def expanding(self: FrameLike, min_periods: int=1) -> 'Expanding[FrameLike]':
    "\n        Provide expanding transformations.\n\n        .. note:: 'min_periods' in pandas-on-Spark works as a fixed window size unlike pandas.\n            Unlike pandas, NA is also counted as the period. This might be changed\n            in the near future.\n\n        Parameters\n        ----------\n        min_periods : int, default 1\n            Minimum number of observations in window required to have a value\n            (otherwise result is NA).\n\n        Returns\n        -------\n        a Window sub-classed for the particular operation\n        "
    from pyspark.pandas.window import Expanding
    return Expanding(self, min_periods=min_periods)
Provide expanding transformations.

.. note:: 'min_periods' in pandas-on-Spark works as a fixed window size unlike pandas.
    Unlike pandas, NA is also counted as the period. This might be changed
    in the near future.

Parameters
----------
min_periods : int, default 1
    Minimum number of observations in window required to have a value
    (otherwise result is NA).

Returns
-------
a Window sub-classed for the particular operation
def expanding(self: FrameLike, min_periods: int=1) -> 'Expanding[FrameLike]':
    "\n        Provide expanding transformations.\n\n        .. note:: 'min_periods' in pandas-on-Spark works as a fixed window size unlike pandas.\n            Unlike pandas, NA is also counted as the period. This might be changed\n            in the near future.\n\n        Parameters\n        ----------\n        min_periods : int, default 1\n            Minimum number of observations in window required to have a value\n            (otherwise result is NA).\n\n        Returns\n        -------\n        a Window sub-classed for the particular operation\n        "
    from pyspark.pandas.window import Expanding
    return Expanding(self, min_periods=min_periods)