issues

What happened: When creating a new variable based on an existing dimension, the attrs of the dimension are lost.

What you expected to happen: The attrs should be preserved.

Minimal Complete Verifiable Example:

```python import xarray as xr

ds = xr.Dataset() ds['x'] = xr.DataArray(range(10), dims='x') ds['y'] = xr.DataArray(range(len(ds['x'])), dims='x') ds['x'].attrs['foo'] = 'bar' print(ds['x']) # attrs of ds['x'] are preserved

print('\n****\n')

ds = xr.Dataset() ds['x'] = xr.DataArray(range(10), dims='x') ds['x'].attrs['foo'] = 'bar' ds['y'] = xr.DataArray(range(len(ds['x'])), dims='x') print(ds['x']) # attrs of ds['x'] are lost ```

Output of above code: ``` <xarray.DataArray 'x' (x: 10)> array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) Coordinates: * x (x) int64 0 1 2 3 4 5 6 7 8 9 Attributes: foo: bar

<xarray.DataArray 'x' (x: 10)> array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) Coordinates: * x (x) int64 0 1 2 3 4 5 6 7 8 9 ```

Environment:

I've been working with some time-series data with occasional nans peppered throughout. I want to interpolate small gaps of nans (say, when there is a single isolated nan or perhaps a block of two) but leave larger blocks as nans. That is, it's not appropriate to fill large gaps, but it acceptable to do so for small gaps.

I was hoping interpolate_na() with the limit argument would do exactly this, but it turns out that if you specify, say, limit=2, it will fill the first two nans of nan-blocks of any length, no matter how long. There are definitely solutions for dealing with this, but it seems like a common issue, and has cropped up over on Pandas as well.

I'm not able to attempt tackling this right now, but I guess I wanted to put in a feature request for an additional argument to interpolate_na() that would do this.

I recently upgraded to xarray 0.14.0. When running code that used to work in 0.13, I get a TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe'' in 0.14 when running code similar to the MCVE below. The code should return the GroupBy bins instead of the error.

MCVE Code Sample

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

ts = pd.date_range(start='2010-08-01', end='2010-08-15', freq='24.8H')

ds = xr.Dataset() ds['time'] = xr.DataArray(pd.date_range('2010-08-01', '2010-08-15', freq='15min'), dims='time') ds['val'] = xr.DataArray(np.random.rand(*ds['time'].shape), dims='time')

ds.groupby_bins('time', ts) #error thrown here ```

Full error details below.

Output of xr.show_versions()

I'm not sure I understand how the unlimited_dims option to to_netcdf() is supposed to work. Consider the following: python ds = xr.Dataset() ds['time'] = xr.DataArray(pd.date_range('2000-01-01', '2000-01-10'), dims='time') ds.to_netcdf('timedim.cdf', unlimited_dims='time') This results in a file that looks like this: ``` $ ncdump timedim.cdf netcdf timedim { dimensions: t = UNLIMITED ; // (0 currently) i = UNLIMITED ; // (0 currently) m = UNLIMITED ; // (0 currently) e = UNLIMITED ; // (0 currently) time = UNLIMITED ; // (10 currently) variables: int64 time(time) ; time:units = "days since 2000-01-01 00:00:00" ; time:calendar = "proleptic_gregorian" ; data:

time = 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ; } `` Note the dimensions namedt,i,m,eall with zero length. Thetimedimension (which is the only one that should exist) is properly set toUNLIMITEDbut we shouldn't have the four extra dimensions. What's going on here? The same behavior occurs when setting viads.encoding['unlimited_dims'] = 'time'. Everything is as expected without theunlimited_dimsoption (but thetimedimension is notUNLIMITED`, of course).

I thought it could be related to the variable and dimension having the same name, but this also happens when they are different.

Expected Output

There shouldn't be extra 0-length dimensions

Output of xr.show_versions()

Migrated from Stack Overflow.

NetCDF supports the NC_STRING type, which can stores arrays of strings in attributes. Xarray already supports reading arrays of strings from attributes in netCDF files, and It would be great if it also supported writing the same.

Reading already works

```python import xarray as xr import netCDF4 as nc

rg = nc.Dataset('test_string.nc', 'w', format='NETCDF4') rg.setncattr_string('testing', ['a', 'b']) rg.close() ds = xr.open_dataset('test_string.nc') print(ds) gives <xarray.Dataset> Dimensions: () Data variables: empty Attributes: testing: ['a', 'b'] ```

This works because I used the setncattr_string method. Setting the attributes like rg.testing = ['a', 'b'] does not work and results in a concatenated list (just like the xarray example below).

Writing doesn't work

python import xarray as xr ds = xr.Dataset() ds.attrs['testing'] = ['a', 'b'] ds.to_netcdf('asdf.nc') ds = xr.open_dataset('asdf.nc', autoclose=True) print(ds) gives <xarray.Dataset> Dimensions: () Data variables: *empty* Attributes: testing: ab

Note the list elements have been concatenated. So this is a request for xarray to implement something like netCDF4's setncattr_string. I would be happy to help do this if someone pointed me in the right direction; I looked through the code but got lost pretty quickly. Thanks.