Method fillna

pandas/core/generic.py:6922–7158

Fill NA/NaN values with `value`. 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

(
        self,
        value: Hashable | Mapping | Series | DataFrame,
        *,
        axis: Axis | None = None,
        inplace: bool = False,
        limit: int | None = None,
    )

    @final
    def fillna(
        self,
        value: Hashable | Mapping | Series | DataFrame,
        *,
        axis: Axis | None = None,
        inplace: bool = False,
        limit: int | None = None,
    ) -> Self:
        """
        Fill NA/NaN values with `value`.

        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.
        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.
        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
            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
            Object with missing values filled.

        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 :ref:`Filling missing data<missing_data.fillna>`
        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],
        ...     ],