def corr(self, other, method='<STR_LIT>', 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>if method in ['<STR_LIT>', '<STR_LIT>', '<STR_LIT>'] or callable(method):<EOL><INDENT>return nanops.nancorr(this.values, other.values, method=method,<EOL>min_periods=min_periods)<EOL><DEDENT>raise ValueError("<STR_LIT>"<EOL>"<STR_LIT>"<EOL>"<STR_LIT>".format(method=method))<EOL>
Compute correlation with `other` Series, excluding missing values.

Parameters
----------
other : Series
    Series with which to compute the correlation.
method : {'pearson', 'kendall', 'spearman'} or callable
    * pearson : standard correlation coefficient
    * kendall : Kendall Tau correlation coefficient
    * spearman : Spearman rank correlation
    * callable: callable with input two 1d ndarrays
        and returning a float. Note that the returned matrix from corr
        will have 1 along the diagonals and will be symmetric
        regardless of the callable's behavior
        .. versionadded:: 0.24.0

min_periods : int, optional
    Minimum number of observations needed to have a valid result.

Returns
-------
float
    Correlation with other.

Examples
--------
>>> def histogram_intersection(a, b):
...     v = np.minimum(a, b).sum().round(decimals=1)
...     return v
>>> s1 = pd.Series([.2, .0, .6, .2])
>>> s2 = pd.Series([.3, .6, .0, .1])
>>> s1.corr(s2, method=histogram_intersection)
0.3