[{'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:def get_header():\nheader = [\'YEAR\',\'PLAYER\',\'GP\']\nurl = "http://espn.go.com/mens-college-basketball/team/stats/_/id/153/north-carolina-tar-heels"\npage = requests.get(url).text\nsoup = BeautifulSoup(page, "html.parser")\ntable = soup.find_all(\'table\')[1]\ncols = table.find_all(\'tr\')[2]\ntds = cols.find_all(\'td\')\nheader += [c.text for c in tds[1:]]\nreturn header\ndef player_data_scrape(year):\nheader = get_header()\ndata = []\ndata.append(header)\nif year == 2016:\nurl = "http://espn.go.com/mens-college-basketball/team/stats/_/id/153/north-carolina-tar-heels"\nelse:\nurl = "http://espn.go.com/mens-college-basketball/team/stats/_/id/153/year/{}/north-carolina-tar-heels".format(year)\npage = requests.get(url).text\nsoup = BeautifulSoup(page, "html.parser")\ntable1 = soup.find_all(\'table\')[0]\ntable2 = soup.find_all(\'table\')[1]\ntrs = table1.find_all(\'tr\')[2:-1]\nfor tr in trs:\nrow = []\nplayer = tr.find_all(\'td\')[0].text.upper()\ngp = tr.find_all(\'td\')[1].text\nrow.append(int(year))\nrow.append(player)\nrow.append(int(gp))\ndata.append(row)\ntots = table2.find_all(\'tr\')[3:-1]\nfor i,tr in enumerate(tots):\ntot_row = [int(c.text) for c in tr.find_all(\'td\')[1:]]\ndata[i+1] += tot_row\nreturn data'}, {'role': 'assistant', 'content': "['helper_functions', 'load_data', 'data_preprocessing']"}]