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

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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.

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