arman correlation are currently computed using pairwise complete observations.

        * `Pearson correlation coefficient <https://en.wikipedia.org/wiki/Pearson_correlation_coefficient>`_
        * `Kendall rank correlation coefficient <https://en.wikipedia.org/wiki/Kendall_rank_correlation_coefficient>`_
        * `Spearman's rank correlation coefficient <https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient>`_

        Examples
        --------
        >>> def histogram_intersection(a, b):
        ...     v = np.minimum(a, b).sum().round(decimals=1)
        ...     return v
        >>> df = pd.DataFrame([(.2, .3), (.0, .6), (.6, .0), (.2, .1)],
        ...                   columns=['dogs', 'cats'])
        >>> df.corr(method=histogram_intersection)
              dogs  cats
        dogs   1.0   0.3
        cats   0.3   1.0

        >>> df = pd.DataFrame([(1, 1), (2, np.nan), (np.nan, 3), (4, 4)],
        ...                   columns=['dogs', 'cats'])
        >>> df.corr(min_periods=3)
              dogs  cats
        dogs   1.0   NaN
        cats   NaN   1.0
        Fİ