issue_comments

I'm on a train wifi, so not really better. However, I think this is because xarray=2023.04.0 is not on conda-forge, yet (the PR to the feedstock still has to be merged), so you can't install xarray and pandas=2 into the same environment. As a workaround, you can try installing xarray using pip.

can you share a bit more about the environment you're trying to create? Is that by chance a py38 environment, or does one of the libraries you're trying to install have a upper bound for xarray? xarray>=2023.04.0 does not have the pin on pandas anymore, so you should be able to install that even if the repodata patches didn't work.

CI says these are the tests we'd need to fix: FAILED xarray/tests/test_coding_times.py::test_should_cftime_be_used_source_outside_range - Failed: DID NOT RAISE <class 'ValueError'> FAILED xarray/tests/test_cftimeindex.py::test_to_datetimeindex_out_of_range[360_day] - Failed: DID NOT RAISE <class 'ValueError'> FAILED xarray/tests/test_cftimeindex.py::test_to_datetimeindex_out_of_range[365_day] - Failed: DID NOT RAISE <class 'ValueError'> FAILED xarray/tests/test_cftimeindex.py::test_to_datetimeindex_out_of_range[366_day] - Failed: DID NOT RAISE <class 'ValueError'> FAILED xarray/tests/test_cftimeindex.py::test_to_datetimeindex_out_of_range[all_leap] - Failed: DID NOT RAISE <class 'ValueError'> FAILED xarray/tests/test_cftimeindex.py::test_to_datetimeindex_out_of_range[gregorian] - Failed: DID NOT RAISE <class 'ValueError'> FAILED xarray/tests/test_cftimeindex.py::test_to_datetimeindex_out_of_range[julian] - Failed: DID NOT RAISE <class 'ValueError'> FAILED xarray/tests/test_cftimeindex.py::test_to_datetimeindex_out_of_range[noleap] - Failed: DID NOT RAISE <class 'ValueError'> FAILED xarray/tests/test_cftimeindex.py::test_to_datetimeindex_out_of_range[proleptic_gregorian] - Failed: DID NOT RAISE <class 'ValueError'> FAILED xarray/tests/test_cftimeindex.py::test_to_datetimeindex_out_of_range[standard] - Failed: DID NOT RAISE <class 'ValueError'> FAILED xarray/tests/test_dataarray.py::TestDataArray::test_sel_float - NotImplementedError: float16 indexes are not supported

Edit: one more on windows: FAILED xarray/tests/test_utils.py::test_maybe_coerce_to_str[1-2-expected1] - AssertionError: assert dtype('int64') == dtype('int32') + where dtype('int64') = Index([1, 2], dtype='int64').dtype + and dtype('int32') = Index([1, 2], dtype='int32').dtype Edit2: the doctests also fail: FAILED xarray/core/accessor_dt.py::xarray.core.accessor_dt.DatetimeAccessor FAILED xarray/core/accessor_dt.py::xarray.core.accessor_dt.TimedeltaAccessor FAILED xarray/core/dataarray.py::xarray.core.dataarray.DataArray.groupby

For test_should_cftime_be_used_source_outside_range and test_to_datetimeindex_out_of_range I'd probably use a date that is outside the s resolution range (not sure if that actually makes sense, though). What do you think, @spencerkclark?

For test_maybe_coerce_to_str I think the reason is that we use np.array to cast a python int to an array, but the default resolution is different on windows. Apparently, pandas still uses int64 if constructed directly from python ints, but numpy uses int32 on windows, and as you say pandas does not insist on int64 anymore. The fix would be to explicitly specify the dtype in the array calls.

And finally, I'm not sure what to do with test_sel_float. Maybe we can split the monolithic test into one parametrized by dtype and skip the float16 test variant for pandas>=2.0?

with the merge of #7441 we should already support pandas=2.0 on main. I think we can try unpinning in a PR to see how many of the errors from #7707 are related to pandas (none, hopefully, but I'm not sure).