docs: def _create_cont_describe(self,data):
          """
          Describe the continuous data.
  
          Parameters
          ----------
              data : pandas DataFrame
                  The input dataset.
  
          Returns
          ----------
              df_cont : pandas DataFrame
                  Summarise the continuous variables.
          """
          aggfuncs = [pd.Series.count,np.mean,np.median,self._std,
              self._q25,self._q75,min,max,self._t1_summary,self._diptest,
              self._outliers,self._far_outliers,self._normaltest]
  
          # coerce continuous data to numeric
          cont_data = data[self._continuous].apply(pd.to_numeric, errors='coerce')
          # check all data in each continuous column is numeric
          bad_cols = cont_data.count() != data[self._continuous].count()
          bad_cols = cont_data.columns[bad_cols]
          if len(bad_cols)>0:
              raise InputError("""The following continuous column(s) have non-numeric values: {}.
              Either specify the column(s) as categorical or remove the non-numeric values.""".format(bad_cols.values))
  
          # check for coerced column containing all NaN to warn user
          for column in cont_data.columns[cont_data.count() == 0]:
              self._non_continuous_warning(column)
  
          if self._groupby:
              # add the groupby column back
              cont_data = cont_data.merge(data[[self._groupby]],
                  left_index=True, right_index=True)
  
              # group and aggregate data
              df_cont = pd.pivot_table(cont_data,
                  columns=[self._groupby],
                  aggfunc=aggfuncs)
          else:
              # if no groupby, just add single group column
              df_cont = cont_data.apply(aggfuncs).T
              df_cont.columns.name = 'overall'
              df_cont.columns = pd.MultiIndex.from_product([df_cont.columns,
                  ['overall']])
  
          df_cont.index.rename('variable',inplace=True)
  
          # remove prefix underscore from column names (e.g. _std -> std)
          agg_rename = df_cont.columns.levels[0]
          agg_rename = [x[1:] if x[0]=='_' else x for x in agg_rename]
          df_cont.columns.set_levels(agg_rename, level=0, inplace=True)
  
          return df_cont