From the Python client
Most Go to glossary for Aura Graph Analytics (opens in a new tab)The Aura service that runs graph algorithms in a separate session, with no plugin to install. workflows run from the Go to glossary for graph data science (opens in a new tab)Analysing data through the structure of its connections. Also the name of the Neo4j library that implements it. Python client, not the Go to glossary for Aura (opens in a new tab)Neo4j's fully managed cloud service. Workspace. The Python client gives you the same gds object you've used against local Neo4j or Go to glossary for AuraDS (opens in a new tab)The Aura product for data science workloads, with the Graph Data Science library installed. — same algorithms, same DataFrames, same mental model — pointed at an Full definition for Aura Graph Analytics session (opens in a new tab)A managed, temporary compute environment that holds a projection and runs algorithms against it..
The lessons in this module walk you through the code line-by-line. Nothing runs here in the browser — the first lesson gets you set up in a GitHub Codespace to run the code yourself.
In this module, you'll learn:
- How to authenticate to AGA from Python, estimate session memory, and create a session
- How the
gdsobject behaves inside an AGA session and which parts of its surface are AGA-specific - How to project from Go to glossary for AuraDB (opens in a new tab)The Aura product for transactional workloads., run algorithms, and write results back
- How to build a graph from Pandas DataFrames for data that isn't in Neo4j
- How to drive AGA from the plain
neo4jdriver — the same Full definition for pattern (opens in a new tab)A graph structure written in Cypher, such as a node joined to another node by a relationship. works in any language
Before you begin, you will need:
- A GitHub account
By the end of this module, you'll be able to script any AGA workflow end to end.