# xarray.core.groupby.DatasetGroupBy.min

#### DatasetGroupBy.min(dim=None, , skipna=None, keep_attrs=None, \*\*kwargs)[[source]](https://github.com/pydata/xarray/blob/main/xarray/core/_aggregations.py#L4150-L4263)

Reduce this Dataset’s data by applying `min` (i.e., minimum) along some dimension(s).

* **Parameters:**
  * **dim** ([`str`](https://docs.python.org/3/builtins/stdtypes.html#str), `Iterable` of `Hashable`, `"..."` or [`None`](https://docs.python.org/3/builtins/constants.html#None), *default*: [`None`](https://docs.python.org/3/builtins/constants.html#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`](https://docs.python.org/3/builtins/functions.html#bool) or [`None`](https://docs.python.org/3/builtins/constants.html#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`](https://docs.python.org/3/builtins/functions.html#bool) or [`None`](https://docs.python.org/3/builtins/constants.html#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** ([`Dataset`](xarray.Dataset.html.md#xarray.Dataset)) – New Dataset with `min` applied to its data and the
  indicated dimension(s) removed

#### SEE ALSO
[`numpy.min`](https://numpy.org/doc/stable/reference/generated/numpy.min.html#numpy.min), [`dask.array.min`](https://docs.dask.org/en/latest/generated/dask.array.min.html#dask.array.min), `Dataset.min`

[GroupBy: Group and Bin Data](../user-guide/groupby.html.md#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

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```pycon
>>> da = xr.DataArray(
...     np.array([1, 2, 3, 0, 2, np.nan]),
...     dims="time",
...     coords=dict(
...         time=("time", pd.date_range("2001-01-01", freq="ME", periods=6)),
...         labels=("time", np.array(["a", "b", "c", "c", "b", "a"])),
...     ),
... )
>>> ds = xr.Dataset(dict(da=da))
>>> ds
<xarray.Dataset> Size: 120B
Dimensions:  (time: 6)
Coordinates:
  * time     (time) datetime64[us] 48B 2001-01-31 2001-02-28 ... 2001-06-30
    labels   (time) <U1 24B 'a' 'b' 'c' 'c' 'b' 'a'
Data variables:
    da       (time) float64 48B 1.0 2.0 3.0 0.0 2.0 nan
```

```pycon
>>> ds.groupby("labels").min()
<xarray.Dataset> Size: 48B
Dimensions:  (labels: 3)
Coordinates:
  * labels   (labels) object 24B 'a' 'b' 'c'
Data variables:
    da       (labels) float64 24B 1.0 2.0 0.0
```

Use `skipna` to control whether NaNs are ignored.

```pycon
>>> ds.groupby("labels").min(skipna=False)
<xarray.Dataset> Size: 48B
Dimensions:  (labels: 3)
Coordinates:
  * labels   (labels) object 24B 'a' 'b' 'c'
Data variables:
    da       (labels) float64 24B nan 2.0 0.0
```

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