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.
Affiliation: Quansight Labs
Marco is a Senior Software Engineer at Quansight Labs.
He is core dev of pandas and Polars, and is a leading voice in the Consortium for Python Data API Standards.
He has worked extensively with time series in his previous roles as data scientist. He was one of the prize winners of the M6 Forecasting Competition, winning $6,000 for his Q1 result. His background's in mathematics and he holds an MSc from the University of Oxford.
visit the speaker at: Github