While I agree the speed benefit is marginal, the API is the differentiator.
Polars is a v2 of a dataframe API with a lot of thought put into offering a consistent experience. Variable names are seemingly regular across the board (eg no `sep` on this method, but `delimiter` here), no Numpy int nan baggage, and no silent data type conversions do a lot to improve the robustness of the code. That it is faster is nice, but a big shrug for my typical use cases.
The loss of the index is probably the right move - the implicit column has some subtle logic which I do not miss after switching to polars.
Source: over a decade of pandas experience. There are still a few idioms for which I do not have a good polars alternative, but nothing that is a deal breaker. The syntax is overall more verbose, but I am ok with it.
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[ 3.5 ms ] story [ 11.7 ms ] threadPolars is a v2 of a dataframe API with a lot of thought put into offering a consistent experience. Variable names are seemingly regular across the board (eg no `sep` on this method, but `delimiter` here), no Numpy int nan baggage, and no silent data type conversions do a lot to improve the robustness of the code. That it is faster is nice, but a big shrug for my typical use cases.
The loss of the index is probably the right move - the implicit column has some subtle logic which I do not miss after switching to polars.
Source: over a decade of pandas experience. There are still a few idioms for which I do not have a good polars alternative, but nothing that is a deal breaker. The syntax is overall more verbose, but I am ok with it.