# \nplt.rcParams[\'figure.figsize\'] = (18.0, 6.0)\nsns.boxplot(x="city_var", y="means", data=city_pvars)\n\n# From this we observe that P1, P2, P11, P19, P20, P23, and P30 are approximately a good\n# proxy for geographical location.'}, {'role': 'assistant', 'content': "['helper_functions', 'comment_only']"}]