[{'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 name_replace(s, r1, r2):\n    s = str(s).replace(r1,r2)\n    for r3 in r1.split(' '):\n        s = str(s).replace(r3,r2)\n    return s\n\ndef get_features(df):\n    df['section_min'] = df[['Pronoun-offset', 'A-offset', 'B-offset']].min(axis=1)\n    df['Pronoun-offset2'] = df['Pronoun-offset'] + df['Pronoun'].map(len)\n    df['A-offset2'] = df['A-offset'] + df['A_Noun'].map(len)\n    df['B-offset2'] = df['B-offset'] + df['B_Noun'].map(len) \n    df['A-dist_abs'] = (df['Pronoun-offset'] - df['A-offset']).abs()\n    df['B-dist_abs'] = (df['Pronoun-offset'] - df['B-offset']).abs()\n    df['A-dist'] = (df['Pronoun-offset'] - df['A-offset'])\n    df['B-dist'] = (df['Pronoun-offset'] - df['B-offset'])\n    df['section_max'] = df[['Pronoun-offset2', 'A-offset2', 'B-offset2']].max(axis=1)\n    df['A_max'] = (df['A-offset2'] == df['section_max']).astype(int)\n    df['A_min'] = (df['A-offset2'] == df['section_min']).astype(int)\n    df['B_max'] = (df['B-offset2'] == df['section_max']).astype(int)\n    df['B_min'] = (df['B-offset2'] == df['section_min']).astype(int)\n    df['wc'] = df.apply(lambda r: len(str(r['Text'][r['section_min']: r['section_max']]).split(' ')), axis=1)\n    #df['Text'] = df.apply(lambda r: r['Text'][: r['Pronoun-offset']] + 'pronountarget' + r['Text'][r['Pronoun-offset'] + len(str(r['Pronoun'])): ], axis=1)\n    df['Text'] = df.apply(lambda r: name_replace(r['Text'], r['A_Noun'], 'subjectone'), axis=1)\n    df['Text'] = df.apply(lambda r: name_replace(r['Text'], r['B_Noun'], 'subjecttwo'), axis=1)\n    return(df)\n\ntrain = get_features(train)\ntest = get_features(test)"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]