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RAPIDS cuDF is an open-source Python library for GPU accelerated DataFrames. cuDF supplies a Pandas-like API that permits information engineers, analysts, and information engineers can use carry out information manipulation and evaluation duties on giant datasets and time sequence information utilizing the ability of NVIDIA GPUs permitting for sooner information processing and evaluation.
Getting began with cuDF is easy, particularly you probably have expertise utilizing Python and libraries like Pandas. Whereas each cuDF and Pandas supply comparable APIs for information manipulation, there are particular forms of issues by which cuDF can present vital efficiency enhancements over Pandas, together with giant scale datasets, information preprocessing and engineering, real-time analytics, and, after all, parallel processing. The larger the dataset, the higher the efficiency advantages.
For extra on utilizing cuDF for information science, check out our latest cheat sheet.
This cheat sheet covers the next points of RAPIDS cuDF:
- Set up
- Studying information
- Writing information
- Deciding on information
- Dealing with lacking information
- Making use of capabilities
- Processing information
- and extra
Try the RAPIDS cuDF Cheat Sheet now, and examine again quickly for extra.
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