def append(self, key, value, format=None, append=True, columns=None,<EOL>dropna=None, **kwargs):
if columns is not None:<EOL><INDENT>raise TypeError("<STR_LIT>"<EOL>"<STR_LIT>")<EOL><DEDENT>if dropna is None:<EOL><INDENT>dropna = get_option("<STR_LIT>")<EOL><DEDENT>if format is None:<EOL><INDENT>format = get_option("<STR_LIT>") or '<STR_LIT>'<EOL><DEDENT>kwargs = self._validate_format(format, kwargs)<EOL>self._write_to_group(key, value, append=append, dropna=dropna,<EOL>**kwargs)<EOL>
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