Replace time zone in polars dataframe

dt.replace_time_zone

If you are working remotely and your team spans multiple time zones, it is important to ensure that the data you are analyzing matches the time zone in which it originated. For example, if you are in Chicago and a client in New York sends you a dataset to analyze, loading the dataset may display timestamps in your local time zone.

shape: (4, 1)

Chicago_Time
datetime[ms, America/Chicago]
2023-12-31 18:00:00 CST
2024-01-01 19:00:00 CST
2024-01-02 20:00:00 CST
2024-01-03 21:00:00 CST

Replace the time zone

To replace your local time zone with the time zone of the location where the data originated, you can use the date component replace_time_zone, as shown below:

(df
 .with_columns(pl.col('Chicago_Time').dt.replace_time_zone('America/New_York')
               .alias('New_York_Time'))
 )

shape: (4, 2)

Chicago_Time	New_York_Time
datetime[ms, America/Chicago]	datetime[ms, America/New_York]
2023-12-31 18:00:00 CST	2023-12-31 18:00:00 EST
2024-01-01 19:00:00 CST	2024-01-01 19:00:00 EST
2024-01-02 20:00:00 CST	2024-01-02 20:00:00 EST
2024-01-03 21:00:00 CST	2024-01-03 21:00:00 EST

The time zone is now changed from Chicago time to New York time.

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