RAPIDS cuDF (pronounced “KOO-dee-eff”) is a Python GPU DataFrame library built on the Apache Arrow columnar memory format. It is designed for loading, joining, aggregating, filtering, and manipulating data at high speeds. cuDF provides a pandas-like API, making it familiar to data engineers and data scientists, enabling them to accelerate their workflows without delving into CUDA programming. The library supports 100% of the pandas API, using the GPU for supported operations and automatically falling back to pandas for other operations.

Features

• Pandas-Like API: Offers a familiar interface for data engineers and scientists, allowing them to use their existing knowledge to accelerate workflows.
• GPU Acceleration: Utilizes the power of NVIDIA GPUs to perform data operations at high speeds, significantly reducing processing time.
• Apache Arrow Integration: Built on the Apache Arrow columnar memory format, ensuring efficient data handling and interoperability with other Arrow-based libraries.
• Comprehensive Functionality: Supports a wide range of data manipulation operations, including loading, joining, aggregating, filtering, and more.
• Automatic Fallback: Automatically falls back to pandas for operations not supported on the GPU, ensuring seamless execution of workflows.

Benefits

• High Performance: Accelerates data manipulation tasks, enabling faster data processing and analysis.
• Ease of Use: Provides a pandas-like API, making it easy for users to transition to GPU-accelerated workflows.
• Flexibility: Supports a wide range of data operations, making it suitable for various data manipulation tasks.
• Seamless Integration: Built on Apache Arrow, ensuring compatibility with other Arrow-based libraries and tools.