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SUMMARY:Vectorized statistical distributions\, built on the Polars engine 
 - Francesco Bruzzesi
DTSTART;TZID=Europe/Paris:20261125T144000
DTEND;TZID=Europe/Paris:20261125T151000
DTSTAMP:20260930T121250Z
UID:pretalx-compute-paris-2026-SKB9ET@pretalx.com
DESCRIPTION:If you do statistical work in Polars today\, you leave Polars 
 to do it: `.to_numpy()`\, a trip through `scipy.stats`\, and back. That cu
 ts the lazy query in half and materializes everything. The other way out\,
  a `map_elements` UDF\, is slower still and holds the GIL.\n\nscipy broadc
 asts parameter arrays fine\, so a different distribution per row\, where t
 he mean and standard deviation are themselves columns\, is already vectori
 zed there. It hands the result back as a NumPy array\, and the lazy plan e
 nds there.\n\nThis talk walks through a Polars expression plugin that expo
 ses `scipy.stats`-style distributions natively inside Polars expressions\,
  with column-valued parameters as a first-class feature. The math is Rust 
 (`statrs`)\; the surface is Polars. We will cover the plugin architecture 
 (`pyo3-polars`)\, why column-valued parameters reshape the API\, how seede
 d sampling stays reproducible across platforms\, chunk layouts and both en
 gines\, and the null and error contract.\n\nThe thread running through it 
 is what we got wrong before we got it right: the abstraction we built and 
 then changed\, the parameterization we flipped\, the contract we had to re
 write. Expect concrete code\, real benchmarks against scipy + polars.
LOCATION:Room 108
URL:https://pretalx.com/compute-paris-2026/talk/SKB9ET/
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