def cumsum(self: FrameLike, skipna: bool=True) -> FrameLike:
    "\n        Return cumulative sum over a DataFrame or Series axis.\n\n        Returns a DataFrame or Series of the same size containing the cumulative sum.\n\n        .. note:: the current implementation of cumsum uses Spark's Window without\n            specifying partition specification. This leads to move all data into\n            single partition in single machine and could cause serious\n            performance degradation. Avoid this method against very large dataset.\n\n        Parameters\n        ----------\n        skipna : boolean, default True\n            Exclude NA/null values. If an entire row/column is NA, the result will be NA.\n\n        Returns\n        -------\n        DataFrame or Series\n\n        See Also\n        --------\n        DataFrame.sum : Return the sum over DataFrame axis.\n        DataFrame.cummax : Return cumulative maximum over DataFrame axis.\n        DataFrame.cummin : Return cumulative minimum over DataFrame axis.\n        DataFrame.cumsum : Return cumulative sum over DataFrame axis.\n        DataFrame.cumprod : Return cumulative product over DataFrame axis.\n        Series.sum : Return the sum over Series axis.\n        Series.cummax : Return cumulative maximum over Series axis.\n        Series.cummin : Return cumulative minimum over Series axis.\n        Series.cumsum : Return cumulative sum over Series axis.\n        Series.cumprod : Return cumulative product over Series axis.\n\n        Examples\n        --------\n        >>> df = ps.DataFrame([[2.0, 1.0], [3.0, None], [1.0, 0.0]], columns=list('AB'))\n        >>> df\n             A    B\n        0  2.0  1.0\n        1  3.0  NaN\n        2  1.0  0.0\n\n        By default, iterates over rows and finds the sum in each column.\n\n        >>> df.cumsum()\n             A    B\n        0  2.0  1.0\n        1  5.0  NaN\n        2  6.0  1.0\n\n        It works identically in Series.\n\n        >>> df.A.cumsum()\n        0    2.0\n        1    5.0\n        2    6.0\n        Name: A, dtype: float64\n        "
    return self._apply_series_op((lambda psser: psser._cumsum(skipna)), should_resolve=True)
Return cumulative sum over a DataFrame or Series axis.

Returns a DataFrame or Series of the same size containing the cumulative sum.

.. note:: the current implementation of cumsum uses Spark's Window without
    specifying partition specification. This leads to move all data into
    single partition in single machine and could cause serious
    performance degradation. Avoid this method against very large dataset.

Parameters
----------
skipna : boolean, default True
    Exclude NA/null values. If an entire row/column is NA, the result will be NA.

Returns
-------
DataFrame or Series

See Also
--------
DataFrame.sum : Return the sum over DataFrame axis.
DataFrame.cummax : Return cumulative maximum over DataFrame axis.
DataFrame.cummin : Return cumulative minimum over DataFrame axis.
DataFrame.cumsum : Return cumulative sum over DataFrame axis.
DataFrame.cumprod : Return cumulative product over DataFrame axis.
Series.sum : Return the sum over Series axis.
Series.cummax : Return cumulative maximum over Series axis.
Series.cummin : Return cumulative minimum over Series axis.
Series.cumsum : Return cumulative sum over Series axis.
Series.cumprod : Return cumulative product over Series axis.

Examples
--------
>>> df = ps.DataFrame([[2.0, 1.0], [3.0, None], [1.0, 0.0]], columns=list('AB'))
>>> df
     A    B
0  2.0  1.0
1  3.0  NaN
2  1.0  0.0

By default, iterates over rows and finds the sum in each column.

>>> df.cumsum()
     A    B
0  2.0  1.0
1  5.0  NaN
2  6.0  1.0

It works identically in Series.

>>> df.A.cumsum()
0    2.0
1    5.0
2    6.0
Name: A, dtype: float64
def cumsum(self: FrameLike, skipna: bool=True) -> FrameLike:
    "\n        Return cumulative sum over a DataFrame or Series axis.\n\n        Returns a DataFrame or Series of the same size containing the cumulative sum.\n\n        .. note:: the current implementation of cumsum uses Spark's Window without\n            specifying partition specification. This leads to move all data into\n            single partition in single machine and could cause serious\n            performance degradation. Avoid this method against very large dataset.\n\n        Parameters\n        ----------\n        skipna : boolean, default True\n            Exclude NA/null values. If an entire row/column is NA, the result will be NA.\n\n        Returns\n        -------\n        DataFrame or Series\n\n        See Also\n        --------\n        DataFrame.sum : Return the sum over DataFrame axis.\n        DataFrame.cummax : Return cumulative maximum over DataFrame axis.\n        DataFrame.cummin : Return cumulative minimum over DataFrame axis.\n        DataFrame.cumsum : Return cumulative sum over DataFrame axis.\n        DataFrame.cumprod : Return cumulative product over DataFrame axis.\n        Series.sum : Return the sum over Series axis.\n        Series.cummax : Return cumulative maximum over Series axis.\n        Series.cummin : Return cumulative minimum over Series axis.\n        Series.cumsum : Return cumulative sum over Series axis.\n        Series.cumprod : Return cumulative product over Series axis.\n\n        Examples\n        --------\n        >>> df = ps.DataFrame([[2.0, 1.0], [3.0, None], [1.0, 0.0]], columns=list('AB'))\n        >>> df\n             A    B\n        0  2.0  1.0\n        1  3.0  NaN\n        2  1.0  0.0\n\n        By default, iterates over rows and finds the sum in each column.\n\n        >>> df.cumsum()\n             A    B\n        0  2.0  1.0\n        1  5.0  NaN\n        2  6.0  1.0\n\n        It works identically in Series.\n\n        >>> df.A.cumsum()\n        0    2.0\n        1    5.0\n        2    6.0\n        Name: A, dtype: float64\n        "
    return self._apply_series_op((lambda psser: psser._cumsum(skipna)), should_resolve=True)