January 28, 2026
No warning, all warring
Pandas 3.0
Goodbye annoying warning; hello Polars vs Pandas brawl
TLDR: Pandas 3.0 arrives with faster text handling and a new copy-on-write model that ends a notorious warning. The comments explode into a Polars vs. Pandas showdown, with speed fans declaring Pandas obsolete, grammar cops nitpicking, and others worried the new time settings will break things for real users.
Pandas 3.0 just dropped with big promises—faster text handling, a cleaner “copy-on-write” model that kills a notorious warning, and fewer date errors by using microseconds. But the real action is in the comments, where it’s less “release notes” and more “steel cage match.”
One camp is already holding a Polars victory parade. A speed-chasing commenter bragged about a “10–20x” boost after switching, while another went nuclear, calling Pandas “inferior in every way.” The message: performance wins, and some say AI helpers make learning new syntax so easy that loyalty is optional. Meanwhile, a grammar vigilante marched in to fix a typo (“impactfull”), proving nothing ignites the internet like a misspelling.
On Team Pandas, relief is bubbling: the dreaded “SettingWithCopy” warning is gone, chained edits are out, and code should now behave more predictably—no mystery edits lurking in the shadows. The new string type (with optional help from the Arrow library for speed) feels grown-up, and there’s even a simpler way to build columns. But not everyone is chill: a timestamp change has skeptics warning of “so many problems.”
Bottom line: Pandas 3.0 is a big move, but the comments turned it into a Polars vs. Pandas street fight, with AI, typos, and time itself dragged into the fray.
Key Points
- •Pandas 3.0.0 introduces a default dedicated string dtype (str) instead of NumPy’s object dtype.
- •Copy-on-Write is now the default and only mode, removing chained assignment and the SettingWithCopyWarning.
- •Datetime-like data defaults to microsecond resolution (or input resolution), avoiding certain out-of-bounds errors.
- •Initial support for pd.col() provides simplified syntax for creating callables in DataFrame.assign.
- •Users are advised to upgrade to pandas 2.3 first, review migration guides, and install via PyPI or conda-forge; pyarrow is recommended for performance.