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
480753417,MDU6SXNzdWU0ODA3NTM0MTc=,3216,Feature request: time-based rolling window functionality,7360639,open,0,,,11,2019-08-14T15:43:31Z,2023-04-06T20:34:47Z,,NONE,,,,"Hi,

I was hoping you would consider extending the rolling window functionality to time windows; as far as I can tell any rolling window functions simply work across a number of nearby points, and not points within, (say) an hour or minute window. This means that I can't even find a reliable rolling mean without writing code myself (and as I am relatively new to Python, this inevitably ends up uselessly slow)

This would extend all rolling functionality to unevenly sampled data, and to buggy data with quality gaps. It would also allow me/others to fix such datagaps by averaging and downsampling where appropriate.

(Context: basically all space physics data and probably other fields too. Really, this would need to be a centred window - I think pandas has a non-centred time window but that doesn't help much.)

Thanks for reading this! And sorry if this is already available - I couldn't find any settings for it.

(PS the multidimensionality of xarray is so useful for me, I have so many vector observations in so many different co-ordinate systems!)
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645443880,MDU6SXNzdWU2NDU0NDM4ODA=,4180,to_netcdf very slow for some single character data types,7360639,closed,0,,,2,2020-06-25T10:18:53Z,2020-06-26T09:45:39Z,2020-06-26T09:45:39Z,NONE,,,,"Hi,

I'm not entirely sure if this is a bug, my misuse, or a feature request. However, I don't think that this is desired behaviour: saving a dataset with single characters in the initial data type is very slow compared to a less desirable alternative.




Example:
make a smaller fake dataset, save it as it is (tester has dtype '<U1'), then change data type and do it again

``` python
import xarray as xr
import numpy as np
import cProfile


ds = xr.Dataset({'tester':('index',np.full((1000000),'.'))},coords = {'index':np.arange(0,1000000)})


cProfile.run(""""""ds.to_netcdf('somefilename')"""""")


ds.tester.values = ds.tester.values.astype('|S1')
cProfile.run(""""""ds.to_netcdf('somefilename')"""""")
```



I find that the first option takes around 8s and the second around 0.076s. This is a massive difference - my own dataset is much larger than this so I am obliged to save it using the |S1 dtype. However, this is much more difficult to use. I am using this field for quality control flags, and now == '.' doesn't work, so I have to wrap saving and loading these datasets with a function changing the datatype.

So I have a workaround but I don't think it makes sense to be this much slower to save single character strings. I also tried pickling the dataset with '<U1' datatype - that is very fast, but netCDF would be better.




<details><summary>Output of <tt>xr.show_versions()</tt></summary>
python: 3.7.6 | packaged by conda-forge | (default, Jun  1 2020, 18:57:50) 
[GCC 7.5.0]
python-bits: 64
OS: Linux
OS-release: 3.10.0-957.27.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8
libhdf5: 1.10.5
libnetcdf: 4.7.4

xarray: 0.15.1
pandas: 1.0.4
numpy: 1.18.5
scipy: 1.4.1
netCDF4: 1.5.3
pydap: None
h5netcdf: None
h5py: 2.10.0
Nio: None
zarr: None
cftime: 1.1.3
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.3.2
dask: 2.18.1
distributed: 2.18.0
matplotlib: 3.2.1
cartopy: None
seaborn: None
numbagg: None
setuptools: 47.1.1.post20200529
pip: 20.1.1
conda: None
pytest: None
IPython: None
sphinx: None


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