def ffill(self: FrameLike, axis: Optional[Axis]=None, inplace: bool_type=False, limit: Optional[int]=None) -> FrameLike:
    "\n        Synonym for `DataFrame.fillna()` or `Series.fillna()` with ``method=`ffill```.\n\n        .. note:: the current implementation of 'ffill' uses Spark's Window\n            without 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        axis : {0 or `index`}\n            1 and `columns` are not supported.\n        inplace : boolean, default False\n            Fill in place (do not create a new object)\n        limit : int, default None\n            If method is specified, this is the maximum number of consecutive NaN values to\n            forward/backward fill. In other words, if there is a gap with more than this number of\n            consecutive NaNs, it will only be partially filled. If method is not specified,\n            this is the maximum number of entries along the entire axis where NaNs will be filled.\n            Must be greater than 0 if not None\n\n        Returns\n        -------\n        DataFrame or Series\n            DataFrame or Series with NA entries filled.\n\n        Examples\n        --------\n        >>> psdf = ps.DataFrame({\n        ...     'A': [None, 3, None, None],\n        ...     'B': [2, 4, None, 3],\n        ...     'C': [None, None, None, 1],\n        ...     'D': [0, 1, 5, 4]\n        ...     },\n        ...     columns=['A', 'B', 'C', 'D'])\n        >>> psdf\n             A    B    C  D\n        0  NaN  2.0  NaN  0\n        1  3.0  4.0  NaN  1\n        2  NaN  NaN  NaN  5\n        3  NaN  3.0  1.0  4\n\n        Propagate non-null values forward.\n\n        >>> psdf.ffill()\n             A    B    C  D\n        0  NaN  2.0  NaN  0\n        1  3.0  4.0  NaN  1\n        2  3.0  4.0  NaN  5\n        3  3.0  3.0  1.0  4\n\n        For Series\n\n        >>> psser = ps.Series([2, 4, None, 3])\n        >>> psser\n        0    2.0\n        1    4.0\n        2    NaN\n        3    3.0\n        dtype: float64\n\n        >>> psser.ffill()\n        0    2.0\n        1    4.0\n        2    4.0\n        3    3.0\n        dtype: float64\n        "
    return self.fillna(method='ffill', axis=axis, inplace=inplace, limit=limit)
Synonym for `DataFrame.fillna()` or `Series.fillna()` with ``method=`ffill```.

.. note:: the current implementation of 'ffill' 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
----------
axis : {0 or `index`}
    1 and `columns` are not supported.
inplace : boolean, default False
    Fill in place (do not create a new object)
limit : int, default None
    If method is specified, this is the maximum number of consecutive NaN values to
    forward/backward fill. In other words, if there is a gap with more than this number of
    consecutive NaNs, it will only be partially filled. If method is not specified,
    this is the maximum number of entries along the entire axis where NaNs will be filled.
    Must be greater than 0 if not None

Returns
-------
DataFrame or Series
    DataFrame or Series with NA entries filled.

Examples
--------
>>> psdf = ps.DataFrame({
...     'A': [None, 3, None, None],
...     'B': [2, 4, None, 3],
...     'C': [None, None, None, 1],
...     'D': [0, 1, 5, 4]
...     },
...     columns=['A', 'B', 'C', 'D'])
>>> psdf
     A    B    C  D
0  NaN  2.0  NaN  0
1  3.0  4.0  NaN  1
2  NaN  NaN  NaN  5
3  NaN  3.0  1.0  4

Propagate non-null values forward.

>>> psdf.ffill()
     A    B    C  D
0  NaN  2.0  NaN  0
1  3.0  4.0  NaN  1
2  3.0  4.0  NaN  5
3  3.0  3.0  1.0  4

For Series

>>> psser = ps.Series([2, 4, None, 3])
>>> psser
0    2.0
1    4.0
2    NaN
3    3.0
dtype: float64

>>> psser.ffill()
0    2.0
1    4.0
2    4.0
3    3.0
dtype: float64
def ffill(self: FrameLike, axis: Optional[Axis]=None, inplace: bool_type=False, limit: Optional[int]=None) -> FrameLike:
    "\n        Synonym for `DataFrame.fillna()` or `Series.fillna()` with ``method=`ffill```.\n\n        .. note:: the current implementation of 'ffill' uses Spark's Window\n            without 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        axis : {0 or `index`}\n            1 and `columns` are not supported.\n        inplace : boolean, default False\n            Fill in place (do not create a new object)\n        limit : int, default None\n            If method is specified, this is the maximum number of consecutive NaN values to\n            forward/backward fill. In other words, if there is a gap with more than this number of\n            consecutive NaNs, it will only be partially filled. If method is not specified,\n            this is the maximum number of entries along the entire axis where NaNs will be filled.\n            Must be greater than 0 if not None\n\n        Returns\n        -------\n        DataFrame or Series\n            DataFrame or Series with NA entries filled.\n\n        Examples\n        --------\n        >>> psdf = ps.DataFrame({\n        ...     'A': [None, 3, None, None],\n        ...     'B': [2, 4, None, 3],\n        ...     'C': [None, None, None, 1],\n        ...     'D': [0, 1, 5, 4]\n        ...     },\n        ...     columns=['A', 'B', 'C', 'D'])\n        >>> psdf\n             A    B    C  D\n        0  NaN  2.0  NaN  0\n        1  3.0  4.0  NaN  1\n        2  NaN  NaN  NaN  5\n        3  NaN  3.0  1.0  4\n\n        Propagate non-null values forward.\n\n        >>> psdf.ffill()\n             A    B    C  D\n        0  NaN  2.0  NaN  0\n        1  3.0  4.0  NaN  1\n        2  3.0  4.0  NaN  5\n        3  3.0  3.0  1.0  4\n\n        For Series\n\n        >>> psser = ps.Series([2, 4, None, 3])\n        >>> psser\n        0    2.0\n        1    4.0\n        2    NaN\n        3    3.0\n        dtype: float64\n\n        >>> psser.ffill()\n        0    2.0\n        1    4.0\n        2    4.0\n        3    3.0\n        dtype: float64\n        "
    return self.fillna(method='ffill', axis=axis, inplace=inplace, limit=limit)