Summarizing Parquet data

The first Parquet-based report is the digital checkout summary that the team built in Chapter 1, but now starting from a scan_parquet query. Build the same lazy pipeline so the team can reuse this pattern across their archive.

The LazyFrame requests is already built for you from the Parquet file.

This exercise is part of the course

Scaling and Optimizing Data Pipelines with Polars

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Exercise instructions

Filter requests to rows where use is "Digital".
Group the filtered rows by format.
Trigger execution at the very end of the pipeline.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

result = (
    requests
    # Filter to digital
    .filter(pl.col("use") == "____")
    # Group by format
    .group_by("____")
    .agg(pl.col("checkouts").sum().alias("total"))
    .sort("total", descending=True)
    # Trigger execution at the end
    .____()
)
print(result)

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