(self, n=None, value=None, with_state=False, start=None)
return Window(self, n=n, value=value, with_state=with_state, start=start)
Sliding window operations

        Windowed operations are defined over a sliding window of data, either
        with a fixed number of elements::

            >>> df.window(n=10).sum()  # sum of the last ten elements

        or over an index value range (index must be monotonic)::

            >>> df.window(value='2h').mean()  # average over the last two hours

        Windowed dataframes support all normal arithmetic, aggregations, and
        groupby-aggregations.

        Parameters
        ----------
        n: int
            Window of number of elements over which to roll
        value: str
            Window of time 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: None or resulting Python object type from the operation
            Accepts a valid start state.

        Examples
        --------
        >>> df.window(n=10).std()
        >>> df.window(value='2h').count()

        >>> w = df.window(n=100)
        >>> w.groupby(w.name).amount.sum()
        >>> w.groupby(w.x % 10).y.var()

        See Also
        --------
        DataFrame.rolling: mimic's Pandas rolling aggregations
Sliding window operations
Sliding window operations
def window(self, n=None, value=None, with_state=False, start=None):
        """ Sliding window operations

        Windowed operations are defined over a sliding window of data, either
        with a fixed number of elements::

            >>> df.window(n=10).sum()  # sum of the last ten elements

        or over an index value range (index must be monotonic)::

            >>> df.window(value='2h').mean()  # average over the last two hours

        Windowed dataframes support all normal arithmetic, aggregations, and
        groupby-aggregations.

        Parameters
        ----------
        n: int
            Window of number of elements over which to roll
        value: str
            Window of time 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: None or resulting Python object type from the operation
            Accepts a valid start state.

        Examples
        --------
        >>> df.window(n=10).std()
        >>> df.window(value='2h').count()

        >>> w = df.window(n=100)
        >>> w.groupby(w.name).amount.sum()
        >>> w.groupby(w.x % 10).y.var()

        See Also
        --------
        DataFrame.rolling: mimic's Pandas rolling aggregations
        """
        return Window(self, n=n, value=value, with_state=with_state, start=start)
def window ( self , n = None , value = None , with_state = False , start = None ) : return Window ( self , n = n , value = value , with_state = with_state , start = start )