datatree.DataTree.bfill

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Attention

This repository has been archived. Please use xarray.DataTree instead.

datatree.DataTree.bfill#

DataTree.bfill(dim: Hashable, limit: int | None = None) → Self[source]#

Fill NaN values by propagating values backward

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 backward 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[ns] 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

# filled dataset, fills NaN values by propagating values backward

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

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

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

Dataset

See also

Dataset.ffill

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