I think #2298 is what I'm really waiting for and would solve the use cases I listed above. I'll have no trouble using the accessor methods in the time being. Thanks, @shoyer

I think the cleanest way to do this in the long term would be to combine some sort of "lazy array" object with caching, e.g., along the lines of what's described in https://github.com/pydata/xarray/issues/2298. I'm not sure what the best solution in the short-term is, though.

That's true, but unless you start subclassing dataset then isn't that always going to be the case?

You have some quantity which you can only calculate with either a function or an accessor method on the dataset, wouldn't you need to alter the __getitem__ method on the dataset object (or some subclass of it) in order to get the behaviour you're describing?

The only problem I see with this is that actual.acc.altitude must be called before actual.altitude, otherwise it will result in the data_var being used before it is created.

If you want to return your newly-calculated altitude and also have it be a full data_var in your dataset, one way would be to just alter the original dataset in-place. Something like

```python

import xarray as xr import pandas as pd import xarray.testing as xrt

@xr.register_dataset_accessor('acc') class Accessor(object): def init(self, xarray_ds): self._ds = xarray_ds self._altitude = None

@property
def altitude(self):
    """ An expensive calculation that results in data that not everyone needs. """
    if self._altitude is None:
        self._altitude = xr.DataArray([5, 10, 15, 20, 25],
                                      coords=[('time', self._ds.time)])

        # Here we add the calculated altitude to the dataset as a new data variable
        self._ds['altitude'] = self._altitude

    # Return just the altitude dataarray
    return self._altitude

expected = xr.Dataset({'data': (['time'], [100, 30, 10, 3, 1]), 'altitude': (['time'], [5, 10, 15, 20, 25])}, coords={'time': pd.date_range('2014-09-06', periods=5, freq='1s')})

actual = xr.Dataset({'data': (['time'], [100, 30, 10, 3, 1])}, coords={'time': pd.date_range('2014-09-06', periods=5, freq='1s')})

Return newly-calculated altitude, but also store it in the actual dataset for later

altitude = actual.acc.altitude

Check that worked

xrt.assert_equal(actual, expected) xrt.assert_equal(actual['altitude'], actual.acc.altitude) ```