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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def preprocess_data(self, data, hdh_cpoint=65, cdh_cpoint=65, col_hdh_cdh=None, col_degree=None, degree=None, standardize=False, normalize=False, year=False, month=False, week=False, tod=False, dow=False, save_file=True):
 """ Preprocesses dataframe according to user specifications and stores result in self.preprocessed_data. Parameters data : pd.DataFrame() Dataframe to be preprocessed. hdh_cpoint : int Heating degree hours. Defaults to 65. cdh_cpoint : int Cooling degree hours. Defaults to 65. col_hdh_cdh : str Column name which contains the outdoor air temperature. col_degree : list(str) Column to exponentiate. degree : list(str) Exponentiation degree. standardize : bool Standardize data. normalize : bool Normalize data. year : bool Year. month : bool Month. week : bool Week. tod : bool Time of Day. dow : bool Day of Week. save_file : bool Specifies whether to save file or not. Defaults to True. Returns ------- pd.DataFrame() Dataframe containing preprocessed data. """


        # Check to ensure data is a pandas dataframe
        if not isinstance(data, pd.DataFrame):
            raise SystemError('data has to be a pandas dataframe.')
        
        # Create instance
        preprocess_data_obj = Preprocess_Data(data)
        if col_hdh_cdh:
            preprocess_data_obj.add_degree_days(col=col_hdh_cdh, hdh_cpoint=hdh_cpoint, cdh_cpoint=cdh_cpoint)
        preprocess_data_obj.add_col_features(col=col_degree, degree=degree)

        if standardize:
            preprocess_data_obj.standardize()
        if normalize:
            preprocess_data_obj.normalize()

        preprocess_data_obj.add_time_features(year=year, month=month, week=week, tod=tod, dow=dow)
        
        # Store preprocessed data in wrapper class
        self.preprocessed_data = preprocess_data_obj.preprocessed_data

        # Logging
        self.result['Preprocess'] = {
            'HDH CPoint': hdh_cpoint,
            'CDH CPoint': cdh_cpoint,
            'HDH CDH Calc Col': col_hdh_cdh,
            'Col Degree': col_degree,
            'Degree': degree,
            'Standardize': standardize,
            'Normalize': normalize,
            'Year': year,
            'Month': month,
            'Week': week,
            'Time of Day': tod,
            'Day of Week': dow,
            'Save File': save_file
        }

        if save_file:
            f = self.results_folder_name + '/preprocessed_data-' + str(self.get_global_count()) + '.csv'
            self.preprocessed_data.to_csv(f)
            self.result['Preprocess']['Saved File'] = f
        else:
            self.result['Preprocess']['Saved File'] = ''

        return self.preprocessed_data