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.
Questo esercizio fa parte del corso
Data Transformation with Polars
Istruzioni dell'esercizio
- Parse the
timecolumn to a Datetime dtype using the format"%m-%d-%Y %H:%M".
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# Parse the time column to Datetime
bikes.with_columns(
pl.col("time").____.____(pl.____, "%m-%d-%Y %H:%M")
)