code: def flatten_excel(path='.', ext='xlsx', sheetname=0, skiprows=None, header=0, date_parser=parse_date, verbosity=0, output_ext=None):
      
  
      date_parser = date_parser or (lambda x: x)
      dotted_ext, dotted_output_ext = None, None
      if ext != None and output_ext != None:
          dotted_ext = ('' if ext.startswith('.') else '.') + ext
          dotted_output_ext = ('' if output_ext.startswith('.') else '.') + output_ext
      table = {}
      for file_properties in util.find_files(path, ext=ext or '', verbosity=verbosity):
          file_path = file_properties['path']
          if output_ext and (dotted_output_ext + '.') in file_path:
              continue
          df = dataframe_from_excel(file_path, sheetname=sheetname, header=header, skiprows=skiprows)
          df = flatten_dataframe(df, verbosity=verbosity)
          if dotted_ext != None and dotted_output_ext != None:
              df.to_csv(file_path[:-len(dotted_ext)] + dotted_output_ext + dotted_ext)
      return table
docs: Load all Excel files in the given path, write .flat.csv files, return `DataFrame` dict
  
      Arguments:
        path (str): file or folder to retrieve CSV files and `pandas.DataFrame`s from
        ext (str): file name extension (to filter files by)
        date_parser (function): if the MultiIndex can be interpretted as a datetime, this parser will be used
  
      Returns:
        dict of DataFrame: { file_path: flattened_data_frame }