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.

Cet exercice fait partie du cours

<cours>Scaling and Optimizing Data Pipelines with Polars</cours>

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Instructions de l’exercice

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

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

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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