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
435535284,MDU6SXNzdWU0MzU1MzUyODQ=,2912,Writing a netCDF file is unexpectedly slow,2014301,closed,0,,,12,2019-04-21T18:31:36Z,2023-09-12T15:58:18Z,2023-09-12T15:58:18Z,NONE,,,,"```python
ncdat=xr.open_mfdataset(nclist, concat_dim='time')

ncdat['lat']=ncdat['lat'].isel(time=0).drop('time')
ncdat['lon']=ncdat['lon'].isel(time=0).drop('time')
ncdat=ncdat.rename({'north_south':'lat', 'east_west':'lon'})

lat_coords=ncdat.lat[:,0] #Extract latitudes
lon_coords=ncdat.lon[0,:] #Extract longitudes

ncdat=ncdat.drop(['lat','lon'])

reformatted_ncdat=ncdat.assign_coords(lat=lat_coords,lon=lon_coords, time=ncdat.coords['time'])

ncdat = reformatted_ncdat.sortby('time')
ncdat.to_netcdf('testing.nc')

```
#### Problem description

After some processing, I am left with this xarray dataset `ncdat` which I want to export to a netCDF file.

```
<xarray.Dataset>
Dimensions:                 (lat: 59, lon: 75, time: 500)
Coordinates:
  * time                    (time) datetime64[ns] 2007-01-22 ... 2008-06-04
  * lat                     (lat) float32 -4.25 -4.15 ... 1.4500003 1.5500002
  * lon                     (lon) float32 29.049988 29.149994 ... 36.450012
Data variables:
    Streamflow_tavg         (time, lat, lon) float32 dask.array<shape=(500, 59, 75), chunksize=(1, 59, 75)>
    RiverDepth_tavg         (time, lat, lon) float32 dask.array<shape=(500, 59, 75), chunksize=(1, 59, 75)>
    RiverFlowVelocity_tavg  (time, lat, lon) float32 dask.array<shape=(500, 59, 75), chunksize=(1, 59, 75)>
    FloodedFrac_tavg        (time, lat, lon) float32 dask.array<shape=(500, 59, 75), chunksize=(1, 59, 75)>
    SurfElev_tavg           (time, lat, lon) float32 dask.array<shape=(500, 59, 75), chunksize=(1, 59, 75)>
    SWS_tavg                (time, lat, lon) float32 dask.array<shape=(500, 59, 75), chunksize=(1, 59, 75)>
Attributes:
    missing_value:           -9999.0
    NUM_SOIL_LAYERS:         1
    SOIL_LAYER_THICKNESSES:  1.0
    title:                   LIS land surface model output
    institution:             NASA GSFC
    source:                  model_not_specified
    history:                 created on date: 2019-04-19T09:11:12.992
    references:              Kumar_etal_EMS_2006, Peters-Lidard_etal_ISSE_2007
    conventions:             CF-1.6
    comment:                 website: http://lis.gsfc.nasa.gov/
    MAP_PROJECTION:          EQUIDISTANT CYLINDRICAL
    SOUTH_WEST_CORNER_LAT:   -4.25
    SOUTH_WEST_CORNER_LON:   29.05
    DX:                      0.1
    DY:                      0.1
```
But the problem is it takes an inordinately long time to export. Almost 10 mins for this particular file which is only 35M.

How can I expedite this process? Is there anything wrong with the structure of `ncdat`?

#### Expected Output
A netCDF file

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3 | packaged by conda-forge | (default, Mar 27 2019, 23:01:00)
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 3.0.101-0.47.105-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.4
libnetcdf: 4.6.2

xarray: 0.12.1
pandas: 0.24.2
numpy: 1.16.2
scipy: 1.2.1
netCDF4: 1.5.0.1
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.0.3.4
nc_time_axis: None
PseudonetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.2.1
dask: 1.2.0
distributed: 1.27.0
matplotlib: 3.0.3
cartopy: 0.17.0
seaborn: 0.9.0
setuptools: 41.0.0
pip: 19.0.3
conda: None
pytest: None
IPython: 7.4.0
sphinx: None

</details>
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455200681,MDU6SXNzdWU0NTUyMDA2ODE=,3017,Why am I running into a IndexVariable error message while assigning dims?,2014301,closed,0,,,1,2019-06-12T13:01:44Z,2020-04-06T22:43:53Z,2020-04-06T22:43:53Z,NONE,,,,"#### Code Sample, a copy-pastable example if possible

I am trying to remove the (north_south, east_west) from `lat` and `lon` and and make them coordinate dimensions. The following piece of code was working fine earlier. But now I am running into an error message. I think I updated xarray. Would like to know what I am doing wrong. Or if there is another way to do this?

```python
ncdat = xr.open_mfdataset(files_lis)
ncdat['lat']=ncdat['lat'].isel(time=0).drop('time')
ncdat['lon']=ncdat['lon'].isel(time=0).drop('time')
ncdat=ncdat.rename({'north_south':'lat', 'east_west':'lon'})

lat_coords = ncdat['lat'].values[:,-1] #Extract latitudes
lon_coords = ncdat['lon'].values[-1,:] #Extract longitudes

reformatted_ncdat=ncdat.assign_coords(lat=lat_coords, lon=lon_coords, time=ncdat.coords['time'])

```
#### Problem description
This is `ncdat` after `xr.open_mfdataset(files_lis)`
```
<xarray.Dataset>
Dimensions:           (SoilMoist_profiles: 3, east_west: 172, north_south: 92, time: 10)
Coordinates:
  * time              (time) datetime64[ns] 2016-08-01 2016-08-02 ... 2016-08-10
Dimensions without coordinates: SoilMoist_profiles, east_west, north_south
Data variables:
    lat               (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    lon               (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    Snowf_tavg        (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    Rainf_tavg        (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    Evap_tavg         (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    Qs_tavg           (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    Qsb_tavg          (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    Qsm_tavg          (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    SWE_tavg          (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    SnowDepth_tavg    (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    SoilMoist_tavg    (time, SoilMoist_profiles, north_south, east_west) float32 dask.array<shape=(10, 3, 92, 172), chunksize=(1, 3, 92, 172)>
    CanopInt_tavg     (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    WaterTableD_tavg  (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    TWS_tavg          (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    GWS_tavg          (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    SnowCover_tavg    (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
    TotalPrecip_tavg  (time, north_south, east_west) float32 dask.array<shape=(10, 92, 172), chunksize=(1, 92, 172)>
```
#### Error
`ValueError: IndexVariable objects must be 1-dimensional`

#### Expected Output

Please ignore values. Just an illustration.
```
<xarray.Dataset>
Dimensions:           (SoilMoist_profiles: 3, lat: 92, lon: 172, time: 10)
Coordinates:
  * lat               (lat) float32 4.1249995 4.375 4.625 ... 26.624996 26.875
  * lon               (lon) float32 nan nan nan ... 24.375 24.625004 24.874996
  * time              (time) datetime64[ns] 2016-08-01 2016-08-02 ... 2017-12-31
Dimensions without coordinates: SoilMoist_profiles
Data variables:
    Snowf_tavg        (time, lat, lon) float32 ...
    Rainf_tavg        (time, lat, lon) float32 ...
    Evap_tavg         (time, lat, lon) float32 ...
    Qs_tavg           (time, lat, lon) float32 ...
    Qsb_tavg          (time, lat, lon) float32 ...
    Qsm_tavg          (time, lat, lon) float32 ...
    SWE_tavg          (time, lat, lon) float32 ...
    SnowDepth_tavg    (time, lat, lon) float32 ...
    SoilMoist_tavg    (time, SoilMoist_profiles, lat, lon) float32 ...
    CanopInt_tavg     (time, lat, lon) float32 ...
    WaterTableD_tavg  (time, lat, lon) float32 ...
    TWS_tavg          (time, lat, lon) float32 ...
    GWS_tavg          (time, lat, lon) float32 ...
    SnowCover_tavg    (time, lat, lon) float32 ...
    TotalPrecip_tavg  (time, lat, lon) float32 ...
```
#### Output of ``xr.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.3 (default, Mar 27 2019, 16:54:48)
[Clang 4.0.1 (tags/RELEASE_401/final)]
python-bits: 64
OS: Darwin
OS-release: 18.6.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.4
libnetcdf: 4.6.2

xarray: 0.12.1
pandas: 0.24.2
numpy: 1.16.4
scipy: 1.2.1
netCDF4: 1.4.2
pydap: None
h5netcdf: None
h5py: 2.9.0
Nio: None
zarr: None
cftime: 1.0.3.4
nc_time_axis: None
PseudonetCDF: None
rasterio: 1.0.21
cfgrib: None
iris: None
bottleneck: None
dask: 1.2.2
distributed: 1.28.1
matplotlib: 3.1.0
cartopy: None
seaborn: 0.9.0
setuptools: 41.0.1
pip: 19.1.1
conda: None
pytest: None
IPython: 7.5.0
sphinx: None

</details>
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392816513,MDU6SXNzdWUzOTI4MTY1MTM=,2623,Why is my export to netcdf command leading to a __truediv__ error?,2014301,closed,0,,,7,2018-12-19T23:16:14Z,2018-12-24T15:58:03Z,2018-12-24T15:58:03Z,NONE,,,,"#### Code Sample, a copy-pastable example if possible
```python
import numpy as np
import xarray as xr
import glob, os

NCDIR = './output/out/'
finalfile = 'summaout.nc'

outfilelist = glob.glob((NCDIR+'/*{}*.nc').format('basin_*timestep'))

ds=xr.open_mfdataset(outfilelist, concat_dim='hru')

replace = ds['pptrate']
runoff = ds['averageInstantRunoff'].values
runoff = np.squeeze(runoffdata, axis=2)
runoff = runoff.transpose()
replace.values = runoff

ncconvert = ds.drop('averageInstantRunoff')
runoffarray = xr.DataArray(runoff, dims=['time','hru'])
ds['averageInstantRunoff'] = runoffarray
ds.to_netcdf('test.nc')

```
#### Problem description

This is `ds` just before export.
```
<xarray.Dataset>
Dimensions:               (hru: 17, time: 233)
Coordinates:
  * hru                   (hru) int64 9 17 11 8 3 2 6 4 7 12 1 13 10 16 15 5 14
  * time                  (time) datetime64[ns] 2010-01-01 ... 2010-01-30
Data variables:
    pptrate               (time, hru) float64 9.241e-05 9.241e-05 ... 2.717e-09
    hruId                 (hru) int64 dask.array<shape=(17,), chunksize=(1,)>
    averageInstantRunoff  (time, hru) float64 9.241e-05 9.241e-05 ... 2.717e-09
    nSnow                 (time, hru) int32 dask.array<shape=(233, 17), chunksize=(233, 1)>
    nSoil                 (time, hru) int32 dask.array<shape=(233, 17), chunksize=(233, 1)>
    nLayers               (time, hru) int32 dask.array<shape=(233, 17), chunksize=(233, 1)>
```
I get this error message:

```
TypeError: cannot perform __truediv__ with this index type: <class 'pandas.core.indexes.datetimes.DatetimeIndex'>
```

#### Expected Output
netCDF

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

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.4.final.0
python-bits: 64
OS: Linux
OS-release: 3.12.62-60.64.8-default
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8

xarray: 0.11.0
pandas: 0.21.0
numpy: 1.13.3
scipy: 1.0.0
netCDF4: 1.4.0
h5netcdf: 0.5.0
h5py: 2.7.1
Nio: None
zarr: None
cftime: 1.0.0
PseudonetCDF: None
rasterio: None
iris: None
bottleneck: 1.2.1
cyordereddict: None
dask: 0.19.3
distributed: 1.23.3
matplotlib: 2.1.2
cartopy: 0.15.1
seaborn: 0.9.0
setuptools: 36.7.2
pip: 18.1
conda: 4.5.11
pytest: 3.2.5
IPython: 6.2.1
sphinx: 1.6.5

</details>
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