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
448478648,MDExOlB1bGxSZXF1ZXN0MjgyMjM5MDI0,2991,ENH: str accessor,3621629,closed,0,,,6,2019-05-25T16:10:22Z,2019-06-10T13:11:14Z,2019-06-10T13:11:11Z,CONTRIBUTOR,,0,pydata/xarray/pulls/2991,"Hello,

Some of the pandas str functionalities. Instead of wrapping pandas internal as in #2983 I copy/pasted the code since it's simple and tiny.

Currently it's a bit more restrictive than pandas since it expects all elements to be string like.

 - [x] Closes #2983 
 - [x] Tests added
 - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2991/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
447856992,MDU6SXNzdWU0NDc4NTY5OTI=,2983,string accessor,3621629,closed,0,,,1,2019-05-23T20:25:01Z,2019-06-10T13:11:10Z,2019-06-10T13:11:10Z,CONTRIBUTOR,,,,"Hello,

I have written a small wrapper around pandas internal methods
https://gist.github.com/0x0L/ef78c80a42892c0f832c91357914a5a4

Missing methods are those involving list or list of arrays (join, split, partition, ...)

Let me know if there's enough interest to turn this into a full commit","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2983/reactions"", ""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
452734140,MDExOlB1bGxSZXF1ZXN0Mjg1NTY0OTEz,3001,BUG: fix safe_cast_to_index,3621629,closed,0,,,3,2019-06-05T21:52:57Z,2019-06-10T04:48:45Z,2019-06-10T04:48:45Z,CONTRIBUTOR,,0,pydata/xarray/pulls/3001," - [x] Closes #3000
 - [x] Tests added
 - [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3001/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
452729969,MDU6SXNzdWU0NTI3Mjk5Njk=,3000,Slowness when cftime is installed,3621629,closed,0,,,0,2019-06-05T21:40:42Z,2019-06-10T04:48:44Z,2019-06-10T04:48:44Z,CONTRIBUTOR,,,,"With `cftime` installed

```python
import numpy as np
import pandas as pd
import xarray as xr

da = xr.DataArray(np.random.randn(5000, 500))
df = da.to_pandas()

# with pandas
%time df_stacked = df.stack()
# Wall time: 48.3 ms
%time df_unstacked = df_stacked.unstack()
# Wall time: 368 ms

# with xarray
%time da_stacked = da.stack(stacked_dim=('dim_0', 'dim_1'))
# Wall time: 1.03 s
%time da_unstacked = da_stacked.unstack('stacked_dim')
# Wall time: 78.2 ms
```

prun points to `CFTimeIndex(index)` in `_maybe_cast_to_cftimeindex`

The behaviour is also incorrect for empty indexes:
```python
da[:0].stack(dim=['dim_0', 'dim_1']).dim.to_index()
# CFTimeIndex([], dtype='object', name='dim')
```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3000/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
276688437,MDU6SXNzdWUyNzY2ODg0Mzc=,1742,Performance regression when selecting,3621629,closed,0,,,1,2017-11-24T19:34:29Z,2019-06-06T19:08:06Z,2019-06-06T19:08:06Z,CONTRIBUTOR,,,,"Hello,

I just noticed a performance drop in 0.10 after a `conda update xarray`

```python
import numpy as np
import pandas as pd
import xarray as xr

np.random.seed(1234)
ds = xr.Dataset({k: pd.DataFrame(np.random.randn(2500, 2000)) for k in range(20)})
mask = (np.random.randn(2000) > -0.2).astype(bool)

%timeit ds.sel(dim_0=slice(50, 1250), dim_1=mask)
%timeit ds[0].sel(dim_0=slice(50, 1250), dim_1=mask)

# xarray 0.9.6 -> 120 ± 0.4 ms, 4.2 ± 0.02 ms
# xarray 0.10 -> 190 ± 0.4 ms, 6.8 ± 0.03 ms
```

This was run in a docker image. Strangely I can't reproduce it natively on macos (performance is the same as in 0.10 in docker for both versions). On a window box, with similar but ""real"" netcdf dataset performance is halved.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1742/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
274996832,MDU6SXNzdWUyNzQ5OTY4MzI=,1726,Behavior of dataarray with no dimensions,3621629,closed,0,,,3,2017-11-17T21:07:55Z,2018-01-11T21:24:43Z,2018-01-11T21:24:43Z,CONTRIBUTOR,,,,"Consider

```python
type(np.array([1.0]).mean())
# -> numpy.float64

type(pd.Series([1.0]).mean())
# -> float

type(xr.DataArray([1.0]).mean())
# -> xarray.core.dataarray.DataArray
```

The issue is that this dimensionless data array won't be cast into float by numpy/pandas when constructing a new ndarray/dataframe. You'll have to do it explicitly. Not a big deal but it feels weird.

I'm sure there's a real technical reason (keeping metadata ?) behind this behavior but I couldn't find any discussion about it.
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1726/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
275813162,MDExOlB1bGxSZXF1ZXN0MTUzOTY2MTk0,1734,pandas casting issues,3621629,closed,0,,,7,2017-11-21T18:21:56Z,2018-01-11T21:24:43Z,2018-01-11T21:24:43Z,CONTRIBUTOR,,0,pydata/xarray/pulls/1734," - [x] Closes #1726 

Added a comment about constructing `pandas` objects from `xarray` objects","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1734/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
275789502,MDExOlB1bGxSZXF1ZXN0MTUzOTQ4Njk1,1733,Rank Methods,3621629,closed,0,,,9,2017-11-21T17:03:41Z,2017-12-18T16:51:05Z,2017-12-18T16:51:00Z,CONTRIBUTOR,,0,pydata/xarray/pulls/1733,"- [x] Closes #1731
- [x] Tests added / passed
- [x] Passes ``git diff upstream/master **/*py | flake8 --diff``
- [x] Fully documented, including `whats-new.rst` for all changes and `api.rst` for new API
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1733/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
275461273,MDU6SXNzdWUyNzU0NjEyNzM=,1731,Rank function,3621629,closed,0,,,4,2017-11-20T18:55:33Z,2017-12-18T16:51:00Z,2017-12-18T16:51:00Z,CONTRIBUTOR,,,,"Hi,

I think xarray is missing a rank function.
Is there any reason not to expose a wrapper to `bottleneck.nanrankdata` ?

See also https://github.com/pydata/xarray/issues/1635

[edit]
Although moving rank is mentioned in the whats-new for v0.9.2 I wasn't able to find that functionality nor a trace of it in the code :)","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1731/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue