Will Dean
Will Dean
Thank you @FBruzzesi for that info. Don't think we leverage index to full extent but only use for that reindex operation. So working around should be possible Maybe you can...
Is the goal to make the notebook logic easy accessible?
Would be good to make it work with the scikit-learn / generator approach for returning the spilts for example : https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.TimeSeriesSplit.html
Yeah, I would raise with the arviz team
Are you able to convert your data to floats?
A good one to hit would be `rfm_summary` https://github.com/pymc-labs/pymc-marketing/blob/5ae96ebcefdac268be0ebf0f9dfab6548cc74cab/pymc_marketing/clv/utils.py?plain=1#L267-L278 If this can be ported to Narwhals, someone can do the aggregation in spark or duckdb
> Do you think this would also be useful in the other modeling modules? We don't provide an aggregation step in MMM or Bass-Diffusion like in CLV. Cannot speak for...
I added another issue. The fivetran adaptors would be easy use for narwhals since it is just some dataframe logic
Heard about this https://docs.codspeed.io/benchmarks/python Free for opensource and would have a web interface Maybe similar thing to codecov
It seems like there needs to be a similar hotfix to [this](https://github.com/pymc-labs/pymc-marketing/blob/6e37d3b6d2cba703d7f844301232309df5c68cd2/pymc_marketing/clv/utils.py#L120-L138). These methods do not clash in names so consolidation for the time being doesn't seem super difficult at...