Master Data Preparation for Graphs with Paco Nathan | PyData Global 2022 📊

www.pydata.org Data science practitioners have a saying that a 80% of their time gets spent on data prep. Often this involves tools such as Pandas and Jupyter. Graph Data Science is similar, except the data prep techniques are highly specialized and computationally expensive. Moreover, data prep for graphs is required before commercial tools such as graph databases or visualization can be used effectively. This talk shows examples of data prep for graphs. A progressive example illustrates the challenges plus techniques that leverage open source integrations with the PyData stack: Arrow/Parquet, PSL, Ray, Keyvi, Datasketch, etc. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVideoTimestamps