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UID:pretalx-python-asia-2026-BVMAMJ@pretalx.com
DTSTART;TZID=PST:20260321T153000
DTEND;TZID=PST:20260321T160000
DESCRIPTION:Pandas is one of the most widely used tools in Python\, yet man
 y developers unintentionally write slow or memory heavy DataFrame code. Th
 is talk covers practical performance techniques that can significantly spe
 ed up Pandas workflows: vectorization\, avoiding apply\, optimizing data t
 ypes\, reducing memory usage\, minimizing DataFrame copies\, improving joi
 ns and groupbys\, and using chunked loading for large files. We also look 
 at when to extend Pandas with Polars\, Apache Arrow\, or DuckDB for faster
  execution. If you work with data at any scale\, this session gives you si
 mple\, actionable tricks to make your Pandas pipelines faster and more pro
 duction ready.
DTSTAMP:20260501T070408Z
LOCATION:Yuchengco Hall 5th Flr. Y507 (Workshop Room 1)
SUMMARY:Fast Pandas: Performance Tricks You Wish You Knew Earlier - SOORAJ 
 TS\, Allen Y
URL:https://pretalx.com/python-asia-2026/talk/BVMAMJ/
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