pandas.DataFrame.fillna#

DataFrame.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.