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Laziness in this context has huge advantages in reducing memory allocation. Many operations can be fused together, so there's less of a need to allocate huge intermediate data structures at every step.


yeah, totally, I can see that. I think that polars is the first library to do this locally, which is surprising if it has so many advantages.


It's been around in R-land for a while with dplyr and its variety of backends (including Arrow, the same as Polars). Pandas is just an incredibly mediocre library in nearly all respects.


> It's been around in R-land for a while with dplyr and its variety of backends

Only for SQL databases, so not really. Source: have been running dplyr since 2011.


The Arrow backend does allow for lazy eval.

https://arrow.apache.org/cookbook/r/manipulating-data---tabl...




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