code: def throughput_data(self, cycle_data, frequency='1D',pointscolumn= None):
          
          if len(cycle_data)<1:
             return None # Note completed items yet, return None
  
          if pointscolumn:
              return cycle_data[['completed_timestamp', pointscolumn]] \
                  .rename(columns={pointscolumn: 'sum'}) \
                  .groupby('completed_timestamp').sum() \
                  .resample(frequency).sum() \
                  .fillna(0)
          else:
              return cycle_data[['completed_timestamp', 'key']] \
                  .rename(columns={'key': 'count'}) \
                  .groupby('completed_timestamp').count() \
                  .resample(frequency).sum() \
                  .fillna(0)
docs: Return a data frame with columns `completed_timestamp` of the
          given frequency, either
          `count`, where count is the number of items
          'sum', where sum is the sum of value specified by pointscolumn. Expected to be 'StoryPoints'
          completed at that timestamp (e.g. daily).