html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1490#issuecomment-318079611,https://api.github.com/repos/pydata/xarray/issues/1490,318079611,MDEyOklzc3VlQ29tbWVudDMxODA3OTYxMQ==,4992424,2017-07-26T14:57:58Z,2017-07-26T14:57:58Z,NONE,"Did some digging. 

Note here that the **dtype**s of `time1` and `time2` are different; the first is a **datetime64[ns]** but the second is a **datetime64[ns, UTC]**. For the sake of illustration, I'm going to change the timezone to EST. If we print `time2`, we get something that looks like this:

``` python
>>> time2
DatetimeIndex(['2000-01-01 00:00:00-05:00', '2000-01-01 01:00:00-05:00',
               '2000-01-01 02:00:00-05:00', '2000-01-01 03:00:00-05:00',
               '2000-01-01 04:00:00-05:00', '2000-01-01 05:00:00-05:00',
               '2000-01-01 06:00:00-05:00', '2000-01-01 07:00:00-05:00',
               '2000-01-01 08:00:00-05:00', '2000-01-01 09:00:00-05:00',
               ...
               '2000-12-30 14:00:00-05:00', '2000-12-30 15:00:00-05:00',
               '2000-12-30 16:00:00-05:00', '2000-12-30 17:00:00-05:00',
               '2000-12-30 18:00:00-05:00', '2000-12-30 19:00:00-05:00',
               '2000-12-30 20:00:00-05:00', '2000-12-30 21:00:00-05:00',
               '2000-12-30 22:00:00-05:00', '2000-12-30 23:00:00-05:00'],
              dtype='datetime64[ns, EST]', length=8760, freq='H')
```

But, if we directly print its *values*, we get something slightly different:

``` python
>>> time2.values
array(['2000-01-01T05:00:00.000000000', '2000-01-01T06:00:00.000000000',
       '2000-01-01T07:00:00.000000000', ...,
       '2000-12-31T02:00:00.000000000', '2000-12-31T03:00:00.000000000',
       '2000-12-31T04:00:00.000000000'], dtype='datetime64[ns]')
``` 

The difference is that the timezone delta has been automatically added in terms of hours to each value in `time2`. This brings up something to note: if you construct your `Dataset` using `time1.values` and `time2.values`, there is no problem:

``` python
import pandas as pd
import xarray as xr
time1 = pd.date_range('2000-01-01', freq='H', periods=365 * 24)  #timezone naïve
time2 = pd.date_range('2000-01-01', freq='H', periods=365 * 24, tz='UTC')  #timezone aware
ds1 = xr.Dataset({'foo': ('time', np.arange(365 * 24)), 'time': time1.values})
ds2 = xr.Dataset({'foo': ('time', np.arange(365 * 24)), 'time': time2.values})
ds1.resample('3H', 'time', how='mean')  # works fine
ds2.resample('3H', 'time', how='mean')  # works fine
```

Both `time1` and `time2` are instances of `pd.DatetimeIndex` which are subclasses of `pd.Index`. When xarray tries to turn them into `Variable`s, it ultimately uses a `PandasIndexAdapter` to decode the contents of `time1` and `time2`, and this is where the trouble happens. The `PandasIndexAdapter` tries to safely cast the dtype of the array it is passed, which works just fine for `time1`. But for some weird reason, numpy doesn't recognize its own datetime dtypes when they have timezone information. That is, this will work:

``` python
>>> np.dtype('datetime64[ns]')
dtype('<M8[ns]')
```
But this won't:

``` python
>>> np.dtype('datetime64[ns, UTC]')
TypeError: Invalid datetime unit in metadata string ""[ns, UC]""
```

But also, the type of `time2.dtype` is a `pandas.types.dtypes.DatetimeTZDtype`, which NumPy doesn't know what to do with (it doesn't know how to map that type to its own `datetime64`).

So what happens is that the resulting `Variable` which defines the **time** coordinate on your `ds2` has an array with the correct values, but is explicitly told to have the dtype `object`. When the array is decoded, then, bad things happen. 

One solution would be to catch this potential glitch in either [`is_valid_numpy_dtype()`](https://github.com/pydata/xarray/blob/a943419b86c1c952d07cef6acf0b10ea5784a4cc/xarray/core/utils.py#L193) or the [`PandasIndexAdapter` constructor](https://github.com/pydata/xarray/blob/c2588dadff82f2e56b9ec9c10d6d57661dbcce15/xarray/core/indexing.py#L506). Alternatively, we could eagerly coerce arrays with type `pandas.types.dtypes.DatetimeTZDtype` into numpy-compliant types at some earlier point.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,245649333