Method bfill

pandas/core/generic.py:7263–7373

Fill NA/NaN values by using the next valid observation to fill the gap. This method fills missing values in a backward direction along the specified axis, propagating non-null values from later positions to earlier positions containing NaN. Parameters

(
        self,
        *,
        axis: None | Axis = None,
        inplace: bool = False,
        limit: None | int = None,
        limit_area: Literal["inside", "outside"] | None = None,
    )

    @final
    def bfill(
        self,
        *,
        axis: None | Axis = None,
        inplace: bool = False,
        limit: None | int = None,
        limit_area: Literal["inside", "outside"] | None = None,
    ) -> Self:
        """
        Fill NA/NaN values by using the next valid observation to fill the gap.

        This method fills missing values in a backward direction along the
        specified axis, propagating non-null values from later positions to
        earlier positions containing NaN.

        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.
        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.
        limit_area : {`None`, 'inside', 'outside'}, default None
            If limit is specified, consecutive NaNs will be filled with this
            restriction.

            * ``None``: No fill restriction.
            * 'inside': Only fill NaNs surrounded by valid values
              (interpolate).
            * 'outside': Only fill NaNs outside valid values (extrapolate).

            .. versionadded:: 2.2.0

        Returns
        -------
        Series/DataFrame
            Object with missing values filled.

        See Also
        --------
        DataFrame.ffill : Fill NA/NaN values by propagating the last valid
            observation to next valid.

        Examples
        --------
        For Series:

        >>> s = pd.Series([1, None, None, 2])
        >>> s.bfill()