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Comments (44)

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  tomrod
  Well done, Polars team!Everything that I build greenfield moving forward I plan to use DuckDB, Polars, or PyArrow. Pandas was a great grandfather of a project (I actually cut my OSS contrib teeth on it, how the time flies)! I'll always appreciate the improvement pandas brought over SAS.
• yshvrdhn
  what about daft project ?
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  gozzoo
  I'm not following the trends closely, but has Polars become a full replacement for Pandas? Are there use cases where one is better suited than the other?
• therno
  I use Polars 2.0(rc) to (pre)calculate billions of weather scores on https://therno.com and it has been a lifesaverHappy that I can upgrade to 2.0 final tonight.
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  niltecedu
  A bit surprised about the datafusion results from the post, I have tried it time and time again, but datafusion has always been the leading/trading blowers with polars for our workfloads with duckdb being vastly slower.
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  sgarland
  TIL that Polars supports SQL. Amazing.
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  dkgs
  A coincidence with the fact duckdb is supposed to release 2.0.0 very soon? :)
• hnd9q09qk4
  Thing I care about most is whether the old eager-vs-lazy footguns got cleaned up. Half my bugs were a stray collect() in a loop killing the query plan.
• Centigonal
  out of core sounds awesome! biggest thing that forced me to switch from pandas/polars to other solutions back in the day.
• dartharva
  Been using polars for over a year now, it is fantastic.
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  Kinrany
  How does Polars relate to DataFusion these days? There's no reason for them not to converge into a single ecosystem, is there?
• jt-s
  Although I find pandas a bit aggravating in many ways, for myself and my equally idiotic laboratory scientist pals, seems that it is the default way you might interface with other libraries like SciPy (i.e. they expect things as NumPy arrays or pandas dataframes). Is this a real issue or will most things happily accept a polars dataframe? We don’t work with such large datasets that speed is likely a huge concern tbh.
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  Vaslo
  My team is all moving over to polars and DuckDB
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  tonyhart7
  finally long time comingcant wait to upgrade my Quant trading bot
• mrtimo
  I can use pandas to clean a dataset, but each cleaning task is usually one line of code. OTOH, With DuckDB with one SQL statement I can replace 40+ lines of polars/pandas. You may reply, SQL isn't as easy to understand! Fair point, it's a declarative language... which is why I use Malloy. Malloy is to TypeScript as Javascript is to SQL. Malloy is much easier to read and write (just as TypeScript is) because it has a built in semantic model -- all the joins, measures, and dimensions are done in one place.Here is an example [1] of visualizing college football games. Here are all the queries, and semantic model that power all the visualizations [2] Here is the AI generated typescript/react that does the visualizations [3]. The Malloy ecosystem has Malloyyo and Publisher which are replacements for PowerBI and Tableau and Looker. Here is another example for visualizing global trade [4].[1] - https://mrtimo.github.io/cfb-games/games-2026.html?week=Week... [2] - https://github.com/mrtimo/cfb-games/blob/main/drives.malloy [3] - https://github.com/mrtimo/cfb-games/blob/main/dashboards/gam... [4] - https://tradeexplorer.org/