pandas.Series.fillna#

Series.fillna(value, *, axis=None, inplace=False, limit=None)[source]#

Fill NA/NaN values with value.

Parameters:
valuescalar, 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.

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

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:
Series/DataFrame or None

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

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.

Notes

For non-object dtype, value=None will use the NA value of the dtype. See more details in the Filling missing data section.

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

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

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

Only replace the first NaN element.

>>> df.fillna(value=values, limit=1)
     A    B    C    D
0  0.0  2.0  2.0  0.0
1  3.0  4.0  NaN  1.0
2  NaN  1.0  NaN  3.0
3  NaN  3.0  NaN  4.0

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

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

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