Calculating the difference from hourly mean

Totals are useful, but deviations tell a richer story. Calculate how much each hour's rentals differ from the average for that hour across all days to quickly flag above-normal or below-normal demand.

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

This exercise is part of the course

Data Transformation with Polars

View Course

Exercise instructions

Add a column diff_from_avg showing rentals minus the hourly mean of rentals.

Hands-on interactive exercise

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

# Calculate how each rental differs from its hourly average
bikes.with_columns(
    (pl.col("rentals") - pl.col("rentals").____().over("____")).alias("____")
)

Edit and Run Code