[{'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:from mpl_toolkits.basemap import Basemap\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\n%matplotlib inline\ndef plot_city(winner):\nfig = plt.figure(figsize = (12,12))\nax = fig.add_axes([0.1,0.1,0.8,0.8])\nlllat = 49.8; urlat = 55.5; lllon = -7.7; urlon = 2.7\nm = Basemap(ax=ax, projection = 'stere', lon_0 = (urlon+lllon)/2,\nlat_0 = (urlat+lllat)/2, llcrnrlat = lllat, urcrnrlat = urlat,\nllcrnrlon = lllon, urcrnrlon = urlon, resolution = 'l')\nm.drawcoastlines()\nm.drawcountries()\nm.fillcontinents(color = 'coral')\nm.drawmapboundary()\nwinner_city = team_city['city'][team_city.team[team_city.team == winner].index.tolist()[0]]\nlon1, lat1 = cordlist[winner_city]\nx1,y1 = m(lat1, lon1)\nm.plot(x1, y1, 'bo', markersize=24)\nlabel1 = winner + '(' + winner_city + ')'\nplt.text(x1-50000, y1+30000, label1)\nred_patch = mpatches.Patch(color='blue', label='Winner on this Season')\nplt.legend(handles=[red_patch])\nplt.show()"}, {'role': 'assistant', 'content': "['helper_functions', 'data_exploration', 'result_visualization']"}]