modin/pandas/series.py
def dot(self, other):  # noqa: PR01, RT01, D200
        """
        Compute the dot product between the Series and the columns of `other`.
        """
        if isinstance(other, BasePandasDataset):
            common = self.index.union(other.index)
            if len(common) > len(self.index) or len(common) > len(other.index):
                raise ValueError("Matrices are not aligned")

            qc = other.reindex(index=common)._query_compiler
            if isinstance(other, Series):
                return self._reduce_dimension(
                    query_compiler=self._query_compiler.dot(
                        qc, squeeze_self=True, squeeze_other=True
                    )
                )
            else:
                return self.__constructor__(
                    query_compiler=self._query_compiler.dot(
                        qc, squeeze_self=True, squeeze_other=False
                    )
                )

        other = np.asarray(other)
        if self.shape[0] != other.shape[0]:
            raise ValueError(
                "Dot product shape mismatch, {} vs {}".format(self.shape, other.shape)
            )

        if len(other.shape) > 1:
            return (
                self._query_compiler.dot(other, squeeze_self=True).to_numpy().squeeze()
            )

        return self._reduce_dimension(
            query_compiler=self._query_compiler.dot(other, squeeze_self=True)
        )

        Compute the dot product between the Series and the columns of `other`.
        
Compute the dot product between the Series and the columns of `other`.
Compute the dot product between the Series and the columns of ` other ` .
def dot(self, other):  
        if isinstance(other, BasePandasDataset):
            common = self.index.union(other.index)
            if len(common) > len(self.index) or len(common) > len(other.index):
                raise ValueError("Matrices are not aligned")
            qc = other.reindex(index=common)._query_compiler
            if isinstance(other, Series):
                return self._reduce_dimension(
                    query_compiler=self._query_compiler.dot(
                        qc, squeeze_self=True, squeeze_other=True
                    )
                )
            else:
                return self.__constructor__(
                    query_compiler=self._query_compiler.dot(
                        qc, squeeze_self=True, squeeze_other=False
                    )
                )
        other = np.asarray(other)
        if self.shape[0] != other.shape[0]:
            raise ValueError(
                "Dot product shape mismatch, {} vs {}".format(self.shape, other.shape)
            )
        if len(other.shape) > 1:
            return (
                self._query_compiler.dot(other, squeeze_self=True).to_numpy().squeeze()
            )
        return self._reduce_dimension(
            query_compiler=self._query_compiler.dot(other, squeeze_self=True)
        )
def dot ( self , other ) : if isinstance ( other , BasePandasDataset ) : common = self . index . union ( other . index ) if len ( common ) > len ( self . index ) or len ( common ) > len ( other . index ) : raise ValueError ( "Matrices are not aligned" ) qc = other . reindex ( index = common ) . _query_compiler if isinstance ( other , Series ) : return self . _reduce_dimension ( query_compiler = self . _query_compiler . dot ( qc , squeeze_self = True , squeeze_other = True ) ) else : return self . __constructor__ ( query_compiler = self . _query_compiler . dot ( qc , squeeze_self = True , squeeze_other = False ) ) other = np . asarray ( other ) if self . shape [ 0 ] != other . shape [ 0 ] : raise ValueError ( "Dot product shape mismatch, {} vs {}" . format ( self . shape , other . shape ) ) if len ( other . shape ) > 1 : return ( self . _query_compiler . dot ( other , squeeze_self = True ) . to_numpy ( ) . squeeze ( ) ) return self . _reduce_dimension ( query_compiler = self . _query_compiler . dot ( other , squeeze_self = True ) )
Compute the dot product between the Series and the columns of `other`.
Compute the dot product between the Series and the columns of ` other ` .
# noqa: PR01, RT01, D200 """
        Compute the dot product between the Series and the columns of `other`.
        """
self
other