code: def cov(self, other, min_periods=None):
          
          this, other = self.align(other, join='inner', copy=False)
          if len(this) == 0:
              return np.nan
          return nanops.nancov(this.values, other.values,
                               min_periods=min_periods)
docs: Compute covariance with Series, excluding missing values.
  
          Parameters
          ----------
          other : Series
              Series with which to compute the covariance.
          min_periods : int, optional
              Minimum number of observations needed to have a valid result.
  
          Returns
          -------
          float
              Covariance between Series and other normalized by N-1
              (unbiased estimator).
  
          Examples
          --------
          >>> s1 = pd.Series([0.90010907, 0.13484424, 0.62036035])
          >>> s2 = pd.Series([0.12528585, 0.26962463, 0.51111198])
          >>> s1.cov(s2)
          -0.01685762652715874