pyTigerGraph is a Python package for connecting to TigerGraph databases.

Already familiar with pyTigerGraph? Join the pyTigerGraph Community, else, get to know pyTigerGraph below.

Get to Know pyTigerGraph

admin Get Started

Step-by-step guides to help you get up and running.

Get Started | pyTiger 101

admin Core Functions

Core Functions allow you to use the core features of the TigerGraph database through pyTigerGraph.

admin GDS Functions

Graph Data Science (GDS) Functions perform machine learning tasks.

admin Datasets

Data Ingestion Functions ingest stock datasets into a TigerGraph database.

admin Visualizations

Use Visualizations to visualize graphs.

admin Object-Oriented Schema

The Object-Oriented Schema functionality allows users to manipulate schema elements in the database in an object-oriented approach in Python.

admin Contribute

Checkout the Contributing section for instructions on how to contribute.

admin Release Notes

See Release Notes for the most up-to-date news on pyTigerGraph.

pyTigerGraph Community

There are many community resources that you can use for help and support using pyTigerGraph:

pyTigerGraph vs. pyTigerGraph[gds]

We offer two versions of the package: pyTigerGraph and pyTigerGraph[gds].

pyTigerGraph is the default version and contains the core functionality of pyTigerGraph, including the following:

  • Data manipulation functions:inserting, updating, upserting, deleting, and retrieving vertices and edges.

  • Query functions: running and managing queries inside the TigerGraph database

  • Metadata functions: fetching details of graphs/schemas, vertex and edge types, and other schema objects and object types

  • Authentication and authorization functions

  • Miscellaneous utility functions

The pyTigerGraph[gds] version of pyTigerGraph is a drop-in replacement for pyTigerGraph, but adds support for Graph Data Science and Graph machine learning capabilities. This includes:

  • Graph feature engineering using algorithms from the GSQL Graph Data Science Library.

  • Data loaders for training and inference of Graph Neural Network (GNN) models using PyTorch Geometric and DGL.

  • Machine learning utilities for splitting vertices into training, validation, and testing sets.