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.
Diese Übung ist Teil des Kurses
Data Transformation with Polars
Anleitung zur Übung
- Add a column
diff_from_avgshowingrentalsminus the hourly mean ofrentals.
Interaktive Übung
Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.
# Calculate how each rental differs from its hourly average
bikes.with_columns(
(pl.col("rentals") - pl.col("rentals").____().over("____")).alias("____")
)