class_docstr: Student's t-distribution (sktime native).
  
  Parameters
  ----------
  mean : float or array of float (1D or 2D)
      mean of the t-distribution distribution
  sd : float or array of float (1D or 2D), must be positive
      standard deviation of the t-distribution distribution
  df : float or array of float (1D or 2D), must be positive
      Degrees of freedom of the t-distribution
  index : pd.Index, optional, default = RangeIndex
  columns : pd.Index, optional, default = RangeIndex
  
  Example
  -------
  >>> from sktime.proba.t import TDistribution
  
  >>> n = TDistribution(mu=[[0, 1], [2, 3], [4, 5]], sigma=1, df=10)
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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]`. The expectation,
  :math:`\mathbb{E}[X]`, as infinite if :math:`\nu \le 1`.
  
  Returns
  -------
  pd.DataFrame with same rows, columns as `self`
  expected value of distribution (entry-wise)
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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]`. The expectation,
      :math:`\mathbb{E}[X]`, as infinite if :math:`\nu \le 1`.
  
      Returns
      -------
      pd.DataFrame with same rows, columns as `self`
      expected value of distribution (entry-wise)
      """
      mean_arr = self._mu.copy()
      if (self._df <= 1).any():
          mean_arr = mean_arr.astype(np.float32)
          mean_arr[self._df <= 1] = np.inf
      return pd.DataFrame(
          mean_arr, index=self.index, columns=self.columns
      )
  
file_path: sktime/sktime/proba/t.py
imports: import numpy
  import pandas
  from scipy.special import betaincinv, gamma, hyp2f1, loggamma
  from sktime.proba.base import BaseDistribution
label_desc: Use image node_id 2 for calling the TDistribution obj's underlying member method code with example usage: obj.mean() and returns: pd