pyTigerGraph
pyTigerGraph is a Python package for connecting to TigerGraph databases.
Already familiar with pyTigerGraph? Join the community, else, get to know pyTigerGraph below.
Get to Know pyTigerGraph
Get Started Step-by-step guides to help you get up and running. |
Core Functions Core Functions allow you to use the core features of the TigerGraph database through pyTigerGraph. |
GDS Functions Graph Data Science (GDS) Functions perform machine learning tasks. |
TigerGraph Co-Pilot TigerGraph Co-Pilot is a natural language query service that allows users to ask questions about their graph data in plain English. |
Datasets Data Ingestion Functions ingest stock datasets into a TigerGraph database. |
Visualizations Use Visualizations to visualize graphs. |
Object-Oriented Schema The Object-Oriented Schema functionality allows users to manipulate schema elements in the database in an object-oriented approach in Python. |
Contribute Checkout the Contributing section for instructions on how to contribute. |
pyTigerGraph Community Utilize community resources for help and support when using pyTigerGraph. Community Forum | Community Discord | pyTigerGraph GitHub Issues |
Release Notes See Release Notes for the most up-to-date news on 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:
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Data manipulation functions:inserting, updating, upserting, deleting, and retrieving vertices and edges.
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Query functions: running and managing queries inside the TigerGraph database
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Metadata functions: fetching details of graphs/schemas, vertex and edge types, and other schema objects and object types
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Authentication and authorization functions
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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:
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Graph feature engineering using algorithms from the GSQL Graph Data Science Library.
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Data loaders for training and inference of Graph Neural Network (GNN) models using PyTorch Geometric and DGL.
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Machine learning utilities for splitting vertices into training, validation, and testing sets.