@pnsaevik If the approach we adopt in scipp could be ported to xarray you would be able to to something like (assuming that the ragged array representation you have in mind is "list of lists"):

```python data = my_load_netcdf(...) # list of lists

assume 'x' is the dimension of the nested lists

bin_edges = sc.Variable(dims=['x'], values=[0.1,0.3,0.5,0.7,0.9]) realigned = sc.realign(data, {'x':bin_edges}) filtered = realigned['x', 1:3].copy() my_store_netcdf(filtered.unaligned, ...) ``` Basically, we have slicing for the "realigned" wrapper. It performs a filter operation when copied.

Edit 2021: Above example is very outdated, we have cleaned up the mechanism, see https://scipp.github.io/user-guide/binned-data/binned-data.html.

I am not familiar with the details of the various applications people in this discussion have, but here is an approach we are taking, trying to solve variations of the problem "data scattered in multi-dimensional space" or irregular time-series data. See https://scipp.github.io/user-guide/binned-data/binned-data.html for an illustrated description.

The basic idea is to keep data in a linear representation and wrap it in a "realigned" wrapper. One reason for this development was to provide a pathway to use dask with our type of data (independent time series at a large number of points in space, with chunking along the "time-series", which is not a dimension since every time series has a different length). With the linked approach we could use dask to distribute the linear underlying representation, keeping the lightweight realigned wrapper on all workers. We are still in early experimentation with this (the dask part is not actually in development yet). It probably has performance issues if more than "millions" of points are realigned --- our case is millions of time series with thousands/millions of time points in each, but the two do not mix (not both are realigned, and if they are it is independently), so we do not run into the performance issue in most cases.

In principle I could imagine this non-destructive realignment approach could be mapped to xarray, so it may be of interest to people here.