class_docstr: Imputed Data
  
  This class should not be instantiated directly.
  Instead, it is returned when ImputationKernel.impute_new_data() is called.
  For parameter arguments, see ImputationKernel documentation.
class_docstr_tok: ["Imputed","Data","This","class","should","not","be","instantiated","directly",".","Instead",",","it","is","returned","when","ImputationKernel.impute_new_data","(",")","is","called",".","For","parameter","arguments",",","see","ImputationKernel","documentation","."]
docstr: Return a dict containing the average imputation value
  for specified variables at each iteration.
docstr_tok: ["Return","a","dict","containing","the","average","imputation","value","for","specified","variables","at","each","iteration","."]
code: def get_means(self, datasets, variables=None):
      """
      Return a dict containing the average imputation value
      for specified variables at each iteration.
      """
      num_vars = self._get_num_vars(variables)
  
      # For every variable, get the correlations between every dataset combination
      # at each iteration
      curr_iteration = self.iteration_count(datasets=datasets)
      if self.save_all_iterations:
          iter_range = list(range(curr_iteration + 1))
      else:
          iter_range = [curr_iteration]
      mean_dict = {
          ds: {
              var: {
                  itr: np.mean(self[ds, var, itr]) for itr in iter_range
              }
              for var in num_vars
          }
          for ds in datasets
      }
  
      return mean_dict
  
imports: import numpy
  from .compat import pd_DataFrame
  from .utils import _t_dat, _t_var_list, _t_var_dict, _ensure_iterable, _dict_set_diff, _assign_col_values_without_copy, _slice, _subset_data
  from itertools import combinations
  from typing import Dict, List, Union, Any, Optional
  from warnings import warn
label_desc: Use image node_id 20 for calling the ImputedData obj's underlying member method code with example usage: obj.get_means(datasets, variables) and returns: mean_dict