Label-based "fancy indexing" function for DataFrame.

        Given equal-length arrays of row and column labels, return an
        array of the values corresponding to each (row, col) pair.

        Parameters
        ----------
        row_labels : sequence
            The row labels to use for lookup
        col_labels : sequence
            The column labels to use for lookup

        Notes
        -----
        Akin to::

            result = [df.get_value(row, col)
                      for row, col in zip(row_labels, col_labels)]

        Examples
        --------
        values : ndarray
            The found values

def lookup(self, row_labels, col_labels):
        """
        Label-based "fancy indexing" function for DataFrame.

        Given equal-length arrays of row and column labels, return an
        array of the values corresponding to each (row, col) pair.

        Parameters
        ----------
        row_labels : sequence
            The row labels to use for lookup
        col_labels : sequence
            The column labels to use for lookup

        Notes
        -----
        Akin to::

            result = [df.get_value(row, col)
                      for row, col in zip(row_labels, col_labels)]

        Examples
        --------
        values : ndarray
            The found values
        """
        n = len(row_labels)
        if n != len(col_labels):
            raise ValueError('Row labels must have same size as column labels')

        thresh = 1000
        if not self._is_mixed_type or n > thresh:
            values = self.values
            ridx = self.index.get_indexer(row_labels)
            cidx = self.columns.get_indexer(col_labels)
            if (ridx == -1).any():
                raise KeyError('One or more row labels was not found')
            if (cidx == -1).any():
                raise KeyError('One or more column labels was not found')
            flat_index = ridx * len(self.columns) + cidx
            result = values.flat[flat_index]
        else:
            result = np.empty(n, dtype='O')
            for i, (r, c) in enumerate(zip(row_labels, col_labels)):
                result[i] = self._get_value(r, c)

        if is_object_dtype(result):
            result = lib.maybe_convert_objects(result)

        return result