What happened: datetime was in np.datetime64 formet. When converted t datetime.datetime format it returned an int What you expected to happen: `to get a datetime returned Minimal Complete Verifiable Example:

```python

Put your MCVE code here

import xarray as xr import numpy as np import datetime date_frame = xr.DataArray(dims='time',coords={'time':pd.date_range('2000-01-01',periods=365)},data=np.zeros(365)) print('pandas date range (datetime): ',pd.date_range('2000-01-01',periods=365)[0]) print('dataframe datetime converted to datetime (int): ',date_frame.coords['time'].data[0].astype(datetime.datetime)) print("normal numpy datetime64 converted to datetime (datetime): ",np.datetime64(datetime.datetime(2000,1,1)).astype(datetime.datetime)) output: pandas date range (datetime): 2000-01-01 00:00:00 dataframe datetime converted to datetime (int): 946684800000000000 normal numpy datetime64 converted to datetime (datetime): 2000-01-01 00:00:00 ```

if converted to int, it also gives different lengths of int : date_frame: 946684800000000000 946684800000000 normal datetime64^ Anything else we need to know?:

it is also mentioned in this SO thread appears to be a problem in the datetime64....

numpy version 1.20.0 pandas version 1.2.1

Environment:

What happened: Tests failed, also reported in #5564 and relatd to #5559 What you expected to happen:

Minimal Complete Verifiable Example:

python cd xarray pip install -e . pip install h5netcdf pytest-xdist netcdf4 py.test -n 4 ```python ======================================================= FAILURES ======================================================= ______ TestH5NetCDFFileObject.testopen_fileobj _______ [gw3] linux -- Python 3.9.5 /home/joeperdefloep/miniconda3/envs/xr-dev/bin/python3.9

self = <xarray.tests.test_backends.TestH5NetCDFFileObject object at 0x7f051a6b59a0>

def test_open_fileobj(self):
    # open in-memory datasets instead of local file paths
    expected = create_test_data().drop_vars("dim3")
    expected.attrs["foo"] = "bar"
    with create_tmp_file() as tmp_file:
        expected.to_netcdf(tmp_file, engine="h5netcdf")

        with open(tmp_file, "rb") as f:
            with open_dataset(f, engine="h5netcdf") as actual:
                assert_identical(expected, actual)

            f.seek(0)
            with open_dataset(f) as actual:
                assert_identical(expected, actual)

            f.seek(0)
            with BytesIO(f.read()) as bio:
                with open_dataset(bio, engine="h5netcdf") as actual:
                    assert_identical(expected, actual)

            f.seek(0)
            with pytest.raises(TypeError, match="not a valid NetCDF 3"):

              open_dataset(f, engine="scipy")

/mnt/e/Git/xarray/xarray/tests/test_backends.py:2887:

/mnt/e/Git/xarray/xarray/backends/api.py:483: in open_dataset backend = plugins.get_backend(engine)

engine = 'scipy'

def get_backend(engine):
    """Select open_dataset method based on current engine."""
    if isinstance(engine, str):
        engines = list_engines()
        if engine not in engines:

          raise ValueError(
                f"unrecognized engine {engine} must be one of: {list(engines)}"
            )

E ValueError: unrecognized engine scipy must be one of: ['netcdf4', 'h5netcdf', 'store']

/mnt/e/Git/xarray/xarray/backends/plugins.py:156: ValueError ________ test_3641 ___________ [gw0] linux -- Python 3.9.5 /home/joeperdefloep/miniconda3/envs/xr-dev/bin/python3.9

@requires_cftime
def test_3641():
    times = xr.cftime_range("0001", periods=3, freq="500Y")
    da = xr.DataArray(range(3), dims=["time"], coords=[times])

  da.interp(time=["0002-05-01"])

/mnt/e/Git/xarray/xarray/tests/test_interp.py:733:

/mnt/e/Git/xarray/xarray/core/dataarray.py:1687: in interp ds = self._to_temp_dataset().interp( /mnt/e/Git/xarray/xarray/core/dataset.py:3146: in interp variables[name] = missing.interp(var, var_indexers, method, kwargs) /mnt/e/Git/xarray/xarray/core/missing.py:633: in interp interped = interp_func( /mnt/e/Git/xarray/xarray/core/missing.py:752: in interp_func return _interpnd(var, x, new_x, func, kwargs) /mnt/e/Git/xarray/xarray/core/missing.py:770: in _interpnd return _interp1d(var, x, new_x, func, kwargs) /mnt/e/Git/xarray/xarray/core/missing.py:758: in _interp1d rslt = func(x, var, assume_sorted=True, kwargs)(np.ravel(new_x))

self = <[AttributeError("'ScipyInterpolator' object has no attribute 'method'") raised in repr()] ScipyInterpolator object at 0x7f1aa547fc40> xi = <xarray.IndexVariable 'time' (time: 2)> array([0.00000e+00, 1.57788e+19]), yi = array([0, 1]), method = 'linear' fill_value = None, assume_sorted = True, copy = False, bounds_error = False, order = None, kwargs = {}

def __init__(
    self,
    xi,
    yi,
    method=None,
    fill_value=None,
    assume_sorted=True,
    copy=False,
    bounds_error=False,
    order=None,
    **kwargs,
):

  from scipy.interpolate import interp1d

E ModuleNotFoundError: No module named 'scipy'

/mnt/e/Git/xarray/xarray/core/missing.py:129: ModuleNotFoundError =================================================== warnings summary =================================================== xarray/tests/test_dataarray.py::TestReduce1D::test_min[x3-5-2-1] xarray/tests/test_dataarray.py::TestReduce1D::test_max[x3-5-2-1] xarray/tests/test_dataarray.py::TestReduce2D::test_min[x2-minindex2-maxindex2-nanindex2] xarray/tests/test_dataarray.py::TestReduce2D::test_max[x2-minindex2-maxindex2-nanindex2] /home/joeperdefloep/miniconda3/envs/xr-dev/lib/python3.9/site-packages/numpy/core/fromnumeric.py:86: RuntimeWarning: invalid value encountered in reduce return ufunc.reduce(obj, axis, dtype, out, **passkwargs)

-- Docs: https://docs.pytest.org/en/stable/warnings.html =============================================== short test summary info ================================================ FAILED xarray/tests/test_backends.py::TestH5NetCDFFileObject::test_open_fileobj - ValueError: unrecognized engine sci... FAILED xarray/tests/test_interp.py::test_3641 - ModuleNotFoundError: No module named 'scipy' ============= 2 failed, 8243 passed, 3776 skipped, 29 xfailed, 26 xpassed, 4 warnings in 229.01s (0:03:49) ============= ``` Anything else we need to know?:

Environment:

• [x] Closes #4985 #5565
• [x] Tests added
• [x] Passes pre-commit run --all-files
• [x] User visible changes (including notable bug fixes) are documented in whats-new.rst
• [x] New functions/methods are listed in api.rst

What happened: It returned an array of nan values What you expected to happen: an array of zeroes Minimal Complete Verifiable Example:

ZeroToNan.zip ```python

Your code here

z = zarr.open('ZeroToNan.zip') print(z['foo__a'][:]) print(xr.open_zarr(z.store).foo__a.values) ```

Anything else we need to know?: I also posted this problem here and here Environment: