code: def _write_core_tables(options, module, core_results):
      
  
      table_name = 'core_result.csv'
      single_file_path = os.path.join(options['run_dir'], table_name)
  
      if module == 'emp':  # List of tuples
          for i, core_result in enumerate(core_results):
              file_path = _get_file_path(i, options, table_name)
              core_result[1].to_csv(file_path, index=False, float_format='%.4f')
  
      elif type(core_results) == type(pd.DataFrame()):  # DataFrame
          core_results.to_csv(single_file_path, index=False, float_format='%.4f')
  
      else:  # Array or single value (atleast_1d corrects for unsized array)
          df = pd.DataFrame({'y': np.atleast_1d(core_results)})
          df.to_csv(single_file_path, index=False, float_format='%.4f')
docs: Notes
      -----
      Depending on function that was called for analysis, core_results may be a
      list of tuples (empirical), a dataframe, an array, or a single value.
  
      For the list of tuples from empirical, the second element of each tuple is
      the raw result, and we write them all with the appropriate prefix. For
      dataframes, we write them. For arrays or single values, we convert to data
      frames and write them.