Calculating a rolling sum

Continuing with the London bike-sharing data, hourly rental counts can jump dramatically from one hour to the next, making it hard to spot trends. Rolling statistics smooth out this noise by summing over a window of consecutive rows.

polars is loaded as pl. The DataFrame bikes is available with columns time, rentals, and temp.

Cet exercice fait partie du cours

<cours>Data Transformation with Polars</cours>

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Instructions de l’exercice

Add a column rolling_total with the 3-hour rolling sum of rentals.

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

# Add a 3-hour rolling sum of rentals
bikes.with_columns(
    pl.col("rentals").____(window_size=____).alias("____")
)

Modifier et exécuter le code