Grouping by multiple columns with window functions

Taking the analysis further, you want to break down bike demand by both time of day and weather. Warm sunny hours likely behave differently from cool cloudy ones. Create a simple weather category, then calculate total rentals for each hour-weather combination.

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

本练习是课程的一部分

Data Transformation with Polars

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练习说明

Create a weather column: "warm" when temp exceeds 20, otherwise "cool".
Add total_by_hour_weather with the sum of rentals grouped by hour and weather.

交互式实操练习

通过完成这段示例代码来试试这个练习。

# Add weather category based on temperature
bikes.with_columns(
    pl.when(pl.col("temp") > 20)
    .then(pl.lit("____"))
    .otherwise(pl.lit("____"))
    .alias("weather")
).with_columns(

    # Calculate total rentals by hour and weather
    pl.col("rentals").____().over("____", "____").alias("total_by_hour_weather")
)

编辑并运行代码