From the Python client

Most Aura Graph AnalyticsThe Aura service that runs graph algorithms in a separate session, with no plugin to install. workflows run from the GDSAnalysing data through the structure of its connections. Also the name of the Neo4j library that implements it. Python client, not the AuraNeo4j's fully managed cloud service. Workspace. The Python client gives you the same gds object you've used against local Neo4j or AuraDSThe Aura product for data science workloads, with the Graph Data Science library installed. — same algorithms, same DataFrames, same mental model — pointed at an AGA sessionA 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 gds object behaves inside an AGA session and which parts of its surface are AGA-specific
• How to project from AuraDBThe 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 neo4j driver — the same pattern 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.