def _result_aggregated(self, column_labels: List[Label], scols: List[Column]) -> 'Series':
    '\n        Given aggregated Spark columns and respective column labels from the original\n        pandas-on-Spark DataFrame, construct the result Series.\n        '
    from pyspark.pandas.series import first_series
    cols = []
    result_scol_name = 'value'
    for (label, applied_col) in zip(column_labels, scols):
        cols.append(F.struct(*[SF.lit(col).alias(SPARK_INDEX_NAME_FORMAT(i)) for (i, col) in enumerate(label)], *[applied_col.alias(result_scol_name)]))
    sdf = self._internal.spark_frame.select(F.array(*cols).alias('arrays')).select(F.explode(F.col('arrays')))
    sdf = sdf.selectExpr('col.*')
    internal = InternalFrame(spark_frame=sdf, index_spark_columns=[scol_for(sdf, SPARK_INDEX_NAME_FORMAT(i)) for i in range(self._internal.column_labels_level)], index_names=self._internal.column_label_names, column_labels=[None], data_spark_columns=[scol_for(sdf, result_scol_name)])
    return first_series(DataFrame(internal))
Given aggregated Spark columns and respective column labels from the original
pandas-on-Spark DataFrame, construct the result Series.
def _result_aggregated(self, column_labels: List[Label], scols: List[Column]) -> 'Series':
    '\n        Given aggregated Spark columns and respective column labels from the original\n        pandas-on-Spark DataFrame, construct the result Series.\n        '
    from pyspark.pandas.series import first_series
    cols = []
    result_scol_name = 'value'
    for (label, applied_col) in zip(column_labels, scols):
        cols.append(F.struct(*[SF.lit(col).alias(SPARK_INDEX_NAME_FORMAT(i)) for (i, col) in enumerate(label)], *[applied_col.alias(result_scol_name)]))
    sdf = self._internal.spark_frame.select(F.array(*cols).alias('arrays')).select(F.explode(F.col('arrays')))
    sdf = sdf.selectExpr('col.*')
    internal = InternalFrame(spark_frame=sdf, index_spark_columns=[scol_for(sdf, SPARK_INDEX_NAME_FORMAT(i)) for i in range(self._internal.column_labels_level)], index_names=self._internal.column_label_names, column_labels=[None], data_spark_columns=[scol_for(sdf, result_scol_name)])
    return first_series(DataFrame(internal))