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 ejercicio forma parte del curso

Scaling and Optimizing Data Pipelines with Polars

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Instrucciones del ejercicio

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

ejercicio interactivo práctico

Prueba este ejercicio completando este código de ejemplo.

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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