(self, window, min_periods=1, with_state=False, start=())
return Rolling(self, window, min_periods, with_state, start)
Compute rolling aggregations

        When followed by an aggregation method like ``sum``, ``mean``, or
        ``std`` this produces a new Streaming dataframe whose values are
        aggregated over that window.

        The window parameter can be either a number of rows or a timedelta like
        ``"2 minutes"` in which case the index should be a datetime index.

        This operates by keeping enough of a backlog of records to maintain an
        accurate stream.  It performs a copy at every added dataframe.  Because
        of this it may be slow if the rolling window is much larger than the
        average stream element.

        Parameters
        ----------
        window: int or timedelta
            Window over which to roll
        with_state: bool (False)
            Whether to return the state along with the result as a tuple (state, result).
            State may be needed downstream for a number of reasons like checkpointing.
        start: () or resulting Python object type from the operation
            Accepts a valid start state.

        Returns
        -------
        Rolling object

        See Also
        --------
        DataFrame.window: more generic window operations
Compute rolling aggregations
Compute rolling aggregations
def rolling(self, window, min_periods=1, with_state=False, start=()):
        """ Compute rolling aggregations

        When followed by an aggregation method like ``sum``, ``mean``, or
        ``std`` this produces a new Streaming dataframe whose values are
        aggregated over that window.

        The window parameter can be either a number of rows or a timedelta like
        ``"2 minutes"` in which case the index should be a datetime index.

        This operates by keeping enough of a backlog of records to maintain an
        accurate stream.  It performs a copy at every added dataframe.  Because
        of this it may be slow if the rolling window is much larger than the
        average stream element.

        Parameters
        ----------
        window: int or timedelta
            Window over which to roll
        with_state: bool (False)
            Whether to return the state along with the result as a tuple (state, result).
            State may be needed downstream for a number of reasons like checkpointing.
        start: () or resulting Python object type from the operation
            Accepts a valid start state.

        Returns
        -------
        Rolling object

        See Also
        --------
        DataFrame.window: more generic window operations
        """
        return Rolling(self, window, min_periods, with_state, start)
def rolling ( self , window , min_periods = 1 , with_state = False , start = ( ) ) : return Rolling ( self , window , min_periods , with_state , start )