code: def get_metric_history_as_columns(self, slugs, since=None,
                                        granularity='daily'):
          
          history = self.get_metric_history(slugs, since, granularity=granularity)
          _history = []  # new, columnar history
          periods = ['Period']  # A separate, single column for the time period
          for s in slugs:
              column = [s]  # story all the data for a single slug
              for key, value in history:
                  # ``metric_slug`` extracts the slug from the Redis Key
                  if template_tags.metric_slug(key) == s:
                      column.append(value)
  
                  # Get time period value as first column; This value is
                  # duplicated in the Redis key for each value, so this is a bit
                  # inefficient, but... oh well.
                  period = template_tags.strip_metric_prefix(key)
                  if period not in periods:
                      periods.append(period)
  
              _history.append(column)  # Remember that slug's column of data
  
          # Finally, stick the time periods in the first column.
          _history.insert(0, periods)
          return list(zip(*_history))
docs: Provides the same data as ``get_metric_history``, but in a columnar
          format. If you had the following yearly history, for example::
  
              [
                  ('m:bar:y:2012', '1'),
                  ('m:bar:y:2013', '2'),
                  ('m:foo:y:2012', '3'),
                  ('m:foo:y:2013', '4')
              ]
  
          this method would provide you with the following data structure::
  
              [
                  ['Period',  'bar',  'foo']
                  ['y:2012',  '1',    '3'],
                  ['y:2013',  '2',    '4'],
              ]
  
          Note that this also includes a header column. Data in this format may
          be useful for certain graphing libraries (I'm looking at you Google
          Charts LineChart).