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/1265#issuecomment-751364613,https://api.github.com/repos/pydata/xarray/issues/1265,751364613,MDEyOklzc3VlQ29tbWVudDc1MTM2NDYxMw==,14808389,2020-12-26T15:05:59Z,2020-12-26T15:05:59Z,MEMBER,instead of the workarounds mentioned in https://github.com/pydata/xarray/issues/1265#issuecomment-279464724 this should work once the integration with `awkward` is implemented (see also #4285),"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,207283854
https://github.com/pydata/xarray/issues/1265#issuecomment-751244884,https://api.github.com/repos/pydata/xarray/issues/1265,751244884,MDEyOklzc3VlQ29tbWVudDc1MTI0NDg4NA==,26384082,2020-12-25T12:49:53Z,2020-12-25T12:49:53Z,NONE,"In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity

If this issue remains relevant, please comment here or remove the `stale` label; otherwise it will be marked as closed automatically
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,207283854
https://github.com/pydata/xarray/issues/1265#issuecomment-456683093,https://api.github.com/repos/pydata/xarray/issues/1265,456683093,MDEyOklzc3VlQ29tbWVudDQ1NjY4MzA5Mw==,26384082,2019-01-23T06:12:26Z,2019-01-23T06:12:26Z,NONE,"In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity If this issue remains relevant, please comment here; otherwise it will be marked as closed automatically
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,207283854
https://github.com/pydata/xarray/issues/1265#issuecomment-280422799,https://api.github.com/repos/pydata/xarray/issues/1265,280422799,MDEyOklzc3VlQ29tbWVudDI4MDQyMjc5OQ==,10928117,2017-02-16T18:51:30Z,2017-02-16T18:51:30Z,NONE,"Hi, I tried to came with a bit more interesting but still simple example

```python
from itertools import product
import numpy as np
import pandas as pd

import holoviews as hv
hv.notebook_extension()


def energies(L, a):
    k = np.pi * np.arange(1, L//a) / L
    return {'exact': k**2, 'approx': 2*(1 - np.cos(k * a)) / a**2}

L = np.arange(10, 21, 2)
a = np.array([1, .5, .25])

data = []
for Li, ai in product(L, a):
    output = dict(L=Li, a=ai)
    output.update(**energies(Li, ai))
    data.append(output)

df = pd.DataFrame(data)

hmap_data = {}
for n, row in df.iterrows():
    key = row.L, row.a
    val = (hv.Points((np.arange(len(row.exact)), row.exact), kdims=['n', 'E']) * 
           hv.Points((np.arange(len(row.approx)), row.approx), kdims=['n', 'E']))
    hmap_data[key] = val
    
hv.HoloMap(hmap_data, kdims=['L', 'a']).select(n=(0, 20), E=(0, 20))
```

example is simple and don't include any serious simulation. I compare here energies of particle in 1D box vs what would came out from tight-binding simulation. Example is very simple but it captures situation that happens often when calculating spectrum of a finite system: for different system size we get different amount of energy levels.

That simple example is manageable without any pandas or xarray machinery but imagine real simulation made with [kwant](https://kwant-project.org/) for series of different input parameters (system dimensions, gate voltages, chemical potentials, etc...)","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,207283854
https://github.com/pydata/xarray/issues/1265#issuecomment-279516519,https://api.github.com/repos/pydata/xarray/issues/1265,279516519,MDEyOklzc3VlQ29tbWVudDI3OTUxNjUxOQ==,1217238,2017-02-13T20:45:14Z,2017-02-13T20:45:14Z,MEMBER,I'm definitely happy to look at a more realistic / complete example. My PhD work was actually doing quantum simulations.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,207283854
https://github.com/pydata/xarray/issues/1265#issuecomment-279514589,https://api.github.com/repos/pydata/xarray/issues/1265,279514589,MDEyOklzc3VlQ29tbWVudDI3OTUxNDU4OQ==,10928117,2017-02-13T20:37:48Z,2017-02-13T20:37:48Z,NONE,I believe that this is a common problem in simulation of quantum mechanical problems. I will try to come with a bit more realistic / practical example that I hope will help with choosing the best solution.,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,207283854
https://github.com/pydata/xarray/issues/1265#issuecomment-279464724,https://api.github.com/repos/pydata/xarray/issues/1265,279464724,MDEyOklzc3VlQ29tbWVudDI3OTQ2NDcyNA==,1217238,2017-02-13T17:40:02Z,2017-02-13T17:40:02Z,MEMBER,"Xarray adds labels to NumPy array, so it can't handle variable length arrays any better than NumPy.

Basically, your options are to either (a) store stored numpy arrays using dtype=object (not really recommended), (b) pad each array up to a common length with NaNs (used to mark missing values in xarray) or (c) put multiple variables in an `xarray.Dataset` and use different dimension names for the variable length dimension.

Depending on your exact use case, either (b) or (c) could be a good solution.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,207283854