Calculating the hourly total with window functions
Continuing with the London bike-sharing data, you want to spot unusual demand patterns like events or disruptions. To do this, compare each hour's rentals to its typical total across all days. Window functions let you add group statistics while keeping every row intact.
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
- Calculate the sum of
rentalsgrouped byhourand name the columnhourly_total.
Interaktive Übung
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# Add the hourly total using a window function
bikes.with_columns(
pl.col("rentals").____().over("____").alias("____")
)