# xarray.Dataset.pipe

#### Dataset.pipe(func, \*args, \*\*kwargs)[[source]](https://github.com/pydata/xarray/blob/main/xarray/core/common.py#L745-L879)

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`.

### 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

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```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
<xarray.Dataset> 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)
<xarray.Dataset> 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)
<xarray.Dataset> 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)
... )
<xarray.Dataset> 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
```

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#### SEE ALSO
[`pandas.DataFrame.pipe`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.pipe.html#pandas.DataFrame.pipe)