Python for Data Analysis, 3rd Edition – The Open Access Version Online

1. star-history

   1 48 7,237 9.1 TypeScript

   The missing star history graph of GitHub repos - https://star-history.com

   

2. CodeRabbit

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3. db-benchmark

   2 92 332 0.0 R

   reproducible benchmark of database-like ops

   

   I think that neither "outlier toolchain" nor "some percentage increase" are fair. This benchmark [0] show significant speedup while lowering the memory needs. You still need to reach dask/spark for really big data where you need a cluster of beefy computers for your tasks.

   If you use an r5d.24xlarge-like[1] instance, you can skip spark/dask for most workflows as 768 GB is plenty enough. On top of that, polars will efficiently use the 96 available cores when you are computing your join, groupby, etc.

   Also polars is getting more and more popular[2]

   [0] -- https://h2oai.github.io/db-benchmark/

4. xarray

   3 11 3,767 9.7 Python

   N-D labeled arrays and datasets in Python

   

   Does polars have N-D labelled arrays, and if so can it perform computations on them quickly? I've been thinking of moving from pandas to xarray [0], but might consider poplars too if it has some of that functionality.

   [0] https://xarray.dev/

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