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RAPIDS cuDF is an open-source Python library for GPU accelerated DataFrames. cuDF offers a Pandas-like API that lets knowledge engineers, analysts, and knowledge engineers can use conduct details manipulation and examination tasks on large datasets and time collection details utilizing the electricity of NVIDIA GPUs letting for faster knowledge processing and assessment.
Having started with cuDF is straightforward, specially if you have knowledge utilizing Python and libraries like Pandas. Even though each cuDF and Pandas present similar APIs for facts manipulation, there are distinct kinds of difficulties in which cuDF can offer major overall performance improvements in excess of Pandas, which includes significant scale datasets, details preprocessing and engineering, authentic-time analytics, and, of system, parallel processing. The even bigger the dataset, the increased the effectiveness positive aspects.
For additional on using cuDF for information science, check out our most current cheat sheet.
This cheat sheet handles the pursuing factors of RAPIDS cuDF:
- Installation
- Reading facts
- Composing facts
- Picking information
- Handling lacking info
- Making use of functions
- Processing information
- and much more
Check out the RAPIDS cuDF Cheat Sheet now, and examine back again before long for more.
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