The Real Python Podcast

Testing Python Code for Scalability & What's New in pandas 3.0

15 snips
Jan 30, 2026
Christopher Trudeau, Regular PyCoder’s Weekly contributor who curates Python news and tooling. He covers testing for algorithmic scaling and how to write Big-O unit tests. He walks through pandas 3.0 highlights like a dedicated string dtype, pd.col-style column expressions, and copy-on-write behavior. Short rundowns of new tools, PEPs, profilers, and practical community releases round out the chat.
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INSIGHT

Pandas 3.0: Cleaner Ops And Faster Strings

  • Pandas 3.0 introduces pd.col expressions, Copy-on-Write, and a dedicated PyArrow-backed string dtype for big performance and usability gains.
  • These changes enable cleaner column operations, predictable copy behavior, and 5–10x faster string handling when PyArrow is installed.
ANECDOTE

SettingWithCopyWarning Frustration

  • Christopher Trudeau recalled seeing the recurring "SettingWithCopyWarning" when reviewing other people's Pandas code.
  • He found Pandas 3.0's Copy-on-Write change valuable because filtering now reliably returns a copy, avoiding ambiguity.
ADVICE

Test Big-O, Not Just Timings

  • Write unit tests that assert your function's Big-O scaling rather than absolute timings to catch regressions at scale.
  • Use the Big O library's decorator and assert_bounds to embed scalability checks in your test suite.
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