From the Python client

Most Aura Graph Analytics workflows run from the GDS Python client, not the Aura Workspace. The Python client gives you the same gds object you've used against local Neo4j or AuraDS — same algorithms, same DataFrames, same mental model — pointed at an AGA session.

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 AuraDB, 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.