Parsing datetime strings

You're a London transport analyst tasked with understanding summertime bike-sharing demand patterns. The dataset contains hourly rental data from July, but the time column is stored as strings in "MM-DD-YYYY HH:MM" format. To analyze when demand peaks, you first need to convert it to a proper Datetime dtype.

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

Este exercicio faz parte do curso

Data Transformation with Polars

Ver curso

Instruções do exercicio

Parse the time column to a Datetime dtype using the format "%m-%d-%Y %H:%M".

exercicio interativo prático

Tente este exercicio completando este código de exemplo.

# Parse the time column to Datetime
bikes.with_columns(
    pl.col("time").____.____(pl.____, "%m-%d-%Y %H:%M")
)

Editar e Executar Código