Method ffill

xarray/core/dataset.py:6663–6725

Fill NaN values by propagating values forward *Requires bottleneck.* Parameters ---------- dim : Hashable Specifies the dimension along which to propagate values when filling. limit : int or None, optional The maximum number of consec

(self, dim: Hashable, limit: int | None = None)

        return new

    def ffill(self, dim: Hashable, limit: int | None = None) -> Self:
        """Fill NaN values by propagating values forward

        *Requires bottleneck.*

        Parameters
        ----------
        dim : Hashable
            Specifies the dimension along which to propagate values when filling.
        limit : int or None, optional
            The maximum number of consecutive NaN values to forward fill. In
            other words, if there is a gap with more than this number of
            consecutive NaNs, it will only be partially filled. Must be greater
            than 0 or None for no limit. Must be None or greater than or equal
            to axis length if filling along chunked axes (dimensions).

        Examples
        --------
        >>> time = pd.date_range("2023-01-01", periods=10, freq="D")
        >>> data = np.array(
        ...     [1, np.nan, np.nan, np.nan, 5, np.nan, np.nan, 8, np.nan, 10]
        ... )
        >>> dataset = xr.Dataset({"data": (("time",), data)}, coords={"time": time})
        >>> dataset
        <xarray.Dataset> Size: 160B
        Dimensions:  (time: 10)
        Coordinates:
          * time     (time) datetime64[us] 80B 2023-01-01 2023-01-02 ... 2023-01-10
        Data variables:
            data     (time) float64 80B 1.0 nan nan nan 5.0 nan nan 8.0 nan 10.0

        # Perform forward fill (ffill) on the dataset

        >>> dataset.ffill(dim="time")
        <xarray.Dataset> Size: 160B
        Dimensions:  (time: 10)
        Coordinates:
          * time     (time) datetime64[us] 80B 2023-01-01 2023-01-02 ... 2023-01-10
        Data variables:
            data     (time) float64 80B 1.0 1.0 1.0 1.0 5.0 5.0 5.0 8.0 8.0 10.0

        # Limit the forward filling to a maximum of 2 consecutive NaN values

        >>> dataset.ffill(dim="time", limit=2)
        <xarray.Dataset> Size: 160B
        Dimensions:  (time: 10)
        Coordinates:
          * time     (time) datetime64[us] 80B 2023-01-01 2023-01-02 ... 2023-01-10
        Data variables:
            data     (time) float64 80B 1.0 1.0 1.0 nan 5.0 5.0 5.0 8.0 8.0 10.0

        Returns
        -------
        Dataset

        See Also
        --------
        Dataset.bfill