code: def partial_transform(self, sequence, mode='clip'):
          
          if mode not in ['clip', 'fill']:
              raise ValueError('mode must be one of ["clip", "fill"]: %s' % mode)
          sequence = np.asarray(sequence)
          if sequence.ndim != 1:
              raise ValueError("Each sequence must be 1D")
  
          f = np.vectorize(lambda k: self.mapping_.get(k, np.nan),
                           otypes=[np.float])
  
          a = f(sequence)
          if mode == 'fill':
              if np.all(np.mod(a, 1) == 0):
                  result = a.astype(int)
              else:
                  result = a
          elif mode == 'clip':
              result = [a[s].astype(int) for s in
                        np.ma.clump_unmasked(np.ma.masked_invalid(a))]
          else:
              raise RuntimeError()
  
          return result
docs: Transform a sequence to internal indexing
  
          Recall that `sequence` can be arbitrary labels, whereas ``transmat_``
          and ``countsmat_`` are indexed with integers between 0 and
          ``n_states - 1``. This methods maps a set of sequences from the labels
          onto this internal indexing.
  
          Parameters
          ----------
          sequence : array-like
              A 1D iterable of state labels. Labels can be integers, strings, or
              other orderable objects.
          mode : {'clip', 'fill'}
              Method by which to treat labels in `sequence` which do not have
              a corresponding index. This can be due, for example, to the ergodic
              trimming step.
  
             ``clip``
                 Unmapped labels are removed during transform. If they occur
                 at the beginning or end of a sequence, the resulting transformed
                 sequence will be shorted. If they occur in the middle of a
                 sequence, that sequence will be broken into two (or more)
                 sequences. (Default)
             ``fill``
                 Unmapped labels will be replaced with NaN, to signal missing
                 data. [The use of NaN to signal missing data is not fantastic,
                 but it's consistent with current behavior of the ``pandas``
                 library.]
  
          Returns
          -------
          mapped_sequence : list or ndarray
              If mode is "fill", return an ndarray in internal indexing.
              If mode is "clip", return a list of ndarrays each in internal
              indexing.