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id node_id number title user state locked assignee milestone comments created_at updated_at ▲ closed_at author_association active_lock_reason draft pull_request body reactions performed_via_github_app state_reason repo type
1371769156 I_kwDOAMm_X85Rw4lE 7030 Assigning with .loc indexing: implicit effect of dimension order? darikg 6875882 closed 0     5 2022-09-13T17:19:07Z 2023-09-12T18:17:11Z 2023-09-12T18:17:11Z CONTRIBUTOR      

What happened?

Assigning a DataArray to a subset of another DataArray seems to depend on the order of the dimensions

What did you expect to happen?

I would expect xarray to automatically align the dimensions appropriately.

Minimal Complete Verifiable Example

Example 1:

```python from xarray import DataArray from numpy import zeros, ones, arange

Two arrays abc and acb, identical except for dimensional ordering

abc = DataArray(zeros((2, 3, 4)), dims=('a', 'b', 'c')) acb = DataArray(zeros((2, 4, 3)), dims=('a', 'c', 'b')) assert (abc == acb).all()

Assign a subset

bc = DataArray(ones((3, 4)), dims=('b', 'c')) abc.loc[dict(a=0)] = bc acb.loc[dict(a=0)] = bc # ValueError: could not broadcast input array from shape (3,4) into shape (4,3) ```

Example 2:

Same as example 1, but instead of raising a ValueError, runs without error, but gives incorrect results because it ignores dimension order

```python

This time, make b and c dimensions identical

abc = DataArray(zeros((2, 3, 3)), dims=('a', 'b', 'c')) acb = DataArray(zeros((2, 3, 3)), dims=('a', 'c', 'b')) assert (abc == acb).all()

Assign a subset

bc = DataArray(arange(9).reshape(3, 3), dims=('b', 'c')) abc.loc[dict(a=0)] = bc acb.loc[dict(a=0)] = bc assert (abc == acb).all() # Assertion error ```

MVCE confirmation

  • [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
  • [X] Complete example — the example is self-contained, including all data and the text of any traceback.
  • [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
  • [X] New issue — a search of GitHub Issues suggests this is not a duplicate.

Relevant log output

No response

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS ------------------ commit: None python: 3.9.13 | packaged by conda-forge | (main, May 27 2022, 16:50:36) [MSC v.1929 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 142 Stepping 12, GenuineIntel byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: ('English_United States', '1252') libhdf5: 1.12.1 libnetcdf: 4.8.1 xarray: 2022.6.0 pandas: 1.4.3 numpy: 1.23.1 scipy: 1.9.0 netCDF4: 1.6.0 pydap: None h5netcdf: None h5py: 3.7.0 Nio: None zarr: None cftime: 1.6.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: None distributed: None matplotlib: 3.5.2 cartopy: None seaborn: None numbagg: None fsspec: 2022.7.1 cupy: None pint: None sparse: None flox: None numpy_groupies: None setuptools: 63.4.2 pip: 22.2.2 conda: 4.14.0 pytest: 7.1.2 IPython: 8.4.0 sphinx: None
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  completed xarray 13221727 issue
873713013 MDU6SXNzdWU4NzM3MTMwMTM= 5240 Coord name not set when `concat`ing along a DataArray darikg 6875882 closed 0     4 2021-05-01T16:18:32Z 2021-08-23T17:00:39Z 2021-08-23T17:00:39Z CONTRIBUTOR      

python from xarray import DataArray, concat a = DataArray([0], dims='a') out = concat([a, a], dim=DataArray([0, 1], dims='b')) print(out.coords)

Coordinates:

None (b) int32 0 1

I would've expected the name of the new coordinate to be the name of the DataArray variable but instead it's None.

Environment:

Output of <tt>xr.show_versions()</tt> INSTALLED VERSIONS ------------------ commit: None python: 3.9.2 | packaged by conda-forge | (default, Feb 21 2021, 04:59:43) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 142 Stepping 12, GenuineIntel byteorder: little LC_ALL: None LANG: en_US.UTF-8 LOCALE: English_United States.1252 libhdf5: None libnetcdf: None xarray: 0.17.0 pandas: 1.2.4 numpy: 1.20.2 scipy: None netCDF4: None pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: None distributed: None matplotlib: None cartopy: None seaborn: None numbagg: None pint: None setuptools: 49.6.0.post20210108 pip: 21.1.1 conda: None pytest: None IPython: 7.23.0 sphinx: None
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  completed xarray 13221727 issue
828805728 MDU6SXNzdWU4Mjg4MDU3Mjg= 5022 Extracting `formatting_html` as a standalone library? darikg 6875882 open 0     3 2021-03-11T06:23:04Z 2021-07-16T17:31:20Z   CONTRIBUTOR      

Hi, xarray is a superb library but my favorite icing on the cake is the out-of-the-box rich HTML display in Jupyter. I've been finding myself wanting the same thing for every class I use in python that is fundamentally a hierachically structured numerical dataset.

Has there been any discussion of generalizing the whole thing? I'd be happy to give it a shot myself, but I was hoping to pick your collective brains first, especially @benbovy and @jsignell, (who based on my very preliminary github sleuthing were early heavy-lifters?)

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    xarray 13221727 issue
686495257 MDExOlB1bGxSZXF1ZXN0NDc0MDUyNjc1 4379 Use deepcopy recursively on numpy arrays darikg 6875882 closed 0     12 2020-08-26T17:31:56Z 2020-08-27T15:58:49Z 2020-08-27T14:56:49Z CONTRIBUTOR   0 pydata/xarray/pulls/4379
  • [x] Closes #4362
  • [x] Tests added
  • [x] Passes isort . && black . && mypy . && flake8
  • [ ] User visible changes (including notable bug fixes) are documented in whats-new.rst

(Not sure this is worth noting in whats-new)

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    xarray 13221727 pull
683649612 MDU6SXNzdWU2ODM2NDk2MTI= 4362 Surprising deepcopy semantics with dtype='object' darikg 6875882 closed 0     4 2020-08-21T15:40:59Z 2020-08-27T14:56:48Z 2020-08-27T14:56:48Z CONTRIBUTOR      

```python from copy import deepcopy import numpy as np import xarray as xr

class Dummy: pass

a0 = np.array([Dummy()]) a1 = deepcopy(a0) print(a0[0] is a1[0]) # False, as expected

x0 = xr.DataArray(a0, dims='dummy') x1 = deepcopy(x0) print(x0.values[0] is x1.values[0]) # unexpectedly True ```

I think this is a bug, and would be fixed with an extra deepcopy around here

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  completed xarray 13221727 issue

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