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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 summarize(df,preview_rows = 8, display_max_cols = None,display_width = None, output_path = None, output_safe = True,to_folder = False):
 """ Prints information about the DataFrame to a file or to the prompt. Parameters df - DataFrame The DataFrame to summarize preview_rows - int, default 5 Amount of rows to preview from the head and tail of the DataFrame display_max_cols - int, default None Maximum amount of columns to display. If set to None, all columns will be displayed. If set to 0, only as many as fit in the screen's width will be displayed display_width - int, default None Width of output. Can be width of file or width of console for printing. Set to None for pandas to detect it from console. output_path - path-like, default None If not None, this will be used as the path of the output file, and this function will print to a file instead of to the prompt output_safe - boolean, default True If True and output_file is not None, this function will not overwrite any existing files. output_csv: boolean, default False If True, will output to a directory with name of output_path with all data in csv format. WARNING: If set to true, this function will overwrite existing files in the directory with the following names: ['Preview.csv','Describe.csv','Info.csv','Percentile Details.csv', 'Missing Values Summary.csv','Potential Outliers.csv','Correlation Matrix.csv'] """

    assert type(df) is pd.DataFrame
    
    # Reformat displays
    initial_settings = pd_settings(display_max_cols, None, display_width)

    # --------Values of data-----------
    df_preview = _io.preview(df,preview_rows)
    df_desc_num, df_desc_cat = detailed_desc(df)
    percent_values = stats.percentiles(df)
    potential_outliers = stats.df_outliers(df).dropna(axis = 1,how = 'all')
    potential_outliers = potential_outliers if _utils.rows(potential_outliers) else None
    corr_values = regstats.corr_matrix(df)

    # ----------Build lists------------
    
    title_list = \
    ['Preview','Describe (Numerical)','Describe (Categorical)','Percentile Details',
     'Potential Outliers','Correlation Matrix']
    info_list = \
    [df_preview,df_desc_num, df_desc_cat,percent_values,
     potential_outliers,corr_values]
    error_list = [None,'No numerical data.','All numerical data.','No numerical data.',
                  'No potential outliers.','No categorical, bool, or numerical data.']
    
    # ----------Build output------------
    
    output = ''
    for title, value,error_text in zip(title_list,info_list,error_list):
        if value is None:
            value = "{} skipped: {}".format(title,error_text)
        if str(value).endswith('\n'):
            value = value[:-1]
        output+='{}\n{}\n\n'.format(_io.title_line(title),value)

    # ----------Send to file/print to console------------
    if output_path is None: 
        # Potentially could change this to allow for output_safe to work with directories
         print(output)
    else:
        if not to_folder:
            print('Outputting to file...')
            _io.output_to_file(output,output_path,output_safe)
        else:
            print('Outputting to folder...')
            if not os.path.exists(output_path):
                os.mkdir(output_path)
            for title, value,error_text in zip(title_list,info_list,error_list):
                if value is None:
                    print("{} skipped: {}".format(title,error_text))
                else:
                    file_dir = os.path.join(output_path,"{}.csv".format(title))
                    if type(value) is pd.DataFrame:
                        # Eventually add a check to see if file exists
                        value.to_csv(file_dir)
                    else:
                        _io.output_to_file(value,file_dir,False) 
                        # Change to output_safe when directory output_safe is implemented
        print('Done!')

    # Reset display settings
    pd_settings(*initial_settings)