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.

Este exercicio faz parte do curso

Scaling and Optimizing Data Pipelines with Polars

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Instruções do exercicio

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

exercicio interativo prático

Tente este exercicio completando este código de exemplo.

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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