[{'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:@info\ndef timeseries_features(df):\n    # move to datetime format\n    df.Date = pd.to_datetime(df.Date)\n    df = df.sort_values('Date').reset_index(drop = True)\n    \n    # derive regular for ml task time series features\n    df['month']          = df.Date.dt.month\n    df['dayofmonth']     = df.Date.dt.day\n    df['dayofyear']      = df.Date.dt.dayofyear\n    df['year']           = df.Date.dt.year\n    df['is_weekday']     = df.DayOfWeek.apply(lambda x: 0 if x in (6,7) else 1)\n    df['is_month_start'] = df.Date.dt.is_month_start.astype(int)\n    df['is_month_end']   = df.Date.dt.is_month_end.astype(int)\n\n    # also lets take into account holidays\n    from pandas.tseries.holiday import USFederalHolidayCalendar as calendar\n\n    holidays = calendar().holidays(start = df.Date.min(), end = df.Date.max())\n    df['is_holiday'] = df.Date.isin(holidays).astype(int)\n    \n\n    return df\n\n@info\ndef clean_main(df):\n    # drop days with 0 sales\n    df = df.loc[df.Sales != 0,:].reset_index(drop = True)\n    df = df.drop(['Open'],axis = 1)\n    \n    # beacus unique values contain mixed dtype array(['a', '0', 'b', 'c', 0], dtype=object)\n    # also could be fixed during pandas importing\n    df.StateHoliday = df.StateHoliday.astype(str)\n    \n    return df\n\n@info\ndef clean_store(df):\n    # lets drop columns with high content of nan values\n    df.CompetitionDistance.fillna(df.CompetitionDistance.mean(),inplace = True)\n    df.drop(['CompetitionOpenSinceMonth','CompetitionOpenSinceYear','Promo2SinceWeek','Promo2SinceYear'],axis = 1,inplace = True)\n    \n    import calendar\n    \n    # We have a list of promo monthes and we can derive usefull feature\n    # presence or absence of promo\n    \n    # first create encoded dictionary Month:Number eg Feb:2\n    month_dict = {v: k for k,v in enumerate(calendar.month_abbr)}\n    del month_dict['']\n    del month_dict['Sep']\n    month_dict['NaN']   = 0 # assign absence of promo 0\n    month_dict['Sept']  = 9 # There is no Sep\n\n    # Secondly, we treat PromoInterval columns, making each row list instead of string  now we have smth like ['Feb','Mar','Sept']\n    # and lets apply dictionary\n    df.PromoInterval = df.PromoInterval.fillna('NaN')\n    df.PromoInterval = df.PromoInterval.str.split(',')\n    # Lastly we are applyin transformation\n    df.PromoInterval = df.PromoInterval.apply(lambda x: [month_dict[value] for value in x if month_dict.get(value)])\n    \n    # Lets create new feature that us equal to number of promo monthes\n    df['promo_len']  = df.PromoInterval.apply(lambda x: len(x))\n    return df\n\n\n# Pipeline for train file\ntrain_prep = (train\n             .copy()\n             .pipe(log_transf)\n             .pipe(remove_outliers)\n             .pipe(timeseries_features)\n             .pipe(clean_main)\n             )\n\n# Pipeline for store file\nstore_prep = (stores\n             .copy()\n             .pipe(clean_store)\n             )\n# Now we merge two\ndata_prep               = pd.merge(train_prep,store_prep,how='left',on='Store')\n\n# Using our transformation in PromoInterval interval, we create binary new feature is_promo or not\ndata_prep['is_promo']   = data_prep.apply(lambda x: 1 if x['month'] in x['PromoInterval'] else 0,axis = 1)\ndata_prep               = data_prep.drop('PromoInterval',axis=1).reset_index(drop=True)"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]