Speed up Pandas operations with NO code changes using Nvidia RAPIDS cuDF.

As data scientists, we love using pandas for data manipulation due to its flexibility and ease of use. However, as our datasets grow in size, pandas operations can become slow and cumbersome. RAPIDS cuDF offers a solution that brings GPU acceleration to pandas with zero code change, enabling you to handle large datasets efficiently without sacrificing the familiar pandas API.

Zero Code Change Acceleration

With RAPIDS cuDF, you can write your code with the full flexibility of pandas and automatically accelerate it on the GPU by simply loading cudf.pandas. If needed, the system will fall back to the CPU without any extra effort from your side. This seamless integration allows you to enjoy GPU speed while maintaining compatibility with existing pandas code and third-party libraries.

How to Use It

To accelerate pandas operations in IPython or Jupyter Notebooks, use the following magic command:

%load_ext cudf.pandas
import pandas as pd

For Python scripts, use the module flag on the command line:

python -m cudf.pandas script.py

Alternatively, if you can’t use command line flags, explicitly enable cudf.pandas via import: