This talk gives an overview of the Neo4j graph database and the Cypher query language from the point of view of a Python user. We'll look at how to run queries and visualise or extract those results into software such as Pandas. We'll also explore the property graph data model and look at how it differs from other data models.
Graph databases offer a fresh perspective on data modelling and one that is often closer to the real world than a traditional RDBMS. In this talk, we'll look at how to work with Neo4j's property graph data model from the point of view of a Python user, how this model differs from other database models and we'll also show how to integrate the Cypher query language into a Python application.
This talk will (hopefully!) contain a couple of live demonstrations. We'll explore how to integrate Cypher query results with data analysis tools such as Pandas as well as how to visualise graph data through the Neo4j browser.