Method dropna

xarray/core/dataset.py:6302–6434

Returns a new dataset with dropped labels for missing values along the provided dimension. Parameters ---------- dim : hashable Dimension along which to drop missing values. Dropping along multiple dimensions simultaneously is not yet supporte

(
        self,
        dim: Hashable,
        *,
        how: Literal["any", "all"] = "any",
        thresh: int | None = None,
        subset: Iterable[Hashable] | None = None,
    )

        return ds

    def dropna(
        self,
        dim: Hashable,
        *,
        how: Literal["any", "all"] = "any",
        thresh: int | None = None,
        subset: Iterable[Hashable] | None = None,
    ) -> Self:
        """Returns a new dataset with dropped labels for missing values along
        the provided dimension.

        Parameters
        ----------
        dim : hashable
            Dimension along which to drop missing values. Dropping along
            multiple dimensions simultaneously is not yet supported.
        how : {"any", "all"}, default: "any"
            - any : if any NA values are present, drop that label
            - all : if all values are NA, drop that label

        thresh : int or None, optional
            If supplied, require this many non-NA values (summed over all the subset variables).
        subset : iterable of hashable or None, optional
            Which variables to check for missing values. By default, all
            variables in the dataset are checked.

        Examples
        --------
        >>> dataset = xr.Dataset(
        ...     {
        ...         "temperature": (
        ...             ["time", "location"],
        ...             [[23.4, 24.1], [np.nan, 22.1], [21.8, 24.2], [20.5, 25.3]],
        ...         )
        ...     },
        ...     coords={"time": [1, 2, 3, 4], "location": ["A", "B"]},
        ... )
        >>> dataset
        <xarray.Dataset> Size: 104B
        Dimensions:      (time: 4, location: 2)
        Coordinates:
          * time         (time) int64 32B 1 2 3 4
          * location     (location) <U1 8B 'A' 'B'
        Data variables:
            temperature  (time, location) float64 64B 23.4 24.1 nan ... 24.2 20.5 25.3

        Drop NaN values from the dataset

        >>> dataset.dropna(dim="time")
        <xarray.Dataset> Size: 80B
        Dimensions:      (time: 3, location: 2)
        Coordinates:
          * time         (time) int64 24B 1 3 4
          * location     (location) <U1 8B 'A' 'B'
        Data variables:
            temperature  (time, location) float64 48B 23.4 24.1 21.8 24.2 20.5 25.3

        Drop labels with any NaN values