[{'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:from mpl_toolkits.basemap import Basemap\nimport matplotlib.lines as mlines\ndata_20160725 = df#[df.date == "20160725"] # Filter on a specific date\nfor t in np.unique(data_20160725.datefull):\nfig = plt.figure(figsize=(15,10))\ndata = data_20160725[data_20160725.datefull == t]\ndata = data.groupby(["lat", "lon", "tap"]).sum()["count"]\nvals = data.index.get_values()\nx = [x for x,y,z in vals]\ny = [y for x,y,z in vals]\ntaps = [z for x,y,z in vals]\nnew_data = pd.DataFrame(columns = ["x", "y", "taps", "count"])\nnew_data["x"] = x\nnew_data["y"] = y\nnew_data["taps"] = taps\nnew_data["count"] = data.values\non_data = new_data[new_data.taps == "on"]\noff_data = new_data[new_data.taps == "off"]\nm = Basemap(projection=\'merc\',\nresolution = \'i\' ,llcrnrlon=150.614385, llcrnrlat=-33.949563,\nurcrnrlon=151.324376,urcrnrlat=-33.653431,)\nm.drawcoastlines()\nm.drawcountries()\nm.drawmapboundary(fill_color=\'white\')\nxs,ys = m(list(on_data["y"]), list(on_data["x"]))\ncrimes = on_data["count"]\nmin_marker_size = 10\nfor lon, lat, mag in zip(list(on_data["y"]), list(on_data["x"]), crimes):\nx,y = m(lon, lat)\nmsize = mag/200 * min_marker_size\nm.plot(x, y, \'ro\',markersize=msize)\nfor lon, lat, mag in zip(list(off_data["y"]), list(off_data["x"]), off_data["count"]):\nx,y = m(lon, lat)\nmsize = mag/200 * min_marker_size\nm.plot(x, y,\'bo\', markersize=msize)\nname = pd.to_datetime(str(t)).strftime(\'%Y_%m_%d_%H_%M\')\nplt.annotate(\'%s\'%name,xy=(0.05,0.05),xytext=(0.05,0.05),xycoords=\'axes fraction\',fontsize=12)\nred_patch = mlines.Line2D([],[],color=\'red\', marker = \'o\', label=\'Tap On\')\nblue_patch = mlines.Line2D([],[],color=\'blue\', marker = \'o\', label=\'Tap Off\')\nplt.legend(handles=[red_patch, blue_patch], loc = "upper left")\nstations = ["Penrith Station", "Blacktown Station", "Parramatta Station", "Strathfield Station",\n"Central Station", "Town Hall Station", "Wynyard Station", "North Sydney Station", "Chatswood Station",\n"Circular Quay Station"]\nfor i in stations:\nx,y = m(suburbs[suburbs["loc"] == i]["lon"].values, suburbs[suburbs["loc"] == i]["lat"].values)\nplt.text(x,y, i)\nfig.savefig("image/"+name+".png")\nplt.close(fig)'}, {'role': 'assistant', 'content': 'data_preprocessing'}]