def cov(self, other, min_periods=None):
this, other = self.align(other, join='<STR_LIT>', copy=False)<EOL>if len(this) == <NUM_LIT:0>:<EOL><INDENT>return np.nan<EOL><DEDENT>return nanops.nancov(this.values, other.values,<EOL>min_periods=min_periods)<EOL>
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