Creating multiple columns from a rolling window on a single column

I have a very large dataset of DateTimeIndexed signals that cannot be loaded in memory without using dask.

I am trying to do some feature engineering to derive multiple features from a single column using a rolling window of about “100ms” and other time frames (using fast fourier transform and the original data is in 20ms).
I have looked into rolling but my understanding is that the result type should return a single value according to rolling.apply so it is not usable here.

A solution I found in pandas was to use resample and iterate over the bins generated to create a list of my different features (there is an equivalent in dask here) like so:

f1s, f2s = [], []
bins = result.resample("100ms")
for bin in bins:
    # compute new features from column df["X"]
    f1 = ....
    f2 = ....

However, resample in dask doesn't seem to be iterable so this solution is not applicable either.
I would appreciate any advice on how to best solve my problem.
Thank you for you time.

Hi @carl-krikorian, welcome to Dask Discourse forum!

First, could you precise what you mean by rolling window? Is it rolling in the sense you might have a step different than the windows size, or are each original value in only one window?

You talk about using resample: do you need the original values or the resampled ones?

Could you provide some reproducible example?

One easy solution if your dataset is already sorted by time could be to use map_partitions, and code using Pandas.