code: def standard_deviation(x):
      
      if x.ndim > 1 and len(x[0]) > 1:
          return np.std(x, axis=1)
      return np.std(x)
docs: Return a numpy array of column standard deviation
  
      Parameters
      ----------
      x : ndarray
          A numpy array instance
  
      Returns
      -------
      ndarray
          A 1 x n numpy array instance of column standard deviation
  
      Examples
      --------
      >>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
      >>> np.testing.assert_array_almost_equal(
      ...     standard_deviation(a),
      ...     [0.816496, 0.816496, 0.816496])
      >>> a = np.array([1, 2, 3])
      >>> np.testing.assert_array_almost_equal(
      ...     standard_deviation(a),
      ...     0.816496)