@staticmethod
def _index_normalized_frame(level: int, psser_or_psdf: DataFrameOrSeries) -> 'DataFrame':
    '\n        Returns a frame that is normalized against the current column index level.\n        For example, the name in `pd.Series([...], name="abc")` can be can be\n        ("abc", "", "") if the current DataFrame has a multi-index for its column\n        '
    from pyspark.pandas.series import Series
    if isinstance(psser_or_psdf, Series):
        psdf = psser_or_psdf.to_frame()
    else:
        assert isinstance(psser_or_psdf, DataFrame), type(psser_or_psdf)
        psdf = psser_or_psdf.copy()
    psdf.columns = pd.MultiIndex.from_tuples([tuple(([name_like_string(label)] + ([''] * (level - 1)))) for label in psdf._internal.column_labels])
    return psdf
Returns a frame that is normalized against the current column index level.
For example, the name in `pd.Series([...], name="abc")` can be can be
("abc", "", "") if the current DataFrame has a multi-index for its column
@staticmethod
def _index_normalized_frame(level: int, psser_or_psdf: DataFrameOrSeries) -> 'DataFrame':
    '\n        Returns a frame that is normalized against the current column index level.\n        For example, the name in `pd.Series([...], name="abc")` can be can be\n        ("abc", , ) if the current DataFrame has a multi-index for its column\n        '
    from pyspark.pandas.series import Series
    if isinstance(psser_or_psdf, Series):
        psdf = psser_or_psdf.to_frame()
    else:
        assert isinstance(psser_or_psdf, DataFrame), type(psser_or_psdf)
        psdf = psser_or_psdf.copy()
    psdf.columns = pd.MultiIndex.from_tuples([tuple(([name_like_string(label)] + ([] * (level - 1)))) for label in psdf._internal.column_labels])
    return psdf