code: def append(self, key, value, format=None, append=True, columns=None,
                 dropna=None, **kwargs):
          
          if columns is not None:
              raise TypeError("columns is not a supported keyword in append, "
                              "try data_columns")
  
          if dropna is None:
              dropna = get_option("io.hdf.dropna_table")
          if format is None:
              format = get_option("io.hdf.default_format") or 'table'
          kwargs = self._validate_format(format, kwargs)
          self._write_to_group(key, value, append=append, dropna=dropna,
                               **kwargs)
docs: Append to Table in file. Node must already exist and be Table
          format.
  
          Parameters
          ----------
          key : object
          value : {Series, DataFrame}
          format : 'table' is the default
              table(t) : table format
                         Write as a PyTables Table structure which may perform
                         worse but allow more flexible operations like searching
                         / selecting subsets of the data
          append       : boolean, default True, append the input data to the
              existing
          data_columns :  list of columns, or True, default None
              List of columns to create as indexed data columns for on-disk
              queries, or True to use all columns. By default only the axes
              of the object are indexed. See `here
              <http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns>`__.
          min_itemsize : dict of columns that specify minimum string sizes
          nan_rep      : string to use as string nan represenation
          chunksize    : size to chunk the writing
          expectedrows : expected TOTAL row size of this table
          encoding     : default None, provide an encoding for strings
          dropna       : boolean, default False, do not write an ALL nan row to
              the store settable by the option 'io.hdf.dropna_table'
  
          Notes
          -----
          Does *not* check if data being appended overlaps with existing
          data in the table, so be careful