code: def get_query_result(self, query_object):
          
  
          # Here, we assume that all the queries will use the same datasource, which is
          # is a valid assumption for current setting. In a long term, we may or maynot
          # support multiple queries from different data source.
  
          timestamp_format = None
          if self.datasource.type == 'table':
              dttm_col = self.datasource.get_col(query_object.granularity)
              if dttm_col:
                  timestamp_format = dttm_col.python_date_format
  
          # The datasource here can be different backend but the interface is common
          result = self.datasource.query(query_object.to_dict())
  
          df = result.df
          # Transform the timestamp we received from database to pandas supported
          # datetime format. If no python_date_format is specified, the pattern will
          # be considered as the default ISO date format
          # If the datetime format is unix, the parse will use the corresponding
          # parsing logic
          if df is not None and not df.empty:
              if DTTM_ALIAS in df.columns:
                  if timestamp_format in ('epoch_s', 'epoch_ms'):
                      # Column has already been formatted as a timestamp.
                      df[DTTM_ALIAS] = df[DTTM_ALIAS].apply(pd.Timestamp)
                  else:
                      df[DTTM_ALIAS] = pd.to_datetime(
                          df[DTTM_ALIAS], utc=False, format=timestamp_format)
                  if self.datasource.offset:
                      df[DTTM_ALIAS] += timedelta(hours=self.datasource.offset)
                  df[DTTM_ALIAS] += query_object.time_shift
  
              if self.enforce_numerical_metrics:
                  self.df_metrics_to_num(df, query_object)
  
              df.replace([np.inf, -np.inf], np.nan)
          return {
              'query': result.query,
              'status': result.status,
              'error_message': result.error_message,
              'df': df,
          }
docs: Returns a pandas dataframe based on the query object