docs: def reduce_duplicate_frequencies(self):
          """In case multiple frequencies were measured, average them and compute
          std, min, max values for zt.
  
          In case timesteps were added (i.e., multiple separate measurements),
          group over those and average for each timestep.
  
          Examples
          --------
  
          ::
  
              import tempfile
              import reda
              with tempfile.TemporaryDirectory() as fid:
                  reda.data.download_data('sip04_fs_06', fid)
                  sip = reda.SIP()
                  sip.import_sip04(fid + '/sip_dataA.mat', timestep=0)
                  # well, add the spectrum again as another timestep
                  sip.import_sip04(fid + '/sip_dataA.mat', timestep=1)
              df = sip.reduce_duplicate_frequencies()
  
          """
          group_keys = ['frequency', ]
          if 'timestep' in self.data.columns:
              group_keys = group_keys + ['timestep', ]
  
          g = self.data.groupby(group_keys)
  
          def group_apply(item):
              y = item[['zt_1', 'zt_2', 'zt_3']].values.flatten()
              zt_imag_std = np.std(y.imag)
              zt_real_std = np.std(y.real)
              zt_imag_min = np.min(y.imag)
              zt_real_min = np.min(y.real)
              zt_imag_max = np.max(y.imag)
              zt_real_max = np.max(y.real)
              zt_imag_mean = np.mean(y.imag)
              zt_real_mean = np.mean(y.real)
              dfn = pd.DataFrame(
                  {
                      'zt_real_mean': zt_real_mean,
                      'zt_real_std': zt_real_std,
                      'zt_real_min': zt_real_min,
                      'zt_real_max': zt_real_max,
                      'zt_imag_mean': zt_imag_mean,
                      'zt_imag_std': zt_imag_std,
                      'zt_imag_min': zt_imag_min,
                      'zt_imag_max': zt_imag_max,
                  },
                  index=[0, ]
              )
  
              dfn['count'] = len(y)
              dfn.index.name = 'index'
              return dfn
  
          p = g.apply(group_apply)
          p.index = p.index.droplevel('index')
          if len(group_keys) > 1:
              p = p.swaplevel(0, 1).sort_index()
          return p