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
188996339,MDU6SXNzdWUxODg5OTYzMzk=,1115,Feature request: Compute cross-correlation (similar to pd.Series.corr()) of gridded data,6334793,closed,0,,,31,2016-11-13T21:29:04Z,2020-05-25T16:57:48Z,2020-05-25T16:57:48Z,NONE,,,,"As a earth scientist regularly dealing with 3D data (time, latitude, longitude), I believe it would be great to be able to perform cross-correlation on DataArrays by specifying the axis. It's usage could look like: a.corr(b, axis = 0). It would be even more useful if the two arrays need not have the same dimensions (e.g. 'b' could be a time series). 

Currently, the only way to compute this that I am aware of, is by looping through each grid, converting the time series to pd.Series(), and then computing the correlation. This takes a long time. 
Would also appreciate suggestions to a faster algorithm. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/1115/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
396102183,MDExOlB1bGxSZXF1ZXN0MjQyMzk5MjY1,2652,cov() and corr(),6334793,closed,0,,,20,2019-01-04T23:30:44Z,2020-05-25T16:57:31Z,2020-05-25T16:57:31Z,FIRST_TIMER,,0,pydata/xarray/pulls/2652,"Added da.corr() and da.cov() to dataarray.py. Test added in test_dataarray.py, and tested using pytest.
Concerns issue #1115

The test is based on demo data and can be readily added to the user guide. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2652/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,pull
307903558,MDU6SXNzdWUzMDc5MDM1NTg=,2009,Removing inter-subplot spaces when using cartopy projections,6334793,closed,0,,,2,2018-03-23T05:02:59Z,2018-03-23T16:45:15Z,2018-03-23T16:45:15Z,NONE,,,,"#### Code Sample, a copy-pastable example if possible

```python
import cartopy.crs as ccrs
import xarray as xr
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec


projection = True


ts           = xr.tutorial.load_dataset('air_temperature').air[0, ...]
ncols, nrows = 2, 2
fig          = plt.figure()
# ny,nx        = ts.shape
# dx, dy       = nx/ny, 1
# figsize      = plt.figaspect(float(dy * ncols) / float(dx * nrows))
# fig          = plt.figure(figsize=figsize)
gs           = gridspec.GridSpec(ncols, nrows)


for i in range(4):
    if projection:
        ax = plt.subplot(gs[i], projection=ccrs.PlateCarree())
        ax.coastlines()
        ts.plot(ax=ax, add_colorbar=False, add_labels=False,
                transform=ccrs.PlateCarree())
    else:
        ax = plt.subplot(gs[i])
        ts.plot(ax=ax, add_colorbar=False, add_labels=False)
    ax.set_xticks([])
    ax.set_yticks([])
    # ax.set_aspect('auto', adjustable='box-forced')
    if (i == 0) or (i == 1):
        ax.set_title('title')
    if (i == 0) or (i == 2):
        ax.set_ylabel('ylabel')

# plt.tight_layout()
fig.subplots_adjust(wspace=0, hspace=0)

plt.show()

```
#### Problem description

In the above script, the subplots get plotted with no in-between spaces if no projection information is provided (i.e. projection=False). 
![demo_without_projection](https://user-images.githubusercontent.com/6334793/37812520-b86b0dc6-2e1d-11e8-971a-cb72c7caa656.png)

But when projection info is provided, there is pretty much no way of removing the inter-column (or inter-row) spaces. 
![demo_with_projection](https://user-images.githubusercontent.com/6334793/37812526-c2bfe828-2e1d-11e8-892a-c9072b5c2e18.png)

Commented lines are the different ways that I tried to remove the spaces. 

#### Output of ``xr.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: None
LOCALE: None.None

xarray: 0.10.0
pandas: 0.22.0
numpy: 1.14.0
scipy: 1.0.0
netCDF4: 1.3.1
h5netcdf: 0.5.0
Nio: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.16.1
matplotlib: 2.1.2
cartopy: 0.15.1
seaborn: 0.8.1
setuptools: 38.4.0
pip: 9.0.3
conda: 4.4.10
pytest: 3.3.2
IPython: 6.2.1
sphinx: 1.6.6
</details>
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