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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 values_for_column(self, column_name, limit=10000):
 """Retrieve some values for the given column"""

        logging.info(
            'Getting values for columns [{}] limited to [{}]'
            .format(column_name, limit))
        # TODO: Use Lexicographic TopNMetricSpec once supported by PyDruid
        if self.fetch_values_from:
            from_dttm = utils.parse_human_datetime(self.fetch_values_from)
        else:
            from_dttm = datetime(1970, 1, 1)

        qry = dict(
            datasource=self.datasource_name,
            granularity='all',
            intervals=from_dttm.isoformat() + '/' + datetime.now().isoformat(),
            aggregations=dict(count=count('count')),
            dimension=column_name,
            metric='count',
            threshold=limit,
        )

        client = self.cluster.get_pydruid_client()
        client.topn(**qry)
        df = client.export_pandas()
        return [row[column_name] for row in df.to_records(index=False)]