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: Plots the average value of imputations over each iteration.
  
  Parameters
  ----------
  variables: None or list
      The variables to plot. Must be numeric.
  adj_args
      Passed to matplotlib.pyplot.subplots_adjust()
docstr_tok: ["Plots","the","average","value","of","imputations","over","each","iteration",".","Parameters","--","--","--","--","--","variables",":","None","or","list","The","variables","to","plot",".","Must","be","numeric",".","adj_args","Passed","to","matplotlib.pyplot.subplots_adjust","(",")"]
code: def plot_mean_convergence(
      self, datasets=None, variables=None, **adj_args
  ):
      """
      Plots the average value of imputations over each iteration.
  
      Parameters
      ----------
      variables: None or list
          The variables to plot. Must be numeric.
      adj_args
          Passed to matplotlib.pyplot.subplots_adjust()
  
      """
  
      # Move this to .compat at some point.
      try:
          import matplotlib.pyplot as plt
          from matplotlib import gridspec
      except ImportError:
          raise ImportError(
              "matplotlib must be installed to plot mean convergence"
          )
  
      if self.iteration_count() < 2 or not self.save_all_iterations:
          raise ValueError("There is only one iteration.")
  
      if datasets is None:
          datasets = list(range(self.dataset_count()))
      else:
          datasets = _ensure_iterable(datasets)
      num_vars = self._get_num_vars(variables)
      mean_dict = self.get_means(datasets=datasets, variables=variables)
      plots, plotrows, plotcols = self._prep_multi_plot(num_vars)
      gs = gridspec.GridSpec(plotrows, plotcols)
      fig, ax = plt.subplots(plotrows, plotcols, squeeze=False)
  
      for v in range(plots):
          axr, axc = next(iter(gs[v].rowspan)), next(
              iter(gs[v].colspan)
          )
          var = num_vars[v]
          for d in mean_dict.values():
              ax[axr, axc].plot(list(d[var].values()), color="black")
          ax[axr, axc].set_title(var)
          ax[axr, axc].set_xlabel("Iteration")
          ax[axr, axc].set_ylabel("mean")
      plt.subplots_adjust(**adj_args)
  
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 21 for calling the ImputedData obj's underlying member method code with example usage: obj.plot_mean_convergence(datasets, variables) without return types