Downcasting numeric columns

Now that the ranges look safe, cast the numeric columns to smaller dtypes. Use Int32 for the integer columns and Float32 for the floats where lower precision is still good enough for summary stats.

The movies DataFrame is preloaded for you.

Este exercicio faz parte do curso

Scaling and Optimizing Data Pipelines with Polars

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Instruções do exercicio

Cast vote_count and budget to pl.Int32.
Cast runtime and vote_average to pl.Float32.

exercicio interativo prático

Tente este exercicio completando este código de exemplo.

movies_optimized = movies.with_columns(
    # Integer columns to Int32
    pl.col("vote_count").cast(pl.____),
    pl.col("budget").cast(pl.____),
    # Float columns to Float32
    pl.col("runtime").cast(pl.____),
    pl.col("vote_average").cast(pl.____),
)

result = movies_optimized.select(
    "movie_title", "budget", "runtime", "vote_average", "vote_count"
).head(8)
print(result)

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