[{'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:df_test.fillna(1, inplace = True) #11rows with Nans decided to leave them open since its one store 622 which is \n#usually open\n#Left-join the train to the store dataset since .Why?\n#Because you want to make sure you have all events even if some of them don\'t have their store information ( which shouldn\'t happen)\ndf_test_store = pd.merge(df_test, df_store, how = \'left\', on = \'Store\')\nprint ("The Test_Store dataset has {} Rows and {} Variables".format(str(df_test_store.shape[0]),str(df_test_store.shape[1]))) \ndf_test_store[\'Month\']=df_test_store.Date.dt.month\ndf_test_store[\'Year\']=df_test_store.Date.dt.year\ndf_test_store[\'Day\']=df_test_store.Date.dt.day\n\ndf_test_store[\'StateHoliday\'] = df_test_store[\'StateHoliday\'].astype(\'category\')\ndf_test_store[\'Assortment\'] = df_test_store[\'Assortment\'].astype(\'category\')\ndf_test_store[\'StoreType\'] = df_test_store[\'StoreType\'].astype(\'category\')\ndf_test_store[\'PromoInterval\']= df_test_store[\'PromoInterval\'].astype(\'category\')\ndf_test_store[\'StateHoliday_cat\'] = df_test_store[\'StateHoliday\'].cat.codes\ndf_test_store[\'Assortment_cat\'] = df_test_store[\'Assortment\'].cat.codes\ndf_test_store[\'StoreType_cat\'] = df_test_store[\'StoreType\'].cat.codes\ndf_test_store[\'PromoInterval_cat\'] = df_test_store[\'PromoInterval\'].cat.codes\ndf_test_store[\'StateHoliday_cat\'] = df_test_store[\'StateHoliday_cat\'].astype(\'float\')\ndf_test_store[\'Assortment_cat\'] = df_test_store[\'Assortment_cat\'].astype(\'float\')\ndf_test_store[\'StoreType_cat\'] = df_test_store[\'StoreType_cat\'].astype(\'float\')\ndf_test_store[\'PromoInterval_cat\'] = df_test_store[\'PromoInterval_cat\'].astype(\'float\')\ndf_test_store[\'CompetitionOpenSince\'] = np.where((df_test_store[\'CompetitionOpenSinceMonth\']==0) & (df_test_store[\'CompetitionOpenSinceYear\']==0) , 0,(df_test_store.Month - df_test_store.CompetitionOpenSinceMonth) + \n                                       (12 * (df_test_store.Year - df_test_store.CompetitionOpenSinceYear)) )\n\n\n\ndf_test_store["is_holiday_state"] = df_test_store[\'StateHoliday\'].map({"0": 0, "a": 1, "b": 1, "c": 1})\n\n\n\n\ndf_test_store=pd.get_dummies(df_test_store, columns=["Assortment", "StoreType","PromoInterval"], prefix=["is_Assortment", "is_StoreType","is_PromoInteval"])\n\''}, {'role': 'assistant', 'content': 'data_preprocessing'}]