Examples

Scan a Parquet or CSV file with up to 5 million rows, let Polars push the filter and column selection into the scan, and compare the time with reading the whole file first.

Run one phplyr pipeline on phibble's in-memory DataFrame and on a PolarsTable, check that the results agree, and compare the times.

Price a year of daily sales with an asof join, smooth it with a time-based rolling mean, and summarize it by month or quarter.

The same pipeline on an in-memory data frame and in Polars, with timings for up to 300,000 rows.