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
776520994,MDU6SXNzdWU3NzY1MjA5OTQ=,4741,Attribute style access is slow,32801740,closed,0,,,2,2020-12-30T15:52:07Z,2021-01-05T23:00:29Z,2021-01-05T23:00:29Z,CONTRIBUTOR,,,,"I appreciate xarray's ability to use attribute style access ``ds.foo`` as an alternative to ``ds[""foo""]`` as it requires less characters/keystrokes and has less 'visual clutter'.

A drawback is that it can be much slower as lookup time seems to display `O(n)` behaviour instead of `O(1)` with `n` being the number of variables in the dataset. For e.g. `n=100` it is approximately 100 times slower than dictionary-style access:

    # Dataset with many (100) variables
    ds = xr.Dataset({f'var{v}': [] for v in range(100)})

    %timeit ds['var0']
    462 µs ± 1.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

    %timeit ds.var0
    47.1 ms ± 205 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)


``dir()`` and ``_ipython_key_completions_()`` which are used for e.g. tab completion in iPython are equally slow:

    %timeit dir(ds)
    47 ms ± 163 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

    %timeit ds._ipython_key_completions_()
    46.8 ms ± 210 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

I would like to see xarray having much better performance for attribute style access.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4741/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
755607271,MDU6SXNzdWU3NTU2MDcyNzE=,4644,astype method lost its order parameter,32801740,closed,0,,,7,2020-12-02T21:02:02Z,2020-12-16T16:33:00Z,2020-12-16T16:33:00Z,CONTRIBUTOR,,,,"
**What happened**:
I upgraded from xarray 0.15.1 to 0.16.2 and the `astype` method seems to have lost the `order` parameter.

```python
In [1]: import xarray as xr

In [2]: xr.__version__
Out[2]: '0.16.2'

In [3]: xr.DataArray([[1.0, 2.0], [3.0, 4.0]]).astype(dtype='d', order='F').values.strides
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-3-208ab49008ef> in <module>
----> 1 xr.DataArray([[1.0, 2.0], [3.0, 4.0]]).astype(dtype='d', order='F').values.strides

TypeError: astype() got an unexpected keyword argument 'order'
```

**What you expected to happen**:
I was expecting to get the same result as with xarray 0.15.1:

```python
In [1]: import xarray as xr

In [2]: xr.__version__
Out[2]: '0.15.1'

In [3]: xr.DataArray([[1.0, 2.0], [3.0, 4.0]]).astype(dtype='d', order='F').values.strides
Out[3]: (8, 16)
```

**Anything else we need to know?**:
Looking at the documentation it seems it disappeared between 0.16.0 and 0.16.1. The documentation at http://xarray.pydata.org/en/v0.16.0/generated/xarray.DataArray.astype.html
still has this snippet

> order ({'C', 'F', 'A', 'K'}, optional) – Controls the memory layout order of the result. ‘C’ means C order, ‘F’ means Fortran order, ‘A’ means ‘F’ order if all the arrays are Fortran contiguous, ‘C’ order otherwise, and ‘K’ means as close to the order the array elements appear in memory as possible. Default is ‘K’.

(which was identical to the documentation from numpy.ndarray.astype at https://numpy.org/doc/stable/reference/generated/numpy.ndarray.astype.html)

while http://xarray.pydata.org/en/v0.16.1/generated/xarray.DataArray.astype.html seems to lack that part.


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