class_docstr: Base probability distribution.
docstr: Return expected value of the distribution.
  
  Let :math:`X` be a random variable with the distribution of `self`.
  Returns the expectation :math:`\mathbb{E}[X]`
  
  Returns
  -------
  pd.DataFrame with same rows, columns as `self`
  expected value of distribution (entry-wise)
docstr_tok: ["Return","expected","value","of","the","distribution",".","Let",":","math",":","`","X","`","be","a","random","variable","with","the","distribution","of","`","self","`",".","Returns","the","expectation",":","math",":","`","\\mathbb","{","E","}","[","X","]","`","Returns","--","--","--","-","pd.DataFrame","with","same","rows",",","columns","as","`","self","`","expected","value","of","distribution","(","entry-wise",")"]
code: def mean(self):
      r"""Return expected value of the distribution.
  
      Let :math:`X` be a random variable with the distribution of `self`.
      Returns the expectation :math:`\mathbb{E}[X]`
  
      Returns
      -------
      pd.DataFrame with same rows, columns as `self`
      expected value of distribution (entry-wise)
      """
      approx_spl_size = self.get_tag("approx_mean_spl")
      approx_method = (
          "by approximating the expected value by the arithmetic mean of "
          f"{approx_spl_size} samples"
      )
      warn(self._method_error_msg("mean", fill_in=approx_method))
  
      spl = self.sample(approx_spl_size)
      return spl.groupby(level=1, sort=False).mean()
  
imports: from warnings import warn
  import numpy
  import pandas
  from sktime.base import BaseObject
  from sktime.utils.pandas import df_map
  from sktime.utils.validation._dependencies import _check_estimator_deps
label_desc: Use image node_id 17 for calling the BaseDistribution obj's underlying member method code with example usage: obj.mean() and returns: spl