docs: def parallel_cumulative_blame(self, branch='master', limit=None, skip=None, num_datapoints=None, committer=True,
                                    workers=1, ignore_globs=None, include_globs=None):
          """
          Returns the blame at every revision of interest. Index is a datetime, column per committer, with number of lines
          blamed to each committer at each timestamp as data.
  
          :param branch: (optional, default 'master') the branch to work in
          :param limit: (optional, default None), the maximum number of revisions to return, None for no limit
          :param skip: (optional, default None), the number of revisions to skip. Ex: skip=2 returns every other revision, None for no skipping.
          :param num_datapoints: (optional, default=None) if limit and skip are none, and this isn't, then num_datapoints evenly spaced revs will be used
          :param committer: (optional, defualt=True) true if committer should be reported, false if author
          :param ignore_globs: (optional, default=None) a list of globs to ignore, default none excludes nothing
          :param include_globs: (optinal, default=None) a list of globs to include, default of None includes everything.
          :param workers: (optional, default=1) integer, the number of workers to use in the threadpool, -1 for one per core.
          :return: DataFrame
  
          """
  
          if not _has_joblib:
              raise ImportError('''Must have joblib installed to use parallel_cumulative_blame(), please use
              cumulative_blame() instead.''')
  
          revs = self.revs(branch=branch, limit=limit, skip=skip, num_datapoints=num_datapoints)
  
          if self.verbose:
              print('Beginning processing for cumulative blame:')
  
          revisions = json.loads(revs.to_json(orient='index'))
          revisions = [revisions[key] for key in revisions]
  
          ds = Parallel(n_jobs=workers, backend='threading', verbose=5)(
              delayed(_parallel_cumulative_blame_func)
              (self, x, committer, ignore_globs, include_globs) for x in revisions
          )
  
          revs = DataFrame(ds)
          del revs['rev']
  
          revs['date'] = to_datetime(revs['date'].map(datetime.datetime.fromtimestamp))
          revs.set_index(keys=['date'], drop=True, inplace=True)
          revs = revs.fillna(0.0)
  
          # drop 0 cols
          for col in revs.columns.values:
              if col != 'col':
                  if revs[col].sum() == 0:
                      del revs[col]
  
          # drop 0 rows
          keep_idx = []
          committers = [x for x in revs.columns.values if x != 'date']
          for idx, row in revs.iterrows():
              if sum([row[x] for x in committers]) > 0:
                  keep_idx.append(idx)
  
          revs = revs.ix[keep_idx]
          revs.sort_index(ascending=False, inplace=True)
  
          return revs