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.
Deze oefening maakt deel uit van de cursus
Data Transformation with Polars
Oefeninstructies
- Parse the
timecolumn to a Datetime dtype using the format"%m-%d-%Y %H:%M".
Interactieve oefening met praktijkervaring
Probeer deze oefening door deze voorbeeldcode aan te vullen.
# Parse the time column to Datetime
bikes.with_columns(
pl.col("time").____.____(pl.____, "%m-%d-%Y %H:%M")
)