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ritchie46
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Sep 14, 2024
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| def q2_polars(df): | ||
| return df.with_columns( |
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Can we use the select + explode mapping here?
ritchie46
reviewed
Sep 14, 2024
| Participants typically used pandas (Polars was only just getting started at the time), so here we benchmark how long it have | ||
| taken to do the same feature engineering with Polars (and, coming soon, DuckDB). | ||
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| We believe this to be a useful task to benchmark, because: |
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I think we can remove L9-L12.
I think this can serve as a basis for more time-series related benchmarks on this datasets. I don't think we have to strictly limit to what was used in the kaggle competition.
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Just got back to this - running locally, I'm seeing very good results for Polars: |
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Some results: https://www.kaggle.com/code/marcogorelli/m5-forecasting-feature-engineering-benchmark