code: def execute_series_lead_lag_timedelta(
      op, data, offset, default, aggcontext=None, **kwargs
  ):
      
      # lagging adds time (delayed), leading subtracts time (moved up)
      func = operator.add if isinstance(op, ops.Lag) else operator.sub
      group_by = aggcontext.group_by
      order_by = aggcontext.order_by
  
      # get the parent object from which `data` originated
      parent = aggcontext.parent
  
      # get the DataFrame from the parent object, handling the DataFrameGroupBy
      # case
      parent_df = getattr(parent, 'obj', parent)
  
      # index our parent df by grouping and ordering keys
      indexed_original_df = parent_df.set_index(group_by + order_by)
  
      # perform the time shift
      adjusted_parent_df = parent_df.assign(
          **{k: func(parent_df[k], offset) for k in order_by}
      )
  
      # index the parent *after* adjustment
      adjusted_indexed_parent = adjusted_parent_df.set_index(group_by + order_by)
  
      # get the column we care about
      result = adjusted_indexed_parent[getattr(data, 'obj', data).name]
  
      # reindex the shifted data by the original frame's index
      result = result.reindex(indexed_original_df.index)
  
      # add a default if necessary
      return post_lead_lag(result, default)
docs: An implementation of shifting a column relative to another one that is
      in units of time rather than rows.