While GPUs are often associated with deep learning, their acceleration capabilities are increasingly accessible for tabular data processing, a common task for many data scientists. Traditionally, these workflows run on CPUs, which can become a bottleneck with larger datasets. However, tools like cuDF, cudf.pandas, and the Polars GPU Engine are bringing GPU acceleration to the Python data science stack, allowing many existing workflows to run on GPUs with minimal to no code changes.

This article explores how much of a data science workflow, starting with data preparation, can be accelerated using GPUs. It introduces cuDF, a Python GPU DataFrame library with a pandas-like API, and demonstrates its use with the NYC Yellow Taxi Trip Records dataset. The benefits of using cuDF over pandas are highlighted through performance comparisons of common operations like filtering, column creation, and aggregations, showing significant speedups.

For those with existing pandas codebases, cudf.pandas offers a seamless way to accelerate these workflows. By enabling this accelerator, existing pandas code can run on the GPU without requiring extensive rewriting. This approach allows data scientists to leverage GPU power for data preparation tasks, reducing waiting times and freeing up more time for analysis and problem-solving.