The goal of Polars is being a lightning fast DataFrame library that utilizes all
available cores on your machine.
Polars is semi-lazy. It allows you to do most of your work eagerly, similar to pandas, but
it does provide you with a powerful expression syntax that will be optimized executed on polars' query engine.
Polars also supports full lazy query execution that allows for more query optimization.
Polars keeps track of your query in a logical plan. This
plan is optimized and reordered before running it. When a result is requested Polars
distributes the available work to different executors that use the algorithms available
in the eager API to come up with the result. Because the whole query context is known to
the optimizer and executors of the logical plan, processes dependent on separate data
sources can be parallelized on the fly.
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