modin.pandas.Series.fillna¶

Series.fillna(value: Hashable | Mapping | Series = None, *, method: FillnaOptions | None = None, axis: Axis | None = None, inplace: bool = False, limit: int | None = None, downcast: dict | None = None) → Series | None[source]¶

Fill NA/NaN values using the specified method.

Parameters:

• value (scalar, dict, Series, or DataFrame) – Value to use to fill holes (e.g. 0), alternately a dict/Series/DataFrame of values specifying which value to use for each index (for a Series) or column (for a DataFrame). Values not in the dict/Series/DataFrame will not be filled. This value cannot be a list.

• method ({{'backfill', 'bfill', 'ffill', None}}, default None) –

  Method to use for filling holes in reindexed Series:

  • ffill: propagate last valid observation forward to next valid.

  • backfill / bfill: use next valid observation to fill gap.

  Deprecated since version 2.1.0: Use ffill or bfill instead.

• axis ({axes_single_arg}) – Axis along which to fill missing values. For Series this parameter is unused and defaults to 0.

• inplace (bool, 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).

• 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.

• downcast (dict, 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).

  Deprecated since version 2.2.0.

Returns:

Object with missing values filled or None if inplace=True.

Return type:

{klass} or None

See also

ffill

Fill values by propagating the last valid observation to next valid.

bfill

Fill values by using the next valid observation to fill the gap.

interpolate

Fill NaN values using interpolation.

reindex

Conform object to new index.

asfreq

Convert TimeSeries to specified frequency.

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

Copy

Replace all NaN elements with 0s.

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

Copy

Replace all NaN elements in column ‘A’, ‘B’, ‘C’, and ‘D’, with 0, 1, 2, and 3 respectively.

>>> values = {"A": 0, "B": 1, "C": 2, "D": 3}
>>> df.fillna(value=values)
     A    B    C    D
0  0.0  2.0  2.0  0.0
1  3.0  4.0  2.0  1.0
2  0.0  1.0  2.0  3.0
3  0.0  3.0  2.0  4.0

Copy

When filling using a DataFrame, replacement happens along the same column names and same indices

>>> df2 = pd.DataFrame(np.zeros((4, 4)), columns=list("ABCE"))
>>> df.fillna(df2)
     A    B    C    D
0  0.0  2.0  0.0  0.0
1  3.0  4.0  0.0  1.0
2  0.0  0.0  0.0  NaN
3  0.0  3.0  0.0  4.0

Copy

Note that column D is not affected since it is not present in df2.