Description
What happened?
With a Dataset/DataArray containing a time dimension whose index uses the datetime64[ns, utc] (timezone-aware) dtype, if one calls .isel() to slice say the first element in this dimension, the time coordinate in the resulting Dataset/DataArray will have reverted to datetime64[ns] (i.e. timezone-naive).
What did you expect to happen?
Resulting Dataset/DataArray should retain the timezone-awareness on the coordinate of the sliced time dimension/index and still use datetime64[ns, utc] dtype
Minimal Complete Verifiable Example
import numpy as np
import pandas as pd
import xarray as xr
mydata = xr.DataArray(
data=np.array([
[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11]
]),
coords={
"category": ["A", "B", "C"],
"time": pd.to_datetime([
"2024-08-02T11:00:00+00:00",
"2024-08-02T12:00:00+00:00",
"2024-08-02T13:00:00+00:00",
"2024-08-02T14:00:00+00:00"
])
},
name="volume"
)
print(mydata)
print("---------------------------")
print(f"time index dtype before calling `.isel()`: {mydata.indexes['time'].dtype}")
print(f"time coord dtype before calling `.isel()`: {mydata.coords['time'].dtype}")
print("---------------------------")
# The following will slice the zeroth index in the time dimension - the time index will cease to exist but the corresponding coordinate will remain
subset = mydata.isel(time=0, drop=False)
print("---------------------------")
print(subset)
print("---------------------------")
print(f"time coord dtype after calling `.isel()`: {subset.coords['time'].dtype}")MVCE confirmation
- Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
- Complete example — the example is self-contained, including all data and the text of any traceback.
- Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
- New issue — a search of GitHub Issues suggests this is not a duplicate.
- Recent environment — the issue occurs with the latest version of xarray and its dependencies.
Relevant log output
<xarray.DataArray 'volume' (category: 3, time: 4)> Size: 96B
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
Coordinates:
* category (category) <U1 12B 'A' 'B' 'C'
* time (time) object 32B 1722596400000000000 ... 1722607200000000000
---------------------------
time index dtype before calling `.isel()`: datetime64[ns, UTC]
time coord dtype before calling `.isel()`: object
---------------------------
---------------------------
<xarray.DataArray 'volume' (category: 3)> Size: 24B
array([0, 4, 8])
Coordinates:
* category (category) <U1 12B 'A' 'B' 'C'
time datetime64[ns] 8B 2024-08-02T11:00:00
---------------------------
time coord dtype after calling `.isel()`: datetime64[ns]Anything else we need to know?
Tested with version 2024.3.0 and also 2024.7.0.
Similar to #6416
Environment
INSTALLED VERSIONS
commit: None
python: 3.11.0 (main, Mar 1 2023, 18:26:19) [GCC 11.2.0]
python-bits: 64
OS: Linux
OS-release: 5.15.0-1064-azure
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: C.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: None
libnetcdf: None
xarray: 2024.7.0
pandas: 2.2.2
numpy: 2.0.1
scipy: None
netCDF4: None
pydap: None
h5netcdf: None
h5py: None
zarr: None
cftime: None
nc_time_axis: None
iris: None
bottleneck: None
dask: None
distributed: None
matplotlib: None
cartopy: None
seaborn: None
numbagg: None
fsspec: None
cupy: None
pint: None
sparse: None
flox: None
numpy_groupies: None
setuptools: 69.5.1
pip: 24.0
conda: None
pytest: None
mypy: None
IPython: 8.26.0
sphinx: None