[{'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:coords = pluto[pluto['Address'].map(lambda a: ('BROADWAY' in str(a)) & ('EAST' not in str(a)) & ('WEST' not in str(a)))][['XCoord', 'YCoord', 'Borough']]\ncoords = coords[(coords['Borough'] == 'Manhattan') | (coords['Borough'] == 'Bronx')]\ncoords = coords.dropna()\nprojstr = '+proj=lcc +lat_1=40.66666666666666 +lat_2=41.03333333333333 +lat_0=40.16666666666666 +lon_0=-74 +x_0=300000 +y_0=0 +ellps=GRS80 +datum=NAD83 +to_meter=0.3048006096012192 +no_defs'\npnyc = Proj(projstr,\npreserve_units=True)\ncoords = [pnyc(x, y, inverse=True) for x, y in zip(coords['XCoord'], coords['YCoord'])]\nplt.scatter([c[0] for c in coords], [c[1] for c in coords])\nplt.figure(figsize=(24,10))\nplt.show()"}, {'role': 'assistant', 'content': 'data_exploration'}]