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
1044363666,PR_kwDOAMm_X84uE2qZ,5936,Fix a missing @requires_zarr in tests,490531,closed,0,,,3,2021-11-04T04:49:39Z,2021-11-04T10:12:58Z,2021-11-04T10:12:46Z,CONTRIBUTOR,,0,pydata/xarray/pulls/5936,"When zarr is not available, this test fails with `NameError: name 'zarr' is not defined`.

<details>
<summary>Full trace</summary>

```
__________________________ test_zarr_storage_options ___________________________

    @requires_fsspec
    def test_zarr_storage_options():
        pytest.importorskip(""aiobotocore"")
        ds = create_test_data()
        store_target = ""memory://test.zarr""
>       ds.to_zarr(store_target, storage_options={""test"": ""zarr_write""})

/build/python-xarray/src/xarray-0.20.0/xarray/tests/test_backends.py:2406: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/build/python-xarray/src/xarray-0.20.0/xarray/core/dataset.py:2037: in to_zarr
    return to_zarr(
/build/python-xarray/src/xarray-0.20.0/xarray/backends/api.py:1391: in to_zarr
    zstore = backends.ZarrStore.open_group(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

cls = <class 'xarray.backends.zarr.ZarrStore'>
store = <fsspec.mapping.FSMap object at 0x7f074691cfd0>, mode = 'w-'
synchronizer = None, group = None, consolidated = False
consolidate_on_close = True, chunk_store = None, storage_options = None
append_dim = None, write_region = None, safe_chunks = True, stacklevel = 4

    @classmethod
    def open_group(
        cls,
        store,
        mode=""r"",
        synchronizer=None,
        group=None,
        consolidated=False,
        consolidate_on_close=False,
        chunk_store=None,
        storage_options=None,
        append_dim=None,
        write_region=None,
        safe_chunks=True,
        stacklevel=2,
    ):
    
        # zarr doesn't support pathlib.Path objects yet. zarr-python#601
        if isinstance(store, os.PathLike):
            store = os.fspath(store)
    
        open_kwargs = dict(
            mode=mode,
            synchronizer=synchronizer,
            path=group,
        )
        open_kwargs[""storage_options""] = storage_options
    
        if chunk_store:
            open_kwargs[""chunk_store""] = chunk_store
            if consolidated is None:
                consolidated = False
    
        if consolidated is None:
            try:
                zarr_group = zarr.open_consolidated(store, **open_kwargs)
            except KeyError:
                warnings.warn(
                    ""Failed to open Zarr store with consolidated metadata, ""
                    ""falling back to try reading non-consolidated metadata. ""
                    ""This is typically much slower for opening a dataset. ""
                    ""To silence this warning, consider:\n""
                    ""1. Consolidating metadata in this existing store with ""
                    ""zarr.consolidate_metadata().\n""
                    ""2. Explicitly setting consolidated=False, to avoid trying ""
                    ""to read consolidate metadata, or\n""
                    ""3. Explicitly setting consolidated=True, to raise an ""
                    ""error in this case instead of falling back to try ""
                    ""reading non-consolidated metadata."",
                    RuntimeWarning,
                    stacklevel=stacklevel,
                )
                zarr_group = zarr.open_group(store, **open_kwargs)
        elif consolidated:
            # TODO: an option to pass the metadata_key keyword
            zarr_group = zarr.open_consolidated(store, **open_kwargs)
        else:
>           zarr_group = zarr.open_group(store, **open_kwargs)
E           NameError: name 'zarr' is not defined

/build/python-xarray/src/xarray-0.20.0/xarray/backends/zarr.py:386: NameError
```
</details>","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5936/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
667055142,MDU6SXNzdWU2NjcwNTUxNDI=,4281,Test failure in test_serializable_locks,490531,open,0,,,2,2020-07-28T13:02:56Z,2021-06-05T18:20:30Z,,CONTRIBUTOR,,,,"**What happened**: The mentioned test failed.

**What you expected to happen**: It should work?

**Minimal Complete Verifiable Example**:

```python
python setup.py build
pytest
```

**Anything else we need to know?**:

Full excerpt:
```
___________________________ test_serializable_locks ____________________________

    def test_func():
        result = None
        workers = []
        with clean(timeout=active_rpc_timeout, **clean_kwargs) as loop:
    
            async def coro():
                with dask.config.set(config):
                    s = False
                    for i in range(5):
                        try:
                            s, ws = await start_cluster(
                                nthreads,
                                scheduler,
                                loop,
                                security=security,
                                Worker=Worker,
                                scheduler_kwargs=scheduler_kwargs,
                                worker_kwargs=worker_kwargs,
                            )
                        except Exception as e:
                            logger.error(
                                ""Failed to start gen_cluster, retrying"",
                                exc_info=True,
                            )
                            await asyncio.sleep(1)
                        else:
                            workers[:] = ws
                            args = [s] + workers
                            break
                    if s is False:
                        raise Exception(""Could not start cluster"")
                    if client:
                        c = await Client(
                            s.address,
                            loop=loop,
                            security=security,
                            asynchronous=True,
                            **client_kwargs,
                        )
                        args = [c] + args
                    try:
                        future = func(*args)
                        if timeout:
                            future = asyncio.wait_for(future, timeout)
                        result = await future
                        if s.validate:
                            s.validate_state()
                    finally:
                        if client and c.status not in (""closing"", ""closed""):
                            await c._close(fast=s.status == ""closed"")
                        await end_cluster(s, workers)
                        await asyncio.wait_for(cleanup_global_workers(), 1)
    
                    try:
                        c = await default_client()
                    except ValueError:
                        pass
                    else:
                        await c._close(fast=True)
    
                    def get_unclosed():
                        return [c for c in Comm._instances if not c.closed()] + [
                            c
                            for c in _global_clients.values()
                            if c.status != ""closed""
                        ]
    
                    try:
                        start = time()
                        while time() < start + 5:
                            gc.collect()
                            if not get_unclosed():
                                break
                            await asyncio.sleep(0.05)
                        else:
                            if allow_unclosed:
                                print(f""Unclosed Comms: {get_unclosed()}"")
                            else:
                                raise RuntimeError(""Unclosed Comms"", get_unclosed())
                    finally:
                        Comm._instances.clear()
                        _global_clients.clear()
    
                    return result
    
>           result = loop.run_sync(
                coro, timeout=timeout * 2 if timeout else timeout
            )

/usr/lib/python3.8/site-packages/distributed/utils_test.py:953: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.8/site-packages/tornado/ioloop.py:532: in run_sync
    return future_cell[0].result()
/usr/lib/python3.8/site-packages/distributed/utils_test.py:912: in coro
    result = await future
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

fut = <Task cancelled name='Task-701' coro=<test_serializable_locks() done, defined at /build/python-xarray/src/xarray-0.16.0/xarray/tests/test_distributed.py:220>>
timeout = 10

    async def wait_for(fut, timeout, *, loop=None):
        """"""Wait for the single Future or coroutine to complete, with timeout.
    
        Coroutine will be wrapped in Task.
    
        Returns result of the Future or coroutine.  When a timeout occurs,
        it cancels the task and raises TimeoutError.  To avoid the task
        cancellation, wrap it in shield().
    
        If the wait is cancelled, the task is also cancelled.
    
        This function is a coroutine.
        """"""
        if loop is None:
            loop = events.get_running_loop()
        else:
            warnings.warn(""The loop argument is deprecated since Python 3.8, ""
                          ""and scheduled for removal in Python 3.10."",
                          DeprecationWarning, stacklevel=2)
    
        if timeout is None:
            return await fut
    
        if timeout <= 0:
            fut = ensure_future(fut, loop=loop)
    
            if fut.done():
                return fut.result()
    
            fut.cancel()
            raise exceptions.TimeoutError()
    
        waiter = loop.create_future()
        timeout_handle = loop.call_later(timeout, _release_waiter, waiter)
        cb = functools.partial(_release_waiter, waiter)
    
        fut = ensure_future(fut, loop=loop)
        fut.add_done_callback(cb)
    
        try:
            # wait until the future completes or the timeout
            try:
                await waiter
            except exceptions.CancelledError:
                fut.remove_done_callback(cb)
                fut.cancel()
                raise
    
            if fut.done():
                return fut.result()
            else:
                fut.remove_done_callback(cb)
                # We must ensure that the task is not running
                # after wait_for() returns.
                # See https://bugs.python.org/issue32751
                await _cancel_and_wait(fut, loop=loop)
>               raise exceptions.TimeoutError()
E               asyncio.exceptions.TimeoutError

/usr/lib/python3.8/asyncio/tasks.py:490: TimeoutError
----------------------------- Captured stderr call -----------------------------
distributed.scheduler - INFO - Clear task state
distributed.scheduler - INFO -   Scheduler at:     tcp://127.0.0.1:40483
distributed.scheduler - INFO -   dashboard at:            127.0.0.1:8787
distributed.worker - INFO -       Start worker at:      tcp://127.0.0.1:45589
distributed.worker - INFO -          Listening to:      tcp://127.0.0.1:45589
distributed.worker - INFO -          dashboard at:            127.0.0.1:43247
distributed.worker - INFO - Waiting to connect to:      tcp://127.0.0.1:40483
distributed.worker - INFO - -------------------------------------------------
distributed.worker - INFO -               Threads:                          1
distributed.worker - INFO -                Memory:                  135.05 GB
distributed.worker - INFO -       Local Directory: /build/python-xarray/src/xarray-0.16.0/dask-worker-space/worker-nbb_tie4
distributed.worker - INFO - -------------------------------------------------
distributed.worker - INFO -       Start worker at:      tcp://127.0.0.1:40153
distributed.worker - INFO -          Listening to:      tcp://127.0.0.1:40153
distributed.worker - INFO -          dashboard at:            127.0.0.1:33037
distributed.worker - INFO - Waiting to connect to:      tcp://127.0.0.1:40483
distributed.worker - INFO - -------------------------------------------------
distributed.worker - INFO -               Threads:                          2
distributed.worker - INFO -                Memory:                  135.05 GB
distributed.worker - INFO -       Local Directory: /build/python-xarray/src/xarray-0.16.0/dask-worker-space/worker-qyb14fl2
distributed.worker - INFO - -------------------------------------------------
distributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:45589', name: 0, memory: 0, processing: 0>
distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:45589
distributed.core - INFO - Starting established connection
distributed.scheduler - INFO - Register worker <Worker 'tcp://127.0.0.1:40153', name: 1, memory: 0, processing: 0>
distributed.scheduler - INFO - Starting worker compute stream, tcp://127.0.0.1:40153
distributed.core - INFO - Starting established connection
distributed.worker - INFO -         Registered to:      tcp://127.0.0.1:40483
distributed.worker - INFO - -------------------------------------------------
distributed.worker - INFO -         Registered to:      tcp://127.0.0.1:40483
distributed.worker - INFO - -------------------------------------------------
distributed.core - INFO - Starting established connection
distributed.core - INFO - Starting established connection
distributed.scheduler - INFO - Receive client connection: Client-01142bc3-d0d0-11ea-84a5-6cb311234570
distributed.core - INFO - Starting established connection
distributed.protocol.pickle - INFO - Failed to serialize (<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [0]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>}). Exception: dumps() got an unexpected keyword argument 'buffer_callback'
distributed.protocol.core - CRITICAL - Failed to Serialize
Traceback (most recent call last):
  File ""/usr/lib/python3.8/site-packages/distributed/protocol/core.py"", line 36, in dumps
    data = {
  File ""/usr/lib/python3.8/site-packages/distributed/protocol/core.py"", line 37, in <dictcomp>
    key: serialize(
  File ""/usr/lib/python3.8/site-packages/distributed/protocol/serialize.py"", line 244, in serialize
    raise TypeError(msg, str(x)[:10000])
TypeError: ('Could not serialize object of type tuple.', ""(<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [0]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})"")
distributed.comm.utils - INFO - Unserializable Message: [{'op': 'update-graph', 'tasks': {'f-e71c719fc7843a71d9b6ed77ba5c1139': <Serialize: (<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [0]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-3e2e39666f6b20c1585ae513bd6c58a1': <Serialize: (<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [1]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-92de0310d2e920306d74497bcdbd21f1': <Serialize: (<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [2]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-f210a433c90cbabd4a839e5957c2ef7c': <Serialize: (<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [3]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-5ecad7d214286d3d4cb2c0f51d71596c': <Serialize: (<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [4]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-662771094d226039899ae3b5f21c6184': <Serialize: (<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [5]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-6aa952faad415c3c2dab888f1e97135e': <Serialize: (<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [6]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-c5a149b0b56aa271ac0e5456fcbd0c7c': <Serialize: (<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [7]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-2d7b671f31682ff35e7b351af7c5ca2b': <Serialize: (<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [8]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>, 'f-0f7fc14155cfae899da1d79302c723c1': <Serialize: (<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [9]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})>}, 'dependencies': {}, 'keys': ['f-e71c719fc7843a71d9b6ed77ba5c1139', 'f-3e2e39666f6b20c1585ae513bd6c58a1', 'f-92de0310d2e920306d74497bcdbd21f1', 'f-f210a433c90cbabd4a839e5957c2ef7c', 'f-5ecad7d214286d3d4cb2c0f51d71596c', 'f-662771094d226039899ae3b5f21c6184', 'f-6aa952faad415c3c2dab888f1e97135e', 'f-c5a149b0b56aa271ac0e5456fcbd0c7c', 'f-2d7b671f31682ff35e7b351af7c5ca2b', 'f-0f7fc14155cfae899da1d79302c723c1'], 'restrictions': {}, 'loose_restrictions': [], 'priority': {'f-e71c719fc7843a71d9b6ed77ba5c1139': 0, 'f-3e2e39666f6b20c1585ae513bd6c58a1': 1, 'f-92de0310d2e920306d74497bcdbd21f1': 2, 'f-f210a433c90cbabd4a839e5957c2ef7c': 3, 'f-5ecad7d214286d3d4cb2c0f51d71596c': 4, 'f-662771094d226039899ae3b5f21c6184': 5, 'f-6aa952faad415c3c2dab888f1e97135e': 6, 'f-c5a149b0b56aa271ac0e5456fcbd0c7c': 7, 'f-2d7b671f31682ff35e7b351af7c5ca2b': 8, 'f-0f7fc14155cfae899da1d79302c723c1': 9}, 'user_priority': 0, 'resources': None, 'submitting_task': None, 'retries': None, 'fifo_timeout': '100 ms', 'actors': False}]
distributed.comm.utils - ERROR - ('Could not serialize object of type tuple.', ""(<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [0]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})"")
Traceback (most recent call last):
  File ""/usr/lib/python3.8/site-packages/distributed/comm/utils.py"", line 34, in _to_frames
    protocol.dumps(
  File ""/usr/lib/python3.8/site-packages/distributed/protocol/core.py"", line 36, in dumps
    data = {
  File ""/usr/lib/python3.8/site-packages/distributed/protocol/core.py"", line 37, in <dictcomp>
    key: serialize(
  File ""/usr/lib/python3.8/site-packages/distributed/protocol/serialize.py"", line 244, in serialize
    raise TypeError(msg, str(x)[:10000])
TypeError: ('Could not serialize object of type tuple.', ""(<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [0]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})"")
distributed.batched - ERROR - Error in batched write
Traceback (most recent call last):
  File ""/usr/lib/python3.8/site-packages/distributed/batched.py"", line 92, in _background_send
    nbytes = yield self.comm.write(
  File ""/usr/lib/python3.8/site-packages/tornado/gen.py"", line 735, in run
    value = future.result()
  File ""/usr/lib/python3.8/site-packages/distributed/comm/tcp.py"", line 220, in write
    frames = await to_frames(
  File ""/usr/lib/python3.8/site-packages/distributed/comm/utils.py"", line 54, in to_frames
    return _to_frames()
  File ""/usr/lib/python3.8/site-packages/distributed/comm/utils.py"", line 34, in _to_frames
    protocol.dumps(
  File ""/usr/lib/python3.8/site-packages/distributed/protocol/core.py"", line 36, in dumps
    data = {
  File ""/usr/lib/python3.8/site-packages/distributed/protocol/core.py"", line 37, in <dictcomp>
    key: serialize(
  File ""/usr/lib/python3.8/site-packages/distributed/protocol/serialize.py"", line 244, in serialize
    raise TypeError(msg, str(x)[:10000])
TypeError: ('Could not serialize object of type tuple.', ""(<function apply at 0x7f8c2cb36940>, <function test_serializable_locks.<locals>.f at 0x7f8c1da0e040>, (<class 'tuple'>, [0]), {'lock': <SerializableLock: 789a59f0-0049-40aa-a0ed-0f2fe6c213a8>})"")
distributed.scheduler - INFO - Remove client Client-01142bc3-d0d0-11ea-84a5-6cb311234570
distributed.scheduler - INFO - Remove client Client-01142bc3-d0d0-11ea-84a5-6cb311234570
distributed.scheduler - INFO - Close client connection: Client-01142bc3-d0d0-11ea-84a5-6cb311234570
distributed.worker - INFO - Stopping worker at tcp://127.0.0.1:45589
distributed.worker - INFO - Stopping worker at tcp://127.0.0.1:40153
distributed.scheduler - INFO - Remove worker <Worker 'tcp://127.0.0.1:45589', name: 0, memory: 0, processing: 0>
distributed.core - INFO - Removing comms to tcp://127.0.0.1:45589
distributed.scheduler - INFO - Remove worker <Worker 'tcp://127.0.0.1:40153', name: 1, memory: 0, processing: 0>
distributed.core - INFO - Removing comms to tcp://127.0.0.1:40153
distributed.scheduler - INFO - Lost all workers
distributed.scheduler - INFO - Scheduler closing...
distributed.scheduler - INFO - Scheduler closing all comms
------------------------------ Captured log call -------------------------------
ERROR    asyncio:base_events.py:1707 Task exception was never retrieved
future: <Task finished name='Task-702' coro=<Client._gather.<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()>
Traceback (most recent call last):
  File ""/usr/lib/python3.8/site-packages/distributed/client.py"", line 1821, in wait
    raise AllExit()
distributed.client.AllExit
ERROR    asyncio:base_events.py:1707 Task exception was never retrieved
future: <Task finished name='Task-703' coro=<Client._gather.<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()>
Traceback (most recent call last):
  File ""/usr/lib/python3.8/site-packages/distributed/client.py"", line 1821, in wait
    raise AllExit()
distributed.client.AllExit
ERROR    asyncio:base_events.py:1707 Task exception was never retrieved
future: <Task finished name='Task-704' coro=<Client._gather.<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()>
Traceback (most recent call last):
  File ""/usr/lib/python3.8/site-packages/distributed/client.py"", line 1821, in wait
    raise AllExit()
distributed.client.AllExit
ERROR    asyncio:base_events.py:1707 Task exception was never retrieved
future: <Task finished name='Task-705' coro=<Client._gather.<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()>
Traceback (most recent call last):
  File ""/usr/lib/python3.8/site-packages/distributed/client.py"", line 1821, in wait
    raise AllExit()
distributed.client.AllExit
ERROR    asyncio:base_events.py:1707 Task exception was never retrieved
future: <Task finished name='Task-706' coro=<Client._gather.<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()>
Traceback (most recent call last):
  File ""/usr/lib/python3.8/site-packages/distributed/client.py"", line 1821, in wait
    raise AllExit()
distributed.client.AllExit
ERROR    asyncio:base_events.py:1707 Task exception was never retrieved
future: <Task finished name='Task-707' coro=<Client._gather.<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()>
Traceback (most recent call last):
  File ""/usr/lib/python3.8/site-packages/distributed/client.py"", line 1821, in wait
    raise AllExit()
distributed.client.AllExit
ERROR    asyncio:base_events.py:1707 Task exception was never retrieved
future: <Task finished name='Task-708' coro=<Client._gather.<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()>
Traceback (most recent call last):
  File ""/usr/lib/python3.8/site-packages/distributed/client.py"", line 1821, in wait
    raise AllExit()
distributed.client.AllExit
ERROR    asyncio:base_events.py:1707 Task exception was never retrieved
future: <Task finished name='Task-709' coro=<Client._gather.<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()>
Traceback (most recent call last):
  File ""/usr/lib/python3.8/site-packages/distributed/client.py"", line 1821, in wait
    raise AllExit()
distributed.client.AllExit
ERROR    asyncio:base_events.py:1707 Task exception was never retrieved
future: <Task finished name='Task-710' coro=<Client._gather.<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()>
Traceback (most recent call last):
  File ""/usr/lib/python3.8/site-packages/distributed/client.py"", line 1821, in wait
    raise AllExit()
distributed.client.AllExit
ERROR    asyncio:base_events.py:1707 Task exception was never retrieved
future: <Task finished name='Task-711' coro=<Client._gather.<locals>.wait() done, defined at /usr/lib/python3.8/site-packages/distributed/client.py:1816> exception=AllExit()>
Traceback (most recent call last):
  File ""/usr/lib/python3.8/site-packages/distributed/client.py"", line 1821, in wait
    raise AllExit()
distributed.client.AllExit
```

**Environment**:

<details><summary>Output of <tt>xr.show_versions()</tt></summary>

INSTALLED VERSIONS
------------------
commit: None
python: 3.8.4 (default, Jul 15 2020, 10:38:22) 
[GCC 10.1.0]
python-bits: 64
OS: Linux
OS-release: 5.7.8-arch1-1
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.12.0
libnetcdf: 4.7.4

xarray: 0.16.0
pandas: 1.0.5
numpy: 1.19.1
scipy: 1.5.2
netCDF4: 1.5.4
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.2.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.3.2
dask: 2.21.0
distributed: 2.21.0
matplotlib: 3.3.0
cartopy: None
seaborn: 0.10.1
numbagg: None
pint: 0.0.0
setuptools: 49.2.0
pip: None
conda: None
pytest: 5.4.3
IPython: None
sphinx: None

</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4281/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
832404698,MDU6SXNzdWU4MzI0MDQ2OTg=,5038,[tests] ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed). ,490531,closed,0,,,11,2021-03-16T04:44:23Z,2021-03-16T20:15:38Z,2021-03-16T20:03:33Z,CONTRIBUTOR,,,,"**What happened**: I’m running tests of xarray while building it for packaging (Arch Linux), and they are 6 tests failures, all with this error message.

**What you expected to happen**: Tests should work. I’m not sure why a version 0.0.0 is reported while running tests in the build environment.

<details><summary>Full error log</summary>

```
__________________ TestDataArray.test_from_series_multiindex ___________________

self = <xarray.tests.test_dataarray.TestDataArray object at 0x7fcb3b23c2b0>

    def test_from_series_multiindex(self):
        # GH:3951
        df = pd.DataFrame({""B"": [1, 2, 3], ""A"": [4, 5, 6]})
        df = df.rename_axis(""num"").rename_axis(""alpha"", axis=1)
>       actual = df.stack(""alpha"").to_xarray()

/build/python-xarray/src/xarray-0.17.0/xarray/tests/test_dataarray.py:3697: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.9/site-packages/pandas/core/generic.py:3011: in to_xarray
    xarray = import_optional_dependency(""xarray"")
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'xarray', extra = '', raise_on_missing = True, on_version = 'raise'

    def import_optional_dependency(
        name: str, extra: str = """", raise_on_missing: bool = True, on_version: str = ""raise""
    ):
        """"""
        Import an optional dependency.
    
        By default, if a dependency is missing an ImportError with a nice
        message will be raised. If a dependency is present, but too old,
        we raise.
    
        Parameters
        ----------
        name : str
            The module name. This should be top-level only, so that the
            version may be checked.
        extra : str
            Additional text to include in the ImportError message.
        raise_on_missing : bool, default True
            Whether to raise if the optional dependency is not found.
            When False and the module is not present, None is returned.
        on_version : str {'raise', 'warn'}
            What to do when a dependency's version is too old.
    
            * raise : Raise an ImportError
            * warn : Warn that the version is too old. Returns None
            * ignore: Return the module, even if the version is too old.
              It's expected that users validate the version locally when
              using ``on_version=""ignore""`` (see. ``io/html.py``)
    
        Returns
        -------
        maybe_module : Optional[ModuleType]
            The imported module, when found and the version is correct.
            None is returned when the package is not found and `raise_on_missing`
            is False, or when the package's version is too old and `on_version`
            is ``'warn'``.
        """"""
    
        package_name = INSTALL_MAPPING.get(name)
        install_name = package_name if package_name is not None else name
    
        msg = (
            f""Missing optional dependency '{install_name}'. {extra} ""
            f""Use pip or conda to install {install_name}.""
        )
        try:
            module = importlib.import_module(name)
        except ImportError:
            if raise_on_missing:
                raise ImportError(msg) from None
            else:
                return None
    
        minimum_version = VERSIONS.get(name)
        if minimum_version:
            version = get_version(module)
            if distutils.version.LooseVersion(version) < minimum_version:
                assert on_version in {""warn"", ""raise"", ""ignore""}
                msg = (
                    f""Pandas requires version '{minimum_version}' or newer of '{name}' ""
                    f""(version '{version}' currently installed).""
                )
                if on_version == ""warn"":
                    warnings.warn(msg, UserWarning)
                    return None
                elif on_version == ""raise"":
>                   raise ImportError(msg)
E                   ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed).

/usr/lib/python3.9/site-packages/pandas/compat/_optional.py:126: ImportError
_______________________ TestDataset.test_sel_categorical _______________________

self = <xarray.tests.test_dataset.TestDataset object at 0x7fcb3afb8640>

    def test_sel_categorical(self):
        ind = pd.Series([""foo"", ""bar""], dtype=""category"")
        df = pd.DataFrame({""ind"": ind, ""values"": [1, 2]})
>       ds = df.set_index(""ind"").to_xarray()

/build/python-xarray/src/xarray-0.17.0/xarray/tests/test_dataset.py:1432: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.9/site-packages/pandas/core/generic.py:3011: in to_xarray
    xarray = import_optional_dependency(""xarray"")
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'xarray', extra = '', raise_on_missing = True, on_version = 'raise'

    def import_optional_dependency(
        name: str, extra: str = """", raise_on_missing: bool = True, on_version: str = ""raise""
    ):
        """"""
        Import an optional dependency.
    
        By default, if a dependency is missing an ImportError with a nice
        message will be raised. If a dependency is present, but too old,
        we raise.
    
        Parameters
        ----------
        name : str
            The module name. This should be top-level only, so that the
            version may be checked.
        extra : str
            Additional text to include in the ImportError message.
        raise_on_missing : bool, default True
            Whether to raise if the optional dependency is not found.
            When False and the module is not present, None is returned.
        on_version : str {'raise', 'warn'}
            What to do when a dependency's version is too old.
    
            * raise : Raise an ImportError
            * warn : Warn that the version is too old. Returns None
            * ignore: Return the module, even if the version is too old.
              It's expected that users validate the version locally when
              using ``on_version=""ignore""`` (see. ``io/html.py``)
    
        Returns
        -------
        maybe_module : Optional[ModuleType]
            The imported module, when found and the version is correct.
            None is returned when the package is not found and `raise_on_missing`
            is False, or when the package's version is too old and `on_version`
            is ``'warn'``.
        """"""
    
        package_name = INSTALL_MAPPING.get(name)
        install_name = package_name if package_name is not None else name
    
        msg = (
            f""Missing optional dependency '{install_name}'. {extra} ""
            f""Use pip or conda to install {install_name}.""
        )
        try:
            module = importlib.import_module(name)
        except ImportError:
            if raise_on_missing:
                raise ImportError(msg) from None
            else:
                return None
    
        minimum_version = VERSIONS.get(name)
        if minimum_version:
            version = get_version(module)
            if distutils.version.LooseVersion(version) < minimum_version:
                assert on_version in {""warn"", ""raise"", ""ignore""}
                msg = (
                    f""Pandas requires version '{minimum_version}' or newer of '{name}' ""
                    f""(version '{version}' currently installed).""
                )
                if on_version == ""warn"":
                    warnings.warn(msg, UserWarning)
                    return None
                elif on_version == ""raise"":
>                   raise ImportError(msg)
E                   ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed).

/usr/lib/python3.9/site-packages/pandas/compat/_optional.py:126: ImportError
____________________ TestDataset.test_sel_categorical_error ____________________

self = <xarray.tests.test_dataset.TestDataset object at 0x7fcb3b199220>

    def test_sel_categorical_error(self):
        ind = pd.Series([""foo"", ""bar""], dtype=""category"")
        df = pd.DataFrame({""ind"": ind, ""values"": [1, 2]})
>       ds = df.set_index(""ind"").to_xarray()

/build/python-xarray/src/xarray-0.17.0/xarray/tests/test_dataset.py:1440: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.9/site-packages/pandas/core/generic.py:3011: in to_xarray
    xarray = import_optional_dependency(""xarray"")
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'xarray', extra = '', raise_on_missing = True, on_version = 'raise'

    def import_optional_dependency(
        name: str, extra: str = """", raise_on_missing: bool = True, on_version: str = ""raise""
    ):
        """"""
        Import an optional dependency.
    
        By default, if a dependency is missing an ImportError with a nice
        message will be raised. If a dependency is present, but too old,
        we raise.
    
        Parameters
        ----------
        name : str
            The module name. This should be top-level only, so that the
            version may be checked.
        extra : str
            Additional text to include in the ImportError message.
        raise_on_missing : bool, default True
            Whether to raise if the optional dependency is not found.
            When False and the module is not present, None is returned.
        on_version : str {'raise', 'warn'}
            What to do when a dependency's version is too old.
    
            * raise : Raise an ImportError
            * warn : Warn that the version is too old. Returns None
            * ignore: Return the module, even if the version is too old.
              It's expected that users validate the version locally when
              using ``on_version=""ignore""`` (see. ``io/html.py``)
    
        Returns
        -------
        maybe_module : Optional[ModuleType]
            The imported module, when found and the version is correct.
            None is returned when the package is not found and `raise_on_missing`
            is False, or when the package's version is too old and `on_version`
            is ``'warn'``.
        """"""
    
        package_name = INSTALL_MAPPING.get(name)
        install_name = package_name if package_name is not None else name
    
        msg = (
            f""Missing optional dependency '{install_name}'. {extra} ""
            f""Use pip or conda to install {install_name}.""
        )
        try:
            module = importlib.import_module(name)
        except ImportError:
            if raise_on_missing:
                raise ImportError(msg) from None
            else:
                return None
    
        minimum_version = VERSIONS.get(name)
        if minimum_version:
            version = get_version(module)
            if distutils.version.LooseVersion(version) < minimum_version:
                assert on_version in {""warn"", ""raise"", ""ignore""}
                msg = (
                    f""Pandas requires version '{minimum_version}' or newer of '{name}' ""
                    f""(version '{version}' currently installed).""
                )
                if on_version == ""warn"":
                    warnings.warn(msg, UserWarning)
                    return None
                elif on_version == ""raise"":
>                   raise ImportError(msg)
E                   ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed).

/usr/lib/python3.9/site-packages/pandas/compat/_optional.py:126: ImportError
___________________ TestDataset.test_categorical_multiindex ____________________

self = <xarray.tests.test_dataset.TestDataset object at 0x7fcb3ae86670>

    def test_categorical_multiindex(self):
        i1 = pd.Series([0, 0])
        cat = pd.CategoricalDtype(categories=[""foo"", ""baz"", ""bar""])
        i2 = pd.Series([""baz"", ""bar""], dtype=cat)
    
        df = pd.DataFrame({""i1"": i1, ""i2"": i2, ""values"": [1, 2]}).set_index(
            [""i1"", ""i2""]
        )
>       actual = df.to_xarray()

/build/python-xarray/src/xarray-0.17.0/xarray/tests/test_dataset.py:1487: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.9/site-packages/pandas/core/generic.py:3011: in to_xarray
    xarray = import_optional_dependency(""xarray"")
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'xarray', extra = '', raise_on_missing = True, on_version = 'raise'

    def import_optional_dependency(
        name: str, extra: str = """", raise_on_missing: bool = True, on_version: str = ""raise""
    ):
        """"""
        Import an optional dependency.
    
        By default, if a dependency is missing an ImportError with a nice
        message will be raised. If a dependency is present, but too old,
        we raise.
    
        Parameters
        ----------
        name : str
            The module name. This should be top-level only, so that the
            version may be checked.
        extra : str
            Additional text to include in the ImportError message.
        raise_on_missing : bool, default True
            Whether to raise if the optional dependency is not found.
            When False and the module is not present, None is returned.
        on_version : str {'raise', 'warn'}
            What to do when a dependency's version is too old.
    
            * raise : Raise an ImportError
            * warn : Warn that the version is too old. Returns None
            * ignore: Return the module, even if the version is too old.
              It's expected that users validate the version locally when
              using ``on_version=""ignore""`` (see. ``io/html.py``)
    
        Returns
        -------
        maybe_module : Optional[ModuleType]
            The imported module, when found and the version is correct.
            None is returned when the package is not found and `raise_on_missing`
            is False, or when the package's version is too old and `on_version`
            is ``'warn'``.
        """"""
    
        package_name = INSTALL_MAPPING.get(name)
        install_name = package_name if package_name is not None else name
    
        msg = (
            f""Missing optional dependency '{install_name}'. {extra} ""
            f""Use pip or conda to install {install_name}.""
        )
        try:
            module = importlib.import_module(name)
        except ImportError:
            if raise_on_missing:
                raise ImportError(msg) from None
            else:
                return None
    
        minimum_version = VERSIONS.get(name)
        if minimum_version:
            version = get_version(module)
            if distutils.version.LooseVersion(version) < minimum_version:
                assert on_version in {""warn"", ""raise"", ""ignore""}
                msg = (
                    f""Pandas requires version '{minimum_version}' or newer of '{name}' ""
                    f""(version '{version}' currently installed).""
                )
                if on_version == ""warn"":
                    warnings.warn(msg, UserWarning)
                    return None
                elif on_version == ""raise"":
>                   raise ImportError(msg)
E                   ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed).

/usr/lib/python3.9/site-packages/pandas/compat/_optional.py:126: ImportError
_________________ TestDataset.test_from_dataframe_categorical __________________

self = <xarray.tests.test_dataset.TestDataset object at 0x7fcb3ab0b880>

    def test_from_dataframe_categorical(self):
        cat = pd.CategoricalDtype(
            categories=[""foo"", ""bar"", ""baz"", ""qux"", ""quux"", ""corge""]
        )
        i1 = pd.Series([""foo"", ""bar"", ""foo""], dtype=cat)
        i2 = pd.Series([""bar"", ""bar"", ""baz""], dtype=cat)
    
        df = pd.DataFrame({""i1"": i1, ""i2"": i2, ""values"": [1, 2, 3]})
>       ds = df.set_index(""i1"").to_xarray()

/build/python-xarray/src/xarray-0.17.0/xarray/tests/test_dataset.py:4131: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/usr/lib/python3.9/site-packages/pandas/core/generic.py:3011: in to_xarray
    xarray = import_optional_dependency(""xarray"")
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'xarray', extra = '', raise_on_missing = True, on_version = 'raise'

    def import_optional_dependency(
        name: str, extra: str = """", raise_on_missing: bool = True, on_version: str = ""raise""
    ):
        """"""
        Import an optional dependency.
    
        By default, if a dependency is missing an ImportError with a nice
        message will be raised. If a dependency is present, but too old,
        we raise.
    
        Parameters
        ----------
        name : str
            The module name. This should be top-level only, so that the
            version may be checked.
        extra : str
            Additional text to include in the ImportError message.
        raise_on_missing : bool, default True
            Whether to raise if the optional dependency is not found.
            When False and the module is not present, None is returned.
        on_version : str {'raise', 'warn'}
            What to do when a dependency's version is too old.
    
            * raise : Raise an ImportError
            * warn : Warn that the version is too old. Returns None
            * ignore: Return the module, even if the version is too old.
              It's expected that users validate the version locally when
              using ``on_version=""ignore""`` (see. ``io/html.py``)
    
        Returns
        -------
        maybe_module : Optional[ModuleType]
            The imported module, when found and the version is correct.
            None is returned when the package is not found and `raise_on_missing`
            is False, or when the package's version is too old and `on_version`
            is ``'warn'``.
        """"""
    
        package_name = INSTALL_MAPPING.get(name)
        install_name = package_name if package_name is not None else name
    
        msg = (
            f""Missing optional dependency '{install_name}'. {extra} ""
            f""Use pip or conda to install {install_name}.""
        )
        try:
            module = importlib.import_module(name)
        except ImportError:
            if raise_on_missing:
                raise ImportError(msg) from None
            else:
                return None
    
        minimum_version = VERSIONS.get(name)
        if minimum_version:
            version = get_version(module)
            if distutils.version.LooseVersion(version) < minimum_version:
                assert on_version in {""warn"", ""raise"", ""ignore""}
                msg = (
                    f""Pandas requires version '{minimum_version}' or newer of '{name}' ""
                    f""(version '{version}' currently installed).""
                )
                if on_version == ""warn"":
                    warnings.warn(msg, UserWarning)
                    return None
                elif on_version == ""raise"":
>                   raise ImportError(msg)
E                   ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed).

/usr/lib/python3.9/site-packages/pandas/compat/_optional.py:126: ImportError
_________________________ test_roundtrip_pandas_series _________________________

    @given(numeric_series, st.text())
>   def test_roundtrip_pandas_series(ser, ix_name):

/build/python-xarray/src/xarray-0.17.0/properties/test_pandas_roundtrip.py:73: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
/build/python-xarray/src/xarray-0.17.0/properties/test_pandas_roundtrip.py:79: in test_roundtrip_pandas_series
    xr.testing.assert_identical(arr, roundtripped.to_xarray())
/usr/lib/python3.9/site-packages/pandas/core/generic.py:3011: in to_xarray
    xarray = import_optional_dependency(""xarray"")
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

name = 'xarray', extra = '', raise_on_missing = True, on_version = 'raise'

    def import_optional_dependency(
        name: str, extra: str = """", raise_on_missing: bool = True, on_version: str = ""raise""
    ):
        """"""
        Import an optional dependency.
    
        By default, if a dependency is missing an ImportError with a nice
        message will be raised. If a dependency is present, but too old,
        we raise.
    
        Parameters
        ----------
        name : str
            The module name. This should be top-level only, so that the
            version may be checked.
        extra : str
            Additional text to include in the ImportError message.
        raise_on_missing : bool, default True
            Whether to raise if the optional dependency is not found.
            When False and the module is not present, None is returned.
        on_version : str {'raise', 'warn'}
            What to do when a dependency's version is too old.
    
            * raise : Raise an ImportError
            * warn : Warn that the version is too old. Returns None
            * ignore: Return the module, even if the version is too old.
              It's expected that users validate the version locally when
              using ``on_version=""ignore""`` (see. ``io/html.py``)
    
        Returns
        -------
        maybe_module : Optional[ModuleType]
            The imported module, when found and the version is correct.
            None is returned when the package is not found and `raise_on_missing`
            is False, or when the package's version is too old and `on_version`
            is ``'warn'``.
        """"""
    
        package_name = INSTALL_MAPPING.get(name)
        install_name = package_name if package_name is not None else name
    
        msg = (
            f""Missing optional dependency '{install_name}'. {extra} ""
            f""Use pip or conda to install {install_name}.""
        )
        try:
            module = importlib.import_module(name)
        except ImportError:
            if raise_on_missing:
                raise ImportError(msg) from None
            else:
                return None
    
        minimum_version = VERSIONS.get(name)
        if minimum_version:
            version = get_version(module)
            if distutils.version.LooseVersion(version) < minimum_version:
                assert on_version in {""warn"", ""raise"", ""ignore""}
                msg = (
                    f""Pandas requires version '{minimum_version}' or newer of '{name}' ""
                    f""(version '{version}' currently installed).""
                )
                if on_version == ""warn"":
                    warnings.warn(msg, UserWarning)
                    return None
                elif on_version == ""raise"":
>                   raise ImportError(msg)
E                   ImportError: Pandas requires version '0.12.3' or newer of 'xarray' (version '0.0.0' currently installed).

/usr/lib/python3.9/site-packages/pandas/compat/_optional.py:126: ImportError
```

</details>

**Environment**:

<details><summary>Output of <tt>xr.show_versions()</tt></summary>

commit: None
python: 3.9.2 (default, Feb 20 2021, 18:40:11) 
[GCC 10.2.0]
python-bits: 64
OS: Linux
OS-release: 5.11.5-arch1-1
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.12.0
libnetcdf: 4.7.4

xarray: 0.0.0
pandas: 1.2.3
numpy: 1.20.1
scipy: 1.6.1
netCDF4: 1.5.5.1
pydap: None
h5netcdf: None
h5py: 3.1.0
Nio: None
zarr: None
cftime: 1.4.1
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.3.2
dask: 2021.03.0
distributed: 2021.03.0
matplotlib: 3.3.4
cartopy: None
seaborn: 0.11.1
numbagg: None
pint: 0.16.1
setuptools: 54.1.1
pip: None
conda: None
pytest: 6.2.2
IPython: None
sphinx: None

</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5038/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
567058587,MDU6SXNzdWU1NjcwNTg1ODc=,3778,Two strange errors in tests TestVariable using .copy(),490531,closed,0,,,7,2020-02-18T17:51:22Z,2020-02-28T15:16:14Z,2020-02-28T15:16:14Z,CONTRIBUTOR,,,,"Setup is as described in #3777.

The two failures are the following ones:
```
____________________ TestVariable.test_index_0d_not_a_time _____________________

self = <xarray.tests.test_units.TestVariable object at 0x7f3926b2ad00>

    def test_index_0d_not_a_time(self):
        d = np.datetime64(""NaT"", ""ns"")
        x = self.cls([""x""], [d])
>       self._assertIndexedLikeNDArray(x, d)

xarray/tests/test_variable.py:206: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

self = <xarray.tests.test_units.TestVariable object at 0x7f3926b2ad00>
variable = <xarray.Variable (x: 1)>
<Quantity(['NaT'], 'meter')>
expected_value0 = numpy.datetime64('NaT'), expected_dtype = None

    def _assertIndexedLikeNDArray(self, variable, expected_value0, expected_dtype=None):
        """"""Given a 1-dimensional variable, verify that the variable is indexed
        like a numpy.ndarray.
        """"""
        assert variable[0].shape == ()
        assert variable[0].ndim == 0
        assert variable[0].size == 1
        # test identity
>       assert variable.equals(variable.copy())
E       AssertionError: assert False
E        +  where False = <bound method Variable.equals of <xarray.Variable (x: 1)>\n<Quantity(['NaT'], 'meter')>>(<xarray.Variable (x: 1)>\n<Quantity(['NaT'], 'meter')>)
E        +    where <bound method Variable.equals of <xarray.Variable (x: 1)>\n<Quantity(['NaT'], 'meter')>> = <xarray.Variable (x: 1)>\n<Quantity(['NaT'], 'meter')>.equals
E        +    and   <xarray.Variable (x: 1)>\n<Quantity(['NaT'], 'meter')> = <bound method Variable.copy of <xarray.Variable (x: 1)>\n<Quantity(['NaT'], 'meter')>>()
E        +      where <bound method Variable.copy of <xarray.Variable (x: 1)>\n<Quantity(['NaT'], 'meter')>> = <xarray.Variable (x: 1)>\n<Quantity(['NaT'], 'meter')>.copy

xarray/tests/test_variable.py:151: AssertionError
_____________________ TestVariable.test_equals_all_dtypes ______________________

self = <xarray.tests.test_units.TestVariable object at 0x7f39282b4e50>

    def test_equals_all_dtypes(self):
        for v, _ in self.example_1d_objects():
            v2 = v.copy()
>           assert v.equals(v2)
E           AssertionError: assert False
E            +  where False = <bound method Variable.equals of <xarray.Variable (x: 3)>\n<Quantity(['2000-01-01T00:00:00.000000000' '2000-01-02T00:00:00.000000000'\n '2000-01-03T00:00:00.000000000'], 'meter')>>(<xarray.Variable (x: 3)>\n<Quantity(['2000-01-01T00:00:00.000000000' '2000-01-02T00:00:00.000000000'\n '2000-01-03T00:00:00.000000000'], 'meter')>)
E            +    where <bound method Variable.equals of <xarray.Variable (x: 3)>\n<Quantity(['2000-01-01T00:00:00.000000000' '2000-01-02T00:00:00.000000000'\n '2000-01-03T00:00:00.000000000'], 'meter')>> = <xarray.Variable (x: 3)>\n<Quantity(['2000-01-01T00:00:00.000000000' '2000-01-02T00:00:00.000000000'\n '2000-01-03T00:00:00.000000000'], 'meter')>.equals

xarray/tests/test_variable.py:386: AssertionError
```

I have no idea what’s wrong here.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3778/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
567062305,MDU6SXNzdWU1NjcwNjIzMDU=,3779,Three test_aggregation[int-method_median] tests failing,490531,closed,0,,,2,2020-02-18T17:58:29Z,2020-02-23T19:34:35Z,2020-02-23T19:34:35Z,CONTRIBUTOR,,,,"Follow-up of #3777.

The three failing tests seems to be failing because `dask_array=None`, which is likely the same kind of issue as #3777: dask-dependant tests ran while dask is not available. The other printed error is strange to me, because `numpy` is at version 1.18.1 on this system.

```
_______________ TestVariable.test_aggregation[int-method_median] _______________

values = array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1]), axis = None, skipna = None
kwargs = {}, func = <function _dask_or_eager_func.<locals>.f at 0x7f3927bdbe50>
msg = 'median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None'

    def f(values, axis=None, skipna=None, **kwargs):
        if kwargs.pop(""out"", None) is not None:
            raise TypeError(f""`out` is not valid for {name}"")
    
        values = asarray(values)
    
        if coerce_strings and values.dtype.kind in ""SU"":
            values = values.astype(object)
    
        func = None
        if skipna or (skipna is None and values.dtype.kind in ""cfO""):
            nanname = ""nan"" + name
            func = getattr(nanops, nanname)
        else:
            func = _dask_or_eager_func(name, dask_module=dask_module)
    
        try:
>           return func(values, axis=axis, **kwargs)

xarray/core/duck_array_ops.py:307: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1]),), kwargs = {'axis': None}
dispatch_args = (array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1]),)

    def f(*args, **kwargs):
        if list_of_args:
            dispatch_args = args[0]
        else:
            dispatch_args = args[array_args]
>       if any(isinstance(a, dask_array.Array) for a in dispatch_args):

xarray/core/duck_array_ops.py:40: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

.0 = <tuple_iterator object at 0x7f3927966f70>

>   if any(isinstance(a, dask_array.Array) for a in dispatch_args):
E   AttributeError: 'NoneType' object has no attribute 'Array'

xarray/core/duck_array_ops.py:40: AttributeError

During handling of the above exception, another exception occurred:

self = <xarray.tests.test_units.TestVariable object at 0x7f3927966640>
func = method_median, dtype = <class 'int'>

    @pytest.mark.parametrize(
        ""func"",
        (
            method(""all""),
            method(""any""),
            method(""argmax""),
            method(""argmin""),
            method(""argsort""),
            method(""cumprod""),
            method(""cumsum""),
            method(""max""),
            method(""mean""),
            method(""median""),
            method(""min""),
            pytest.param(
                method(""prod""),
                marks=pytest.mark.xfail(reason=""not implemented by pint""),
            ),
            method(""std""),
            method(""sum""),
            method(""var""),
        ),
        ids=repr,
    )
    def test_aggregation(self, func, dtype):
        array = np.linspace(0, 1, 10).astype(dtype) * (
            unit_registry.m if func.name != ""cumprod"" else unit_registry.dimensionless
        )
        variable = xr.Variable(""x"", array)
    
        units = extract_units(func(array))
>       expected = attach_units(func(strip_units(variable)), units)

xarray/tests/test_units.py:1389: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
xarray/tests/test_units.py:374: in __call__
    return func(*all_args, **all_kwargs)
xarray/core/common.py:46: in wrapped_func
    return self.reduce(func, dim, axis, skipna=skipna, **kwargs)
xarray/core/variable.py:1537: in reduce
    data = func(input_data, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

values = array([0, 0, 0, 0, 0, 0, 0, 0, 0, 1]), axis = None, skipna = None
kwargs = {}, func = <function _dask_or_eager_func.<locals>.f at 0x7f3927bdbe50>
msg = 'median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None'

    def f(values, axis=None, skipna=None, **kwargs):
        if kwargs.pop(""out"", None) is not None:
            raise TypeError(f""`out` is not valid for {name}"")
    
        values = asarray(values)
    
        if coerce_strings and values.dtype.kind in ""SU"":
            values = values.astype(object)
    
        func = None
        if skipna or (skipna is None and values.dtype.kind in ""cfO""):
            nanname = ""nan"" + name
            func = getattr(nanops, nanname)
        else:
            func = _dask_or_eager_func(name, dask_module=dask_module)
    
        try:
            return func(values, axis=axis, **kwargs)
        except AttributeError:
            if isinstance(values, dask_array_type):
                try:  # dask/dask#3133 dask sometimes needs dtype argument
                    # if func does not accept dtype, then raises TypeError
                    return func(values, axis=axis, dtype=values.dtype, **kwargs)
                except (AttributeError, TypeError):
                    msg = ""%s is not yet implemented on dask arrays"" % name
            else:
                msg = (
                    ""%s is not available with skipna=False with the ""
                    ""installed version of numpy; upgrade to numpy 1.12 ""
                    ""or newer to use skipna=True or skipna=None"" % name
                )
>           raise NotImplementedError(msg)
E           NotImplementedError: median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None

xarray/core/duck_array_ops.py:321: NotImplementedError
______________ TestDataArray.test_aggregation[int-method_median] _______________

values = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), axis = None, skipna = None
kwargs = {}, func = <function _dask_or_eager_func.<locals>.f at 0x7f39286dbc10>
msg = 'median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None'

    def f(values, axis=None, skipna=None, **kwargs):
        if kwargs.pop(""out"", None) is not None:
            raise TypeError(f""`out` is not valid for {name}"")
    
        values = asarray(values)
    
        if coerce_strings and values.dtype.kind in ""SU"":
            values = values.astype(object)
    
        func = None
        if skipna or (skipna is None and values.dtype.kind in ""cfO""):
            nanname = ""nan"" + name
            func = getattr(nanops, nanname)
        else:
            func = _dask_or_eager_func(name, dask_module=dask_module)
    
        try:
>           return func(values, axis=axis, **kwargs)

xarray/core/duck_array_ops.py:307: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),), kwargs = {'axis': None}
dispatch_args = (array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),)

    def f(*args, **kwargs):
        if list_of_args:
            dispatch_args = args[0]
        else:
            dispatch_args = args[array_args]
>       if any(isinstance(a, dask_array.Array) for a in dispatch_args):

xarray/core/duck_array_ops.py:40: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

.0 = <tuple_iterator object at 0x7f39288ad880>

>   if any(isinstance(a, dask_array.Array) for a in dispatch_args):
E   AttributeError: 'NoneType' object has no attribute 'Array'

xarray/core/duck_array_ops.py:40: AttributeError

During handling of the above exception, another exception occurred:

self = <xarray.tests.test_units.TestDataArray object at 0x7f39288ad190>
func = method_median, dtype = <class 'int'>

    @pytest.mark.parametrize(
        ""func"",
        (
            pytest.param(
                function(""all""),
                marks=pytest.mark.xfail(reason=""not implemented by pint yet""),
            ),
            pytest.param(
                function(""any""),
                marks=pytest.mark.xfail(reason=""not implemented by pint yet""),
            ),
            function(""argmax""),
            function(""argmin""),
            function(""max""),
            function(""mean""),
            pytest.param(
                function(""median""),
                marks=pytest.mark.xfail(reason=""not implemented by xarray""),
            ),
            function(""min""),
            pytest.param(
                function(""prod""),
                marks=pytest.mark.xfail(reason=""not implemented by pint yet""),
            ),
            function(""sum""),
            function(""std""),
            function(""var""),
            function(""cumsum""),
            pytest.param(
                function(""cumprod""),
                marks=pytest.mark.xfail(reason=""not implemented by pint yet""),
            ),
            pytest.param(
                method(""all""),
                marks=pytest.mark.xfail(reason=""not implemented by pint yet""),
            ),
            pytest.param(
                method(""any""),
                marks=pytest.mark.xfail(reason=""not implemented by pint yet""),
            ),
            method(""argmax""),
            method(""argmin""),
            method(""max""),
            method(""mean""),
            method(""median""),
            method(""min""),
            pytest.param(
                method(""prod""),
                marks=pytest.mark.xfail(
                    reason=""comparison of quantity with ndarrays in nanops not implemented""
                ),
            ),
            method(""sum""),
            method(""std""),
            method(""var""),
            method(""cumsum""),
            pytest.param(
                method(""cumprod""),
                marks=pytest.mark.xfail(reason=""pint does not implement cumprod yet""),
            ),
        ),
        ids=repr,
    )
    def test_aggregation(self, func, dtype):
        array = np.arange(10).astype(dtype) * (
            unit_registry.m if func.name != ""cumprod"" else unit_registry.dimensionless
        )
        data_array = xr.DataArray(data=array, dims=""x"")
    
        # units differ based on the applied function, so we need to
        # first compute the units
        units = extract_units(func(array))
>       expected = attach_units(func(strip_units(data_array)), units)

xarray/tests/test_units.py:2226: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
xarray/tests/test_units.py:374: in __call__
    return func(*all_args, **all_kwargs)
xarray/core/common.py:46: in wrapped_func
    return self.reduce(func, dim, axis, skipna=skipna, **kwargs)
xarray/core/dataarray.py:2235: in reduce
    var = self.variable.reduce(func, dim, axis, keep_attrs, keepdims, **kwargs)
xarray/core/variable.py:1537: in reduce
    data = func(input_data, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

values = array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]), axis = None, skipna = None
kwargs = {}, func = <function _dask_or_eager_func.<locals>.f at 0x7f39286dbc10>
msg = 'median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None'

    def f(values, axis=None, skipna=None, **kwargs):
        if kwargs.pop(""out"", None) is not None:
            raise TypeError(f""`out` is not valid for {name}"")
    
        values = asarray(values)
    
        if coerce_strings and values.dtype.kind in ""SU"":
            values = values.astype(object)
    
        func = None
        if skipna or (skipna is None and values.dtype.kind in ""cfO""):
            nanname = ""nan"" + name
            func = getattr(nanops, nanname)
        else:
            func = _dask_or_eager_func(name, dask_module=dask_module)
    
        try:
            return func(values, axis=axis, **kwargs)
        except AttributeError:
            if isinstance(values, dask_array_type):
                try:  # dask/dask#3133 dask sometimes needs dtype argument
                    # if func does not accept dtype, then raises TypeError
                    return func(values, axis=axis, dtype=values.dtype, **kwargs)
                except (AttributeError, TypeError):
                    msg = ""%s is not yet implemented on dask arrays"" % name
            else:
                msg = (
                    ""%s is not available with skipna=False with the ""
                    ""installed version of numpy; upgrade to numpy 1.12 ""
                    ""or newer to use skipna=True or skipna=None"" % name
                )
>           raise NotImplementedError(msg)
E           NotImplementedError: median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None

xarray/core/duck_array_ops.py:321: NotImplementedError
_______________ TestDataset.test_aggregation[int-method_median] ________________

values = <Quantity([0 0 0 0 0 0 0 0 0 1], 'pascal')>, axis = 0, skipna = None
kwargs = {}, func = <function _dask_or_eager_func.<locals>.f at 0x7f392619b820>
msg = 'median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None'

    def f(values, axis=None, skipna=None, **kwargs):
        if kwargs.pop(""out"", None) is not None:
            raise TypeError(f""`out` is not valid for {name}"")
    
        values = asarray(values)
    
        if coerce_strings and values.dtype.kind in ""SU"":
            values = values.astype(object)
    
        func = None
        if skipna or (skipna is None and values.dtype.kind in ""cfO""):
            nanname = ""nan"" + name
            func = getattr(nanops, nanname)
        else:
            func = _dask_or_eager_func(name, dask_module=dask_module)
    
        try:
>           return func(values, axis=axis, **kwargs)

xarray/core/duck_array_ops.py:307: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

args = (<Quantity([0 0 0 0 0 0 0 0 0 1], 'pascal')>,), kwargs = {'axis': 0}
dispatch_args = (<Quantity([0 0 0 0 0 0 0 0 0 1], 'pascal')>,)

    def f(*args, **kwargs):
        if list_of_args:
            dispatch_args = args[0]
        else:
            dispatch_args = args[array_args]
>       if any(isinstance(a, dask_array.Array) for a in dispatch_args):

xarray/core/duck_array_ops.py:40: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

.0 = <tuple_iterator object at 0x7f39269995e0>

>   if any(isinstance(a, dask_array.Array) for a in dispatch_args):
E   AttributeError: 'NoneType' object has no attribute 'Array'

xarray/core/duck_array_ops.py:40: AttributeError

During handling of the above exception, another exception occurred:

self = <xarray.tests.test_units.TestDataset object at 0x7f3927adf880>
func = method_median, dtype = <class 'int'>

    @pytest.mark.parametrize(
        ""func"",
        (
            pytest.param(
                function(""all""),
                marks=pytest.mark.xfail(reason=""not implemented by pint""),
            ),
            pytest.param(
                function(""any""),
                marks=pytest.mark.xfail(reason=""not implemented by pint""),
            ),
            function(""argmax""),
            function(""argmin""),
            function(""max""),
            function(""min""),
            function(""mean""),
            pytest.param(
                function(""median""),
                marks=pytest.mark.xfail(
                    reason=""np.median does not work with dataset yet""
                ),
            ),
            function(""sum""),
            pytest.param(
                function(""prod""),
                marks=pytest.mark.xfail(reason=""not implemented by pint""),
            ),
            function(""std""),
            function(""var""),
            function(""cumsum""),
            pytest.param(
                function(""cumprod""),
                marks=pytest.mark.xfail(reason=""fails within xarray""),
            ),
            pytest.param(
                method(""all""), marks=pytest.mark.xfail(reason=""not implemented by pint"")
            ),
            pytest.param(
                method(""any""), marks=pytest.mark.xfail(reason=""not implemented by pint"")
            ),
            method(""argmax""),
            method(""argmin""),
            method(""max""),
            method(""min""),
            method(""mean""),
            method(""median""),
            method(""sum""),
            pytest.param(
                method(""prod""),
                marks=pytest.mark.xfail(reason=""not implemented by pint""),
            ),
            method(""std""),
            method(""var""),
            method(""cumsum""),
            pytest.param(
                method(""cumprod""), marks=pytest.mark.xfail(reason=""fails within xarray"")
            ),
        ),
        ids=repr,
    )
    def test_aggregation(self, func, dtype):
        unit_a = (
            unit_registry.Pa if func.name != ""cumprod"" else unit_registry.dimensionless
        )
        unit_b = (
            unit_registry.kg / unit_registry.m ** 3
            if func.name != ""cumprod""
            else unit_registry.dimensionless
        )
        a = xr.DataArray(data=np.linspace(0, 1, 10).astype(dtype) * unit_a, dims=""x"")
        b = xr.DataArray(data=np.linspace(-1, 0, 10).astype(dtype) * unit_b, dims=""x"")
        x = xr.DataArray(data=np.arange(10).astype(dtype) * unit_registry.m, dims=""x"")
        y = xr.DataArray(
            data=np.arange(10, 20).astype(dtype) * unit_registry.s, dims=""x""
        )
    
        ds = xr.Dataset(data_vars={""a"": a, ""b"": b}, coords={""x"": x, ""y"": y})
    
>       actual = func(ds)

xarray/tests/test_units.py:3733: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
xarray/tests/test_units.py:374: in __call__
    return func(*all_args, **all_kwargs)
xarray/core/common.py:83: in wrapped_func
    return self.reduce(
xarray/core/dataset.py:4230: in reduce
    variables[name] = var.reduce(
xarray/core/variable.py:1535: in reduce
    data = func(input_data, axis=axis, **kwargs)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

values = <Quantity([0 0 0 0 0 0 0 0 0 1], 'pascal')>, axis = 0, skipna = None
kwargs = {}, func = <function _dask_or_eager_func.<locals>.f at 0x7f392619b820>
msg = 'median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None'

    def f(values, axis=None, skipna=None, **kwargs):
        if kwargs.pop(""out"", None) is not None:
            raise TypeError(f""`out` is not valid for {name}"")
    
        values = asarray(values)
    
        if coerce_strings and values.dtype.kind in ""SU"":
            values = values.astype(object)
    
        func = None
        if skipna or (skipna is None and values.dtype.kind in ""cfO""):
            nanname = ""nan"" + name
            func = getattr(nanops, nanname)
        else:
            func = _dask_or_eager_func(name, dask_module=dask_module)
    
        try:
            return func(values, axis=axis, **kwargs)
        except AttributeError:
            if isinstance(values, dask_array_type):
                try:  # dask/dask#3133 dask sometimes needs dtype argument
                    # if func does not accept dtype, then raises TypeError
                    return func(values, axis=axis, dtype=values.dtype, **kwargs)
                except (AttributeError, TypeError):
                    msg = ""%s is not yet implemented on dask arrays"" % name
            else:
                msg = (
                    ""%s is not available with skipna=False with the ""
                    ""installed version of numpy; upgrade to numpy 1.12 ""
                    ""or newer to use skipna=True or skipna=None"" % name
                )
>           raise NotImplementedError(msg)
E           NotImplementedError: median is not available with skipna=False with the installed version of numpy; upgrade to numpy 1.12 or newer to use skipna=True or skipna=None

xarray/core/duck_array_ops.py:321: NotImplementedError
```

However I’m not much knowledgeable on all this, so I’ll defer to you for finding the root cause.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3779/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
568320746,MDU6SXNzdWU1NjgzMjA3NDY=,3783,New TestVariable.test_pad failure with pint 0.11,490531,closed,0,,,1,2020-02-20T13:59:35Z,2020-02-23T19:13:08Z,2020-02-23T19:13:08Z,CONTRIBUTOR,,,,"Since y-day, `pint` 0.11 was released and it results in a new test failure (in addition to the ones in #3778 and #3779):
```
____________________________ TestVariable.test_pad _____________________________

self = <xarray.tests.test_units.TestVariable object at 0x7f6bfd8c1b20>

    def test_pad(self):
        data = np.arange(4 * 3 * 2).reshape(4, 3, 2)
        v = self.cls([""x"", ""y"", ""z""], data)
    
        xr_args = [{""x"": (2, 1)}, {""y"": (0, 3)}, {""x"": (3, 1), ""z"": (2, 0)}]
        np_args = [
            ((2, 1), (0, 0), (0, 0)),
            ((0, 0), (0, 3), (0, 0)),
            ((3, 1), (0, 0), (2, 0)),
        ]
        for xr_arg, np_arg in zip(xr_args, np_args):
            actual = v.pad_with_fill_value(**xr_arg)
            expected = np.pad(
                np.array(v.data.astype(float)),
                np_arg,
                mode=""constant"",
                constant_values=np.nan,
            )
            assert_array_equal(actual, expected)
            assert isinstance(actual._data, type(v._data))
    
        # for the boolean array, we pad False
        data = np.full_like(data, False, dtype=bool).reshape(4, 3, 2)
        v = self.cls([""x"", ""y"", ""z""], data)
        for xr_arg, np_arg in zip(xr_args, np_args):
>           actual = v.pad_with_fill_value(fill_value=False, **xr_arg)

xarray/tests/test_variable.py:813: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
xarray/core/variable.py:1210: in pad_with_fill_value
    array = np.pad(
<__array_function__ internals>:5: in pad
    ???
/usr/lib/python3.8/site-packages/pint/quantity.py:1543: in __array_function__
    return numpy_wrap(""function"", func, args, kwargs, types)
/usr/lib/python3.8/site-packages/pint/numpy_func.py:894: in numpy_wrap
    return handled[name](*args, **kwargs)
/usr/lib/python3.8/site-packages/pint/numpy_func.py:671: in _pad
    kwargs[""constant_values""] = _recursive_convert(kwargs[""constant_values""], units)
/usr/lib/python3.8/site-packages/pint/numpy_func.py:659: in _recursive_convert
    arg = unit._REGISTRY.Quantity(arg, unit)
/usr/lib/python3.8/site-packages/pint/quantity.py:200: in __new__
    inst._magnitude = _to_magnitude(
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

value = False, force_ndarray = True, force_ndarray_like = False

    def _to_magnitude(value, force_ndarray=False, force_ndarray_like=False):
        if isinstance(value, (dict, bool)) or value is None:
>           raise TypeError(""Invalid magnitude for Quantity: {0!r}"".format(value))
E           TypeError: Invalid magnitude for Quantity: False

/usr/lib/python3.8/site-packages/pint/compat.py:49: TypeError
```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3783/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
567035590,MDU6SXNzdWU1NjcwMzU1OTA=,3777,test_open_mfdataset_list_attr is ran even when dask is not available,490531,closed,0,,,5,2020-02-18T17:08:23Z,2020-02-19T18:24:43Z,2020-02-19T18:24:43Z,CONTRIBUTOR,,,,"I’m currently packaging xarray (0.15.0) for ArchLinux, and I’m running the test suite with all our currently available packages (in addition to `python-numpy`, `python-pandas` and `python-pytest` of course):

    python-netcdf4
    python-scipy
    python-cftime
    python-bottleneck
    python-matplotlib
    python-seaborn
    python-pint

I was greatly impressed at the automatic selection of tests depending on what is available on the system. :) I’m only seeing 10 tests failures (`10 failed, 8057 passed, 1384 skipped, 518 xfailed, 277 xpassed, 5268 warnings`), and amongst them one is happening because a test requiring dask is run even without it being installed: `test_open_mfdataset_list_attr`.

Corresponding test output:
```
________________________ test_open_mfdataset_list_attr _________________________

    @requires_netCDF4
    def test_open_mfdataset_list_attr():
        """"""
        Case when an attribute of type list differs across the multiple files
        """"""
        from netCDF4 import Dataset
    
        with create_tmp_files(2) as nfiles:
            for i in range(2):
                f = Dataset(nfiles[i], ""w"") 
                f.createDimension(""x"", 3)
                vlvar = f.createVariable(""test_var"", np.int32, (""x""))
                # here create an attribute as a list 
                vlvar.test_attr = [f""string a {i}"", f""string b {i}""]
                vlvar[:] = np.arange(3)
                f.close()
            ds1 = open_dataset(nfiles[0])
            ds2 = open_dataset(nfiles[1])
            original = xr.concat([ds1, ds2], dim=""x"")
>           with xr.open_mfdataset(
                [nfiles[0], nfiles[1]], combine=""nested"", concat_dim=""x""
            ) as actual:                                                                                                                                                                                                                                                       

xarray/tests/test_backends.py:2561: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _  
xarray/backends/api.py:908: in open_mfdataset
    datasets = [open_(p, **open_kwargs) for p in paths]
xarray/backends/api.py:908: in <listcomp>
    datasets = [open_(p, **open_kwargs) for p in paths]
xarray/backends/api.py:538: in open_dataset
    ds = maybe_decode_store(store)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _  

store = <xarray.backends.netCDF4_.NetCDF4DataStore object at 0x7f392aebc640>
lock = False

    def maybe_decode_store(store, lock=False):
        ds = conventions.decode_cf(
            store,
            mask_and_scale=mask_and_scale,
            decode_times=decode_times,
            concat_characters=concat_characters,
            decode_coords=decode_coords,
            drop_variables=drop_variables,
            use_cftime=use_cftime,
        )    
    
        _protect_dataset_variables_inplace(ds, cache)
    
        if chunks is not None:
>           from dask.base import tokenize
E           ModuleNotFoundError: No module named 'dask'

xarray/backends/api.py:459: ModuleNotFoundError
```
I think this test should thus not be selected if dask is not installed. ;)","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3777/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
567510745,MDExOlB1bGxSZXF1ZXN0Mzc3MTIxMjM4,3780,Avoid running test_open_mfdataset_list_attr without dask,490531,closed,0,,,6,2020-02-19T11:41:25Z,2020-02-19T18:24:43Z,2020-02-19T18:24:43Z,CONTRIBUTOR,,0,pydata/xarray/pulls/3780,"Fixes GH-3777.

 - [x] Closes #3777","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3780/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull