Mastering Data Manipulation with CUDA Accelerated Pandas with cuDF - Hands on Guide (Ep. 04)

Join this channel to get access to perks: https://www.youtube.com/channel/UC5vr5PwcXiKX_-6NTteAlXw/join In this video, we delve into data manipulation using CUDA-accelerated Pandas (cuDF) on a powerful Dell Workstation Precision 3680 with an NVIDIA RTX 5000 ADA GPU. The tutorial covers essential data manipulation techniques, including selecting columns, indexing rows, and Boolean indexing, while highlighting the significant speed benefits of using GPUs. We demonstrate the seamless integration of cuDF using the 'load_ext' magic command, making it easier to leverage GPU acceleration without changing existing pandas code. Additionally, we explore the profiling capabilities of cuDF to understand GPU and CPU utilization during data operations. This video is part of a series supported by Dell and NVIDIA, aimed at showcasing effective big data handling on GPUs. Notebook: https://github.com/wjbmattingly/cuda-python/blob/main/notebooks/cudf/02_selecting_data.ipynb 00:00 Introduction and Recap 00:21 Data Manipulation Overview 00:58 Workstation Setup and Sponsorship 02:17 Starting the Notebook 02:26 Speed Benefits of CuDF 04:08 Profiling with CuDF 05:21 Indexing and Filtering Data 08:11 Multi-Conditional Filtering 09:31 Conclusion and Key Takeaways Dell Workstation: https://www.dell.com/en-us/dt/ai-technologies/index.htm?utm_source=William&utm_medium=Content&utm_campaign=Polars RTX 5000 Ada: https://nvda.ws/4gPZUpQ RAPIDS: https://nvda.ws/3CwwcHK If you enjoy this video, please subscribe. ✅Be my Patron: https://www.patreon.com/WJBMattingly ✅PayPal: https://www.paypal.com/cgi-bin/webscr?cmd=_donations&business=AZ73QW52SUX8N&currency_code=USD&source=url If there's a specific video you would like to see or a tutorial series, let me know in the comments and I will try and make it. If you liked this video, check out www.PythonHumanities.com, where I have Coding Exercises, Lessons, on-site Python shells where you can experiment with code, and a text version of the material d