Finding gaps in both DataFrames

For data quality checks, you want to see all rows from both DataFrames - listings without benchmarks and benchmarks without listings. This helps identify gaps before analysis.

polars is loaded as pl, and the DataFrames hotels and type_benchmarks are available for you.

This exercise is part of the course

Data Transformation with Polars

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Exercise instructions

Join hotels with type_benchmarks on type and beach, keeping all rows from both DataFrames.
Use coalesce=True to avoid duplicate join columns.

Hands-on interactive exercise

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

# Keep all rows from both DataFrames
full_view = hotels.____(
    type_benchmarks,
    on=["type", "beach"],
    how="____",
    # Avoid duplicate columns
    coalesce=____
)

print(full_view.head())

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