pandas.Series.ffill#

Series.ffill(*, axis=None, inplace=False, limit=None, downcast=_NoDefault.no_default)[source]#

Fill NA/NaN values by propagating the last valid observation to next valid.

Parameters:

axis{0 or ‘index’} for Series, {0 or ‘index’, 1 or ‘columns’} for DataFrame: Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.
inplacebool, default False: If True, fill in-place. Note: this will modify any other views on this object (e.g., a no-copy slice for a column in a DataFrame).
limitint, 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.
downcastdict, default is None: A dict of item->dtype of what to downcast if possible, or the string ‘infer’ which will try to downcast to an appropriate equal type (e.g. float64 to int64 if possible).

Returns:

Series/DataFrame or None: Object with missing values filled or None if inplace=True.

Examples

>>> df = pd.DataFrame([[np.nan, 2, np.nan, 0],
...                    [3, 4, np.nan, 1],
...                    [np.nan, np.nan, np.nan, np.nan],
...                    [np.nan, 3, np.nan, 4]],
...                   columns=list("ABCD"))
>>> df
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  NaN  NaN NaN  NaN
3  NaN  3.0 NaN  4.0

>>> df.ffill()
     A    B   C    D
0  NaN  2.0 NaN  0.0
1  3.0  4.0 NaN  1.0
2  3.0  4.0 NaN  1.0
3  3.0  3.0 NaN  4.0

>>> ser = pd.Series([1, np.nan, 2, 3])
>>> ser.ffill()
0   1.0
1   1.0
2   2.0
3   3.0
dtype: float64