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.
Diese Übung ist Teil des Kurses
Data Transformation with Polars
Anleitung zur Übung
- Parse the
timecolumn to a Datetime dtype using the format"%m-%d-%Y %H:%M".
Interaktive Übung
Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.
# Parse the time column to Datetime
bikes.with_columns(
pl.col("time").____.____(pl.____, "%m-%d-%Y %H:%M")
)