Coping with large datasets inside Python has all the time been a problem. The language isn’t tailor-made for dealing with large quantities of information as native SQL programs or spark.
Essentially the most well-known library for dealing with 2-D datasets inside Python is, with none query, pandas. Though straightforward to make use of and utilized by each knowledge scientist, Pandas is written in Python and C, making it a bit combersume and gradual to carry out operations on giant knowledge. If you’re a knowledge scientist, you’ve handled the ache of ready 200 years for a group by to complete.
One of many libraries that goals to unravel that is polars —an especially environment friendly Python package deal that is ready to deal with giant datasets, principally for the next causes:
- It’s written in Rust
- It leverages multi-threading routinely
- It defers most calculations by utilizing lazy analysis
And.. after in the present day, now you can leverage NVIDIA’s {hardware} to maximise polars’ GPU engine capabilities.
On this weblog put up, we’ll see how one can leverage polars+GPU and pace up your knowledge pipelines enormously.