[{'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:lx = np.arange(0,45000,1000)\nly = lx\npl.figure(figsize = (6,6))\npl.plot(maleall, femaleall, 'or', markersize = 10, alpha = 0.5, label = 'All')\npl.plot(malewhite, femalewhite, 'ob', markersize = 10, alpha = 0.5, label = 'White')\npl.plot(maleblack, femaleblack, 'og', markersize = 10, alpha = 0.5, label = 'Black')\npl.plot(maleasian, femaleasian, 'oy', markersize = 10, alpha = 0.5, label = 'Asian')\npl.plot(malehis, femalehis, 'oc', markersize = 10, alpha = 0.5, label = 'Hispanic')\npl.plot(lx,ly)\npl.xlabel('Male median income', fontsize = 16)\npl.ylabel('Female median income', fontsize = 16)\npl.xlim(0,45000)\npl.ylim(0,45000)\nplt.legend()"}, {'role': 'assistant', 'content': "['data_exploration', 'result_visualization']"}]