html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/3984#issuecomment-616214519,https://api.github.com/repos/pydata/xarray/issues/3984,616214519,MDEyOklzc3VlQ29tbWVudDYxNjIxNDUxOQ==,34353851,2020-04-19T19:49:19Z,2020-04-19T19:49:19Z,NONE,"I dont try it, but i know your problem.
If you try to create from dataarray df.to_dataset(name='participant_A')
df.to_dataset(name='participant_B')
and after merge them?

xr.merge([ds1, ds2], compat='no_conflicts')

http://xarray.pydata.org/en/stable/combining.html

In potter case you could create nan values to create the same dimensions.

But i have never tried. I found another solution for my data, but it was my
alternative.

El dom., 19 abr. 2020 20:57, (Ray) Jinbiao Yang <notifications@github.com>
escribió:

> I always use Pandas to deal with my neuroscience data (multi-dimension).
> It is annoying to stack and unstack all the time and I heard Xarray is
> designed for multi-dimension data.
>
> In neuroscience research, we usually have multiple participants and we
> will test them different times, which means the data may look like this:
>
>    - participant A:
>       - 2*5*100 matrix
>    - participant B:
>       - 2*5*101 matrix
>
> (100 and 101 are the testing times)
>
> But *Dataset doesn't support to have 2*5*100 DataArray and 2*5*101
> DataArray together*. Is there any solution to deal with that kind of data
> in Xarray?
>
> —
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> Reply to this email directly, view it on GitHub
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