Set one-dimensional data variable as dimension coordinate?

Code Sample

I have this dataset, and I’d like to make it indexable by time:

<xarray.Dataset>
Dimensions:                (station_observations: 46862)
Dimensions without coordinates: station_observations
Data variables:
    time                   (station_observations) datetime64[ns] ...
    SNOW_ON_THE_GROUND     (station_observations) float64 ...
    ONE_DAY_SNOW           (station_observations) float64 ...
    ONE_DAY_RAIN           (station_observations) float64 ...
    ONE_DAY_PRECIPITATION  (station_observations) float64 ...
    MIN_TEMP               (station_observations) float64 ...
    MAX_TEMP               (station_observations) float64 ...
Attributes:
    elevation:     15.0

Problem description

I expected to be able to use ds.set_coords to make the time variable an indexable coordinate. The variable IS converted to a coordinate, but it is not a dimension coordinate, so I can’t index with it. I can use assign_coords(station_observations=ds.time) to make station_observations indexable by time, but then the name in semantically wrong, and the time variable still exists, which makes the code harder to maintain.

Expected Output

ds.set_coords('time', inplace=True)
<xarray.Dataset>
Dimensions:                (station_observations: 46862)
Coordinates:
    time                   (station_observations) datetime64[ns] ...
Dimensions without coordinates: station_observations
Data variables:
    SNOW_ON_THE_GROUND     (station_observations) float64 ...
    ONE_DAY_SNOW           (station_observations) float64 ...
    ONE_DAY_RAIN           (station_observations) float64 ...
    ONE_DAY_PRECIPITATION  (station_observations) float64 ...
    MIN_TEMP               (station_observations) float64 ...
    MAX_TEMP               (station_observations) float64 ...
Attributes:
    elevation:     15.0

In [95]: ds.sel(time='1896')
ValueError: dimensions or multi-index levels ['time'] do not exist

with assign_coords:

In [97]: ds=ds.assign_coords(station_observations=ds.time)

In [98]: ds.sel(station_observations='1896')
Out[98]: 
<xarray.Dataset>
Dimensions:                (station_observations: 366)
Coordinates:
  * station_observations   (station_observations) datetime64[ns] 1896-01-01 ...
Data variables:
    time                   (station_observations) datetime64[ns] ...
    SNOW_ON_THE_GROUND     (station_observations) float64 ...
    ONE_DAY_SNOW           (station_observations) float64 ...
    ONE_DAY_RAIN           (station_observations) float64 ...
    ONE_DAY_PRECIPITATION  (station_observations) float64 ...
    MIN_TEMP               (station_observations) float64 ...
    MAX_TEMP               (station_observations) float64 ...
Attributes:
    elevation:     15.0

works correctly, but looks ugly. It would be nice if the time variable could be assigned as a dimension directly. I can drop the time variable and rename the station_observations, but it’s a little annoying to do so.

Output of xr.show_versions()

INSTALLED VERSIONS

commit: None python: 3.6.6.final.0 python-bits: 64 OS: Linux OS-release: 4.16.0-041600-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: en_AU.UTF-8 LOCALE: en_AU.UTF-8

xarray: 0.10.2 pandas: 0.22.0 numpy: 1.13.3 scipy: 0.19.1 netCDF4: 1.3.1 h5netcdf: None h5py: None Nio: None zarr: None bottleneck: 1.2.0 cyordereddict: None dask: 0.16.0 distributed: None matplotlib: 2.1.1 cartopy: None seaborn: None setuptools: 39.0.1 pip: 9.0.1 conda: None pytest: None IPython: 5.5.0 sphinx: None

[keewis]

to get your example to work, use this:

data=pd.DataFrame({'x': [0, 1, 2], 'y': ['a', 'b', 'c'],'z': ['a', 'b', 'c']})
xr.DataArray(data.x, coords={'y': data.y, 'x':data.z}, dims="y")

to get both as dimensions, use

df = pd.DataFrame({'x': [0, 1, 2], 'y': ['a', 'b', 'c'],'z': ['a', 'b', 'c']})
ds = df.set_index(["y", "z"]).to_xarray()

[fujiisoup]

Hi @nedclimaterisk. Thanks for the raising an issue.

In that case, you can use swap_dims,

In [1]: import xarray as xr
   ...: ds = xr.Dataset({'x': ('i', [0, 1, 2]), 'y': ('i', ['a', 'b', 'c'])})
   ...: ds
   ...: 
   ...: 
Out[1]: 
<xarray.Dataset>
Dimensions:  (i: 3)
Dimensions without coordinates: i
Data variables:
    x        (i) int64 0 1 2
    y        (i) <U1 'a' 'b' 'c'

In [2]: ds.swap_dims({'i': 'x'})
Out[2]: 
<xarray.Dataset>
Dimensions:  (x: 3)
Coordinates:
  * x        (x) int64 0 1 2
Data variables:
    y        (x) <U1 'a' 'b' 'c'