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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def put(self, key, value, format=None, append=False, **kwargs):
 """ Store object in HDFStore Parameters key : object value : {Series, DataFrame} format : 'fixed(f)|table(t)', default is 'fixed' fixed(f) : Fixed format Fast writing/reading. Not-appendable, nor searchable 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 False This will force Table format, append the input data to the existing. data_columns : list of columns to create as data columns, or True to use all columns. See `here <http://pandas.pydata.org/pandas-docs/stable/io.html#query-via-data-columns>`__ # noqa 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' """

        if format is None:
            format = get_option("io.hdf.default_format") or 'fixed'
        kwargs = self._validate_format(format, kwargs)
        self._write_to_group(key, value, append=append, **kwargs)