original_docstring: 
      #input sequencer for repeated file_name_origin
       In case of split origin files, will be different output files
        with the same source file name.
        In this case, it is necessary to differentiate the name of the source
        files in the timestamp, through a numeric sequence.
        e.g.: filename_P01, filename_P02
      
docstring: input sequencer for repeated file_name_origin
  In case of split origin files, will be different output files
  with the same source file name.
  In this case, it is necessary to differentiate the name of the source
  files in the timestamp, through a numeric sequence.
docstring_tokens: input sequencer for repeated file_name_origin In case of split origin files will be different output files with the same source file name . In this case it is necessary to differentiate the name of the source files in the timestamp through a numeric sequence .
code: def create_df_description_without_folder(df):
  
      # create column time_stamp_str
      df['time_stamp'] = pd.to_timedelta(df['time_stamp'], unit='D')
      df['time_stamp_str'] = df['time_stamp'].apply(timedelta_to_string)
  
      # Of column file_name_origin remove extension
      df['file_name_origin'] = \
          df['file_name_origin'].apply(get_file_name_without_extension)
  
      """
      #input sequencer for repeated file_name_origin
       In case of split origin files, will be different output files
        with the same source file name.
        In this case, it is necessary to differentiate the name of the source
        files in the timestamp, through a numeric sequence.
        e.g.: filename_P01, filename_P02
      """
      df = sequencer_file_repeated(df, 'file_name_origin')
  
      # create dataframe description as df_output
      df_output = df.copy()
      df_output = df_output.loc[:, ['file_name_origin', 'file_output',
                                    'duration', 'head_file']]
      df_output = df_output.drop_duplicates(subset='file_output', keep='first')
      df_output['description'] = ''
      for index, row in df_output.iterrows():
          file_output = row['file_output']
          mask_file_output = df['file_output'].isin([file_output])
          df_video_details = df.loc[mask_file_output,
                                    ['file_name_origin_seq', 'time_stamp_str']]
          df_video_details = df_video_details.reset_index()
          description = ''
          for index2, row in df_video_details.iterrows():
              file_name_origin_seq = row['file_name_origin_seq']
  
              folder_name = row['time_stamp_str']
              if folder_name is None:
                  logging.error(f'None Folder name: {folder_name}')
                  folder_name = ''
              if index2 == 0:
                  description = folder_name + ' ' +\
                                file_name_origin_seq
              else:
                  description = description + '\n' + folder_name + \
                                ' ' + file_name_origin_seq
          df_output.loc[index, 'description'] = description
  
      df_output = df_output.reindex(['file_output', 'description'], axis=1)
  
      return df_output
code_tokens: def create_df_description_without_folder ( df ) : df [ 'time_stamp' ] = pd . to_timedelta ( df [ 'time_stamp' ] , unit = 'D' ) df [ 'time_stamp_str' ] = df [ 'time_stamp' ] . apply ( timedelta_to_string ) df [ 'file_name_origin' ] = df [ 'file_name_origin' ] . apply ( get_file_name_without_extension ) df = sequencer_file_repeated ( df , 'file_name_origin' ) df_output = df . copy ( ) df_output = df_output . loc [ : , [ 'file_name_origin' , 'file_output' , 'duration' , 'head_file' ] ] df_output = df_output . drop_duplicates ( subset = 'file_output' , keep = 'first' ) df_output [ 'description' ] = '' for index , row in df_output . iterrows ( ) : file_output = row [ 'file_output' ] mask_file_output = df [ 'file_output' ] . isin ( [ file_output ] ) df_video_details = df . loc [ mask_file_output , [ 'file_name_origin_seq' , 'time_stamp_str' ] ] df_video_details = df_video_details . reset_index ( ) description = '' for index2 , row in df_video_details . iterrows ( ) : file_name_origin_seq = row [ 'file_name_origin_seq' ] folder_name = row [ 'time_stamp_str' ] if folder_name is None : logging . error ( f'None Folder name: {folder_name}' ) folder_name = '' if index2 == 0 : description = folder_name + ' ' + file_name_origin_seq else : description = description + '\n' + folder_name + ' ' + file_name_origin_seq df_output . loc [ index , 'description' ] = description df_output = df_output . reindex ( [ 'file_output' , 'description' ] , axis = 1 ) return df_output
short_docstring: input sequencer for repeated file_name_origin
  In case of split origin files, will be different output files
  with the same source file name.
short_docstring_tokens: input sequencer for repeated file_name_origin In case of split origin files will be different output files with the same source file name .
comment: # create column time_stamp_str$SEP$# Of column file_name_origin remove extension$SEP$"""
      #input sequencer for repeated file_name_origin
       In case of split origin files, will be different output files
        with the same source file name.
        In this case, it is necessary to differentiate the name of the source
        files in the timestamp, through a numeric sequence.
        e.g.: filename_P01, filename_P02
      """$SEP$# create dataframe description as df_output