code: def histogram(self, cycle_data, bins=10):
          
          values, edges = np.histogram(cycle_data['cycle_time'].astype('timedelta64[D]').dropna(), bins=bins)
  
          index = []
          for i, v in enumerate(edges):
              if i == 0:
                  continue
              index.append("%.01f to %.01f" % (edges[i - 1], edges[i],))
  
          return pd.Series(values, name="Items", index=index)
docs: Return histogram data for the cycle times in `cycle_data`. Returns
          a dictionary with keys `bin_values` and `bin_edges` of numpy arrays