[{'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:minmaxfeatures = None\nfor chunk in pd.read_csv('../input/bosch-production-line-performance/train_date.csv.zip',usecols=usefuldatefeatures,chunksize=50000,low_memory=False):\n    features = chunk.columns.values.tolist()\n    features.remove('Id')\n    df_mindate_chunk = chunk[['Id']].copy()\n    df_mindate_chunk['mindate'] = chunk[features].min(axis=1).values\n    df_mindate_chunk['maxdate'] = chunk[features].max(axis=1).values\n    df_mindate_chunk['min_time_station'] =  chunk[features].idxmin(axis = 1).apply(lambda s: int(s.split('_')[1][1:]) if s is not np.nan else -1)\n    df_mindate_chunk['max_time_station'] =  chunk[features].idxmax(axis = 1).apply(lambda s: int(s.split('_')[1][1:]) if s is not np.nan else -1)\n    minmaxfeatures = pd.concat([minmaxfeatures, df_mindate_chunk])\n\ndel chunk\ngc.collect()"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]