issue_comments: 748486801

Thanks for the response. I think reindex would need to be changed as well because this code:

python sensor_data.reindex({ 'time': [1], 'sensor': ['A', 'B'] }, method='ffill')

Is not equivalent to this code: python sensor_data.reindex({ 'time': [1], 'sensor': ['A', 'B'] }).ffill(dim='time').ffill(dim='sensor')

So if I understand your to_dataset idea correctly, you are proposing:

python ds = sensor_data.to_dataset(dim='sensor') xr.concat([ ds[sensor].sel({'time': time}, method='ffill', drop=True) for sensor, time in zip(['A', 'A', 'A', 'B', 'C'], [0, 1, 2, 0, 0]) ], dim='sample')

I guess this works but it's a bit cumbersome and unlikely to be fast. I think there must be something I'm not understanding here - I'm not familiar with all the nuances of the xarray api.

Your idea of reindex followed by sel is an interesting one, but it does do something slightly different than what I was asking for: it does not fail if one of the sensors in the query list is missing, but rather inserts a NaN. I suppose you could fix this by doing an extra check afterwards, assuming that your original pre-reindex data contained no NaNs.

In general min(S*N,T*N) could be much larger than S*T, so for big queries it's quite possible that you wouldn't have enough space to allocate the intermediate even if you could fit 100s of copies of the original S*T matrix. Using a dask cluster would make this situation less likely of course, but it seems like it would be better to avoid all this copying (even on a beefy cluster) even if just for performance reasons.