Utilizing Polars with NVIDIA GPU can velocity up your knowledge pipelines
Coping with large datasets inside Python has at all times been a problem. The language will not be tailor-made for dealing with big quantities of knowledge as native SQL programs or spark.
Probably the most well-known library for dealing with 2-D datasets inside Python is, with none query, pandas. Though simple 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. In case you are an information scientist, you’ve handled the ache of ready 200 years for a group by to complete.
One of many libraries that goals to resolve that is polars —a particularly environment friendly Python bundle 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 as we speak, now you can leverage NVIDIA’s {hardware} to maximise polars’ GPU engine capabilities.
On this weblog submit, we’ll see how one can leverage polars+GPU and velocity up your knowledge pipelines enormously.