Maximilian Roos

Thanks for the clear issue @gerritholl . I agree — it's confusing if those two look the same. Currently, one way of discriminating them: ```python In [6]: da1.indexes['x'] Out[6]: IntervalIndex([(0.0,...

I think I really empathize with the pain here. There's a very real explicitness vs "helpfulness" tradeoff, often depending on whether people are doing exploratory research vs hardened production (a...

That's quite an old version of xarray! Could we confirm it has similar results on a more recent version?

Thanks @d1mach . Could it be related to https://github.com/pydata/xarray/issues/7136 ?

Sorry this didn't get @Illviljan . It looks good, without me having that much context. Do you have any info on whether this has any performance impact? I imagine this...

~Yeah, I guess if someone does `x = y.transpose()` and then `x[0] = 42`, then `y` would be inconsistently updated. Don't think we're likely to get copy-on-write semantics soon!~ ~So...

It's curious that's slow — it's not a deep copy and so should be fast (in python terms!), since it's just copying the class instance. Totally understand re ASV —...

Agree with (1) or (3). I do (1) a lot, no harm in adding it to xarray. I could also imagine (2) with options for backends (e.g. pandas as one...

This does seem very odd. Does anyone have any ideas? As per @forman , changing ```diff -c32 = xr.DataArray(np.linspace(0, 1, 10, dtype=np.float32), dims='x') +c32 = xr.DataArray(np.linspace(0, 1, 10, dtype=np.float64), dims='x')...

It could be coherent to have: - `v32.x.equals(v64.x)` be false — the indexes themselves aren't the same - the join allow some float imprecision (similar to `method=nearest`), which would conveniently...