Method min

xarray/core/_aggregations.py:7200–7304

Reduce this DataArray's data by applying ``min`` along some dimension(s). Parameters ---------- dim : str, Iterable of Hashable, "..." or None, default: None Name of dimension[s] along which to apply ``min``. For e.g. ``dim="x"`` or ``dim=["x

(
        self,
        dim: Dims = None,
        *,
        skipna: bool | None = None,
        keep_attrs: bool | None = None,
        **kwargs: Any,
    )

            return out

    def min(
        self,
        dim: Dims = None,
        *,
        skipna: bool | None = None,
        keep_attrs: bool | None = None,
        **kwargs: Any,
    ) -> DataArray:
        """
        Reduce this DataArray's data by applying ``min`` along some dimension(s).

        Parameters
        ----------
        dim : str, Iterable of Hashable, "..." or None, default: None
            Name of dimension[s] along which to apply ``min``. For e.g. ``dim="x"``
            or ``dim=["x", "y"]``. If None, will reduce over the GroupBy dimensions.
            If "...", will reduce over all dimensions.
        skipna : bool or None, optional
            If True, skip missing values (as marked by NaN). By default, only
            skips missing values for float dtypes; other dtypes either do not
            have a sentinel missing value (int) or ``skipna=True`` has not been
            implemented (object, datetime64 or timedelta64).
        keep_attrs : bool or None, optional
            If True, ``attrs`` will be copied from the original
            object to the new one.  If False, the new object will be
            returned without attributes.
        **kwargs : Any
            Additional keyword arguments passed on to the appropriate array
            function for calculating ``min`` on this object's data.
            These could include dask-specific kwargs like ``split_every``.

        Returns
        -------
        reduced : DataArray
            New DataArray with ``min`` applied to its data and the
            indicated dimension(s) removed

        See Also
        --------
        numpy.min
        dask.array.min
        DataArray.min
        :ref:`groupby`
            User guide on groupby operations.

        Notes
        -----
        Use the ``flox`` package to significantly speed up groupby computations,
        especially with dask arrays. Xarray will use flox by default if installed.
        Pass flox-specific keyword arguments in ``**kwargs``.
        See the `flox documentation <https://flox.readthedocs.io>`_ for more.

        Examples
        --------
        >>> da = xr.DataArray(
        ...     np.array([1, 2, 3, 0, 2, np.nan]),
        ...     dims="time",
        ...     coords=dict(