Time series analysis is ubiquitous in applied data science because of the value it delivers. In order to do effective time series analysis, you need to know your tools well. Polars has excellent built-in time series support, and it's also possible to extend it where necessary.

We will talk about:

• Basic built-in time series operations with Polars (e.g. "what's the average number of sales per month?").
• numba/numpy/scipy interoperability for not-so-basic time series operations (e.g. non-linear interpolation, or cumulative operations).
• Advanced, custom time series operations, and how you can implement them as Polars plugins (e.g. business day arithmetic).

Basic interest and knowledge of Python and data will be assumed, but no prior Polars experience is required.

Anyone working with time series and/or dataframes will likely benefit from the talk.

Marco Gorelli

Affiliation: Quansight Labs

Marco is a core dev of pandas and Polars and works at Quansight Labs as Senior Software Engineer. He also consults and trains clients professionally on Polars. He has also written the first Polars Plugins Tutorial and has taught Polars Plugins to clients.

He has a background in Mathematics and holds an MSc from the University of Oxford, and was one of the prize winners in the M6 Forecasting Competition (2nd place overall Q1).

visit the speaker at: Github