original_docstring: 
          Retrieves the mongodb from the DB, converting them into pandas
          DataFrames within a symbol dictionary.
  
          For this handler it will be assumed that the database structure
          is designed as Data Directory.
  
          :return:
          
docstring: Retrieves the mongodb from the DB, converting them into pandas
  DataFrames within a symbol dictionary.
  
  For this handler it will be assumed that the database structure
  is designed as Data Directory.
docstring_tokens: Retrieves the mongodb from the DB converting them into pandas DataFrames within a symbol dictionary . For this handler it will be assumed that the database structure is designed as Data Directory .
code: def _retrieve_mongodb_data(self):
          """
          Retrieves the mongodb from the DB, converting them into pandas
          DataFrames within a symbol dictionary.
  
          For this handler it will be assumed that the database structure
          is designed as Data Directory.
  
          :return:
          """
          comb_index = None
          for s in self.symbol_list:
              # Load the data from symbol database for specific time period,
              # indexed on datetime
              self.symbol_data[s] = df_fromMongoDB(self.dbname, s)
  
              # Combine the index to pad forward values
              if comb_index is None:
                  comb_index = self.symbol_data[s].index
              else:
                  comb_index.union(self.symbol_data[s].index)
  
              # Set the latest symbol_data to None
              self.latest_symbol_data[s] = []
  
          # Reindex the dataframes
          for s in self.symbol_list:
              self.symbol_data[s] = self.symbol_data[s].\
                  reindex(index=comb_index, method='pad').iterrows()
code_tokens: def _retrieve_mongodb_data ( self ) : comb_index = None for s in self . symbol_list : self . symbol_data [ s ] = df_fromMongoDB ( self . dbname , s ) if comb_index is None : comb_index = self . symbol_data [ s ] . index else : comb_index . union ( self . symbol_data [ s ] . index ) self . latest_symbol_data [ s ] = [ ] for s in self . symbol_list : self . symbol_data [ s ] = self . symbol_data [ s ] . reindex ( index = comb_index , method = 'pad' ) . iterrows ( )
short_docstring: Retrieves the mongodb from the DB, converting them into pandas
  DataFrames within a symbol dictionary.
short_docstring_tokens: Retrieves the mongodb from the DB converting them into pandas DataFrames within a symbol dictionary .
comment: """
          Retrieves the mongodb from the DB, converting them into pandas
          DataFrames within a symbol dictionary.
  
          For this handler it will be assumed that the database structure
          is designed as Data Directory.
  
          :return:
          """$SEP$# Load the data from symbol database for specific time period,$SEP$# indexed on datetime$SEP$# Combine the index to pad forward values$SEP$# Set the latest symbol_data to None$SEP$# Reindex the dataframes