Notebook Setup

In this tutorial, we're going to use the PyGraphistry python library to create and visualize our first graph, then move on to more advanced techniques like creating a graph from any CSV, modifying visualization properties, and analyzing data from within the Graphistry visual environment.

Installing Graphistry locally with Docker

Visualize our first graph using a Jupyter notebook

Learn about hypergraphs and upload any CSV to Graphistry

Discover all the tools available within our visualization

Section 3. Graphing any CSV - Hypergraphs and Your Data



We can transform any CSV-like file into an insightful graph using a hypergraph transformation, wherein we use the structure of the CSV table to create a graph of nodes, edges and properties. The intuition is that every unique value in the datatable is turned into a node and every row (e.g., the event or sample) is also turned into a node, and connected to its value nodes. The resulting graph, when clustered, reveals the relationships between rows and cell values. In this notebook example, we'll be uploading and analyzing a malware file report .

Using the hypergraph transformation on the list of samples reveals phenomena such as:



Example Notebook:





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