title: Scipy.stats t moments extraction question
body: <p>I'm trying to extract the skew &amp; kurtosis from a student t dist that I fitted to some non-normal data.  </p>
  
  <p>I have no problem extracting the mean &amp; stdev, but do not know the code necessary to extract and print the student t skew &amp; kurtosis</p>
  
  <pre><code>mu_norm, sig_norm = norm.fit(returns) # extract the mean and stdev of the closest normal distribution fit
  print("Normal mean is {0:.8f}, stdev is {1:.8f}".format(mu_norm, sig_norm))
  dx = 0.0001 # resolution of the distribution
  x = np.arange(-1, 1, dx)
  pdf_n = norm.pdf(x, mu_norm, sig_norm)
  
  nu, mu_t, sig_t = t.fit(returns) # extract the mean and stdev of the closest Student-t distribution fit
  nu = np.round(nu)
  print("Student-t mean is {0:.8f}, stdev is {1:.8f}, dof is {2}".format(mu_t, sig_t, nu))
  pdf_t = t.pdf(x, nu, mu_t, sig_t)
  
  print('Maximum: ', returns.max())
  print('Minimum: ', returns.min())
  print('Norm_Skew', returns.skew())
  print('Norm_Kurtosis', returns.kurtosis())
  </code></pre>
  
  <p>I would like to add the following output:</p>
  
  <pre><code>print('t_Skew', returns.tskew())
  print('t_Kurtosis', returns.tkurtosis())
  </code></pre>