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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)

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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': k2, 'approx': 2*(1 - np.cos(k * a)) / a2}

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 for series of different input parameters (system dimensions, gate voltages, chemical potentials, etc...)

I'm definitely happy to look at a more realistic / complete example. My PhD work was actually doing quantum simulations.

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.

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.