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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 get_timeseries(self, child_agg_count=0, dataframe=False):
 """ Get time series data for the specified fields and period of analysis :param child_agg_count: the child aggregation count to be used default = 0 :param dataframe: if dataframe=True, return a pandas.DataFrame object :returns: dictionary containing "date", "value" and "unixtime" keys with lists as values containing data from each bucket in the aggregation """


        res = self.fetch_aggregation_results()

        ts = {"date": [], "value": [], "unixtime": []}

        if 'buckets' not in res['aggregations'][str(self.parent_agg_counter - 1)]:
            raise RuntimeError("Aggregation results have no buckets in time series results.")

        for bucket in res['aggregations'][str(self.parent_agg_counter - 1)]['buckets']:
            ts['date'].append(parser.parse(bucket['key_as_string']).date())
            if str(child_agg_count) in bucket:
                # We have a subaggregation with the value
                # If it is percentiles we get the median
                if 'values' in bucket[str(child_agg_count)]:
                    val = bucket[str(child_agg_count)]['values']['50.0']
                    if val == 'NaN':
                        # ES returns NaN. Convert to None for matplotlib graph
                        val = None
                    ts['value'].append(val)
                else:
                    ts['value'].append(bucket[str(child_agg_count)]['value'])
            else:
                ts['value'].append(bucket['doc_count'])
            # unixtime comes in ms from ElasticSearch
            ts['unixtime'].append(bucket['key'] / 1000)

        if dataframe:
            df = pd.DataFrame.from_records(ts, index="date")
            return df.fillna(0)
        return ts