# xarray.Dataset.pipe
#### Dataset.pipe(func, \*args, \*\*kwargs)[[source]](https://github.com/pydata/xarray/blob/main/xarray/core/common.py#L755-L888)
Apply `func(self, *args, **kwargs)`.
This method replicates the pandas method of the same name.
* **Parameters:**
* **func** ([`callable()`](https://docs.python.org/3/builtins/functions.html#callable)) – function to apply to this xarray object (Dataset/DataArray).
`args`, and `kwargs` are passed into `func`.
Alternatively a `(callable, data_keyword)` tuple where
`data_keyword` is a string indicating the keyword of
`callable` that expects the xarray object.
* **\*args** – positional arguments passed into `func`.
* **\*\*kwargs** – a dictionary of keyword arguments passed into `func`.
* **Returns:**
**object** (`Any`) – the return type of `func`.
#### SEE ALSO
[`pandas.DataFrame.pipe`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pipe.html#pandas.DataFrame.pipe)
### Notes
Use `.pipe` when chaining together functions that expect
xarray or pandas objects, e.g., instead of writing
```python
f(g(h(ds), arg1=a), arg2=b, arg3=c)
```
You can write
```python
(ds.pipe(h).pipe(g, arg1=a).pipe(f, arg2=b, arg3=c))
```
If you have a function that takes the data as (say) the second
argument, pass a tuple indicating which keyword expects the
data. For example, suppose `f` takes its data as `arg2`:
```python
(ds.pipe(h).pipe(g, arg1=a).pipe((f, "arg2"), arg1=a, arg3=c))
```
### Examples
```pycon
>>> x = xr.Dataset(
... {
... "temperature_c": (
... ("lat", "lon"),
... 20 * np.random.rand(4).reshape(2, 2),
... ),
... "precipitation": (("lat", "lon"), np.random.rand(4).reshape(2, 2)),
... },
... coords={"lat": [10, 20], "lon": [150, 160]},
... )
>>> x
Size: 96B
Dimensions: (lat: 2, lon: 2)
Coordinates:
* lat (lat) int64 16B 10 20
* lon (lon) int64 16B 150 160
Data variables:
temperature_c (lat, lon) float64 32B 10.98 14.3 12.06 10.9
precipitation (lat, lon) float64 32B 0.4237 0.6459 0.4376 0.8918
```
```pycon
>>> def adder(data, arg):
... return data + arg
...
>>> def div(data, arg):
... return data / arg
...
>>> def sub_mult(data, sub_arg, mult_arg):
... return (data * mult_arg) - sub_arg
...
>>> x.pipe(adder, 2)
Size: 96B
Dimensions: (lat: 2, lon: 2)
Coordinates:
* lat (lat) int64 16B 10 20
* lon (lon) int64 16B 150 160
Data variables:
temperature_c (lat, lon) float64 32B 12.98 16.3 14.06 12.9
precipitation (lat, lon) float64 32B 2.424 2.646 2.438 2.892
```
```pycon
>>> x.pipe(adder, arg=2)
Size: 96B
Dimensions: (lat: 2, lon: 2)
Coordinates:
* lat (lat) int64 16B 10 20
* lon (lon) int64 16B 150 160
Data variables:
temperature_c (lat, lon) float64 32B 12.98 16.3 14.06 12.9
precipitation (lat, lon) float64 32B 2.424 2.646 2.438 2.892
```
```pycon
>>> (
... x.pipe(adder, arg=2)
... .pipe(div, arg=2)
... .pipe(sub_mult, sub_arg=2, mult_arg=2)
... )
Size: 96B
Dimensions: (lat: 2, lon: 2)
Coordinates:
* lat (lat) int64 16B 10 20
* lon (lon) int64 16B 150 160
Data variables:
temperature_c (lat, lon) float64 32B 10.98 14.3 12.06 10.9
precipitation (lat, lon) float64 32B 0.4237 0.6459 0.4376 0.8918
```