Marco Gorelli - Polars and time zones: everything you need to know | PyData Global 2023

Master time zones and datetimes effortlessly with Polars, a powerful Python library that automates time zone handling, ensuring accuracy and efficiency in your data analysis.

Key takeaways
  • Never deal with time zones manually due to their complexity and the potential for errors.

  • Use software like Polars to handle time zones accurately and efficiently.

  • Polars supports datetimes with two components: time unit and time zone.

  • Time unit represents the smallest amount of time that the data type can represent, while the time zone specifies the region’s offset from Coordinated Universal Time (UTC).

  • Polars offers various data types for datetimes, including date, time, datetime, and timestamp, each with its own characteristics and use cases.

  • To read data from a CSV into Polars, use the read_csv() function, which automatically infers the data types, including datetimes, if they are present in the CSV file.

  • Polars provides functions like .dt.offset_by() to manipulate datetimes by adding or subtracting a specified duration, such as hours or days.

  • The .dt.replace_timezone() function allows you to convert datetimes from one time zone to another, while .dt.convert_timezone() helps determine the local datetime in a different time zone.

  • Polars also supports expressions, which are lazy columns or functions that transform data frames into series.

  • Expressions can be used to perform various operations on datetimes, such as arithmetic operations, comparisons, and filtering.

  • Polars offers comprehensive documentation and examples to help users understand and utilize its features effectively.

  • The Polars Discord community is an active and helpful resource for users to ask questions, share experiences, and get assistance from the Polars team and other community members.