[{'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:merge[\'num_description_len\'] = merge[\'description\'].str.len()<br>merge[\'num_description_words\'] = merge[\'description\'].apply(lambda x:len(x.split(\' \')))<br>merge[\'price_per_bedrooms\'] = merge[\'price\']/merge[\'bedrooms\']<br>merge[\'price_per_bathrooms\'] = merge[\'price\']/merge[\'bathrooms\']<br>merge[\'price_per_rooms\'] = merge[\'price\']/merge[\'rooms\']<br>merge[\'beds_percent\'] = merge[\'bedrooms\']/merge[\'rooms\']<br>merge[\'num_capital_letters\'] = merge[\'description\'].apply(lambda x: sum(1 for c in x if c.isupper()))<br>merge[\'num_address_len\'] = merge[\'display_address\'].str.len()<br>merge[\'num_address_words\'] = merge[\'display_address\'].apply(lambda x:len(x.split(\' \')))<br>merge[\'address_east\'] = merge[\'street_address\'].apply(lambda x: x.find(\'East\') > -1).astype(int)<br>merge[\'address_west\'] = merge[\'street_address\'].apply(lambda x: x.find(\'West\') > -1).astype(int)<br>merge[\'num_photos\'] = merge[\'photos\'].str.len()<br>merge[\'num_features\'] = merge[\'features\'].str.len()<br>merge[\'num_photos_low\'] = merge[\'num_photos\'].apply(lambda x:1 if x > 22 else 0)  # all is low<br>merge[\'price_low_medium\'] = merge[\'price\'].apply(lambda x:1 if 7500< x < 10000 else 0)  # all is low or medium<br>merge[\'price_low\'] = merge[\'price\'].apply(lambda x:1 if x >= 10000 else 0)  # all is low<br>def cap_share(x):<br>    return sum(1 for c in x if c.isupper())/float(len(x) + 1)<br>merge[\'num_cap_share\'] = merge[\'description\'].apply(cap_share)<br>merge[\'num_description_lines\'] = merge[\'description\'].apply(lambda x: x.count(\'<br /><br />\'))<br>merge[\'num_redacted\'] = 0<br>merge[\'num_redacted\'].ix[merge[\'description\'].str.contains(\'website_redacted\')] = 1<br>merge[\'num_email\'] = 0<br>merge[\'num_email\'].ix[merge[\'description\'].str.contains(\'@\')] = 1<br><br>reg = re.compile(".*?(\\\\(?\\\\d{3}\\\\D{0,3}\\\\d{3}\\\\D{0,3}\\\\d{4}).*?", re.S)<br>def try_and_find_nr(description):<br>    if reg.match(description) is None:<br>        return 0<br>    return 1<br>merge[\'num_phone_nr\'] = merge[\'description\'].apply(try_and_find_nr)<br><br><br><br>features_to_use = np.concatenate([features_to_use, [\'num_description_len\', \'num_description_words\',<br>                                               \'price_per_bedrooms\', \'price_per_bathrooms\', \'price_per_rooms\', \'num_photos\', \'num_features\',<br>                                               \'num_photos_low\', \'price_low_medium\', \'price_low\',<br>                                                   \'beds_percent\', \'num_capital_letters\', \'num_address_len\',<br>                                                    \'num_address_words\', \'address_east\', \'address_west\',<br>                                                   \'num_cap_share\', \'num_description_lines\', <br>                                                    \'num_redacted\', \'num_email\', \'num_phone_nr\']])<br>features_to_use = np.unique(features_to_use)<br>features_to_use'}, {'role': 'assistant', 'content': 'data_preprocessing'}]