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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def corr(self, other, method='pearson', min_periods=None):
 """ 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 -------- 0.3 """

        this, other = self.align(other, join='inner', copy=False)
        if len(this) == 0:
            return np.nan

        if method in ['pearson', 'spearman', 'kendall'] or callable(method):
            return nanops.nancorr(this.values, other.values, method=method,
                                  min_periods=min_periods)

        raise ValueError("method must be either 'pearson', "
                         "'spearman', 'kendall', or a callable, "
                         "'{method}' was supplied".format(method=method))