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emarx
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Dec 24, 2023
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| @offline(tags="batch-most-recent-revenue") | ||
| def batch_most_recent_revenue( | ||
| records: DataFrame[RApplication.id, RApplication.records, Now] | ||
| ) -> DataFrame[RApplication.id, RApplication.most_recent_revenue]: | ||
| df: pl.LazyFrame = records.to_polars() | ||
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| exploded = df.explode(pl.col(str(RApplication.records))).unnest(str(RApplication.records)) | ||
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| time_consistent = exploded.filter(pl.col(str(RQuickbooksRecord.created_at)) <= pl.col(str(Now))) | ||
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| most_recent_rev_output = ( | ||
| time_consistent.sort(by=str(RQuickbooksRecord.created_at), descending=True) | ||
| .groupby(str(RApplication.id), str(Now), maintain_order=True) | ||
| .agg(pl.first(str(RQuickbooksRecord.revenue)).alias(str(RApplication.most_recent_revenue))) | ||
| .select(str(RApplication.id), str(RApplication.most_recent_revenue)) | ||
| ) | ||
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| return most_recent_rev_output |
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The output DataFrame should include all input application IDs, even when no records exist for an application. Consider using a right join with the input IDs and defaulting most_recent_revenue to 0 for applications without records. This ensures the output maintains the same cardinality as the input.
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