# xarray.Dataset.filter_by_attrs
#### Dataset.filter_by_attrs(\*\*kwargs)[[source]](https://github.com/pydata/xarray/blob/main/xarray/core/dataset.py#L8899-L8992)
Returns a `Dataset` with variables that match specific conditions.
Can pass in `key=value` or `key=callable`. A Dataset is returned
containing only the variables for which all the filter tests pass.
These tests are either `key=value` for which the attribute `key`
has the exact value `value` or the callable passed into
`key=callable` returns True. The callable will be passed a single
value, either the value of the attribute `key` or `None` if the
DataArray does not have an attribute with the name `key`.
* **Parameters:**
**\*\*kwargs** –
key
: Attribute name.
value
: If value is a callable, it should return a boolean in the form
of bool = func(attr) where attr is da.attrs[key].
Otherwise, value will be compared to the each
DataArray’s attrs[key].
* **Returns:**
**new** ([`Dataset`](xarray.Dataset.html.md#xarray.Dataset)) – New dataset with variables filtered by attribute.
### Examples
```pycon
>>> temp = 15 + 8 * np.random.randn(2, 2, 3)
>>> precip = 10 * np.random.rand(2, 2, 3)
>>> lon = [[-99.83, -99.32], [-99.79, -99.23]]
>>> lat = [[42.25, 42.21], [42.63, 42.59]]
>>> dims = ["x", "y", "time"]
>>> temp_attr = dict(standard_name="air_potential_temperature")
>>> precip_attr = dict(standard_name="convective_precipitation_flux")
```
```pycon
>>> ds = xr.Dataset(
... dict(
... temperature=(dims, temp, temp_attr),
... precipitation=(dims, precip, precip_attr),
... ),
... coords=dict(
... lon=(["x", "y"], lon),
... lat=(["x", "y"], lat),
... time=pd.date_range("2014-09-06", periods=3),
... reference_time=pd.Timestamp("2014-09-05"),
... ),
... )
```
Get variables matching a specific standard_name:
```pycon
>>> ds.filter_by_attrs(standard_name="convective_precipitation_flux")
Size: 192B
Dimensions: (x: 2, y: 2, time: 3)
Coordinates:
lon (x, y) float64 32B -99.83 -99.32 -99.79 -99.23
lat (x, y) float64 32B 42.25 42.21 42.63 42.59
* time (time) datetime64[us] 24B 2014-09-06 2014-09-07 2014-09-08
reference_time datetime64[us] 8B 2014-09-05
Dimensions without coordinates: x, y
Data variables:
precipitation (x, y, time) float64 96B 5.68 9.256 0.7104 ... 4.615 7.805
```
Get all variables that have a standard_name attribute:
```pycon
>>> standard_name = lambda v: v is not None
>>> ds.filter_by_attrs(standard_name=standard_name)
Size: 288B
Dimensions: (x: 2, y: 2, time: 3)
Coordinates:
lon (x, y) float64 32B -99.83 -99.32 -99.79 -99.23
lat (x, y) float64 32B 42.25 42.21 42.63 42.59
* time (time) datetime64[us] 24B 2014-09-06 2014-09-07 2014-09-08
reference_time datetime64[us] 8B 2014-09-05
Dimensions without coordinates: x, y
Data variables:
temperature (x, y, time) float64 96B 29.11 18.2 22.83 ... 16.15 26.63
precipitation (x, y, time) float64 96B 5.68 9.256 0.7104 ... 4.615 7.805
```