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.

This exercise is part of the course

Data Transformation with Polars

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Exercise instructions

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

Hands-on interactive exercise

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

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

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