issue_comments: 1546041240

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https://github.com/pydata/xarray/pull/7840#issuecomment-1546041240 https://api.github.com/repos/pydata/xarray/issues/7840 1546041240 IC_kwDOAMm_X85cJreY 30324885 2023-05-12T17:07:08Z 2023-05-12T17:09:44Z NONE

Yeah, as a total new comer, this explanation seems not only unintuitive but also like we're fighting with common terminology used in math and everywhere else.

When you define the coordinates,

data = xr.DataArray(np.random.randn(2,3)*10, dims=("x", "y"), coords={"x": [10, 20]})

It looks like you are defining a specific coordinate system [10,20] on a specific dimension "x". The key of the object-value pair set in coords (ie. "x") MUST refer to a dimension, because "x" is a dimension that we are defining a coordinate system for. As we don't define any coordinate system on "y", asking xarray for data.coords["y"] gives us a array([0,1,2]) which we'd consider to be an intuitive default coordinate system.

Intuitively, we think: An xarray contains dimensions along which specific coordinates can be defined: xarray.dimension.coordinate

In the attribute definition, we say:

data.x.attrs["units"] = "x units" which feels like we are defining an attribute on the entire dimension. As opposed to data.x.10 (I realize the problem here) which would be definiting an attribute on a single coordinate within a dimension.

I'm done, thank you for your time. I will continue reading and learning about xarray.

 
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