def cov(self, other=None, pairwise=None, bias=False, **kwargs):
        """
        Exponential weighted sample covariance.
        """
        if other is None:
            other = self._selected_obj
            # only default unset
            pairwise = True if pairwise is None else pairwise
        other = self._shallow_copy(other)

        def _get_cov(X, Y):
            X = self._shallow_copy(X)
            Y = self._shallow_copy(Y)
            cov = libwindow.ewmcov(X._prep_values(), Y._prep_values(),
                                   self.com, int(self.adjust),
                                   int(self.ignore_na), int(self.min_periods),
                                   int(bias))
            return X._wrap_result(cov)

        return _flex_binary_moment(self._selected_obj, other._selected_obj,
                                   _get_cov, pairwise=bool(pairwise))
def cov ( self , other = None , pairwise = None , bias = False , ** kwargs ) : if other is None : other = self . _selected_obj pairwise = True if pairwise is None else pairwise other = self . _shallow_copy ( other ) def _get_cov ( X , Y ) : X = self . _shallow_copy ( X ) Y = self . _shallow_copy ( Y ) cov = libwindow . ewmcov ( X . _prep_values ( ) , Y . _prep_values ( ) , self . com , int ( self . adjust ) , int ( self . ignore_na ) , int ( self . min_periods ) , int ( bias ) ) return X . _wrap_result ( cov ) return _flex_binary_moment ( self . _selected_obj , other . _selected_obj , _get_cov , pairwise = bool ( pairwise ) )
Exponential weighted sample covariance.