and time zones: everything you need to know
summary
Polars gives concrete answers: it parses CSVs with parse_dates=True, converts offset-aware strings to UTC, and requires an explicit timezone dtype when local semantics matter. Use replace_time_zone vs convert_time_zone to assign versus translate local times, use 1d vs 24h for calendar-day vs literal 24-hour shifts (DST can make them differ), use offset_by for arithmetic, and expect oddities like Nepal’s +5:45 and Samoa skipping a calendar day — Polars handles these reliably and fast.
AI-written summary · about
chapters (8)
- 0:00Introduction
- 1:52The dangers of manual handling
- 7:14Polars crash course
- 9:48Data types and timestamps
- 13:26Time zones in Polars
- 18:03Common pitfalls
- 22:13Conclusion
- 23:01Q&A session
related talks
02h 14mGetting Started with Polars
45mTimes and dates in Pandas
01h 32mAn Introduction to Polars
33mGet your Data prod ready, Fast, with Ruby Polars!
22mDon’t Write Polars Code with a Pandas Accent
36mUnderstanding Polars Expressions when you're used to pandas
35mA Tour of the Many DataFrame Frameworks
36mHow Narwhals brings Polars, DuckDB, PyArrow, & pandas together