Improved inference of names when concatenating arrays #2775
Description
Problem description
Using the name of the first element to concatenate as the name of the concatenated array is only correct if all names are identical. When names vary, using a clear placeholder name or the name of the new dimension would avoid misleading data users.
This came up for me recently when stacking several bands of a satellite image to produce a faceted plot - the resulting colorbar was labelled "blue", even though that was clearly incorrect.
A similar process is probably also desirable for aggregation of units across concatenated arrays - use first if identical, otherwise discard or error depending on the compat argument.
Code Sample, a copy-pastable example if possible
ds = xr.Dataset({
k: xr.DataArray(np.random.random((2, 2)), dims="x y".split(), name=k)
for k in "blue green red".split()
})
# arr.name == "blue", could be "band" or "concat_dim"
arr = xr.concat([ds.blue, ds.green, ds.red], dim="band")
# label of colorbar is "blue", which is meaningless
arr.plot.imshow(col="band")
One implementation that would certainly be nice for this use-case (though perhaps not generally) is that concatenating DataArrays along an entirely new dimension with unique array names and dim passed a string could create a new Index as well, as pd.Index([a.name for a in objs], name=dim).
Details
INSTALLED VERSIONS
commit: None
python: 3.6.5 |Anaconda, Inc.| (default, Mar 29 2018, 13:32:41) [MSC v.1900 64 bit (AMD64)]
python-bits: 64
OS: Windows
OS-release: 10
machine: AMD64
processor: Intel64 Family 6 Model 158 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None
libhdf5: 1.10.3
libnetcdf: 4.4.1.1
xarray: 0.11.2
pandas: 0.23.1
numpy: 1.14.5
scipy: 1.2.1
netCDF4: 1.4.2
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.0.3.4
PseudonetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
cyordereddict: None
dask: None
distributed: None
matplotlib: 3.0.2
cartopy: None
seaborn: 0.9.0
setuptools: 40.6.2
pip: 10.0.1
conda: None
pytest: 4.2.0
IPython: 6.4.0
sphinx: 1.8.0
I'd be happy to write a PR for this if it would be accepted.