[{'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:b"def concat_data():\\n    df_train = pd.read_csv(\'../input/train.csv\')\\n    df_test = pd.read_csv(\'../input/test.csv\')\\n    df_extra = pd.read_csv(\'../input/store.csv\')\\n    df_test[\'Sales\'] = -1\\n    df_full = pd.concat([df_train, df_test]).reset_index(drop=True)\\n\\n    #Merge extra information about stores\\n    df_full = df_full.merge(df_extra, left_on=[\'Store\'], right_on=[\'Store\'], how=\'left\')\\n    \\n    df_full[\'Year\'] = pd.DatetimeIndex(df_full[\'Date\']).year\\n    df_full[\'Month\'] = pd.DatetimeIndex(df_full[\'Date\']).month\\n    df_full[\'Day\'] = pd.DatetimeIndex(df_full[\'Date\']).day\\n    df_full[\'WeekOfYear\'] = pd.DatetimeIndex(df_full[\'Date\']).weekofyear\\n    \\n    # Calculate competition open in months\\n    df_full[\'CompetitionOpen\'] = 12 * (df_full.Year - df_full.CompetitionOpenSinceYear) + \\\\\\n        (df_full.Month - df_full.CompetitionOpenSinceMonth)\\n\\n    # Calculate promo open time in months\\n    df_full[\'PromoOpen\'] = 12 * (df_full.Year - df_full.Promo2SinceYear) + \\\\\\n        (df_full.WeekOfYear - df_full.Promo2SinceWeek) / 4.0\\n    df_full[\'PromoOpen\'] = df_full.PromoOpen.apply(lambda x: x if x > 0 else 0)\\n    df_full.loc[df_full.Promo2SinceYear == 0, \'PromoOpen\'] = 0\\n\\n    # Transform month interval in a boolean column \\n    month2str = {1:\'Jan\', 2:\'Feb\', 3:\'Mar\', 4:\'Apr\', 5:\'May\', 6:\'Jun\',\\n                 7:\'Jul\', 8:\'Aug\', 9:\'Sept\', 10:\'Oct\', 11:\'Nov\', 12:\'Dec\'}\\n    df_full[\'monthStr\'] = df_full.Month.map(month2str)\\n    df_full.loc[df_full.PromoInterval == 0, \'PromoInterval\'] = \'\'\\n    df_full[\'IsPromoMonth\'] = 0\\n    for interval in df_full.PromoInterval.unique():\\n        interval = str(interval)\\n        if interval != \'\':\\n            for month in interval.split(\',\'):\\n                df_full.loc[(df_full.monthStr == month) & (df_full.PromoInterval == interval), \'IsPromoMonth\'] = 1\\n\\n\\n    return df_full\\n\\ndf_full = concat_data()"'}, {'role': 'assistant', 'content': "['load_data']"}]