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.

This exercise is part of the course

Data Transformation with Polars

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Exercise instructions

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

Hands-on interactive exercise

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

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

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