[{'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:# covert object type to float\ndef func(x):\n    x_new = 0\n    try:\n        x_new = float(x.replace(",", ""))\n    except:\n#         print(x)\n        x_new = np.nan\n    return x_new\ncols = [\n    \'Gross_domestic_product__constant_prices\', \n    \'Gross_domestic_product__current_prices\', \n    \'Gross_domestic_product__deflator\', \n    \'Gross_domestic_product_per_capita__constant_prices\', \n    \'Gross_domestic_product_per_capita__current_prices\', \n    \'Output_gap_in_percent_of_potential_GDP\', \n    \'Gross_domestic_product_based_on_purchasing_power_parity__PPP__valuation_of_country_GDP\', \n    \'Gross_domestic_product_based_on_purchasing_power_parity__PPP__per_capita_GDP\', \n    \'Gross_domestic_product_based_on_purchasing_power_parity__PPP__share_of_world_total\', \n    \'Implied_PPP_conversion_rate\', \'Total_investment\', \n    \'Gross_national_savings\', \'Inflation__average_consumer_prices\', \n    \'Inflation__end_of_period_consumer_prices\', \n    \'Six_month_London_interbank_offered_rate__LIBOR_\', \n    \'Volume_of_imports_of_goods_and_services\', \n    \'Volume_of_Imports_of_goods\', \n    \'Volume_of_exports_of_goods_and_services\', \n    \'Volume_of_exports_of_goods\', \'Unemployment_rate\', \'Employment\', \'Population\', \n    \'General_government_revenue\', \'General_government_total_expenditure\', \n    \'General_government_net_lending_borrowing\', \'General_government_structural_balance\', \n    \'General_government_primary_net_lending_borrowing\', \'General_government_net_debt\', \n    \'General_government_gross_debt\', \'Gross_domestic_product_corresponding_to_fiscal_year__current_prices\', \n    \'Current_account_balance\', \'pop\',\n    \'sex0\',\n\'sex14\',\n\'sex25\',\n\'sex54\',\n\'sex64\',\n\'sex65plus\',\n\'sexratio\',\n\'lung\',\n\'femalelung\',\n\'malelung\',\n\'gdp2019\',\n\'healthexp\',\n\'healthperpop\',\n\'fertility\',\n\'firstcase\',\n\'totalcases\',\n\'activecases\',\n\'newcases\',\n\'deaths\',\n\'newdeaths\',\n\'recovered\',\n\'critical\',\n\'casediv1m\',\n\'deathdiv1m\',\n]\nfor col in cols:\n    df_traintest8[col] = df_traintest8[col].apply(lambda x: func(x))  \nprint(df_traintest8[\'pop\'].dtype)\''}, {'role': 'assistant', 'content': "['data_preprocessing']"}]