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

def append(self, key, value, format=None, append=True, columns=None,
               dropna=None, **kwargs):
        """
        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
        """
        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)