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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 merge_and_average(self, platform, expression_column, group_by_column, force=False, merge_on_column=None, gsm_on=None, gpl_on=None):
 """Merge and average GSE samples. For given platform prepare the DataFrame with all the samples present in the GSE annotated with given column from platform and averaged over the column. Args: platform (:obj:`str` or :obj:`GEOparse.GPL`):
 GPL platform to use. expression_column (:obj:`str`):
 Column name in which "expressions" are represented group_by_column (:obj:`str`):
 The data will be grouped and averaged over this column and only this column will be kept force (:obj:`bool`):
 If the name of the GPL does not match the platform name in GSM proceed anyway merge_on_column (:obj:`str`):
 Column to merge the data on - should be present in both GSM and GPL gsm_on (:obj:`str`):
 In the case columns to merge are different in GSM and GPL use this column in GSM gpl_on (:obj:`str`):
 In the case columns to merge are different in GSM and GPL use this column in GPL Returns: :obj:`pandas.DataFrame`: Merged and averaged table of results. """

        if isinstance(platform, str):
            gpl = self.gpls[platform]
        elif isinstance(platform, GPL):
            gpl = platform
        else:
            raise ValueError("Platform has to be of type GPL or string with "
                             "key for platform in GSE")

        data = []
        for gsm in self.gsms.values():
            if gpl.name == gsm.metadata['platform_id'][0]:
                data.append(gsm.annotate_and_average(
                    gpl=gpl,
                    merge_on_column=merge_on_column,
                    expression_column=expression_column,
                    group_by_column=group_by_column,
                    force=force,
                    gpl_on=gpl_on,
                    gsm_on=gsm_on))
        if len(data) == 0:
            logger.warning("No samples for the platform were found\n")
            return None
        elif len(data) == 1:
            return data[0]
        else:
            return data[0].join(data[1:])