Having taken the ideas presented here as inspiration the latest HoloViews release actually extends what we had described above and provides the capability to use arbitrary xarray methods to transform the data and control the parameters of those transforms using Panel based widgets. The HoloViews docs show one such example built on xarray which is built around so call dim expressions:

```python import panel as pn import xarray as xr

air_temp = xr.tutorial.load_dataset('air_temperature')

We declare a dim expression which uses the quantile method from the xr namespace

and provides a panel FloatSlider as the argument to the expression

q = pn.widgets.FloatSlider(name='quantile') quantile_expr = hv.dim('air').xr.quantile(q, dim='time')

We now wrap the xarray Dataset in a HoloViews one, apply the dim expression and cast the result to an image

temp_ds = hv.Dataset(air_temp, ['lon', 'lat']) transformed = temp_ds.apply.transform(air=quantile_expr).apply(hv.Image)

Now we display the resulting transformation pipeline alongside the widget

pn.Column(q, transformed.opts(colorbar=True, width=400)) ```

I am likely to integrate this capability with hvPlot with a more intuitive API, e.g. in this case I'd expect to be able to spell this something like this:

python xrds = xr.tutorial.load_dataset('air_temperature') q = pn.widgets.FloatSlider(name='quantile') quantile_expr = hv.dim('air').xr.quantile(q, dim='time') xrds.hvplot.image(transforms={'air': quantile_expr})