docs: def diagnose(df,preview_rows = 2,
              display_max_cols = 0,display_width = None): 
      """ Prints information about the DataFrame pertinent to data cleaning.
  
      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.
      """
      assert type(df) is pd.DataFrame
      # Diagnose problems with the data formats that can be addressed in cleaning
      
       # Get initial display settings
      initial_max_cols = pd.get_option('display.max_columns')
      initial_max_rows = pd.get_option('display.max_rows')
      initial_width = pd.get_option('display.width')
  
      # Reformat displays
      pd.set_option('display.max_columns', display_max_cols)
      pd.set_option('display.max_rows',None)
      if display_width is not None:
              pd.set_option('display.width',display_width)
  
      # --------Values of data-----------
      df_preview = _io.preview(df,preview_rows)
      
      df_info = _io.get_info(df,verbose = True, max_cols = display_max_cols, 
                                 memory_usage = 'deep',null_counts = True)
      
      dtypes = stats.dtypes_summary(df).apply(_io.format_row,args = [_utils.rows(df)],axis = 1)
      
      potential_outliers = stats.df_outliers(df).dropna(axis = 1,how = 'all')
      potential_outliers = potential_outliers if _utils.rows(potential_outliers) \
                      else None
  
      # ----------Build lists------------
      
      title_list = \
      ['Preview','Info',
       'Data Types Summary','Potential Outliers']
      info_list = \
      [df_preview,df_info,
       dtypes,potential_outliers]
      error_list = [None,None,
                    None,'No potential outliers.']
      
      # ----------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------------
          # Potentially could change this to allow for output_safe to work with directories
      print(output)
  
      # Reset display settings
      pd.set_option('display.max_columns', initial_max_cols)
      pd.set_option('display.max_rows', initial_max_rows)
      pd.set_option('display.width', initial_width)